From 6ddc47a7ef0a0a72e5d55511255ed7e39d46573e Mon Sep 17 00:00:00 2001
From: Vladislav Volkov <vladislav.volkov@intel.com>
Date: Thu, 16 Dec 2021 16:58:19 +0300
Subject: [PATCH 001/160] [CPU] Convert precisions on inputs/outputs (#8805)

---
 .../src/mkldnn_plugin/mkldnn_plugin.cpp       |  31 +-
 .../nodes/common/cpu_convert.cpp              | 616 ++++++++++++++++--
 .../mkldnn_plugin/nodes/common/cpu_convert.h  |  29 +-
 .../nodes/mkldnn_convert_node.cpp             |  21 +-
 .../mkldnn_plugin/nodes/mkldnn_convert_node.h |   1 +
 .../python/tests/test_onnx/test_backend.py    |   7 -
 .../python/tests/test_onnx/test_zoo_models.py |   6 -
 .../interface/ie_iplugin_internal.cpp         |  16 +-
 .../blob_tests/set_blob.cpp                   |  11 +-
 .../single_layer_tests/conversion.cpp         |  28 +-
 .../skip_tests_config.cpp                     |  24 +-
 .../ov_executable_network/exec_graph_info.hpp |   8 +
 .../plugin/shared/src/blob_tests/set_blob.cpp |  22 +-
 .../src/base/layer_test_utils.cpp             |  84 +--
 .../src/single_layer/conversion.cpp           |   3 +
 15 files changed, 725 insertions(+), 182 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index 1c8b40aaa8b..a6bda152e23 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -504,23 +504,24 @@ Engine::LoadExeNetworkImpl(const InferenceEngine::CNNNetwork &network, const std
     OV_ITT_SCOPED_TASK(itt::domains::MKLDNNPlugin, "Engine::LoadExeNetworkImpl");
 
     // verification of supported input
-    InferenceEngine::InputsDataMap _networkInputs = network.getInputsInfo();
-    for (const auto &ii : _networkInputs) {
+    for (const auto &ii : network.getInputsInfo()) {
         auto input_precision = ii.second->getPrecision();
-        if (input_precision != InferenceEngine::Precision::FP64 &&
-            input_precision != InferenceEngine::Precision::FP32 &&
-            input_precision != InferenceEngine::Precision::I32 &&
-            input_precision != InferenceEngine::Precision::U32 &&
-            input_precision != InferenceEngine::Precision::U16 &&
-            input_precision != InferenceEngine::Precision::I16 &&
-            input_precision != InferenceEngine::Precision::I8 &&
-            input_precision != InferenceEngine::Precision::U8 &&
-            input_precision != InferenceEngine::Precision::BF16 &&
-            input_precision != InferenceEngine::Precision::BOOL &&
-            input_precision != InferenceEngine::Precision::I64 &&
-            input_precision != InferenceEngine::Precision::U64) {
+
+        using hash_t = std::hash<typename std::underlying_type<Precision::ePrecision>::type>;
+
+        static const std::unordered_set<Precision::ePrecision, hash_t> supported_precisions = {
+            Precision::U8,   Precision::I8,
+            Precision::U16,  Precision::I16,
+            Precision::U32,  Precision::I32,
+            Precision::U64,  Precision::I64,
+            Precision::BF16, Precision::FP16,
+            Precision::FP32, Precision::FP64,
+            Precision::BOOL
+        };
+
+        if (!supported_precisions.count(input_precision)) {
             IE_THROW(NotImplemented)
-                               << "Input image format " << input_precision << " is not supported yet...";
+                        << "Input image format " << input_precision << " is not supported yet...";
         }
     }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp b/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp
index 8763b551af9..31205ad84e3 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp
@@ -4,27 +4,208 @@
 
 #include "cpu_convert.h"
 #include "cpu_memcpy.h"
-#include "utils/bfloat16.hpp"
+#include <utils/bfloat16.hpp>
+#include <utils/general_utils.h>
 #include <mkldnn_selective_build.h>
+#include <ie_parallel.hpp>
+#include <openvino/core/type/float16.hpp>
+#include <cpu/x64/jit_generator.hpp>
+#include <algorithm>
 #include <type_traits>
 #include <tuple>
-#include <ie_parallel.hpp>
+#include <cmath>
 
+using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
+using namespace dnnl::impl::cpu::x64;
+using namespace dnnl::impl::utils;
+using namespace Xbyak;
 
 namespace {
 
-template<typename srcType, typename dstType>
-void convert(const void *srcPtr, void *dstPtr, const size_t size) {
-    if (std::is_same<srcType, dstType>::value) {
-        cpu_memcpy(dstPtr, srcPtr, size*sizeof(dstType));
-    } else {
-        const srcType *srcData = reinterpret_cast<const srcType *>(srcPtr);
-        dstType *dstData = reinterpret_cast<dstType *>(dstPtr);
+template <typename src_t, typename dst_t>
+void convert_vec(jit_generator & gen,
+                 const RegExp & src,
+                 const RegExp & dst);
 
-        parallel_for(size, [&](size_t i) {
-            dstData[i] = static_cast<dstType>(srcData[i]);
+template <>
+void convert_vec<ov::float16, float>(jit_generator & gen,
+                                     const RegExp & src,
+                                     const RegExp & dst) {
+    auto const & f16vec = gen.xmm3;
+    auto const & f32vec = gen.ymm4;
+
+    gen.movdqu(f16vec, gen.xword[src]);
+    gen.vcvtph2ps(f32vec, f16vec);
+    gen.vmovups(gen.yword[dst], f32vec);
+}
+
+template <>
+void convert_vec<float, ov::float16>(jit_generator & gen,
+                                     const RegExp & src,
+                                     const RegExp & dst) {
+    auto const & f16vec = gen.xmm3;
+    auto const & f32vec = gen.ymm4;
+
+    gen.vmovups(f32vec, gen.yword[src]);
+    gen.vcvtps2ph(f16vec, f32vec, 0);
+    gen.movdqu(gen.xword[dst], f16vec);
+}
+
+class jit_convert_array : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_convert_array)
+
+    void generate() override {
+        const size_t vlen = 8u;
+        const size_t vlen_log2 = 3;
+
+        auto reg_src = rax;
+        auto reg_dst = rbx;
+        auto reg_sz = rdx;
+
+        Label tail, exit;
+
+        preamble();
+
+        mov(reg_src, ptr[param1 + offsetof(args_t, src)]);
+        mov(reg_dst, ptr[param1 + offsetof(args_t, out)]);
+        mov(reg_sz, ptr[param1 + offsetof(args_t, count)]);
+
+        xor_(rsi, rsi);
+        mov(r8, reg_sz);
+        shr(r8, vlen_log2);
+
+        foreach(rsi, 1, r8, [&, this](const Xbyak::Reg64& idx) {
+            _convert_vec(*this, reg_src, reg_dst);
+            add(reg_src, _src_size * vlen);
+            add(reg_dst, _dst_size * vlen);
         });
+
+        L(tail);
+
+        shl(rsi, vlen_log2);
+        sub(reg_sz, rsi);
+        test(reg_sz, reg_sz);
+        jz(exit);
+
+        // allocate array for 8 floats on stack
+        sub(rsp, vlen * sizeof(float));
+        mov(r8, rsp);
+
+        vpxor(ymm4, ymm4, ymm4);
+        vmovups(yword[r8], ymm4);
+
+        // Tail conversion
+        copy(r8, reg_src, reg_sz, _src_size);
+        _convert_vec(*this, r8, r8);
+        copy(reg_dst, r8, reg_sz, _dst_size);
+
+        // Free the array on stack
+        add(rsp, vlen * sizeof(float));
+
+        L(exit);
+
+        postamble();
+    }
+
+    void foreach(const Xbyak::Reg64& idx,
+                 size_t step,
+                 const Xbyak::Reg64& end,
+                 std::function<void(const Xbyak::Reg64&)> && fn) {
+        Label loop, exit;
+
+        L(loop);
+        cmp(idx, end);
+        jge(exit);
+
+        fn(idx);
+
+        add(idx, step);
+        jmp(loop);
+        L(exit);
+    }
+
+    void copy(const Xbyak::Reg64& dst,
+              const Xbyak::Reg64& src,
+              const Xbyak::Reg64& size,
+              size_t item_size) {
+        push(rsi);
+        push(r15);
+
+        xor_(rsi, rsi);
+
+        auto address_frame = [this](size_t size) -> const AddressFrame& {
+            switch (size) {
+                case 1: return byte;
+                case 2: return word;
+                case 4: return dword;
+                case 8: return qword;
+                default:
+                    break;
+            }
+            return ptr;
+        };
+
+        const auto & addr_frame = address_frame(item_size);
+
+        foreach(rsi, 1, size, [&, this](const Xbyak::Reg64& idx) {
+            mov(r15, addr_frame[src + idx * item_size]);
+            mov(addr_frame[dst + idx * item_size], r15);
+        });
+
+        pop(r15);
+        pop(rsi);
+    }
+
+public:
+    typedef struct {
+        const void* src;
+        void* out;
+        const size_t count;
+    } args_t;
+
+    typedef void (*fn_t)(const args_t*);
+
+    typedef void (*convert_vec_t)(jit_generator &,
+                                  const RegExp &,
+                                  const RegExp &);
+
+    jit_convert_array(convert_vec_t convert_vec,
+                      size_t src_size,
+                      size_t dst_size)
+        : _convert_vec(convert_vec)
+        , _src_size(src_size)
+        , _dst_size(dst_size) {}
+
+    template<typename src_t, typename dst_t>
+    static fn_t get() {
+        if (mayiuse(avx2) && cpu().has(util::Cpu::tF16C)) {
+            static jit_convert_array converter(convert_vec<src_t, dst_t>, sizeof(src_t), sizeof(dst_t));
+            auto & generator = static_cast<jit_generator&>(converter);
+            generator.create_kernel();
+            return (fn_t)generator.jit_ker();
+        }
+        return nullptr;
+    }
+
+private:
+    convert_vec_t _convert_vec;
+    size_t _src_size;
+    size_t _dst_size;
+};
+
+template <typename TI, typename TO>
+void jit_convert(const TI* arg, TO* out, size_t count) {
+    using jit_impl = jit_convert_array;
+    static auto converter = jit_impl::get<TI, TO>();
+
+    if (converter) {
+        typename jit_impl::args_t args = { arg, out, count };
+        converter(&args);
+    } else {
+        for (size_t i = 0; i < count; ++i) {
+            out[i] = static_cast<TO>(arg[i]);
+        }
     }
 }
 
@@ -35,84 +216,391 @@ struct PrecisionInfo {
 
 template <>
 struct PrecisionInfo<Precision::BF16> {
-    using value_type = MKLDNNPlugin::bfloat16_t;
+    using value_type = bfloat16_t;
 };
 
+template <>
+struct PrecisionInfo<Precision::FP16> {
+    using value_type = ov::float16;
+};
+
+template <>
+struct PrecisionInfo<Precision::BOOL> {
+    using value_type = uint8_t;
+};
+
+template<typename T,
+         typename U = typename std::conditional<
+                        std::is_same<ov::float16, T>::value
+                        || std::is_same<bfloat16_t, T>::value,
+                        float, T>::type>
+struct Range {
+    const std::tuple<U, U> & fit(const Precision & prec);
+
+private:
+    std::tuple<U, U> _range {
+        std::numeric_limits<T>::lowest(),
+        std::numeric_limits<T>::max()
+    };
+};
+
+template<typename T, typename U>
+const std::tuple<U, U> & Range<T, U>::fit(const Precision & prec) {
+    if (prec.is_float()) {
+        double lbound, ubound;
+        switch (prec) {
+            case Precision::BF16:
+                lbound = static_cast<double>(std::numeric_limits<bfloat16_t>::lowest());
+                ubound = static_cast<double>(std::numeric_limits<bfloat16_t>::max());
+                break;
+            case Precision::FP16:
+                lbound = static_cast<double>(std::numeric_limits<ov::float16>::lowest());
+                ubound = static_cast<double>(std::numeric_limits<ov::float16>::max());
+                break;
+            case Precision::FP32:
+                lbound = static_cast<double>(std::numeric_limits<float>::lowest());
+                ubound = static_cast<double>(std::numeric_limits<float>::max());
+                break;
+            case Precision::FP64:
+                lbound = std::numeric_limits<double>::lowest();
+                ubound = std::numeric_limits<double>::max();
+                break;
+            default:
+                IE_THROW() << "Unsupported precision";
+        }
+        std::get<0>(_range) = static_cast<U>(std::max(static_cast<double>(std::get<0>(_range)), lbound));
+        std::get<1>(_range) = static_cast<U>(std::min(static_cast<double>(std::get<1>(_range)), ubound));
+    } else {
+        int64_t lbound;
+        uint64_t ubound;
+        switch (prec) {
+            case Precision::BOOL:
+            case Precision::U8:
+                lbound = static_cast<int64_t>(std::numeric_limits<uint8_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<uint8_t>::max());
+                break;
+            case Precision::I8:
+                lbound = static_cast<int64_t>(std::numeric_limits<int8_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<int8_t>::max());
+                break;
+            case Precision::U16:
+                lbound = static_cast<int64_t>(std::numeric_limits<uint16_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<uint16_t>::max());
+                break;
+            case Precision::I16:
+                lbound = static_cast<int64_t>(std::numeric_limits<int16_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<int16_t>::max());
+                break;
+            case Precision::U32:
+                lbound = static_cast<int64_t>(std::numeric_limits<uint32_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<uint32_t>::max());
+                break;
+            case Precision::I32:
+                lbound = static_cast<int64_t>(std::numeric_limits<int32_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<int32_t>::max());
+                break;
+            case Precision::U64:
+                lbound = static_cast<int64_t>(std::numeric_limits<uint64_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<uint64_t>::max());
+                break;
+            case Precision::I64:
+                lbound = static_cast<int64_t>(std::numeric_limits<int64_t>::lowest());
+                ubound = static_cast<uint64_t>(std::numeric_limits<int64_t>::max());
+                break;
+            default:
+                IE_THROW() << "Unsupported precision";
+        }
+        using ltype = typename std::conditional<
+                            std::is_floating_point<U>::value,
+                            double, int64_t>::type;
+        using utype = typename std::conditional<
+                            std::is_floating_point<U>::value,
+                            double, uint64_t>::type;
+        std::get<0>(_range) = static_cast<U>(std::max(static_cast<ltype>(std::get<0>(_range)), static_cast<ltype>(lbound)));
+        std::get<1>(_range) = static_cast<U>(std::min(static_cast<utype>(std::get<1>(_range)), static_cast<utype>(ubound)));
+    }
+    return _range;
+}
+
 struct ConvertContext {
     const void *srcPtr;
     void *dstPtr;
     size_t size;
+    Precision interimPrc;
+    Precision dstPrc;
     bool converted;
+
+    template<typename T>
+    std::tuple<T, T> range() const {
+        Range<T> r;
+        r.fit(interimPrc);
+        return r.fit(dstPrc);
+    }
 };
 
 template<typename T>
-struct ConvertPrecision {
-    using src_t = typename std::tuple_element<0, T>::type;
-    using dst_t = typename std::tuple_element<1, T>::type;
+struct ConvertPrecision;
 
+template<typename src_t, typename dst_t>
+struct ConvertPrecision<std::tuple<src_t, dst_t>> {
     void operator()(ConvertContext & ctx) {
-        convert<src_t, dst_t>(ctx.srcPtr, ctx.dstPtr, ctx.size);
+        auto src = static_cast<const src_t *>(ctx.srcPtr);
+        auto dst = static_cast<dst_t *>(ctx.dstPtr);
+        src_t lbound, ubound;
+        std::tie(lbound, ubound) = ctx.range<src_t>();
+
+        if (std::is_integral<src_t>::value
+            || ctx.interimPrc.is_float()
+            || std::is_integral<dst_t>::value) {
+            parallel_for(ctx.size, [&](size_t i) {
+                dst[i] = static_cast<dst_t>(std::max(std::min(src[i], ubound), lbound));
+            });
+        } else {
+            parallel_for(ctx.size, [&](size_t i) {
+                dst[i] = static_cast<dst_t>(std::trunc(std::max(std::min(src[i], ubound), lbound)));
+            });
+        }
+
         ctx.converted = true;
     }
 };
 
+template<>
+struct ConvertPrecision<std::tuple<float, bfloat16_t>> {
+    void operator()(ConvertContext & ctx) {
+        auto src = static_cast<const float *>(ctx.srcPtr);
+        auto dst = static_cast<bfloat16_t *>(ctx.dstPtr);
+
+        if (ctx.interimPrc.is_float()) {
+            parallel_for(ctx.size, [&](size_t i) {
+                dst[i] = static_cast<bfloat16_t>(src[i]);
+            });
+        } else {
+            float lbound, ubound;
+            std::tie(lbound, ubound) = ctx.range<float>();
+            parallel_for(ctx.size, [&](size_t i) {
+                dst[i] = static_cast<bfloat16_t>(std::trunc(std::max(std::min(src[i], ubound), lbound)));
+            });
+        }
+
+        ctx.converted = true;
+    }
+};
+
+template<>
+struct ConvertPrecision<std::tuple<bfloat16_t, float>> {
+    void operator()(ConvertContext & ctx) {
+        auto src = static_cast<const bfloat16_t *>(ctx.srcPtr);
+        auto dst = static_cast<float *>(ctx.dstPtr);
+
+        if (ctx.interimPrc.is_float()) {
+            parallel_for(ctx.size, [&](size_t i) {
+                dst[i] = static_cast<float>(src[i]);
+            });
+        } else {
+            float lbound, ubound;
+            std::tie(lbound, ubound) = ctx.range<bfloat16_t>();
+            parallel_for(ctx.size, [&](size_t i) {
+                dst[i] = std::trunc(std::max(std::min(static_cast<float>(src[i]), ubound), lbound));
+            });
+        }
+
+        ctx.converted = true;
+    }
+};
+
+template<typename src_t>
+struct ConvertPrecision<std::tuple<src_t, ov::float16>> {
+    void operator()(ConvertContext & ctx) {
+        auto src = static_cast<const src_t *>(ctx.srcPtr);
+        auto dst = static_cast<ov::float16 *>(ctx.dstPtr);
+
+        constexpr size_t batch = 64;
+        const size_t iterations = MKLDNNPlugin::div_up(ctx.size, batch);
+        typedef float batch_type[batch];
+
+        src_t lbound, ubound;
+        std::tie(lbound, ubound) = ctx.range<src_t>();
+
+        if (std::is_integral<src_t>::value
+            || ctx.interimPrc.is_float()) {
+            parallel_for(iterations, [&](size_t i) {
+                batch_type tmp;
+                const size_t offset = i * batch;
+                const size_t current_batch_size = std::min(ctx.size - offset, batch);
+                for (size_t j = 0; j < current_batch_size; ++j)         // src_t -> fp32
+                    tmp[j] = static_cast<float>(std::max(std::min(src[offset + j], ubound), lbound));
+                jit_convert(tmp, dst + offset, current_batch_size);     // fp32 -> fp16
+            });
+        } else {
+            parallel_for(iterations, [&](size_t i) {
+                batch_type tmp;
+                const size_t offset = i * batch;
+                const size_t current_batch_size = std::min(ctx.size - offset, batch);
+                for (size_t j = 0; j < current_batch_size; ++j)         // src_t -> fp32
+                    tmp[j] = static_cast<float>(std::trunc(std::max(std::min(src[offset + j], ubound), lbound)));
+                jit_convert(tmp, dst + offset, current_batch_size);     // fp32 -> fp16
+            });
+        }
+
+        ctx.converted = true;
+    }
+};
+
+template<typename dst_t>
+struct ConvertPrecision<std::tuple<ov::float16, dst_t>> {
+    void operator()(ConvertContext & ctx) {
+        auto src = static_cast<const ov::float16 *>(ctx.srcPtr);
+        auto dst = static_cast<dst_t *>(ctx.dstPtr);
+
+        constexpr size_t batch = 64;
+        const size_t iterations = MKLDNNPlugin::div_up(ctx.size, batch);
+        typedef float batch_type[batch];
+
+        float lbound, ubound;
+        std::tie(lbound, ubound) = ctx.range<ov::float16>();
+
+        if (ctx.interimPrc.is_float()
+            || std::is_integral<dst_t>::value) {
+            parallel_for(iterations, [&](size_t i) {
+                batch_type tmp;
+                const size_t offset = i * batch;
+                const size_t current_batch_size = std::min(ctx.size - offset, batch);
+                jit_convert(src + offset, tmp, current_batch_size);     // fp16 -> fp32
+                for (size_t j = 0; j < current_batch_size; ++j)         // fp32 -> dst_t
+                    dst[offset + j] = static_cast<dst_t>(std::max(std::min(tmp[j], ubound), lbound));
+            });
+        } else {
+            parallel_for(iterations, [&](size_t i) {
+                batch_type tmp;
+                const size_t offset = i * batch;
+                const size_t current_batch_size = std::min(ctx.size - offset, batch);
+                jit_convert(src + offset, tmp, current_batch_size);     // fp16 -> fp32
+                for (size_t j = 0; j < current_batch_size; ++j)         // fp32 -> dst_t
+                    dst[offset + j] = static_cast<dst_t>(std::trunc(std::max(std::min(tmp[j], ubound), lbound)));
+            });
+        }
+
+        ctx.converted = true;
+    }
+};
+
+template<>
+struct ConvertPrecision<std::tuple<ov::float16, ov::float16>> {
+    void operator()(ConvertContext & ctx) {
+        auto src = static_cast<const ov::float16 *>(ctx.srcPtr);
+        auto dst = static_cast<ov::float16 *>(ctx.dstPtr);
+
+        constexpr size_t batch = 64;
+        const size_t iterations = MKLDNNPlugin::div_up(ctx.size, batch);
+        typedef float batch_type[batch];
+
+        float lbound, ubound;
+        std::tie(lbound, ubound) = ctx.range<ov::float16>();
+
+        if (ctx.interimPrc.is_float()) {
+            cpu_memcpy(dst, src, ctx.size * sizeof(ov::float16));
+        } else {
+            parallel_for(iterations, [&](size_t i) {
+                batch_type tmp;
+                const size_t offset = i * batch;
+                const size_t current_batch_size = std::min(ctx.size - offset, batch);
+                jit_convert(src + offset, tmp, current_batch_size);     // fp16 -> fp32
+                for (size_t j = 0; j < current_batch_size; ++j)         // truncate fp32
+                    tmp[j] = std::trunc(std::max(std::min(tmp[j], ubound), lbound));
+                jit_convert(tmp, dst + offset, current_batch_size);     // fp32 -> fp16
+            });
+        }
+
+        ctx.converted = true;
+    }
+};
+
+bool isConversionTruncatesRange(const Precision & from, const Precision & to) {
+    return to.bitsSize() < from.bitsSize()
+            || (from.is_float() && !to.is_float())      // float -> integral
+            || (from.isSigned() != to.isSigned())       // signed <-> unsigned
+            || (to == Precision::BOOL && from != to);   // T -> bool
+}
+
 }   // namespace
 
 #define MKLDNN_CVT(ST, DT) OV_CASE2(Precision::ST, Precision::DT, PrecisionInfo<Precision::ST>::value_type, PrecisionInfo<Precision::DT>::value_type)
 
-void cpu_convert(const void *srcPtr, void *dstPtr, Precision srcPrc, Precision dstPrc, const size_t size) {
-    using namespace MKLDNNPlugin;
+#define MKLDNN_CVT_LIST                                                                             \
+    MKLDNN_CVT(U8, I8),     MKLDNN_CVT(U8, U16),    MKLDNN_CVT(U8, I16),    MKLDNN_CVT(U8, U32),    \
+    MKLDNN_CVT(U8, I32),    MKLDNN_CVT(U8, U64),    MKLDNN_CVT(U8, I64),    MKLDNN_CVT(U8, FP32),   \
+    MKLDNN_CVT(U8, FP16),   MKLDNN_CVT(U8, BF16),   MKLDNN_CVT(U8, FP64),   MKLDNN_CVT(U8, BOOL),   \
+    MKLDNN_CVT(I8, U8),     MKLDNN_CVT(I8, U16),    MKLDNN_CVT(I8, I16),    MKLDNN_CVT(I8, U32),    \
+    MKLDNN_CVT(I8, I32),    MKLDNN_CVT(I8, U64),    MKLDNN_CVT(I8, I64),    MKLDNN_CVT(I8, FP32),   \
+    MKLDNN_CVT(I8, FP16),   MKLDNN_CVT(I8, BF16),   MKLDNN_CVT(I8, FP64),   MKLDNN_CVT(I8, BOOL),   \
+    MKLDNN_CVT(U16, U8),    MKLDNN_CVT(U16, I8),    MKLDNN_CVT(U16, I16),   MKLDNN_CVT(U16, U32),   \
+    MKLDNN_CVT(U16, I32),   MKLDNN_CVT(U16, U64),   MKLDNN_CVT(U16, I64),   MKLDNN_CVT(U16, FP32),  \
+    MKLDNN_CVT(U16, FP16),  MKLDNN_CVT(U16, BF16),  MKLDNN_CVT(U16, FP64),  MKLDNN_CVT(U16, BOOL),  \
+    MKLDNN_CVT(I16, U8),    MKLDNN_CVT(I16, I8),    MKLDNN_CVT(I16, U16),   MKLDNN_CVT(I16, U32),   \
+    MKLDNN_CVT(I16, I32),   MKLDNN_CVT(I16, U64),   MKLDNN_CVT(I16, I64),   MKLDNN_CVT(I16, FP32),  \
+    MKLDNN_CVT(I16, FP16),  MKLDNN_CVT(I16, BF16),  MKLDNN_CVT(I16, FP64),  MKLDNN_CVT(I16, BOOL),  \
+    MKLDNN_CVT(U32, U8),    MKLDNN_CVT(U32, I8),    MKLDNN_CVT(U32, U16),   MKLDNN_CVT(U32, I16),   \
+    MKLDNN_CVT(U32, I32),   MKLDNN_CVT(U32, U64),   MKLDNN_CVT(U32, I64),   MKLDNN_CVT(U32, FP32),  \
+    MKLDNN_CVT(U32, FP16),  MKLDNN_CVT(U32, BF16),  MKLDNN_CVT(U32, FP64),  MKLDNN_CVT(U32, BOOL),  \
+    MKLDNN_CVT(I32, U8),    MKLDNN_CVT(I32, I8),    MKLDNN_CVT(I32, U16),   MKLDNN_CVT(I32, I16),   \
+    MKLDNN_CVT(I32, U32),   MKLDNN_CVT(I32, U64),   MKLDNN_CVT(I32, I64),   MKLDNN_CVT(I32, FP32),  \
+    MKLDNN_CVT(I32, FP16),  MKLDNN_CVT(I32, BF16),  MKLDNN_CVT(I32, FP64),  MKLDNN_CVT(I32, BOOL),  \
+    MKLDNN_CVT(U64, U8),    MKLDNN_CVT(U64, I8),    MKLDNN_CVT(U64, U16),   MKLDNN_CVT(U64, I16),   \
+    MKLDNN_CVT(U64, U32),   MKLDNN_CVT(U64, I32),   MKLDNN_CVT(U64, I64),   MKLDNN_CVT(U64, FP32),  \
+    MKLDNN_CVT(U64, FP16),  MKLDNN_CVT(U64, BF16),  MKLDNN_CVT(U64, FP64),  MKLDNN_CVT(U64, BOOL),  \
+    MKLDNN_CVT(I64, U8),    MKLDNN_CVT(I64, I8),    MKLDNN_CVT(I64, U16),   MKLDNN_CVT(I64, I16),   \
+    MKLDNN_CVT(I64, U32),   MKLDNN_CVT(I64, I32),   MKLDNN_CVT(I64, U64),   MKLDNN_CVT(I64, FP32),  \
+    MKLDNN_CVT(I64, FP16),  MKLDNN_CVT(I64, BF16),  MKLDNN_CVT(I64, FP64),  MKLDNN_CVT(I64, BOOL),  \
+    MKLDNN_CVT(FP32, U8),   MKLDNN_CVT(FP32, I8),   MKLDNN_CVT(FP32, U16),  MKLDNN_CVT(FP32, I16),  \
+    MKLDNN_CVT(FP32, U32),  MKLDNN_CVT(FP32, I32),  MKLDNN_CVT(FP32, U64),  MKLDNN_CVT(FP32, I64),  \
+    MKLDNN_CVT(FP32, FP16), MKLDNN_CVT(FP32, BF16), MKLDNN_CVT(FP32, FP64), MKLDNN_CVT(FP32, BOOL), \
+    MKLDNN_CVT(FP16, U8),   MKLDNN_CVT(FP16, I8),   MKLDNN_CVT(FP16, U16),  MKLDNN_CVT(FP16, I16),  \
+    MKLDNN_CVT(FP16, U32),  MKLDNN_CVT(FP16, I32),  MKLDNN_CVT(FP16, U64),  MKLDNN_CVT(FP16, I64),  \
+    MKLDNN_CVT(FP16, FP32), MKLDNN_CVT(FP16, BF16), MKLDNN_CVT(FP16, FP64), MKLDNN_CVT(FP16, BOOL), \
+    MKLDNN_CVT(BF16, U8),   MKLDNN_CVT(BF16, I8),   MKLDNN_CVT(BF16, U16),  MKLDNN_CVT(BF16, I16),  \
+    MKLDNN_CVT(BF16, U32),  MKLDNN_CVT(BF16, I32),  MKLDNN_CVT(BF16, U64),  MKLDNN_CVT(BF16, I64),  \
+    MKLDNN_CVT(BF16, FP32), MKLDNN_CVT(BF16, FP16), MKLDNN_CVT(BF16, FP64), MKLDNN_CVT(BF16, BOOL), \
+    MKLDNN_CVT(FP64, U8),   MKLDNN_CVT(FP64, I8),   MKLDNN_CVT(FP64, U16),  MKLDNN_CVT(FP64, I16),  \
+    MKLDNN_CVT(FP64, U32),  MKLDNN_CVT(FP64, I32),  MKLDNN_CVT(FP64, U64),  MKLDNN_CVT(FP64, I64),  \
+    MKLDNN_CVT(FP64, FP32), MKLDNN_CVT(FP64, FP16), MKLDNN_CVT(FP64, BF16), MKLDNN_CVT(FP64, BOOL), \
+    MKLDNN_CVT(BOOL, U8),   MKLDNN_CVT(BOOL, I8),   MKLDNN_CVT(BOOL, U16),  MKLDNN_CVT(BOOL, I16),  \
+    MKLDNN_CVT(BOOL, U32),  MKLDNN_CVT(BOOL, I32),  MKLDNN_CVT(BOOL, U64),  MKLDNN_CVT(BOOL, I64),  \
+    MKLDNN_CVT(BOOL, FP32), MKLDNN_CVT(BOOL, FP16), MKLDNN_CVT(BOOL, BF16), MKLDNN_CVT(BOOL, FP64), \
+    MKLDNN_CVT(U8, U8),     MKLDNN_CVT(I8, I8),     MKLDNN_CVT(U16, U16),   MKLDNN_CVT(I16, I16),   \
+    MKLDNN_CVT(U32, U32),   MKLDNN_CVT(I32, I32),   MKLDNN_CVT(U64, U64),   MKLDNN_CVT(I64, I64),   \
+    MKLDNN_CVT(FP32, FP32), MKLDNN_CVT(FP16, FP16), MKLDNN_CVT(BF16, BF16), MKLDNN_CVT(FP64, FP64), \
+    MKLDNN_CVT(BOOL, BOOL)
 
+void cpu_convert(const void *srcPtr, void *dstPtr, Precision srcPrc, Precision dstPrc, const size_t size) {
+    cpu_convert(srcPtr, dstPtr, srcPrc, dstPrc, dstPrc, size);
+}
+
+void cpu_convert(const void *srcPtr,
+                 void *dstPtr,
+                 InferenceEngine::Precision srcPrc,
+                 InferenceEngine::Precision interimPrc,
+                 InferenceEngine::Precision dstPrc,
+                 const size_t size) {
     if (srcPtr == nullptr || dstPtr == nullptr)
         IE_THROW() << "cpu_convert has null data pointer";
 
-    if (srcPrc == dstPrc) {
-        cpu_memcpy(dstPtr, srcPtr, size*dstPrc.size());
-        return;
+    if (srcPrc == dstPrc && srcPrc == interimPrc) {
+        cpu_memcpy(dstPtr, srcPtr, size * dstPrc.size());
+    } else {
+        ConvertContext ctx = {
+            srcPtr,
+            dstPtr,
+            size,
+            interimPrc,
+            dstPrc,
+            false
+        };
+        OV_SWITCH(MKLDNNPlugin, ConvertPrecision, ctx, std::tie(srcPrc, dstPrc), MKLDNN_CVT_LIST);
+        if (!ctx.converted)
+            IE_THROW() << "cpu_convert can't convert from: " << srcPrc << " precision to: " << dstPrc;
     }
-
-    ConvertContext ctx = { srcPtr, dstPtr, size, false };
-
-    OV_SWITCH(MKLDNNPlugin, ConvertPrecision, ctx, std::tie(srcPrc, dstPrc),
-    MKLDNN_CVT(U8, I8),    MKLDNN_CVT(U8, U16),    MKLDNN_CVT(U8, I16),
-    MKLDNN_CVT(U8, I32),   MKLDNN_CVT(U8, U64),    MKLDNN_CVT(U8, I64),
-    MKLDNN_CVT(U8, FP32),  MKLDNN_CVT(U8, BF16),   MKLDNN_CVT(U8, BOOL),
-    MKLDNN_CVT(I8, U8),    MKLDNN_CVT(I8, U16),    MKLDNN_CVT(I8, I16),
-    MKLDNN_CVT(I8, I32),   MKLDNN_CVT(I8, U64),    MKLDNN_CVT(I8, I64),
-    MKLDNN_CVT(I8, FP32),  MKLDNN_CVT(I8, BF16),   MKLDNN_CVT(I8, BOOL),
-    MKLDNN_CVT(U16, U8),   MKLDNN_CVT(U16, I8),    MKLDNN_CVT(U16, I16),
-    MKLDNN_CVT(U16, I32),  MKLDNN_CVT(U16, U64),   MKLDNN_CVT(U16, I64),
-    MKLDNN_CVT(U16, FP32), MKLDNN_CVT(U16, BF16),  MKLDNN_CVT(U16, BOOL),
-    MKLDNN_CVT(I16, U8),   MKLDNN_CVT(I16, I8),    MKLDNN_CVT(I16, U16),
-    MKLDNN_CVT(I16, I32),  MKLDNN_CVT(I16, U64),   MKLDNN_CVT(I16, I64),
-    MKLDNN_CVT(I16, FP32), MKLDNN_CVT(I16, BF16),  MKLDNN_CVT(I16, BOOL),
-    MKLDNN_CVT(I32, U8),   MKLDNN_CVT(I32, I8),    MKLDNN_CVT(I32, U16),
-    MKLDNN_CVT(I32, I16),  MKLDNN_CVT(I32, U64),   MKLDNN_CVT(I32, I64),
-    MKLDNN_CVT(I32, FP32), MKLDNN_CVT(I32, BF16),  MKLDNN_CVT(I32, BOOL),
-    MKLDNN_CVT(U64, U8),   MKLDNN_CVT(U64, I8),    MKLDNN_CVT(U64, U16),
-    MKLDNN_CVT(U64, I16),  MKLDNN_CVT(U64, I32),   MKLDNN_CVT(U64, I64),
-    MKLDNN_CVT(U64, FP32), MKLDNN_CVT(U64, BF16),  MKLDNN_CVT(U64, BOOL),
-    MKLDNN_CVT(I64, U8),   MKLDNN_CVT(I64, I8),    MKLDNN_CVT(I64, U16),
-    MKLDNN_CVT(I64, I16),  MKLDNN_CVT(I64, I32),   MKLDNN_CVT(I64, U64),
-    MKLDNN_CVT(I64, FP32), MKLDNN_CVT(I64, BF16),  MKLDNN_CVT(I64, BOOL),
-    MKLDNN_CVT(FP32, U8),  MKLDNN_CVT(FP32, I8),   MKLDNN_CVT(FP32, U16),
-    MKLDNN_CVT(FP32, I16), MKLDNN_CVT(FP32, I32),  MKLDNN_CVT(FP32, U64),
-    MKLDNN_CVT(FP32, I64), MKLDNN_CVT(FP32, BF16), MKLDNN_CVT(FP32, BOOL),
-    MKLDNN_CVT(BF16, U8),  MKLDNN_CVT(BF16, I8),   MKLDNN_CVT(BF16, U16),
-    MKLDNN_CVT(BF16, I16), MKLDNN_CVT(BF16, I32),  MKLDNN_CVT(BF16, U64),
-    MKLDNN_CVT(BF16, I64), MKLDNN_CVT(BF16, FP32), MKLDNN_CVT(BF16, BOOL),
-    MKLDNN_CVT(BOOL, U8),  MKLDNN_CVT(BOOL, I8),   MKLDNN_CVT(BOOL, U16),
-    MKLDNN_CVT(BOOL, I16), MKLDNN_CVT(BOOL, I32),  MKLDNN_CVT(BOOL, U64),
-    MKLDNN_CVT(BOOL, I64), MKLDNN_CVT(BOOL, FP32), MKLDNN_CVT(BOOL, BF16),
-    MKLDNN_CVT(FP64, U8),  MKLDNN_CVT(FP64, I8),   MKLDNN_CVT(FP64, U16),
-    MKLDNN_CVT(FP64, I16), MKLDNN_CVT(FP64, I32),  MKLDNN_CVT(FP64, U64),
-    MKLDNN_CVT(FP64, I64), MKLDNN_CVT(FP64, FP32), MKLDNN_CVT(FP64, BF16), MKLDNN_CVT(FP64, BOOL),
-    MKLDNN_CVT(U32, U8),  MKLDNN_CVT(U32, I8),   MKLDNN_CVT(U32, U16),
-    MKLDNN_CVT(U32, I16), MKLDNN_CVT(U32, I32),  MKLDNN_CVT(U32, U64),
-    MKLDNN_CVT(U32, I64), MKLDNN_CVT(U32, FP32), MKLDNN_CVT(U32, BF16), MKLDNN_CVT(U32, BOOL));
-
-    if (!ctx.converted)
-        IE_THROW() << "cpu_convert can't convert from: " << srcPrc << " precision to: " << dstPrc;
 }
 
 #undef MKLDNN_CVT
+#undef MKLDNN_CVT_LIST
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.h b/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.h
index dd4ef59a38b..8ed46cab7a0 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.h
@@ -19,5 +19,32 @@
  * number of elements in buffers to be converted
  * @return none.
  */
+void cpu_convert(const void *srcPtr,
+                 void *dstPtr,
+                 InferenceEngine::Precision srcPrc,
+                 InferenceEngine::Precision dstPrc,
+                 const size_t size);
 
-void cpu_convert(const void *srcPtr, void *dstPtr, InferenceEngine::Precision srcPrc, InferenceEngine::Precision dstPrc, const size_t size);
+/**
+ * @brief Copy size elements from buffer specified srcPtr pointer to buffer specified dstPtr.
+ * If the precisions srcPrc and dstPrc are different, a conversion from srcPrc to dstPrc is performed.
+ * @param srcPtr
+ * pointer to the buffer to convert from
+ * @param dstPtr
+ * pointer to the buffer to convert to
+ * @param srcPrc
+ * precision the buffer from which convert
+ * @param interimPrc
+ * intermediate precision used for type truncation
+ * @param dstPrc
+ * precision the buffer to which convert
+ * @param size
+ * number of elements in buffers to be converted
+ * @return none.
+ */
+void cpu_convert(const void *srcPtr,
+                 void *dstPtr,
+                 InferenceEngine::Precision srcPrc,
+                 InferenceEngine::Precision interimPrc,
+                 InferenceEngine::Precision dstPrc,
+                 const size_t size);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
index 2faf969c1ea..2a20f45ff29 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
@@ -7,7 +7,8 @@
 #include "common/cpu_convert.h"
 #include "common/blocked_desc_creator.h"
 #include <ngraph/opsets/opset1.hpp>
-#include "utils/ngraph_utils.hpp"
+#include <ie_ngraph_utils.hpp>
+#include <utils/ngraph_utils.hpp>
 
 using namespace mkldnn;
 using namespace MKLDNNPlugin;
@@ -26,14 +27,17 @@ bool MKLDNNConvertNode::isSupportedOperation(const std::shared_ptr<const ngraph:
     return true;
 }
 
-MKLDNNConvertNode::MKLDNNConvertNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache) :
-        MKLDNNNode(op, eng, cache) {
+MKLDNNConvertNode::MKLDNNConvertNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
+        : MKLDNNNode(op, eng, cache) {
     std::string errorMessage;
     if (isSupportedOperation(op, errorMessage)) {
         errorPrefix = "Convert node with name '" + getName() + "'";
     } else {
         IE_THROW(NotImplemented) << errorMessage;
     }
+
+    auto convert = ov::as_type_ptr<const ngraph::opset1::Convert>(op);
+    origPrc = details::convertPrecision(convert->get_destination_type());
 }
 
 std::vector<VectorDims> MKLDNNConvertNode::shapeInfer() const {
@@ -42,7 +46,8 @@ std::vector<VectorDims> MKLDNNConvertNode::shapeInfer() const {
 
 MKLDNNConvertNode::MKLDNNConvertNode(const Shape &shape, const InferenceEngine::Precision &inPrc, const InferenceEngine::Precision &outPrc,
                                      const std::string &nodeName, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
-        : MKLDNNNode("Convert", nodeName, eng, cache) {
+        : MKLDNNNode("Convert", nodeName, eng, cache)
+        , origPrc(outPrc) {
     inputShapes.push_back(shape);
     addOriginalInputPrecision(inPrc);
     outputShapes.push_back(shape);
@@ -147,7 +152,13 @@ void MKLDNNConvertNode::execute(mkldnn::stream strm) {
 
     void* srcPtr = parentMem.GetPtr();
     void* dstPtr = childMem.GetPtr();
-    cpu_convert(srcPtr, dstPtr, parentMem.getDesc().getPrecision(), childMem.getDesc().getPrecision(), parentPaddElemCount);
+
+    cpu_convert(srcPtr,
+                dstPtr,
+                parentMem.getDesc().getPrecision(),
+                origPrc,
+                childMem.getDesc().getPrecision(),
+                parentPaddElemCount);
 }
 
 bool MKLDNNConvertNode::created() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h
index bffb2447280..08042187788 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h
@@ -49,6 +49,7 @@ public:
 private:
     MemoryDescPtr input;
     MemoryDescPtr output;
+    InferenceEngine::Precision origPrc;
 
     std::string errorPrefix;
 };
diff --git a/src/bindings/python/tests/test_onnx/test_backend.py b/src/bindings/python/tests/test_onnx/test_backend.py
index a3f88676852..7385c5057ef 100644
--- a/src/bindings/python/tests/test_onnx/test_backend.py
+++ b/src/bindings/python/tests/test_onnx/test_backend.py
@@ -101,16 +101,9 @@ tests_expected_to_fail = [
     (
         xfail_issue_FLOAT_LIKE,
         "OnnxBackendNodeModelTest.test_cast_BFLOAT16_to_FLOAT_cpu",
-        "OnnxBackendNodeModelTest.test_cast_FLOAT16_to_DOUBLE_cpu",
-        "OnnxBackendNodeModelTest.test_cast_FLOAT16_to_FLOAT_cpu",
         "OnnxBackendNodeModelTest.test_cast_FLOAT_to_BFLOAT16_cpu",
         "OnnxBackendNodeModelTest.test_castlike_BFLOAT16_to_FLOAT_expanded_cpu",
-        "OnnxBackendNodeModelTest.test_castlike_FLOAT16_to_DOUBLE_expanded_cpu",
-        "OnnxBackendNodeModelTest.test_castlike_FLOAT16_to_FLOAT_expanded_cpu",
         "OnnxBackendNodeModelTest.test_castlike_FLOAT_to_BFLOAT16_expanded_cpu",
-        "OnnxBackendNodeModelTest.test_max_float16_cpu",
-        "OnnxBackendNodeModelTest.test_min_float16_cpu",
-        "OnnxBackendNodeModelTest.test_mod_mixed_sign_float16_cpu",
     ),
     (
         xfail_issue_49207,
diff --git a/src/bindings/python/tests/test_onnx/test_zoo_models.py b/src/bindings/python/tests/test_onnx/test_zoo_models.py
index ee16fa4724a..ac455d76168 100644
--- a/src/bindings/python/tests/test_onnx/test_zoo_models.py
+++ b/src/bindings/python/tests/test_onnx/test_zoo_models.py
@@ -171,12 +171,6 @@ if len(zoo_models) > 0:
     test_cases = backend_test.test_cases["OnnxBackendModelExecutionTest"]
     if tests.MODEL_ZOO_XFAIL:
         execution_xfail_list = [
-            # New Python API - fp16 blob
-            (xfail_issue_67415, "test_MSFT_opset7_fp16_inception_v1_onnxzoo_lotus_inception_v1_cpu"),
-            (xfail_issue_67415, "test_MSFT_opset7_fp16_shufflenet_onnxzoo_lotus_shufflenet_cpu"),
-            (xfail_issue_67415, "test_MSFT_opset8_fp16_inception_v1_onnxzoo_lotus_inception_v1_cpu"),
-            (xfail_issue_67415, "test_MSFT_opset8_fp16_shufflenet_onnxzoo_lotus_shufflenet_cpu"),
-
             # ONNX Model Zoo
             (xfail_issue_39669, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_encoder_12_t5_encoder_cpu"),
             (xfail_issue_39669, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_decoder_with_lm_head_12_t5_decoder_with_lm_head_cpu"),
diff --git a/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
index a3f27eb8ea2..945f06ef6e8 100644
--- a/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
+++ b/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
@@ -309,16 +309,15 @@ void IInferencePlugin::SetExeNetworkInfo(const std::shared_ptr<IExecutableNetwor
     OPENVINO_ASSERT(outputsInfo.size() == function->get_output_size());
 
     for (const auto& param : function->get_parameters()) {
-        auto new_param = param->copy_with_new_inputs({});
+        auto new_param = ov::as_type_ptr<ov::op::v0::Parameter>(param->copy_with_new_inputs({}));
         new_param->set_friendly_name(param->get_friendly_name());
         if (add_operation_names)
             new_param->output(0).get_tensor().add_names({new_param->get_friendly_name()});
         // WA: use CNNNetwork's precisions since plugins sometimes override their precisions
         // after transformation pipeline is run
-        new_param->set_output_type(
-            0,
-            InferenceEngine::details::convertPrecision(inputsInfo.at(new_param->get_friendly_name())->getPrecision()),
-            new_param->get_output_partial_shape(0));
+        new_param->set_element_type(
+            InferenceEngine::details::convertPrecision(inputsInfo.at(new_param->get_friendly_name())->getPrecision()));
+        new_param->validate_and_infer_types();
         const_params.emplace_back(new_param);
     }
     for (const auto& result : function->get_results()) {
@@ -326,10 +325,9 @@ void IInferencePlugin::SetExeNetworkInfo(const std::shared_ptr<IExecutableNetwor
                                                                   result->get_output_partial_shape(0));
         const std::string param_name = ngraph::op::util::create_ie_output_name(result->input_value(0));
         fake_param->set_friendly_name(param_name);
-        fake_param->set_output_type(
-            0,
-            InferenceEngine::details::convertPrecision(outputsInfo.at(param_name)->getPrecision()),
-            fake_param->get_output_partial_shape(0));
+        fake_param->set_element_type(
+            InferenceEngine::details::convertPrecision(outputsInfo.at(param_name)->getPrecision()));
+        fake_param->validate_and_infer_types();
         auto new_result = result->copy_with_new_inputs({fake_param});
         new_result->set_friendly_name(result->get_friendly_name());
         if (add_operation_names) {
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/blob_tests/set_blob.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/blob_tests/set_blob.cpp
index 5e27ee86b77..641d25dacb8 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/blob_tests/set_blob.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/blob_tests/set_blob.cpp
@@ -8,8 +8,15 @@
 using namespace BehaviorTestsDefinitions;
 using namespace InferenceEngine;
 
-const std::vector<Precision> precisionSet = {Precision::FP32, Precision::I16, Precision::U8, Precision::I8, Precision::U16, Precision::I32, Precision::BOOL,
-                                             Precision::I64, Precision::U64};
+const std::vector<Precision> precisionSet = {
+    Precision::U8,   Precision::I8,
+    Precision::U16,  Precision::I16,
+    Precision::U32,  Precision::I32,
+    Precision::U64,  Precision::I64,
+    Precision::BF16, Precision::FP16,
+    Precision::FP32, Precision::FP64,
+    Precision::BOOL
+};
 
 const std::vector<setType> typeSet = {setType::INPUT, setType::OUTPUT, setType::BOTH};
 
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/conversion.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/conversion.cpp
index 03be6f86285..868f22db789 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/conversion.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/conversion.cpp
@@ -18,24 +18,26 @@ const std::vector<ngraph::helpers::ConversionTypes> conversionOpTypes = {
 const std::vector<std::vector<size_t>> inShape = {{1, 2, 3, 4}};
 
 const std::vector<InferenceEngine::Precision> netPrecisions = {
-    // Ticket: 59594
-    // InferenceEngine::Precision::I4,
-    InferenceEngine::Precision::I8,
-    InferenceEngine::Precision::I16,
-    InferenceEngine::Precision::I32,
-    InferenceEngine::Precision::I64,
-    // Ticket: 59594
-    // InferenceEngine::Precision::BIN,
-    // InferenceEngine::Precision::BOOL,
-    // InferenceEngine::Precision::U4,
     InferenceEngine::Precision::U8,
+    InferenceEngine::Precision::I8,
     InferenceEngine::Precision::U16,
-    // Ticket: 59594
-    // InferenceEngine::Precision::U32,
+    InferenceEngine::Precision::I16,
+    InferenceEngine::Precision::U32,
+    InferenceEngine::Precision::I32,
     InferenceEngine::Precision::U64,
+    InferenceEngine::Precision::I64,
     InferenceEngine::Precision::BF16,
     InferenceEngine::Precision::FP16,
-    InferenceEngine::Precision::FP32};
+    InferenceEngine::Precision::FP32,
+    InferenceEngine::Precision::FP64,
+    InferenceEngine::Precision::BOOL,
+    InferenceEngine::Precision::MIXED,
+    InferenceEngine::Precision::Q78,
+    InferenceEngine::Precision::U4,
+    InferenceEngine::Precision::I4,
+    InferenceEngine::Precision::BIN,
+    InferenceEngine::Precision::CUSTOM,
+};
 
 INSTANTIATE_TEST_SUITE_P(smoke_ConversionLayerTest,
                          ConversionLayerTest,
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index e9d0e22bc4e..b6857ff3671 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -104,17 +104,6 @@ std::vector<std::string> disabledTestPatterns() {
 
         // CPU plugin does not support some precisions
         R"(smoke_CachingSupportCase_CPU/LoadNetworkCacheTestBase.CompareWithRefImpl/ReadConcatSplitAssign_f32_batch1_CPU)",
-        // CPU plugin does not support some precisions
-        R"(.*Behavior.*OVExecGraphImportExportTest.*elementType=(i8|u32).*)",
-        R"(.*Behavior.*OVExecGraphImportExportTest.*elementType=(f16).*)",
-        R"(.*EltwiseLayerTest.*NetType=f16.*)",
-
-        // TODO: CVS-66526 overrides i/o precisions in execution graph
-        // as WA we used GetInputsInfo() precisions instead of ngraph ones
-        // R"(.*smoke_BehaviorTests.*OVExecGraphImportExportTest.*importExportedFunction.*type=(i16|u16).*)",
-        // R"(.*smoke_BehaviorTests.*OVExecGraphImportExportTest.*importExportedFunction.*type=(i64|u64).*)",
-        // R"(.*smoke_BehaviorTests.*OVExecGraphImportExportTest.*importExportedIENetwork.*type=(i16|u16).*)",
-        // R"(.*smoke_BehaviorTests.*OVExecGraphImportExportTest.*importExportedIENetwork.*type=(i64|u64).*)",
 
         // CPU does not support dynamic rank
         // Issue: CVS-66778
@@ -168,7 +157,18 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*CTCLossLayerCPUTest.*ctcMergeRepeated=1.*)",
         // Issue: 71756
         R"(.*Deconv_.*D_(Blocked|DW|1x1)_.*DeconvolutionLayerCPUTest\.CompareWithRefs.*inFmts=(nChw16c|nCdhw16c)_outFmts=(nChw16c|nCdhw16c)_primitive=jit_avx512_.*Fused=Multiply\(PerChannel\)\.Add\(PerChannel\).*)",
-        R"(.*smoke_GroupDeconv_(2|3)D_Blocked_BF16.*S=(\(2\.2\)|\(2\.2\.2\))_PB=(\(0\.0\)|\(0\.0\.0\))_PE=(\(0\.0\)|\(0\.0\.0\))_D=(\(1\.1\)|\(1\.1\.1\))_.*_O=64_G=4.*)"
+        R"(.*smoke_GroupDeconv_(2|3)D_Blocked_BF16.*S=(\(2\.2\)|\(2\.2\.2\))_PB=(\(0\.0\)|\(0\.0\.0\))_PE=(\(0\.0\)|\(0\.0\.0\))_D=(\(1\.1\)|\(1\.1\.1\))_.*_O=64_G=4.*)",
+        // Issue: 72150
+        R"(.*smoke_SetBlobCPU/SetBlobTest.CompareWithRefs/Type=.*_Device=CPU_PrecisionInNet=BOOL.*)",
+        // Issue: 59594
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*BOOL.*)",
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*MIXED.*)",
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*Q78.*)",
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*U4.*)",
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*I4.*)",
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*BIN.*)",
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*CUSTOM.*)",
+        R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*UNSPECIFIED.*)",
     };
 
 #define FIX_62820 0
diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_graph_info.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_graph_info.hpp
index 65aac7c4766..2478213bd7d 100644
--- a/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_graph_info.hpp
+++ b/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_graph_info.hpp
@@ -108,11 +108,15 @@ TEST_P(OVExecGraphImportExportTest, importExportedFunction) {
               importedExecNet.input(0).get_tensor().get_partial_shape());
     EXPECT_EQ(function->input(0).get_tensor().get_element_type(),
               importedExecNet.input(0).get_tensor().get_element_type());
+    EXPECT_EQ(function->input(0).get_element_type(),
+              importedExecNet.input(0).get_tensor().get_element_type());
     EXPECT_EQ(function->input(1).get_tensor().get_names(), importedExecNet.input(1).get_tensor().get_names());
     EXPECT_EQ(function->input(1).get_tensor().get_partial_shape(),
               importedExecNet.input(1).get_tensor().get_partial_shape());
     EXPECT_EQ(function->input(1).get_tensor().get_element_type(),
               importedExecNet.input(1).get_tensor().get_element_type());
+    EXPECT_EQ(function->input(1).get_element_type(),
+              importedExecNet.input(1).get_tensor().get_element_type());
     EXPECT_EQ(importedExecNet.input(0).get_node(), importedExecNet.input("data1").get_node());
     EXPECT_NE(importedExecNet.input(1).get_node(), importedExecNet.input("data1").get_node());
     EXPECT_EQ(importedExecNet.input(1).get_node(), importedExecNet.input("data2").get_node());
@@ -125,11 +129,15 @@ TEST_P(OVExecGraphImportExportTest, importExportedFunction) {
               importedExecNet.output(0).get_tensor().get_partial_shape());
     EXPECT_EQ(function->output(0).get_tensor().get_element_type(),
               importedExecNet.output(0).get_tensor().get_element_type());
+    EXPECT_EQ(function->output(0).get_element_type(),
+              importedExecNet.output(0).get_tensor().get_element_type());
     EXPECT_EQ(function->output(1).get_tensor().get_names(), importedExecNet.output(1).get_tensor().get_names());
     EXPECT_EQ(function->output(1).get_tensor().get_partial_shape(),
               importedExecNet.output(1).get_tensor().get_partial_shape());
     EXPECT_EQ(function->output(1).get_tensor().get_element_type(),
               importedExecNet.output(1).get_tensor().get_element_type());
+    EXPECT_EQ(function->output(1).get_element_type(),
+              importedExecNet.output(1).get_tensor().get_element_type());
     EXPECT_EQ(importedExecNet.output(0).get_node(), importedExecNet.output("relu").get_node());
     EXPECT_NE(importedExecNet.output(1).get_node(), importedExecNet.output("relu").get_node());
     EXPECT_EQ(importedExecNet.output(1).get_node(), importedExecNet.output("concat").get_node());
diff --git a/src/tests/functional/plugin/shared/src/blob_tests/set_blob.cpp b/src/tests/functional/plugin/shared/src/blob_tests/set_blob.cpp
index 073b78c66c5..ad8e0555683 100644
--- a/src/tests/functional/plugin/shared/src/blob_tests/set_blob.cpp
+++ b/src/tests/functional/plugin/shared/src/blob_tests/set_blob.cpp
@@ -43,15 +43,19 @@ std::string SetBlobTest::getTestCaseName(testing::TestParamInfo<SetBlobParams> o
 inline void fillBlob(Blob::Ptr &blob) {
     switch (blob->getTensorDesc().getPrecision()) {
 #define CASE(X) case X: CommonTestUtils::fill_data_random<X>(blob); break;
-        CASE(InferenceEngine::Precision::FP32)
-        CASE(InferenceEngine::Precision::U8)
-        CASE(InferenceEngine::Precision::U16)
-        CASE(InferenceEngine::Precision::I8)
-        CASE(InferenceEngine::Precision::I16)
-        CASE(InferenceEngine::Precision::I64)
-        CASE(InferenceEngine::Precision::U64)
-        CASE(InferenceEngine::Precision::I32)
-        CASE(InferenceEngine::Precision::BOOL)
+    CASE(Precision::U8)
+    CASE(Precision::I8)
+    CASE(Precision::U16)
+    CASE(Precision::I16)
+    CASE(Precision::U32)
+    CASE(Precision::I32)
+    CASE(Precision::U64)
+    CASE(Precision::I64)
+    CASE(Precision::BF16)
+    CASE(Precision::FP16)
+    CASE(Precision::FP32)
+    CASE(Precision::FP64)
+    CASE(Precision::BOOL)
 #undef CASE
         default:
             IE_THROW() << "Can't fill blob with precision: " << blob->getTensorDesc().getPrecision();
diff --git a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
index 67dc3d5cb2c..7292ae0eb78 100644
--- a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
+++ b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
@@ -131,54 +131,54 @@ inline void callCompare(const std::pair<ngraph::element::Type, std::vector<std::
                         const T_IE* actualBuffer, size_t size, float threshold, float abs_threshold) {
     auto expectedBuffer = expected.second.data();
     switch (expected.first) {
-        case ngraph::element::Type_t::i64:
-            LayerTestsCommon::Compare<T_IE, int64_t>(reinterpret_cast<const int64_t *>(expectedBuffer),
-                                                     actualBuffer, size, threshold, abs_threshold);
-            break;
-        case ngraph::element::Type_t::i32:
-            LayerTestsCommon::Compare<T_IE, int32_t>(reinterpret_cast<const int32_t *>(expectedBuffer),
-                                                     actualBuffer, size, threshold, abs_threshold);
-            break;
-        case ngraph::element::Type_t::i16:
-            LayerTestsCommon::Compare<T_IE, int16_t>(reinterpret_cast<const int16_t *>(expectedBuffer),
+        case ngraph::element::Type_t::boolean:
+        case ngraph::element::Type_t::u8:
+            LayerTestsCommon::Compare<T_IE, uint8_t>(reinterpret_cast<const uint8_t *>(expectedBuffer),
                                                      actualBuffer, size, threshold, abs_threshold);
             break;
         case ngraph::element::Type_t::i8:
             LayerTestsCommon::Compare<T_IE, int8_t>(reinterpret_cast<const int8_t *>(expectedBuffer),
                                                     actualBuffer, size, threshold, abs_threshold);
             break;
-        case ngraph::element::Type_t::u64:
-            LayerTestsCommon::Compare<T_IE, uint64_t>(reinterpret_cast<const uint64_t *>(expectedBuffer),
+        case ngraph::element::Type_t::u16:
+            LayerTestsCommon::Compare<T_IE, uint16_t>(reinterpret_cast<const uint16_t *>(expectedBuffer),
                                                       actualBuffer, size, threshold, abs_threshold);
             break;
+        case ngraph::element::Type_t::i16:
+            LayerTestsCommon::Compare<T_IE, int16_t>(reinterpret_cast<const int16_t *>(expectedBuffer),
+                                                     actualBuffer, size, threshold, abs_threshold);
+            break;
         case ngraph::element::Type_t::u32:
             LayerTestsCommon::Compare<T_IE, uint32_t>(reinterpret_cast<const uint32_t *>(expectedBuffer),
                                                       actualBuffer, size, threshold, abs_threshold);
             break;
-        case ngraph::element::Type_t::u16:
-            LayerTestsCommon::Compare<T_IE, uint16_t>(reinterpret_cast<const uint16_t *>(expectedBuffer),
-                                                      actualBuffer, size, threshold, abs_threshold);
-            break;
-        case ngraph::element::Type_t::boolean:
-        case ngraph::element::Type_t::u8:
-            LayerTestsCommon::Compare<T_IE, uint8_t>(reinterpret_cast<const uint8_t *>(expectedBuffer),
+        case ngraph::element::Type_t::i32:
+            LayerTestsCommon::Compare<T_IE, int32_t>(reinterpret_cast<const int32_t *>(expectedBuffer),
                                                      actualBuffer, size, threshold, abs_threshold);
             break;
-        case ngraph::element::Type_t::f64:
-            LayerTestsCommon::Compare<T_IE, double>(reinterpret_cast<const double *>(expectedBuffer),
-                                                   actualBuffer, size, threshold, abs_threshold);
+        case ngraph::element::Type_t::u64:
+            LayerTestsCommon::Compare<T_IE, uint64_t>(reinterpret_cast<const uint64_t *>(expectedBuffer),
+                                                      actualBuffer, size, threshold, abs_threshold);
             break;
-        case ngraph::element::Type_t::f32:
-            LayerTestsCommon::Compare<T_IE, float>(reinterpret_cast<const float *>(expectedBuffer),
-                                                   actualBuffer, size, threshold, abs_threshold);
+        case ngraph::element::Type_t::i64:
+            LayerTestsCommon::Compare<T_IE, int64_t>(reinterpret_cast<const int64_t *>(expectedBuffer),
+                                                     actualBuffer, size, threshold, abs_threshold);
+            break;
+        case ngraph::element::Type_t::bf16:
+            LayerTestsCommon::Compare<T_IE, ngraph::bfloat16>(reinterpret_cast<const ngraph::bfloat16 *>(expectedBuffer),
+                                                              actualBuffer, size, threshold, abs_threshold);
             break;
         case ngraph::element::Type_t::f16:
             LayerTestsCommon::Compare<T_IE, ngraph::float16>(reinterpret_cast<const ngraph::float16 *>(expectedBuffer),
                                                              actualBuffer, size, threshold, abs_threshold);
             break;
-        case ngraph::element::Type_t::bf16:
-            LayerTestsCommon::Compare<T_IE, ngraph::bfloat16>(reinterpret_cast<const ngraph::bfloat16 *>(expectedBuffer),
-                                                              actualBuffer, size, threshold, abs_threshold);
+        case ngraph::element::Type_t::f32:
+            LayerTestsCommon::Compare<T_IE, float>(reinterpret_cast<const float *>(expectedBuffer),
+                                                   actualBuffer, size, threshold, abs_threshold);
+            break;
+        case ngraph::element::Type_t::f64:
+            LayerTestsCommon::Compare<T_IE, double>(reinterpret_cast<const double *>(expectedBuffer),
+                                                   actualBuffer, size, threshold, abs_threshold);
             break;
         case ngraph::element::Type_t::i4: {
             auto expectedOut = ngraph::helpers::convertOutputPrecision(
@@ -230,14 +230,9 @@ void LayerTestsCommon::Compare(const std::pair<ngraph::element::Type, std::vecto
 
     const auto &size = actual->size();
     switch (precision) {
-        case InferenceEngine::Precision::FP32:
-            callCompare<float>(expected, reinterpret_cast<const float *>(actualBuffer), size, threshold, abs_threshold);
-            break;
-        case InferenceEngine::Precision::I32:
-            callCompare<int32_t>(expected, reinterpret_cast<const int32_t *>(actualBuffer), size, threshold, abs_threshold);
-            break;
-        case InferenceEngine::Precision::I64:
-            callCompare<int64_t>(expected, reinterpret_cast<const int64_t *>(actualBuffer), size, threshold, abs_threshold);
+        case InferenceEngine::Precision::BOOL:
+        case InferenceEngine::Precision::U8:
+            callCompare<uint8_t>(expected, reinterpret_cast<const uint8_t *>(actualBuffer), size, threshold, abs_threshold);
             break;
         case InferenceEngine::Precision::I8:
             callCompare<int8_t>(expected, reinterpret_cast<const int8_t *>(actualBuffer), size, threshold, abs_threshold);
@@ -248,19 +243,30 @@ void LayerTestsCommon::Compare(const std::pair<ngraph::element::Type, std::vecto
         case InferenceEngine::Precision::I16:
             callCompare<int16_t>(expected, reinterpret_cast<const int16_t *>(actualBuffer), size, threshold, abs_threshold);
             break;
-        case InferenceEngine::Precision::BOOL:
-        case InferenceEngine::Precision::U8:
-            callCompare<uint8_t>(expected, reinterpret_cast<const uint8_t *>(actualBuffer), size, threshold, abs_threshold);
+        case InferenceEngine::Precision::U32:
+            callCompare<uint32_t>(expected, reinterpret_cast<const uint32_t *>(actualBuffer), size, threshold, abs_threshold);
+            break;
+        case InferenceEngine::Precision::I32:
+            callCompare<int32_t>(expected, reinterpret_cast<const int32_t *>(actualBuffer), size, threshold, abs_threshold);
             break;
         case InferenceEngine::Precision::U64:
             callCompare<uint64_t>(expected, reinterpret_cast<const uint64_t *>(actualBuffer), size, threshold, abs_threshold);
             break;
+        case InferenceEngine::Precision::I64:
+            callCompare<int64_t>(expected, reinterpret_cast<const int64_t *>(actualBuffer), size, threshold, abs_threshold);
+            break;
         case InferenceEngine::Precision::BF16:
             callCompare<ngraph::bfloat16>(expected, reinterpret_cast<const ngraph::bfloat16 *>(actualBuffer), size, threshold, abs_threshold);
             break;
         case InferenceEngine::Precision::FP16:
             callCompare<ngraph::float16>(expected, reinterpret_cast<const ngraph::float16 *>(actualBuffer), size, threshold, abs_threshold);
             break;
+        case InferenceEngine::Precision::FP32:
+            callCompare<float>(expected, reinterpret_cast<const float *>(actualBuffer), size, threshold, abs_threshold);
+            break;
+        case InferenceEngine::Precision::FP64:
+            callCompare<double>(expected, reinterpret_cast<const double *>(actualBuffer), size, threshold, abs_threshold);
+            break;
         default:
             FAIL() << "Comparator for " << precision << " precision isn't supported";
     }
diff --git a/src/tests/functional/shared_test_classes/src/single_layer/conversion.cpp b/src/tests/functional/shared_test_classes/src/single_layer/conversion.cpp
index 0c5e7a387a7..1320abbd362 100644
--- a/src/tests/functional/shared_test_classes/src/single_layer/conversion.cpp
+++ b/src/tests/functional/shared_test_classes/src/single_layer/conversion.cpp
@@ -28,6 +28,9 @@ std::string ConversionLayerTest::getTestCaseName(const testing::TestParamInfo<Co
 }
 
 void ConversionLayerTest::SetUp() {
+    if (FuncTestUtils::SkipTestsConfig::currentTestIsDisabled()) {
+        GTEST_SKIP() << "Disabled test due to configuration" << std::endl;
+    }
     ngraph::helpers::ConversionTypes conversionOpType;
     InferenceEngine::Precision inputPrecision, targetPrecision;
     std::vector<std::vector<size_t>> inputShape;

From bd2e3de2953e4e379ec5cc41949a8c1af9088a49 Mon Sep 17 00:00:00 2001
From: Steve Yoo <steve.yoo@intel.com>
Date: Thu, 16 Dec 2021 23:48:10 +0900
Subject: [PATCH 002/160] Create LSTMCell-1 (#9073)

---
 .../template_plugin/backend/evaluates_map.cpp |  24 +
 .../template_plugin/backend/opset_int_tbl.hpp |   1 +
 .../functional/op_reference/lstm_cell.cpp     | 589 ++++++++++++++----
 3 files changed, 477 insertions(+), 137 deletions(-)

diff --git a/docs/template_plugin/backend/evaluates_map.cpp b/docs/template_plugin/backend/evaluates_map.cpp
index 789cff5b4b1..c6a864f9727 100644
--- a/docs/template_plugin/backend/evaluates_map.cpp
+++ b/docs/template_plugin/backend/evaluates_map.cpp
@@ -1961,6 +1961,30 @@ bool evaluate(const shared_ptr<op::v0::RNNCell>& op, const HostTensorVector& out
     return true;
 }
 
+template <element::Type_t ET>
+bool evaluate(const shared_ptr<op::v0::LSTMCell>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
+    using T = typename element_type_traits<ET>::value_type;
+    runtime::reference::lstm_cell<T>(inputs[0]->get_data_ptr<ET>(),
+                                     inputs[0]->get_shape(),
+                                     inputs[1]->get_data_ptr<ET>(),
+                                     inputs[1]->get_shape(),
+                                     inputs[2]->get_data_ptr<ET>(),
+                                     inputs[2]->get_shape(),
+                                     inputs[3]->get_data_ptr<ET>(),
+                                     inputs[3]->get_shape(),
+                                     inputs[4]->get_data_ptr<ET>(),
+                                     inputs[4]->get_shape(),
+                                     inputs[5]->get_data_ptr<ET>(),
+                                     inputs[5]->get_shape(),
+                                     outputs[0]->get_data_ptr<ET>(),
+                                     outputs[1]->get_data_ptr<ET>(),
+                                     op->get_activations()[0],
+                                     op->get_activations()[1],
+                                     op->get_activations()[2],
+                                     op->get_clip());
+    return true;
+}
+
 template <element::Type_t ET>
 bool evaluate(const shared_ptr<op::v4::LSTMCell>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
     using T = typename element_type_traits<ET>::value_type;
diff --git a/docs/template_plugin/backend/opset_int_tbl.hpp b/docs/template_plugin/backend/opset_int_tbl.hpp
index fe1230d79d4..287bf9a0d11 100644
--- a/docs/template_plugin/backend/opset_int_tbl.hpp
+++ b/docs/template_plugin/backend/opset_int_tbl.hpp
@@ -20,6 +20,7 @@ NGRAPH_OP(Gelu, op::v0)
 NGRAPH_OP(GRN, op::v0)
 NGRAPH_OP(HardSigmoid, op::v0)
 NGRAPH_OP(LRN, ngraph::op::v0)
+NGRAPH_OP(LSTMCell, op::v0)
 NGRAPH_OP(MVN, ngraph::op::v0)
 NGRAPH_OP(NormalizeL2, op::v0)
 NGRAPH_OP(PriorBox, ngraph::op::v0)
diff --git a/docs/template_plugin/tests/functional/op_reference/lstm_cell.cpp b/docs/template_plugin/tests/functional/op_reference/lstm_cell.cpp
index c28bbf0ba94..493224da17b 100644
--- a/docs/template_plugin/tests/functional/op_reference/lstm_cell.cpp
+++ b/docs/template_plugin/tests/functional/op_reference/lstm_cell.cpp
@@ -4,7 +4,8 @@
 
 #include <gtest/gtest.h>
 
-#include "openvino/op/lstm_cell.hpp"
+#include "openvino/opsets/opset4.hpp"
+#include "openvino/opsets/opset1.hpp"
 #include "base_reference_test.hpp"
 
 using namespace reference_tests;
@@ -12,13 +13,6 @@ using namespace ov;
 
 namespace {
 struct LSTMCellParams {
-    LSTMCellParams(
-        int32_t batchSize, int32_t inputSize, int32_t hiddenSize, int32_t gatesCount,
-        const Tensor& X, const Tensor& W, const Tensor& R, const Tensor& H_t, const Tensor& C_t, const Tensor& B,
-        const Tensor& Ho, const Tensor& Co, const std::string& testcaseName = "") :
-        batchSize(batchSize), inputSize(inputSize), hiddenSize(hiddenSize), gatesCount(gatesCount),
-        X(X), W(W), R(R), H_t(H_t), C_t(C_t), B(B), Ho(Ho), Co(Co), testcaseName(testcaseName) {}
-
     int32_t batchSize;
     int32_t inputSize;
     int32_t hiddenSize;
@@ -34,6 +28,22 @@ struct LSTMCellParams {
     std::string testcaseName;
 };
 
+struct Builder : ParamsBuilder<LSTMCellParams> {
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, batchSize);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, inputSize);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, hiddenSize);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, gatesCount);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, X);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, W);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, R);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, H_t);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, C_t);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, B);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, Ho);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, Co);
+    REFERENCE_TESTS_ADD_SET_PARAM(Builder, testcaseName);
+};
+
 class ReferenceLSTMCellTest : public testing::TestWithParam<LSTMCellParams>, public CommonReferenceTest {
 public:
     void SetUp() override {
@@ -63,26 +73,24 @@ public:
         result << "_hoType=" << param.Ho.type;
         result << "_hoShape=" << param.Ho.shape;
         result << "_coType=" << param.Co.type;
+        result << "_coShape=" << param.Co.shape;
         if (param.testcaseName != "") {
-            result << "_coShape=" << param.Co.shape;
             result << "_=" << param.testcaseName;
-        } else {
-            result << "_coShape=" << param.Co.shape;
         }
         return result.str();
     }
 
 private:
     static std::shared_ptr<Model> CreateFunction(const LSTMCellParams& params) {
-        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
-        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
-        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
-        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
-        const auto C_t = std::make_shared<op::v0::Parameter>(params.C_t.type, params.C_t.shape);
-        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+        const auto X = std::make_shared<opset1::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<opset1::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<opset1::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<opset1::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<opset1::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<opset1::Parameter>(params.B.type, params.B.shape);
 
         const auto lstm_cell =
-            std::make_shared<op::v4::LSTMCell>(X,
+            std::make_shared<opset4::LSTMCell>(X,
                                                H_t,
                                                C_t,
                                                op::util::convert_lstm_node_format(W, op::util::LSTMWeightsFormat::IOFC),
@@ -107,15 +115,15 @@ public:
 
 private:
     static std::shared_ptr<Model> CreateFunction(const LSTMCellParams& params) {
-        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
-        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
-        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
-        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
-        const auto C_t = std::make_shared<op::v0::Parameter>(params.C_t.type, params.C_t.shape);
-        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+        const auto X = std::make_shared<opset1::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<opset1::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<opset1::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<opset1::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<opset1::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<opset1::Parameter>(params.B.type, params.B.shape);
 
         const auto lstm_cell =
-            std::make_shared<op::v4::LSTMCell>(X,
+            std::make_shared<opset4::LSTMCell>(X,
                                                H_t,
                                                C_t,
                                                op::util::convert_lstm_node_format(W, op::util::LSTMWeightsFormat::IOFC),
@@ -142,15 +150,15 @@ private:
     static std::shared_ptr<Model> CreateFunction(const LSTMCellParams& params) {
         const float clip_threshold = 3.5f;
 
-        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
-        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
-        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
-        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
-        const auto C_t = std::make_shared<op::v0::Parameter>(params.C_t.type, params.C_t.shape);
-        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+        const auto X = std::make_shared<opset1::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<opset1::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<opset1::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<opset1::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<opset1::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<opset1::Parameter>(params.B.type, params.B.shape);
 
         const auto lstm_cell =
-            std::make_shared<op::v4::LSTMCell>(X,
+            std::make_shared<opset4::LSTMCell>(X,
                                                H_t,
                                                C_t,
                                                W,
@@ -179,36 +187,130 @@ TEST_P(ReferenceLSTMCellTestBiasClip, CompareWithRefs) {
     Exec();
 }
 
+class ReferenceLSTMCellV1Test : public ReferenceLSTMCellTest {
+private:
+    static std::shared_ptr<Model> CreateFunction(const LSTMCellParams& params) {
+        const auto X = std::make_shared<opset1::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<opset1::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<opset1::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<opset1::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<opset1::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<opset1::Parameter>(params.B.type, params.B.shape);
+
+        const auto lstm_cell =
+            std::make_shared<opset1::LSTMCell>(X,
+                                               H_t,
+                                               C_t,
+                                               op::util::convert_lstm_node_format(W, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(R, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(B, op::util::LSTMWeightsFormat::IOFC),
+                                               params.hiddenSize);
+
+        auto function = std::make_shared<Model>(lstm_cell->outputs(), ParameterVector{X, H_t, C_t, W, R, B});
+        return function;
+    }
+};
+
+class ReferenceLSTMCellV1TestBiasDefaultAttrs : public ReferenceLSTMCellTestBiasDefaultAttrs {
+private:
+    static std::shared_ptr<Model> CreateFunction(const LSTMCellParams& params) {
+        const auto X = std::make_shared<opset1::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<opset1::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<opset1::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<opset1::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<opset1::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<opset1::Parameter>(params.B.type, params.B.shape);
+
+        const auto lstm_cell =
+            std::make_shared<opset1::LSTMCell>(X,
+                                               H_t,
+                                               C_t,
+                                               op::util::convert_lstm_node_format(W, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(R, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(B, op::util::LSTMWeightsFormat::IOFC),
+                                               params.hiddenSize);
+
+        auto function = std::make_shared<Model>(lstm_cell->outputs(), ParameterVector{X, H_t, C_t, W, R, B});
+        return function;
+    }
+};
+
+class ReferenceLSTMCellV1TestBiasClip : public ReferenceLSTMCellTestBiasClip {
+private:
+    static std::shared_ptr<Model> CreateFunction(const LSTMCellParams& params) {
+        const float clip_threshold = 3.5f;
+
+        const auto X = std::make_shared<opset1::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<opset1::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<opset1::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<opset1::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<opset1::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<opset1::Parameter>(params.B.type, params.B.shape);
+
+        const auto lstm_cell =
+            std::make_shared<opset1::LSTMCell>(X,
+                                               H_t,
+                                               C_t,
+                                               W,
+                                               R,
+                                               B,
+                                               params.hiddenSize,
+                                               op::LSTMWeightsFormat::IFCO,
+                                               std::vector<std::string>{"sigmoid", "tanh", "tanh"},
+                                               std::vector<float>{},
+                                               std::vector<float>{},
+                                               clip_threshold);
+
+        auto function = std::make_shared<Model>(lstm_cell->outputs(), ParameterVector{X, H_t, C_t, W, R, B});
+        return function;
+    }
+};
+
+TEST_P(ReferenceLSTMCellV1Test, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceLSTMCellV1TestBiasDefaultAttrs, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceLSTMCellV1TestBiasClip, CompareWithRefs) {
+    Exec();
+}
+
 template <element::Type_t ET>
 std::vector<LSTMCellParams> generateParams() {
     using T = typename element_type_traits<ET>::value_type;
     std::vector<LSTMCellParams> params {
-        LSTMCellParams(
-            2, 3, 3, 4,
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}),
-            Tensor(ET, {4 * 3, 3}, std::vector<T>{
-                3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
-                9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
-                6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
-                8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
-                5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
-                3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}),
-            Tensor(ET, {4 * 3, 3}, std::vector<T>{
-                0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
-                0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
-                0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
-                0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
-                0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
-                0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}),
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}),
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}),
-            Tensor(ET, {4 * 3}, std::vector<T>(4 * 3, 0.f)),
-            Tensor(ET, {2, 3}, std::vector<T>{0.81457126f, 0.61109227f, 0.769522f, 0.52239674f, 0.4324641f, 0.63183f}),
-            Tensor(ET, {2, 3}, std::vector<T>{1.4444952f, 0.9635685f, 1.2875274f, 0.8053419f, 0.7184521f, 0.95803297f}),
-            "lstm_cell_zero_bias_default_attrs"),
+        Builder {}
+        .batchSize(2)
+        .inputSize(3)
+        .hiddenSize(3)
+        .gatesCount(4)
+        .X(Tensor(ET, {2, 3}, std::vector<T>{
+            0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}))
+        .W(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+            9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+            6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+            8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+            5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+            3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}))
+        .R(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            0.0987983f, 0.52032113f, 0.5848073f, 0.5356095f, 0.74497133f, 0.73260087f,
+            0.1700787f, 0.45684233f, 0.1495722f, 0.42734373f, 0.4433832f, 0.25906256f,
+            0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+            0.6881257f, 0.8170279f, 0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+            0.86843914f, 0.45967972f, 0.6237719f, 0.11074839f, 0.6029616f, 0.3149305f,
+            0.46504205f, 0.5843412f, 0.8733427f, 0.7687243f, 0.07074859f, 0.39188156f}))
+        .H_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}))
+        .C_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}))
+        .B(Tensor(ET, {4 * 3}, std::vector<T>(4 * 3, 0.f)))
+        .Ho(Tensor(ET, {2, 3}, std::vector<T>{0.81457126f, 0.61109227f, 0.769522f, 0.52239674f, 0.4324641f, 0.63183f}))
+        .Co(Tensor(ET, {2, 3}, std::vector<T>{1.4444952f, 0.9635685f, 1.2875274f, 0.8053419f, 0.7184521f, 0.95803297f}))
+        .testcaseName("lstm_cell_zero_bias_default_attrs")
     };
     return params;
 }
@@ -232,53 +334,56 @@ template <element::Type_t ET>
 std::vector<LSTMCellParams> generateParamsBiasDefaultAttrs() {
     using T = typename element_type_traits<ET>::value_type;
     std::vector<LSTMCellParams> params {
-        LSTMCellParams(
-            2, 3, 3, 4,
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}),
-            Tensor(ET, {4 * 3, 3}, std::vector<T>{
-                3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
-                9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
-                6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
-                8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
-                5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
-                3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}),
-            Tensor(ET, {4 * 3, 3}, std::vector<T>{
-                0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
-                0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
-                0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
-                0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
-                0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
-                0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}),
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}),
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}),
-            Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
-                                               1.15248052f,
-                                               1.16671345f,
-                                               0.21450312f,
-                                               1.2380678f,
-                                               1.51688835f,
-                                               0.46718366f,
-                                               0.91810346f,
-                                               1.1274234f,
-                                               0.51022074f,
-                                               1.11389844f,
-                                               0.74174305f}),
-            Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
+        Builder {}
+        .batchSize(2)
+        .inputSize(3)
+        .hiddenSize(3)
+        .gatesCount(4)
+        .X(Tensor(ET, {2, 3}, std::vector<T>{
+            0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}))
+        .W(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+            9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+            6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+            8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+            5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+            3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}))
+        .R(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
+            0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
+            0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+            0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+            0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
+            0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}))
+        .H_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}))
+        .C_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}))
+        .B(Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
+                                              1.15248052f,
+                                              1.16671345f,
+                                              0.21450312f,
+                                              1.2380678f,
+                                              1.51688835f,
+                                              0.46718366f,
+                                              0.91810346f,
+                                              1.1274234f,
+                                              0.51022074f,
+                                              1.11389844f,
+                                              0.74174305f}))
+        .Ho(Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
                                               0.76665538549423218,
                                               0.82509011030197144,
                                               0.6479143500328064,
                                               0.66586339473724365,
-                                              0.74838578701019287}),
-            Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
+                                              0.74838578701019287}))
+        .Co(Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
                                               1.1150213479995728,
                                               1.4578367471694946,
                                               1.0649888515472412,
                                               0.93761754035949707,
-                                              1.3659683465957642}),
-            "lstm_cell_bias_default_attrs"),
+                                              1.3659683465957642}))
+        .testcaseName("lstm_cell_bias_default_attrs"),
     };
     return params;
 }
@@ -302,53 +407,56 @@ template <element::Type_t ET>
 std::vector<LSTMCellParams> generateParamsBiasClip() {
     using T = typename element_type_traits<ET>::value_type;
     std::vector<LSTMCellParams> params {
-        LSTMCellParams(
-            2, 3, 3, 4,
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}),
-            Tensor(ET, {4 * 3, 3}, std::vector<T>{
-                3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
-                9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
-                6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
-                8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
-                5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
-                3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}),
-            Tensor(ET, {4 * 3, 3}, std::vector<T>{
-                0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
-                0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
-                0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
-                0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
-                0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
-                0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}),
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}),
-            Tensor(ET, {2, 3}, std::vector<T>{
-                0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}),
-            Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
-                                               1.15248052f,
-                                               1.16671345f,
-                                               0.21450312f,
-                                               1.2380678f,
-                                               1.51688835f,
-                                               0.46718366f,
-                                               0.91810346f,
-                                               1.1274234f,
-                                               0.51022074f,
-                                               1.11389844f,
-                                               0.74174305f}),
-            Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
+        Builder {}
+        .batchSize(2)
+        .inputSize(3)
+        .hiddenSize(3)
+        .gatesCount(4)
+        .X(Tensor(ET, {2, 3}, std::vector<T>{
+            0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}))
+        .W(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+            9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+            6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+            8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+            5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+            3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}))
+        .R(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
+            0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
+            0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+            0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+            0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
+            0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}))
+        .H_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}))
+        .C_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}))
+        .B(Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
+                                              1.15248052f,
+                                              1.16671345f,
+                                              0.21450312f,
+                                              1.2380678f,
+                                              1.51688835f,
+                                              0.46718366f,
+                                              0.91810346f,
+                                              1.1274234f,
+                                              0.51022074f,
+                                              1.11389844f,
+                                              0.74174305f}))
+        .Ho(Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
                                               0.76665538549423218,
                                               0.82387429475784302,
                                               0.6479143500328064,
                                               0.66586339473724365,
-                                              0.74838578701019287}),
-            Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
+                                              0.74838578701019287}))
+        .Co(Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
                                               1.1150213479995728,
                                               1.4510968923568726,
                                               1.0649888515472412,
                                               0.93761754035949707,
-                                              1.3659683465957642}),
-            "lstm_cell_bias_clip"),
+                                              1.3659683465957642}))
+        .testcaseName("lstm_cell_bias_clip"),
     };
     return params;
 }
@@ -376,4 +484,211 @@ INSTANTIATE_TEST_SUITE_P(smoke_LSTMCell_With_Hardcoded_Refs, ReferenceLSTMCellTe
 
 INSTANTIATE_TEST_SUITE_P(smoke_LSTMCell_With_Hardcoded_Refs, ReferenceLSTMCellTestBiasClip,
     testing::ValuesIn(generateCombinedParamsBiasClip()), ReferenceLSTMCellTest::getTestCaseName);
-} // namespace
+
+template <element::Type_t ET>
+std::vector<LSTMCellParams> generateParamsV1() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<LSTMCellParams> params {
+        Builder {}
+        .batchSize(2)
+        .inputSize(3)
+        .hiddenSize(3)
+        .gatesCount(4)
+        .X(Tensor(ET, {2, 3}, std::vector<T>{
+            0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}))
+        .W(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+            9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+            6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+            8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+            5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+            3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}))
+        .R(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            0.0987983f, 0.52032113f, 0.5848073f, 0.5356095f, 0.74497133f, 0.73260087f,
+            0.1700787f, 0.45684233f, 0.1495722f, 0.42734373f, 0.4433832f, 0.25906256f,
+            0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+            0.6881257f, 0.8170279f, 0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+            0.86843914f, 0.45967972f, 0.6237719f, 0.11074839f, 0.6029616f, 0.3149305f,
+            0.46504205f, 0.5843412f, 0.8733427f, 0.7687243f, 0.07074859f, 0.39188156f}))
+        .H_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}))
+        .C_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}))
+        .B(Tensor(ET, {4 * 3}, std::vector<T>(4 * 3, 0.f)))
+        .Ho(Tensor(ET, {2, 3}, std::vector<T>{0.81457126f, 0.61109227f, 0.769522f, 0.52239674f, 0.4324641f, 0.63183f}))
+        .Co(Tensor(ET, {2, 3}, std::vector<T>{1.4444952f, 0.9635685f, 1.2875274f, 0.8053419f, 0.7184521f, 0.95803297f}))
+        .testcaseName("lstm_cell_v1_zero_bias_default_attrs")
+    };
+    return params;
+}
+
+std::vector<LSTMCellParams> generateCombinedParamsV1() {
+    const std::vector<std::vector<LSTMCellParams>> generatedParams {
+        generateParamsV1<element::Type_t::bf16>(),
+        generateParamsV1<element::Type_t::f16>(),
+        generateParamsV1<element::Type_t::f32>(),
+        generateParamsV1<element::Type_t::f64>(),
+    };
+    std::vector<LSTMCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<LSTMCellParams> generateParamsBiasDefaultAttrsV1() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<LSTMCellParams> params {
+        Builder {}
+        .batchSize(2)
+        .inputSize(3)
+        .hiddenSize(3)
+        .gatesCount(4)
+        .X(Tensor(ET, {2, 3}, std::vector<T>{
+            0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}))
+        .W(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+            9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+            6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+            8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+            5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+            3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}))
+        .R(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
+            0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
+            0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+            0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+            0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
+            0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}))
+        .H_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}))
+        .C_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}))
+        .B(Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
+                                              1.15248052f,
+                                              1.16671345f,
+                                              0.21450312f,
+                                              1.2380678f,
+                                              1.51688835f,
+                                              0.46718366f,
+                                              0.91810346f,
+                                              1.1274234f,
+                                              0.51022074f,
+                                              1.11389844f,
+                                              0.74174305f}))
+        .Ho(Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
+                                              0.76665538549423218,
+                                              0.82509011030197144,
+                                              0.6479143500328064,
+                                              0.66586339473724365,
+                                              0.74838578701019287}))
+        .Co(Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
+                                              1.1150213479995728,
+                                              1.4578367471694946,
+                                              1.0649888515472412,
+                                              0.93761754035949707,
+                                              1.3659683465957642}))
+        .testcaseName("lstm_cell_v1_bias_default_attrs"),
+    };
+    return params;
+}
+
+std::vector<LSTMCellParams> generateCombinedParamsBiasDefaultAttrsV1() {
+    const std::vector<std::vector<LSTMCellParams>> generatedParams {
+        generateParamsBiasDefaultAttrsV1<element::Type_t::bf16>(),
+        generateParamsBiasDefaultAttrsV1<element::Type_t::f16>(),
+        generateParamsBiasDefaultAttrsV1<element::Type_t::f32>(),
+        generateParamsBiasDefaultAttrsV1<element::Type_t::f64>(),
+    };
+    std::vector<LSTMCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<LSTMCellParams> generateParamsBiasClipV1() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<LSTMCellParams> params {
+        Builder {}
+        .batchSize(2)
+        .inputSize(3)
+        .hiddenSize(3)
+        .gatesCount(4)
+        .X(Tensor(ET, {2, 3}, std::vector<T>{
+            0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}))
+        .W(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+            9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+            6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+            8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+            5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+            3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}))
+        .R(Tensor(ET, {4 * 3, 3}, std::vector<T>{
+            0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
+            0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
+            0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+            0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+            0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
+            0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}))
+        .H_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}))
+        .C_t(Tensor(ET, {2, 3}, std::vector<T>{
+            0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}))
+        .B(Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
+                                              1.15248052f,
+                                              1.16671345f,
+                                              0.21450312f,
+                                              1.2380678f,
+                                              1.51688835f,
+                                              0.46718366f,
+                                              0.91810346f,
+                                              1.1274234f,
+                                              0.51022074f,
+                                              1.11389844f,
+                                              0.74174305f}))
+        .Ho(Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
+                                              0.76665538549423218,
+                                              0.82387429475784302,
+                                              0.6479143500328064,
+                                              0.66586339473724365,
+                                              0.74838578701019287}))
+        .Co(Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
+                                              1.1150213479995728,
+                                              1.4510968923568726,
+                                              1.0649888515472412,
+                                              0.93761754035949707,
+                                              1.3659683465957642}))
+        .testcaseName("lstm_cell_v1_bias_clip"),
+    };
+    return params;
+}
+
+std::vector<LSTMCellParams> generateCombinedParamsBiasClipV1() {
+    const std::vector<std::vector<LSTMCellParams>> generatedParams {
+        generateParamsBiasClipV1<element::Type_t::bf16>(),
+        generateParamsBiasClipV1<element::Type_t::f16>(),
+        generateParamsBiasClipV1<element::Type_t::f32>(),
+        generateParamsBiasClipV1<element::Type_t::f64>(),
+    };
+    std::vector<LSTMCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_LSTMCellV1_With_Hardcoded_Refs, ReferenceLSTMCellV1Test,
+    testing::ValuesIn(generateCombinedParamsV1()), ReferenceLSTMCellV1Test::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_LSTMCellV1_With_Hardcoded_Refs, ReferenceLSTMCellV1TestBiasDefaultAttrs,
+    testing::ValuesIn(generateCombinedParamsBiasDefaultAttrsV1()), ReferenceLSTMCellV1Test::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_LSTMCellV1_With_Hardcoded_Refs, ReferenceLSTMCellV1TestBiasClip,
+    testing::ValuesIn(generateCombinedParamsBiasClipV1()), ReferenceLSTMCellV1Test::getTestCaseName);
+} // namespace
\ No newline at end of file

From 0668ea07795585b95f4d7f513f86533df80d43d6 Mon Sep 17 00:00:00 2001
From: Alexander Zhogov <alexander.zhogov@intel.com>
Date: Thu, 16 Dec 2021 18:54:08 +0300
Subject: [PATCH 003/160] Azure CI: Fix "access denied" issue with certutil on
 Windows (#9262)

* Azure CI: Fix "access denied" issue with certutil on Windows

* Use Invoke-WebRequest for GitHub
---
 .ci/azure/windows.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index b9738b6a7bb..81e2b47a622 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -83,7 +83,7 @@ jobs:
     displayName: 'Make dir'
 
   - script: |
-      certutil -urlcache -split -f https://openvinoweb.z5.web.core.windows.net/incredibuild/install_ib_console.bat install_ib_console.bat
+      curl -O https://openvinoweb.z5.web.core.windows.net/incredibuild/install_ib_console.bat
       call install_ib_console.bat
     workingDirectory: $(WORK_DIR)
     displayName: 'Install IncrediBuild'
@@ -117,9 +117,9 @@ jobs:
       python -m pip install -r $(REPO_DIR)\tools\mo\requirements.txt
       python -m pip install -r $(REPO_DIR)\tools\mo\requirements_dev.txt
       rem Speed up build
-      certutil -urlcache -split -f https://github.com/Kitware/CMake/releases/download/v$(CMAKE_VERSION)/cmake-$(CMAKE_VERSION)-windows-x86_64.zip cmake-$(CMAKE_VERSION)-windows-x86_64.zip
+      powershell -command "Invoke-WebRequest https://github.com/Kitware/CMake/releases/download/v$(CMAKE_VERSION)/cmake-$(CMAKE_VERSION)-windows-x86_64.zip -OutFile cmake-$(CMAKE_VERSION)-windows-x86_64.zip"
       powershell -command "Expand-Archive -Force cmake-$(CMAKE_VERSION)-windows-x86_64.zip"
-      certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip ninja-win.zip
+      powershell -command "Invoke-WebRequest https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip -OutFile ninja-win.zip"
       powershell -command "Expand-Archive -Force ninja-win.zip"
       git clone https://github.com/google/gtest-parallel.git
     workingDirectory: $(WORK_DIR)

From 012e513f750679c939158e783b94670cd7ee9f28 Mon Sep 17 00:00:00 2001
From: Ekaterina Aidova <ekaterina.aidova@intel.com>
Date: Thu, 16 Dec 2021 19:05:36 +0300
Subject: [PATCH 004/160] update POT submodule (#8740)

* update POT submodule

* update tests
---
 tools/pot/tests/utils/open_model_zoo.py | 4 ++--
 tools/pot/thirdparty/open_model_zoo     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/pot/tests/utils/open_model_zoo.py b/tools/pot/tests/utils/open_model_zoo.py
index 02582e9d145..e49b7ec17c8 100644
--- a/tools/pot/tests/utils/open_model_zoo.py
+++ b/tools/pot/tests/utils/open_model_zoo.py
@@ -28,7 +28,7 @@ OMZ_DEFINITIONS_PATH = LIBS_ROOT/'open_model_zoo'/'data'/'dataset_definitions.ym
 sys.path.append(str(OMZ_DOWNLOADER_PATH / 'src'))
 # pylint: disable=E0611,C0413,C0411,E0401
 importlib.reload(openvino)
-from openvino.model_zoo._configuration import load_models
+from openvino.model_zoo._configuration import load_models, ModelLoadingMode
 from openvino.model_zoo._common import MODEL_ROOT
 is_platform_windows = sys.platform.startswith('win')
 
@@ -83,7 +83,7 @@ def convert(config):
 
 
 def get_models_list():
-    return load_models(MODEL_ROOT, Dict(config=None))
+    return load_models(MODEL_ROOT, Dict(config=None), mode=ModelLoadingMode.ignore_composite)
 
 
 def download_engine_config(model_name):
diff --git a/tools/pot/thirdparty/open_model_zoo b/tools/pot/thirdparty/open_model_zoo
index b7c83530a47..a04512d8553 160000
--- a/tools/pot/thirdparty/open_model_zoo
+++ b/tools/pot/thirdparty/open_model_zoo
@@ -1 +1 @@
-Subproject commit b7c83530a470734e3e4bde8fe0d80dcc2d9e9b2c
+Subproject commit a04512d8553aed4b7bde5032141f28bfd26f6f5c

From 58f0c75b91587dd8d3855b6ba5966d086d06f4b4 Mon Sep 17 00:00:00 2001
From: Yury Gaydaychuk <yury.gaydaychuk@intel.com>
Date: Thu, 16 Dec 2021 22:02:09 +0300
Subject: [PATCH 005/160] [CPU] RoiAlign: case of integer tensor handled
 (#8069)

---
 .../src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp    | 9 ++++++---
 .../plugin/cpu/single_layer_tests/roialign.cpp           | 4 ++--
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
index bbd4d12ac5e..91ee9cf58fd 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
@@ -108,9 +108,12 @@ void MKLDNNROIAlignNode::initSupportedPrimitiveDescriptors() {
     Precision inputPrec0 = getOriginalInputPrecisionAtPort(0);
     Precision outputPrec = getOriginalOutputPrecisionAtPort(0);
 
-    if (!mayiuse(avx512_core)) {
-        if (outputPrec == Precision::BF16 || inputPrec0 == Precision::BF16)
+    if (inputPrec0 != Precision::FP32 || outputPrec != Precision::FP32) {
+        if ((outputPrec == Precision::BF16 || inputPrec0 == Precision::BF16) && mayiuse(avx512_core)) {
+            outputPrec = inputPrec0 = Precision::BF16;
+        } else {
             outputPrec = inputPrec0 = Precision::FP32;
+        }
     }
 
     NodeConfig config;
@@ -130,7 +133,7 @@ void MKLDNNROIAlignNode::initSupportedPrimitiveDescriptors() {
                               {LayoutType::ncsp, Precision::FP32},
                               {LayoutType::ncsp, Precision::I32}},
                              {{fmts.second, outputPrec}},
-                              impl_desc_type::unknown);
+                              impl_desc_type::ref);
     }
 }
 
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp
index a8587e46f1e..4492a458519 100644
--- a/src/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp
@@ -149,7 +149,7 @@ protected:
         auto roialign = std::make_shared<ngraph::opset3::ROIAlign>(float_params[0], float_params[1], int_params[0], pooledH, pooledW,
                                                                    samplingRatio, spatialScale, mode);
 
-        selectedType = makeSelectedTypeStr("unknown", inputPrecision);
+        selectedType = makeSelectedTypeStr("ref", inputPrecision);
         if (inputPrecision == ElementType::bf16) {
             rel_threshold = 1e-2;
         }
@@ -182,7 +182,7 @@ std::vector<CPUSpecificParams> filterCPUInfoForDevice() {
 
 const std::vector<ElementType> netPrecisions = {
         ElementType::f32,
-        ElementType::bf16,
+        ElementType::bf16
 };
 
 const std::vector<int> spatialBinXVector = { 2 };

From d421bc6c4f0e058e2d938fc424082978a0dfd1aa Mon Sep 17 00:00:00 2001
From: Alexander Zhogov <alexander.zhogov@intel.com>
Date: Thu, 16 Dec 2021 22:09:26 +0300
Subject: [PATCH 006/160] Azure CI: Fix "access denied" issue with certutil on
 Windows CC (#9269)

---
 .ci/azure/windows_conditional_compilation.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.ci/azure/windows_conditional_compilation.yml b/.ci/azure/windows_conditional_compilation.yml
index cc310068d11..3f1e152a4d6 100644
--- a/.ci/azure/windows_conditional_compilation.yml
+++ b/.ci/azure/windows_conditional_compilation.yml
@@ -59,7 +59,7 @@ jobs:
 
   - script: |
       rem Speed up build
-      certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip ninja-win.zip
+      powershell -command "Invoke-WebRequest https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip -OutFile ninja-win.zip"
       powershell -command "Expand-Archive -Force ninja-win.zip"
     workingDirectory: $(WORK_DIR)
     displayName: 'Install dependencies'

From 4fea4024b4ce6c2ea66902f6aa6191a0929d5cdb Mon Sep 17 00:00:00 2001
From: Mikhail Nosov <mikhail.nosov@intel.com>
Date: Fri, 17 Dec 2021 00:55:47 +0300
Subject: [PATCH 007/160] Overwrite 'model' layout with preprocessing when
 layout is already set (#9258)

---
 src/core/src/preprocess/pre_post_process.cpp |  6 ++--
 src/core/tests/preprocess.cpp                | 34 ++++++++++++++++++++
 2 files changed, 38 insertions(+), 2 deletions(-)

diff --git a/src/core/src/preprocess/pre_post_process.cpp b/src/core/src/preprocess/pre_post_process.cpp
index db9395534e6..090708274d2 100644
--- a/src/core/src/preprocess/pre_post_process.cpp
+++ b/src/core/src/preprocess/pre_post_process.cpp
@@ -367,7 +367,8 @@ std::shared_ptr<Model> PrePostProcessor::build() {
     for (const auto& input_info : m_impl->m_inputs) {
         auto& input = input_info.m_impl;
         // Set parameter layout from 'model' information
-        if (input->get_model()->is_layout_set() && input->m_resolved_param->get_layout().empty()) {
+        if (input->get_model()->is_layout_set()) {
+            // Overwrite existing model's layout here (fix 74065)
             input->m_resolved_param->set_layout(input->get_model()->get_layout());
         }
     }
@@ -563,7 +564,8 @@ std::shared_ptr<Model> PrePostProcessor::build() {
         node.get_tensor().set_names({});
         result = std::dynamic_pointer_cast<op::v0::Result>(node.get_node_shared_ptr());
         // Set result layout from 'model' information
-        if (output->get_model_data()->is_layout_set() && result->get_layout().empty()) {
+        if (output->get_model_data()->is_layout_set()) {
+            // Overwrite existing model's layout here (fix 74065)
             result->set_layout(output->get_model_data()->get_layout());
         }
         auto parent = result->get_input_source_output(0);
diff --git a/src/core/tests/preprocess.cpp b/src/core/tests/preprocess.cpp
index 856dd597dce..532f1427de7 100644
--- a/src/core/tests/preprocess.cpp
+++ b/src/core/tests/preprocess.cpp
@@ -534,6 +534,23 @@ TEST(pre_post_process, reuse_model_layout_no_tensor_info) {
     EXPECT_EQ(f->get_parameters().front()->get_layout(), "NC??");
 }
 
+TEST(pre_post_process, set_model_layout_when_already_exists) {
+    auto m = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 2, 1});
+    {
+        auto p = PrePostProcessor(m);
+        p.input().model().set_layout("N???");
+        m = p.build();
+    }
+    EXPECT_EQ(m->input().get_partial_shape(), (PartialShape{Dimension::dynamic(), 3, 2, 1}));
+    {
+        auto p = PrePostProcessor(m);
+        p.input().tensor().set_layout("NHWC");
+        p.input().model().set_layout("NCHW");  // Expect "N???" will be overwritten by "NCHW"
+        m = p.build();
+    }
+    EXPECT_EQ(m->input().get_partial_shape(), (PartialShape{Dimension::dynamic(), 2, 1, 3}));
+}
+
 TEST(pre_post_process, set_layout_out_of_bounds) {
     auto shape = PartialShape{1, 2};
     std::stringstream shape_str;
@@ -1146,6 +1163,23 @@ TEST(pre_post_process, postprocess_convert_layout_implicit) {
     EXPECT_EQ(f->get_results()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
 }
 
+TEST(pre_post_process, postprocess_set_model_layout_when_already_exists) {
+    auto m = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 2, 1});
+    {
+        auto p = PrePostProcessor(m);
+        p.output().model().set_layout("N???");
+        m = p.build();
+    }
+    EXPECT_EQ(m->output().get_partial_shape(), (PartialShape{Dimension::dynamic(), 3, 2, 1}));
+    {
+        auto p = PrePostProcessor(m);
+        p.output().model().set_layout("NCHW");  // Expect "N???" will be overwritten by "NCHW"
+        p.output().tensor().set_layout("NHWC");
+        m = p.build();
+    }
+    EXPECT_EQ(m->output().get_partial_shape(), (PartialShape{Dimension::dynamic(), 2, 1, 3}));
+}
+
 TEST(pre_post_process, postprocess_convert_layout_explicit_no_target) {
     auto f = create_n_inputs<2>(element::f32, Shape{1, 3, 2, 2});
     auto p = PrePostProcessor(f);

From dbd2b5dc08796a25dcd78a659a915e3dbd563029 Mon Sep 17 00:00:00 2001
From: Ivan Tikhonov <ivan.tikhonov@intel.com>
Date: Fri, 17 Dec 2021 08:05:30 +0300
Subject: [PATCH 008/160] ONNX/IR/Common FrontEnds Refactoring (#9174)

* Refactor ONNX,IR,Common FrontEnds

* Rename frontends dir to frontend

* Rename frontend_manager, frontend_defs -> manager, defs; move Place, InputModel to src for Paddle FE

* remove exports for ir/input_model

* fix unit tests

* Fix unit tests

* revert ONNX_API/IR_API defines

* move manager.hpp to frontend dir

* delete common folder; rename defs to visibility; fix includes

* revert local changes

* codestyle

* fix build: set correct includes

* fix includes

* fix build; resolve review comments

* fix install paths

* correct installation folder for onnx_import

* use IE_THROW in ir frontend
---
 .../frontends/frontends.cmake                 |   4 +-
 .../frontends/ov_frontends.hpp.in             |   2 +-
 .../{manager.cpp => frontend_manager.cpp}     |   3 +-
 .../src/pyopenvino/frontend/frontend.cpp      |   6 +-
 .../src/pyopenvino/frontend/inputmodel.cpp    |   4 +-
 .../{frontend_manager.cpp => manager.cpp}     |   5 +-
 .../{frontend_manager.hpp => manager.hpp}     |   0
 .../python/src/pyopenvino/frontend/place.cpp  |   5 +-
 .../python/src/pyopenvino/pyopenvino.cpp      |   2 +-
 .../mock_py_ov_frontend/mock_py_frontend.cpp  |   4 +-
 .../mock_py_ov_frontend/mock_py_frontend.hpp  |   4 +-
 src/core/tests/frontend/frontend_manager.cpp  |  22 +-
 src/core/tests/frontend/mock_frontend.cpp     |   4 +-
 .../paddlepaddle/convert_unsupported.cpp      |   4 +-
 .../paddlepaddle/incorrect_cut_model.cpp      |   4 +-
 .../tests/frontend/paddlepaddle/places.cpp    |   2 +-
 .../test_models/gen_scripts/generate_slice.py |   2 +-
 .../paddlepaddle/throw_in_conversion.cpp      |   4 +-
 .../frontend/shared/include/basic_api.hpp     |   2 +-
 .../frontend/shared/include/convert_model.hpp |   2 +-
 .../shared/include/cut_specific_model.hpp     |   2 +-
 .../frontend/shared/include/load_from.hpp     |   2 +-
 .../frontend/shared/include/op_fuzzy.hpp      |   2 +-
 .../frontend/shared/include/partial_shape.hpp |   2 +-
 .../shared/include/set_element_type.hpp       |   2 +-
 .../frontend/shared/include/telemetry.hpp     |   2 +-
 .../tests/frontend/shared/include/utils.hpp   |   2 +-
 .../tests/frontend/shared/src/telemetry.cpp   |   2 +-
 .../tensorflow/convert_unsupported.cpp        |   4 +-
 .../frontend/tensorflow/transpose_sinking.cpp |   2 +-
 src/core/tests/pass/serialization/read_ir.hpp |   2 +-
 src/frontends/common/CMakeLists.txt           |  23 +-
 .../frontend/exception.hpp}                   |   2 +-
 .../frontend}/frontend.hpp                    |  26 +-
 .../frontend}/input_model.hpp                 |  10 +-
 .../{ => openvino/frontend}/manager.hpp       |   7 +-
 .../{common => openvino/frontend}/place.hpp   |   4 +-
 .../frontend}/telemetry_extension.hpp         |   2 +-
 .../frontend/visibility.hpp}                  |   7 +-
 src/frontends/common/src/frontend.cpp         |  72 +++++
 src/frontends/common/src/input_model.cpp      | 114 +++++++
 .../src/{frontend_manager.cpp => manager.cpp} | 293 +-----------------
 src/frontends/common/src/place.cpp            | 139 +++++++++
 src/frontends/common/src/plugin_loader.cpp    |  16 +-
 src/frontends/common/src/plugin_loader.hpp    |   2 +-
 .../common/src/telemetry_extension.cpp        |   2 +-
 src/frontends/common/src/utils.cpp            |   2 +-
 src/frontends/common/src/utils.hpp            |   2 +-
 .../ir/include/ir_frontend/model.hpp          |  31 --
 .../frontend/ir}/frontend.hpp                 |  14 +-
 .../frontend/ir/visibility.hpp}               |  10 -
 src/frontends/ir/src/frontend.cpp             |  32 +-
 .../ir/src/{model.cpp => input_model.cpp}     |  18 +-
 src/frontends/ir/src/input_model.hpp          |  33 ++
 src/frontends/ir/src/ir_deserializer.cpp      |   1 -
 src/frontends/ir/src/ir_deserializer.hpp      |   2 +-
 src/frontends/ir/src/rt_info_deserializer.cpp |   5 +-
 src/frontends/ir/src/rt_info_deserializer.hpp |   1 -
 src/frontends/onnx/frontend/CMakeLists.txt    |   2 +-
 .../frontend/onnx}/frontend.hpp               |   8 +-
 .../onnx/frontend/src/core/graph.hpp          |   2 +-
 src/frontends/onnx/frontend/src/editor.hpp    |   2 +-
 src/frontends/onnx/frontend/src/frontend.cpp  |  46 +--
 .../onnx/frontend/src/input_model.cpp         |  99 +++---
 .../onnx/frontend/src/input_model.hpp         |  26 +-
 src/frontends/onnx/frontend/src/place.cpp     | 184 ++++++-----
 src/frontends/onnx/frontend/src/place.hpp     |  30 +-
 .../onnx/frontend/src/utils/onnx_internal.hpp |   2 +-
 src/frontends/paddlepaddle/CMakeLists.txt     |   2 +-
 .../paddlepaddle_frontend/frontend.hpp        |   4 +-
 .../include/paddlepaddle_frontend/model.hpp   |   4 +-
 .../include/paddlepaddle_frontend/place.hpp   |   4 +-
 .../include/paddlepaddle_frontend/utility.hpp |   2 +-
 .../src/{exceptions.cpp => exception.cpp}     |   3 +-
 .../exceptions.hpp                            |   2 +-
 src/frontends/paddlepaddle/src/frontend.cpp   |   2 +-
 src/frontends/paddlepaddle/src/model.cpp      |   2 +-
 .../paddlepaddle/src/node_context.hpp         |   2 +-
 src/frontends/paddlepaddle/src/pdpd_utils.hpp |   2 +-
 src/frontends/tensorflow/CMakeLists.txt       |   2 +-
 .../include/tensorflow_frontend/frontend.hpp  |   6 +-
 .../include/tensorflow_frontend/utility.hpp   |   2 +-
 src/frontends/tensorflow/src/exceptions.hpp   |   3 +-
 src/frontends/tensorflow/src/model.cpp        |   2 +-
 src/frontends/tensorflow/src/model.hpp        |   6 +-
 src/frontends/tensorflow/src/place.cpp        |   2 +-
 src/frontends/tensorflow/src/place.hpp        |   2 +-
 src/frontends/tensorflow/src/tensorflow.cpp   |   2 +-
 src/inference/src/ie_network_reader.cpp       |   2 +-
 .../partial_shape_deserialization.cpp         |   2 +-
 .../partial_shape_serialization.cpp           |   2 +-
 .../rt_info_deserialization.cpp               |   2 +-
 .../rt_info_serialization.cpp                 |   2 +-
 tests/fuzz/src/import_pdpd-fuzzer.cc          |   2 +-
 .../mock_mo_ov_frontend/mock_mo_frontend.cpp  |   4 +-
 .../mock_mo_ov_frontend/mock_mo_frontend.hpp  |   4 +-
 96 files changed, 746 insertions(+), 710 deletions(-)
 rename src/bindings/python/src/compatibility/pyngraph/passes/{manager.cpp => frontend_manager.cpp} (99%)
 rename src/bindings/python/src/pyopenvino/frontend/{frontend_manager.cpp => manager.cpp} (98%)
 rename src/bindings/python/src/pyopenvino/frontend/{frontend_manager.hpp => manager.hpp} (100%)
 rename src/frontends/common/include/{common/frontend_exceptions.hpp => openvino/frontend/exception.hpp} (99%)
 rename src/frontends/common/include/{common => openvino/frontend}/frontend.hpp (86%)
 rename src/frontends/common/include/{common => openvino/frontend}/input_model.hpp (97%)
 rename src/frontends/common/include/{ => openvino/frontend}/manager.hpp (94%)
 rename src/frontends/common/include/{common => openvino/frontend}/place.hpp (99%)
 rename src/frontends/common/include/{common => openvino/frontend}/telemetry_extension.hpp (97%)
 rename src/frontends/common/include/{common/frontend_defs.hpp => openvino/frontend/visibility.hpp} (68%)
 create mode 100644 src/frontends/common/src/frontend.cpp
 create mode 100644 src/frontends/common/src/input_model.cpp
 rename src/frontends/common/src/{frontend_manager.cpp => manager.cpp} (51%)
 create mode 100644 src/frontends/common/src/place.cpp
 delete mode 100644 src/frontends/ir/include/ir_frontend/model.hpp
 rename src/frontends/ir/include/{ir_frontend => openvino/frontend/ir}/frontend.hpp (82%)
 rename src/frontends/ir/include/{ir_frontend/utility.hpp => openvino/frontend/ir/visibility.hpp} (60%)
 rename src/frontends/ir/src/{model.cpp => input_model.cpp} (95%)
 create mode 100644 src/frontends/ir/src/input_model.hpp
 rename src/frontends/onnx/frontend/include/{onnx_frontend => openvino/frontend/onnx}/frontend.hpp (86%)
 rename src/frontends/paddlepaddle/src/{exceptions.cpp => exception.cpp} (90%)
 rename src/frontends/paddlepaddle/{include/paddlepaddle_frontend => src}/exceptions.hpp (96%)

diff --git a/cmake/developer_package/frontends/frontends.cmake b/cmake/developer_package/frontends/frontends.cmake
index b36db7b0d78..b5b07559bea 100644
--- a/cmake/developer_package/frontends/frontends.cmake
+++ b/cmake/developer_package/frontends/frontends.cmake
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(FRONTEND_INSTALL_INCLUDE "runtime/include/ngraph/frontend")
+set(FRONTEND_INSTALL_INCLUDE "runtime/include/")
 set(FRONTEND_NAME_SUFFIX "_ov_frontend")
 
 set(FRONTEND_NAMES "" CACHE INTERNAL "")
@@ -225,7 +225,7 @@ macro(ov_add_frontend)
 
         if(OV_FRONTEND_LINKABLE_FRONTEND)
             # install -dev part
-            install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/${OV_FRONTEND_NAME}_frontend
+            install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/
                     DESTINATION ${FRONTEND_INSTALL_INCLUDE}
                     COMPONENT core_dev
                     FILES_MATCHING PATTERN "*.hpp")
diff --git a/cmake/developer_package/frontends/ov_frontends.hpp.in b/cmake/developer_package/frontends/ov_frontends.hpp.in
index 0293054160d..82c1f1d010f 100644
--- a/cmake/developer_package/frontends/ov_frontends.hpp.in
+++ b/cmake/developer_package/frontends/ov_frontends.hpp.in
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include "common/frontend.hpp"
+#include "openvino/frontend/frontend.hpp"
 
 @OV_FRONTEND_DECLARATIONS@
 
diff --git a/src/bindings/python/src/compatibility/pyngraph/passes/manager.cpp b/src/bindings/python/src/compatibility/pyngraph/passes/frontend_manager.cpp
similarity index 99%
rename from src/bindings/python/src/compatibility/pyngraph/passes/manager.cpp
rename to src/bindings/python/src/compatibility/pyngraph/passes/frontend_manager.cpp
index dd3ae2b6ad6..a85c4be5645 100644
--- a/src/bindings/python/src/compatibility/pyngraph/passes/manager.cpp
+++ b/src/bindings/python/src/compatibility/pyngraph/passes/frontend_manager.cpp
@@ -2,12 +2,11 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph/pass/manager.hpp"
-
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 
 #include "ngraph/pass/constant_folding.hpp"
+#include "ngraph/pass/manager.hpp"
 #include "ngraph/pass/pass.hpp"
 #include "ngraph/pass/validate.hpp"
 #include "pyngraph/passes/manager.hpp"
diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
index 096bc3e5bf6..5adeed0ce1c 100644
--- a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
@@ -7,9 +7,9 @@
 #include <pybind11/stl.h>
 #include <pybind11/stl_bind.h>
 
-#include "common/frontend_exceptions.hpp"
-#include "common/telemetry_extension.hpp"
-#include "manager.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/telemetry_extension.hpp"
 #include "pyopenvino/graph/function.hpp"
 
 namespace py = pybind11;
diff --git a/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp b/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
index 45ba73c7fbf..db6c9c55c74 100644
--- a/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
@@ -6,8 +6,8 @@
 #include <pybind11/stl.h>
 #include <pybind11/stl_bind.h>
 
-#include "common/frontend_exceptions.hpp"
-#include "manager.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/manager.hpp"
 
 namespace py = pybind11;
 
diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend_manager.cpp b/src/bindings/python/src/pyopenvino/frontend/manager.cpp
similarity index 98%
rename from src/bindings/python/src/pyopenvino/frontend/frontend_manager.cpp
rename to src/bindings/python/src/pyopenvino/frontend/manager.cpp
index 2f0a40903e3..38c1bf64ef6 100644
--- a/src/bindings/python/src/pyopenvino/frontend/frontend_manager.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/manager.cpp
@@ -2,14 +2,13 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "frontend_manager.hpp"
+#include "openvino/frontend/manager.hpp"
 
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 #include <pybind11/stl_bind.h>
 
-#include "common/frontend_exceptions.hpp"
-#include "manager.hpp"
+#include "openvino/frontend/exception.hpp"
 
 namespace py = pybind11;
 
diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend_manager.hpp b/src/bindings/python/src/pyopenvino/frontend/manager.hpp
similarity index 100%
rename from src/bindings/python/src/pyopenvino/frontend/frontend_manager.hpp
rename to src/bindings/python/src/pyopenvino/frontend/manager.hpp
diff --git a/src/bindings/python/src/pyopenvino/frontend/place.cpp b/src/bindings/python/src/pyopenvino/frontend/place.cpp
index f0f00972c0b..69744140a2a 100644
--- a/src/bindings/python/src/pyopenvino/frontend/place.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/place.cpp
@@ -6,9 +6,8 @@
 #include <pybind11/stl.h>
 #include <pybind11/stl_bind.h>
 
-#include "common/frontend_exceptions.hpp"
-#include "frontend_manager.hpp"
-#include "manager.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/manager.hpp"
 #include "pyopenvino/graph/function.hpp"
 
 namespace py = pybind11;
diff --git a/src/bindings/python/src/pyopenvino/pyopenvino.cpp b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
index e86d64a88f9..46bc6e1eb8f 100644
--- a/src/bindings/python/src/pyopenvino/pyopenvino.cpp
+++ b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
@@ -32,8 +32,8 @@
 #include "pyopenvino/core/variable_state.hpp"
 #include "pyopenvino/core/version.hpp"
 #include "pyopenvino/frontend/frontend.hpp"
-#include "pyopenvino/frontend/frontend_manager.hpp"
 #include "pyopenvino/frontend/inputmodel.hpp"
+#include "pyopenvino/frontend/manager.hpp"
 #include "pyopenvino/frontend/place.hpp"
 #include "pyopenvino/graph/any.hpp"
 #include "pyopenvino/graph/descriptors/tensor.hpp"
diff --git a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.cpp b/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.cpp
index 6491ac6a43e..fd48b138bff 100644
--- a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.cpp
+++ b/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.cpp
@@ -4,8 +4,8 @@
 
 #include "mock_py_frontend.hpp"
 
-#include "common/frontend_defs.hpp"
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 using namespace ngraph;
 using namespace ov::frontend;
diff --git a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp b/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
index 32f9794bec6..3bc8d8f423a 100644
--- a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
+++ b/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
@@ -4,9 +4,9 @@
 
 #pragma once
 
-#include "common/frontend_defs.hpp"
-#include "manager.hpp"
 #include "ngraph/visibility.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 // Defined if we are building the plugin DLL (instead of using it)
 #ifdef mock_py_ov_frontend_EXPORTS
diff --git a/src/core/tests/frontend/frontend_manager.cpp b/src/core/tests/frontend/frontend_manager.cpp
index 12c8748f35a..b4255de3085 100644
--- a/src/core/tests/frontend/frontend_manager.cpp
+++ b/src/core/tests/frontend/frontend_manager.cpp
@@ -2,9 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <common/frontend_exceptions.hpp>
-#include <manager.hpp>
 #include <memory>
+#include <openvino/frontend/exception.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #include "gmock/gmock.h"
 #include "gtest/gtest.h"
@@ -66,8 +66,9 @@ static int set_test_env(const char* name, const char* value) {
 
 TEST(FrontEndManagerTest, testAvailableFrontEnds) {
     FrontEndManager fem;
+    class MockFrontEnd : public FrontEnd {};
     ASSERT_NO_THROW(fem.register_front_end("mock", []() {
-        return std::make_shared<FrontEnd>();
+        return std::make_shared<MockFrontEnd>();
     }));
     auto frontends = fem.get_available_front_ends();
     ASSERT_NE(std::find(frontends.begin(), frontends.end(), "mock"), frontends.end());
@@ -105,8 +106,9 @@ TEST(FrontEndManagerTest, testDefaultFrontEnd) {
     ASSERT_NO_THROW(fe = fem.load_by_model(""));
     ASSERT_FALSE(fe);
 
-    std::unique_ptr<FrontEnd> fePtr(new FrontEnd());  // to verify base destructor
-    fe = std::make_shared<FrontEnd>();
+    class MockFrontEnd : public FrontEnd {};
+    std::unique_ptr<FrontEnd> fePtr(new MockFrontEnd());  // to verify base destructor
+    fe = std::make_shared<MockFrontEnd>();
     ASSERT_ANY_THROW(fe->load(""));
     ASSERT_ANY_THROW(fe->convert(std::shared_ptr<Function>(nullptr)));
     ASSERT_ANY_THROW(fe->convert(InputModel::Ptr(nullptr)));
@@ -117,8 +119,9 @@ TEST(FrontEndManagerTest, testDefaultFrontEnd) {
 }
 
 TEST(FrontEndManagerTest, testDefaultInputModel) {
-    std::unique_ptr<InputModel> imPtr(new InputModel());  // to verify base destructor
-    InputModel::Ptr im = std::make_shared<InputModel>();
+    class MockInputModel : public InputModel {};
+    std::unique_ptr<InputModel> imPtr(new MockInputModel());  // to verify base destructor
+    InputModel::Ptr im = std::make_shared<MockInputModel>();
     ASSERT_EQ(im->get_inputs(), std::vector<Place::Ptr>{});
     ASSERT_EQ(im->get_outputs(), std::vector<Place::Ptr>{});
     ASSERT_ANY_THROW(im->override_all_inputs({nullptr}));
@@ -146,8 +149,9 @@ TEST(FrontEndManagerTest, testDefaultInputModel) {
 }
 
 TEST(FrontEndManagerTest, testDefaultPlace) {
-    std::unique_ptr<Place> placePtr(new Place());  // to verify base destructor
-    Place::Ptr place = std::make_shared<Place>();
+    class MockPlace : public Place {};
+    std::unique_ptr<Place> placePtr(new MockPlace());  // to verify base destructor
+    Place::Ptr place = std::make_shared<MockPlace>();
     ASSERT_ANY_THROW(place->get_names());
     ASSERT_EQ(place->get_consuming_operations(), std::vector<Place::Ptr>{});
     ASSERT_EQ(place->get_consuming_operations(0), std::vector<Place::Ptr>{});
diff --git a/src/core/tests/frontend/mock_frontend.cpp b/src/core/tests/frontend/mock_frontend.cpp
index ad7a205f3a7..7d4e24290be 100644
--- a/src/core/tests/frontend/mock_frontend.cpp
+++ b/src/core/tests/frontend/mock_frontend.cpp
@@ -2,9 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "common/frontend_defs.hpp"
-#include "manager.hpp"
 #include "ngraph/visibility.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 // Defined if we are building the plugin DLL (instead of using it)
 #ifdef mock1_ov_frontend_EXPORTS
diff --git a/src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp b/src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp
index 940bb3e72f2..764e752ed06 100644
--- a/src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp
+++ b/src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <common/frontend_exceptions.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/exception.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "paddle_utils.hpp"
diff --git a/src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp b/src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp
index 595181f3edc..6005ad3ffea 100644
--- a/src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp
+++ b/src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp
@@ -4,8 +4,8 @@
 
 #include <gtest/gtest.h>
 
-#include <common/frontend_exceptions.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/exception.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #include "paddle_utils.hpp"
 #include "utils.hpp"
diff --git a/src/core/tests/frontend/paddlepaddle/places.cpp b/src/core/tests/frontend/paddlepaddle/places.cpp
index 925fb068eae..2092920cbee 100644
--- a/src/core/tests/frontend/paddlepaddle/places.cpp
+++ b/src/core/tests/frontend/paddlepaddle/places.cpp
@@ -3,7 +3,7 @@
 //
 
 #include <frontend/shared/include/utils.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #include "gtest/gtest.h"
 #include "paddle_utils.hpp"
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
index 8c6b5fd1eeb..75328e48fca 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
+++ b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
@@ -26,7 +26,7 @@ def slice(name : str, x, axes : list, start : list, end : list):
 
         outs = exe.run(
             feed={'x': x},
-            fetch_list=[out])             
+            fetch_list=[out])
 
         saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
 
diff --git a/src/core/tests/frontend/paddlepaddle/throw_in_conversion.cpp b/src/core/tests/frontend/paddlepaddle/throw_in_conversion.cpp
index a67950514c6..88c8128baab 100644
--- a/src/core/tests/frontend/paddlepaddle/throw_in_conversion.cpp
+++ b/src/core/tests/frontend/paddlepaddle/throw_in_conversion.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <common/frontend_exceptions.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/exception.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "paddle_utils.hpp"
diff --git a/src/core/tests/frontend/shared/include/basic_api.hpp b/src/core/tests/frontend/shared/include/basic_api.hpp
index 40c11081c33..045c302d908 100644
--- a/src/core/tests/frontend/shared/include/basic_api.hpp
+++ b/src/core/tests/frontend/shared/include/basic_api.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 using BasicTestParam = std::tuple<std::string,   // FrontEnd name
                                   std::string,   // Base path to models
diff --git a/src/core/tests/frontend/shared/include/convert_model.hpp b/src/core/tests/frontend/shared/include/convert_model.hpp
index 51dd1d2f95d..14c174e4a94 100644
--- a/src/core/tests/frontend/shared/include/convert_model.hpp
+++ b/src/core/tests/frontend/shared/include/convert_model.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 using ConvertParam = std::tuple<std::string,   // FrontEnd name
                                 std::string,   // Base path to models
diff --git a/src/core/tests/frontend/shared/include/cut_specific_model.hpp b/src/core/tests/frontend/shared/include/cut_specific_model.hpp
index ea8f54b73b2..5a6fb27696c 100644
--- a/src/core/tests/frontend/shared/include/cut_specific_model.hpp
+++ b/src/core/tests/frontend/shared/include/cut_specific_model.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 struct CutModelParam {
     std::string m_frontEndName;
diff --git a/src/core/tests/frontend/shared/include/load_from.hpp b/src/core/tests/frontend/shared/include/load_from.hpp
index 77d89a65e11..db037d27953 100644
--- a/src/core/tests/frontend/shared/include/load_from.hpp
+++ b/src/core/tests/frontend/shared/include/load_from.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 struct LoadFromFEParam {
     std::string m_frontEndName;
diff --git a/src/core/tests/frontend/shared/include/op_fuzzy.hpp b/src/core/tests/frontend/shared/include/op_fuzzy.hpp
index fae7726a266..0b29df5f901 100644
--- a/src/core/tests/frontend/shared/include/op_fuzzy.hpp
+++ b/src/core/tests/frontend/shared/include/op_fuzzy.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 using FuzzyOpTestParam = std::tuple<std::string,   // FrontEnd name
                                     std::string,   // Base path to models
diff --git a/src/core/tests/frontend/shared/include/partial_shape.hpp b/src/core/tests/frontend/shared/include/partial_shape.hpp
index 5eac24ce61b..1890b1680fc 100644
--- a/src/core/tests/frontend/shared/include/partial_shape.hpp
+++ b/src/core/tests/frontend/shared/include/partial_shape.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 struct BaseFEParam {
     BaseFEParam() {}
diff --git a/src/core/tests/frontend/shared/include/set_element_type.hpp b/src/core/tests/frontend/shared/include/set_element_type.hpp
index 3ae4fce68cf..7e345420d9d 100644
--- a/src/core/tests/frontend/shared/include/set_element_type.hpp
+++ b/src/core/tests/frontend/shared/include/set_element_type.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 struct SetTypeFEParam {
     std::string m_frontEndName;
diff --git a/src/core/tests/frontend/shared/include/telemetry.hpp b/src/core/tests/frontend/shared/include/telemetry.hpp
index 770967455f2..747af456b3d 100644
--- a/src/core/tests/frontend/shared/include/telemetry.hpp
+++ b/src/core/tests/frontend/shared/include/telemetry.hpp
@@ -6,7 +6,7 @@
 
 #include <gtest/gtest.h>
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 class TelemetryMock {
 public:
diff --git a/src/core/tests/frontend/shared/include/utils.hpp b/src/core/tests/frontend/shared/include/utils.hpp
index 6c9c9d6072e..4a82ca1356d 100644
--- a/src/core/tests/frontend/shared/include/utils.hpp
+++ b/src/core/tests/frontend/shared/include/utils.hpp
@@ -5,7 +5,7 @@
 #pragma once
 
 #include <fstream>
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 #include <string>
 
 #include "common_test_utils/file_utils.hpp"
diff --git a/src/core/tests/frontend/shared/src/telemetry.cpp b/src/core/tests/frontend/shared/src/telemetry.cpp
index 088a3074b27..c96b05b990e 100644
--- a/src/core/tests/frontend/shared/src/telemetry.cpp
+++ b/src/core/tests/frontend/shared/src/telemetry.cpp
@@ -4,7 +4,7 @@
 
 #include "telemetry.hpp"
 
-#include <common/telemetry_extension.hpp>
+#include <openvino/frontend/telemetry_extension.hpp>
 
 #include "utils.hpp"
 
diff --git a/src/core/tests/frontend/tensorflow/convert_unsupported.cpp b/src/core/tests/frontend/tensorflow/convert_unsupported.cpp
index 6374912e74d..391305ea0ed 100644
--- a/src/core/tests/frontend/tensorflow/convert_unsupported.cpp
+++ b/src/core/tests/frontend/tensorflow/convert_unsupported.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <common/frontend_exceptions.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/exception.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "tf_utils.hpp"
diff --git a/src/core/tests/frontend/tensorflow/transpose_sinking.cpp b/src/core/tests/frontend/tensorflow/transpose_sinking.cpp
index b1f08b28aa9..44f50d2c80b 100644
--- a/src/core/tests/frontend/tensorflow/transpose_sinking.cpp
+++ b/src/core/tests/frontend/tensorflow/transpose_sinking.cpp
@@ -5,7 +5,7 @@
 #include "transpose_sinking.hpp"
 
 #include <frontend/shared/include/utils.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 #include <openvino/opsets/opset7.hpp>
 #include <openvino/opsets/opset8.hpp>
 #include <openvino/pass/manager.hpp>
diff --git a/src/core/tests/pass/serialization/read_ir.hpp b/src/core/tests/pass/serialization/read_ir.hpp
index 31a81999d84..e763c93ebc8 100644
--- a/src/core/tests/pass/serialization/read_ir.hpp
+++ b/src/core/tests/pass/serialization/read_ir.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 #include "openvino/core/except.hpp"
 #include "openvino/core/model.hpp"
 
diff --git a/src/frontends/common/CMakeLists.txt b/src/frontends/common/CMakeLists.txt
index 869db527261..3750dc92e92 100644
--- a/src/frontends/common/CMakeLists.txt
+++ b/src/frontends/common/CMakeLists.txt
@@ -13,7 +13,7 @@ file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
 file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
 
 # Add include path to so_extension.hpp
-set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/frontend_manager.cpp
+set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/frontend.cpp
                     APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
 
 set(FRONTEND_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
@@ -25,7 +25,6 @@ source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
 # create library
 
 add_library(${TARGET_NAME} ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
-add_library(ngraph::${TARGET_NAME} ALIAS ${TARGET_NAME})
 add_library(openvino::frontend::common ALIAS ${TARGET_NAME})
 
 target_include_directories(${TARGET_NAME}
@@ -37,10 +36,10 @@ target_include_directories(${TARGET_NAME}
         "${CMAKE_CURRENT_BINARY_DIR}/src")
 
 target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
-target_link_libraries(${TARGET_NAME} PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC ngraph)
+target_link_libraries(${TARGET_NAME} PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC openvino::core)
 set_property(SOURCE
             ${CMAKE_CURRENT_SOURCE_DIR}/src/plugin_loader.cpp
-            ${CMAKE_CURRENT_SOURCE_DIR}/src/frontend_manager.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/manager.cpp
         APPEND PROPERTY COMPILE_DEFINITIONS
         FRONTEND_LIB_PREFIX="${CMAKE_SHARED_LIBRARY_PREFIX}"
         FRONTEND_LIB_SUFFIX="${FRONTEND_NAME_SUFFIX}${IE_BUILD_POSTFIX}${CMAKE_SHARED_LIBRARY_SUFFIX}")
@@ -48,9 +47,7 @@ set_property(SOURCE
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
 ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
-                    INCLUDE_DIRECTORY "${FRONTEND_INCLUDE_DIR}"
-                    ADDITIONAL_INCLUDE_DIRECTORIES
-                        $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>)
+                    INCLUDE_DIRECTORY "${FRONTEND_INCLUDE_DIR}")
 
 ie_add_vs_version_file(NAME ${TARGET_NAME}
                        FILEDESCRIPTION "Manager of OpenVINO Frontends")
@@ -61,7 +58,7 @@ ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 if(BUILD_SHARED_LIBS)
     add_library(${TARGET_NAME}_static STATIC ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
     add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME}_static)
-    target_link_libraries(${TARGET_NAME}_static PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC ngraph)
+    target_link_libraries(${TARGET_NAME}_static PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC openvino::core)
     target_include_directories(${TARGET_NAME}_static PUBLIC
         $<BUILD_INTERFACE:${FRONTEND_INCLUDE_DIR}>)
     target_include_directories(${TARGET_NAME}_static PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
@@ -82,15 +79,9 @@ install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
         ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
         LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
 
-install(DIRECTORY ${FRONTEND_INCLUDE_DIR}/common
+install(DIRECTORY ${FRONTEND_INCLUDE_DIR}/openvino
         DESTINATION ${FRONTEND_INSTALL_INCLUDE}
-        COMPONENT core_dev
-        FILES_MATCHING PATTERN "*.hpp")
-
-install(DIRECTORY ${FRONTEND_INCLUDE_DIR}/
-        DESTINATION ${FRONTEND_INSTALL_INCLUDE}
-        COMPONENT core_dev
-        FILES_MATCHING PATTERN "manager.hpp")
+        COMPONENT core_dev)
 
 export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
        APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
diff --git a/src/frontends/common/include/common/frontend_exceptions.hpp b/src/frontends/common/include/openvino/frontend/exception.hpp
similarity index 99%
rename from src/frontends/common/include/common/frontend_exceptions.hpp
rename to src/frontends/common/include/openvino/frontend/exception.hpp
index 67dfe85d4f1..71d14238d81 100644
--- a/src/frontends/common/include/common/frontend_exceptions.hpp
+++ b/src/frontends/common/include/openvino/frontend/exception.hpp
@@ -7,8 +7,8 @@
 #include <memory>
 #include <string>
 
-#include "frontend_defs.hpp"
 #include "openvino/core/except.hpp"
+#include "visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/common/include/common/frontend.hpp b/src/frontends/common/include/openvino/frontend/frontend.hpp
similarity index 86%
rename from src/frontends/common/include/common/frontend.hpp
rename to src/frontends/common/include/openvino/frontend/frontend.hpp
index 1ba63e6f593..13b30254a39 100644
--- a/src/frontends/common/include/common/frontend.hpp
+++ b/src/frontends/common/include/openvino/frontend/frontend.hpp
@@ -8,12 +8,12 @@
 #include <string>
 #include <vector>
 
-#include "frontend_defs.hpp"
 #include "input_model.hpp"
 #include "openvino/core/any.hpp"
 #include "openvino/core/extension.hpp"
 #include "openvino/core/model.hpp"
 #include "openvino/core/op_extension.hpp"
+#include "visibility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -25,7 +25,7 @@ public:
 
     FrontEnd();
 
-    virtual ~FrontEnd();
+    virtual ~FrontEnd() = 0;
 
     /// \brief Validates if FrontEnd can recognize model with parameters specified.
     /// Same parameters should be used to load model.
@@ -55,34 +55,34 @@ public:
         return load_impl(vars);
     }
 
-    /// \brief Completely convert and normalize entire function, throws if it is not
+    /// \brief Completely convert and normalize entire Model, throws if it is not
     /// possible
     /// \param model Input model
-    /// \return fully converted nGraph function
+    /// \return fully converted OV Model
     virtual std::shared_ptr<ov::Model> convert(InputModel::Ptr model) const;
 
-    /// \brief Completely convert the remaining, not converted part of a function.
-    /// \param partiallyConverted partially converted nGraph function
+    /// \brief Completely convert the remaining, not converted part of a Model.
+    /// \param partiallyConverted partially converted OV Model
     virtual void convert(std::shared_ptr<ov::Model> partially_converted) const;
 
     /// \brief Convert only those parts of the model that can be converted leaving others
     /// as-is. Converted parts are not normalized by additional transformations; normalize
-    /// function or another form of convert function should be called to finalize the
+    /// Model or another form of convert Model should be called to finalize the
     /// conversion process.
     /// \param model Input model
-    /// \return partially converted nGraph function
+    /// \return partially converted OV Model
     virtual std::shared_ptr<ov::Model> convert_partially(InputModel::Ptr model) const;
 
     /// \brief Convert operations with one-to-one mapping with decoding nodes.
-    /// Each decoding node is an nGraph node representing a single FW operation node with
+    /// Each decoding node is an OV node representing a single FW operation node with
     /// all attributes represented in FW-independent way.
     /// \param model Input model
-    /// \return nGraph function after decoding
+    /// \return OV Model after decoding
     virtual std::shared_ptr<ov::Model> decode(InputModel::Ptr model) const;
 
-    /// \brief Runs normalization passes on function that was loaded with partial conversion
-    /// \param function partially converted nGraph function
-    virtual void normalize(std::shared_ptr<ov::Model> function) const;
+    /// \brief Runs normalization passes on Model that was loaded with partial conversion
+    /// \param Model partially converted OV Model
+    virtual void normalize(std::shared_ptr<ov::Model> model) const;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
     /// if frontend is selected automatically by FrontEndManager::load_by_model
diff --git a/src/frontends/common/include/common/input_model.hpp b/src/frontends/common/include/openvino/frontend/input_model.hpp
similarity index 97%
rename from src/frontends/common/include/common/input_model.hpp
rename to src/frontends/common/include/openvino/frontend/input_model.hpp
index 1642e3b10f2..c8327b0252c 100644
--- a/src/frontends/common/include/common/input_model.hpp
+++ b/src/frontends/common/include/openvino/frontend/input_model.hpp
@@ -8,10 +8,10 @@
 #include <string>
 #include <vector>
 
-#include "frontend_defs.hpp"
 #include "openvino/core/partial_shape.hpp"
 #include "openvino/core/type/element_type.hpp"
 #include "place.hpp"
+#include "visibility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -22,7 +22,7 @@ namespace frontend {
 /// \note Class methods are divided into several groups: searching for places, naming and
 /// annotation, topology editing, setting tensor properties.
 ///
-/// Editing requests may affect ability to convert the original model to nGraph function.
+/// Editing requests may affect ability to convert the original model to OV Model.
 /// Aim to provide these editing capabilities is to unlock conversion for models that
 /// are not natively supported "as-is" because of undefined shapes, types or operations.
 ///
@@ -39,7 +39,7 @@ class FRONTEND_API InputModel {
 public:
     typedef std::shared_ptr<InputModel> Ptr;
 
-    virtual ~InputModel() = default;
+    virtual ~InputModel() = 0;
 
     /////  Searching for places  /////
 
@@ -169,8 +169,8 @@ public:
 
     /// \brief Defines all possible shape that may be used for this place; place should be
     /// uniquely refer to some data. This partial shape will be converted to corresponding
-    /// shape of results ngraph nodes and will define shape inference when the model is
-    /// converted to ngraph.
+    /// shape of results OV nodes and will define shape inference when the model is
+    /// converted to OV.
     /// \param place Model place
     /// \param shape Partial shape for this place
     virtual void set_partial_shape(Place::Ptr place, const ov::PartialShape& shape);
diff --git a/src/frontends/common/include/manager.hpp b/src/frontends/common/include/openvino/frontend/manager.hpp
similarity index 94%
rename from src/frontends/common/include/manager.hpp
rename to src/frontends/common/include/openvino/frontend/manager.hpp
index b79fc89ba2c..af6a3778aac 100644
--- a/src/frontends/common/include/manager.hpp
+++ b/src/frontends/common/include/openvino/frontend/manager.hpp
@@ -8,9 +8,9 @@
 #include <sstream>
 #include <string>
 
-#include "common/frontend.hpp"
-#include "common/frontend_defs.hpp"
 #include "openvino/core/any.hpp"
+#include "openvino/frontend/frontend.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -22,8 +22,7 @@ using FrontEndFactory = std::function<FrontEnd::Ptr()>;
 /// frontends This is a main frontend entry point for client applications
 class FRONTEND_API FrontEndManager final {
 public:
-    /// \brief Default constructor. Searches and loads of available frontends
-    FrontEndManager();
+    /*/ \brief Default constructor. Searches and loads of available frontends*/ FrontEndManager();
 
     /// \brief Default move constructor
     FrontEndManager(FrontEndManager&&) noexcept;
diff --git a/src/frontends/common/include/common/place.hpp b/src/frontends/common/include/openvino/frontend/place.hpp
similarity index 99%
rename from src/frontends/common/include/common/place.hpp
rename to src/frontends/common/include/openvino/frontend/place.hpp
index f5c00263764..7fdab781996 100644
--- a/src/frontends/common/include/common/place.hpp
+++ b/src/frontends/common/include/openvino/frontend/place.hpp
@@ -8,7 +8,7 @@
 #include <string>
 #include <vector>
 
-#include "frontend_defs.hpp"
+#include "visibility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -61,7 +61,7 @@ class FRONTEND_API Place {
 public:
     typedef std::shared_ptr<Place> Ptr;
 
-    virtual ~Place() = default;
+    virtual ~Place() = 0;
 
     /// \brief All associated names (synonyms) that identify this place in the graph in a
     /// framework specific way
diff --git a/src/frontends/common/include/common/telemetry_extension.hpp b/src/frontends/common/include/openvino/frontend/telemetry_extension.hpp
similarity index 97%
rename from src/frontends/common/include/common/telemetry_extension.hpp
rename to src/frontends/common/include/openvino/frontend/telemetry_extension.hpp
index 914ad41c4bb..b1454414b3e 100644
--- a/src/frontends/common/include/common/telemetry_extension.hpp
+++ b/src/frontends/common/include/openvino/frontend/telemetry_extension.hpp
@@ -8,11 +8,11 @@
 #include <iostream>
 #include <type_traits>
 
-#include "frontend_defs.hpp"
 #include "openvino/core/extension.hpp"
 #include "openvino/pass/graph_rewrite.hpp"
 #include "openvino/pass/manager.hpp"
 #include "openvino/pass/pass.hpp"
+#include "visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/common/include/common/frontend_defs.hpp b/src/frontends/common/include/openvino/frontend/visibility.hpp
similarity index 68%
rename from src/frontends/common/include/common/frontend_defs.hpp
rename to src/frontends/common/include/openvino/frontend/visibility.hpp
index 23d2fa86998..b578a596673 100644
--- a/src/frontends/common/include/common/frontend_defs.hpp
+++ b/src/frontends/common/include/openvino/frontend/visibility.hpp
@@ -11,11 +11,14 @@
 
 #if defined(USE_STATIC_FRONTEND_COMMON) || defined(OPENVINO_STATIC_LIBRARY)
 #    define FRONTEND_API
+#    define FRONTEND_C_API
 #else
 // Defined if cmake is building the frontend_common DLL (instead of using it)
 #    ifdef frontend_common_EXPORTS
-#        define FRONTEND_API OPENVINO_CORE_EXPORTS
+#        define FRONTEND_API   OPENVINO_CORE_EXPORTS
+#        define FRONTEND_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    else
-#        define FRONTEND_API OPENVINO_CORE_IMPORTS
+#        define FRONTEND_API   OPENVINO_CORE_IMPORTS
+#        define FRONTEND_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
 #    endif  // frontend_common_EXPORTS
 #endif      // USE_STATIC_FRONTEND_COMMON || OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/common/src/frontend.cpp b/src/frontends/common/src/frontend.cpp
new file mode 100644
index 00000000000..2ab96691ee9
--- /dev/null
+++ b/src/frontends/common/src/frontend.cpp
@@ -0,0 +1,72 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/util/env_util.hpp>
+#include <openvino/util/file_util.hpp>
+
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/place.hpp"
+#include "openvino/util/env_util.hpp"
+#include "plugin_loader.hpp"
+#include "so_extension.hpp"
+#include "utils.hpp"
+
+using namespace ov;
+using namespace ov::frontend;
+
+FrontEnd::FrontEnd() = default;
+
+FrontEnd::~FrontEnd() = default;
+
+bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
+    return false;
+}
+
+InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& params) const {
+    FRONT_END_NOT_IMPLEMENTED(load_impl);
+}
+std::shared_ptr<Model> FrontEnd::convert(InputModel::Ptr model) const {
+    FRONT_END_NOT_IMPLEMENTED(convert);
+}
+
+void FrontEnd::convert(std::shared_ptr<Model>) const {
+    FRONT_END_NOT_IMPLEMENTED(convert);
+}
+
+std::shared_ptr<Model> FrontEnd::convert_partially(InputModel::Ptr model) const {
+    FRONT_END_NOT_IMPLEMENTED(convert_partially);
+}
+
+std::shared_ptr<Model> FrontEnd::decode(InputModel::Ptr model) const {
+    FRONT_END_NOT_IMPLEMENTED(decode);
+}
+
+void FrontEnd::normalize(std::shared_ptr<Model> model) const {
+    FRONT_END_NOT_IMPLEMENTED(normalize);
+}
+
+void FrontEnd::add_extension(const std::shared_ptr<ov::Extension>& extension) {
+    // Left unimplemented intentionally.
+    // Each frontend can support own set of extensions, so this method should be implemented on the frontend side
+}
+
+void FrontEnd::add_extension(const std::vector<std::shared_ptr<ov::Extension>>& extensions) {
+    for (const auto& ext : extensions)
+        add_extension(ext);
+}
+
+void FrontEnd::add_extension(const std::string& library_path) {
+    add_extension(ov::detail::load_extensions(library_path));
+}
+
+#ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
+void FrontEnd::add_extension(const std::wstring& library_path) {
+    add_extension(ov::detail::load_extensions(library_path));
+}
+#endif
+
+std::string FrontEnd::get_name() const {
+    return std::string();
+}
diff --git a/src/frontends/common/src/input_model.cpp b/src/frontends/common/src/input_model.cpp
new file mode 100644
index 00000000000..9c1cc7a3629
--- /dev/null
+++ b/src/frontends/common/src/input_model.cpp
@@ -0,0 +1,114 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/util/env_util.hpp>
+
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/place.hpp"
+#include "utils.hpp"
+
+using namespace ov;
+using namespace ov::frontend;
+
+InputModel::~InputModel() = default;
+
+std::vector<Place::Ptr> InputModel::get_inputs() const {
+    return {};
+}
+
+std::vector<Place::Ptr> InputModel::get_outputs() const {
+    return {};
+}
+
+Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensor_name) const {
+    return nullptr;
+}
+
+Place::Ptr InputModel::get_place_by_operation_name(const std::string& operation_name) const {
+    return nullptr;
+}
+
+Place::Ptr InputModel::get_place_by_operation_name_and_input_port(const std::string& operation_name,
+                                                                  int input_port_index) {
+    return nullptr;
+}
+
+Place::Ptr InputModel::get_place_by_operation_name_and_output_port(const std::string& operation_name,
+                                                                   int output_port_index) {
+    return nullptr;
+}
+
+void InputModel::set_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
+    FRONT_END_NOT_IMPLEMENTED(set_name_for_tensor);
+}
+
+void InputModel::add_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
+    FRONT_END_NOT_IMPLEMENTED(add_name_for_tensor);
+}
+
+void InputModel::set_name_for_operation(Place::Ptr operation, const std::string& new_name) {
+    FRONT_END_NOT_IMPLEMENTED(set_name_for_operation);
+}
+
+void InputModel::free_name_for_tensor(const std::string& name) {
+    FRONT_END_NOT_IMPLEMENTED(free_name_for_tensor);
+}
+
+void InputModel::free_name_for_operation(const std::string& name) {
+    FRONT_END_NOT_IMPLEMENTED(free_name_for_operation);
+}
+
+void InputModel::set_name_for_dimension(Place::Ptr place, size_t shape_dim_index, const std::string& dim_name) {
+    FRONT_END_NOT_IMPLEMENTED(set_name_for_dimension);
+}
+
+void InputModel::cut_and_add_new_input(Place::Ptr place, const std::string& new_name_optional) {
+    FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_input);
+}
+
+void InputModel::cut_and_add_new_output(Place::Ptr place, const std::string& new_name_optional) {
+    FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_output);
+}
+
+Place::Ptr InputModel::add_output(Place::Ptr place) {
+    FRONT_END_NOT_IMPLEMENTED(add_output);
+}
+
+void InputModel::remove_output(Place::Ptr place) {
+    FRONT_END_NOT_IMPLEMENTED(remove_output);
+}
+
+void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
+    FRONT_END_NOT_IMPLEMENTED(override_all_outputs);
+}
+
+void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
+    FRONT_END_NOT_IMPLEMENTED(override_all_inputs);
+}
+
+void InputModel::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
+    FRONT_END_NOT_IMPLEMENTED(extract_subgraph);
+}
+
+// Setting tensor properties
+void InputModel::set_partial_shape(Place::Ptr place, const PartialShape&) {
+    FRONT_END_NOT_IMPLEMENTED(set_partial_shape);
+}
+
+PartialShape InputModel::get_partial_shape(Place::Ptr place) const {
+    FRONT_END_NOT_IMPLEMENTED(get_partial_shape);
+}
+
+void InputModel::set_element_type(Place::Ptr place, const element::Type&) {
+    FRONT_END_NOT_IMPLEMENTED(set_element_type);
+}
+
+void InputModel::set_tensor_value(Place::Ptr place, const void* value) {
+    FRONT_END_NOT_IMPLEMENTED(set_tensor_value);
+}
+
+void InputModel::set_tensor_partial_value(Place::Ptr place, const void* min_value, const void* max_value) {
+    FRONT_END_NOT_IMPLEMENTED(set_tensor_partial_value);
+}
diff --git a/src/frontends/common/src/frontend_manager.cpp b/src/frontends/common/src/manager.cpp
similarity index 51%
rename from src/frontends/common/src/frontend_manager.cpp
rename to src/frontends/common/src/manager.cpp
index f50781418a4..7b79721603a 100644
--- a/src/frontends/common/src/frontend_manager.cpp
+++ b/src/frontends/common/src/manager.cpp
@@ -2,22 +2,19 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/frontend/manager.hpp"
+
 #include <openvino/util/env_util.hpp>
 #include <openvino/util/file_util.hpp>
 
-#include "common/frontend_exceptions.hpp"
-#include "common/place.hpp"
-#include "manager.hpp"
-#include "ngraph/except.hpp"
-#include "openvino/util/env_util.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/place.hpp"
 #include "plugin_loader.hpp"
-#include "so_extension.hpp"
 #include "utils.hpp"
 
 using namespace ov;
 using namespace ov::frontend;
 
-//----------- FrontEndManager ---------------------------
 class FrontEndManager::Impl {
     std::mutex m_loading_mutex;
     std::vector<PluginInfo> m_plugins;
@@ -232,285 +229,3 @@ template <>
 FrontEnd::Ptr FrontEndManager::load_by_model(const std::vector<ov::Any>& variants) {
     return load_by_model_impl(variants);
 }
-
-//----------- FrontEnd ---------------------------
-
-FrontEnd::FrontEnd() = default;
-
-FrontEnd::~FrontEnd() = default;
-
-bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
-    return false;
-}
-
-InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& params) const {
-    FRONT_END_NOT_IMPLEMENTED(load_impl);
-}
-std::shared_ptr<Model> FrontEnd::convert(InputModel::Ptr model) const {
-    FRONT_END_NOT_IMPLEMENTED(convert);
-}
-
-void FrontEnd::convert(std::shared_ptr<Model>) const {
-    FRONT_END_NOT_IMPLEMENTED(convert);
-}
-
-std::shared_ptr<Model> FrontEnd::convert_partially(InputModel::Ptr model) const {
-    FRONT_END_NOT_IMPLEMENTED(convert_partially);
-}
-
-std::shared_ptr<Model> FrontEnd::decode(InputModel::Ptr model) const {
-    FRONT_END_NOT_IMPLEMENTED(decode);
-}
-
-void FrontEnd::normalize(std::shared_ptr<Model> model) const {
-    FRONT_END_NOT_IMPLEMENTED(normalize);
-}
-
-void FrontEnd::add_extension(const std::shared_ptr<ov::Extension>& extension) {
-    // Left unimplemented intentionally.
-    // Each frontend can support own set of extensions, so this method should be implemented on the frontend side
-}
-
-void FrontEnd::add_extension(const std::vector<std::shared_ptr<ov::Extension>>& extensions) {
-    for (const auto& ext : extensions)
-        add_extension(ext);
-}
-
-void FrontEnd::add_extension(const std::string& library_path) {
-    add_extension(ov::detail::load_extensions(library_path));
-}
-
-#ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-void FrontEnd::add_extension(const std::wstring& library_path) {
-    add_extension(ov::detail::load_extensions(library_path));
-}
-#endif
-
-std::string FrontEnd::get_name() const {
-    return std::string();
-}
-
-//----------- InputModel ---------------------------
-std::vector<Place::Ptr> InputModel::get_inputs() const {
-    return {};
-}
-
-std::vector<Place::Ptr> InputModel::get_outputs() const {
-    return {};
-}
-
-Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensor_name) const {
-    return nullptr;
-}
-
-Place::Ptr InputModel::get_place_by_operation_name(const std::string& operation_name) const {
-    return nullptr;
-}
-
-Place::Ptr InputModel::get_place_by_operation_name_and_input_port(const std::string& operation_name,
-                                                                  int input_port_index) {
-    return nullptr;
-}
-
-Place::Ptr InputModel::get_place_by_operation_name_and_output_port(const std::string& operation_name,
-                                                                   int output_port_index) {
-    return nullptr;
-}
-
-void InputModel::set_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
-    FRONT_END_NOT_IMPLEMENTED(set_name_for_tensor);
-}
-
-void InputModel::add_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
-    FRONT_END_NOT_IMPLEMENTED(add_name_for_tensor);
-}
-
-void InputModel::set_name_for_operation(Place::Ptr operation, const std::string& new_name) {
-    FRONT_END_NOT_IMPLEMENTED(set_name_for_operation);
-}
-
-void InputModel::free_name_for_tensor(const std::string& name) {
-    FRONT_END_NOT_IMPLEMENTED(free_name_for_tensor);
-}
-
-void InputModel::free_name_for_operation(const std::string& name) {
-    FRONT_END_NOT_IMPLEMENTED(free_name_for_operation);
-}
-
-void InputModel::set_name_for_dimension(Place::Ptr place, size_t shape_dim_index, const std::string& dim_name) {
-    FRONT_END_NOT_IMPLEMENTED(set_name_for_dimension);
-}
-
-void InputModel::cut_and_add_new_input(Place::Ptr place, const std::string& new_name_optional) {
-    FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_input);
-}
-
-void InputModel::cut_and_add_new_output(Place::Ptr place, const std::string& new_name_optional) {
-    FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_output);
-}
-
-Place::Ptr InputModel::add_output(Place::Ptr place) {
-    FRONT_END_NOT_IMPLEMENTED(add_output);
-}
-
-void InputModel::remove_output(Place::Ptr place) {
-    FRONT_END_NOT_IMPLEMENTED(remove_output);
-}
-
-void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
-    FRONT_END_NOT_IMPLEMENTED(override_all_outputs);
-}
-
-void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
-    FRONT_END_NOT_IMPLEMENTED(override_all_inputs);
-}
-
-void InputModel::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
-    FRONT_END_NOT_IMPLEMENTED(extract_subgraph);
-}
-
-// Setting tensor properties
-void InputModel::set_partial_shape(Place::Ptr place, const PartialShape&) {
-    FRONT_END_NOT_IMPLEMENTED(set_partial_shape);
-}
-
-PartialShape InputModel::get_partial_shape(Place::Ptr place) const {
-    FRONT_END_NOT_IMPLEMENTED(get_partial_shape);
-}
-
-void InputModel::set_element_type(Place::Ptr place, const element::Type&) {
-    FRONT_END_NOT_IMPLEMENTED(set_element_type);
-}
-
-void InputModel::set_tensor_value(Place::Ptr place, const void* value) {
-    FRONT_END_NOT_IMPLEMENTED(set_tensor_value);
-}
-
-void InputModel::set_tensor_partial_value(Place::Ptr place, const void* min_value, const void* max_value) {
-    FRONT_END_NOT_IMPLEMENTED(set_tensor_partial_value);
-}
-
-//----------- Place ---------------------------
-std::vector<std::string> Place::get_names() const {
-    FRONT_END_NOT_IMPLEMENTED(get_names);
-}
-
-std::vector<Place::Ptr> Place::get_consuming_operations() const {
-    return {};
-}
-
-std::vector<Place::Ptr> Place::get_consuming_operations(int output_port_index) const {
-    return {};
-}
-
-std::vector<Place::Ptr> Place::get_consuming_operations(const std::string& outputPortName, int outputPortIndex) const {
-    return {};
-}
-
-Place::Ptr Place::get_target_tensor() const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_target_tensor(int output_port_index) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_producing_operation() const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_producing_operation(int input_port_index) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_producing_port() const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_input_port() const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_input_port(int input_port_index) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_input_port(const std::string& input_name) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_input_port(const std::string& input_name, int input_port_index) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_output_port() const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_output_port(int output_port_index) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_output_port(const std::string& output_name) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_output_port(const std::string& output_name, int output_port_index) const {
-    return nullptr;
-}
-
-std::vector<Place::Ptr> Place::get_consuming_ports() const {
-    return {};
-}
-
-bool Place::is_input() const {
-    FRONT_END_NOT_IMPLEMENTED(is_input);
-}
-
-bool Place::is_output() const {
-    FRONT_END_NOT_IMPLEMENTED(is_output);
-}
-
-bool Place::is_equal(Ptr another) const {
-    FRONT_END_NOT_IMPLEMENTED(is_equal);
-}
-
-bool Place::is_equal_data(Ptr another) const {
-    FRONT_END_NOT_IMPLEMENTED(is_equal_data);
-}
-
-Place::Ptr Place::get_source_tensor() const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_source_tensor(int input_port_index) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_source_tensor(const std::string& inputName) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_target_tensor(const std::string& outputPortName) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_target_tensor(const std::string& outputPortName, int outputPortIndex) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_producing_operation(const std::string& inputName) const {
-    return nullptr;
-}
-
-Place::Ptr Place::get_producing_operation(const std::string& inputName, int inputPortIndex) const {
-    return nullptr;
-}
-
-std::vector<Place::Ptr> Place::get_consuming_operations(const std::string& outputPortName) const {
-    return {};
-}
diff --git a/src/frontends/common/src/place.cpp b/src/frontends/common/src/place.cpp
new file mode 100644
index 00000000000..9b8f611f9ce
--- /dev/null
+++ b/src/frontends/common/src/place.cpp
@@ -0,0 +1,139 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/place.hpp"
+
+#include <openvino/util/env_util.hpp>
+
+#include "openvino/frontend/exception.hpp"
+#include "utils.hpp"
+
+using namespace ov;
+using namespace ov::frontend;
+
+Place::~Place() = default;
+
+std::vector<std::string> Place::get_names() const {
+    FRONT_END_NOT_IMPLEMENTED(get_names);
+}
+
+std::vector<Place::Ptr> Place::get_consuming_operations() const {
+    return {};
+}
+
+std::vector<Place::Ptr> Place::get_consuming_operations(int output_port_index) const {
+    return {};
+}
+
+std::vector<Place::Ptr> Place::get_consuming_operations(const std::string& outputPortName, int outputPortIndex) const {
+    return {};
+}
+
+Place::Ptr Place::get_target_tensor() const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_target_tensor(int output_port_index) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_producing_operation() const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_producing_operation(int input_port_index) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_producing_port() const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_input_port() const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_input_port(int input_port_index) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_input_port(const std::string& input_name) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_input_port(const std::string& input_name, int input_port_index) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_output_port() const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_output_port(int output_port_index) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_output_port(const std::string& output_name) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_output_port(const std::string& output_name, int output_port_index) const {
+    return nullptr;
+}
+
+std::vector<Place::Ptr> Place::get_consuming_ports() const {
+    return {};
+}
+
+bool Place::is_input() const {
+    FRONT_END_NOT_IMPLEMENTED(is_input);
+}
+
+bool Place::is_output() const {
+    FRONT_END_NOT_IMPLEMENTED(is_output);
+}
+
+bool Place::is_equal(Ptr another) const {
+    FRONT_END_NOT_IMPLEMENTED(is_equal);
+}
+
+bool Place::is_equal_data(Ptr another) const {
+    FRONT_END_NOT_IMPLEMENTED(is_equal_data);
+}
+
+Place::Ptr Place::get_source_tensor() const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_source_tensor(int input_port_index) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_source_tensor(const std::string& inputName) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_target_tensor(const std::string& outputPortName) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_target_tensor(const std::string& outputPortName, int outputPortIndex) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_producing_operation(const std::string& inputName) const {
+    return nullptr;
+}
+
+Place::Ptr Place::get_producing_operation(const std::string& inputName, int inputPortIndex) const {
+    return nullptr;
+}
+
+std::vector<Place::Ptr> Place::get_consuming_operations(const std::string& outputPortName) const {
+    return {};
+}
diff --git a/src/frontends/common/src/plugin_loader.cpp b/src/frontends/common/src/plugin_loader.cpp
index e3d3cb1c2f4..e7233ec2542 100644
--- a/src/frontends/common/src/plugin_loader.cpp
+++ b/src/frontends/common/src/plugin_loader.cpp
@@ -16,7 +16,7 @@
 
 #include <sys/stat.h>
 
-#include <ngraph/log.hpp>
+#include <openvino/util/log.hpp>
 #include <string>
 #include <vector>
 
@@ -61,7 +61,6 @@ void load_static_plugins(std::vector<PluginInfo>& res) {
 
 // TODO: change to std::filesystem for C++17
 static std::vector<std::string> list_files(const std::string& path) {
-    NGRAPH_SUPPRESS_DEPRECATED_START
     std::vector<std::string> res;
     try {
         const auto prefix = std::string(FRONTEND_LIB_PREFIX);
@@ -82,7 +81,6 @@ static std::vector<std::string> list_files(const std::string& path) {
         // Ignore exceptions
     }
     return res;
-    NGRAPH_SUPPRESS_DEPRECATED_END
 }
 
 void ov::frontend::find_plugins(const std::string& dir_name, std::vector<PluginInfo>& res) {
@@ -99,7 +97,7 @@ void ov::frontend::find_plugins(const std::string& dir_name, std::vector<PluginI
             }) == res.end()) {
             res.emplace_back(std::move(plugin_info));
         } else {
-            NGRAPH_DEBUG << "Static frontend for '" << plugin_info.m_file_name << "' is already loaded\n";
+            OPENVINO_DEBUG << "Static frontend for '" << plugin_info.m_file_name << "' is already loaded\n";
         }
     }
 }
@@ -139,27 +137,27 @@ bool PluginInfo::load_internal() {
     try {
         so = ov::util::load_shared_object(m_file_path.c_str());
     } catch (const std::exception& ex) {
-        NGRAPH_DEBUG << "Error loading FrontEnd '" << m_file_path << "': " << ex.what() << std::endl;
+        OPENVINO_DEBUG << "Error loading FrontEnd '" << m_file_path << "': " << ex.what() << std::endl;
         return false;
     }
 
     auto info_addr = reinterpret_cast<void* (*)()>(ov::util::get_symbol(so, "GetAPIVersion"));
     if (!info_addr) {
-        NGRAPH_DEBUG << "Loaded FrontEnd [" << m_file_path << "] doesn't have API version" << std::endl;
+        OPENVINO_DEBUG << "Loaded FrontEnd [" << m_file_path << "] doesn't have API version" << std::endl;
         return false;
     }
     FrontEndVersion plug_info{reinterpret_cast<FrontEndVersion>(info_addr())};
 
     if (plug_info != OV_FRONTEND_API_VERSION) {
         // Plugin has incompatible API version, do not load it
-        NGRAPH_DEBUG << "Loaded FrontEnd [" << m_file_path << "] has incompatible API version" << plug_info
-                     << std::endl;
+        OPENVINO_DEBUG << "Loaded FrontEnd [" << m_file_path << "] has incompatible API version" << plug_info
+                       << std::endl;
         return false;
     }
 
     auto creator_addr = reinterpret_cast<void* (*)()>(ov::util::get_symbol(so, "GetFrontEndData"));
     if (!creator_addr) {
-        NGRAPH_DEBUG << "Loaded FrontEnd [" << m_file_path << "] doesn't have Frontend Data" << std::endl;
+        OPENVINO_DEBUG << "Loaded FrontEnd [" << m_file_path << "] doesn't have Frontend Data" << std::endl;
         return false;
     }
 
diff --git a/src/frontends/common/src/plugin_loader.hpp b/src/frontends/common/src/plugin_loader.hpp
index 32340d05729..481b6dd434a 100644
--- a/src/frontends/common/src/plugin_loader.hpp
+++ b/src/frontends/common/src/plugin_loader.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #ifdef _WIN32
 static const char PathSeparator[] = ";";
diff --git a/src/frontends/common/src/telemetry_extension.cpp b/src/frontends/common/src/telemetry_extension.cpp
index 134836ac701..8dfa0c3725d 100644
--- a/src/frontends/common/src/telemetry_extension.cpp
+++ b/src/frontends/common/src/telemetry_extension.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "common/telemetry_extension.hpp"
+#include "openvino/frontend/telemetry_extension.hpp"
 
 using namespace ov::frontend;
 
diff --git a/src/frontends/common/src/utils.cpp b/src/frontends/common/src/utils.cpp
index 8fceccf2718..35a54723e6e 100644
--- a/src/frontends/common/src/utils.cpp
+++ b/src/frontends/common/src/utils.cpp
@@ -4,7 +4,7 @@
 
 #include "utils.hpp"
 
-#include "common/frontend_exceptions.hpp"
+#include "openvino/frontend/exception.hpp"
 #include "openvino/util/file_util.hpp"
 #include "plugin_loader.hpp"
 
diff --git a/src/frontends/common/src/utils.hpp b/src/frontends/common/src/utils.hpp
index c5a4f30475f..100e4abb207 100644
--- a/src/frontends/common/src/utils.hpp
+++ b/src/frontends/common/src/utils.hpp
@@ -4,7 +4,7 @@
 
 #include <string>
 
-#include "common/frontend_defs.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/ir/include/ir_frontend/model.hpp b/src/frontends/ir/include/ir_frontend/model.hpp
deleted file mode 100644
index 243bb327d9b..00000000000
--- a/src/frontends/ir/include/ir_frontend/model.hpp
+++ /dev/null
@@ -1,31 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <istream>
-#include <memory>
-
-#include "ir_frontend/utility.hpp"
-#include "manager.hpp"
-#include "ngraph/runtime/aligned_buffer.hpp"
-
-namespace ov {
-namespace frontend {
-
-class IR_API InputModelIR : public InputModel {
-    friend class FrontEndIR;
-    class InputModelIRImpl;
-    std::shared_ptr<InputModelIRImpl> _impl;
-
-public:
-    InputModelIR(std::istream& stream,
-                 const std::shared_ptr<ngraph::runtime::AlignedBuffer>& weights,
-                 const std::unordered_map<ov::DiscreteTypeInfo, ov::BaseOpExtension::Ptr>& extensions);
-
-    std::shared_ptr<Model> convert();
-};
-
-}  // namespace frontend
-}  // namespace ov
diff --git a/src/frontends/ir/include/ir_frontend/frontend.hpp b/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
similarity index 82%
rename from src/frontends/ir/include/ir_frontend/frontend.hpp
rename to src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
index ac79bca3ff1..4139e37b929 100644
--- a/src/frontends/ir/include/ir_frontend/frontend.hpp
+++ b/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
@@ -4,16 +4,19 @@
 
 #pragma once
 
-#include "common/frontend.hpp"
-#include "common/telemetry_extension.hpp"
-#include "utility.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/frontend.hpp"
+#include "openvino/frontend/ir/visibility.hpp"
+#include "openvino/frontend/telemetry_extension.hpp"
+#include "openvino/openvino.hpp"
 
 namespace ov {
 namespace frontend {
+namespace ir {
 
-class IR_API FrontEndIR : public FrontEnd {
+class IR_API FrontEnd : public ov::frontend::FrontEnd {
 public:
-    FrontEndIR() = default;
+    FrontEnd() = default;
 
     /// \brief Completely convert the remaining, not converted part of a function.
     /// \param partiallyConverted partially converted nGraph function
@@ -47,5 +50,6 @@ private:
     std::shared_ptr<TelemetryExtension> m_telemetry;
 };
 
+}  // namespace ir
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/ir/include/ir_frontend/utility.hpp b/src/frontends/ir/include/openvino/frontend/ir/visibility.hpp
similarity index 60%
rename from src/frontends/ir/include/ir_frontend/utility.hpp
rename to src/frontends/ir/include/openvino/frontend/ir/visibility.hpp
index 5e9555fc50d..a90f0e1d529 100644
--- a/src/frontends/ir/include/ir_frontend/utility.hpp
+++ b/src/frontends/ir/include/openvino/frontend/ir/visibility.hpp
@@ -18,13 +18,3 @@
 #        define IR_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
 #    endif  // ir_ov_frontend_EXPORTS
 #endif      // OPENVINO_STATIC_LIBRARY
-
-#define IR_ASSERT(ex, msg)                 \
-    {                                      \
-        if (!(ex))                         \
-            throw std::runtime_error(msg); \
-    }
-
-#define IR_THROW(msg) throw std::runtime_error(std::string("ERROR: ") + msg)
-
-#define NOT_IMPLEMENTED(msg) throw std::runtime_error(std::string(msg) + " is not implemented")
diff --git a/src/frontends/ir/src/frontend.cpp b/src/frontends/ir/src/frontend.cpp
index ff32cde6dee..4ce3da25c5a 100644
--- a/src/frontends/ir/src/frontend.cpp
+++ b/src/frontends/ir/src/frontend.cpp
@@ -2,13 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ir_frontend/frontend.hpp"
+#include "openvino/frontend/ir/frontend.hpp"
 
 #include <array>
 #include <vector>
 
-#include "ir_frontend/model.hpp"
-#include "ir_frontend/utility.hpp"
+#include "input_model.hpp"
 #include "ngraph/runtime/aligned_buffer.hpp"
 #include "ngraph/runtime/shared_buffer.hpp"
 #include "openvino/core/any.hpp"
@@ -20,6 +19,7 @@ using namespace ov;
 
 namespace ov {
 namespace frontend {
+namespace ir {
 namespace {
 
 inline size_t GetIRVersion(pugi::xml_node& root) {
@@ -59,7 +59,7 @@ size_t GetIRVersion(std::istream& model) {
 
 }  // namespace
 
-bool FrontEndIR::supported_impl(const std::vector<ov::Any>& variants) const {
+bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
     std::ifstream local_model_stream;
     std::istream* provided_model_stream = nullptr;
 
@@ -99,7 +99,7 @@ bool FrontEndIR::supported_impl(const std::vector<ov::Any>& variants) const {
     return version >= 10 && version <= 11;
 }
 
-void FrontEndIR::add_extension(const ov::Extension::Ptr& ext) {
+void FrontEnd::add_extension(const ov::Extension::Ptr& ext) {
     if (auto telemetry = std::dynamic_pointer_cast<TelemetryExtension>(ext)) {
         m_telemetry = telemetry;
     } else if (auto so_ext = std::dynamic_pointer_cast<ov::detail::SOExtension>(ext)) {
@@ -111,7 +111,7 @@ void FrontEndIR::add_extension(const ov::Extension::Ptr& ext) {
         extensions.emplace_back(ext);
 }
 
-InputModel::Ptr FrontEndIR::load_impl(const std::vector<ov::Any>& variants) const {
+InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const {
     std::ifstream local_model_stream;
     std::istream* provided_model_stream = nullptr;
     std::shared_ptr<ngraph::runtime::AlignedBuffer> weights;
@@ -125,11 +125,11 @@ InputModel::Ptr FrontEndIR::load_impl(const std::vector<ov::Any>& variants) cons
         return exts;
     };
 
-    auto create_input_model = [&]() -> std::shared_ptr<InputModelIR> {
+    auto create_input_model = [&]() -> std::shared_ptr<InputModel> {
         if (provided_model_stream) {
-            return std::make_shared<InputModelIR>(*provided_model_stream, weights, create_extensions_map());
+            return std::make_shared<InputModel>(*provided_model_stream, weights, create_extensions_map());
         } else if (local_model_stream.is_open()) {
-            auto input_model = std::make_shared<InputModelIR>(local_model_stream, weights, create_extensions_map());
+            auto input_model = std::make_shared<InputModel>(local_model_stream, weights, create_extensions_map());
             local_model_stream.close();
             return input_model;
         }
@@ -203,9 +203,9 @@ InputModel::Ptr FrontEndIR::load_impl(const std::vector<ov::Any>& variants) cons
         bin_stream.open(weights_path, std::ios::binary);
         if (!bin_stream.is_open())
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-            IR_THROW("Weights file " + ov::util::wstring_to_string(weights_path) + " cannot be opened!");
+            IE_THROW() << "Weights file " + ov::util::wstring_to_string(weights_path) + " cannot be opened!";
 #else
-            IR_THROW("Weights file " + weights_path + " cannot be opened!");
+            IE_THROW() << "Weights file " + weights_path + " cannot be opened!";
 #endif
 
         bin_stream.seekg(0, std::ios::end);
@@ -225,15 +225,17 @@ InputModel::Ptr FrontEndIR::load_impl(const std::vector<ov::Any>& variants) cons
     return create_input_model();
 }
 
-std::shared_ptr<ov::Model> FrontEndIR::convert(InputModel::Ptr model) const {
-    auto ir_model = std::dynamic_pointer_cast<InputModelIR>(model);
+std::shared_ptr<ov::Model> FrontEnd::convert(InputModel::Ptr model) const {
+    auto ir_model = std::dynamic_pointer_cast<InputModel>(model);
     OPENVINO_ASSERT(ir_model != nullptr);
     return ir_model->convert();
 }
 
-std::string FrontEndIR::get_name() const {
+std::string FrontEnd::get_name() const {
     return "ir";
 }
+
+}  // namespace ir
 }  // namespace frontend
 }  // namespace ov
 
@@ -245,7 +247,7 @@ IR_C_API void* GetFrontEndData() {
     frontend::FrontEndPluginInfo* res = new frontend::FrontEndPluginInfo();
     res->m_name = "ir";
     res->m_creator = []() {
-        return std::make_shared<frontend::FrontEndIR>();
+        return std::make_shared<ov::frontend::ir::FrontEnd>();
     };
     return res;
 }
diff --git a/src/frontends/ir/src/model.cpp b/src/frontends/ir/src/input_model.cpp
similarity index 95%
rename from src/frontends/ir/src/model.cpp
rename to src/frontends/ir/src/input_model.cpp
index 341cb6ea54a..96b38ce906c 100644
--- a/src/frontends/ir/src/model.cpp
+++ b/src/frontends/ir/src/input_model.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ir_frontend/model.hpp"
+#include "input_model.hpp"
 
 #include <xml_parse_utils.h>
 
@@ -186,7 +186,9 @@ void ParsePreProcess(pugi::xml_node& root,
 
 namespace ov {
 namespace frontend {
-class InputModelIR::InputModelIRImpl {
+namespace ir {
+
+class InputModel::InputModelIRImpl {
     std::shared_ptr<ngraph::runtime::AlignedBuffer> m_weights;
     std::unordered_map<ov::DiscreteTypeInfo, ov::BaseOpExtension::Ptr> m_extensions;
     std::unordered_map<std::string, ngraph::OpSet> m_opsets;
@@ -217,17 +219,17 @@ public:
     std::shared_ptr<Function> convert();
 };
 
-InputModelIR::InputModelIR(std::istream& stream,
-                           const std::shared_ptr<ngraph::runtime::AlignedBuffer>& weights,
-                           const std::unordered_map<ov::DiscreteTypeInfo, ov::BaseOpExtension::Ptr>& extensions) {
+InputModel::InputModel(std::istream& stream,
+                       const std::shared_ptr<ngraph::runtime::AlignedBuffer>& weights,
+                       const std::unordered_map<ov::DiscreteTypeInfo, ov::BaseOpExtension::Ptr>& extensions) {
     _impl = std::make_shared<InputModelIRImpl>(stream, weights, extensions);
 }
 
-std::shared_ptr<Function> InputModelIR::convert() {
+std::shared_ptr<Function> InputModel::convert() {
     return _impl->convert();
 }
 
-std::shared_ptr<Function> InputModelIR::InputModelIRImpl::convert() {
+std::shared_ptr<Function> InputModel::InputModelIRImpl::convert() {
     std::unordered_map<std::string, std::shared_ptr<ngraph::Variable>> variables;
 
     // Load default opsets
@@ -240,5 +242,7 @@ std::shared_ptr<Function> InputModelIR::InputModelIRImpl::convert() {
 
     return function;
 }
+
+}  // namespace ir
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/ir/src/input_model.hpp b/src/frontends/ir/src/input_model.hpp
new file mode 100644
index 00000000000..6fae50ea6cd
--- /dev/null
+++ b/src/frontends/ir/src/input_model.hpp
@@ -0,0 +1,33 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <istream>
+#include <memory>
+
+#include "ngraph/runtime/aligned_buffer.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/visibility.hpp"
+
+namespace ov {
+namespace frontend {
+namespace ir {
+
+class InputModel : public ov::frontend::InputModel {
+    friend class FrontEnd;
+    class InputModelIRImpl;
+    std::shared_ptr<InputModelIRImpl> _impl;
+
+public:
+    InputModel(std::istream& stream,
+               const std::shared_ptr<ngraph::runtime::AlignedBuffer>& weights,
+               const std::unordered_map<ov::DiscreteTypeInfo, ov::BaseOpExtension::Ptr>& extensions);
+
+    std::shared_ptr<Model> convert();
+};
+
+}  // namespace ir
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/ir/src/ir_deserializer.cpp b/src/frontends/ir/src/ir_deserializer.cpp
index 555d32997a7..166c9a92808 100644
--- a/src/frontends/ir/src/ir_deserializer.cpp
+++ b/src/frontends/ir/src/ir_deserializer.cpp
@@ -7,7 +7,6 @@
 #include <pugixml.hpp>
 
 #include "ie_ngraph_utils.hpp"
-#include "ir_frontend/model.hpp"
 #include "ngraph/op/util/framework_node.hpp"
 #include "ngraph/opsets/opset1.hpp"
 #include "rt_info_deserializer.hpp"
diff --git a/src/frontends/ir/src/ir_deserializer.hpp b/src/frontends/ir/src/ir_deserializer.hpp
index 869a980fed2..27836bb5220 100644
--- a/src/frontends/ir/src/ir_deserializer.hpp
+++ b/src/frontends/ir/src/ir_deserializer.hpp
@@ -9,7 +9,7 @@
 #include <pugixml.hpp>
 
 #include "ie_ngraph_utils.hpp"
-#include "ir_frontend/model.hpp"
+#include "input_model.hpp"
 #include "ngraph/opsets/opset.hpp"
 #include "openvino/core/attribute_visitor.hpp"
 #include "openvino/core/op_extension.hpp"
diff --git a/src/frontends/ir/src/rt_info_deserializer.cpp b/src/frontends/ir/src/rt_info_deserializer.cpp
index 0b7bee7c329..1c39be259ba 100644
--- a/src/frontends/ir/src/rt_info_deserializer.cpp
+++ b/src/frontends/ir/src/rt_info_deserializer.cpp
@@ -2,11 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ir_frontend/utility.hpp>
 #include <pugixml.hpp>
 #include <rt_info_deserializer.hpp>
 #include <transformations/rt_info/attributes.hpp>
 
+#include "openvino/frontend/exception.hpp"
+
 using namespace ov;
 
 void RTInfoDeserializer::on_adapter(const std::string& name, ValueAccessor<void>& adapter) {
@@ -19,6 +20,6 @@ void RTInfoDeserializer::on_adapter(const std::string& name, ValueAccessor<void>
         str_to_container(val, ss);
         a->set(ss);
     } else {
-        IR_THROW("Not implemented");
+        IE_THROW() << "Not implemented";
     }
 }
diff --git a/src/frontends/ir/src/rt_info_deserializer.hpp b/src/frontends/ir/src/rt_info_deserializer.hpp
index f706fbc7c7f..22d5550ac63 100644
--- a/src/frontends/ir/src/rt_info_deserializer.hpp
+++ b/src/frontends/ir/src/rt_info_deserializer.hpp
@@ -7,7 +7,6 @@
 #include <istream>
 #include <memory>
 
-#include "ir_frontend/utility.hpp"
 #include "openvino/core/attribute_visitor.hpp"
 #include "utils.hpp"
 
diff --git a/src/frontends/onnx/frontend/CMakeLists.txt b/src/frontends/onnx/frontend/CMakeLists.txt
index 5fe802a7f3f..aaf2b473682 100644
--- a/src/frontends/onnx/frontend/CMakeLists.txt
+++ b/src/frontends/onnx/frontend/CMakeLists.txt
@@ -20,6 +20,6 @@ ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
                         $<TARGET_PROPERTY:frontend_common::static,INTERFACE_INCLUDE_DIRECTORIES>)
 
 install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/include/onnx_import
-        DESTINATION ${FRONTEND_INSTALL_INCLUDE}
+        DESTINATION ${FRONTEND_INSTALL_INCLUDE}/ngraph/frontend
         COMPONENT core_dev
         FILES_MATCHING PATTERN "*.hpp")
diff --git a/src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
similarity index 86%
rename from src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp
rename to src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
index a82d7056a70..131bff7aa79 100644
--- a/src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp
+++ b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
@@ -4,7 +4,8 @@
 
 #pragma once
 
-#include <common/frontend.hpp>
+#include <openvino/frontend/frontend.hpp>
+#include <openvino/frontend/telemetry_extension.hpp>
 
 #ifdef OPENVINO_STATIC_LIBRARY
 #    define ONNX_FRONTEND_API
@@ -21,7 +22,9 @@
 
 namespace ov {
 namespace frontend {
-class ONNX_FRONTEND_API FrontEndONNX : public FrontEnd {
+namespace onnx {
+
+class ONNX_FRONTEND_API FrontEnd : public ov::frontend::FrontEnd {
 public:
     std::shared_ptr<ov::Model> convert(InputModel::Ptr model) const override;
     void convert(std::shared_ptr<ov::Model> partially_converted) const override;
@@ -37,5 +40,6 @@ private:
     std::shared_ptr<TelemetryExtension> m_telemetry;
 };
 
+}  // namespace onnx
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/onnx/frontend/src/core/graph.hpp b/src/frontends/onnx/frontend/src/core/graph.hpp
index 0e5c2378d32..49991b29754 100644
--- a/src/frontends/onnx/frontend/src/core/graph.hpp
+++ b/src/frontends/onnx/frontend/src/core/graph.hpp
@@ -10,12 +10,12 @@
 #include <string>
 #include <vector>
 
-#include "common/telemetry_extension.hpp"
 #include "core/graph_cache.hpp"
 #include "core/model.hpp"
 #include "ngraph/function.hpp"
 #include "ngraph/op/parameter.hpp"
 #include "onnx_import/core/operator_set.hpp"
+#include "openvino/frontend/telemetry_extension.hpp"
 
 namespace ngraph {
 namespace onnx_import {
diff --git a/src/frontends/onnx/frontend/src/editor.hpp b/src/frontends/onnx/frontend/src/editor.hpp
index 133a6dcc743..d72d03a64ad 100644
--- a/src/frontends/onnx/frontend/src/editor.hpp
+++ b/src/frontends/onnx/frontend/src/editor.hpp
@@ -4,10 +4,10 @@
 
 #pragma once
 
-#include <common/telemetry_extension.hpp>
 #include <istream>
 #include <map>
 #include <memory>
+#include <openvino/frontend/telemetry_extension.hpp>
 
 #include "editor_types.hpp"
 #include "ngraph/function.hpp"
diff --git a/src/frontends/onnx/frontend/src/frontend.cpp b/src/frontends/onnx/frontend/src/frontend.cpp
index 6acec3bd5f3..38aca686dde 100644
--- a/src/frontends/onnx/frontend/src/frontend.cpp
+++ b/src/frontends/onnx/frontend/src/frontend.cpp
@@ -2,82 +2,82 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <common/frontend_exceptions.hpp>
-#include <common/telemetry_extension.hpp>
 #include <fstream>
 #include <input_model.hpp>
-#include <manager.hpp>
-#include <onnx_frontend/frontend.hpp>
 #include <onnx_import/onnx.hpp>
+#include <openvino/frontend/exception.hpp>
+#include <openvino/frontend/manager.hpp>
+#include <openvino/frontend/onnx/frontend.hpp>
+#include <openvino/frontend/telemetry_extension.hpp>
 #include <sstream>
 #include <utils/onnx_internal.hpp>
 
 #include "onnx_common/onnx_model_validator.hpp"
 
 using namespace ov;
-using namespace ov::frontend;
+using namespace ov::frontend::onnx;
 
-ONNX_FRONTEND_C_API FrontEndVersion GetAPIVersion() {
+ONNX_FRONTEND_C_API ov::frontend::FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
 ONNX_FRONTEND_C_API void* GetFrontEndData() {
-    FrontEndPluginInfo* res = new FrontEndPluginInfo();
+    ov::frontend::FrontEndPluginInfo* res = new ov::frontend::FrontEndPluginInfo();
     res->m_name = "onnx";
     res->m_creator = []() {
-        return std::make_shared<FrontEndONNX>();
+        return std::make_shared<FrontEnd>();
     };
     return res;
 }
 
-InputModel::Ptr FrontEndONNX::load_impl(const std::vector<ov::Any>& variants) const {
-    if (variants.size() == 0) {
+InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const {
+    if (variants.empty()) {
         return nullptr;
     }
     if (variants[0].is<std::string>()) {
         const auto path = variants[0].as<std::string>();
-        return std::make_shared<InputModelONNX>(path, m_telemetry);
+        return std::make_shared<InputModel>(path, m_telemetry);
     }
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
     if (variants[0].is<std::wstring>()) {
         const auto path = variants[0].as<std::wstring>();
-        return std::make_shared<InputModelONNX>(path, m_telemetry);
+        return std::make_shared<InputModel>(path, m_telemetry);
     }
 #endif
     if (variants[0].is<std::istream*>()) {
         const auto stream = variants[0].as<std::istream*>();
         if (variants.size() > 1 && variants[1].is<std::string>()) {
             const auto path = variants[0].as<std::string>();
-            return std::make_shared<InputModelONNX>(*stream, path, m_telemetry);
+            return std::make_shared<InputModel>(*stream, path, m_telemetry);
         }
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
         if (variants.size() > 1 && variants[1].is<std::wstring>()) {
             const auto path = variants[1].as<std::wstring>();
-            return std::make_shared<InputModelONNX>(*stream, path, m_telemetry);
+            return std::make_shared<InputModel>(*stream, path, m_telemetry);
         }
 #endif
-        return std::make_shared<InputModelONNX>(*stream, m_telemetry);
+        return std::make_shared<InputModel>(*stream, m_telemetry);
     }
     return nullptr;
 }
 
-std::shared_ptr<ngraph::Function> FrontEndONNX::convert(InputModel::Ptr model) const {
-    auto model_onnx = std::dynamic_pointer_cast<InputModelONNX>(model);
+std::shared_ptr<ngraph::Function> FrontEnd::convert(InputModel::Ptr model) const {
+    auto model_onnx = std::dynamic_pointer_cast<InputModel>(model);
     NGRAPH_CHECK(model_onnx != nullptr, "Invalid input model");
     return model_onnx->convert();
 }
 
-void FrontEndONNX::convert(std::shared_ptr<ngraph::Function> partially_converted) const {
+void FrontEnd::convert(std::shared_ptr<ngraph::Function> partially_converted) const {
     ngraph::onnx_import::detail::convert_decoded_function(partially_converted);
 }
 
-std::shared_ptr<ngraph::Function> FrontEndONNX::decode(InputModel::Ptr model) const {
-    auto model_onnx = std::dynamic_pointer_cast<InputModelONNX>(model);
+std::shared_ptr<ngraph::Function> FrontEnd::decode(InputModel::Ptr model) const {
+    auto model_onnx = std::dynamic_pointer_cast<InputModel>(model);
     NGRAPH_CHECK(model_onnx != nullptr, "Invalid input model");
     return model_onnx->decode();
 }
 
-std::string FrontEndONNX::get_name() const {
+std::string FrontEnd::get_name() const {
     return "onnx";
 }
 
@@ -103,7 +103,7 @@ private:
 };
 }  // namespace
 
-bool FrontEndONNX::supported_impl(const std::vector<ov::Any>& variants) const {
+bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
     if (variants.size() == 0) {
         return false;
     }
@@ -133,7 +133,7 @@ bool FrontEndONNX::supported_impl(const std::vector<ov::Any>& variants) const {
     return false;
 }
 
-void FrontEndONNX::add_extension(const std::shared_ptr<ov::Extension>& extension) {
+void FrontEnd::add_extension(const std::shared_ptr<ov::Extension>& extension) {
     if (auto telemetry = std::dynamic_pointer_cast<TelemetryExtension>(extension)) {
         m_telemetry = telemetry;
     }
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index 38fda9ae8a7..8a3fe3ee286 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -4,79 +4,76 @@
 
 #include "input_model.hpp"
 
-#include <common/frontend_exceptions.hpp>
+#include <openvino/frontend/exception.hpp>
 #include <openvino/util/file_util.hpp>
 
 #include "place.hpp"
 
 using namespace ov;
-using namespace ov::frontend;
+using namespace ov::frontend::onnx;
 
 NGRAPH_SUPPRESS_DEPRECATED_START
 
-InputModelONNX::InputModelONNX(const std::string& path,
-                               const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
+InputModel::InputModel(const std::string& path, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
     : m_editor{std::make_shared<onnx_editor::ONNXModelEditor>(path, telemetry)} {}
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-InputModelONNX::InputModelONNX(const std::wstring& path,
-                               const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
+InputModel::InputModel(const std::wstring& path, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
     : m_editor{std::make_shared<onnx_editor::ONNXModelEditor>(path, telemetry)} {}
 #endif
 
-InputModelONNX::InputModelONNX(std::istream& model_stream,
-                               const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
+InputModel::InputModel(std::istream& model_stream, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
     : m_editor{std::make_shared<onnx_editor::ONNXModelEditor>(model_stream, "", telemetry)} {}
 
-InputModelONNX::InputModelONNX(std::istream& model_stream,
-                               const std::string& path,
-                               const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
+InputModel::InputModel(std::istream& model_stream,
+                       const std::string& path,
+                       const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
     : m_editor{std::make_shared<onnx_editor::ONNXModelEditor>(model_stream, path, telemetry)} {}
 
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-InputModelONNX::InputModelONNX(std::istream& model_stream,
-                               const std::wstring& path,
-                               const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
-    : InputModelONNX(model_stream, ov::util::wstring_to_string(path), telemetry) {}
+InputModel::InputModel(std::istream& model_stream,
+                       const std::wstring& path,
+                       const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry)
+    : InputModel(model_stream, ov::util::wstring_to_string(path), telemetry) {}
 #endif
 
-std::vector<Place::Ptr> InputModelONNX::get_inputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModel::get_inputs() const {
     const auto& inputs = m_editor->model_inputs();
     std::vector<Place::Ptr> in_places;
     in_places.reserve(inputs.size());
     for (const auto& input : inputs) {
-        in_places.push_back(std::make_shared<PlaceTensorONNX>(input, m_editor));
+        in_places.push_back(std::make_shared<PlaceTensor>(input, m_editor));
     }
     return in_places;
 }
 
-std::vector<Place::Ptr> InputModelONNX::get_outputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModel::get_outputs() const {
     const auto& outputs = m_editor->model_outputs();
     std::vector<Place::Ptr> out_places;
     out_places.reserve(outputs.size());
     for (const auto& output : outputs) {
-        out_places.push_back(std::make_shared<PlaceTensorONNX>(output, m_editor));
+        out_places.push_back(std::make_shared<PlaceTensor>(output, m_editor));
     }
     return out_places;
 }
 
-Place::Ptr InputModelONNX::get_place_by_tensor_name(const std::string& tensor_name) const {
+ov::frontend::Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensor_name) const {
     if (m_editor->is_correct_tensor_name(tensor_name)) {
-        return std::make_shared<PlaceTensorONNX>(tensor_name, m_editor);
+        return std::make_shared<PlaceTensor>(tensor_name, m_editor);
     }
     return nullptr;
 }
 
-Place::Ptr InputModelONNX::get_place_by_operation_name(const std::string& operation_name) const {
+ov::frontend::Place::Ptr InputModel::get_place_by_operation_name(const std::string& operation_name) const {
     if (m_editor->is_correct_and_unambiguous_node(operation_name)) {
         const auto node_index = m_editor->get_node_index(onnx_editor::EditorNode{operation_name});
-        return std::make_shared<PlaceOpONNX>(onnx_editor::EditorNode{node_index}, m_editor);
+        return std::make_shared<PlaceOp>(onnx_editor::EditorNode{node_index}, m_editor);
     }
     return nullptr;
 }
 
-Place::Ptr InputModelONNX::get_place_by_operation_name_and_input_port(const std::string& operation_name,
-                                                                      int input_port_index) {
+ov::frontend::Place::Ptr InputModel::get_place_by_operation_name_and_input_port(const std::string& operation_name,
+                                                                                int input_port_index) {
     const auto op = get_place_by_operation_name(operation_name);
     if (op != nullptr) {
         return op->get_input_port(input_port_index);
@@ -84,8 +81,8 @@ Place::Ptr InputModelONNX::get_place_by_operation_name_and_input_port(const std:
     return nullptr;
 }
 
-Place::Ptr InputModelONNX::get_place_by_operation_name_and_output_port(const std::string& operation_name,
-                                                                       int output_port_index) {
+ov::frontend::Place::Ptr InputModel::get_place_by_operation_name_and_output_port(const std::string& operation_name,
+                                                                                 int output_port_index) {
     const auto op = get_place_by_operation_name(operation_name);
     if (op != nullptr) {
         return op->get_output_port(output_port_index);
@@ -93,62 +90,62 @@ Place::Ptr InputModelONNX::get_place_by_operation_name_and_output_port(const std
     return nullptr;
 }
 
-void InputModelONNX::set_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
-    const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensorONNX>(tensor);
+void InputModel::set_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
+    const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor);
     FRONT_END_GENERAL_CHECK(onnx_tensor, __FUNCTION__, " expects a pointer to place of ONNX tensor type.");
     onnx_tensor->set_name(new_name);
 }
 
-void InputModelONNX::set_name_for_operation(Place::Ptr operation, const std::string& new_name) {
-    const auto onnx_operation = std::dynamic_pointer_cast<PlaceOpONNX>(operation);
+void InputModel::set_name_for_operation(Place::Ptr operation, const std::string& new_name) {
+    const auto onnx_operation = std::dynamic_pointer_cast<PlaceOp>(operation);
     FRONT_END_GENERAL_CHECK(onnx_operation, __FUNCTION__, " expects a pointer to place of ONNX operation type.");
     onnx_operation->set_name(new_name);
 }
 
-void InputModelONNX::free_name_for_operation(const std::string& name) {
+void InputModel::free_name_for_operation(const std::string& name) {
     m_editor->clear_nodes_name(name);
 }
 
-void InputModelONNX::set_name_for_dimension(Place::Ptr tensor, size_t shape_dim_index, const std::string& dim_name) {
-    const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensorONNX>(tensor);
+void InputModel::set_name_for_dimension(Place::Ptr tensor, size_t shape_dim_index, const std::string& dim_name) {
+    const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor);
     FRONT_END_GENERAL_CHECK(onnx_tensor, __FUNCTION__, " expects a pointer to place of ONNX tensor type.");
     onnx_tensor->set_name_for_dimension(shape_dim_index, dim_name);
 }
 
-void InputModelONNX::add_name_for_tensor(Place::Ptr, const std::string&) {
+void InputModel::add_name_for_tensor(Place::Ptr, const std::string&) {
     FRONT_END_THROW("Method add_name_for_tensor is not applicable for ONNX model. ONNX tensor has just one name.");
 }
 
-void InputModelONNX::free_name_for_tensor(const std::string&) {
+void InputModel::free_name_for_tensor(const std::string&) {
     FRONT_END_THROW("Method free_name_for_tensor is not applicable for ONNX model. ONNX tensor name is an identifier.");
 }
 
-void InputModelONNX::set_partial_shape(Place::Ptr place, const ngraph::PartialShape& shape) {
+void InputModel::set_partial_shape(Place::Ptr place, const ngraph::PartialShape& shape) {
     std::map<std::string, ngraph::PartialShape> m;
     m[place->get_names()[0]] = shape;
     m_editor->set_input_shapes(m);
 }
 
-ngraph::PartialShape InputModelONNX::get_partial_shape(Place::Ptr place) const {
+ngraph::PartialShape InputModel::get_partial_shape(Place::Ptr place) const {
     return m_editor->get_tensor_shape(place->get_names().at(0));
 }
 
-void InputModelONNX::set_element_type(Place::Ptr place, const ngraph::element::Type& type) {
+void InputModel::set_element_type(Place::Ptr place, const ngraph::element::Type& type) {
     std::map<std::string, ngraph::element::Type_t> m;
     m[place->get_names()[0]] = type;
     m_editor->set_input_types(m);
 }
 
-std::shared_ptr<Model> InputModelONNX::decode() {
+std::shared_ptr<Model> InputModel::decode() {
     return m_editor->decode();
 }
 
-std::shared_ptr<Model> InputModelONNX::convert() {
+std::shared_ptr<Model> InputModel::convert() {
     return m_editor->get_function();
 }
 
 // Editor features
-void InputModelONNX::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
+void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
     extract_subgraph({}, outputs);
     NGRAPH_CHECK(m_editor->model_outputs().size() == outputs.size(),
                  "Unexpected number of outputs after override_all_outputs");
@@ -160,7 +157,7 @@ void InputModelONNX::override_all_outputs(const std::vector<Place::Ptr>& outputs
                  "Not all provided arguments of override_all_outputs are new outputs of the model");
 }
 
-void InputModelONNX::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
+void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
     const auto outputs_before_extraction = m_editor->model_outputs();
     extract_subgraph({inputs}, {});
     NGRAPH_CHECK(std::equal(std::begin(outputs_before_extraction),
@@ -172,13 +169,13 @@ void InputModelONNX::override_all_inputs(const std::vector<Place::Ptr>& inputs)
                  "Unexpected number of inputs after override_all_inputs");
 }
 
-void InputModelONNX::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
+void InputModel::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
     std::vector<onnx_editor::InputEdge> onnx_inputs;
     onnx_inputs.reserve(inputs.size());
     for (const auto& input : inputs) {
-        if (const auto input_port = std::dynamic_pointer_cast<PlaceInputEdgeONNX>(input)) {
+        if (const auto input_port = std::dynamic_pointer_cast<PlaceInputEdge>(input)) {
             onnx_inputs.push_back(input_port->get_input_edge());
-        } else if (const auto tensor = std::dynamic_pointer_cast<PlaceTensorONNX>(input)) {
+        } else if (const auto tensor = std::dynamic_pointer_cast<PlaceTensor>(input)) {
             auto name = tensor->get_names()[0];
             const auto consumers = m_editor->find_output_consumers(name);
             std::transform(std::begin(consumers),
@@ -187,7 +184,7 @@ void InputModelONNX::extract_subgraph(const std::vector<Place::Ptr>& inputs, con
                            [](const onnx_editor::InputEdge& edge) {
                                return edge;
                            });
-        } else if (const auto op = std::dynamic_pointer_cast<PlaceOpONNX>(input)) {
+        } else if (const auto op = std::dynamic_pointer_cast<PlaceOp>(input)) {
             const auto editor_node = op->get_editor_node();
             const auto op_inputs = m_editor->get_input_ports(editor_node);
             int node_idx = m_editor->get_node_index(editor_node);
@@ -204,14 +201,14 @@ void InputModelONNX::extract_subgraph(const std::vector<Place::Ptr>& inputs, con
     std::vector<onnx_editor::OutputEdge> onnx_outputs;
     onnx_outputs.reserve(outputs.size());
     for (const auto& output : outputs) {
-        if (const auto output_port = std::dynamic_pointer_cast<PlaceOutputEdgeONNX>(output)) {
+        if (const auto output_port = std::dynamic_pointer_cast<PlaceOutputEdge>(output)) {
             onnx_outputs.push_back(output_port->get_output_edge());
-        } else if (const auto tensor = std::dynamic_pointer_cast<PlaceTensorONNX>(output)) {
+        } else if (const auto tensor = std::dynamic_pointer_cast<PlaceTensor>(output)) {
             const auto output_port = tensor->get_producing_port();
-            const auto onnx_output_edge = std::dynamic_pointer_cast<PlaceOutputEdgeONNX>(output_port);
+            const auto onnx_output_edge = std::dynamic_pointer_cast<PlaceOutputEdge>(output_port);
             NGRAPH_CHECK(onnx_output_edge, "Non-onnx output place was passed as extraction subgraph argument");
             onnx_outputs.push_back(onnx_output_edge->get_output_edge());
-        } else if (const auto op = std::dynamic_pointer_cast<PlaceOpONNX>(output)) {
+        } else if (const auto op = std::dynamic_pointer_cast<PlaceOp>(output)) {
             const auto editor_node = op->get_editor_node();
             const auto op_outputs = m_editor->get_output_ports(editor_node);
             int node_idx = m_editor->get_node_index(editor_node);
diff --git a/src/frontends/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
index a3875b2adba..1e1c86427ed 100644
--- a/src/frontends/onnx/frontend/src/input_model.hpp
+++ b/src/frontends/onnx/frontend/src/input_model.hpp
@@ -4,28 +4,30 @@
 
 #pragma once
 
-#include <common/input_model.hpp>
 #include <editor.hpp>
 #include <fstream>
+#include <openvino/frontend/input_model.hpp>
 
 namespace ov {
 namespace frontend {
-class InputModelONNX : public InputModel {
+namespace onnx {
+
+class InputModel : public ov::frontend::InputModel {
 public:
-    InputModelONNX(const std::string& path, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
+    InputModel(const std::string& path, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-    InputModelONNX(const std::wstring& path, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
+    InputModel(const std::wstring& path, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
 #endif
-    InputModelONNX(std::istream& model_stream, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
+    InputModel(std::istream& model_stream, const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
     // The path can be required even if the model is passed as a stream because it is necessary
     // for ONNX external data feature
-    InputModelONNX(std::istream& model_stream,
-                   const std::string& path,
-                   const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
+    InputModel(std::istream& model_stream,
+               const std::string& path,
+               const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-    InputModelONNX(std::istream& model_stream,
-                   const std::wstring& path,
-                   const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
+    InputModel(std::istream& model_stream,
+               const std::wstring& path,
+               const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
 #endif
 
     std::vector<Place::Ptr> get_inputs() const override;
@@ -62,5 +64,7 @@ public:
 private:
     std::shared_ptr<ov::onnx_editor::ONNXModelEditor> m_editor;
 };
+
+}  // namespace onnx
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/onnx/frontend/src/place.cpp b/src/frontends/onnx/frontend/src/place.cpp
index 5c2c25fbb1c..6e73fd450ac 100644
--- a/src/frontends/onnx/frontend/src/place.cpp
+++ b/src/frontends/onnx/frontend/src/place.cpp
@@ -4,160 +4,157 @@
 
 #include "place.hpp"
 
-#include <common/frontend_exceptions.hpp>
+#include <openvino/frontend/exception.hpp>
 
 using namespace ov;
-using namespace ov::frontend;
+using namespace ov::frontend::onnx;
 
-PlaceInputEdgeONNX::PlaceInputEdgeONNX(const onnx_editor::InputEdge& edge,
-                                       std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceInputEdge::PlaceInputEdge(const onnx_editor::InputEdge& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_edge{edge},
       m_editor{std::move(editor)} {}
 
-PlaceInputEdgeONNX::PlaceInputEdgeONNX(onnx_editor::InputEdge&& edge,
-                                       std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceInputEdge::PlaceInputEdge(onnx_editor::InputEdge&& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_edge{std::move(edge)},
       m_editor{std::move(editor)} {}
 
-onnx_editor::InputEdge PlaceInputEdgeONNX::get_input_edge() const {
+onnx_editor::InputEdge PlaceInputEdge::get_input_edge() const {
     return m_edge;
 }
 
-bool PlaceInputEdgeONNX::is_input() const {
+bool PlaceInputEdge::is_input() const {
     return m_editor->is_input(m_edge);
 }
 
-bool PlaceInputEdgeONNX::is_output() const {
+bool PlaceInputEdge::is_output() const {
     return false;
 }
 
-bool PlaceInputEdgeONNX::is_equal(Place::Ptr another) const {
-    if (const auto in_edge = std::dynamic_pointer_cast<PlaceInputEdgeONNX>(another)) {
+bool PlaceInputEdge::is_equal(Place::Ptr another) const {
+    if (const auto in_edge = std::dynamic_pointer_cast<PlaceInputEdge>(another)) {
         const auto& editor_edge = in_edge->get_input_edge();
         return (editor_edge.m_node_idx == m_edge.m_node_idx) && (editor_edge.m_port_idx == m_edge.m_port_idx);
     }
     return false;
 }
 
-bool PlaceInputEdgeONNX::is_equal_data(Place::Ptr another) const {
+bool PlaceInputEdge::is_equal_data(Place::Ptr another) const {
     return get_source_tensor()->is_equal_data(another);
 }
 
-Place::Ptr PlaceInputEdgeONNX::get_source_tensor() const {
-    return std::make_shared<PlaceTensorONNX>(m_editor->get_source_tensor_name(m_edge), m_editor);
+ov::frontend::Place::Ptr PlaceInputEdge::get_source_tensor() const {
+    return std::make_shared<PlaceTensor>(m_editor->get_source_tensor_name(m_edge), m_editor);
 }
 
-std::vector<Place::Ptr> PlaceInputEdgeONNX::get_consuming_operations() const {
-    return {std::make_shared<PlaceOpONNX>(onnx_editor::EditorNode{m_edge.m_node_idx}, m_editor)};
+std::vector<ov::frontend::Place::Ptr> PlaceInputEdge::get_consuming_operations() const {
+    return {std::make_shared<PlaceOp>(onnx_editor::EditorNode{m_edge.m_node_idx}, m_editor)};
 }
 
-Place::Ptr PlaceInputEdgeONNX::get_producing_operation() const {
+ov::frontend::Place::Ptr PlaceInputEdge::get_producing_operation() const {
     return get_source_tensor()->get_producing_operation();
 }
 
-Place::Ptr PlaceInputEdgeONNX::get_producing_port() const {
+ov::frontend::Place::Ptr PlaceInputEdge::get_producing_port() const {
     return get_source_tensor()->get_producing_port();
 }
 
-PlaceOutputEdgeONNX::PlaceOutputEdgeONNX(const onnx_editor::OutputEdge& edge,
-                                         std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceOutputEdge::PlaceOutputEdge(const onnx_editor::OutputEdge& edge,
+                                 std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_edge{edge},
       m_editor{std::move(editor)} {}
 
-PlaceOutputEdgeONNX::PlaceOutputEdgeONNX(onnx_editor::OutputEdge&& edge,
-                                         std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceOutputEdge::PlaceOutputEdge(onnx_editor::OutputEdge&& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_edge{std::move(edge)},
       m_editor{std::move(editor)} {}
 
-onnx_editor::OutputEdge PlaceOutputEdgeONNX::get_output_edge() const {
+onnx_editor::OutputEdge PlaceOutputEdge::get_output_edge() const {
     return m_edge;
 }
 
-bool PlaceOutputEdgeONNX::is_input() const {
+bool PlaceOutputEdge::is_input() const {
     return false;
 }
 
-bool PlaceOutputEdgeONNX::is_output() const {
+bool PlaceOutputEdge::is_output() const {
     return m_editor->is_output(m_edge);
 }
 
-bool PlaceOutputEdgeONNX::is_equal(Place::Ptr another) const {
-    if (const auto out_edge = std::dynamic_pointer_cast<PlaceOutputEdgeONNX>(another)) {
+bool PlaceOutputEdge::is_equal(Place::Ptr another) const {
+    if (const auto out_edge = std::dynamic_pointer_cast<PlaceOutputEdge>(another)) {
         const auto& editor_edge = out_edge->get_output_edge();
         return (editor_edge.m_node_idx == m_edge.m_node_idx) && (editor_edge.m_port_idx == m_edge.m_port_idx);
     }
     return false;
 }
 
-bool PlaceOutputEdgeONNX::is_equal_data(Place::Ptr another) const {
+bool PlaceOutputEdge::is_equal_data(Place::Ptr another) const {
     return get_target_tensor()->is_equal_data(another);
 }
 
-Place::Ptr PlaceOutputEdgeONNX::get_target_tensor() const {
-    return std::make_shared<PlaceTensorONNX>(m_editor->get_target_tensor_name(m_edge), m_editor);
+ov::frontend::Place::Ptr PlaceOutputEdge::get_target_tensor() const {
+    return std::make_shared<PlaceTensor>(m_editor->get_target_tensor_name(m_edge), m_editor);
 }
 
-std::vector<Place::Ptr> PlaceOutputEdgeONNX::get_consuming_ports() const {
+std::vector<ov::frontend::Place::Ptr> PlaceOutputEdge::get_consuming_ports() const {
     return get_target_tensor()->get_consuming_ports();
 }
 
-Place::Ptr PlaceOutputEdgeONNX::get_producing_operation() const {
-    return std::make_shared<PlaceOpONNX>(onnx_editor::EditorNode{m_edge.m_node_idx}, m_editor);
+ov::frontend::Place::Ptr PlaceOutputEdge::get_producing_operation() const {
+    return std::make_shared<PlaceOp>(onnx_editor::EditorNode{m_edge.m_node_idx}, m_editor);
 }
 
-std::vector<Place::Ptr> PlaceOutputEdgeONNX::get_consuming_operations() const {
+std::vector<ov::frontend::Place::Ptr> PlaceOutputEdge::get_consuming_operations() const {
     return get_target_tensor()->get_consuming_operations();
 }
 
-PlaceTensorONNX::PlaceTensorONNX(const std::string& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceTensor::PlaceTensor(const std::string& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_name{name},
       m_editor{std::move(editor)} {}
 
-PlaceTensorONNX::PlaceTensorONNX(std::string&& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceTensor::PlaceTensor(std::string&& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_name{std::move(name)},
       m_editor{std::move(editor)} {}
 
-std::vector<std::string> PlaceTensorONNX::get_names() const {
+std::vector<std::string> PlaceTensor::get_names() const {
     return {m_name};
 }
 
-Place::Ptr PlaceTensorONNX::get_producing_port() const {
+ov::frontend::Place::Ptr PlaceTensor::get_producing_port() const {
     FRONT_END_GENERAL_CHECK(!is_input(),
                             "Tensor: " + m_name + " is an input of the model and doesn't have producing port.");
-    return std::make_shared<PlaceOutputEdgeONNX>(m_editor->find_output_edge(m_name), m_editor);
+    return std::make_shared<PlaceOutputEdge>(m_editor->find_output_edge(m_name), m_editor);
 }
 
-std::vector<Place::Ptr> PlaceTensorONNX::get_consuming_ports() const {
-    std::vector<Place::Ptr> ret;
+std::vector<ov::frontend::Place::Ptr> PlaceTensor::get_consuming_ports() const {
+    std::vector<ov::frontend::Place::Ptr> ret;
     auto edges = m_editor->find_output_consumers(m_name);
     std::transform(edges.begin(), edges.end(), std::back_inserter(ret), [this](const onnx_editor::InputEdge& edge) {
-        return std::make_shared<PlaceInputEdgeONNX>(edge, this->m_editor);
+        return std::make_shared<PlaceInputEdge>(edge, this->m_editor);
     });
     return ret;
 }
 
-Place::Ptr PlaceTensorONNX::get_producing_operation() const {
+ov::frontend::Place::Ptr PlaceTensor::get_producing_operation() const {
     return get_producing_port()->get_producing_operation();
 }
 
-bool PlaceTensorONNX::is_input() const {
+bool PlaceTensor::is_input() const {
     const auto inputs = m_editor->model_inputs();
     return std::find(std::begin(inputs), std::end(inputs), m_name) != std::end(inputs);
 }
 
-bool PlaceTensorONNX::is_output() const {
+bool PlaceTensor::is_output() const {
     const auto outputs = m_editor->model_outputs();
     return std::find(std::begin(outputs), std::end(outputs), m_name) != std::end(outputs);
 }
 
-bool PlaceTensorONNX::is_equal(Place::Ptr another) const {
-    if (const auto tensor = std::dynamic_pointer_cast<PlaceTensorONNX>(another)) {
+bool PlaceTensor::is_equal(Place::Ptr another) const {
+    if (const auto tensor = std::dynamic_pointer_cast<PlaceTensor>(another)) {
         return m_name == tensor->get_names().at(0);
     }
     return false;
 }
 
-bool PlaceTensorONNX::is_equal_data(Place::Ptr another) const {
+bool PlaceTensor::is_equal_data(Place::Ptr another) const {
     const auto consuming_ports = get_consuming_ports();
     const auto eq_to_consuming_port = [&consuming_ports](const Ptr& another) {
         return std::any_of(consuming_ports.begin(), consuming_ports.end(), [&another](const Ptr& place) {
@@ -168,9 +165,9 @@ bool PlaceTensorONNX::is_equal_data(Place::Ptr another) const {
            eq_to_consuming_port(another);
 }
 
-std::vector<Place::Ptr> PlaceTensorONNX::get_consuming_operations() const {
-    std::vector<Place::Ptr> consuming_ports = get_consuming_ports();
-    std::vector<Place::Ptr> consuming_ops;
+std::vector<ov::frontend::Place::Ptr> PlaceTensor::get_consuming_operations() const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ports = get_consuming_ports();
+    std::vector<ov::frontend::Place::Ptr> consuming_ops;
     std::transform(std::begin(consuming_ports),
                    std::end(consuming_ports),
                    std::back_inserter(consuming_ops),
@@ -181,87 +178,86 @@ std::vector<Place::Ptr> PlaceTensorONNX::get_consuming_operations() const {
     return consuming_ops;
 }
 
-void PlaceTensorONNX::set_name(const std::string& new_name) {
+void PlaceTensor::set_name(const std::string& new_name) {
     if (m_name == new_name)
         return;
     m_editor->set_tensor_name(m_name, new_name);
     m_name = new_name;
 }
 
-void PlaceTensorONNX::set_name_for_dimension(size_t shape_dim_index, const std::string& dim_name) {
+void PlaceTensor::set_name_for_dimension(size_t shape_dim_index, const std::string& dim_name) {
     m_editor->set_name_for_dimension(m_name, shape_dim_index, dim_name);
 }
 
-PlaceOpONNX::PlaceOpONNX(const onnx_editor::EditorNode& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceOp::PlaceOp(const onnx_editor::EditorNode& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_node{node},
       m_editor{std::move(editor)} {}
 
-PlaceOpONNX::PlaceOpONNX(onnx_editor::EditorNode&& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
+PlaceOp::PlaceOp(onnx_editor::EditorNode&& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
     : m_node{std::move(node)},
       m_editor{std::move(editor)} {}
 
-std::vector<std::string> PlaceOpONNX::get_names() const {
+std::vector<std::string> PlaceOp::get_names() const {
     return {m_node.m_node_name};
 }
 
-const onnx_editor::EditorNode& PlaceOpONNX::get_editor_node() const {
+const onnx_editor::EditorNode& PlaceOp::get_editor_node() const {
     return m_node;
 }
 
-Place::Ptr PlaceOpONNX::get_output_port() const {
+ov::frontend::Place::Ptr PlaceOp::get_output_port() const {
     if (m_editor->get_output_ports(m_node).size() == 1) {
         return get_output_port(0);
     }
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_output_port(int output_port_index) const {
+ov::frontend::Place::Ptr PlaceOp::get_output_port(int output_port_index) const {
     if (output_port_index < m_editor->get_output_ports(m_node).size()) {
-        return std::make_shared<PlaceOutputEdgeONNX>(
+        return std::make_shared<PlaceOutputEdge>(
             m_editor->find_output_edge(m_node, onnx_editor::EditorOutput{output_port_index}),
             m_editor);
     }
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_output_port(const std::string& output_port_name) const {
+ov::frontend::Place::Ptr PlaceOp::get_output_port(const std::string& output_port_name) const {
     const auto output_ports = m_editor->get_output_ports(m_node);
     if (std::count(std::begin(output_ports), std::end(output_ports), output_port_name) == 1) {
-        return std::make_shared<PlaceOutputEdgeONNX>(
+        return std::make_shared<PlaceOutputEdge>(
             m_editor->find_output_edge(m_node, onnx_editor::EditorOutput{output_port_name}),
             m_editor);
     }
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_input_port() const {
+ov::frontend::Place::Ptr PlaceOp::get_input_port() const {
     if (m_editor->get_input_ports(m_node).size() == 1) {
         return get_input_port(0);
     }
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_input_port(int input_port_index) const {
+ov::frontend::Place::Ptr PlaceOp::get_input_port(int input_port_index) const {
     if (input_port_index < m_editor->get_input_ports(m_node).size()) {
-        return std::make_shared<PlaceInputEdgeONNX>(
+        return std::make_shared<PlaceInputEdge>(
             m_editor->find_input_edge(m_node, onnx_editor::EditorInput{input_port_index}),
             m_editor);
     }
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_input_port(const std::string& input_name) const {
+ov::frontend::Place::Ptr PlaceOp::get_input_port(const std::string& input_name) const {
     const auto input_ports = m_editor->get_input_ports(m_node);
     if (std::count(std::begin(input_ports), std::end(input_ports), input_name) == 1) {
-        return std::make_shared<PlaceInputEdgeONNX>(
-            m_editor->find_input_edge(m_node, onnx_editor::EditorInput{input_name}),
-            m_editor);
+        return std::make_shared<PlaceInputEdge>(m_editor->find_input_edge(m_node, onnx_editor::EditorInput{input_name}),
+                                                m_editor);
     }
     return nullptr;
 }
 
-std::vector<Place::Ptr> PlaceOpONNX::get_consuming_ports() const {
-    std::vector<Place::Ptr> consuming_ports;
+std::vector<ov::frontend::Place::Ptr> PlaceOp::get_consuming_ports() const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ports;
     const auto out_ports_size = m_editor->get_output_ports(m_node).size();
     for (int out_idx = 0; out_idx < out_ports_size; ++out_idx) {
         auto consuming_ops_out = get_output_port(out_idx)->get_consuming_ports();
@@ -271,12 +267,12 @@ std::vector<Place::Ptr> PlaceOpONNX::get_consuming_ports() const {
 }
 
 namespace {
-std::vector<Place::Ptr> get_consuming_ops(std::vector<Place::Ptr> input_ports) {
-    std::vector<Place::Ptr> consuming_ops;
+std::vector<ov::frontend::Place::Ptr> get_consuming_ops(std::vector<ov::frontend::Place::Ptr> input_ports) {
+    std::vector<ov::frontend::Place::Ptr> consuming_ops;
     std::transform(std::begin(input_ports),
                    std::end(input_ports),
                    std::back_inserter(consuming_ops),
-                   [](const Place::Ptr place) {
+                   [](const ov::frontend::Place::Ptr place) {
                        return place->get_consuming_operations().at(0);
                    });
 
@@ -284,22 +280,22 @@ std::vector<Place::Ptr> get_consuming_ops(std::vector<Place::Ptr> input_ports) {
 }
 }  // namespace
 
-std::vector<Place::Ptr> PlaceOpONNX::get_consuming_operations() const {
-    std::vector<Place::Ptr> consuming_ports = get_consuming_ports();
+std::vector<ov::frontend::Place::Ptr> PlaceOp::get_consuming_operations() const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ports = get_consuming_ports();
     return get_consuming_ops(consuming_ports);
 }
 
-std::vector<Place::Ptr> PlaceOpONNX::get_consuming_operations(int output_port_index) const {
-    std::vector<Place::Ptr> consuming_ports = get_output_port(output_port_index)->get_consuming_ports();
+std::vector<ov::frontend::Place::Ptr> PlaceOp::get_consuming_operations(int output_port_index) const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ports = get_output_port(output_port_index)->get_consuming_ports();
     return get_consuming_ops(consuming_ports);
 }
 
-std::vector<Place::Ptr> PlaceOpONNX::get_consuming_operations(const std::string& output_port_name) const {
-    std::vector<Place::Ptr> consuming_ports = get_output_port(output_port_name)->get_consuming_ports();
+std::vector<ov::frontend::Place::Ptr> PlaceOp::get_consuming_operations(const std::string& output_port_name) const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ports = get_output_port(output_port_name)->get_consuming_ports();
     return get_consuming_ops(consuming_ports);
 }
 
-Place::Ptr PlaceOpONNX::get_producing_operation() const {
+ov::frontend::Place::Ptr PlaceOp::get_producing_operation() const {
     const auto input_port = get_input_port();
     if (input_port != nullptr) {
         return input_port->get_producing_operation();
@@ -307,7 +303,7 @@ Place::Ptr PlaceOpONNX::get_producing_operation() const {
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_producing_operation(int input_port_index) const {
+ov::frontend::Place::Ptr PlaceOp::get_producing_operation(int input_port_index) const {
     const auto input_port = get_input_port(input_port_index);
     if (input_port != nullptr) {
         return input_port->get_producing_operation();
@@ -315,7 +311,7 @@ Place::Ptr PlaceOpONNX::get_producing_operation(int input_port_index) const {
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_producing_operation(const std::string& input_port_name) const {
+ov::frontend::Place::Ptr PlaceOp::get_producing_operation(const std::string& input_port_name) const {
     const auto input_port = get_input_port(input_port_name);
     if (input_port != nullptr) {
         return input_port->get_producing_operation();
@@ -323,8 +319,8 @@ Place::Ptr PlaceOpONNX::get_producing_operation(const std::string& input_port_na
     return nullptr;
 }
 
-bool PlaceOpONNX::is_equal(Place::Ptr another) const {
-    if (const auto place_op = std::dynamic_pointer_cast<PlaceOpONNX>(another)) {
+bool PlaceOp::is_equal(Place::Ptr another) const {
+    if (const auto place_op = std::dynamic_pointer_cast<PlaceOp>(another)) {
         const auto& another_node = place_op->get_editor_node();
         if (m_editor->is_correct_and_unambiguous_node(m_node) ||
             m_editor->is_correct_and_unambiguous_node(another_node)) {
@@ -334,7 +330,7 @@ bool PlaceOpONNX::is_equal(Place::Ptr another) const {
     return false;
 }
 
-Place::Ptr PlaceOpONNX::get_target_tensor() const {
+ov::frontend::Place::Ptr PlaceOp::get_target_tensor() const {
     const auto output_port = get_output_port();
     if (output_port != nullptr) {
         return output_port->get_target_tensor();
@@ -342,7 +338,7 @@ Place::Ptr PlaceOpONNX::get_target_tensor() const {
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_target_tensor(int output_port_index) const {
+ov::frontend::Place::Ptr PlaceOp::get_target_tensor(int output_port_index) const {
     const auto output_port = get_output_port(output_port_index);
     if (output_port != nullptr) {
         return output_port->get_target_tensor();
@@ -350,7 +346,7 @@ Place::Ptr PlaceOpONNX::get_target_tensor(int output_port_index) const {
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_target_tensor(const std::string& output_name) const {
+ov::frontend::Place::Ptr PlaceOp::get_target_tensor(const std::string& output_name) const {
     const auto output_port = get_output_port(output_name);
     if (output_port != nullptr) {
         return output_port->get_target_tensor();
@@ -358,7 +354,7 @@ Place::Ptr PlaceOpONNX::get_target_tensor(const std::string& output_name) const
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_source_tensor() const {
+ov::frontend::Place::Ptr PlaceOp::get_source_tensor() const {
     const auto input_port = get_input_port();
     if (input_port != nullptr) {
         return input_port->get_source_tensor();
@@ -366,7 +362,7 @@ Place::Ptr PlaceOpONNX::get_source_tensor() const {
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_source_tensor(int input_port_index) const {
+ov::frontend::Place::Ptr PlaceOp::get_source_tensor(int input_port_index) const {
     const auto input_port = get_input_port(input_port_index);
     if (input_port != nullptr) {
         return input_port->get_source_tensor();
@@ -374,7 +370,7 @@ Place::Ptr PlaceOpONNX::get_source_tensor(int input_port_index) const {
     return nullptr;
 }
 
-Place::Ptr PlaceOpONNX::get_source_tensor(const std::string& input_name) const {
+ov::frontend::Place::Ptr PlaceOp::get_source_tensor(const std::string& input_name) const {
     const auto input_port = get_input_port(input_name);
     if (input_port != nullptr) {
         return input_port->get_source_tensor();
@@ -382,15 +378,15 @@ Place::Ptr PlaceOpONNX::get_source_tensor(const std::string& input_name) const {
     return nullptr;
 }
 
-bool PlaceOpONNX::is_input() const {
+bool PlaceOp::is_input() const {
     return false;
 }
 
-bool PlaceOpONNX::is_output() const {
+bool PlaceOp::is_output() const {
     return false;
 }
 
-void PlaceOpONNX::set_name(const std::string& new_name) {
+void PlaceOp::set_name(const std::string& new_name) {
     m_editor->set_node_name(m_node, new_name);
     m_node.m_node_name = new_name;
 }
diff --git a/src/frontends/onnx/frontend/src/place.hpp b/src/frontends/onnx/frontend/src/place.hpp
index 063a18867d8..02bd737e16f 100644
--- a/src/frontends/onnx/frontend/src/place.hpp
+++ b/src/frontends/onnx/frontend/src/place.hpp
@@ -4,17 +4,19 @@
 
 #pragma once
 
-#include <common/place.hpp>
 #include <editor.hpp>
 #include <memory>
+#include <openvino/frontend/place.hpp>
 #include <sstream>
 
 namespace ov {
 namespace frontend {
-class PlaceInputEdgeONNX : public Place {
+namespace onnx {
+
+class PlaceInputEdge : public Place {
 public:
-    PlaceInputEdgeONNX(const onnx_editor::InputEdge& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
-    PlaceInputEdgeONNX(onnx_editor::InputEdge&& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceInputEdge(const onnx_editor::InputEdge& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceInputEdge(onnx_editor::InputEdge&& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
 
     // internal usage
     onnx_editor::InputEdge get_input_edge() const;
@@ -34,10 +36,10 @@ private:
     const std::shared_ptr<onnx_editor::ONNXModelEditor> m_editor;
 };
 
-class PlaceOutputEdgeONNX : public Place {
+class PlaceOutputEdge : public Place {
 public:
-    PlaceOutputEdgeONNX(const onnx_editor::OutputEdge& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
-    PlaceOutputEdgeONNX(onnx_editor::OutputEdge&& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceOutputEdge(const onnx_editor::OutputEdge& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceOutputEdge(onnx_editor::OutputEdge&& edge, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
 
     // internal usage
     onnx_editor::OutputEdge get_output_edge() const;
@@ -57,10 +59,10 @@ private:
     std::shared_ptr<onnx_editor::ONNXModelEditor> m_editor;
 };
 
-class PlaceTensorONNX : public Place {
+class PlaceTensor : public Place {
 public:
-    PlaceTensorONNX(const std::string& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
-    PlaceTensorONNX(std::string&& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceTensor(const std::string& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceTensor(std::string&& name, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
 
     // external usage
     std::vector<std::string> get_names() const override;
@@ -81,10 +83,10 @@ private:
     std::shared_ptr<onnx_editor::ONNXModelEditor> m_editor;
 };
 
-class PlaceOpONNX : public Place {
+class PlaceOp : public Place {
 public:
-    PlaceOpONNX(const onnx_editor::EditorNode& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
-    PlaceOpONNX(onnx_editor::EditorNode&& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceOp(const onnx_editor::EditorNode& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
+    PlaceOp(onnx_editor::EditorNode&& node, std::shared_ptr<onnx_editor::ONNXModelEditor> editor);
     std::vector<std::string> get_names() const override;
 
     // internal usage
@@ -125,5 +127,7 @@ private:
     onnx_editor::EditorNode m_node;
     std::shared_ptr<onnx_editor::ONNXModelEditor> m_editor;
 };
+
+}  // namespace onnx
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp b/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
index 9813e82fa4b..dba12e3b2c6 100644
--- a/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
+++ b/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
@@ -7,8 +7,8 @@
 #include <memory>
 #include <string>
 
-#include "common/telemetry_extension.hpp"
 #include "ngraph/function.hpp"
+#include "openvino/frontend/telemetry_extension.hpp"
 
 namespace ONNX_NAMESPACE {
 class ModelProto;
diff --git a/src/frontends/paddlepaddle/CMakeLists.txt b/src/frontends/paddlepaddle/CMakeLists.txt
index b3d46d67d1a..d6c5e4999f5 100644
--- a/src/frontends/paddlepaddle/CMakeLists.txt
+++ b/src/frontends/paddlepaddle/CMakeLists.txt
@@ -2,8 +2,8 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
+# TODO: Add LINKABLE_FRONTEND option when tensorflow frontend directory is moved to openvino folder
 ov_add_frontend(NAME paddlepaddle
-                LINKABLE_FRONTEND
                 PROTOBUF_LITE
                 FILEDESCRIPTION "FrontEnd to load and convert PaddlePaddle file format"
                 LINK_LIBRARIES inference_engine_transformations)
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
index 2b10a622ff7..0cabd02cd2b 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
@@ -4,8 +4,8 @@
 
 #pragma once
 
-#include <common/telemetry_extension.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
+#include <openvino/frontend/telemetry_extension.hpp>
 
 #include "exceptions.hpp"
 #include "model.hpp"
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
index 8cc63a5540a..ba8659e04b4 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
@@ -4,8 +4,8 @@
 
 #pragma once
 
-#include <common/telemetry_extension.hpp>
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
+#include <openvino/frontend/telemetry_extension.hpp>
 
 #include "paddlepaddle_frontend/utility.hpp"
 
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
index e41e39564db..d99a9db6f4d 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
@@ -4,9 +4,9 @@
 
 #pragma once
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
-#include "paddlepaddle_frontend/exceptions.hpp"
+#include "exceptions.hpp"
 
 namespace paddle {
 namespace framework {
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
index 8d61af08ff6..a9c273cb392 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include <manager.hpp>
+#include <openvino/frontend/manager.hpp>
 
 #ifdef OPENVINO_STATIC_LIBRARY
 #    define PDPD_API
diff --git a/src/frontends/paddlepaddle/src/exceptions.cpp b/src/frontends/paddlepaddle/src/exception.cpp
similarity index 90%
rename from src/frontends/paddlepaddle/src/exceptions.cpp
rename to src/frontends/paddlepaddle/src/exception.cpp
index 0d172c6f4b5..ddd097ba5ef 100644
--- a/src/frontends/paddlepaddle/src/exceptions.cpp
+++ b/src/frontends/paddlepaddle/src/exception.cpp
@@ -2,8 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "paddlepaddle_frontend/exceptions.hpp"
-
+#include "exceptions.hpp"
 #include "node_context.hpp"
 
 namespace ov {
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/exceptions.hpp b/src/frontends/paddlepaddle/src/exceptions.hpp
similarity index 96%
rename from src/frontends/paddlepaddle/include/paddlepaddle_frontend/exceptions.hpp
rename to src/frontends/paddlepaddle/src/exceptions.hpp
index 2501ffafa35..78ce6937d83 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/exceptions.hpp
+++ b/src/frontends/paddlepaddle/src/exceptions.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include <common/frontend_exceptions.hpp>
+#include <openvino/frontend/exception.hpp>
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/paddlepaddle/src/frontend.cpp b/src/frontends/paddlepaddle/src/frontend.cpp
index b9df7dce3a1..a552dcaa06a 100644
--- a/src/frontends/paddlepaddle/src/frontend.cpp
+++ b/src/frontends/paddlepaddle/src/frontend.cpp
@@ -10,11 +10,11 @@
 #include <vector>
 
 #include "decoder.hpp"
+#include "exceptions.hpp"
 #include "framework.pb.h"
 #include "node_context.hpp"
 #include "op_table.hpp"
 #include "openvino/opsets/opset7.hpp"
-#include "paddlepaddle_frontend/exceptions.hpp"
 #include "paddlepaddle_frontend/model.hpp"
 #include "paddlepaddle_frontend/place.hpp"
 #include "pdpd_fw_node.hpp"
diff --git a/src/frontends/paddlepaddle/src/model.cpp b/src/frontends/paddlepaddle/src/model.cpp
index e089e836048..48ccef9643e 100644
--- a/src/frontends/paddlepaddle/src/model.cpp
+++ b/src/frontends/paddlepaddle/src/model.cpp
@@ -8,10 +8,10 @@
 #include <queue>
 
 #include "decoder.hpp"
+#include "exceptions.hpp"
 #include "framework.pb.h"
 #include "node_context.hpp"
 #include "openvino/opsets/opset7.hpp"
-#include "paddlepaddle_frontend/exceptions.hpp"
 #include "paddlepaddle_frontend/place.hpp"
 #include "pdpd_utils.hpp"
 
diff --git a/src/frontends/paddlepaddle/src/node_context.hpp b/src/frontends/paddlepaddle/src/node_context.hpp
index 8b363d1f6ba..6dfe727f6ee 100644
--- a/src/frontends/paddlepaddle/src/node_context.hpp
+++ b/src/frontends/paddlepaddle/src/node_context.hpp
@@ -3,9 +3,9 @@
 //
 
 #pragma once
+#include "exceptions.hpp"
 #include "ngraph/compatibility.hpp"
 #include "openvino/core/any.hpp"
-#include "paddlepaddle_frontend/exceptions.hpp"
 #include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
diff --git a/src/frontends/paddlepaddle/src/pdpd_utils.hpp b/src/frontends/paddlepaddle/src/pdpd_utils.hpp
index 6f98dcd16db..5fcaaa6c188 100644
--- a/src/frontends/paddlepaddle/src/pdpd_utils.hpp
+++ b/src/frontends/paddlepaddle/src/pdpd_utils.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include "common/frontend_exceptions.hpp"
+#include "openvino/frontend/exception.hpp"
 #include "openvino/opsets/opset6.hpp"
 
 namespace ov {
diff --git a/src/frontends/tensorflow/CMakeLists.txt b/src/frontends/tensorflow/CMakeLists.txt
index 1e049c6d66d..b285b4ed459 100644
--- a/src/frontends/tensorflow/CMakeLists.txt
+++ b/src/frontends/tensorflow/CMakeLists.txt
@@ -2,8 +2,8 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
+# TODO: Add LINKABLE_FRONTEND option when tensorflow frontend directory is moved to openvino folder
 ov_add_frontend(NAME tensorflow
-                LINKABLE_FRONTEND
                 SKIP_INSTALL
                 FILEDESCRIPTION "FrontEnd to load and convert TensorFlow file format"
                 LINK_LIBRARIES openvino::util)
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp b/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
index 14c8ea8922c..8c53ecb4a9d 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
+++ b/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
@@ -7,11 +7,11 @@
 #include <functional>
 #include <map>
 
-#include "common/frontend.hpp"
-#include "common/input_model.hpp"
-#include "common/telemetry_extension.hpp"
 #include "openvino/core/any.hpp"
 #include "openvino/core/node_vector.hpp"
+#include "openvino/frontend/frontend.hpp"
+#include "openvino/frontend/input_model.hpp"
+#include "openvino/frontend/telemetry_extension.hpp"
 #include "tensorflow_frontend/utility.hpp"
 
 namespace ov {
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp b/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
index 2e6c3abf6b2..1990785fc30 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
+++ b/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include "common/frontend_exceptions.hpp"
+#include "openvino/frontend/exception.hpp"
 
 #ifdef OPENVINO_STATIC_LIBRARY
 #    define TF_API
diff --git a/src/frontends/tensorflow/src/exceptions.hpp b/src/frontends/tensorflow/src/exceptions.hpp
index 0a72e27ad7e..d43995a765d 100644
--- a/src/frontends/tensorflow/src/exceptions.hpp
+++ b/src/frontends/tensorflow/src/exceptions.hpp
@@ -3,8 +3,7 @@
 //
 #pragma once
 
-#include "common/frontend_exceptions.hpp"
-#include "openvino/core/node.hpp"
+#include "openvino/frontend/exception.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/tensorflow/src/model.cpp b/src/frontends/tensorflow/src/model.cpp
index e3e1aa76531..3fc273d38cd 100644
--- a/src/frontends/tensorflow/src/model.cpp
+++ b/src/frontends/tensorflow/src/model.cpp
@@ -7,8 +7,8 @@
 #include <fstream>
 #include <queue>
 
-#include "common/frontend_exceptions.hpp"
 #include "node_context.hpp"
+#include "openvino/frontend/exception.hpp"
 #include "openvino/opsets/opset7.hpp"
 #include "place.hpp"
 #include "tensorflow_frontend/graph_iterator.hpp"
diff --git a/src/frontends/tensorflow/src/model.hpp b/src/frontends/tensorflow/src/model.hpp
index d2edc1dc5d3..6c88056131e 100644
--- a/src/frontends/tensorflow/src/model.hpp
+++ b/src/frontends/tensorflow/src/model.hpp
@@ -4,9 +4,9 @@
 
 #pragma once
 
-#include "common/input_model.hpp"
-#include "common/place.hpp"
-#include "common/telemetry_extension.hpp"
+#include "openvino/frontend/input_model.hpp"
+#include "openvino/frontend/place.hpp"
+#include "openvino/frontend/telemetry_extension.hpp"
 #include "tensorflow_frontend/graph_iterator.hpp"
 
 namespace ov {
diff --git a/src/frontends/tensorflow/src/place.cpp b/src/frontends/tensorflow/src/place.cpp
index 8b7a3f9ac55..bdddede95f7 100644
--- a/src/frontends/tensorflow/src/place.cpp
+++ b/src/frontends/tensorflow/src/place.cpp
@@ -4,9 +4,9 @@
 
 #include "place.hpp"
 
-#include "common/frontend_exceptions.hpp"
 #include "node_context.hpp"
 #include "op_def.pb.h"
+#include "openvino/frontend/exception.hpp"
 #include "tensor.pb.h"
 #include "types.pb.h"
 
diff --git a/src/frontends/tensorflow/src/place.hpp b/src/frontends/tensorflow/src/place.hpp
index 733e0c25262..f6197ee3a90 100644
--- a/src/frontends/tensorflow/src/place.hpp
+++ b/src/frontends/tensorflow/src/place.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include "common/frontend.hpp"
+#include "openvino/frontend/frontend.hpp"
 #include "tensorflow_frontend/decoder.hpp"
 
 namespace ov {
diff --git a/src/frontends/tensorflow/src/tensorflow.cpp b/src/frontends/tensorflow/src/tensorflow.cpp
index 24824017e42..e462ef7b92e 100644
--- a/src/frontends/tensorflow/src/tensorflow.cpp
+++ b/src/frontends/tensorflow/src/tensorflow.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 #include "tensorflow_frontend/frontend.hpp"
 
 TF_C_API ov::frontend::FrontEndVersion GetAPIVersion() {
diff --git a/src/inference/src/ie_network_reader.cpp b/src/inference/src/ie_network_reader.cpp
index 6fa28421f66..e7bc26c8e99 100644
--- a/src/inference/src/ie_network_reader.cpp
+++ b/src/inference/src/ie_network_reader.cpp
@@ -18,7 +18,7 @@
 #include "ie_common.h"
 #include "ie_icnn_network.hpp"
 #include "ie_input_info.hpp"
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 #ifdef ENABLE_IR_V7_READER
 #    include "legacy/ie_ir_version.hpp"
 #endif
diff --git a/src/tests/functional/inference_engine/ir_serialization/partial_shape_deserialization.cpp b/src/tests/functional/inference_engine/ir_serialization/partial_shape_deserialization.cpp
index c9eddbf6e21..4f1e879c966 100644
--- a/src/tests/functional/inference_engine/ir_serialization/partial_shape_deserialization.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/partial_shape_deserialization.cpp
@@ -7,7 +7,7 @@
 #include <inference_engine.hpp>
 #include <memory>
 #include <string>
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 
 #include "graph_comparator.hpp"
 #include "ie_blob.h"
diff --git a/src/tests/functional/inference_engine/ir_serialization/partial_shape_serialization.cpp b/src/tests/functional/inference_engine/ir_serialization/partial_shape_serialization.cpp
index 445c672ce69..7303fce5346 100644
--- a/src/tests/functional/inference_engine/ir_serialization/partial_shape_serialization.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/partial_shape_serialization.cpp
@@ -9,7 +9,7 @@
 #include <openvino/core/preprocess/pre_post_process.hpp>
 #include <openvino/opsets/opset8.hpp>
 #include <transformations/rt_info/attributes.hpp>
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "ngraph/ngraph.hpp"
diff --git a/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp b/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
index 4a2f8e5e775..ff203e3a1fa 100644
--- a/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
@@ -15,7 +15,7 @@
 #include <transformations/rt_info/fused_names_attribute.hpp>
 #include <transformations/rt_info/old_api_map_order_attribute.hpp>
 #include <transformations/rt_info/old_api_map_element_type_attribute.hpp>
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 #include "graph_comparator.hpp"
 #include "ie_blob.h"
 #include "ie_precision.hpp"
diff --git a/src/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp b/src/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp
index d72ad3b4a1a..fcb884ca442 100644
--- a/src/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp
@@ -15,7 +15,7 @@
 #include <openvino/core/preprocess/pre_post_process.hpp>
 #include <openvino/opsets/opset8.hpp>
 #include <transformations/rt_info/attributes.hpp>
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 
 using namespace ngraph;
 
diff --git a/tests/fuzz/src/import_pdpd-fuzzer.cc b/tests/fuzz/src/import_pdpd-fuzzer.cc
index ae85d3c3f42..5cb685847f9 100644
--- a/tests/fuzz/src/import_pdpd-fuzzer.cc
+++ b/tests/fuzz/src/import_pdpd-fuzzer.cc
@@ -1,7 +1,7 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-#include "manager.hpp"
+#include "openvino/frontend/manager.hpp"
 #include "ngraph/ngraph.hpp"
 #include "tokenizer.h"
 #include <string>
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.cpp b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.cpp
index e23910ef623..2820d8e8ebd 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.cpp
+++ b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.cpp
@@ -3,9 +3,9 @@
 //
 
 #include "mock_mo_frontend.hpp"
-#include "common/frontend_defs.hpp"
-#include "manager.hpp"
 #include "ngraph/visibility.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 using namespace ngraph;
 using namespace ov::frontend;
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
index 553870dd423..2a0c6dddab4 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
+++ b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
@@ -4,9 +4,9 @@
 
 #pragma once
 
-#include "common/frontend_defs.hpp"
-#include "manager.hpp"
 #include "ngraph/visibility.hpp"
+#include "openvino/frontend/manager.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 // Defined if we are building the plugin DLL (instead of using it)
 #ifdef mock_mo_ov_frontend_EXPORTS

From b41cb57773737bb905852eb389d3b417c0e73ec1 Mon Sep 17 00:00:00 2001
From: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>
Date: Fri, 17 Dec 2021 10:42:21 +0300
Subject: [PATCH 009/160] Fix build issue for openvino wheel package on Windows
 (#9265)

* Fix build issue for openvino wheel package on Windows

* fix env
---
 .../ie_bridges/python/wheel/.env.in           | 15 -------
 .../ie_bridges/python/wheel/CMakeLists.txt    | 43 ++++++-------------
 .../wheel/meta/openvino-dev.requirements.txt  | 28 ------------
 .../python/wheel/meta/openvino-dev.setup.cfg  | 22 ----------
 .../wheel/meta/openvino.requirements.txt      |  1 -
 .../python/wheel/meta/pypi_overview.md        | 32 --------------
 .../python/wheel/requirements-dev.txt         |  2 +-
 .../ie_bridges/python/wheel/setup.cfg         | 14 +++---
 .../ie_bridges/python/wheel/setup.py          | 34 +++++++--------
 tools/openvino_dev/setup.py                   |  2 +-
 10 files changed, 41 insertions(+), 152 deletions(-)
 delete mode 100644 inference-engine/ie_bridges/python/wheel/.env.in
 delete mode 100644 inference-engine/ie_bridges/python/wheel/meta/openvino-dev.requirements.txt
 delete mode 100644 inference-engine/ie_bridges/python/wheel/meta/openvino-dev.setup.cfg
 delete mode 100644 inference-engine/ie_bridges/python/wheel/meta/openvino.requirements.txt
 delete mode 100644 inference-engine/ie_bridges/python/wheel/meta/pypi_overview.md

diff --git a/inference-engine/ie_bridges/python/wheel/.env.in b/inference-engine/ie_bridges/python/wheel/.env.in
deleted file mode 100644
index 5dc313f6b1d..00000000000
--- a/inference-engine/ie_bridges/python/wheel/.env.in
+++ /dev/null
@@ -1,15 +0,0 @@
-WHEEL_PACKAGE_NAME=@WHEEL_PACKAGE_NAME@
-WHEEL_VERSION=@WHEEL_VERSION@
-WHEEL_BUILD=@WHEEL_BUILD@
-WHEEL_LICENCE_TYPE=@WHEEL_LICENCE_TYPE@
-WHEEL_AUTHOR=@WHEEL_AUTHOR@
-WHEEL_AUTHOR_EMAIL=@WHEEL_AUTHOR_EMAIL@
-WHEEL_DESC=@WHEEL_DESC@
-WHEEL_LICENSE=@WHEEL_LICENSE@
-WHEEL_REQUIREMENTS=@WHEEL_REQUIREMENTS@
-WHEEL_OVERVIEW=@WHEEL_OVERVIEW@
-
-CMAKE_BUILD_DIR=@CMAKE_BINARY_DIR@
-OV_RUNTIME_LIBS_DIR=@IE_CPACK_RUNTIME_PATH@
-TBB_LIBS_DIR=@TBB_LIBS_DIR@
-PY_PACKAGES_DIR=@PY_PACKAGES_DIR@
diff --git a/inference-engine/ie_bridges/python/wheel/CMakeLists.txt b/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
index 53d8207c347..7bb7bc2ab79 100644
--- a/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
+++ b/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
@@ -1,40 +1,14 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 #
-
-set(WHEEL_PACKAGE_NAME "openvino" CACHE STRING "Name of the package")
-set(WHEEL_LICENCE_TYPE "OSI Approved :: Apache Software License" CACHE STRING "License type for the package")
-set(WHEEL_AUTHOR "Intel Corporation" CACHE STRING "Package author’s name")
-set(WHEEL_AUTHOR_EMAIL "openvino_pushbot@intel.com" CACHE STRING "Email address of the package author")
-set(WHEEL_DESC "Inference Engine Python* API" CACHE STRING "Short, summary description of the package")
-set(WHEEL_URL "https://docs.openvinotoolkit.org/latest/index.html" CACHE STRING "Home page url")
-set(WHEEL_DOWNLOAD_URL "https://github.com/openvinotoolkit/openvino/tags" CACHE STRING "Download page url")
 set(WHEEL_VERSION "${IE_VERSION}" CACHE STRING "Version of this release" FORCE)
 set(WHEEL_BUILD "${IE_VERSION_BUILD}" CACHE STRING "Build number of this release" FORCE)
-set(WHEEL_LICENSE "${CMAKE_SOURCE_DIR}/LICENSE" CACHE STRING "Wheel license file")
-set(WHEEL_REQUIREMENTS "${CMAKE_CURRENT_SOURCE_DIR}/meta/openvino.requirements.txt" CACHE STRING "Wheel requirements.txt file")
-set(WHEEL_OVERVIEW "${CMAKE_CURRENT_SOURCE_DIR}/meta/pypi_overview.md" CACHE STRING "Detailed description")
-
-set(SETUP_PY "${CMAKE_CURRENT_SOURCE_DIR}/setup.py")
-set(SETUP_ENV "${CMAKE_CURRENT_SOURCE_DIR}/.env.in")
-set(SETUP_ENV_OUT "${CMAKE_CURRENT_SOURCE_DIR}/.env")
-
 set(PY_PACKAGES_DIR ${PYTHON_BRIDGE_CPACK_PATH}/${PYTHON_VERSION})
 set(TBB_LIBS_DIR runtime/3rdparty/tbb/lib)
-
-if(APPLE)
-    set(WHEEL_PLATFORM macosx_10_15_x86_64)
-elseif(UNIX)
-    set(WHEEL_PLATFORM manylinux2014_x86_64)
-elseif(WIN32)
-    set(WHEEL_PLATFORM win_amd64)
+if(WIN32)
     set(TBB_LIBS_DIR runtime/3rdparty/tbb/bin)
-else()
-    message(FATAL_ERROR "This platform is not supported")
 endif()
 
-configure_file(${SETUP_ENV} ${SETUP_ENV_OUT} @ONLY)
-
 if(LINUX)
     find_host_program(patchelf_program
                       NAMES patchelf
@@ -55,21 +29,30 @@ endforeach()
 
 execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import wheel.bdist_wheel ; print(f'{wheel.bdist_wheel.get_abi_tag()}')" OUTPUT_VARIABLE PYTHON_ABI)
 execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import wheel.vendored.packaging.tags as tags ; print(f'{tags.interpreter_name()}{tags.interpreter_version()}')" OUTPUT_VARIABLE INTERPRETER)
+execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import wheel.vendored.packaging.tags as tags ; print(f'{next(tags._platform_tags())}')" OUTPUT_VARIABLE WHEEL_PLATFORM)
 string(STRIP ${PYTHON_ABI} PYTHON_ABI)
 string(STRIP ${INTERPRETER} INTERPRETER)
+string(STRIP ${WHEEL_PLATFORM} WHEEL_PLATFORM)
 
 set(openvino_wheel_name "openvino-${WHEEL_VERSION}-${WHEEL_BUILD}-${INTERPRETER}-${PYTHON_ABI}-${WHEEL_PLATFORM}.whl")
 set(openvino_wheels_output_dir "${CMAKE_BINARY_DIR}/wheels")
 set(openvino_wheel_path "${openvino_wheels_output_dir}/${openvino_wheel_name}")
 
 add_custom_command(OUTPUT ${openvino_wheel_path}
+    COMMAND ${CMAKE_COMMAND} -E copy_directory "${CMAKE_CURRENT_SOURCE_DIR}" "${CMAKE_CURRENT_BINARY_DIR}"
+    COMMAND ${CMAKE_COMMAND} -E copy_directory "${CMAKE_SOURCE_DIR}/licensing" "${CMAKE_BINARY_DIR}/licensing"
     COMMAND ${CMAKE_COMMAND} -E remove_directory "${CMAKE_CURRENT_BINARY_DIR}/site-packages"
-    COMMAND ${PYTHON_EXECUTABLE} ${SETUP_PY} clean bdist_wheel
+    COMMAND ${CMAKE_COMMAND} -E env WHEEL_VERSION=${WHEEL_VERSION}
+        WHEEL_BUILD=${WHEEL_BUILD}
+        CMAKE_BUILD_DIR=${CMAKE_BINARY_DIR}
+        OV_RUNTIME_LIBS_DIR=${IE_CPACK_RUNTIME_PATH}
+        TBB_LIBS_DIR=${TBB_LIBS_DIR}
+        PY_PACKAGES_DIR=${PY_PACKAGES_DIR}
+        ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/setup.py" clean bdist_wheel
         --dist-dir ${openvino_wheels_output_dir}
         --build=${WHEEL_BUILD}
         --plat-name=${WHEEL_PLATFORM}
-    # COMMAND ${CMAKE_COMMAND} -E remove ${SETUP_ENV_OUT}
-    DEPENDS ${openvino_wheel_deps} ${SETUP_ENV_OUT}
+    DEPENDS ${openvino_wheel_deps} 
     WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}"
     COMMENT "Building Python wheel ${openvino_wheel_name}"
     VERBATIM)
diff --git a/inference-engine/ie_bridges/python/wheel/meta/openvino-dev.requirements.txt b/inference-engine/ie_bridges/python/wheel/meta/openvino-dev.requirements.txt
deleted file mode 100644
index ccc569a0194..00000000000
--- a/inference-engine/ie_bridges/python/wheel/meta/openvino-dev.requirements.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-defusedxml>=0.7.1
-scipy~=1.5.4
-jstyleson~=0.0.2
-numpy>=1.16.6,<1.20
-addict>=2.4.0
-pandas~=1.1.5
-hyperopt~=0.1.2
-networkx~=2.5
-tqdm>=4.54.1
-texttable~=1.6.3
-py-cpuinfo>=7.0.0
-PyYAML>=5.4.1
-pillow>=8.1.2
-scikit-image>=0.17.2
-scikit-learn>=0.24.1
-yamlloader>=0.5
-shapely>=1.7.1
-nibabel>=3.2.1
-pydicom>=2.1.2
-sentencepiece>=0.1.95
-tokenizers>=0.10.1
-editdistance>=0.5.3
-parasail>=1.2.4
-fast-ctc-decode>=0.2.5
-rawpy>=0.16.0
-nltk>=3.5
-opencv-python==4.5.*
-progress>=1.5
diff --git a/inference-engine/ie_bridges/python/wheel/meta/openvino-dev.setup.cfg b/inference-engine/ie_bridges/python/wheel/meta/openvino-dev.setup.cfg
deleted file mode 100644
index d6789c4a084..00000000000
--- a/inference-engine/ie_bridges/python/wheel/meta/openvino-dev.setup.cfg
+++ /dev/null
@@ -1,22 +0,0 @@
-[options]
-py_modules =
-    mo
-    mo_tf
-    mo_caffe
-    mo_mxnet
-    mo_onnx
-    mo_kaldi
-
-[options.package_data]
-    * = *
-
-[options.entry_points]
-console_scripts =
-
-[metadata]
-license_files =
-    readme*
-    *LICENSE*
-    *license*
-    *third-party-programs*
-    *EULA*
diff --git a/inference-engine/ie_bridges/python/wheel/meta/openvino.requirements.txt b/inference-engine/ie_bridges/python/wheel/meta/openvino.requirements.txt
deleted file mode 100644
index 63012dd1739..00000000000
--- a/inference-engine/ie_bridges/python/wheel/meta/openvino.requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-numpy>=1.16.6,<1.20
diff --git a/inference-engine/ie_bridges/python/wheel/meta/pypi_overview.md b/inference-engine/ie_bridges/python/wheel/meta/pypi_overview.md
deleted file mode 100644
index 418a04bc16a..00000000000
--- a/inference-engine/ie_bridges/python/wheel/meta/pypi_overview.md
+++ /dev/null
@@ -1,32 +0,0 @@
-## OpenVINO™ Toolkit
-
-OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The OpenVINO™ toolkit includes the Deep Learning Deployment Toolkit (DLDT).
-
-OpenVINO™ toolkit:
-
-- Enables CNN-based deep learning inference on the edge
-- Supports heterogeneous execution across an Intel® CPU, Intel® Integrated Graphics, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
-- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
-- Includes optimized calls for computer vision standards, including OpenCV\* and OpenCL™
-
-Operating Systems:
-- Ubuntu* 18.04 long-term support (LTS), 64-bit
-- Windows* 10, 64-bit
-- macOS* 10.15, 64-bit
-
-## Install the Runtime Package Using the PyPI Repository
-1. Set up and update pip to the highest version:
-   ```sh
-   python3 -m pip install --upgrade pip
-   ```
-2. Install the Intel® distribution of OpenVINO™ toolkit:
-   ```sh
-   pip install openvino
-   ```
-
-3. Verify that the package is installed:
-   ```sh
-   python3 -c "from openvino.inference_engine import IECore"
-   ```
-   
-Now you are ready to develop and run your application.
\ No newline at end of file
diff --git a/inference-engine/ie_bridges/python/wheel/requirements-dev.txt b/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
index 8c4ce47c35f..b7574b392d2 100644
--- a/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
+++ b/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
@@ -1,3 +1,3 @@
 setuptools>=53.0.0
 wheel>=0.36.2
-python-decouple>=3.4
+
diff --git a/inference-engine/ie_bridges/python/wheel/setup.cfg b/inference-engine/ie_bridges/python/wheel/setup.cfg
index abb1790b67f..c6893c93c42 100644
--- a/inference-engine/ie_bridges/python/wheel/setup.cfg
+++ b/inference-engine/ie_bridges/python/wheel/setup.cfg
@@ -1,7 +1,11 @@
 [metadata]
 license_files =
-	readme* 
-	*LICENSE*
-	*license*
-	*third-party-programs*
-	*EULA*
+    readme* 
+    *LICENSE*
+    *license*
+    *third-party-programs*
+    ../../../../licensing/runtime-third-party-programs.txt
+    ../../../../licensing/tbb_third-party-programs.txt
+    ../../../../licensing/onednn_third-party-programs.txt
+    ../../../../LICENSE
+
diff --git a/inference-engine/ie_bridges/python/wheel/setup.py b/inference-engine/ie_bridges/python/wheel/setup.py
index 517dce7560e..eb8d573dfba 100644
--- a/inference-engine/ie_bridges/python/wheel/setup.py
+++ b/inference-engine/ie_bridges/python/wheel/setup.py
@@ -21,7 +21,6 @@ from setuptools import setup, find_namespace_packages, Extension
 from setuptools.command.build_ext import build_ext
 from setuptools.command.build_clib import build_clib
 from setuptools.command.install import install
-from decouple import config
 
 WHEEL_LIBS_INSTALL_DIR = os.path.join('openvino', 'libs')
 WHEEL_LIBS_PACKAGE = 'openvino.libs'
@@ -41,10 +40,11 @@ elif machine == 'aarch64':
     ARCH = 'arm64'
 
 # The following variables can be defined in environment or .env file
-CMAKE_BUILD_DIR = config('CMAKE_BUILD_DIR', '.')
-OV_RUNTIME_LIBS_DIR = config('OV_RUNTIME_LIBS_DIR', f'runtime/{LIBS_DIR}/{ARCH}/{CONFIG}')
-TBB_LIBS_DIR = config('TBB_LIBS_DIR', f'runtime/3rdparty/tbb/{LIBS_DIR}')
-PY_PACKAGES_DIR = config('PY_PACKAGES_DIR', f'python/{PYTHON_VERSION}')
+SCRIPT_DIR = Path(__file__).resolve().parents[0]
+CMAKE_BUILD_DIR = os.getenv('CMAKE_BUILD_DIR', '.')
+OV_RUNTIME_LIBS_DIR = os.getenv('OV_RUNTIME_LIBS_DIR', f'runtime/{LIBS_DIR}/{ARCH}/{CONFIG}')
+TBB_LIBS_DIR = os.getenv('TBB_LIBS_DIR', f'runtime/3rdparty/tbb/{LIBS_DIR}')
+PY_PACKAGES_DIR = os.getenv('PY_PACKAGES_DIR', f'python/{PYTHON_VERSION}')
 LIBS_RPATH = '$ORIGIN' if sys.platform == 'linux' else '@loader_path'
 
 LIB_INSTALL_CFG = {
@@ -428,28 +428,28 @@ if not any(pl in sys.platform for pl in platforms):
     sys.exit(f'Unsupported platform: {sys.platform}, expected: linux, win32, darwin')
 
 # copy license file into the build directory
-package_license = config('WHEEL_LICENSE', '')
+package_license = os.getenv('WHEEL_LICENSE', SCRIPT_DIR.parents[3] / 'LICENSE')
 if os.path.exists(package_license):
     copyfile(package_license, 'LICENSE')
 
 packages = find_namespace_packages(get_package_dir(PY_INSTALL_CFG))
 package_data: typing.Dict[str, list] = {}
-pkg_name = config('WHEEL_PACKAGE_NAME', 'openvino')
+pkg_name = os.getenv('WHEEL_PACKAGE_NAME', 'openvino')
 ext_modules = find_prebuilt_extensions(get_dir_list(PY_INSTALL_CFG)) if pkg_name == 'openvino' else []
 
 setup(
-    version=config('WHEEL_VERSION', '0.0.0'),
-    build=config('WHEEL_BUILD', '000'),
-    author_email=config('WHEEL_AUTHOR_EMAIL', 'openvino_pushbot@intel.com'),
+    version=os.getenv('WHEEL_VERSION', '0.0.0'),
+    build=os.getenv('WHEEL_BUILD', '000'),
+    author_email=os.getenv('WHEEL_AUTHOR_EMAIL', 'openvino_pushbot@intel.com'),
     name=pkg_name,
-    license=config('WHEEL_LICENCE_TYPE', 'OSI Approved :: Apache Software License'),
-    author=config('WHEEL_AUTHOR', 'Intel Corporation'),
-    description=config('WHEEL_DESC', 'Inference Engine Python* API'),
-    install_requires=get_dependencies(config('WHEEL_REQUIREMENTS', 'meta/openvino.requirements.txt')),
-    long_description=get_description(config('WHEEL_OVERVIEW', 'meta/pypi_overview.md')),
+    license=os.getenv('WHEEL_LICENCE_TYPE', 'OSI Approved :: Apache Software License'),
+    author=os.getenv('WHEEL_AUTHOR', 'Intel(R) Corporation'),
+    description=os.getenv('WHEEL_DESC', 'OpenVINO(TM) Runtime'),
+    install_requires=get_dependencies(os.getenv('WHEEL_REQUIREMENTS', SCRIPT_DIR.parents[0] / 'requirements.txt')),
+    long_description=get_description(os.getenv('WHEEL_OVERVIEW', SCRIPT_DIR.parents[3] / 'docs/install_guides/pypi-openvino-rt.md')),
     long_description_content_type='text/markdown',
-    download_url=config('WHEEL_DOWNLOAD_URL', 'https://github.com/openvinotoolkit/openvino/tags'),
-    url=config('WHEEL_URL', 'https://docs.openvinotoolkit.org/latest/index.html'),
+    download_url=os.getenv('WHEEL_DOWNLOAD_URL', 'https://github.com/openvinotoolkit/openvino/tags'),
+    url=os.getenv('WHEEL_URL', 'https://docs.openvinotoolkit.org/latest/index.html'),
     cmdclass={
         'build': CustomBuild,
         'install': CustomInstall,
diff --git a/tools/openvino_dev/setup.py b/tools/openvino_dev/setup.py
index a18d6991a75..44861d6a3ed 100644
--- a/tools/openvino_dev/setup.py
+++ b/tools/openvino_dev/setup.py
@@ -195,7 +195,7 @@ setup(
     author_email='openvino_pushbot@intel.com',
     url='https://docs.openvinotoolkit.org/latest/index.html',
     download_url='https://github.com/openvinotoolkit/openvino/tags',
-    description='OpenVINO™ Developer Package',
+    description='OpenVINO(TM) Development Tools',
     long_description=get_description(SCRIPT_DIR.parents[1] / 'docs/install_guides/pypi-openvino-dev.md'),
     long_description_content_type='text/markdown',
     classifiers=[

From 94c3731b50771737a945a7d65907d8f73581fcbe Mon Sep 17 00:00:00 2001
From: "Min, Byungil" <byungil.min@intel.com>
Date: Fri, 17 Dec 2021 16:43:21 +0900
Subject: [PATCH 010/160] [GPU] support multiple sum post-op of onednn kernel
 (#9221)

+ Applied WA for multiple sum post-ops of nednn kernel

Signed-off-by: Min, Byungil <byungil.min@intel.com>
---
 .../thirdparty/clDNN/src/layout_optimizer.cpp   | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
index 1c4518b1654..1646ca99a89 100644
--- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
+++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
@@ -1283,6 +1283,23 @@ impl_types layout_optimizer::get_preferred_impl_type(program_node& node, format
             impl_candidate = impl_types::ocl;
         }
 
+        // [WA] to avoid an onednn kernel issue of multiple sum post-ops
+        if (!node.get_fused_primitives().empty()) {
+            size_t sum_post_op_cnt = 0;
+            for (auto& fused_op : node.get_fused_primitives()) {
+                if (fused_op.node->is_type<eltwise>() && node.get_dependencies().size() > fused_op.dep_start_idx && fused_op.deps.size() == 1)  {
+                    auto& eltw_in = node.get_dependency(fused_op.dep_start_idx);
+                    if (program_helpers::are_layouts_identical_for_onednn_sum_post_op(eltw_in.get_output_layout(), node.get_output_layout()) &&
+                        fused_op.node->as<eltwise>().get_primitive()->needs_onednn_sum_post_op(eltw_in.get_output_layout())) {
+                        if (sum_post_op_cnt > 0)
+                            return impl_types::ocl;
+
+                        sum_post_op_cnt += 1;
+                    }
+                }
+            }
+        }
+
         if (node.is_type<convolution>()) {
             // oneDNN doesn't have good support for groups with fsv16 fmt
             auto& conv = node.as<convolution>();

From 04dc16f861216e3d4a528b916fdbf072d671234d Mon Sep 17 00:00:00 2001
From: Maxim Andronov <maxim.andronov@intel.com>
Date: Fri, 17 Dec 2021 11:12:07 +0300
Subject: [PATCH 011/160] [CPU] General fixes for dynamic shapes. Part 2
 (#8871)

---
 .../src/mkldnn_plugin/cpu_shape.h             |  10 +
 .../memory_desc/cpu_blocked_memory_desc.cpp   |  50 +++-
 .../memory_desc/cpu_blocked_memory_desc.h     |   1 +
 .../memory_desc/cpu_memory_desc.h             |   7 +-
 .../memory_desc/cpu_memory_desc_utils.cpp     |  43 ++-
 .../memory_desc/dnnl_blocked_memory_desc.cpp  |  61 ++++-
 .../memory_desc/dnnl_blocked_memory_desc.h    |   2 +-
 .../memory_desc/dnnl_memory_desc.cpp          |   4 +
 .../memory_desc/dnnl_memory_desc.h            |   1 +
 .../src/mkldnn_plugin/mkldnn_graph.cpp        |  29 +-
 .../src/mkldnn_plugin/mkldnn_graph.h          |   3 -
 .../mkldnn_plugin/mkldnn_infer_request.cpp    |   3 +-
 .../src/mkldnn_plugin/mkldnn_node.cpp         |  62 ++++-
 .../src/mkldnn_plugin/mkldnn_node.h           |  10 +-
 .../mkldnn_plugin/mkldnn_nodes_factory.cpp    |   2 +
 .../nodes/common/dnnl_executor.cpp            |  45 +++
 .../nodes/common/dnnl_executor.h              |  39 +++
 .../nodes/mkldnn_adaptive_pooling.cpp         |   6 +-
 .../nodes/mkldnn_adaptive_pooling.h           |   3 +-
 .../nodes/mkldnn_batch_to_space_node.cpp      |   4 +
 .../nodes/mkldnn_batch_to_space_node.h        |   3 +-
 .../nodes/mkldnn_broadcast_node.cpp           |  16 +-
 .../nodes/mkldnn_broadcast_node.h             |   6 +-
 .../nodes/mkldnn_bucketize_node.cpp           |   8 +-
 .../nodes/mkldnn_bucketize_node.h             |   3 +-
 .../nodes/mkldnn_concat_node.cpp              |  49 ++--
 .../mkldnn_plugin/nodes/mkldnn_concat_node.h  |   5 +-
 .../mkldnn_plugin/nodes/mkldnn_conv_node.cpp  | 175 ++++++++----
 .../mkldnn_plugin/nodes/mkldnn_conv_node.h    |  20 +-
 .../nodes/mkldnn_convert_node.cpp             |  11 +-
 .../mkldnn_plugin/nodes/mkldnn_convert_node.h |   3 +-
 .../nodes/mkldnn_ctc_greedy_decoder_node.cpp  |  10 +-
 .../nodes/mkldnn_ctc_greedy_decoder_node.h    |   1 -
 ...mkldnn_ctc_greedy_decoder_seq_len_node.cpp |  10 +-
 .../mkldnn_ctc_greedy_decoder_seq_len_node.h  |   1 -
 .../nodes/mkldnn_ctc_loss_node.cpp            |   8 +-
 .../nodes/mkldnn_ctc_loss_node.h              |   3 +-
 .../nodes/mkldnn_cum_sum_node.cpp             |   8 +-
 .../mkldnn_plugin/nodes/mkldnn_cum_sum_node.h |   1 -
 .../nodes/mkldnn_deconv_node.cpp              | 131 ++++-----
 .../mkldnn_plugin/nodes/mkldnn_deconv_node.h  |  51 +---
 .../nodes/mkldnn_detection_output_node.cpp    |  14 +-
 .../nodes/mkldnn_detection_output_node.h      |   3 +-
 .../nodes/mkldnn_eltwise_node.cpp             |  12 +-
 .../mkldnn_plugin/nodes/mkldnn_eltwise_node.h |   4 +-
 .../mkldnn_embedding_bag_offset_sum_node.cpp  |  16 +-
 .../mkldnn_embedding_bag_offset_sum_node.h    |   4 +-
 .../mkldnn_embedding_bag_packed_sum_node.cpp  |  16 +-
 .../mkldnn_embedding_bag_packed_sum_node.h    |   4 +-
 .../mkldnn_embedding_segments_sum_node.cpp    |  16 +-
 .../mkldnn_embedding_segments_sum_node.h      |   4 +-
 ...mental_detectron_detection_output_node.cpp |   6 -
 ...rimental_detectron_detection_output_node.h |   1 -
 ...n_generate_proposals_single_image_node.cpp |   6 -
 ...ron_generate_proposals_single_image_node.h |   1 -
 ...ntal_detectron_priorgridgenerator_node.cpp |   6 -
 ...mental_detectron_priorgridgenerator_node.h |   1 -
 ...ental_detectron_roifeatureextractor_node.h |   1 -
 ...dnn_experimental_detectron_topkrois_node.h |   1 -
 .../mkldnn_extract_image_patches_node.cpp     |  10 +-
 .../nodes/mkldnn_extract_image_patches_node.h |   1 -
 .../nodes/mkldnn_fake_quantize_node.cpp       |  11 +-
 .../nodes/mkldnn_fake_quantize_node.h         |   3 +-
 .../nodes/mkldnn_gather_elements_node.cpp     |  12 +-
 .../nodes/mkldnn_gather_elements_node.h       |   3 +-
 .../nodes/mkldnn_gather_nd_node.cpp           |  10 +-
 .../nodes/mkldnn_gather_nd_node.h             |   1 -
 .../nodes/mkldnn_gather_node.cpp              |  10 +-
 .../mkldnn_plugin/nodes/mkldnn_gather_node.h  |   1 -
 .../nodes/mkldnn_interpolate_node.cpp         |   2 +-
 .../nodes/mkldnn_log_softmax_node.cpp         |  12 +-
 .../nodes/mkldnn_log_softmax_node.h           |   3 +-
 .../mkldnn_plugin/nodes/mkldnn_lrn_node.cpp   |  12 +-
 .../src/mkldnn_plugin/nodes/mkldnn_lrn_node.h |   1 -
 .../mkldnn_plugin/nodes/mkldnn_math_node.cpp  |   4 +
 .../mkldnn_plugin/nodes/mkldnn_math_node.h    |   3 +-
 .../nodes/mkldnn_matmul_node.cpp              |   8 -
 .../mkldnn_plugin/nodes/mkldnn_matmul_node.h  |   1 -
 .../nodes/mkldnn_matrix_nms_node.cpp          |  24 +-
 .../nodes/mkldnn_matrix_nms_node.h            |   4 +-
 .../nodes/mkldnn_multiclass_nms.cpp           |  24 +-
 .../nodes/mkldnn_multiclass_nms.hpp           |   4 +-
 .../mkldnn_plugin/nodes/mkldnn_mvn_node.cpp   |  12 +-
 .../src/mkldnn_plugin/nodes/mkldnn_mvn_node.h |   3 +-
 .../nodes/mkldnn_non_max_suppression_node.cpp |  19 +-
 .../nodes/mkldnn_non_max_suppression_node.h   |   2 +-
 .../mkldnn_plugin/nodes/mkldnn_non_zero.cpp   |   6 +
 .../src/mkldnn_plugin/nodes/mkldnn_non_zero.h |   6 +-
 .../nodes/mkldnn_normalize_node.cpp           |   8 +
 .../nodes/mkldnn_normalize_node.h             |   4 +-
 .../nodes/mkldnn_one_hot_node.cpp             |   4 +
 .../mkldnn_plugin/nodes/mkldnn_one_hot_node.h |   2 +-
 .../mkldnn_plugin/nodes/mkldnn_pad_node.cpp   |  53 ++--
 .../src/mkldnn_plugin/nodes/mkldnn_pad_node.h |   4 +
 .../nodes/mkldnn_pooling_node.cpp             |   8 +-
 .../mkldnn_plugin/nodes/mkldnn_pooling_node.h |   3 +-
 .../nodes/mkldnn_proposal_node.cpp            |   4 +
 .../nodes/mkldnn_proposal_node.h              |   3 +-
 .../mkldnn_plugin/nodes/mkldnn_range_node.cpp |   4 +
 .../mkldnn_plugin/nodes/mkldnn_range_node.h   |   6 +-
 .../nodes/mkldnn_reduce_node.cpp              |  11 +
 .../mkldnn_plugin/nodes/mkldnn_reduce_node.h  |   3 +-
 .../nodes/mkldnn_reorder_node.cpp             |  12 +
 .../mkldnn_plugin/nodes/mkldnn_reorder_node.h |   6 +-
 .../nodes/mkldnn_reorg_yolo_node.cpp          |   4 +
 .../nodes/mkldnn_reorg_yolo_node.h            |   5 +-
 .../nodes/mkldnn_reshape_node.cpp             |  14 +-
 .../mkldnn_plugin/nodes/mkldnn_reshape_node.h |   1 -
 .../nodes/mkldnn_roi_align_node.cpp           |   8 +-
 .../nodes/mkldnn_roi_align_node.h             |   1 -
 .../nodes/mkldnn_roi_pooling_node.cpp         |   4 +-
 .../nodes/mkldnn_scatter_update_node.cpp      |  34 +--
 .../nodes/mkldnn_scatter_update_node.h        |   2 +-
 .../nodes/mkldnn_select_node.cpp              |  11 +-
 .../mkldnn_plugin/nodes/mkldnn_select_node.h  |   3 +-
 .../mkldnn_plugin/nodes/mkldnn_shapeof.cpp    |   4 +
 .../src/mkldnn_plugin/nodes/mkldnn_shapeof.h  |   6 +-
 .../nodes/mkldnn_shuffle_channels_node.cpp    |   6 +-
 .../nodes/mkldnn_shuffle_channels_node.h      |   2 +-
 .../nodes/mkldnn_softmax_node.cpp             |  12 +-
 .../mkldnn_plugin/nodes/mkldnn_softmax_node.h |   1 -
 .../nodes/mkldnn_space_to_batch_node.cpp      |   4 +
 .../nodes/mkldnn_space_to_batch_node.h        |   3 +-
 .../nodes/mkldnn_space_to_depth_node.cpp      |   2 +-
 .../mkldnn_plugin/nodes/mkldnn_split_node.cpp |  48 ++--
 .../mkldnn_plugin/nodes/mkldnn_split_node.h   |  11 +-
 .../nodes/mkldnn_strided_slice_node.cpp       |  12 +-
 .../nodes/mkldnn_strided_slice_node.h         |   2 +
 .../mkldnn_plugin/nodes/mkldnn_tile_node.cpp  |  12 +-
 .../mkldnn_plugin/nodes/mkldnn_tile_node.h    |   5 +-
 .../mkldnn_plugin/nodes/mkldnn_topk_node.cpp  |   8 +-
 .../mkldnn_plugin/nodes/mkldnn_topk_node.h    |   1 -
 .../nodes/mkldnn_transpose_node.cpp           |   8 +-
 .../nodes/mkldnn_transpose_node.h             |   1 +
 src/bindings/python/tests/__init__.py         |   6 -
 .../python/tests/test_onnx/test_backend.py    |   1 -
 .../python/tests/test_onnx/test_zoo_models.py |   6 -
 .../test_onnx/test_backend.py                 |   1 -
 .../test_onnx/test_zoo_models.py              |   2 -
 src/core/src/op/topk.cpp                      |   4 +-
 .../skip_tests_config.cpp                     |   2 +
 .../plugin/cpu/single_layer_tests/concat.cpp  |  60 +++-
 .../cpu/single_layer_tests/convolution.cpp    |  37 +++
 .../plugin/cpu/single_layer_tests/nonzero.cpp |  47 +++-
 .../cpu/single_layer_tests/variadic_split.cpp |  88 +++++-
 .../subgraph_tests/src/static_zero_dims.cpp   |  62 +++++
 src/tests/unit/cpu/mkldnn_zero_dims_test.cpp  | 258 ++++++++++++++++++
 147 files changed, 1438 insertions(+), 742 deletions(-)
 create mode 100644 inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.cpp
 create mode 100644 inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.h
 create mode 100644 src/tests/functional/plugin/cpu/subgraph_tests/src/static_zero_dims.cpp
 create mode 100644 src/tests/unit/cpu/mkldnn_zero_dims_test.cpp

diff --git a/inference-engine/src/mkldnn_plugin/cpu_shape.h b/inference-engine/src/mkldnn_plugin/cpu_shape.h
index 0972ebd227f..9fbc0a55616 100644
--- a/inference-engine/src/mkldnn_plugin/cpu_shape.h
+++ b/inference-engine/src/mkldnn_plugin/cpu_shape.h
@@ -25,6 +25,8 @@ public:
         type = shape.is_static() ? ShapeType::Static : ShapeType::Dynamic;
 
         initDims();
+
+        hasZeroDimensions = std::any_of(dims.begin(), dims.end(), [](size_t dim) { return dim == 0; } );
     }
 
     explicit Shape(const InferenceEngine::SizeVector& shape) {
@@ -33,6 +35,8 @@ public:
         type = ShapeType::Static;
 
         initDims();
+
+        hasZeroDimensions = std::any_of(dims.begin(), dims.end(), [](size_t dim) { return dim == 0; } );
     }
 
     /**
@@ -106,6 +110,10 @@ public:
         return type == ShapeType::Dynamic;
     }
 
+    bool hasZeroDims() const {
+        return hasZeroDimensions;
+    }
+
     size_t getRank() const {
         return minDims.size();
     }
@@ -169,6 +177,8 @@ private:
         Dynamic
     } type {ShapeType::Static};
 
+    bool hasZeroDimensions = false;
+
     VectorDims minDims;
     VectorDims maxDims;
     VectorDims dims;
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.cpp b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.cpp
index 4718151ea84..dd8e3597598 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.cpp
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.cpp
@@ -16,7 +16,8 @@ CpuBlockedMemoryDesc::CpuBlockedMemoryDesc(InferenceEngine::Precision prc, const
     offsetPadding = 0;
     offsetPaddingToData.resize(dims.size(), 0);
     strides.resize(order.size());
-    strides[strides.size() - 1] = 1;
+    // for empty tensor case we fill all strides with 0 values
+    strides[strides.size() - 1] = shape.hasZeroDims() ? 0 : 1;
     for (size_t i = 2; i <= order.size(); i++) {
         strides[strides.size() - i] = strides[strides.size() - (i - 1)] * blockedDims[blockedDims.size() - (i - 1)];
     }
@@ -33,6 +34,15 @@ CpuBlockedMemoryDesc::CpuBlockedMemoryDesc(InferenceEngine::Precision prc, const
         IE_THROW() << "CpuBlockedMemoryDesc doesn't support undefined blockedDims.";
     }
 
+    if (shape.hasZeroDims()) {
+        const auto& dims = shape.getDims();
+        for (size_t i = 0; i < shape.getRank(); i++) {
+            if (dims[order[i]] == 0 && !dimsEqualWeak(blockedDims[i], 0)) {
+                IE_THROW() << "Can't create CpuBlockedMemoryDesc. Mistmatch zero dims in dims and blocked dims";
+            }
+        }
+    }
+
     this->order = order;
     this->blockedDims = blockedDims;
     this->offsetPadding = offsetPadding;
@@ -44,7 +54,9 @@ CpuBlockedMemoryDesc::CpuBlockedMemoryDesc(InferenceEngine::Precision prc, const
     }
 
     if (strides.empty() && !order.empty()) {
-        if (std::any_of(this->blockedDims.begin(), this->blockedDims.end(), [](size_t val) { return val == Shape::UNDEFINED_DIM; })) {
+        if (shape.hasZeroDims()) {
+            this->strides.resize(order.size(), 0);
+        } else if (std::any_of(this->blockedDims.begin(), this->blockedDims.end(), [](size_t val) { return val == Shape::UNDEFINED_DIM; })) {
             this->strides.resize(order.size(), Shape::UNDEFINED_DIM);
         } else {
             this->strides.resize(order.size());
@@ -54,6 +66,9 @@ CpuBlockedMemoryDesc::CpuBlockedMemoryDesc(InferenceEngine::Precision prc, const
             }
         }
     } else {
+        if (shape.hasZeroDims() && std::any_of(strides.begin(), strides.end(), [](size_t stride) { return stride != 0; } )) {
+            IE_THROW() << "Can't create CpuBlockedMemoryDesc with zero dim, but with non zero strides";
+        }
         this->strides = strides;
     }
 
@@ -92,11 +107,16 @@ bool CpuBlockedMemoryDesc::isCompatible(const DnnlBlockedMemoryDesc &rhs) const
     return rhs.isCompatible(*this);
 }
 
+bool CpuBlockedMemoryDesc::canComputeMemSizeZeroDims() const {
+    return getShape().hasZeroDims() && getOffsetPadding() != Shape::UNDEFINED_DIM;
+}
+
 size_t CpuBlockedMemoryDesc::getCurrentMemSizeImp() const {
     int64_t e_size = getOffsetPadding() + 1;  // size in bytes (from begin of data to last element)
-    for (int j = 0; j < getBlockDims().size(); j++)
-        e_size += (getBlockDims()[j] - 1) * getStrides()[j];
-
+    if (!getShape().hasZeroDims()) {
+        for (int j = 0; j < getBlockDims().size(); j++)
+            e_size += (getBlockDims()[j] - 1) * getStrides()[j];
+    }
 
     e_size *= getPrecision() == InferenceEngine::Precision::BIN ? 1 : getPrecision().size();
 
@@ -104,14 +124,14 @@ size_t CpuBlockedMemoryDesc::getCurrentMemSizeImp() const {
 }
 
 size_t CpuBlockedMemoryDesc::getMaxMemSize() const {
-    if (shape.isStatic()) {
+    if (shape.isStatic() || shape.hasZeroDims()) {
         return getCurrentMemSize();
     }
 
-    auto& maxDims = shape.getMaxDims();
+    const auto& maxDims = shape.getMaxDims();
     if (std::any_of(maxDims.begin(), maxDims.end(), [](size_t x){ return Shape::UNDEFINED_DIM == x ||
                                                                          // WA: for some nodes ngraph compute upper bound depending on precision max value
-                                                                         std::numeric_limits<int32_t>::max() == x; })) {
+                                                                         x >= std::numeric_limits<int32_t>::max(); })) {
         return UNDEFINED_SIZE;
     }
 
@@ -270,15 +290,23 @@ bool CpuBlockedMemoryDesc::blocksExtended() const {
 }
 
 size_t CpuBlockedMemoryDesc::getPaddedElementsCount() const {
-    if (std::any_of(blockedDims.begin(), blockedDims.end(), [](Dim dim) { return dim == Shape::UNDEFINED_DIM; }))
+    if (getShape().hasZeroDims()) {
+        return 0;
+    }
+    if (std::any_of(blockedDims.begin(), blockedDims.end(), [](Dim dim) { return dim == Shape::UNDEFINED_DIM; })) {
         IE_THROW() << "Can't compute padded elements count for non undefined blocked dims";
+    }
     return std::accumulate(blockedDims.begin(), blockedDims.end(), size_t{1}, std::multiplies<size_t>());
 }
 
 MemoryDescPtr CpuBlockedMemoryDesc::cloneWithUndefStridesAndOffset() const {
     const auto orderSize = getOrder().size();
-    return std::make_shared<CpuBlockedMemoryDesc>(getPrecision(), getShape(), getBlockDims(), getOrder(), Shape::UNDEFINED_DIM,
-                                                  VectorDims(orderSize, 0), VectorDims(orderSize, Shape::UNDEFINED_DIM));
+    CpuBlockedMemoryDescPtr newDesc = std::make_shared<CpuBlockedMemoryDesc>(*this);
+    newDesc->strides = VectorDims(orderSize, Shape::UNDEFINED_DIM);
+    newDesc->offsetPadding = Shape::UNDEFINED_DIM;
+    newDesc->offsetPaddingToData =  VectorDims(orderSize, 0);
+    newDesc->status = descStatus::Undefined;
+    return newDesc;
 }
 
 MemoryDescPtr CpuBlockedMemoryDesc::cloneWithDefaultStridesAndOffset() const {
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.h b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.h
index 6f8cb9b9e45..28515ff03b4 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.h
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.h
@@ -84,6 +84,7 @@ public:
 
 private:
     size_t getElementOffset(size_t elemNumber) const override;
+    bool canComputeMemSizeZeroDims() const override;
     size_t getCurrentMemSizeImp() const override;
     size_t getOffset(const InferenceEngine::SizeVector& v) const;
     bool isPlainFormat() const;
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc.h b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc.h
index 660a65117fb..ff79c8e4426 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc.h
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc.h
@@ -93,7 +93,7 @@ public:
      */
     size_t getCurrentMemSize() const {
         size_t retVal = UNDEFINED_SIZE;
-        if (isDefined()) {
+        if (canComputeMemSize()) {
             retVal = getCurrentMemSizeImp();
         }
         return retVal;
@@ -140,8 +140,13 @@ protected:
     // Get offset to the n'th element. Returns physical index of the element by the logical one considering padding, layout, blocking etc.
     virtual size_t getElementOffset(size_t elemNumber) const = 0;
 
+    virtual bool canComputeMemSizeZeroDims() const = 0;
     virtual bool isDefinedImp() const = 0;
 
+    bool canComputeMemSize() const {
+        return isDefined() || canComputeMemSizeZeroDims();
+    }
+
     virtual MemoryDescPtr cloneWithNewDimsImp(const VectorDims& dims) const = 0;
 
     MemoryDescType type;
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp
index 38dd1220e29..ee38482a9cc 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp
@@ -48,17 +48,37 @@ DnnlBlockedMemoryDesc MemoryDescUtils::convertToDnnlBlockedMemoryDesc(const Memo
 CpuBlockedMemoryDesc MemoryDescUtils::convertToCpuBlockedMemoryDesc(const InferenceEngine::TensorDesc& desc) {
     if (desc.getLayout() == InferenceEngine::Layout::ANY)
         IE_THROW() << "Cannot convert InferenceEngine::TensorDesc with ANY layout to CpuBlockedMemoryDesc";
-    const auto &blkDesc = desc.getBlockingDesc();
-    return CpuBlockedMemoryDesc(desc.getPrecision(), Shape(desc.getDims()), blkDesc.getBlockDims(), blkDesc.getOrder(), blkDesc.getOffsetPadding(),
-                                blkDesc.getOffsetPaddingToData(), blkDesc.getStrides());
+
+    const auto& blkDesc = desc.getBlockingDesc();
+    const auto& dims = desc.getDims();
+
+    auto strides = blkDesc.getStrides();
+    // for empty tensor case InferenceEngine::TensorDesc fill strides with non zero values before first 0 dims
+    // i.e. dims[1, 0, 2, 3] -> strides [0, 6, 3, 1]
+    if (std::any_of(dims.begin(), dims.end(), [](size_t dim){ return dim == 0; })) {
+        std::fill(strides.begin(), strides.end(), 0);
+    }
+
+    return CpuBlockedMemoryDesc(desc.getPrecision(), Shape(dims), blkDesc.getBlockDims(), blkDesc.getOrder(), blkDesc.getOffsetPadding(),
+                                blkDesc.getOffsetPaddingToData(), strides);
 }
 
 DnnlBlockedMemoryDesc MemoryDescUtils::convertToDnnlBlockedMemoryDesc(const InferenceEngine::TensorDesc& desc) {
-    const auto &blkDesc = desc.getBlockingDesc();
     if (desc.getLayout() == InferenceEngine::Layout::ANY)
         IE_THROW() << "Cannot convert InferenceEngine::TensorDesc with ANY layout to DnnlBlockedMemoryDesc";
+
+    const auto& blkDesc = desc.getBlockingDesc();
+    const auto& dims = desc.getDims();
+
+    auto strides = blkDesc.getStrides();
+    // for empty tensor case InferenceEngine::TensorDesc fill strides with non zero values before first 0 dims
+    // i.e. dims[1, 0, 2, 3] -> strides [0, 6, 3, 1]
+    if (std::any_of(dims.begin(), dims.end(), [](size_t dim){ return dim == 0; })) {
+        std::fill(strides.begin(), strides.end(), 0);
+    }
+
     return DnnlBlockedMemoryDesc(desc.getPrecision(), Shape(desc.getDims()), blkDesc.getBlockDims(), blkDesc.getOrder(), blkDesc.getOffsetPadding(),
-                                 blkDesc.getOffsetPaddingToData(), blkDesc.getStrides());
+                                 blkDesc.getOffsetPaddingToData(), strides);
 }
 
 BlockedMemoryDescPtr MemoryDescUtils::convertToBlockedMemoryDesc(const MemoryDescPtr &desc) {
@@ -80,9 +100,16 @@ InferenceEngine::Blob::Ptr MemoryDescUtils::interpretAsBlob(const MKLDNNMemory &
 
 InferenceEngine::TensorDesc MemoryDescUtils::convertToTensorDesc(const MemoryDesc& desc) {
     if (auto blockingDesc = dynamic_cast<const BlockedMemoryDesc*>(&desc)) {
-        return InferenceEngine::TensorDesc(blockingDesc->getPrecision(), blockingDesc->getShape().getStaticDims(),
-                                           {blockingDesc->getBlockDims(), blockingDesc->getOrder(), blockingDesc->getOffsetPadding(),
-                                            blockingDesc->getOffsetPaddingToData(), blockingDesc->getStrides()});
+        InferenceEngine::BlockingDesc blkDesc = desc.getShape().hasZeroDims() ? InferenceEngine::BlockingDesc(blockingDesc->getBlockDims(),
+                                                                                                              blockingDesc->getOrder(),
+                                                                                                              blockingDesc->getOffsetPadding(),
+                                                                                                              blockingDesc->getOffsetPaddingToData()) :
+                                                                                InferenceEngine::BlockingDesc(blockingDesc->getBlockDims(),
+                                                                                                              blockingDesc->getOrder(),
+                                                                                                              blockingDesc->getOffsetPadding(),
+                                                                                                              blockingDesc->getOffsetPaddingToData(),
+                                                                                                              blockingDesc->getStrides());
+        return InferenceEngine::TensorDesc(blockingDesc->getPrecision(), blockingDesc->getShape().getStaticDims(), blkDesc);
     } else {
         IE_THROW() << "Cannot convert MemoryDesc to InferenceEngine::TensorDesc";
     }
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp
index f4eed00661a..4b1efda623a 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp
@@ -15,12 +15,17 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(InferenceEngine::Precision prc, con
     const auto &dims = shape.getDims();
 
     if (!strides.empty()) { // custom strides
+        if (shape.hasZeroDims() && std::any_of(strides.begin(), strides.end(), [](size_t stride) { return stride != 0; } )) {
+            IE_THROW() << "Can't create DnnlBlockedMemoryDesc with zero dim, but with non zero strides";
+        }
         desc = {MKLDNNExtensionUtils::convertToDnnlDims(dims),
                 MKLDNNExtensionUtils::IEPrecisionToDataType(prc),
                 MKLDNNExtensionUtils::convertToDnnlDims(strides)};
     } else {
         mkldnn::memory::dims plain_strides;
-        if (std::any_of(dims.begin(), dims.end(), [](size_t val) { return val == Shape::UNDEFINED_DIM; })) {
+        if (shape.hasZeroDims()) {
+            plain_strides.resize(ndims, 0);
+        } else if (std::any_of(dims.begin(), dims.end(), [](size_t val) { return val == Shape::UNDEFINED_DIM; })) {
             plain_strides.resize(ndims, DNNL_RUNTIME_DIM_VAL);
         } else {
             plain_strides.resize(ndims, 1);
@@ -58,8 +63,8 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(InferenceEngine::Precision prc, con
  *   Limitation of conversion first N elements of order should be permutation of [0,1,2 ... N]
  */
 DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(InferenceEngine::Precision prc, const Shape& shape, const VectorDims& blockedDims,
-                                                 const VectorDims& order, size_t offsetPadding, const VectorDims& offsetPaddingToData,
-                                                 const VectorDims& strides) : MemoryDesc(shape, DnnlBlocked) {
+                                             const VectorDims& order, size_t offsetPadding, const VectorDims& offsetPaddingToData,
+                                             const VectorDims& strides) : MemoryDesc(shape, DnnlBlocked) {
     using namespace mkldnn;
     // scalar case
     if (shape.getRank() == 0) {
@@ -90,8 +95,8 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(InferenceEngine::Precision prc, con
         IE_THROW() << "DnnlBlockedMemoryDesc doesn't support undefined order.";
     }
 
-    if (std::any_of(blockedDims.begin() + shape.getRank(), blockedDims.end(), [](size_t val) { return val == Shape::UNDEFINED_DIM; })) {
-        IE_THROW() << "DnnlBlockedMemoryDesc doesn't support undefined blockedDims.";
+    if (std::any_of(blockedDims.begin() + shape.getRank(), blockedDims.end(), [](size_t val) { return val == Shape::UNDEFINED_DIM || val == 0; })) {
+        IE_THROW() << "DnnlBlockedMemoryDesc doesn't support undefined or zero blockedDims.";
     }
 
     auto dims = MKLDNNExtensionUtils::convertToDnnlDims(shape.getDims());
@@ -106,7 +111,12 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(InferenceEngine::Precision prc, con
 
     size_t inner_ndims = order.size() - dims.size();
 
+    const bool emptyDesc = shape.hasZeroDims();
     if (!strides.empty()) {
+        if (emptyDesc && std::any_of(strides.begin(), strides.end(), [](size_t dim) { return dim != 0; } )) {
+            IE_THROW() << "Can't create DnnlBlockedMemoryDesc with zero dim, but with non zero strides";
+        }
+
         bool is_descending_strides = true;
         for (int i = 1; i < strides.size(); i++) {
             is_descending_strides &= (strides[i - 1] >= strides[i]);
@@ -118,7 +128,7 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(InferenceEngine::Precision prc, con
             IE_THROW() << "Can not construct DnnlBlockedMemoryDesc from strides: " << vec2str(strides);
     }
 
-    if (!strides.empty() && std::none_of(strides.begin(), strides.end(), [](size_t x) { return Shape::UNDEFINED_DIM == x; })) {
+    if (!strides.empty() && !emptyDesc && std::none_of(strides.begin(), strides.end(), [](size_t x) { return Shape::UNDEFINED_DIM == x; })) {
         bool inner_block_are_dense = one_of(strides.back(), 0, 1);  // stride 1 - is dense case, 0 - broad casted
         for (int i = outer_ndims; i < strides.size() - 1; i++) {
             inner_block_are_dense &= (strides[i] == strides[i + 1] * blockedDims[i + 1]);
@@ -203,6 +213,11 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(const Shape& shape, mkldnn::memory:
     order.swap(perm);
     order.insert(order.end(), inner_idxs.begin(), inner_idxs.end());
 
+    if (shape.hasZeroDims()) {
+        auto& blk = desc.data.format_desc.blocking;
+        std::fill(std::begin(blk.strides), std::begin(blk.strides) + desc.data.ndims, 0);
+    }
+
     initBlockedParams();
 }
 
@@ -296,6 +311,12 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(const mkldnn::memory::desc& mdesc)
         IE_THROW(Unexpected) << "Can't create DnnlBlockedMemoryDesc from not blocking desc";
 
     order = extractOrder(desc);
+
+    if (getShape().hasZeroDims()) {
+        auto& blk = desc.data.format_desc.blocking;
+        std::fill(std::begin(blk.strides), std::begin(blk.strides) + desc.data.ndims, 0);
+    }
+
     initBlockedParams();
 }
 
@@ -368,6 +389,7 @@ bool DnnlBlockedMemoryDesc::isTailCFormat() const {
 static mkldnn::memory::desc cloneDescWithNewDims(const mkldnn::memory::desc& desc, const VectorDims& dims, const VectorDims& order) {
     using namespace dnnl::impl::utils;
     auto mklDims = MKLDNNExtensionUtils::convertToDnnlDims(dims);
+    const auto offsetPadding = desc.data.offset0;
     mkldnn::memory::desc newMklDesc = desc;
     array_copy(newMklDesc.data.dims, mklDims.data(), mklDims.size());
     std::vector<int> perm(order.begin(), order.begin() + mklDims.size());
@@ -379,6 +401,9 @@ static mkldnn::memory::desc cloneDescWithNewDims(const mkldnn::memory::desc& des
     if (retCode != dnnl::impl::status::success) {
         IE_THROW() << "Can not clone DnnlBlockedMemoryDesc with dims: " << MemoryDescUtils::dims2str(dims);
     }
+    // dnnl::impl::fill_blocked always set offset0 to 0
+    // so we need to restore actual value
+    newMklDesc.data.offset0 = offsetPadding;
     return newMklDesc;
 }
 
@@ -476,14 +501,14 @@ bool DnnlBlockedMemoryDesc::isSame(mkldnn::memory::format_tag fmt) const {
 }
 
 size_t DnnlBlockedMemoryDesc::getMaxMemSize() const {
-    if (shape.isStatic()) {
+    if (shape.isStatic() || shape.hasZeroDims()) {
         return getCurrentMemSize();
     }
 
-    auto& maxDims = shape.getMaxDims();
+    const auto& maxDims = shape.getMaxDims();
     if (std::any_of(maxDims.begin(), maxDims.end(), [](size_t x){ return Shape::UNDEFINED_DIM == x ||
                                                                          // WA: for some nodes ngraph compute upper bound depending on precision max value
-                                                                         std::numeric_limits<int32_t>::max() == x; })) {
+                                                                         x >= std::numeric_limits<int32_t>::max(); })) {
         return UNDEFINED_SIZE;
     }
 
@@ -492,6 +517,13 @@ size_t DnnlBlockedMemoryDesc::getMaxMemSize() const {
 }
 
 size_t DnnlBlockedMemoryDesc::getPaddedElementsCount() const {
+    if (getShape().hasZeroDims()) {
+        return 0;
+    }
+    if (std::any_of(std::begin(desc.data.padded_dims), std::begin(desc.data.padded_dims) + desc.data.ndims,
+            [](dnnl_dim_t dim) { return dim == DNNL_RUNTIME_DIM_VAL; })) {
+        IE_THROW() << "Can't compute padded elements count for non undefined blocked dims";
+    }
     return std::accumulate(std::begin(desc.data.padded_dims), std::begin(desc.data.padded_dims) + desc.data.ndims, size_t{1},
                            std::multiplies<int64_t>());
 }
@@ -548,7 +580,7 @@ void DnnlBlockedMemoryDesc::initStrides() {
     const size_t total_ndims = outer_ndims + inner_ndims;
 
     // strides of inner dims. In case of 4i16o4i will be {64, 4, 1}
-    VectorDims inner_strides(inner_ndims, 1);
+    VectorDims inner_strides(inner_ndims, getShape().hasZeroDims() ? 0 : 1);
     for (size_t i = 1; i < blk_desc.inner_nblks; i++) {
         inner_strides[blk_desc.inner_nblks - 1 - i] = inner_strides[blk_desc.inner_nblks - i] * blk_desc.inner_blks[blk_desc.inner_nblks - i];
     }
@@ -600,7 +632,9 @@ void DnnlBlockedMemoryDesc::recomputeDefaultStrides() {
         IE_THROW() << "Can't recompute stride: order size != blocked dims size";
 
     auto &oneDnnStrides = desc.data.format_desc.blocking.strides;
-    if (std::any_of(blockedDims.begin(), blockedDims.end(), [](Dim val) { return val == Shape::UNDEFINED_DIM; })) {
+    if (getShape().hasZeroDims()) {
+        std::fill(std::begin(oneDnnStrides), std::begin(oneDnnStrides) + getShape().getRank(), 0);
+    } else if (std::any_of(blockedDims.begin(), blockedDims.end(), [](Dim val) { return val == Shape::UNDEFINED_DIM; })) {
         std::fill(std::begin(oneDnnStrides), std::begin(oneDnnStrides) + rank, DNNL_RUNTIME_DIM_VAL);
         initStrides();
     } else {
@@ -633,6 +667,11 @@ DnnlBlockedMemoryDesc::DnnlBlockedMemoryDesc(const mkldnn::memory::desc& mdesc,
 
     desc = cloneDescWithNewDims(mdesc, shape.getDims(), order);
 
+    if (shape.hasZeroDims()) {
+        auto& blk = desc.data.format_desc.blocking;
+        std::fill(std::begin(blk.strides), std::begin(blk.strides) + desc.data.ndims, 0);
+    }
+
     initBlockedParams();
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.h b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.h
index 770686c6317..f78707e03d6 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.h
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.h
@@ -71,7 +71,7 @@ private:
     explicit DnnlBlockedMemoryDesc(const mkldnn::memory::desc& mdesc);
 
     // Creates DnnlBlockedMemoryDesc using the shape parameter as a true shape but all other params (layout, blocks, etc.) are used from the mdesc, but
-    // the mdesc own shape is ignored. The main purpose of this constructor is making dynamic descriptor form some dummy mdesc, which stores info about
+    // the mdesc own shape is ignored. The main purpose of this constructor is making dynamic descriptor from some dummy mdesc, which stores info about
     // layout, blocking, strides, etc., and the provided dynamic shape.
     DnnlBlockedMemoryDesc(const mkldnn::memory::desc& mdesc, const Shape& shape);
 
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.cpp b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.cpp
index 0e060912df2..5747529e587 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.cpp
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.cpp
@@ -15,6 +15,10 @@ DnnlMemoryDesc::DnnlMemoryDesc(const mkldnn::memory::desc& desc) :
         IE_THROW(Unexpected) << "Memory format any is prohibited!";
 }
 
+bool DnnlMemoryDesc::canComputeMemSizeZeroDims() const {
+    return getShape().hasZeroDims() && desc.data.offset0 != DNNL_RUNTIME_DIM_VAL;
+}
+
 size_t DnnlMemoryDesc::getCurrentMemSizeImp() const {
     return MKLDNNExtensionUtils::getMemSizeForDnnlDesc(desc);
 }
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.h b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.h
index c17b847dc16..d796c7ef234 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.h
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.h
@@ -63,6 +63,7 @@ private:
 
     size_t getElementOffset(size_t elemNumber) const override;
 
+    bool canComputeMemSizeZeroDims() const override;
     size_t getCurrentMemSizeImp() const override;
     bool isDefinedImp() const override;
     MemoryDescPtr cloneWithNewDimsImp(const VectorDims& dims) const override;
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
index 6765a1c0b67..37d8633bd5c 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
@@ -384,15 +384,16 @@ void MKLDNNGraph::InitOptimalPrimitiveDescriptors() {
 void MKLDNNGraph::ExtractConstantAndExecutableNodes() {
     OV_ITT_SCOPE(FIRST_INFERENCE, itt::domains::MKLDNN_LT, "MKLDNNGraph::ExtractConstantAndExecutableNodes");
     for (const auto& graphNode : graphNodes) {
-        if (graphNode->isConstant())
+        if (graphNode->isConstant()) {
             constantGraphNodes.emplace_back(graphNode);
-        else if (CPU_DEBUG_CAPS_ALWAYS_TRUE(graphNode->isExecutable()))
+        } else if (CPU_DEBUG_CAPS_ALWAYS_TRUE(graphNode->isExecutable())) {
             /* @todo
              * Revise implementation.
              * With current way it is possible that with debug_caps enabled
              * we execute a node, which is not ready to be executed
              */
             executableGraphNodes.emplace_back(graphNode);
+        }
     }
 }
 
@@ -792,7 +793,7 @@ void MKLDNNGraph::PullOutputData(BlobMap &out) {
 
         // check for empty output blob
         if (std::any_of(outDims.begin(), outDims.end(), [](const Dim dim) {return dim == 0;})) {
-            return;
+            continue;
         }
 
         auto srcPrec = actualDesc.getPrecision();
@@ -835,10 +836,11 @@ inline void MKLDNNGraph::ExecuteNode(const MKLDNNNodePtr& node, const mkldnn::st
     DUMP(node, infer_count);
     OV_ITT_SCOPED_TASK(itt::domains::MKLDNNPlugin, node->profiling.execute);
 
-    if (node->isDynamicNode())
+    if (node->isDynamicNode()) {
         node->executeDynamic(stream);
-    else
+    } else {
         node->execute(stream);
+    }
 }
 
 void MKLDNNGraph::Infer(MKLDNNInferRequest* request, int batch) {
@@ -854,7 +856,6 @@ void MKLDNNGraph::Infer(MKLDNNInferRequest* request, int batch) {
 
         if (request)
             request->ThrowIfCanceled();
-
         ExecuteNode(node, stream);
     }
 
@@ -993,22 +994,6 @@ Config MKLDNNGraph::getProperty() const {
     return config;
 }
 
-Blob::Ptr MKLDNNGraph::getInputBlob(const std::string& name) {
-    auto itr = inputNodesMap.find(name);
-    if (itr != inputNodesMap.end()) {
-        return MemoryDescUtils::interpretAsBlob(itr->second->getChildEdgeAt(0)->getMemory());
-    }
-    return nullptr;
-}
-
-Blob::Ptr MKLDNNGraph::getOutputBlob(const std::string& name) {
-    auto itr = outputNodesMap.find(name);
-    if (itr != outputNodesMap.end()) {
-        return MemoryDescUtils::interpretAsBlob(itr->second->getParentEdgeAt(0)->getMemory());
-    }
-    return nullptr;
-}
-
 void MKLDNNGraph::RemoveEdge(MKLDNNEdgePtr& edge) {
     for (auto it = graphEdges.begin(); it != graphEdges.end(); it++) {
         if ((*it) == edge) {
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph.h b/inference-engine/src/mkldnn_plugin/mkldnn_graph.h
index 38e1bb01dc4..9d6703df665 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph.h
@@ -44,9 +44,6 @@ public:
     void setProperty(const std::map<std::string, std::string> &properties);
     Config getProperty() const;
 
-    InferenceEngine::Blob::Ptr getInputBlob(const std::string& name);
-    InferenceEngine::Blob::Ptr getOutputBlob(const std::string& name);
-
     template<typename NET>
     void CreateGraph(NET &network,
                      const MKLDNNExtensionManager::Ptr& extMgr,
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
index ccd9c96e675..6179f52394a 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
@@ -190,8 +190,9 @@ void MKLDNNPlugin::MKLDNNInferRequest::redefineMemoryForInputNodes() {
         const auto inputNode = cpuInputNodes.find(blob.first);
         if (inputNode == cpuInputNodes.end())
             IE_THROW() << "CPU execution graph doesn't contain input node with name: " << blob.first;
-        if (inputNode->second->isDynamicNode())
+        if (inputNode->second->isDynamicNode()) {
             inputNode->second->redefineOutputMemory({blob.second->getTensorDesc().getDims()});
+        }
     }
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
index a0a10a93ec4..32e64b625ca 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
@@ -84,7 +84,7 @@ MKLDNNNode::MKLDNNNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::en
     for (size_t i = 0; i < op->get_input_size(); i++) {
         const auto &shape = op->get_input_partial_shape(i);
         if (shape.rank().is_dynamic()) {
-            IE_THROW(Unexpected) << "CPU plug-in doesn't support operation with dynamic rank";
+            IE_THROW(Unexpected) << "CPU plug-in doesn't support " << getTypeStr() << " operation with dynamic rank. Operation name: " << getName();
         }
 
         bool isScalar = shape.rank().get_length() == 0;
@@ -99,7 +99,7 @@ MKLDNNNode::MKLDNNNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::en
         for (size_t i = 0; i < op->get_output_size(); i++) {
             const auto &shape = op->get_output_partial_shape(i);
             if (shape.rank().is_dynamic()) {
-                IE_THROW(Unexpected) << "CPU plug-in doesn't support operation with dynamic rank";
+                IE_THROW(Unexpected) << "CPU plug-in doesn't support " << getTypeStr() << " operation with dynamic rank. Operation name: " << getName();
             }
 
             bool isScalar = shape.rank().get_length() == 0;
@@ -230,6 +230,15 @@ bool MKLDNNNode::isEdgesEmpty(const std::vector<MKLDNNEdgeWeakPtr>& edges) const
     return true;
 }
 
+void MKLDNNNode::createPrimitive() {
+    if (inputShapesDefined() && isExecutable()) {
+        if (needPrepareParams()) {
+            prepareParams();
+        }
+        updateLastInputDims();
+    }
+}
+
 void MKLDNNNode::selectOptimalPrimitiveDescriptor() {
     selectPreferPrimitiveDescriptor(getPrimitivesPriority(), false);
 }
@@ -510,12 +519,14 @@ void MKLDNNNode::executeDynamic(mkldnn::stream strm) {
     if (needShapeInfer()) {
         redefineOutputMemory(shapeInfer());
     }
-    if (needPrepareParams()) {
-        IE_ASSERT(inputShapesDefined()) << "Can't prepare params for " << getTypeStr() << " node with name: " << getName() <<
-            " since the input shapes are not defined.";
-        prepareParams();
+    if (isExecutable()) {
+        if (needPrepareParams()) {
+            IE_ASSERT(inputShapesDefined()) << "Can't prepare params for " << getTypeStr() << " node with name: " << getName() <<
+                " since the input shapes are not defined.";
+            prepareParams();
+        }
+        executeDynamicImpl(strm);
     }
-    executeDynamicImpl(strm);
     updateLastInputDims();
 }
 
@@ -1333,6 +1344,36 @@ std::pair<std::vector<float>, std::vector<float>> MKLDNNNode::getScalesAndShifts
     return {scales, shifts};
 }
 
+bool MKLDNNNode::isInputTensorAtPortEmpty(size_t port) const {
+    if (inputShapes.size() <= port) {
+        IE_THROW() << "Incorrect input port number for node " << getName();
+    }
+    return getParentEdgesAtPort(port)[0]->getMemory().GetShape().hasZeroDims();
+}
+
+bool MKLDNNNode::isOutputTensorAtPortEmpty(size_t port) const {
+    if (outputShapes.size() <= port) {
+        IE_THROW() << "Incorrect output port number for node " << getName();
+    }
+    return getChildEdgesAtPort(port)[0]->getMemory().GetShape().hasZeroDims();
+}
+
+bool MKLDNNNode::hasEmptyInputTensors() const {
+    for (size_t i = 0; i < getParentEdges().size(); i++) {
+        if (isInputTensorAtPortEmpty(i))
+            return true;
+    }
+    return false;
+}
+
+bool MKLDNNNode::hasEmptyOutputTensors() const {
+    for (size_t i = 0; i < outputShapes.size(); i++) {
+        if (isOutputTensorAtPortEmpty(i))
+            return true;
+    }
+    return false;
+}
+
 bool MKLDNNNode::inputShapesDefined() const {
     for (size_t i = 0; i < getParentEdges().size(); i++) {
         if (!getParentEdgesAtPort(i)[0]->getMemory().getDesc().isDefined())
@@ -1406,8 +1447,11 @@ std::vector<VectorDims> MKLDNNNode::shapeInferGeneric(const std::vector<Shape>&
     std::vector<VectorDims> newOutputShapes(opToShapeInfer->get_output_size());
     for (size_t i = 0; i < newOutputShapes.size(); i++) {
         const auto &partShape = opToShapeInfer->get_output_partial_shape(i);
-        if (partShape.is_dynamic())
-            IE_THROW(NotImplemented) << "CPU plug-in doesn't support default shape infer for nodes with internal dynamism";
+        if (partShape.is_dynamic()) {
+            IE_THROW(NotImplemented) << "CPU plug-in doesn't support default shape infer for node " << getTypeStr()
+                                     << " with internal dynamism. Operation name: " << getName();
+        }
+
         newOutputShapes[i] = partShape.get_shape();
     }
     return newOutputShapes;
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.h b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
index 97517d54fbc..85475733b13 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
@@ -199,7 +199,7 @@ public:
 
     // must be called only after MKLDNNGraph::InitEdges()
     virtual bool isExecutable() const {
-        return true;
+        return !hasEmptyInputTensors();
     }
 
     bool isConstant();
@@ -370,7 +370,7 @@ public:
      */
     virtual void filterSupportedPrimitiveDescriptors();
 
-    virtual void createPrimitive() = 0;
+    virtual void createPrimitive();
 
     virtual void selectOptimalPrimitiveDescriptor();
     virtual void initOptimalPrimitiveDescriptor();
@@ -728,6 +728,12 @@ protected:
 
     bool isDynamic = false;
 
+    bool isInputTensorAtPortEmpty(size_t port) const;
+    bool isOutputTensorAtPortEmpty(size_t port) const;
+
+    bool hasEmptyInputTensors() const;
+    bool hasEmptyOutputTensors() const;
+
     bool inputShapesDefined() const;
     bool outputShapesDefined() const;
     bool shapesDefined() const;
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
index f7f8aa2c7c7..f3ff2beba9a 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
@@ -80,6 +80,7 @@
 #include "nodes/mkldnn_reduce_node.h"
 #include "nodes/mkldnn_if_node.h"
 #include "nodes/mkldnn_ctc_greedy_decoder_node.h"
+#include "nodes/mkldnn_non_zero.h"
 
 #define MKLDNN_NODE(__prim, __type) \
     registerNodeIfRequired(MKLDNNPlugin, __prim, __type, MKLDNNNodeImpl<__prim>)
@@ -168,4 +169,5 @@ MKLDNNPlugin::MKLDNNNode::NodesFactory::NodesFactory()
     MKLDNN_NODE(MKLDNNTopKNode, TopK);
     MKLDNN_NODE(MKLDNNStridedSliceNode, StridedSlice);
     MKLDNN_NODE(MKLDNNGRNNode, GRN);
+    MKLDNN_NODE(MKLDNNNonZeroNode, NonZero);
 }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.cpp b/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.cpp
new file mode 100644
index 00000000000..6f001fe8db1
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.cpp
@@ -0,0 +1,45 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "dnnl_executor.h"
+
+using namespace mkldnn;
+using namespace MKLDNNPlugin;
+
+DnnlExecutor::IntermReorder::IntermReorder(const mkldnn::memory::desc& descSrc,
+                                           const mkldnn::memory::desc& descDst,
+                                           const mkldnn::engine& engine) : m_descSrc(descSrc), m_descDst(descDst) {
+    auto reorderPd = mkldnn::reorder::primitive_desc(engine, descSrc, engine, descDst);
+    m_reorder = mkldnn::reorder(reorderPd);
+}
+
+void DnnlExecutor::IntermReorder::exec(mkldnn::memory& memSrc, mkldnn::memory& memDst, mkldnn::stream strm) {
+    m_reorder.execute(strm, memSrc, memDst);
+}
+
+void DnnlExecutor::exec(std::unordered_map<int, mkldnn::memory> primArgs, mkldnn::stream strm) {
+    for (auto &inReorder : inputReorders) {
+        if (primArgs.count(inReorder.first)) {
+            mkldnn::memory memDst(inReorder.second.getDstDesc(), strm.get_engine());
+            inReorder.second.exec(primArgs[inReorder.first], memDst, strm);
+            primArgs[inReorder.first] = memDst;
+        } else {
+            IE_THROW() << "DnnlExecutor has reorder for input " << inReorder.first << ", but doesn't have source memory";
+        }
+    }
+    std::unordered_map<int, mkldnn::memory> outputMem;
+    for (auto &outReorder : outputReorders) {
+        if (primArgs.count(outReorder.first)) {
+            mkldnn::memory memSrc(outReorder.second.getSrcDesc(), strm.get_engine());
+            outputMem[outReorder.first] = primArgs[outReorder.first];
+            primArgs[outReorder.first] = memSrc;
+        } else {
+            IE_THROW() << "DnnlExecutor has reorder for output " << outReorder.first << ", but doesn't have destination memory";
+        }
+    }
+    (*execPrim).execute(strm, primArgs);
+    for (auto &outReorder : outputReorders) {
+        outReorder.second.exec(primArgs[outReorder.first], outputMem[outReorder.first], strm);
+    }
+}
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.h b/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.h
new file mode 100644
index 00000000000..b6e6c83030d
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.h
@@ -0,0 +1,39 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "mkldnn_memory.h"
+#include "mkldnn_primitive.h"
+
+namespace MKLDNNPlugin {
+
+class DnnlExecutor {
+    protected:
+        class IntermReorder {
+            public:
+                IntermReorder(const mkldnn::memory::desc& descSrc, const mkldnn::memory::desc& descDst, const mkldnn::engine& engine);
+                void exec(mkldnn::memory& memSrc, mkldnn::memory& memDst, mkldnn::stream strm);
+                const mkldnn::memory::desc& getSrcDesc() const { return m_descSrc; }
+                const mkldnn::memory::desc& getDstDesc() const { return m_descDst; }
+
+            private:
+                mkldnn::reorder m_reorder;
+                mkldnn::memory::desc m_descSrc;
+                mkldnn::memory::desc m_descDst;
+        };
+
+    public:
+        void exec(std::unordered_map<int, mkldnn::memory> primArgs, mkldnn::stream strm);
+        virtual ~DnnlExecutor() = default;
+
+    protected:
+        DnnlExecutor() = default;
+        MKLDNNPrimitive execPrim;
+        // key is the port number for the primitive that needs memory reordering
+        std::unordered_map<int, IntermReorder> inputReorders;
+        std::unordered_map<int, IntermReorder> outputReorders;
+};
+
+}  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp
index 64ed60cbdb9..0909f99deb2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp
@@ -145,6 +145,10 @@ void MKLDNNAdaptivePoolingNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+void MKLDNNAdaptivePoolingNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNAdaptivePoolingNode::execute(mkldnn::stream strm) {
     auto inputPrec = getParentEdgeAt(0)->getMemory().GetDataType();
     auto outputPrec = getChildEdgeAt(0)->getMemory().GetDataType();
@@ -283,8 +287,6 @@ bool MKLDNNAdaptivePoolingNode::created() const {
     return getType() == AdaptivePooling;
 }
 
-void MKLDNNAdaptivePoolingNode::createPrimitive() {}
-
 inline void MKLDNNAdaptivePoolingNode::setBinBorders(size_t *startPtr, size_t *endPtr, size_t idx, size_t inputLength, size_t outputLength) {
     *(startPtr) = idx * inputLength / outputLength;
     *(endPtr) = ceil(static_cast<float>((idx + 1) * inputLength) / outputLength);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h
index c47740e75ec..88a3e9a9324 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h
@@ -18,7 +18,6 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
@@ -36,7 +35,7 @@ protected:
     bool needShapeInfer() const override;
     std::vector<VectorDims> shapeInfer() const override;
     bool needPrepareParams() const override { return false; };
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp
index a4811c30aff..60a37375cdc 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp
@@ -225,6 +225,10 @@ void MKLDNNBatchToSpaceNode::batchToSpaceKernel() {
     });
 }
 
+void MKLDNNBatchToSpaceNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNBatchToSpaceNode::execute(mkldnn::stream strm) {
     switch (getParentEdgeAt(0)->getMemory().getDesc().getPrecision().size()) {
         case 1: batchToSpaceKernel<PrecisionTrait<Precision::U8>::value_type>();  break;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h
index 0c0ef5ee06c..91fe87e4f71 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h
@@ -18,12 +18,11 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     bool needPrepareParams() const override { return false; };
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp
index 898c03f9b40..eba9e00666f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp
@@ -107,14 +107,6 @@ void MKLDNNBroadcastNode::initSupportedPrimitiveDescriptors() {
     supportedPrimitiveDescriptors = getSupportedConfigs(this);
 }
 
-void MKLDNNBroadcastNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 bool MKLDNNBroadcastNode::needPrepareParams() const {
     return needPrepareParamsVar;
 }
@@ -215,6 +207,14 @@ std::vector<VectorDims> MKLDNNBroadcastNode::shapeInfer() const {
     return newOutputShapes;
 }
 
+bool MKLDNNBroadcastNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
+}
+
+void MKLDNNBroadcastNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNBroadcastNode::execute(mkldnn::stream strm) {
     if (optimizedCase) {
         optimizedExecute(getParentEdgeAt(INPUT_DATA_IDX)->getMemoryPtr(), getChildEdgeAt(0)->getMemoryPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h
index 4851cf19c9a..93cbddb0b45 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h
@@ -19,13 +19,11 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
-    void executeDynamicImpl(mkldnn::stream strm) override {
-        execute(strm);
-    }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool isExecutable() const override;
     static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
 
 protected:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.cpp
index 86e29b292cb..d875d228191 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.cpp
@@ -203,12 +203,8 @@ void MKLDNNBucketizeNode::prepareParams() {
         std::accumulate(input_tensor_dims.begin(), input_tensor_dims.end(), size_t(1), std::multiplies<size_t>());
 }
 
-void MKLDNNBucketizeNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
+bool MKLDNNBucketizeNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
 }
 
 std::vector<VectorDims> MKLDNNBucketizeNode::shapeInfer() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.h
index ae4a4030bfd..04f0b0df3f0 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.h
@@ -15,15 +15,16 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
     void executeDynamicImpl(mkldnn::stream strm) override {
         execute(strm);
     }
+
     void prepareParams() override;
     std::vector<VectorDims> shapeInfer() const override;
 
+    bool isExecutable() const override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
index 3b0e1e481c8..a6e044b4353 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
@@ -31,6 +31,10 @@ namespace {
     constexpr size_t channelAxis = 1lu;
 }
 
+bool MKLDNNConcatNode::isExecutable() const {
+    return !hasEmptyOutputTensors() && !isOptimized();
+}
+
 bool MKLDNNConcatNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
         const auto concatOp = ngraph::as_type_ptr<const ngraph::op::v0::Concat>(op);
@@ -173,7 +177,7 @@ void MKLDNNConcatNode::initSupportedPrimitiveDescriptors() {
     }
 
     // TODO [DS]: inplace
-    if (!canBeInPlace)
+    if (!canBeInPlace || std::any_of(inputShapes.begin(), inputShapes.end(), [](const Shape& shape) { return shape.hasZeroDims(); }))
         return;
 
     // Optimized inplace case
@@ -353,7 +357,6 @@ void MKLDNNConcatNode::prepareParams() {
         IE_THROW() << "Preferable primitive descriptor is not set.";
 
     std::vector<memory::desc> srcs_d;
-
     for (size_t i = 0; i < getParentEdges().size(); i++) {
         const auto& srcMemPtr = getParentEdgesAtPort(i)[0]->getMemoryPtr();
         if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr()) {
@@ -362,6 +365,10 @@ void MKLDNNConcatNode::prepareParams() {
                                << getName() << ".";
         }
 
+        if (srcMemPtr->GetShape().hasZeroDims()) {
+            continue;
+        }
+
         auto desc = srcMemPtr->GetDescWithType<DnnlMemoryDesc>()->getDnnlDesc();
         const auto& dims = srcMemPtr->getStaticDims();
         for (size_t j = 0; j < dims.size(); j++) {
@@ -382,14 +389,6 @@ void MKLDNNConcatNode::prepareParams() {
     prim.reset(new concat(primitive_desc));
 }
 
-void MKLDNNConcatNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 size_t MKLDNNConcatNode::inverseOrder(const SizeVector& order, size_t axis) {
     for (size_t i = 0; i < order.size(); i++) {
         if (axis == order[i]) {
@@ -489,16 +488,23 @@ void MKLDNNConcatNode::execute(mkldnn::stream strm) {
         return;
     }
 
+    const MKLDNNMemory& dst_memory = getChildEdgeAt(0)->getMemory();
     if (canOptimizeNspc) {
         execNspcSpecCase();
         return;
     }
 
-    const MKLDNNMemory& dst_memory = getChildEdgeAt(0)->getMemory();
     const size_t num_src = getParentEdges().size();
     std::unordered_map<int, memory> mem_ags {{DNNL_ARG_DST, dst_memory.GetPrimitive()}};
-    for (int i = 0; i < num_src; i++)
-        mem_ags[DNNL_ARG_MULTIPLE_SRC + i] = getParentEdgeAt(i)->getMemory().GetPrimitive();
+    size_t nonZeroInShapes = 0;
+    for (int i = 0; i < num_src; i++) {
+        const auto& srcMem = getParentEdgesAtPort(i)[0]->getMemory();
+        if (srcMem.GetShape().hasZeroDims()) {
+            continue;
+        }
+        mem_ags[DNNL_ARG_MULTIPLE_SRC + nonZeroInShapes] = srcMem.GetPrimitive();
+        nonZeroInShapes++;
+    }
 
     (*prim).execute(strm, mem_ags);
 }
@@ -518,21 +524,32 @@ void MKLDNNConcatNode::execNspcSpecCase() {
     std::vector<const uint8_t*> src_ptrs;
     std::vector<uint8_t*> dst_ptrs;
 
+    size_t nonZeroInShapes = 0;
+    int firstNonZeroEdge = -1;
     for (size_t i = 0; i < num_src; i++) {
-        const MKLDNNMemory& src_mem = getParentEdgeAt(i)->getMemory();
+        const MKLDNNMemory& src_mem = getParentEdgesAtPort(i)[0]->getMemory();
+        if (src_mem.GetShape().hasZeroDims()) {
+            continue;
+        }
         const size_t num_channels = src_mem.getStaticDims()[channelAxis];
 
         channelsDataSize.push_back(num_channels * dataSize);
         src_ptrs.push_back(reinterpret_cast<const uint8_t*>(src_mem.GetData()));
         dst_ptrs.push_back(dst_ptr + channels_size);
         channels_size += num_channels * dataSize;
+
+        if (firstNonZeroEdge == -1) {
+            firstNonZeroEdge = i;
+        }
+
+        nonZeroInShapes++;
     }
 
-    const size_t iter_count = getParentEdgeAt(0)->getMemory().GetSize() / channelsDataSize[0];
+    const size_t iter_count = getParentEdgeAt(firstNonZeroEdge)->getMemory().GetSize() / channelsDataSize[0];
 
     parallel_for(iter_count, [&](int i) {
         const size_t dst_off = i * channels_size;
-        for (int j = 0; j < num_src; j++) {
+        for (int j = 0; j < nonZeroInShapes; j++) {
             cpu_memcpy(dst_ptrs[j] + dst_off, src_ptrs[j] + i * channelsDataSize[j], channelsDataSize[j]);
         }
     });
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h
index 09686ca5f24..5e51f9c8a6e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h
@@ -19,7 +19,6 @@ public:
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
     void initOptimalPrimitiveDescriptor() override;
-    void createPrimitive() override;
     void selectOptimalPrimitiveDescriptor() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
@@ -28,10 +27,8 @@ public:
     bool isOptimized() const;
 
     InferenceEngine::Precision getRuntimePrecision() const override;
-    bool isExecutable() const override {
-        return !isOptimized();
-    }
 
+    bool isExecutable() const override;
     bool needPrepareParams() const override;
     void prepareParams() override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
index 03f7700e7af..6e46b42b4a8 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
@@ -487,15 +487,6 @@ void MKLDNNConvolutionNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
-
-void MKLDNNConvolutionNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 bool MKLDNNConvolutionNode::created() const {
     return getType() == Convolution;
 }
@@ -545,7 +536,14 @@ MKLDNNConvolutionNode::createDescriptorInternal(const mkldnn::memory::desc& inpu
 
 void MKLDNNConvolutionNode::createDescriptor(const std::vector<MemoryDescPtr>& inputDesc,
                                              const std::vector<MemoryDescPtr>& outputDesc) {
-    auto inpDesc = inputDesc[0]->isDefined() ? inputDesc[0] : MemoryDescUtils::makeDummyDesc(*inputDesc[0]);
+    MemoryDescPtr inpDesc;
+    if (inputDesc[0]->isDefined()) {
+        inpDesc = inputDesc[0];
+    } else {
+        auto dummyInDims = MemoryDescUtils::makeDummyShape(inputDesc[0]->getShape()).getStaticDims();
+        dummyInDims[1] = IC;
+        inpDesc = inputDesc[0]->cloneWithNewDims(dummyInDims);
+    }
     DnnlMemoryDescPtr definedInpMemDesc = MemoryDescUtils::convertToDnnlMemoryDesc(inpDesc);
     DnnlMemoryDescPtr definedOutMemDesc;
 
@@ -906,14 +904,57 @@ InferenceEngine::Blob::Ptr MKLDNNConvolutionNode::createInternalBlob(InferenceEn
     return internalBlob;
 }
 
+std::shared_ptr<MKLDNNDescriptor> MKLDNNConvolutionNode::createMkldnnConvDesc(const mkldnn::memory::desc& srcDesc,
+                                                                              const mkldnn::memory::desc& wghDesc,
+                                                                              const mkldnn::memory::desc& dstDesc,
+                                                                              const mkldnn::memory::desc& biasDesc) {
+    std::shared_ptr<mkldnn::convolution_forward::desc> dnnlConvDesc;
+    auto alg = isWinograd() ? mkldnn::algorithm::convolution_winograd : mkldnn::algorithm::convolution_direct;
+
+    if (withBiases) {
+        // WA to align IR bias representation (3 to 5 rank tensors) to oneDNN representation (1 rank tensor)
+        mkldnn::memory::desc dnnlBiasDesc = biasDesc.reshape(MKLDNNExtensionUtils::convertToDnnlDims(biasesDims));
+        return std::make_shared<MKLDNNDescriptor>(createDescriptorInternal(srcDesc,
+                                                  wghDesc,
+                                                  dnnlBiasDesc,
+                                                  dstDesc,
+                                                  alg));
+    } else {
+        return std::make_shared<MKLDNNDescriptor>(createDescriptorInternal(srcDesc,
+                                                  wghDesc,
+                                                  dstDesc,
+                                                  alg));
+    }
+}
+
 void MKLDNNConvolutionNode::prepareParams() {
+    auto srcMemPtr = getParentEdgesAtPort(0)[0]->getMemoryPtr();
+    auto wghMemPtr = getParentEdgesAtPort(1)[0]->getMemoryPtr();
+    auto dstMemPtr = getChildEdgesAtPort(0)[0]->getMemoryPtr();
+    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
+        IE_THROW() << "Destination memory didn't allocate.";
+    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
+        IE_THROW() << "Input memory didn't allocate.";
+    if (!wghMemPtr || !wghMemPtr->GetPrimitivePtr())
+        IE_THROW() << "Weight memory didn't allocate.";
+    MKLDNNMemoryPtr biasMemPtr = nullptr;
+    if (withBiases) {
+        biasMemPtr = getParentEdgesAtPort(2)[0]->getMemoryPtr();
+        if (!biasMemPtr || !biasMemPtr->GetPrimitivePtr())
+            IE_THROW() << "Input memory didn't allocate.";
+    }
+
     const NodeDesc *selected_pd = getSelectedPrimitiveDescriptor();
     if (selected_pd == nullptr)
         IE_THROW() << "Preferable primitive descriptor is not set for node " << getName() << ".";
 
-    auto inMemoryDesc = getParentEdgesAtPort(0).front()->getMemory().GetDescWithType<DnnlMemoryDesc>();
-    auto weightMemoryDesc = getParentEdgesAtPort(1).front()->getMemory().GetDescWithType<DnnlMemoryDesc>();
-    auto outMemoryDesc = getChildEdgesAtPort(0).front()->getMemory().GetDescWithType<DnnlMemoryDesc>();
+    auto inMemoryDesc = srcMemPtr->GetDescWithType<DnnlMemoryDesc>();
+    auto weightMemoryDesc = wghMemPtr->GetDescWithType<DnnlMemoryDesc>();
+    auto outMemoryDesc = dstMemPtr->GetDescWithType<DnnlMemoryDesc>();
+    mkldnn::memory::desc biasDesc;
+    if (biasMemPtr) {
+        biasDesc = biasMemPtr->GetDescWithType<DnnlMemoryDesc>()->getDnnlDesc();
+    }
 
     auto initPrimitiveAttr = [&]() {
         mkldnn::primitive_attr attr;
@@ -934,55 +975,95 @@ void MKLDNNConvolutionNode::prepareParams() {
         pAttrLocal = initPrimitiveAttr();
     }
 
-    std::shared_ptr<mkldnn::convolution_forward::desc> dnnlConvDesc;
-    auto alg = isWinograd() ? mkldnn::algorithm::convolution_winograd : mkldnn::algorithm::convolution_direct;
+    std::shared_ptr<MKLDNNDescriptor> desc = createMkldnnConvDesc(inMemoryDesc->getDnnlDesc(),
+                                                                  weightMemoryDesc->getDnnlDesc(),
+                                                                  outMemoryDesc->getDnnlDesc(),
+                                                                  biasDesc);
 
-    if (withBiases) {
-        auto biasMemoryDesc = getParentEdgesAtPort(2).front()->getMemory().GetDescWithType<DnnlMemoryDesc>();
-        // WA to align IR bias representation (3 to 5 rank tensors) to oneDNN representation (1 rank tensor)
-        mkldnn::memory::desc dnnlBiasDesc = biasMemoryDesc->getDnnlDesc().reshape(MKLDNNExtensionUtils::convertToDnnlDims(biasesDims));
-        dnnlConvDesc = createDescriptorInternal(inMemoryDesc->getDnnlDesc(),
-                                                weightMemoryDesc->getDnnlDesc(),
-                                                dnnlBiasDesc,
-                                                outMemoryDesc->getDnnlDesc(),
-                                                alg);
-    } else {
-        dnnlConvDesc = createDescriptorInternal(inMemoryDesc->getDnnlDesc(),
-                                                weightMemoryDesc->getDnnlDesc(),
-                                                outMemoryDesc->getDnnlDesc(),
-                                                alg);
-    }
-
-    MKLDNNDescriptor desc(dnnlConvDesc);
-
-    auto itpd = desc.createPrimitiveDescriptorIterator(getEngine(), *pAttrLocal);
+    auto itpd = desc->createPrimitiveDescriptorIterator(getEngine(), *pAttrLocal);
 
     convolution_forward::primitive_desc prim_desc;
-    while (static_cast<bool>(itpd))  {
+
+    execPtr = nullptr;
+    while (static_cast<bool>(itpd)) {
         impl_desc_type impl_type = parse_impl_name(itpd.impl_info_str());
 
         if (impl_type == selected_pd->getImplementationType()) {
             prim_desc = convolution_forward::primitive_desc(itpd.get());
+            execPtr = std::make_shared<ConvolutionExecutor>(prim_desc,
+                                                            srcMemPtr->GetPrimitive().get_desc(),
+                                                            wghMemPtr->GetPrimitive().get_desc(),
+                                                            dstMemPtr->GetPrimitive().get_desc(),
+                                                            getEngine());
             break;
         }
-        if (!itpd.next_impl())
-            IE_THROW() << "Primitive descriptor was not found for node " << getName() << ".";
+
+        if (!itpd.next_impl()) {
+            auto inDesc = mkldnn::memory::desc(MKLDNNExtensionUtils::convertToDnnlDims(srcMemPtr->getStaticDims()),
+                                                                                       srcMemPtr->GetDataType(),
+                                                                                       memory::format_tag::any);
+            auto wghDesc = mkldnn::memory::desc(MKLDNNExtensionUtils::convertToDnnlDims(wghMemPtr->getStaticDims()),
+                                                                                        wghMemPtr->GetDataType(),
+                                                                                        memory::format_tag::any);
+            auto outDesc = mkldnn::memory::desc(MKLDNNExtensionUtils::convertToDnnlDims(dstMemPtr->getStaticDims()),
+                                                                                        dstMemPtr->GetDataType(),
+                                                                                        memory::format_tag::any);
+
+            std::shared_ptr<MKLDNNDescriptor> reorderConvDesc = createMkldnnConvDesc(inDesc, wghDesc, outDesc, biasDesc);
+            auto reordItpd = reorderConvDesc->createPrimitiveDescriptorIterator(getEngine(), *pAttrLocal);
+            if (static_cast<bool>(reordItpd)) {
+                auto prim_desc = convolution_forward::primitive_desc(reordItpd.get());
+                execPtr = std::make_shared<ConvolutionExecutor>(prim_desc, srcMemPtr->GetPrimitive().get_desc(),
+                                                                wghMemPtr->GetPrimitive().get_desc(),
+                                                                dstMemPtr->GetPrimitive().get_desc(),
+                                                                getEngine());
+                break;
+            }
+        }
     }
+    if (execPtr) {
+        primArgs[DNNL_ARG_SRC] = srcMemPtr->GetPrimitive();
+        primArgs[DNNL_ARG_WEIGHTS] = wghMemPtr->GetPrimitive();
+        primArgs[DNNL_ARG_DST] = dstMemPtr->GetPrimitive();
 
-    prim.reset(new convolution_forward(prim_desc));
+        if (withBiases) {
+            primArgs[DNNL_ARG_BIAS] = biasMemPtr->GetPrimitive();
+        }
 
-    primArgs[DNNL_ARG_SRC] = getParentEdgesAtPort(0)[0]->getMemoryPtr()->GetPrimitive();
-    primArgs[DNNL_ARG_WEIGHTS] = getWeights();
-    primArgs[DNNL_ARG_DST] = getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPrimitive();
-
-    if (withBiases) {
-        primArgs[DNNL_ARG_BIAS] = getBias();
+        MKLDNNNode::appendPostOpArgs(*pAttrLocal, primArgs, binaryPostOpsArgs);
+    } else {
+        IE_THROW() << "Primitive descriptor was not found for node " << getName() << ".";
     }
-
-    appendPostOpArgs(*pAttrLocal, primArgs, binaryPostOpsArgs);
 }
 
-void MKLDNNConvolutionNode::executeDynamicImpl(dnnl::stream strm) {
+MKLDNNConvolutionNode::ConvolutionExecutor::ConvolutionExecutor(const mkldnn::convolution_forward::primitive_desc& pd,
+                                                                const mkldnn::memory::desc& inMemDesc,
+                                                                const mkldnn::memory::desc& weightMemDesc,
+                                                                const mkldnn::memory::desc& outMemDesc,
+                                                                const mkldnn::engine& engine) {
+    execPrim.reset(new mkldnn::convolution_forward(pd));
+
+    if (inMemDesc != pd.src_desc()) {
+        inputReorders.insert({DNNL_ARG_SRC, IntermReorder(inMemDesc, pd.src_desc(), engine)});
+    }
+
+    if (weightMemDesc != pd.weights_desc()) {
+        inputReorders.insert({DNNL_ARG_WEIGHTS, IntermReorder(weightMemDesc, pd.weights_desc(), engine)});
+    }
+
+    if (outMemDesc != pd.dst_desc()) {
+        outputReorders.insert({DNNL_ARG_DST, IntermReorder(pd.dst_desc(), outMemDesc, engine)});
+    }
+}
+
+void MKLDNNConvolutionNode::execute(mkldnn::stream strm) {
+    if (!execPtr) {
+        IE_THROW() << "Can't execute Convolution node with name: " << getName() << ", because executor is not compiled";
+    }
+    execPtr->exec(primArgs, strm);
+}
+
+void MKLDNNConvolutionNode::executeDynamicImpl(mkldnn::stream strm) {
     execute(strm);
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.h
index dcdd18092d5..f70fd806617 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.h
@@ -9,6 +9,7 @@
 #include <memory>
 #include <string>
 #include <vector>
+#include "common/dnnl_executor.h"
 
 namespace MKLDNNPlugin {
 
@@ -23,7 +24,6 @@ public:
     void createDescriptor(const std::vector<MemoryDescPtr>& inputDesc,
                           const std::vector<MemoryDescPtr>& outputDesc) override;
     void initDescriptor(const NodeConfig& config) override;
-    void createPrimitive() override;
     void selectOptimalPrimitiveDescriptor() override;
     void initSupportedPrimitiveDescriptors() override;
     void filterSupportedPrimitiveDescriptors() override;
@@ -65,7 +65,25 @@ protected:
     InferenceEngine::Precision fusedEltwisePrecision(const MKLDNNNodePtr& fusingNode) const;
 
 private:
+    using executorPtr = std::shared_ptr<DnnlExecutor>;
+    executorPtr execPtr = nullptr;
+
+    class ConvolutionExecutor : public DnnlExecutor {
+        public:
+            ConvolutionExecutor(const mkldnn::convolution_forward::primitive_desc& pd,
+                                const mkldnn::memory::desc& inMemDesc,
+                                const mkldnn::memory::desc& weightMemDesc,
+                                const mkldnn::memory::desc& outMemDesc,
+                                const mkldnn::engine& engine);
+    };
+
+    std::shared_ptr<MKLDNNDescriptor> createMkldnnConvDesc(const mkldnn::memory::desc& srcDesc,
+                                                           const mkldnn::memory::desc& wghDesc,
+                                                           const mkldnn::memory::desc& dstDesc,
+                                                           const mkldnn::memory::desc& biasDesc);
+
     void prepareParams() override;
+    void execute(mkldnn::stream strm) override;
     void executeDynamicImpl(mkldnn::stream strm) override;
 
     void addZeroPoints(mkldnn::primitive_attr& attr) const;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
index 2a20f45ff29..00150f7eba4 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
@@ -129,15 +129,8 @@ void MKLDNNConvertNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
-void MKLDNNConvertNode::createPrimitive() {
-    auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
-    auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
-    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " has not allocated destination memory";
-    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " has not allocated input memory";
-    if (getSelectedPrimitiveDescriptor() == nullptr)
-        IE_THROW() << errorPrefix << " has nullable preferable primitive descriptor";
+void MKLDNNConvertNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 void MKLDNNConvertNode::execute(mkldnn::stream strm) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h
index 08042187788..b345259fa5f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h
@@ -19,9 +19,8 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     bool created() const override;
     bool canBeInPlace() const override {
         return false;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp
index b6881da496f..33b3859cc06 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp
@@ -165,14 +165,8 @@ bool MKLDNNCTCGreedyDecoderNode::created() const {
     return getType() == CTCGreedyDecoder;
 }
 
-void MKLDNNCTCGreedyDecoderNode::executeDynamicImpl(dnnl::stream strm) {
-    MKLDNNCTCGreedyDecoderNode::execute(strm);
-}
-
-void MKLDNNCTCGreedyDecoderNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
+void MKLDNNCTCGreedyDecoderNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 bool MKLDNNCTCGreedyDecoderNode::needPrepareParams() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h
index a936d47d75d..84da43a9e5a 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h
@@ -15,7 +15,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
     void executeDynamicImpl(dnnl::stream strm) override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
index 81e94cee450..e9de4e7d5fd 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
@@ -168,14 +168,8 @@ bool MKLDNNCTCGreedyDecoderSeqLenNode::created() const {
     return getType() == CTCGreedyDecoderSeqLen;
 }
 
-void MKLDNNCTCGreedyDecoderSeqLenNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
-}
-
-void MKLDNNCTCGreedyDecoderSeqLenNode::executeDynamicImpl(dnnl::stream strm) {
-    MKLDNNCTCGreedyDecoderSeqLenNode::execute(strm);
+void MKLDNNCTCGreedyDecoderSeqLenNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 bool MKLDNNCTCGreedyDecoderSeqLenNode::needPrepareParams() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
index 023a9e8f409..6d997ae600d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
@@ -15,7 +15,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
     void executeDynamicImpl(dnnl::stream strm) override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp
index 3019cf20e86..0647f235155 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp
@@ -57,12 +57,8 @@ void MKLDNNCTCLossNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
-void MKLDNNCTCLossNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
+void MKLDNNCTCLossNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 void MKLDNNCTCLossNode::execute(mkldnn::stream strm) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h
index b37f4500f3e..65df24e6457 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h
@@ -15,13 +15,12 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
     bool needPrepareParams() const override { return false; };
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp
index a99e30a1db0..0613f10cb70 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp
@@ -258,13 +258,7 @@ bool MKLDNNCumSumNode::needPrepareParams() const {
 }
 
 void MKLDNNCumSumNode::executeDynamicImpl(mkldnn::stream strm) {
-    return execute(strm);
-}
-
-void MKLDNNCumSumNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
+    execute(strm);
 }
 
 REG_MKLDNN_PRIM_FOR(MKLDNNCumSumNode, CumSum)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h
index f917a53ef34..dc1d1b219d7 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h
@@ -15,7 +15,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
index f81e4601eab..19684324fbf 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
@@ -475,7 +475,7 @@ void MKLDNNDeconvolutionNode::execute(mkldnn::stream strm) {
     if (!execPtr) {
         IE_THROW() << "Can't execute Deconvolution node with name: " << getName() << ", because executor is not compiled";
     }
-    execPtr->exec(strm);
+    execPtr->exec(primArgs, strm);
 
     if (externOutShape) {
         lastOutputSpatialDims = readOutputSpatialDims();
@@ -519,12 +519,18 @@ void MKLDNNDeconvolutionNode::createDeconvPrim(std::shared_ptr<MKLDNNDescriptor>
                     prepareMemory(itpd);
                 }
                 auto prim_desc = deconvolution_forward::primitive_desc(itpd.get());
-                execPtr = std::make_shared<DeconvExecutorInt8>(prim_desc, srcMemPtr, internalBlobMemory.front(), dstMemPtr, *attr,
-                                                               binaryPostOpsArgs, getEngine());
+                execPtr = std::make_shared<DeconvExecutorInt8>(prim_desc,
+                                                               srcMemPtr->GetPrimitive().get_desc(),
+                                                               internalBlobMemory.front()->GetPrimitive().get_desc(),
+                                                               dstMemPtr->GetPrimitive().get_desc(),
+                                                               getEngine());
             } else {
                 auto prim_desc = convolution_backward_data::primitive_desc(itpd.get());
-                execPtr = std::make_shared<DeconvExecutorDefault>(prim_desc, srcMemPtr, wghMemPtr, dstMemPtr, *attr,
-                                                                  binaryPostOpsArgs, getEngine());
+                execPtr = std::make_shared<DeconvExecutorDefault>(prim_desc,
+                                                                  srcMemPtr->GetPrimitive().get_desc(),
+                                                                  wghMemPtr->GetPrimitive().get_desc(),
+                                                                  dstMemPtr->GetPrimitive().get_desc(),
+                                                                  getEngine());
             }
             return;
         }
@@ -544,8 +550,11 @@ void MKLDNNDeconvolutionNode::createDeconvPrim(std::shared_ptr<MKLDNNDescriptor>
             auto anyDeconvItpd = anyDeconvDesc->createPrimitiveDescriptorIterator(getEngine(), *attr);
             if (static_cast<bool>(anyDeconvItpd)) {
                 auto prim_desc = convolution_backward_data::primitive_desc(anyDeconvItpd.get());
-                execPtr = std::make_shared<DeconvExecutorDefault>(prim_desc, srcMemPtr, wghMemPtr, dstMemPtr, *attr,
-                                                                  binaryPostOpsArgs, getEngine());
+                execPtr = std::make_shared<DeconvExecutorDefault>(prim_desc,
+                                                                  srcMemPtr->GetPrimitive().get_desc(),
+                                                                  wghMemPtr->GetPrimitive().get_desc(),
+                                                                  dstMemPtr->GetPrimitive().get_desc(),
+                                                                  getEngine());
                 return;
             }
         }
@@ -555,12 +564,15 @@ void MKLDNNDeconvolutionNode::createDeconvPrim(std::shared_ptr<MKLDNNDescriptor>
 
 void MKLDNNDeconvolutionNode::prepareParams() {
     auto srcMemPtr = getParentEdgesAtPort(0)[0]->getMemoryPtr();
+    auto wghMemPtr = getParentEdgesAtPort(1)[0]->getMemoryPtr();
     auto dstMemPtr = getChildEdgesAtPort(0)[0]->getMemoryPtr();
     if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
         IE_THROW() << "Destination memory didn't allocate.";
     if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
         IE_THROW() << "Input memory didn't allocate.";
     const NodeDesc *selected_pd = getSelectedPrimitiveDescriptor();
+    if (!wghMemPtr || !wghMemPtr->GetPrimitivePtr())
+        IE_THROW() << "Weight memory didn't allocate.";
     if (selected_pd == nullptr)
         IE_THROW() << "Preferable primitive descriptor is not set for node " << getName() << ".";
 
@@ -610,7 +622,18 @@ void MKLDNNDeconvolutionNode::prepareParams() {
                                              selected_pd->getImplementationType() == MKLDNNPlugin::impl_desc_type::jit_avx512_winograd);
     }
 
-    createDeconvPrim(desc, srcMemPtr, getParentEdgesAtPort(1)[0]->getMemoryPtr(), dstMemPtr, pAttrLocal, selected_pd->getImplementationType());
+    createDeconvPrim(desc, srcMemPtr, wghMemPtr, dstMemPtr, pAttrLocal, selected_pd->getImplementationType());
+
+    if (std::dynamic_pointer_cast<DeconvExecutorInt8>(execPtr)) {
+        primArgs = {{DNNL_ARG_SRC, srcMemPtr->GetPrimitive()},
+                    {DNNL_ARG_WEIGHTS, internalBlobMemory.front()->GetPrimitive()},
+                    {DNNL_ARG_DST, dstMemPtr->GetPrimitive()}};
+    } else {
+        primArgs = {{DNNL_ARG_DIFF_DST, srcMemPtr->GetPrimitive()},
+                    {DNNL_ARG_WEIGHTS, wghMemPtr->GetPrimitive()},
+                    {DNNL_ARG_DIFF_SRC, dstMemPtr->GetPrimitive()}};
+    }
+    MKLDNNNode::appendPostOpArgs(attr, primArgs, binaryPostOpsArgs);
 }
 
 void MKLDNNDeconvolutionNode::createPrimitive() {
@@ -738,100 +761,44 @@ InferenceEngine::Precision MKLDNNDeconvolutionNode::getRuntimePrecision() const
     return getMaxPrecision(inputPrecisions);
 }
 
-MKLDNNDeconvolutionNode::DeconvExecutor::IntermReorder::IntermReorder(MKLDNNMemoryPtr memFrom,
-                                                                      const mkldnn::memory::desc& descTo,
-                                                                      const mkldnn::engine& engine) : m_memFrom(memFrom) {
-    m_memTo = std::make_shared<MKLDNNMemory>(engine);
-    m_memTo->Create(MKLDNNExtensionUtils::makeDescriptor(descTo));
-    m_reorder = mkldnn::reorder(m_memFrom->GetPrimitive(), m_memTo->GetPrimitive());
-}
-
-MKLDNNDeconvolutionNode::DeconvExecutor::IntermReorder::IntermReorder(const mkldnn::memory::desc& descFrom,
-                                                                      MKLDNNMemoryPtr memTo,
-                                                                      const mkldnn::engine& engine) : m_memTo(memTo) {
-    m_memFrom = std::make_shared<MKLDNNMemory>(engine);
-    m_memFrom->Create(MKLDNNExtensionUtils::makeDescriptor(descFrom));
-    m_reorder = mkldnn::reorder(m_memFrom->GetPrimitive(), m_memTo->GetPrimitive());
-}
-
-void MKLDNNDeconvolutionNode::DeconvExecutor::IntermReorder::exec(mkldnn::stream strm) {
-    auto src = m_memFrom->GetPrimitive();
-    auto dst = m_memTo->GetPrimitive();
-    m_reorder.execute(strm, src, dst);
-}
-
-void MKLDNNDeconvolutionNode::DeconvExecutor::exec(mkldnn::stream strm) {
-    for (auto &inReorder : inputReorders) {
-        inReorder.exec(strm);
-    }
-    (*execPrim).execute(strm, primArgs);
-    for (auto &outReorder : outputReorders) {
-        outReorder.exec(strm);
-    }
-}
-
 MKLDNNDeconvolutionNode::DeconvExecutorDefault::DeconvExecutorDefault(const mkldnn::convolution_backward_data::primitive_desc& pd,
-                                                                      MKLDNNMemoryPtr inMem,
-                                                                      MKLDNNMemoryPtr weightMem,
-                                                                      MKLDNNMemoryPtr outMem,
-                                                                      const mkldnn::primitive_attr &attr,
-                                                                      const std::vector<MKLDNNMemoryPtr>& binPostOpsArgs,
+                                                                      const mkldnn::memory::desc& inMemDesc,
+                                                                      const mkldnn::memory::desc& weightMemDesc,
+                                                                      const mkldnn::memory::desc& outMemDesc,
                                                                       const mkldnn::engine& engine) {
     execPrim.reset(new mkldnn::convolution_backward_data(pd));
 
-    if (inMem->GetPrimitive().get_desc() != pd.diff_dst_desc()) {
-        inputReorders.push_back(IntermReorder(inMem, pd.diff_dst_desc(), engine));
-        primArgs[DNNL_ARG_DIFF_DST] = inputReorders.back().getToMem()->GetPrimitive();
-    } else {
-        primArgs[DNNL_ARG_DIFF_DST] = inMem->GetPrimitive();
+    if (inMemDesc != pd.diff_dst_desc()) {
+        inputReorders.insert({DNNL_ARG_DIFF_DST, IntermReorder(inMemDesc, pd.diff_dst_desc(), engine)});
     }
 
-    if (weightMem->GetPrimitive().get_desc() != pd.weights_desc()) {
-        inputReorders.push_back(IntermReorder(weightMem, pd.weights_desc(), engine));
-        primArgs[DNNL_ARG_WEIGHTS] = inputReorders.back().getToMem()->GetPrimitive();
-    } else {
-        primArgs[DNNL_ARG_WEIGHTS] = weightMem->GetPrimitive();
+    if (weightMemDesc != pd.weights_desc()) {
+        inputReorders.insert({DNNL_ARG_WEIGHTS, IntermReorder(weightMemDesc, pd.weights_desc(), engine)});
     }
 
-    if (outMem->GetPrimitive().get_desc() != pd.diff_src_desc()) {
-        outputReorders.push_back(IntermReorder(pd.diff_src_desc(), outMem, engine));
-        primArgs[DNNL_ARG_DIFF_SRC] = outputReorders.back().getFromMem()->GetPrimitive();
-    } else {
-        primArgs[DNNL_ARG_DIFF_SRC] = outMem->GetPrimitive();
+    if (outMemDesc != pd.diff_src_desc()) {
+        outputReorders.insert({DNNL_ARG_DIFF_SRC, IntermReorder(pd.diff_src_desc(), outMemDesc, engine)});
     }
-    MKLDNNNode::appendPostOpArgs(attr, primArgs, binPostOpsArgs);
 }
 
 MKLDNNDeconvolutionNode::DeconvExecutorInt8::DeconvExecutorInt8(const mkldnn::deconvolution_forward::primitive_desc& pd,
-                                                                MKLDNNMemoryPtr inMem,
-                                                                MKLDNNMemoryPtr weightMem,
-                                                                MKLDNNMemoryPtr outMem,
-                                                                const mkldnn::primitive_attr &attr,
-                                                                const std::vector<MKLDNNMemoryPtr>& binPostOpsArgs,
+                                                                const mkldnn::memory::desc& inMemDesc,
+                                                                const mkldnn::memory::desc& weightMemDesc,
+                                                                const mkldnn::memory::desc& outMemDesc,
                                                                 const mkldnn::engine& engine) {
     execPrim.reset(new mkldnn::deconvolution_forward(pd));
 
-    if (inMem->GetPrimitive().get_desc() != pd.src_desc()) {
-        inputReorders.push_back(IntermReorder(inMem, pd.src_desc(), engine));
-        primArgs[DNNL_ARG_SRC] = inputReorders.back().getToMem()->GetPrimitive();
-    } else {
-        primArgs[DNNL_ARG_SRC] = inMem->GetPrimitive();
+    if (inMemDesc != pd.src_desc()) {
+        inputReorders.insert({DNNL_ARG_SRC, IntermReorder(inMemDesc, pd.src_desc(), engine)});
     }
 
-    if (weightMem->GetPrimitive().get_desc() != pd.weights_desc()) {
-        inputReorders.push_back(IntermReorder(weightMem, pd.weights_desc(), engine));
-        primArgs[DNNL_ARG_WEIGHTS] = inputReorders.back().getToMem()->GetPrimitive();
-    } else {
-        primArgs[DNNL_ARG_WEIGHTS] = weightMem->GetPrimitive();
+    if (weightMemDesc != pd.weights_desc()) {
+        inputReorders.insert({DNNL_ARG_WEIGHTS, IntermReorder(weightMemDesc, pd.weights_desc(), engine)});
     }
 
-    if (outMem->GetPrimitive().get_desc() != pd.dst_desc()) {
-        outputReorders.push_back(IntermReorder(pd.dst_desc(), outMem, engine));
-        primArgs[DNNL_ARG_DST] = outputReorders.back().getFromMem()->GetPrimitive();
-    } else {
-        primArgs[DNNL_ARG_DST] = outMem->GetPrimitive();
+    if (outMemDesc != pd.dst_desc()) {
+        outputReorders.insert({DNNL_ARG_DST, IntermReorder(pd.dst_desc(), outMemDesc, engine)});
     }
-    MKLDNNNode::appendPostOpArgs(attr, primArgs, binPostOpsArgs);
 }
 
 std::vector<int32_t> MKLDNNDeconvolutionNode::readOutputSpatialDims() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h
index 6af4a3d35ed..9b3bab44676 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h
@@ -9,6 +9,7 @@
 #include <memory>
 #include <string>
 #include <vector>
+#include "common/dnnl_executor.h"
 
 namespace MKLDNNPlugin {
 
@@ -53,56 +54,24 @@ public:
     std::vector<VectorDims> shapeInfer() const override;
 
 private:
-    class DeconvExecutor {
-        protected:
-            class IntermReorder {
-                public:
-                    IntermReorder(MKLDNNMemoryPtr memFrom, const mkldnn::memory::desc& descTo, const mkldnn::engine& engine);
-                    IntermReorder(const mkldnn::memory::desc& descFrom, MKLDNNMemoryPtr memTo, const mkldnn::engine& engine);
-                    MKLDNNMemoryPtr getFromMem() const { return m_memFrom; }
-                    MKLDNNMemoryPtr getToMem() const { return m_memTo; }
-                    void exec(mkldnn::stream strm);
-
-                private:
-                    MKLDNNMemoryPtr m_memFrom;
-                    MKLDNNMemoryPtr m_memTo;
-                    mkldnn::reorder m_reorder;
-            };
-
-        public:
-            void exec(mkldnn::stream strm);
-            virtual ~DeconvExecutor() = default;
-
-        protected:
-            DeconvExecutor() = default;
-            std::vector<IntermReorder> inputReorders;
-            MKLDNNPrimitive execPrim;
-            std::vector<IntermReorder> outputReorders;
-            std::unordered_map<int, mkldnn::memory> primArgs;
-    };
-
-    using executorPtr = std::shared_ptr<DeconvExecutor>;
+    using executorPtr = std::shared_ptr<DnnlExecutor>;
     executorPtr execPtr = nullptr;
 
-    class DeconvExecutorDefault : public DeconvExecutor {
+    class DeconvExecutorDefault : public DnnlExecutor {
         public:
             DeconvExecutorDefault(const mkldnn::convolution_backward_data::primitive_desc& pd,
-                                  MKLDNNMemoryPtr inMem,
-                                  MKLDNNMemoryPtr weightMem,
-                                  MKLDNNMemoryPtr outMem,
-                                  const mkldnn::primitive_attr &attr,
-                                  const std::vector<MKLDNNMemoryPtr>& binPostOpsArgs,
+                                  const mkldnn::memory::desc& inMemDesc,
+                                  const mkldnn::memory::desc& weightMemDesc,
+                                  const mkldnn::memory::desc& outMemDesc,
                                   const mkldnn::engine& engine);
     };
 
-    class DeconvExecutorInt8 : public DeconvExecutor {
+    class DeconvExecutorInt8 : public DnnlExecutor {
         public:
             DeconvExecutorInt8(const mkldnn::deconvolution_forward::primitive_desc& pd,
-                               MKLDNNMemoryPtr inMem,
-                               MKLDNNMemoryPtr weightMem,
-                               MKLDNNMemoryPtr outMem,
-                               const mkldnn::primitive_attr &attr,
-                               const std::vector<MKLDNNMemoryPtr>& binPostOpsArgs,
+                               const mkldnn::memory::desc& inMemDesc,
+                               const mkldnn::memory::desc& weightMemDesc,
+                               const mkldnn::memory::desc& outMemDesc,
                                const mkldnn::engine& engine);
     };
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp
index 3d5e8a24744..2d20f4ee1ea 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp
@@ -49,15 +49,7 @@ bool MKLDNNDetectionOutputNode::isSupportedOperation(const std::shared_ptr<const
     return true;
 }
 
-void MKLDNNDetectionOutputNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
-MKLDNNDetectionOutputNode::MKLDNNDetectionOutputNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng,
+MKLDNNDetectionOutputNode::MKLDNNDetectionOutputNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng,
         MKLDNNWeightsSharing::Ptr &cache) : MKLDNNNode(op, eng, cache) {
     std::string errorMessage;
     if (!isSupportedOperation(op, errorMessage)) {
@@ -170,6 +162,10 @@ struct ConfidenceComparatorDO {
     const float* confData;
 };
 
+void MKLDNNDetectionOutputNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNDetectionOutputNode::execute(mkldnn::stream strm) {
     float *dstData = reinterpret_cast<float *>(getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPtr());
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.h
index 41fc5478328..c602e636259 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.h
@@ -16,7 +16,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
@@ -24,7 +23,7 @@ public:
 
 protected:
     void prepareParams() override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
 private:
     static const int ID_LOC = 0;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
index f87e32c1476..c046fb968f9 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
@@ -1525,14 +1525,6 @@ void MKLDNNEltwiseNode::selectOptimalPrimitiveDescriptor() {
     selectPreferPrimitiveDescriptor(getPrimitivesPriority(), true);
 }
 
-void MKLDNNEltwiseNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNEltwiseNode::initOptimalPrimitiveDescriptor() {
     auto selected_pd = getSelectedPrimitiveDescriptor();
     if (selected_pd == nullptr)
@@ -1683,6 +1675,10 @@ void MKLDNNEltwiseNode::executeReference(const jit_eltwise_params &jep, const ji
     });
 }
 
+void MKLDNNEltwiseNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNEltwiseNode::execute(mkldnn::stream strm) {
     if (execPtr) {
         jit_eltwise_call_args_ptrs args_ptrs = {};
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h
index b5e7768b52a..ce34a2096fc 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h
@@ -90,13 +90,11 @@ public:
     bool isWithBroadcast();
     bool isSpecialConvolutionAddFusing() const { return specialConvolutionAddFusing; }
 
-    void createPrimitive() override;
-
     std::vector<VectorDims> shapeInfer() const override;
     bool needPrepareParams() const override;
     void prepareParams() override;
 
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     enum BroadcastingPolicy {
         PerChannel,
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
index 86cb02d45d7..499d9b5b0de 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
@@ -70,14 +70,6 @@ void MKLDNNEmbeddingBagOffsetSumNode::initSupportedPrimitiveDescriptors() {
     addSupportedPrimDesc(inDataConfigurators, {{LayoutType::ncsp, inDataPrecision}}, impl_desc_type::ref_any);
 }
 
-void MKLDNNEmbeddingBagOffsetSumNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNEmbeddingBagOffsetSumNode::prepareParams() {
     _indicesLen = getParentEdgesAtPort(INDICES_IDX)[0]->getMemory().getStaticDims()[0];
     _offsetsLen = getParentEdgesAtPort(OFFSETS_IDX)[0]->getMemory().getStaticDims()[0];
@@ -126,6 +118,14 @@ void MKLDNNEmbeddingBagOffsetSumNode::getIndices(int embIndex, const int*& indic
         weightsIdx = offsetsData_[embIndex];
 }
 
+void MKLDNNEmbeddingBagOffsetSumNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
+bool MKLDNNEmbeddingBagOffsetSumNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
+}
+
 void MKLDNNEmbeddingBagOffsetSumNode::execute(mkldnn::stream strm) {
     const auto *srcData = reinterpret_cast<const uint8_t *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
     auto *dstData = reinterpret_cast<uint8_t *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h
index 146003c0b41..9fd0e2ff4fe 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h
@@ -19,15 +19,15 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool isExecutable() const override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 protected:
     void prepareParams() override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
 private:
     void initFromInputs() override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
index 9dbaad08d80..bda4ebc7c28 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
@@ -64,14 +64,6 @@ void MKLDNNEmbeddingBagPackedSumNode::initSupportedPrimitiveDescriptors() {
     addSupportedPrimDesc(inDataConfigurators, {{LayoutType::ncsp, inDataPrecision}}, impl_desc_type::ref_any);
 }
 
-void MKLDNNEmbeddingBagPackedSumNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNEmbeddingBagPackedSumNode::prepareParams() {
     _batch = getParentEdgesAtPort(INDICES_IDX)[0]->getMemory().getStaticDims()[0];
     _indicesPerBag = getParentEdgesAtPort(INDICES_IDX)[0]->getMemory().getStaticDims()[1];
@@ -94,6 +86,14 @@ void MKLDNNEmbeddingBagPackedSumNode::getIndices(int embIndex, const int*& indic
     weightsIdx = embIndex * _indicesPerBag;
 }
 
+void MKLDNNEmbeddingBagPackedSumNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
+bool MKLDNNEmbeddingBagPackedSumNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
+}
+
 void MKLDNNEmbeddingBagPackedSumNode::execute(mkldnn::stream strm) {
     const auto *srcData = reinterpret_cast<const uint8_t *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
     auto *dstData = reinterpret_cast<uint8_t *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h
index fb01897debb..fddec417c65 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h
@@ -19,15 +19,15 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool isExecutable() const override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 protected:
     void prepareParams() override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
 private:
     void initFromInputs() override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp
index 2dca1793df1..58e5de78882 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp
@@ -11,14 +11,6 @@
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
-void MKLDNNEmbeddingSegmentsSumNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 bool MKLDNNEmbeddingSegmentsSumNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
         const auto embBagSegSumOp = ngraph::as_type_ptr<const ngraph::op::v3::EmbeddingSegmentsSum>(op);
@@ -129,6 +121,14 @@ void MKLDNNEmbeddingSegmentsSumNode::getIndices(int embIndex, const int*& indice
     }
 }
 
+void MKLDNNEmbeddingSegmentsSumNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
+bool MKLDNNEmbeddingSegmentsSumNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
+}
+
 void MKLDNNEmbeddingSegmentsSumNode::execute(mkldnn::stream strm) {
     const auto *srcData = reinterpret_cast<const uint8_t *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
     auto *dstData = reinterpret_cast<uint8_t *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h
index 942d337d099..31b3f4163e1 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h
@@ -19,15 +19,15 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool isExecutable() const override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 protected:
     void prepareParams() override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
 private:
     void initFromInputs() override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.cpp
index 96d8c48be47..96f1c85e738 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.cpp
@@ -272,12 +272,6 @@ void MKLDNNExperimentalDetectronDetectionOutputNode::initSupportedPrimitiveDescr
                          impl_desc_type::ref_any);
 }
 
-void MKLDNNExperimentalDetectronDetectionOutputNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNExperimentalDetectronDetectionOutputNode::execute(mkldnn::stream strm) {
     const int rois_num = getParentEdgeAt(INPUT_ROIS)->getMemory().getStaticDims()[0];
     assert(classes_num_ == static_cast<int>(getParentEdgeAt(INPUT_SCORES)->getMemory().getStaticDims()[1]));
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.h
index 3c73bd036bb..eeb9ccb9399 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.h
@@ -15,7 +15,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp
index fc36c163484..88fd2f44abd 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp
@@ -320,12 +320,6 @@ void MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::initSupportedP
                          impl_desc_type::ref_any);
 }
 
-void MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::execute(mkldnn::stream strm) {
     try {
         if (inputShapes.size() != 4 || outputShapes.size() != 2) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
index a18f41e5a94..d00ec14601c 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
@@ -16,7 +16,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp
index a30031d9b84..c4d544e0b39 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp
@@ -56,12 +56,6 @@ void MKLDNNExperimentalDetectronPriorGridGeneratorNode::initSupportedPrimitiveDe
                          impl_desc_type::ref_any);
 }
 
-void MKLDNNExperimentalDetectronPriorGridGeneratorNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNExperimentalDetectronPriorGridGeneratorNode::execute(mkldnn::stream strm) {
     const int num_priors_ = getParentEdgeAt(INPUT_PRIORS)->getMemory().getStaticDims()[0];
     assert(getParentEdgeAt(INPUT_PRIORS)->getMemory().getStaticDims()[1] == 4);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h
index c908add3223..b1a39dade02 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h
@@ -15,7 +15,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
index aceba74e694..4ebf1467366 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
@@ -15,7 +15,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.h
index 2f66469502e..7cefc63ee8d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.h
@@ -15,7 +15,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp
index 29faa1dac52..ccd98c5c2ae 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp
@@ -327,14 +327,6 @@ MKLDNNExtractImagePatchesNode::MKLDNNExtractImagePatchesNode(const std::shared_p
         IE_THROW() << errorPrefix << "must have the following attributes with shape {2}: sizes, strides, rates.";
 }
 
-void MKLDNNExtractImagePatchesNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNExtractImagePatchesNode::prepareParams() {
     const auto& srcMemPtr0 = getParentEdgeAt(0)->getMemoryPtr();
     const auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
@@ -381,7 +373,7 @@ void MKLDNNExtractImagePatchesNode::execute(mkldnn::stream strm) {
 }
 
 void MKLDNNExtractImagePatchesNode::executeDynamicImpl(mkldnn::stream strm) {
-    return execute(strm);
+    execute(strm);
 }
 
 void MKLDNNExtractImagePatchesNode::ExtractImagePatchesRefExecutor::executeReference(
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h
index 62df89c9822..d35d9f48d3b 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h
@@ -45,7 +45,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp
index 3597719521b..b658f1b82ab 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp
@@ -1387,13 +1387,6 @@ void MKLDNNFakeQuantizeNode::prepareParams() {
     }
 }
 
-void MKLDNNFakeQuantizeNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNFakeQuantizeNode::executeReference() {
     auto &srcMemory = getParentEdgeAt(0)->getMemoryPtr();
     auto &dstMemory = getChildEdgeAt(0)->getMemoryPtr();
@@ -1652,6 +1645,10 @@ void MKLDNNFakeQuantizeNode::executeQuantization(const std::unique_ptr<jit_uni_q
     }
 }
 
+void MKLDNNFakeQuantizeNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNFakeQuantizeNode::execute(mkldnn::stream strm) {
     auto selectedPrimitiveDescriptor = getSelectedPrimitiveDescriptor();
     if (!selectedPrimitiveDescriptor)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h
index a56b94fdf40..8f5770d720f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h
@@ -70,10 +70,9 @@ public:
 
     void initSupportedPrimitiveDescriptors() override;
     void getSupportedDescriptors() override;
-    void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     size_t getAxis() const { return axis; }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.cpp
index a13c1f76f8b..3dc51699118 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.cpp
@@ -54,14 +54,6 @@ MKLDNNGatherElementsNode::MKLDNNGatherElementsNode(const std::shared_ptr<ngraph:
     axis_ = axis;
 }
 
-void MKLDNNGatherElementsNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNGatherElementsNode::prepareParams() {
     const auto& dataDims = getParentEdgesAtPort(dataIndex_)[0]->getMemory().getStaticDims();
     const auto& dstDims = getChildEdgesAtPort(0)[0]->getMemory().getStaticDims();
@@ -102,6 +94,10 @@ void MKLDNNGatherElementsNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
+void MKLDNNGatherElementsNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 template <typename dataType>
 void MKLDNNGatherElementsNode::directExecution() {
     const auto *srcData = reinterpret_cast<const dataType *>(getParentEdgeAt(dataIndex_)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.h
index 632076b08d7..f5d38944795 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.h
@@ -18,14 +18,13 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
 
 protected:
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     void prepareParams() override;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp
index ae98c0e74ab..fc72e152713 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp
@@ -80,14 +80,6 @@ void MKLDNNGatherNDNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
-void MKLDNNGatherNDNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNGatherNDNode::prepareParams() {
     auto& srcMemPtr = getParentEdgeAt(GATHERND_DATA)->getMemoryPtr();
     auto& idxMemPtr = getParentEdgeAt(GATHERND_INDEXES)->getMemoryPtr();
@@ -227,7 +219,7 @@ void MKLDNNGatherNDNode::GatherNDExecutor::gatherElementwise(const MKLDNNMemoryP
     });
 }
 
-void MKLDNNGatherNDNode::executeDynamicImpl(dnnl::stream strm) {
+void MKLDNNGatherNDNode::executeDynamicImpl(mkldnn::stream strm) {
     execute(strm);
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h
index 53661c4d342..29ff88b1466 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h
@@ -18,7 +18,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.cpp
index 0e44c0e4c3b..706ad37f8dc 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.cpp
@@ -110,20 +110,12 @@ void MKLDNNGatherNode::prepareParams() {
 }
 
 bool MKLDNNGatherNode::needPrepareParams() const {
-    bool result = MKLDNNNode::needPrepareParams();
+    bool result = inputShapesModified();
     if (!isAxisInputConst)
         result = result || axis != (reinterpret_cast<const int32_t*>(getParentEdgeAt(GATHER_AXIS)->getMemoryPtr()->GetPtr()))[0];
     return result;
 }
 
-void MKLDNNGatherNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNGatherNode::execute(mkldnn::stream strm) {
     const int32_t* srcIndexes = reinterpret_cast<const int32_t*>(getParentEdgeAt(GATHER_INDEXES)->getMemoryPtr()->GetPtr());
     const uint8_t* srcData = reinterpret_cast<const uint8_t*>(getParentEdgeAt(GATHER_DATA)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.h
index 1477f9325f2..df66ac52578 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.h
@@ -18,7 +18,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
index d828878bbb3..0f102e6dd28 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
@@ -2075,7 +2075,7 @@ void MKLDNNInterpolateNode::createPrimitive() {
     interpAttrs.inPrc = srcMemPtr->getDesc().getPrecision();
     interpAttrs.outPrc = dstMemPtr->getDesc().getPrecision();
 
-    if (shapesDefined()) {
+    if (shapesDefined() && isExecutable()) {
         if (needPrepareParams())
             prepareParams();
         updateLastInputDims();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp
index 9f537b490fc..9ab047473a6 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp
@@ -60,14 +60,6 @@ void MKLDNNLogSoftmaxNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
-void MKLDNNLogSoftmaxNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNLogSoftmaxNode::prepareParams() {
     const auto &dims = getParentEdgesAtPort(0)[0]->getMemory().getStaticDims();
     reducedAxisStride = 1;
@@ -87,6 +79,10 @@ void MKLDNNLogSoftmaxNode::prepareParams() {
         reducedAxisStride *= dims[i];
 }
 
+void MKLDNNLogSoftmaxNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNLogSoftmaxNode::execute(mkldnn::stream strm) {
     const float *srcData = reinterpret_cast<const float *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
     float* dstData = reinterpret_cast<float *>(getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h
index c7fd5aac3c2..b51e72ed0ff 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h
@@ -16,12 +16,11 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     void prepareParams() override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp
index e1430776725..72bc3e2d61a 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp
@@ -110,14 +110,6 @@ std::shared_ptr<MemoryDesc> MKLDNNLrnNode::getSrcMemDesc(mkldnn::primitive_desc_
     }
 }
 
-void MKLDNNLrnNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNLrnNode::prepareParams() {
     auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
     auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
@@ -174,8 +166,8 @@ std::vector<VectorDims> MKLDNNLrnNode::shapeInfer() const {
     return { getParentEdgesAtPort(0).front()->getMemory().getStaticDims() };
 }
 
-void MKLDNNLrnNode::executeDynamicImpl(dnnl::stream strm) {
-    MKLDNNNode::execute(strm);
+void MKLDNNLrnNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 REG_MKLDNN_PRIM_FOR(MKLDNNLrnNode, Lrn);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h
index 97ef5163f43..348ecc3ae3a 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h
@@ -23,7 +23,6 @@ public:
         return static_cast<size_t>(getOriginalInputsNumber());
     }
     std::shared_ptr<MemoryDesc> getSrcMemDesc(mkldnn::primitive_desc_iterator &primitive_desc_it, size_t idx) override;
-    void createPrimitive() override;
     bool created() const override;
     bool canBeInPlace() const override {
         return false;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp
index 163e76ca483..b90b14d1423 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp
@@ -63,6 +63,10 @@ std::vector<VectorDims> MKLDNNMathNode::shapeInfer() const {
     return std::vector<VectorDims>{getParentEdgesAtPort(0)[0]->getMemory().getStaticDims()};
 }
 
+void MKLDNNMathNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNMathNode::execute(mkldnn::stream strm) {
     size_t dataSize = getChildEdgesAtPort(0)[0]->getMemory().GetShape().getElementsCount();
     const float *src_data = reinterpret_cast<const float *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h
index ee2ce5d75b1..c580e7a1a64 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h
@@ -15,13 +15,12 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     std::vector<VectorDims> shapeInfer() const override;
     bool needPrepareParams() const override { return false; };
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp
index c443eedf2c2..5513a27f0d2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp
@@ -313,14 +313,6 @@ void MKLDNNMatMulNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
-void MKLDNNMatMulNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 MemoryDescPtr MKLDNNMatMulNode::getSrcMemDesc(mkldnn::primitive_desc_iterator &primitive_desc_it, size_t idx) {
     auto desc = idx > 0 ? primitive_desc_it.weights_desc(idx - 1): primitive_desc_it.src_desc(idx);
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h
index ab3abd3a35c..2d7e8c00e60 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h
@@ -22,7 +22,6 @@ public:
                           const std::vector<MemoryDescPtr>& outputDesc) override;
     void initSupportedPrimitiveDescriptors() override;
     MemoryDescPtr getSrcMemDesc(mkldnn::primitive_desc_iterator &primitive_desc_it, size_t idx) override;
-    void createPrimitive() override;
     bool canFuse(const MKLDNNNodePtr& node) const override;
     bool created() const override;
     size_t getMaxBatch() const override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp
index b01aa731e68..c9668785548 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp
@@ -240,13 +240,6 @@ size_t MKLDNNMatrixNmsNode::nmsMatrix(const float* boxesData, const float* score
     return numDet;
 }
 
-void MKLDNNMatrixNmsNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNMatrixNmsNode::prepareParams() {
     const auto& boxes_dims = getParentEdgeAt(NMS_BOXES)->getMemory().getStaticDims();
     const auto& scores_dims = getParentEdgeAt(NMS_SCORES)->getMemory().getStaticDims();
@@ -288,6 +281,23 @@ void MKLDNNMatrixNmsNode::prepareParams() {
     }
 }
 
+bool MKLDNNMatrixNmsNode::isExecutable() const {
+    return isDynamicNode() || MKLDNNNode::isExecutable();
+}
+
+void MKLDNNMatrixNmsNode::executeDynamicImpl(mkldnn::stream strm) {
+    if (hasEmptyInputTensors()) {
+        getChildEdgesAtPort(NMS_SELECTED_OUTPUTS)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_SELECTED_OUTPUTS)->cloneWithNewDims({0, 6}));
+        getChildEdgesAtPort(NMS_SELECTED_INDICES)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_SELECTED_INDICES)->cloneWithNewDims({0, 1}));
+        getChildEdgesAtPort(NMS_VALID_OUTPUTS)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_VALID_OUTPUTS)->cloneWithNewDims({0}));
+        return;
+    }
+    execute(strm);
+}
+
 void MKLDNNMatrixNmsNode::execute(mkldnn::stream strm) {
     const float* boxes = reinterpret_cast<const float*>(getParentEdgeAt(NMS_BOXES)->getMemoryPtr()->GetPtr());
     const float* scores = reinterpret_cast<const float*>(getParentEdgeAt(NMS_SCORES)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
index 338247cf103..add276f3dc6 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
@@ -27,13 +27,13 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    bool isExecutable() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     bool needShapeInfer() const override { return false; }
     void prepareParams() override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
index 85d13de3829..15d810fbb4d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
@@ -105,13 +105,6 @@ void MKLDNNMultiClassNmsNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
-void MKLDNNMultiClassNmsNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNMultiClassNmsNode::prepareParams() {
     const auto& boxes_dims = getParentEdgeAt(NMS_BOXES)->getMemory().getStaticDims();
     const auto& scores_dims = getParentEdgeAt(NMS_SCORES)->getMemory().getStaticDims();
@@ -145,6 +138,23 @@ void MKLDNNMultiClassNmsNode::prepareParams() {
     m_numBoxOffset.resize(m_numBatches);
 }
 
+bool MKLDNNMultiClassNmsNode::isExecutable() const {
+    return isDynamicNode() || MKLDNNNode::isExecutable();
+}
+
+void MKLDNNMultiClassNmsNode::executeDynamicImpl(mkldnn::stream strm) {
+    if (hasEmptyInputTensors()) {
+        getChildEdgesAtPort(NMS_SELECTEDOUTPUTS)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_SELECTEDOUTPUTS)->cloneWithNewDims({0, 6}));
+        getChildEdgesAtPort(NMS_SELECTEDINDICES)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_SELECTEDINDICES)->cloneWithNewDims({0, 1}));
+        getChildEdgesAtPort(NMS_SELECTEDNUM)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_SELECTEDNUM)->cloneWithNewDims({0}));
+        return;
+    }
+    execute(strm);
+}
+
 void MKLDNNMultiClassNmsNode::execute(mkldnn::stream strm) {
     const float* boxes = reinterpret_cast<const float*>(getParentEdgeAt(NMS_BOXES)->getMemoryPtr()->GetPtr());
     const float* scores = reinterpret_cast<const float*>(getParentEdgeAt(NMS_SCORES)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
index 6acf3755001..3c1e928bbf0 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
@@ -23,13 +23,13 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    bool isExecutable() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     bool needShapeInfer() const override { return false; }
     void prepareParams() override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
index e8e913ecb9a..cb6496100eb 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
@@ -849,14 +849,6 @@ void MKLDNNMVNNode::prepareParams() {
         }
 }
 
-void MKLDNNMVNNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNMVNNode::transformTo5DCase(const SizeVector& shape) {
     switch (shape.size()) {
         // for 1 and 2 rank, if initAcrossChannels_ is true, adjust shape to fully vectorize under unified 5d procedure.
@@ -908,6 +900,10 @@ void MKLDNNMVNNode::setPostOps(mkldnn::primitive_attr &attr, bool initWeights) {
     attr.set_post_ops(ops);
 }
 
+void MKLDNNMVNNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNMVNNode::execute(mkldnn::stream strm) {
     auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto &srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.h
index 0c740ba9fb1..9b4fe9fff14 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.h
@@ -77,10 +77,9 @@ public:
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     bool canBeInPlace() const override {
         return false;
     }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp
index 6bd0e44ae8c..5cb090a6951 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp
@@ -147,14 +147,20 @@ void MKLDNNNonMaxSuppressionNode::prepareParams() {
         i.resize(num_classes);
 }
 
-void MKLDNNNonMaxSuppressionNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        prepareParams();
-        updateLastInputDims();
-    }
+bool MKLDNNNonMaxSuppressionNode::isExecutable() const {
+    return isDynamicNode() || MKLDNNNode::isExecutable();
 }
 
 void MKLDNNNonMaxSuppressionNode::executeDynamicImpl(mkldnn::stream strm) {
+    if (hasEmptyInputTensors() || (inputShapes.size() > NMS_MAXOUTPUTBOXESPERCLASS &&
+            reinterpret_cast<int *>(getParentEdgeAt(NMS_MAXOUTPUTBOXESPERCLASS)->getMemoryPtr()->GetPtr())[0] == 0)) {
+        getChildEdgesAtPort(NMS_SELECTEDINDICES)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_SELECTEDINDICES)->cloneWithNewDims({0, 3}));
+        getChildEdgesAtPort(NMS_SELECTEDSCORES)[0]->getMemoryPtr()->redefineDesc(
+            getBaseMemDescAtOutputPort(NMS_SELECTEDSCORES)->cloneWithNewDims({0, 3}));
+        *reinterpret_cast<int *>(getChildEdgesAtPort(NMS_VALIDOUTPUTS)[0]->getMemoryPtr()->GetPtr()) = 0;
+        return;
+    }
     execute(strm);
 }
 
@@ -168,8 +174,9 @@ void MKLDNNNonMaxSuppressionNode::execute(mkldnn::stream strm) {
 
     max_output_boxes_per_class = std::min(max_output_boxes_per_class, num_boxes);
 
-    if (max_output_boxes_per_class == 0)
+    if (max_output_boxes_per_class == 0) {
         return;
+    }
 
     if (inputShapes.size() > NMS_IOUTHRESHOLD)
         iou_threshold = reinterpret_cast<float *>(getParentEdgeAt(NMS_IOUTHRESHOLD)->getMemoryPtr()->GetPtr())[0];
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h
index fd8ecc94d6b..e30ffcdc65d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h
@@ -20,7 +20,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
@@ -52,6 +51,7 @@ public:
 
     void executeDynamicImpl(mkldnn::stream strm) override;
 
+    bool isExecutable() const override;
     bool needShapeInfer() const override { return false; }
     void prepareParams() override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp
index f6e0624a4ba..5e270ba56cc 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp
@@ -85,6 +85,11 @@ struct MKLDNNNonZeroNode::NonZeroExecute {
         ctx.node.executeSpecified<T>();
     }
 };
+
+void MKLDNNNonZeroNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNNonZeroNode::execute(mkldnn::stream strm) {
     auto inputPrec = getParentEdgesAtPort(0)[0]->getMemory().getDesc().getPrecision();
     NonZeroContext ctx = {*this };
@@ -104,6 +109,7 @@ void MKLDNNNonZeroNode::executeSpecified() {
     Shape inShape = getParentEdgeAt(0)->getMemory().GetShape();
     size_t inRank = inShape.getRank();
     size_t nonZeroCount = getNonZeroElementsCount(src, inShape);
+
     if (isDynamicNode()) {
         VectorDims newDims{inRank, nonZeroCount};
         dstMemPtr->redefineDesc(getBaseMemDescAtOutputPort(0)->cloneWithNewDims(newDims));
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h
index 2f46042a331..a454a2da268 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h
@@ -18,15 +18,11 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {
-        if (inputShapesDefined())
-            updateLastInputDims();
-    };
     void execute(mkldnn::stream strm) override;
     bool created() const override;
     bool needShapeInfer() const override {return false;};
     bool needPrepareParams() const override {return false;};
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
index 90ff314da8a..3e350b39613 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
@@ -854,12 +854,20 @@ void MKLDNNNormalizeL2Node::createPrimitive() {
     }
 }
 
+bool MKLDNNNormalizeL2Node::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
+}
+
 void MKLDNNNormalizeL2Node::prepareParams() {
     const auto& dims = getParentEdgeAt(DATA)->getMemoryPtr()->getStaticDims();
     setPostOps(kernel_attrs, dims, true);
     execPtr = NormalizeL2Executor::getNormalizeL2Executor(attrs, kernel_attrs, dims);
 }
 
+void MKLDNNNormalizeL2Node::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNNormalizeL2Node::execute(mkldnn::stream strm) {
     if (!execPtr)
         THROW_ERROR << "doesn't have a compiled executor.";
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.h
index 1086acdcf8c..dd6a44f9c16 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.h
@@ -91,7 +91,9 @@ public:
 
     std::vector<VectorDims> shapeInfer() const override;
     void prepareParams() override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
+
+    bool isExecutable() const override;
 
 private:
     enum class NormEpsMode {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.cpp
index dbd6f0fafc6..f016d80c349 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.cpp
@@ -137,6 +137,10 @@ void MKLDNNOneHotNode::one_hot(size_t prefix_size, size_t suffix_size) {
     });
 }
 
+void MKLDNNOneHotNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNOneHotNode::execute(mkldnn::stream strm) {
     std::size_t prefix_size = 1;
     auto input_dims = getParentEdgeAt(0)->getMemory().getStaticDims();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.h
index 52e44acf026..9d43911d3f2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.h
@@ -26,7 +26,7 @@ public:
     bool needShapeInfer() const override;
     std::vector<VectorDims> shapeInfer() const override;
     bool needPrepareParams() const override { return false; };
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
index db4eb862921..40d80ca409b 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
@@ -227,6 +227,10 @@ void MKLDNNPadNode::createPrimitive() {
     }
 }
 
+bool MKLDNNPadNode::isExecutable() const {
+    return !isOutputTensorAtPortEmpty(0);
+}
+
 void MKLDNNPadNode::prepareParams() {
     execPtr = std::make_shared<PadExecutor>(attrs,
                                             getParentEdgeAt(0)->getMemoryPtr()->GetDescWithType<BlockedMemoryDesc>()->getBlockDims(),
@@ -237,8 +241,15 @@ MKLDNNPadNode::PadExecutor::PadExecutor(const PadAttrs& attrs,
                                         const VectorDims& srcDims,
                                         const VectorDims& dstDims) {
     params.attrs = attrs;
-    params.srcDims = srcDims;
     params.dstDims = dstDims;
+
+    zeroInputDimsCase = std::any_of(srcDims.begin(), srcDims.end(), [](size_t dim) { return dim == 0; } ) &&
+                        std::none_of(dstDims.begin(), dstDims.end(), [](size_t dim) { return dim == 0; } );
+    if (zeroInputDimsCase) {
+        return;
+    }
+
+    params.srcDims = srcDims;
     params.dataSize = attrs.prc.size();
 
     size_t nDims = params.srcDims.size();
@@ -289,19 +300,23 @@ MKLDNNPadNode::PadExecutor::PadExecutor(const PadAttrs& attrs,
 }
 
 void MKLDNNPadNode::PadExecutor::exec(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
-    switch (params.attrs.padMode) {
-        case CONSTANT:
-            padConstant(srcMemPtr, dstMemPtr);
-            break;
-        case EDGE:
-            padEdge(srcMemPtr, dstMemPtr);
-            break;
-        case REFLECT:
-            padReflectOrSymmetric(srcMemPtr, dstMemPtr);
-            break;
-        case SYMMETRIC:
-            padReflectOrSymmetric(srcMemPtr, dstMemPtr, true);
-            break;
+    if (zeroInputDimsCase) {
+        padConstant(srcMemPtr, dstMemPtr);
+    } else {
+        switch (params.attrs.padMode) {
+            case CONSTANT:
+                padConstant(srcMemPtr, dstMemPtr);
+                break;
+            case EDGE:
+                padEdge(srcMemPtr, dstMemPtr);
+                break;
+            case REFLECT:
+                padReflectOrSymmetric(srcMemPtr, dstMemPtr);
+                break;
+            case SYMMETRIC:
+                padReflectOrSymmetric(srcMemPtr, dstMemPtr, true);
+                break;
+        }
     }
 }
 
@@ -335,7 +350,7 @@ static inline void parallel_step(size_t nDims, const VectorDims& dims, VectorDim
 }
 
 void MKLDNNPadNode::PadExecutor::padConstant(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
-    if (params.attrs.padValue == 0) {
+    if (params.attrs.padValue == 0 && !zeroInputDimsCase) {
         padConstantZero(srcMemPtr, dstMemPtr);
         return;
     }
@@ -351,10 +366,16 @@ void MKLDNNPadNode::PadExecutor::padConstant(MKLDNNMemoryPtr& srcMemPtr, MKLDNNM
 
 template<typename T>
 void MKLDNNPadNode::PadExecutor::padConstantCommon(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
-    const T* srcData = reinterpret_cast<const T*>(srcMemPtr->GetPtr());
     T* dstData = reinterpret_cast<T*>(dstMemPtr->GetPtr());
     const T value = static_cast<T>(params.attrs.padValue);
+    if (zeroInputDimsCase) {
+        const auto workAmount = dstMemPtr->GetDescWithType<BlockedMemoryDesc>()->getPaddedElementsCount();
+        parallel_for(workAmount, [&](size_t i) {
+            dstData[i] = value;
+        });
+    }
 
+    const T* srcData = reinterpret_cast<const T*>(srcMemPtr->GetPtr());
     const size_t beginShift = params.attrs.padsBegin[params.nDimsForWork] * params.shift;
     const size_t copySize = params.srcDims[params.nDimsForWork] * params.shift;
     const size_t endShift = params.attrs.padsEnd[params.nDimsForWork] * params.shift;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
index 74189292fd8..d8beaac96cd 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
@@ -23,6 +23,8 @@ public:
 
     void prepareParams() override;
 
+    bool isExecutable() const override;
+
 protected:
     void executeDynamicImpl(mkldnn::stream strm) override;
 
@@ -71,6 +73,8 @@ private:
             }
         };
 
+        bool zeroInputDimsCase = false;
+
         struct {
             PadAttrs attrs;
             VectorDims srcDims;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp
index 0a95c61d86e..2de486d6f8d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp
@@ -273,12 +273,8 @@ void MKLDNNPoolingNode::prepareParams() {
     primArgs = {{DNNL_ARG_SRC, src}, {DNNL_ARG_DST, dst}};
 }
 
-void MKLDNNPoolingNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
+void MKLDNNPoolingNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 bool MKLDNNPoolingNode::created() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
index 75f726d567b..a60423cd036 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
@@ -22,14 +22,13 @@ public:
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
     void initDescriptor(const NodeConfig& config) override;
-    void createPrimitive() override;
     bool created() const override;
     bool canBeInPlace() const override {
         return false;
     }
 
     void prepareParams() override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp
index 1d3f2ff60d1..7309d578c33 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp
@@ -155,6 +155,10 @@ void MKLDNNProposalNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+void MKLDNNProposalNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNProposalNode::execute(mkldnn::stream strm) {
     try {
         const float* probabilitiesData = reinterpret_cast<const float *>(getParentEdgeAt(PROBABILITIES_IN_IDX)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h
index 0c0c6d443a8..ac6272f8a51 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h
@@ -18,12 +18,11 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     bool needPrepareParams() const override { return false; };
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.cpp
index bfc0d63f446..10e9d14c6ba 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.cpp
@@ -83,6 +83,10 @@ void MKLDNNRangeNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+void MKLDNNRangeNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNRangeNode::execute(mkldnn::stream strm) {
     StatusCode retcode = OK;
     switch (getParentEdgeAt(0)->getMemory().getDesc().getPrecision()) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.h
index d7b600e3f27..720c3111729 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.h
@@ -15,15 +15,11 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {
-        if (inputShapesDefined())
-            updateLastInputDims();
-    };
     void execute(mkldnn::stream strm) override;
     bool created() const override;
     bool needPrepareParams() const override {return false;};
     bool needShapeInfer() const override {return false;};
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
index 4ccaf709471..aa78c2e9304 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
@@ -1827,6 +1827,10 @@ void MKLDNNReduceNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+bool MKLDNNReduceNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(REDUCE_DATA);
+}
+
 void MKLDNNReduceNode::prepareParams() {
     src_dims = getParentEdgesAtPort(REDUCE_DATA)[0]->getMemory().getDesc().getShape().getDims();
     std::vector<int> reduce_axes;
@@ -1864,6 +1868,9 @@ void MKLDNNReduceNode::prepareParams() {
 }
 
 void MKLDNNReduceNode::createPrimitive() {
+    if (!isExecutable()) {
+        return;
+    }
     auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto &srcMemPtr = getParentEdgeAt(REDUCE_DATA)->getMemoryPtr();
     if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
@@ -1919,6 +1926,10 @@ void MKLDNNReduceNode::createPrimitive() {
     jit_mode = jit_mode && reduce_kernel;
 }
 
+void MKLDNNReduceNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNReduceNode::execute(mkldnn::stream strm) {
     auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto &srcMemPtr = getParentEdgeAt(REDUCE_DATA)->getMemoryPtr();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h
index 07effa0891f..9e3541a5a1d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h
@@ -90,12 +90,13 @@ public:
     void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     bool canFuse(const MKLDNNNodePtr& node) const override;
     bool canBeInPlace() const override {
         return false;
     }
 
+    bool isExecutable() const override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.cpp
index 02ccf1b546b..4ebeba5c587 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.cpp
@@ -19,6 +19,10 @@ using namespace mkldnn;
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
+bool MKLDNNReorderNode::isExecutable() const {
+    return MKLDNNNode::isExecutable() && !isOptimized;
+}
+
 MKLDNNReorderNode::MKLDNNReorderNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &w_cache) :
         MKLDNNNode(op, eng, w_cache) {
     IE_THROW() << "Can't create reorder node from ngraph node";
@@ -100,6 +104,10 @@ void MKLDNNReorderNode::createPrimitive() {
     }
 }
 
+void MKLDNNReorderNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNReorderNode::prepareParams() {
     if (!isOptimized) {
         auto &srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
@@ -348,6 +356,10 @@ void MKLDNNReorderNode::reorderData(const MKLDNNMemory &input, const MKLDNNMemor
     if (!input.getDesc().isDefined() || !output.getDesc().isDefined())
         IE_THROW() << "Can't reorder data with dynamic shapes";
 
+    if (input.GetShape().hasZeroDims() || output.GetShape().hasZeroDims()) {
+        return;
+    }
+
     if (size != 0)
         IE_ASSERT(size <= output.GetSize());
     if (input.getDesc().isCompatible(output.getDesc())) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.h
index 1ccb23768d3..cad52fe3998 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.h
@@ -24,9 +24,7 @@ public:
     bool created() const override;
     const std::vector<impl_desc_type>& getPrimitivesPriority() override;
 
-    bool isExecutable() const override {
-        return !isOptimized;
-    }
+    bool isExecutable() const override;
 
     void createPrimitive() override;
 
@@ -34,7 +32,7 @@ public:
 
     void prepareParams() override;
 
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     void setDescs(const MemoryDesc& input, const MemoryDesc& output) {
         this->input = input.clone();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp
index 3ebc977bc41..65ce1beae8c 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp
@@ -51,6 +51,10 @@ void MKLDNNReorgYoloNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
+void MKLDNNReorgYoloNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNReorgYoloNode::execute(mkldnn::stream strm) {
     const auto *src_data = reinterpret_cast<const float *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
     auto *dst_data = reinterpret_cast<float *>(getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h
index c3be7362484..e83dbde7288 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h
@@ -15,13 +15,10 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
     bool needPrepareParams() const override { return false; }
-    void executeDynamicImpl(mkldnn::stream strm) override {
-        execute(strm);
-    }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.cpp
index 10a9a07b480..35dd1e74946 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.cpp
@@ -135,21 +135,13 @@ void MKLDNNReshapeNode::initSupportedPrimitiveDescriptors() {
     supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
 }
 
-void MKLDNNReshapeNode::createPrimitive() {
-    auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
-    auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
-    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
-        IE_THROW() << "Destination memory didn't allocate.";
-    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
-        IE_THROW() << "Input memory didn't allocate.";
-    if (getSelectedPrimitiveDescriptor() == nullptr)
-        IE_THROW() << "Preferable primitive descriptor is not set.";
-}
-
 void MKLDNNReshapeNode::executeDynamicImpl(mkldnn::stream strm) {
     auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
     auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     const auto count = srcMemPtr->GetShape().getElementsCount();
+    if (count == 0) {
+        return;
+    }
     if (count != dstMemPtr->GetShape().getElementsCount())
         IE_THROW() << errorPrefix << " has different elements number in input and output buffers";
     cpu_memcpy(dstMemPtr->GetPtr(), srcMemPtr->GetPtr(), count * MKLDNNExtensionUtils::sizeOfDataType(srcMemPtr->GetDataType()));
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.h
index 86770c13cea..aed9c18cc6b 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.h
@@ -19,7 +19,6 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     bool created() const override;
     bool isExecutable() const override {
         return isDynamicNode();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
index 91ee9cf58fd..836c2f7656e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
@@ -372,13 +372,7 @@ bool MKLDNNROIAlignNode::needPrepareParams() const {
 }
 
 void MKLDNNROIAlignNode::executeDynamicImpl(mkldnn::stream strm) {
-    return execute(strm);
-}
-
-void MKLDNNROIAlignNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
+    execute(strm);
 }
 
 REG_MKLDNN_PRIM_FOR(MKLDNNROIAlignNode, ROIAlign)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h
index 31d470d4bca..e8bd963bc80 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h
@@ -19,7 +19,6 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.cpp
index 6f314411c9c..ee126838ca6 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.cpp
@@ -419,7 +419,7 @@ void MKLDNNROIPoolingNode::createPrimitive() {
     refParams.dst_data_size = refParams.dst_prc.size();
 
     if (inputShapesDefined()) {
-        if (needPrepareParams())
+        if (needPrepareParams() && isExecutable())
             prepareParams();
         updateLastInputDims();
     }
@@ -437,7 +437,7 @@ void MKLDNNROIPoolingNode::execute(mkldnn::stream strm) {
 }
 
 void MKLDNNROIPoolingNode::executeDynamicImpl(mkldnn::stream strm) {
-    return execute(strm);
+    execute(strm);
 }
 
 void MKLDNNROIPoolingNode::prepareParams() {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp
index 412d3853f1a..6ceed6869ca 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp
@@ -35,6 +35,10 @@ bool MKLDNNScatterUpdateNode::isSupportedOperation(const std::shared_ptr<const n
     return true;
 }
 
+bool MKLDNNScatterUpdateNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(DATA_ID);
+}
+
 MKLDNNScatterUpdateNode::MKLDNNScatterUpdateNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
         : MKLDNNNode(op, eng, cache), dataSize(0lu), indicesSize(0lu), axisSize(0lu), dataPrec(Precision::UNSPECIFIED), indicesPrec(Precision::UNSPECIFIED),
           axisPrec(Precision::UNSPECIFIED) {
@@ -211,34 +215,12 @@ void MKLDNNScatterUpdateNode::initSupportedPrimitiveDescriptors() {
                           impl_desc_type::unknown);
 }
 
-void MKLDNNScatterUpdateNode::createPrimitive() {
-    auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
-    auto &srcMemPtr = getParentEdgeAt(DATA_ID)->getMemoryPtr();
-    auto &indicesMemPtr = getParentEdgeAt(INDICES_ID)->getMemoryPtr();
-    auto &updateMemPtr = getParentEdgeAt(UPDATE_ID)->getMemoryPtr();
-
-    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " did not allocate destination memory";
-    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " did not allocate input memory";
-    if (!indicesMemPtr || !indicesMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " did not allocate indices memory";
-    if (!updateMemPtr || !updateMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " did not allocate update memory";
-    if (getSelectedPrimitiveDescriptor() == nullptr)
-        IE_THROW() << errorPrefix << " did not set preferable primitive descriptor";
-
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
-}
-
 bool MKLDNNScatterUpdateNode::needPrepareParams() const {
     return false;
 }
 
 void MKLDNNScatterUpdateNode::executeDynamicImpl(mkldnn::stream strm) {
-    return execute(strm);
+    execute(strm);
 }
 
 int64_t MKLDNNScatterUpdateNode::getIndicesValue(uint8_t *indices, size_t offset) {
@@ -268,8 +250,8 @@ static std::vector<size_t> getBlockND(const VectorDims& shape) {
 }
 
 void MKLDNNScatterUpdateNode::execute(mkldnn::stream strm) {
-    auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto &srcMemPtr = getParentEdgeAt(DATA_ID)->getMemoryPtr();
+    auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto &indicesMemPtr = getParentEdgeAt(INDICES_ID)->getMemoryPtr();
     auto &updateMemPtr = getParentEdgeAt(UPDATE_ID)->getMemoryPtr();
 
@@ -354,6 +336,10 @@ void MKLDNNScatterUpdateNode::execute(mkldnn::stream strm) {
         });
     }
 
+    if (isInputTensorAtPortEmpty(INDICES_ID)) {
+        return;
+    }
+
     switch (scatterUpdateMode) {
         case ScatterUpdateMode::ScatterUpdate: {
             scatterUpdate(indicesPtr, updatePtr, axis, dstPtr);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h
index 30b0ca5735d..59ba5054b74 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h
@@ -24,7 +24,6 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
     bool canBeInPlace() const override {
@@ -34,6 +33,7 @@ public:
     bool needPrepareParams() const override;
     void executeDynamicImpl(mkldnn::stream strm) override;
 
+    bool isExecutable() const override;
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.cpp
index d907d76b369..3752dae38ca 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.cpp
@@ -152,13 +152,6 @@ void MKLDNNSelectNode::prepareParams() {
     }
 }
 
-void MKLDNNSelectNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        prepareParams();
-        updateLastInputDims();
-    }
-}
-
 void MKLDNNSelectNode::calcOutOffset(VectorDims& offset, const VectorDims& dims) {
     int k = 1;
     for (int i = dims.size() - 1; i >= 0; i--) {
@@ -200,6 +193,10 @@ void MKLDNNSelectNode::execute_impl() {
     }
 }
 
+void MKLDNNSelectNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNSelectNode::execute(mkldnn::stream strm) {
     const size_t condPrecSize = getParentEdgeAt(CONDITION)->getMemory().getDesc().getPrecision().size();
     const size_t inputsPrecSize = getParentEdgeAt(THEN)->getMemory().getDesc().getPrecision().size();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.h
index 6602195f122..4312a95bf04 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.h
@@ -18,11 +18,10 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     void prepareParams() override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.cpp
index 28097fcaf58..20df75a421d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.cpp
@@ -57,6 +57,10 @@ void MKLDNNShapeOfNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+bool MKLDNNShapeOfNode::isExecutable() const {
+    return true;
+}
+
 void MKLDNNShapeOfNode::execute(mkldnn::stream strm) {
     auto inPtr = getParentEdgeAt(0)->getMemoryPtr();
     auto outPtr = getChildEdgeAt(0)->getMemoryPtr();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.h
index 2a7eb9560e6..0152e3f1bb9 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.h
@@ -18,10 +18,6 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {
-        if (inputShapesDefined())
-            updateLastInputDims();
-    };
     void execute(mkldnn::stream strm) override;
     bool created() const override;
     bool needPrepareParams() const override {return false;};
@@ -30,6 +26,8 @@ public:
         return {VectorDims{getParentEdgesAtPort(0)[0]->getMemory().getStaticDims().size()}};
     }
 
+    bool isExecutable() const override;
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.cpp
index b6b68d59221..4909eb8ccc6 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.cpp
@@ -114,7 +114,7 @@ void MKLDNNShuffleChannelsNode::createPrimitive() {
                        memoryDesc.hasLayoutType(LayoutType::nCsp8c) ? LayoutType::nCsp8c :
                        memoryDesc.hasLayoutType(LayoutType::nspc) ? LayoutType::nspc : LayoutType::ncsp;
 
-    if (inputShapesDefined()) {
+    if (inputShapesDefined() && isExecutable()) {
         if (needPrepareParams())
             prepareParams();
         updateLastInputDims();
@@ -245,6 +245,10 @@ void MKLDNNShuffleChannelsNode::ShuffleChannelsExecutor::exec(const uint8_t* src
         permuteKernel->execute(srcData, dstData);
 }
 
+void MKLDNNShuffleChannelsNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNShuffleChannelsNode::execute(mkldnn::stream strm) {
     if (!execPtr)
         THROW_SHCH_ERROR << "doesn't have a compiled executor.";
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.h
index dbac77aaa05..91362b9a941 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.h
@@ -28,7 +28,7 @@ public:
     void prepareParams() override;
 
 protected:
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
 private:
     struct ShuffleChannelsAttributes {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.cpp
index 8463344a52d..3732640e09e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.cpp
@@ -66,14 +66,6 @@ void MKLDNNSoftMaxNode::getSupportedDescriptors() {
     }
 }
 
-void MKLDNNSoftMaxNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 bool MKLDNNSoftMaxNode::created() const {
     return getType() == Softmax;
 }
@@ -148,8 +140,8 @@ void MKLDNNSoftMaxNode::prepareParams() {
     primArgs = {{DNNL_ARG_SRC, src}, {DNNL_ARG_DST, dst}};
 }
 
-void MKLDNNSoftMaxNode::executeDynamicImpl(dnnl::stream strm) {
-    MKLDNNNode::execute(strm);
+void MKLDNNSoftMaxNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 std::vector<VectorDims> MKLDNNSoftMaxNode::shapeInfer() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.h
index 0b95aab8123..72372ca3f5e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.h
@@ -20,7 +20,6 @@ public:
     void createDescriptor(const std::vector<MemoryDescPtr>& inputDesc,
                           const std::vector<MemoryDescPtr>& outputDesc) override;
     void getSupportedDescriptors() override;
-    void createPrimitive() override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.cpp
index 85fc3e114d7..83ab528e1b2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.cpp
@@ -229,6 +229,10 @@ void MKLDNNSpaceToBatchNode::SpaceToBatchKernel() {
     });
 }
 
+void MKLDNNSpaceToBatchNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNSpaceToBatchNode::execute(mkldnn::stream strm) {
     switch (getParentEdgeAt(0)->getMemory().getDesc().getPrecision().size()) {
         case 1: SpaceToBatchKernel<PrecisionTrait<Precision::U8>::value_type>();  break;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.h
index 8bbf4f1fb73..33c893b0c94 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.h
@@ -18,12 +18,11 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     bool needPrepareParams() const override { return false; };
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.cpp
index c0ac5db9468..0d64170c406 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.cpp
@@ -154,7 +154,7 @@ void MKLDNNSpaceToDepthNode::createPrimitive() {
                                  ? LayoutType::nCsp8c
                                  : memoryDesc.hasLayoutType(LayoutType::nspc) ? LayoutType::nspc : LayoutType::ncsp;
 
-    if (inputShapesDefined()) {
+    if (inputShapesDefined() && isExecutable()) {
         if (needPrepareParams())
             prepareParams();
         updateLastInputDims();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp
index 1412d0d5afe..348c1238fe9 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp
@@ -194,10 +194,12 @@ void MKLDNNSplitNode::initSupportedPrimitiveDescriptors() {
             for (size_t i = 0; i < outputShapes.size(); i++) {
                 auto outBlockingDesc = refConfig.outConfs[i].desc->as<CpuBlockedMemoryDesc>();
                 const auto& outBlkDims = outBlockingDesc->getBlockDims();
-                const auto& dims = outBlockingDesc->getShape().getStaticDims();
+                const auto& shape = outBlockingDesc->getShape();
+                const auto& dims = shape.getStaticDims();
 
                 config.outConfs[i].inPlace = 0;
-                config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(outPrecision, Shape(dims), outBlkDims, order, offset, offsets, strides);
+                config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(outPrecision, Shape(dims), outBlkDims, order, offset, offsets,
+                                                                                 shape.hasZeroDims() ? SizeVector(numOfDim, 0) : strides);
             }
             supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
         }
@@ -245,16 +247,20 @@ void MKLDNNSplitNode::prepareParams() {
 
     dstMemPtrs.clear();
     std::vector<BlockedMemoryDescCPtr> outDescs;
-    for (size_t i = 0; i < outputShapes.size(); ++i) {
-        const auto &outMemPtr = this->getChildEdgesAtPort(i)[0]->getMemoryPtr();
+    for (size_t port = 0; port < outputShapes.size(); ++port) {
+        const auto &outMemPtr = this->getChildEdgesAtPort(port)[0]->getMemoryPtr();
         if (!outMemPtr || !outMemPtr->GetPrimitivePtr()) {
             THROW_ERROR << "has not allocated destination memory";
         }
 
+        if (outMemPtr->GetShape().hasZeroDims()) {
+            continue;
+        }
+
         if (uint8_t* dstData = reinterpret_cast<uint8_t*>(outMemPtr->GetPtr())) {
-            dstMemPtrs.push_back(dstData);
+            dstMemPtrs.emplace_back(port, dstData);
         } else {
-            THROW_ERROR << "can't get child edge indx " << i << "data.";
+            THROW_ERROR << "can't get child edge indx " << port << "data.";
         }
 
         if (!canUseOptimizedNspc2Ncsp) {
@@ -268,15 +274,8 @@ void MKLDNNSplitNode::prepareParams() {
     }
 }
 
-void MKLDNNSplitNode::createPrimitive() {
-    if (getSelectedPrimitiveDescriptor() == nullptr)
-        THROW_ERROR << "Preferable primitive descriptor is not set.";
-
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
+bool MKLDNNSplitNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0) && !isOptimized();
 }
 
 void MKLDNNSplitNode::execute(mkldnn::stream strm) {
@@ -346,13 +345,16 @@ void MKLDNNSplitNode::initOptimalPrimitiveDescriptor() {
         for (size_t i = 0; i < outputShapes.size(); i++) {
             auto oldDesc = config.outConfs[i].desc;
             auto outBlockingDesc = oldDesc->as<BlockedMemoryDesc>();
+            const auto& shape = outBlockingDesc->getShape();
+            const auto& blkDims = outBlockingDesc->getBlockDims();
             config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(outBlockingDesc->getPrecision(),
-                                                                             outBlockingDesc->getShape(),
-                                                                             outBlockingDesc->getBlockDims(),
+                                                                             shape,
+                                                                             blkDims,
                                                                              outBlockingDesc->getOrder(),
                                                                              firstInBlockingDesc->getOffsetPadding() + offset,
                                                                              firstInBlockingDesc->getOffsetPaddingToData(),
-                                                                             firstInBlockingDesc->getStrides());
+                                                                             (shape.hasZeroDims() ? VectorDims(blkDims.size(), 0) :
+                                                                              firstInBlockingDesc->getStrides()));
 
             size_t axisSize = 1;
             for (size_t j = axis; j < outBlockingDesc->getBlockDims().size(); j++) {
@@ -497,11 +499,11 @@ void MKLDNNSplitNode::optimizedNspc2Ncsp(size_t MB) {
     const size_t strideIW = IC*dataSize;
     const size_t strideOC = DHW * dataSize;
 
-    for (size_t i = 0, sIdx = 0; i < outputShapes.size(); i++) {
-        auto dstData = dstMemPtrs[i];
+    for (size_t i = 0, sIdx = 0; i < dstMemPtrs.size(); i++) {
+        auto dstData = dstMemPtrs[i].second;
 
         size_t innerSize = 1;
-        auto dims = getChildEdgesAtPort(i)[0]->getMemory().getStaticDims();
+        auto dims = getChildEdgesAtPort(dstMemPtrs[i].first)[0]->getMemory().getStaticDims();
 
         for (size_t j = axis; j < dims.size(); j++) {
             innerSize *= dims[j];
@@ -568,14 +570,14 @@ MKLDNNSplitNode::SplitOptimizedExecutor::SplitOptimizedExecutor(BlockedMemoryDes
     }
 }
 
-void MKLDNNSplitNode::SplitOptimizedExecutor::exec(const uint8_t* srcData, const std::vector<uint8_t*> &dstMemPtrs,
+void MKLDNNSplitNode::SplitOptimizedExecutor::exec(const uint8_t* srcData, const std::vector<std::pair<size_t, uint8_t*>> &dstMemPtrs,
                                                    const Dim origBatch, const Dim perInferBatch) {
     size_t execCountStrides = countStrides;
     if (origBatch != perInferBatch)
         execCountStrides = execCountStrides / origBatch * perInferBatch;
 
     parallel_for2d(dstMemPtrs.size(), execCountStrides, [&](size_t i, size_t j) {
-        uint8_t* dstData = dstMemPtrs[i];
+        uint8_t* dstData = dstMemPtrs[i].second;
 
         cpu_memcpy(&dstData[j * dataSize[i]],
                    &srcData[srcDataOffsets[i] + j * srcDataStride],
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h
index aef06929cd2..00c0f719bf2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h
@@ -18,7 +18,6 @@ public:
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
     void selectOptimalPrimitiveDescriptor() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
@@ -26,9 +25,7 @@ public:
     void initOptimalPrimitiveDescriptor() override;
 
     void setDynamicBatchLim(int lim) override;
-    bool isExecutable() const override {
-        return !isOptimized();
-    }
+    bool isExecutable() const override;
 
     bool needPrepareParams() const override;
     void prepareParams() override;
@@ -36,7 +33,7 @@ public:
 
 private:
     struct SplitExecutor {
-        virtual void exec(const uint8_t* srcData, const std::vector<uint8_t*> &dstMemPtrs,
+        virtual void exec(const uint8_t* srcData, const std::vector<std::pair<size_t, uint8_t*>> &dstMemPtrs,
                           const Dim origBatch, const Dim perInferBatch) = 0;
         virtual ~SplitExecutor() = default;
     };
@@ -45,7 +42,7 @@ private:
     struct SplitOptimizedExecutor : public SplitExecutor {
         public:
             SplitOptimizedExecutor(BlockedMemoryDescCPtr inDesc, const std::vector<BlockedMemoryDescCPtr> &outDescs, const size_t axis);
-            void exec(const uint8_t* srcData, const std::vector<uint8_t*> &dstMemPtrs,
+            void exec(const uint8_t* srcData, const std::vector<std::pair<size_t, uint8_t*>> &dstMemPtrs,
                       const Dim origBatch, const Dim perInferBatch) override;
 
         private:
@@ -60,7 +57,7 @@ private:
     bool canUseOptimizedNspc2Ncsp = false;
 
     size_t axis = 1;
-    std::vector<uint8_t*> dstMemPtrs;
+    std::vector<std::pair<size_t, uint8_t*>> dstMemPtrs;
 
     size_t INPUTS_NUM = 2;
 };
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.cpp
index ebfbab56329..2fd04d85146 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.cpp
@@ -300,7 +300,14 @@ void MKLDNNStridedSliceNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+bool MKLDNNStridedSliceNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
+}
+
 void MKLDNNStridedSliceNode::createPrimitive() {
+    if (!isExecutable()) {
+        return;
+    }
     auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto& srcMemPtr = getParentEdgeAt(DATA_ID)->getMemoryPtr();
     if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
@@ -670,9 +677,8 @@ void MKLDNNStridedSliceNode::StridedSliceExecutor::exec(const uint8_t* srcData,
 void MKLDNNStridedSliceNode::execute(mkldnn::stream strm) {
     if (!execPtr)
         THROW_ERROR << "doesn't have compiled executor!";
-
-    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    uint8_t* dstData = reinterpret_cast<uint8_t*>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
+    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(getParentEdgeAt(0)->getMemory().GetPtr());
+    uint8_t* dstData = reinterpret_cast<uint8_t*>(getChildEdgeAt(0)->getMemory().GetPtr());
     execPtr->exec(srcData, dstData);
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.h
index 9feadce871d..18e9c757431 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.h
@@ -24,6 +24,8 @@ public:
         return false;
     }
 
+    bool isExecutable() const override;
+
 protected:
     void prepareParams() override;
     void executeDynamicImpl(mkldnn::stream strm) override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp
index 74a1d2911c2..4a035681c25 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp
@@ -80,14 +80,6 @@ void MKLDNNTileNode::initSupportedPrimitiveDescriptors() {
     supportedPrimitiveDescriptors = getSupportedConfigs(this);
 }
 
-void MKLDNNTileNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        if (needPrepareParams())
-            prepareParams();
-        updateLastInputDims();
-    }
-}
-
 bool MKLDNNTileNode::needPrepareParams() const {
     return needPrepareParamsVar;
 }
@@ -150,6 +142,10 @@ std::vector<VectorDims> MKLDNNTileNode::shapeInfer() const {
     return newOutputShapes;
 }
 
+void MKLDNNTileNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNTileNode::execute(mkldnn::stream strm) {
     if (optimizedCase) {
         optimizedExecute(getParentEdgeAt(TILE_INPUT)->getMemoryPtr(), getChildEdgeAt(0)->getMemoryPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h
index b973a3a27c9..3af3a0b9c0f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h
@@ -16,11 +16,8 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
-    void executeDynamicImpl(mkldnn::stream strm) override {
-        execute(strm);
-    }
+    void executeDynamicImpl(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp
index c1d200f79c3..14746d819a4 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp
@@ -158,13 +158,7 @@ bool MKLDNNTopKNode::needPrepareParams() const {
 }
 
 void MKLDNNTopKNode::executeDynamicImpl(mkldnn::stream strm) {
-    return execute(strm);
-}
-
-void MKLDNNTopKNode::createPrimitive() {
-    if (inputShapesDefined()) {
-        updateLastInputDims();
-    }
+    execute(strm);
 }
 
 bool MKLDNNTopKNode::needShapeInfer() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h
index bd2a72824cc..7d288c4fb8b 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h
@@ -17,7 +17,6 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.cpp
index 93a2ab8f299..d11261fd55d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.cpp
@@ -106,10 +106,14 @@ void MKLDNNTransposeNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+bool MKLDNNTransposeNode::isExecutable() const {
+    return !isInputTensorAtPortEmpty(0);
+}
+
 bool MKLDNNTransposeNode::needPrepareParams() const {
     if (isOptimized)
         return false;
-    return MKLDNNNode::needPrepareParams();
+    return inputShapesModified();
 }
 
 void MKLDNNTransposeNode::prepareParams() {
@@ -151,7 +155,7 @@ void MKLDNNTransposeNode::createPrimitive() {
     auto dstDesc = getChildEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>();
     params.dst_block_order = dstDesc->getOrder();
 
-    if (inputShapesDefined()) {
+    if (inputShapesDefined() && isExecutable()) {
         prepareParams();
         updateLastInputDims();
     }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.h
index 0b45d826159..a5af65e8131 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.h
@@ -31,6 +31,7 @@ public:
         return order;
     }
 
+    bool isExecutable() const override;
     bool needPrepareParams() const override;
     void prepareParams() override;
 
diff --git a/src/bindings/python/tests/__init__.py b/src/bindings/python/tests/__init__.py
index f8522ff5f7d..08eb6fdd7b5 100644
--- a/src/bindings/python/tests/__init__.py
+++ b/src/bindings/python/tests/__init__.py
@@ -51,12 +51,6 @@ xfail_issue_35923 = xfail_test(reason="RuntimeError: PReLU without weights is no
 xfail_issue_35927 = xfail_test(reason="RuntimeError: B has zero dimension that is not allowable")
 xfail_issue_36486 = xfail_test(reason="RuntimeError: HardSigmoid operation should be converted "
                                       "to HardSigmoid_IE")
-xfail_issue_38084 = xfail_test(reason="RuntimeError: AssertionFailed: layer->get_output_partial_shape(i)."
-                                      "is_static() nGraph <value> operation with name: <value> cannot be "
-                                      "converted to <value> layer with name: <value> because output "
-                                      "with index 0 contains dynamic shapes: {<value>}. Try to use "
-                                      "CNNNetwork::reshape() method in order to specialize shapes "
-                                      "before the conversion.")
 xfail_issue_38091 = xfail_test(reason="AssertionError: Mismatched elements")
 xfail_issue_38699 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations: "
                                       "ai.onnx.preview.training.Gradient")
diff --git a/src/bindings/python/tests/test_onnx/test_backend.py b/src/bindings/python/tests/test_onnx/test_backend.py
index 7385c5057ef..1f7ab1d76c4 100644
--- a/src/bindings/python/tests/test_onnx/test_backend.py
+++ b/src/bindings/python/tests/test_onnx/test_backend.py
@@ -202,7 +202,6 @@ tests_expected_to_fail = [
     (
         xfail_issue_38706,
         "OnnxBackendNodeModelTest.test_split_zero_size_splits_cpu",
-        "OnnxBackendNodeModelTest.test_slice_start_out_of_bounds_cpu",
     ),
     (
         xfail_issue_33538,
diff --git a/src/bindings/python/tests/test_onnx/test_zoo_models.py b/src/bindings/python/tests/test_onnx/test_zoo_models.py
index ac455d76168..9530e73be2e 100644
--- a/src/bindings/python/tests/test_onnx/test_zoo_models.py
+++ b/src/bindings/python/tests/test_onnx/test_zoo_models.py
@@ -15,7 +15,6 @@ from tests import (
     xfail_issue_38701,
     xfail_issue_45457,
     xfail_issue_37957,
-    xfail_issue_38084,
     xfail_issue_39669,
     xfail_issue_37973,
     xfail_issue_47430,
@@ -174,8 +173,6 @@ if len(zoo_models) > 0:
             # ONNX Model Zoo
             (xfail_issue_39669, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_encoder_12_t5_encoder_cpu"),
             (xfail_issue_39669, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_decoder_with_lm_head_12_t5_decoder_with_lm_head_cpu"),
-            (xfail_issue_38084, "test_onnx_model_zoo_vision_object_detection_segmentation_mask_rcnn_model_MaskRCNN_10_mask_rcnn_R_50_FPN_1x_cpu"),
-            (xfail_issue_38084, "test_onnx_model_zoo_vision_object_detection_segmentation_faster_rcnn_model_FasterRCNN_10_faster_rcnn_R_50_FPN_1x_cpu"),
             (xfail_issue_47430, "test_onnx_model_zoo_vision_object_detection_segmentation_fcn_model_fcn_resnet50_11_fcn_resnet50_11_model_cpu"),
             (xfail_issue_47430, "test_onnx_model_zoo_vision_object_detection_segmentation_fcn_model_fcn_resnet101_11_fcn_resnet101_11_model_cpu"),
             (xfail_issue_48145, "test_onnx_model_zoo_text_machine_comprehension_bert_squad_model_bertsquad_8_download_sample_8_bertsquad8_cpu"),
@@ -193,9 +190,6 @@ if len(zoo_models) > 0:
             (xfail_issue_58676, "test_MSFT_opset7_fp16_tiny_yolov2_onnxzoo_winmlperf_tiny_yolov2_cpu"),
             (xfail_issue_58676, "test_MSFT_opset8_fp16_tiny_yolov2_onnxzoo_winmlperf_tiny_yolov2_cpu"),
 
-            (xfail_issue_38084, "test_MSFT_opset10_mask_rcnn_mask_rcnn_R_50_FPN_1x_cpu"),
-            (xfail_issue_38084, "test_MSFT_opset10_faster_rcnn_faster_rcnn_R_50_FPN_1x_cpu"),
-
             (xfail_issue_39669, "test_MSFT_opset9_cgan_cgan_cpu"),
             (xfail_issue_47495, "test_MSFT_opset10_BERT_Squad_bertsquad10_cpu"),
             (xfail_issue_45457, "test_MSFT_opset10_mlperf_ssd_resnet34_1200_ssd_resnet34_mAP_20.2_cpu"),
diff --git a/src/bindings/python/tests_compatibility/test_onnx/test_backend.py b/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
index 3da0078c75b..58d36643606 100644
--- a/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
+++ b/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
@@ -194,7 +194,6 @@ tests_expected_to_fail = [
     (
         xfail_issue_38706,
         "OnnxBackendNodeModelTest.test_split_zero_size_splits_cpu",
-        "OnnxBackendNodeModelTest.test_slice_start_out_of_bounds_cpu",
     ),
     (
         xfail_issue_33538,
diff --git a/src/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py b/src/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py
index 6f7f90b2970..f24c16e5a28 100644
--- a/src/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py
+++ b/src/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py
@@ -162,7 +162,6 @@ if len(zoo_models) > 0:
             # ONNX Model Zoo
             (xfail_issue_39669, "test_onnx_model_zoo_text_machine_comprehension_t5_model_t5_encoder_12_t5_encoder_cpu"),
             (xfail_issue_38084, "test_onnx_model_zoo_vision_object_detection_segmentation_mask_rcnn_model_MaskRCNN_10_mask_rcnn_R_50_FPN_1x_cpu"),
-            (xfail_issue_38084, "test_onnx_model_zoo_vision_object_detection_segmentation_faster_rcnn_model_FasterRCNN_10_faster_rcnn_R_50_FPN_1x_cpu"),
             (xfail_issue_47430, "test_onnx_model_zoo_vision_object_detection_segmentation_fcn_model_fcn_resnet50_11_fcn_resnet50_11_model_cpu"),
             (xfail_issue_47430, "test_onnx_model_zoo_vision_object_detection_segmentation_fcn_model_fcn_resnet101_11_fcn_resnet101_11_model_cpu"),
             (xfail_issue_48145, "test_onnx_model_zoo_text_machine_comprehension_bert_squad_model_bertsquad_8_download_sample_8_bertsquad8_cpu"),
@@ -181,7 +180,6 @@ if len(zoo_models) > 0:
             (xfail_issue_58676, "test_MSFT_opset8_fp16_tiny_yolov2_onnxzoo_winmlperf_tiny_yolov2_cpu"),
 
             (xfail_issue_38084, "test_MSFT_opset10_mask_rcnn_mask_rcnn_R_50_FPN_1x_cpu"),
-            (xfail_issue_38084, "test_MSFT_opset10_faster_rcnn_faster_rcnn_R_50_FPN_1x_cpu"),
 
             (xfail_issue_39669, "test_MSFT_opset9_cgan_cgan_cpu"),
             (xfail_issue_47495, "test_MSFT_opset10_BERT_Squad_bertsquad10_cpu"),
diff --git a/src/core/src/op/topk.cpp b/src/core/src/op/topk.cpp
index ef7b46f7290..ad95d9703df 100644
--- a/src/core/src/op/topk.cpp
+++ b/src/core/src/op/topk.cpp
@@ -319,8 +319,8 @@ size_t op::v1::TopK::validate_and_get_k(const shared_ptr<op::v0::Constant>& k_co
                           " elements).");
 
     NODE_VALIDATION_CHECK(this,
-                          k_const_contents[0] > 0,
-                          "The value of 'K' must be a positive number.",
+                          k_const_contents[0] >= 0,
+                          "The value of 'K' must be more or equal zero.",
                           " (got ",
                           k_const_contents[0],
                           ").");
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index b6857ff3671..d661cd45182 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -169,6 +169,8 @@ std::vector<std::string> disabledTestPatterns() {
         R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*BIN.*)",
         R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*CUSTOM.*)",
         R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*UNSPECIFIED.*)",
+        // Issue:
+        R"(.*smoke_VariadicSplit4D_CPU_zero_dims.*)",
     };
 
 #define FIX_62820 0
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/concat.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/concat.cpp
index d7281975f06..81094e09f1b 100644
--- a/src/tests/functional/plugin/cpu/single_layer_tests/concat.cpp
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/concat.cpp
@@ -49,7 +49,21 @@ public:
         return result.str();
     }
 
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override {
+        if (actual.front().get_size() == 0) {
+            ASSERT_EQ(0, expected.front().get_size());
+            for (const auto& shape : targetStaticShapes[inferNum]) {
+                ASSERT_EQ(shape_size(shape), 0);
+            }
+        } else {
+            SubgraphBaseTest::compare(expected, actual);
+        }
+        inferNum++;
+    }
+
 protected:
+    size_t inferNum = 0;
+
     void SetUp() override {
         targetDevice = CommonTestUtils::DEVICE_CPU;
 
@@ -77,7 +91,7 @@ TEST_P(ConcatLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
     run();
-//     CheckPluginRelatedresult(executableNetwork, "Concatenation");
+    CheckPluginRelatedResults(executableNetwork, "Concatenation");
 }
 
 namespace {
@@ -150,9 +164,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block_dynamic_axis_1, ConcatLayerCPU
 
 const std::vector<std::vector<InputShape>> inputShapes4D_axis1 = {
         {
-            {{-1, -1, -1, -1}, {{2, 32, 5, 7}, {1, 18, 10, 2}, {3, 8, 1, 8}}},
-            {{-1, -1, -1, -1}, {{2, 16, 5, 7}, {1, 5, 10, 2}, {3, 3, 1, 8}}},
-            {{-1, -1, -1, -1}, {{2, 64, 5, 7}, {1, 45, 10, 2}, {3, 1, 1, 8}}}
+            {{-1, -1, -1, -1}, {{2, 32, 0, 7}, {2, 32, 5, 7}, {2, 32, 5, 7}, {1, 18, 10, 2}, {2, 32, 5, 7}, {3, 8, 1, 8}, {2, 0, 5, 7}}},
+            {{-1, -1, -1, -1}, {{2, 16, 0, 7}, {2, 16, 5, 7}, {2, 16, 5, 7}, {1, 5, 10, 2}, {2, 0, 5, 7}, {3, 3, 1, 8}, {2, 16, 5, 7}}},
+            {{-1, -1, -1, -1}, {{2, 64, 0, 7}, {2, 64, 5, 7}, {2, 0, 5, 7}, {1, 45, 10, 2}, {2, 64, 5, 7}, {3, 1, 1, 8}, {2, 64, 5, 7}}}
         },
         {
             {{{1, 3}, {8, 32}, {1, 10}, {2, 8}}, {{2, 32, 5, 7}, {1, 18, 10, 2}, {3, 8, 1, 8}}},
@@ -447,10 +461,13 @@ INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_dynamic_axis_4, ConcatLayerCPUTest,
                                 ::testing::Values(planar_5D_ref, planarChannels_5D)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-const std::vector<std::vector<InputShape>> inputShapes_byBatch = {
+const std::vector<std::vector<InputShape>> inputShapes_byBatch_static = {
         static_shapes_to_test_representation({{5, 2, 2, 2}, {2, 2, 2, 2}}),
         static_shapes_to_test_representation({{1, 3, 5}, {3, 3, 5}}),
-        static_shapes_to_test_representation({{4, 3, 2}, {1, 3, 2}}),
+        static_shapes_to_test_representation({{4, 3, 2}, {1, 3, 2}})
+};
+
+const std::vector<std::vector<InputShape>> inputShapes_byBatch_dynamic = {
         // 5D
         {
             {{-1, -1, -1, -1, -1}, {{10, 32, 4, 5, 5}, {4, 7, 1, 1, 3},  {3, 20, 7, 9, 1}, }},
@@ -475,14 +492,22 @@ const std::vector<std::vector<InputShape>> inputShapes_byBatch = {
         }
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat_byBatch, ConcatLayerCPUTest,
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_byBatch_static, ConcatLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Values(0),
-                                 ::testing::ValuesIn(inputShapes_byBatch),
+                                 ::testing::ValuesIn(inputShapes_byBatch_static),
                                  ::testing::ValuesIn(netPrecisions),
                                  ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"})),
                                  ConcatLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_byBatch_dynamic, ConcatLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Values(0),
+                                 ::testing::ValuesIn(inputShapes_byBatch_dynamic),
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                                 ConcatLayerCPUTest::getTestCaseName);
+
 const std::vector<std::vector<InputShape>> inputShapes3D_axis1 = {
         static_shapes_to_test_representation({{2, 4, 5}, {2, 4, 5}}),
         {
@@ -549,11 +574,14 @@ INSTANTIATE_TEST_SUITE_P(smoke_Concat_2D_axis1, ConcatLayerCPUTest,
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
                         ConcatLayerCPUTest::getTestCaseName);
 
-const std::vector<std::vector<InputShape>> inputShapes1D = {
+const std::vector<std::vector<InputShape>> inputShapes1D_static = {
         static_shapes_to_test_representation({ov::Shape{5}, ov::Shape{5}}),
         static_shapes_to_test_representation({ov::Shape{2}, ov::Shape{2}}),
         static_shapes_to_test_representation({ov::Shape{1}, ov::Shape{1}}),
-        static_shapes_to_test_representation({ov::Shape{3}, ov::Shape{3}}),
+        static_shapes_to_test_representation({ov::Shape{3}, ov::Shape{3}})
+};
+
+const std::vector<std::vector<InputShape>> inputShapes1D_dynamic = {
         {
             {{-1}, {{19}, {8}, {5}}},
             {{-1}, {{19}, {8}, {5}}},
@@ -566,14 +594,22 @@ const std::vector<std::vector<InputShape>> inputShapes1D = {
         },
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat_1D, ConcatLayerCPUTest,
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_1D_static, ConcatLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(0),
-                                ::testing::ValuesIn(inputShapes1D),
+                                ::testing::ValuesIn(inputShapes1D_static),
                                 ::testing::ValuesIn(netPrecisions),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"})),
                         ConcatLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_1D_dynamic, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(0),
+                                ::testing::ValuesIn(inputShapes1D_dynamic),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                        ConcatLayerCPUTest::getTestCaseName);
+
 // ============================================== inPlace cases ============================================
 INSTANTIATE_TEST_SUITE_P(concat_Concat4D_CPU_Block8inPlace, ConcatLayerCPUTest,
                         ::testing::Combine(
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/convolution.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/convolution.cpp
index e71139b48db..add0d6d08ae 100755
--- a/src/tests/functional/plugin/cpu/single_layer_tests/convolution.cpp
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/convolution.cpp
@@ -974,6 +974,43 @@ INSTANTIATE_TEST_SUITE_P(Conv_PlainToBlocked_2D_BF16_dilated, ConvolutionLayerCP
                                  ::testing::Values(cpuEmptyPluginConfig)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+/* ============= Reorder + Convolution ============= */
+const auto convParams_Reorder_2D = ::testing::Combine(
+        ::testing::Values(SizeVector{1, 1}),
+        ::testing::Values(SizeVector{2, 2}),
+        ::testing::Values(std::vector<ptrdiff_t>{0, 0}),
+        ::testing::Values(std::vector<ptrdiff_t>{0, 0}),
+        ::testing::Values(SizeVector{1, 1}),
+        ::testing::Values(64),
+        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+);
+
+std::vector<InputShape> inputShapes_Reorder_2D = {
+        {
+            // dynamic shape
+            { -1, 32, -1, -1 },
+            // target static shapes
+            {
+                { 1, 32, 39, 40 },
+                { 2, 32, 20, 20}
+            }
+        }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_reorder_Conv_2D, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_Reorder_2D,
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes_Reorder_2D),
+                                         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice({conv_avx512_2D_1x1})),
+                                 ::testing::Values(emptyFusingSpec),
+                                 ::testing::Values(cpuEmptyPluginConfig)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 /* ============= Convolution (3D) ============= */
 const auto convParams_ExplicitPadding_3D = ::testing::Combine(
         ::testing::ValuesIn(kernels3d),
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/nonzero.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/nonzero.cpp
index a0f9f609ce1..eb4853a54d8 100644
--- a/src/tests/functional/plugin/cpu/single_layer_tests/nonzero.cpp
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/nonzero.cpp
@@ -7,6 +7,7 @@
 
 #include "ngraph_functions/builders.hpp"
 #include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
 
 using namespace InferenceEngine;
 using namespace CPUTestUtils;
@@ -21,6 +22,7 @@ typedef std::tuple<
 
 typedef std::tuple<
         NonZeroLayerTestParams,
+        std::pair<size_t, size_t>, // start from, range
         CPUSpecificParams> NonZeroLayerCPUTestParamsSet;
 
 class NonZeroLayerCPUTest : public testing::WithParamInterface<NonZeroLayerCPUTestParamsSet>,
@@ -28,8 +30,9 @@ class NonZeroLayerCPUTest : public testing::WithParamInterface<NonZeroLayerCPUTe
 public:
     static std::string getTestCaseName(testing::TestParamInfo<NonZeroLayerCPUTestParamsSet> obj) {
         NonZeroLayerTestParams basicParamsSet;
+        std::pair<size_t, size_t> genData;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = obj.param;
+        std::tie(basicParamsSet, genData, cpuParams) = obj.param;
         std::string td;
         ElementType netType = ElementType::undefined;
         InputShape inputShape;
@@ -44,28 +47,55 @@ public:
             result << CommonTestUtils::vec2str(shape) << "_";
         }
         result << ")_";
+        result << "StartFrom=" << genData.first << "_";
+        result << "Range=" << genData.second << "_";
         result << "netPRC=" << netType;
         result << CPUTestsBase::getTestCaseName(cpuParams);
         return result.str();
     }
+
+    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], range, startFrom);
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override {
+        const auto dims = targetStaticShapes[inferNum].front();
+        if (!((startFrom == 0 && range == 1) || std::any_of(dims.begin(), dims.end(), [](size_t dim) { return dim == 0; } ))) {
+            SubgraphBaseTest::compare(expected, actual);
+        }
+        inferNum++;
+    }
+
 protected:
+    size_t startFrom = 0, range = 10;
+    size_t inferNum = 0;
+
     void SetUp() override {
         targetDevice = CommonTestUtils::DEVICE_CPU;
         NonZeroLayerTestParams basicParamsSet;
+        std::pair<size_t, size_t> genData;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+        std::tie(basicParamsSet, genData, cpuParams) = this->GetParam();
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
         ElementType netType = ElementType::undefined;
         InputShape inputShape;
         std::tie(inputShape, netType) = basicParamsSet;
 
+        std::tie(startFrom, range) = genData;
+
         init_input_shapes({inputShape});
         auto inputParams = ngraph::builder::makeDynamicParams(netType, inputDynamicShapes);
 
         auto nonZero = std::make_shared<ngraph::opset3::NonZero>(inputParams[0]);
         // I8 was used as a special placeholder during calculating of primitive type if input was U8,
         // real runtime precision is still U8
-        selectedType = makeSelectedTypeStr("ref_", netType == ElementType::u8 ? ElementType::i8 : netType);
+        selectedType = makeSelectedTypeStr("ref", netType == ElementType::u8 ? ElementType::i8 : netType);
         inputParams[0]->set_friendly_name("input");
         function = makeNgraphFunction(netType, inputParams, nonZero, "NonZero");
     }
@@ -92,6 +122,11 @@ const std::vector<ElementType> netPrecisions = {
         ElementType::u8
 };
 
+const std::vector<std::pair<size_t, size_t>> genData = {
+    {0, 10},
+    {0, 1}
+};
+
 std::vector<InputShape> inShapesDynamic = {
         {
             //dynamic shape
@@ -116,6 +151,7 @@ std::vector<InputShape> inShapesDynamic = {
             {-1, -1, -1},
             { //target static shapes
                 {4, 4, 100},
+                {5, 0, 2},
                 {4, 4, 200},
                 {4, 4, 300}
             }
@@ -126,6 +162,7 @@ std::vector<InputShape> inShapesDynamic = {
             { //target static shapes
                 {4, 4, 4, 100},
                 {4, 4, 4, 200},
+                {5, 0, 0, 2},
                 {4, 4, 4, 300}
             }
         },
@@ -160,16 +197,20 @@ const auto paramsStatic = ::testing::Combine(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation(inShapesStatic)),
                 ::testing::ValuesIn(netPrecisions)),
+        ::testing::ValuesIn(genData),
         ::testing::ValuesIn(filterCPUInfoForDevice()));
 const auto paramsDynamic = ::testing::Combine(
         ::testing::Combine(
                 ::testing::ValuesIn(inShapesDynamic),
                 ::testing::ValuesIn(netPrecisions)),
+        ::testing::ValuesIn(genData),
         ::testing::ValuesIn(filterCPUInfoForDevice()));
 
 INSTANTIATE_TEST_SUITE_P(smoke_NonZeroStaticCPUTest, NonZeroLayerCPUTest,
                          paramsStatic, NonZeroLayerCPUTest::getTestCaseName);
 INSTANTIATE_TEST_SUITE_P(smoke_NonZeroDynamicCPUTest, NonZeroLayerCPUTest,
                          paramsDynamic, NonZeroLayerCPUTest::getTestCaseName);
+
 } // namespace
+
 } // namespace CPULayerTestsDefinitions
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/variadic_split.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/variadic_split.cpp
index d7395f31597..99d3a9a141b 100644
--- a/src/tests/functional/plugin/cpu/single_layer_tests/variadic_split.cpp
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/variadic_split.cpp
@@ -78,7 +78,7 @@ TEST_P(VariadicSplitLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
     run();
-//     CheckPluginRelatedResults(executableNetwork, "Split");
+    CheckPluginRelatedResults(executableNetwork, "Split");
 }
 
 namespace {
@@ -180,8 +180,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_Nspc2NcspSpecial, VariadicSpl
                                 ::testing::Values(perChannelsToPlanar_5D)),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_planar_static, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(InputShape{ {}, {{3, 6, 5, 6}} }),
+                                ::testing::Values(2, 3),
+                                ::testing::Values(std::vector<int>{1, 3, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_4D, planar_4D_ref, perChannels_4D)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
 const std::vector<InputShape> inputShapes4D_planar = {
-        { {}, {{3, 6, 5, 6}} },
         {
             // dynamic
             {-1, -1, -1, -1},
@@ -210,7 +218,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_planar, VariadicSplitLayerCPU
                                 ::testing::Values(2, 3),
                                 ::testing::Values(std::vector<int>{1, 3, -1}),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(planar_4D, planar_4D_ref, perChannels_4D)),
+                                ::testing::Values(planar_4D_ref, perChannels_4D)),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
 const std::vector<InputShape> inputShapes4D_block = {
@@ -255,8 +263,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_Block16, VariadicSplitLayerCP
                                 ::testing::Values(blocked16_4D_ref)),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_planar_static, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(InputShape{ {}, {{3, 24, 4, 5, 6}} }),
+                                ::testing::Values(2, 3, 4),
+                                ::testing::Values(std::vector<int>{2, 1, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D, planar_5D_ref, perChannels_5D)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
 const std::vector<InputShape> inputShapes5D_planar = {
-        { {}, {{3, 24, 4, 5, 6}} },
         {
             // dynamic
             {-1, -1, -1, -1, -1},
@@ -285,7 +301,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_planar, VariadicSplitLayerCPU
                                 ::testing::Values(2, 3, 4),
                                 ::testing::Values(std::vector<int>{2, 1, -1}),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(planar_5D, planar_5D_ref, perChannels_5D)),
+                                ::testing::Values(planar_5D_ref, perChannels_5D)),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
 const std::vector<InputShape> inputShapes5D_block = {
@@ -330,8 +346,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_Block16, VariadicSplitLayerCP
                                 ::testing::Values(blocked16_5D_ref)),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit3D_static, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(InputShape{ {}, {{14, 7, 21}} }),
+                                ::testing::Values(0, 1, 2),
+                                ::testing::Values(std::vector<int>{2, 4, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
 const std::vector<InputShape> inputShapes3D = {
-        { {}, {{14, 7, 21}} },
         {
             // dynamic
             {-1, -1, -1},
@@ -360,11 +384,19 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit3D, VariadicSplitLayerCPUTest,
                                 ::testing::Values(0, 1, 2),
                                 ::testing::Values(std::vector<int>{2, 4, -1}),
                                 ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit2D_static, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(InputShape{ {}, {{6, 12}} }),
+                                ::testing::Values(0, 1),
+                                ::testing::Values(std::vector<int>{2, -1}),
+                                ::testing::ValuesIn(netPrecisions),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
 const std::vector<InputShape> inputShapes2D = {
-        { {}, {{6, 12}} },
         {
             // dynamic
             {-1, -1},
@@ -393,11 +425,19 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit2D, VariadicSplitLayerCPUTest,
                                 ::testing::Values(0, 1),
                                 ::testing::Values(std::vector<int>{2, -1}),
                                 ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit1D_static, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(InputShape{ {}, {{10}} }),
+                                ::testing::Values(0),
+                                ::testing::Values(std::vector<int>{2, 1, 1, -1}),
+                                ::testing::ValuesIn(netPrecisions),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
 const std::vector<InputShape> inputShapes1D = {
-        { {}, {{10}} },
         {
             // dynamic
             {-1},
@@ -426,7 +466,37 @@ INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit1D, VariadicSplitLayerCPUTest,
                                 ::testing::Values(0),
                                 ::testing::Values(std::vector<int>{2, 1, 1, -1}),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes4D_zero_dims = {
+        {
+            // dynamic
+            {-1, -1, -1, -1},
+            // target
+            {
+                {1, 7, 7, 7},
+                {3, 7, 7, 7},
+            }
+        }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_zero_dims, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes4D_zero_dims),
+                                ::testing::Values(1, 2, 3),
+                                ::testing::Values(std::vector<int>{3, 4, -1}, std::vector<int>{3, -1, 4}, std::vector<int>{-1, 3, 4}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_4D_ref)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_zero_dims_nspc_ncsp, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes4D_zero_dims),
+                                ::testing::Values(1),
+                                ::testing::Values(std::vector<int>{3, 4, -1}, std::vector<int>{3, -1, 4}, std::vector<int>{-1, 3, 4}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(perChannelsToPlanar_4D)),
                         VariadicSplitLayerCPUTest::getTestCaseName);
 
 } // namespace
diff --git a/src/tests/functional/plugin/cpu/subgraph_tests/src/static_zero_dims.cpp b/src/tests/functional/plugin/cpu/subgraph_tests/src/static_zero_dims.cpp
new file mode 100644
index 00000000000..1082dfa8a2f
--- /dev/null
+++ b/src/tests/functional/plugin/cpu/subgraph_tests/src/static_zero_dims.cpp
@@ -0,0 +1,62 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "functional_test_utils/skip_tests_config.hpp"
+
+using namespace ov::test;
+
+namespace SubgraphTestsDefinitions {
+
+class StaticZeroDims : public SubgraphBaseTest {
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        InputShape inputShapes{{}, {{7, 4}}};
+
+        init_input_shapes({inputShapes});
+
+        auto ngPrc = ngraph::element::f32;
+        auto inputParams = ngraph::builder::makeDynamicParams(ngPrc, inputDynamicShapes);
+
+        auto splitAxisOp = std::make_shared<ngraph::opset3::Constant>(ngraph::element::i64, ngraph::Shape{}, std::vector<int64_t>{0});
+        std::vector<int> splitLenght = {1, 0, 6};
+        auto splitLengthsOp = std::make_shared<ngraph::opset3::Constant>(ngraph::element::i32, ngraph::Shape{splitLenght.size()}, splitLenght);
+        auto varSplit = std::make_shared<ngraph::opset3::VariadicSplit>(inputParams[0], splitAxisOp, splitLengthsOp);
+
+        auto relu1 = std::make_shared<ngraph::opset5::Relu>(varSplit->output(0));
+
+        auto numInRoi = ngraph::builder::makeConstant(ngPrc, {0}, std::vector<float>{}, false);
+        auto expDet = std::make_shared<ov::op::v6::ExperimentalDetectronTopKROIs>(varSplit->output(1), numInRoi, 10);
+        auto relu2 = std::make_shared<ngraph::opset5::Relu>(expDet);
+
+        auto relu3 = std::make_shared<ngraph::opset5::Relu>(varSplit->output(2));
+
+        ngraph::NodeVector results{relu1, relu2, relu3};
+        function = std::make_shared<ngraph::Function>(results, inputParams, "StaticZeroDims");
+    }
+
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override {
+        ASSERT_EQ(expected.size(), actual.size());
+        for (size_t i = 0; i < expected.size(); i++) {
+            // skip second output tensor because it's output ExperimentalDetectronTopKROIs: input dims [0, 4]
+            // so according to spec output values undefined
+            if (i == 1) {
+                continue;
+            }
+            ov::test::utils::compare(expected[i], actual[i], abs_threshold, rel_threshold);
+        }
+    }
+};
+
+TEST_F(StaticZeroDims, smoke_CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+}
+
+} // namespace SubgraphTestsDefinitions
\ No newline at end of file
diff --git a/src/tests/unit/cpu/mkldnn_zero_dims_test.cpp b/src/tests/unit/cpu/mkldnn_zero_dims_test.cpp
new file mode 100644
index 00000000000..cb2944dc6b6
--- /dev/null
+++ b/src/tests/unit/cpu/mkldnn_zero_dims_test.cpp
@@ -0,0 +1,258 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "mkldnn_memory.h"
+#include "memory_desc/cpu_memory_desc_utils.h"
+#include "nodes/common/blocked_desc_creator.h"
+#include "mkldnn_extension_utils.h"
+#include "memory_desc/dnnl_blocked_memory_desc.h"
+
+using namespace MKLDNNPlugin;
+using namespace InferenceEngine;
+using namespace testing;
+
+/* ======================================= BASE ZERO DIM TEST ======================================= */
+class MemDescWithZeroDimsBaseTest: public ::testing::Test {
+protected:
+    Shape shape;
+    dnnl::memory::format_tag fmt;
+    const InferenceEngine::Precision precision = InferenceEngine::Precision::FP32;
+
+    void validate(const BlockedMemoryDesc& desc, const VectorDims& expectedStrieds, size_t offsetSize, size_t offsetPaddingSize,
+                  size_t maxMemSize, bool orderCheckSkip = false) {
+        VectorDims expectedBlkDims;
+        VectorDims expectedOrder;
+        {
+            auto origShape = shape.toPartialShape();
+            auto replaceShape = origShape;
+            std::replace(replaceShape.begin(), replaceShape.end(), ngraph::Dimension(0), ngraph::Dimension(3));
+            Shape dummyShape(replaceShape);
+            DnnlBlockedMemoryDesc dummyDesc(dummyShape, MKLDNNExtensionUtils::IEPrecisionToDataType(precision), fmt);
+            expectedBlkDims = dummyDesc.getBlockDims();
+            expectedOrder = dummyDesc.getOrder();
+            for (size_t i = 0; i < dummyShape.getRank(); i++) {
+                if (origShape[expectedOrder[i]] == ngraph::Dimension(0)) {
+                    expectedBlkDims[i] = 0;
+                }
+            }
+        }
+
+        ASSERT_EQ(shape.getDims(), desc.getShape().getDims());
+        ASSERT_EQ(shape.getMinDims(), desc.getShape().getMinDims());
+        ASSERT_EQ(shape.getMaxDims(), desc.getShape().getMaxDims());
+
+        ASSERT_EQ(expectedStrieds, desc.getStrides());
+        ASSERT_EQ(expectedBlkDims, desc.getBlockDims());
+        if (!orderCheckSkip) {
+            ASSERT_EQ(expectedOrder, desc.getOrder());
+        }
+
+        ASSERT_EQ(0, desc.getPaddedElementsCount());
+        ASSERT_EQ(maxMemSize, desc.getMaxMemSize());
+        ASSERT_EQ(maxMemSize, desc.getCurrentMemSize());
+
+        ASSERT_EQ(offsetSize, desc.getOffsetPadding());
+        ASSERT_EQ(VectorDims(expectedBlkDims.size(), offsetPaddingSize), desc.getOffsetPaddingToData());
+    }
+
+    virtual std::pair<DnnlBlockedMemoryDesc, CpuBlockedMemoryDesc> createDescs() const {
+        DnnlBlockedMemoryDesc descDnnl(precision, shape);
+        CpuBlockedMemoryDesc descCpu(precision, shape);
+        return {descDnnl, descCpu};
+    }
+
+    void Run() {
+        const size_t offset = 0, offsetPadding = 0;
+
+        auto descs = createDescs();
+        DnnlBlockedMemoryDesc descDnnl(descs.first);
+        CpuBlockedMemoryDesc descCpu(descs.second);
+
+        VectorDims zeroStrides(descDnnl.getBlockDims().size(), 0);
+        validate(descDnnl, zeroStrides, offset, offsetPadding, 0);
+        validate(descCpu, zeroStrides, offset, offsetPadding, precision.size());
+
+        ASSERT_TRUE(descDnnl.isCompatible(descCpu));
+        ASSERT_TRUE(descCpu.isCompatible(descDnnl));
+
+        // undefined
+        VectorDims undefDnnlStrides(descDnnl.getBlockDims().size(), Shape::UNDEFINED_DIM);
+        std::fill(undefDnnlStrides.begin() + descDnnl.getShape().getRank(), undefDnnlStrides.end(), 0);
+        const auto undefDnnl = descDnnl.cloneWithUndefStridesAndOffset();
+        validate(*undefDnnl->as<BlockedMemoryDesc>(), undefDnnlStrides, Shape::UNDEFINED_DIM, offsetPadding, Shape::UNDEFINED_DIM);
+
+        VectorDims undefCpuStrides(descCpu.getBlockDims().size(), Shape::UNDEFINED_DIM);
+        const auto undefCpu = descCpu.cloneWithUndefStridesAndOffset();
+        validate(*undefCpu->as<BlockedMemoryDesc>(), undefCpuStrides, Shape::UNDEFINED_DIM, offsetPadding,
+                  Shape::UNDEFINED_DIM);
+
+        // defined
+        const auto definedDnnl = descDnnl.cloneWithDefaultStridesAndOffset();
+        validate(*definedDnnl->as<BlockedMemoryDesc>(), zeroStrides, offset, offsetPadding, 0);
+
+        const auto definedCpu = descCpu.cloneWithDefaultStridesAndOffset();
+        validate(*definedCpu->as<BlockedMemoryDesc>(), zeroStrides, offset, offsetPadding, precision.size());
+    }
+};
+
+/* ======================================= TEST DATA ======================================= */
+const std::vector<Shape> staticShapes = {
+    Shape(VectorDims{0, 32, 48, 64}),
+    Shape(VectorDims{16, 0, 48, 64}),
+    Shape(VectorDims{16, 32, 0, 64}),
+    Shape(VectorDims{16, 32, 48, 0}),
+    Shape(VectorDims{16, 32, 0, 0}),
+    Shape(VectorDims{0, 0, 48, 64}),
+    Shape(VectorDims{16, 0, 0, 64}),
+    Shape(VectorDims{0, 0, 0, 64}),
+    Shape(VectorDims{16, 0, 0, 0}),
+    Shape(VectorDims{0, 0, 0, 0})
+};
+
+const std::vector<Shape> dynamicShapes = {
+    Shape(ngraph::PartialShape{0, -1, {0, 48}, -1}),
+    Shape(ngraph::PartialShape{16, 0, -1, {0, 64}}),
+    Shape(ngraph::PartialShape{-1, -1, 0, -1}),
+    Shape(ngraph::PartialShape{{0, 16}, -1, {0, 48}, 0}),
+    Shape(ngraph::PartialShape{-1, 32, 0, 0}),
+    Shape(ngraph::PartialShape{0, 0, 48, -1}),
+    Shape(ngraph::PartialShape{{0, 16}, 0, 0, 64}),
+    Shape(ngraph::PartialShape{0, 0, 0, -1}),
+    Shape(ngraph::PartialShape{{0, 16}, 0, 0, 0}),
+    Shape(ngraph::PartialShape{0, 0, 0, 0})
+};
+
+const std::vector<dnnl::memory::format_tag> fmts = {
+    dnnl::memory::format_tag::nchw,
+    dnnl::memory::format_tag::nhwc,
+    dnnl::memory::format_tag::nChw8c,
+    dnnl::memory::format_tag::nChw16c,
+    dnnl::memory::format_tag::NChw16n16c,
+    dnnl::memory::format_tag::Acdb16a
+};
+
+/* ======================================= SPECIFIC TEST CASES ======================================= */
+using MemDescWithZeroDimsParams = std::tuple<dnnl::memory::format_tag,
+                                             Shape>;
+
+class MemDescWithZeroDimsFmtTest: public testing::WithParamInterface<MemDescWithZeroDimsParams>,
+                                  public MemDescWithZeroDimsBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<MemDescWithZeroDimsParams> &obj) {
+        Shape shape;
+        dnnl::memory::format_tag fmt;
+        std::tie(fmt, shape) = obj.param;
+        std::ostringstream result;
+        result << "Shape=" << shape.toString();
+        result << "_Fmt=" << mkldnn::utils::fmt2str(fmt);
+        return result.str();
+    }
+
+    std::pair<DnnlBlockedMemoryDesc, CpuBlockedMemoryDesc> createDescs() const override {
+        DnnlBlockedMemoryDesc descDnnl(shape, MKLDNNExtensionUtils::IEPrecisionToDataType(precision), fmt);
+        CpuBlockedMemoryDesc descCpu(precision, shape, descDnnl.getBlockDims(), descDnnl.getOrder());
+        return {descDnnl, descCpu};
+    }
+
+protected:
+    void SetUp() override {
+        std::tie(fmt, shape) = this->GetParam();
+        ASSERT_TRUE(shape.hasZeroDims()) << "Can't run MemDescWithZeroDimsTest, because shape doesn't contain zero dims";
+    }
+};
+
+TEST_P(MemDescWithZeroDimsFmtTest, CreateDescWithFmt) {
+    Run();
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_MemDescWithZeroDimsFmtTest_static, MemDescWithZeroDimsFmtTest,
+                         ::testing::Combine(::testing::ValuesIn(fmts),
+                                            ::testing::ValuesIn(staticShapes)),
+                         MemDescWithZeroDimsFmtTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MemDescWithZeroDimsFmtTest_dynamic, MemDescWithZeroDimsFmtTest,
+                         ::testing::Combine(::testing::ValuesIn(fmts),
+                                            ::testing::ValuesIn(dynamicShapes)),
+                         MemDescWithZeroDimsFmtTest::getTestCaseName);
+
+class MemDescWithZeroDimsPlanarTest: public testing::WithParamInterface<Shape>,
+                                     public MemDescWithZeroDimsBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<Shape> &obj) {
+        Shape shape;
+        shape = obj.param;
+        std::ostringstream result;
+        result << "Shape=" << shape.toString();
+        return result.str();
+    }
+protected:
+    void SetUp() override {
+        shape = this->GetParam();
+        fmt = dnnl::memory::format_tag::nchw;
+        ASSERT_TRUE(shape.hasZeroDims()) << "Can't run MemDescWithZeroDimsTest, because shape doesn't contain zero dims";
+    }
+};
+
+TEST_P(MemDescWithZeroDimsPlanarTest, CreateDescPlanar) {
+    Run();
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_MemDescWithZeroDimsPlanarTest, MemDescWithZeroDimsPlanarTest,
+                         ::testing::ValuesIn(staticShapes),
+                         MemDescWithZeroDimsPlanarTest::getTestCaseName);
+
+using MemDescWithZeroDimsCloneNewDimsParams = std::tuple<dnnl::memory::format_tag, // memory format
+                                              Shape,                               // dynamic shapes
+                                              Shape>;                              // static shapes
+
+class MemDescWithZeroDimsCloneNewDimsTest: public testing::WithParamInterface<MemDescWithZeroDimsCloneNewDimsParams>,
+                                           public MemDescWithZeroDimsBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<MemDescWithZeroDimsCloneNewDimsParams> &obj) {
+        Shape shapeDynamic, shapeClone;
+        dnnl::memory::format_tag fmt;
+        std::tie(fmt, shapeDynamic, shapeClone) = obj.param;
+        std::ostringstream result;
+        result << "ShapeDynamic=" << shapeDynamic.toString();
+        result << "_ShapeClone=" << shapeClone.toString();
+        result << "_Fmt=" << mkldnn::utils::fmt2str(fmt);
+        return result.str();
+    }
+protected:
+    Shape shapeDynamic;
+
+    void SetUp() override {
+        std::tie(fmt, shapeDynamic, shape) = this->GetParam();
+        ASSERT_TRUE(shape.hasZeroDims()) << "Can't run MemDescWithZeroDimsTest, because shape doesn't contain zero dims";
+    }
+};
+
+TEST_P(MemDescWithZeroDimsCloneNewDimsTest, CloneWithNewDims) {
+    DnnlBlockedMemoryDesc dynamicDescDnnl(shapeDynamic, MKLDNNExtensionUtils::IEPrecisionToDataType(precision), fmt);
+    CpuBlockedMemoryDesc dynamicDescCpu(precision, shape, dynamicDescDnnl.getBlockDims(), dynamicDescDnnl.getOrder());
+    const size_t offset = 0, offsetPadding = 0;
+    VectorDims zeroStrides(dynamicDescDnnl.getBlockDims().size(), 0);
+
+    const auto clonedDescDnnl = dynamicDescDnnl.cloneWithNewDims(shape.getStaticDims());
+    const auto clonedDescCpu = dynamicDescCpu.cloneWithNewDims(shape.getStaticDims());
+
+    // can't compute order correct since strides equal
+    const auto& dims = shape.getDims();
+    bool skipOrderCheck = std::all_of(dims.begin() + 1, dims.end(), [](const size_t& dim) { return dim == 0; });
+    validate(*clonedDescDnnl->as<BlockedMemoryDesc>(), zeroStrides, offset, offsetPadding, 0, skipOrderCheck);
+    validate(*clonedDescCpu->as<BlockedMemoryDesc>(), zeroStrides, offset, offsetPadding, precision.size());
+}
+
+const std::vector<Shape> srcDynShapes = {
+    Shape(ngraph::PartialShape({-1, -1, -1, -1})),
+    Shape(ngraph::PartialShape({{0, 16}, {0, 32}, {0, 48}, {0, 64}}))
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_MemDescWithZeroDimsCloneNewDimsTest, MemDescWithZeroDimsCloneNewDimsTest,
+                         ::testing::Combine(::testing::ValuesIn(fmts),
+                                            ::testing::ValuesIn(srcDynShapes),
+                                            ::testing::ValuesIn(staticShapes)),
+                         MemDescWithZeroDimsCloneNewDimsTest::getTestCaseName);

From 0644aaa947442143640f3bb8563f265b92e915a8 Mon Sep 17 00:00:00 2001
From: Indira Salyahova <indira.salyahova@intel.com>
Date: Fri, 17 Dec 2021 12:05:27 +0300
Subject: [PATCH 012/160] [POT] Add new special metrics in aa (#9251)

* Update utils.py

* Update utils.py
---
 .../pot/algorithms/quantization/accuracy_aware_common/utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py
index 554425326e8..48aaf9ae825 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py
@@ -17,7 +17,8 @@ from ....samplers.creator import create_sampler
 
 SPECIAL_METRICS = ['cmc', 'reid_map', 'pairwise_accuracy_subsets', 'pairwise_accuracy', 'normalized_embedding_accuracy',
                    'face_recognition_tafa_pair_metric', 'localization_recall',
-                   'coco_orig_keypoints_precision', 'coco_orig_segm_precision', 'coco_orig_keypoints_precision']
+                   'coco_orig_keypoints_precision', 'coco_orig_segm_precision', 'coco_orig_keypoints_precision',
+                   'spearman_correlation_coef', 'pearson_correlation_coef']
 
 METRICS_CONFIGS = {'sigmoid_recom_loss': {'metrics': 'log_loss',
                                           'postprocessing': 'sigmoid_normalize_recommendation'},

From 4c4d006c5ae654e920c2356ad76ef875c6924393 Mon Sep 17 00:00:00 2001
From: Aleksandr Korolev <aleksandr.korolev@intel.com>
Date: Fri, 17 Dec 2021 12:14:08 +0300
Subject: [PATCH 013/160] [VPU] restore network P/R info in Import method
 (#8844)

In the new API, we need to recover information about function inputs/outputs.
---
 .../include/vpu/backend/backend.hpp           |   5 +
 .../include/vpu/backend/blob_format.hpp       |  12 ++
 .../include/vpu/blob_reader.hpp               |   8 ++
 .../include/vpu/graph_transformer.hpp         |   1 +
 .../src/backend/serialize.cpp                 | 121 ++++++++++++++++++
 .../vpu/graph_transformer/src/blob_reader.cpp |  60 ++++++++-
 .../src/frontend/frontend.cpp                 |   9 +-
 .../myriad_executable_network.cpp             |  13 +-
 .../ov_executable_network/exec_graph_info.cpp |  47 +++++++
 .../skip_tests_config.cpp                     |   9 +-
 10 files changed, 279 insertions(+), 6 deletions(-)
 create mode 100644 src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp

diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/backend/backend.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/backend/backend.hpp
index 5fa54bd478c..6b4d29ab04b 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/backend/backend.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/backend/backend.hpp
@@ -54,6 +54,11 @@ private:
             const mv_blob_header& blobHdr,
             std::vector<char>& blob);
 
+    void serializeParamsAndResults(
+            const Model& model,
+            const mv_blob_header& blobHdr,
+            std::vector<char>& blob);
+
     ElfN_Ehdr createElfHeader();
 
     void getMetaData(
diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/backend/blob_format.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/backend/blob_format.hpp
index 2a7f72adb32..8607fdb238b 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/backend/blob_format.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/backend/blob_format.hpp
@@ -62,4 +62,16 @@ VPU_PACKED(mv_stage_header {
     uint32_t numShaves;
 };)
 
+VPU_PACKED(network_info_header {
+    uint32_t parameters_size;
+    uint32_t results_size;
+};)
+
+VPU_PACKED(network_params_header {
+    uint32_t name_lenght;
+    uint32_t shape_size;
+    uint32_t element_type_bytesize;
+    uint32_t output_tensor_names_size;
+};)
+
 }  //  namespace vpu
diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/blob_reader.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/blob_reader.hpp
index 37f2ec63dfc..9b7b3bb7833 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/blob_reader.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/blob_reader.hpp
@@ -26,6 +26,9 @@ public:
     const ie::InputsDataMap& getNetworkInputs() const { return _networkInputs; }
     const ie::OutputsDataMap& getNetworkOutputs() const { return _networkOutputs; }
 
+    const std::vector<std::shared_ptr<const ov::Node>>& getNetworkParemeters() const { return _parameters; }
+    const std::vector<std::shared_ptr<const ov::Node>>& getNetworkResults() const { return _results; }
+
     uint32_t getStageCount() const { return _blobHeader.stages_count; }
 
     uint32_t getMagicNumber() const { return _blobHeader.magic_number; }
@@ -36,6 +39,8 @@ public:
     uint32_t getNumberOfShaves() const { return _blobHeader.number_of_shaves; }
     uint32_t getNumberOfSlices() const { return _blobHeader.number_of_cmx_slices; }
 
+    uint32_t getFileSize() const { return _blobHeader.file_size; }
+
     const DataInfo& getInputInfo()  const { return _inputInfo; }
     const DataInfo& getOutputInfo() const { return _outputInfo; }
 
@@ -49,6 +54,9 @@ private:
     ie::InputsDataMap  _networkInputs;
     ie::OutputsDataMap _networkOutputs;
 
+    std::vector<std::shared_ptr<const ov::Node>> _parameters = {};
+    std::vector<std::shared_ptr<const ov::Node>> _results = {};
+
     DataInfo _inputInfo;
     DataInfo _outputInfo;
 };
diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp
index 6566e5d5265..625faefbc65 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp
@@ -89,6 +89,7 @@ std::set<std::string> getSupportedLayers(const ie::CNNNetwork& network, const Pl
 
 const uint32_t BLOB_MAGIC_NUMBER  = 9709;
 const uint32_t BLOB_VERSION_MAJOR = 6;
+// Must be changed when possible
 const uint32_t BLOB_VERSION_MINOR = 0;
 
 }  // namespace vpu
diff --git a/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp b/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp
index 6d45a968b9f..1bdf80c4276 100644
--- a/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp
@@ -15,6 +15,9 @@
 #include <description_buffer.hpp>
 #include <xml_parse_utils.h>
 
+#include <ngraph/ops.hpp>
+#include <transformations/utils/utils.hpp>
+
 #include <climits>
 #include <cstring>
 #include <string>
@@ -161,6 +164,118 @@ void BackEnd::serializeConstShapes(const Model& model, const mv_blob_header& blo
     }
 }
 
+void BackEnd::serializeParamsAndResults(const Model& model, const mv_blob_header& blobHdr,
+                        std::vector<char>& blob) {
+    const auto networkParams = model->attrs().getOrDefault<ov::ParameterVector>("networkParameters");
+    const auto networkResults = model->attrs().getOrDefault<ov::ResultVector>("networkResults");
+
+    auto getNetworkParameterHeader = [](const std::shared_ptr<ov::Node>& node) {
+        network_params_header nph;
+        nph.element_type_bytesize = sizeof(node->get_element_type().operator ov::element::Type_t());
+        nph.name_lenght = node->get_friendly_name().size();
+        nph.shape_size = node->get_shape().size();
+        nph.output_tensor_names_size = node->get_output_tensor(0).get_names().size();
+        return nph;
+    };
+
+    uint32_t networkInfoOffset = blob.size();
+    auto serializeParameters = [&blob, &networkInfoOffset,
+                                &getNetworkParameterHeader](
+                                const std::shared_ptr<ov::Node>& node) {
+        BlobSerializer headerSerializer;
+        BlobSerializer shapeSerializer;
+        BlobSerializer elementTypeSerializer;
+        BlobSerializer tensorNamesSerializer;
+        BlobSerializer inputNameForResultSerializer;
+
+        const auto nph = getNetworkParameterHeader(node);
+        const bool isResult = ov::is_type<ov::op::v0::Result>(node);
+        int totalNetworkInfoOffset =
+            networkInfoOffset + sizeof(nph) + nph.name_lenght +
+            nph.element_type_bytesize +
+            sizeof(size_t) * (nph.output_tensor_names_size + nph.shape_size);
+
+        for (const auto& name : node->get_output_tensor(0).get_names()) {
+            totalNetworkInfoOffset += sizeof(size_t) + name.size();
+        }
+        if (isResult) {
+            totalNetworkInfoOffset +=
+                sizeof(size_t) +
+                ngraph::op::util::create_ie_output_name(node->input_value(0)).size();
+        }
+
+        blob.resize(totalNetworkInfoOffset);
+
+        headerSerializer.append(nph);
+        std::copy_n(headerSerializer.data(), sizeof(nph),
+                    blob.data() + networkInfoOffset);
+
+        networkInfoOffset += sizeof(nph);
+        const auto nodeName = node->get_friendly_name();
+        VPU_THROW_UNLESS(
+            node->get_output_partial_shape(0).rank().is_static(),
+            "Serialization of shapes with dynamic rank is not supported");
+        const auto nodeShape = node->get_output_partial_shape(0).get_shape();
+        const auto nodeElType =
+            node->get_element_type().operator ov::element::Type_t();
+
+        std::copy_n(nodeName.data(), nodeName.size(),
+                    blob.data() + networkInfoOffset);
+        networkInfoOffset += nph.name_lenght;
+
+        for (const auto shapeIdx : nodeShape) {
+            shapeSerializer.append(shapeIdx);
+        }
+        std::copy_n(shapeSerializer.data(),
+                    shapeSerializer.size(), blob.data() + networkInfoOffset);
+        networkInfoOffset += shapeSerializer.size();
+        elementTypeSerializer.append(nodeElType);
+        std::copy_n(elementTypeSerializer.data(), nph.element_type_bytesize,
+                    blob.data() + networkInfoOffset);
+        networkInfoOffset += nph.element_type_bytesize;
+
+        for (const auto& name : node->get_output_tensor(0).get_names()) {
+            tensorNamesSerializer.append(name.size());
+            for (const auto ch : name) {
+                tensorNamesSerializer.append(ch);
+            }
+        }
+        std::copy_n(tensorNamesSerializer.data(), tensorNamesSerializer.size(),
+                    blob.data() + networkInfoOffset);
+        networkInfoOffset += tensorNamesSerializer.size();
+
+        if (isResult) {
+            const auto inputNameForResult =
+                ngraph::op::util::create_ie_output_name(node->input_value(0));
+            inputNameForResultSerializer.append(inputNameForResult.size());
+            for (const auto ch : inputNameForResult) {
+                inputNameForResultSerializer.append(ch);
+            }
+            std::copy_n(inputNameForResultSerializer.data(),
+                        inputNameForResultSerializer.size(),
+                        blob.data() + networkInfoOffset);
+            networkInfoOffset += inputNameForResultSerializer.size();
+        }
+    };
+
+    BlobSerializer networkInfoSerializer;
+    network_info_header nih;
+    nih.parameters_size = networkParams.size();
+    nih.results_size = networkResults.size();
+    blob.resize(networkInfoOffset + sizeof(nih));
+    networkInfoSerializer.append(nih);
+    std::copy_n(networkInfoSerializer.data(), sizeof(nih), blob.data() + networkInfoOffset);
+    networkInfoOffset += sizeof(nih);
+
+    for (const auto& param : networkParams) {
+        serializeParameters(param);
+    }
+
+    for (const auto& result : networkResults) {
+        serializeParameters(result);
+    }
+}
+
 void BackEnd::serialize(
         const Model& model,
         std::vector<char>& blob,
@@ -271,6 +386,12 @@ void BackEnd::serialize(
 
     serializeConstData(model, blobHdr, blob);
     serializeConstShapes(model, blobHdr, blob);
+    const auto networkParams = model->attrs().getOrDefault<ov::ParameterVector>("networkParameters");
+    const auto networkResults = model->attrs().getOrDefault<ov::ResultVector>("networkResults");
+    // To avoid constant network case
+    if (!networkParams.empty() && !networkResults.empty()) {
+        serializeParamsAndResults(model, blobHdr, blob);
+    }
 
     blobHeader.first = blob.data();
     blobHeader.second = sizeof(ElfN_Ehdr) + sizeof(mv_blob_header);
diff --git a/inference-engine/src/vpu/graph_transformer/src/blob_reader.cpp b/inference-engine/src/vpu/graph_transformer/src/blob_reader.cpp
index fe57214b3ba..3c056e2006b 100644
--- a/inference-engine/src/vpu/graph_transformer/src/blob_reader.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/blob_reader.cpp
@@ -10,7 +10,7 @@
 #include <string>
 
 #include <ie_input_info.hpp>
-
+#include <ie_ngraph_utils.hpp>
 #include <vpu/graph_transformer.hpp>
 #include <vpu/backend/blob_format.hpp>
 #include <vpu/model/data.hpp>
@@ -116,6 +116,64 @@ void BlobReader::parse(const std::vector<char>& blob) {
             _networkOutputs[processedOutput.getName()] = std::make_shared<ie::Data>(processedOutput);
         }
     }
+    if (blob.size() != _blobHeader.file_size) {
+        auto networkInfoOffset = _blobHeader.file_size;
+        const auto nih = readFromBlob<network_info_header>(blob, networkInfoOffset);
+        auto extractParameter = [&blob, &networkInfoOffset](bool isResult) {
+            const auto nph = readFromBlob<network_params_header>(blob, networkInfoOffset);
+            std::string parameterFriendlyName(nph.name_lenght, '0');
+
+            for (auto idx = 0; idx < nph.name_lenght; ++idx) {
+                parameterFriendlyName[idx] = readFromBlob<char>(blob, networkInfoOffset);
+            }
+
+            ov::Shape parameterShape(nph.shape_size);
+            for (auto idx = 0; idx < nph.shape_size; ++idx) {
+                parameterShape[idx] = readFromBlob<size_t>(blob, networkInfoOffset);
+            }
+
+            ov::element::Type_t parameterType = readFromBlob<ov::element::Type_t>(blob, networkInfoOffset);
+            std::shared_ptr<ov::Node> parameter =
+                std::make_shared<ov::op::v0::Parameter>(parameterType,
+                                                        parameterShape);
+
+            std::unordered_set<std::string> tensorNames;
+            for (auto idx = 0; idx < nph.output_tensor_names_size; ++idx) {
+                const auto nameLenght = readFromBlob<size_t>(blob, networkInfoOffset);
+                std::string tensorName;
+                for (auto nameSymbolIdx = 0; nameSymbolIdx < nameLenght; ++nameSymbolIdx) {
+                    tensorName += readFromBlob<char>(blob, networkInfoOffset);
+                }
+                tensorNames.insert(tensorName);
+            }
+            if (isResult) {
+                auto fakeParameter = parameter;
+                parameter = std::make_shared<ov::op::v0::Result>(parameter);
+
+                const auto inputNameLenght = readFromBlob<size_t>(blob, networkInfoOffset);
+
+                std::string inputName;
+                for (auto nameSymbolIdx = 0; nameSymbolIdx < inputNameLenght; ++nameSymbolIdx) {
+                    inputName += readFromBlob<char>(blob, networkInfoOffset);
+                }
+                fakeParameter->set_friendly_name(inputName);
+
+                parameter = parameter->copy_with_new_inputs({fakeParameter});
+            }
+            parameter->set_friendly_name(parameterFriendlyName);
+            parameter->output(0).get_tensor().set_names(tensorNames);
+
+            return parameter;
+        };
+
+        for (auto paramIdx = 0; paramIdx < nih.parameters_size; ++paramIdx) {
+            _parameters.emplace_back(extractParameter(false));
+        }
+
+        for (auto paramIdx = 0; paramIdx < nih.results_size; ++paramIdx) {
+            _results.emplace_back(extractParameter(true));
+        }
+    }
 }
 
 }  // namespace vpu
diff --git a/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp b/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp
index 447ba461280..c1960da95e4 100644
--- a/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp
@@ -492,7 +492,14 @@ ModelPtr FrontEnd::runCommonPasses(ie::CNNNetwork network,
 
     model->attrs().set<int>("index", g_counter.fetch_add(1));
     model->attrs().set<Resources>("resources", env.resources);
-
+    // Transmitting Information about the parameters/results of the network for
+    // the possibility of importing it
+    if (network.getFunction() != nullptr) {
+        model->attrs().set<ov::ParameterVector>(
+            "networkParameters", network.getFunction()->get_parameters());
+        model->attrs().set<ov::ResultVector>(
+            "networkResults", network.getFunction()->get_results());
+    }
     //
     // Update IE Network
     //
diff --git a/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp b/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
index 90c12142c23..83e48a22595 100644
--- a/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
+++ b/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
@@ -164,11 +164,20 @@ void ExecutableNetwork::Import(std::istream& strm, std::vector<DevicePtr> &devic
 
     this->_networkInputs  = blobReader.getNetworkInputs();
     this->_networkOutputs = blobReader.getNetworkOutputs();
-    std::size_t numStages = blobReader.getStageCount();
-    auto blobHeader = blobReader.getHeader();
+    if (blobSize == blobReader.getFileSize()) {
+        _log->warning(
+            "Older version of blob. Unable to get information about network "
+            "parameters/results. Please recompile blob");
+    }
+    this->setInputs(blobReader.getNetworkParemeters());
+    this->setOutputs(blobReader.getNetworkResults());
 
     _inputInfo  = blobReader.getInputInfo();
     _outputInfo = blobReader.getOutputInfo();
+
+    std::size_t numStages = blobReader.getStageCount();
+    auto blobHeader = blobReader.getHeader();
+
     openDevice(devicePool);
     _executor->allocateGraph(_device, _graphDesc, _graphBlob, blobHeader, numStages, networkName, _actualNumExecutors);
     _graphMetaData.stagesMeta.resize(numStages);
diff --git a/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp
new file mode 100644
index 00000000000..6ca8d1df490
--- /dev/null
+++ b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "behavior/ov_executable_network/exec_graph_info.hpp"
+
+#include "ie_plugin_config.hpp"
+#include <common_test_utils/test_constants.hpp>
+
+using namespace ov::test::behavior;
+namespace {
+const std::vector<ov::element::Type_t> netPrecisions = {
+        ov::element::f16,
+        ov::element::f32,
+};
+const std::vector<std::map<std::string, std::string>> configs = {
+        {},
+};
+const std::vector<std::map<std::string, std::string>> multiConfigs = {
+        {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, CommonTestUtils::DEVICE_MYRIAD}}};
+
+const std::vector<std::map<std::string, std::string>> heteroConfigs = {
+        {{"TARGET_FALLBACK", CommonTestUtils::DEVICE_MYRIAD}}};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests,
+                         OVExecGraphImportExportTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_MYRIAD),
+                                 ::testing::ValuesIn(configs)),
+                         OVExecGraphImportExportTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests,
+         OVExecGraphImportExportTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(netPrecisions),
+                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                ::testing::ValuesIn(multiConfigs)),
+        OVExecGraphImportExportTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests,
+         OVExecGraphImportExportTest,
+        ::testing::Combine(::testing::ValuesIn(netPrecisions),
+                           ::testing::Values(CommonTestUtils::DEVICE_HETERO),
+                           ::testing::ValuesIn(heteroConfigs)),
+        OVExecGraphImportExportTest::getTestCaseName);
+
+}  // namespace
diff --git a/src/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp
index 1a7695a867a..aaacb7b8df1 100644
--- a/src/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp
@@ -52,14 +52,19 @@ std::vector<std::string> disabledTestPatterns() {
         // Not implemented yet:
         R"(.*Behavior.*ExecutableNetworkBaseTest.*canSetConfigToExecNet.*)",
         R"(.*Behavior.*ExecutableNetworkBaseTest.*canExport.*)",
-        // TODO: CVS-65013
+        // TODO: Issue 69529
+        R"(.*Behavior.*OVExecGraphImportExportTest.*ieImport.*)",
+        R"(.*Behavior.*OVExecGraphImportExportTest.*ExportedIENetwork.*)",
+        // TODO: Issue 73501
+        R"(.*_Hetero_Behavior.*OVExecGraphImportExportTest.*)",
+        // TODO: Issue 65013
         R"(.*LoadNetworkCreateDefaultExecGraphResult.*)",
         // Not expected behavior
         R"(.*Behavior.*ExecNetSetPrecision.*canSetOutputPrecisionForNetwork.*U8.*)",
         R"(.*CoreThreadingTestsWithIterations.*)",
         R"(.*OVExecutableNetworkBaseTest.*CanSetConfigToExecNet.*)",
         R"(.*OVClassNetworkTestP.*(SetAffinityWithConstantBranches|SetAffinityWithKSO).*)",
-        // TODO: Issue: CVS-69640
+        // TODO: Issue 69640
         R"(.*EltwiseLayerTest.*OpType=Prod.*)",
         R"(.*EltwiseLayerTest.*OpType=SqDiff.*PARAMETER.*SCALAR.*)",
         R"(.*EltwiseLayerTest.*TS=\(\(16\.16\.96\)_\(96\)_\).*OpType=SqDiff.*)",

From e9874ec1d41bfee303e0bdd0b9ba92c3b88425c6 Mon Sep 17 00:00:00 2001
From: Fedor Zharinov <fedor.zharinov@intel.com>
Date: Fri, 17 Dec 2021 12:20:43 +0300
Subject: [PATCH 014/160] Dynamic reshapes (#7788)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Merged and compiling

* Fix for dynamic shape type

* review fixes

* renamed blob shape to tensor shape, small improvements

* fix code style

* added parsing of multiple shapes

* store latency per group, add isIdleRequestAvailable() to Infer Queue

* added cached random inputs

* redesign pipeline, added new metrics(avg, max, min), added metrics per groups

* fixed code style

* small improvements

* modified tensor parameters parsing

* modified -i parameter parsing: added possibility to specify input names

* implemented image cashing

* added cashed blobs creating

* added -pcseq flag, modified batch filling, changes fps formula

* improvements

* code formatting

* code formatting2

* apply suggestions from review

* replaced Buffer class with InferenceEngine Blobs

* use batch size in blobs filling

* added shared blob allocator to handle blob's data

* fixed warnings & code style

* allocate blobs

* fix for networks with image info input

* added comments & fixed codestyle

* clear data in free() in SharedBlobAllocator

* remove unnecessary check

* Delimeter is changed to ::

* stylefix

* added layout from string function, small improvements

* modified parsing to enable : in input parameters

* small fixes

* small fixes

* added missed blob allocation, fixes

* [TEST]added support for remote blobs

* fix remote blobs

* new inputs/files output format

* removed vectors resize which caused bugs

* made cl::Buffer type under ifdef, fix inputs filling

* changed batch() function to not throwing exceptions

* removed unused var

* fix code style

* replace empty name in input files with name from net input

* restored old behaviour for static models

* fix code style

* fix warning - made const iterator

* fix warning - remove reference in loop variable

* added random and image_info input types to -i, fix problem with layout

* replaced batch() with getBatchSize() in main

* fix layout, shape, tensor shape parameters parsing

* upd help messages for input, tensor shape and pcseq command

* added buffer for cl output blobs, small fixes

Signed-off-by: ivikhrev <ivan.vikhrev@intel.com>

* added legacy mode

* restore setBlob

* code style formatting

* move collecting latency for groups under flag

* removed not applicable layouts

* added hint to error message when wrong input name in -tensor_shape was specified

* added new metrics to statistics report

* Apply suggestions from code review

* fix binary blobs filling when layout is CN

* apply suggestions

* moved file in the right place after rebase

* improved -pcseq output

* updated args and readme

* removed TEMPLATE plugin registration

* fix -shape arg  decsription

* enable providing several -i args as input

* renamed legacy_mode to inference_only and made it default for static models, renamed tensor_shape to data_shape

* upd readme

* use getBlob() in inference only mode

* fix old input type for static case

* fix typo

* upd readme

* move log about benchmark mode to the measuring perfomance step

* added class for latency metrics

* upd readme, fix typos, renamed funcs

* fix warning and upd parsing to avoid error with : in file paths

* fix error on centos : error: use of deleted function ‘std::basic_stringstream<char>::basic_stringstream(const std::basic_stringstream<char>&)

* added check for key in inputs

* renamed input to inputs

* adjust batch size for binary blobs

* replaced warning with exception in bench mode defining

* align measurement cycle with master

Co-authored-by: ivikhrev <ivan.vikhrev@intel.com>
---
 samples/cpp/benchmark_app/README.md           | 102 ++-
 samples/cpp/benchmark_app/benchmark_app.hpp   |  47 +-
 .../cpp/benchmark_app/infer_request_wrap.hpp  |  61 +-
 samples/cpp/benchmark_app/inputs_filling.cpp  | 859 +++++++++++-------
 samples/cpp/benchmark_app/inputs_filling.hpp  |  15 +-
 samples/cpp/benchmark_app/main.cpp            | 368 ++++++--
 .../benchmark_app/remote_blobs_filling.cpp    |  89 +-
 .../benchmark_app/remote_blobs_filling.hpp    |  21 +-
 .../benchmark_app/shared_blob_allocator.hpp   |  43 +
 .../cpp/benchmark_app/statistics_report.hpp   |  50 +
 samples/cpp/benchmark_app/utils.cpp           | 170 +++-
 samples/cpp/benchmark_app/utils.hpp           | 268 ++++--
 src/inference/include/ie/ie_input_info.hpp    |   2 +-
 13 files changed, 1533 insertions(+), 562 deletions(-)
 create mode 100644 samples/cpp/benchmark_app/shared_blob_allocator.hpp

diff --git a/samples/cpp/benchmark_app/README.md b/samples/cpp/benchmark_app/README.md
index 67fdfb73535..5b67cbf13ac 100644
--- a/samples/cpp/benchmark_app/README.md
+++ b/samples/cpp/benchmark_app/README.md
@@ -79,11 +79,14 @@ Options:
     -h, --help                  Print a usage message
     -m "<path>"                 Required. Path to an .xml/.onnx/.prototxt file with a trained model or to a .blob files with a trained compiled model.
     -i "<path>"                 Optional. Path to a folder with images and/or binaries or to specific image or binary file.
+                                In case of dynamic shapes networks with several inputs provide the same number of files for each input (except cases with single file for any input):
+                                "input1:1.jpg input2:1.bin", "input1:1.bin,2.bin input2:3.bin input3:4.bin,5.bin ".
+                                Also you can pass specific keys for inputs: "random" - for fillling input with random data, "image_info" - for filling input with image size.
     -d "<device>"               Optional. Specify a target device to infer on (the list of available devices is shown below). Default value is CPU.
                                 Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin.
                                 Use "-d MULTI:<comma-separated_devices_list>" format to specify MULTI plugin.
                                 Use "-d GPU.X" format to specify device id for GPU devices.
-    The application looks for a suitable plugin for the specified device.
+                                The application looks for a suitable plugin for the specified device.
     -l "<absolute_path>"        Required for CPU custom layers. Absolute path to a shared library with the kernels implementations.
           Or
     -c "<absolute_path>"        Required for GPU custom kernels. Absolute path to an .xml file with the kernels description.
@@ -99,11 +102,23 @@ Options:
     -stream_output              Optional. Print progress as a plain text. When specified, an interactive progress bar is replaced with a multiline output.
     -t                          Optional. Time, in seconds, to execute topology.
     -progress                   Optional. Show progress bar (can affect performance measurement). Default values is "false".
-    -shape                      Optional. Set shape for input. For example, "input1[1,3,224,224],input2[1,4]" or "[1,3,224,224]" in case of one input size.
+    -shape                      Optional. Set shape for network input. For example, "input1[1,3,224,224],input2[1,4]" or "[1,3,224,224]" in case of one input size.
+                                This parameter affect model input shape and can be dynamic. For dynamic dimensions use symbol `?` or '-1'. Ex. [?,3,?,?].
+                                For bounded dimensions specify range 'min..max'. Ex. [1..10,3,?,?].
+    -data_shape                 Required for networks with dynamic shapes. Set shape for input blobs.
+                                In case of one input size: "[1,3,224,224]" or "input1[1,3,224,224],input2[1,4]".
+                                In case of several input sizes provide the same number for
+                                each input (except cases with single shape for any input): "[1,3,128,128][3,3,128,128][1,3,320,320]",
+                                "input1[1,1,128,128][1,1,256,256],input2[80,1]" or "input1[1,192][1,384],input2[1,192][1,384],input3[1,192][1,384],input4[1,192][1,384]".
+                                If network shapes are all static specifying the option will cause an exception.
     -layout                     Optional. Prompts how network layouts should be treated by application. For example, "input1[NCHW],input2[NC]" or "[NCHW]" in case of one input size.
     -cache_dir "<path>"         Optional. Enables caching of loaded models to specified directory.
     -load_from_file             Optional. Loads model from file directly without ReadNetwork.
     -latency_percentile         Optional. Defines the percentile to be reported in latency metric. The valid range is [1, 100]. The default value is 50 (median).
+    -inference_only             Optional. Measure only inference stage. Default option for static models.
+                                Dynamic models are measured in full mode which includes inputs setup stage,
+                                inference only mode available for them with single input data shape only.
+                                To enable full mode for static models pass \"false\" value to this argument: ex. -inference_only=false".
 
   CPU-specific performance options:
     -nstreams "<integer>"       Optional. Number of streams to use for inference on the CPU, GPU or MYRIAD devices
@@ -117,16 +132,19 @@ Options:
     -enforcebf16="<true/false>" Optional. By default floating point operations execution in bfloat16 precision are enforced if supported by platform.
     -pin "YES"/"HYBRID_AWARE"/"NUMA"/"NO"
                                 Optional. Explicit inference threads binding options (leave empty to let the OpenVINO to make a choice):
-					            enabling threads->cores pinning ("YES", which is already default for a conventional CPU),
-			                    letting the runtime to decide on the threads->different core types ("HYBRID_AWARE", which is default on the hybrid CPUs)
-			                    threads->(NUMA)nodes ("NUMA") or
-			      	            completely disable ("NO") CPU inference threads pinning.
+                                enabling threads->cores pinning ("YES", which is already default for a conventional CPU),
+                                letting the runtime to decide on the threads->different core types ("HYBRID_AWARE", which is default on the hybrid CPUs)
+                                threads->(NUMA)nodes ("NUMA") or completely disable ("NO") CPU inference threads pinning.
     -ip "U8"/"FP16"/"FP32"      Optional. Specifies precision for all input layers of the network.
     -op "U8"/"FP16"/"FP32"      Optional. Specifies precision for all output layers of the network.
-    -iop                        Optional. Specifies precision for input and output layers by name. Example: -iop "input:FP16, output:FP16". Notice that quotes are required. Overwrites precision from ip and op options for specified layers.
+    -iop                        Optional. Specifies precision for input and output layers by name. Example: -iop "input:FP16, output:FP16". Notice that quotes are required.
+                                Overwrites precision from ip and op options for specified layers.
 
   Statistics dumping options:
-    -report_type "<type>"       Optional. Enable collecting statistics report. "no_counters" report contains configuration options specified, resulting FPS and latency. "average_counters" report extends "no_counters" report and additionally includes average PM counters values for each layer from the network. "detailed_counters" report extends "average_counters" report and additionally includes per-layer PM counters and latency for each executed infer request.
+    -report_type "<type>"       Optional. Enable collecting statistics report. "no_counters" report contains configuration options specified, resulting FPS and latency.
+                                "average_counters" report extends "no_counters" report and additionally includes average PM counters values for each layer from the network.
+                                "detailed_counters" report extends "average_counters" report and additionally includes per-layer PM counters
+                                and latency for each executed infer request.
     -report_folder              Optional. Path to a folder where statistics report is stored.
     -exec_graph_path            Optional. Path to a file where to store executable graph information serialized.
     -pc                         Optional. Report performance counters.
@@ -181,33 +199,55 @@ This section provides step-by-step instructions on how to run the Benchmark Tool
 The application outputs the number of executed iterations, total duration of execution, latency, and throughput.
 Additionally, if you set the `-report_type` parameter, the application outputs statistics report. If you set the `-pc` parameter, the application outputs performance counters. If you set `-exec_graph_path`, the application reports executable graph information serialized. All measurements including per-layer PM counters are reported in milliseconds.
 
-Below are fragments of sample output for CPU and GPU devices:
+Below are fragments of sample output static and dynamic networks:
 
-* For CPU:
+* For static network:
    ```
-   [Step 8/9] Measuring performance (Start inference asynchronously, 60000 ms duration, 4 inference requests in parallel using 4 streams)
-   Progress: [....................] 100.00% done
-
-   [Step 9/9] Dumping statistics report
-   [ INFO ] Statistics collecting was not requested. No reports are dumped.
-   Progress: [....................] 100.00% done
-
-   Count:      4612 iterations
-   Duration:   60110.04 ms
-   Latency:    50.99 ms
-   Throughput: 76.73 FPS
-   ```
-
-* For GPU:
-   ```
-   [Step 10/11] Measuring performance (Start inference asynchronously, 5 inference requests using 4 streams for CPU, limits: 120000 ms duration)
-   Progress: [....................] 100% done
+   [Step 10/11] Measuring performance (Start inference asynchronously, 4 inference requests using 4 streams for CPU, limits: 60000 ms duration)
+   [ INFO ] BENCHMARK IS IN INFERENCE ONLY MODE.
+   [ INFO ] Input blobs will be filled once before performance measurements.
+   [ INFO ] First inference took 26.26 ms
+   Progress: [................... ]  99% done
 
    [Step 11/11] Dumping statistics report
-   Count:      102515 iterations
-   Duration:   120007.38 ms
-   Latency:    5.84 ms
-   Throughput: 854.24 FP
+   [ INFO ] Count:      6640 iterations
+   [ INFO ] Duration:   60039.70 ms
+   [ INFO ] Latency:
+   [ INFO ]        Median:  35.36 ms
+   [ INFO ]        Avg:    36.12 ms
+   [ INFO ]        Min:    18.55 ms
+   [ INFO ]        Max:    88.96 ms
+   [ INFO ] Throughput: 110.59 FPS
+   ```
+
+* For dynamic network:
+   ```
+   [Step 10/11] Measuring performance (Start inference asynchronously, 4 inference requests using 4 streams for CPU, limits: 60000 ms duration)
+   [ INFO ] BENCHMARK IS IN FULL MODE.
+   [ INFO ] Inputs setup stage will be included in performance measurements.
+   [ INFO ] First inference took 26.80 ms
+   Progress: [................... ]  99% done
+
+   [Step 11/11] Dumping statistics report
+   [ INFO ] Count:      5199 iterations
+   [ INFO ] Duration:   60043.34 ms
+   [ INFO ] Latency:
+   [ INFO ]        Median:  41.58 ms
+   [ INFO ]        Avg:    46.07 ms
+   [ INFO ]        Min:    8.44 ms
+   [ INFO ]        Max:    115.65 ms
+   [ INFO ] Latency for each data shape group:
+   [ INFO ] 1. data : [1, 3, 224, 224]
+   [ INFO ]        Median:  38.37 ms
+   [ INFO ]        Avg:    30.29 ms
+   [ INFO ]        Min:    8.44 ms
+   [ INFO ]        Max:    61.30 ms
+   [ INFO ] 2. data : [1, 3, 448, 448]
+   [ INFO ]        Median:  68.21 ms
+   [ INFO ]        Avg:    61.85 ms
+   [ INFO ]        Min:    29.58 ms
+   [ INFO ]        Max:    115.65 ms
+   [ INFO ] Throughput: 86.59 FPS
    ```
 
 ## See Also
diff --git a/samples/cpp/benchmark_app/benchmark_app.hpp b/samples/cpp/benchmark_app/benchmark_app.hpp
index 511b03c4751..35ed1c05ee7 100644
--- a/samples/cpp/benchmark_app/benchmark_app.hpp
+++ b/samples/cpp/benchmark_app/benchmark_app.hpp
@@ -19,7 +19,12 @@ static const char help_message[] = "Print a usage message";
 
 /// @brief message for images argument
 static const char input_message[] =
-    "Optional. Path to a folder with images and/or binaries or to specific image or binary file.";
+    "Optional. Path to a folder with images and/or binaries or to specific image or binary file.\n"
+    "                              In case of dynamic shapes networks with several inputs provide the same number"
+    " of files for each input (except cases with single file for any input):"
+    "\"input1:1.jpg input2:1.bin\", \"input1:1.bin,2.bin input2:3.bin input3:4.bin,5.bin \"."
+    " Also you can pass specific keys for inputs: \"random\" - for fillling input with random data,"
+    " \"image_info\" - for filling input with image size.";
 
 /// @brief message for model argument
 static const char model_message[] =
@@ -136,6 +141,9 @@ static const char progress_message[] =
 // @brief message for performance counters option
 static const char pc_message[] = "Optional. Report performance counters.";
 
+// @brief message for performance counters for sequence option
+static const char pcseq_message[] = "Optional. Report latencies for each shape in -data_shape sequence.";
+
 #ifdef HAVE_DEVICE_MEM_SUPPORT
 // @brief message for switching memory allocation type option
 static const char use_device_mem_message[] =
@@ -155,9 +163,19 @@ static const char dump_config_message[] =
 #endif
 
 static const char shape_message[] =
-    "Optional. Set shape for input. For example, \"input1[1,3,224,224],input2[1,4]\" or "
-    "\"[1,3,224,224]\""
-    " in case of one input size.";
+    "Optional. Set shape for network input. For example, \"input1[1,3,224,224],input2[1,4]\" or \"[1,3,224,224]\""
+    " in case of one input size. This parameter affect model input shape and can be dynamic."
+    " For dynamic dimensions use symbol `?` or '-1'. Ex. [?,3,?,?]."
+    " For bounded dimensions specify range 'min..max'. Ex. [1..10,3,?,?].";
+
+static const char data_shape_message[] =
+    " Required for networks with dynamic shapes. Set shape for input blobs."
+    " In case of one input size: \"[1,3,224,224]\" or \"input1[1,3,224,224],input2[1,4]\"."
+    " In case of several input sizes provide the same number for each input (except cases with single shape for any "
+    "input):"
+    " \"[1,3,128,128][3,3,128,128][1,3,320,320]\", \"input1[1,1,128,128][1,1,256,256],input2[80,1]\""
+    " or \"input1[1,192][1,384],input2[1,192][1,384],input3[1,192][1,384],input4[1,192][1,384]\"."
+    " If network shapes are all static specifying the option will cause an exception.";
 
 static const char layout_message[] =
     "Optional. Prompts how network layouts should be treated by application. "
@@ -196,6 +214,13 @@ static constexpr char input_image_mean_message[] =
     "Values to be provided in the [R, G, B] format. Can be defined for desired input of the model,\n"
     "Example: -imean data[255,255,255],info[255,255,255]\n";
 
+static constexpr char inference_only_message[] =
+    "Optional. Measure only inference stage. Default option for static models. Dynamic models"
+    " are measured in full mode which includes inputs setup stage,"
+    " inference only mode available for them with single input data shape only."
+    " To enable full mode for static models pass \"false\" value to this argument:"
+    " ex. \"-inference_only=false\".\n";
+
 /// @brief Define flag for showing help message <br>
 DEFINE_bool(h, false, help_message);
 
@@ -276,6 +301,9 @@ DEFINE_bool(progress, false, progress_message);
 /// @brief Define flag for showing performance counters <br>
 DEFINE_bool(pc, false, pc_message);
 
+/// @brief Define flag for showing performance sequence counters <br>
+DEFINE_bool(pcseq, false, pcseq_message);
+
 #ifdef HAVE_DEVICE_MEM_SUPPORT
 /// @brief Define flag for switching beetwen host and device memory allocation for input and output buffers
 DEFINE_bool(use_device_mem, false, use_device_mem_message);
@@ -292,6 +320,9 @@ DEFINE_string(dump_config, "", dump_config_message);
 /// @brief Define flag for input shape <br>
 DEFINE_string(shape, "", shape_message);
 
+/// @brief Define flag for input blob shape <br>
+DEFINE_string(data_shape, "", data_shape_message);
+
 /// @brief Define flag for layout shape <br>
 DEFINE_string(layout, "", layout_message);
 
@@ -322,6 +353,9 @@ DEFINE_string(iscale, "", input_image_scale_message);
 /// @brief Define flag for using input image mean <br>
 DEFINE_string(imean, "", input_image_mean_message);
 
+/// @brief Define flag for inference only mode <br>
+DEFINE_bool(inference_only, true, inference_only_message);
+
 /**
  * @brief This function show a help message
  */
@@ -346,8 +380,9 @@ static void showUsage() {
     std::cout << "    -t                        " << execution_time_message << std::endl;
     std::cout << "    -progress                 " << progress_message << std::endl;
     std::cout << "    -shape                    " << shape_message << std::endl;
+    std::cout << "    -data_shape             " << data_shape_message << std::endl;
     std::cout << "    -layout                   " << layout_message << std::endl;
-    std::cout << "    -cache_dir \"<path>\"        " << cache_dir_message << std::endl;
+    std::cout << "    -cache_dir \"<path>\"       " << cache_dir_message << std::endl;
     std::cout << "    -load_from_file           " << load_from_file_message << std::endl;
     std::cout << "    -latency_percentile       " << infer_latency_percentile_message << std::endl;
     std::cout << std::endl << "  device-specific performance options:" << std::endl;
@@ -363,6 +398,7 @@ static void showUsage() {
     std::cout << "    -report_folder            " << report_folder_message << std::endl;
     std::cout << "    -exec_graph_path          " << exec_graph_path_message << std::endl;
     std::cout << "    -pc                       " << pc_message << std::endl;
+    std::cout << "    -pcseq                    " << pcseq_message << std::endl;
 #ifdef USE_OPENCV
     std::cout << "    -dump_config              " << dump_config_message << std::endl;
     std::cout << "    -load_config              " << load_config_message << std::endl;
@@ -373,4 +409,5 @@ static void showUsage() {
     std::cout << "    -iop                        \"<value>\"    " << iop_message << std::endl;
     std::cout << "    -iscale                    " << input_image_scale_message << std::endl;
     std::cout << "    -imean                     " << input_image_mean_message << std::endl;
+    std::cout << "    -inference_only              " << inference_only_message << std::endl;
 }
diff --git a/samples/cpp/benchmark_app/infer_request_wrap.hpp b/samples/cpp/benchmark_app/infer_request_wrap.hpp
index fb142a08da8..774ef8f2153 100644
--- a/samples/cpp/benchmark_app/infer_request_wrap.hpp
+++ b/samples/cpp/benchmark_app/infer_request_wrap.hpp
@@ -18,13 +18,12 @@
 // clang-format off
 #include "inference_engine.hpp"
 
+#include "remote_blobs_filling.hpp"
 #include "statistics_report.hpp"
+#include "utils.hpp"
 // clang-format on
 
-typedef std::chrono::high_resolution_clock Time;
-typedef std::chrono::nanoseconds ns;
-
-typedef std::function<void(size_t id, const double latency)> QueueCallbackFunction;
+typedef std::function<void(size_t id, size_t group_id, const double latency)> QueueCallbackFunction;
 
 /// @brief Wrapper class for InferenceEngine::InferRequest. Handles asynchronous callbacks and calculates execution
 /// time.
@@ -37,10 +36,12 @@ public:
     explicit InferReqWrap(InferenceEngine::ExecutableNetwork& net, size_t id, QueueCallbackFunction callbackQueue)
         : _request(net.CreateInferRequest()),
           _id(id),
-          _callbackQueue(callbackQueue) {
+          _lat_group_id(0),
+          _callbackQueue(callbackQueue),
+          outputClBuffer() {
         _request.SetCompletionCallback([&]() {
             _endTime = Time::now();
-            _callbackQueue(_id, getExecutionTimeInMilliseconds());
+            _callbackQueue(_id, _lat_group_id, getExecutionTimeInMilliseconds());
         });
     }
 
@@ -57,7 +58,7 @@ public:
         _startTime = Time::now();
         _request.Infer();
         _endTime = Time::now();
-        _callbackQueue(_id, getExecutionTimeInMilliseconds());
+        _callbackQueue(_id, _lat_group_id, getExecutionTimeInMilliseconds());
     }
 
     std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> getPerformanceCounts() {
@@ -77,26 +78,48 @@ public:
         return static_cast<double>(execTime.count()) * 0.000001;
     }
 
+    void setLatencyGroupId(size_t id) {
+        _lat_group_id = id;
+    }
+
+    // in case of using GPU memory we need to allocate CL buffer for
+    // output blobs. By encapsulating cl buffer inside InferReqWrap
+    // we will control the number of output buffers and access to it.
+    std::map<std::string, ::gpu::BufferType>& getOutputClBuffer() {
+        return outputClBuffer;
+    }
+
 private:
     InferenceEngine::InferRequest _request;
     Time::time_point _startTime;
     Time::time_point _endTime;
     size_t _id;
+    size_t _lat_group_id;
     QueueCallbackFunction _callbackQueue;
+    std::map<std::string, ::gpu::BufferType> outputClBuffer;
 };
 
 class InferRequestsQueue final {
 public:
-    InferRequestsQueue(InferenceEngine::ExecutableNetwork& net, size_t nireq) {
+    InferRequestsQueue(InferenceEngine::ExecutableNetwork& net,
+                       size_t nireq,
+                       size_t lat_group_n,
+                       bool enable_lat_groups)
+        : enable_lat_groups(enable_lat_groups) {
         for (size_t id = 0; id < nireq; id++) {
-            requests.push_back(std::make_shared<InferReqWrap>(
-                net,
-                id,
-                std::bind(&InferRequestsQueue::putIdleRequest, this, std::placeholders::_1, std::placeholders::_2)));
+            requests.push_back(std::make_shared<InferReqWrap>(net,
+                                                              id,
+                                                              std::bind(&InferRequestsQueue::putIdleRequest,
+                                                                        this,
+                                                                        std::placeholders::_1,
+                                                                        std::placeholders::_2,
+                                                                        std::placeholders::_3)));
             _idleIds.push(id);
         }
+        _latency_groups.resize(lat_group_n);
         resetTimes();
     }
+
     ~InferRequestsQueue() {
         // Inference Request guarantee that it will wait for all asynchronous internal tasks in destructor
         // So it should be released before any context that the request can use inside internal asynchronous tasks
@@ -111,15 +134,21 @@ public:
         _startTime = Time::time_point::max();
         _endTime = Time::time_point::min();
         _latencies.clear();
+        for (auto& group : _latency_groups) {
+            group.clear();
+        }
     }
 
     double getDurationInMilliseconds() {
         return std::chrono::duration_cast<ns>(_endTime - _startTime).count() * 0.000001;
     }
 
-    void putIdleRequest(size_t id, const double latency) {
+    void putIdleRequest(size_t id, size_t lat_group_id, const double latency) {
         std::unique_lock<std::mutex> lock(_mutex);
         _latencies.push_back(latency);
+        if (enable_lat_groups) {
+            _latency_groups[lat_group_id].push_back(latency);
+        }
         _idleIds.push(id);
         _endTime = std::max(Time::now(), _endTime);
         _cv.notify_one();
@@ -147,6 +176,10 @@ public:
         return _latencies;
     }
 
+    std::vector<std::vector<double>> getLatencyGroups() {
+        return _latency_groups;
+    }
+
     std::vector<InferReqWrap::Ptr> requests;
 
 private:
@@ -156,4 +189,6 @@ private:
     Time::time_point _startTime;
     Time::time_point _endTime;
     std::vector<double> _latencies;
+    std::vector<std::vector<double>> _latency_groups;
+    bool enable_lat_groups;
 };
diff --git a/samples/cpp/benchmark_app/inputs_filling.cpp b/samples/cpp/benchmark_app/inputs_filling.cpp
index a9bce26089f..93a8e3895ef 100644
--- a/samples/cpp/benchmark_app/inputs_filling.cpp
+++ b/samples/cpp/benchmark_app/inputs_filling.cpp
@@ -3,7 +3,10 @@
 //
 
 #include <algorithm>
+#include <fstream>
+#include <iomanip>
 #include <memory>
+#include <random>
 #include <string>
 #include <utility>
 #include <vector>
@@ -13,6 +16,8 @@
 #include "format_reader_ptr.h"
 
 #include "inputs_filling.hpp"
+#include "shared_blob_allocator.hpp"
+#include "utils.hpp"
 // clang-format on
 
 using namespace InferenceEngine;
@@ -42,207 +47,464 @@ std::vector<std::string> filterFilesByExtensions(const std::vector<std::string>&
     return filtered;
 }
 
-template <typename T>
-void fillBlobImage(Blob::Ptr& inputBlob,
-                   const std::vector<std::string>& filePaths,
-                   const size_t& batchSize,
-                   const benchmark_app::InputInfo& app_info,
-                   const size_t& requestId,
-                   const size_t& inputId,
-                   const size_t& inputSize) {
-    MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
-    if (!minput) {
-        IE_THROW() << "We expect inputBlob to be inherited from MemoryBlob in "
-                      "fillBlobImage, "
-                   << "but by fact we were not able to cast inputBlob to MemoryBlob";
-    }
-    // locked memory holder should be alive all time while access to its buffer
-    // happens
-    auto minputHolder = minput->wmap();
-    auto inputBlobData = minputHolder.as<T*>();
-
-    /** Collect images data ptrs **/
-    std::vector<std::shared_ptr<uint8_t>> vreader;
-    vreader.reserve(batchSize);
-
-    for (size_t i = 0ULL, inputIndex = requestId * batchSize * inputSize + inputId; i < batchSize;
-         i++, inputIndex += inputSize) {
-        inputIndex %= filePaths.size();
-
-        slog::info << "Prepare image " << filePaths[inputIndex] << slog::endl;
-        FormatReader::ReaderPtr reader(filePaths[inputIndex].c_str());
-        if (reader.get() == nullptr) {
-            slog::warn << "Image " << filePaths[inputIndex] << " cannot be read!" << slog::endl << slog::endl;
-            continue;
-        }
-
-        /** Getting image data **/
-        std::shared_ptr<uint8_t> imageData(reader->getData(app_info.width(), app_info.height()));
-        if (imageData) {
-            vreader.push_back(imageData);
-        }
-    }
-
-    /** Fill input tensor with images. First b channel, then g and r channels **/
-    const size_t numChannels = app_info.channels();
-    const size_t width = app_info.width();
-    const size_t height = app_info.height();
-    /** Iterate over all input images **/
-    for (size_t imageId = 0; imageId < vreader.size(); ++imageId) {
-        /** Iterate over all width **/
-        for (size_t w = 0; w < app_info.width(); ++w) {
-            /** Iterate over all height **/
-            for (size_t h = 0; h < app_info.height(); ++h) {
-                /** Iterate over all channels **/
-                for (size_t ch = 0; ch < numChannels; ++ch) {
-                    /**          [images stride + channels stride + pixel id ] all in
-                     * bytes            **/
-                    size_t offset = imageId * numChannels * width * height +
-                                    (((app_info.layout == "NCHW") || (app_info.layout == "CHW"))
-                                         ? (ch * width * height + h * width + w)
-                                         : (h * width * numChannels + w * numChannels + ch));
-                    inputBlobData[offset] =
-                        (static_cast<T>(vreader.at(imageId).get()[h * width * numChannels + w * numChannels + ch]) -
-                         static_cast<T>(app_info.mean[ch])) /
-                        static_cast<T>(app_info.scale[ch]);
-                }
-            }
-        }
-    }
-}
-
-template <typename T>
-void fillBlobBinary(Blob::Ptr& inputBlob,
-                    const std::vector<std::string>& filePaths,
-                    const size_t& batchSize,
-                    const size_t& requestId,
-                    const size_t& inputId,
-                    const size_t& inputSize) {
-    MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
-    auto adjBatchSize = batchSize;
-
-    // Check layout
-    std::stringstream ss;
-    auto tensorDesc = inputBlob->getTensorDesc();
-    ss << tensorDesc.getLayout();
-    auto layout = ss.str();
-    std::size_t batchIndex = layout.find("N");
-    if (batchIndex == std::string::npos) {
-        adjBatchSize = 1;
-    } else if (tensorDesc.getDims().at(batchIndex) != batchSize) {
-        adjBatchSize = tensorDesc.getDims().at(batchIndex);
-    }
-
-    if (!minput) {
-        IE_THROW() << "We expect inputBlob to be inherited from MemoryBlob in "
-                      "fillBlobBinary, "
-                   << "but by fact we were not able to cast inputBlob to MemoryBlob";
-    }
-    // locked memory holder should be alive all time while access to its buffer
-    // happens
-    auto minputHolder = minput->wmap();
-
-    auto inputBlobData = minputHolder.as<char*>();
-    for (size_t i = 0ULL, inputIndex = requestId * adjBatchSize * inputSize + inputId; i < adjBatchSize;
-         i++, inputIndex += inputSize) {
-        inputIndex %= filePaths.size();
-
-        slog::info << "Prepare binary file " << filePaths[inputIndex] << slog::endl;
-        std::ifstream binaryFile(filePaths[inputIndex], std::ios_base::binary | std::ios_base::ate);
-        if (!binaryFile) {
-            IE_THROW() << "Cannot open " << filePaths[inputIndex];
-        }
-
-        auto fileSize = static_cast<std::size_t>(binaryFile.tellg());
-        binaryFile.seekg(0, std::ios_base::beg);
-        if (!binaryFile.good()) {
-            IE_THROW() << "Can not read " << filePaths[inputIndex];
-        }
-        auto inputSize = inputBlob->size() * sizeof(T) / adjBatchSize;
-        if (fileSize != inputSize) {
-            IE_THROW() << "File " << filePaths[inputIndex] << " contains " << std::to_string(fileSize)
-                       << " bytes "
-                          "but the network expects "
-                       << std::to_string(inputSize);
-        }
-        binaryFile.read(&inputBlobData[i * inputSize], inputSize);
-    }
-}
-
 template <typename T>
 using uniformDistribution = typename std::conditional<
     std::is_floating_point<T>::value,
     std::uniform_real_distribution<T>,
     typename std::conditional<std::is_integral<T>::value, std::uniform_int_distribution<T>, void>::type>::type;
 
-template <typename T, typename T2>
-void fillBlobRandom(Blob::Ptr& inputBlob,
-                    T rand_min = std::numeric_limits<uint8_t>::min(),
-                    T rand_max = std::numeric_limits<uint8_t>::max()) {
-    MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
-    if (!minput) {
-        IE_THROW() << "We expect inputBlob to be inherited from MemoryBlob in "
-                      "fillBlobRandom, "
-                   << "but by fact we were not able to cast inputBlob to MemoryBlob";
-    }
-    // locked memory holder should be alive all time while access to its buffer
-    // happens
-    auto minputHolder = minput->wmap();
+template <typename T>
+InferenceEngine::Blob::Ptr createBlobFromImage(const std::vector<std::string>& files,
+                                               size_t inputId,
+                                               size_t batchSize,
+                                               const benchmark_app::InputInfo& inputInfo,
+                                               std::string* filenames_used = nullptr) {
+    size_t blob_size =
+        std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<int>());
+    T* data = new T[blob_size];
 
-    auto inputBlobData = minputHolder.as<T*>();
-    std::mt19937 gen(0);
-    uniformDistribution<T2> distribution(rand_min, rand_max);
-    for (size_t i = 0; i < inputBlob->size(); i++) {
-        inputBlobData[i] = static_cast<T>(distribution(gen));
+    /** Collect images data ptrs **/
+    std::vector<std::shared_ptr<uint8_t>> vreader;
+    vreader.reserve(batchSize);
+
+    for (size_t b = 0; b < batchSize; ++b) {
+        auto inputIndex = (inputId + b) % files.size();
+        if (filenames_used) {
+            *filenames_used += (filenames_used->empty() ? "" : ", ") + files[inputIndex];
+        }
+        FormatReader::ReaderPtr reader(files[inputIndex].c_str());
+        if (reader.get() == nullptr) {
+            slog::warn << "Image " << files[inputIndex] << " cannot be read!" << slog::endl << slog::endl;
+            continue;
+        }
+
+        /** Getting image data **/
+        std::shared_ptr<uint8_t> imageData(reader->getData(inputInfo.width(), inputInfo.height()));
+        if (imageData) {
+            vreader.push_back(imageData);
+        }
     }
+
+    /** Fill input tensor with image. First b channel, then g and r channels **/
+    const size_t numChannels = inputInfo.channels();
+    const size_t width = inputInfo.width();
+    const size_t height = inputInfo.height();
+    /** Iterate over all input images **/
+    for (size_t b = 0; b < batchSize; ++b) {
+        /** Iterate over all width **/
+        for (size_t w = 0; w < width; ++w) {
+            /** Iterate over all height **/
+            for (size_t h = 0; h < height; ++h) {
+                /** Iterate over all channels **/
+                for (size_t ch = 0; ch < numChannels; ++ch) {
+                    /**          [images stride + channels stride + pixel id ] all in
+                     * bytes            **/
+                    size_t offset = b * numChannels * width * height +
+                                    (((inputInfo.layout == "NCHW") || (inputInfo.layout == "CHW"))
+                                         ? (ch * width * height + h * width + w)
+                                         : (h * width * numChannels + w * numChannels + ch));
+                    data[offset] =
+                        (static_cast<T>(vreader.at(b).get()[h * width * numChannels + w * numChannels + ch]) -
+                         static_cast<T>(inputInfo.mean[ch])) /
+                        static_cast<T>(inputInfo.scale[ch]);
+                }
+            }
+        }
+    }
+
+    InferenceEngine::TensorDesc tDesc(inputInfo.precision, inputInfo.dataShape, inputInfo.originalLayout);
+    auto blob =
+        InferenceEngine::make_shared_blob<T>(tDesc,
+                                             std::make_shared<SharedBlobAllocator<T>>(data, blob_size * sizeof(T)));
+    blob->allocate();
+    return blob;
 }
 
 template <typename T>
-void fillBlobImInfo(Blob::Ptr& inputBlob, const size_t& batchSize, std::pair<size_t, size_t> image_size) {
-    MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
-    if (!minput) {
-        IE_THROW() << "We expect inputBlob to be inherited from MemoryBlob in "
-                      "fillBlobImInfo, "
-                   << "but by fact we were not able to cast inputBlob to MemoryBlob";
-    }
-    // locked memory holder should be alive all time while access to its buffer
-    // happens
-    auto minputHolder = minput->wmap();
+InferenceEngine::Blob::Ptr createBlobImInfo(const std::pair<size_t, size_t>& image_size,
+                                            size_t batchSize,
+                                            const benchmark_app::InputInfo& inputInfo) {
+    size_t blob_size =
+        std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<int>());
+    T* data = new T[blob_size];
 
-    auto inputBlobData = minputHolder.as<T*>();
     for (size_t b = 0; b < batchSize; b++) {
-        size_t iminfoSize = inputBlob->size() / batchSize;
+        size_t iminfoSize = blob_size / batchSize;
         for (size_t i = 0; i < iminfoSize; i++) {
             size_t index = b * iminfoSize + i;
             if (0 == i)
-                inputBlobData[index] = static_cast<T>(image_size.first);
+                data[index] = static_cast<T>(image_size.first);
             else if (1 == i)
-                inputBlobData[index] = static_cast<T>(image_size.second);
+                data[index] = static_cast<T>(image_size.second);
             else
-                inputBlobData[index] = 1;
+                data[index] = 1;
         }
     }
+
+    InferenceEngine::TensorDesc tDesc(inputInfo.precision, inputInfo.dataShape, inputInfo.originalLayout);
+    InferenceEngine::Blob::Ptr blob =
+        InferenceEngine::make_shared_blob<T>(tDesc,
+                                             std::make_shared<SharedBlobAllocator<T>>(data, blob_size * sizeof(T)));
+    blob->allocate();
+    return blob;
+}
+
+template <typename T>
+InferenceEngine::Blob::Ptr createBlobFromBinary(const std::vector<std::string>& files,
+                                                size_t inputId,
+                                                size_t batchSize,
+                                                const benchmark_app::InputInfo& inputInfo,
+                                                std::string* filenames_used = nullptr) {
+    size_t blob_size =
+        std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<int>());
+    char* data = new char[blob_size * sizeof(T)];
+
+    // adjust batch size
+    std::stringstream ss;
+    ss << inputInfo.originalLayout;
+    std::string layout = ss.str();
+    if (layout.find("N") == std::string::npos) {
+        batchSize = 1;
+    } else if (inputInfo.batch() != batchSize) {
+        batchSize = inputInfo.batch();
+    }
+
+    for (size_t b = 0; b < batchSize; ++b) {
+        size_t inputIndex = (inputId + b) % files.size();
+        std::ifstream binaryFile(files[inputIndex], std::ios_base::binary | std::ios_base::ate);
+        if (!binaryFile) {
+            IE_THROW() << "Cannot open " << files[inputIndex];
+        }
+
+        auto fileSize = static_cast<std::size_t>(binaryFile.tellg());
+        binaryFile.seekg(0, std::ios_base::beg);
+        if (!binaryFile.good()) {
+            IE_THROW() << "Can not read " << files[inputIndex];
+        }
+        auto inputSize = blob_size * sizeof(T) / batchSize;
+        if (fileSize != inputSize) {
+            IE_THROW() << "File " << files[inputIndex] << " contains " << std::to_string(fileSize)
+                       << " bytes "
+                          "but the network expects "
+                       << std::to_string(inputSize);
+        }
+
+        if (inputInfo.layout != "CN") {
+            binaryFile.read(&data[b * inputSize], inputSize);
+        } else {
+            for (int i = 0; i < inputInfo.channels(); i++) {
+                binaryFile.read(&data[(i * batchSize + b) * sizeof(T)], sizeof(T));
+            }
+        }
+
+        if (filenames_used) {
+            *filenames_used += (filenames_used->empty() ? "" : ", ") + files[inputIndex];
+        }
+    }
+
+    InferenceEngine::TensorDesc tDesc(inputInfo.precision, inputInfo.dataShape, inputInfo.originalLayout);
+    InferenceEngine::Blob::Ptr blob =
+        InferenceEngine::make_shared_blob<T>(tDesc,
+                                             std::make_shared<SharedBlobAllocator<T>>((T*)data, blob_size * sizeof(T)));
+    blob->allocate();
+    return blob;
+}
+
+template <typename T, typename T2>
+InferenceEngine::Blob::Ptr createBlobRandom(const benchmark_app::InputInfo& inputInfo,
+                                            T rand_min = std::numeric_limits<uint8_t>::min(),
+                                            T rand_max = std::numeric_limits<uint8_t>::max()) {
+    size_t blob_size =
+        std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<int>());
+    T* data = new T[blob_size];
+
+    std::mt19937 gen(0);
+    uniformDistribution<T2> distribution(rand_min, rand_max);
+    for (size_t i = 0; i < blob_size; i++) {
+        data[i] = static_cast<T>(distribution(gen));
+    }
+
+    InferenceEngine::TensorDesc tDesc(inputInfo.precision, inputInfo.dataShape, inputInfo.originalLayout);
+    InferenceEngine::Blob::Ptr blob =
+        InferenceEngine::make_shared_blob<T>(tDesc,
+                                             std::make_shared<SharedBlobAllocator<T>>(data, blob_size * sizeof(T)));
+    blob->allocate();
+    return blob;
+}
+
+InferenceEngine::Blob::Ptr getImageBlob(const std::vector<std::string>& files,
+                                        size_t inputId,
+                                        size_t batchSize,
+                                        const std::pair<std::string, benchmark_app::InputInfo>& inputInfo,
+                                        std::string* filenames_used = nullptr) {
+    auto precision = inputInfo.second.precision;
+    if (precision == InferenceEngine::Precision::FP32) {
+        return createBlobFromImage<float>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if (precision == InferenceEngine::Precision::FP16) {
+        return createBlobFromImage<short>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if (precision == InferenceEngine::Precision::I32) {
+        return createBlobFromImage<int32_t>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if (precision == InferenceEngine::Precision::I64) {
+        return createBlobFromImage<int64_t>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if (precision == InferenceEngine::Precision::U8) {
+        return createBlobFromImage<uint8_t>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else {
+        IE_THROW() << "Input precision is not supported for " << inputInfo.first;
+    }
 }
 
-void fillBlobs(const std::vector<std::string>& inputFiles,
-               const size_t& batchSize,
-               benchmark_app::InputsInfo& app_inputs_info,
-               std::vector<InferReqWrap::Ptr> requests) {
-    std::vector<std::pair<size_t, size_t>> input_image_sizes;
-    for (auto& item : app_inputs_info) {
-        if (item.second.isImage()) {
-            input_image_sizes.push_back(std::make_pair(item.second.width(), item.second.height()));
-        }
-        slog::info << "Network input '" << item.first << "' precision " << item.second.precision << ", dimensions ("
-                   << item.second.layout << "): ";
-        for (const auto& i : item.second.shape) {
-            slog::info << i << " ";
-        }
-        slog::info << slog::endl;
+InferenceEngine::Blob::Ptr getImInfoBlob(const std::pair<size_t, size_t>& image_size,
+                                         size_t batchSize,
+                                         const std::pair<std::string, benchmark_app::InputInfo>& inputInfo) {
+    auto precision = inputInfo.second.precision;
+    if (precision == InferenceEngine::Precision::FP32) {
+        return createBlobImInfo<float>(image_size, batchSize, inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::FP16) {
+        return createBlobImInfo<short>(image_size, batchSize, inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::I32) {
+        return createBlobImInfo<int32_t>(image_size, batchSize, inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::I64) {
+        return createBlobImInfo<int64_t>(image_size, batchSize, inputInfo.second);
+    } else {
+        IE_THROW() << "Input precision is not supported for " << inputInfo.first;
+    }
+}
+
+InferenceEngine::Blob::Ptr getBinaryBlob(const std::vector<std::string>& files,
+                                         size_t inputId,
+                                         size_t batchSize,
+                                         const std::pair<std::string, benchmark_app::InputInfo>& inputInfo,
+                                         std::string* filenames_used = nullptr) {
+    auto precision = inputInfo.second.precision;
+    if (precision == InferenceEngine::Precision::FP32) {
+        return createBlobFromBinary<float>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if (precision == InferenceEngine::Precision::FP16) {
+        return createBlobFromBinary<short>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if (precision == InferenceEngine::Precision::I32) {
+        return createBlobFromBinary<int32_t>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if (precision == InferenceEngine::Precision::I64) {
+        return createBlobFromBinary<int64_t>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else if ((precision == InferenceEngine::Precision::U8) || (precision == InferenceEngine::Precision::BOOL)) {
+        return createBlobFromBinary<uint8_t>(files, inputId, batchSize, inputInfo.second, filenames_used);
+    } else {
+        IE_THROW() << "Input precision is not supported for " << inputInfo.first;
+    }
+}
+
+InferenceEngine::Blob::Ptr getRandomBlob(const std::pair<std::string, benchmark_app::InputInfo>& inputInfo) {
+    auto precision = inputInfo.second.precision;
+    if (precision == InferenceEngine::Precision::FP32) {
+        return createBlobRandom<float, float>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::FP16) {
+        return createBlobRandom<short, short>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::I32) {
+        return createBlobRandom<int32_t, int32_t>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::I64) {
+        return createBlobRandom<int64_t, int64_t>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::U8) {
+        // uniform_int_distribution<uint8_t> is not allowed in the C++17
+        // standard and vs2017/19
+        return createBlobRandom<uint8_t, uint32_t>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::I8) {
+        // uniform_int_distribution<int8_t> is not allowed in the C++17 standard
+        // and vs2017/19
+        return createBlobRandom<int8_t, int32_t>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::U16) {
+        return createBlobRandom<uint16_t, uint16_t>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::I16) {
+        return createBlobRandom<int16_t, int16_t>(inputInfo.second);
+    } else if (precision == InferenceEngine::Precision::BOOL) {
+        return createBlobRandom<uint8_t, uint32_t>(inputInfo.second, 0, 1);
+    } else {
+        IE_THROW() << "Input precision is not supported for " << inputInfo.first;
+    }
+}
+
+std::string getTestInfoStreamHeader(benchmark_app::InputInfo& inputInfo) {
+    std::stringstream strOut;
+    strOut << "(" << inputInfo.layout << ", " << inputInfo.precision << ", " << getShapeString(inputInfo.dataShape)
+           << ", ";
+    if (inputInfo.partialShape.is_dynamic()) {
+        strOut << std::string("dyn:") << inputInfo.partialShape << "):\t";
+    } else {
+        strOut << "static):\t";
+    }
+    return strOut.str();
+}
+
+std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> getBlobs(
+    std::map<std::string, std::vector<std::string>>& inputFiles,
+    std::vector<benchmark_app::InputsInfo>& app_inputs_info) {
+    std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> blobs;
+    if (app_inputs_info.empty()) {
+        throw std::logic_error("Inputs Info for network is empty!");
     }
 
-    size_t imageInputCount = input_image_sizes.size();
-    size_t binaryInputCount = app_inputs_info.size() - imageInputCount;
+    if (!inputFiles.empty() && inputFiles.size() != app_inputs_info[0].size()) {
+        throw std::logic_error("Number of inputs specified in -i must be equal number of network inputs!");
+    }
+
+    // count image type inputs of network
+    std::vector<std::pair<size_t, size_t>> net_input_im_sizes;
+    for (auto& inputs_info : app_inputs_info) {
+        for (auto& input : inputs_info) {
+            if (input.second.isImage()) {
+                net_input_im_sizes.push_back(std::make_pair(input.second.width(), input.second.height()));
+            }
+        }
+    }
+
+    for (auto& files : inputFiles) {
+        if (!files.first.empty() && app_inputs_info[0].find(files.first) == app_inputs_info[0].end()) {
+            throw std::logic_error("Input name \"" + files.first +
+                                   "\" used in -i parameter doesn't match any network's input");
+        }
+
+        std::string input_name = files.first.empty() ? app_inputs_info[0].begin()->first : files.first;
+        auto input = app_inputs_info[0].at(input_name);
+        if (!files.second.empty() && files.second[0] != "random" && files.second[0] != "image_info") {
+            if (input.isImage()) {
+                files.second = filterFilesByExtensions(files.second, supported_image_extensions);
+            } else if (input.isImageInfo() && net_input_im_sizes.size() == app_inputs_info.size()) {
+                slog::info << "Input '" << input_name
+                           << "' probably is image info. All files for this input will"
+                              " be ignored."
+                           << slog::endl;
+                files.second = {"image_info"};
+                continue;
+            } else {
+                files.second = filterFilesByExtensions(files.second, supported_binary_extensions);
+            }
+        }
+
+        if (files.second.empty()) {
+            slog::warn << "No suitable files for input found! Random data will be used for input " << input_name
+                       << slog::endl;
+            files.second = {"random"};
+        }
+
+        size_t filesToBeUsed = 0;
+        size_t shapesToBeUsed = 0;
+        if (files.second.size() > app_inputs_info.size()) {
+            shapesToBeUsed = app_inputs_info.size();
+            filesToBeUsed = files.second.size() - files.second.size() % app_inputs_info.size();
+            if (filesToBeUsed != files.second.size()) {
+                slog::warn << "Number of files must be a multiple of the number of shapes for certain input. Only " +
+                                  std::to_string(filesToBeUsed) + " files will be added."
+                           << slog::endl;
+            }
+            while (files.second.size() != filesToBeUsed) {
+                files.second.pop_back();
+            }
+        } else {
+            shapesToBeUsed = app_inputs_info.size() - app_inputs_info.size() % files.second.size();
+            filesToBeUsed = files.second.size();
+            if (shapesToBeUsed != app_inputs_info.size()) {
+                slog::warn << "Number of data shapes must be a multiple of the number of files. For input "
+                           << files.first << " only " + std::to_string(shapesToBeUsed) + " files will be added."
+                           << slog::endl;
+            }
+            while (app_inputs_info.size() != shapesToBeUsed) {
+                app_inputs_info.pop_back();
+                net_input_im_sizes.pop_back();
+            }
+        }
+    }
+
+    std::vector<std::map<std::string, std::string>> logOutput;
+    // All inputs should process equal number of files, so for the case of N, 1, N number of files,
+    // second input also should have N blobs cloned from 1 file
+    size_t filesNum = 0;
+    if (!inputFiles.empty()) {
+        filesNum = std::max_element(inputFiles.begin(),
+                                    inputFiles.end(),
+                                    [](const std::pair<std::string, std::vector<std::string>>& a,
+                                       const std::pair<std::string, std::vector<std::string>>& b) {
+                                        return a.second.size() < b.second.size();
+                                    })
+                       ->second.size();
+    } else {
+        std::vector<std::pair<size_t, size_t>> net_input_im_sizes;
+        for (auto& input_info : app_inputs_info[0]) {
+            inputFiles[input_info.first] = {"random"};
+        }
+    }
+
+    for (const auto& files : inputFiles) {
+        std::string input_name = files.first.empty() ? app_inputs_info[0].begin()->first : files.first;
+        size_t n_shape = 0, m_file = 0;
+        while (n_shape < app_inputs_info.size() || m_file < filesNum) {
+            size_t batchSize = getBatchSize(app_inputs_info[n_shape % app_inputs_info.size()]);
+            size_t inputId = m_file % files.second.size();
+            auto input_info = app_inputs_info[n_shape % app_inputs_info.size()].at(input_name);
+
+            std::string blob_src_info;
+            if (files.second[0] == "random") {
+                // Fill random
+                blob_src_info =
+                    "random (" + std::string((input_info.isImage() ? "image" : "binary data")) + " is expected)";
+                blobs[input_name].push_back(getRandomBlob({input_name, input_info}));
+            } else if (files.second[0] == "image_info") {
+                // Most likely it is image info: fill with image information
+                auto image_size = net_input_im_sizes.at(n_shape % app_inputs_info.size());
+                blob_src_info =
+                    "Image size blob " + std::to_string(image_size.first) + " x " + std::to_string(image_size.second);
+                blobs[input_name].push_back(getImInfoBlob(image_size, batchSize, {input_name, input_info}));
+            } else if (input_info.isImage()) {
+                // Fill with Images
+                blobs[input_name].push_back(
+                    getImageBlob(files.second, inputId, batchSize, {input_name, input_info}, &blob_src_info));
+            } else {
+                // Fill with binary files
+                blobs[input_name].push_back(
+                    getBinaryBlob(files.second, inputId, batchSize, {input_name, input_info}, &blob_src_info));
+            }
+
+            // Preparing info
+            std::string strOut = getTestInfoStreamHeader(input_info) + blob_src_info;
+            if (n_shape >= logOutput.size()) {
+                logOutput.resize(n_shape + 1);
+            }
+            logOutput[n_shape][input_name] += strOut;
+
+            ++n_shape;
+            m_file += batchSize;
+        }
+    }
+
+    for (int i = 0; i < logOutput.size(); i++) {
+        slog::info << "Test Config " << i << slog::endl;
+        auto maxNameWidth = std::max_element(logOutput[i].begin(),
+                                             logOutput[i].end(),
+                                             [](const std::pair<std::string, std::string>& a,
+                                                const std::pair<std::string, std::string>& b) {
+                                                 return a.first.size() < b.first.size();
+                                             })
+                                ->first.size();
+        for (auto inputLog : logOutput[i]) {
+            slog::info << std::left << std::setw(maxNameWidth + 2) << inputLog.first << inputLog.second << slog::endl;
+        }
+    }
+
+    return blobs;
+}
+
+std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> getBlobsStaticCase(
+    const std::vector<std::string>& inputFiles,
+    const size_t& batchSize,
+    benchmark_app::InputsInfo& app_inputs_info,
+    size_t requestsNum) {
+    std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> blobs;
+
+    std::vector<std::pair<size_t, size_t>> net_input_im_sizes;
+    for (auto& item : app_inputs_info) {
+        if (item.second.isImage()) {
+            net_input_im_sizes.push_back(std::make_pair(item.second.width(), item.second.height()));
+        }
+    }
+
+    size_t imageInputsNum = net_input_im_sizes.size();
+    size_t binaryInputsNum = app_inputs_info.size() - imageInputsNum;
 
     std::vector<std::string> binaryFiles;
     std::vector<std::string> imageFiles;
@@ -255,7 +517,7 @@ void fillBlobs(const std::vector<std::string>& inputFiles,
         binaryFiles = filterFilesByExtensions(inputFiles, supported_binary_extensions);
         std::sort(std::begin(binaryFiles), std::end(binaryFiles));
 
-        auto binaryToBeUsed = binaryInputCount * batchSize * requests.size();
+        auto binaryToBeUsed = binaryInputsNum * batchSize * requestsNum;
         if (binaryToBeUsed > 0 && binaryFiles.empty()) {
             std::stringstream ss;
             for (auto& ext : supported_binary_extensions) {
@@ -278,7 +540,7 @@ void fillBlobs(const std::vector<std::string>& inputFiles,
         imageFiles = filterFilesByExtensions(inputFiles, supported_image_extensions);
         std::sort(std::begin(imageFiles), std::end(imageFiles));
 
-        auto imagesToBeUsed = imageInputCount * batchSize * requests.size();
+        auto imagesToBeUsed = imageInputsNum * batchSize * requestsNum;
         if (imagesToBeUsed > 0 && imageFiles.empty()) {
             std::stringstream ss;
             for (auto& ext : supported_image_extensions) {
@@ -299,156 +561,129 @@ void fillBlobs(const std::vector<std::string>& inputFiles,
         }
     }
 
-    for (size_t requestId = 0; requestId < requests.size(); requestId++) {
-        slog::info << "Infer Request " << requestId << " filling" << slog::endl;
+    std::map<std::string, std::vector<std::string>> mappedFiles;
+    size_t imageInputsCount = 0;
+    size_t binaryInputsCount = 0;
+    for (auto& input : app_inputs_info) {
+        if (input.second.isImage()) {
+            mappedFiles[input.first] = {};
+            for (size_t i = 0; i < imageFiles.size(); i += imageInputsNum) {
+                mappedFiles[input.first].push_back(
+                    imageFiles[(imageInputsCount + i) * imageInputsNum % imageFiles.size()]);
+            }
+            ++imageInputsCount;
+        } else {
+            mappedFiles[input.first] = {};
+            if (!binaryFiles.empty()) {
+                for (size_t i = 0; i < binaryFiles.size(); i += binaryInputsNum) {
+                    mappedFiles[input.first].push_back(binaryFiles[(binaryInputsCount + i) % binaryFiles.size()]);
+                }
+            }
+            ++binaryInputsCount;
+        }
+    }
 
+    size_t filesNum = 0;
+    if (!inputFiles.empty()) {
+        filesNum = std::max_element(mappedFiles.begin(),
+                                    mappedFiles.end(),
+                                    [](const std::pair<std::string, std::vector<std::string>>& a,
+                                       const std::pair<std::string, std::vector<std::string>>& b) {
+                                        return a.second.size() < b.second.size();
+                                    })
+                       ->second.size();
+    }
+    size_t test_configs_num = filesNum / batchSize == 0 ? 1 : filesNum / batchSize;
+    std::vector<std::map<std::string, std::string>> logOutput(test_configs_num);
+    for (const auto& files : mappedFiles) {
         size_t imageInputId = 0;
         size_t binaryInputId = 0;
-        for (auto& item : app_inputs_info) {
-            Blob::Ptr inputBlob = requests.at(requestId)->getBlob(item.first);
-            auto app_info = app_inputs_info.at(item.first);
-            auto precision = app_info.precision;
-            if (app_info.isImage()) {
+        auto input_name = files.first;
+        auto input_info = app_inputs_info.at(files.first);
+
+        for (size_t i = 0; i < test_configs_num; ++i) {
+            std::string blob_src_info;
+            if (input_info.isImage()) {
                 if (!imageFiles.empty()) {
                     // Fill with Images
-                    if (precision == InferenceEngine::Precision::FP32) {
-                        fillBlobImage<float>(inputBlob,
-                                             imageFiles,
-                                             batchSize,
-                                             app_info,
-                                             requestId,
-                                             imageInputId++,
-                                             imageInputCount);
-                    } else if (precision == InferenceEngine::Precision::FP16) {
-                        fillBlobImage<short>(inputBlob,
-                                             imageFiles,
-                                             batchSize,
-                                             app_info,
-                                             requestId,
-                                             imageInputId++,
-                                             imageInputCount);
-                    } else if (precision == InferenceEngine::Precision::I32) {
-                        fillBlobImage<int32_t>(inputBlob,
-                                               imageFiles,
-                                               batchSize,
-                                               app_info,
-                                               requestId,
-                                               imageInputId++,
-                                               imageInputCount);
-                    } else if (precision == InferenceEngine::Precision::I64) {
-                        fillBlobImage<int64_t>(inputBlob,
-                                               imageFiles,
-                                               batchSize,
-                                               app_info,
-                                               requestId,
-                                               imageInputId++,
-                                               imageInputCount);
-                    } else if (precision == InferenceEngine::Precision::U8) {
-                        fillBlobImage<uint8_t>(inputBlob,
-                                               imageFiles,
-                                               batchSize,
-                                               app_info,
-                                               requestId,
-                                               imageInputId++,
-                                               imageInputCount);
-                    } else {
-                        IE_THROW() << "Input precision is not supported for " << item.first;
-                    }
+                    blobs[input_name].push_back(
+                        getImageBlob(files.second, imageInputId, batchSize, {input_name, input_info}, &blob_src_info));
+                    imageInputId = (imageInputId + batchSize) % files.second.size();
+                    logOutput[i][input_name] += getTestInfoStreamHeader(input_info) + blob_src_info;
                     continue;
                 }
             } else {
                 if (!binaryFiles.empty()) {
                     // Fill with binary files
-                    if (precision == InferenceEngine::Precision::FP32) {
-                        fillBlobBinary<float>(inputBlob,
-                                              binaryFiles,
-                                              batchSize,
-                                              requestId,
-                                              binaryInputId++,
-                                              binaryInputCount);
-                    } else if (precision == InferenceEngine::Precision::FP16) {
-                        fillBlobBinary<short>(inputBlob,
-                                              binaryFiles,
-                                              batchSize,
-                                              requestId,
-                                              binaryInputId++,
-                                              binaryInputCount);
-                    } else if (precision == InferenceEngine::Precision::I32) {
-                        fillBlobBinary<int32_t>(inputBlob,
-                                                binaryFiles,
-                                                batchSize,
-                                                requestId,
-                                                binaryInputId++,
-                                                binaryInputCount);
-                    } else if (precision == InferenceEngine::Precision::I64) {
-                        fillBlobBinary<int64_t>(inputBlob,
-                                                binaryFiles,
-                                                batchSize,
-                                                requestId,
-                                                binaryInputId++,
-                                                binaryInputCount);
-                    } else if ((precision == InferenceEngine::Precision::U8) ||
-                               (precision == InferenceEngine::Precision::BOOL)) {
-                        fillBlobBinary<uint8_t>(inputBlob,
-                                                binaryFiles,
-                                                batchSize,
-                                                requestId,
-                                                binaryInputId++,
-                                                binaryInputCount);
-                    } else {
-                        IE_THROW() << "Input precision is not supported for " << item.first;
-                    }
+                    blobs[input_name].push_back(getBinaryBlob(files.second,
+                                                              binaryInputId,
+                                                              batchSize,
+                                                              {input_name, input_info},
+                                                              &blob_src_info));
+                    binaryInputId = (binaryInputId + batchSize) % files.second.size();
+                    logOutput[i][input_name] += getTestInfoStreamHeader(input_info) + blob_src_info;
                     continue;
                 }
-
-                if (app_info.isImageInfo() && (input_image_sizes.size() == 1)) {
+                if (input_info.isImageInfo() && (net_input_im_sizes.size() == 1)) {
                     // Most likely it is image info: fill with image information
-                    auto image_size = input_image_sizes.at(0);
-                    slog::info << "Fill input '" << item.first << "' with image size " << image_size.first << "x"
-                               << image_size.second << slog::endl;
-                    if (precision == InferenceEngine::Precision::FP32) {
-                        fillBlobImInfo<float>(inputBlob, batchSize, image_size);
-                    } else if (precision == InferenceEngine::Precision::FP16) {
-                        fillBlobImInfo<short>(inputBlob, batchSize, image_size);
-                    } else if (precision == InferenceEngine::Precision::I32) {
-                        fillBlobImInfo<int32_t>(inputBlob, batchSize, image_size);
-                    } else if (precision == InferenceEngine::Precision::I64) {
-                        fillBlobImInfo<int64_t>(inputBlob, batchSize, image_size);
-                    } else {
-                        IE_THROW() << "Input precision is not supported for image info!";
-                    }
+                    auto image_size = net_input_im_sizes.at(0);
+                    blob_src_info = "Image size blob " + std::to_string(image_size.first) + " x " +
+                                    std::to_string(image_size.second);
+                    blobs[input_name].push_back(getImInfoBlob(image_size, batchSize, {input_name, input_info}));
+                    logOutput[i][input_name] += getTestInfoStreamHeader(input_info) + blob_src_info;
                     continue;
                 }
             }
             // Fill random
-            slog::info << "Fill input '" << item.first << "' with random values ("
-                       << std::string((app_info.isImage() ? "image" : "some binary data")) << " is expected)"
-                       << slog::endl;
-            if (precision == InferenceEngine::Precision::FP32) {
-                fillBlobRandom<float, float>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::FP16) {
-                fillBlobRandom<short, short>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::I32) {
-                fillBlobRandom<int32_t, int32_t>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::I64) {
-                fillBlobRandom<int64_t, int64_t>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::U8) {
-                // uniform_int_distribution<uint8_t> is not allowed in the C++17
-                // standard and vs2017/19
-                fillBlobRandom<uint8_t, uint32_t>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::I8) {
-                // uniform_int_distribution<int8_t> is not allowed in the C++17 standard
-                // and vs2017/19
-                fillBlobRandom<int8_t, int32_t>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::U16) {
-                fillBlobRandom<uint16_t, uint16_t>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::I16) {
-                fillBlobRandom<int16_t, int16_t>(inputBlob);
-            } else if (precision == InferenceEngine::Precision::BOOL) {
-                fillBlobRandom<uint8_t, uint32_t>(inputBlob, 0, 1);
-            } else {
-                IE_THROW() << "Input precision is not supported for " << item.first;
-            }
+            blob_src_info =
+                "random (" + std::string((input_info.isImage() ? "image" : "binary data")) + " is expected)";
+            blobs[input_name].push_back(getRandomBlob({input_name, input_info}));
+            logOutput[i][input_name] += getTestInfoStreamHeader(input_info) + blob_src_info;
         }
     }
+
+    for (int i = 0; i < logOutput.size(); i++) {
+        slog::info << "Test Config " << i << slog::endl;
+        auto maxNameWidth = std::max_element(logOutput[i].begin(),
+                                             logOutput[i].end(),
+                                             [](const std::pair<std::string, std::string>& a,
+                                                const std::pair<std::string, std::string>& b) {
+                                                 return a.first.size() < b.first.size();
+                                             })
+                                ->first.size();
+        for (auto inputLog : logOutput[i]) {
+            slog::info << std::left << std::setw(maxNameWidth + 2) << inputLog.first << inputLog.second << slog::endl;
+        }
+    }
+
+    return blobs;
+}
+
+void copyBlobData(InferenceEngine::Blob::Ptr& dst, const InferenceEngine::Blob::Ptr& src) {
+    if (src->getTensorDesc() != dst->getTensorDesc()) {
+        throw std::runtime_error(
+            "Source and destination blobs tensor descriptions are expected to be equal for data copying.");
+    }
+
+    InferenceEngine::MemoryBlob::Ptr srcMinput = as<InferenceEngine::MemoryBlob>(src);
+    if (!srcMinput) {
+        IE_THROW() << "We expect source blob to be inherited from MemoryBlob in "
+                      "fillBlobImage, "
+                   << "but by fact we were not able to cast source blob to MemoryBlob";
+    }
+    // locked memory holder should be alive all time while access to its buffer
+    // happens
+    auto srcMinputHolder = srcMinput->wmap();
+    auto srcBlobData = srcMinputHolder.as<void*>();
+
+    InferenceEngine::MemoryBlob::Ptr dstMinput = as<InferenceEngine::MemoryBlob>(dst);
+    if (!dstMinput) {
+        IE_THROW() << "We expect destination blob to be inherited from MemoryBlob in "
+                      "fillBlobImage, "
+                   << "but by fact we were not able to cast destination blob to MemoryBlob";
+    }
+    auto dstMinputHolder = dstMinput->wmap();
+    auto dstBlobData = dstMinputHolder.as<void*>();
+
+    std::memcpy(dstBlobData, srcBlobData, src->byteSize());
 }
diff --git a/samples/cpp/benchmark_app/inputs_filling.hpp b/samples/cpp/benchmark_app/inputs_filling.hpp
index 4f6f1a55272..fdbcce07fb3 100644
--- a/samples/cpp/benchmark_app/inputs_filling.hpp
+++ b/samples/cpp/benchmark_app/inputs_filling.hpp
@@ -14,7 +14,14 @@
 #include "utils.hpp"
 // clang-format on
 
-void fillBlobs(const std::vector<std::string>& inputFiles,
-               const size_t& batchSize,
-               benchmark_app::InputsInfo& app_inputs_info,
-               std::vector<InferReqWrap::Ptr> requests);
\ No newline at end of file
+std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> getBlobs(
+    std::map<std::string, std::vector<std::string>>& inputFiles,
+    std::vector<benchmark_app::InputsInfo>& app_inputs_info);
+
+std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> getBlobsStaticCase(
+    const std::vector<std::string>& inputFiles,
+    const size_t& batchSize,
+    benchmark_app::InputsInfo& app_inputs_info,
+    size_t requestsNum);
+
+void copyBlobData(InferenceEngine::Blob::Ptr& dst, const InferenceEngine::Blob::Ptr& src);
diff --git a/samples/cpp/benchmark_app/main.cpp b/samples/cpp/benchmark_app/main.cpp
index 3fc33804197..0debab7af1b 100644
--- a/samples/cpp/benchmark_app/main.cpp
+++ b/samples/cpp/benchmark_app/main.cpp
@@ -34,14 +34,6 @@ using namespace InferenceEngine;
 
 static const size_t progressBarDefaultTotalCount = 1000;
 
-uint64_t getDurationInMilliseconds(uint32_t duration) {
-    return duration * 1000LL;
-}
-
-uint64_t getDurationInNanoseconds(uint32_t duration) {
-    return duration * 1000000000LL;
-}
-
 bool ParseAndCheckCommandLine(int argc, char* argv[]) {
     // ---------------------------Parsing and validating input
     // arguments--------------------------------------
@@ -104,7 +96,7 @@ static void next_step(const std::string additional_info = "") {
         {6, "Configuring input of the model"},
         {7, "Loading the model to the device"},
         {8, "Setting optimal runtime parameters"},
-        {9, "Creating infer requests and filling input blobs with images"},
+        {9, "Creating infer requests and preparing input blobs with data"},
         {10, "Measuring performance"},
         {11, "Dumping statistics report"}};
 
@@ -116,13 +108,6 @@ static void next_step(const std::string additional_info = "") {
               << (additional_info.empty() ? "" : " (" + additional_info + ")") << std::endl;
 }
 
-template <typename T>
-T getMedianValue(const std::vector<T>& vec, std::size_t percentile) {
-    std::vector<T> sortedVec(vec);
-    std::sort(sortedVec.begin(), sortedVec.end());
-    return sortedVec[(sortedVec.size() / 100) * percentile];
-}
-
 /**
  * @brief The entry point of the benchmark application
  */
@@ -180,15 +165,15 @@ int main(int argc, char* argv[]) {
             load_config(FLAGS_load_config, config);
         }
 #endif
-        /** This vector stores paths to the processed images **/
-        std::vector<std::string> inputFiles;
-        parseInputFilesArguments(inputFiles);
+        /** This vector stores paths to the processed images with input names**/
+        auto inputFiles = parseInputArguments(gflags::GetArgvs());
 
         // ----------------- 2. Loading the Inference Engine
         // -----------------------------------------------------------
         next_step();
 
         Core ie;
+
         if (FLAGS_d.find("CPU") != std::string::npos && !FLAGS_l.empty()) {
             // CPU (MKLDNN) extensions is loaded as a shared library and passed as a
             // pointer to base extension
@@ -378,19 +363,10 @@ int main(int argc, char* argv[]) {
             ie.SetConfig(item.second, item.first);
         }
 
-        auto double_to_string = [](const double number) {
-            std::stringstream ss;
-            ss << std::fixed << std::setprecision(2) << number;
-            return ss.str();
-        };
-        auto get_total_ms_time = [](Time::time_point& startTime) {
-            return std::chrono::duration_cast<ns>(Time::now() - startTime).count() * 0.000001;
-        };
-
         size_t batchSize = FLAGS_b;
         Precision precision = Precision::UNSPECIFIED;
         std::string topology_name = "";
-        benchmark_app::InputsInfo app_inputs_info;
+        std::vector<benchmark_app::InputsInfo> app_inputs_info;
         std::string output_name;
 
         // Takes priority over config from file
@@ -398,6 +374,7 @@ int main(int argc, char* argv[]) {
             ie.SetConfig({{CONFIG_KEY(CACHE_DIR), FLAGS_cache_dir}});
         }
 
+        bool isDynamicNetwork = false;
         if (FLAGS_load_from_file && !isNetworkCompiled) {
             next_step();
             slog::info << "Skipping the step for loading network from file" << slog::endl;
@@ -407,14 +384,15 @@ int main(int argc, char* argv[]) {
             slog::info << "Skipping the step for loading network from file" << slog::endl;
             auto startTime = Time::now();
             exeNetwork = ie.LoadNetwork(FLAGS_m, device_name);
-            auto duration_ms = double_to_string(get_total_ms_time(startTime));
+            auto duration_ms = double_to_string(get_duration_ms_till_now(startTime));
             slog::info << "Load network took " << duration_ms << " ms" << slog::endl;
             if (statistics)
                 statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
                                           {{"load network time (ms)", duration_ms}});
             app_inputs_info = getInputsInfo<InputInfo::CPtr>(FLAGS_shape,
                                                              FLAGS_layout,
-                                                             FLAGS_b,
+                                                             batchSize,
+                                                             FLAGS_data_shape,
                                                              FLAGS_iscale,
                                                              FLAGS_imean,
                                                              exeNetwork.GetInputsInfo());
@@ -430,7 +408,7 @@ int main(int argc, char* argv[]) {
 
             auto startTime = Time::now();
             CNNNetwork cnnNetwork = ie.ReadNetwork(FLAGS_m);
-            auto duration_ms = double_to_string(get_total_ms_time(startTime));
+            auto duration_ms = double_to_string(get_duration_ms_till_now(startTime));
             slog::info << "Read network took " << duration_ms << " ms" << slog::endl;
             if (statistics)
                 statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
@@ -444,34 +422,45 @@ int main(int argc, char* argv[]) {
             // ----------------- 5. Resizing network to match image sizes and given
             // batch ----------------------------------
             next_step();
-            batchSize = cnnNetwork.getBatchSize();
             // Parse input shapes if specified
             bool reshape = false;
             app_inputs_info = getInputsInfo<InputInfo::Ptr>(FLAGS_shape,
                                                             FLAGS_layout,
                                                             FLAGS_b,
+                                                            FLAGS_data_shape,
                                                             FLAGS_iscale,
                                                             FLAGS_imean,
                                                             inputInfo,
                                                             reshape);
             if (reshape) {
-                InferenceEngine::ICNNNetwork::InputShapes shapes = {};
-                for (auto& item : app_inputs_info)
-                    shapes[item.first] = item.second.shape;
+                benchmark_app::PartialShapes shapes = {};
+                for (auto& item : app_inputs_info[0])
+                    shapes[item.first] = item.second.partialShape;
                 slog::info << "Reshaping network: " << getShapesString(shapes) << slog::endl;
                 startTime = Time::now();
                 cnnNetwork.reshape(shapes);
-                duration_ms = double_to_string(get_total_ms_time(startTime));
+                duration_ms = double_to_string(get_duration_ms_till_now(startTime));
                 slog::info << "Reshape network took " << duration_ms << " ms" << slog::endl;
                 if (statistics)
                     statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
                                               {{"reshape network time (ms)", duration_ms}});
             }
-            // use batch size according to provided layout and shapes
-            batchSize = (!FLAGS_layout.empty()) ? getBatchSize(app_inputs_info) : cnnNetwork.getBatchSize();
-
             topology_name = cnnNetwork.getName();
-            slog::info << (FLAGS_b != 0 ? "Network batch size was changed to: " : "Network batch size: ") << batchSize
+
+            // Check if network has dynamic shapes
+            auto input_info = app_inputs_info[0];
+            isDynamicNetwork = std::any_of(input_info.begin(),
+                                           input_info.end(),
+                                           [](const std::pair<std::string, benchmark_app::InputInfo>& i) {
+                                               return i.second.partialShape.is_dynamic();
+                                           });
+
+            // use batch size according to provided layout and shapes (static case)
+            if (batchSize == 0 || !isDynamicNetwork) {
+                batchSize = (!FLAGS_layout.empty()) ? getBatchSize(app_inputs_info[0]) : cnnNetwork.getBatchSize();
+            }
+
+            slog::info << (batchSize != 0 ? "Network batch size was changed to: " : "Network batch size: ") << batchSize
                        << slog::endl;
 
             // ----------------- 6. Configuring inputs and outputs
@@ -482,11 +471,12 @@ int main(int argc, char* argv[]) {
             for (auto& item : cnnNetwork.getInputsInfo()) {
                 // if precision for input set by user, then set it to app_inputs
                 // if it an image, set U8
-                if (!FLAGS_ip.empty() || FLAGS_iop.find(item.first) != std::string::npos) {
-                    app_inputs_info.at(item.first).precision = item.second->getPrecision();
-                } else if (app_inputs_info.at(item.first).isImage()) {
-                    app_inputs_info.at(item.first).precision = Precision::U8;
-                    item.second->setPrecision(app_inputs_info.at(item.first).precision);
+                if (!FLAGS_ip.empty() || FLAGS_iop.find(item.first) != std::string::npos ||
+                    item.second->getPartialShape().is_dynamic()) {
+                    app_inputs_info[0].at(item.first).precision = item.second->getPrecision();
+                } else if (app_inputs_info[0].at(item.first).isImage()) {
+                    app_inputs_info[0].at(item.first).precision = Precision::U8;
+                    item.second->setPrecision(app_inputs_info[0].at(item.first).precision);
                 }
             }
 
@@ -496,7 +486,7 @@ int main(int argc, char* argv[]) {
             next_step();
             startTime = Time::now();
             exeNetwork = ie.LoadNetwork(cnnNetwork, device_name);
-            duration_ms = double_to_string(get_total_ms_time(startTime));
+            duration_ms = double_to_string(get_duration_ms_till_now(startTime));
             slog::info << "Load network took " << duration_ms << " ms" << slog::endl;
             if (statistics)
                 statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
@@ -513,7 +503,7 @@ int main(int argc, char* argv[]) {
             next_step();
             auto startTime = Time::now();
             exeNetwork = ie.ImportNetwork(FLAGS_m, device_name, {});
-            auto duration_ms = double_to_string(get_total_ms_time(startTime));
+            auto duration_ms = double_to_string(get_duration_ms_till_now(startTime));
             slog::info << "Import network took " << duration_ms << " ms" << slog::endl;
             if (statistics)
                 statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
@@ -521,6 +511,7 @@ int main(int argc, char* argv[]) {
             app_inputs_info = getInputsInfo<InputInfo::CPtr>(FLAGS_shape,
                                                              FLAGS_layout,
                                                              FLAGS_b,
+                                                             FLAGS_data_shape,
                                                              FLAGS_iscale,
                                                              FLAGS_imean,
                                                              exeNetwork.GetInputsInfo());
@@ -528,6 +519,23 @@ int main(int argc, char* argv[]) {
                 batchSize = 1;
             }
         }
+
+        if (isDynamicNetwork && FLAGS_api == "sync") {
+            throw std::logic_error("Benchmarking of the model with dynamic shapes is available for async API only."
+                                   "Please use -api async -nstreams 1 -nireq 1 to emulate sync behavior");
+        }
+
+        // Defining of benchmark mode
+        // for static models inference only mode is used as default one
+        bool inferenceOnly = FLAGS_inference_only;
+        if (isDynamicNetwork) {
+            if (isFlagSetInCommandLine("inference_only") && inferenceOnly && app_inputs_info.size() != 1) {
+                throw std::logic_error(
+                    "Dynamic models with different input data shapes must be benchmarked only in full mode.");
+            }
+            inferenceOnly = isFlagSetInCommandLine("inference_only") && inferenceOnly && app_inputs_info.size() == 1;
+        }
+
         // ----------------- 8. Querying optimal runtime parameters
         // -----------------------------------------------------
         next_step();
@@ -573,11 +581,21 @@ int main(int argc, char* argv[]) {
 
         // Iteration limit
         uint32_t niter = FLAGS_niter;
+        size_t shape_groups_num = app_inputs_info.size();
         if ((niter > 0) && (FLAGS_api == "async")) {
-            niter = ((niter + nireq - 1) / nireq) * nireq;
-            if (FLAGS_niter != niter) {
-                slog::warn << "Number of iterations was aligned by request number from " << FLAGS_niter << " to "
-                           << niter << " using number of requests " << nireq << slog::endl;
+            if (shape_groups_num > nireq) {
+                niter = ((niter + shape_groups_num - 1) / shape_groups_num) * shape_groups_num;
+                if (FLAGS_niter != niter) {
+                    slog::warn << "Number of iterations was aligned by data shape groups number from " << FLAGS_niter
+                               << " to " << niter << " using number of possible input shapes " << shape_groups_num
+                               << slog::endl;
+                }
+            } else {
+                niter = ((niter + nireq - 1) / nireq) * nireq;
+                if (FLAGS_niter != niter) {
+                    slog::warn << "Number of iterations was aligned by request number from " << FLAGS_niter << " to "
+                               << niter << " using number of requests " << nireq << slog::endl;
+                }
             }
         }
 
@@ -596,6 +614,7 @@ int main(int argc, char* argv[]) {
             statistics->addParameters(
                 StatisticsReport::Category::RUNTIME_CONFIG,
                 {
+                    {"benchmark mode", inferenceOnly ? "inference only" : "full"},
                     {"topology", topology_name},
                     {"target device", device_name},
                     {"API", FLAGS_api},
@@ -619,18 +638,46 @@ int main(int argc, char* argv[]) {
         // ----------------------------------------
         next_step();
 
-        InferRequestsQueue inferRequestsQueue(exeNetwork, nireq);
-        if (isFlagSetInCommandLine("use_device_mem")) {
-            if (device_name.find("GPU") == 0)
-                ::gpu::fillRemoteBlobs(inputFiles, batchSize, app_inputs_info, inferRequestsQueue.requests, exeNetwork);
-            else if (device_name.find("CPU") == 0)
-                fillBlobs(inputFiles, batchSize, app_inputs_info, inferRequestsQueue.requests);
-            else
-                IE_THROW() << "Requested device doesn't support `use_device_mem` option.";
-        } else {
-            fillBlobs(inputFiles, batchSize, app_inputs_info, inferRequestsQueue.requests);
-        }
+        InferRequestsQueue inferRequestsQueue(exeNetwork, nireq, app_inputs_info.size(), FLAGS_pcseq);
 
+        bool inputHasName = false;
+        if (inputFiles.size() > 0) {
+            inputHasName = inputFiles.begin()->first != "";
+        }
+        bool newInputType = isDynamicNetwork || inputHasName;
+        // create vector to store remote input blobs buffer
+        std::vector<::gpu::BufferType> clInputsBuffer;
+        bool useGpuMem = false;
+
+        std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> inputsData;
+        if (isFlagSetInCommandLine("use_device_mem")) {
+            if (device_name.find("GPU") == 0) {
+                inputsData = ::gpu::getRemoteInputBlobs(inputFiles, app_inputs_info, exeNetwork, clInputsBuffer);
+                useGpuMem = true;
+            } else if (device_name.find("CPU") == 0) {
+                if (newInputType) {
+                    inputsData = getBlobs(inputFiles, app_inputs_info);
+                } else {
+                    inputsData =
+                        getBlobsStaticCase(inputFiles.empty() ? std::vector<std::string>{} : inputFiles.begin()->second,
+                                           batchSize,
+                                           app_inputs_info[0],
+                                           nireq);
+                }
+            } else {
+                IE_THROW() << "Requested device doesn't support `use_device_mem` option.";
+            }
+        } else {
+            if (newInputType) {
+                inputsData = getBlobs(inputFiles, app_inputs_info);
+            } else {
+                inputsData =
+                    getBlobsStaticCase(inputFiles.empty() ? std::vector<std::string>{} : inputFiles.begin()->second,
+                                       batchSize,
+                                       app_inputs_info[0],
+                                       nireq);
+            }
+        }
         // ----------------- 10. Measuring performance
         // ------------------------------------------------------------------
         size_t progressCnt = 0;
@@ -668,26 +715,91 @@ int main(int argc, char* argv[]) {
             }
             ss << niter << " iterations";
         }
+
         next_step(ss.str());
 
+        if (inferenceOnly) {
+            slog::info << "BENCHMARK IS IN INFERENCE ONLY MODE." << slog::endl;
+            slog::info << "Input blobs will be filled once before performance measurements." << slog::endl;
+        } else {
+            slog::info << "BENCHMARK IS IN FULL MODE." << slog::endl;
+            slog::info << "Inputs setup stage will be included in performance measurements." << slog::endl;
+        }
+
+        // copy prepared data straight into inferRequest->getBlob()
+        // for inference only mode
+        if (inferenceOnly) {
+            if (nireq < inputsData.begin()->second.size())
+                slog::warn << "Only " << nireq << " test configs will be used." << slog::endl;
+            size_t i = 0;
+            for (auto& inferRequest : inferRequestsQueue.requests) {
+                auto inputs = app_inputs_info[i % app_inputs_info.size()];
+                for (auto& item : inputs) {
+                    auto inputName = item.first;
+                    const auto& inputBlob = inputsData.at(inputName)[i % inputsData.at(inputName).size()];
+                    // for remote blobs setBlob is used, they are already allocated on the device
+                    if (useGpuMem) {
+                        inferRequest->setBlob(inputName, inputBlob);
+                    } else {
+                        InferenceEngine::Blob::Ptr requestBlob = inferRequest->getBlob(inputName);
+                        if (isDynamicNetwork) {
+                            requestBlob->setShape(inputBlob->getTensorDesc().getDims());
+                        }
+                        copyBlobData(requestBlob, inputBlob);
+                    }
+                }
+
+                if (useGpuMem) {
+                    auto outputBlobs = ::gpu::getRemoteOutputBlobs(exeNetwork, inferRequest->getOutputClBuffer());
+                    for (auto& output : exeNetwork.GetOutputsInfo()) {
+                        inferRequest->setBlob(output.first, outputBlobs[output.first]);
+                    }
+                }
+                ++i;
+            }
+        }
+
         // warming up - out of scope
         auto inferRequest = inferRequestsQueue.getIdleRequest();
         if (!inferRequest) {
             IE_THROW() << "No idle Infer Requests!";
         }
+
+        if (!inferenceOnly) {
+            auto inputs = app_inputs_info[0];
+
+            for (auto& item : inputs) {
+                auto inputName = item.first;
+                const auto& data = inputsData.at(inputName)[0];
+                inferRequest->setBlob(inputName, data);
+            }
+
+            if (useGpuMem) {
+                auto outputBlobs = ::gpu::getRemoteOutputBlobs(exeNetwork, inferRequest->getOutputClBuffer());
+                for (auto& output : exeNetwork.GetOutputsInfo()) {
+                    inferRequest->setBlob(output.first, outputBlobs[output.first]);
+                }
+            }
+        }
+
         if (FLAGS_api == "sync") {
             inferRequest->infer();
         } else {
             inferRequest->startAsync();
         }
+
         inferRequestsQueue.waitAll();
+
         auto duration_ms = double_to_string(inferRequestsQueue.getLatencies()[0]);
         slog::info << "First inference took " << duration_ms << " ms" << slog::endl;
-        if (statistics)
+
+        if (statistics) {
             statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
                                       {{"first inference time (ms)", duration_ms}});
+        }
         inferRequestsQueue.resetTimes();
 
+        size_t processedFramesN = 0;
         auto startTime = Time::now();
         auto execTime = std::chrono::duration_cast<ns>(Time::now() - startTime).count();
 
@@ -695,7 +807,6 @@ int main(int argc, char* argv[]) {
         /** to align number if iterations to guarantee that last infer requests are
          * executed in the same conditions **/
         ProgressBar progressBar(progressBarTotalCount, FLAGS_stream_output, FLAGS_progress);
-
         while ((niter != 0LL && iteration < niter) ||
                (duration_nanoseconds != 0LL && (uint64_t)execTime < duration_nanoseconds) ||
                (FLAGS_api == "async" && iteration % nireq != 0)) {
@@ -704,6 +815,31 @@ int main(int argc, char* argv[]) {
                 IE_THROW() << "No idle Infer Requests!";
             }
 
+            if (!inferenceOnly) {
+                auto inputs = app_inputs_info[iteration % app_inputs_info.size()];
+
+                if (FLAGS_pcseq) {
+                    inferRequest->setLatencyGroupId(iteration % app_inputs_info.size());
+                }
+
+                if (isDynamicNetwork) {
+                    batchSize = getBatchSize(inputs);
+                }
+
+                for (auto& item : inputs) {
+                    auto inputName = item.first;
+                    const auto& data = inputsData.at(inputName)[iteration % inputsData.at(inputName).size()];
+                    inferRequest->setBlob(inputName, data);
+                }
+
+                if (useGpuMem) {
+                    auto outputBlobs = ::gpu::getRemoteOutputBlobs(exeNetwork, inferRequest->getOutputClBuffer());
+                    for (auto& output : exeNetwork.GetOutputsInfo()) {
+                        inferRequest->setBlob(output.first, outputBlobs[output.first]);
+                    }
+                }
+            }
+
             if (FLAGS_api == "sync") {
                 inferRequest->infer();
             } else {
@@ -716,9 +852,10 @@ int main(int argc, char* argv[]) {
                 inferRequest->wait();
                 inferRequest->startAsync();
             }
-            iteration++;
+            ++iteration;
 
             execTime = std::chrono::duration_cast<ns>(Time::now() - startTime).count();
+            processedFramesN += batchSize;
 
             if (niter > 0) {
                 progressBar.addProgress(1);
@@ -737,10 +874,17 @@ int main(int argc, char* argv[]) {
         // wait the latest inference executions
         inferRequestsQueue.waitAll();
 
-        double latency = getMedianValue<double>(inferRequestsQueue.getLatencies(), FLAGS_latency_percentile);
+        LatencyMetrics generalLatency(inferRequestsQueue.getLatencies());
+        std::vector<LatencyMetrics> groupLatencies = {};
+        if (FLAGS_pcseq && app_inputs_info.size() > 1) {
+            for (auto lats : inferRequestsQueue.getLatencyGroups()) {
+                groupLatencies.push_back(LatencyMetrics(lats));
+            }
+        }
+
         double totalDuration = inferRequestsQueue.getDurationInMilliseconds();
-        double fps =
-            (FLAGS_api == "sync") ? batchSize * 1000.0 / latency : batchSize * 1000.0 * iteration / totalDuration;
+        double fps = (FLAGS_api == "sync") ? batchSize * 1000.0 / generalLatency.percentile(FLAGS_latency_percentile)
+                                           : 1000.0 * processedFramesN / totalDuration;
 
         if (statistics) {
             statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
@@ -751,19 +895,67 @@ int main(int argc, char* argv[]) {
             if (device_name.find("MULTI") == std::string::npos) {
                 std::string latency_label;
                 if (FLAGS_latency_percentile == 50) {
-                    latency_label = "latency (ms)";
+                    latency_label = "Median latency (ms)";
                 } else {
                     latency_label = "latency (" + std::to_string(FLAGS_latency_percentile) + " percentile) (ms)";
                 }
+                statistics->addParameters(
+                    StatisticsReport::Category::EXECUTION_RESULTS,
+                    {
+                        {latency_label, double_to_string(generalLatency.percentile(FLAGS_latency_percentile))},
+                    });
                 statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
                                           {
-                                              {latency_label, double_to_string(latency)},
+                                              {"Average latency (ms)", double_to_string(generalLatency.average())},
                                           });
+                statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
+                                          {
+                                              {"Min latency (ms)", double_to_string(generalLatency.min())},
+                                          });
+                statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
+                                          {
+                                              {"Max latency (ms)", double_to_string(generalLatency.max())},
+                                          });
+
+                if (FLAGS_pcseq && app_inputs_info.size() > 1) {
+                    statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
+                                              {
+                                                  {"Latency for each data shape group:", ""},
+                                              });
+                    for (size_t i = 0; i < app_inputs_info.size(); ++i) {
+                        std::string data_shapes_string = "";
+                        data_shapes_string += std::to_string(i + 1) + ". ";
+                        for (auto& item : app_inputs_info[i]) {
+                            data_shapes_string += item.first + " : " + getShapeString(item.second.dataShape) + " ";
+                        }
+                        statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
+                                                  {
+                                                      {data_shapes_string, ""},
+                                                  });
+                        statistics->addParameters(
+                            StatisticsReport::Category::EXECUTION_RESULTS,
+                            {
+                                {latency_label,
+                                 double_to_string(groupLatencies[i].percentile(FLAGS_latency_percentile))},
+                            });
+                        statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
+                                                  {
+                                                      {"Average (ms)", double_to_string(groupLatencies[i].average())},
+                                                  });
+                        statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
+                                                  {
+                                                      {"Min (ms)", double_to_string(groupLatencies[i].min())},
+                                                  });
+                        statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
+                                                  {
+                                                      {"Max (ms)", double_to_string(groupLatencies[i].max())},
+                                                  });
+                    }
+                }
             }
             statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS,
                                       {{"throughput", double_to_string(fps)}});
         }
-
         progressBar.finish();
 
         // ----------------- 11. Dumping statistics report
@@ -805,18 +997,32 @@ int main(int argc, char* argv[]) {
         if (statistics)
             statistics->dump();
 
-        std::cout << "Count:      " << iteration << " iterations" << std::endl;
-        std::cout << "Duration:   " << double_to_string(totalDuration) << " ms" << std::endl;
+        // Performance metrics report
+        slog::info << "Count:      " << iteration << " iterations" << slog::endl;
+        slog::info << "Duration:   " << double_to_string(totalDuration) << " ms" << slog::endl;
         if (device_name.find("MULTI") == std::string::npos) {
-            std::cout << "Latency";
-            if (FLAGS_latency_percentile == 50) {
-                std::cout << ":    ";
-            } else {
-                std::cout << " (" << FLAGS_latency_percentile << " percentile):    ";
+            slog::info << "Latency: " << slog::endl;
+            generalLatency.logTotal(FLAGS_latency_percentile);
+
+            if (FLAGS_pcseq && app_inputs_info.size() > 1) {
+                slog::info << "Latency for each data shape group:" << slog::endl;
+                for (size_t i = 0; i < app_inputs_info.size(); ++i) {
+                    slog::info << (i + 1) << ".";
+                    for (auto& item : app_inputs_info[i]) {
+                        std::stringstream input_shape;
+                        auto shape = item.second.dataShape;
+                        std::copy(shape.begin(), shape.end() - 1, std::ostream_iterator<int>(input_shape, ","));
+                        input_shape << shape.back();
+                        slog::info << " " << item.first << " : " << getShapeString(item.second.dataShape);
+                    }
+                    slog::info << slog::endl;
+
+                    groupLatencies[i].logTotal(FLAGS_latency_percentile);
+                }
             }
-            std::cout << double_to_string(latency) << " ms" << std::endl;
         }
-        std::cout << "Throughput: " << double_to_string(fps) << " FPS" << std::endl;
+        slog::info << "Throughput: " << double_to_string(fps) << " FPS" << slog::endl;
+
     } catch (const std::exception& ex) {
         slog::err << ex.what() << slog::endl;
 
diff --git a/samples/cpp/benchmark_app/remote_blobs_filling.cpp b/samples/cpp/benchmark_app/remote_blobs_filling.cpp
index 924d400ba4c..cdb30ceb7e4 100644
--- a/samples/cpp/benchmark_app/remote_blobs_filling.cpp
+++ b/samples/cpp/benchmark_app/remote_blobs_filling.cpp
@@ -2,12 +2,15 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-// clang-format off
 #include <memory>
+#include <random>
 #include <string>
 #include <utility>
 #include <vector>
 
+// clang-format off
+#include <samples/slog.hpp>
+
 #include "remote_blobs_filling.hpp"
 // clang-format on
 
@@ -85,58 +88,98 @@ size_t getBytesPerElement(InferenceEngine::Precision precision) {
     }
 }
 
-void fillRemoteBlobs(const std::vector<std::string>& inputFiles,
-                     const size_t& batchSize,
-                     benchmark_app::InputsInfo& app_inputs_info,
-                     std::vector<InferReqWrap::Ptr> requests,
-                     const InferenceEngine::ExecutableNetwork& exeNetwork) {
+std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> getRemoteInputBlobs(
+    const std::map<std::string, std::vector<std::string>>& inputFiles,
+    const std::vector<benchmark_app::InputsInfo>& app_inputs_info,
+    const InferenceEngine::ExecutableNetwork& exeNetwork,
+    std::vector<BufferType>& clBuffer) {
 #ifdef HAVE_DEVICE_MEM_SUPPORT
     slog::info << "Device memory will be used for input and output blobs" << slog::endl;
     if (inputFiles.size()) {
         slog::warn << "Device memory supports only random data at this moment, input images will be ignored"
                    << slog::endl;
     }
+
+    std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> remoteBlobs;
     auto context = exeNetwork.GetContext();
     auto oclContext = std::dynamic_pointer_cast<InferenceEngine::gpu::ClContext>(context)->get();
     auto oclInstance = std::make_shared<OpenCL>(oclContext);
 
-    auto setShared = [&](size_t requestId,
-                         const std::string name,
-                         const InferenceEngine::TensorDesc& desc,
-                         bool fillRandom = false) {
+    auto setShared = [&](const std::string name, const InferenceEngine::TensorDesc& desc, bool fillRandom = false) {
         cl_int err;
         auto inputDims = desc.getDims();
         auto elementsNum = std::accumulate(begin(inputDims), end(inputDims), 1, std::multiplies<size_t>());
         auto inputSize = elementsNum * getBytesPerElement(desc.getPrecision());
 
-        cl::Buffer sharedBuffer =
-            cl::Buffer(oclInstance->_context, CL_MEM_READ_WRITE, (cl::size_type)inputSize, NULL, &err);
+        clBuffer.push_back(cl::Buffer(oclInstance->_context, CL_MEM_READ_WRITE, (cl::size_type)inputSize, NULL, &err));
 
         if (fillRandom) {
-            void* mappedPtr = oclInstance->_queue.enqueueMapBuffer(sharedBuffer,
+            void* mappedPtr = oclInstance->_queue.enqueueMapBuffer(clBuffer.back(),
                                                                    CL_TRUE,
                                                                    CL_MEM_READ_WRITE,
                                                                    0,
                                                                    (cl::size_type)inputSize);
             fillBuffer(mappedPtr, elementsNum, desc.getPrecision());
-            oclInstance->_queue.enqueueUnmapMemObject(sharedBuffer, mappedPtr);
+            oclInstance->_queue.enqueueUnmapMemObject(clBuffer.back(), mappedPtr);
         }
 
-        InferenceEngine::Blob::Ptr sharedBlob = InferenceEngine::gpu::make_shared_blob(desc, context, sharedBuffer);
-
-        requests.at(requestId)->setBlob(name, sharedBlob);
+        auto blob = InferenceEngine::gpu::make_shared_blob(desc, context, clBuffer.back());
+        remoteBlobs[name].push_back(blob);
     };
 
-    for (size_t requestId = 0; requestId < requests.size(); requestId++) {
-        for (auto& item : exeNetwork.GetInputsInfo())
-            setShared(requestId, item.first, item.second->getTensorDesc(), true);
-
-        for (auto& item : exeNetwork.GetOutputsInfo())
-            setShared(requestId, item.first, item.second->getTensorDesc());
+    for (auto& inputs_info : app_inputs_info) {
+        for (auto& input : inputs_info) {
+            // Fill random
+            slog::info << "Prepare remote blob for input '" << input.first << "' with random values ("
+                       << std::string((input.second.isImage() ? "image" : "some binary data")) << " is expected)"
+                       << slog::endl;
+            setShared(input.first,
+                      InferenceEngine::TensorDesc(input.second.precision,
+                                                  input.second.dataShape,
+                                                  getLayoutFromString(input.second.layout)),
+                      true);
+        }
     }
+
+    return remoteBlobs;
 #else
     IE_THROW() << "Device memory requested for GPU device, but OpenCL was not linked";
 #endif
 }
 
+std::map<std::string, InferenceEngine::Blob::Ptr> getRemoteOutputBlobs(
+    const InferenceEngine::ExecutableNetwork& exeNetwork,
+    std::map<std::string, ::gpu::BufferType>& clBuffer) {
+#ifdef HAVE_DEVICE_MEM_SUPPORT
+    std::map<std::string, InferenceEngine::Blob::Ptr> outputBlobs;
+    for (auto& output : exeNetwork.GetOutputsInfo()) {
+        cl_int err;
+        auto context = exeNetwork.GetContext();
+        auto oclContext = std::dynamic_pointer_cast<InferenceEngine::gpu::ClContext>(context)->get();
+        auto oclInstance = std::make_shared<OpenCL>(oclContext);
+
+        auto desc = output.second->getTensorDesc();
+        auto inputDims = desc.getDims();
+        auto elementsNum = std::accumulate(begin(inputDims), end(inputDims), 1, std::multiplies<size_t>());
+        auto inputSize = elementsNum * getBytesPerElement(desc.getPrecision());
+
+        cl::size_type bufferSize = 0;
+        if (clBuffer.find(output.first) == clBuffer.end()) {
+            clBuffer[output.first] =
+                cl::Buffer(oclInstance->_context, CL_MEM_READ_WRITE, (cl::size_type)inputSize, NULL, &err);
+        } else {
+            auto& buff = clBuffer[output.first];
+            buff.getInfo(CL_MEM_SIZE, &bufferSize);
+            if (inputSize != bufferSize) {
+                buff = cl::Buffer(oclInstance->_context, CL_MEM_READ_WRITE, (cl::size_type)inputSize, NULL, &err);
+            }
+        }
+        outputBlobs[output.first] = InferenceEngine::gpu::make_shared_blob(desc, context, clBuffer[output.first]);
+    }
+
+    return outputBlobs;
+#else
+    IE_THROW() << "Device memory requested for GPU device, but OpenCL was not linked";
+#endif
+}
 }  // namespace gpu
diff --git a/samples/cpp/benchmark_app/remote_blobs_filling.hpp b/samples/cpp/benchmark_app/remote_blobs_filling.hpp
index e3a10e96263..73ce0e5e4ab 100644
--- a/samples/cpp/benchmark_app/remote_blobs_filling.hpp
+++ b/samples/cpp/benchmark_app/remote_blobs_filling.hpp
@@ -2,6 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#pragma once
+
 #if defined(HAVE_GPU_DEVICE_MEM_SUPPORT)
 #    define HAVE_DEVICE_MEM_SUPPORT
 #    include "gpu/gpu_context_api_ocl.hpp"
@@ -10,13 +12,14 @@
 // clang-format off
 #include "inference_engine.hpp"
 
-#include "infer_request_wrap.hpp"
 #include "utils.hpp"
 // clang-format on
 
 namespace gpu {
 
 #ifdef HAVE_DEVICE_MEM_SUPPORT
+using BufferType = cl::Buffer;
+
 struct OpenCL {
     cl::Context _context;
     cl::Device _device;
@@ -55,12 +58,18 @@ struct OpenCL {
         _queue = cl::CommandQueue(_context, _device, props);
     }
 };
+#else
+using BufferType = void*;
 #endif
 
-void fillRemoteBlobs(const std::vector<std::string>& inputFiles,
-                     const size_t& batchSize,
-                     benchmark_app::InputsInfo& app_inputs_info,
-                     std::vector<InferReqWrap::Ptr> requests,
-                     const InferenceEngine::ExecutableNetwork& exeNetwork);
+std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> getRemoteInputBlobs(
+    const std::map<std::string, std::vector<std::string>>& inputFiles,
+    const std::vector<benchmark_app::InputsInfo>& app_inputs_info,
+    const InferenceEngine::ExecutableNetwork& exeNetwork,
+    std::vector<BufferType>& clBuffer);
+
+std::map<std::string, InferenceEngine::Blob::Ptr> getRemoteOutputBlobs(
+    const InferenceEngine::ExecutableNetwork& exeNetwork,
+    std::map<std::string, ::gpu::BufferType>& clBuffer);
 
 }  // namespace gpu
diff --git a/samples/cpp/benchmark_app/shared_blob_allocator.hpp b/samples/cpp/benchmark_app/shared_blob_allocator.hpp
new file mode 100644
index 00000000000..189c522e0d4
--- /dev/null
+++ b/samples/cpp/benchmark_app/shared_blob_allocator.hpp
@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "ie_allocator.hpp"
+
+template <class T>
+class SharedBlobAllocator : public InferenceEngine::IAllocator {
+public:
+    SharedBlobAllocator(const T* data, size_t size) : data(data), size(size){};
+
+    ~SharedBlobAllocator() {
+        free((void*)data);
+    };
+
+    void* lock(void* handle, InferenceEngine::LockOp op = InferenceEngine::LOCK_FOR_WRITE) noexcept override {
+        if (handle == data) {
+            return (void*)data;
+        }
+        return nullptr;
+    }
+
+    void unlock(void* handle) noexcept override{};
+
+    void* alloc(size_t size) noexcept override {
+        return size <= this->size ? (void*)data : nullptr;
+    };
+
+    bool free(void* handle) noexcept override {
+        if (handle == data) {
+            delete[] data;
+            data = nullptr;
+            return true;
+        }
+        return false;
+    };
+
+private:
+    const T* data;
+    size_t size;
+};
diff --git a/samples/cpp/benchmark_app/statistics_report.hpp b/samples/cpp/benchmark_app/statistics_report.hpp
index 0325c7e58de..9ae3c34143e 100644
--- a/samples/cpp/benchmark_app/statistics_report.hpp
+++ b/samples/cpp/benchmark_app/statistics_report.hpp
@@ -11,9 +11,12 @@
 
 // clang-format off
 #include "inference_engine.hpp"
+
 #include "samples/common.hpp"
 #include "samples/csv_dumper.hpp"
 #include "samples/slog.hpp"
+
+#include "utils.hpp"
 // clang-format on
 
 // @brief statistics reports types
@@ -21,6 +24,53 @@ static constexpr char noCntReport[] = "no_counters";
 static constexpr char averageCntReport[] = "average_counters";
 static constexpr char detailedCntReport[] = "detailed_counters";
 
+/// @brief Responsible for calculating different latency metrics
+class LatencyMetrics {
+public:
+    LatencyMetrics() = delete;
+
+    LatencyMetrics(const std::vector<double>& latencies) : latencies(latencies) {
+        if (latencies.empty()) {
+            throw std::logic_error("Latency metrics class expects non-empty vector of latencies at consturction.");
+        }
+        std::sort(this->latencies.begin(), this->latencies.end());
+    }
+
+    LatencyMetrics(std::vector<double>&& latencies) : latencies(latencies) {
+        if (latencies.empty()) {
+            throw std::logic_error("Latency metrics class expects non-empty vector of latencies at consturction.");
+        }
+        std::sort(this->latencies.begin(), this->latencies.end());
+    }
+
+    double min() {
+        return latencies[0];
+    }
+
+    double average() {
+        return std::accumulate(latencies.begin(), latencies.end(), 0.0) / latencies.size();
+    }
+
+    double percentile(std::size_t p) {
+        return latencies[size_t(latencies.size() / 100.0 * p)];
+    }
+
+    double max() {
+        return latencies.back();
+    }
+
+    void logTotal(size_t p) {
+        std::string percentileStr = (p == 50) ? "\tMedian:  " : "\t" + std::to_string(p) + " percentile:    ";
+        slog::info << percentileStr << double_to_string(percentile(p)) << " ms" << slog::endl;
+        slog::info << "\tAvg:    " << double_to_string(average()) << " ms" << slog::endl;
+        slog::info << "\tMin:    " << double_to_string(min()) << " ms" << slog::endl;
+        slog::info << "\tMax:    " << double_to_string(max()) << " ms" << slog::endl;
+    }
+
+private:
+    std::vector<double> latencies;
+};
+
 /// @brief Responsible for collecting of statistics and dumping to .csv file
 class StatisticsReport {
 public:
diff --git a/samples/cpp/benchmark_app/utils.cpp b/samples/cpp/benchmark_app/utils.cpp
index ed21d6e9dd9..752539b5873 100644
--- a/samples/cpp/benchmark_app/utils.cpp
+++ b/samples/cpp/benchmark_app/utils.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-// clang-format off
 #include <algorithm>
 #include <map>
 #include <regex>
@@ -10,8 +9,10 @@
 #include <utility>
 #include <vector>
 
-#include "samples/common.hpp"
-#include "samples/slog.hpp"
+// clang-format off
+#include <samples/args_helper.hpp>
+#include <samples/common.hpp>
+#include <samples/slog.hpp>
 
 #include "utils.hpp"
 // clang-format on
@@ -35,7 +36,7 @@ size_t InputInfo::getDimentionByLayout(char character) const {
     size_t pos = layout.find(character);
     if (pos == std::string::npos)
         throw std::runtime_error("Error: Can't get " + std::string(character, 1) + " from layout " + layout);
-    return shape.at(pos);
+    return dataShape.at(pos);
 }
 size_t InputInfo::width() const {
     return getDimentionByLayout('W');
@@ -152,8 +153,8 @@ size_t getBatchSize(const benchmark_app::InputsInfo& inputs_info) {
         std::size_t batch_index = info.second.layout.find("N");
         if (batch_index != std::string::npos) {
             if (batch_size == 0)
-                batch_size = info.second.shape[batch_index];
-            else if (batch_size != info.second.shape[batch_index])
+                batch_size = info.second.dataShape[batch_index];
+            else if (batch_size != info.second.dataShape[batch_index])
                 throw std::logic_error("Can't deterimine batch size: batch is "
                                        "different for different inputs!");
         }
@@ -163,6 +164,47 @@ size_t getBatchSize(const benchmark_app::InputsInfo& inputs_info) {
     return batch_size;
 }
 
+InferenceEngine::Layout getLayoutFromString(const std::string& string_layout) {
+    static const std::unordered_map<std::string, InferenceEngine::Layout> layouts = {
+        {"NCHW", InferenceEngine::Layout::NCHW},
+        {"NHWC", InferenceEngine::Layout::NHWC},
+        {"NCDHW", InferenceEngine::Layout::NCDHW},
+        {"NDHWC", InferenceEngine::Layout::NDHWC},
+        {"C", InferenceEngine::Layout::C},
+        {"CHW", InferenceEngine::Layout::CHW},
+        {"HWC", InferenceEngine::Layout::HWC},
+        {"HW", InferenceEngine::Layout::HW},
+        {"NC", InferenceEngine::Layout::NC},
+        {"CN", InferenceEngine::Layout::CN}};
+    auto it = layouts.find(string_layout);
+    if (it != layouts.end()) {
+        return it->second;
+    }
+    IE_THROW() << "Unknown layout with name '" << string_layout << "'.";
+}
+
+std::string getShapeString(const InferenceEngine::SizeVector& shape) {
+    std::stringstream ss;
+    ss << "[";
+    for (size_t i = 0; i < shape.size(); ++i) {
+        if (i > 0)
+            ss << ", ";
+        ss << shape.at(i);
+    }
+    ss << "]";
+    return ss.str();
+}
+
+std::string getShapesString(const benchmark_app::PartialShapes& shapes) {
+    std::stringstream ss;
+    for (auto& shape : shapes) {
+        if (!ss.str().empty())
+            ss << ", ";
+        ss << "\'" << shape.first << "': " << shape.second;
+    }
+    return ss.str();
+}
+
 std::string getShapesString(const InferenceEngine::ICNNNetwork::InputShapes& shapes) {
     std::stringstream ss;
     for (auto& shape : shapes) {
@@ -218,6 +260,120 @@ std::map<std::string, std::vector<float>> parseScaleOrMean(const std::string& sc
     return return_value;
 }
 
+std::vector<ngraph::Dimension> parsePartialShape(const std::string& partial_shape) {
+    std::vector<ngraph::Dimension> shape;
+    for (auto& dim : split(partial_shape, ',')) {
+        if (dim == "?" || dim == "-1") {
+            shape.push_back(ngraph::Dimension::dynamic());
+        } else {
+            const std::string range_divider = "..";
+            size_t range_index = dim.find(range_divider);
+            if (range_index != std::string::npos) {
+                std::string min = dim.substr(0, range_index);
+                std::string max = dim.substr(range_index + range_divider.length());
+                shape.push_back(ngraph::Dimension(min.empty() ? 0 : std::stoi(min),
+                                                  max.empty() ? ngraph::Interval::s_max : std::stoi(max)));
+            } else {
+                shape.push_back(std::stoi(dim));
+            }
+        }
+    }
+
+    return shape;
+}
+
+InferenceEngine::SizeVector parseTensorShape(const std::string& dataShape) {
+    std::vector<size_t> shape;
+    for (auto& dim : split(dataShape, ',')) {
+        shape.push_back(std::stoi(dim));
+    }
+    return shape;
+}
+
+std::pair<std::string, std::vector<std::string>> parseInputFiles(const std::string& file_paths_string) {
+    auto search_string = file_paths_string;
+    std::string input_name = "";
+    std::vector<std::string> file_paths;
+
+    // parse strings like <input1>:file1,file2,file3 and get name from them
+    size_t semicolon_pos = search_string.find_first_of(":");
+    size_t quote_pos = search_string.find_first_of("\"");
+    if (semicolon_pos != std::string::npos && quote_pos != std::string::npos && semicolon_pos > quote_pos) {
+        // if : is found after opening " symbol - this means that " belongs to pathname
+        semicolon_pos = std::string::npos;
+    }
+    if (search_string.length() > 2 && semicolon_pos == 1 && search_string[2] == '\\') {
+        // Special case like C:\ denotes drive name, not an input name
+        semicolon_pos = std::string::npos;
+    }
+
+    if (semicolon_pos != std::string::npos) {
+        input_name = search_string.substr(0, semicolon_pos);
+        search_string = search_string.substr(semicolon_pos + 1);
+    }
+
+    // parse file1,file2,file3 and get vector of paths
+    size_t coma_pos = 0;
+    do {
+        coma_pos = search_string.find_first_of(',');
+        file_paths.push_back(search_string.substr(0, coma_pos));
+        if (coma_pos == std::string::npos) {
+            search_string = "";
+            break;
+        }
+        search_string = search_string.substr(coma_pos + 1);
+    } while (coma_pos != std::string::npos);
+
+    if (!search_string.empty())
+        throw std::logic_error("Can't parse file paths for input " + input_name +
+                               " in input parameter string: " + file_paths_string);
+
+    return {input_name, file_paths};
+}
+
+std::map<std::string, std::vector<std::string>> parseInputArguments(const std::vector<std::string>& args) {
+    std::map<std::string, std::vector<std::string>> mapped_files = {};
+    auto args_it = begin(args);
+    const auto is_image_arg = [](const std::string& s) {
+        return s == "-i";
+    };
+    const auto is_arg = [](const std::string& s) {
+        return s.front() == '-';
+    };
+    while (args_it != args.end()) {
+        const auto files_start = std::find_if(args_it, end(args), is_image_arg);
+        if (files_start == end(args)) {
+            break;
+        }
+        const auto files_begin = std::next(files_start);
+        const auto files_end = std::find_if(files_begin, end(args), is_arg);
+        for (auto f = files_begin; f != files_end; ++f) {
+            auto files = parseInputFiles(*f);
+            if (mapped_files.find(files.first) == mapped_files.end()) {
+                mapped_files[files.first] = {};
+            }
+
+            for (auto& file : files.second) {
+                readInputFilesArguments(mapped_files[files.first], file);
+            }
+        }
+        args_it = files_end;
+    }
+    size_t max_files = 20;
+    for (auto& files : mapped_files) {
+        if (files.second.size() <= max_files) {
+            slog::info << "For input " << files.first << " " << files.second.size() << " files were added. "
+                       << slog::endl;
+        } else {
+            slog::info << "For input " << files.first << " " << files.second.size() << " files were added. "
+                       << " The number of files will be limited to " << max_files << "." << slog::endl;
+            files.second.resize(20);
+        }
+    }
+
+    return mapped_files;
+}
+
 #ifdef USE_OPENCV
 void dump_config(const std::string& filename, const std::map<std::string, std::map<std::string, std::string>>& config) {
     auto plugin_to_opencv_format = [](const std::string& str) -> std::string {
@@ -270,4 +426,4 @@ void load_config(const std::string& filename, std::map<std::string, std::map<std
         }
     }
 }
-#endif
\ No newline at end of file
+#endif
diff --git a/samples/cpp/benchmark_app/utils.hpp b/samples/cpp/benchmark_app/utils.hpp
index 0440648a63b..b6c62ff4c21 100644
--- a/samples/cpp/benchmark_app/utils.hpp
+++ b/samples/cpp/benchmark_app/utils.hpp
@@ -4,15 +4,43 @@
 
 #pragma once
 
+#include <chrono>
+#include <iomanip>
 #include <map>
+#include <samples/slog.hpp>
 #include <string>
 #include <vector>
 
+#include "ngraph/partial_shape.hpp"
+
+typedef std::chrono::high_resolution_clock Time;
+typedef std::chrono::nanoseconds ns;
+
+inline uint64_t getDurationInMilliseconds(uint32_t duration) {
+    return duration * 1000LL;
+}
+
+inline uint64_t getDurationInNanoseconds(uint32_t duration) {
+    return duration * 1000000000LL;
+}
+
+inline double get_duration_ms_till_now(Time::time_point& startTime) {
+    return std::chrono::duration_cast<ns>(Time::now() - startTime).count() * 0.000001;
+};
+
+inline std::string double_to_string(const double number) {
+    std::stringstream ss;
+    ss << std::fixed << std::setprecision(2) << number;
+    return ss.str();
+};
+
 namespace benchmark_app {
 struct InputInfo {
     InferenceEngine::Precision precision;
-    InferenceEngine::SizeVector shape;
+    ngraph::PartialShape partialShape;
+    InferenceEngine::SizeVector dataShape;
     std::string layout;
+    InferenceEngine::Layout originalLayout;
     std::vector<float> scale;
     std::vector<float> mean;
     bool isImage() const;
@@ -25,43 +53,56 @@ struct InputInfo {
     size_t depth() const;
 };
 using InputsInfo = std::map<std::string, InputInfo>;
+using PartialShapes = std::map<std::string, ngraph::PartialShape>;
 }  // namespace benchmark_app
 
 std::vector<std::string> parseDevices(const std::string& device_string);
 uint32_t deviceDefaultDeviceDurationInSeconds(const std::string& device);
 std::map<std::string, std::string> parseNStreamsValuePerDevice(const std::vector<std::string>& devices,
                                                                const std::string& values_string);
+
+InferenceEngine::Layout getLayoutFromString(const std::string& string_layout);
+std::string getShapeString(const InferenceEngine::SizeVector& shape);
+std::string getShapesString(const benchmark_app::PartialShapes& shapes);
 std::string getShapesString(const InferenceEngine::ICNNNetwork::InputShapes& shapes);
 size_t getBatchSize(const benchmark_app::InputsInfo& inputs_info);
 std::vector<std::string> split(const std::string& s, char delim);
+
 std::map<std::string, std::vector<float>> parseScaleOrMean(const std::string& scale_mean,
                                                            const benchmark_app::InputsInfo& inputs_info);
+std::vector<ngraph::Dimension> parsePartialShape(const std::string& partial_shape);
+InferenceEngine::SizeVector parseTensorShape(const std::string& data_shape);
+std::pair<std::string, std::vector<std::string>> parseInputFiles(const std::string& file_paths_string);
+std::map<std::string, std::vector<std::string>> parseInputArguments(const std::vector<std::string>& args);
 
 template <typename T>
-std::map<std::string, std::string> parseInputParameters(const std::string parameter_string,
-                                                        const std::map<std::string, T>& input_info) {
-    // Parse parameter string like "input0[value0],input1[value1]" or "[value]" (applied to all
-    // inputs)
-    std::map<std::string, std::string> return_value;
+std::map<std::string, std::vector<std::string>> parseInputParameters(const std::string parameter_string,
+                                                                     const std::map<std::string, T>& input_info) {
+    // Parse parameter string like "[value0]", "[value0][value1]" or "input0[value0][value1],input1[value2][value3]"
+    // (applied to all inputs)
+    std::map<std::string, std::vector<std::string>> return_value;
     std::string search_string = parameter_string;
     auto start_pos = search_string.find_first_of('[');
+    auto input_name = search_string.substr(0, start_pos);
     while (start_pos != std::string::npos) {
         auto end_pos = search_string.find_first_of(']');
         if (end_pos == std::string::npos)
             break;
-        auto input_name = search_string.substr(0, start_pos);
+        if (start_pos)
+            input_name = search_string.substr(0, start_pos);
         auto input_value = search_string.substr(start_pos + 1, end_pos - start_pos - 1);
         if (!input_name.empty()) {
-            return_value[input_name] = input_value;
+            return_value[input_name].push_back(input_value);
         } else {
             for (auto& item : input_info) {
-                return_value[item.first] = input_value;
+                return_value[item.first].push_back(input_value);
             }
         }
         search_string = search_string.substr(end_pos + 1);
-        if (search_string.empty() || search_string.front() != ',')
+        if (search_string.empty() || (search_string.front() != ',' && search_string.front() != '['))
             break;
-        search_string = search_string.substr(1);
+        if (search_string.front() == ',')
+            search_string = search_string.substr(1);
         start_pos = search_string.find_first_of('[');
     }
     if (!search_string.empty())
@@ -70,87 +111,156 @@ std::map<std::string, std::string> parseInputParameters(const std::string parame
 }
 
 template <typename T>
-benchmark_app::InputsInfo getInputsInfo(const std::string& shape_string,
-                                        const std::string& layout_string,
-                                        const size_t batch_size,
-                                        const std::string& scale_string,
-                                        const std::string& mean_string,
-                                        const std::map<std::string, T>& input_info,
-                                        bool& reshape_required) {
-    std::map<std::string, std::string> shape_map = parseInputParameters(shape_string, input_info);
-    std::map<std::string, std::string> layout_map = parseInputParameters(layout_string, input_info);
+std::vector<benchmark_app::InputsInfo> getInputsInfo(const std::string& shape_string,
+                                                     const std::string& layout_string,
+                                                     const size_t batch_size,
+                                                     const std::string& data_shapes_string,
+                                                     const std::string& scale_string,
+                                                     const std::string& mean_string,
+                                                     const std::map<std::string, T>& input_info,
+                                                     bool& reshape_required) {
+    std::map<std::string, std::vector<std::string>> shape_map = parseInputParameters(shape_string, input_info);
+    std::map<std::string, std::vector<std::string>> data_shapes_map =
+        parseInputParameters(data_shapes_string, input_info);
+    std::map<std::string, std::vector<std::string>> layout_map = parseInputParameters(layout_string, input_info);
+
+    size_t min_size = 1, max_size = 1;
+    if (!data_shapes_map.empty()) {
+        min_size = std::min_element(data_shapes_map.begin(),
+                                    data_shapes_map.end(),
+                                    [](std::pair<std::string, std::vector<std::string>> a,
+                                       std::pair<std::string, std::vector<std::string>> b) {
+                                        return a.second.size() < b.second.size() && a.second.size() != 1;
+                                    })
+                       ->second.size();
+
+        max_size = std::max_element(data_shapes_map.begin(),
+                                    data_shapes_map.end(),
+                                    [](std::pair<std::string, std::vector<std::string>> a,
+                                       std::pair<std::string, std::vector<std::string>> b) {
+                                        return a.second.size() < b.second.size();
+                                    })
+                       ->second.size();
+        if (min_size != max_size) {
+            throw std::logic_error(
+                "Shapes number for every input should be either 1 or should be equal to shapes number of other inputs");
+        }
+    }
 
     reshape_required = false;
-    benchmark_app::InputsInfo info_map;
-    for (auto& item : input_info) {
-        benchmark_app::InputInfo info;
-        auto name = item.first;
-        auto descriptor = item.second->getTensorDesc();
-        // Precision
-        info.precision = descriptor.getPrecision();
-        // Shape
-        if (shape_map.count(name)) {
-            std::vector<size_t> parsed_shape;
-            for (auto& dim : split(shape_map.at(name), ',')) {
-                parsed_shape.push_back(std::stoi(dim));
-            }
-            info.shape = parsed_shape;
-            reshape_required = true;
-        } else {
-            info.shape = descriptor.getDims();
-        }
-        // Layout
-        if (layout_map.count(name)) {
-            info.layout = layout_map.at(name);
-            std::transform(info.layout.begin(), info.layout.end(), info.layout.begin(), ::toupper);
-        } else {
-            std::stringstream ss;
-            ss << descriptor.getLayout();
-            info.layout = ss.str();
-        }
-        // Update shape with batch if needed
-        if (batch_size != 0) {
-            std::size_t batch_index = info.layout.find("N");
-            if ((batch_index != std::string::npos) && (info.shape.at(batch_index) != batch_size)) {
-                info.shape[batch_index] = batch_size;
+
+    std::vector<benchmark_app::InputsInfo> info_maps;
+
+    for (size_t i = 0; i < min_size; ++i) {
+        benchmark_app::InputsInfo info_map;
+        for (auto& item : input_info) {
+            benchmark_app::InputInfo info;
+            auto name = item.first;
+            auto descriptor = item.second->getTensorDesc();
+            // Precision
+            info.precision = descriptor.getPrecision();
+            // Partial Shape
+            if (shape_map.count(name)) {
+                std::vector<ngraph::Dimension> parsed_shape;
+                if (shape_map.at(name).size() > 1) {
+                    throw std::logic_error(
+                        "shape command line parameter doesn't support multiple shapes for one input.");
+                }
+                info.partialShape = parsePartialShape(shape_map.at(name)[0]);
                 reshape_required = true;
+            } else {
+                info.partialShape = item.second->getPartialShape();
+            }
+
+            if (info.partialShape.is_dynamic() && info.isImage()) {
+                throw std::logic_error(
+                    "benchmark_app supports only binary and random data as input for dynamic models at this moment.");
+            }
+
+            // Tensor Shape
+            if (info.partialShape.is_dynamic() && data_shapes_map.count(name)) {
+                info.dataShape = parseTensorShape(data_shapes_map.at(name)[i % data_shapes_map.at(name).size()]);
+            } else if (info.partialShape.is_static()) {
+                info.dataShape = info.partialShape.get_shape();
+                if (data_shapes_map.find(name) != data_shapes_map.end()) {
+                    throw std::logic_error(
+                        "Network's input \"" + name +
+                        "\" is static. Use -shape argument for static inputs instead of -data_shape.");
+                }
+            } else if (!data_shapes_map.empty()) {
+                throw std::logic_error("Can't find network input name \"" + name + "\" in \"-data_shape " +
+                                       data_shapes_string + "\" command line parameter");
+            } else {
+                throw std::logic_error(
+                    "data_shape command line parameter should be set in case of network with dynamic shapes.");
+            }
+
+            // Layout
+            info.originalLayout = descriptor.getLayout();
+            if (layout_map.count(name)) {
+                if (layout_map.at(name).size() > 1) {
+                    throw std::logic_error(
+                        "layout command line parameter doesn't support multiple layouts for one input.");
+                }
+                info.layout = layout_map.at(name)[0];
+                std::transform(info.layout.begin(), info.layout.end(), info.layout.begin(), ::toupper);
+            } else {
+                std::stringstream ss;
+                ss << descriptor.getLayout();
+                info.layout = ss.str();
+            }
+            // Update shape with batch if needed (only in static shape case)
+            // Update blob shape only not affecting network shape to trigger dynamic batch size case
+            if (batch_size != 0) {
+                std::size_t batch_index = info.layout.find("N");
+                if ((batch_index != std::string::npos) && (info.dataShape.at(batch_index) != batch_size)) {
+                    if (info.partialShape.is_static()) {
+                        info.partialShape[batch_index] = batch_size;
+                    }
+                    info.dataShape[batch_index] = batch_size;
+                    reshape_required = true;
+                }
+            }
+            info_map[name] = info;
+        }
+
+        // Update scale and mean
+        std::map<std::string, std::vector<float>> scale_map = parseScaleOrMean(scale_string, info_map);
+        std::map<std::string, std::vector<float>> mean_map = parseScaleOrMean(mean_string, info_map);
+
+        for (auto& item : info_map) {
+            if (item.second.isImage()) {
+                item.second.scale.assign({1, 1, 1});
+                item.second.mean.assign({0, 0, 0});
+
+                if (scale_map.count(item.first)) {
+                    item.second.scale = scale_map.at(item.first);
+                }
+                if (mean_map.count(item.first)) {
+                    item.second.mean = mean_map.at(item.first);
+                }
             }
         }
-        info_map[name] = info;
+
+        info_maps.push_back(info_map);
     }
 
-    // Update scale and mean
-    std::map<std::string, std::vector<float>> scale_map = parseScaleOrMean(scale_string, info_map);
-    std::map<std::string, std::vector<float>> mean_map = parseScaleOrMean(mean_string, info_map);
-
-    for (auto& item : info_map) {
-        if (item.second.isImage()) {
-            item.second.scale.assign({1, 1, 1});
-            item.second.mean.assign({0, 0, 0});
-
-            if (scale_map.count(item.first)) {
-                item.second.scale = scale_map.at(item.first);
-            }
-            if (mean_map.count(item.first)) {
-                item.second.mean = mean_map.at(item.first);
-            }
-        }
-    }
-
-    return info_map;
+    return info_maps;
 }
 
 template <typename T>
-benchmark_app::InputsInfo getInputsInfo(const std::string& shape_string,
-                                        const std::string& layout_string,
-                                        const size_t batch_size,
-                                        const std::string& scale_string,
-                                        const std::string& mean_string,
-                                        const std::map<std::string, T>& input_info) {
+std::vector<benchmark_app::InputsInfo> getInputsInfo(const std::string& shape_string,
+                                                     const std::string& layout_string,
+                                                     const size_t batch_size,
+                                                     const std::string& data_shapes_string,
+                                                     const std::string& scale_string,
+                                                     const std::string& mean_string,
+                                                     const std::map<std::string, T>& input_info) {
     bool reshape_required = false;
     return getInputsInfo<T>(shape_string,
                             layout_string,
                             batch_size,
+                            data_shapes_string,
                             scale_string,
                             mean_string,
                             input_info,
@@ -160,4 +270,4 @@ benchmark_app::InputsInfo getInputsInfo(const std::string& shape_string,
 #ifdef USE_OPENCV
 void dump_config(const std::string& filename, const std::map<std::string, std::map<std::string, std::string>>& config);
 void load_config(const std::string& filename, std::map<std::string, std::map<std::string, std::string>>& config);
-#endif
\ No newline at end of file
+#endif
diff --git a/src/inference/include/ie/ie_input_info.hpp b/src/inference/include/ie/ie_input_info.hpp
index 94965b6f5ff..52f86890dc8 100644
--- a/src/inference/include/ie/ie_input_info.hpp
+++ b/src/inference/include/ie/ie_input_info.hpp
@@ -145,7 +145,7 @@ public:
      * @return PartialShape object describing input shape.
      */
     INFERENCE_ENGINE_DEPRECATED("Use OpenVINO 2.0 API for dynamic shapes support")
-    ngraph::PartialShape getPartialShape() {
+    ngraph::PartialShape getPartialShape() const {
         if (!_inputData) {
             IE_THROW() << "Data is empty!";
         }

From 062d86f71f49922885274aff0fbdb26161977c9a Mon Sep 17 00:00:00 2001
From: Sofya Balandina <sofya.balandina@intel.com>
Date: Fri, 17 Dec 2021 12:25:47 +0300
Subject: [PATCH 015/160] [IE TESTS] Fix fill blob with random data for
 precision U4/I4/BIN (#9241)

---
 .../cpu/shared_tests_instances/skip_tests_config.cpp       | 5 -----
 .../gpu/shared_tests_instances/skip_tests_config.cpp       | 5 -----
 .../shared/include/behavior/infer_request/io_blob.hpp      | 4 ++--
 src/tests/ie_test_utils/common_test_utils/data_utils.hpp   | 7 ++++++-
 4 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index d661cd45182..cfdb50a9208 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -127,11 +127,6 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*smoke_PrePostProcess.*two_inputs_trivial.*)",
         // TODO: CVS-67255
         R"(smoke_If.*SimpleIf2OutTest.*)",
-        // TODO: CVS-68525
-        R"(.*CanSetInBlobWithDifferentPrecision/netPRC=(I4|U4).*)",
-        R"(.*CanSetInBlobWithDifferentPrecision/netPRC=BIN.*)",
-        R"(.*CanSetOutBlobWithDifferentPrecision/netPRC=(I4|U4).*)",
-        R"(.*CanSetOutBlobWithDifferentPrecision/netPRC=BIN.*)",
 
         // Issue: 69086
         // need to add support convert BIN -> FP32
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
index 0c5c61b35e8..eeb729c5bdd 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
@@ -79,11 +79,6 @@ std::vector<std::string> disabledTestPatterns() {
             R"(.*EltwiseLayerTest.*OpType=Pow.*NetType=i64.*)",
             // TODO: Issue 67910
             R"(.*smoke_PrePostProcess_GPU.*two_inputs_trivial.*)",
-            // TODO: 68525
-            R"(.*CanSetInBlobWithDifferentPrecision/netPRC=(I4|U4).*)",
-            R"(.*CanSetInBlobWithDifferentPrecision/netPRC=BIN.*)",
-            R"(.*CanSetOutBlobWithDifferentPrecision/netPRC=(I4|U4).*)",
-            R"(.*CanSetOutBlobWithDifferentPrecision/netPRC=BIN.*)",
             // TODO: Issue: 67486
             R"(.*(SoftMaxLayerTest).*)",
             // TODO: Issue: 68712
diff --git a/src/tests/functional/plugin/shared/include/behavior/infer_request/io_blob.hpp b/src/tests/functional/plugin/shared/include/behavior/infer_request/io_blob.hpp
index 94dfc0aaf07..6770563f583 100644
--- a/src/tests/functional/plugin/shared/include/behavior/infer_request/io_blob.hpp
+++ b/src/tests/functional/plugin/shared/include/behavior/infer_request/io_blob.hpp
@@ -346,7 +346,7 @@ protected:
 };
 
 
-TEST_P(InferRequestIOBBlobSetPrecisionTest, CanSetInBlobWithDifferentPrecision) {
+TEST_P(InferRequestIOBBlobSetPrecisionTest, CanSetOutBlobWithDifferentPrecision) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
     // Create InferRequest
     InferenceEngine::InferRequest req;
@@ -362,7 +362,7 @@ TEST_P(InferRequestIOBBlobSetPrecisionTest, CanSetInBlobWithDifferentPrecision)
     }
 }
 
-TEST_P(InferRequestIOBBlobSetPrecisionTest, CanSetOutBlobWithDifferentPrecision) {
+TEST_P(InferRequestIOBBlobSetPrecisionTest, CanSetInBlobWithDifferentPrecision) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
     // Create InferRequest
     InferenceEngine::InferRequest req;
diff --git a/src/tests/ie_test_utils/common_test_utils/data_utils.hpp b/src/tests/ie_test_utils/common_test_utils/data_utils.hpp
index 7bcee22e8f3..7453017077b 100644
--- a/src/tests/ie_test_utils/common_test_utils/data_utils.hpp
+++ b/src/tests/ie_test_utils/common_test_utils/data_utils.hpp
@@ -262,7 +262,12 @@ void inline fill_data_random(InferenceEngine::Blob::Ptr &blob, const uint32_t ra
                              const int32_t k = 1, const int seed = 1) {
     using dataType = typename InferenceEngine::PrecisionTrait<PRC>::value_type;
     auto *rawBlobDataPtr = blob->buffer().as<dataType *>();
-    fill_data_random(rawBlobDataPtr, blob->size(), range, start_from, k, seed);
+    if (PRC == InferenceEngine::Precision::U4 || PRC == InferenceEngine::Precision::I4 ||
+        PRC == InferenceEngine::Precision::BIN) {
+        fill_data_random(rawBlobDataPtr, blob->byteSize(), range, start_from, k, seed);
+    } else {
+        fill_data_random(rawBlobDataPtr, blob->size(), range, start_from, k, seed);
+    }
 }
 
 /** @brief Fill blob with a sorted sequence of unique elements randomly generated.

From 3f5fca80db28f0a4e767bb270b2966324b6e5845 Mon Sep 17 00:00:00 2001
From: Yaroslav Torzuk <yaroslav.torzuk2@altran.com>
Date: Fri, 17 Dec 2021 11:41:05 +0200
Subject: [PATCH 016/160] [GPU] Slice operation (#8267)

---
 .../clDNN/api/intel_gpu/primitives/slice.hpp  |  37 +++++
 .../kernel_selector/common/common_types.h     |   1 +
 .../actual_kernels/slice/slice_kernel_ref.cpp | 111 ++++++++++++++
 .../actual_kernels/slice/slice_kernel_ref.h   |  42 +++++
 .../slice/slice_kernel_selector.cpp           |  18 +++
 .../slice/slice_kernel_selector.h             |  23 +++
 .../core/cl_kernels/slice_ref.cl              |  36 +++++
 .../clDNN/src/impls/ocl/register.cpp          |   1 +
 .../clDNN/src/impls/ocl/register.hpp          |   4 +-
 .../thirdparty/clDNN/src/impls/ocl/slice.cpp  | 138 +++++++++++++++++
 .../thirdparty/clDNN/src/include/slice_inst.h |  38 +++++
 .../thirdparty/clDNN/src/slice.cpp            |  40 +++++
 .../clDNN/tests/test_cases/slice.cpp          | 144 ++++++++++++++++++
 src/core/include/openvino/op/slice.hpp        |   2 +
 src/core/src/op/slice.cpp                     |   2 +
 .../intel_gpu/plugin/primitives_list.hpp      |   1 +
 .../intel_gpu/src/plugin/ops/slice.cpp        |  37 +++++
 17 files changed, 674 insertions(+), 1 deletion(-)
 create mode 100644 inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/slice.hpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.h
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.h
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/slice_ref.cl
 create mode 100644 inference-engine/thirdparty/clDNN/src/impls/ocl/slice.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/src/include/slice_inst.h
 create mode 100644 inference-engine/thirdparty/clDNN/src/slice.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/tests/test_cases/slice.cpp
 create mode 100644 src/plugins/intel_gpu/src/plugin/ops/slice.cpp

diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/slice.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/slice.hpp
new file mode 100644
index 00000000000..1594b4e2393
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/slice.hpp
@@ -0,0 +1,37 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "primitive.hpp"
+
+namespace cldnn {
+/// @addtogroup cpp_api C++ API
+/// @{
+/// @addtogroup cpp_topology Network Topology
+/// @{
+/// @addtogroup cpp_primitives Primitives
+/// @{
+
+/// @brief
+/// @details
+struct slice : public primitive_base<slice> {
+    CLDNN_DECLARE_PRIMITIVE(slice)
+
+    /// @brief Constructs slice primitive.
+    /// @param id This primitive id.
+    /// @param inputs List of primitive ids.
+    slice(const primitive_id& id,
+                  const std::vector<primitive_id>& inputs,
+                  const tensor output_shape,
+                  const primitive_id& ext_prim_id = "",
+                  const padding& output_padding = padding())
+        : primitive_base{id, inputs, ext_prim_id, output_padding},
+          output_shape {output_shape}
+    {}
+
+    tensor output_shape;
+};
+/// @}
+/// @}
+/// @}
+}  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
index c4745cbb1ca..775dc355661 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
@@ -55,6 +55,7 @@ enum class KernelType {
     DEPTH_TO_SPACE,
     BATCH_TO_SPACE,
     SHUFFLE_CHANNELS,
+    SLICE,
     STRIDED_SLICE,
     REVERSE_SEQUENCE,
     BINARY_CONVOLUTION,
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.cpp
new file mode 100644
index 00000000000..5d8d3030e6e
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.cpp
@@ -0,0 +1,111 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include"slice_kernel_ref.h"
+#include <kernel_selector_utils.h>
+#include <vector>
+
+namespace {
+
+void addJitConstantsForAttribute(kernel_selector::JitConstants &jit,
+        const std::string &name, const std::vector<std::int32_t> &attribute) {
+    using namespace kernel_selector;
+    jit.AddConstant(MakeJitConstant(name + "_BATCH", attribute[0]));
+    jit.AddConstant(MakeJitConstant(name + "_FEATURE", attribute[1]));
+    if (attribute.size() == 5) {  // BFZYX
+        jit.AddConstant(MakeJitConstant(name + "_Z", attribute[2]));
+        jit.AddConstant(MakeJitConstant(name + "_Y", attribute[3]));
+        jit.AddConstant(MakeJitConstant(name + "_X", attribute[4]));
+    } else {  // BFYX
+        jit.AddConstant(MakeJitConstant(name + "_Y", attribute[2]));
+        jit.AddConstant(MakeJitConstant(name + "_X", attribute[3]));
+    }
+}
+
+} // anonymous namespace
+
+namespace kernel_selector {
+
+KernelsData SliceKernelRef::GetKernelsData(const Params &params,
+        const optional_params &options) const {
+    if (!Validate(params, options)) {
+        return {};
+    }
+    KernelData kernel_data = KernelData::Default<slice_params>(params);
+    slice_params &new_params =
+            dynamic_cast<slice_params&>(*kernel_data.params.get());
+    auto dispatch_data = SetDefault(new_params, options);
+    auto entry_point = GetEntryPoint(kernelName, new_params.layerID, params, options);
+    auto slice_specific_jit = GetJitConstants(new_params);
+    auto jit = CreateJit(kernelName, slice_specific_jit, entry_point);
+
+    FillCLKernelData(kernel_data.kernels[0], dispatch_data, params.engineInfo,
+            kernelName, jit, entry_point);
+
+    return {kernel_data};
+}
+
+KernelsPriority SliceKernelRef::GetKernelsPriority(const Params&/*params*/,
+        const optional_params&/*options*/) const {
+    return DONT_USE_IF_HAVE_SOMETHING_ELSE;
+}
+
+ParamsKey SliceKernelRef::GetSupportedKey() const {
+    ParamsKey k;
+    k.EnableInputDataType(Datatype::INT8);
+    k.EnableInputDataType(Datatype::UINT8);
+    k.EnableInputDataType(Datatype::F16);
+    k.EnableInputDataType(Datatype::F32);
+    k.EnableInputDataType(Datatype::INT32);
+    k.EnableInputDataType(Datatype::INT64);
+    k.EnableOutputDataType(Datatype::F16);
+    k.EnableOutputDataType(Datatype::F32);
+    k.EnableOutputDataType(Datatype::INT32);
+    k.EnableOutputDataType(Datatype::INT64);
+    k.EnableInputLayout(DataLayout::bfyx);
+    k.EnableInputLayout(DataLayout::bfzyx);
+    k.EnableOutputLayout(DataLayout::bfyx);
+    k.EnableOutputLayout(DataLayout::bfzyx);
+    k.EnableTensorOffset();
+    k.EnableTensorPitches();
+    k.EnableBatching();
+    return k;
+}
+
+bool SliceKernelRef::Validate(const Params &p, const optional_params &o) const {
+    if (p.GetType() != KernelType::SLICE || o.GetType() != KernelType::SLICE) {
+        return false;
+    }
+
+    const slice_params &params = dynamic_cast<const slice_params&>(p);
+    if (params.inputs.empty())
+        return false;
+
+    if (params.output.Dimentions() > 5 || params.inputs[0].Dimentions() > 5)
+        return false;
+
+    return true;
+}
+
+JitConstants SliceKernelRef::GetJitConstants(const slice_params &params) const {
+    JitConstants jit = MakeBaseParamsJitConstants(params);
+    addJitConstantsForAttribute(jit, "SLICE_BEGIN", params.start);
+    addJitConstantsForAttribute(jit, "SLICE_END", params.end);
+    addJitConstantsForAttribute(jit, "SLICE_STEP", params.step);
+    return jit;
+}
+
+CommonDispatchData SliceKernelRef::SetDefault(const slice_params &params,
+        const optional_params&) const {
+    CommonDispatchData dispatchData;
+    dispatchData.gws = { params.output.Batch().v, params.output.Feature().v,
+            params.output.Z().v * params.output.Y().v * params.output.X().v };
+
+    dispatchData.lws = GetOptimalLocalWorkGroupSizes(dispatchData.gws,
+            params.engineInfo);
+
+    return dispatchData;
+}
+
+} // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.h
new file mode 100644
index 00000000000..5cfb3533090
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_ref.h
@@ -0,0 +1,42 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "kernel_base_opencl.h"
+#include <vector>
+
+namespace kernel_selector {
+
+struct slice_params: public base_params {
+    slice_params() : base_params(KernelType::SLICE) {}
+
+    std::vector<std::int32_t> start;
+    std::vector<std::int32_t> end;
+    std::vector<std::int32_t> step;
+};
+
+struct slice_optional_params : optional_params {
+    slice_optional_params() : optional_params(KernelType::SLICE) {}
+};
+
+class SliceKernelRef: public KernelBaseOpenCL {
+public:
+    SliceKernelRef() :
+            KernelBaseOpenCL { "slice_ref" } {
+    }
+    KernelsData GetKernelsData(const Params &params,
+            const optional_params &options) const override;
+    KernelsPriority GetKernelsPriority(const Params &params,
+            const optional_params &options) const override;
+    ParamsKey GetSupportedKey() const override;
+    bool Validate(const Params &p, const optional_params &o) const override;
+
+private:
+    JitConstants GetJitConstants(const slice_params &params) const;
+    CommonDispatchData SetDefault(const slice_params &params,
+            const optional_params&) const;
+};
+
+} // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.cpp
new file mode 100644
index 00000000000..4b6adcec2b9
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.cpp
@@ -0,0 +1,18 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "slice_kernel_selector.h"
+#include "slice_kernel_ref.h"
+
+namespace kernel_selector {
+
+slice_kernel_selector::slice_kernel_selector() {
+    Attach<SliceKernelRef>();
+}
+
+KernelsData slice_kernel_selector::GetBestKernels(const Params &params,
+        const optional_params &options) const {
+    return GetNaiveBestKernel(params, options, KernelType::SLICE);
+}
+
+} // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.h
new file mode 100644
index 00000000000..b6e9c88c137
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/slice/slice_kernel_selector.h
@@ -0,0 +1,23 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <kernel_selector.h>
+
+namespace kernel_selector {
+
+class slice_kernel_selector : public kernel_selector_base {
+public:
+    static slice_kernel_selector& Instance() {
+        static slice_kernel_selector instance_;
+        return instance_;
+    }
+
+    slice_kernel_selector();
+
+    KernelsData GetBestKernels(const Params& params, const optional_params& options) const override;
+};
+
+} // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/slice_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/slice_ref.cl
new file mode 100644
index 00000000000..5ba13f890c0
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/slice_ref.cl
@@ -0,0 +1,36 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "include/batch_headers/fetch_data.cl"
+
+KERNEL(slice_ref)(const __global INPUT0_TYPE* input, __global OUTPUT_TYPE* output)
+{
+    const uint batch = get_global_id(0);
+    const uint feature = get_global_id(1);
+#if INPUT0_DIMS <= 4
+    const uint xy = get_global_id(2);
+    const uint y = xy / OUTPUT_SIZE_X;
+    const uint x = xy % OUTPUT_SIZE_X;
+    const uint output_index = OUTPUT_GET_INDEX(batch, feature, y, x);
+    const uint input_index = INPUT0_GET_INDEX(
+        SLICE_BEGIN_BATCH + batch * SLICE_STEP_BATCH,
+        SLICE_BEGIN_FEATURE + feature * SLICE_STEP_FEATURE,
+        SLICE_BEGIN_Y + y * SLICE_STEP_Y,
+        SLICE_BEGIN_X + x * SLICE_STEP_X);
+#elif INPUT0_DIMS == 5
+    const uint xyz = get_global_id(2);
+    const uint yx = xyz % (OUTPUT_SIZE_X * OUTPUT_SIZE_Y);
+    const uint z = xyz / (OUTPUT_SIZE_X * OUTPUT_SIZE_Y);
+    const uint y = yx / OUTPUT_SIZE_X;
+    const uint x = yx % OUTPUT_SIZE_X;
+    const uint output_index = OUTPUT_GET_INDEX(batch, feature, z, y, x);
+    const uint input_index = INPUT0_GET_INDEX(
+        SLICE_BEGIN_BATCH + batch * SLICE_STEP_BATCH,
+        SLICE_BEGIN_FEATURE + feature * SLICE_STEP_FEATURE,
+        SLICE_BEGIN_Z + z * SLICE_STEP_Z,
+        SLICE_BEGIN_Y + y * SLICE_STEP_Y,
+        SLICE_BEGIN_X + x * SLICE_STEP_X);
+#endif
+    output[output_index] = ACTIVATION(input[input_index], ACTIVATION_PARAMS);
+}
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
index 329d31b9574..b07d40e4134 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
@@ -65,6 +65,7 @@ void register_implementations() {
     REGISTER_OCL(softmax);
     REGISTER_OCL(space_to_batch);
     REGISTER_OCL(space_to_depth);
+    REGISTER_OCL(slice);
     REGISTER_OCL(strided_slice);
     REGISTER_OCL(tile);
     REGISTER_OCL(lstm_dynamic_input);
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
index a0b46ef8931..6232a937ccf 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
@@ -53,6 +53,7 @@
 #include "intel_gpu/primitives/scatter_nd_update.hpp"
 #include "intel_gpu/primitives/select.hpp"
 #include "intel_gpu/primitives/shuffle_channels.hpp"
+#include "intel_gpu/primitives/slice.hpp"
 #include "intel_gpu/primitives/softmax.hpp"
 #include "intel_gpu/primitives/space_to_batch.hpp"
 #include "intel_gpu/primitives/strided_slice.hpp"
@@ -73,7 +74,7 @@ void register_implementations();
 
 namespace detail {
 
-#define REGISTER_OCL(prim)              \
+#define REGISTER_OCL(prim)               \
     struct attach_##prim##_impl {        \
         attach_##prim##_impl();          \
     }
@@ -130,6 +131,7 @@ REGISTER_OCL(scatter_elements_update);
 REGISTER_OCL(scatter_nd_update);
 REGISTER_OCL(select);
 REGISTER_OCL(shuffle_channels);
+REGISTER_OCL(slice);
 REGISTER_OCL(softmax);
 REGISTER_OCL(space_to_batch);
 REGISTER_OCL(space_to_depth);
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/slice.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/slice.cpp
new file mode 100644
index 00000000000..8c1f2e9dec9
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/slice.cpp
@@ -0,0 +1,138 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <slice_inst.h>
+#include <slice/slice_kernel_ref.h>
+#include <data_inst.h>
+#include <intel_gpu/runtime/error_handler.hpp>
+#include <impls/implementation_map.hpp>
+#include <slice/slice_kernel_selector.h>
+#include "primitive_base.hpp"
+#include <vector>
+#include <algorithm>
+#include <cstddef>
+
+namespace cldnn {
+namespace ocl {
+
+namespace {
+template<typename T, class = typename std::enable_if<std::is_integral<T>::value>::type>
+std::vector<std::int32_t> extractIntegerData(const data_node& node, const stream& stream) {
+    mem_lock<T> lock{node.get_attached_memory_ptr(), stream};
+    T* data = lock.data();
+    std::vector<std::int32_t> integer_data;
+    integer_data.reserve(node.get_output_layout().count());
+    std::copy(data, data + node.get_output_layout().count(), std::back_inserter(integer_data));
+    return integer_data;
+}
+
+std::vector<std::int32_t> extractIntegerData(const data_node& node, const stream& stream) {
+    switch (node.get_output_layout().data_type) {
+    case data_types::u8:
+        return extractIntegerData<std::uint8_t>(node, stream);
+    case data_types::i8:
+        return extractIntegerData<std::int8_t>(node, stream);
+    case data_types::i32:
+        return extractIntegerData<std::int32_t>(node, stream);
+    case data_types::i64:
+        return extractIntegerData<std::int64_t>(node, stream);
+    default:
+        CLDNN_ERROR_DATA_TYPES_MISMATCH(node.id(), "Slice parameter",
+                node.get_output_layout().data_type, "Any integral type",
+                data_types::i32, "Slice parameters should be of integral type.");
+    }
+    return {};
+}
+
+std::vector<std::int32_t> extractShape(kernel_selector::Tensor::DataTensor& tensor) {
+    auto logical_dims = tensor.LogicalDims();
+    // LogicalDims method returns dims in reversed order
+    return {logical_dims.rbegin(), logical_dims.rend()};
+}
+
+} // namespace
+
+struct slice_impl : typed_primitive_impl_ocl<slice> {
+    using parent = typed_primitive_impl_ocl<slice>;
+    using parent::parent;
+
+    enum InputIndices {
+        kData,
+        kStart,
+        kEnd,
+        kStep,
+        kAxes,
+        kInputsNum
+    };
+
+    std::unique_ptr<primitive_impl> clone() const override {
+        return make_unique<slice_impl>(*this);
+    }
+
+    static primitive_impl* create(const slice_node& arg) {
+        auto params = get_default_params<kernel_selector::slice_params>(
+                arg);
+        auto op_params = get_default_optional_params<
+                kernel_selector::slice_optional_params>(
+                arg.get_program());
+        const auto& inputs = arg.get_dependencies();
+        const stream& stream = arg.get_program().get_stream();
+        auto start_elts = extractIntegerData(inputs[InputIndices::kStart]->as<data>(), stream);
+        auto end_elts = extractIntegerData(inputs[InputIndices::kEnd]->as<data>(), stream);
+        auto step_elts = extractIntegerData(inputs[InputIndices::kStep]->as<data>(), stream);
+        auto data_shape = extractShape(params.inputs[0]);
+        std::vector<std::int32_t> axes(data_shape.size());
+        if (inputs.size() == InputIndices::kInputsNum)
+            axes = std::move(extractIntegerData(inputs[InputIndices::kAxes]->as<data>(), stream));
+        else
+            std::iota(axes.begin(), axes.end(), 0);
+        std::vector<std::int32_t> selected_start(data_shape.size(), 0);
+        std::vector<std::int32_t> selected_step(data_shape.size(), 1);
+        std::vector<std::int32_t> selected_end(data_shape);
+        for (int axe = 0; axe < axes.size(); axe++) {
+            auto transformed_axe = axes[axe] < 0 ? data_shape.size() + axes[axe] : axes[axe];
+            auto start = start_elts[axe];
+            auto end = end_elts[axe];
+            auto dim_size = data_shape[transformed_axe];
+            selected_start[transformed_axe] = std::max(std::min(start < 0 ? dim_size + start : start, dim_size - 1), 0);
+            selected_end[transformed_axe] = std::max(std::min(end < 0 ? dim_size + end : end, dim_size - 1), 0);
+            selected_step[transformed_axe] = step_elts[axe];
+        }
+        params.start = std::move(selected_start);
+        params.end = std::move(selected_end);
+        params.step = std::move(selected_step);
+        auto &kernel_selector =
+                kernel_selector::slice_kernel_selector::Instance();
+        auto best_kernels = kernel_selector.GetBestKernels(params, op_params);
+
+        CLDNN_ERROR_BOOL(arg.id(), "Best_kernel.empty()", best_kernels.empty(),
+                "Cannot find a proper kernel with this arguments");
+
+        return new slice_impl(arg, best_kernels[0]);
+    }
+};
+
+namespace detail {
+
+attach_slice_impl::attach_slice_impl() {
+    implementation_map<slice>::add(impl_types::ocl, slice_impl::create, {
+        std::make_tuple(data_types::f16, format::bfyx),
+        std::make_tuple(data_types::f32, format::bfyx),
+        std::make_tuple(data_types::u8, format::bfyx),
+        std::make_tuple(data_types::i8, format::bfyx),
+        std::make_tuple(data_types::i32, format::bfyx),
+        std::make_tuple(data_types::i64, format::bfyx),
+        std::make_tuple(data_types::f16, format::bfzyx),
+        std::make_tuple(data_types::f32, format::bfzyx),
+        std::make_tuple(data_types::u8, format::bfyx),
+        std::make_tuple(data_types::i8, format::bfyx),
+        std::make_tuple(data_types::i32, format::bfzyx),
+        std::make_tuple(data_types::i64, format::bfzyx),
+    });
+}
+
+}  // namespace detail
+
+} // namespace ocl
+} // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/include/slice_inst.h b/inference-engine/thirdparty/clDNN/src/include/slice_inst.h
new file mode 100644
index 00000000000..2b3c382b2b7
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/include/slice_inst.h
@@ -0,0 +1,38 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+#include <intel_gpu/primitives/slice.hpp>
+#include "primitive_inst.h"
+#include <intel_gpu/runtime/error_handler.hpp>
+
+namespace cldnn {
+
+template <>
+struct typed_program_node<slice> : public typed_program_node_base<slice> {
+    using parent = typed_program_node_base<slice>;
+
+public:
+    using parent::parent;
+
+    program_node& input(std::size_t index = 0) const { return get_dependency(index); }
+};
+
+using slice_node = typed_program_node<slice>;
+
+template <>
+class typed_primitive_inst<slice> : public typed_primitive_inst_base<slice> {
+    using parent = typed_primitive_inst_base<slice>;
+
+public:
+    static layout calc_output_layout(slice_node const& node);
+    static std::string to_string(slice_node const& node);
+
+public:
+    typed_primitive_inst(network& network, slice_node const& desc);
+};
+
+using slice_inst = typed_primitive_inst<slice>;
+
+} // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/slice.cpp b/inference-engine/thirdparty/clDNN/src/slice.cpp
new file mode 100644
index 00000000000..fc3856c82e0
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/slice.cpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <slice_inst.h>
+#include "primitive_type_base.h"
+#include <sstream>
+#include <json_object.h>
+
+namespace cldnn {
+
+primitive_type_id slice::type_id() {
+    static primitive_type_base<slice> instance;
+    return &instance;
+}
+
+slice_inst::typed_primitive_inst(network& network, slice_node const& node)
+    : parent(network, node) {}
+
+layout slice_inst::calc_output_layout(slice_node const& node) {
+    auto primitive = node.get_primitive();
+    auto input_layout = node.input(0).get_output_layout();
+    return {input_layout.data_type, input_layout.format, primitive->output_shape};
+}
+
+std::string slice_inst::to_string(slice_node const& node) {
+    auto node_info = node.desc_to_json();
+    json_composite slice_info;
+    slice_info.add("input id", node.input().id());
+    slice_info.add("begin_param id", node.get_dependency(1).id());
+    slice_info.add("end_param id", node.get_dependency(2).id());
+    slice_info.add("step_param id", node.get_dependency(3).id());
+    slice_info.add("axis_param id", node.get_dependency(4).id());
+    node_info->add("slice info", slice_info);
+    std::stringstream primitive_description;
+    node_info->dump(primitive_description);
+    return primitive_description.str();
+}
+
+} // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/slice.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/slice.cpp
new file mode 100644
index 00000000000..5b1aa017ccc
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/slice.cpp
@@ -0,0 +1,144 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "test_utils.h"
+
+#include <intel_gpu/primitives/input_layout.hpp>
+#include <intel_gpu/primitives/slice.hpp>
+#include <intel_gpu/primitives/data.hpp>
+
+#include <random>
+#include <algorithm>
+#include <vector>
+
+using namespace cldnn;
+using namespace ::tests;
+
+namespace {
+
+template<typename T>
+class SliceTest : public ::testing::Test {
+public:
+    static std::vector<T> GenInput(int size) {
+        std::vector<T> result;
+        for (int i = 0; i < size; i++)
+            result.push_back(i);
+        return result;
+    }
+
+    void TearDown() override {
+        assert(input_shape_.size() == 4 || input_shape_.size() == 5);
+        format input_format = input_shape_.size() == 4 ? format::bfyx : format::bfzyx;
+        layout data_layout ( input_type_, input_format, tensor{input_shape_} );
+        std::vector<T> input_vals = GenInput(data_layout.get_linear_size());
+        memory::ptr input = engine_.allocate_memory(data_layout);
+        set_values(input, input_vals);
+        topology topology;
+        topology.add(input_layout("input", input->get_layout()));
+        topology.add(data("start", start_));
+        topology.add(data("stop", stop_));
+        topology.add(data("step", step_));
+        std::vector<primitive_id> inputs {"input", "start", "stop", "step"};
+        if (axes_) {
+            topology.add(data("axes", axes_));
+            inputs.push_back("axes");
+        }
+        topology.add(slice("slice", inputs, tensor{output_shape_}));
+
+        network network(engine_, topology);
+
+        network.set_input_data("input", input);
+
+        auto outputs = network.execute();
+
+        EXPECT_EQ(outputs.size(), size_t(1));
+        EXPECT_EQ(outputs.begin()->first, "slice");
+
+        auto output = outputs.at("slice").get_memory();
+
+        cldnn::mem_lock<T> output_ptr(output, get_test_stream());
+
+        ASSERT_EQ(output_ptr.size(), expected_output_.size());
+        for (size_t i = 0; i < output_ptr.size(); ++i)
+            EXPECT_TRUE(are_equal(expected_output_[i], output_ptr[i], 2e-3));
+    }
+
+    data_types DataType() const;
+
+protected:
+    engine& engine_ = get_test_engine();
+    std::vector<std::int32_t> input_shape_;
+    data_types input_type_ {DataType()};
+    memory::ptr start_;
+    memory::ptr stop_;
+    memory::ptr step_;
+    memory::ptr axes_;
+    std::vector<std::int32_t> output_shape_;
+    std::vector<T> expected_output_;
+};
+
+template<>
+data_types SliceTest<float>::DataType() const {return data_types::f32;}
+
+template<>
+data_types SliceTest<int>::DataType() const { return data_types::i32; }
+
+template<>
+data_types SliceTest<long long>::DataType() const { return data_types::i64; }
+
+using testing::Types;
+typedef Types<float, int, long long> DataTypes;
+TYPED_TEST_SUITE(SliceTest, DataTypes);
+
+TYPED_TEST(SliceTest, bfyx_positive_step) {
+    this->input_shape_ = { 1, 2, 100, 12 };
+    this->start_ = this->engine_.allocate_memory({ data_types::i64, format::bfyx, { 4, 1, 1, 1 } });
+    set_values<int64_t>(this->start_, {0, 1, 0, 1});
+    this->stop_ = this->engine_.allocate_memory({ data_types::i64, format::bfyx, { 4, 1, 1, 1 } });
+    set_values<int64_t>(this->stop_, { 1, 2, 5, 100 });
+    this->step_ = this->engine_.allocate_memory({ data_types::i64, format::bfyx, { 4, 1, 1, 1 } });
+    set_values<int64_t>(this->step_, { 1, 1, 1, 10 });
+    this->output_shape_ = { 1, 1, 5, 10 };
+    this->expected_output_ = {
+            1201, 1211, 1221, 1231, 1241, 1301, 1311, 1321, 1331, 1341,
+            1401, 1411, 1421, 1431, 1441, 1501, 1511, 1521, 1531, 1541,
+            1601, 1611, 1621, 1631, 1641, 1701, 1711, 1721, 1731, 1741,
+            1801, 1811, 1821, 1831, 1841, 1901, 1911, 1921, 1931, 1941,
+            2001, 2011, 2021, 2031, 2041, 2101, 2111, 2121, 2131, 2141
+    };
+}
+
+TYPED_TEST(SliceTest, bfyx_negative_step) {
+    this->input_shape_ = { 1, 2, 100, 12 };
+    this->start_ = this->engine_.allocate_memory({ data_types::i64, format::bfyx, { 4, 1, 1, 1 } });
+    set_values<int64_t>(this->start_, { 1, 2, 5, 100 });
+    this->stop_ = this->engine_.allocate_memory({ data_types::i64, format::bfyx, { 4, 1, 1, 1 } });
+    set_values<int64_t>(this->stop_, {0, 1, 0, 1});
+    this->step_ = this->engine_.allocate_memory({ data_types::i64, format::bfyx, { 4, 1, 1, 1 } });
+    set_values<int64_t>(this->step_, { -1, -1, -1, -10 });
+    this->output_shape_ = { 1, 1, 5, 10 };
+    this->expected_output_ = {
+            1799, 1789, 1779, 1769, 1759, 1699, 1689, 1679, 1669, 1659,
+            1599, 1589, 1579, 1569, 1559, 1499, 1489, 1479, 1469, 1459,
+            1399, 1389, 1379, 1369, 1359, 1299, 1289, 1279, 1269, 1259,
+            1199, 1189, 1179, 1169, 1159, 1099, 1089, 1079, 1069, 1059,
+             999,   989,  979, 969,  959,  899,  889,  879,  869,  859
+    };
+}
+
+TYPED_TEST(SliceTest, bfzyx) {
+    this->input_shape_ = { 2, 3, 10, 12, 5 };
+    this->start_ = this->engine_.allocate_memory({ data_types::i64, format::bfzyx, { 5, 1, 1, 1 } });
+    set_values<int64_t>(this->start_, { 0, 0, 0, 0, 0 });
+    this->stop_ = this->engine_.allocate_memory({ data_types::i64, format::bfzyx, { 5, 1, 1, 1 } });
+    set_values<int64_t>(this->stop_, {1, 2, 2, 2, 2});
+    this->step_ = this->engine_.allocate_memory({ data_types::i64, format::bfzyx, { 5, 1, 1, 1 } });
+    set_values<int64_t>(this->step_, { 1, 1, 1, 1, 1 });
+    this->output_shape_ = { 1, 2, 2, 2, 2 };
+    this->expected_output_ = {
+              0,   1,  10,  11, 120, 121, 130, 131,
+            600, 601, 610, 611, 720, 721, 730, 731
+    };
+}
+
+} // anonymous namespace
diff --git a/src/core/include/openvino/op/slice.hpp b/src/core/include/openvino/op/slice.hpp
index 45d7bfec2b1..5bbb36c4987 100644
--- a/src/core/include/openvino/op/slice.hpp
+++ b/src/core/include/openvino/op/slice.hpp
@@ -15,6 +15,8 @@ class OPENVINO_API Slice : public Op {
 public:
     OPENVINO_OP("Slice", "opset8");
 
+    BWDCMP_RTTI_DECLARATION;
+
     Slice() = default;
 
     ///
diff --git a/src/core/src/op/slice.cpp b/src/core/src/op/slice.cpp
index 3c382718426..e4a7f2c54e4 100644
--- a/src/core/src/op/slice.cpp
+++ b/src/core/src/op/slice.cpp
@@ -16,6 +16,8 @@
 using namespace std;
 using namespace ngraph;
 
+BWDCMP_RTTI_DEFINITION(ov::op::v8::Slice);
+
 op::v8::Slice::Slice(const Output<Node>& data,
                      const Output<Node>& start,
                      const Output<Node>& stop,
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
index 7ec7a460615..3962ffa1b23 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
@@ -211,6 +211,7 @@ REGISTER_FACTORY(v6, ExperimentalDetectronROIFeatureExtractor);
 REGISTER_FACTORY(v7, Gather);
 
 // ------------------------------ Supported v8 ops ------------------------------ //
+REGISTER_FACTORY(v8, Slice);
 REGISTER_FACTORY(v8, Gather);
 REGISTER_FACTORY(v8, GatherND);
 REGISTER_FACTORY(v8, DeformableConvolution);
diff --git a/src/plugins/intel_gpu/src/plugin/ops/slice.cpp b/src/plugins/intel_gpu/src/plugin/ops/slice.cpp
new file mode 100644
index 00000000000..84a4106048c
--- /dev/null
+++ b/src/plugins/intel_gpu/src/plugin/ops/slice.cpp
@@ -0,0 +1,37 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "intel_gpu/plugin/program.hpp"
+#include "intel_gpu/plugin/common_utils.hpp"
+
+#include "ngraph/op/slice.hpp"
+
+#include "intel_gpu/primitives/slice.hpp"
+
+#include <memory>
+
+namespace ov {
+namespace runtime {
+namespace intel_gpu {
+
+namespace {
+
+static void CreateSliceOp(Program& p, const std::shared_ptr<ngraph::op::v8::Slice>& op) {
+    p.ValidateInputs(op, { 4, 5 });
+    auto input_primitives = p.GetInputPrimitiveIDs(op);
+    auto output_shape = tensor_from_dims(op->get_output_shape(0));
+    auto slice_prim = cldnn::slice(layer_type_name_ID(op),
+            input_primitives, output_shape,
+            op->get_friendly_name());
+    p.AddPrimitive(slice_prim);
+    p.AddPrimitiveToProfiler(op);
+}
+
+} // namespace
+
+REGISTER_FACTORY_IMPL(v8, Slice);
+
+}  // namespace intel_gpu
+}  // namespace runtime
+}  // namespace ov

From e212a1ac83637dd23e142ffc1104e12c56849f78 Mon Sep 17 00:00:00 2001
From: Victor Kuznetsov <victor.kuznetsov@intel.com>
Date: Fri, 17 Dec 2021 13:06:56 +0300
Subject: [PATCH 017/160] [TimeTests] Disable VPU option, rename cache option
 (#9114)

* add performance hint to time infer

* Update time tests with options

* fix mistake in help

* update test conf with GPU models

* move ie configs from load plugin timer
---
 .../.automation/desktop_test_config_cache.yml | 272 ++++++++++++++++++
 .../include/timetests_helper/utils.h          |   2 +-
 tests/time_tests/scripts/run_timetest.py      |  28 +-
 .../src/timetests/timetest_infer.cpp          |  34 +--
 tests/time_tests/src/timetests_helper/cli.h   |  46 +--
 .../time_tests/src/timetests_helper/main.cpp  |   5 +-
 tests/time_tests/test_runner/conftest.py      |  35 +--
 tests/time_tests/test_runner/test_timetest.py |   6 +-
 8 files changed, 312 insertions(+), 116 deletions(-)

diff --git a/tests/time_tests/.automation/desktop_test_config_cache.yml b/tests/time_tests/.automation/desktop_test_config_cache.yml
index b7b8417a869..802f26a182c 100644
--- a/tests/time_tests/.automation/desktop_test_config_cache.yml
+++ b/tests/time_tests/.automation/desktop_test_config_cache.yml
@@ -6,6 +6,14 @@
     precision: FP16
     framework: caffe2
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/caffe2/FP16/resnet-50-pytorch.xml
+    name: resnet-50-pytorch
+    precision: FP16
+    framework: caffe2
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -14,6 +22,14 @@
     precision: FP16-INT8
     framework: caffe2
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/caffe2/FP16-INT8/resnet-50-pytorch.xml
+    name: resnet-50-pytorch
+    precision: FP16-INT8
+    framework: caffe2
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -22,6 +38,14 @@
     precision: FP16
     framework: caffe2
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe2/FP16/mobilenet-v2.xml
+    name: mobilenet-v2
+    precision: FP16
+    framework: caffe2
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -30,6 +54,14 @@
     precision: FP16-INT8
     framework: caffe2
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe2/FP16-INT8/mobilenet-v2.xml
+    name: mobilenet-v2
+    precision: FP16-INT8
+    framework: caffe2
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -38,6 +70,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/faster_rcnn_resnet101_coco/tf/FP16/faster_rcnn_resnet101_coco.xml
+    name: faster_rcnn_resnet101_coco
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -46,6 +86,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/faster_rcnn_resnet101_coco/tf/FP16-INT8/faster_rcnn_resnet101_coco.xml
+    name: faster_rcnn_resnet101_coco
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -54,6 +102,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml
+    name: faster-rcnn-resnet101-coco-sparse-60-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -62,6 +118,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml
+    name: faster-rcnn-resnet101-coco-sparse-60-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -70,6 +134,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml
+    name: googlenet-v1
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -78,6 +150,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml
+    name: googlenet-v1
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -86,6 +166,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml
+    name: googlenet-v3
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -94,6 +182,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml
+    name: googlenet-v3
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -102,6 +198,14 @@
     precision: FP16
     framework: caffe
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml
+    name: ssd512
+    precision: FP16
+    framework: caffe
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -110,6 +214,14 @@
     precision: FP16-INT8
     framework: caffe
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml
+    name: ssd512
+    precision: FP16-INT8
+    framework: caffe
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -118,6 +230,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml
+    name: yolo-v2-ava-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -126,6 +246,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml
+    name: yolo-v2-ava-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -134,6 +262,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml
+    name: yolo-v2-ava-sparse-35-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -142,6 +278,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml
+    name: yolo-v2-ava-sparse-35-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -150,6 +294,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml
+    name: yolo-v2-ava-sparse-70-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -158,6 +310,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml
+    name: yolo-v2-ava-sparse-70-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -166,6 +326,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml
+    name: yolo-v2-tiny-ava-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -174,6 +342,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml
+    name: yolo-v2-tiny-ava-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -182,6 +358,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml
+    name: yolo-v2-tiny-ava-sparse-30-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -190,6 +374,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml
+    name: yolo-v2-tiny-ava-sparse-30-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -198,6 +390,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml
+    name: yolo-v2-tiny-ava-sparse-60-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -206,6 +406,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml
+    name: yolo-v2-tiny-ava-sparse-60-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -214,6 +422,14 @@
     precision: FP16
     framework: caffe2
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe2/FP16/squeezenet1.1.xml
+    name: squeezenet1.1
+    precision: FP16
+    framework: caffe2
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -222,6 +438,14 @@
     precision: FP16-INT8
     framework: caffe2
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe2/FP16-INT8/squeezenet1.1.xml
+    name: squeezenet1.1
+    precision: FP16-INT8
+    framework: caffe2
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -230,6 +454,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml
+    name: icnet-camvid-ava-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -238,6 +470,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml
+    name: icnet-camvid-ava-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -246,6 +486,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml
+    name: icnet-camvid-ava-sparse-30-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -254,6 +502,14 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml
+    name: icnet-camvid-ava-sparse-30-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -262,6 +518,14 @@
     precision: FP16
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml
+    name: icnet-camvid-ava-sparse-60-0001
+    precision: FP16
+    framework: tf
+  use_model_cache: true
 - device:
     name: CPU
   model:
@@ -270,3 +534,11 @@
     precision: FP16-INT8
     framework: tf
   use_model_cache: true
+- device:
+    name: GPU
+  model:
+    path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml
+    name: icnet-camvid-ava-sparse-60-0001
+    precision: FP16-INT8
+    framework: tf
+  use_model_cache: true
diff --git a/tests/time_tests/include/timetests_helper/utils.h b/tests/time_tests/include/timetests_helper/utils.h
index 86c9addaab6..585b91f4a04 100644
--- a/tests/time_tests/include/timetests_helper/utils.h
+++ b/tests/time_tests/include/timetests_helper/utils.h
@@ -22,7 +22,7 @@ std::string fileExt(const std::string &filename) {
 }
 
 /**
- * @brief Function that enables performance hint for specified device.
+ * @brief Function that enables Latency performance hint for specified device.
  */
 void setPerformanceConfig(InferenceEngine::Core ie, const std::string &device) {
   std::vector<std::string> supported_config_keys = ie.GetMetric(device, METRIC_KEY(SUPPORTED_CONFIG_KEYS));
diff --git a/tests/time_tests/scripts/run_timetest.py b/tests/time_tests/scripts/run_timetest.py
index 1f60c7d6289..fad7a66bca2 100644
--- a/tests/time_tests/scripts/run_timetest.py
+++ b/tests/time_tests/scripts/run_timetest.py
@@ -62,9 +62,7 @@ def prepare_executable_cmd(args: dict):
         str(args["executable"].resolve(strict=True)),
         "-m", str(args["model"].resolve(strict=True)),
         "-d", args["device"],
-        "-p" if args["perf_hint"] else "",
-        "-v" if args["vpu_compiler"] else "", args['vpu_compiler'] if args["vpu_compiler"] else "",
-        "-c" if args["cpu_cache"] else "",
+        "-c" if args["model_cache"] else "",
     ]
 
 
@@ -135,21 +133,11 @@ def cli_parser():
     parser.add_argument("-s",
                         dest="stats_path",
                         type=Path,
-                        help="path to a file to save aggregated statistics")
-    parser.add_argument("-p",
-                        dest="perf_hint",
+                        help="Path to a file to save aggregated statistics")
+    parser.add_argument("-c",
+                        dest="model_cache",
                         action="store_true",
-                        help="Enables 'LATENCY' performance hint for specified device.")
-    exclusive_group = parser.add_mutually_exclusive_group(required=False)
-    exclusive_group.add_argument("-c",
-                                 dest="cpu_cache",
-                                 action="store_true",
-                                 help="Enable CPU model cache usage")
-    exclusive_group.add_argument("-v",
-                                 dest="vpu_compiler",
-                                 choices=["MCM", "MLIR"],
-                                 type=str,
-                                 help="Change VPUX compiler type")
+                        help="Enable model cache usage")
 
     args = parser.parse_args()
 
@@ -162,12 +150,6 @@ if __name__ == "__main__":
     logging.basicConfig(format="[ %(levelname)s ] %(message)s",
                         level=logging.DEBUG, stream=sys.stdout)
 
-    assert not (args.cpu_cache and args.device != "CPU"), \
-        "The cache option is used only for the CPU device."
-
-    assert not (args.vpu_compiler and "VPUX" not in args.device), \
-        "The VPUX compiler option is used only for the VPUX device."
-
     exit_code, _, aggr_stats, _ = run_timetest(
         dict(args._get_kwargs()), log=logging)  # pylint: disable=protected-access
     if args.stats_path:
diff --git a/tests/time_tests/src/timetests/timetest_infer.cpp b/tests/time_tests/src/timetests/timetest_infer.cpp
index 5612c5dd0f2..e9b45bfb48c 100644
--- a/tests/time_tests/src/timetests/timetest_infer.cpp
+++ b/tests/time_tests/src/timetests/timetest_infer.cpp
@@ -16,40 +16,28 @@ using namespace InferenceEngine;
  * main(). The function should not throw any exceptions and responsible for
  * handling it by itself.
  */
-int runPipeline(const std::string &model, const std::string &device, const bool performanceHint,
-                const bool isCacheEnabled, const std::string &vpuCompiler) {
-  auto pipeline = [](const std::string &model, const std::string &device, const bool performanceHint,
-                     const bool isCacheEnabled, const std::string &vpuCompiler) {
+int runPipeline(const std::string &model, const std::string &device, const bool isCacheEnabled) {
+  auto pipeline = [](const std::string &model, const std::string &device, const bool isCacheEnabled) {
     Core ie;
     CNNNetwork cnnNetwork;
     ExecutableNetwork exeNetwork;
     InferRequest inferRequest;
     size_t batchSize = 0;
 
-    // set config for VPUX device
-    std::map<std::string, std::string> vpuConfig = {};
-    if (vpuCompiler == "MCM")
-      vpuConfig = {{"VPUX_COMPILER_TYPE", "MCM"}};
-    else if (vpuCompiler == "MLIR")
-      vpuConfig = {{"VPUX_COMPILER_TYPE", "MLIR"}};
-
     // first_inference_latency = time_to_inference + first_inference
     {
       SCOPED_TIMER(time_to_inference);
       {
         SCOPED_TIMER(load_plugin);
-        if (performanceHint) {
-          TimeTest::setPerformanceConfig(ie, device);
-        }
         ie.GetVersions(device);
-
-        if (isCacheEnabled)
-          ie.SetConfig({{CONFIG_KEY(CACHE_DIR), "models_cache"}});
       }
+      // Set performance and model cache configs
+      TimeTest::setPerformanceConfig(ie, device);
+      if (isCacheEnabled)
+          ie.SetConfig({{CONFIG_KEY(CACHE_DIR), "models_cache"}});
       {
+        SCOPED_TIMER(create_exenetwork);
         if (!isCacheEnabled) {
-          SCOPED_TIMER(create_exenetwork);
-
           if (TimeTest::fileExt(model) == "blob") {
             SCOPED_TIMER(import_network);
             exeNetwork = ie.ImportNetwork(model, device);
@@ -60,21 +48,19 @@ int runPipeline(const std::string &model, const std::string &device, const bool
               cnnNetwork = ie.ReadNetwork(model);
               batchSize = cnnNetwork.getBatchSize();
             }
-
             {
               SCOPED_TIMER(load_network);
-              exeNetwork = ie.LoadNetwork(cnnNetwork, device, vpuConfig);
+              exeNetwork = ie.LoadNetwork(cnnNetwork, device);
             }
           }
         }
         else {
-          SCOPED_TIMER(load_network);
+          SCOPED_TIMER(load_network_cache);
           exeNetwork = ie.LoadNetwork(model, device);
         }
       }
       inferRequest = exeNetwork.CreateInferRequest();
     }
-
     {
       SCOPED_TIMER(first_inference);
       {
@@ -88,7 +74,7 @@ int runPipeline(const std::string &model, const std::string &device, const bool
   };
 
   try {
-    pipeline(model, device, performanceHint, isCacheEnabled, vpuCompiler);
+    pipeline(model, device, isCacheEnabled);
   } catch (const InferenceEngine::Exception &iex) {
     std::cerr
         << "Inference Engine pipeline failed with Inference Engine exception:\n"
diff --git a/tests/time_tests/src/timetests_helper/cli.h b/tests/time_tests/src/timetests_helper/cli.h
index 3ebf2a8f924..c2fb200e62c 100644
--- a/tests/time_tests/src/timetests_helper/cli.h
+++ b/tests/time_tests/src/timetests_helper/cli.h
@@ -10,7 +10,8 @@
 #include <vector>
 
 /// @brief message for help argument
-static const char help_message[] = "Print a usage message";
+static const char help_message[] =
+    "Print a usage message.";
 
 /// @brief message for model argument
 static const char model_message[] =
@@ -19,24 +20,17 @@ static const char model_message[] =
 
 /// @brief message for target device argument
 static const char target_device_message[] =
-    "Required. Specify a target device to infer on. "
+    "Required. Specify a target device to infer on. \n"
     "Use \"-d HETERO:<comma-separated_devices_list>\" format to specify HETERO "
-    "plugin. "
+    "plugin. \n"
     "Use \"-d MULTI:<comma-separated_devices_list>\" format to specify MULTI "
-    "plugin. "
+    "plugin. \n"
     "The application looks for a suitable plugin for the specified device.";
 
-/// @brief message for vpu argument
-static const char performance_hint_message[] =
-    "Not required. Enables performance hint 'LATENCY' for specified device.";
-
 /// @brief message for cache argument
-static const char cpu_cache_message[] =
-    "Not required. Use this key to run timetests with CPU models caching.";
-
-/// @brief message for vpu argument
-static const char vpu_compiler_message[] =
-    "Not required. Use this key to run timetests with 'MLIR' or 'MCM' VPUX compiler type.";
+static const char model_cache_message[] =
+    "Not required. Use this key to run timetests with models caching. \n"
+    "TimeInfer executable should be run twice - the second run will use cache prepared from first run.";
 
 /// @brief message for statistics path argument
 static const char statistics_path_message[] =
@@ -56,17 +50,9 @@ DEFINE_string(m, "", model_message);
 /// It is a required parameter
 DEFINE_string(d, "", target_device_message);
 
-/// @brief Define parameter for set performance hint for target device <br>
-/// It is a non-required parameter
-DEFINE_bool(p, false, performance_hint_message);
-
 /// @brief Define parameter for set CPU models caching <br>
 /// It is a non-required parameter
-DEFINE_bool(c, false, cpu_cache_message);
-
-/// @brief Define parameter VPU compiler type <br>
-/// It is a non-required parameter
-DEFINE_string(v, "", vpu_compiler_message);
+DEFINE_bool(c, false, model_cache_message);
 
 /// @brief Define parameter for set path to a file to write statistics <br>
 /// It is a required parameter
@@ -77,16 +63,12 @@ DEFINE_string(s, "", statistics_path_message);
  */
 static void showUsage() {
   std::cout << std::endl;
-  std::cout << "TimeTests [OPTION]" << std::endl;
+  std::cout << "TimeInfer [OPTION]" << std::endl;
   std::cout << "Options:" << std::endl;
   std::cout << std::endl;
-  std::cout << "    -h, --help                  " << help_message << std::endl;
+  std::cout << "    -h, --help                " << help_message << std::endl;
   std::cout << "    -m \"<path>\"               " << model_message << std::endl;
-  std::cout << "    -d \"<device>\"             " << target_device_message
-            << std::endl;
-  std::cout << "    -s \"<path>\"               " << statistics_path_message
-            << std::endl;
-  std::cout << "    -p                          " << performance_hint_message << std::endl;
-  std::cout << "    -c                          " << cpu_cache_message << std::endl;
-  std::cout << "    -v \"<compiler_type>\"      " << vpu_compiler_message << std::endl;
+  std::cout << "    -d \"<device>\"             " << target_device_message << std::endl;
+  std::cout << "    -s \"<path>\"               " << statistics_path_message << std::endl;
+  std::cout << "    -c                        " << model_cache_message << std::endl;
 }
diff --git a/tests/time_tests/src/timetests_helper/main.cpp b/tests/time_tests/src/timetests_helper/main.cpp
index 193e5339711..2263324c743 100644
--- a/tests/time_tests/src/timetests_helper/main.cpp
+++ b/tests/time_tests/src/timetests_helper/main.cpp
@@ -8,8 +8,7 @@
 
 #include <iostream>
 
-int runPipeline(const std::string &model, const std::string &device, const bool performanceHint,
-                const bool isCacheEnabled, const std::string &vpuCompiler);
+int runPipeline(const std::string &model, const std::string &device, const bool isCacheEnabled);
 
 /**
  * @brief Parses command line and check required arguments
@@ -41,7 +40,7 @@ bool parseAndCheckCommandLine(int argc, char **argv) {
  */
 int _runPipeline() {
   SCOPED_TIMER(full_run);
-  return runPipeline(FLAGS_m, FLAGS_d, FLAGS_p, FLAGS_c, FLAGS_v);
+  return runPipeline(FLAGS_m, FLAGS_d, FLAGS_c);
 }
 
 /**
diff --git a/tests/time_tests/test_runner/conftest.py b/tests/time_tests/test_runner/conftest.py
index 647e603857f..cb9a457d58c 100644
--- a/tests/time_tests/test_runner/conftest.py
+++ b/tests/time_tests/test_runner/conftest.py
@@ -60,20 +60,9 @@ def pytest_addoption(parser):
         default=3
     )
     test_args_parser.addoption(
-        "--cpu_cache",
+        "--model_cache",
         action='store_true',
-        help="Enable model CPU cache usage",
-    )
-    test_args_parser.addoption(
-        "--perf_hint",
-        action='store_true',
-        help="Enables 'LATENCY' performance hint for specified device."
-    )
-    test_args_parser.addoption(
-        "--vpu_compiler",
-        choices=["MCM", "MLIR"],
-        type=str,
-        help="Change VPUX compiler type",
+        help="Enable model cache usage",
     )
     db_args_parser = parser.getgroup("timetest database use")
     db_args_parser.addoption(
@@ -131,22 +120,11 @@ def niter(request):
 
 
 @pytest.fixture(scope="session")
-def cpu_cache(request):
+def model_cache(request):
     """Fixture function for command-line option."""
-    return request.config.getoption('cpu_cache')
+    return request.config.getoption('model_cache')
 
 
-@pytest.fixture(scope="session")
-def perf_hint(request):
-    """Fixture function for command-line option."""
-    return request.config.getoption('perf_hint')
-
-
-@pytest.fixture(scope="session")
-def vpu_compiler(request):
-    """Fixture function for command-line option."""
-    return request.config.getoption('vpu_compiler')
-
 # -------------------- CLI options --------------------
 
 
@@ -272,9 +250,8 @@ def prepare_db_info(request, test_info, executable, niter, manifest_metadata):
         with open(db_meta_path, "r") as db_meta_f:
             test_info["db_info"].update(json.load(db_meta_f))
 
-    # add cpu cache status
-    cpu_cache = True if request.config.getoption("cpu_cache") else False
-    test_info["db_info"].update({"use_cpu_cache": cpu_cache})
+    # add model cache status
+    test_info["db_info"].update({"model_cache": request.config.getoption("model_cache")})
 
     # add test info
     info = {
diff --git a/tests/time_tests/test_runner/test_timetest.py b/tests/time_tests/test_runner/test_timetest.py
index f84c53dd051..ef546ac3dfb 100644
--- a/tests/time_tests/test_runner/test_timetest.py
+++ b/tests/time_tests/test_runner/test_timetest.py
@@ -34,7 +34,7 @@ from scripts.run_timetest import run_timetest
 REFS_FACTOR = 1.2      # 120%
 
 
-def test_timetest(instance, executable, niter, cl_cache_dir, cpu_cache, vpu_compiler, perf_hint, model_cache_dir,
+def test_timetest(instance, executable, niter, cl_cache_dir, model_cache, model_cache_dir,
                   test_info, temp_dir, validate_test_case, prepare_db_info):
     """Parameterized test.
 
@@ -67,9 +67,7 @@ def test_timetest(instance, executable, niter, cl_cache_dir, cpu_cache, vpu_comp
         "model": Path(model_path),
         "device": instance["device"]["name"],
         "niter": niter,
-        "perf_hint": perf_hint,
-        "cpu_cache": cpu_cache,
-        "vpu_compiler": vpu_compiler if vpu_compiler else ""
+        "model_cache": model_cache,
     }
     logging.info("Run timetest once to generate any cache")
     retcode, msg, _, _ = run_timetest({**exe_args, "niter": 1}, log=logging)

From 71f3e2da10ed7832d7921dd16744bb06d6e7442d Mon Sep 17 00:00:00 2001
From: Sergey Shlyapnikov <sergey.shlyapnikov@intel.com>
Date: Fri, 17 Dec 2021 13:26:59 +0300
Subject: [PATCH 018/160] [GPU] Fix fused ops reshaping for OneDNN FC (#9217)

---
 .../add_onednn_optimization_attributes.cpp    | 22 +++++++
 .../impls/onednn/fully_connected_onednn.cpp   | 14 ----
 .../tests/test_cases/fusings_gpu_test.cpp     | 64 +++++++++++++------
 3 files changed, 68 insertions(+), 32 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/add_onednn_optimization_attributes.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/add_onednn_optimization_attributes.cpp
index bb7b396467c..5b8e65015b7 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/add_onednn_optimization_attributes.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/add_onednn_optimization_attributes.cpp
@@ -7,12 +7,34 @@
 #include "pass_manager.h"
 #include "program_node.h"
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#include "fully_connected_inst.h"
+#include <impls/onednn/utils.hpp>
+#endif
+
 using namespace cldnn;
 
 void add_onednn_optimization_attributes::run(program& p) {
 #ifdef ENABLE_ONEDNN_FOR_GPU
     for (auto& node : p.get_processing_order()) {
         if (node->get_preferred_impl_type() == impl_types::onednn) {
+            if (node->is_type<fully_connected>()) {
+                auto fc_prim = node->as<fully_connected>().get_primitive();
+
+                // Reshape fused ops tensors for OneDNN FC if needed
+                if (fc_prim->input_size == 3) {
+                    for (auto& fused_prim : node->get_fused_primitives()) {
+                        auto fused_node = fused_prim.node;
+                        if (fused_node->is_type<eltwise>()) {
+                            auto& dependency = node->get_dependency(fused_prim.dep_start_idx);
+                            auto original_layout = dependency.get_output_layout();
+                            onednn::combine_bf_with_first_spatial_dim(original_layout);
+                            dependency.set_output_layout(original_layout, false);
+                        }
+                    }
+                }
+            }
+
             node->init_onednn_primitive_attributes();
         }
     }
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp b/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp
index 2796070a51b..81a0fcf32a3 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp
@@ -128,20 +128,6 @@ public:
     static primitive_impl* create(const fully_connected_node& arg) {
         auto& engine = arg.get_program().get_engine();
         auto desc = get_fully_connected_descriptor(arg);
-        auto prim = arg.get_primitive();
-
-        if (prim->input_size == 3) {
-            for (auto& fused_node : arg.get_fused_primitives()) {
-                auto node = fused_node.node;
-                if (node->is_type<eltwise>()) {
-                    auto& dependency = arg.get_dependency(fused_node.dep_start_idx);
-                    auto original_layout = dependency.get_output_layout();
-                    onednn::combine_bf_with_first_spatial_dim(original_layout);
-                    dependency.set_output_layout(original_layout, false);
-                }
-            }
-        }
-
         auto attr = arg.get_onednn_primitive_attributes();
         dnnl::primitive_desc prim_desc{&desc->data, attr.get(), engine.get_onednn_engine(), nullptr};
 
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
index 35e4fe25e08..48c42e4eae5 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
@@ -608,6 +608,7 @@ public:
 #define CASE_FC_U8S8_3D_1 {2, 32, 1, 3}, {2, 32, 1, 16}, {16, 3, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
 #define CASE_FC_U8S8_3D_2 {1, 1, 1, 3}, {1, 1, 1, 32}, {32, 3, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
 #define CASE_FC_U8S8_3D_3 {2, 3, 1, 1}, {2, 3, 1, 15}, {15, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_3D_4 {1, 512, 1, 1024}, {1, 384, 1, 1024}, {1024, 1024, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
 
 #define CASE_NORMALIZE_I8_1 {1, 2, 3, 3}, data_types::u8, format::bfyx, data_types::f32, format::bfyx
 
@@ -9258,7 +9259,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_elements_activation_scale_eltwise,
 }));
 
 #ifdef ENABLE_ONEDNN_FOR_GPU
-class ConvFusingTestOneDNN : public WeightsPrimitiveFusingTest<bc_test_params> {
+class WeightsPrimitiveFusingTestOneDNN : public WeightsPrimitiveFusingTest<bc_test_params> {
 public:
     void execute(bc_test_params& p) {
         // Onednn post operation has issue in a machine that does not support imad.
@@ -9299,7 +9300,7 @@ public:
     }
 };
 
-class conv_int8_eltwise_onednn : public ConvFusingTestOneDNN {};
+class conv_int8_eltwise_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_eltwise_onednn, u8_eltwise_sum_out) {
     auto p = GetParam();
 
@@ -9364,7 +9365,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_onednn,
                                 bc_test_params{CASE_CONV3D_S8S8_5, 3, 4},
                         }));
 
-class conv_fp32_activation_abs_onednn : public ConvFusingTestOneDNN {};
+class conv_fp32_activation_abs_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_abs_onednn, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9387,7 +9388,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_abs_onednn,
                                 bc_test_params{CASE_CONV_FP16_4, 2, 3},
                           }));
 
-class conv_fp32_activation_mish_onednn : public ConvFusingTestOneDNN {};
+class conv_fp32_activation_mish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_mish_onednn, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9410,7 +9411,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_mish_onednn,
                                 bc_test_params{CASE_CONV_FP16_4, 2, 3},
                           }));
 
-class conv_fp32_activation_swish_onednn : public ConvFusingTestOneDNN {};
+class conv_fp32_activation_swish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_swish_onednn, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9433,7 +9434,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_swish_onednn,
                                 bc_test_params{CASE_CONV_FP16_4, 2, 3},
                           }));
 
-class conv_fp32_activation_hswish_onednn : public ConvFusingTestOneDNN {};
+class conv_fp32_activation_hswish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_hswish_onednn, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9456,7 +9457,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_hswish_onednn,
                                 bc_test_params{CASE_CONV_FP16_4, 2, 3},
                           }));
 
-class conv_fp32_activation_exp_onednn : public ConvFusingTestOneDNN {};
+class conv_fp32_activation_exp_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_exp_onednn, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9479,7 +9480,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_exp_onednn,
                                 bc_test_params{CASE_CONV_FP16_4, 2, 3},
                           }));
 
-class conv_int8_quantize_u8_onednn : public ConvFusingTestOneDNN {};
+class conv_int8_quantize_u8_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_quantize_u8_onednn, per_channel) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9526,7 +9527,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_quantize_u8_onednn,
                                 bc_test_params{CASE_CONV_S8S8_3, 2, 3},
                         }));
 
-class conv_int8_activation_eltwise_quantize_onednn : public ConvFusingTestOneDNN {};
+class conv_int8_activation_eltwise_quantize_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_activation_eltwise_quantize_onednn, bsv32_fsv32) {
     auto p = GetParam();
     layout eltwise_layout = get_output_layout(p);
@@ -9578,7 +9579,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_activation_eltwise_quantize_oned
                                 bc_test_params{CASE_CONV_S8S8_15, 2, 5},
                         }));
 
-class conv_int8_scale_shift_swish_onednn : public ConvFusingTestOneDNN {};
+class conv_int8_scale_shift_swish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_scale_shift_swish_onednn, bsv32_fsv32) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9617,7 +9618,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_shift_swish_onednn,
                                 bc_test_params{CASE_CONV_S8S8_15, 2, 7},
                         }));
 
-class conv_int8_eltwise_scale_onednn : public ConvFusingTestOneDNN {};
+class conv_int8_eltwise_scale_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_eltwise_scale_onednn, u8_eltwise_prod_out_reuse) {
     auto p = GetParam();
 
@@ -9667,7 +9668,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_scale_onednn,
 // Limitations: no
 // DNNL_VERBOSE log without optimization: attr-post-ops:eltwise_linear:12.75:127.5+eltwise_linear:1:-128
 // DNNL_VERBOSE log with optimization:    attr-post-ops:eltwise_linear:12.75:-0.5
-class post_ops_optimizations_onednn_eltw_linear_eltw_linear : public ConvFusingTestOneDNN {};
+class post_ops_optimizations_onednn_eltw_linear_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_eltw_linear_eltw_linear, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9718,7 +9719,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_linear_
 // Limitations: beta = 0 in eltw_linear
 // DNNL_VERBOSE log without optimization: attr-post-ops:eltwise_linear:12.75:127.5+eltwise_round+eltwise_linear:2.00784+eltwise_clip:0:512
 // DNNL_VERBOSE log with optimization:    attr-post-ops:eltwise_linear:12.75:127.5+eltwise_round:0:0:2.00784+eltwise_clip:0:512
-class post_ops_optimizations_onednn_eltw_non_linear_eltw_linear : public ConvFusingTestOneDNN {};
+class post_ops_optimizations_onednn_eltw_non_linear_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_eltw_non_linear_eltw_linear, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9769,7 +9770,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_non_lin
 // Limitations: alpha = 1 and scale = 1 in eltw_linear; binary_add is a constant compile-time buffer
 // DNNL_VERBOSE log without optimization: attr-oscale:2 attr-post-ops:binary_add:f32:2+eltwise_linear:1:-127+eltwise_clip:-127:127
 // DNNL_VERBOSE log with optimization:    attr-oscale:2 attr-post-ops:binary_add:f32:2+eltwise_clip:-127:127
-class post_ops_optimizations_onednn_binary_add_eltw_linear : public ConvFusingTestOneDNN {};
+class post_ops_optimizations_onednn_binary_add_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_binary_add_eltw_linear, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9820,7 +9821,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_add_e
 // Limitations: beta = 0 in eltw_linear; binary_mul is a constant compile-time buffer
 // DNNL_VERBOSE log without optimization: attr-oscale:2 attr-post-ops:binary_mul:f32:2+eltwise_linear:2.01575+eltwise_clip:0:512
 // DNNL_VERBOSE log with optimization:    attr-oscale:2 attr-post-ops:binary_mul:f32:2+eltwise_clip:0:512
-class post_ops_optimizations_onednn_binary_mul_eltw_linear : public ConvFusingTestOneDNN {};
+class post_ops_optimizations_onednn_binary_mul_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_binary_mul_eltw_linear, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9871,7 +9872,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_mul_e
 // Limitations: beta = 0 in eltw_linear
 // DNNL_VERBOSE log without optimization: attr-oscale:2 attr-post-ops:eltwise_linear:2.01575+eltwise_clip:0:512
 // DNNL_VERBOSE log with optimization:    attr-oscale:2 attr-post-ops:eltwise_clip:0:512
-class post_ops_optimizations_onednn_oscale_eltw_linear : public ConvFusingTestOneDNN {};
+class post_ops_optimizations_onednn_oscale_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_oscale_eltw_linear, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9920,7 +9921,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_oscale_eltw_
 // Limitations: beta = 0 in eltw_linear
 // DNNL_VERBOSE log without optimization: attr-post-ops:eltwise_relu+sum:1:0:u8+eltwise_linear:12.7+eltwise_clip:0:127
 // DNNL_VERBOSE log with optimization:    attr-post-ops:eltwise_relu:0:0:12.7+sum:12.7:0:u8+eltwise_clip:0:127
-class post_ops_optimizations_onednn_eltw_any_sum_eltw_linear : public ConvFusingTestOneDNN {};
+class post_ops_optimizations_onednn_eltw_any_sum_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_eltw_any_sum_eltw_linear, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -9970,7 +9971,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_any_sum
 // Input range uses in 2 cases: not per-tensor output range or out_lo > out_hi
 // Here's out_lo > out_hi and no optimizations
 // DNNL_VERBOSE log: attr-post-ops:eltwise_linear:12.75:127.5+eltwise_round+eltwise_linear:-1:127
-class post_ops_optimizations_input_range : public ConvFusingTestOneDNN {};
+class post_ops_optimizations_input_range : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_input_range, basic) {
     auto p = GetParam();
     create_topologies(input_layout("input", get_input_layout(p)),
@@ -10015,6 +10016,33 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_input_range,
                                 bc_test_params{CASE_CONV_S8S8_14, 2, 3},
                                 bc_test_params{CASE_CONV_S8S8_15, 2, 3},
                         }));
+
+class fc_int8_inputs_fused_fp32_sum : public WeightsPrimitiveFusingTestOneDNN {};
+TEST_P(fc_int8_inputs_fused_fp32_sum, basic) {
+    auto p = GetParam();
+    auto shift_layout = layout{ p.default_type, p.default_format, tensor{1, 1, 1, p.kernel.batch[0]} };
+
+    create_topologies(input_layout("input", get_input_layout(p)),
+                data("weights", get_mem(get_fc_weights_layout(p))),
+                data("bias", get_mem(get_fc_bias_layout(p))),
+                data("shift_data", get_mem(shift_layout, 1)),
+                fully_connected("fc_prim", "input", "weights", "bias", cldnn::data_types::f32, "", padding(), get_fc_output_dim_size(p)),
+                eltwise("shift", {"fc_prim", "shift_data"}, eltwise_mode::sum, cldnn::data_types::f32),
+                crop("crop", "shift", get_output_layout(p).size, {0, 0, 0, 0}),
+                reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
+    );
+
+    tolerance = 1e-5f;
+    execute(p);
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_inputs_fused_fp32_sum, ::testing::ValuesIn(std::vector<bc_test_params>{
+                                                                            // OneDNN has issue with small shapes - ticket 7064
+                                                                            // bc_test_params{ CASE_FC_U8S8_3D_1, 2, 4 },
+                                                                            // bc_test_params{ CASE_FC_U8S8_3D_2, 2, 4 },
+                                                                            bc_test_params{ CASE_FC_U8S8_3D_4, 2, 4 },
+
+}));
 #endif
 
 

From c047da07c012714f554676109e4d91c58a74e4d7 Mon Sep 17 00:00:00 2001
From: serhii-pavlovskyi-altran
 <82883030+serhii-pavlovskyi-altran@users.noreply.github.com>
Date: Fri, 17 Dec 2021 12:31:01 +0200
Subject: [PATCH 019/160] fix lohika ci checkout in the face of changed
 submodules (#9266)

* fix ci checkout in the face of changed submodules

* update cldnn plugin name in lohika ci script

Co-authored-by: Serhii Pavlovskyi <spavlovskyi@lohika.com>
---
 .ci/azure/linux_lohika.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.ci/azure/linux_lohika.yml b/.ci/azure/linux_lohika.yml
index 3e04658ebd6..198e24433c9 100644
--- a/.ci/azure/linux_lohika.yml
+++ b/.ci/azure/linux_lohika.yml
@@ -35,7 +35,7 @@ jobs:
 
   - checkout: none
 
-  - script: git -C ~/work/openvino checkout -m --recurse-submodules $(Build.SourceVersion)
+  - script: git -C ~/work/openvino checkout -m $(Build.SourceVersion) && git -C ~/work/openvino submodule update --init --recursive
     displayName: checkout
 
     # Should be after 'Install dependencies' because Git lfs is not installed
@@ -71,7 +71,7 @@ jobs:
       ./buildreleasenolto.sh
       libinference_engine_preproc.so
       MKLDNNPlugin
-      clDNNPlugin
+      ov_intel_gpu_plugin
       clDNN_unit_tests64
       gpuFuncTests
     displayName: Build Lin

From 59fca61dd58ae403eb6793228ab910d8f2eae35a Mon Sep 17 00:00:00 2001
From: Ivan Tikhonov <ivan.tikhonov@intel.com>
Date: Fri, 17 Dec 2021 13:37:16 +0300
Subject: [PATCH 020/160] [FrontEnds]: Fix build issue on Win platforms (#9274)

* include place.hpp into input_model.hpp

* resolve issues in api validator

* set full namespaces for Place::Ptr in input_model
---
 .../api_validator/api_validator.cmake         | 16 +++++++-
 .../onnx/frontend/src/input_model.cpp         | 29 +++++++-------
 .../onnx/frontend/src/input_model.hpp         | 39 ++++++++++---------
 3 files changed, 51 insertions(+), 33 deletions(-)

diff --git a/cmake/developer_package/api_validator/api_validator.cmake b/cmake/developer_package/api_validator/api_validator.cmake
index d58ba745742..2d707dd6a46 100644
--- a/cmake/developer_package/api_validator/api_validator.cmake
+++ b/cmake/developer_package/api_validator/api_validator.cmake
@@ -79,8 +79,20 @@ function(_ie_add_api_validator_post_build_step)
     _ie_add_api_validator_post_build_step_recursive(TARGET ${API_VALIDATOR_TARGET})
 
     # remove targets which were tested before
-
-    foreach(item IN LISTS VALIDATED_LIBRARIES)
+    foreach(target IN LISTS API_VALIDATOR_TARGETS)
+        list(FIND VALIDATED_LIBRARIES ${target} index)
+        if (NOT index EQUAL -1)
+            list(APPEND VALIDATED_TARGETS ${target})
+        endif()
+        if(TARGET "${target}")
+            get_target_property(orig_target ${target} ALIASED_TARGET)
+            list(FIND VALIDATED_LIBRARIES ${orig_target} index)
+            if (NOT index EQUAL -1)
+                list(APPEND VALIDATED_TARGETS ${target})
+            endif()
+        endif()
+    endforeach()
+    foreach(item IN LISTS VALIDATED_TARGETS)
         list(REMOVE_ITEM API_VALIDATOR_TARGETS ${item})
     endforeach()
 
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index 8a3fe3ee286..a3df6da6a93 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -39,7 +39,7 @@ InputModel::InputModel(std::istream& model_stream,
 
 std::vector<ov::frontend::Place::Ptr> InputModel::get_inputs() const {
     const auto& inputs = m_editor->model_inputs();
-    std::vector<Place::Ptr> in_places;
+    std::vector<ov::frontend::Place::Ptr> in_places;
     in_places.reserve(inputs.size());
     for (const auto& input : inputs) {
         in_places.push_back(std::make_shared<PlaceTensor>(input, m_editor));
@@ -49,7 +49,7 @@ std::vector<ov::frontend::Place::Ptr> InputModel::get_inputs() const {
 
 std::vector<ov::frontend::Place::Ptr> InputModel::get_outputs() const {
     const auto& outputs = m_editor->model_outputs();
-    std::vector<Place::Ptr> out_places;
+    std::vector<ov::frontend::Place::Ptr> out_places;
     out_places.reserve(outputs.size());
     for (const auto& output : outputs) {
         out_places.push_back(std::make_shared<PlaceTensor>(output, m_editor));
@@ -90,13 +90,13 @@ ov::frontend::Place::Ptr InputModel::get_place_by_operation_name_and_output_port
     return nullptr;
 }
 
-void InputModel::set_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
+void InputModel::set_name_for_tensor(ov::frontend::Place::Ptr tensor, const std::string& new_name) {
     const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor);
     FRONT_END_GENERAL_CHECK(onnx_tensor, __FUNCTION__, " expects a pointer to place of ONNX tensor type.");
     onnx_tensor->set_name(new_name);
 }
 
-void InputModel::set_name_for_operation(Place::Ptr operation, const std::string& new_name) {
+void InputModel::set_name_for_operation(ov::frontend::Place::Ptr operation, const std::string& new_name) {
     const auto onnx_operation = std::dynamic_pointer_cast<PlaceOp>(operation);
     FRONT_END_GENERAL_CHECK(onnx_operation, __FUNCTION__, " expects a pointer to place of ONNX operation type.");
     onnx_operation->set_name(new_name);
@@ -106,13 +106,15 @@ void InputModel::free_name_for_operation(const std::string& name) {
     m_editor->clear_nodes_name(name);
 }
 
-void InputModel::set_name_for_dimension(Place::Ptr tensor, size_t shape_dim_index, const std::string& dim_name) {
+void InputModel::set_name_for_dimension(ov::frontend::Place::Ptr tensor,
+                                        size_t shape_dim_index,
+                                        const std::string& dim_name) {
     const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor);
     FRONT_END_GENERAL_CHECK(onnx_tensor, __FUNCTION__, " expects a pointer to place of ONNX tensor type.");
     onnx_tensor->set_name_for_dimension(shape_dim_index, dim_name);
 }
 
-void InputModel::add_name_for_tensor(Place::Ptr, const std::string&) {
+void InputModel::add_name_for_tensor(ov::frontend::Place::Ptr, const std::string&) {
     FRONT_END_THROW("Method add_name_for_tensor is not applicable for ONNX model. ONNX tensor has just one name.");
 }
 
@@ -120,17 +122,17 @@ void InputModel::free_name_for_tensor(const std::string&) {
     FRONT_END_THROW("Method free_name_for_tensor is not applicable for ONNX model. ONNX tensor name is an identifier.");
 }
 
-void InputModel::set_partial_shape(Place::Ptr place, const ngraph::PartialShape& shape) {
+void InputModel::set_partial_shape(ov::frontend::Place::Ptr place, const ngraph::PartialShape& shape) {
     std::map<std::string, ngraph::PartialShape> m;
     m[place->get_names()[0]] = shape;
     m_editor->set_input_shapes(m);
 }
 
-ngraph::PartialShape InputModel::get_partial_shape(Place::Ptr place) const {
+ngraph::PartialShape InputModel::get_partial_shape(ov::frontend::Place::Ptr place) const {
     return m_editor->get_tensor_shape(place->get_names().at(0));
 }
 
-void InputModel::set_element_type(Place::Ptr place, const ngraph::element::Type& type) {
+void InputModel::set_element_type(ov::frontend::Place::Ptr place, const ngraph::element::Type& type) {
     std::map<std::string, ngraph::element::Type_t> m;
     m[place->get_names()[0]] = type;
     m_editor->set_input_types(m);
@@ -145,19 +147,19 @@ std::shared_ptr<Model> InputModel::convert() {
 }
 
 // Editor features
-void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
+void InputModel::override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
     extract_subgraph({}, outputs);
     NGRAPH_CHECK(m_editor->model_outputs().size() == outputs.size(),
                  "Unexpected number of outputs after override_all_outputs");
     NGRAPH_CHECK(std::all_of(std::begin(outputs),
                              std::end(outputs),
-                             [](const Place::Ptr& place) {
+                             [](const ov::frontend::Place::Ptr& place) {
                                  return place->is_output();
                              }),
                  "Not all provided arguments of override_all_outputs are new outputs of the model");
 }
 
-void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
+void InputModel::override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) {
     const auto outputs_before_extraction = m_editor->model_outputs();
     extract_subgraph({inputs}, {});
     NGRAPH_CHECK(std::equal(std::begin(outputs_before_extraction),
@@ -169,7 +171,8 @@ void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
                  "Unexpected number of inputs after override_all_inputs");
 }
 
-void InputModel::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
+void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                                  const std::vector<ov::frontend::Place::Ptr>& outputs) {
     std::vector<onnx_editor::InputEdge> onnx_inputs;
     onnx_inputs.reserve(inputs.size());
     for (const auto& input : inputs) {
diff --git a/src/frontends/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
index 1e1c86427ed..f69ef4f4a52 100644
--- a/src/frontends/onnx/frontend/src/input_model.hpp
+++ b/src/frontends/onnx/frontend/src/input_model.hpp
@@ -30,36 +30,39 @@ public:
                const std::shared_ptr<ov::frontend::TelemetryExtension>& telemetry = {});
 #endif
 
-    std::vector<Place::Ptr> get_inputs() const override;
-    std::vector<Place::Ptr> get_outputs() const override;
-    Place::Ptr get_place_by_tensor_name(const std::string& tensor_name) const override;
-    Place::Ptr get_place_by_operation_name(const std::string& operation_name) const override;
-    Place::Ptr get_place_by_operation_name_and_input_port(const std::string& operation_name,
-                                                          int input_port_index) override;
-    Place::Ptr get_place_by_operation_name_and_output_port(const std::string& operation_name,
-                                                           int output_port_index) override;
+    std::vector<ov::frontend::Place::Ptr> get_inputs() const override;
+    std::vector<ov::frontend::Place::Ptr> get_outputs() const override;
+    ov::frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensor_name) const override;
+    ov::frontend::Place::Ptr get_place_by_operation_name(const std::string& operation_name) const override;
+    ov::frontend::Place::Ptr get_place_by_operation_name_and_input_port(const std::string& operation_name,
+                                                                        int input_port_index) override;
+    ov::frontend::Place::Ptr get_place_by_operation_name_and_output_port(const std::string& operation_name,
+                                                                         int output_port_index) override;
 
-    void set_name_for_tensor(Place::Ptr tensor, const std::string& new_name) override;
-    void set_name_for_operation(Place::Ptr operation, const std::string& new_name) override;
+    void set_name_for_tensor(ov::frontend::Place::Ptr tensor, const std::string& new_name) override;
+    void set_name_for_operation(ov::frontend::Place::Ptr operation, const std::string& new_name) override;
     void free_name_for_operation(const std::string& name) override;
-    void set_name_for_dimension(Place::Ptr place, size_t shape_dim_index, const std::string& dim_name) override;
+    void set_name_for_dimension(ov::frontend::Place::Ptr place,
+                                size_t shape_dim_index,
+                                const std::string& dim_name) override;
 
     /// \brief  Not applicable for ONNX model. Throws immediately
-    void add_name_for_tensor(Place::Ptr tensor, const std::string& new_name) override;
+    void add_name_for_tensor(ov::frontend::Place::Ptr tensor, const std::string& new_name) override;
     /// \brief  Not applicable for ONNX model. Throws immediately
     void free_name_for_tensor(const std::string& name) override;
 
-    void set_partial_shape(Place::Ptr place, const ngraph::PartialShape& shape) override;
-    ngraph::PartialShape get_partial_shape(Place::Ptr place) const override;
-    void set_element_type(Place::Ptr place, const ngraph::element::Type& type) override;
+    void set_partial_shape(ov::frontend::Place::Ptr place, const ngraph::PartialShape& shape) override;
+    ngraph::PartialShape get_partial_shape(ov::frontend::Place::Ptr place) const override;
+    void set_element_type(ov::frontend::Place::Ptr place, const ngraph::element::Type& type) override;
 
     std::shared_ptr<Model> decode();
     std::shared_ptr<Model> convert();
 
     // Editor features
-    void override_all_outputs(const std::vector<Place::Ptr>& outputs) override;
-    void override_all_inputs(const std::vector<Place::Ptr>& inputs) override;
-    void extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) override;
+    void override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) override;
+    void override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) override;
+    void extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                          const std::vector<ov::frontend::Place::Ptr>& outputs) override;
 
 private:
     std::shared_ptr<ov::onnx_editor::ONNXModelEditor> m_editor;

From 9e8c8dbd940cd9508cffc5f9c65bf076a2a8946f Mon Sep 17 00:00:00 2001
From: Piotr Szmelczynski <piotr.szmelczynski@intel.com>
Date: Fri, 17 Dec 2021 12:37:59 +0100
Subject: [PATCH 021/160] [PYTHON] Improve Tensor-to-numpy converts (#9138)

* add support for f16 and bf16 for outputs_to_dict

* create test for f16

* add test for infer new request return type

* fix infer return type test

* update Type import

* update asserts in infer_new_request_return_type

* fix import in test_infer_request

* clean test file

* style fix
---
 .../python/src/pyopenvino/core/common.cpp     | 48 +++++------
 .../src/pyopenvino/core/infer_request.cpp     |  1 -
 .../tests/test_inference_engine/test_core.py  | 15 ++++
 .../test_infer_request.py                     | 84 ++++++++++++++++++-
 4 files changed, 120 insertions(+), 28 deletions(-)

diff --git a/src/bindings/python/src/pyopenvino/core/common.cpp b/src/bindings/python/src/pyopenvino/core/common.cpp
index 987456b34ee..795b385a07c 100644
--- a/src/bindings/python/src/pyopenvino/core/common.cpp
+++ b/src/bindings/python/src/pyopenvino/core/common.cpp
@@ -321,68 +321,64 @@ py::dict outputs_to_dict(const std::vector<ov::Output<const ov::Node>>& outputs,
         ov::runtime::Tensor t{request.get_tensor(out)};
         switch (t.get_element_type()) {
         case ov::element::Type_t::i8: {
-            py::array arr(t.get_shape(), t.data<int8_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<int8_t>(t.get_shape(), t.data<int8_t>());
+            ;
             break;
         }
         case ov::element::Type_t::i16: {
-            py::array arr(t.get_shape(), t.data<int16_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<int16_t>(t.get_shape(), t.data<int16_t>());
+            ;
             break;
         }
         case ov::element::Type_t::i32: {
-            py::array arr(t.get_shape(), t.data<int32_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<int32_t>(t.get_shape(), t.data<int32_t>());
+            ;
             break;
         }
         case ov::element::Type_t::i64: {
-            py::array arr(t.get_shape(), t.data<int64_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<int64_t>(t.get_shape(), t.data<int64_t>());
+            ;
             break;
         }
         case ov::element::Type_t::u8: {
-            py::array arr(t.get_shape(), t.data<uint8_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<uint8_t>(t.get_shape(), t.data<uint8_t>());
+            ;
             break;
         }
         case ov::element::Type_t::u16: {
-            py::array arr(t.get_shape(), t.data<uint16_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<uint16_t>(t.get_shape(), t.data<uint16_t>());
             break;
         }
         case ov::element::Type_t::u32: {
-            py::array arr(t.get_shape(), t.data<uint32_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<uint32_t>(t.get_shape(), t.data<uint32_t>());
+            ;
             break;
         }
         case ov::element::Type_t::u64: {
-            py::array arr(t.get_shape(), t.data<uint64_t>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<uint64_t>(t.get_shape(), t.data<uint64_t>());
             break;
         }
         case ov::element::Type_t::bf16: {
-            py::array arr(t.get_shape(), t.data<ov::bfloat16>());
-            res[py::cast(out)] = arr.view("int16");
+            res[py::cast(out)] = py::array(py::dtype("float16"), t.get_shape(), t.data<ov::bfloat16>());
             break;
         }
         case ov::element::Type_t::f16: {
-            py::array arr(t.get_shape(), t.data<ov::float16>());
-            res[py::cast(out)] = arr.view("int16");
+            res[py::cast(out)] = py::array(py::dtype("float16"), t.get_shape(), t.data<ov::float16>());
             break;
         }
         case ov::element::Type_t::f32: {
-            py::array arr(t.get_shape(), t.data<float>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<float>(t.get_shape(), t.data<float>());
+            ;
             break;
         }
         case ov::element::Type_t::f64: {
-            py::array arr(t.get_shape(), t.data<double>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<double>(t.get_shape(), t.data<double>());
+            ;
             break;
         }
         case ov::element::Type_t::boolean: {
-            py::array arr(t.get_shape(), t.data<bool*>());
-            res[py::cast(out)] = arr;
+            res[py::cast(out)] = py::array_t<bool>(t.get_shape(), t.data<bool>());
+            ;
             break;
         }
         default: {
diff --git a/src/bindings/python/src/pyopenvino/core/infer_request.cpp b/src/bindings/python/src/pyopenvino/core/infer_request.cpp
index 14dc2befeef..6bb7186c586 100644
--- a/src/bindings/python/src/pyopenvino/core/infer_request.cpp
+++ b/src/bindings/python/src/pyopenvino/core/infer_request.cpp
@@ -65,7 +65,6 @@ void regclass_InferRequest(py::module m) {
             self._start_time = Time::now();
             self._request.infer();
             self._end_time = Time::now();
-
             return Common::outputs_to_dict(self._outputs, self._request);
         },
         py::arg("inputs"));
diff --git a/src/bindings/python/tests/test_inference_engine/test_core.py b/src/bindings/python/tests/test_inference_engine/test_core.py
index 93058708005..3806cd2348b 100644
--- a/src/bindings/python/tests/test_inference_engine/test_core.py
+++ b/src/bindings/python/tests/test_inference_engine/test_core.py
@@ -380,3 +380,18 @@ def test_read_model_from_buffer_no_weights(device):
     core = Core()
     func = core.read_model(model=model)
     assert isinstance(func, Model)
+
+
+def test_infer_new_request_return_type(device):
+    ie = Core()
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    img = read_image()
+    exec_net = ie.compile_model(func, device)
+    res = exec_net.infer_new_request({"data": img})
+    arr = res[list(res)[0]][0]
+
+    assert isinstance(arr, np.ndarray)
+    assert arr.itemsize == 4
+    assert arr.shape == (10,)
+    assert arr.dtype == "float32"
+    assert arr.nbytes == 40
diff --git a/src/bindings/python/tests/test_inference_engine/test_infer_request.py b/src/bindings/python/tests/test_inference_engine/test_infer_request.py
index 224bfe077e0..8d9d3c0b50a 100644
--- a/src/bindings/python/tests/test_inference_engine/test_infer_request.py
+++ b/src/bindings/python/tests/test_inference_engine/test_infer_request.py
@@ -8,7 +8,8 @@ import datetime
 import time
 
 import openvino.runtime.opset8 as ops
-from openvino.runtime import Core, AsyncInferQueue, Tensor, ProfilingInfo, Model
+from openvino.runtime import Core, AsyncInferQueue, Tensor, ProfilingInfo, Model, Type
+from openvino.preprocess import PrePostProcessor
 
 from ..conftest import model_path, read_image
 
@@ -351,3 +352,84 @@ def test_results_async_infer(device):
 
     for i in range(num_request):
         np.allclose(list(outputs.values()), list(infer_queue[i].results.values()))
+
+
+@pytest.mark.skipif(os.environ.get("TEST_DEVICE") not in ["GPU, FPGA", "MYRIAD"],
+                    reason="Device independent test")
+def test_infer_float16(device):
+    model = bytes(b"""<net name="add_model" version="10">
+    <layers>
+    <layer id="0" name="x" type="Parameter" version="opset1">
+        <data element_type="f16" shape="2,2,2"/>
+        <output>
+            <port id="0" precision="FP16">
+                <dim>2</dim>
+                <dim>2</dim>
+                <dim>2</dim>
+            </port>
+        </output>
+    </layer>
+    <layer id="1" name="y" type="Parameter" version="opset1">
+        <data element_type="f16" shape="2,2,2"/>
+        <output>
+            <port id="0" precision="FP16">
+                <dim>2</dim>
+                <dim>2</dim>
+                <dim>2</dim>
+            </port>
+        </output>
+    </layer>
+    <layer id="2" name="sum" type="Add" version="opset1">
+        <input>
+            <port id="0">
+                <dim>2</dim>
+                <dim>2</dim>
+                <dim>2</dim>
+            </port>
+            <port id="1">
+                <dim>2</dim>
+                <dim>2</dim>
+                <dim>2</dim>
+            </port>
+        </input>
+        <output>
+            <port id="2" precision="FP16">
+                <dim>2</dim>
+                <dim>2</dim>
+                <dim>2</dim>
+            </port>
+        </output>
+    </layer>
+    <layer id="3" name="sum/sink_port_0" type="Result" version="opset1">
+        <input>
+            <port id="0">
+                <dim>2</dim>
+                <dim>2</dim>
+                <dim>2</dim>
+            </port>
+        </input>
+    </layer>
+    </layers>
+    <edges>
+    <edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
+    <edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
+    <edge from-layer="2" from-port="2" to-layer="3" to-port="0"/>
+    </edges>
+</net>""")
+    core = Core()
+    func = core.read_model(model=model)
+    p = PrePostProcessor(func)
+    p.input(0).tensor().set_element_type(Type.f16)
+    p.input(0).preprocess().convert_element_type(Type.f16)
+    p.input(1).tensor().set_element_type(Type.f16)
+    p.input(1).preprocess().convert_element_type(Type.f16)
+    p.output(0).tensor().set_element_type(Type.f16)
+    p.output(0).postprocess().convert_element_type(Type.f16)
+
+    func = p.build()
+    exec_net = core.compile_model(func, device)
+    input_data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]).astype(np.float16)
+    request = exec_net.create_infer_request()
+    outputs = request.infer({0: input_data, 1: input_data})
+    assert np.allclose(list(outputs.values()), list(request.results.values()))
+    assert np.allclose(list(outputs.values()), input_data + input_data)

From 123c99ca528e1a44b2a31060277074cee1bab83f Mon Sep 17 00:00:00 2001
From: Smirnov Grigorii <grigorii.smirnov@intel.com>
Date: Fri, 17 Dec 2021 15:08:50 +0300
Subject: [PATCH 022/160] move conv_ie deconv_ie to legacy with related passes.
 (#9117)

* move conv_ie deconv_ie to legacy with related passes. Change headers to legacy

* update convert_nms_gather_path_to_unsigned.cpp

* add legacy dependecy and revert changes in tests

* remove INFERENCE_ENGINE_API_CLASS
---
 .../legacy}/ngraph_ops/convolution_ie.hpp     |   4 +-
 .../legacy}/ngraph_ops/deconvolution_ie.hpp   |   4 +-
 .../conv_bias_fusion.hpp                      |  14 +-
 .../convert_convolutions.hpp                  |  12 +-
 .../src/convert_function_to_cnn_network.cpp   |   4 +-
 .../src/ngraph_ops/convolution_ie.cpp         |   6 +-
 .../src/ngraph_ops/deconvolution_ie.cpp       |   6 +-
 .../conv_bias_fusion.cpp                      |  16 +-
 .../convert_convolutions.cpp                  |  19 +-
 .../convert_opset1_to_legacy.cpp              |   4 +-
 .../reshape_1d_ops.cpp                        |   2 +-
 .../convert_nms_gather_path_to_unsigned.cpp   | 179 ++++++++----------
 .../transformations/conv_fusion_test.cpp      |   2 +-
 .../convert_broadcast3_test.cpp               |   2 +-
 .../convert_convolution_test.cpp              |   4 +-
 .../convert_deconvolution_test.cpp            |   6 +-
 .../convert_ngraph_to_cnn_network_tests.cpp   |   2 +-
 .../keep_constant_inputs_tests.cpp            |   2 +-
 .../mul_add_conversion_test.cpp               |   2 +-
 .../shared_test_classes/CMakeLists.txt        |   1 +
 .../src/subgraph/conv_eltwise_fusion.cpp      |   4 +-
 21 files changed, 128 insertions(+), 167 deletions(-)
 rename src/common/{transformations/include => legacy/include/legacy}/ngraph_ops/convolution_ie.hpp (97%)
 rename src/common/{transformations/include => legacy/include/legacy}/ngraph_ops/deconvolution_ie.hpp (97%)
 rename src/common/{transformations/include/transformations/common_optimizations => legacy/include/legacy/transformations/convert_opset1_to_legacy}/conv_bias_fusion.hpp (80%)
 rename src/common/{transformations/include/transformations/op_conversions => legacy/include/legacy/transformations/convert_opset1_to_legacy}/convert_convolutions.hpp (81%)
 rename src/common/{transformations => legacy}/src/ngraph_ops/convolution_ie.cpp (96%)
 rename src/common/{transformations => legacy}/src/ngraph_ops/deconvolution_ie.cpp (96%)
 rename src/common/{transformations/src/transformations/common_optimizations => legacy/src/transformations/convert_opset1_to_legacy}/conv_bias_fusion.cpp (94%)
 rename src/common/{transformations/src/transformations/op_conversions => legacy/src/transformations/convert_opset1_to_legacy}/convert_convolutions.cpp (93%)

diff --git a/src/common/transformations/include/ngraph_ops/convolution_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/convolution_ie.hpp
similarity index 97%
rename from src/common/transformations/include/ngraph_ops/convolution_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/convolution_ie.hpp
index 06274442a02..44bcc0e0b3d 100644
--- a/src/common/transformations/include/ngraph_ops/convolution_ie.hpp
+++ b/src/common/legacy/include/legacy/ngraph_ops/convolution_ie.hpp
@@ -8,15 +8,13 @@
 #include <vector>
 #include <algorithm>
 
-#include <transformations_visibility.hpp>
-
 #include "ngraph/coordinate_diff.hpp"
 #include "ngraph/op/op.hpp"
 
 namespace ngraph {
 namespace op {
 
-class TRANSFORMATIONS_API ConvolutionIE : public Op {
+class ConvolutionIE : public Op {
 public:
     OPENVINO_OP("ConvolutionIE", "util");
     BWDCMP_RTTI_DECLARATION;
diff --git a/src/common/transformations/include/ngraph_ops/deconvolution_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/deconvolution_ie.hpp
similarity index 97%
rename from src/common/transformations/include/ngraph_ops/deconvolution_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/deconvolution_ie.hpp
index 7ecb8df95da..6f0f78fd303 100644
--- a/src/common/transformations/include/ngraph_ops/deconvolution_ie.hpp
+++ b/src/common/legacy/include/legacy/ngraph_ops/deconvolution_ie.hpp
@@ -8,15 +8,13 @@
 #include <vector>
 #include <algorithm>
 
-#include <transformations_visibility.hpp>
-
 #include "ngraph/coordinate_diff.hpp"
 #include "ngraph/op/op.hpp"
 
 namespace ngraph {
 namespace op {
 
-class TRANSFORMATIONS_API DeconvolutionIE : public Op {
+class DeconvolutionIE : public Op {
 public:
     OPENVINO_OP("DeconvolutionIE", "util");
     BWDCMP_RTTI_DECLARATION;
diff --git a/src/common/transformations/include/transformations/common_optimizations/conv_bias_fusion.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp
similarity index 80%
rename from src/common/transformations/include/transformations/common_optimizations/conv_bias_fusion.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp
index d18f1bc2a6c..14f0e3826a5 100644
--- a/src/common/transformations/include/transformations/common_optimizations/conv_bias_fusion.hpp
+++ b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp
@@ -7,8 +7,6 @@
 #include <memory>
 #include <functional>
 
-#include <transformations_visibility.hpp>
-
 #include <ngraph/ngraph.hpp>
 
 #include "ngraph/pattern/matcher.hpp"
@@ -16,8 +14,8 @@
 #include "ngraph/op/reshape.hpp"
 #include "ngraph/op/add.hpp"
 
-#include "ngraph_ops/convolution_ie.hpp"
-#include "ngraph_ops/deconvolution_ie.hpp"
+#include "legacy/ngraph_ops/convolution_ie.hpp"
+#include "legacy/ngraph_ops/deconvolution_ie.hpp"
 #include "ngraph/op/group_conv.hpp"
 #include "ngraph/rt_info.hpp"
 
@@ -26,10 +24,10 @@
 namespace ngraph {
 namespace pass {
 
-class TRANSFORMATIONS_API ConvFusion;
-class TRANSFORMATIONS_API ConvAddFusion;
-class TRANSFORMATIONS_API ConvMultiplyFusion;
-class TRANSFORMATIONS_API DeconvAddFusion;
+class ConvFusion;
+class ConvAddFusion;
+class ConvMultiplyFusion;
+class DeconvAddFusion;
 
 }  // namespace pass
 }  // namespace ngraph
diff --git a/src/common/transformations/include/transformations/op_conversions/convert_convolutions.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp
similarity index 81%
rename from src/common/transformations/include/transformations/op_conversions/convert_convolutions.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp
index 5d4d888ad87..0e04abc5858 100644
--- a/src/common/transformations/include/transformations/op_conversions/convert_convolutions.hpp
+++ b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp
@@ -7,19 +7,19 @@
 #include <vector>
 #include <memory>
 
-#include <transformations_visibility.hpp>
+#include <ie_api.h>
 
 #include <ngraph/pass/graph_rewrite.hpp>
 
 namespace ngraph {
 namespace pass {
 
-class TRANSFORMATIONS_API ConvertConvolutions;
+class ConvertConvolutions;
 
-class TRANSFORMATIONS_API ConvertConvolution;
-class TRANSFORMATIONS_API ConvertGroupConvolution;
-class TRANSFORMATIONS_API ConvertDeconvolution;
-class TRANSFORMATIONS_API ConvertGroupDeconvolution;
+class ConvertConvolution;
+class ConvertGroupConvolution;
+class ConvertDeconvolution;
+class ConvertGroupDeconvolution;
 
 }  // namespace pass
 }  // namespace ngraph
diff --git a/src/common/legacy/src/convert_function_to_cnn_network.cpp b/src/common/legacy/src/convert_function_to_cnn_network.cpp
index a93939159f8..87328cea152 100644
--- a/src/common/legacy/src/convert_function_to_cnn_network.cpp
+++ b/src/common/legacy/src/convert_function_to_cnn_network.cpp
@@ -10,8 +10,8 @@
 #include <sstream>
 
 #include <cnn_network_ngraph_impl.hpp>
-#include "ngraph_ops/convolution_ie.hpp"
-#include "ngraph_ops/deconvolution_ie.hpp"
+#include "legacy/ngraph_ops/convolution_ie.hpp"
+#include "legacy/ngraph_ops/deconvolution_ie.hpp"
 #include "legacy/ngraph_ops/eltwise.hpp"
 #include "legacy/ngraph_ops/fully_connected.hpp"
 #include "legacy/ngraph_ops/gather_ie.hpp"
diff --git a/src/common/transformations/src/ngraph_ops/convolution_ie.cpp b/src/common/legacy/src/ngraph_ops/convolution_ie.cpp
similarity index 96%
rename from src/common/transformations/src/ngraph_ops/convolution_ie.cpp
rename to src/common/legacy/src/ngraph_ops/convolution_ie.cpp
index 37ff1b6928f..48a6749cc0c 100644
--- a/src/common/transformations/src/ngraph_ops/convolution_ie.cpp
+++ b/src/common/legacy/src/ngraph_ops/convolution_ie.cpp
@@ -2,8 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph_ops/convolution_ie.hpp"
-#include "itt.hpp"
+#include "legacy/ngraph_ops/convolution_ie.hpp"
 
 #include <algorithm>
 #include <memory>
@@ -100,7 +99,6 @@ op::ConvolutionIE::ConvolutionIE(const Output<Node>& data_batch,
 }
 
 void op::ConvolutionIE::validate_and_infer_types() {
-    INTERNAL_OP_SCOPE(ConvolutionIE_validate_and_infer_types);
     PartialShape data_batch_shape = get_input_partial_shape(0);
     PartialShape filters_shape = get_input_partial_shape(1);
 
@@ -147,7 +145,6 @@ void op::ConvolutionIE::validate_and_infer_types() {
 }
 
 shared_ptr<Node> op::ConvolutionIE::clone_with_new_inputs(const ngraph::OutputVector & new_args) const {
-    INTERNAL_OP_SCOPE(ConvolutionIE_clone_with_new_inputs);
     if (new_args.size() == 2) {
         return make_shared<ConvolutionIE>(new_args.at(0),
                                           new_args.at(1),
@@ -175,7 +172,6 @@ shared_ptr<Node> op::ConvolutionIE::clone_with_new_inputs(const ngraph::OutputVe
 }
 
 bool op::ConvolutionIE::visit_attributes(AttributeVisitor& visitor) {
-    INTERNAL_OP_SCOPE(ConvolutionIE_visit_attributes);
     visitor.on_attribute("strides", m_strides);
     visitor.on_attribute("dilations", m_dilations);
     visitor.on_attribute("pads_begin", m_pads_begin);
diff --git a/src/common/transformations/src/ngraph_ops/deconvolution_ie.cpp b/src/common/legacy/src/ngraph_ops/deconvolution_ie.cpp
similarity index 96%
rename from src/common/transformations/src/ngraph_ops/deconvolution_ie.cpp
rename to src/common/legacy/src/ngraph_ops/deconvolution_ie.cpp
index bbd1415e91c..3ec0fc71723 100644
--- a/src/common/transformations/src/ngraph_ops/deconvolution_ie.cpp
+++ b/src/common/legacy/src/ngraph_ops/deconvolution_ie.cpp
@@ -6,9 +6,8 @@
 #include <memory>
 #include <vector>
 #include <ngraph/ops.hpp>
-#include "itt.hpp"
 
-#include "ngraph_ops/deconvolution_ie.hpp"
+#include "legacy/ngraph_ops/deconvolution_ie.hpp"
 
 #include "ngraph/util.hpp"
 #include "ngraph/validation_util.hpp"
@@ -70,7 +69,6 @@ op::DeconvolutionIE::DeconvolutionIE(const Output<Node>& data,
 }
 
 void op::DeconvolutionIE::validate_and_infer_types() {
-    INTERNAL_OP_SCOPE(DeconvolutionIE_validate_and_infer_types);
     // To calculate output shape we use opset1::GroupConvolutionBackPropData
     // but before we need to reshape weights from I(G*O)YX to GIOYX
     auto weights = input_value(1);
@@ -115,7 +113,6 @@ void op::DeconvolutionIE::validate_and_infer_types() {
 }
 
 shared_ptr<Node> op::DeconvolutionIE::clone_with_new_inputs(const ngraph::OutputVector &new_args) const {
-    INTERNAL_OP_SCOPE(DeconvolutionIE_clone_with_new_inputs);
     if (new_args.size() == 2) {
         return make_shared<DeconvolutionIE>(new_args.at(0),
                                             new_args.at(1),
@@ -146,7 +143,6 @@ shared_ptr<Node> op::DeconvolutionIE::clone_with_new_inputs(const ngraph::Output
 }
 
 bool op::DeconvolutionIE::visit_attributes(AttributeVisitor& visitor) {
-    INTERNAL_OP_SCOPE(DeconvolutionIE_visit_attributes);
     visitor.on_attribute("strides", m_strides);
     visitor.on_attribute("dilations", m_dilations);
     visitor.on_attribute("pads_begin", m_pads_begin);
diff --git a/src/common/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/conv_bias_fusion.cpp
similarity index 94%
rename from src/common/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/conv_bias_fusion.cpp
index 2cec36d7bf5..36ed9e4261f 100644
--- a/src/common/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp
+++ b/src/common/legacy/src/transformations/convert_opset1_to_legacy/conv_bias_fusion.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "transformations/common_optimizations/conv_bias_fusion.hpp"
+#include "legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp"
 
 #include <memory>
 #include <numeric>
@@ -13,11 +13,10 @@
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pattern/op/wrap_type.hpp>
 
-#include <ngraph_ops/convolution_ie.hpp>
-#include <ngraph_ops/deconvolution_ie.hpp>
+#include <legacy/ngraph_ops/convolution_ie.hpp>
+#include <legacy/ngraph_ops/deconvolution_ie.hpp>
 
 #include <transformations/utils/utils.hpp>
-#include "itt.hpp"
 
 using namespace ngraph;
 
@@ -162,7 +161,6 @@ bool conv_callback(ngraph::pattern::Matcher &m) {
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvAddFusion, "ConvAddFusion", 0);
 
 ngraph::pass::ConvAddFusion::ConvAddFusion() {
-    MATCHER_SCOPE(ConvAddFusion);
     auto conv = ngraph::pattern::wrap_type<op::ConvolutionIE>(pattern::consumers_count(1));
     auto add = ngraph::pattern::wrap_type<opset1::Add>({conv, pattern::any_input()});
 
@@ -170,14 +168,13 @@ ngraph::pass::ConvAddFusion::ConvAddFusion() {
         return conv_callback<op::ConvolutionIE>(m);
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(add, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(add, "ConvAddFusion");
     register_matcher(m, callback);
 }
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvMultiplyFusion, "ConvMultiplyFusion", 0);
 
 ngraph::pass::ConvMultiplyFusion::ConvMultiplyFusion() {
-    MATCHER_SCOPE(ConvMultiplyFusion);
     auto conv = ngraph::pattern::wrap_type<op::ConvolutionIE>(pattern::consumers_count(1));
     auto add = ngraph::pattern::wrap_type<opset1::Multiply>({conv, pattern::any_input()});
 
@@ -185,14 +182,13 @@ ngraph::pass::ConvMultiplyFusion::ConvMultiplyFusion() {
         return conv_callback<op::ConvolutionIE>(m);
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(add, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(add, "ConvMultiplyFusion");
     register_matcher(m, callback);
 }
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::DeconvAddFusion, "DeconvAddFusion", 0);
 
 ngraph::pass::DeconvAddFusion::DeconvAddFusion() {
-    MATCHER_SCOPE(DeconvAddFusion);
     auto conv = ngraph::pattern::wrap_type<op::DeconvolutionIE>(pattern::consumers_count(1));
     auto add = ngraph::pattern::wrap_type<opset1::Add>({conv, pattern::any_input()});
 
@@ -200,6 +196,6 @@ ngraph::pass::DeconvAddFusion::DeconvAddFusion() {
         return conv_callback<op::DeconvolutionIE>(m);
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(add, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(add, "DeconvAddFusion");
     register_matcher(m, callback);
 }
diff --git a/src/common/transformations/src/transformations/op_conversions/convert_convolutions.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_convolutions.cpp
similarity index 93%
rename from src/common/transformations/src/transformations/op_conversions/convert_convolutions.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_convolutions.cpp
index 1f0fb32ae6b..4101f28b495 100644
--- a/src/common/transformations/src/transformations/op_conversions/convert_convolutions.cpp
+++ b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_convolutions.cpp
@@ -2,8 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
-#include "transformations/op_conversions/convert_convolutions.hpp"
+#include "legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp"
 
 #include <memory>
 #include <vector>
@@ -11,8 +10,8 @@
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/rt_info.hpp>
 
-#include <ngraph_ops/convolution_ie.hpp>
-#include <ngraph_ops/deconvolution_ie.hpp>
+#include <legacy/ngraph_ops/convolution_ie.hpp>
+#include <legacy/ngraph_ops/deconvolution_ie.hpp>
 
 #include <ngraph/pattern/op/wrap_type.hpp>
 
@@ -20,7 +19,6 @@ NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertConvolutions, "ConvertConvolutions",
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertConvolution, "ConvertConvolution", 0);
 
 ngraph::pass::ConvertConvolution::ConvertConvolution() {
-    MATCHER_SCOPE(ConvertConvolution);
     auto conv = ngraph::pattern::wrap_type<opset1::Convolution>();
 
     ngraph::matcher_pass_callback callback = [](pattern::Matcher& m) {
@@ -44,14 +42,13 @@ ngraph::pass::ConvertConvolution::ConvertConvolution() {
         return true;
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(conv, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(conv, "ConvertConvolution");
     this->register_matcher(m, callback);
 }
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertGroupConvolution, "ConvertGroupConvolution", 0);
 
 ngraph::pass::ConvertGroupConvolution::ConvertGroupConvolution() {
-    MATCHER_SCOPE(ConvertGroupConvolution);
     auto gconv = ngraph::pattern::wrap_type<opset1::GroupConvolution>();
 
     ngraph::matcher_pass_callback callback = [](pattern::Matcher& m) {
@@ -91,14 +88,13 @@ ngraph::pass::ConvertGroupConvolution::ConvertGroupConvolution() {
         return true;
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(gconv, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(gconv, "ConvertGroupConvolution");
     this->register_matcher(m, callback);
 }
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertDeconvolution, "ConvertDeconvolution", 0);
 
 ngraph::pass::ConvertDeconvolution::ConvertDeconvolution() {
-    MATCHER_SCOPE(ConvertDeconvolution);
     auto conv = ngraph::pattern::wrap_type<opset1::ConvolutionBackpropData>();
 
     ngraph::matcher_pass_callback callback = [](pattern::Matcher& m) {
@@ -125,14 +121,13 @@ ngraph::pass::ConvertDeconvolution::ConvertDeconvolution() {
         return true;
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(conv, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(conv, "ConvertDeconvolution");
     this->register_matcher(m, callback);
 }
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertGroupDeconvolution, "ConvertGroupDeconvolution", 0);
 
 ngraph::pass::ConvertGroupDeconvolution::ConvertGroupDeconvolution() {
-    MATCHER_SCOPE(ConvertGroupDeconvolution);
     auto gconv = ngraph::pattern::wrap_type<opset1::GroupConvolutionBackpropData>();
 
     ngraph::matcher_pass_callback callback = [](pattern::Matcher& m) {
@@ -171,6 +166,6 @@ ngraph::pass::ConvertGroupDeconvolution::ConvertGroupDeconvolution() {
         return true;
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(gconv, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(gconv, "ConvertGroupDeconvolution");
     this->register_matcher(m, callback);
 }
diff --git a/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp
index 29d8f1dd199..0eb5da216bb 100644
--- a/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp
+++ b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp
@@ -42,8 +42,8 @@
 #include <transformations/op_conversions/convert_minimum_to_power_and_max.hpp>
 #include <transformations/op_conversions/convert_subtract.hpp>
 
-#include <transformations/common_optimizations/conv_bias_fusion.hpp>
-#include <transformations/op_conversions/convert_convolutions.hpp>
+#include "legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp"
+#include "legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp"
 #include <transformations/op_conversions/convert_previous_nms_to_nms_5.hpp>
 
 #include <ngraph/pass/constant_folding.hpp>
diff --git a/src/common/legacy/src/transformations/convert_opset1_to_legacy/reshape_1d_ops.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/reshape_1d_ops.cpp
index ae52fd0dd05..b1113da4639 100644
--- a/src/common/legacy/src/transformations/convert_opset1_to_legacy/reshape_1d_ops.cpp
+++ b/src/common/legacy/src/transformations/convert_opset1_to_legacy/reshape_1d_ops.cpp
@@ -11,7 +11,7 @@
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pattern/op/wrap_type.hpp>
 
-#include "ngraph_ops/convolution_ie.hpp"
+#include "legacy/ngraph_ops/convolution_ie.hpp"
 #include "transformations/utils/utils.hpp"
 
 using namespace ngraph;
diff --git a/src/common/transformations/src/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.cpp b/src/common/transformations/src/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.cpp
index 8cf9091fcd5..35994a0af5d 100644
--- a/src/common/transformations/src/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.cpp
@@ -1,7 +1,6 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-
 #include "transformations/common_optimizations/convert_nms_gather_path_to_unsigned.hpp"
 #include <ngraph/pattern/op/wrap_type.hpp>
 #include <ngraph/opsets/opset1.hpp>
@@ -16,111 +15,95 @@
 #include "itt.hpp"
 #include "ngraph/node.hpp"
 
-using namespace ngraph;
 using namespace std;
 
-class InitNMSPath: public pass::MatcherPass {
-public:
-    NGRAPH_RTTI_DECLARATION;
-
-    InitNMSPath() {
-        MATCHER_SCOPE(InitNMSPath);
-
-        auto nms_pattern = pattern::wrap_type<opset1::NonMaxSuppression,
-                opset3::NonMaxSuppression,
-                opset5::NonMaxSuppression>();
-
-        matcher_pass_callback callback = [=](pattern::Matcher &m) {
-            const auto& out_nodes = m.get_match_root()->output(0).get_target_inputs();
-            for (const auto& out_node : out_nodes) {
-                ov::set_nms_selected_indices(out_node.get_node());
-            }
-            return true;
-        };
-
-        auto m = make_shared<pattern::Matcher>(nms_pattern, matcher_name);
-        register_matcher(m, callback);
-    }
-};
-
-NGRAPH_RTTI_DEFINITION(InitNMSPath, "InitNMSPath", 0);
-
-
+namespace ngraph {
+namespace pass {
+class InitNMSPath : public pass::MatcherPass {
+    public:
+        NGRAPH_RTTI_DECLARATION;
+        InitNMSPath() {
+            MATCHER_SCOPE(InitNMSPath);
+            auto nms_pattern = pattern::wrap_type<opset1::NonMaxSuppression,
+                    opset3::NonMaxSuppression,
+                    opset5::NonMaxSuppression>();
+            matcher_pass_callback callback = [=](pattern::Matcher &m) {
+                const auto &out_nodes = m.get_match_root()->output(0).get_target_inputs();
+                for (const auto & out_node : out_nodes) {
+                    ov::set_nms_selected_indices(out_node.get_node());
+                }
+                return true;
+            };
+            auto m = make_shared<pattern::Matcher>(nms_pattern, matcher_name);
+            register_matcher(m, callback);
+        }
+    };
 class PropagateNMSPath: public pass::MatcherPass {
-public:
-    NGRAPH_RTTI_DECLARATION;
-
-    PropagateNMSPath(){
-        MATCHER_SCOPE(PropagateNMSPath);
-
-        auto node_pattern = pattern::wrap_type<
-                opset8::Squeeze,
-                opset8::Unsqueeze,
-                opset8::Reshape,
-                op::util::BroadcastBase,
-                opset8::StridedSlice,
-                opset8::VariadicSplit,
-                opset8::Concat,
-                opset8::Convert>();
-
-        matcher_pass_callback callback = [=](pattern::Matcher &m) {
-            auto node = m.get_match_root();
-            const auto & inputs = node->input_values();
-            if (any_of(inputs.begin(), inputs.end(), [](const Output<Node> & output) {
-                return ov::has_nms_selected_indices(output.get_node());
-            })) {
-                ov::set_nms_selected_indices(node.get());
-            }
-            return true;
-        };
-
-        auto m = make_shared<pattern::Matcher>(node_pattern, matcher_name);
-        register_matcher(m, callback);
-    }
-};
-
-NGRAPH_RTTI_DEFINITION(PropagateNMSPath, "PropagateNMSPath", 0);
-
+    public:
+        NGRAPH_RTTI_DECLARATION;
+        PropagateNMSPath(){
+            MATCHER_SCOPE(PropagateNMSPath);
+            auto node_pattern = pattern::wrap_type<
+                    opset8::Squeeze,
+                    opset8::Unsqueeze,
+                    opset8::Reshape,
+                    op::util::BroadcastBase,
+                    opset8::StridedSlice,
+                    opset8::VariadicSplit,
+                    opset8::Concat,
+                    opset8::Convert>();
+            matcher_pass_callback callback = [=](pattern::Matcher &m) {
+                auto node = m.get_match_root();
+                const auto & inputs = node->input_values();
+                if (any_of(inputs.begin(), inputs.end(), [](const Output<Node> & output) {
+                    return ov::has_nms_selected_indices(output.get_node());
+                })) {
+                    ov::set_nms_selected_indices(node.get());
+                }
+                return true;
+            };
+            auto m = make_shared<pattern::Matcher>(node_pattern, matcher_name);
+            register_matcher(m, callback);
+        }
+    };
 class UpdateConvertGather: public pass::MatcherPass {
-public:
-    NGRAPH_RTTI_DECLARATION;
+    public:
+        NGRAPH_RTTI_DECLARATION;
+        UpdateConvertGather(){
+            MATCHER_SCOPE(UpdateConvertGather);
+            auto node_pattern = pattern::wrap_type<op::util::GatherBase>();
+            matcher_pass_callback callback = [=](pattern::Matcher &m) {
+                auto gather = m.get_match_root();
+                auto indices = gather->input_value(1);
+                if (!ov::has_nms_selected_indices(indices.get_node()))
+                    return false;
+                auto out_type = (indices.get_element_type() == element::i64 ?  element::u64 : element::u32);
+                auto existing_convert = dynamic_pointer_cast<opset8::Convert>(indices.get_node_shared_ptr());
+                if (existing_convert && indices.get_target_inputs().size() == 1) {
+                    existing_convert->set_convert_element_type(out_type);
+                    existing_convert->validate_and_infer_types();
+                } else {
+                    auto new_convert_to_unsigned = make_shared<opset8::Convert>(indices, out_type);
+                    gather->input(1).replace_source_output(new_convert_to_unsigned);
+                    copy_runtime_info(gather, new_convert_to_unsigned);
+                }
+                return true;
+            };
+            auto m = make_shared<pattern::Matcher>(node_pattern, matcher_name);
+            register_matcher(m, callback);
+        }
+    };
+}// namespace pass
+}// namespace ngraph
 
-    UpdateConvertGather(){
-        MATCHER_SCOPE(UpdateConvertGather);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::InitNMSPath, "InitNMSPath", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::PropagateNMSPath, "PropagateNMSPath", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::UpdateConvertGather, "UpdateConvertGather", 0);
 
-        auto node_pattern = pattern::wrap_type<op::util::GatherBase>();
-
-        matcher_pass_callback callback = [=](pattern::Matcher &m) {
-            auto gather = m.get_match_root();
-            auto indices = gather->input_value(1);
-
-            if (!ov::has_nms_selected_indices(indices.get_node()))
-                return false;
-
-            auto out_type = (indices.get_element_type() == element::i64 ?  element::u64 : element::u32);
-            auto existing_convert = dynamic_pointer_cast<opset8::Convert>(indices.get_node_shared_ptr());
-            if (existing_convert && indices.get_target_inputs().size() == 1) {
-                existing_convert->set_convert_element_type(out_type);
-                existing_convert->validate_and_infer_types();
-            } else {
-                auto new_convert_to_unsigned = make_shared<opset8::Convert>(indices, out_type);
-                gather->input(1).replace_source_output(new_convert_to_unsigned);
-                copy_runtime_info(gather, new_convert_to_unsigned);
-            }
-            return true;
-        };
-
-        auto m = make_shared<pattern::Matcher>(node_pattern, matcher_name);
-        register_matcher(m, callback);
-    }
-};
-
-NGRAPH_RTTI_DEFINITION(UpdateConvertGather, "UpdateConvertGather", 0);
-
-pass::ConvertNmsGatherPathToUnsigned::ConvertNmsGatherPathToUnsigned() {
+ngraph::pass::ConvertNmsGatherPathToUnsigned::ConvertNmsGatherPathToUnsigned() {
     add_matcher<InitNMSPath>();
     add_matcher<PropagateNMSPath>();
     add_matcher<UpdateConvertGather>();
 }
 
-NGRAPH_RTTI_DEFINITION(pass::ConvertNmsGatherPathToUnsigned, "ConvertNmsGatherPathToUnsigned", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertNmsGatherPathToUnsigned, "ConvertNmsGatherPathToUnsigned", 0);
\ No newline at end of file
diff --git a/src/tests/functional/inference_engine/transformations/conv_fusion_test.cpp b/src/tests/functional/inference_engine/transformations/conv_fusion_test.cpp
index 0fa792b6115..c74e01a416e 100644
--- a/src/tests/functional/inference_engine/transformations/conv_fusion_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/conv_fusion_test.cpp
@@ -17,7 +17,7 @@
 #include <ngraph/pass/constant_folding.hpp>
 #include <transformations/utils/utils.hpp>
 #include <transformations/init_node_info.hpp>
-#include <transformations/common_optimizations/conv_bias_fusion.hpp>
+#include <legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp>
 #include <transformations/common_optimizations/conv_mul_fusion.hpp>
 #include <ngraph/pass/visualize_tree.hpp>
 
diff --git a/src/tests/functional/inference_engine/transformations/convert_broadcast3_test.cpp b/src/tests/functional/inference_engine/transformations/convert_broadcast3_test.cpp
index 95b54fe0461..061986ccb94 100644
--- a/src/tests/functional/inference_engine/transformations/convert_broadcast3_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/convert_broadcast3_test.cpp
@@ -19,7 +19,7 @@
 #include <transformations/init_node_info.hpp>
 #include <ngraph/pass/visualize_tree.hpp>
 #include <transformations/op_conversions/convert_broadcast3.hpp>
-#include <ngraph_ops/convolution_ie.hpp>
+#include <legacy/ngraph_ops/convolution_ie.hpp>
 #include <ngraph/pass/manager.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
diff --git a/src/tests/functional/inference_engine/transformations/convert_convolution_test.cpp b/src/tests/functional/inference_engine/transformations/convert_convolution_test.cpp
index 27f173304d1..7414005dc23 100644
--- a/src/tests/functional/inference_engine/transformations/convert_convolution_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/convert_convolution_test.cpp
@@ -18,8 +18,8 @@
 #include <transformations/utils/utils.hpp>
 #include <transformations/init_node_info.hpp>
 #include <ngraph/pass/visualize_tree.hpp>
-#include <transformations/op_conversions/convert_convolutions.hpp>
-#include <ngraph_ops/convolution_ie.hpp>
+#include <legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp>
+#include <legacy/ngraph_ops/convolution_ie.hpp>
 #include <ngraph/pass/manager.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
diff --git a/src/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp b/src/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp
index 242325390d2..5be2ffe257a 100644
--- a/src/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp
@@ -18,9 +18,9 @@
 #include <transformations/utils/utils.hpp>
 #include <transformations/init_node_info.hpp>
 #include <ngraph/pass/visualize_tree.hpp>
-#include <transformations/op_conversions/convert_convolutions.hpp>
-#include <ngraph_ops/convolution_ie.hpp>
-#include <ngraph_ops/deconvolution_ie.hpp>
+#include <legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp>
+#include <legacy/ngraph_ops/convolution_ie.hpp>
+#include <legacy/ngraph_ops/deconvolution_ie.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
diff --git a/src/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp b/src/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
index 26ea74e7f97..524570623b3 100644
--- a/src/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
+++ b/src/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
@@ -19,7 +19,7 @@
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/opsets/opset4.hpp>
 #include <ngraph/opsets/opset5.hpp>
-#include <ngraph_ops/convolution_ie.hpp>
+#include <legacy/ngraph_ops/convolution_ie.hpp>
 #include <transformations/init_node_info.hpp>
 #include <legacy/convert_function_to_cnn_network.hpp>
 
diff --git a/src/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp b/src/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp
index bb97268d787..526d3b82b78 100644
--- a/src/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp
+++ b/src/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp
@@ -9,7 +9,7 @@
 
 #include <ngraph/function.hpp>
 #include <ngraph/opsets/opset1.hpp>
-#include <ngraph_ops/convolution_ie.hpp>
+#include <legacy/ngraph_ops/convolution_ie.hpp>
 #include <transformations/init_node_info.hpp>
 
 #include <ie_precision.hpp>
diff --git a/src/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp b/src/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp
index 9358886a63a..008b64f17bc 100644
--- a/src/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp
@@ -17,7 +17,7 @@
 #include <ngraph/pass/constant_folding.hpp>
 #include <transformations/utils/utils.hpp>
 #include <transformations/init_node_info.hpp>
-#include <transformations/common_optimizations/conv_bias_fusion.hpp>
+#include <legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp>
 #include <ngraph/pass/visualize_tree.hpp>
 #include <legacy/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.hpp>
 #include <legacy/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.hpp>
diff --git a/src/tests/functional/shared_test_classes/CMakeLists.txt b/src/tests/functional/shared_test_classes/CMakeLists.txt
index 652f2ea8f39..1c11d10b69c 100644
--- a/src/tests/functional/shared_test_classes/CMakeLists.txt
+++ b/src/tests/functional/shared_test_classes/CMakeLists.txt
@@ -21,6 +21,7 @@ addIeTarget(
                 pugixml::static
                 funcTestUtils
                 ngraphFunctions
+                inference_engine_legacy
 )
 
 ie_faster_build(${TARGET_NAME}
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/conv_eltwise_fusion.cpp b/src/tests/functional/shared_test_classes/src/subgraph/conv_eltwise_fusion.cpp
index 6e9e4578356..e45137f960c 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/conv_eltwise_fusion.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/conv_eltwise_fusion.cpp
@@ -3,10 +3,10 @@
 //
 
 #include "transformations/common_optimizations/conv_mul_fusion.hpp"
-#include "transformations/op_conversions/convert_convolutions.hpp"
-#include "transformations/common_optimizations/conv_bias_fusion.hpp"
 #include "ngraph/pass/constant_folding.hpp"
 #include "shared_test_classes/subgraph/conv_eltwise_fusion.hpp"
+#include <legacy/transformations/convert_opset1_to_legacy/conv_bias_fusion.hpp>
+#include <legacy/transformations/convert_opset1_to_legacy/convert_convolutions.hpp>
 
 namespace SubgraphTestsDefinitions {
 

From b394441c79d219d7cef775149bf9cbb8816a0a57 Mon Sep 17 00:00:00 2001
From: Egor Duplensky <egor.duplenskii@intel.com>
Date: Fri, 17 Dec 2021 15:13:16 +0300
Subject: [PATCH 023/160] [CPU] Fix sse4 uni_ methods (Normalize and Input
 nodes) (#9268)

---
 inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.cpp  | 2 +-
 inference-engine/thirdparty/mkl-dnn                             | 2 +-
 .../functional/plugin/cpu/single_layer_tests/normalize.cpp      | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.cpp
index e5f8e5691eb..4f73fb7c1ab 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.cpp
@@ -109,7 +109,7 @@ protected:
         uni_vmovdqu(b, a);                   // b = a
         uni_vmovdqu(c, a);                   // c = a
         uni_vpcmpeqd(b, b, zero);               // if (a == 0) b = 1 else b = 0
-        uni_vpand(c, mask);                  // c = a & 01111111100000000000000000000000
+        uni_vpand(c, c, mask);                  // c = a & 01111111100000000000000000000000
         uni_vpcmpeqd(c, c, zero);               // if (c == 0) c = 1 else c = 0
         uni_vtestps(b, c);                    // if ((!b & c) == 0) CF = 1 else CF = 0
     }
diff --git a/inference-engine/thirdparty/mkl-dnn b/inference-engine/thirdparty/mkl-dnn
index 5adbcb757c7..acee807d849 160000
--- a/inference-engine/thirdparty/mkl-dnn
+++ b/inference-engine/thirdparty/mkl-dnn
@@ -1 +1 @@
-Subproject commit 5adbcb757c77f1bf0cd11ad58dd92e93ea2e3561
+Subproject commit acee807d84944008df6741677ab52e01d790d58a
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/normalize.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/normalize.cpp
index 4c4ae1d3bd6..a857cc94541 100755
--- a/src/tests/functional/plugin/cpu/single_layer_tests/normalize.cpp
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/normalize.cpp
@@ -270,7 +270,7 @@ std::vector<CPUSpecificParams> getCPUSpecificParams() {
         result.push_back(CPUSpecificParams({nhwc}, {nhwc}, {}, {}));
         if (with_cpu_x86_avx512f()) {
             result.push_back(CPUSpecificParams({nChw16c}, {nChw16c}, {}, {}));
-        } else if (with_cpu_x86_avx2()) {
+        } else {
             result.push_back(CPUSpecificParams({nChw8c}, {nChw8c}, {}, {}));
         }
     }

From ba4802554e888b9b8bac47572ae8f2a20946c9b5 Mon Sep 17 00:00:00 2001
From: Pavel Esir <pavel.esir@intel.com>
Date: Fri, 17 Dec 2021 15:56:40 +0300
Subject: [PATCH 024/160] [MO] Azure CI, unit-test dirs and howto doc fixes
 (#9146)

* Azure CI fixes for MO

* leftover: update imports in docs

* moved unit-tests from extensions/ into mo/
---
 .ci/azure/linux.yml                           |  6 +-
 docs/HOWTO/mo_extensions/front/tf/Complex.py  |  8 +--
 .../mo_extensions/front/tf/ComplexAbs.py      | 10 ++--
 docs/HOWTO/mo_extensions/front/tf/FFT_ext.py  |  3 +-
 docs/HOWTO/mo_extensions/ops/FFT.py           |  6 +-
 .../Customize_Model_Optimizer.md              | 58 +++++++++----------
 ...Net_Model_Optimizer_with_New_Primitives.md |  6 +-
 ...odel_Optimizer_with_Caffe_Python_Layers.md |  4 +-
 .../mo/unit_tests/extensions/back/__init__.py |  0
 .../unit_tests/extensions/front/__init__.py   |  0
 .../extensions/front/caffe/__init__.py        |  0
 .../extensions/front/kaldi/__init__.py        |  0
 .../extensions/front/mxnet/__init__.py        |  0
 .../extensions/front/onnx/__init__.py         |  0
 .../extensions/front/tf/__init__.py           |  0
 .../unit_tests/extensions/middle/__init__.py  |  0
 .../mo/unit_tests/extensions/ops/__init__.py  |  0
 .../analysis/Iterator_get_next_test.py        |  0
 .../{extensions => mo/analysis}/__init__.py   |  0
 .../back/ChangeOutputTypeAttributes_test.py   |  0
 .../ChangeRandomUniformOutputType_test.py     |  0
 .../back/ClampNormalizer_test.py              |  0
 .../back/ConvolutionNormalizer_test.py        |  0
 .../{extensions => mo}/back/CutMemory_test.py |  0
 .../back/FakeOutputResolver_test.py           |  0
 .../back/FuseTransposesSequence_test.py       |  0
 .../back/GatherTreeNormalizer_test.py         |  0
 .../back/InterpolateReshape_test.py           |  0
 .../back/LayoutChangeForGatherND_test.py      |  0
 .../back/MarkNodesWithShapeValues_test.py     |  0
 .../back/MatMulNormalizer_test.py             |  0
 .../OptimizeTransposeReshapeSequence_test.py  |  0
 .../back/ReduceTransposeDimensions_test.py    |  0
 .../back/ResultRename_test.py                 |  0
 .../back/ReverseInputChannels_test.py         |  0
 .../back/ShapeOfConstFolding_test.py          |  0
 .../ShuffleChannelPatternOptimization_test.py |  0
 .../back/SpecialNodesFinalization_test.py     |  0
 .../back/TransposeDFT_test.py                 |  0
 .../back/TransposeReduceFusing_test.py        |  0
 .../back/add_outputs_recursive_test.py        |  0
 .../back/compress_quantized_weights_test.py   |  0
 ...sert_compatibility_l2normalization_test.py |  0
 .../back/kaldi_remove_memory_output_test.py   |  0
 .../back/names_uniqueness_check_test.py       |  0
 .../back/remove_last_softmax_test.py          |  0
 .../front/ATenToEmbeddingBag_test.py          |  0
 .../front/AttributedClampNormalizer_test.py   |  0
 .../front/AttributedPadToPad_test.py          |  0
 ...ibutedRandomUniformToRandomUniform_test.py |  0
 .../front/AttributedRollToRoll_test.py        |  0
 .../front/GeLUMerger_Erf_test.py              |  0
 .../front/GeLUMerger_Tanh_test.py             |  0
 .../front/GlobalPoolingToReduce_test.py       |  0
 .../front/HSigmoid_fusion_test.py             |  0
 .../front/HSwish_fusing_test.py               |  0
 .../front/LayerNorm_test.py                   |  0
 .../{extensions => mo}/front/Log1p_test.py    |  0
 .../front/OneHotDepthNormalizer_test.py       |  0
 .../{extensions => mo}/front/Pack_test.py     |  0
 .../front/RollWithEmptyAxesReplacer_test.py   |  0
 .../ThresholdedReluDecomposition_test.py      |  0
 .../binary_quantize_normalization_test.py     |  0
 .../front/broadcast_with_range_test.py        |  0
 .../front/caffe/MVNCaffeToMVN_test.py         |  0
 .../front/caffe/argmax_ext_test.py            |  0
 .../front/caffe/axpy_test.py                  |  0
 .../{extensions => mo}/front/caffe/bn_test.py |  0
 .../front/caffe/conv_ext_test.py              |  0
 .../front/caffe/crop_ext_test.py              |  0
 .../front/caffe/ctcgreedydecoder_ext_test.py  |  0
 .../front/caffe/elementwise_ext_test.py       |  0
 .../front/caffe/eltwise_add_normalize_test.py |  0
 .../front/caffe/elu_test.py                   |  0
 .../front/caffe/grn_ext_test.py               |  0
 .../front/caffe/normalize_ext_test.py         |  0
 .../front/caffe/pooling_ext_test.py           |  0
 .../front/caffe/prelu_ext_test.py             |  0
 .../caffe/priorbox_clustered_ext_test.py      |  0
 .../front/caffe/priorbox_ext_test.py          |  0
 .../front/caffe/proposal_ext_test.py          |  0
 .../front/caffe/proposal_python_ext_test.py   |  0
 .../front/caffe/regionyolo_ext_test.py        |  0
 .../front/caffe/reorgyolo_ext_test.py         |  0
 .../{extensions => mo}/front/div_test.py      |  0
 .../front/eltwise_n_test.py                   |  0
 .../front/freeze_placeholder_value_test.py    |  0
 .../front/image_scaler_test.py                |  0
 .../front/instance_normalization_test.py      |  0
 .../front/interpolate_reshape_test.py         |  0
 .../add_permute_after_convolution_test.py     |  0
 .../front/kaldi/apply_counts_test.py          |  0
 .../kaldi/memory_offset_adjustment_test.py    |  0
 .../kaldi/replace_lstm_nonlinearity_test.py   |  0
 .../replace_timeheightconvolution_test.py     |  0
 .../front/kaldi/set_ports_test.py             |  0
 .../front/kaldi/sigmoid_ext_test.py           |  0
 .../front/kaldi/tanh_ext_test.py              |  0
 .../kaldi/tdnn_component_replacer_test.py     |  0
 .../front/mxnet/MXFFTToDFT_test.py            |  0
 .../front/mxnet/RNN_ext_test.py               |  0
 .../front/mxnet/activation_test.py            |  0
 .../add_input_data_to_prior_boxes_test.py     |  0
 .../mxnet/check_softmax_node_inputs_test.py   |  0
 .../front/mxnet/conv_ext_test.py              |  0
 .../front/mxnet/custom_test.py                |  0
 .../front/mxnet/gather_test.py                |  0
 .../front/mxnet/gluoncv_ssd_anchors_test.py   |  0
 .../front/mxnet/leaky_relu_test.py            |  0
 .../front/mxnet/multibox_detection_test.py    |  0
 .../front/mxnet/mx_reshape_reverse_test.py    |  0
 .../front/mxnet/mx_reshape_to_reshape_test.py |  0
 .../front/mxnet/pooling_ext_test.py           |  0
 .../front/mxnet/sigmoid_test.py               |  0
 ...pattern_flatten_softmax_activation_test.py |  0
 .../mxnet/ssd_pattern_remove_flatten_test.py  |  0
 .../mxnet/ssd_pattern_remove_reshape_test.py  |  0
 .../ssd_pattern_remove_transpose_test.py      |  0
 .../ssd_reorder_detection_out_inputs_test.py  |  0
 .../front/onnx/AttributedSliceToSlice_test.py |  0
 .../front/onnx/MvnOnnxToMvn_test.py           |  0
 .../analysis => mo/front/onnx}/__init__.py    |  0
 .../front/onnx/activation_ext_test.py         |  0
 .../front/onnx/affine_ext_test.py             |  0
 .../front/onnx/conv_ext_test.py               |  0
 .../front/onnx/crop_ext_test.py               |  0
 .../front/onnx/detection_output_test.py       |  0
 .../front/onnx/gru_ext_test.py                |  0
 .../front/onnx/image_scaler_ext_test.py       |  0
 .../onnx/instance_normalization_ext_test.py   |  0
 .../front/onnx/lstm_ext_test.py               |  0
 .../front/onnx/normalize_ext_test.py          |  0
 .../front/onnx/pad_converter_test.py          |  0
 .../front/onnx/pad_ext_test.py                |  0
 .../front/onnx/priorbox_clustered_ext_test.py |  0
 .../front/onnx/priorbox_ext_test.py           |  0
 .../front/onnx/rnn_ext_test.py                |  0
 .../front/onnx/squeeze_ext_test.py            |  0
 .../front/onnx/transpose_ext_test.py          |  0
 .../front/onnx/unsqueeze_ext_test.py          |  0
 .../front/onnx/upsample_ext_test.py           |  0
 .../front/output_cut_test.py                  |  0
 .../front/rank_decomposer_test.py             |  0
 .../front/reciprocal_test.py                  |  0
 .../front/reduce_axis_normalizer_test.py      |  0
 .../front/scatter_normalizer_test.py          |  0
 .../front/size_replacer_test.py               |  0
 .../front/softsign_replacer_test.py           |  0
 .../front/split_normalizer_test.py            |  0
 .../{extensions => mo}/front/sub_test.py      |  0
 .../tf/CTCGreedyDecoderReplacement_test.py    |  0
 .../front/tf/CTCLossReplacement_test.py       |  0
 .../front/tf/ComplexAbsAfterComplex_test.py   |  0
 .../front/tf/ComplexAbs_test.py               |  0
 .../front/tf/CorrectRollAxes_test.py          |  0
 .../front/tf/IteratorGetNextCut_test.py       |  0
 ...nConstBeginStridedSliceReplacement_test.py |  0
 .../front/tf/ObjectDetectionAPI_test.py       |  0
 .../front/tf/RollRealImagPack_test.py         |  0
 .../front/tf/SSliceComplex_test.py            |  0
 .../front/tf/SwitchMergeOptimization_test.py  |  0
 .../front/tf/TFFFTToDFT_test.py               |  0
 .../front/tf/TFSliceToSlice_test.py           |  0
 .../front/tf/WhereDecomposition_test.py       |  0
 .../front/tf/concat_ext_test.py               |  0
 .../front/tf/concat_test.py                   |  0
 .../front/tf/conv_ext_test.py                 |  0
 .../front/tf/deconv_ext_test.py               |  0
 ...mbedding_segments_operation_fusing_test.py |  0
 .../front/tf/embedding_segments_sum_test.py   |  0
 .../front/tf/fifo_replacer_test.py            |  0
 .../front/tf/floor_div_test.py                |  0
 .../front/tf/identityN_to_identity_test.py    |  0
 .../front/tf/mvn_unrolled_test.py             |  0
 .../front/tf/next_iteration_ext_test.py       |  0
 .../front/tf/pad_tf_to_pad_test.py            |  0
 .../front/tf/sparse_to_dense_replacer_test.py |  0
 .../load/tf => mo/load}/loader_test.py        |  0
 tools/mo/unit_tests/mo/main_test_actual.py    |  2 +-
 .../middle/AddIsCyclicAttribute_test.py       |  0
 .../middle/AddMeanScaleValues_test.py         |  0
 .../middle/ArgOpsToTopK_test.py               |  0
 .../middle/CheckForCycle_test.py              |  0
 .../middle/ConcatOptimization_test.py         |  0
 .../middle/ConvertGroupedStridedSlice_test.py |  0
 .../CutInputHavingZeroDimFromConcat_test.py   |  0
 .../middle/EltwiseInputReshape_test.py        |  0
 .../middle/FakeSplitOutputs_test.py           |  0
 .../middle/FusedBatchNormTraining_test.py     |  0
 .../middle/GroupNorm_test.py                  |  0
 .../middle/InsertSelect_test.py               |  0
 .../InterpolateSequenceToInterpolate_test.py  |  0
 .../middle/L2NormFusing_test.py               |  0
 .../middle/LayoutChangeForEinsum_test.py      |  0
 .../middle/LeakyReluPattern_test.py           |  0
 .../middle/MXTileReplacer_test.py             |  0
 .../middle/MakeKaldiConstReshapable_test.py   |  0
 .../middle/MulQuantizeFuse_test.py            |  0
 .../middle/PoolV2ToAttributedPool_test.py     |  0
 .../middle/PreserveRuntimeInfo_test.py        |  0
 .../middle/ReluQuantizeFuse_test.py           |  0
 .../middle/RemoveDuplicationMemory_test.py    |  0
 .../middle/RemoveUselessConcatSplit_test.py   |  0
 .../middle/RemoveUselessCrops_test.py         |  0
 .../middle/RemoveUselessPad_test.py           |  0
 .../ReplaceMemoryOffsetWithSplice_test.py     |  0
 .../middle/ReplacePNormNodePattern_test.py    |  0
 .../middle/ReplaceSpliceNodePattern_test.py   |  0
 .../ReverseTransposeNormalization_test.py     |  0
 .../middle/SharedWeightsDuplication_test.py   |  0
 .../middle/SliceConverter_test.py             |  0
 .../middle/SliceLikeToStridedSlice_test.py    |  0
 .../SplitConcatPairToInterpolate_test.py      |  0
 .../middle/StridedSliceNormalizer_test.py     |  0
 .../middle/StridedSliceReplacer_test.py       |  0
 .../middle/TensorIteratorBackEdge_test.py     |  0
 .../middle/TensorIteratorCondition_test.py    |  0
 .../middle/TensorIteratorInput_test.py        |  0
 .../middle/TensorIteratorOutput_test.py       |  0
 ...ueezeTileReshapeBlockToInterpolate_test.py |  0
 .../middle/UpsampleToResample_test.py         |  0
 .../middle/dequantize_linear_resolver_test.py |  0
 .../middle/quantize_fuses_test.py             |  0
 .../middle/quantize_linear_resolver_test.py   |  0
 .../middle/reverse_tensor_iterator_test.py    |  0
 .../middle/sparse_reshape_test.py             |  0
 .../ops/ExtractImagePatches_test.py           |  0
 .../{extensions => mo}/ops/If_test.py         |  0
 .../ops/LookupTableInsert_test.py             |  0
 .../{extensions => mo}/ops/MatMul_test.py     |  0
 .../ops/MatMul_value_propagation_test.py      |  0
 .../ops/ONNXResize11_test.py                  |  0
 .../{extensions => mo}/ops/ReduceOps_test.py  |  0
 .../{extensions => mo}/ops/Reverse_test.py    |  0
 .../{extensions => mo}/ops/activation_test.py |  0
 .../{extensions => mo}/ops/argmax_test.py     |  0
 .../{extensions => mo}/ops/assert_test.py     |  0
 .../{extensions => mo}/ops/bucketize_test.py  |  0
 .../{extensions => mo}/ops/cast_test.py       |  0
 .../ops/ctc_greedy_decoder_test.py            |  0
 .../{extensions => mo}/ops/ctc_loss_test.py   |  0
 .../{extensions => mo}/ops/cumsum_test.py     |  0
 .../ops/depth_to_space_test.py                |  0
 .../dft_signal_size_canonicalization_test.py  |  0
 .../ops/div_value_propagation_test.py         |  0
 .../{extensions => mo}/ops/einsum_test.py     |  0
 .../ops/elementwise_test.py                   |  0
 .../ops/embedding_bag_test.py                 |  0
 .../{extensions => mo}/ops/gather_test.py     |  0
 .../ops/gatherelements_test.py                |  0
 .../{extensions => mo}/ops/gathernd_test.py   |  0
 .../{extensions => mo}/ops/grn_test.py        |  0
 .../ops/instance_normalization_test.py        |  0
 .../ops/interpolate_test.py                   |  0
 .../{extensions => mo}/ops/merge_test.py      |  0
 .../ops/non_max_suppression_test.py           |  0
 .../{extensions => mo}/ops/normalize_test.py  |  0
 .../{extensions => mo}/ops/one_hot_test.py    |  0
 .../ops/priorbox_clustered_test.py            |  0
 .../{extensions => mo}/ops/priorbox_test.py   |  0
 .../{extensions => mo}/ops/proposal_test.py   |  0
 .../ops/psroipooling_test.py                  |  0
 .../{extensions => mo}/ops/quantize_test.py   |  0
 .../{extensions => mo}/ops/regionyolo_test.py |  0
 .../{extensions => mo}/ops/reorgyolo_test.py  |  0
 .../{extensions => mo}/ops/scatter_test.py    |  0
 .../{extensions => mo}/ops/scatternd_test.py  |  0
 .../{extensions => mo}/ops/select_test.py     |  0
 .../{extensions => mo}/ops/slice_like_test.py |  0
 .../ops/space_to_depth_test.py                |  0
 .../ops/sparse_fill_empty_rows_test.py        |  0
 .../ops/sparse_reshape_test.py                |  0
 .../ops/sparse_segment_mean_test.py           |  0
 .../ops/sparse_segment_sqrtn_test.py          |  0
 .../ops/sparse_segment_sum_test.py            |  0
 .../{extensions => mo}/ops/split_test.py      |  0
 .../{extensions => mo}/ops/switch_test.py     |  0
 .../{extensions => mo}/ops/topk_test.py       |  0
 .../{extensions => mo}/ops/transpose_test.py  |  0
 .../{extensions => mo}/ops/unique_test.py     |  0
 .../{extensions => mo}/ops/upsample_test.py   |  0
 281 files changed, 49 insertions(+), 54 deletions(-)
 delete mode 100644 tools/mo/unit_tests/extensions/back/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/front/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/front/caffe/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/front/kaldi/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/front/mxnet/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/front/onnx/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/front/tf/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/middle/__init__.py
 delete mode 100644 tools/mo/unit_tests/extensions/ops/__init__.py
 rename tools/mo/unit_tests/{extensions => mo}/analysis/Iterator_get_next_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo/analysis}/__init__.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ChangeOutputTypeAttributes_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ChangeRandomUniformOutputType_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ClampNormalizer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ConvolutionNormalizer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/CutMemory_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/FakeOutputResolver_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/FuseTransposesSequence_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/GatherTreeNormalizer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/InterpolateReshape_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/LayoutChangeForGatherND_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/MarkNodesWithShapeValues_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/MatMulNormalizer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/OptimizeTransposeReshapeSequence_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ReduceTransposeDimensions_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ResultRename_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ReverseInputChannels_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ShapeOfConstFolding_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/ShuffleChannelPatternOptimization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/SpecialNodesFinalization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/TransposeDFT_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/TransposeReduceFusing_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/add_outputs_recursive_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/compress_quantized_weights_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/insert_compatibility_l2normalization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/kaldi_remove_memory_output_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/back/names_uniqueness_check_test.py (100%)
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 rename tools/mo/unit_tests/{extensions => mo}/front/kaldi/apply_counts_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/kaldi/memory_offset_adjustment_test.py (100%)
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 rename tools/mo/unit_tests/{extensions => mo}/front/kaldi/replace_timeheightconvolution_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/kaldi/set_ports_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/kaldi/sigmoid_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/kaldi/tanh_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/kaldi/tdnn_component_replacer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/MXFFTToDFT_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/RNN_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/activation_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/add_input_data_to_prior_boxes_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/check_softmax_node_inputs_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/conv_ext_test.py (100%)
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 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/gather_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/gluoncv_ssd_anchors_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/leaky_relu_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/multibox_detection_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/mx_reshape_reverse_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/mx_reshape_to_reshape_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/pooling_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/sigmoid_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/ssd_pattern_flatten_softmax_activation_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/ssd_pattern_remove_flatten_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/ssd_pattern_remove_reshape_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/ssd_pattern_remove_transpose_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/mxnet/ssd_reorder_detection_out_inputs_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/AttributedSliceToSlice_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/MvnOnnxToMvn_test.py (100%)
 rename tools/mo/unit_tests/{extensions/analysis => mo/front/onnx}/__init__.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/activation_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/affine_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/conv_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/crop_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/detection_output_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/gru_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/image_scaler_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/instance_normalization_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/lstm_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/normalize_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/pad_converter_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/pad_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/priorbox_clustered_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/priorbox_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/rnn_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/squeeze_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/onnx/transpose_ext_test.py (100%)
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 rename tools/mo/unit_tests/{extensions => mo}/front/output_cut_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/rank_decomposer_test.py (100%)
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 rename tools/mo/unit_tests/{extensions => mo}/front/reduce_axis_normalizer_test.py (100%)
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 rename tools/mo/unit_tests/{extensions => mo}/front/size_replacer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/softsign_replacer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/split_normalizer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/sub_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/CTCGreedyDecoderReplacement_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/CTCLossReplacement_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/ComplexAbsAfterComplex_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/ComplexAbs_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/CorrectRollAxes_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/IteratorGetNextCut_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/NonConstBeginStridedSliceReplacement_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/ObjectDetectionAPI_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/RollRealImagPack_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/SSliceComplex_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/SwitchMergeOptimization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/TFFFTToDFT_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/TFSliceToSlice_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/WhereDecomposition_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/concat_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/concat_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/conv_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/deconv_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/embedding_segments_operation_fusing_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/embedding_segments_sum_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/fifo_replacer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/floor_div_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/identityN_to_identity_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/mvn_unrolled_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/next_iteration_ext_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/pad_tf_to_pad_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/front/tf/sparse_to_dense_replacer_test.py (100%)
 rename tools/mo/unit_tests/{extensions/load/tf => mo/load}/loader_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/AddIsCyclicAttribute_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/AddMeanScaleValues_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ArgOpsToTopK_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/CheckForCycle_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ConcatOptimization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ConvertGroupedStridedSlice_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/CutInputHavingZeroDimFromConcat_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/EltwiseInputReshape_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/FakeSplitOutputs_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/FusedBatchNormTraining_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/GroupNorm_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/InsertSelect_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/InterpolateSequenceToInterpolate_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/L2NormFusing_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/LayoutChangeForEinsum_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/LeakyReluPattern_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/MXTileReplacer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/MakeKaldiConstReshapable_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/MulQuantizeFuse_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/PoolV2ToAttributedPool_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/PreserveRuntimeInfo_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ReluQuantizeFuse_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/RemoveDuplicationMemory_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/RemoveUselessConcatSplit_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/RemoveUselessCrops_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/RemoveUselessPad_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ReplaceMemoryOffsetWithSplice_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ReplacePNormNodePattern_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ReplaceSpliceNodePattern_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/ReverseTransposeNormalization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/SharedWeightsDuplication_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/SliceConverter_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/SliceLikeToStridedSlice_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/SplitConcatPairToInterpolate_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/StridedSliceNormalizer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/StridedSliceReplacer_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/TensorIteratorBackEdge_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/TensorIteratorCondition_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/TensorIteratorInput_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/TensorIteratorOutput_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/UnsqueezeTileReshapeBlockToInterpolate_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/UpsampleToResample_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/dequantize_linear_resolver_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/quantize_fuses_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/quantize_linear_resolver_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/reverse_tensor_iterator_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/middle/sparse_reshape_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/ExtractImagePatches_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/If_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/LookupTableInsert_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/MatMul_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/MatMul_value_propagation_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/ONNXResize11_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/ReduceOps_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/Reverse_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/activation_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/argmax_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/assert_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/bucketize_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/cast_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/ctc_greedy_decoder_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/ctc_loss_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/cumsum_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/depth_to_space_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/dft_signal_size_canonicalization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/div_value_propagation_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/einsum_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/elementwise_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/embedding_bag_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/gather_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/gatherelements_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/gathernd_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/grn_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/instance_normalization_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/interpolate_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/merge_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/non_max_suppression_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/normalize_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/one_hot_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/priorbox_clustered_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/priorbox_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/proposal_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/psroipooling_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/quantize_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/regionyolo_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/reorgyolo_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/scatter_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/scatternd_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/select_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/slice_like_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/space_to_depth_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/sparse_fill_empty_rows_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/sparse_reshape_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/sparse_segment_mean_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/sparse_segment_sqrtn_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/sparse_segment_sum_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/split_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/switch_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/topk_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/transpose_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/unique_test.py (100%)
 rename tools/mo/unit_tests/{extensions => mo}/ops/upsample_test.py (100%)

diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
index d48aa25f5bb..90c9aa11d17 100644
--- a/.ci/azure/linux.yml
+++ b/.ci/azure/linux.yml
@@ -241,7 +241,7 @@ jobs:
       . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_DIR)/tests/mo/unit_tests --junitxml=TEST-ModelOptimizer.xml
     displayName: 'Model Optimizer UT'
     continueOnError: false
-    enabled: false
+    enabled: true
 
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/ov_core_unit_tests --gtest_print_time=1 --gtest_filter=-*IE_GPU* --gtest_output=xml:TEST-NGraphUT.xml
     workingDirectory: $(INSTALL_TEST_DIR)
@@ -334,7 +334,7 @@ jobs:
     displayName: 'Samples Smoke Tests'
     continueOnError: false
     condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
-    enabled: false
+    enabled: true
 
   - script: |
       export DATA_PATH=$(MODELS_PATH)
@@ -353,7 +353,7 @@ jobs:
     workingDirectory: $(LAYER_TESTS_DIR)
     displayName: 'Layer Tests'
     continueOnError: false
-    enabled: false
+    enabled: true
 
   - task: PublishTestResults@2
     condition: always()
diff --git a/docs/HOWTO/mo_extensions/front/tf/Complex.py b/docs/HOWTO/mo_extensions/front/tf/Complex.py
index fa23d9b1f4d..60167aad6c0 100644
--- a/docs/HOWTO/mo_extensions/front/tf/Complex.py
+++ b/docs/HOWTO/mo_extensions/front/tf/Complex.py
@@ -2,12 +2,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
 #! [complex:transformation]
-import logging as log
 
-import numpy as np
-
-from mo.front.common.replacement import FrontReplacementSubgraph
-from mo.graph.graph import Graph
+from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
+from openvino.tools.mo.graph.graph import Graph
 
 
 class Complex(FrontReplacementSubgraph):
@@ -41,4 +38,3 @@ class Complex(FrontReplacementSubgraph):
         # change the connection so now all consumers of "complex_node" get data from input node of strided slice nodes
         complex_node.out_port(0).get_connection().set_source(input_node_output_port)
 #! [complex:transformation]
-
diff --git a/docs/HOWTO/mo_extensions/front/tf/ComplexAbs.py b/docs/HOWTO/mo_extensions/front/tf/ComplexAbs.py
index 4fe0368fd66..ea6c94ed1e2 100644
--- a/docs/HOWTO/mo_extensions/front/tf/ComplexAbs.py
+++ b/docs/HOWTO/mo_extensions/front/tf/ComplexAbs.py
@@ -4,11 +4,11 @@
 #! [complex_abs:transformation]
 import numpy as np
 
-from extensions.ops.elementwise import Pow
-from extensions.ops.ReduceOps import ReduceSum
-from mo.front.common.replacement import FrontReplacementOp
-from mo.graph.graph import Graph, Node
-from mo.ops.const import Const
+from openvino.tools.mo.ops.elementwise import Pow
+from openvino.tools.mo.ops.ReduceOps import ReduceSum
+from openvino.tools.mo.front.common.replacement import FrontReplacementOp
+from openvino.tools.mo.graph.graph import Graph, Node
+from openvino.tools.mo.ops.const import Const
 
 
 class ComplexAbs(FrontReplacementOp):
diff --git a/docs/HOWTO/mo_extensions/front/tf/FFT_ext.py b/docs/HOWTO/mo_extensions/front/tf/FFT_ext.py
index e5216d2ea3e..c15c4e38903 100644
--- a/docs/HOWTO/mo_extensions/front/tf/FFT_ext.py
+++ b/docs/HOWTO/mo_extensions/front/tf/FFT_ext.py
@@ -3,8 +3,7 @@
 
 # ! [fft_ext:extractor]
 from ...ops.FFT import FFT
-from mo.front.extractor import FrontExtractorOp
-from mo.utils.error import Error
+from openvino.tools.mo.front.extractor import FrontExtractorOp
 
 
 class FFT2DFrontExtractor(FrontExtractorOp):
diff --git a/docs/HOWTO/mo_extensions/ops/FFT.py b/docs/HOWTO/mo_extensions/ops/FFT.py
index 92645cd6967..e0f018fa165 100644
--- a/docs/HOWTO/mo_extensions/ops/FFT.py
+++ b/docs/HOWTO/mo_extensions/ops/FFT.py
@@ -2,9 +2,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
 #! [fft:operation]
-from mo.front.common.partial_infer.elemental import copy_shape_infer
-from mo.graph.graph import Node, Graph
-from mo.ops.op import Op
+from openvino.tools.mo.front.common.partial_infer.elemental import copy_shape_infer
+from openvino.tools.mo.graph.graph import Graph
+from openvino.tools.mo.ops.op import Op
 
 
 class FFT(Op):
diff --git a/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md b/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
index d133654b8df..d29b2edeb6c 100644
--- a/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
+++ b/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
@@ -639,9 +639,9 @@ graph. Consider the extractor for the TensorFlow\* operation `Const` (refer to t
 `extensions/front/tf/const_ext.py`):
 
 ```py
-from mo.front.extractor import FrontExtractorOp
-from mo.front.tf.extractors.utils import tf_dtype_extractor, tf_tensor_shape, tf_tensor_content
-from mo.ops.const import Const
+from openvino.tools.mo.front.extractor import FrontExtractorOp
+from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor, tf_tensor_shape, tf_tensor_content
+from openvino.tools.mo.ops.const import Const
 
 
 class ConstExtractor(FrontExtractorOp):
@@ -679,9 +679,9 @@ Consider another example with an extractor of ONNX\* operation `Constant` (refer
 from onnx import numpy_helper
 from onnx.numpy_helper import to_array
 
-from mo.front.extractor import FrontExtractorOp
-from mo.front.onnx.extractors.utils import onnx_attr
-from mo.ops.const import Const
+from openvino.tools.mo.front.extractor import FrontExtractorOp
+from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
+from openvino.tools.mo.ops.const import Const
 
 
 class ConstantExtractor(FrontExtractorOp):
@@ -814,11 +814,11 @@ fusing of the sub-graph defining the [Mish](../../../ops/activation/Mish_4.md) a
 operation:
 
 ```py
-from extensions.front.Softplus_fusion import SoftplusFusion
-from extensions.ops.activation_ops import Mish
-from mo.front.common.replacement import FrontReplacementSubgraph
-from mo.front.subgraph_matcher import SubgraphMatch
-from mo.graph.graph import Graph, rename_nodes
+from openvino.tools.mo.front.Softplus_fusion import SoftplusFusion
+from openvino.tools.mo.ops.activation_ops import Mish
+from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
+from openvino.tools.mo.front.subgraph_matcher import SubgraphMatch
+from openvino.tools.mo.graph.graph import Graph, rename_nodes
 
 
 class MishFusion(FrontReplacementSubgraph):
@@ -886,12 +886,12 @@ transformation.
 Consider an example transformation from the file is `extensions/front/Pack.py`  which replaces operation `Pack` from
 the TensorFlow\*:
 ```py
-from mo.front.common.partial_infer.utils import int64_array
-from mo.front.common.replacement import FrontReplacementOp
-from mo.front.tf.graph_utils import create_op_with_const_inputs
-from mo.graph.graph import Node, Graph, rename_nodes
-from mo.ops.concat import Concat
-from mo.ops.unsqueeze import Unsqueeze
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.replacement import FrontReplacementOp
+from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
+from openvino.tools.mo.graph.graph import Node, Graph, rename_nodes
+from openvino.tools.mo.ops.concat import Concat
+from openvino.tools.mo.ops.unsqueeze import Unsqueeze
 
 
 class Pack(FrontReplacementOp):
@@ -932,11 +932,11 @@ specification.
 ```py
 import logging as log
 
-from mo.front.common.partial_infer.utils import int64_array
-from mo.front.common.replacement import FrontReplacementPattern
-from mo.graph.graph import Graph
-from mo.ops.const import Const
-from mo.utils.error import Error
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
+from openvino.tools.mo.graph.graph import Graph
+from openvino.tools.mo.ops.const import Const
+from openvino.tools.mo.utils.error import Error
 
 
 class SqueezeNormalize(FrontReplacementPattern):
@@ -1200,13 +1200,13 @@ The example of the configuration file for this type of transformation is `extens
 and the corresponding transformation file is `./extensions/front/YOLO.py`:
 
 ```py
-from extensions.front.no_op_eraser import NoOpEraser
-from extensions.front.standalone_const_eraser import StandaloneConstEraser
-from extensions.ops.regionyolo import RegionYoloOp
-from mo.front.tf.replacement import FrontReplacementFromConfigFileGeneral
-from mo.graph.graph import Node, Graph
-from mo.ops.result import Result
-from mo.utils.error import Error
+from openvino.tools.mo.front.no_op_eraser import NoOpEraser
+from openvino.tools.mo.front.standalone_const_eraser import StandaloneConstEraser
+from openvino.tools.mo.ops.regionyolo import RegionYoloOp
+from openvino.tools.mo.front.tf.replacement import FrontReplacementFromConfigFileGeneral
+from openvino.tools.mo.graph.graph import Node, Graph
+from openvino.tools.mo.ops.result import Result
+from openvino.tools.mo.utils.error import Error
 
 
 class YoloRegionAddon(FrontReplacementFromConfigFileGeneral):
diff --git a/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_MXNet_Model_Optimizer_with_New_Primitives.md b/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_MXNet_Model_Optimizer_with_New_Primitives.md
index e20a44969cf..9c4774715e4 100644
--- a/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_MXNet_Model_Optimizer_with_New_Primitives.md
+++ b/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_MXNet_Model_Optimizer_with_New_Primitives.md
@@ -20,9 +20,9 @@ assume that we have already created the `CustomOp` class (inherited from `Op` cl
 for this MXNet custom operation as described in the [Customize_Model_Optimizer](Customize_Model_Optimizer.md).
 
 ```py
-from extension.ops.custom_op import CustomOp  # implementation of the MO operation class
-from mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
-from mo.front.extractor import MXNetCustomFrontExtractorOp
+from openvino.tools.mo.ops.custom_op import CustomOp  # implementation of the MO operation class
+from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
+from openvino.tools.mo.front.extractor import MXNetCustomFrontExtractorOp
 
 class CustomProposalFrontExtractor(MXNetCustomFrontExtractorOp):  # inherit from specific base class
     op = 'MyCustomOp'  # the value corresponding to the `op_type` value of the MXNet operation
diff --git a/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_Model_Optimizer_with_Caffe_Python_Layers.md b/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_Model_Optimizer_with_Caffe_Python_Layers.md
index 579437aeb5a..f95fae23e2b 100644
--- a/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_Model_Optimizer_with_Caffe_Python_Layers.md
+++ b/docs/MO_DG/prepare_model/customize_model_optimizer/Extending_Model_Optimizer_with_Caffe_Python_Layers.md
@@ -40,8 +40,8 @@ operation `ProposalOp` which corresponds to `Proposal` operation described in th
 document. Refer to the source code below for a detailed explanation of the extractor.
 
 ```py
-from extensions.ops.proposal import ProposalOp
-from mo.front.extractor import CaffePythonFrontExtractorOp
+from openvino.tools.mo.ops.proposal import ProposalOp
+from openvino.tools.mo.front.extractor import CaffePythonFrontExtractorOp
 
 
 class ProposalPythonFrontExtractor(CaffePythonFrontExtractorOp):
diff --git a/tools/mo/unit_tests/extensions/back/__init__.py b/tools/mo/unit_tests/extensions/back/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/front/__init__.py b/tools/mo/unit_tests/extensions/front/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/front/caffe/__init__.py b/tools/mo/unit_tests/extensions/front/caffe/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/front/kaldi/__init__.py b/tools/mo/unit_tests/extensions/front/kaldi/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/front/mxnet/__init__.py b/tools/mo/unit_tests/extensions/front/mxnet/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/front/onnx/__init__.py b/tools/mo/unit_tests/extensions/front/onnx/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/front/tf/__init__.py b/tools/mo/unit_tests/extensions/front/tf/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/middle/__init__.py b/tools/mo/unit_tests/extensions/middle/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/ops/__init__.py b/tools/mo/unit_tests/extensions/ops/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tools/mo/unit_tests/extensions/analysis/Iterator_get_next_test.py b/tools/mo/unit_tests/mo/analysis/Iterator_get_next_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/analysis/Iterator_get_next_test.py
rename to tools/mo/unit_tests/mo/analysis/Iterator_get_next_test.py
diff --git a/tools/mo/unit_tests/extensions/__init__.py b/tools/mo/unit_tests/mo/analysis/__init__.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/__init__.py
rename to tools/mo/unit_tests/mo/analysis/__init__.py
diff --git a/tools/mo/unit_tests/extensions/back/ChangeOutputTypeAttributes_test.py b/tools/mo/unit_tests/mo/back/ChangeOutputTypeAttributes_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/ChangeOutputTypeAttributes_test.py
rename to tools/mo/unit_tests/mo/back/ChangeOutputTypeAttributes_test.py
diff --git a/tools/mo/unit_tests/extensions/back/ChangeRandomUniformOutputType_test.py b/tools/mo/unit_tests/mo/back/ChangeRandomUniformOutputType_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/ChangeRandomUniformOutputType_test.py
rename to tools/mo/unit_tests/mo/back/ChangeRandomUniformOutputType_test.py
diff --git a/tools/mo/unit_tests/extensions/back/ClampNormalizer_test.py b/tools/mo/unit_tests/mo/back/ClampNormalizer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/ClampNormalizer_test.py
rename to tools/mo/unit_tests/mo/back/ClampNormalizer_test.py
diff --git a/tools/mo/unit_tests/extensions/back/ConvolutionNormalizer_test.py b/tools/mo/unit_tests/mo/back/ConvolutionNormalizer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/ConvolutionNormalizer_test.py
rename to tools/mo/unit_tests/mo/back/ConvolutionNormalizer_test.py
diff --git a/tools/mo/unit_tests/extensions/back/CutMemory_test.py b/tools/mo/unit_tests/mo/back/CutMemory_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/CutMemory_test.py
rename to tools/mo/unit_tests/mo/back/CutMemory_test.py
diff --git a/tools/mo/unit_tests/extensions/back/FakeOutputResolver_test.py b/tools/mo/unit_tests/mo/back/FakeOutputResolver_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/FakeOutputResolver_test.py
rename to tools/mo/unit_tests/mo/back/FakeOutputResolver_test.py
diff --git a/tools/mo/unit_tests/extensions/back/FuseTransposesSequence_test.py b/tools/mo/unit_tests/mo/back/FuseTransposesSequence_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/FuseTransposesSequence_test.py
rename to tools/mo/unit_tests/mo/back/FuseTransposesSequence_test.py
diff --git a/tools/mo/unit_tests/extensions/back/GatherTreeNormalizer_test.py b/tools/mo/unit_tests/mo/back/GatherTreeNormalizer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/GatherTreeNormalizer_test.py
rename to tools/mo/unit_tests/mo/back/GatherTreeNormalizer_test.py
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rename to tools/mo/unit_tests/mo/back/InterpolateReshape_test.py
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rename to tools/mo/unit_tests/mo/back/LayoutChangeForGatherND_test.py
diff --git a/tools/mo/unit_tests/extensions/back/MarkNodesWithShapeValues_test.py b/tools/mo/unit_tests/mo/back/MarkNodesWithShapeValues_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/MarkNodesWithShapeValues_test.py
rename to tools/mo/unit_tests/mo/back/MarkNodesWithShapeValues_test.py
diff --git a/tools/mo/unit_tests/extensions/back/MatMulNormalizer_test.py b/tools/mo/unit_tests/mo/back/MatMulNormalizer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/MatMulNormalizer_test.py
rename to tools/mo/unit_tests/mo/back/MatMulNormalizer_test.py
diff --git a/tools/mo/unit_tests/extensions/back/OptimizeTransposeReshapeSequence_test.py b/tools/mo/unit_tests/mo/back/OptimizeTransposeReshapeSequence_test.py
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diff --git a/tools/mo/unit_tests/extensions/back/ReduceTransposeDimensions_test.py b/tools/mo/unit_tests/mo/back/ReduceTransposeDimensions_test.py
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rename to tools/mo/unit_tests/mo/back/ResultRename_test.py
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diff --git a/tools/mo/unit_tests/extensions/back/ShapeOfConstFolding_test.py b/tools/mo/unit_tests/mo/back/ShapeOfConstFolding_test.py
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rename from tools/mo/unit_tests/extensions/back/ShapeOfConstFolding_test.py
rename to tools/mo/unit_tests/mo/back/ShapeOfConstFolding_test.py
diff --git a/tools/mo/unit_tests/extensions/back/ShuffleChannelPatternOptimization_test.py b/tools/mo/unit_tests/mo/back/ShuffleChannelPatternOptimization_test.py
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rename from tools/mo/unit_tests/extensions/back/SpecialNodesFinalization_test.py
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diff --git a/tools/mo/unit_tests/extensions/back/TransposeDFT_test.py b/tools/mo/unit_tests/mo/back/TransposeDFT_test.py
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rename to tools/mo/unit_tests/mo/back/TransposeDFT_test.py
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diff --git a/tools/mo/unit_tests/extensions/back/add_outputs_recursive_test.py b/tools/mo/unit_tests/mo/back/add_outputs_recursive_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/back/add_outputs_recursive_test.py
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diff --git a/tools/mo/unit_tests/extensions/back/compress_quantized_weights_test.py b/tools/mo/unit_tests/mo/back/compress_quantized_weights_test.py
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rename to tools/mo/unit_tests/mo/back/compress_quantized_weights_test.py
diff --git a/tools/mo/unit_tests/extensions/back/insert_compatibility_l2normalization_test.py b/tools/mo/unit_tests/mo/back/insert_compatibility_l2normalization_test.py
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diff --git a/tools/mo/unit_tests/extensions/back/kaldi_remove_memory_output_test.py b/tools/mo/unit_tests/mo/back/kaldi_remove_memory_output_test.py
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diff --git a/tools/mo/unit_tests/extensions/front/AttributedRandomUniformToRandomUniform_test.py b/tools/mo/unit_tests/mo/front/AttributedRandomUniformToRandomUniform_test.py
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rename from tools/mo/unit_tests/extensions/front/AttributedRandomUniformToRandomUniform_test.py
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diff --git a/tools/mo/unit_tests/extensions/front/AttributedRollToRoll_test.py b/tools/mo/unit_tests/mo/front/AttributedRollToRoll_test.py
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rename to tools/mo/unit_tests/mo/front/AttributedRollToRoll_test.py
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diff --git a/tools/mo/unit_tests/extensions/front/GeLUMerger_Tanh_test.py b/tools/mo/unit_tests/mo/front/GeLUMerger_Tanh_test.py
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rename from tools/mo/unit_tests/extensions/front/HSigmoid_fusion_test.py
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rename from tools/mo/unit_tests/extensions/front/LayerNorm_test.py
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rename from tools/mo/unit_tests/extensions/front/onnx/crop_ext_test.py
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diff --git a/tools/mo/unit_tests/extensions/front/onnx/detection_output_test.py b/tools/mo/unit_tests/mo/front/onnx/detection_output_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/detection_output_test.py
rename to tools/mo/unit_tests/mo/front/onnx/detection_output_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/gru_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/gru_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/gru_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/gru_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/image_scaler_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/image_scaler_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/image_scaler_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/image_scaler_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/instance_normalization_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/instance_normalization_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/instance_normalization_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/instance_normalization_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/lstm_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/lstm_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/lstm_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/lstm_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/normalize_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/normalize_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/normalize_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/normalize_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/pad_converter_test.py b/tools/mo/unit_tests/mo/front/onnx/pad_converter_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/pad_converter_test.py
rename to tools/mo/unit_tests/mo/front/onnx/pad_converter_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/pad_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/pad_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/pad_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/pad_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/priorbox_clustered_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/priorbox_clustered_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/priorbox_clustered_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/priorbox_clustered_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/priorbox_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/priorbox_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/priorbox_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/priorbox_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/rnn_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/rnn_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/rnn_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/rnn_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/squeeze_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/squeeze_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/squeeze_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/squeeze_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/transpose_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/transpose_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/transpose_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/transpose_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/unsqueeze_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/unsqueeze_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/unsqueeze_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/unsqueeze_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/onnx/upsample_ext_test.py b/tools/mo/unit_tests/mo/front/onnx/upsample_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/onnx/upsample_ext_test.py
rename to tools/mo/unit_tests/mo/front/onnx/upsample_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/output_cut_test.py b/tools/mo/unit_tests/mo/front/output_cut_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/output_cut_test.py
rename to tools/mo/unit_tests/mo/front/output_cut_test.py
diff --git a/tools/mo/unit_tests/extensions/front/rank_decomposer_test.py b/tools/mo/unit_tests/mo/front/rank_decomposer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/rank_decomposer_test.py
rename to tools/mo/unit_tests/mo/front/rank_decomposer_test.py
diff --git a/tools/mo/unit_tests/extensions/front/reciprocal_test.py b/tools/mo/unit_tests/mo/front/reciprocal_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/reciprocal_test.py
rename to tools/mo/unit_tests/mo/front/reciprocal_test.py
diff --git a/tools/mo/unit_tests/extensions/front/reduce_axis_normalizer_test.py b/tools/mo/unit_tests/mo/front/reduce_axis_normalizer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/reduce_axis_normalizer_test.py
rename to tools/mo/unit_tests/mo/front/reduce_axis_normalizer_test.py
diff --git a/tools/mo/unit_tests/extensions/front/scatter_normalizer_test.py b/tools/mo/unit_tests/mo/front/scatter_normalizer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/scatter_normalizer_test.py
rename to tools/mo/unit_tests/mo/front/scatter_normalizer_test.py
diff --git a/tools/mo/unit_tests/extensions/front/size_replacer_test.py b/tools/mo/unit_tests/mo/front/size_replacer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/size_replacer_test.py
rename to tools/mo/unit_tests/mo/front/size_replacer_test.py
diff --git a/tools/mo/unit_tests/extensions/front/softsign_replacer_test.py b/tools/mo/unit_tests/mo/front/softsign_replacer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/softsign_replacer_test.py
rename to tools/mo/unit_tests/mo/front/softsign_replacer_test.py
diff --git a/tools/mo/unit_tests/extensions/front/split_normalizer_test.py b/tools/mo/unit_tests/mo/front/split_normalizer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/split_normalizer_test.py
rename to tools/mo/unit_tests/mo/front/split_normalizer_test.py
diff --git a/tools/mo/unit_tests/extensions/front/sub_test.py b/tools/mo/unit_tests/mo/front/sub_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/sub_test.py
rename to tools/mo/unit_tests/mo/front/sub_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/CTCGreedyDecoderReplacement_test.py b/tools/mo/unit_tests/mo/front/tf/CTCGreedyDecoderReplacement_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/CTCGreedyDecoderReplacement_test.py
rename to tools/mo/unit_tests/mo/front/tf/CTCGreedyDecoderReplacement_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/CTCLossReplacement_test.py b/tools/mo/unit_tests/mo/front/tf/CTCLossReplacement_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/CTCLossReplacement_test.py
rename to tools/mo/unit_tests/mo/front/tf/CTCLossReplacement_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/ComplexAbsAfterComplex_test.py b/tools/mo/unit_tests/mo/front/tf/ComplexAbsAfterComplex_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/ComplexAbsAfterComplex_test.py
rename to tools/mo/unit_tests/mo/front/tf/ComplexAbsAfterComplex_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/ComplexAbs_test.py b/tools/mo/unit_tests/mo/front/tf/ComplexAbs_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/ComplexAbs_test.py
rename to tools/mo/unit_tests/mo/front/tf/ComplexAbs_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/CorrectRollAxes_test.py b/tools/mo/unit_tests/mo/front/tf/CorrectRollAxes_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/CorrectRollAxes_test.py
rename to tools/mo/unit_tests/mo/front/tf/CorrectRollAxes_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/IteratorGetNextCut_test.py b/tools/mo/unit_tests/mo/front/tf/IteratorGetNextCut_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/IteratorGetNextCut_test.py
rename to tools/mo/unit_tests/mo/front/tf/IteratorGetNextCut_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/NonConstBeginStridedSliceReplacement_test.py b/tools/mo/unit_tests/mo/front/tf/NonConstBeginStridedSliceReplacement_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/NonConstBeginStridedSliceReplacement_test.py
rename to tools/mo/unit_tests/mo/front/tf/NonConstBeginStridedSliceReplacement_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/ObjectDetectionAPI_test.py b/tools/mo/unit_tests/mo/front/tf/ObjectDetectionAPI_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/ObjectDetectionAPI_test.py
rename to tools/mo/unit_tests/mo/front/tf/ObjectDetectionAPI_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/RollRealImagPack_test.py b/tools/mo/unit_tests/mo/front/tf/RollRealImagPack_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/RollRealImagPack_test.py
rename to tools/mo/unit_tests/mo/front/tf/RollRealImagPack_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/SSliceComplex_test.py b/tools/mo/unit_tests/mo/front/tf/SSliceComplex_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/SSliceComplex_test.py
rename to tools/mo/unit_tests/mo/front/tf/SSliceComplex_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/SwitchMergeOptimization_test.py b/tools/mo/unit_tests/mo/front/tf/SwitchMergeOptimization_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/SwitchMergeOptimization_test.py
rename to tools/mo/unit_tests/mo/front/tf/SwitchMergeOptimization_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/TFFFTToDFT_test.py b/tools/mo/unit_tests/mo/front/tf/TFFFTToDFT_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/TFFFTToDFT_test.py
rename to tools/mo/unit_tests/mo/front/tf/TFFFTToDFT_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/TFSliceToSlice_test.py b/tools/mo/unit_tests/mo/front/tf/TFSliceToSlice_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/TFSliceToSlice_test.py
rename to tools/mo/unit_tests/mo/front/tf/TFSliceToSlice_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/WhereDecomposition_test.py b/tools/mo/unit_tests/mo/front/tf/WhereDecomposition_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/WhereDecomposition_test.py
rename to tools/mo/unit_tests/mo/front/tf/WhereDecomposition_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/concat_ext_test.py b/tools/mo/unit_tests/mo/front/tf/concat_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/concat_ext_test.py
rename to tools/mo/unit_tests/mo/front/tf/concat_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/concat_test.py b/tools/mo/unit_tests/mo/front/tf/concat_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/concat_test.py
rename to tools/mo/unit_tests/mo/front/tf/concat_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/conv_ext_test.py b/tools/mo/unit_tests/mo/front/tf/conv_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/conv_ext_test.py
rename to tools/mo/unit_tests/mo/front/tf/conv_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/deconv_ext_test.py b/tools/mo/unit_tests/mo/front/tf/deconv_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/deconv_ext_test.py
rename to tools/mo/unit_tests/mo/front/tf/deconv_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/embedding_segments_operation_fusing_test.py b/tools/mo/unit_tests/mo/front/tf/embedding_segments_operation_fusing_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/embedding_segments_operation_fusing_test.py
rename to tools/mo/unit_tests/mo/front/tf/embedding_segments_operation_fusing_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/embedding_segments_sum_test.py b/tools/mo/unit_tests/mo/front/tf/embedding_segments_sum_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/embedding_segments_sum_test.py
rename to tools/mo/unit_tests/mo/front/tf/embedding_segments_sum_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/fifo_replacer_test.py b/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/fifo_replacer_test.py
rename to tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/floor_div_test.py b/tools/mo/unit_tests/mo/front/tf/floor_div_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/floor_div_test.py
rename to tools/mo/unit_tests/mo/front/tf/floor_div_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/identityN_to_identity_test.py b/tools/mo/unit_tests/mo/front/tf/identityN_to_identity_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/identityN_to_identity_test.py
rename to tools/mo/unit_tests/mo/front/tf/identityN_to_identity_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/mvn_unrolled_test.py b/tools/mo/unit_tests/mo/front/tf/mvn_unrolled_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/mvn_unrolled_test.py
rename to tools/mo/unit_tests/mo/front/tf/mvn_unrolled_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/next_iteration_ext_test.py b/tools/mo/unit_tests/mo/front/tf/next_iteration_ext_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/next_iteration_ext_test.py
rename to tools/mo/unit_tests/mo/front/tf/next_iteration_ext_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/pad_tf_to_pad_test.py b/tools/mo/unit_tests/mo/front/tf/pad_tf_to_pad_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/pad_tf_to_pad_test.py
rename to tools/mo/unit_tests/mo/front/tf/pad_tf_to_pad_test.py
diff --git a/tools/mo/unit_tests/extensions/front/tf/sparse_to_dense_replacer_test.py b/tools/mo/unit_tests/mo/front/tf/sparse_to_dense_replacer_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/front/tf/sparse_to_dense_replacer_test.py
rename to tools/mo/unit_tests/mo/front/tf/sparse_to_dense_replacer_test.py
diff --git a/tools/mo/unit_tests/extensions/load/tf/loader_test.py b/tools/mo/unit_tests/mo/load/loader_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/load/tf/loader_test.py
rename to tools/mo/unit_tests/mo/load/loader_test.py
diff --git a/tools/mo/unit_tests/mo/main_test_actual.py b/tools/mo/unit_tests/mo/main_test_actual.py
index 76b9c6c9f05..3756dcf46d4 100644
--- a/tools/mo/unit_tests/mo/main_test_actual.py
+++ b/tools/mo/unit_tests/mo/main_test_actual.py
@@ -22,7 +22,7 @@ ngraph_needed = pytest.mark.skipif(not ngraph_available,
 
 class TestMainErrors(unittest.TestCase):
     @patch('argparse.ArgumentParser.parse_args', return_value=argparse.Namespace())
-    @patch('mo.main.driver', side_effect=FrameworkError('FW ERROR MESSAGE'))
+    @patch('openvino.tools.mo.main.driver', side_effect=FrameworkError('FW ERROR MESSAGE'))
     @ngraph_needed
     def test_FrameworkError(self, mock_argparse, mock_driver):
         with self.assertLogs() as logger:
diff --git a/tools/mo/unit_tests/extensions/middle/AddIsCyclicAttribute_test.py b/tools/mo/unit_tests/mo/middle/AddIsCyclicAttribute_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/AddIsCyclicAttribute_test.py
rename to tools/mo/unit_tests/mo/middle/AddIsCyclicAttribute_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/AddMeanScaleValues_test.py b/tools/mo/unit_tests/mo/middle/AddMeanScaleValues_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/AddMeanScaleValues_test.py
rename to tools/mo/unit_tests/mo/middle/AddMeanScaleValues_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/ArgOpsToTopK_test.py b/tools/mo/unit_tests/mo/middle/ArgOpsToTopK_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/ArgOpsToTopK_test.py
rename to tools/mo/unit_tests/mo/middle/ArgOpsToTopK_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/CheckForCycle_test.py b/tools/mo/unit_tests/mo/middle/CheckForCycle_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/CheckForCycle_test.py
rename to tools/mo/unit_tests/mo/middle/CheckForCycle_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/ConcatOptimization_test.py b/tools/mo/unit_tests/mo/middle/ConcatOptimization_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/ConcatOptimization_test.py
rename to tools/mo/unit_tests/mo/middle/ConcatOptimization_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/ConvertGroupedStridedSlice_test.py b/tools/mo/unit_tests/mo/middle/ConvertGroupedStridedSlice_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/ConvertGroupedStridedSlice_test.py
rename to tools/mo/unit_tests/mo/middle/ConvertGroupedStridedSlice_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/CutInputHavingZeroDimFromConcat_test.py b/tools/mo/unit_tests/mo/middle/CutInputHavingZeroDimFromConcat_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/CutInputHavingZeroDimFromConcat_test.py
rename to tools/mo/unit_tests/mo/middle/CutInputHavingZeroDimFromConcat_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/EltwiseInputReshape_test.py b/tools/mo/unit_tests/mo/middle/EltwiseInputReshape_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/EltwiseInputReshape_test.py
rename to tools/mo/unit_tests/mo/middle/EltwiseInputReshape_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/FakeSplitOutputs_test.py b/tools/mo/unit_tests/mo/middle/FakeSplitOutputs_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/FakeSplitOutputs_test.py
rename to tools/mo/unit_tests/mo/middle/FakeSplitOutputs_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/FusedBatchNormTraining_test.py b/tools/mo/unit_tests/mo/middle/FusedBatchNormTraining_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/FusedBatchNormTraining_test.py
rename to tools/mo/unit_tests/mo/middle/FusedBatchNormTraining_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/GroupNorm_test.py b/tools/mo/unit_tests/mo/middle/GroupNorm_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/GroupNorm_test.py
rename to tools/mo/unit_tests/mo/middle/GroupNorm_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/InsertSelect_test.py b/tools/mo/unit_tests/mo/middle/InsertSelect_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/InsertSelect_test.py
rename to tools/mo/unit_tests/mo/middle/InsertSelect_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/InterpolateSequenceToInterpolate_test.py b/tools/mo/unit_tests/mo/middle/InterpolateSequenceToInterpolate_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/InterpolateSequenceToInterpolate_test.py
rename to tools/mo/unit_tests/mo/middle/InterpolateSequenceToInterpolate_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/L2NormFusing_test.py b/tools/mo/unit_tests/mo/middle/L2NormFusing_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/L2NormFusing_test.py
rename to tools/mo/unit_tests/mo/middle/L2NormFusing_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/LayoutChangeForEinsum_test.py b/tools/mo/unit_tests/mo/middle/LayoutChangeForEinsum_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/LayoutChangeForEinsum_test.py
rename to tools/mo/unit_tests/mo/middle/LayoutChangeForEinsum_test.py
diff --git a/tools/mo/unit_tests/extensions/middle/LeakyReluPattern_test.py b/tools/mo/unit_tests/mo/middle/LeakyReluPattern_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/middle/LeakyReluPattern_test.py
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similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/sparse_fill_empty_rows_test.py
rename to tools/mo/unit_tests/mo/ops/sparse_fill_empty_rows_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/sparse_reshape_test.py b/tools/mo/unit_tests/mo/ops/sparse_reshape_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/sparse_reshape_test.py
rename to tools/mo/unit_tests/mo/ops/sparse_reshape_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/sparse_segment_mean_test.py b/tools/mo/unit_tests/mo/ops/sparse_segment_mean_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/sparse_segment_mean_test.py
rename to tools/mo/unit_tests/mo/ops/sparse_segment_mean_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/sparse_segment_sqrtn_test.py b/tools/mo/unit_tests/mo/ops/sparse_segment_sqrtn_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/sparse_segment_sqrtn_test.py
rename to tools/mo/unit_tests/mo/ops/sparse_segment_sqrtn_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/sparse_segment_sum_test.py b/tools/mo/unit_tests/mo/ops/sparse_segment_sum_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/sparse_segment_sum_test.py
rename to tools/mo/unit_tests/mo/ops/sparse_segment_sum_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/split_test.py b/tools/mo/unit_tests/mo/ops/split_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/split_test.py
rename to tools/mo/unit_tests/mo/ops/split_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/switch_test.py b/tools/mo/unit_tests/mo/ops/switch_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/switch_test.py
rename to tools/mo/unit_tests/mo/ops/switch_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/topk_test.py b/tools/mo/unit_tests/mo/ops/topk_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/topk_test.py
rename to tools/mo/unit_tests/mo/ops/topk_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/transpose_test.py b/tools/mo/unit_tests/mo/ops/transpose_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/transpose_test.py
rename to tools/mo/unit_tests/mo/ops/transpose_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/unique_test.py b/tools/mo/unit_tests/mo/ops/unique_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/unique_test.py
rename to tools/mo/unit_tests/mo/ops/unique_test.py
diff --git a/tools/mo/unit_tests/extensions/ops/upsample_test.py b/tools/mo/unit_tests/mo/ops/upsample_test.py
similarity index 100%
rename from tools/mo/unit_tests/extensions/ops/upsample_test.py
rename to tools/mo/unit_tests/mo/ops/upsample_test.py

From ad10edbd2e372c13a201073bf39bdf45b63d42db Mon Sep 17 00:00:00 2001
From: Liubov Talamanova <piccione-mail@yandex.ru>
Date: Fri, 17 Dec 2021 18:46:57 +0300
Subject: [PATCH 025/160] Set fullname instead of name in fq_configuration
 (#9281)

---
 .../algorithms/quantization/fake_quantize_configuration.py  | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py
index 5f93c533c79..b796fcf2f82 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py
@@ -398,11 +398,11 @@ def _fake_quantize_to_types(model, hardware_config):
                 if not _is_quantizable(child):
                     queue.append(child)
                 elif child.type not in descendants:
-                    descendants.append((child.name,
+                    descendants.append((child.fullname,
                                         get_hardware_config_operation_type(child, available_types)))
                 if current.type == 'Split' \
                         and child.type == 'Concat' \
-                        and len({child_.name for child_ in children}) == 1:
+                        and len({child_.fullname for child_ in children}) == 1:
                     break
         return descendants
 
@@ -414,7 +414,7 @@ def _fake_quantize_to_types(model, hardware_config):
     available_types = [layer['type'] for layer in hardware_config]
     for fq in get_nodes_by_type(model, ['FakeQuantize']):
         node_input = get_node_input(fq, 0)
-        out[fq.name] = (_get_node_valuable_descendant(fq), node_input.type == 'Const')
+        out[fq.fullname] = (_get_node_valuable_descendant(fq), node_input.type == 'Const')
 
     return out
 

From 36a6774067c3238bffee18f0d02354dce5e26721 Mon Sep 17 00:00:00 2001
From: Maxim Vafin <maxim.vafin@intel.com>
Date: Fri, 17 Dec 2021 19:05:43 +0300
Subject: [PATCH 026/160] Fix preprocessing in MO to use friendly names as well
 as tensor names (#9206)

* Fix preprocessing in MO to use friendly names as well as tensor names

* Apply review comments

* Revert not necessary changes

* Apply review feedback
---
 .../tools/mo/back/offline_transformations.py  | 71 +++++++------------
 .../openvino/tools/mo/back/preprocessing.py   | 29 ++++++--
 .../mo/back/moc_preprocessing_test_actual.py  | 16 ++++-
 3 files changed, 64 insertions(+), 52 deletions(-)

diff --git a/tools/mo/openvino/tools/mo/back/offline_transformations.py b/tools/mo/openvino/tools/mo/back/offline_transformations.py
index ff4f3ac8bb0..19970a0f1f2 100644
--- a/tools/mo/openvino/tools/mo/back/offline_transformations.py
+++ b/tools/mo/openvino/tools/mo/back/offline_transformations.py
@@ -38,52 +38,6 @@ def compress_model(func: object):
     compress_model_transformation(func)
 
 
-def add_layouts(ov_function, argv: argparse.Namespace):
-    from openvino.preprocess import PrePostProcessor  # pylint: disable=no-name-in-module,import-error
-    from openvino.runtime import Layout  # pylint: disable=import-error,no-name-in-module
-
-    prep = PrePostProcessor(ov_function)
-    layout_values = argv.layout_values
-    if '' in layout_values:
-        if len(ov_function.inputs) == 1:
-            layout_values = {
-                list(ov_function.input().get_tensor().get_names())[0]: {
-                    'source_layout': layout_values[''].get('source_layout'),
-                    'target_layout': layout_values[''].get('target_layout')
-                }
-            }
-        else:
-            input_names = [list(ov_input.get_tensor().get_names())[0] for ov_input in ov_function.inputs]
-            raise Error('Layout without name can be specified for models with only one input, '
-                        'but provided model has {} inputs: \'{}\'. '
-                        'Please specify explicitly input/output name for --layout option'
-                        .format(len(input_names), input_names))
-
-    set_layout_names = set(layout_values.keys())
-    for idx, ov_input in enumerate(ov_function.inputs):
-        found = set.intersection(set(ov_input.get_tensor().get_names()), set_layout_names)
-        assert len(found) <= 1, 'More then one name point to the same node'
-        if len(found) == 1:
-            node_name = list(found)[0]
-            found_layout = layout_values[node_name]
-            if found_layout['source_layout']:
-                prep.input(node_name).model().set_layout(Layout(found_layout['source_layout']))
-            if found_layout['target_layout']:
-                prep.input(node_name).tensor().set_layout(Layout(found_layout['target_layout']))
-
-    for idx, ov_output in enumerate(ov_function.outputs):
-        found = set.intersection(set(ov_output.get_tensor().get_names()), set_layout_names)
-        assert len(found) <= 1, 'More then one name point to the same node'
-        if len(found) == 1:
-            node_name = list(found)[0]
-            found_layout = layout_values[node_name]
-            if found_layout['source_layout']:
-                prep.output(node_name).model().set_layout(Layout(found_layout['source_layout']))
-            if found_layout['target_layout']:
-                prep.output(node_name).tensor().set_layout(Layout(found_layout['target_layout']))
-    prep.build()
-
-
 def apply_offline_transformations(input_model: str, argv: argparse.Namespace):
     # This variable is only needed by GenerateMappingFile transformation
     # to produce correct mapping
@@ -91,6 +45,7 @@ def apply_offline_transformations(input_model: str, argv: argparse.Namespace):
 
     from openvino.offline_transformations_pybind import generate_mapping_file, serialize  # pylint: disable=import-error,no-name-in-module
     from openvino.frontend import FrontEndManager, FrontEnd  # pylint: disable=no-name-in-module,import-error
+    from openvino.tools.mo.back.preprocessing import apply_preprocessing  # pylint: disable=no-name-in-module,import-error
 
     fem = FrontEndManager()
 
@@ -104,7 +59,29 @@ def apply_offline_transformations(input_model: str, argv: argparse.Namespace):
 
     func = read_model(input_model + "_tmp.xml")
 
-    add_layouts(func, argv)  # TODO: replace with preprocessing
+    # TODO: use ngraph preprocessing (Mean/Scale/ReverseInputChannels) for legacy frontends
+    reverse_input_channels = False
+    if 'reverse_input_channels' in argv:
+        reverse_input_channels = argv.reverse_input_channels
+        argv.reverse_input_channels = False
+    mean_scale_values = {}
+    if 'mean_scale_values' in argv:
+        mean_scale_values = argv.mean_scale_values
+        argv.mean_scale_values = {}
+    scale = None
+    if 'scale' in argv:
+        scale = argv.scale
+        argv.scale = None
+
+    # Apply preprocessing for layouts only
+    apply_preprocessing(ov_function=func, argv=argv)
+
+    if 'reverse_input_channels' in argv:
+        argv.reverse_input_channels = reverse_input_channels
+    if 'mean_scale_values' in argv:
+        argv.mean_scale_values = mean_scale_values
+    if 'scale' in argv:
+        argv.scale = scale
 
     apply_user_transformations(func, parse_transform(argv.transform))
     apply_moc_transformations(func)
diff --git a/tools/mo/openvino/tools/mo/back/preprocessing.py b/tools/mo/openvino/tools/mo/back/preprocessing.py
index c61da606d4a..7d657debed5 100644
--- a/tools/mo/openvino/tools/mo/back/preprocessing.py
+++ b/tools/mo/openvino/tools/mo/back/preprocessing.py
@@ -59,7 +59,7 @@ def update_mean_scale_to_dict(input_nodes: list, mean_scale_val, scale):
     return mean_scale_val
 
 
-def check_keys_valid(ov_function: Model, keys: list, search_outputs: bool):
+def check_keys_valid(ov_function: Model, dict_to_validate: dict, search_outputs: bool):
     """
     Internal function: checks if keys from cmd line arguments correspond to ov_function's inputs/outputs
     Throws if some key is not found
@@ -70,7 +70,28 @@ def check_keys_valid(ov_function: Model, keys: list, search_outputs: bool):
     if search_outputs:
         nodes += ov_function.outputs
 
-    for name in keys:
+    # We need to replace all node names from dict to tensor names
+    rename_dict = {}
+    # Find names for replacing
+    for name in dict_to_validate.keys():
+        for ov_node in nodes:
+            if name in ov_node.get_tensor().get_names():
+                break
+            elif name == ov_node.get_node().get_friendly_name():
+                assert len(ov_node.get_tensor().get_names()) > 0, 'Node must have at least one tensor name'
+                new_name = list(ov_node.get_tensor().get_names())[0]
+                rename_dict[name] = new_name
+                break
+
+    # Replace found node names with tensor names
+    for name, new_name in rename_dict.items():
+        assert name in dict_to_validate, 'Key {} is not in initial dict'.format(name)
+        assert new_name not in dict_to_validate, 'Key {} is already in initial dict'.format(new_name)
+        dict_to_validate[new_name] = dict_to_validate[name]
+        del dict_to_validate[name]
+
+    # validate the dict
+    for name in dict_to_validate.keys():
         node_found = False
         for ov_node in nodes:
             if name in ov_node.get_tensor().get_names():
@@ -349,8 +370,8 @@ def apply_preprocessing(ov_function: Model, argv: argparse.Namespace):
                 'target_layout': layout_values[''].get('target_layout')
             }
         }
-    check_keys_valid(ov_function=ov_function, keys=mean_scale_values.keys(), search_outputs=False)
-    check_keys_valid(ov_function=ov_function, keys=layout_values.keys(), search_outputs=True)
+    check_keys_valid(ov_function=ov_function, dict_to_validate=mean_scale_values, search_outputs=False)
+    check_keys_valid(ov_function=ov_function, dict_to_validate=layout_values, search_outputs=True)
 
     layout_values = update_layout_is_input_flag(ov_function, layout_values)
     layout_values = guess_source_layouts_by_mean_scale(ov_function, layout_values, mean_scale_values)
diff --git a/tools/mo/unit_tests/mo/back/moc_preprocessing_test_actual.py b/tools/mo/unit_tests/mo/back/moc_preprocessing_test_actual.py
index 5a1288b482a..7d979ecf3f5 100644
--- a/tools/mo/unit_tests/mo/back/moc_preprocessing_test_actual.py
+++ b/tools/mo/unit_tests/mo/back/moc_preprocessing_test_actual.py
@@ -40,7 +40,7 @@ def create_function2(shape1=[2, 2], shape2=[2, 2], dtype1=np.float32, dtype2=np.
 
 def create_function1(shape1=[2, 2]):
     input1 = ops.parameter(shape1, dtype=np.float32, name="input1")
-    input1.get_output_tensor(0).set_names({'input1', 'input1a'})
+    input1.get_output_tensor(0).set_names({'input1a', 'input1b'})
     relu1 = ops.relu(input1)
     res1 = ops.result(relu1, "res1")
     res1.get_output_tensor(0).set_names({'res1', 'res1a'})
@@ -651,3 +651,17 @@ class TestPreprocessingMOC(unittest.TestCase):
 
         # Verify that guessed layout (?C??) is not appeared in input2
         self.assertEqual(function.get_parameters()[1].layout, Layout())
+
+    def test_friendly_name(self):
+        argv = Namespace(mean_scale_values={'input1': {'mean': np.array([2., 4., 8.]), 'scale': None}},
+                         layout_values={'input1': {'source_layout': 'nchw'}},
+                         scale=None)
+        function = create_function1(shape1=[1, 3, 224, 224])
+        process_function(ov_function=function, argv=argv)
+        op_node = list(function.get_parameters()[0].output(0).get_target_inputs())[0].get_node()
+        self.assertTrue(op_node.get_type_name() == 'Subtract' or op_node.get_type_name() == 'Add')
+        self.check_mean_constant(op_node, expected=[2., 4., 8.], shape=[1, 3, 1, 1])
+
+        # Verify that layout (nchw) is appeared in input1
+        self.assertEqual(function.get_parameters()[0].layout, Layout('nchw'))
+

From c7a3c93d3f3c04afe83974dd6213dbbd9921abc8 Mon Sep 17 00:00:00 2001
From: Mikhail Nosov <mikhail.nosov@intel.com>
Date: Fri, 17 Dec 2021 22:39:13 +0300
Subject: [PATCH 027/160] FrontEnd API - change 'shared_ptr' to 'const
 shared_ptr&' (#9287)

* FrontEnd API - change 'shared_ptr' to 'const shared_ptr&' in public arguments

* Fix clang-format
---
 .../src/pyopenvino/frontend/frontend.cpp      |  4 +--
 .../mock_py_ov_frontend/mock_py_frontend.hpp  | 36 +++++++++----------
 .../include/openvino/frontend/frontend.hpp    | 10 +++---
 .../include/openvino/frontend/input_model.hpp | 26 +++++++-------
 .../include/openvino/frontend/place.hpp       |  4 +--
 src/frontends/common/src/frontend.cpp         | 10 +++---
 src/frontends/common/src/input_model.cpp      | 26 +++++++-------
 src/frontends/common/src/place.cpp            |  4 +--
 .../include/openvino/frontend/ir/frontend.hpp |  2 +-
 src/frontends/ir/src/frontend.cpp             |  2 +-
 .../openvino/frontend/onnx/frontend.hpp       |  6 ++--
 src/frontends/onnx/frontend/src/frontend.cpp  |  6 ++--
 .../onnx/frontend/src/input_model.cpp         | 14 ++++----
 .../onnx/frontend/src/input_model.hpp         | 14 ++++----
 src/frontends/onnx/frontend/src/place.cpp     | 14 ++++----
 src/frontends/onnx/frontend/src/place.hpp     | 14 ++++----
 .../paddlepaddle_frontend/frontend.hpp        |  8 ++---
 .../include/paddlepaddle_frontend/model.hpp   |  8 ++---
 .../include/paddlepaddle_frontend/place.hpp   |  8 ++---
 src/frontends/paddlepaddle/src/frontend.cpp   |  8 ++---
 src/frontends/paddlepaddle/src/model.cpp      |  8 ++---
 src/frontends/paddlepaddle/src/place.cpp      |  6 ++--
 .../include/tensorflow_frontend/frontend.hpp  | 10 +++---
 src/frontends/tensorflow/src/frontend.cpp     | 10 +++---
 src/frontends/tensorflow/src/model.cpp        |  8 ++---
 src/frontends/tensorflow/src/model.hpp        |  8 ++---
 src/frontends/tensorflow/src/place.cpp        |  6 ++--
 src/frontends/tensorflow/src/place.hpp        |  8 ++---
 .../mock_mo_ov_frontend/mock_mo_frontend.hpp  | 12 +++----
 29 files changed, 150 insertions(+), 150 deletions(-)

diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
index 5adeed0ce1c..c3e49181793 100644
--- a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
@@ -41,7 +41,7 @@ void regclass_frontend_FrontEnd(py::module m) {
              )");
 
     fem.def("convert",
-            static_cast<std::shared_ptr<ov::Model> (FrontEnd::*)(InputModel::Ptr) const>(&FrontEnd::convert),
+            static_cast<std::shared_ptr<ov::Model> (FrontEnd::*)(const InputModel::Ptr&) const>(&FrontEnd::convert),
             py::arg("model"),
             R"(
                 Completely convert and normalize entire function, throws if it is not possible.
@@ -58,7 +58,7 @@ void regclass_frontend_FrontEnd(py::module m) {
              )");
 
     fem.def("convert",
-            static_cast<void (FrontEnd::*)(std::shared_ptr<ov::Model>) const>(&FrontEnd::convert),
+            static_cast<void (FrontEnd::*)(const std::shared_ptr<ov::Model>&) const>(&FrontEnd::convert),
             py::arg("function"),
             R"(
                 Completely convert the remaining, not converted part of a function.
diff --git a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp b/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
index 3bc8d8f423a..a9d61f99275 100644
--- a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
+++ b/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
@@ -257,13 +257,13 @@ public:
         return false;
     }
 
-    bool is_equal(Ptr another) const override {
+    bool is_equal(const Ptr& another) const override {
         m_stat.m_is_equal++;
         m_stat.m_lastArgPlace = another;
         return false;
     }
 
-    bool is_equal_data(Ptr another) const override {
+    bool is_equal_data(const Ptr& another) const override {
         m_stat.m_is_equal_data++;
         m_stat.m_lastArgPlace = another;
         return false;
@@ -471,19 +471,19 @@ public:
         return std::make_shared<PlaceMockPy>();
     }
 
-    void set_name_for_tensor(Place::Ptr tensor, const std::string& newName) override {
+    void set_name_for_tensor(const Place::Ptr& tensor, const std::string& newName) override {
         m_stat.m_set_name_for_tensor++;
         m_stat.m_lastArgPlace = tensor;
         m_stat.m_lastArgString = newName;
     }
 
-    void add_name_for_tensor(Place::Ptr tensor, const std::string& newName) override {
+    void add_name_for_tensor(const Place::Ptr& tensor, const std::string& newName) override {
         m_stat.m_add_name_for_tensor++;
         m_stat.m_lastArgPlace = tensor;
         m_stat.m_lastArgString = newName;
     }
 
-    void set_name_for_operation(Place::Ptr operation, const std::string& newName) override {
+    void set_name_for_operation(const Place::Ptr& operation, const std::string& newName) override {
         m_stat.m_set_name_for_operation++;
         m_stat.m_lastArgPlace = operation;
         m_stat.m_lastArgString = newName;
@@ -499,32 +499,32 @@ public:
         m_stat.m_lastArgString = name;
     }
 
-    void set_name_for_dimension(Place::Ptr place, size_t shapeDimIndex, const std::string& dimName) override {
+    void set_name_for_dimension(const Place::Ptr& place, size_t shapeDimIndex, const std::string& dimName) override {
         m_stat.m_set_name_for_dimension++;
         m_stat.m_lastArgPlace = place;
         m_stat.m_lastArgInt = static_cast<int>(shapeDimIndex);
         m_stat.m_lastArgString = dimName;
     }
 
-    void cut_and_add_new_input(Place::Ptr place, const std::string& newNameOptional) override {
+    void cut_and_add_new_input(const Place::Ptr& place, const std::string& newNameOptional) override {
         m_stat.m_cut_and_add_new_input++;
         m_stat.m_lastArgPlace = place;
         m_stat.m_lastArgString = newNameOptional;
     }
 
-    void cut_and_add_new_output(Place::Ptr place, const std::string& newNameOptional) override {
+    void cut_and_add_new_output(const Place::Ptr& place, const std::string& newNameOptional) override {
         m_stat.m_cut_and_add_new_output++;
         m_stat.m_lastArgPlace = place;
         m_stat.m_lastArgString = newNameOptional;
     }
 
-    Place::Ptr add_output(Place::Ptr place) override {
+    Place::Ptr add_output(const Place::Ptr& place) override {
         m_stat.m_add_output++;
         m_stat.m_lastArgPlace = place;
         return std::make_shared<PlaceMockPy>();
     }
 
-    void remove_output(Place::Ptr place) override {
+    void remove_output(const Place::Ptr& place) override {
         m_stat.m_remove_output++;
         m_stat.m_lastArgPlace = place;
     }
@@ -546,19 +546,19 @@ public:
     }
 
     // Setting tensor properties
-    void set_partial_shape(Place::Ptr place, const ngraph::PartialShape& shape) override {
+    void set_partial_shape(const Place::Ptr& place, const ngraph::PartialShape& shape) override {
         m_stat.m_set_partial_shape++;
         m_stat.m_lastArgPlace = place;
         m_stat.m_lastArgPartialShape = shape;
     }
 
-    ngraph::PartialShape get_partial_shape(Place::Ptr place) const override {
+    ngraph::PartialShape get_partial_shape(const Place::Ptr& place) const override {
         m_stat.m_get_partial_shape++;
         m_stat.m_lastArgPlace = place;
         return {};
     }
 
-    void set_element_type(Place::Ptr place, const ngraph::element::Type& type) override {
+    void set_element_type(const Place::Ptr& place, const ngraph::element::Type& type) override {
         m_stat.m_set_element_type++;
         m_stat.m_lastArgPlace = place;
         m_stat.m_lastArgElementType = type;
@@ -631,26 +631,26 @@ public:
         return false;
     }
 
-    std::shared_ptr<ov::Model> convert(InputModel::Ptr model) const override {
+    std::shared_ptr<ov::Model> convert(const InputModel::Ptr& model) const override {
         m_stat.m_convert_model++;
         return std::make_shared<ov::Model>(ov::NodeVector{}, ov::ParameterVector{});
     }
 
-    void convert(std::shared_ptr<ov::Model> func) const override {
+    void convert(const std::shared_ptr<ov::Model>& func) const override {
         m_stat.m_convert++;
     }
 
-    std::shared_ptr<ov::Model> convert_partially(InputModel::Ptr model) const override {
+    std::shared_ptr<ov::Model> convert_partially(const InputModel::Ptr& model) const override {
         m_stat.m_convert_partially++;
         return std::make_shared<ov::Model>(ov::NodeVector{}, ov::ParameterVector{});
     }
 
-    std::shared_ptr<ov::Model> decode(InputModel::Ptr model) const override {
+    std::shared_ptr<ov::Model> decode(const InputModel::Ptr& model) const override {
         m_stat.m_decode++;
         return std::make_shared<ov::Model>(ov::NodeVector{}, ov::ParameterVector{});
     }
 
-    void normalize(std::shared_ptr<ov::Model> function) const override {
+    void normalize(const std::shared_ptr<ov::Model>& function) const override {
         m_stat.m_normalize++;
     }
 
diff --git a/src/frontends/common/include/openvino/frontend/frontend.hpp b/src/frontends/common/include/openvino/frontend/frontend.hpp
index 13b30254a39..15793500fc0 100644
--- a/src/frontends/common/include/openvino/frontend/frontend.hpp
+++ b/src/frontends/common/include/openvino/frontend/frontend.hpp
@@ -59,11 +59,11 @@ public:
     /// possible
     /// \param model Input model
     /// \return fully converted OV Model
-    virtual std::shared_ptr<ov::Model> convert(InputModel::Ptr model) const;
+    virtual std::shared_ptr<ov::Model> convert(const InputModel::Ptr& model) const;
 
     /// \brief Completely convert the remaining, not converted part of a Model.
     /// \param partiallyConverted partially converted OV Model
-    virtual void convert(std::shared_ptr<ov::Model> partially_converted) const;
+    virtual void convert(const std::shared_ptr<ov::Model>& partially_converted) const;
 
     /// \brief Convert only those parts of the model that can be converted leaving others
     /// as-is. Converted parts are not normalized by additional transformations; normalize
@@ -71,18 +71,18 @@ public:
     /// conversion process.
     /// \param model Input model
     /// \return partially converted OV Model
-    virtual std::shared_ptr<ov::Model> convert_partially(InputModel::Ptr model) const;
+    virtual std::shared_ptr<ov::Model> convert_partially(const InputModel::Ptr& model) const;
 
     /// \brief Convert operations with one-to-one mapping with decoding nodes.
     /// Each decoding node is an OV node representing a single FW operation node with
     /// all attributes represented in FW-independent way.
     /// \param model Input model
     /// \return OV Model after decoding
-    virtual std::shared_ptr<ov::Model> decode(InputModel::Ptr model) const;
+    virtual std::shared_ptr<ov::Model> decode(const InputModel::Ptr& model) const;
 
     /// \brief Runs normalization passes on Model that was loaded with partial conversion
     /// \param Model partially converted OV Model
-    virtual void normalize(std::shared_ptr<ov::Model> model) const;
+    virtual void normalize(const std::shared_ptr<ov::Model>& model) const;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
     /// if frontend is selected automatically by FrontEndManager::load_by_model
diff --git a/src/frontends/common/include/openvino/frontend/input_model.hpp b/src/frontends/common/include/openvino/frontend/input_model.hpp
index c8327b0252c..ae104d30ea5 100644
--- a/src/frontends/common/include/openvino/frontend/input_model.hpp
+++ b/src/frontends/common/include/openvino/frontend/input_model.hpp
@@ -95,17 +95,17 @@ public:
     /// \brief Sets name for tensor. Overwrites existing names of this place
     /// \param tensor Tensor place
     /// \param new_name New name for this tensor
-    virtual void set_name_for_tensor(Place::Ptr tensor, const std::string& new_name);
+    virtual void set_name_for_tensor(const Place::Ptr& tensor, const std::string& new_name);
 
     /// \brief Adds new name for tensor
     /// \param tensor Tensor place
     /// \param new_name New name to be added to this place
-    virtual void add_name_for_tensor(Place::Ptr tensor, const std::string& new_name);
+    virtual void add_name_for_tensor(const Place::Ptr& tensor, const std::string& new_name);
 
     /// \brief Sets name for operation. Overwrites existing names of this place
     /// \param operation Operation place
     /// \param new_name New name for this operation
-    virtual void set_name_for_operation(Place::Ptr operation, const std::string& new_name);
+    virtual void set_name_for_operation(const Place::Ptr& operation, const std::string& new_name);
 
     /// \brief Unassign specified name from tensor place(s)
     /// \param name Name of tensor
@@ -119,7 +119,7 @@ public:
     /// \param place Model's place
     /// \param shape_dim_index Dimension index
     /// \param dim_name Name to assign on this dimension
-    virtual void set_name_for_dimension(Place::Ptr place, size_t shape_dim_index, const std::string& dim_name);
+    virtual void set_name_for_dimension(const Place::Ptr& place, size_t shape_dim_index, const std::string& dim_name);
 
     ///// Topology Editing  /////
 
@@ -127,25 +127,25 @@ public:
     /// all nodes that don't contribute to any output.
     /// \param place New place to be assigned as input
     /// \param new_name_optional Optional new name assigned to this input place
-    virtual void cut_and_add_new_input(Place::Ptr place, const std::string& new_name_optional = "");
+    virtual void cut_and_add_new_input(const Place::Ptr& place, const std::string& new_name_optional = "");
 
     /// \brief Cut immediately after this place and assign this place as new output; prune
     /// all nodes that don't contribute to any output.
     /// \param place New place to be assigned as output
     /// \param new_name_optional Optional new name assigned to this output place
-    virtual void cut_and_add_new_output(Place::Ptr place, const std::string& new_name_optional = "");
+    virtual void cut_and_add_new_output(const Place::Ptr& place, const std::string& new_name_optional = "");
 
     /// \brief Assign this place as new output or add necessary nodes to represent a new
     /// output.
     ///
     /// \param place Anchor point to add an output
     /// \return new output place, may be the same as a given place
-    virtual Place::Ptr add_output(Place::Ptr place);
+    virtual Place::Ptr add_output(const Place::Ptr& place);
 
     /// \brief Removes any sinks directly attached to this place with all inbound data flow
     /// if it is not required by any other output.
     /// \param place Model place
-    virtual void remove_output(Place::Ptr place);
+    virtual void remove_output(const Place::Ptr& place);
 
     /// \brief Replaces all existing outputs with new ones removing all data flow that is
     /// not required for new outputs.
@@ -173,30 +173,30 @@ public:
     /// converted to OV.
     /// \param place Model place
     /// \param shape Partial shape for this place
-    virtual void set_partial_shape(Place::Ptr place, const ov::PartialShape& shape);
+    virtual void set_partial_shape(const Place::Ptr& place, const ov::PartialShape& shape);
 
     /// \brief Returns current partial shape used for this place
     /// \param place Model place
     /// \return Partial shape for this place
-    virtual ov::PartialShape get_partial_shape(Place::Ptr place) const;
+    virtual ov::PartialShape get_partial_shape(const Place::Ptr& place) const;
 
     /// \brief Sets new element type for a place
     /// \param place Model place
     /// \param type New element type
-    virtual void set_element_type(Place::Ptr place, const ov::element::Type& type);
+    virtual void set_element_type(const Place::Ptr& place, const ov::element::Type& type);
 
     /// \brief Freezes a tensor with statically defined value or replace existing value for
     /// already constant node or tensor
     /// \param place Tensor place
     /// \param value Value for tensor place representing a memory buffer
-    virtual void set_tensor_value(Place::Ptr place, const void* value);
+    virtual void set_tensor_value(const Place::Ptr& place, const void* value);
 
     /// \brief Defines partial value (lower bound and upper bound) for a tensor place
     /// TODO: more details for min_value and max_value format; who defines shape?
     /// \param place Tensor place
     /// \param min_value Lower bound of partial value for tensor place
     /// \param max_value Upper bound of partial value for tensor place
-    virtual void set_tensor_partial_value(Place::Ptr place, const void* min_value, const void* max_value);
+    virtual void set_tensor_partial_value(const Place::Ptr& place, const void* min_value, const void* max_value);
 };
 
 }  // namespace frontend
diff --git a/src/frontends/common/include/openvino/frontend/place.hpp b/src/frontends/common/include/openvino/frontend/place.hpp
index 7fdab781996..8b9eb039b23 100644
--- a/src/frontends/common/include/openvino/frontend/place.hpp
+++ b/src/frontends/common/include/openvino/frontend/place.hpp
@@ -292,7 +292,7 @@ public:
     /// \brief Returns true if another place is the same as this place.
     ///
     /// \param another Another place object
-    virtual bool is_equal(Ptr another) const;
+    virtual bool is_equal(const Ptr& another) const;
 
     /// \brief Returns true if another place points to the same data.
     ///
@@ -300,7 +300,7 @@ public:
     /// -> input edge -> input port.
     ///
     /// \param another Another place object
-    virtual bool is_equal_data(Ptr another) const;
+    virtual bool is_equal_data(const Ptr& another) const;
 };
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/common/src/frontend.cpp b/src/frontends/common/src/frontend.cpp
index 2ab96691ee9..7ba6de2e78e 100644
--- a/src/frontends/common/src/frontend.cpp
+++ b/src/frontends/common/src/frontend.cpp
@@ -27,23 +27,23 @@ bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
 InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& params) const {
     FRONT_END_NOT_IMPLEMENTED(load_impl);
 }
-std::shared_ptr<Model> FrontEnd::convert(InputModel::Ptr model) const {
+std::shared_ptr<Model> FrontEnd::convert(const InputModel::Ptr& model) const {
     FRONT_END_NOT_IMPLEMENTED(convert);
 }
 
-void FrontEnd::convert(std::shared_ptr<Model>) const {
+void FrontEnd::convert(const std::shared_ptr<Model>&) const {
     FRONT_END_NOT_IMPLEMENTED(convert);
 }
 
-std::shared_ptr<Model> FrontEnd::convert_partially(InputModel::Ptr model) const {
+std::shared_ptr<Model> FrontEnd::convert_partially(const InputModel::Ptr& model) const {
     FRONT_END_NOT_IMPLEMENTED(convert_partially);
 }
 
-std::shared_ptr<Model> FrontEnd::decode(InputModel::Ptr model) const {
+std::shared_ptr<Model> FrontEnd::decode(const InputModel::Ptr& model) const {
     FRONT_END_NOT_IMPLEMENTED(decode);
 }
 
-void FrontEnd::normalize(std::shared_ptr<Model> model) const {
+void FrontEnd::normalize(const std::shared_ptr<Model>& model) const {
     FRONT_END_NOT_IMPLEMENTED(normalize);
 }
 
diff --git a/src/frontends/common/src/input_model.cpp b/src/frontends/common/src/input_model.cpp
index 9c1cc7a3629..385b50afb83 100644
--- a/src/frontends/common/src/input_model.cpp
+++ b/src/frontends/common/src/input_model.cpp
@@ -40,15 +40,15 @@ Place::Ptr InputModel::get_place_by_operation_name_and_output_port(const std::st
     return nullptr;
 }
 
-void InputModel::set_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
+void InputModel::set_name_for_tensor(const Place::Ptr& tensor, const std::string& new_name) {
     FRONT_END_NOT_IMPLEMENTED(set_name_for_tensor);
 }
 
-void InputModel::add_name_for_tensor(Place::Ptr tensor, const std::string& new_name) {
+void InputModel::add_name_for_tensor(const Place::Ptr& tensor, const std::string& new_name) {
     FRONT_END_NOT_IMPLEMENTED(add_name_for_tensor);
 }
 
-void InputModel::set_name_for_operation(Place::Ptr operation, const std::string& new_name) {
+void InputModel::set_name_for_operation(const Place::Ptr& operation, const std::string& new_name) {
     FRONT_END_NOT_IMPLEMENTED(set_name_for_operation);
 }
 
@@ -60,23 +60,23 @@ void InputModel::free_name_for_operation(const std::string& name) {
     FRONT_END_NOT_IMPLEMENTED(free_name_for_operation);
 }
 
-void InputModel::set_name_for_dimension(Place::Ptr place, size_t shape_dim_index, const std::string& dim_name) {
+void InputModel::set_name_for_dimension(const Place::Ptr& place, size_t shape_dim_index, const std::string& dim_name) {
     FRONT_END_NOT_IMPLEMENTED(set_name_for_dimension);
 }
 
-void InputModel::cut_and_add_new_input(Place::Ptr place, const std::string& new_name_optional) {
+void InputModel::cut_and_add_new_input(const Place::Ptr& place, const std::string& new_name_optional) {
     FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_input);
 }
 
-void InputModel::cut_and_add_new_output(Place::Ptr place, const std::string& new_name_optional) {
+void InputModel::cut_and_add_new_output(const Place::Ptr& place, const std::string& new_name_optional) {
     FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_output);
 }
 
-Place::Ptr InputModel::add_output(Place::Ptr place) {
+Place::Ptr InputModel::add_output(const Place::Ptr& place) {
     FRONT_END_NOT_IMPLEMENTED(add_output);
 }
 
-void InputModel::remove_output(Place::Ptr place) {
+void InputModel::remove_output(const Place::Ptr& place) {
     FRONT_END_NOT_IMPLEMENTED(remove_output);
 }
 
@@ -93,22 +93,22 @@ void InputModel::extract_subgraph(const std::vector<Place::Ptr>& inputs, const s
 }
 
 // Setting tensor properties
-void InputModel::set_partial_shape(Place::Ptr place, const PartialShape&) {
+void InputModel::set_partial_shape(const Place::Ptr& place, const PartialShape&) {
     FRONT_END_NOT_IMPLEMENTED(set_partial_shape);
 }
 
-PartialShape InputModel::get_partial_shape(Place::Ptr place) const {
+PartialShape InputModel::get_partial_shape(const Place::Ptr& place) const {
     FRONT_END_NOT_IMPLEMENTED(get_partial_shape);
 }
 
-void InputModel::set_element_type(Place::Ptr place, const element::Type&) {
+void InputModel::set_element_type(const Place::Ptr& place, const element::Type&) {
     FRONT_END_NOT_IMPLEMENTED(set_element_type);
 }
 
-void InputModel::set_tensor_value(Place::Ptr place, const void* value) {
+void InputModel::set_tensor_value(const Place::Ptr& place, const void* value) {
     FRONT_END_NOT_IMPLEMENTED(set_tensor_value);
 }
 
-void InputModel::set_tensor_partial_value(Place::Ptr place, const void* min_value, const void* max_value) {
+void InputModel::set_tensor_partial_value(const Place::Ptr& place, const void* min_value, const void* max_value) {
     FRONT_END_NOT_IMPLEMENTED(set_tensor_partial_value);
 }
diff --git a/src/frontends/common/src/place.cpp b/src/frontends/common/src/place.cpp
index 9b8f611f9ce..008a66348ea 100644
--- a/src/frontends/common/src/place.cpp
+++ b/src/frontends/common/src/place.cpp
@@ -94,11 +94,11 @@ bool Place::is_output() const {
     FRONT_END_NOT_IMPLEMENTED(is_output);
 }
 
-bool Place::is_equal(Ptr another) const {
+bool Place::is_equal(const Ptr& another) const {
     FRONT_END_NOT_IMPLEMENTED(is_equal);
 }
 
-bool Place::is_equal_data(Ptr another) const {
+bool Place::is_equal_data(const Ptr& another) const {
     FRONT_END_NOT_IMPLEMENTED(is_equal_data);
 }
 
diff --git a/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp b/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
index 4139e37b929..5a393a0c2cb 100644
--- a/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
+++ b/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
@@ -21,7 +21,7 @@ public:
     /// \brief Completely convert the remaining, not converted part of a function.
     /// \param partiallyConverted partially converted nGraph function
     /// \return fully converted nGraph function
-    std::shared_ptr<Model> convert(InputModel::Ptr model) const override;
+    std::shared_ptr<Model> convert(const InputModel::Ptr& model) const override;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
     /// if frontend is selected automatically by FrontEndManager::load_by_model
diff --git a/src/frontends/ir/src/frontend.cpp b/src/frontends/ir/src/frontend.cpp
index 4ce3da25c5a..abd722047d3 100644
--- a/src/frontends/ir/src/frontend.cpp
+++ b/src/frontends/ir/src/frontend.cpp
@@ -225,7 +225,7 @@ InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const
     return create_input_model();
 }
 
-std::shared_ptr<ov::Model> FrontEnd::convert(InputModel::Ptr model) const {
+std::shared_ptr<ov::Model> FrontEnd::convert(const InputModel::Ptr& model) const {
     auto ir_model = std::dynamic_pointer_cast<InputModel>(model);
     OPENVINO_ASSERT(ir_model != nullptr);
     return ir_model->convert();
diff --git a/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
index 131bff7aa79..21b6f6b375d 100644
--- a/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
+++ b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
@@ -26,9 +26,9 @@ namespace onnx {
 
 class ONNX_FRONTEND_API FrontEnd : public ov::frontend::FrontEnd {
 public:
-    std::shared_ptr<ov::Model> convert(InputModel::Ptr model) const override;
-    void convert(std::shared_ptr<ov::Model> partially_converted) const override;
-    std::shared_ptr<ov::Model> decode(InputModel::Ptr model) const override;
+    std::shared_ptr<ov::Model> convert(const InputModel::Ptr& model) const override;
+    void convert(const std::shared_ptr<ov::Model>& partially_converted) const override;
+    std::shared_ptr<ov::Model> decode(const InputModel::Ptr& model) const override;
     std::string get_name() const override;
     bool supported_impl(const std::vector<ov::Any>& variants) const override;
     void add_extension(const std::shared_ptr<ov::Extension>& extension) override;
diff --git a/src/frontends/onnx/frontend/src/frontend.cpp b/src/frontends/onnx/frontend/src/frontend.cpp
index 38aca686dde..c8e2ba554f1 100644
--- a/src/frontends/onnx/frontend/src/frontend.cpp
+++ b/src/frontends/onnx/frontend/src/frontend.cpp
@@ -61,17 +61,17 @@ InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const
     return nullptr;
 }
 
-std::shared_ptr<ngraph::Function> FrontEnd::convert(InputModel::Ptr model) const {
+std::shared_ptr<ngraph::Function> FrontEnd::convert(const InputModel::Ptr& model) const {
     auto model_onnx = std::dynamic_pointer_cast<InputModel>(model);
     NGRAPH_CHECK(model_onnx != nullptr, "Invalid input model");
     return model_onnx->convert();
 }
 
-void FrontEnd::convert(std::shared_ptr<ngraph::Function> partially_converted) const {
+void FrontEnd::convert(const std::shared_ptr<ov::Model>& partially_converted) const {
     ngraph::onnx_import::detail::convert_decoded_function(partially_converted);
 }
 
-std::shared_ptr<ngraph::Function> FrontEnd::decode(InputModel::Ptr model) const {
+std::shared_ptr<ngraph::Function> FrontEnd::decode(const InputModel::Ptr& model) const {
     auto model_onnx = std::dynamic_pointer_cast<InputModel>(model);
     NGRAPH_CHECK(model_onnx != nullptr, "Invalid input model");
     return model_onnx->decode();
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index a3df6da6a93..500e4acde16 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -90,13 +90,13 @@ ov::frontend::Place::Ptr InputModel::get_place_by_operation_name_and_output_port
     return nullptr;
 }
 
-void InputModel::set_name_for_tensor(ov::frontend::Place::Ptr tensor, const std::string& new_name) {
+void InputModel::set_name_for_tensor(const ov::frontend::Place::Ptr& tensor, const std::string& new_name) {
     const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor);
     FRONT_END_GENERAL_CHECK(onnx_tensor, __FUNCTION__, " expects a pointer to place of ONNX tensor type.");
     onnx_tensor->set_name(new_name);
 }
 
-void InputModel::set_name_for_operation(ov::frontend::Place::Ptr operation, const std::string& new_name) {
+void InputModel::set_name_for_operation(const ov::frontend::Place::Ptr& operation, const std::string& new_name) {
     const auto onnx_operation = std::dynamic_pointer_cast<PlaceOp>(operation);
     FRONT_END_GENERAL_CHECK(onnx_operation, __FUNCTION__, " expects a pointer to place of ONNX operation type.");
     onnx_operation->set_name(new_name);
@@ -106,7 +106,7 @@ void InputModel::free_name_for_operation(const std::string& name) {
     m_editor->clear_nodes_name(name);
 }
 
-void InputModel::set_name_for_dimension(ov::frontend::Place::Ptr tensor,
+void InputModel::set_name_for_dimension(const ov::frontend::Place::Ptr& tensor,
                                         size_t shape_dim_index,
                                         const std::string& dim_name) {
     const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor);
@@ -114,7 +114,7 @@ void InputModel::set_name_for_dimension(ov::frontend::Place::Ptr tensor,
     onnx_tensor->set_name_for_dimension(shape_dim_index, dim_name);
 }
 
-void InputModel::add_name_for_tensor(ov::frontend::Place::Ptr, const std::string&) {
+void InputModel::add_name_for_tensor(const ov::frontend::Place::Ptr&, const std::string&) {
     FRONT_END_THROW("Method add_name_for_tensor is not applicable for ONNX model. ONNX tensor has just one name.");
 }
 
@@ -122,17 +122,17 @@ void InputModel::free_name_for_tensor(const std::string&) {
     FRONT_END_THROW("Method free_name_for_tensor is not applicable for ONNX model. ONNX tensor name is an identifier.");
 }
 
-void InputModel::set_partial_shape(ov::frontend::Place::Ptr place, const ngraph::PartialShape& shape) {
+void InputModel::set_partial_shape(const ov::frontend::Place::Ptr& place, const ngraph::PartialShape& shape) {
     std::map<std::string, ngraph::PartialShape> m;
     m[place->get_names()[0]] = shape;
     m_editor->set_input_shapes(m);
 }
 
-ngraph::PartialShape InputModel::get_partial_shape(ov::frontend::Place::Ptr place) const {
+ngraph::PartialShape InputModel::get_partial_shape(const ov::frontend::Place::Ptr& place) const {
     return m_editor->get_tensor_shape(place->get_names().at(0));
 }
 
-void InputModel::set_element_type(ov::frontend::Place::Ptr place, const ngraph::element::Type& type) {
+void InputModel::set_element_type(const ov::frontend::Place::Ptr& place, const ngraph::element::Type& type) {
     std::map<std::string, ngraph::element::Type_t> m;
     m[place->get_names()[0]] = type;
     m_editor->set_input_types(m);
diff --git a/src/frontends/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
index f69ef4f4a52..53a2d81ac24 100644
--- a/src/frontends/onnx/frontend/src/input_model.hpp
+++ b/src/frontends/onnx/frontend/src/input_model.hpp
@@ -39,21 +39,21 @@ public:
     ov::frontend::Place::Ptr get_place_by_operation_name_and_output_port(const std::string& operation_name,
                                                                          int output_port_index) override;
 
-    void set_name_for_tensor(ov::frontend::Place::Ptr tensor, const std::string& new_name) override;
-    void set_name_for_operation(ov::frontend::Place::Ptr operation, const std::string& new_name) override;
+    void set_name_for_tensor(const ov::frontend::Place::Ptr& tensor, const std::string& new_name) override;
+    void set_name_for_operation(const ov::frontend::Place::Ptr& operation, const std::string& new_name) override;
     void free_name_for_operation(const std::string& name) override;
-    void set_name_for_dimension(ov::frontend::Place::Ptr place,
+    void set_name_for_dimension(const ov::frontend::Place::Ptr& place,
                                 size_t shape_dim_index,
                                 const std::string& dim_name) override;
 
     /// \brief  Not applicable for ONNX model. Throws immediately
-    void add_name_for_tensor(ov::frontend::Place::Ptr tensor, const std::string& new_name) override;
+    void add_name_for_tensor(const ov::frontend::Place::Ptr& tensor, const std::string& new_name) override;
     /// \brief  Not applicable for ONNX model. Throws immediately
     void free_name_for_tensor(const std::string& name) override;
 
-    void set_partial_shape(ov::frontend::Place::Ptr place, const ngraph::PartialShape& shape) override;
-    ngraph::PartialShape get_partial_shape(ov::frontend::Place::Ptr place) const override;
-    void set_element_type(ov::frontend::Place::Ptr place, const ngraph::element::Type& type) override;
+    void set_partial_shape(const ov::frontend::Place::Ptr& place, const ngraph::PartialShape& shape) override;
+    ngraph::PartialShape get_partial_shape(const ov::frontend::Place::Ptr& place) const override;
+    void set_element_type(const ov::frontend::Place::Ptr& place, const ngraph::element::Type& type) override;
 
     std::shared_ptr<Model> decode();
     std::shared_ptr<Model> convert();
diff --git a/src/frontends/onnx/frontend/src/place.cpp b/src/frontends/onnx/frontend/src/place.cpp
index 6e73fd450ac..fd4c7a97601 100644
--- a/src/frontends/onnx/frontend/src/place.cpp
+++ b/src/frontends/onnx/frontend/src/place.cpp
@@ -29,7 +29,7 @@ bool PlaceInputEdge::is_output() const {
     return false;
 }
 
-bool PlaceInputEdge::is_equal(Place::Ptr another) const {
+bool PlaceInputEdge::is_equal(const Place::Ptr& another) const {
     if (const auto in_edge = std::dynamic_pointer_cast<PlaceInputEdge>(another)) {
         const auto& editor_edge = in_edge->get_input_edge();
         return (editor_edge.m_node_idx == m_edge.m_node_idx) && (editor_edge.m_port_idx == m_edge.m_port_idx);
@@ -37,7 +37,7 @@ bool PlaceInputEdge::is_equal(Place::Ptr another) const {
     return false;
 }
 
-bool PlaceInputEdge::is_equal_data(Place::Ptr another) const {
+bool PlaceInputEdge::is_equal_data(const Place::Ptr& another) const {
     return get_source_tensor()->is_equal_data(another);
 }
 
@@ -78,7 +78,7 @@ bool PlaceOutputEdge::is_output() const {
     return m_editor->is_output(m_edge);
 }
 
-bool PlaceOutputEdge::is_equal(Place::Ptr another) const {
+bool PlaceOutputEdge::is_equal(const Place::Ptr& another) const {
     if (const auto out_edge = std::dynamic_pointer_cast<PlaceOutputEdge>(another)) {
         const auto& editor_edge = out_edge->get_output_edge();
         return (editor_edge.m_node_idx == m_edge.m_node_idx) && (editor_edge.m_port_idx == m_edge.m_port_idx);
@@ -86,7 +86,7 @@ bool PlaceOutputEdge::is_equal(Place::Ptr another) const {
     return false;
 }
 
-bool PlaceOutputEdge::is_equal_data(Place::Ptr another) const {
+bool PlaceOutputEdge::is_equal_data(const Place::Ptr& another) const {
     return get_target_tensor()->is_equal_data(another);
 }
 
@@ -147,14 +147,14 @@ bool PlaceTensor::is_output() const {
     return std::find(std::begin(outputs), std::end(outputs), m_name) != std::end(outputs);
 }
 
-bool PlaceTensor::is_equal(Place::Ptr another) const {
+bool PlaceTensor::is_equal(const Place::Ptr& another) const {
     if (const auto tensor = std::dynamic_pointer_cast<PlaceTensor>(another)) {
         return m_name == tensor->get_names().at(0);
     }
     return false;
 }
 
-bool PlaceTensor::is_equal_data(Place::Ptr another) const {
+bool PlaceTensor::is_equal_data(const Place::Ptr& another) const {
     const auto consuming_ports = get_consuming_ports();
     const auto eq_to_consuming_port = [&consuming_ports](const Ptr& another) {
         return std::any_of(consuming_ports.begin(), consuming_ports.end(), [&another](const Ptr& place) {
@@ -319,7 +319,7 @@ ov::frontend::Place::Ptr PlaceOp::get_producing_operation(const std::string& inp
     return nullptr;
 }
 
-bool PlaceOp::is_equal(Place::Ptr another) const {
+bool PlaceOp::is_equal(const Place::Ptr& another) const {
     if (const auto place_op = std::dynamic_pointer_cast<PlaceOp>(another)) {
         const auto& another_node = place_op->get_editor_node();
         if (m_editor->is_correct_and_unambiguous_node(m_node) ||
diff --git a/src/frontends/onnx/frontend/src/place.hpp b/src/frontends/onnx/frontend/src/place.hpp
index 02bd737e16f..2b323d6c1a9 100644
--- a/src/frontends/onnx/frontend/src/place.hpp
+++ b/src/frontends/onnx/frontend/src/place.hpp
@@ -24,8 +24,8 @@ public:
     // external usage
     bool is_input() const override;
     bool is_output() const override;
-    bool is_equal(Place::Ptr another) const override;
-    bool is_equal_data(Place::Ptr another) const override;
+    bool is_equal(const Place::Ptr& another) const override;
+    bool is_equal_data(const Place::Ptr& another) const override;
     Place::Ptr get_source_tensor() const override;
     std::vector<Place::Ptr> get_consuming_operations() const override;
     Place::Ptr get_producing_operation() const override;
@@ -47,8 +47,8 @@ public:
     // external usage
     bool is_input() const override;
     bool is_output() const override;
-    bool is_equal(Place::Ptr another) const override;
-    bool is_equal_data(Place::Ptr another) const override;
+    bool is_equal(const Place::Ptr& another) const override;
+    bool is_equal_data(const Place::Ptr& another) const override;
     Place::Ptr get_target_tensor() const override;
     std::vector<Place::Ptr> get_consuming_ports() const override;
     Place::Ptr get_producing_operation() const override;
@@ -71,8 +71,8 @@ public:
     Place::Ptr get_producing_operation() const override;
     bool is_input() const override;
     bool is_output() const override;
-    bool is_equal(Place::Ptr another) const override;
-    bool is_equal_data(Place::Ptr another) const override;
+    bool is_equal(const Place::Ptr& another) const override;
+    bool is_equal_data(const Place::Ptr& another) const override;
     std::vector<Place::Ptr> get_consuming_operations() const override;
 
     void set_name(const std::string& new_name);
@@ -119,7 +119,7 @@ public:
     Place::Ptr get_source_tensor(int input_port_index) const override;
     Place::Ptr get_source_tensor(const std::string& input_name) const override;
 
-    bool is_equal(Place::Ptr another) const override;
+    bool is_equal(const Place::Ptr& another) const override;
     bool is_input() const override;
     bool is_output() const override;
 
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
index 0cabd02cd2b..a88f13c779e 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
@@ -21,11 +21,11 @@ public:
     /// \brief Completely convert the remaining, not converted part of a function.
     /// \param partiallyConverted partially converted OV Model
     /// \return fully converted OV Model
-    std::shared_ptr<ov::Model> convert(InputModel::Ptr model) const override;
+    std::shared_ptr<ov::Model> convert(const InputModel::Ptr& model) const override;
 
     /// \brief Completely convert the remaining, not converted part of a function.
     /// \param partiallyConverted partially converted OV Model
-    void convert(std::shared_ptr<Model> partiallyConverted) const override;
+    void convert(const std::shared_ptr<Model>& partiallyConverted) const override;
 
     /// \brief Convert only those parts of the model that can be converted leaving others
     /// as-is. Converted parts are not normalized by additional transformations; normalize
@@ -33,14 +33,14 @@ public:
     /// conversion process.
     /// \param model Input model
     /// \return partially converted OV Model
-    std::shared_ptr<Model> convert_partially(InputModel::Ptr model) const override;
+    std::shared_ptr<Model> convert_partially(const InputModel::Ptr& model) const override;
 
     /// \brief Convert operations with one-to-one mapping with decoding nodes.
     /// Each decoding node is an OV node representing a single FW operation node with
     /// all attributes represented in FW-independent way.
     /// \param model Input model
     /// \return OV Model after decoding
-    std::shared_ptr<Model> decode(InputModel::Ptr model) const override;
+    std::shared_ptr<Model> decode(const InputModel::Ptr& model) const override;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
     /// if frontend is selected automatically by FrontEndManager::load_by_model
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
index ba8659e04b4..3fa9dbe7a99 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
@@ -35,10 +35,10 @@ public:
     void override_all_outputs(const std::vector<Place::Ptr>& outputs) override;
     void override_all_inputs(const std::vector<Place::Ptr>& inputs) override;
     void extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) override;
-    void set_partial_shape(Place::Ptr place, const ov::PartialShape&) override;
-    ov::PartialShape get_partial_shape(Place::Ptr place) const override;
-    void set_element_type(Place::Ptr place, const ov::element::Type&) override;
-    void set_tensor_value(Place::Ptr place, const void* value) override;
+    void set_partial_shape(const Place::Ptr& place, const ov::PartialShape&) override;
+    ov::PartialShape get_partial_shape(const Place::Ptr& place) const override;
+    void set_element_type(const Place::Ptr& place, const ov::element::Type&) override;
+    void set_tensor_value(const Place::Ptr& place, const void* value) override;
 };
 
 }  // namespace frontend
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
index d99a9db6f4d..4fdd245d005 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
@@ -35,7 +35,7 @@ public:
 
     bool is_input() const override;
     bool is_output() const override;
-    bool is_equal(Ptr another) const override {
+    bool is_equal(const Ptr& another) const override {
         return this == another.get();
     }
 
@@ -67,7 +67,7 @@ public:
     Place::Ptr get_source_tensor() const override;
     Ptr get_producing_port() const override;
 
-    bool is_equal_data(Ptr another) const override;
+    bool is_equal_data(const Ptr& another) const override;
 
 private:
     std::weak_ptr<TensorPlacePDPD> m_source_tensor;
@@ -90,7 +90,7 @@ public:
     Place::Ptr get_producing_operation() const override;
     std::vector<Place::Ptr> get_consuming_ports() const override;
     Ptr get_target_tensor() const override;
-    bool is_equal_data(Ptr another) const override;
+    bool is_equal_data(const Ptr& another) const override;
 
 private:
     std::weak_ptr<OpPlacePDPD> m_op;
@@ -185,7 +185,7 @@ public:
     std::vector<Place::Ptr> get_consuming_operations() const override;
     std::vector<Place::Ptr> get_consuming_ports() const override;
     Ptr get_producing_port() const override;
-    bool is_equal_data(Ptr another) const override;
+    bool is_equal_data(const Ptr& another) const override;
 
 private:
     const paddle::framework::proto::VarDesc& m_var_desc;
diff --git a/src/frontends/paddlepaddle/src/frontend.cpp b/src/frontends/paddlepaddle/src/frontend.cpp
index a552dcaa06a..e9a27c643f1 100644
--- a/src/frontends/paddlepaddle/src/frontend.cpp
+++ b/src/frontends/paddlepaddle/src/frontend.cpp
@@ -276,7 +276,7 @@ InputModel::Ptr FrontEndPDPD::load_impl(const std::vector<ov::Any>& variants) co
     PDPD_THROW("Model can be loaded either from 1 or 2 files/streams");
 }
 
-std::shared_ptr<ov::Model> FrontEndPDPD::convert(InputModel::Ptr model) const {
+std::shared_ptr<ov::Model> FrontEndPDPD::convert(const InputModel::Ptr& model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(
@@ -287,7 +287,7 @@ std::shared_ptr<ov::Model> FrontEndPDPD::convert(InputModel::Ptr model) const {
     return f;
 }
 
-void FrontEndPDPD::convert(std::shared_ptr<ov::Model> partiallyConverted) const {
+void FrontEndPDPD::convert(const std::shared_ptr<ov::Model>& partiallyConverted) const {
     for (const auto& node : partiallyConverted->get_ordered_ops()) {
         if (ov::is_type<PDPDFrameworkNode>(node)) {
             pdpd::normalize_framework_node(std::dynamic_pointer_cast<PDPDFrameworkNode>(node),
@@ -299,7 +299,7 @@ void FrontEndPDPD::convert(std::shared_ptr<ov::Model> partiallyConverted) const
     }
 }
 
-std::shared_ptr<ov::Model> FrontEndPDPD::convert_partially(InputModel::Ptr model) const {
+std::shared_ptr<ov::Model> FrontEndPDPD::convert_partially(const InputModel::Ptr& model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(
@@ -316,7 +316,7 @@ std::shared_ptr<ov::Model> FrontEndPDPD::convert_partially(InputModel::Ptr model
     return f;
 }
 
-std::shared_ptr<ov::Model> FrontEndPDPD::decode(InputModel::Ptr model) const {
+std::shared_ptr<ov::Model> FrontEndPDPD::decode(const InputModel::Ptr& model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(pdpd_model, pdpd::make_framework_node);
diff --git a/src/frontends/paddlepaddle/src/model.cpp b/src/frontends/paddlepaddle/src/model.cpp
index 48ccef9643e..b499aedd719 100644
--- a/src/frontends/paddlepaddle/src/model.cpp
+++ b/src/frontends/paddlepaddle/src/model.cpp
@@ -472,19 +472,19 @@ void InputModelPDPD::extract_subgraph(const std::vector<Place::Ptr>& inputs, con
     _impl->extractSubgraph(inputs, outputs);
 }
 
-void InputModelPDPD::set_partial_shape(Place::Ptr place, const ov::PartialShape& p_shape) {
+void InputModelPDPD::set_partial_shape(const Place::Ptr& place, const ov::PartialShape& p_shape) {
     _impl->setPartialShape(place, p_shape);
 }
 
-ov::PartialShape InputModelPDPD::get_partial_shape(Place::Ptr place) const {
+ov::PartialShape InputModelPDPD::get_partial_shape(const Place::Ptr& place) const {
     return _impl->getPartialShape(place);
 }
 
-void InputModelPDPD::set_element_type(Place::Ptr place, const ov::element::Type& type) {
+void InputModelPDPD::set_element_type(const Place::Ptr& place, const ov::element::Type& type) {
     _impl->setElementType(place, type);
 }
 
-void InputModelPDPD::set_tensor_value(Place::Ptr place, const void* value) {
+void InputModelPDPD::set_tensor_value(const Place::Ptr& place, const void* value) {
     _impl->setTensorValue(place, value);
 }
 
diff --git a/src/frontends/paddlepaddle/src/place.cpp b/src/frontends/paddlepaddle/src/place.cpp
index cfc4ecf2585..59a3e571ee2 100644
--- a/src/frontends/paddlepaddle/src/place.cpp
+++ b/src/frontends/paddlepaddle/src/place.cpp
@@ -256,7 +256,7 @@ std::vector<Place::Ptr> TensorPlacePDPD::get_consuming_operations() const {
     return consuming_ops;
 }
 
-bool TensorPlacePDPD::is_equal_data(Place::Ptr another) const {
+bool TensorPlacePDPD::is_equal_data(const Place::Ptr& another) const {
     auto consuming_ports = get_consuming_ports();
     bool eq_to_consuming_port =
         std::any_of(consuming_ports.begin(), consuming_ports.end(), [&another](const Ptr& place) {
@@ -305,7 +305,7 @@ Place::Ptr InPortPlacePDPD::get_producing_port() const {
     return get_source_tensor()->get_producing_port();
 }
 
-bool InPortPlacePDPD::is_equal_data(Place::Ptr another) const {
+bool InPortPlacePDPD::is_equal_data(const Place::Ptr& another) const {
     return get_source_tensor()->is_equal_data(another);
 }
 
@@ -338,7 +338,7 @@ std::vector<Place::Ptr> OutPortPlacePDPD::get_consuming_ports() const {
     FRONT_END_THROW("Tensor has expired.");
 }
 
-bool OutPortPlacePDPD::is_equal_data(Place::Ptr another) const {
+bool OutPortPlacePDPD::is_equal_data(const Place::Ptr& another) const {
     return get_target_tensor()->is_equal_data(another);
 }
 
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp b/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
index 8c53ecb4a9d..59b91d0f9d1 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
+++ b/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
@@ -37,11 +37,11 @@ public:
 
     /// \brief Completely convert the model
     /// \return fully converted ov Model
-    std::shared_ptr<Model> convert(ov::frontend::InputModel::Ptr model) const override;
+    std::shared_ptr<Model> convert(const ov::frontend::InputModel::Ptr& model) const override;
 
     /// \brief Completely convert the remaining, not converted part of a function.
     /// \param partiallyConverted partially converted ov Model
-    void convert(std::shared_ptr<Model> partiallyConverted) const override;
+    void convert(const std::shared_ptr<Model>& partiallyConverted) const override;
 
     /// \brief Convert only those parts of the model that can be converted leaving others
     /// as-is. Converted parts are not normalized by additional transformations; normalize
@@ -49,18 +49,18 @@ public:
     /// conversion process.
     /// \param model Input model
     /// \return partially converted ov Model
-    std::shared_ptr<Model> convert_partially(ov::frontend::InputModel::Ptr model) const override;
+    std::shared_ptr<Model> convert_partially(const ov::frontend::InputModel::Ptr& model) const override;
 
     /// \brief Convert operations with one-to-one mapping with decoding nodes.
     /// Each decoding node is an ov node representing a single FW operation node with
     /// all attributes represented in FW-independent way.
     /// \param model Input model
     /// \return ov Model after decoding
-    std::shared_ptr<Model> decode(ov::frontend::InputModel::Ptr model) const override;
+    std::shared_ptr<Model> decode(const ov::frontend::InputModel::Ptr& model) const override;
 
     /// \brief Runs normalization passes on function that was loaded with partial conversion
     /// \param Model partially converted ov Model
-    void normalize(std::shared_ptr<ov::Model> function) const override;
+    void normalize(const std::shared_ptr<ov::Model>& function) const override;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
     std::string get_name() const override {
diff --git a/src/frontends/tensorflow/src/frontend.cpp b/src/frontends/tensorflow/src/frontend.cpp
index 8a158c27ae2..d9bbf954c02 100644
--- a/src/frontends/tensorflow/src/frontend.cpp
+++ b/src/frontends/tensorflow/src/frontend.cpp
@@ -306,7 +306,7 @@ ov::frontend::InputModel::Ptr FrontEndTF::load_impl(const std::vector<ov::Any>&
     return nullptr;
 }
 
-std::shared_ptr<ov::Model> FrontEndTF::convert(ov::frontend::InputModel::Ptr model) const {
+std::shared_ptr<ov::Model> FrontEndTF::convert(const ov::frontend::InputModel::Ptr& model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
     std::shared_ptr<ov::Model> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", true, false, f);
@@ -316,7 +316,7 @@ std::shared_ptr<ov::Model> FrontEndTF::convert(ov::frontend::InputModel::Ptr mod
     return f;
 }
 
-std::shared_ptr<ov::Model> FrontEndTF::convert_partially(ov::frontend::InputModel::Ptr model) const {
+std::shared_ptr<ov::Model> FrontEndTF::convert_partially(const ov::frontend::InputModel::Ptr& model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
     std::shared_ptr<ov::Model> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", false, false, f);
@@ -324,14 +324,14 @@ std::shared_ptr<ov::Model> FrontEndTF::convert_partially(ov::frontend::InputMode
     return f;
 }
 
-std::shared_ptr<ov::Model> FrontEndTF::decode(ov::frontend::InputModel::Ptr model) const {
+std::shared_ptr<ov::Model> FrontEndTF::decode(const ov::frontend::InputModel::Ptr& model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
     std::shared_ptr<ov::Model> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", false, true, f);
     return f;
 }
 
-void FrontEndTF::convert(std::shared_ptr<ov::Model> partiallyConverted) const {
+void FrontEndTF::convert(const std::shared_ptr<ov::Model>& partiallyConverted) const {
     for (const auto& node : partiallyConverted->get_ordered_ops()) {
         if (ov::is_type<TFFrameworkNode>(node)) {
             translate_framework_node(std::dynamic_pointer_cast<TFFrameworkNode>(node), m_op_translators);
@@ -344,7 +344,7 @@ void FrontEndTF::convert(std::shared_ptr<ov::Model> partiallyConverted) const {
     normalize(partiallyConverted);
 }
 
-void FrontEndTF::normalize(std::shared_ptr<ov::Model> function) const {
+void FrontEndTF::normalize(const std::shared_ptr<ov::Model>& function) const {
     ov::pass::Manager manager;
     manager.register_pass<ov::frontend::tf::pass::TransposeSinkingOVTF>();
     manager.run_passes(function);
diff --git a/src/frontends/tensorflow/src/model.cpp b/src/frontends/tensorflow/src/model.cpp
index 3fc273d38cd..4c502eef5e6 100644
--- a/src/frontends/tensorflow/src/model.cpp
+++ b/src/frontends/tensorflow/src/model.cpp
@@ -392,19 +392,19 @@ void InputModelTF::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>&
     _impl->extractSubgraph(inputs, outputs);
 }
 
-void InputModelTF::set_partial_shape(ov::frontend::Place::Ptr place, const ov::PartialShape& p_shape) {
+void InputModelTF::set_partial_shape(const ov::frontend::Place::Ptr& place, const ov::PartialShape& p_shape) {
     _impl->setPartialShape(place, p_shape);
 }
 
-ov::PartialShape InputModelTF::get_partial_shape(ov::frontend::Place::Ptr place) const {
+ov::PartialShape InputModelTF::get_partial_shape(const ov::frontend::Place::Ptr& place) const {
     return _impl->getPartialShape(place);
 }
 
-void InputModelTF::set_element_type(ov::frontend::Place::Ptr place, const ov::element::Type& type) {
+void InputModelTF::set_element_type(const ov::frontend::Place::Ptr& place, const ov::element::Type& type) {
     _impl->setElementType(place, type);
 }
 
-void InputModelTF::set_tensor_value(ov::frontend::Place::Ptr place, const void* value) {
+void InputModelTF::set_tensor_value(const ov::frontend::Place::Ptr& place, const void* value) {
     _impl->setTensorValue(place, value);
 }
 }  // namespace frontend
diff --git a/src/frontends/tensorflow/src/model.hpp b/src/frontends/tensorflow/src/model.hpp
index 6c88056131e..631eb76f116 100644
--- a/src/frontends/tensorflow/src/model.hpp
+++ b/src/frontends/tensorflow/src/model.hpp
@@ -35,10 +35,10 @@ public:
     void override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) override;
     void extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
                           const std::vector<ov::frontend::Place::Ptr>& outputs) override;
-    void set_partial_shape(ov::frontend::Place::Ptr place, const ov::PartialShape&) override;
-    ov::PartialShape get_partial_shape(ov::frontend::Place::Ptr place) const override;
-    void set_element_type(ov::frontend::Place::Ptr place, const ov::element::Type&) override;
-    void set_tensor_value(ov::frontend::Place::Ptr place, const void* value) override;
+    void set_partial_shape(const ov::frontend::Place::Ptr& place, const ov::PartialShape&) override;
+    ov::PartialShape get_partial_shape(const ov::frontend::Place::Ptr& place) const override;
+    void set_element_type(const ov::frontend::Place::Ptr& place, const ov::element::Type&) override;
+    void set_tensor_value(const ov::frontend::Place::Ptr& place, const void* value) override;
 };
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/place.cpp b/src/frontends/tensorflow/src/place.cpp
index bdddede95f7..5a933f14320 100644
--- a/src/frontends/tensorflow/src/place.cpp
+++ b/src/frontends/tensorflow/src/place.cpp
@@ -204,7 +204,7 @@ std::vector<ov::frontend::Place::Ptr> TensorPlaceTF::get_consuming_operations()
     return consuming_ops;
 }
 
-bool TensorPlaceTF::is_equal_data(ov::frontend::Place::Ptr another) const {
+bool TensorPlaceTF::is_equal_data(const ov::frontend::Place::Ptr& another) const {
     auto consuming_ports = get_consuming_ports();
     bool eq_to_consuming_port =
         std::any_of(consuming_ports.begin(), consuming_ports.end(), [&another](const Ptr& place) {
@@ -253,7 +253,7 @@ ov::frontend::Place::Ptr InPortPlaceTF::get_producing_port() const {
     return get_source_tensor()->get_producing_port();
 }
 
-bool InPortPlaceTF::is_equal_data(ov::frontend::Place::Ptr another) const {
+bool InPortPlaceTF::is_equal_data(const ov::frontend::Place::Ptr& another) const {
     return get_source_tensor()->is_equal_data(another);
 }
 
@@ -286,7 +286,7 @@ std::vector<ov::frontend::Place::Ptr> OutPortPlaceTF::get_consuming_ports() cons
     FRONT_END_THROW("Tensor has expired.");
 }
 
-bool OutPortPlaceTF::is_equal_data(ov::frontend::Place::Ptr another) const {
+bool OutPortPlaceTF::is_equal_data(const ov::frontend::Place::Ptr& another) const {
     return get_target_tensor()->is_equal_data(another);
 }
 
diff --git a/src/frontends/tensorflow/src/place.hpp b/src/frontends/tensorflow/src/place.hpp
index f6197ee3a90..0366342b9d8 100644
--- a/src/frontends/tensorflow/src/place.hpp
+++ b/src/frontends/tensorflow/src/place.hpp
@@ -25,7 +25,7 @@ public:
 
     bool is_input() const override;
     bool is_output() const override;
-    bool is_equal(Ptr another) const override {
+    bool is_equal(const Ptr& another) const override {
         return this == another.get();
     }
 
@@ -57,7 +57,7 @@ public:
     ov::frontend::Place::Ptr get_source_tensor() const override;
     Ptr get_producing_port() const override;
 
-    bool is_equal_data(Ptr another) const override;
+    bool is_equal_data(const Ptr& another) const override;
 
 private:
     std::weak_ptr<TensorPlaceTF> m_source_tensor;
@@ -80,7 +80,7 @@ public:
     ov::frontend::Place::Ptr get_producing_operation() const override;
     std::vector<ov::frontend::Place::Ptr> get_consuming_ports() const override;
     Ptr get_target_tensor() const override;
-    bool is_equal_data(Ptr another) const override;
+    bool is_equal_data(const Ptr& another) const override;
 
 private:
     std::weak_ptr<OpPlaceTF> m_op;
@@ -162,7 +162,7 @@ public:
     std::vector<ov::frontend::Place::Ptr> get_consuming_operations() const override;
     std::vector<ov::frontend::Place::Ptr> get_consuming_ports() const override;
     Ptr get_producing_port() const override;
-    bool is_equal_data(Ptr another) const override;
+    bool is_equal_data(const Ptr& another) const override;
 
 private:
     PartialShape m_pshape;
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
index 2a0c6dddab4..a705e57623e 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
+++ b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
@@ -188,7 +188,7 @@ public:
         return m_name.find("output") != std::string::npos;
     }
 
-    bool is_equal(Ptr another) const override
+    bool is_equal(const Ptr& another) const override
     {
         m_stat.m_is_equal++;
         m_stat.m_lastArgPlace = another;
@@ -197,7 +197,7 @@ public:
                m_portIndex == mock->m_portIndex;
     }
 
-    bool is_equal_data(Ptr another) const override
+    bool is_equal_data(const Ptr& another) const override
     {
         if (m_is_op)
             throw "Not implemented";
@@ -358,21 +358,21 @@ public:
     }
 
     // Setting tensor properties
-    void set_partial_shape(Place::Ptr place, const ngraph::PartialShape& shape) override
+    void set_partial_shape(const Place::Ptr& place, const ngraph::PartialShape& shape) override
     {
         m_stat.m_set_partial_shape++;
         m_stat.m_lastArgPlace = place;
         m_stat.m_lastArgPartialShape = shape;
     }
 
-    ngraph::PartialShape get_partial_shape(Place::Ptr place) const override
+    ngraph::PartialShape get_partial_shape(const Place::Ptr& place) const override
     {
         m_stat.m_get_partial_shape++;
         m_stat.m_lastArgPlace = place;
         return m_returnShape;
     }
 
-    void set_element_type(Place::Ptr place, const ngraph::element::Type& type) override
+    void set_element_type(const Place::Ptr& place, const ngraph::element::Type& type) override
     {
         m_stat.m_set_element_type++;
         m_stat.m_lastArgPlace = place;
@@ -414,7 +414,7 @@ class MOCK_API FrontEndMockPy : public FrontEnd
 public:
     FrontEndMockPy() {}
 
-    std::shared_ptr<ov::Model> convert(InputModel::Ptr model) const override
+    std::shared_ptr<ov::Model> convert(const InputModel::Ptr& model) const override
     {
         m_stat.m_convert_model++;
         return std::make_shared<ov::Model>(ov::NodeVector{}, ov::ParameterVector{});

From e02e777df3ef385f1c9419390a08c759b324d689 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 18 Dec 2021 02:03:09 +0300
Subject: [PATCH 028/160] Fixed codeowners (#9283)

---
 CODEOWNERS | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 34388f4086c..76c0ccf5c8a 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -66,7 +66,7 @@ Jenkinsfile  @openvinotoolkit/openvino-admins
 /src/inference/include/ie/gna/  @openvinotoolkit/openvino-ie-gna-maintainers
 
 # IE MULTI:
-/inference-engine/src/multi_device/  @openvinotoolkit/openvino-ie-multi-maintainers
+/src/plugins/auto/  @openvinotoolkit/openvino-ie-multi-maintainers
 /src/inference/include/ie/multi-device/  @openvinotoolkit/openvino-ie-multi-maintainers
 
 # IE Tests:

From be86ec51ab6965a9a7309f326dba614861f64fcb Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 18 Dec 2021 13:25:42 +0300
Subject: [PATCH 029/160] Renamed frontends from *ov_frontend to ov_*_frontend
 (#9275)

* Renamed frontends from *ov_frontend to ov_*_frontend

* Fixed MO

* Fixed include
---
 .../frontends/frontends.cmake                 |  7 +++--
 cmake/developer_package/plugins/plugins.cmake | 16 +++++-----
 cmake/templates/ngraphConfig.cmake.in         | 30 +++++++++----------
 ...Deep_Learning_Inference_Engine_DevGuide.md | 12 ++++----
 .../IE_DG/Extensibility_DG/Custom_ONNX_Ops.md |  2 +-
 docs/IE_DG/inference_engine_intro.md          | 12 ++++----
 docs/snippets/CMakeLists.txt                  |  2 +-
 src/CMakeLists.txt                            | 10 +++----
 src/bindings/python/CMakeLists.txt            |  6 ++--
 .../CMakeLists.txt                            |  2 +-
 .../mock_py_frontend.cpp                      |  0
 .../mock_py_frontend.hpp                      |  4 +--
 .../mock/pyngraph_fe_mock_api/CMakeLists.txt  |  2 +-
 .../pyngraph_mock_frontend_api.cpp            |  2 +-
 src/core/tests/CMakeLists.txt                 |  6 ++--
 src/core/tests/frontend/CMakeLists.txt        |  2 +-
 src/core/tests/frontend/mock_frontend.cpp     |  4 +--
 src/core/tests/frontend/onnx/CMakeLists.txt   |  2 +-
 .../frontend/paddlepaddle/CMakeLists.txt      |  4 +--
 .../tests/frontend/tensorflow/CMakeLists.txt  |  6 ++--
 src/frontends/common/CMakeLists.txt           |  2 +-
 src/frontends/common/src/manager.cpp          |  2 +-
 src/frontends/common/src/plugin_loader.hpp    |  2 +-
 .../openvino/frontend/ir/visibility.hpp       |  4 +--
 .../onnx_import/onnx_importer_visibility.hpp  |  4 +--
 .../openvino/frontend/onnx/frontend.hpp       |  4 +--
 .../include/paddlepaddle_frontend/utility.hpp |  4 +--
 .../include/tensorflow_frontend/utility.hpp   |  4 +--
 .../inference_engine/CMakeLists.txt           |  6 ++--
 .../subgraphs_dumper/CMakeLists.txt           |  4 +--
 .../functional/plugin/shared/CMakeLists.txt   |  2 +-
 .../unit/frontends/onnx_import/CMakeLists.txt |  2 +-
 tools/deployment_manager/configs/darwin.json  |  6 ++--
 tools/deployment_manager/configs/linux.json   |  6 ++--
 tools/deployment_manager/configs/windows.json |  6 ++--
 tools/mo/CMakeLists.txt                       |  6 ++--
 .../mo/frontend_ngraph_test_actual.py         | 24 +++++++--------
 .../moc_frontend/moc_extractor_test_actual.py |  2 +-
 .../mock_mo_python_api/CMakeLists.txt         |  2 +-
 .../mock_mo_python_api/mock_mo_python_api.cpp |  2 +-
 .../CMakeLists.txt                            |  2 +-
 .../mock_mo_frontend.cpp                      |  2 +-
 .../mock_mo_frontend.hpp                      |  6 ++--
 43 files changed, 118 insertions(+), 117 deletions(-)
 rename src/bindings/python/tests/mock/{mock_py_ov_frontend => ov_mock_py_frontend}/CMakeLists.txt (95%)
 rename src/bindings/python/tests/mock/{mock_py_ov_frontend => ov_mock_py_frontend}/mock_py_frontend.cpp (100%)
 rename src/bindings/python/tests/mock/{mock_py_ov_frontend => ov_mock_py_frontend}/mock_py_frontend.hpp (99%)
 rename tools/mo/unit_tests/mock_mo_frontend/{mock_mo_ov_frontend => ov_mock_mo_frontend}/CMakeLists.txt (95%)
 rename tools/mo/unit_tests/mock_mo_frontend/{mock_mo_ov_frontend => ov_mock_mo_frontend}/mock_mo_frontend.cpp (95%)
 rename tools/mo/unit_tests/mock_mo_frontend/{mock_mo_ov_frontend => ov_mock_mo_frontend}/mock_mo_frontend.hpp (99%)

diff --git a/cmake/developer_package/frontends/frontends.cmake b/cmake/developer_package/frontends/frontends.cmake
index b5b07559bea..2474859dc77 100644
--- a/cmake/developer_package/frontends/frontends.cmake
+++ b/cmake/developer_package/frontends/frontends.cmake
@@ -3,7 +3,8 @@
 #
 
 set(FRONTEND_INSTALL_INCLUDE "runtime/include/")
-set(FRONTEND_NAME_SUFFIX "_ov_frontend")
+set(FRONTEND_NAME_PREFIX "ov_")
+set(FRONTEND_NAME_SUFFIX "_frontend")
 
 set(FRONTEND_NAMES "" CACHE INTERNAL "")
 
@@ -20,7 +21,7 @@ function(ov_target_link_frontends TARGET_NAME)
     endif()
 
     foreach(name IN LISTS FRONTEND_NAMES)
-        set(frontend_target_name "${name}${FRONTEND_NAME_SUFFIX}")
+        set(frontend_target_name "${FRONTEND_NAME_PREFIX}${name}${FRONTEND_NAME_SUFFIX}")
         target_link_libraries(${TARGET_NAME} PRIVATE ${frontend_target_name})
     endforeach()
 endfunction()
@@ -99,7 +100,7 @@ macro(ov_add_frontend)
         endif()
     endforeach()
 
-    set(TARGET_NAME "${OV_FRONTEND_NAME}${FRONTEND_NAME_SUFFIX}")
+    set(TARGET_NAME "${FRONTEND_NAME_PREFIX}${OV_FRONTEND_NAME}${FRONTEND_NAME_SUFFIX}")
 
     list(APPEND FRONTEND_NAMES ${OV_FRONTEND_NAME})
     set(FRONTEND_NAMES "${FRONTEND_NAMES}" CACHE INTERNAL "" FORCE)
diff --git a/cmake/developer_package/plugins/plugins.cmake b/cmake/developer_package/plugins/plugins.cmake
index caef045711f..9f915610332 100644
--- a/cmake/developer_package/plugins/plugins.cmake
+++ b/cmake/developer_package/plugins/plugins.cmake
@@ -117,20 +117,20 @@ function(ie_add_plugin)
         # fake dependencies to build in the following order:
         # IE -> IE readers -> IE inference plugins -> IE-based apps
         if(BUILD_SHARED_LIBS)
-            if(TARGET ir_ov_frontend)
-                add_dependencies(${IE_PLUGIN_NAME} ir_ov_frontend)
+            if(TARGET ov_ir_frontend)
+                add_dependencies(${IE_PLUGIN_NAME} ov_ir_frontend)
             endif()
             if(TARGET inference_engine_ir_v7_reader)
                 add_dependencies(${IE_PLUGIN_NAME} inference_engine_ir_v7_reader)
             endif()
-            if(TARGET onnx_ov_frontend)
-                add_dependencies(${IE_PLUGIN_NAME} onnx_ov_frontend)
+            if(TARGET ov_onnx_frontend)
+                add_dependencies(${IE_PLUGIN_NAME} ov_onnx_frontend)
             endif()
-            if(TARGET paddlepaddle_ov_frontend)
-                add_dependencies(${IE_PLUGIN_NAME} paddlepaddle_ov_frontend)
+            if(TARGET ov_paddlepaddle_frontend)
+                add_dependencies(${IE_PLUGIN_NAME} ov_paddlepaddle_frontend)
             endif()
-            if(TARGET tensorflow_ov_frontend)
-                add_dependencies(${IE_PLUGIN_NAME} tensorflow_ov_frontend)
+            if(TARGET ov_tensorflow_frontend)
+                add_dependencies(${IE_PLUGIN_NAME} ov_tensorflow_frontend)
             endif()
         endif()
 
diff --git a/cmake/templates/ngraphConfig.cmake.in b/cmake/templates/ngraphConfig.cmake.in
index e663df45bb0..f243dca140d 100644
--- a/cmake/templates/ngraphConfig.cmake.in
+++ b/cmake/templates/ngraphConfig.cmake.in
@@ -28,11 +28,11 @@
 #
 #   ngraph::common                         - nGraph frontend common
 #
-#   ngraph_onnx_ov_frontend_FOUND          - True if the system has onnx_ov_frontend library
-#   ngraph::onnx_ov_frontend               - ONNX FrontEnd target (optional)
+#   ngraph_ov_onnx_frontend_FOUND          - True if the system has ov_onnx_frontend library
+#   ngraph::ov_onnx_frontend               - ONNX FrontEnd target (optional)
 #
 #   ngraph_paddlepaddle_frontend_FOUND     - True if the system has PDPD frontend
-#   ngraph::paddlepaddle_ov_frontend       - nGraph PDPD frontend (optional)
+#   ngraph::ov_paddlepaddle_frontend       - nGraph PDPD frontend (optional)
 #
 
 @PACKAGE_INIT@
@@ -58,38 +58,38 @@ if(TARGET openvino::frontend::common AND NOT TARGET ngraph::frontend_common)
         INTERFACE_LINK_LIBRARIES openvino::frontend::common)
 endif()
 
-if(TARGET openvino::frontend::onnx AND NOT TARGET ngraph::onnx_ov_frontend)
-    add_library(ngraph::onnx_ov_frontend INTERFACE IMPORTED)
-    set_target_properties(ngraph::onnx_ov_frontend PROPERTIES
+if(TARGET openvino::frontend::onnx AND NOT TARGET ngraph::ov_onnx_frontend)
+    add_library(ngraph::ov_onnx_frontend INTERFACE IMPORTED)
+    set_target_properties(ngraph::ov_onnx_frontend PROPERTIES
         INTERFACE_LINK_LIBRARIES openvino::frontend::onnx)
 endif()
 
-if(TARGET openvino::frontend::paddlepaddle AND NOT TARGET ngraph::paddlepaddle_ov_frontend)
-    add_library(ngraph::paddlepaddle_ov_frontend INTERFACE IMPORTED)
-    set_target_properties(ngraph::paddlepaddle_ov_frontend PROPERTIES
+if(TARGET openvino::frontend::paddlepaddle AND NOT TARGET ngraph::ov_paddlepaddle_frontend)
+    add_library(ngraph::ov_paddlepaddle_frontend INTERFACE IMPORTED)
+    set_target_properties(ngraph::ov_paddlepaddle_frontend PROPERTIES
         INTERFACE_LINK_LIBRARIES openvino::frontend::paddlepaddle)
 endif()
 
-if(TARGET openvino::frontend::tensorflow AND NOT TARGET ngraph::tensorflow_ov_frontend)
-    add_library(ngraph::tensorflow_ov_frontend INTERFACE IMPORTED)
-    set_target_properties(ngraph::tensorflow_ov_frontend PROPERTIES
+if(TARGET openvino::frontend::tensorflow AND NOT TARGET ngraph::ov_tensorflow_frontend)
+    add_library(ngraph::ov_tensorflow_frontend INTERFACE IMPORTED)
+    set_target_properties(ngraph::ov_tensorflow_frontend PROPERTIES
         INTERFACE_LINK_LIBRARIES openvino::frontend::tensorflow)
 endif()
 
 set(ngraph_ngraph_FOUND ON)
 set(NGRAPH_LIBRARIES ngraph::ngraph)
 
-set(ngraph_onnx_ov_frontend_FOUND ${OpenVINO_Frontend_ONNX_FOUND})
+set(ngraph_ov_onnx_frontend_FOUND ${OpenVINO_Frontend_ONNX_FOUND})
 set(ngraph_onnx_importer_FOUND ${OpenVINO_Frontend_ONNX_FOUND})
 
 if(ngraph_onnx_importer_FOUND)
-    set(ONNX_IMPORTER_LIBRARIES ngraph::onnx_ov_frontend)
+    set(ONNX_IMPORTER_LIBRARIES ngraph::ov_onnx_frontend)
     # ngraph::onnx_importer target and variables are deprecated
     # but need to create a dummy target for BW compatibility
     if(NOT TARGET ngraph::onnx_importer)
         add_library(ngraph::onnx_importer INTERFACE IMPORTED)
         set_target_properties(ngraph::onnx_importer PROPERTIES
-            INTERFACE_LINK_LIBRARIES ngraph::onnx_ov_frontend)
+            INTERFACE_LINK_LIBRARIES ngraph::ov_onnx_frontend)
     endif()
 endif()
 
diff --git a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
index 12d5746514c..8297a16d1f5 100644
--- a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
+++ b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
@@ -40,13 +40,13 @@ This library contains the classes to:
 
 Starting from 2022.1 release, OpenVINO Runtime introduced a concept of frontend plugins. Such plugins can be automatically dynamically loaded by OpenVINO Runtime dynamically depending on file format:
 * Linux* OS:
-    - `libir_ov_frontend.so` to read a network from IR
-    - `libpaddlepaddle_ov_frontend.so` to read a network from PaddlePaddle model format
-    - `libonnx_ov_frontend.so` to read a network from ONNX model format
+    - `libov_ir_frontend.so` to read a network from IR
+    - `libov_paddlepaddle_frontend.so` to read a network from PaddlePaddle model format
+    - `libov_onnx_frontend.so` to read a network from ONNX model format
 * Windows* OS:
-    - `ir_ov_frontend.dll` to read a network from IR
-    - `paddlepaddle_ov_frontend.dll` to read a network from PaddlePaddle model format
-    - `onnx_ov_frontend.dll` to read a network from ONNX model format
+    - `ov_ir_frontend.dll` to read a network from IR
+    - `ov_paddlepaddle_frontend.dll` to read a network from PaddlePaddle model format
+    - `ov_onnx_frontend.dll` to read a network from ONNX model format
 
 ### Device-Specific Plugin Libraries
 
diff --git a/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md b/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
index 6aa2448db32..3d276ede298 100644
--- a/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
+++ b/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
@@ -62,7 +62,7 @@ The example below demonstrates how to unregister an operator from the destructor
 ## Requirements for Building with CMake
 
 A program that uses the `register_operator` functionality requires `openvino::core` and `openvino::frontend::onnx` libraries in addition to the OpenVINO Inference Runtime.
-The `onnx_ov_frontend` is a component of the `OpenVINO` package , so `find_package(OpenVINO REQUIRED COMPONENTS ONNX)` can find both.
+The `ov_onnx_frontend` is a component of the `OpenVINO` package , so `find_package(OpenVINO REQUIRED COMPONENTS ONNX)` can find both.
 Those libraries need to be passed to the `target_link_libraries` command in the CMakeLists.txt file.
 
 See CMakeLists.txt below for reference:
diff --git a/docs/IE_DG/inference_engine_intro.md b/docs/IE_DG/inference_engine_intro.md
index 9e0671f87c5..f0fae908d51 100644
--- a/docs/IE_DG/inference_engine_intro.md
+++ b/docs/IE_DG/inference_engine_intro.md
@@ -45,13 +45,13 @@ This library contains the classes to:
 
 Starting from 2022.1 release, OpenVINO Runtime introduced a concept of frontend plugins. Such plugins can be automatically dynamically loaded by OpenVINO Runtime dynamically depending on file format:
 * Unix* OS:
-    - `libir_ov_frontend.so` to read a network from IR
-    - `libpaddlepaddle_ov_frontend.so` to read a network from PaddlePaddle model format
-    - `libonnx_ov_frontend.so` to read a network from ONNX model format
+    - `libov_ir_frontend.so` to read a network from IR
+    - `libov_paddlepaddle_frontend.so` to read a network from PaddlePaddle model format
+    - `libov_onnx_frontend.so` to read a network from ONNX model format
 * Windows* OS:
-    - `ir_ov_frontend.dll` to read a network from IR
-    - `paddlepaddle_ov_frontend.dll` to read a network from PaddlePaddle model format
-    - `onnx_ov_frontend.dll` to read a network from ONNX model format
+    - `ov_ir_frontend.dll` to read a network from IR
+    - `ov_paddlepaddle_frontend.dll` to read a network from PaddlePaddle model format
+    - `ov_onnx_frontend.dll` to read a network from ONNX model format
 
 ### Device-specific Plugin Libraries ###
 
diff --git a/docs/snippets/CMakeLists.txt b/docs/snippets/CMakeLists.txt
index 9cda8490a47..e240d0b3cfe 100644
--- a/docs/snippets/CMakeLists.txt
+++ b/docs/snippets/CMakeLists.txt
@@ -46,7 +46,7 @@ if(OpenCV_FOUND)
 endif()
 
 if(ENABLE_OV_ONNX_FRONTEND)
-    target_link_libraries(${TARGET_NAME} PRIVATE onnx_ov_frontend)
+    target_link_libraries(${TARGET_NAME} PRIVATE ov_onnx_frontend)
 endif()
 
 if(NOT MSVC)
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 0b75eaf3398..8c2e3f3be69 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -31,19 +31,19 @@ endif()
 
 if(ENABLE_OV_IR_FRONTEND)
     if(BUILD_SHARED_LIBS)
-        add_dependencies(ov_runtime_libraries ir_ov_frontend)
+        add_dependencies(ov_runtime_libraries ov_ir_frontend)
     endif()
     # use this one once CVS-69781 is fixed
-    # add_dependencies(inference_engine ir_ov_frontend)
+    # add_dependencies(inference_engine ov_ir_frontend)
 endif()
 if(ENABLE_OV_ONNX_FRONTEND)
-    add_dependencies(inference_engine onnx_ov_frontend)
+    add_dependencies(inference_engine ov_onnx_frontend)
 endif()
 
 if(ENABLE_OV_PDPD_FRONTEND)
-    add_dependencies(inference_engine paddlepaddle_ov_frontend)
+    add_dependencies(inference_engine ov_paddlepaddle_frontend)
 endif()
 
 if(ENABLE_OV_TF_FRONTEND)
-    add_dependencies(inference_engine tensorflow_ov_frontend)
+    add_dependencies(inference_engine ov_tensorflow_frontend)
 endif()
diff --git a/src/bindings/python/CMakeLists.txt b/src/bindings/python/CMakeLists.txt
index 8d1003710f7..0d54e471203 100644
--- a/src/bindings/python/CMakeLists.txt
+++ b/src/bindings/python/CMakeLists.txt
@@ -17,9 +17,9 @@ set(LIBRARY_OUTPUT_DIRECTORY_BIN ${CMAKE_LIBRARY_OUTPUT_DIRECTORY})
 add_subdirectory(src)
 
 if(ENABLE_OV_CORE_UNIT_TESTS)
-    add_subdirectory(tests/mock/mock_py_ov_frontend)
-    add_dependencies(pyopenvino mock_py_ov_frontend)
-    set_target_properties(mock_py_ov_frontend PROPERTIES
+    add_subdirectory(tests/mock/ov_mock_py_frontend)
+    add_dependencies(pyopenvino ov_mock_py_frontend)
+    set_target_properties(ov_mock_py_frontend PROPERTIES
             LIBRARY_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_DIRECTORY_BIN}
             ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_DIRECTORY_BIN}
             COMPILE_PDB_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_DIRECTORY_BIN}
diff --git a/src/bindings/python/tests/mock/mock_py_ov_frontend/CMakeLists.txt b/src/bindings/python/tests/mock/ov_mock_py_frontend/CMakeLists.txt
similarity index 95%
rename from src/bindings/python/tests/mock/mock_py_ov_frontend/CMakeLists.txt
rename to src/bindings/python/tests/mock/ov_mock_py_frontend/CMakeLists.txt
index 65651339ee1..146637a6714 100644
--- a/src/bindings/python/tests/mock/mock_py_ov_frontend/CMakeLists.txt
+++ b/src/bindings/python/tests/mock/ov_mock_py_frontend/CMakeLists.txt
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_FE_NAME "mock_py_ov_frontend")
+set(TARGET_FE_NAME "ov_mock_py_frontend")
 
 file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
diff --git a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.cpp b/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.cpp
similarity index 100%
rename from src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.cpp
rename to src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.cpp
diff --git a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp b/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.hpp
similarity index 99%
rename from src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
rename to src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.hpp
index a9d61f99275..a9598d9da9a 100644
--- a/src/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp
+++ b/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.hpp
@@ -9,11 +9,11 @@
 #include "openvino/frontend/visibility.hpp"
 
 // Defined if we are building the plugin DLL (instead of using it)
-#ifdef mock_py_ov_frontend_EXPORTS
+#ifdef ov_mock_py_frontend_EXPORTS
 #    define MOCK_API OPENVINO_CORE_EXPORTS
 #else
 #    define MOCK_API OPENVINO_CORE_IMPORTS
-#endif  // mock_py_ov_frontend_EXPORTS
+#endif  // ov_mock_py_frontend_EXPORTS
 
 // OK to have 'using' in mock header
 
diff --git a/src/bindings/python/tests/mock/pyngraph_fe_mock_api/CMakeLists.txt b/src/bindings/python/tests/mock/pyngraph_fe_mock_api/CMakeLists.txt
index e9442967797..07bfd2c2e0c 100644
--- a/src/bindings/python/tests/mock/pyngraph_fe_mock_api/CMakeLists.txt
+++ b/src/bindings/python/tests/mock/pyngraph_fe_mock_api/CMakeLists.txt
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_FE_NAME "mock_py_ov_frontend")
+set(TARGET_FE_NAME "ov_mock_py_frontend")
 set(PYBIND_FE_NAME "pybind_mock_frontend")
 
 set(PYBIND_FE_SRC ${CMAKE_CURRENT_SOURCE_DIR}/pyngraph_mock_frontend_api.cpp)
diff --git a/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp b/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
index 19c81e85d96..3d71be89673 100644
--- a/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
+++ b/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
@@ -5,7 +5,7 @@
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 
-#include "../mock_py_ov_frontend/mock_py_frontend.hpp"
+#include "../ov_mock_py_frontend/mock_py_frontend.hpp"
 
 namespace py = pybind11;
 using namespace ngraph;
diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt
index 75caabd2a95..ec539ce6c11 100644
--- a/src/core/tests/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -602,9 +602,9 @@ if (OV_COMPILER_IS_CLANG)
 endif()
 
 if (ENABLE_OV_ONNX_FRONTEND)
-    get_target_property(ONNX_FRONTEND_SRC_DIR onnx_ov_frontend SOURCE_DIR)
+    get_target_property(ONNX_FRONTEND_SRC_DIR ov_onnx_frontend SOURCE_DIR)
     target_include_directories(ov_core_unit_tests PRIVATE ${ONNX_FRONTEND_SRC_DIR}/src)
-    target_link_libraries(ov_core_unit_tests PRIVATE onnx_ov_frontend onnx_test_util)
+    target_link_libraries(ov_core_unit_tests PRIVATE ov_onnx_frontend onnx_test_util)
     if (LINUX)
         target_link_options(ov_core_unit_tests PRIVATE -Wl,--exclude-libs,ALL)
     elseif(APPLE)
@@ -613,7 +613,7 @@ if (ENABLE_OV_ONNX_FRONTEND)
 endif()
 
 if(ENABLE_OV_IR_FRONTEND)
-    add_dependencies(ov_core_unit_tests ir_ov_frontend)
+    add_dependencies(ov_core_unit_tests ov_ir_frontend)
 endif()
 
 install(TARGETS ov_core_unit_tests
diff --git a/src/core/tests/frontend/CMakeLists.txt b/src/core/tests/frontend/CMakeLists.txt
index 4baa5e86181..723776d0407 100644
--- a/src/core/tests/frontend/CMakeLists.txt
+++ b/src/core/tests/frontend/CMakeLists.txt
@@ -16,7 +16,7 @@ if (ENABLE_OV_TF_FRONTEND)
 endif()
 
 set(SRC ${CMAKE_CURRENT_SOURCE_DIR}/mock_frontend.cpp)
-set(MOCK1_FE_NAME mock1_ov_frontend)
+set(MOCK1_FE_NAME ov_mock1_frontend)
 add_library(${MOCK1_FE_NAME} SHARED EXCLUDE_FROM_ALL ${SRC})
 
 target_compile_definitions(${MOCK1_FE_NAME} PRIVATE "-DMOCK_VARIANT=\"1\"")
diff --git a/src/core/tests/frontend/mock_frontend.cpp b/src/core/tests/frontend/mock_frontend.cpp
index 7d4e24290be..eccd09e91c8 100644
--- a/src/core/tests/frontend/mock_frontend.cpp
+++ b/src/core/tests/frontend/mock_frontend.cpp
@@ -7,11 +7,11 @@
 #include "openvino/frontend/visibility.hpp"
 
 // Defined if we are building the plugin DLL (instead of using it)
-#ifdef mock1_ov_frontend_EXPORTS
+#ifdef ov_mock1_frontend_EXPORTS
 #    define MOCK_API OPENVINO_CORE_EXPORTS
 #else
 #    define MOCK_API OPENVINO_CORE_IMPORTS
-#endif  // mock1_ov_frontend_EXPORTS
+#endif  // ov_mock1_frontend_EXPORTS
 
 using namespace ngraph;
 using namespace ov::frontend;
diff --git a/src/core/tests/frontend/onnx/CMakeLists.txt b/src/core/tests/frontend/onnx/CMakeLists.txt
index 6bbf19ea106..e46bdddf0b2 100644
--- a/src/core/tests/frontend/onnx/CMakeLists.txt
+++ b/src/core/tests/frontend/onnx/CMakeLists.txt
@@ -22,5 +22,5 @@ target_compile_definitions(${TARGET_NAME} PRIVATE -D TEST_ONNX_MODELS_DIRNAME=\"
 set(MANIFEST ${CMAKE_CURRENT_SOURCE_DIR}/unit_test.manifest)
 target_compile_definitions(${TARGET_NAME} PRIVATE -D MANIFEST=\"${MANIFEST}\")
 
-add_dependencies(${TARGET_NAME} onnx_ov_frontend)
+add_dependencies(${TARGET_NAME} ov_onnx_frontend)
 add_dependencies(${TARGET_NAME} test_model_zoo)
diff --git a/src/core/tests/frontend/paddlepaddle/CMakeLists.txt b/src/core/tests/frontend/paddlepaddle/CMakeLists.txt
index 4ef43b1802d..1f759130daa 100644
--- a/src/core/tests/frontend/paddlepaddle/CMakeLists.txt
+++ b/src/core/tests/frontend/paddlepaddle/CMakeLists.txt
@@ -55,9 +55,9 @@ else()
 endif()
 
 add_dependencies(${TARGET_NAME} paddlepaddle_test_models)
-add_dependencies(${TARGET_NAME} paddlepaddle_ov_frontend)
+add_dependencies(${TARGET_NAME} ov_paddlepaddle_frontend)
 
 # Fuzzy tests for PaddlePaddle use IE_CPU engine
 if (ENABLE_MKL_DNN)
     add_dependencies(${TARGET_NAME} MKLDNNPlugin)
-endif()
\ No newline at end of file
+endif()
diff --git a/src/core/tests/frontend/tensorflow/CMakeLists.txt b/src/core/tests/frontend/tensorflow/CMakeLists.txt
index 3dd64556607..fd53f140d68 100644
--- a/src/core/tests/frontend/tensorflow/CMakeLists.txt
+++ b/src/core/tests/frontend/tensorflow/CMakeLists.txt
@@ -8,7 +8,7 @@ file(GLOB_RECURSE SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
 add_executable(${TARGET_NAME} ${SRC})
 
-target_link_libraries(${TARGET_NAME} PRIVATE frontend_shared_test_classes tensorflow_ov_frontend)
+target_link_libraries(${TARGET_NAME} PRIVATE frontend_shared_test_classes ov_tensorflow_frontend)
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
@@ -63,7 +63,7 @@ else()
 endif()
 
 add_dependencies(${TARGET_NAME} tensorflow_test_models)
-add_dependencies(${TARGET_NAME} tensorflow_ov_frontend)
+add_dependencies(${TARGET_NAME} ov_tensorflow_frontend)
 
-get_target_property(TENSORFLOW_FRONTEND_SRC_DIR tensorflow_ov_frontend SOURCE_DIR)
+get_target_property(TENSORFLOW_FRONTEND_SRC_DIR ov_tensorflow_frontend SOURCE_DIR)
 target_include_directories(${TARGET_NAME} PRIVATE ${TENSORFLOW_FRONTEND_SRC_DIR}/src/pass/)
diff --git a/src/frontends/common/CMakeLists.txt b/src/frontends/common/CMakeLists.txt
index 3750dc92e92..9cdf6bcd710 100644
--- a/src/frontends/common/CMakeLists.txt
+++ b/src/frontends/common/CMakeLists.txt
@@ -41,7 +41,7 @@ set_property(SOURCE
             ${CMAKE_CURRENT_SOURCE_DIR}/src/plugin_loader.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/manager.cpp
         APPEND PROPERTY COMPILE_DEFINITIONS
-        FRONTEND_LIB_PREFIX="${CMAKE_SHARED_LIBRARY_PREFIX}"
+        FRONTEND_LIB_PREFIX="${CMAKE_SHARED_LIBRARY_PREFIX}${FRONTEND_NAME_PREFIX}"
         FRONTEND_LIB_SUFFIX="${FRONTEND_NAME_SUFFIX}${IE_BUILD_POSTFIX}${CMAKE_SHARED_LIBRARY_SUFFIX}")
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
diff --git a/src/frontends/common/src/manager.cpp b/src/frontends/common/src/manager.cpp
index 7b79721603a..4a27068a8c7 100644
--- a/src/frontends/common/src/manager.cpp
+++ b/src/frontends/common/src/manager.cpp
@@ -99,7 +99,7 @@ public:
 
 private:
     // Helper structure for searching plugin either by name or by file name
-    // File name here doesn't contain prefix/suffix (like "_ov_frontend.so")
+    // File name here doesn't contain prefix/suffix (like "ov_*_frontend.so")
     struct FrontEndNames {
         FrontEndNames(std::string n, std::string f) : name(std::move(n)), file_name(std::move(f)) {}
         bool operator==(const FrontEndNames& other) const {
diff --git a/src/frontends/common/src/plugin_loader.hpp b/src/frontends/common/src/plugin_loader.hpp
index 481b6dd434a..117adfe6c66 100644
--- a/src/frontends/common/src/plugin_loader.hpp
+++ b/src/frontends/common/src/plugin_loader.hpp
@@ -24,7 +24,7 @@ class PluginInfo {
     bool load_internal();
 
 public:
-    std::string m_file_name;  // Plugin file name, e.g. "libir_ov_frontend.so"
+    std::string m_file_name;  // Plugin file name, e.g. "libov_ir_frontend.so"
     std::string m_file_path;  // Plugin file full path
 
     PluginInfo() = default;
diff --git a/src/frontends/ir/include/openvino/frontend/ir/visibility.hpp b/src/frontends/ir/include/openvino/frontend/ir/visibility.hpp
index a90f0e1d529..2eacb5c9e66 100644
--- a/src/frontends/ir/include/openvino/frontend/ir/visibility.hpp
+++ b/src/frontends/ir/include/openvino/frontend/ir/visibility.hpp
@@ -10,11 +10,11 @@
 #    define IR_API
 #    define IR_C_API
 #else
-#    ifdef ir_ov_frontend_EXPORTS
+#    ifdef ov_ir_frontend_EXPORTS
 #        define IR_API   OPENVINO_CORE_EXPORTS
 #        define IR_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    else
 #        define IR_API   OPENVINO_CORE_IMPORTS
 #        define IR_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
-#    endif  // ir_ov_frontend_EXPORTS
+#    endif  // ov_ir_frontend_EXPORTS
 #endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp b/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
index fcce9aaa746..2c9ce604a9b 100644
--- a/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
+++ b/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
@@ -7,9 +7,9 @@
 #ifdef OPENVINO_STATIC_LIBRARY
 #    define ONNX_IMPORTER_API
 #else
-#    ifdef onnx_ov_frontend_EXPORTS
+#    ifdef ov_onnx_frontend_EXPORTS
 #        define ONNX_IMPORTER_API OPENVINO_CORE_EXPORTS
 #    else
 #        define ONNX_IMPORTER_API OPENVINO_CORE_IMPORTS
-#    endif  // onnx_ov_frontend_EXPORTS
+#    endif  // ov_onnx_frontend_EXPORTS
 #endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
index 21b6f6b375d..defe06c97b9 100644
--- a/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
+++ b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
@@ -11,13 +11,13 @@
 #    define ONNX_FRONTEND_API
 #    define ONNX_FRONTEND_C_API
 #else
-#    ifdef onnx_ov_frontend_EXPORTS
+#    ifdef ov_onnx_frontend_EXPORTS
 #        define ONNX_FRONTEND_API   OPENVINO_CORE_EXPORTS
 #        define ONNX_FRONTEND_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    else
 #        define ONNX_FRONTEND_API   OPENVINO_CORE_IMPORTS
 #        define ONNX_FRONTEND_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
-#    endif  // onnx_ov_frontend_EXPORTS
+#    endif  // ov_onnx_frontend_EXPORTS
 #endif      // OPENVINO_STATIC_LIBRARY
 
 namespace ov {
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
index a9c273cb392..9ab066dc759 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
@@ -10,13 +10,13 @@
 #    define PDPD_API
 #    define PDPD_C_API
 #else
-#    ifdef paddlepaddle_ov_frontend_EXPORTS
+#    ifdef ov_paddlepaddle_frontend_EXPORTS
 #        define PDPD_API   OPENVINO_CORE_EXPORTS
 #        define PDPD_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    else
 #        define PDPD_API   OPENVINO_CORE_IMPORTS
 #        define PDPD_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
-#    endif  // paddlepaddle_ov_frontend_EXPORTS
+#    endif  // ov_paddlepaddle_frontend_EXPORTS
 #endif      // OPENVINO_STATIC_LIBRARY
 
 #define PDPD_ASSERT(ex, msg)               \
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp b/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
index 1990785fc30..ed07e8fe4a2 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
+++ b/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
@@ -10,11 +10,11 @@
 #    define TF_API
 #    define TF_C_API
 #else
-#    ifdef tensorflow_ov_frontend_EXPORTS
+#    ifdef ov_tensorflow_frontend_EXPORTS
 #        define TF_API   OPENVINO_CORE_EXPORTS
 #        define TF_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    else
 #        define TF_API   OPENVINO_CORE_IMPORTS
 #        define TF_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
-#    endif  // tensorflow_ov_frontend_EXPORTS
+#    endif  // ov_tensorflow_frontend_EXPORTS
 #endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/tests/functional/inference_engine/CMakeLists.txt b/src/tests/functional/inference_engine/CMakeLists.txt
index b1fccae7f83..cf17b0b78cc 100644
--- a/src/tests/functional/inference_engine/CMakeLists.txt
+++ b/src/tests/functional/inference_engine/CMakeLists.txt
@@ -33,7 +33,7 @@ set(DEPENDENCIES
 )
 
 if(ENABLE_OV_IR_FRONTEND)
-    list(APPEND DEPENDENCIES ir_ov_frontend)
+    list(APPEND DEPENDENCIES ov_ir_frontend)
 endif()
 
 if(ENABLE_IR_V7_READER)
@@ -74,13 +74,13 @@ if(ENABLE_OV_ONNX_FRONTEND)
     target_compile_definitions(${TARGET_NAME} PRIVATE
         ENABLE_OV_ONNX_FRONTEND
         ONNX_TEST_MODELS="${TEST_MODEL_ZOO}/onnx_reader/models/")
-    add_dependencies(${TARGET_NAME} onnx_ov_frontend)
+    add_dependencies(${TARGET_NAME} ov_onnx_frontend)
 endif()
 
 if(ENABLE_OV_PDPD_FRONTEND)
     target_compile_definitions(${TARGET_NAME} PRIVATE
             PADDLE_TEST_MODELS="${CMAKE_CURRENT_SOURCE_DIR}/paddle_reader/models/")
-    add_dependencies(${TARGET_NAME} paddlepaddle_ov_frontend)
+    add_dependencies(${TARGET_NAME} ov_paddlepaddle_frontend)
 endif()
 
 ie_faster_build(${TARGET_NAME}
diff --git a/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt b/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
index 6d1c07b38fb..f9a5a29ddef 100644
--- a/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
+++ b/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
@@ -18,8 +18,8 @@ addIeTargetTest(
         ADD_CPPLINT
 )
 
-if(TARGET ir_ov_frontend)
-    add_dependencies(${TARGET_NAME} ir_ov_frontend)
+if(TARGET ov_ir_frontend)
+    add_dependencies(${TARGET_NAME} ov_ir_frontend)
 endif()
 
 ie_faster_build(${TARGET_NAME} UNITY)
diff --git a/src/tests/functional/plugin/shared/CMakeLists.txt b/src/tests/functional/plugin/shared/CMakeLists.txt
index 8a906017208..bca4f9d39e6 100644
--- a/src/tests/functional/plugin/shared/CMakeLists.txt
+++ b/src/tests/functional/plugin/shared/CMakeLists.txt
@@ -18,7 +18,7 @@ endif()
 
 # remove once CVS-69781 is fixed
 if(ENABLE_OV_IR_FRONTEND)
-    list(APPEND DEPENDENCIES ir_ov_frontend)
+    list(APPEND DEPENDENCIES ov_ir_frontend)
 endif()
 
 if (ENABLE_OV_ONNX_FRONTEND)
diff --git a/src/tests/unit/frontends/onnx_import/CMakeLists.txt b/src/tests/unit/frontends/onnx_import/CMakeLists.txt
index b7c8c10a956..904c8407ef2 100644
--- a/src/tests/unit/frontends/onnx_import/CMakeLists.txt
+++ b/src/tests/unit/frontends/onnx_import/CMakeLists.txt
@@ -11,7 +11,7 @@ addIeTargetTest(
             gtest
             gtest_main
             commonTestUtils
-            onnx_ov_frontend
+            ov_onnx_frontend
             openvino::util
         DEFINES
             ONNX_MODELS_DIR=\"${TEST_MODEL_ZOO}/onnx_import\"
diff --git a/tools/deployment_manager/configs/darwin.json b/tools/deployment_manager/configs/darwin.json
index ea7ecdfbf8d..98e1c3c6b67 100644
--- a/tools/deployment_manager/configs/darwin.json
+++ b/tools/deployment_manager/configs/darwin.json
@@ -24,9 +24,9 @@
         "runtime/lib/intel64/libov_auto_plugin.so",
         "runtime/lib/intel64/libngraph.dylib",
         "runtime/lib/intel64/libfrontend_common.dylib",
-        "runtime/lib/intel64/libir_ov_frontend.dylib",
-        "runtime/lib/intel64/libonnx_ov_frontend.dylib",
-        "runtime/lib/intel64/libpaddlepaddle_ov_frontend.dylib",
+        "runtime/lib/intel64/libov_ir_frontend.dylib",
+        "runtime/lib/intel64/libov_onnx_frontend.dylib",
+        "runtime/lib/intel64/libov_paddlepaddle_frontend.dylib",
         "runtime/lib/intel64/plugins.xml",
         "runtime/3rdparty/tbb"
       ]
diff --git a/tools/deployment_manager/configs/linux.json b/tools/deployment_manager/configs/linux.json
index 13f33024f6d..5e71b7153e9 100644
--- a/tools/deployment_manager/configs/linux.json
+++ b/tools/deployment_manager/configs/linux.json
@@ -30,9 +30,9 @@
         "runtime/lib/intel64/libov_auto_plugin.so",
         "runtime/lib/intel64/libngraph.so",
         "runtime/lib/intel64/libfrontend_common.so",
-        "runtime/lib/intel64/libir_ov_frontend.so",
-        "runtime/lib/intel64/libonnx_ov_frontend.so",
-        "runtime/lib/intel64/libpaddlepaddle_ov_frontend.so",
+        "runtime/lib/intel64/libov_ir_frontend.so",
+        "runtime/lib/intel64/libov_onnx_frontend.so",
+        "runtime/lib/intel64/libov_paddlepaddle_frontend.so",
         "runtime/lib/intel64/plugins.xml",
         "runtime/3rdparty/tbb"
       ]
diff --git a/tools/deployment_manager/configs/windows.json b/tools/deployment_manager/configs/windows.json
index 45ad25794db..776a666755a 100644
--- a/tools/deployment_manager/configs/windows.json
+++ b/tools/deployment_manager/configs/windows.json
@@ -24,9 +24,9 @@
         "runtime/bin/intel64/Release/ov_auto_plugin.dll",
         "runtime/bin/intel64/Release/ngraph.dll",
         "runtime/bin/intel64/Release/frontend_common.dll",
-        "runtime/bin/intel64/Release/ir_ov_frontend.dll",
-        "runtime/bin/intel64/Release/onnx_ov_frontend.dll",
-        "runtime/bin/intel64/Release/paddlepaddle_ov_frontend.dll",
+        "runtime/bin/intel64/Release/ov_ir_frontend.dll",
+        "runtime/bin/intel64/Release/ov_onnx_frontend.dll",
+        "runtime/bin/intel64/Release/ov_paddlepaddle_frontend.dll",
         "runtime/bin/intel64/Release/plugins.xml",
         "runtime/3rdparty/tbb"
       ]
diff --git a/tools/mo/CMakeLists.txt b/tools/mo/CMakeLists.txt
index 5d4748fa404..02fc9186b29 100644
--- a/tools/mo/CMakeLists.txt
+++ b/tools/mo/CMakeLists.txt
@@ -4,10 +4,10 @@
 if(NOT ENABLE_PYTHON)
     message(WARNING "Please enable IE & nGraph Python API (ie_api and offline_transformations_api) targets to enable Model Optimizer target")
 else()
-    add_custom_target(model_optimizer DEPENDS ie_api offline_transformations_api ir_ov_frontend pyopenvino)
+    add_custom_target(model_optimizer DEPENDS ie_api offline_transformations_api ov_ir_frontend pyopenvino)
     if(ENABLE_TESTS)
-        add_subdirectory(unit_tests/mock_mo_frontend/mock_mo_ov_frontend)
-        add_dependencies(model_optimizer mock_mo_ov_frontend)
+        add_subdirectory(unit_tests/mock_mo_frontend/ov_mock_mo_frontend)
+        add_dependencies(model_optimizer ov_mock_mo_frontend)
 
         add_subdirectory(unit_tests/mock_mo_frontend/mock_mo_python_api)
         add_dependencies(model_optimizer mock_mo_python_api)
diff --git a/tools/mo/unit_tests/mo/frontend_ngraph_test_actual.py b/tools/mo/unit_tests/mo/frontend_ngraph_test_actual.py
index ecb6bc6a4ea..eacc2794091 100644
--- a/tools/mo/unit_tests/mo/frontend_ngraph_test_actual.py
+++ b/tools/mo/unit_tests/mo/frontend_ngraph_test_actual.py
@@ -97,7 +97,7 @@ class TestMainFrontend(unittest.TestCase):
     def test_simple_convert(self, mock_argparse):
         f = io.StringIO()
         with redirect_stdout(f):
-            main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+            main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
             out = f.getvalue()
 
         xml_file = re.search(r'\[ SUCCESS \] XML file: (.*)', out).\
@@ -112,7 +112,7 @@ class TestMainFrontend(unittest.TestCase):
         assert stat.supported == 0
         # verify that meta info is added to XML file
         with open(xml_file) as file:
-            assert 'mock_mo_ov_frontend' in file.read()
+            assert 'ov_mock_mo_frontend' in file.read()
 
     @mock_needed
     @patch('argparse.ArgumentParser.parse_args',
@@ -137,14 +137,14 @@ class TestMainFrontend(unittest.TestCase):
 
         # verify that meta info is added to XML file
         with open(xml_file) as file:
-            assert 'mock_mo_ov_frontend' in file.read()
+            assert 'ov_mock_mo_frontend' in file.read()
 
     @mock_needed
     @patch('argparse.ArgumentParser.parse_args',
            return_value=replaceArgsHelper(_input='newInput1,mock_input2'))
     def test_override_inputs(self, mock_argparse):
 
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'override_all_inputs' was called
@@ -156,7 +156,7 @@ class TestMainFrontend(unittest.TestCase):
     @patch('argparse.ArgumentParser.parse_args',
            return_value=replaceArgsHelper(output='newOut1,mock_output2'))
     def test_override_outputs(self, mock_argparse):
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'override_all_outputs' was called
@@ -169,7 +169,7 @@ class TestMainFrontend(unittest.TestCase):
            return_value=replaceArgsHelper(_input='newIn1,newIn2',
                                           output='newOut1,newOut2'))
     def test_extract_subgraph(self, mock_argparse):
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'extract_subgraph' was called
@@ -183,7 +183,7 @@ class TestMainFrontend(unittest.TestCase):
                                           output='new_output2,mock_output1'))
     def test_override_same_inputs(self, mock_argparse):
 
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'override_all_outputs' was called
@@ -198,7 +198,7 @@ class TestMainFrontend(unittest.TestCase):
                                           output='mock_output2,mock_output1'))
     def test_override_same_outputs(self, mock_argparse):
 
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'override_all_inputs' was called
@@ -213,7 +213,7 @@ class TestMainFrontend(unittest.TestCase):
                                           input_shape='[1,2,3,4]'))
     @pytest.mark.skip(reason="Unskip as 8301 will be merged")
     def test_input_shape(self, mock_argparse):
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'set_partial_shape' was called
@@ -225,7 +225,7 @@ class TestMainFrontend(unittest.TestCase):
            return_value=replaceArgsHelper(_input='newIn1{i8}'))
     @pytest.mark.skip(reason="Unskip as 8301 will be merged")
     def test_element_type(self, mock_argparse):
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'set_element_type' was called
@@ -238,7 +238,7 @@ class TestMainFrontend(unittest.TestCase):
     @pytest.mark.skip(reason="Unskip as 8301 will be merged")
     def test_set_batch_size(self, mock_argparse):
         mock_return_partial_shape(PartialShape([-1, 2, 3, 4]))
-        main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+        main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
         stat = get_model_statistic()
 
         # verify that 'set_element_type' was called
@@ -255,7 +255,7 @@ class TestMainFrontend(unittest.TestCase):
         # so MO shall not convert anything and produce specified error
         mock_return_partial_shape(PartialShape([122, 2, 3, 4]))
         with self.assertLogs() as logger:
-            main(argparse.ArgumentParser(), fem, 'mock_mo_ov_frontend')
+            main(argparse.ArgumentParser(), fem, 'ov_mock_mo_frontend')
 
         stat = get_model_statistic()
 
diff --git a/tools/mo/unit_tests/mo/moc_frontend/moc_extractor_test_actual.py b/tools/mo/unit_tests/mo/moc_frontend/moc_extractor_test_actual.py
index 3dee3299363..1bf4aa7e749 100644
--- a/tools/mo/unit_tests/mo/moc_frontend/moc_extractor_test_actual.py
+++ b/tools/mo/unit_tests/mo/moc_frontend/moc_extractor_test_actual.py
@@ -42,7 +42,7 @@ class TestMainFrontend(unittest.TestCase):
         clear_place_statistic()
         clear_setup()
         set_max_port_counts(10, 10)
-        self.fe = fem.load_by_framework('mock_mo_ov_frontend')
+        self.fe = fem.load_by_framework('ov_mock_mo_frontend')
         self.model = self.fe.load('abc.bin')
 
     # Mock model has 'tensor' tensor place
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
index 3be41f71ad2..83ad038bf43 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
+++ b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
@@ -21,7 +21,7 @@ set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
 set(CMAKE_COMPILE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
 set(CMAKE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
 
-set(TARGET_FE_NAME "mock_mo_ov_frontend")
+set(TARGET_FE_NAME "ov_mock_mo_frontend")
 set(PYBIND_FE_NAME "mock_mo_python_api")
 
 set(PYBIND_FE_SRC ${CMAKE_CURRENT_SOURCE_DIR}/mock_mo_python_api.cpp)
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp
index 511c6fe39c0..eca1b6847da 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp
+++ b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp
@@ -5,7 +5,7 @@
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 
-#include "../mock_mo_ov_frontend/mock_mo_frontend.hpp"
+#include "../ov_mock_mo_frontend/mock_mo_frontend.hpp"
 
 namespace py = pybind11;
 using namespace ngraph;
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/CMakeLists.txt b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
similarity index 95%
rename from tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/CMakeLists.txt
rename to tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
index c2991e441b2..aae056a2fd7 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/CMakeLists.txt
+++ b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_FE_NAME "mock_mo_ov_frontend")
+set(TARGET_FE_NAME "ov_mock_mo_frontend")
 
 file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.cpp b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.cpp
similarity index 95%
rename from tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.cpp
rename to tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.cpp
index 2820d8e8ebd..ac43e30f56b 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.cpp
+++ b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.cpp
@@ -29,7 +29,7 @@ extern "C" MOCK_API FrontEndVersion GetAPIVersion()
 extern "C" MOCK_API void* GetFrontEndData()
 {
     FrontEndPluginInfo* res = new FrontEndPluginInfo();
-    res->m_name = "mock_mo_ov_frontend";
+    res->m_name = "ov_mock_mo_frontend";
     res->m_creator = []() { return std::make_shared<FrontEndMockPy>(); };
 
     return res;
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.hpp
similarity index 99%
rename from tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
rename to tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.hpp
index a705e57623e..4343606c80e 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_ov_frontend/mock_mo_frontend.hpp
+++ b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.hpp
@@ -9,11 +9,11 @@
 #include "openvino/frontend/visibility.hpp"
 
 // Defined if we are building the plugin DLL (instead of using it)
-#ifdef mock_mo_ov_frontend_EXPORTS
+#ifdef ov_mock_mo_frontend_EXPORTS
 #define MOCK_API OPENVINO_CORE_EXPORTS
 #else
 #define MOCK_API OPENVINO_CORE_IMPORTS
-#endif // mock_mo_ov_frontend_EXPORTS
+#endif // ov_mock_mo_frontend_EXPORTS
 
 // OK to have 'using' in mock header
 
@@ -452,6 +452,6 @@ private:
     std::string get_name() const override
     {
         m_stat.m_get_name++;
-        return "mock_mo_ov_frontend";
+        return "ov_mock_mo_frontend";
     }
 };

From d82b58a408ff9fa0504d2951dd0d35f4499562de Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomasz=20Do=C5=82bniak?= <tomasz.dolbniak@intel.com>
Date: Sat, 18 Dec 2021 13:29:40 +0100
Subject: [PATCH 030/160] Handling of cut + reshape in MO using ONNX FE (#9102)

---
 .../test_frontend_onnx_editor.py              | 47 ++++++++++++-
 src/core/tests/onnx/onnx_editor.cpp           | 68 +++++++++----------
 src/frontends/onnx/frontend/src/editor.cpp    | 14 +++-
 src/frontends/onnx/frontend/src/editor.hpp    |  7 +-
 .../onnx/frontend/src/input_model.cpp         | 42 ++++++++++--
 src/frontends/onnx/frontend/src/place.cpp     | 14 +++-
 src/frontends/onnx/frontend/src/place.hpp     |  2 +
 .../tools/mo/moc_frontend/extractor.py        |  6 +-
 .../tools/mo/moc_frontend/pipeline.py         | 10 +++
 9 files changed, 160 insertions(+), 50 deletions(-)

diff --git a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
index ec94ada893d..24ba146aa06 100644
--- a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
+++ b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
@@ -51,7 +51,7 @@ from openvino.frontend import FrontEndManager
 def create_test_onnx_models():
     models = {}
     # Input model 1
-    add = onnx.helper.make_node("Add", inputs=["in1", "in2"], outputs=["add_out"])
+    add = onnx.helper.make_node("Add", inputs=["in1", "in2"], outputs=["add_out"], name="onnx_add_op")
     split = onnx.helper.make_node("Split", inputs=["add_out"],
                                   outputs=["out1", "out2"], name="split1", axis=0)
     relu = onnx.helper.make_node("Relu", inputs=["in3"], outputs=["out3"])
@@ -1205,3 +1205,48 @@ def test_set_name_for_dimension():
     with pytest.raises(Exception) as e:
         model.set_name_for_dimension(one_const, 0, dim_name)
     assert "ONNX initializer shape dimension cannot be dynamic." in str(e)
+
+
+def test_set_input_partial_shape_using_input_edge():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    add_operator = model.get_place_by_operation_name("onnx_add_op")
+    add_input_edge = add_operator.get_input_port(inputPortIndex=0)
+    model.set_partial_shape(add_input_edge, PartialShape([10, 10]))
+    add_input_edge = add_operator.get_input_port(inputPortIndex=1)
+    model.set_partial_shape(add_input_edge, PartialShape([1]))
+
+    ov_model = fe.convert(model)
+    assert ov_model.input("in1").get_partial_shape() == PartialShape([10, 10])
+    assert ov_model.input("in2").get_partial_shape() == PartialShape([1])
+
+    assert ov_model.output("out4").get_partial_shape() == PartialShape([10, 10])
+
+
+def test_get_partial_shape_using_input_edge():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    add_operator = model.get_place_by_operation_name("onnx_add_op")
+    add_input_edge = add_operator.get_input_port(inputPortIndex=0)
+
+    pshape = model.get_partial_shape(add_input_edge)
+    assert pshape == PartialShape([2, 2])
+
+
+def test_get_partial_shape_using_output_edge():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    add_operator = model.get_place_by_operation_name("onnx_add_op")
+    add_output_edge = add_operator.get_output_port(outputPortIndex=0)
+
+    assert model.get_partial_shape(add_output_edge) == PartialShape([2, 2])
+
+    split_operator = model.get_place_by_tensor_name("out1").get_producing_operation()
+    out2_edge = split_operator.get_output_port(outputPortIndex=1)
+    assert model.get_partial_shape(out2_edge) == PartialShape([1, 2])
diff --git a/src/core/tests/onnx/onnx_editor.cpp b/src/core/tests/onnx/onnx_editor.cpp
index 66cc8a76058..2931a66c840 100644
--- a/src/core/tests/onnx/onnx_editor.cpp
+++ b/src/core/tests/onnx/onnx_editor.cpp
@@ -250,7 +250,7 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_head_cut) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge(1, 0)}}, {});
+    editor.extract_subgraph({{InputEdge(1, 0)}}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -265,7 +265,7 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_head_cut_ins_and_outs) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge(1, 0)}}, {{OutputEdge(2, 0)}});
+    editor.extract_subgraph({{InputEdge(1, 0)}}, {{OutputEdge(2, 0)}});
 
     // expected to behave the same way as subgraph__linear_model_head_cut
     const auto ref_model =
@@ -281,7 +281,7 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_deeper_head_cut) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge(2, 0)}}, {});
+    editor.extract_subgraph({{InputEdge(2, 0)}}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -296,7 +296,7 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_tail_cut) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
-    editor.cut_graph_fragment({}, {{OutputEdge{1, 0}}});
+    editor.extract_subgraph({}, {{OutputEdge{1, 0}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -311,7 +311,7 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_tail_cut_ins_and_outs) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{0, 0}}}, {{OutputEdge{1, 0}}});
+    editor.extract_subgraph({{InputEdge{0, 0}}}, {{OutputEdge{1, 0}}});
 
     // expected to behave the same way as subgraph__linear_model_tail_cut
     const auto ref_model =
@@ -328,7 +328,7 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_with_initializer_tail_cut) {
         ngraph::file_util::path_join(SERIALIZED_ZOO,
                                      "onnx/model_editor/subgraph__inception_head_with_initializer.onnx")};
 
-    editor.cut_graph_fragment({}, {{OutputEdge{1, 0}}});
+    editor.extract_subgraph({}, {{OutputEdge{1, 0}}});
 
     const auto ref_model = ngraph::file_util::path_join(
         SERIALIZED_ZOO,
@@ -344,7 +344,7 @@ NGRAPH_TEST(onnx_editor, subgraph__initializer_without_matching_input_tail_cut)
         ngraph::file_util::path_join(SERIALIZED_ZOO,
                                      "onnx/model_editor/subgraph__initializer_without_matching_input.onnx")};
 
-    editor.cut_graph_fragment({}, {{OutputEdge{1, 0}}});
+    editor.extract_subgraph({}, {{OutputEdge{1, 0}}});
 
     const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
                                                         "onnx/model_editor/reference/"
@@ -359,7 +359,7 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_deeper_tail_cut) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
-    editor.cut_graph_fragment({}, {{OutputEdge{0, 0}}});
+    editor.extract_subgraph({}, {{OutputEdge{0, 0}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -376,7 +376,7 @@ NGRAPH_TEST(onnx_editor, subgraph__no_input_params) {
 
     ONNXModelEditor editor{model_path};
 
-    editor.cut_graph_fragment({}, {});
+    editor.extract_subgraph({}, {});
 
     const auto result = compare_onnx_models(editor.model_string(), model_path);
 
@@ -388,7 +388,7 @@ NGRAPH_TEST(onnx_editor, subgraph__initializer_to_input_replacement) {
         ngraph::file_util::path_join(SERIALIZED_ZOO,
                                      "onnx/model_editor/subgraph__inception_head_with_initializer.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{0, 2}}}, {{OutputEdge{0, 0}}});
+    editor.extract_subgraph({{InputEdge{0, 2}}}, {{OutputEdge{0, 0}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -404,7 +404,7 @@ NGRAPH_TEST(onnx_editor, subgraph__initializer_to_input_replacement_2) {
         ngraph::file_util::path_join(SERIALIZED_ZOO,
                                      "onnx/model_editor/subgraph__initializer_without_matching_input.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{0, 2}}}, {{OutputEdge{0, 0}}});
+    editor.extract_subgraph({{InputEdge{0, 2}}}, {{OutputEdge{0, 0}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -419,7 +419,7 @@ NGRAPH_TEST(onnx_editor, subgraph__multiout_op_output_edge) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({}, {{OutputEdge{5, 1}}});
+    editor.extract_subgraph({}, {{OutputEdge{5, 1}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -434,7 +434,7 @@ NGRAPH_TEST(onnx_editor, subgraph__existing_inputs_and_outputs_based_extraction)
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{1, 1}, InputEdge{2, 0}}}, {{OutputEdge{4, 0}}});
+    editor.extract_subgraph({{InputEdge{1, 1}, InputEdge{2, 0}}}, {{OutputEdge{4, 0}}});
 
     const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
                                                         "onnx/model_editor/reference/"
@@ -448,7 +448,7 @@ NGRAPH_TEST(onnx_editor, subgraph__existing_inputs_and_outputs_based_extraction)
 NGRAPH_TEST(onnx_editor, subgraph__twice_input_edge_from_tensor_with_single_consumer) {
     ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
 
-    editor.cut_graph_fragment({InputEdge{1, 1}}, {});
+    editor.extract_subgraph({InputEdge{1, 1}}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -464,7 +464,7 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{1, 0}, InputEdge{6, 0}}}, {{OutputEdge{6, 0}, OutputEdge{4, 0}}});
+    editor.extract_subgraph({{InputEdge{1, 0}, InputEdge{6, 0}}}, {{OutputEdge{6, 0}, OutputEdge{4, 0}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -480,7 +480,7 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{3, 0}, InputEdge{3, 1}}}, {{OutputEdge{3, 0}, OutputEdge{4, 0}}});
+    editor.extract_subgraph({{InputEdge{3, 0}, InputEdge{3, 1}}}, {{OutputEdge{3, 0}, OutputEdge{4, 0}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -496,7 +496,7 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{3, 0}, InputEdge{6, 0}}}, {{OutputEdge{6, 0}, OutputEdge{5, 1}}});
+    editor.extract_subgraph({{InputEdge{3, 0}, InputEdge{6, 0}}}, {{OutputEdge{6, 0}, OutputEdge{5, 1}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -512,7 +512,7 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{1, 0}, InputEdge{3, 0}}}, {});
+    editor.extract_subgraph({{InputEdge{1, 0}, InputEdge{3, 0}}}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -528,7 +528,7 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({InputEdge{3, 0}}, {{OutputEdge{6, 0}, OutputEdge{5, 1}}});
+    editor.extract_subgraph({InputEdge{3, 0}}, {{OutputEdge{6, 0}, OutputEdge{5, 1}}});
 
     // expected to behave the same way as the test above
     const auto ref_model =
@@ -545,8 +545,8 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{1, 0, "new_name_1"}, InputEdge{6, 0, "new_name_2"}}},
-                              {{OutputEdge{6, 0}, OutputEdge{4, 0}}});
+    editor.extract_subgraph({{InputEdge{1, 0, "new_name_1"}, InputEdge{6, 0, "new_name_2"}}},
+                            {{OutputEdge{6, 0}, OutputEdge{4, 0}}});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -562,7 +562,7 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_input_relu2) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{4, 0}}}, {});
+    editor.extract_subgraph({{InputEdge{4, 0}}}, {});
 
     const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
                                                         "onnx/model_editor/reference/"
@@ -577,7 +577,7 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{2, 0}}}, {});
+    editor.extract_subgraph({{InputEdge{2, 0}}}, {});
 
     const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
                                                         "onnx/model_editor/reference/"
@@ -592,7 +592,7 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer_2) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{2, 0}, InputEdge{3, 0}}}, {});
+    editor.extract_subgraph({{InputEdge{2, 0}, InputEdge{3, 0}}}, {});
 
     // same as above
     const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -608,7 +608,7 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer_relu2
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{5, 0}, InputEdge{3, 0}}}, {});
+    editor.extract_subgraph({{InputEdge{5, 0}, InputEdge{3, 0}}}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -626,7 +626,7 @@ NGRAPH_TEST(onnx_editor, subgraph__invalid_edge_idx) {
 
     ONNXModelEditor editor{model_path};
     try {
-        editor.cut_graph_fragment({{InputEdge{15, 0}}}, {});
+        editor.extract_subgraph({{InputEdge{15, 0}}}, {});
     } catch (const std::exception& e) {
         std::string msg{e.what()};
         EXPECT_TRUE(msg.find("The specified node index is out of range of nodes in the original model") !=
@@ -640,7 +640,7 @@ NGRAPH_TEST(onnx_editor, subgraph__invalid_port_idx) {
 
     ONNXModelEditor editor{model_path};
     try {
-        editor.cut_graph_fragment({{InputEdge{0, 3}}}, {});
+        editor.extract_subgraph({{InputEdge{0, 3}}}, {});
     } catch (const std::exception& e) {
         std::string msg{e.what()};
         EXPECT_TRUE(msg.find("The specified node with index: 0 has not input port with index: 3") != std::string::npos);
@@ -653,7 +653,7 @@ NGRAPH_TEST(onnx_editor, subgraph__inputs_getter) {
 
     EXPECT_EQ(editor.model_inputs(), (std::vector<std::string>{"data_0", "conv1/7x7_s2_w_0", "conv1/7x7_s2_b_0"}));
 
-    editor.cut_graph_fragment({{InputEdge{1, 0}}}, {});
+    editor.extract_subgraph({{InputEdge{1, 0}}}, {});
 
     EXPECT_EQ(editor.model_inputs(), (std::vector<std::string>{"conv1/7x7_s2_1"}));
 }
@@ -664,7 +664,7 @@ NGRAPH_TEST(onnx_editor, subgraph__custom_input_name_already_exist) {
 
     ONNXModelEditor editor{model_path};
     try {
-        editor.cut_graph_fragment({{InputEdge{1, 0, "conv1/7x7_s2_b_0"}}}, {});
+        editor.extract_subgraph({{InputEdge{1, 0, "conv1/7x7_s2_b_0"}}}, {});
     } catch (const std::exception& e) {
         std::string msg{e.what()};
         EXPECT_TRUE(msg.find("New custom input name: conv1/7x7_s2_b_0 already exist in the graph") !=
@@ -1050,7 +1050,7 @@ NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter) {
 
     const auto output_edge = editor.find_output_edge(EditorNode(EditorOutput("mul2")), EditorOutput(0));
     // OutputEdge{4, "mul2"}
-    editor.cut_graph_fragment({input_edge_1, input_edge_2}, {output_edge});
+    editor.extract_subgraph({input_edge_1, input_edge_2}, {output_edge});
 
     const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
                                                         "onnx/model_editor/reference/"
@@ -1082,7 +1082,7 @@ NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter_custom_nam
     const auto input_edge_2 =
         editor.find_input_edge(EditorNode{EditorOutput{"split2"}}, EditorInput{"add2", "new_name_2"});
 
-    editor.cut_graph_fragment({input_edge_1, input_edge_2}, {});
+    editor.extract_subgraph({input_edge_1, input_edge_2}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -1201,7 +1201,7 @@ NGRAPH_TEST(onnx_editor, editor_api_input_edge_from_tensor_with_single_consumer)
     ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
 
     const auto edge = editor.find_input_edge(EditorNode{EditorOutput{"Y"}}, EditorInput{1});
-    editor.cut_graph_fragment({edge}, {});
+    editor.extract_subgraph({edge}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO,
@@ -1346,7 +1346,7 @@ NGRAPH_TEST(onnx_editor, combined__cut_and_replace_shape) {
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     const auto new_shape = PartialShape({1, 64, 112, 112});
-    editor.cut_graph_fragment({{InputEdge(1, 0)}}, {});
+    editor.extract_subgraph({{InputEdge(1, 0)}}, {});
     editor.set_input_shapes({{"conv1/7x7_s2_1", new_shape}});
 
     const auto ref_model =
@@ -1365,7 +1365,7 @@ NGRAPH_TEST(onnx_editor, cut_operator_with_no_schema) {
     ONNXModelEditor editor{
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/unknown_input_value_info.onnx")};
 
-    editor.cut_graph_fragment({{InputEdge{1, 0}}}, {});
+    editor.extract_subgraph({{InputEdge{1, 0}}}, {});
 
     const auto ref_model =
         ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/unknown_input_value_info.onnx");
diff --git a/src/frontends/onnx/frontend/src/editor.cpp b/src/frontends/onnx/frontend/src/editor.cpp
index 94027d0fda0..911ae6ed4a3 100644
--- a/src/frontends/onnx/frontend/src/editor.cpp
+++ b/src/frontends/onnx/frontend/src/editor.cpp
@@ -336,8 +336,8 @@ PartialShape onnx_editor::ONNXModelEditor::get_tensor_shape(const std::string& t
     }
 }
 
-void onnx_editor::ONNXModelEditor::cut_graph_fragment(const std::vector<InputEdge>& inputs,
-                                                      const std::vector<OutputEdge>& outputs) {
+void onnx_editor::ONNXModelEditor::extract_subgraph(const std::vector<InputEdge>& inputs,
+                                                    const std::vector<OutputEdge>& outputs) {
     if (inputs.empty() && outputs.empty()) {
         return;
     }
@@ -494,6 +494,16 @@ void onnx_editor::ONNXModelEditor::set_node_name(const EditorNode& node, const s
     *graph->mutable_node(node_idx)->mutable_name() = new_name;
 }
 
+std::string onnx_editor::ONNXModelEditor::get_node_name(const EditorNode& node) const {
+    if (node.m_node_index >= 0) {
+        OPENVINO_ASSERT(node.m_node_index < m_pimpl->m_model_proto->graph().node().size(), "XXX");
+        const auto& n = m_pimpl->m_model_proto->graph().node(node.m_node_index);
+        return n.has_name() ? n.name() : "";
+    } else {
+        return node.m_node_name;
+    }
+}
+
 void onnx_editor::ONNXModelEditor::clear_nodes_name(const std::string& name) {
     const auto graph = m_pimpl->m_model_proto->mutable_graph();
 
diff --git a/src/frontends/onnx/frontend/src/editor.hpp b/src/frontends/onnx/frontend/src/editor.hpp
index d72d03a64ad..39ec6125ef6 100644
--- a/src/frontends/onnx/frontend/src/editor.hpp
+++ b/src/frontends/onnx/frontend/src/editor.hpp
@@ -80,7 +80,7 @@ public:
     ///
     /// \param inputs A collection of input edges which become new inputs to the graph
     /// \param outputs A collection of output edges which become new outputs of the graph
-    void cut_graph_fragment(const std::vector<InputEdge>& inputs, const std::vector<OutputEdge>& outputs);
+    void extract_subgraph(const std::vector<InputEdge>& inputs, const std::vector<OutputEdge>& outputs);
 
     /// \brief Modifies the in-memory representation of the model by setting custom input
     ///        values for inputs specified in the provided map.
@@ -112,6 +112,11 @@ public:
     /// \param new_name New name of the node.
     void set_node_name(const EditorNode& node, const std::string& new_name);
 
+    /// \brief Retrieves a node name from the in-memory ONNX model.
+    ///
+    /// \param node Node descriptor for which the lookup should be performed.
+    std::string get_node_name(const EditorNode& node) const;
+
     /// \brief Removes node name for all nodes with given name.
     ///
     /// \note Empty and not present names are accepted.
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index 500e4acde16..d35dfd2b090 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -123,18 +123,46 @@ void InputModel::free_name_for_tensor(const std::string&) {
 }
 
 void InputModel::set_partial_shape(const ov::frontend::Place::Ptr& place, const ngraph::PartialShape& shape) {
-    std::map<std::string, ngraph::PartialShape> m;
-    m[place->get_names()[0]] = shape;
-    m_editor->set_input_shapes(m);
+    std::string input_name;  // name of the model input which should be reshaped
+    const auto input_edge = std::dynamic_pointer_cast<PlaceInputEdge>(place);
+    if (input_edge) {
+        const auto tensor_names = input_edge->get_source_tensor()->get_names();
+        OPENVINO_ASSERT(!tensor_names.empty(), "Cannot retrieve input name. Setting new input shape is not possible.");
+        input_name = tensor_names[0];
+    } else {
+        // fallback in case something else than an InputEdge is passed in - try to retrieve its name and reshape
+        OPENVINO_ASSERT(!place->get_names().empty(),
+                        "Cannot retrieve input name. Setting new input shape is not possible.");
+        input_name = place->get_names()[0];
+    }
+
+    m_editor->set_input_shapes({{input_name, shape}});
 }
 
 ngraph::PartialShape InputModel::get_partial_shape(const ov::frontend::Place::Ptr& place) const {
-    return m_editor->get_tensor_shape(place->get_names().at(0));
+    std::string tensor_name;  // name of the model input which should be reshaped
+    const auto input_edge = std::dynamic_pointer_cast<PlaceInputEdge>(place);
+    const auto output_edge = std::dynamic_pointer_cast<PlaceOutputEdge>(place);
+    if (input_edge) {
+        const auto tensor_names = input_edge->get_source_tensor()->get_names();
+        OPENVINO_ASSERT(!tensor_names.empty(),
+                        "Cannot retrieve source tensor name for this InputEdge and thus partial shape.");
+        tensor_name = tensor_names[0];
+    } else if (output_edge) {
+        const auto tensor_names = output_edge->get_target_tensor()->get_names();
+        OPENVINO_ASSERT(!tensor_names.empty(),
+                        "Cannot retrieve target tensor name for this OutputEdge and thus partial shape.");
+        tensor_name = tensor_names[0];
+    } else {
+        tensor_name = place->get_names().at(0);
+    }
+
+    return m_editor->get_tensor_shape(tensor_name);
 }
 
 void InputModel::set_element_type(const ov::frontend::Place::Ptr& place, const ngraph::element::Type& type) {
     std::map<std::string, ngraph::element::Type_t> m;
-    m[place->get_names()[0]] = type;
+    m[place->get_names().at(0)] = type;
     m_editor->set_input_types(m);
 }
 
@@ -179,7 +207,7 @@ void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& i
         if (const auto input_port = std::dynamic_pointer_cast<PlaceInputEdge>(input)) {
             onnx_inputs.push_back(input_port->get_input_edge());
         } else if (const auto tensor = std::dynamic_pointer_cast<PlaceTensor>(input)) {
-            auto name = tensor->get_names()[0];
+            const auto name = tensor->get_names().at(0);
             const auto consumers = m_editor->find_output_consumers(name);
             std::transform(std::begin(consumers),
                            std::end(consumers),
@@ -224,5 +252,5 @@ void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& i
                            });
         }
     }
-    m_editor->cut_graph_fragment(onnx_inputs, onnx_outputs);
+    m_editor->extract_subgraph(onnx_inputs, onnx_outputs);
 }
diff --git a/src/frontends/onnx/frontend/src/place.cpp b/src/frontends/onnx/frontend/src/place.cpp
index fd4c7a97601..76d70bc8021 100644
--- a/src/frontends/onnx/frontend/src/place.cpp
+++ b/src/frontends/onnx/frontend/src/place.cpp
@@ -21,6 +21,10 @@ onnx_editor::InputEdge PlaceInputEdge::get_input_edge() const {
     return m_edge;
 }
 
+std::vector<std::string> PlaceInputEdge::get_names() const {
+    return {"InputEdge{" + std::to_string(m_edge.m_node_idx) + ", " + std::to_string(m_edge.m_port_idx) + "}"};
+}
+
 bool PlaceInputEdge::is_input() const {
     return m_editor->is_input(m_edge);
 }
@@ -70,6 +74,10 @@ onnx_editor::OutputEdge PlaceOutputEdge::get_output_edge() const {
     return m_edge;
 }
 
+std::vector<std::string> PlaceOutputEdge::get_names() const {
+    return {"OutputEdge{" + std::to_string(m_edge.m_node_idx) + ", " + std::to_string(m_edge.m_port_idx) + "}"};
+}
+
 bool PlaceOutputEdge::is_input() const {
     return false;
 }
@@ -198,7 +206,11 @@ PlaceOp::PlaceOp(onnx_editor::EditorNode&& node, std::shared_ptr<onnx_editor::ON
       m_editor{std::move(editor)} {}
 
 std::vector<std::string> PlaceOp::get_names() const {
-    return {m_node.m_node_name};
+    if (!m_node.m_node_name.empty()) {
+        return {m_node.m_node_name};
+    } else {
+        return {m_editor->get_node_name(m_node)};
+    }
 }
 
 const onnx_editor::EditorNode& PlaceOp::get_editor_node() const {
diff --git a/src/frontends/onnx/frontend/src/place.hpp b/src/frontends/onnx/frontend/src/place.hpp
index 2b323d6c1a9..59a254ad7a5 100644
--- a/src/frontends/onnx/frontend/src/place.hpp
+++ b/src/frontends/onnx/frontend/src/place.hpp
@@ -22,6 +22,7 @@ public:
     onnx_editor::InputEdge get_input_edge() const;
 
     // external usage
+    std::vector<std::string> get_names() const override;
     bool is_input() const override;
     bool is_output() const override;
     bool is_equal(const Place::Ptr& another) const override;
@@ -45,6 +46,7 @@ public:
     onnx_editor::OutputEdge get_output_edge() const;
 
     // external usage
+    std::vector<std::string> get_names() const override;
     bool is_input() const override;
     bool is_output() const override;
     bool is_equal(const Place::Ptr& another) const override;
diff --git a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
index a29a957aa25..8f69c123b6d 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
@@ -32,8 +32,7 @@ def decode_name_with_port(input_model: InputModel, node_name: str):
     if match_post:
         node_post = input_model.get_place_by_operation_name(match_post.group(1))
         if node_post:
-            node_post = node_post.get_output_port(
-                outputPortIndex=int(match_post.group(2)))
+            node_post = node_post.get_output_port(outputPortIndex=int(match_post.group(2)))
             if node_post:
                 found_node_names.append(match_post.group(1))
                 found_nodes.append(node_post)
@@ -43,8 +42,7 @@ def decode_name_with_port(input_model: InputModel, node_name: str):
     if match_pre:
         node_pre = input_model.get_place_by_operation_name(match_pre.group(2))
         if node_pre:
-            node_pre = node_pre.get_input_port(
-                inputPortIndex=int(match_pre.group(1)))
+            node_pre = node_pre.get_input_port(inputPortIndex=int(match_pre.group(1)))
             if node_pre:
                 found_node_names.append(match_pre.group(2))
                 found_nodes.append(node_pre)
diff --git a/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py b/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
index e8773662edc..0e3c816c92c 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
@@ -55,10 +55,20 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
         new_output_places = [x['node'] for x in outputs]
         log.debug('Using extract subgraph')
         input_model.extract_subgraph(new_input_places, new_output_places)
+        # invalidation of existing Place objects could have happened in the operation above
+        if user_shapes:
+            user_shapes, outputs, freeze_placeholder = fe_user_data_repack(
+                input_model, argv.placeholder_shapes, argv.placeholder_data_types,
+                argv.output, argv.freeze_placeholder_with_value)
     elif not inputs_equal:
         new_input_places = [x['node'] for x in user_shapes]
         log.debug('Using override_all_inputs')
         input_model.override_all_inputs(new_input_places)
+        # invalidation of existing Place objects could have happened in the operation above
+        if user_shapes:
+            user_shapes, outputs, freeze_placeholder = fe_user_data_repack(
+                input_model, argv.placeholder_shapes, argv.placeholder_data_types,
+                argv.output, argv.freeze_placeholder_with_value)
     elif not outputs_equal:
         new_output_places = [x['node'] for x in outputs]
         log.debug('Using override_all_outputs')

From 526fe3098d729930ced3de72003fd5e7e0235913 Mon Sep 17 00:00:00 2001
From: Ivan Tikhonov <ivan.tikhonov@intel.com>
Date: Sat, 18 Dec 2021 18:19:25 +0300
Subject: [PATCH 031/160] JsonConfig/JsonTranformation/DecoderTransformation
 Extensions (#9042)

* Squash commits: JsonConfig/JsonTranformation/DecoderTransformation extensions,
Unit tests, pybindings

* Call transformation_extensions in convert_partially method

* fix codestyle

* move json extensions to offline transformations

* Fix unit tests

* fix cmake files

* Refactor JsonConfig extensions and FrontEnds

* Update python bindings

* Add missing files

* correct mkl hash

* fix conflicts with master branch

* Resolve review comments

* Revert local changes

* code style

* Add lib destinations

* fix cmake

* correct nlohmann_json hash

* FunctionPass to ModelPass

* fix unit tests and pyopenvino

* fix pyopenvino

* resolve review comments

* revert local change

* Fix dynamic Win build

* Try to fix linux builds

* Fix Lin build: set correct lib directory

* Resolve review commets

* code style

* resolve review comments

* ubuntu20 issue investigation: temporary disable LTO for frontends

* ubuntu20 issue investigation: temporary disable LTO for unit tests

* ubuntu20 issue investigation: temporary disable LTO for thirdparty and core parts

* Revert "ubuntu20 issue investigation: temporary disable LTO for thirdparty and core parts"

This reverts commit 5489072e4abce2656de82458470ff7f16e9885a9.

* Revert "ubuntu20 issue investigation: temporary disable LTO for unit tests"

This reverts commit 73bd3c6bac986f075f2c584f7fc7c076b3390548.

* Revert "ubuntu20 issue investigation: temporary disable LTO for frontends"

This reverts commit dce2ba4830134c732fd301b28533a079db354d23.

* resolve review comments

* try to disable lto for 3rd party json libs

* fix missprint; codestyle
---
 .../python/src/openvino/frontend/__init__.py  |   2 +
 .../python/src/pyopenvino/core/extension.cpp  |  16 ++
 .../python/src/pyopenvino/core/extension.hpp  |  11 +
 .../src/pyopenvino/frontend/extensions.cpp    |  56 +++++
 .../src/pyopenvino/frontend/extensions.hpp    |  13 ++
 .../src/pyopenvino/frontend/frontend.cpp      |  25 +--
 .../src/pyopenvino/frontend/frontend.hpp      |   2 -
 .../src/pyopenvino/frontend/manager.hpp       |   1 -
 .../python/src/pyopenvino/pyopenvino.cpp      |   6 +-
 .../offline_transformations/CMakeLists.txt    |   8 +-
 .../include/extension/json_config.hpp         |  28 +++
 .../include/extension/json_transformation.hpp |  54 +++++
 .../src/json_extension/json_config.cpp        | 204 ++++++++++++++++++
 src/core/include/openvino/pass/pass.hpp       |   2 +-
 src/core/tests/CMakeLists.txt                 |   3 +-
 .../decoder_transformation_extension.cpp      |  55 +++++
 .../frontend/onnx/json_config_extension.cpp   |  23 ++
 .../paddlepaddle/json_config_extension.cpp    |  23 ++
 src/core/tests/frontend/shared/CMakeLists.txt |   6 +-
 .../frontend/shared/include/json_config.hpp   |  48 +++++
 .../tests/frontend/shared/src/json_config.cpp | 194 +++++++++++++++++
 .../tests/frontend/shared/src/telemetry.cpp   |   3 +-
 .../test_builtin_extensions_1/CMakeLists.txt  |  26 +++
 .../builtin_extensions.cpp                    |   9 +
 .../test_extension.cpp                        |  14 ++
 .../test_extension.hpp                        |  15 ++
 .../test_builtin_extensions_2/CMakeLists.txt  |  26 +++
 .../builtin_extensions.cpp                    |  10 +
 .../test_extensions.cpp                       |  21 ++
 .../test_extensions.hpp                       |  22 ++
 .../tensorflow/json_config_extension.cpp      |  23 ++
 .../extension/decoder_transformation.hpp      |  56 +++++
 .../telemetry.hpp}                            |   2 +-
 .../src/extension/decoder_transformation.cpp  |  47 ++++
 .../telemetry.cpp}                            |   2 +-
 .../include/openvino/frontend/ir/frontend.hpp |   2 +-
 .../openvino/frontend/onnx/frontend.hpp       |   3 +-
 .../onnx/frontend/src/core/graph.hpp          |   2 +-
 src/frontends/onnx/frontend/src/editor.hpp    |   2 +-
 src/frontends/onnx/frontend/src/frontend.cpp  |   2 +-
 .../onnx/frontend/src/utils/onnx_internal.hpp |   2 +-
 .../paddlepaddle_frontend/frontend.hpp        |   4 +-
 .../include/paddlepaddle_frontend/model.hpp   |   2 +-
 src/frontends/paddlepaddle/src/frontend.cpp   |  34 ++-
 .../include/tensorflow_frontend/frontend.hpp  |   4 +-
 src/frontends/tensorflow/src/frontend.cpp     |  30 +++
 src/frontends/tensorflow/src/model.hpp        |   2 +-
 thirdparty/json/CMakeLists.txt                |   2 +
 48 files changed, 1099 insertions(+), 48 deletions(-)
 create mode 100644 src/bindings/python/src/pyopenvino/core/extension.cpp
 create mode 100644 src/bindings/python/src/pyopenvino/core/extension.hpp
 create mode 100644 src/bindings/python/src/pyopenvino/frontend/extensions.cpp
 create mode 100644 src/bindings/python/src/pyopenvino/frontend/extensions.hpp
 create mode 100644 src/common/offline_transformations/include/extension/json_config.hpp
 create mode 100644 src/common/offline_transformations/include/extension/json_transformation.hpp
 create mode 100644 src/common/offline_transformations/src/json_extension/json_config.cpp
 create mode 100644 src/core/tests/frontend/decoder_transformation_extension.cpp
 create mode 100644 src/core/tests/frontend/onnx/json_config_extension.cpp
 create mode 100644 src/core/tests/frontend/paddlepaddle/json_config_extension.cpp
 create mode 100644 src/core/tests/frontend/shared/include/json_config.hpp
 create mode 100644 src/core/tests/frontend/shared/src/json_config.cpp
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_1/CMakeLists.txt
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_1/builtin_extensions.cpp
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.cpp
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.hpp
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_2/CMakeLists.txt
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_2/builtin_extensions.cpp
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.cpp
 create mode 100644 src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.hpp
 create mode 100644 src/core/tests/frontend/tensorflow/json_config_extension.cpp
 create mode 100644 src/frontends/common/include/openvino/frontend/extension/decoder_transformation.hpp
 rename src/frontends/common/include/openvino/frontend/{telemetry_extension.hpp => extension/telemetry.hpp} (96%)
 create mode 100644 src/frontends/common/src/extension/decoder_transformation.cpp
 rename src/frontends/common/src/{telemetry_extension.cpp => extension/telemetry.cpp} (95%)

diff --git a/src/bindings/python/src/openvino/frontend/__init__.py b/src/bindings/python/src/openvino/frontend/__init__.py
index a008675fce0..156b240e00a 100644
--- a/src/bindings/python/src/openvino/frontend/__init__.py
+++ b/src/bindings/python/src/openvino/frontend/__init__.py
@@ -41,6 +41,8 @@ from openvino.pyopenvino import FrontEnd
 from openvino.pyopenvino import InputModel
 from openvino.pyopenvino import Place
 from openvino.pyopenvino import TelemetryExtension
+from openvino.pyopenvino import DecoderTransformationExtension
+from openvino.pyopenvino import JsonConfigExtension
 
 # exceptions
 from openvino.pyopenvino import NotImplementedFailure
diff --git a/src/bindings/python/src/pyopenvino/core/extension.cpp b/src/bindings/python/src/pyopenvino/core/extension.cpp
new file mode 100644
index 00000000000..0d4bafd28e8
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/core/extension.cpp
@@ -0,0 +1,16 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <pybind11/functional.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+#include "openvino/frontend/manager.hpp"
+
+namespace py = pybind11;
+
+void regclass_Extension(py::module m) {
+    py::class_<ov::Extension, std::shared_ptr<ov::Extension>> ext(m, "Extension", py::dynamic_attr());
+}
diff --git a/src/bindings/python/src/pyopenvino/core/extension.hpp b/src/bindings/python/src/pyopenvino/core/extension.hpp
new file mode 100644
index 00000000000..537396df2f3
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/core/extension.hpp
@@ -0,0 +1,11 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+void regclass_Extension(py::module m);
diff --git a/src/bindings/python/src/pyopenvino/frontend/extensions.cpp b/src/bindings/python/src/pyopenvino/frontend/extensions.cpp
new file mode 100644
index 00000000000..9bcf4090e8c
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/frontend/extensions.cpp
@@ -0,0 +1,56 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <pybind11/functional.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+#include "extension/json_config.hpp"
+#include "manager.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/extension/decoder_transformation.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
+#include "pyopenvino/graph/function.hpp"
+
+namespace py = pybind11;
+
+using namespace ov::frontend;
+
+void regclass_frontend_TelemetryExtension(py::module m) {
+    py::class_<TelemetryExtension, std::shared_ptr<TelemetryExtension>, ov::Extension> ext(m,
+                                                                                           "TelemetryExtension",
+                                                                                           py::dynamic_attr());
+
+    ext.def(py::init([](const std::string& event_category,
+                        const TelemetryExtension::event_callback& send_event,
+                        const TelemetryExtension::error_callback& send_error,
+                        const TelemetryExtension::error_callback& send_stack_trace) {
+        return std::make_shared<TelemetryExtension>(event_category, send_event, send_error, send_stack_trace);
+    }));
+
+    ext.def("send_event", &TelemetryExtension::send_event);
+    ext.def("send_error", &TelemetryExtension::send_error);
+    ext.def("send_stack_trace", &TelemetryExtension::send_stack_trace);
+}
+
+void regclass_frontend_DecoderTransformationExtension(py::module m) {
+    py::class_<ov::frontend::DecoderTransformationExtension,
+               std::shared_ptr<ov::frontend::DecoderTransformationExtension>,
+               ov::Extension>
+        ext(m, "DecoderTransformationExtension", py::dynamic_attr());
+}
+
+void regclass_frontend_JsonConfigExtension(py::module m) {
+    py::class_<ov::frontend::JsonConfigExtension,
+               std::shared_ptr<ov::frontend::JsonConfigExtension>,
+               ov::frontend::DecoderTransformationExtension>
+        ext(m, "JsonConfigExtension", py::dynamic_attr());
+
+    ext.doc() = "Extension class to load and process ModelOptimizer JSON config file";
+
+    ext.def(py::init([](const std::string& path) {
+        return std::make_shared<ov::frontend::JsonConfigExtension>(path);
+    }));
+}
diff --git a/src/bindings/python/src/pyopenvino/frontend/extensions.hpp b/src/bindings/python/src/pyopenvino/frontend/extensions.hpp
new file mode 100644
index 00000000000..03133fd0be6
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/frontend/extensions.hpp
@@ -0,0 +1,13 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+void regclass_frontend_TelemetryExtension(py::module m);
+void regclass_frontend_DecoderTransformationExtension(py::module m);
+void regclass_frontend_JsonConfigExtension(py::module m);
diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
index c3e49181793..a510fa45998 100644
--- a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
@@ -8,8 +8,8 @@
 #include <pybind11/stl_bind.h>
 
 #include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/manager.hpp"
-#include "openvino/frontend/telemetry_extension.hpp"
 #include "pyopenvino/graph/function.hpp"
 
 namespace py = pybind11;
@@ -143,26 +143,3 @@ void regclass_frontend_FrontEnd(py::module m) {
         return "<FrontEnd '" + self.get_name() + "'>";
     });
 }
-
-void regclass_frontend_Extension(py::module m) {
-    py::class_<ov::Extension, std::shared_ptr<ov::Extension>> ext(m, "Extension", py::dynamic_attr());
-}
-
-void regclass_frontend_TelemetryExtension(py::module m) {
-    {
-        py::class_<TelemetryExtension, std::shared_ptr<TelemetryExtension>, ov::Extension> ext(m,
-                                                                                               "TelemetryExtension",
-                                                                                               py::dynamic_attr());
-
-        ext.def(py::init([](const std::string& event_category,
-                            const TelemetryExtension::event_callback& send_event,
-                            const TelemetryExtension::error_callback& send_error,
-                            const TelemetryExtension::error_callback& send_stack_trace) {
-            return std::make_shared<TelemetryExtension>(event_category, send_event, send_error, send_stack_trace);
-        }));
-
-        ext.def("send_event", &TelemetryExtension::send_event);
-        ext.def("send_error", &TelemetryExtension::send_error);
-        ext.def("send_stack_trace", &TelemetryExtension::send_stack_trace);
-    }
-}
diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend.hpp b/src/bindings/python/src/pyopenvino/frontend/frontend.hpp
index b6df63e9c26..50b5b252d8c 100644
--- a/src/bindings/python/src/pyopenvino/frontend/frontend.hpp
+++ b/src/bindings/python/src/pyopenvino/frontend/frontend.hpp
@@ -9,5 +9,3 @@
 namespace py = pybind11;
 
 void regclass_frontend_FrontEnd(py::module m);
-void regclass_frontend_Extension(py::module m);
-void regclass_frontend_TelemetryExtension(py::module m);
diff --git a/src/bindings/python/src/pyopenvino/frontend/manager.hpp b/src/bindings/python/src/pyopenvino/frontend/manager.hpp
index aa2e0a16a04..7ab6abfd51f 100644
--- a/src/bindings/python/src/pyopenvino/frontend/manager.hpp
+++ b/src/bindings/python/src/pyopenvino/frontend/manager.hpp
@@ -14,4 +14,3 @@ void regclass_frontend_InitializationFailureFrontEnd(py::module m);
 void regclass_frontend_OpConversionFailureFrontEnd(py::module m);
 void regclass_frontend_OpValidationFailureFrontEnd(py::module m);
 void regclass_frontend_GeneralFailureFrontEnd(py::module m);
-
diff --git a/src/bindings/python/src/pyopenvino/pyopenvino.cpp b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
index 46bc6e1eb8f..e028e3ad389 100644
--- a/src/bindings/python/src/pyopenvino/pyopenvino.cpp
+++ b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
@@ -24,6 +24,7 @@
 #include "pyopenvino/core/compiled_model.hpp"
 #include "pyopenvino/core/containers.hpp"
 #include "pyopenvino/core/core.hpp"
+#include "pyopenvino/core/extension.hpp"
 #include "pyopenvino/core/ie_parameter.hpp"
 #include "pyopenvino/core/infer_request.hpp"
 #include "pyopenvino/core/offline_transformations.hpp"
@@ -31,6 +32,7 @@
 #include "pyopenvino/core/tensor.hpp"
 #include "pyopenvino/core/variable_state.hpp"
 #include "pyopenvino/core/version.hpp"
+#include "pyopenvino/frontend/extensions.hpp"
 #include "pyopenvino/frontend/frontend.hpp"
 #include "pyopenvino/frontend/inputmodel.hpp"
 #include "pyopenvino/frontend/manager.hpp"
@@ -124,6 +126,7 @@ PYBIND11_MODULE(pyopenvino, m) {
     regclass_Parameter(m);
     regclass_AsyncInferQueue(m);
     regclass_ProfilingInfo(m);
+    regclass_Extension(m);
 
     regclass_frontend_Place(m);
     regclass_frontend_InitializationFailureFrontEnd(m);
@@ -131,11 +134,12 @@ PYBIND11_MODULE(pyopenvino, m) {
     regclass_frontend_OpConversionFailureFrontEnd(m);
     regclass_frontend_OpValidationFailureFrontEnd(m);
     regclass_frontend_NotImplementedFailureFrontEnd(m);
-    regclass_frontend_Extension(m);
     regclass_frontend_FrontEndManager(m);
     regclass_frontend_FrontEnd(m);
     regclass_frontend_InputModel(m);
     regclass_frontend_TelemetryExtension(m);
+    regclass_frontend_DecoderTransformationExtension(m);
+    regclass_frontend_JsonConfigExtension(m);
 
     regmodule_offline_transformations(m);
 }
diff --git a/src/common/offline_transformations/CMakeLists.txt b/src/common/offline_transformations/CMakeLists.txt
index 36e41831f76..359cda2fa77 100644
--- a/src/common/offline_transformations/CMakeLists.txt
+++ b/src/common/offline_transformations/CMakeLists.txt
@@ -20,14 +20,18 @@ source_group("include" FILES ${PUBLIC_HEADERS})
 add_library(${TARGET_NAME} STATIC EXCLUDE_FROM_ALL ${LIBRARY_SRC} ${PUBLIC_HEADERS})
 
 target_link_libraries(${TARGET_NAME} PUBLIC ngraph inference_engine_transformations ngraph::reference
-                                            nlohmann_json_schema_validator
-                                     PRIVATE openvino::itt pugixml::static)
+                                     PRIVATE openvino::itt pugixml::static openvino::frontend::common
+                                             nlohmann_json_schema_validator nlohmann_json)
 
 target_include_directories(${TARGET_NAME} PUBLIC ${PUBLIC_HEADERS_DIR}
                                           PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src")
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
 
+# Add include path to so_extension.hpp
+set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/src/json_extension/json_config.cpp
+        PROPERTIES INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
+
 # developer package
 
 openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
diff --git a/src/common/offline_transformations/include/extension/json_config.hpp b/src/common/offline_transformations/include/extension/json_config.hpp
new file mode 100644
index 00000000000..863a12e8ba8
--- /dev/null
+++ b/src/common/offline_transformations/include/extension/json_config.hpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <fstream>
+#include <iostream>
+#include <type_traits>
+
+#include "openvino/frontend/extension/decoder_transformation.hpp"
+#include "openvino/frontend/visibility.hpp"
+
+namespace ov {
+namespace frontend {
+
+/// \brief JsonConfigExtension reads MO config file and delegate transformation functionality to specified
+/// transformation ID specified in the config.
+class JsonConfigExtension : public DecoderTransformationExtension {
+public:
+    explicit JsonConfigExtension(const std::string& config_path);
+
+protected:
+    std::vector<Extension::Ptr> m_loaded_extensions;
+    std::vector<std::pair<std::shared_ptr<DecoderTransformationExtension>, std::string>> m_target_extensions;
+};
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/common/offline_transformations/include/extension/json_transformation.hpp b/src/common/offline_transformations/include/extension/json_transformation.hpp
new file mode 100644
index 00000000000..604c7e74cd4
--- /dev/null
+++ b/src/common/offline_transformations/include/extension/json_transformation.hpp
@@ -0,0 +1,54 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <fstream>
+#include <iostream>
+#include <type_traits>
+
+#include "openvino/frontend/extension/decoder_transformation.hpp"
+#include "openvino/core/extension.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+#include "openvino/pass/manager.hpp"
+#include "openvino/pass/pass.hpp"
+
+namespace ov {
+namespace frontend {
+
+/// \brief Describes transformation that JsonConfigExtension can use as a target transformation specified by ID.
+/// JsonTransformationExtension passes JSON parsed object and matched points in the graph to JsonTransformationExtension
+/// instance, which is derived from DecoderTransformationExtension. DecoderTransformationExtension itself cannot be
+/// used for this purpose because we need to pass those additional objects which are the result of JSON parsing and
+/// graph matching that JsonTransformationExtension performs.
+///
+/// This class is left for backward compatibility only. In the future we would like to get rid off this class as well
+/// as from JsonConfigExtension, and users will use DecoderTransformationExtension only to match sub-graph and modify it
+/// like we do in any other transformations.
+///
+/// Unlike DecoderTransformationExtension, which is initialized by some ready-to-use transformation code and is not used
+/// to derive new classes by regular users, this class is intended to be derived from and it doesn't have convenient
+/// ctos to be initialized. So it is intended for more advanced, internal users inside such components like Model
+/// Optimizer.
+class JsonTransformationExtension : public DecoderTransformationExtension {
+public:
+    explicit JsonTransformationExtension(const std::string& id) : m_id(id) {}
+
+    /// \brief The name of the transformation to identify it from JSON file field 'id'
+    const std::string& id() const {
+        return m_id;
+    }
+
+    /// \brief Modifies OV Model according to the provided rules.
+    ///
+    /// \param[in]  function                   The OV Model object.
+    /// \param[in]  replacement_descriptions   The rules to modify the model in .json format.
+    virtual bool transform(const std::shared_ptr<ov::Model>& function,
+                           const std::string& replacement_descriptions) const = 0;
+
+private:
+    std::string m_id;
+};
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/common/offline_transformations/src/json_extension/json_config.cpp b/src/common/offline_transformations/src/json_extension/json_config.cpp
new file mode 100644
index 00000000000..25d05ca2085
--- /dev/null
+++ b/src/common/offline_transformations/src/json_extension/json_config.cpp
@@ -0,0 +1,204 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "extension/json_config.hpp"
+
+#include "nlohmann/json-schema.hpp"
+#include "openvino/frontend/extension/decoder_transformation.hpp"
+#include "extension/json_transformation.hpp"
+#include "so_extension.hpp"
+
+namespace {
+static const nlohmann::json validation_schema =
+R"(
+{
+        "definitions": {},
+        "$schema": "http://json-schema.org/draft-07/schema#",
+        "title": "Root",
+        "type": "array",
+        "default": [],
+        "items": {
+            "$id": "#root/items",
+                    "title": "Items",
+                    "type": "object",
+                    "required": [
+            "id",
+                    "match_kind"
+            ],
+            "properties": {
+                "custom_attributes": {
+                    "$id": "#root/items/custom_attributes",
+                            "title": "Custom_attributes",
+                            "type": "object",
+                            "properties": {
+                    }
+                },
+                "id": {
+                    "$id": "#root/items/id",
+                            "title": "Id",
+                            "type": "string",
+                            "pattern": "^.*$",
+                            "minLength": 1
+                },
+                "inputs": {
+                    "$id": "#root/items/inputs",
+                            "title": "Inputs",
+                            "type": "array",
+                            "default": [],
+                            "items": {
+                        "$id": "#root/items/inputs/items",
+                                "title": "Items",
+                                "type": "array",
+                                "default": [],
+                                "items": {
+                            "$id": "#root/items/inputs/items/items",
+                                    "title": "Items",
+                                    "type": "object",
+                                    "properties": {
+                                "node": {
+                                    "$id": "#root/items/inputs/items/items/node",
+                                            "title": "Node",
+                                            "type": "string",
+                                            "default": "",
+                                            "pattern": "^.*$"
+                                },
+                                "port": {
+                                    "$id": "#root/items/inputs/items/items/port",
+                                            "title": "Port",
+                                            "type": "integer",
+                                            "default": 0
+                                }
+                            },
+                            "required": ["node", "port"]
+                        }
+
+                    }
+                },
+                "instances": {
+                    "$id": "#root/items/instances",
+                            "title": "Instances",
+                            "type": ["array", "object"],
+                    "items": {
+                        "$id": "#root/items/instances/items",
+                                "title": "Items",
+                                "type": "string",
+                                "default": "",
+                                "pattern": "^.*$"
+                    }
+                },
+                "match_kind": {
+                    "$id": "#root/items/match_kind",
+                            "title": "Match_kind",
+                            "type": "string",
+                            "enum": ["points", "scope", "general"],
+                    "default": "points",
+                            "pattern": "^.*$"
+                },
+                "outputs": {
+                    "$id": "#root/items/outputs",
+                            "title": "Outputs",
+                            "type": "array",
+                            "default": [],
+                            "items": {
+                        "$id": "#root/items/outputs/items",
+                                "title": "Items",
+                                "type": "object",
+                                "properties": {
+                            "node": {
+                                "$id": "#root/items/outputs/items/node",
+                                        "title": "Node",
+                                        "type": "string",
+                                        "default": "",
+                                        "pattern": "^.*$"
+                            },
+                            "port": {
+                                "$id": "#root/items/outputs/items/port",
+                                        "title": "Port",
+                                        "type": "integer",
+                                        "default": 0
+                            }
+                        },
+                        "required": ["node", "port"]
+                    }
+
+                },
+                "include_inputs_to_sub_graph": {
+                    "$id": "#root/items/include_inputs_to_sub_graph",
+                            "title": "Include_inputs_to_sub_graph",
+                            "type": "boolean",
+                            "default": false
+                },
+                "include_outputs_to_sub_graph": {
+                    "$id": "#root/items/include_outputs_to_sub_graph",
+                            "title": "Include_outputs_to_sub_graph",
+                            "type": "boolean",
+                            "default": false
+                }
+            }
+        }
+}
+)"_json;
+} //  namespace
+
+using namespace ov;
+using namespace ov::frontend;
+
+JsonConfigExtension::JsonConfigExtension(const std::string& config_path)
+    : DecoderTransformationExtension([this](std::shared_ptr<ov::Model> f) {
+          bool res = false;
+          for (const auto& target_extension : m_target_extensions) {
+              if (auto extension = std::dynamic_pointer_cast<JsonTransformationExtension>(target_extension.first)) {
+                  res |= extension->transform(f, target_extension.second);
+              }
+          }
+          return res;
+      }) {
+    // Load JSON config
+    nlohmann::json config_json;
+    std::ifstream config_file(config_path);
+    config_file >> config_json;
+
+    // Validate JSON config
+    nlohmann::json_schema::json_validator validator;
+    try {
+        validator.set_root_schema(validation_schema);
+    } catch (const std::exception& e) {
+        OPENVINO_ASSERT(false, "Invalid json schema : ", e.what());
+    }
+
+    try {
+        validator.validate(config_json);
+    } catch (const std::exception& e) {
+        OPENVINO_ASSERT(false, "Json schema validation failed: ", e.what());
+    }
+
+    // Parse JSON Extensions
+
+    // Group sections describing transformations by library.
+    std::unordered_map<std::string, nlohmann::json> lib_to_sections;
+    for (const auto& section : config_json) {
+        lib_to_sections[section["library"]].push_back(section);
+    }
+
+    // Load all extensions in each library and select required
+    for (const auto& it : lib_to_sections) {
+        const auto& lib = it.first;
+        const auto& sections = it.second;
+
+        auto extensions = ov::detail::load_extensions(lib);
+        m_loaded_extensions.insert(m_loaded_extensions.end(), extensions.begin(), extensions.end());
+        for (const auto& ext : extensions) {
+            auto so_extension = std::dynamic_pointer_cast<ov::detail::SOExtension>(ext);
+            OPENVINO_ASSERT(so_extension, "Unexpected extension type loaded from shared library.");
+            auto extension = so_extension->extension();
+            if (auto json_ext = std::dynamic_pointer_cast<JsonTransformationExtension>(extension)) {
+                for (const auto& section : sections) {
+                    if (section["id"] == json_ext->id()) {
+                        m_target_extensions.push_back({json_ext, section.dump()});
+                    }
+                }
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/src/core/include/openvino/pass/pass.hpp b/src/core/include/openvino/pass/pass.hpp
index ad0b9d65b81..fd96c27db03 100644
--- a/src/core/include/openvino/pass/pass.hpp
+++ b/src/core/include/openvino/pass/pass.hpp
@@ -89,7 +89,7 @@ private:
 
 class OPENVINO_API ModelPass : public PassBase {
 public:
-    OPENVINO_RTTI("ov::pass::FunctionPass");
+    OPENVINO_RTTI("ov::pass::ModelPass");
     ~ModelPass() override;
     OPENVINO_DEPRECATED("run_on_function() method is deprecated. Please use run_on_model() instead.")
     virtual bool run_on_function(std::shared_ptr<ov::Model> m);
diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt
index ec539ce6c11..63c977f86fe 100644
--- a/src/core/tests/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -517,7 +517,8 @@ if (ENABLE_OV_ONNX_FRONTEND)
 endif()
 
 # SOURCE FOR FRONTEND TESTING
-file(GLOB FRONTEND_TESTS_SRC ${CMAKE_CURRENT_SOURCE_DIR}/frontend/frontend_manager.cpp)
+file(GLOB FRONTEND_TESTS_SRC ${CMAKE_CURRENT_SOURCE_DIR}/frontend/frontend_manager.cpp
+                             ${CMAKE_CURRENT_SOURCE_DIR}/frontend/decoder_transformation_extension.cpp)
 list(APPEND SRC ${FRONTEND_TESTS_SRC})
 
 foreach(src IN LISTS SRC MULTI_TEST_SRC OP_EVAL_TEST_SRC)
diff --git a/src/core/tests/frontend/decoder_transformation_extension.cpp b/src/core/tests/frontend/decoder_transformation_extension.cpp
new file mode 100644
index 00000000000..c4f59ffeb1b
--- /dev/null
+++ b/src/core/tests/frontend/decoder_transformation_extension.cpp
@@ -0,0 +1,55 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/pass/manager.hpp>
+
+#include "gtest/gtest.h"
+#include "openvino/frontend/extension/decoder_transformation.hpp"
+
+using namespace ov::frontend;
+
+TEST(DecoderTransformation, MatcherPass) {
+    bool flag = false;
+    DecoderTransformationExtension decoder_ext([&](ov::pass::MatcherPass* matcher) {
+        flag = true;
+    });
+
+    ov::pass::Manager manager;
+    decoder_ext.register_pass(manager);
+    manager.run_passes(std::make_shared<ov::Model>(ov::ResultVector{}, ov::ParameterVector{}));
+    EXPECT_EQ(flag, true);
+}
+
+TEST(DecoderTransformation, FunctionPass) {
+    bool flag = false;
+    DecoderTransformationExtension decoder_ext([&](const std::shared_ptr<ov::Model>&) {
+        flag = true;
+        return flag;
+    });
+
+    ov::pass::Manager manager;
+    decoder_ext.register_pass(manager);
+    manager.run_passes(std::make_shared<ov::Model>(ov::ResultVector{}, ov::ParameterVector{}));
+    EXPECT_EQ(flag, true);
+}
+
+TEST(DecoderTransformation, TestPass) {
+    class TestPass : public ov::pass::ModelPass {
+    public:
+        OPENVINO_RTTI("ov::pass::TestPass");
+        TestPass() = default;
+        TestPass(const TestPass& tp) = default;
+        bool run_on_model(const std::shared_ptr<ov::Model>&) override {
+            *m_flag = true;
+            return *m_flag;
+        }
+        std::shared_ptr<bool> m_flag = std::make_shared<bool>(false);
+    } test_pass;
+    DecoderTransformationExtension decoder_ext(test_pass);
+
+    ov::pass::Manager manager;
+    decoder_ext.register_pass(manager);
+    manager.run_passes(std::make_shared<ov::Model>(ov::ResultVector{}, ov::ParameterVector{}));
+    EXPECT_EQ(*test_pass.m_flag, true);
+}
diff --git a/src/core/tests/frontend/onnx/json_config_extension.cpp b/src/core/tests/frontend/onnx/json_config_extension.cpp
new file mode 100644
index 00000000000..a30a1df137e
--- /dev/null
+++ b/src/core/tests/frontend/onnx/json_config_extension.cpp
@@ -0,0 +1,23 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "json_config.hpp"
+#include "onnx_utils.hpp"
+
+using namespace ov::frontend;
+
+using ONNXJsonConfigTest = FrontEndJsonConfigTest;
+
+static JsonConfigFEParam getTestData() {
+    JsonConfigFEParam res;
+    res.m_frontEndName = ONNX_FE;
+    res.m_modelsPath = std::string(TEST_ONNX_MODELS_DIRNAME);
+    res.m_modelName = "controlflow/loop_2d_add.onnx";
+    return res;
+}
+
+INSTANTIATE_TEST_SUITE_P(ONNXJsonConfigTest,
+                         FrontEndJsonConfigTest,
+                         ::testing::Values(getTestData()),
+                         FrontEndJsonConfigTest::getTestCaseName);
diff --git a/src/core/tests/frontend/paddlepaddle/json_config_extension.cpp b/src/core/tests/frontend/paddlepaddle/json_config_extension.cpp
new file mode 100644
index 00000000000..507a3f9db23
--- /dev/null
+++ b/src/core/tests/frontend/paddlepaddle/json_config_extension.cpp
@@ -0,0 +1,23 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "json_config.hpp"
+#include "paddle_utils.hpp"
+
+using namespace ov::frontend;
+
+using PDPDJsonConfigTest = FrontEndJsonConfigTest;
+
+static JsonConfigFEParam getTestData() {
+    JsonConfigFEParam res;
+    res.m_frontEndName = PADDLE_FE;
+    res.m_modelsPath = std::string(TEST_PADDLE_MODELS_DIRNAME);
+    res.m_modelName = "relu/relu.pdmodel";
+    return res;
+}
+
+INSTANTIATE_TEST_SUITE_P(PDPDJsonConfigTest,
+                         FrontEndJsonConfigTest,
+                         ::testing::Values(getTestData()),
+                         FrontEndJsonConfigTest::getTestCaseName);
diff --git a/src/core/tests/frontend/shared/CMakeLists.txt b/src/core/tests/frontend/shared/CMakeLists.txt
index ae4bcb5ac04..71cf6f5c92a 100644
--- a/src/core/tests/frontend/shared/CMakeLists.txt
+++ b/src/core/tests/frontend/shared/CMakeLists.txt
@@ -9,10 +9,14 @@ file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
 
 add_library(${TARGET_NAME} STATIC EXCLUDE_FROM_ALL ${LIBRARY_SRC} ${LIBRARY_HEADERS})
 
+add_subdirectory(test_builtin_extensions_1)
+add_subdirectory(test_builtin_extensions_2)
+
 target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
 target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/../..)
 target_link_libraries(${TARGET_NAME} PUBLIC frontend_common engines_test_util
-        ngraph cnpy commonTestUtils ngraph_test_util openvino::util)
+        offline_transformations ngraph cnpy commonTestUtils ngraph_test_util openvino::util
+        nlohmann_json_schema_validator test_builtin_extensions_1 test_builtin_extensions_2)
 
 target_compile_definitions(${TARGET_NAME}
     PRIVATE
diff --git a/src/core/tests/frontend/shared/include/json_config.hpp b/src/core/tests/frontend/shared/include/json_config.hpp
new file mode 100644
index 00000000000..9ca0b130602
--- /dev/null
+++ b/src/core/tests/frontend/shared/include/json_config.hpp
@@ -0,0 +1,48 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <gtest/gtest.h>
+
+#include <extension/json_config.hpp>
+#include <openvino/frontend/manager.hpp>
+
+class JsonConfigExtensionWrapper : public ov::frontend::JsonConfigExtension {
+public:
+    explicit JsonConfigExtensionWrapper(const std::string& config_path)
+        : ov::frontend::JsonConfigExtension(config_path){};
+    ~JsonConfigExtensionWrapper() override = default;
+
+    std::vector<Extension::Ptr> get_loaded_extensions() {
+        return m_loaded_extensions;
+    };
+
+    std::vector<std::pair<std::shared_ptr<DecoderTransformationExtension>, std::string>> get_target_extensions() {
+        return m_target_extensions;
+    }
+};
+
+struct JsonConfigFEParam {
+    std::string m_frontEndName;
+    std::string m_modelsPath;
+    std::string m_modelName;
+};
+
+class FrontEndJsonConfigTest : public ::testing::TestWithParam<JsonConfigFEParam> {
+public:
+    std::string m_file_name = "test_json.json";
+    std::ofstream m_output_json;
+    JsonConfigFEParam m_param;
+    ov::frontend::FrontEndManager m_fem;
+
+    static std::string getTestCaseName(const testing::TestParamInfo<JsonConfigFEParam>& obj);
+
+    void SetUp() override;
+    void TearDown() override;
+
+protected:
+    void generate_json_config();
+    void initParamTest();
+};
diff --git a/src/core/tests/frontend/shared/src/json_config.cpp b/src/core/tests/frontend/shared/src/json_config.cpp
new file mode 100644
index 00000000000..a12dab60425
--- /dev/null
+++ b/src/core/tests/frontend/shared/src/json_config.cpp
@@ -0,0 +1,194 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "json_config.hpp"
+
+#include <extension/json_config.hpp>
+#include <extension/json_transformation.hpp>
+#include <nlohmann/json-schema.hpp>
+#include <ostream>
+#include <util/graph_comparator.hpp>
+
+#include "utils.hpp"
+
+using namespace ov::frontend;
+
+std::string FrontEndJsonConfigTest::getTestCaseName(const testing::TestParamInfo<JsonConfigFEParam>& obj) {
+    std::string res = obj.param.m_frontEndName + "_" + obj.param.m_modelName;
+    return FrontEndTestUtils::fileToTestName(res);
+}
+
+void FrontEndJsonConfigTest::SetUp() {
+    FrontEndTestUtils::setupTestEnv();
+    m_fem = FrontEndManager();  // re-initialize after setting up environment
+    initParamTest();
+}
+
+void FrontEndJsonConfigTest::TearDown() {
+    m_output_json.close();
+    std::remove(m_file_name.c_str());
+}
+
+void FrontEndJsonConfigTest::initParamTest() {
+    m_param = GetParam();
+    m_param.m_modelName = FrontEndTestUtils::make_model_path(m_param.m_modelsPath + m_param.m_modelName);
+}
+
+inline std::string get_lib_path(const std::string& lib_name) {
+    return ov::util::make_plugin_library_name<char>(ov::util::get_ov_lib_path(), lib_name + IE_BUILD_POSTFIX);
+}
+
+void FrontEndJsonConfigTest::generate_json_config() {
+    std::string json = R"(
+    [
+      {
+        "custom_attributes": {
+          "test_attribute": true
+        },
+        "id": "buildin_extensions_1::TestExtension1",
+        "library": "test_builtin_extensions_1",
+        "match_kind": "scope"
+      },
+      {
+        "custom_attributes": {
+          "test_attribute": true
+        },
+        "id": "buildin_extensions_2::TestExtension1",
+        "library": "test_builtin_extensions_2",
+        "match_kind": "scope"
+      },
+      {
+        "custom_attributes": {
+          "test_attribute": true
+        },
+        "id": "buildin_extensions_2::TestExtension2",
+        "library": "test_builtin_extensions_2",
+        "match_kind": "scope"
+      }
+    ]
+    )";
+    nlohmann::json config_json = nlohmann::json::parse(json);
+
+    // update lib path
+    for (auto& section : config_json) {
+        section["library"] = get_lib_path(section["library"]);
+    }
+
+    m_output_json.open(m_file_name);
+    m_output_json << std::setw(4) << config_json << std::endl;
+}
+
+///////////////////////////////////////////////////////////////////
+
+TEST_P(FrontEndJsonConfigTest, testJsonConfig) {
+    EXPECT_NO_THROW(generate_json_config());
+}
+
+TEST_P(FrontEndJsonConfigTest, testAddJsonConfigExtension) {
+    EXPECT_NO_THROW(generate_json_config());
+    std::map<std::string, nlohmann::json> reference_map = {
+        {"buildin_extensions_1::TestExtension1",
+         nlohmann::json::parse(
+             R"(
+                 {
+                    "custom_attributes": {
+                        "test_attribute": true
+                    },
+                    "id": "buildin_extensions_1::TestExtension1",
+                    "library": "test_builtin_extensions_1",
+                    "match_kind": "scope"
+                 }
+                 )")},
+        {"buildin_extensions_2::TestExtension1",
+         nlohmann::json::parse(
+             R"(
+                 {
+                    "custom_attributes": {
+                        "test_attribute": true
+                    },
+                    "id": "buildin_extensions_2::TestExtension1",
+                    "library": "test_builtin_extensions_2",
+                    "match_kind": "scope"
+                 }
+                 )")},
+        {"buildin_extensions_2::TestExtension2",
+         nlohmann::json::parse(
+             R"(
+                 {
+                    "custom_attributes": {
+                        "test_attribute": true
+                    },
+                    "id": "buildin_extensions_2::TestExtension2",
+                    "library": "test_builtin_extensions_2",
+                    "match_kind": "scope"
+                 }
+                 )")},
+    };
+
+    for (auto& ref_val : reference_map) {
+        ref_val.second["library"] = get_lib_path(ref_val.second["library"]);
+    }
+
+    std::shared_ptr<ov::Model> function;
+    {
+        ov::frontend::FrontEnd::Ptr m_frontEnd;
+        ov::frontend::InputModel::Ptr m_inputModel;
+        m_frontEnd = m_fem.load_by_framework(m_param.m_frontEndName);
+
+        auto json_config_ext = std::make_shared<JsonConfigExtensionWrapper>(m_file_name);
+        m_frontEnd->add_extension(json_config_ext);
+
+        auto loaded_ext = json_config_ext->get_loaded_extensions();
+        auto target_ext = json_config_ext->get_target_extensions();
+
+        EXPECT_EQ(loaded_ext.size(), 3);
+        EXPECT_EQ(target_ext.size(), 3);
+
+        for (const auto& target : target_ext) {
+            auto transformation = std::dynamic_pointer_cast<JsonTransformationExtension>(target.first);
+            EXPECT_NE(transformation, nullptr);
+            EXPECT_EQ(reference_map.at(transformation->id()).dump(), target.second);
+        }
+        ASSERT_NO_THROW(m_inputModel = m_frontEnd->load(m_param.m_modelName));
+        ASSERT_NE(m_inputModel, nullptr);
+
+        ASSERT_NO_THROW(function = m_frontEnd->convert(m_inputModel));
+        ASSERT_NE(function, nullptr);
+        EXPECT_EQ(function->get_friendly_name(), "TestFunction");
+    }
+}
+
+TEST_P(FrontEndJsonConfigTest, compareFunctions) {
+    EXPECT_NO_THROW(generate_json_config());
+
+    std::shared_ptr<ov::Model> function;
+    {
+        ov::frontend::FrontEnd::Ptr m_frontEnd;
+        ov::frontend::InputModel::Ptr m_inputModel;
+        m_frontEnd = m_fem.load_by_framework(m_param.m_frontEndName);
+
+        auto json_config_ext = std::make_shared<JsonConfigExtensionWrapper>(m_file_name);
+        m_frontEnd->add_extension(json_config_ext);
+
+        ASSERT_NO_THROW(m_inputModel = m_frontEnd->load(m_param.m_modelName));
+        ASSERT_NE(m_inputModel, nullptr);
+
+        ASSERT_NO_THROW(function = m_frontEnd->convert(m_inputModel));
+        ASSERT_NE(function, nullptr);
+    }
+
+    std::shared_ptr<ov::Model> function_ref;
+    {
+        ov::frontend::FrontEnd::Ptr m_frontEnd;
+        ov::frontend::InputModel::Ptr m_inputModel;
+        m_frontEnd = m_fem.load_by_framework(m_param.m_frontEndName);
+
+        ASSERT_NO_THROW(m_inputModel = m_frontEnd->load(m_param.m_modelName));
+        ASSERT_NE(m_inputModel, nullptr);
+
+        ASSERT_NO_THROW(function_ref = m_frontEnd->convert(m_inputModel));
+        ASSERT_NE(function, nullptr);
+    }
+    compare_functions(function, function_ref);
+}
diff --git a/src/core/tests/frontend/shared/src/telemetry.cpp b/src/core/tests/frontend/shared/src/telemetry.cpp
index c96b05b990e..38188dae7e8 100644
--- a/src/core/tests/frontend/shared/src/telemetry.cpp
+++ b/src/core/tests/frontend/shared/src/telemetry.cpp
@@ -4,8 +4,7 @@
 
 #include "telemetry.hpp"
 
-#include <openvino/frontend/telemetry_extension.hpp>
-
+#include "openvino/frontend/extension/telemetry.hpp"
 #include "utils.hpp"
 
 using namespace ov::frontend;
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_1/CMakeLists.txt b/src/core/tests/frontend/shared/test_builtin_extensions_1/CMakeLists.txt
new file mode 100644
index 00000000000..cd843295ae2
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_1/CMakeLists.txt
@@ -0,0 +1,26 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(TARGET_NAME "test_builtin_extensions_1")
+
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
+
+# Create library
+add_library(${TARGET_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS})
+
+target_link_libraries(${TARGET_NAME} PUBLIC nlohmann_json inference_engine_transformations frontend_common
+        offline_transformations)
+
+add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
+
+if(WIN32)
+    install(TARGETS ${TARGET_NAME}
+            RUNTIME DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+else()
+    install(TARGETS ${TARGET_NAME}
+            RUNTIME DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
+endif()
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_1/builtin_extensions.cpp b/src/core/tests/frontend/shared/test_builtin_extensions_1/builtin_extensions.cpp
new file mode 100644
index 00000000000..ada294f7fc2
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_1/builtin_extensions.cpp
@@ -0,0 +1,9 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/core/extension.hpp>
+
+#include "test_extension.hpp"
+
+OPENVINO_CREATE_EXTENSIONS(std::vector<ov::Extension::Ptr>({std::make_shared<TestExtension1>()}));
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.cpp b/src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.cpp
new file mode 100644
index 00000000000..796bd3c2df9
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.cpp
@@ -0,0 +1,14 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_extension.hpp"
+
+#include <openvino/core/core.hpp>
+
+bool TestExtension1::transform(const std::shared_ptr<ov::Model>& function, const std::string& config) const {
+    function->set_friendly_name("TestFunction");
+    return true;
+}
+
+TestExtension1::TestExtension1() : ov::frontend::JsonTransformationExtension("buildin_extensions_1::TestExtension1") {}
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.hpp b/src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.hpp
new file mode 100644
index 00000000000..83b69a55be3
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_1/test_extension.hpp
@@ -0,0 +1,15 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <extension/json_transformation.hpp>
+#include <nlohmann/json.hpp>
+
+class TestExtension1 : public ov::frontend::JsonTransformationExtension {
+public:
+    TestExtension1();
+
+    bool transform(const std::shared_ptr<ov::Model>& function, const std::string& config) const override;
+};
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_2/CMakeLists.txt b/src/core/tests/frontend/shared/test_builtin_extensions_2/CMakeLists.txt
new file mode 100644
index 00000000000..e773d4fd02b
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_2/CMakeLists.txt
@@ -0,0 +1,26 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(TARGET_NAME "test_builtin_extensions_2")
+
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
+
+# Create library
+add_library(${TARGET_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS})
+
+target_link_libraries(${TARGET_NAME} PRIVATE nlohmann_json inference_engine_transformations frontend_common
+        offline_transformations)
+
+add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
+
+if(WIN32)
+    install(TARGETS ${TARGET_NAME}
+            RUNTIME DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+else()
+    install(TARGETS ${TARGET_NAME}
+            RUNTIME DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
+endif()
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_2/builtin_extensions.cpp b/src/core/tests/frontend/shared/test_builtin_extensions_2/builtin_extensions.cpp
new file mode 100644
index 00000000000..f601200dbff
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_2/builtin_extensions.cpp
@@ -0,0 +1,10 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/core/extension.hpp>
+
+#include "test_extensions.hpp"
+
+OPENVINO_CREATE_EXTENSIONS(std::vector<ov::Extension::Ptr>({std::make_shared<TestExtension1>(),
+                                                            std::make_shared<TestExtension2>()}));
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.cpp b/src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.cpp
new file mode 100644
index 00000000000..e94716c6416
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.cpp
@@ -0,0 +1,21 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_extensions.hpp"
+
+#include <openvino/core/core.hpp>
+
+bool TestExtension1::transform(const std::shared_ptr<ov::Model>& function, const std::string& config) const {
+    function->set_friendly_name("TestFunction");
+    return true;
+}
+
+TestExtension1::TestExtension1() : ov::frontend::JsonTransformationExtension("buildin_extensions_2::TestExtension1") {}
+
+bool TestExtension2::transform(const std::shared_ptr<ov::Model>& function, const std::string& config) const {
+    function->set_friendly_name("TestFunction");
+    return true;
+}
+
+TestExtension2::TestExtension2() : ov::frontend::JsonTransformationExtension("buildin_extensions_2::TestExtension2") {}
diff --git a/src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.hpp b/src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.hpp
new file mode 100644
index 00000000000..337f1f2e9b2
--- /dev/null
+++ b/src/core/tests/frontend/shared/test_builtin_extensions_2/test_extensions.hpp
@@ -0,0 +1,22 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <extension/json_transformation.hpp>
+#include <nlohmann/json.hpp>
+
+class TestExtension1 : public ov::frontend::JsonTransformationExtension {
+public:
+    TestExtension1();
+
+    bool transform(const std::shared_ptr<ov::Model>& function, const std::string& config) const override;
+};
+
+class TestExtension2 : public ov::frontend::JsonTransformationExtension {
+public:
+    TestExtension2();
+
+    bool transform(const std::shared_ptr<ov::Model>& function, const std::string& config) const override;
+};
diff --git a/src/core/tests/frontend/tensorflow/json_config_extension.cpp b/src/core/tests/frontend/tensorflow/json_config_extension.cpp
new file mode 100644
index 00000000000..76a68045efc
--- /dev/null
+++ b/src/core/tests/frontend/tensorflow/json_config_extension.cpp
@@ -0,0 +1,23 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "json_config.hpp"
+#include "tf_utils.hpp"
+
+using namespace ov::frontend;
+
+using TFJsonConfigTest = FrontEndJsonConfigTest;
+
+static JsonConfigFEParam getTestData() {
+    JsonConfigFEParam res;
+    res.m_frontEndName = TF_FE;
+    res.m_modelsPath = std::string(TEST_TENSORFLOW_MODELS_DIRNAME);
+    res.m_modelName = "2in_2out/2in_2out.pb";
+    return res;
+}
+
+INSTANTIATE_TEST_SUITE_P(TFJsonConfigTest,
+                         FrontEndJsonConfigTest,
+                         ::testing::Values(getTestData()),
+                         FrontEndJsonConfigTest::getTestCaseName);
diff --git a/src/frontends/common/include/openvino/frontend/extension/decoder_transformation.hpp b/src/frontends/common/include/openvino/frontend/extension/decoder_transformation.hpp
new file mode 100644
index 00000000000..2b0d1672b31
--- /dev/null
+++ b/src/frontends/common/include/openvino/frontend/extension/decoder_transformation.hpp
@@ -0,0 +1,56 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <fstream>
+#include <iostream>
+#include <type_traits>
+
+#include "openvino/core/extension.hpp"
+#include "openvino/frontend/visibility.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+#include "openvino/pass/manager.hpp"
+#include "openvino/pass/pass.hpp"
+
+namespace ov {
+namespace frontend {
+
+/// \brief Holds a transformation that is applied just after the original model graph is decoded.
+/// This class is a holder for transformation. The transformation can be specified as
+/// FunctionPass or MathcerPass derivatives or as a function that can be used to build corresponding
+/// FunctionPass or MatcherPass object. The type of the extension is determined in the moment of creation by
+/// calling corresponding ctor.
+class FRONTEND_API DecoderTransformationExtension : public ov::Extension {
+public:
+    DecoderTransformationExtension() = default;
+
+    /// \brief Create a custom functional pass where code of the pass is implemented as a function.
+    explicit DecoderTransformationExtension(const std::function<bool(std::shared_ptr<ov::Model>)>& function_pass);
+
+    /// \brief Create a custom matcher pass where the code of matcher pass initialization is a given function.
+    explicit DecoderTransformationExtension(
+        const std::function<void(ov::pass::MatcherPass*)>& matcher_pass_initializer);
+
+    /// \brief Register existing transformation object which will be copied and kept for further registration.
+    template <typename Transformation,
+              typename std::enable_if<std::is_base_of<ov::pass::PassBase, Transformation>::value, bool>::type = true>
+    explicit DecoderTransformationExtension(const Transformation& transformation)
+        : m_registration([=](ov::pass::Manager& manager) {
+              manager.register_pass<Transformation>(transformation);
+          }) {}
+
+    /// \brief Register pass from this object in a given pass manager object
+    void register_pass(ov::pass::Manager& manager) const;
+
+protected:
+    void set_registration(const std::function<void(ov::pass::Manager&)>& registration) {
+        m_registration = registration;
+    }
+
+private:
+    std::function<void(ov::pass::Manager&)> m_registration;
+};
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/common/include/openvino/frontend/telemetry_extension.hpp b/src/frontends/common/include/openvino/frontend/extension/telemetry.hpp
similarity index 96%
rename from src/frontends/common/include/openvino/frontend/telemetry_extension.hpp
rename to src/frontends/common/include/openvino/frontend/extension/telemetry.hpp
index b1454414b3e..22a03d93b51 100644
--- a/src/frontends/common/include/openvino/frontend/telemetry_extension.hpp
+++ b/src/frontends/common/include/openvino/frontend/extension/telemetry.hpp
@@ -9,10 +9,10 @@
 #include <type_traits>
 
 #include "openvino/core/extension.hpp"
+#include "openvino/frontend/visibility.hpp"
 #include "openvino/pass/graph_rewrite.hpp"
 #include "openvino/pass/manager.hpp"
 #include "openvino/pass/pass.hpp"
-#include "visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/common/src/extension/decoder_transformation.cpp b/src/frontends/common/src/extension/decoder_transformation.cpp
new file mode 100644
index 00000000000..3f66563ab36
--- /dev/null
+++ b/src/frontends/common/src/extension/decoder_transformation.cpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/extension/decoder_transformation.hpp"
+
+#include <utility>
+
+using namespace ov;
+using namespace ov::frontend;
+
+/// \brief Helper class to register user function as a FunctionPass
+class CustomModelPass : public ov::pass::ModelPass {
+public:
+    explicit CustomModelPass(std::function<bool(std::shared_ptr<ov::Model>)> pass) : m_pass(std::move(pass)) {}
+
+    bool run_on_model(const std::shared_ptr<ov::Model>& f) override {
+        return m_pass(f);
+    }
+
+private:
+    std::function<bool(std::shared_ptr<ov::Model>)> m_pass;
+};
+
+/// \brief Helper class to register user matcher pass initialization as a MatcherPass
+class CustomMatcherPass : public ov::pass::MatcherPass {
+public:
+    explicit CustomMatcherPass(const std::function<void(ov::pass::MatcherPass*)>& matcher_pass_initializer) {
+        matcher_pass_initializer(this);
+    }
+};
+
+DecoderTransformationExtension::DecoderTransformationExtension(
+    const std::function<bool(const std::shared_ptr<ov::Model>)>& function_pass)
+    : m_registration([=](ov::pass::Manager& manager) {
+          manager.register_pass<CustomModelPass>(function_pass);
+      }) {}
+
+DecoderTransformationExtension::DecoderTransformationExtension(
+    const std::function<void(ov::pass::MatcherPass*)>& matcher_pass_initializer)
+    : m_registration([=](ov::pass::Manager& manager) {
+          manager.register_pass<CustomMatcherPass>(matcher_pass_initializer);
+      }) {}
+
+void DecoderTransformationExtension::register_pass(ov::pass::Manager& manager) const {
+    m_registration(manager);
+}
diff --git a/src/frontends/common/src/telemetry_extension.cpp b/src/frontends/common/src/extension/telemetry.cpp
similarity index 95%
rename from src/frontends/common/src/telemetry_extension.cpp
rename to src/frontends/common/src/extension/telemetry.cpp
index 8dfa0c3725d..7cb172ec631 100644
--- a/src/frontends/common/src/telemetry_extension.cpp
+++ b/src/frontends/common/src/extension/telemetry.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "openvino/frontend/telemetry_extension.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 
 using namespace ov::frontend;
 
diff --git a/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp b/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
index 5a393a0c2cb..17d7f1cf494 100644
--- a/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
+++ b/src/frontends/ir/include/openvino/frontend/ir/frontend.hpp
@@ -5,9 +5,9 @@
 #pragma once
 
 #include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/frontend.hpp"
 #include "openvino/frontend/ir/visibility.hpp"
-#include "openvino/frontend/telemetry_extension.hpp"
 #include "openvino/openvino.hpp"
 
 namespace ov {
diff --git a/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
index defe06c97b9..cfa4016e4b5 100644
--- a/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
+++ b/src/frontends/onnx/frontend/include/openvino/frontend/onnx/frontend.hpp
@@ -5,7 +5,8 @@
 #pragma once
 
 #include <openvino/frontend/frontend.hpp>
-#include <openvino/frontend/telemetry_extension.hpp>
+
+#include "openvino/frontend/extension/telemetry.hpp"
 
 #ifdef OPENVINO_STATIC_LIBRARY
 #    define ONNX_FRONTEND_API
diff --git a/src/frontends/onnx/frontend/src/core/graph.hpp b/src/frontends/onnx/frontend/src/core/graph.hpp
index 49991b29754..35d9a20d2b7 100644
--- a/src/frontends/onnx/frontend/src/core/graph.hpp
+++ b/src/frontends/onnx/frontend/src/core/graph.hpp
@@ -15,7 +15,7 @@
 #include "ngraph/function.hpp"
 #include "ngraph/op/parameter.hpp"
 #include "onnx_import/core/operator_set.hpp"
-#include "openvino/frontend/telemetry_extension.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 
 namespace ngraph {
 namespace onnx_import {
diff --git a/src/frontends/onnx/frontend/src/editor.hpp b/src/frontends/onnx/frontend/src/editor.hpp
index 39ec6125ef6..1f4a37027ff 100644
--- a/src/frontends/onnx/frontend/src/editor.hpp
+++ b/src/frontends/onnx/frontend/src/editor.hpp
@@ -7,7 +7,6 @@
 #include <istream>
 #include <map>
 #include <memory>
-#include <openvino/frontend/telemetry_extension.hpp>
 
 #include "editor_types.hpp"
 #include "ngraph/function.hpp"
@@ -15,6 +14,7 @@
 #include "ngraph/partial_shape.hpp"
 #include "ngraph/type/element_type.hpp"
 #include "onnx_import/onnx_importer_visibility.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 
 namespace ov {
 namespace onnx_editor {
diff --git a/src/frontends/onnx/frontend/src/frontend.cpp b/src/frontends/onnx/frontend/src/frontend.cpp
index c8e2ba554f1..9f7180b86ce 100644
--- a/src/frontends/onnx/frontend/src/frontend.cpp
+++ b/src/frontends/onnx/frontend/src/frontend.cpp
@@ -8,11 +8,11 @@
 #include <openvino/frontend/exception.hpp>
 #include <openvino/frontend/manager.hpp>
 #include <openvino/frontend/onnx/frontend.hpp>
-#include <openvino/frontend/telemetry_extension.hpp>
 #include <sstream>
 #include <utils/onnx_internal.hpp>
 
 #include "onnx_common/onnx_model_validator.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 
 using namespace ov;
 using namespace ov::frontend::onnx;
diff --git a/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp b/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
index dba12e3b2c6..c564cfe7210 100644
--- a/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
+++ b/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
@@ -8,7 +8,7 @@
 #include <string>
 
 #include "ngraph/function.hpp"
-#include "openvino/frontend/telemetry_extension.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 
 namespace ONNX_NAMESPACE {
 class ModelProto;
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
index a88f13c779e..fd705b2913e 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
@@ -4,8 +4,9 @@
 
 #pragma once
 
+#include <openvino/frontend/extension/decoder_transformation.hpp>
+#include <openvino/frontend/extension/telemetry.hpp>
 #include <openvino/frontend/manager.hpp>
-#include <openvino/frontend/telemetry_extension.hpp>
 
 #include "exceptions.hpp"
 #include "model.hpp"
@@ -70,6 +71,7 @@ private:
         std::function<std::map<std::string, OutputVector>(const std::map<std::string, Output<Node>>&,
                                                           const std::shared_ptr<OpPlacePDPD>&)> func);
     std::shared_ptr<TelemetryExtension> m_telemetry;
+    std::vector<std::shared_ptr<DecoderTransformationExtension>> m_transformation_extensions;
 };
 
 }  // namespace frontend
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
index 3fa9dbe7a99..491af81b3ba 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
@@ -4,8 +4,8 @@
 
 #pragma once
 
+#include <openvino/frontend/extension/telemetry.hpp>
 #include <openvino/frontend/manager.hpp>
-#include <openvino/frontend/telemetry_extension.hpp>
 
 #include "paddlepaddle_frontend/utility.hpp"
 
diff --git a/src/frontends/paddlepaddle/src/frontend.cpp b/src/frontends/paddlepaddle/src/frontend.cpp
index e9a27c643f1..c6e41123bdd 100644
--- a/src/frontends/paddlepaddle/src/frontend.cpp
+++ b/src/frontends/paddlepaddle/src/frontend.cpp
@@ -278,6 +278,20 @@ InputModel::Ptr FrontEndPDPD::load_impl(const std::vector<ov::Any>& variants) co
 
 std::shared_ptr<ov::Model> FrontEndPDPD::convert(const InputModel::Ptr& model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
+    FRONT_END_GENERAL_CHECK(pdpd_model != nullptr, "Invalid input model");
+
+    if (!m_transformation_extensions.empty()) {
+        auto function = decode(model);
+
+        pass::Manager manager;
+        for (const auto& transformation : m_transformation_extensions) {
+            transformation->register_pass(manager);
+        }
+        manager.run_passes(function);
+        convert(function);
+        return function;
+    }
+
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(
         pdpd_model,
@@ -294,13 +308,27 @@ void FrontEndPDPD::convert(const std::shared_ptr<ov::Model>& partiallyConverted)
                                            pdpd::get_supported_ops());
         }
     }
-    for (auto result : partiallyConverted->get_results()) {
+    for (const auto& result : partiallyConverted->get_results()) {
         result->validate_and_infer_types();
     }
 }
 
 std::shared_ptr<ov::Model> FrontEndPDPD::convert_partially(const InputModel::Ptr& model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
+    FRONT_END_GENERAL_CHECK(pdpd_model != nullptr, "Invalid input model");
+
+    if (!m_transformation_extensions.empty()) {
+        auto function = decode(model);
+
+        pass::Manager manager;
+        for (const auto& transformation : m_transformation_extensions) {
+            transformation->register_pass(manager);
+        }
+        manager.run_passes(function);
+        convert(function);
+        return function;
+    }
+
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(
         pdpd_model,
@@ -318,6 +346,8 @@ std::shared_ptr<ov::Model> FrontEndPDPD::convert_partially(const InputModel::Ptr
 
 std::shared_ptr<ov::Model> FrontEndPDPD::decode(const InputModel::Ptr& model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
+    FRONT_END_GENERAL_CHECK(pdpd_model != nullptr, "Invalid input model");
+
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(pdpd_model, pdpd::make_framework_node);
     return f;
@@ -330,6 +360,8 @@ std::string FrontEndPDPD::get_name() const {
 void FrontEndPDPD::add_extension(const std::shared_ptr<ov::Extension>& extension) {
     if (auto telemetry = std::dynamic_pointer_cast<TelemetryExtension>(extension)) {
         m_telemetry = telemetry;
+    } else if (auto transformation = std::dynamic_pointer_cast<DecoderTransformationExtension>(extension)) {
+        m_transformation_extensions.push_back(transformation);
     }
 }
 
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp b/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
index 59b91d0f9d1..274f47bd041 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
+++ b/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
@@ -9,9 +9,10 @@
 
 #include "openvino/core/any.hpp"
 #include "openvino/core/node_vector.hpp"
+#include "openvino/frontend/extension/decoder_transformation.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/frontend.hpp"
 #include "openvino/frontend/input_model.hpp"
-#include "openvino/frontend/telemetry_extension.hpp"
 #include "tensorflow_frontend/utility.hpp"
 
 namespace ov {
@@ -83,6 +84,7 @@ private:
                          std::shared_ptr<ov::Model>& ng_function) const;
 
     std::shared_ptr<TelemetryExtension> m_telemetry;
+    std::vector<std::shared_ptr<DecoderTransformationExtension>> m_transformation_extensions;
 };
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/frontend.cpp b/src/frontends/tensorflow/src/frontend.cpp
index d9bbf954c02..51ee27fcc12 100644
--- a/src/frontends/tensorflow/src/frontend.cpp
+++ b/src/frontends/tensorflow/src/frontend.cpp
@@ -308,6 +308,20 @@ ov::frontend::InputModel::Ptr FrontEndTF::load_impl(const std::vector<ov::Any>&
 
 std::shared_ptr<ov::Model> FrontEndTF::convert(const ov::frontend::InputModel::Ptr& model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
+    FRONT_END_GENERAL_CHECK(model_tf != nullptr, "Invalid input model");
+
+    if (!m_transformation_extensions.empty()) {
+        auto function = decode(model);
+
+        pass::Manager manager;
+        for (const auto& transformation : m_transformation_extensions) {
+            transformation->register_pass(manager);
+        }
+        manager.run_passes(function);
+        convert(function);
+        return function;
+    }
+
     std::shared_ptr<ov::Model> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", true, false, f);
     normalize(f);
@@ -318,6 +332,20 @@ std::shared_ptr<ov::Model> FrontEndTF::convert(const ov::frontend::InputModel::P
 
 std::shared_ptr<ov::Model> FrontEndTF::convert_partially(const ov::frontend::InputModel::Ptr& model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
+    FRONT_END_GENERAL_CHECK(model_tf != nullptr, "Invalid input model");
+
+    if (!m_transformation_extensions.empty()) {
+        auto function = decode(model);
+
+        pass::Manager manager;
+        for (const auto& transformation : m_transformation_extensions) {
+            transformation->register_pass(manager);
+        }
+        manager.run_passes(function);
+        convert(function);
+        return function;
+    }
+
     std::shared_ptr<ov::Model> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", false, false, f);
     normalize(f);
@@ -353,5 +381,7 @@ void FrontEndTF::normalize(const std::shared_ptr<ov::Model>& function) const {
 void FrontEndTF::add_extension(const std::shared_ptr<ov::Extension>& extension) {
     if (auto telemetry = std::dynamic_pointer_cast<TelemetryExtension>(extension)) {
         m_telemetry = telemetry;
+    } else if (auto transformation = std::dynamic_pointer_cast<DecoderTransformationExtension>(extension)) {
+        m_transformation_extensions.push_back(transformation);
     }
 }
diff --git a/src/frontends/tensorflow/src/model.hpp b/src/frontends/tensorflow/src/model.hpp
index 631eb76f116..2ff9d73706c 100644
--- a/src/frontends/tensorflow/src/model.hpp
+++ b/src/frontends/tensorflow/src/model.hpp
@@ -4,9 +4,9 @@
 
 #pragma once
 
+#include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/input_model.hpp"
 #include "openvino/frontend/place.hpp"
-#include "openvino/frontend/telemetry_extension.hpp"
 #include "tensorflow_frontend/graph_iterator.hpp"
 
 namespace ov {
diff --git a/thirdparty/json/CMakeLists.txt b/thirdparty/json/CMakeLists.txt
index 18107b7ef82..ef256d2061d 100644
--- a/thirdparty/json/CMakeLists.txt
+++ b/thirdparty/json/CMakeLists.txt
@@ -4,6 +4,8 @@
 
 # suppress shadowing names warning
 set(JSON_SystemInclude ON CACHE BOOL "" FORCE)
+# disabled due to compiler issues
+set(CMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE OFF)
 add_subdirectory(nlohmann_json EXCLUDE_FROM_ALL)
 
 set(BUILD_SHARED_LIBS OFF)

From 6b0d1525fd3b8e67c42ea901d9001cf11597d642 Mon Sep 17 00:00:00 2001
From: Anton Chetverikov <Anton.Chetverikov@intel.com>
Date: Sat, 18 Dec 2021 19:55:06 +0300
Subject: [PATCH 032/160] [MO]Implement additional shape extraction for TF
 Placeholder operation (#8817)

* Implement additional shape extraction for TF placeholder operation

* Update checks and add comments

* Use more accurate check

* Extract _output_shapes attr only if we have problems with shape attr

* Add missed import

* Apply suggestions from code review

Update code comments

Co-authored-by: Anastasia Popova <anastasia.popova@intel.com>

* Add update condition to avoid unnecessary checks

* Remove unnecessary checks

Co-authored-by: Anastasia Popova <anastasia.popova@intel.com>
---
 .../tools/mo/front/tf/placeholder_ext.py      | 27 +++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py b/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py
index bb0f24f6fd8..1628bb6067f 100644
--- a/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py
+++ b/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py
@@ -1,10 +1,13 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from openvino.tools.mo.ops.parameter import Parameter
+import logging as log
+
+from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor, tf_tensor_shape
 from openvino.tools.mo.ops.op import PermuteAttrs
+from openvino.tools.mo.ops.parameter import Parameter
 
 
 class PlaceholderFrontExtractor(FrontExtractorOp):
@@ -13,9 +16,29 @@ class PlaceholderFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
+        shape = shape_array([])
+        # Extract output shape from `shape` attribute
+        extracted_shape = tf_tensor_shape(node.pb.attr["shape"].shape)
+        if len(extracted_shape) != 0:
+            shape = extracted_shape
+        else:
+            # Extract output shape from `_output_shapes` attribute if it is possible
+            extracted_output_shapes = node.pb.attr["_output_shapes"].list.shape
+            if len(extracted_output_shapes) == 1:   # check if attribute not empty
+                extracted_output_shapes = tf_tensor_shape(extracted_output_shapes[0])
+
+                # Check equality of extracted shapes. We know some cases then Placeholder operation has empty `shape`
+                # attribute value and non-empty `_output_shapes` attribute value and need co handle and support it.
+                if len(extracted_output_shapes) > len(extracted_shape):
+                    log.warning('Extracted shapes for Placeholder operation {} have different lengths: `shape` {} and '
+                                '`_output_shapes` {}. Please, check if model is consistent'.format(
+                        node.pb.name, extracted_shape, extracted_output_shapes))
+                    if len(extracted_output_shapes) != 0:
+                        shape = extracted_output_shapes
+
         attrs = {
             'data_type': tf_dtype_extractor(node.pb.attr["dtype"].type),
-            'shape': tf_tensor_shape(node.pb.attr["shape"].shape),
+            'shape': shape,
             'permute_attrs': PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
         }
         if node.pb.attr["shape"].shape.unknown_rank:

From abcd7486a9584e4c7782501b40aad61197915387 Mon Sep 17 00:00:00 2001
From: Maxim Gordeev <maxim.gordeev@intel.com>
Date: Sun, 19 Dec 2021 20:56:40 +0300
Subject: [PATCH 033/160] Moving of C++ speech sample to OpenVINO API 2.0
 (#9027)

* Moved Speech sample to OpenVINO 2.0

* improved samples's score

* changed code style

* added GNA configs

* renamed function due to new API

* added dynamic batch

* reordered includes

* speech_sample has more than 1 input

* added oname processing

* added multi input

* fixed notes

* removed getFullDeviceName with old api

* getFullDeviceName for benchmark
---
 .../common/utils/include/samples/common.hpp   |   9 +
 samples/cpp/speech_sample/main.cpp            | 840 ++----------------
 samples/cpp/speech_sample/speech_sample.hpp   |  96 +-
 samples/cpp/speech_sample/utils.hpp           | 406 +++++++++
 4 files changed, 600 insertions(+), 751 deletions(-)
 create mode 100644 samples/cpp/speech_sample/utils.hpp

diff --git a/samples/cpp/common/utils/include/samples/common.hpp b/samples/cpp/common/utils/include/samples/common.hpp
index eef3f502bec..c2f37fc8f8d 100644
--- a/samples/cpp/common/utils/include/samples/common.hpp
+++ b/samples/cpp/common/utils/include/samples/common.hpp
@@ -679,6 +679,15 @@ inline std::string getFullDeviceName(InferenceEngine::Core& ie, std::string devi
     }
 }
 
+inline std::string getFullDeviceName(ov::runtime::Core& ie, std::string device) {
+    InferenceEngine::Parameter p;
+    try {
+        p = ie.get_metric(device, METRIC_KEY(FULL_DEVICE_NAME));
+        return p.as<std::string>();
+    } catch (InferenceEngine::Exception&) {
+        return "";
+    }
+}
 /**
  * @brief This class represents an object that is found by an object detection net
  */
diff --git a/samples/cpp/speech_sample/main.cpp b/samples/cpp/speech_sample/main.cpp
index 870e9f93d50..0db982f5676 100644
--- a/samples/cpp/speech_sample/main.cpp
+++ b/samples/cpp/speech_sample/main.cpp
@@ -1,523 +1,35 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-
-#include <gflags/gflags.h>
 #include <time.h>
 
 #include <chrono>
 #include <fstream>
 #include <functional>
-#include <gna/gna_config.hpp>
-#include <inference_engine.hpp>
 #include <iomanip>
 #include <iostream>
 #include <limits>
 #include <map>
 #include <memory>
 #include <random>
-#include <samples/args_helper.hpp>
-#include <samples/common.hpp>
-#include <samples/slog.hpp>
 #include <string>
 #include <thread>
 #include <utility>
 #include <vector>
 
+// clang-format off
+#include <openvino/openvino.hpp>
+#include <gna/gna_config.hpp>
+
+#include <samples/args_helper.hpp>
+#include <samples/slog.hpp>
+
 #include "fileutils.hpp"
 #include "speech_sample.hpp"
+#include "utils.hpp"
+// clang-format on
 
-#define MAX_SCORE_DIFFERENCE 0.0001f  // max score difference for frame error threshold
-#define MAX_VAL_2B_FEAT      16384    // max to find scale factor
-
-using namespace InferenceEngine;
-
-typedef std::chrono::high_resolution_clock Time;
-typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;
-typedef std::chrono::duration<float> fsec;
-/**
- * @brief struct to store score error
- */
-typedef struct {
-    uint32_t numScores;
-    uint32_t numErrors;
-    float threshold;
-    float maxError;
-    float rmsError;
-    float sumError;
-    float sumRmsError;
-    float sumSquaredError;
-    float maxRelError;
-    float sumRelError;
-    float sumSquaredRelError;
-} score_error_t;
-
-/**
- * @brief struct to store infer request data per frame
- */
-struct InferRequestStruct {
-    InferRequest inferRequest;
-    int frameIndex;
-    uint32_t numFramesThisBatch;
-};
-
-/**
- * @brief Check number of input files and model network inputs
- * @param numInputs number model inputs
- * @param numInputFiles number of input files
- * @return none.
- */
-void CheckNumberOfInputs(size_t numInputs, size_t numInputFiles) {
-    if (numInputs != numInputFiles) {
-        throw std::logic_error("Number of network inputs (" + std::to_string(numInputs) +
-                               ")"
-                               " is not equal to number of input files (" +
-                               std::to_string(numInputFiles) + ")");
-    }
-}
-
-/**
- * @brief Get scale factor for quantization
- * @param ptrFloatMemory pointer to float memory with speech feature vector
- * @param targetMax max scale factor
- * @param numElements number of elements in speech feature vector
- * @return scale factor
- */
-float ScaleFactorForQuantization(void* ptrFloatMemory, float targetMax, uint32_t numElements) {
-    float* ptrFloatFeat = reinterpret_cast<float*>(ptrFloatMemory);
-    float max = 0.0;
-    float scaleFactor;
-
-    for (uint32_t i = 0; i < numElements; i++) {
-        if (fabs(ptrFloatFeat[i]) > max) {
-            max = fabs(ptrFloatFeat[i]);
-        }
-    }
-
-    if (max == 0) {
-        scaleFactor = 1.0;
-    } else {
-        scaleFactor = targetMax / max;
-    }
-
-    return (scaleFactor);
-}
-
-/**
- * @brief Clean score error
- * @param error pointer to score error struct
- * @return none.
- */
-void ClearScoreError(score_error_t* error) {
-    error->numScores = 0;
-    error->numErrors = 0;
-    error->maxError = 0.0;
-    error->rmsError = 0.0;
-    error->sumError = 0.0;
-    error->sumRmsError = 0.0;
-    error->sumSquaredError = 0.0;
-    error->maxRelError = 0.0;
-    error->sumRelError = 0.0;
-    error->sumSquaredRelError = 0.0;
-}
-
-/**
- * @brief Update total score error
- * @param error pointer to score error struct
- * @param totalError pointer to total score error struct
- * @return none.
- */
-void UpdateScoreError(score_error_t* error, score_error_t* totalError) {
-    totalError->numErrors += error->numErrors;
-    totalError->numScores += error->numScores;
-    totalError->sumRmsError += error->rmsError;
-    totalError->sumError += error->sumError;
-    totalError->sumSquaredError += error->sumSquaredError;
-    if (error->maxError > totalError->maxError) {
-        totalError->maxError = error->maxError;
-    }
-    totalError->sumRelError += error->sumRelError;
-    totalError->sumSquaredRelError += error->sumSquaredRelError;
-    if (error->maxRelError > totalError->maxRelError) {
-        totalError->maxRelError = error->maxRelError;
-    }
-}
-
-/**
- * @brief Compare score errors, array should be the same length
- * @param ptrScoreArray - pointer to score error struct array
- * @param ptrRefScoreArray - pointer to score error struct array to compare
- * @param scoreError - pointer to score error struct to save a new error
- * @param numRows - number rows in score error arrays
- * @param numColumns - number columns in score error arrays
- * @return none.
- */
-void CompareScores(float* ptrScoreArray,
-                   void* ptrRefScoreArray,
-                   score_error_t* scoreError,
-                   uint32_t numRows,
-                   uint32_t numColumns) {
-    uint32_t numErrors = 0;
-
-    ClearScoreError(scoreError);
-
-    float* A = ptrScoreArray;
-    float* B = reinterpret_cast<float*>(ptrRefScoreArray);
-    for (uint32_t i = 0; i < numRows; i++) {
-        for (uint32_t j = 0; j < numColumns; j++) {
-            float score = A[i * numColumns + j];
-            float refscore = B[i * numColumns + j];
-            float error = fabs(refscore - score);
-            float rel_error = error / (static_cast<float>(fabs(refscore)) + 1e-20f);
-            float squared_error = error * error;
-            float squared_rel_error = rel_error * rel_error;
-            scoreError->numScores++;
-            scoreError->sumError += error;
-            scoreError->sumSquaredError += squared_error;
-            if (error > scoreError->maxError) {
-                scoreError->maxError = error;
-            }
-            scoreError->sumRelError += rel_error;
-            scoreError->sumSquaredRelError += squared_rel_error;
-            if (rel_error > scoreError->maxRelError) {
-                scoreError->maxRelError = rel_error;
-            }
-            if (error > scoreError->threshold) {
-                numErrors++;
-            }
-        }
-    }
-    scoreError->rmsError = sqrt(scoreError->sumSquaredError / (numRows * numColumns));
-    scoreError->sumRmsError += scoreError->rmsError;
-    scoreError->numErrors = numErrors;
-}
-
-/**
- * @brief Get total stdev error
- * @param error pointer to score error struct
- * @return error
- */
-float StdDevError(score_error_t error) {
-    return (sqrt(error.sumSquaredError / error.numScores -
-                 (error.sumError / error.numScores) * (error.sumError / error.numScores)));
-}
-
-#if !defined(__arm__) && !defined(_M_ARM) && !defined(__aarch64__) && !defined(_M_ARM64)
-#    ifdef _WIN32
-#        include <intrin.h>
-#        include <windows.h>
-#    else
-
-#        include <cpuid.h>
-
-#    endif
-
-inline void native_cpuid(unsigned int* eax, unsigned int* ebx, unsigned int* ecx, unsigned int* edx) {
-    size_t level = *eax;
-#    ifdef _WIN32
-    int regs[4] = {static_cast<int>(*eax), static_cast<int>(*ebx), static_cast<int>(*ecx), static_cast<int>(*edx)};
-    __cpuid(regs, level);
-    *eax = static_cast<uint32_t>(regs[0]);
-    *ebx = static_cast<uint32_t>(regs[1]);
-    *ecx = static_cast<uint32_t>(regs[2]);
-    *edx = static_cast<uint32_t>(regs[3]);
-#    else
-    __get_cpuid(level, eax, ebx, ecx, edx);
-#    endif
-}
-
-/**
- * @brief Get GNA module frequency
- * @return GNA module frequency in MHz
- */
-float getGnaFrequencyMHz() {
-    uint32_t eax = 1;
-    uint32_t ebx = 0;
-    uint32_t ecx = 0;
-    uint32_t edx = 0;
-    uint32_t family = 0;
-    uint32_t model = 0;
-    const uint8_t sixth_family = 6;
-    const uint8_t cannon_lake_model = 102;
-    const uint8_t gemini_lake_model = 122;
-    const uint8_t ice_lake_model = 126;
-    const uint8_t tgl_model = 140;
-    const uint8_t next_model = 151;
-
-    native_cpuid(&eax, &ebx, &ecx, &edx);
-    family = (eax >> 8) & 0xF;
-
-    // model is the concatenation of two fields
-    // | extended model | model |
-    // copy extended model data
-    model = (eax >> 16) & 0xF;
-    // shift
-    model <<= 4;
-    // copy model data
-    model += (eax >> 4) & 0xF;
-
-    if (family == sixth_family) {
-        switch (model) {
-        case cannon_lake_model:
-        case ice_lake_model:
-        case tgl_model:
-        case next_model:
-            return 400;
-        case gemini_lake_model:
-            return 200;
-        default:
-            return 1;
-        }
-    } else {
-        // counters not supported and we returns just default value
-        return 1;
-    }
-}
-
-#endif  // if not ARM
-
-/**
- * @brief Print a report on the statistical score error
- * @param totalError reference to a total score error struct
- * @param framesNum number of frames in utterance
- * @param stream output stream
- * @return none.
- */
-void printReferenceCompareResults(score_error_t const& totalError, size_t framesNum, std::ostream& stream) {
-    stream << "         max error: " << totalError.maxError << std::endl;
-    stream << "         avg error: " << totalError.sumError / totalError.numScores << std::endl;
-    stream << "     avg rms error: " << totalError.sumRmsError / framesNum << std::endl;
-    stream << "       stdev error: " << StdDevError(totalError) << std::endl << std::endl;
-    stream << std::endl;
-}
-
-/**
- * @brief Print a report on the performance counts
- * @param utterancePerfMap reference to a map to store performance counters
- * @param numberOfFrames number of frames
- * @param stream output stream
- * @param fullDeviceName full device name string
- * @param numberOfFramesOnHw number of frames delivered to GNA HW
- * @return none.
- */
-void printPerformanceCounters(
-    std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> const& utterancePerfMap,
-    size_t numberOfFrames,
-    std::ostream& stream,
-    std::string fullDeviceName,
-    const uint64_t numberOfFramesOnHw) {
-#if !defined(__arm__) && !defined(_M_ARM) && !defined(__aarch64__) && !defined(_M_ARM64)
-    if (numberOfFrames == 0)
-        throw std::runtime_error("printPerformanceCounters: numberOfFrames=0, incorrect input");
-    std::ios::fmtflags fmt(std::cout.flags());
-    stream << std::endl << "Performance counts:" << std::endl;
-    stream << std::setw(10) << std::right << ""
-           << "Counter descriptions";
-    stream << std::setw(22) << "Utt scoring time";
-    stream << std::setw(18) << "Avg infer time";
-    stream << std::endl;
-
-    stream << std::setw(46) << "(ms)";
-    stream << std::setw(24) << "(us per call)";
-    stream << std::endl;
-    // if GNA HW counters
-    // get frequency of GNA module
-    float freq = getGnaFrequencyMHz();
-    for (const auto& it : utterancePerfMap) {
-        std::string const& counter_name = it.first;
-        float current_units_us = static_cast<float>(it.second.realTime_uSec) / freq;
-        float call_units_us = current_units_us / numberOfFrames;
-        if (FLAGS_d.find("GNA") != std::string::npos) {
-            stream << std::setw(30) << std::left << counter_name.substr(4, counter_name.size() - 1);
-        } else {
-            stream << std::setw(30) << std::left << counter_name;
-        }
-        stream << std::setw(16) << std::right << current_units_us / 1000;
-        stream << std::setw(21) << std::right << call_units_us;
-        stream << std::endl;
-    }
-    stream << std::endl;
-    std::cout << std::endl;
-    std::cout << "Full device name: " << fullDeviceName << std::endl;
-    std::cout << std::endl;
-    stream << "Number of frames delivered to GNA HW: " << numberOfFramesOnHw;
-    stream << "/" << numberOfFrames;
-    stream << std::endl;
-    std::cout.flags(fmt);
-#endif
-}
-
-/**
- * @brief Get performance counts
- * @param request reference to infer request
- * @param perfCounters reference to a map to save performance counters
- * @return none.
- */
-void getPerformanceCounters(InferenceEngine::InferRequest& request,
-                            std::map<std::string, InferenceEngine::InferenceEngineProfileInfo>& perfCounters) {
-    auto retPerfCounters = request.GetPerformanceCounts();
-
-    for (const auto& pair : retPerfCounters) {
-        perfCounters[pair.first] = pair.second;
-    }
-}
-
-/**
- * @brief Summarize performance counts and total number of frames executed on the GNA HW device
- * @param perfCounters reference to a map to get performance counters
- * @param totalPerfCounters reference to a map to save total performance counters
- * @param totalRunsOnHw reference to a total number of frames computed on GNA HW
- * @return none.
- */
-void sumPerformanceCounters(std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> const& perfCounters,
-                            std::map<std::string, InferenceEngine::InferenceEngineProfileInfo>& totalPerfCounters,
-                            uint64_t& totalRunsOnHw) {
-    auto runOnHw = false;
-    for (const auto& pair : perfCounters) {
-        totalPerfCounters[pair.first].realTime_uSec += pair.second.realTime_uSec;
-        runOnHw |= pair.second.realTime_uSec >
-                   0;  // if realTime is above zero, that means that a primitive was executed on the device
-    }
-    totalRunsOnHw += runOnHw;
-}
-
-/**
- * @brief Parse scale factors
- * @param str reference to user-specified input scale factor for quantization, can be separated by comma
- * @return vector scale factors
- */
-std::vector<std::string> ParseScaleFactors(const std::string& str) {
-    std::vector<std::string> scaleFactorInput;
-
-    if (!str.empty()) {
-        std::string outStr;
-        std::istringstream stream(str);
-        int i = 0;
-        while (getline(stream, outStr, ',')) {
-            auto floatScaleFactor = std::stof(outStr);
-            if (floatScaleFactor <= 0.0f) {
-                throw std::logic_error("Scale factor for input #" + std::to_string(i) +
-                                       " (counting from zero) is out of range (must be positive).");
-            }
-            scaleFactorInput.push_back(outStr);
-            i++;
-        }
-    } else {
-        throw std::logic_error("Scale factor need to be specified via -sf option if you are using -q user");
-    }
-    return scaleFactorInput;
-}
-
-/**
- * @brief Parse string of file names separated by comma to save it to vector of file names
- * @param str file names separated by comma
- * @return vector of file names
- */
-std::vector<std::string> ConvertStrToVector(std::string str) {
-    std::vector<std::string> blobName;
-    if (!str.empty()) {
-        size_t pos_last = 0;
-        size_t pos_next = 0;
-        while ((pos_next = str.find(",", pos_last)) != std::string::npos) {
-            blobName.push_back(str.substr(pos_last, pos_next - pos_last));
-            pos_last = pos_next + 1;
-        }
-        blobName.push_back(str.substr(pos_last));
-    }
-    return blobName;
-}
-
-/**
- * @brief Checks input arguments
- * @param argc number of args
- * @param argv list of input arguments
- * @return bool status true(Success) or false(Fail)
- */
-bool ParseAndCheckCommandLine(int argc, char* argv[]) {
-    slog::info << "Parsing input parameters" << slog::endl;
-
-    gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
-    if (FLAGS_h) {
-        showUsage();
-        showAvailableDevices();
-        return false;
-    }
-    bool isDumpMode = !FLAGS_wg.empty() || !FLAGS_we.empty();
-
-    // input not required only in dump mode and if external scale factor provided
-    if (FLAGS_i.empty() && (!isDumpMode || FLAGS_q.compare("user") != 0)) {
-        showUsage();
-        if (isDumpMode) {
-            throw std::logic_error("In model dump mode either static quantization is used (-i) or user scale"
-                                   " factor need to be provided. See -q user option");
-        }
-        throw std::logic_error("Input file not set. Please use -i.");
-    }
-
-    if (FLAGS_m.empty() && FLAGS_rg.empty()) {
-        showUsage();
-        throw std::logic_error("Either IR file (-m) or GNAModel file (-rg) need to be set.");
-    }
-
-    if ((!FLAGS_m.empty() && !FLAGS_rg.empty())) {
-        throw std::logic_error("Only one of -m and -rg is allowed.");
-    }
-
-    std::vector<std::string> supportedDevices = {"CPU",
-                                                 "GPU",
-                                                 "GNA_AUTO",
-                                                 "GNA_HW",
-                                                 "GNA_HW_WITH_SW_FBACK",
-                                                 "GNA_SW_EXACT",
-                                                 "GNA_SW",
-                                                 "GNA_SW_FP32",
-                                                 "HETERO:GNA,CPU",
-                                                 "HETERO:GNA_HW,CPU",
-                                                 "HETERO:GNA_SW_EXACT,CPU",
-                                                 "HETERO:GNA_SW,CPU",
-                                                 "HETERO:GNA_SW_FP32,CPU",
-                                                 "MYRIAD"};
-
-    if (std::find(supportedDevices.begin(), supportedDevices.end(), FLAGS_d) == supportedDevices.end()) {
-        throw std::logic_error("Specified device is not supported.");
-    }
-
-    uint32_t batchSize = (uint32_t)FLAGS_bs;
-    if ((batchSize < 1) || (batchSize > 8)) {
-        throw std::logic_error("Batch size out of range (1..8).");
-    }
-
-    /** default is a static quantization **/
-    if ((FLAGS_q.compare("static") != 0) && (FLAGS_q.compare("dynamic") != 0) && (FLAGS_q.compare("user") != 0)) {
-        throw std::logic_error("Quantization mode not supported (static, dynamic, user).");
-    }
-
-    if (FLAGS_q.compare("dynamic") == 0) {
-        throw std::logic_error("Dynamic quantization not yet supported.");
-    }
-
-    if (FLAGS_qb != 16 && FLAGS_qb != 8) {
-        throw std::logic_error("Only 8 or 16 bits supported.");
-    }
-
-    if (FLAGS_nthreads <= 0) {
-        throw std::logic_error("Invalid value for 'nthreads' argument. It must be greater that or equal to 0");
-    }
-
-    if (FLAGS_cw_r < 0) {
-        throw std::logic_error("Invalid value for 'cw_r' argument. It must be greater than or equal to 0");
-    }
-
-    if (FLAGS_cw_l < 0) {
-        throw std::logic_error("Invalid value for 'cw_l' argument. It must be greater than or equal to 0");
-    }
-
-    if (FLAGS_pwl_me < 0.0 || FLAGS_pwl_me > 100.0) {
-        throw std::logic_error("Invalid value for 'pwl_me' argument. It must be greater than 0.0 and less than 100.0");
-    }
-
-    return true;
-}
+using namespace ov::preprocess;
 
 /**
  * @brief The entry point for inference engine automatic speech recognition sample
@@ -526,15 +38,13 @@ bool ParseAndCheckCommandLine(int argc, char* argv[]) {
  */
 int main(int argc, char* argv[]) {
     try {
-        // ------------------------------ Get Inference Engine version
-        // ------------------------------------------------------
-        slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << slog::endl;
+        // ------------------------------ Get Inference Engine version ----------------------------------------------
+        slog::info << "OpenVINO runtime: " << ov::get_openvino_version() << slog::endl;
 
         // ------------------------------ Parsing and validation of input arguments ---------------------------------
         if (!ParseAndCheckCommandLine(argc, argv)) {
             return 0;
         }
-
         BaseFile* file;
         BaseFile* fileOutput;
         ArkFile arkFile;
@@ -547,19 +57,16 @@ int main(int argc, char* argv[]) {
         } else {
             throw std::logic_error("Invalid input file");
         }
-
         std::vector<std::string> inputFiles;
         std::vector<uint32_t> numBytesThisUtterance;
         uint32_t numUtterances(0);
         if (!FLAGS_i.empty()) {
             std::string outStr;
             std::istringstream stream(FLAGS_i);
-
             uint32_t currentNumUtterances(0), currentNumBytesThisUtterance(0);
             while (getline(stream, outStr, ',')) {
                 std::string filename(fileNameNoExt(outStr) + "." + extInputFile);
                 inputFiles.push_back(filename);
-
                 file->GetFileInfo(filename.c_str(), 0, &currentNumUtterances, &currentNumBytesThisUtterance);
                 if (numUtterances == 0) {
                     numUtterances = currentNumUtterances;
@@ -571,49 +78,30 @@ int main(int argc, char* argv[]) {
             }
         }
         size_t numInputFiles(inputFiles.size());
-        // -----------------------------------------------------------------------------------------------------
-
-        // --------------------------- Step 1. Initialize inference engine core -------------------------------------
-        slog::info << "Loading Inference Engine" << slog::endl;
-        Core ie;
-        CNNNetwork network;
-        ExecutableNetwork executableNet;
 
+        // --------------------------- Step 1. Initialize inference engine core and read model
+        // -------------------------------------
+        ov::runtime::Core core;
+        slog::info << "Loading model files:" << slog::endl << FLAGS_m << slog::endl;
+        std::shared_ptr<ov::Model> model = core.read_model(FLAGS_m);
+        CheckNumberOfInputs(model->inputs().size(), numInputFiles);
+        const ov::Layout tensor_layout{"NC"};
+        ov::preprocess::PrePostProcessor proc(model);
+        for (int i = 0; i < model->inputs().size(); i++) {
+            proc.input(i).tensor().set_element_type(ov::element::f32).set_layout(tensor_layout);
+            proc.output(i).tensor().set_element_type(ov::element::f32);
+        }
+        model = proc.build();
         // ------------------------------ Get Available Devices ------------------------------------------------------
         auto isFeature = [&](const std::string xFeature) {
             return FLAGS_d.find(xFeature) != std::string::npos;
         };
-
         bool useGna = isFeature("GNA");
         bool useHetero = isFeature("HETERO");
-
         std::string deviceStr = useHetero && useGna ? "HETERO:GNA,CPU" : FLAGS_d.substr(0, (FLAGS_d.find("_")));
-
-        slog::info << "Device info: " << slog::endl;
-        slog::info << ie.GetVersions(deviceStr) << slog::endl;
-        // -----------------------------------------------------------------------------------------------------
-
-        // --------------------------- Step 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin
-        // files)
-        slog::info << "Loading network files:" << slog::endl << FLAGS_m << slog::endl;
-
         uint32_t batchSize = (FLAGS_cw_r > 0 || FLAGS_cw_l > 0) ? 1 : (uint32_t)FLAGS_bs;
-
-        if (!FLAGS_m.empty()) {
-            /** Read network model **/
-            network = ie.ReadNetwork(FLAGS_m);
-            CheckNumberOfInputs(network.getInputsInfo().size(), numInputFiles);
-            // -------------------------------------------------------------------------------------------------
-
-            // --------------------------- Set batch size ---------------------------------------------------
-            /** Set batch size.  Unlike in imaging, batching in time (rather than space) is done for speech recognition.
-             * **/
-            network.setBatchSize(batchSize);
-            slog::info << "Batch size is " << std::to_string(network.getBatchSize()) << slog::endl;
-        }
-
+        ov::set_batch(model, batchSize);
         // -----------------------------------------------------------------------------------------------------
-
         // --------------------------- Set parameters and scale factors -------------------------------------
         /** Setting parameter for per layer metrics **/
         std::map<std::string, std::string> gnaPluginConfig;
@@ -621,19 +109,17 @@ int main(int argc, char* argv[]) {
         if (useGna) {
             std::string gnaDevice =
                 useHetero ? FLAGS_d.substr(FLAGS_d.find("GNA"), FLAGS_d.find(",") - FLAGS_d.find("GNA")) : FLAGS_d;
-            gnaPluginConfig[GNAConfigParams::KEY_GNA_DEVICE_MODE] =
+            gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_DEVICE_MODE] =
                 gnaDevice.find("_") == std::string::npos ? "GNA_AUTO" : gnaDevice;
         }
-
         if (FLAGS_pc) {
-            genericPluginConfig[PluginConfigParams::KEY_PERF_COUNT] = PluginConfigParams::YES;
+            genericPluginConfig[InferenceEngine::PluginConfigParams::KEY_PERF_COUNT] =
+                InferenceEngine::PluginConfigParams::YES;
         }
-
         if (FLAGS_q.compare("user") == 0) {
             if (!FLAGS_rg.empty()) {
                 slog::warn << "Custom scale factor will be used for imported gna model: " << FLAGS_rg << slog::endl;
             }
-
             auto scaleFactorInput = ParseScaleFactors(FLAGS_sf);
             if (numInputFiles != scaleFactorInput.size()) {
                 std::string errMessage(
@@ -641,7 +127,6 @@ int main(int argc, char* argv[]) {
                     " scale factors provided for " + std::to_string(numInputFiles) + " input files.");
                 throw std::logic_error(errMessage);
             }
-
             for (size_t i = 0; i < scaleFactorInput.size(); ++i) {
                 slog::info << "For input " << i << " using scale factor of " << scaleFactorInput[i] << slog::endl;
                 std::string scaleFactorConfigKey = GNA_CONFIG_KEY(SCALE_FACTOR) + std::string("_") + std::to_string(i);
@@ -676,37 +161,31 @@ int main(int argc, char* argv[]) {
                 }
             }
         }
-
         if (FLAGS_qb == 8) {
-            gnaPluginConfig[GNAConfigParams::KEY_GNA_PRECISION] = "I8";
+            gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_PRECISION] = "I8";
         } else {
-            gnaPluginConfig[GNAConfigParams::KEY_GNA_PRECISION] = "I16";
+            gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_PRECISION] = "I16";
         }
-
-        gnaPluginConfig[GNAConfigParams::KEY_GNA_EXEC_TARGET] = FLAGS_exec_target;
-        gnaPluginConfig[GNAConfigParams::KEY_GNA_COMPILE_TARGET] = FLAGS_compile_target;
-        gnaPluginConfig[PluginConfigParams::KEY_LOG_LEVEL] = FLAGS_log;
-        gnaPluginConfig[GNAConfigParams::KEY_GNA_LIB_N_THREADS] =
+        gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_EXEC_TARGET] = FLAGS_exec_target;
+        gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_COMPILE_TARGET] = FLAGS_compile_target;
+        gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_LIB_N_THREADS] =
             std::to_string((FLAGS_cw_r > 0 || FLAGS_cw_l > 0) ? 1 : FLAGS_nthreads);
         gnaPluginConfig[GNA_CONFIG_KEY(COMPACT_MODE)] = CONFIG_VALUE(NO);
         gnaPluginConfig[GNA_CONFIG_KEY(PWL_MAX_ERROR_PERCENT)] = std::to_string(FLAGS_pwl_me);
         // -----------------------------------------------------------------------------------------------------
-
         // --------------------------- Write model to file --------------------------------------------------
         // Embedded GNA model dumping (for Intel(R) Speech Enabling Developer Kit)
         if (!FLAGS_we.empty()) {
-            gnaPluginConfig[GNAConfigParams::KEY_GNA_FIRMWARE_MODEL_IMAGE] = FLAGS_we;
-            gnaPluginConfig[GNAConfigParams::KEY_GNA_FIRMWARE_MODEL_IMAGE_GENERATION] = FLAGS_we_gen;
+            gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_FIRMWARE_MODEL_IMAGE] = FLAGS_we;
+            gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_FIRMWARE_MODEL_IMAGE_GENERATION] = FLAGS_we_gen;
         }
         // -----------------------------------------------------------------------------------------------------
-
-        // --------------------------- Step 4. Loading model to the device ------------------------------------------
+        // --------------------------- Step 2. Loading model to the device ------------------------------------------
         if (useGna) {
             genericPluginConfig.insert(std::begin(gnaPluginConfig), std::end(gnaPluginConfig));
         }
         auto t0 = Time::now();
         std::vector<std::string> outputs;
-
         if (!FLAGS_oname.empty()) {
             std::vector<std::string> output_names = ConvertStrToVector(FLAGS_oname);
             std::vector<size_t> ports;
@@ -723,33 +202,34 @@ int main(int argc, char* argv[]) {
                     throw std::logic_error("Ports should have integer type");
                 }
             }
-
             if (!FLAGS_m.empty()) {
                 for (size_t i = 0; i < outputs.size(); i++) {
-                    network.addOutput(outputs[i], ports[i]);
+                    model->add_output(outputs[i], ports[i]);
                 }
             }
         }
-        if (!FLAGS_m.empty()) {
-            slog::info << "Loading model to the device" << slog::endl;
-            executableNet = ie.LoadNetwork(network, deviceStr, genericPluginConfig);
-        } else {
-            slog::info << "Importing model to the device" << slog::endl;
-            executableNet = ie.ImportNetwork(FLAGS_rg.c_str(), deviceStr, genericPluginConfig);
-        }
         ms loadTime = std::chrono::duration_cast<ms>(Time::now() - t0);
         slog::info << "Model loading time " << loadTime.count() << " ms" << slog::endl;
-
+        slog::info << "Loading model to the device " << FLAGS_d << slog::endl;
+        ov::runtime::CompiledModel executableNet;
+        if (!FLAGS_m.empty()) {
+            slog::info << "Loading model to the device" << slog::endl;
+            executableNet = core.compile_model(model, deviceStr, genericPluginConfig);
+        } else {
+            slog::info << "Importing model to the device" << slog::endl;
+            std::istringstream streamrq(FLAGS_rg);
+            executableNet = core.import_model(streamrq, deviceStr, genericPluginConfig);
+        }
         // --------------------------- Exporting gna model using InferenceEngine AOT API---------------------
         if (!FLAGS_wg.empty()) {
             slog::info << "Writing GNA Model to file " << FLAGS_wg << slog::endl;
             t0 = Time::now();
-            executableNet.Export(FLAGS_wg);
+            std::ostringstream streamwq(FLAGS_wg);
+            executableNet.export_model(streamwq);
             ms exportTime = std::chrono::duration_cast<ms>(Time::now() - t0);
             slog::info << "Exporting time " << exportTime.count() << " ms" << slog::endl;
             return 0;
         }
-
         if (!FLAGS_we.empty()) {
             slog::info << "Exported GNA embedded model to file " << FLAGS_we << slog::endl;
             if (!FLAGS_we_gen.empty()) {
@@ -758,24 +238,17 @@ int main(int argc, char* argv[]) {
             return 0;
         }
         // ---------------------------------------------------------------------------------------------------------
-
-        // --------------------------- Step 5. Create infer request --------------------------------------------------
+        // --------------------------- Step 3. Create infer request --------------------------------------------------
         std::vector<InferRequestStruct> inferRequests((FLAGS_cw_r > 0 || FLAGS_cw_l > 0) ? 1 : FLAGS_nthreads);
+
         for (auto& inferRequest : inferRequests) {
-            inferRequest = {executableNet.CreateInferRequest(), -1, batchSize};
+            inferRequest = {executableNet.create_infer_request(), -1, batchSize};
         }
-        // ---------------------------------------------------------------------------------------------------------
-
-        // --------------------------- Step 3. Configure input & output
-        // -------------------------------------------------- This step executed after creating infer request to check
-        // input/output layers mentioned via -iname and -oname args
-        // --------------------------- Prepare input blobs -----------------------------------------------------
-        /** Taking information about all topology inputs **/
-        ConstInputsDataMap cInputInfo = executableNet.GetInputsInfo();
+        // --------------------------- Step 4. Configure input & output
+        // --------------------------------------------------
+        std::vector<ov::runtime::Tensor> ptrInputBlobs;
+        auto cInputInfo = executableNet.inputs();
         CheckNumberOfInputs(cInputInfo.size(), numInputFiles);
-
-        /** Stores all input blobs data **/
-        std::vector<Blob::Ptr> ptrInputBlobs;
         if (!FLAGS_iname.empty()) {
             std::vector<std::string> inputNameBlobs = ConvertStrToVector(FLAGS_iname);
             if (inputNameBlobs.size() != cInputInfo.size()) {
@@ -785,7 +258,7 @@ int main(int argc, char* argv[]) {
                 throw std::logic_error(errMessage);
             }
             for (const auto& input : inputNameBlobs) {
-                Blob::Ptr blob = inferRequests.begin()->inferRequest.GetBlob(input);
+                ov::runtime::Tensor blob = inferRequests.begin()->inferRequest.get_tensor(input);
                 if (!blob) {
                     std::string errMessage("No blob with name : " + input);
                     throw std::logic_error(errMessage);
@@ -794,51 +267,9 @@ int main(int argc, char* argv[]) {
             }
         } else {
             for (const auto& input : cInputInfo) {
-                ptrInputBlobs.push_back(inferRequests.begin()->inferRequest.GetBlob(input.first));
+                ptrInputBlobs.push_back(inferRequests.begin()->inferRequest.get_tensor(input));
             }
         }
-        InputsDataMap inputInfo;
-        if (!FLAGS_m.empty()) {
-            inputInfo = network.getInputsInfo();
-        }
-        /** Configure input precision if model is loaded from IR **/
-        for (auto& item : inputInfo) {
-            Precision inputPrecision = Precision::FP32;  // specify Precision::I16 to provide quantized inputs
-            item.second->setPrecision(inputPrecision);
-        }
-        // ---------------------------------------------------------------------
-
-        // ------------------------------ Prepare output blobs -------------------------------------------------
-        ConstOutputsDataMap cOutputInfo(executableNet.GetOutputsInfo());
-        OutputsDataMap outputInfo;
-        if (!FLAGS_m.empty()) {
-            outputInfo = network.getOutputsInfo();
-        }
-        std::vector<Blob::Ptr> ptrOutputBlob;
-        if (!outputs.empty()) {
-            for (const auto& output : outputs) {
-                Blob::Ptr blob = inferRequests.begin()->inferRequest.GetBlob(output);
-                if (!blob) {
-                    std::string errMessage("No blob with name : " + output);
-                    throw std::logic_error(errMessage);
-                }
-                ptrOutputBlob.push_back(blob);
-            }
-        } else {
-            for (auto& output : cOutputInfo) {
-                ptrOutputBlob.push_back(inferRequests.begin()->inferRequest.GetBlob(output.first));
-            }
-        }
-
-        for (auto& item : outputInfo) {
-            DataPtr outData = item.second;
-            if (!outData) {
-                throw std::logic_error("output data pointer is not valid");
-            }
-
-            Precision outputPrecision = Precision::FP32;  // specify Precision::I32 to retrieve quantized outputs
-            outData->setPrecision(outputPrecision);
-        }
         std::vector<std::string> output_name_files;
         std::vector<std::string> reference_name_files;
         size_t count_file = 1;
@@ -856,40 +287,31 @@ int main(int argc, char* argv[]) {
             }
             count_file = reference_name_files.empty() ? 1 : reference_name_files.size();
         }
-        // ---------------------------------------------------------------------
         // -----------------------------------------------------------------------------------------------------
-
-        // --------------------------- Step 7. Do inference --------------------------------------------------------
+        // --------------------------- Step 5. Do inference --------------------------------------------------------
         for (size_t next_output = 0; next_output < count_file; next_output++) {
             std::vector<std::vector<uint8_t>> ptrUtterances;
             std::vector<uint8_t> ptrScores;
             std::vector<uint8_t> ptrReferenceScores;
             score_error_t frameError, totalError;
-
             ptrUtterances.resize(inputFiles.size());
-
             // initialize memory state before starting
-            for (auto&& state : inferRequests.begin()->inferRequest.QueryState()) {
-                state.Reset();
+            for (auto&& state : inferRequests.begin()->inferRequest.query_state()) {
+                state.reset();
             }
-
             /** Work with each utterance **/
             for (uint32_t utteranceIndex = 0; utteranceIndex < numUtterances; ++utteranceIndex) {
-                std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> utterancePerfMap;
+                std::map<std::string, ov::runtime::ProfilingInfo> utterancePerfMap;
                 uint64_t totalNumberOfRunsOnHw = 0;
                 std::string uttName;
                 uint32_t numFrames(0), n(0);
                 std::vector<uint32_t> numFrameElementsInput;
-
                 uint32_t numFramesReference(0), numFrameElementsReference(0), numBytesPerElementReference(0),
                     numBytesReferenceScoreThisUtterance(0);
-                auto dims = outputs.empty() ? cOutputInfo.rbegin()->second->getDims()
-                                            : cOutputInfo[outputs[next_output]]->getDims();
+                auto dims = executableNet.outputs()[0].get_shape();
                 const auto numScoresPerFrame =
                     std::accumulate(std::begin(dims), std::end(dims), size_t{1}, std::multiplies<size_t>());
-
                 slog::info << "Number scores per frame : " << numScoresPerFrame << slog::endl;
-
                 /** Get information from input file for current utterance **/
                 numFrameElementsInput.resize(numInputFiles);
                 for (size_t i = 0; i < inputFiles.size(); i++) {
@@ -912,20 +334,17 @@ int main(int argc, char* argv[]) {
                                                std::to_string(numFrames) + " and " + std::to_string(currentNumFrames));
                         throw std::logic_error(errMessage);
                     }
-
                     ptrUtterances[i] = ptrUtterance;
                     numFrameElementsInput[i] = currentNumFrameElementsInput;
                 }
-
                 int i = 0;
                 for (auto& ptrInputBlob : ptrInputBlobs) {
-                    if (ptrInputBlob->size() != numFrameElementsInput[i++] * batchSize) {
-                        throw std::logic_error("network input size(" + std::to_string(ptrInputBlob->size()) +
+                    if (ptrInputBlob.get_size() != numFrameElementsInput[i++] * batchSize) {
+                        throw std::logic_error("network input size(" + std::to_string(ptrInputBlob.get_size()) +
                                                ") mismatch to input file size (" +
                                                std::to_string(numFrameElementsInput[i - 1] * batchSize) + ")");
                     }
                 }
-
                 ptrScores.resize(numFrames * numScoresPerFrame * sizeof(float));
                 if (!FLAGS_r.empty()) {
                     /** Read file with reference scores **/
@@ -952,11 +371,8 @@ int main(int argc, char* argv[]) {
                                                   &numFrameElementsReference,
                                                   &numBytesPerElementReference);
                 }
-
                 double totalTime = 0.0;
-
                 std::cout << "Utterance " << utteranceIndex << ": " << std::endl;
-
                 ClearScoreError(&totalError);
                 totalError.threshold = frameError.threshold = MAX_SCORE_DIFFERENCE;
                 auto outputFrame = &ptrScores.front();
@@ -964,17 +380,13 @@ int main(int argc, char* argv[]) {
                 for (auto& ut : ptrUtterances) {
                     inputFrame.push_back(&ut.front());
                 }
-
-                std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> callPerfMap;
-
+                std::map<std::string, ov::runtime::ProfilingInfo> callPerfMap;
                 size_t frameIndex = 0;
                 uint32_t numFramesFile = numFrames;
                 numFrames += FLAGS_cw_l + FLAGS_cw_r;
                 uint32_t numFramesThisBatch{batchSize};
-
                 auto t0 = Time::now();
                 auto t1 = t0;
-
                 while (frameIndex <= numFrames) {
                     if (frameIndex == numFrames) {
                         if (std::find_if(inferRequests.begin(), inferRequests.end(), [&](InferRequestStruct x) {
@@ -983,7 +395,6 @@ int main(int argc, char* argv[]) {
                             break;
                         }
                     }
-
                     bool inferRequestFetched = false;
                     /** Start inference loop **/
                     for (auto& inferRequest : inferRequests) {
@@ -993,63 +404,34 @@ int main(int argc, char* argv[]) {
                             numFramesThisBatch =
                                 (numFrames - frameIndex < batchSize) ? (numFrames - frameIndex) : batchSize;
                         }
+
                         /* waits until inference result becomes available */
                         if (inferRequest.frameIndex != -1) {
-                            StatusCode code =
-                                inferRequest.inferRequest.Wait(InferenceEngine::InferRequest::WaitMode::RESULT_READY);
-
-                            if (code != StatusCode::OK) {
-                                if (!useHetero)
-                                    continue;
-                                if (code != StatusCode::INFER_NOT_STARTED)
-                                    continue;
-                            }
-                            // --------------------------- Step 8. Process output part 1
-                            // -------------------------------------------------------
-                            ConstOutputsDataMap newOutputInfo;
+                            inferRequest.inferRequest.wait();
                             if (inferRequest.frameIndex >= 0) {
                                 if (!FLAGS_o.empty()) {
                                     /* Prepare output data for save to file in future */
                                     outputFrame = &ptrScores.front() +
                                                   numScoresPerFrame * sizeof(float) * (inferRequest.frameIndex);
-                                    if (!outputs.empty()) {
-                                        newOutputInfo[outputs[next_output]] = cOutputInfo[outputs[next_output]];
-                                    } else {
-                                        newOutputInfo = cOutputInfo;
-                                    }
-                                    Blob::Ptr outputBlob =
-                                        inferRequest.inferRequest.GetBlob(newOutputInfo.rbegin()->first);
-                                    MemoryBlob::CPtr moutput = as<MemoryBlob>(outputBlob);
 
-                                    if (!moutput) {
-                                        throw std::logic_error(
-                                            "We expect output to be inherited from MemoryBlob, "
-                                            "but in fact we were not able to cast output to MemoryBlob");
-                                    }
+                                    ov::runtime::Tensor outputBlob =
+                                        inferRequest.inferRequest.get_tensor(executableNet.outputs()[0]);
+                                    if (!FLAGS_oname.empty())
+                                        outputBlob =
+                                            inferRequest.inferRequest.get_tensor(executableNet.outputs().back());
                                     // locked memory holder should be alive all time while access to its buffer happens
-                                    auto moutputHolder = moutput->rmap();
                                     auto byteSize = numScoresPerFrame * sizeof(float);
-                                    std::memcpy(outputFrame, moutputHolder.as<const void*>(), byteSize);
+                                    std::memcpy(outputFrame, outputBlob.data<float>(), byteSize);
                                 }
                                 if (!FLAGS_r.empty()) {
                                     /** Compare output data with reference scores **/
-                                    if (!outputs.empty()) {
-                                        newOutputInfo[outputs[next_output]] = cOutputInfo[outputs[next_output]];
-                                    } else {
-                                        newOutputInfo = cOutputInfo;
-                                    }
-                                    Blob::Ptr outputBlob =
-                                        inferRequest.inferRequest.GetBlob(newOutputInfo.rbegin()->first);
-                                    MemoryBlob::CPtr moutput = as<MemoryBlob>(outputBlob);
-                                    if (!moutput) {
-                                        throw std::logic_error(
-                                            "We expect output to be inherited from MemoryBlob, "
-                                            "but in fact we were not able to cast output to MemoryBlob");
-                                    }
-                                    // locked memory holder should be alive all time while access to its buffer happens
-                                    auto moutputHolder = moutput->rmap();
+                                    ov::runtime::Tensor outputBlob =
+                                        inferRequest.inferRequest.get_tensor(executableNet.outputs()[0]);
+                                    if (!FLAGS_oname.empty())
+                                        outputBlob =
+                                            inferRequest.inferRequest.get_tensor(executableNet.outputs().back());
                                     CompareScores(
-                                        moutputHolder.as<float*>(),
+                                        outputBlob.data<float>(),
                                         &ptrReferenceScores[inferRequest.frameIndex * numFrameElementsReference *
                                                             numBytesPerElementReference],
                                         &frameError,
@@ -1066,53 +448,21 @@ int main(int argc, char* argv[]) {
                             }
                             // -----------------------------------------------------------------------------------------------------
                         }
-
                         if (frameIndex == numFrames) {
                             inferRequest.frameIndex = -1;
                             continue;
                         }
-
-                        // --------------------------- Step 6. Prepare input
-                        // --------------------------------------------------------
-                        ptrInputBlobs.clear();
-                        if (FLAGS_iname.empty()) {
-                            for (auto& input : cInputInfo) {
-                                ptrInputBlobs.push_back(inferRequest.inferRequest.GetBlob(input.first));
-                            }
-                        } else {
-                            std::vector<std::string> inputNameBlobs = ConvertStrToVector(FLAGS_iname);
-                            for (const auto& input : inputNameBlobs) {
-                                Blob::Ptr blob = inferRequests.begin()->inferRequest.GetBlob(input);
-                                if (!blob) {
-                                    std::string errMessage("No blob with name : " + input);
-                                    throw std::logic_error(errMessage);
-                                }
-                                ptrInputBlobs.push_back(blob);
-                            }
-                        }
-
-                        /** Iterate over all the input blobs **/
-                        for (size_t i = 0; i < numInputFiles; ++i) {
-                            MemoryBlob::Ptr minput = as<MemoryBlob>(ptrInputBlobs[i]);
-                            if (!minput) {
-                                std::string errMessage("We expect ptrInputBlobs[" + std::to_string(i) +
-                                                       "] to be inherited from MemoryBlob, " +
-                                                       "but in fact we were not able to cast input blob to MemoryBlob");
-                                throw std::logic_error(errMessage);
-                            }
-                            // locked memory holder should be alive all time while access to its buffer happens
-                            auto minputHolder = minput->wmap();
-
-                            std::memcpy(minputHolder.as<void*>(), inputFrame[i], minput->byteSize());
-                        }
                         // -----------------------------------------------------------------------------------------------------
-
                         int index = static_cast<int>(frameIndex) - (FLAGS_cw_l + FLAGS_cw_r);
+                        for (int i = 0; i < model->inputs().size(); i++) {
+                            inferRequest.inferRequest.set_input_tensor(
+                                i,
+                                ov::runtime::Tensor(ov::element::f32, model->inputs()[i].get_shape(), inputFrame[0]));
+                        }
                         /* Starting inference in asynchronous mode*/
-                        inferRequest.inferRequest.StartAsync();
+                        inferRequest.inferRequest.start_async();
                         inferRequest.frameIndex = index < 0 ? -2 : index;
                         inferRequest.numFramesThisBatch = numFramesThisBatch;
-
                         frameIndex += numFramesThisBatch;
                         for (size_t j = 0; j < inputFiles.size(); j++) {
                             if (FLAGS_cw_l > 0 || FLAGS_cw_r > 0) {
@@ -1139,18 +489,16 @@ int main(int argc, char* argv[]) {
                     }
                 }
                 t1 = Time::now();
-
                 fsec fs = t1 - t0;
                 ms d = std::chrono::duration_cast<ms>(fs);
                 totalTime += d.count();
-
                 // resetting state between utterances
-                for (auto&& state : inferRequests.begin()->inferRequest.QueryState()) {
-                    state.Reset();
+                for (auto&& state : inferRequests.begin()->inferRequest.query_state()) {
+                    state.reset();
                 }
                 // -----------------------------------------------------------------------------------------------------
 
-                // --------------------------- Step 8. Process output part 2
+                // --------------------------- Step 6. Process output
                 // -------------------------------------------------------
 
                 if (!FLAGS_o.empty()) {
@@ -1171,7 +519,6 @@ int main(int argc, char* argv[]) {
                                          numFramesFile,
                                          numScoresPerFrame);
                 }
-
                 /** Show performance results **/
                 std::cout << "Total time in Infer (HW and SW):\t" << totalTime << " ms" << std::endl;
                 std::cout << "Frames in utterance:\t\t\t" << numFrames << " frames" << std::endl;
@@ -1182,8 +529,9 @@ int main(int argc, char* argv[]) {
                     printPerformanceCounters(utterancePerfMap,
                                              frameIndex,
                                              std::cout,
-                                             getFullDeviceName(ie, FLAGS_d),
-                                             totalNumberOfRunsOnHw);
+                                             getFullDeviceName(core, FLAGS_d),
+                                             totalNumberOfRunsOnHw,
+                                             FLAGS_d);
                 }
                 if (!FLAGS_r.empty()) {
                     // print statistical score error
@@ -1201,8 +549,6 @@ int main(int argc, char* argv[]) {
         slog::err << "Unknown/internal exception happened" << slog::endl;
         return 1;
     }
-
     slog::info << "Execution successful" << slog::endl;
-
     return 0;
 }
diff --git a/samples/cpp/speech_sample/speech_sample.hpp b/samples/cpp/speech_sample/speech_sample.hpp
index 336aa654c0b..e93d6efd817 100644
--- a/samples/cpp/speech_sample/speech_sample.hpp
+++ b/samples/cpp/speech_sample/speech_sample.hpp
@@ -1,7 +1,3 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
 #pragma once
 
 #include <gflags/gflags.h>
@@ -228,3 +224,95 @@ static void showUsage() {
     std::cout << "    -iname \"<string>\"       " << input_layer_names_message << std::endl;
     std::cout << "    -pwl_me \"<double>\"      " << pwl_max_error_percent_message << std::endl;
 }
+
+/**
+ * @brief Checks input arguments
+ * @param argc number of args
+ * @param argv list of input arguments
+ * @return bool status true(Success) or false(Fail)
+ */
+bool ParseAndCheckCommandLine(int argc, char* argv[]) {
+    slog::info << "Parsing input parameters" << slog::endl;
+
+    gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
+    if (FLAGS_h) {
+        showUsage();
+        showAvailableDevices();
+        return false;
+    }
+    bool isDumpMode = !FLAGS_wg.empty() || !FLAGS_we.empty();
+
+    // input not required only in dump mode and if external scale factor provided
+    if (FLAGS_i.empty() && (!isDumpMode || FLAGS_q.compare("user") != 0)) {
+        showUsage();
+        if (isDumpMode) {
+            throw std::logic_error("In model dump mode either static quantization is used (-i) or user scale"
+                                   " factor need to be provided. See -q user option");
+        }
+        throw std::logic_error("Input file not set. Please use -i.");
+    }
+
+    if (FLAGS_m.empty() && FLAGS_rg.empty()) {
+        showUsage();
+        throw std::logic_error("Either IR file (-m) or GNAModel file (-rg) need to be set.");
+    }
+
+    if ((!FLAGS_m.empty() && !FLAGS_rg.empty())) {
+        throw std::logic_error("Only one of -m and -rg is allowed.");
+    }
+
+    std::vector<std::string> supportedDevices = {"CPU",
+                                                 "GPU",
+                                                 "GNA_AUTO",
+                                                 "GNA_HW",
+                                                 "GNA_HW_WITH_SW_FBACK",
+                                                 "GNA_SW_EXACT",
+                                                 "GNA_SW",
+                                                 "GNA_SW_FP32",
+                                                 "HETERO:GNA,CPU",
+                                                 "HETERO:GNA_HW,CPU",
+                                                 "HETERO:GNA_SW_EXACT,CPU",
+                                                 "HETERO:GNA_SW,CPU",
+                                                 "HETERO:GNA_SW_FP32,CPU",
+                                                 "MYRIAD"};
+
+    if (std::find(supportedDevices.begin(), supportedDevices.end(), FLAGS_d) == supportedDevices.end()) {
+        throw std::logic_error("Specified device is not supported.");
+    }
+
+    uint32_t batchSize = (uint32_t)FLAGS_bs;
+    if ((batchSize < 1) || (batchSize > 8)) {
+        throw std::logic_error("Batch size out of range (1..8).");
+    }
+
+    /** default is a static quantization **/
+    if ((FLAGS_q.compare("static") != 0) && (FLAGS_q.compare("dynamic") != 0) && (FLAGS_q.compare("user") != 0)) {
+        throw std::logic_error("Quantization mode not supported (static, dynamic, user).");
+    }
+
+    if (FLAGS_q.compare("dynamic") == 0) {
+        throw std::logic_error("Dynamic quantization not yet supported.");
+    }
+
+    if (FLAGS_qb != 16 && FLAGS_qb != 8) {
+        throw std::logic_error("Only 8 or 16 bits supported.");
+    }
+
+    if (FLAGS_nthreads <= 0) {
+        throw std::logic_error("Invalid value for 'nthreads' argument. It must be greater that or equal to 0");
+    }
+
+    if (FLAGS_cw_r < 0) {
+        throw std::logic_error("Invalid value for 'cw_r' argument. It must be greater than or equal to 0");
+    }
+
+    if (FLAGS_cw_l < 0) {
+        throw std::logic_error("Invalid value for 'cw_l' argument. It must be greater than or equal to 0");
+    }
+
+    if (FLAGS_pwl_me < 0.0 || FLAGS_pwl_me > 100.0) {
+        throw std::logic_error("Invalid value for 'pwl_me' argument. It must be greater than 0.0 and less than 100.0");
+    }
+
+    return true;
+}
diff --git a/samples/cpp/speech_sample/utils.hpp b/samples/cpp/speech_sample/utils.hpp
new file mode 100644
index 00000000000..3216ce42531
--- /dev/null
+++ b/samples/cpp/speech_sample/utils.hpp
@@ -0,0 +1,406 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+#include <cnpy.h>
+
+#include <samples/common.hpp>
+
+#define MAX_SCORE_DIFFERENCE 0.0001f  // max score difference for frame error threshold
+#define MAX_VAL_2B_FEAT      16384    // max to find scale factor
+
+typedef std::chrono::high_resolution_clock Time;
+typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;
+typedef std::chrono::duration<float> fsec;
+
+/**
+ * @brief struct to store score error
+ */
+typedef struct {
+    uint32_t numScores;
+    uint32_t numErrors;
+    float threshold;
+    float maxError;
+    float rmsError;
+    float sumError;
+    float sumRmsError;
+    float sumSquaredError;
+    float maxRelError;
+    float sumRelError;
+    float sumSquaredRelError;
+} score_error_t;
+
+/**
+ * @brief struct to store infer request data per frame
+ */
+struct InferRequestStruct {
+    ov::runtime::InferRequest inferRequest;
+    int frameIndex;
+    uint32_t numFramesThisBatch;
+};
+
+/**
+ * @brief Check number of input files and model network inputs
+ * @param numInputs number model inputs
+ * @param numInputFiles number of input files
+ * @return none.
+ */
+void CheckNumberOfInputs(size_t numInputs, size_t numInputFiles) {
+    if (numInputs != numInputFiles) {
+        throw std::logic_error("Number of network inputs (" + std::to_string(numInputs) +
+                               ")"
+                               " is not equal to number of input files (" +
+                               std::to_string(numInputFiles) + ")");
+    }
+}
+
+/**
+ * @brief Get scale factor for quantization
+ * @param ptrFloatMemory pointer to float memory with speech feature vector
+ * @param targetMax max scale factor
+ * @param numElements number of elements in speech feature vector
+ * @return scale factor
+ */
+float ScaleFactorForQuantization(void* ptrFloatMemory, float targetMax, uint32_t numElements) {
+    float* ptrFloatFeat = reinterpret_cast<float*>(ptrFloatMemory);
+    float max = 0.0;
+    float scaleFactor;
+
+    for (uint32_t i = 0; i < numElements; i++) {
+        if (fabs(ptrFloatFeat[i]) > max) {
+            max = fabs(ptrFloatFeat[i]);
+        }
+    }
+
+    if (max == 0) {
+        scaleFactor = 1.0;
+    } else {
+        scaleFactor = targetMax / max;
+    }
+
+    return (scaleFactor);
+}
+
+/**
+ * @brief Clean score error
+ * @param error pointer to score error struct
+ * @return none.
+ */
+void ClearScoreError(score_error_t* error) {
+    error->numScores = 0;
+    error->numErrors = 0;
+    error->maxError = 0.0;
+    error->rmsError = 0.0;
+    error->sumError = 0.0;
+    error->sumRmsError = 0.0;
+    error->sumSquaredError = 0.0;
+    error->maxRelError = 0.0;
+    error->sumRelError = 0.0;
+    error->sumSquaredRelError = 0.0;
+}
+
+/**
+ * @brief Update total score error
+ * @param error pointer to score error struct
+ * @param totalError pointer to total score error struct
+ * @return none.
+ */
+void UpdateScoreError(score_error_t* error, score_error_t* totalError) {
+    totalError->numErrors += error->numErrors;
+    totalError->numScores += error->numScores;
+    totalError->sumRmsError += error->rmsError;
+    totalError->sumError += error->sumError;
+    totalError->sumSquaredError += error->sumSquaredError;
+    if (error->maxError > totalError->maxError) {
+        totalError->maxError = error->maxError;
+    }
+    totalError->sumRelError += error->sumRelError;
+    totalError->sumSquaredRelError += error->sumSquaredRelError;
+    if (error->maxRelError > totalError->maxRelError) {
+        totalError->maxRelError = error->maxRelError;
+    }
+}
+
+/**
+ * @brief Compare score errors, array should be the same length
+ * @param ptrScoreArray - pointer to score error struct array
+ * @param ptrRefScoreArray - pointer to score error struct array to compare
+ * @param scoreError - pointer to score error struct to save a new error
+ * @param numRows - number rows in score error arrays
+ * @param numColumns - number columns in score error arrays
+ * @return none.
+ */
+void CompareScores(float* ptrScoreArray,
+                   void* ptrRefScoreArray,
+                   score_error_t* scoreError,
+                   uint32_t numRows,
+                   uint32_t numColumns) {
+    uint32_t numErrors = 0;
+
+    ClearScoreError(scoreError);
+
+    float* A = ptrScoreArray;
+    float* B = reinterpret_cast<float*>(ptrRefScoreArray);
+    for (uint32_t i = 0; i < numRows; i++) {
+        for (uint32_t j = 0; j < numColumns; j++) {
+            float score = A[i * numColumns + j];
+            // std::cout << "score" << score << std::endl;
+            float refscore = B[i * numColumns + j];
+            float error = fabs(refscore - score);
+            float rel_error = error / (static_cast<float>(fabs(refscore)) + 1e-20f);
+            float squared_error = error * error;
+            float squared_rel_error = rel_error * rel_error;
+            scoreError->numScores++;
+            scoreError->sumError += error;
+            scoreError->sumSquaredError += squared_error;
+            if (error > scoreError->maxError) {
+                scoreError->maxError = error;
+            }
+            scoreError->sumRelError += rel_error;
+            scoreError->sumSquaredRelError += squared_rel_error;
+            if (rel_error > scoreError->maxRelError) {
+                scoreError->maxRelError = rel_error;
+            }
+            if (error > scoreError->threshold) {
+                numErrors++;
+            }
+        }
+    }
+    scoreError->rmsError = sqrt(scoreError->sumSquaredError / (numRows * numColumns));
+    scoreError->sumRmsError += scoreError->rmsError;
+    scoreError->numErrors = numErrors;
+    // std::cout << "rmsError=" << scoreError->rmsError << "sumRmsError="<<scoreError->sumRmsError;
+}
+
+/**
+ * @brief Get total stdev error
+ * @param error pointer to score error struct
+ * @return error
+ */
+float StdDevError(score_error_t error) {
+    return (sqrt(error.sumSquaredError / error.numScores -
+                 (error.sumError / error.numScores) * (error.sumError / error.numScores)));
+}
+
+#if !defined(__arm__) && !defined(_M_ARM) && !defined(__aarch64__) && !defined(_M_ARM64)
+#    ifdef _WIN32
+#        include <intrin.h>
+#        include <windows.h>
+#    else
+
+#        include <cpuid.h>
+
+#    endif
+
+inline void native_cpuid(unsigned int* eax, unsigned int* ebx, unsigned int* ecx, unsigned int* edx) {
+    size_t level = *eax;
+#    ifdef _WIN32
+    int regs[4] = {static_cast<int>(*eax), static_cast<int>(*ebx), static_cast<int>(*ecx), static_cast<int>(*edx)};
+    __cpuid(regs, level);
+    *eax = static_cast<uint32_t>(regs[0]);
+    *ebx = static_cast<uint32_t>(regs[1]);
+    *ecx = static_cast<uint32_t>(regs[2]);
+    *edx = static_cast<uint32_t>(regs[3]);
+#    else
+    __get_cpuid(level, eax, ebx, ecx, edx);
+#    endif
+}
+
+/**
+ * @brief Get GNA module frequency
+ * @return GNA module frequency in MHz
+ */
+float getGnaFrequencyMHz() {
+    uint32_t eax = 1;
+    uint32_t ebx = 0;
+    uint32_t ecx = 0;
+    uint32_t edx = 0;
+    uint32_t family = 0;
+    uint32_t model = 0;
+    const uint8_t sixth_family = 6;
+    const uint8_t cannon_lake_model = 102;
+    const uint8_t gemini_lake_model = 122;
+    const uint8_t ice_lake_model = 126;
+    const uint8_t tgl_model = 140;
+    const uint8_t next_model = 151;
+
+    native_cpuid(&eax, &ebx, &ecx, &edx);
+    family = (eax >> 8) & 0xF;
+
+    // model is the concatenation of two fields
+    // | extended model | model |
+    // copy extended model data
+    model = (eax >> 16) & 0xF;
+    // shift
+    model <<= 4;
+    // copy model data
+    model += (eax >> 4) & 0xF;
+
+    if (family == sixth_family) {
+        switch (model) {
+        case cannon_lake_model:
+        case ice_lake_model:
+        case tgl_model:
+        case next_model:
+            return 400;
+        case gemini_lake_model:
+            return 200;
+        default:
+            return 1;
+        }
+    } else {
+        // counters not supported and we returns just default value
+        return 1;
+    }
+}
+
+#endif  // if not ARM
+
+/**
+ * @brief Print a report on the statistical score error
+ * @param totalError reference to a total score error struct
+ * @param framesNum number of frames in utterance
+ * @param stream output stream
+ * @return none.
+ */
+void printReferenceCompareResults(score_error_t const& totalError, size_t framesNum, std::ostream& stream) {
+    stream << "         max error: " << totalError.maxError << std::endl;
+    stream << "         avg error: " << totalError.sumError / totalError.numScores << std::endl;
+    stream << "     avg rms error: " << totalError.sumRmsError / framesNum << std::endl;
+    stream << "       stdev error: " << StdDevError(totalError) << std::endl << std::endl;
+    stream << std::endl;
+}
+
+/**
+ * @brief Print a report on the performance counts
+ * @param utterancePerfMap reference to a map to store performance counters
+ * @param numberOfFrames number of frames
+ * @param stream output stream
+ * @param fullDeviceName full device name string
+ * @param numberOfFramesOnHw number of frames delivered to GNA HW
+ * @param FLAGS_d flag of device
+ * @return none.
+ */
+void printPerformanceCounters(std::map<std::string, ov::runtime::ProfilingInfo> const& utterancePerfMap,
+                              size_t numberOfFrames,
+                              std::ostream& stream,
+                              std::string fullDeviceName,
+                              const uint64_t numberOfFramesOnHw,
+                              std::string FLAGS_d) {
+#if !defined(__arm__) && !defined(_M_ARM) && !defined(__aarch64__) && !defined(_M_ARM64)
+    stream << std::endl << "Performance counts:" << std::endl;
+    stream << std::setw(10) << std::right << ""
+           << "Counter descriptions";
+    stream << std::setw(22) << "Utt scoring time";
+    stream << std::setw(18) << "Avg infer time";
+    stream << std::endl;
+
+    stream << std::setw(46) << "(ms)";
+    stream << std::setw(24) << "(us per call)";
+    stream << std::endl;
+    // if GNA HW counters
+    // get frequency of GNA module
+    float freq = getGnaFrequencyMHz();
+    for (const auto& it : utterancePerfMap) {
+        std::string const& counter_name = it.first;
+        float current_units_us = static_cast<float>(it.second.real_time.count()) / freq;
+        float call_units_us = current_units_us / numberOfFrames;
+        if (FLAGS_d.find("GNA") != std::string::npos) {
+            stream << std::setw(30) << std::left << counter_name.substr(4, counter_name.size() - 1);
+        } else {
+            stream << std::setw(30) << std::left << counter_name;
+        }
+        stream << std::setw(16) << std::right << current_units_us / 1000;
+        stream << std::setw(21) << std::right << call_units_us;
+        stream << std::endl;
+    }
+    stream << std::endl;
+    std::cout << std::endl;
+    std::cout << "Full device name: " << fullDeviceName << std::endl;
+    std::cout << std::endl;
+    stream << "Number of frames delivered to GNA HW: " << numberOfFramesOnHw;
+    stream << "/" << numberOfFrames;
+    stream << std::endl;
+#endif
+}
+
+/**
+ * @brief Get performance counts
+ * @param request reference to infer request
+ * @param perfCounters reference to a map to save performance counters
+ * @return none.
+ */
+void getPerformanceCounters(ov::runtime::InferRequest& request,
+                            std::map<std::string, ov::runtime::ProfilingInfo>& perfCounters) {
+    auto retPerfCounters = request.get_profiling_info();
+
+    for (const auto& element : retPerfCounters) {
+        perfCounters[element.node_name] = element;
+    }
+}
+
+/**
+ * @brief Summarize performance counts and total number of frames executed on the GNA HW device
+ * @param perfCounters reference to a map to get performance counters
+ * @param totalPerfCounters reference to a map to save total performance counters
+ * @param totalRunsOnHw reference to a total number of frames computed on GNA HW
+ * @return none.
+ */
+void sumPerformanceCounters(std::map<std::string, ov::runtime::ProfilingInfo> const& perfCounters,
+                            std::map<std::string, ov::runtime::ProfilingInfo>& totalPerfCounters,
+                            uint64_t& totalRunsOnHw) {
+    auto runOnHw = false;
+    for (const auto& pair : perfCounters) {
+        totalPerfCounters[pair.first].real_time += pair.second.real_time;
+        runOnHw |= pair.second.real_time > std::chrono::microseconds(0);  // if realTime is above zero, that means that
+                                                                          // a primitive was executed on the device
+    }
+    totalRunsOnHw += runOnHw;
+}
+
+/**
+ * @brief Parse scale factors
+ * @param str reference to user-specified input scale factor for quantization, can be separated by comma
+ * @return vector scale factors
+ */
+std::vector<std::string> ParseScaleFactors(const std::string& str) {
+    std::vector<std::string> scaleFactorInput;
+
+    if (!str.empty()) {
+        std::string outStr;
+        std::istringstream stream(str);
+        int i = 0;
+        while (getline(stream, outStr, ',')) {
+            auto floatScaleFactor = std::stof(outStr);
+            if (floatScaleFactor <= 0.0f) {
+                throw std::logic_error("Scale factor for input #" + std::to_string(i) +
+                                       " (counting from zero) is out of range (must be positive).");
+            }
+            scaleFactorInput.push_back(outStr);
+            i++;
+        }
+    } else {
+        throw std::logic_error("Scale factor need to be specified via -sf option if you are using -q user");
+    }
+    return scaleFactorInput;
+}
+
+/**
+ * @brief Parse string of file names separated by comma to save it to vector of file names
+ * @param str file names separated by comma
+ * @return vector of file names
+ */
+std::vector<std::string> ConvertStrToVector(std::string str) {
+    std::vector<std::string> blobName;
+    if (!str.empty()) {
+        size_t pos_last = 0;
+        size_t pos_next = 0;
+        while ((pos_next = str.find(",", pos_last)) != std::string::npos) {
+            blobName.push_back(str.substr(pos_last, pos_next - pos_last));
+            pos_last = pos_next + 1;
+        }
+        blobName.push_back(str.substr(pos_last));
+    }
+    return blobName;
+}

From a4518ae5953832d8b4cb239744f14138e3afbb6e Mon Sep 17 00:00:00 2001
From: Jade Cho <jade.cho@intel.com>
Date: Mon, 20 Dec 2021 14:19:53 +0900
Subject: [PATCH 034/160] [GPU] Add DumpLayersLimitBatch config to GPU debug
 utils. (#9196)

* Add DumpLayersLimitBatch config to GPU debug utils.

+ Support OV_GPU_Help config
+ Only run first inference if OV_GPU_DumpLayersPath is set.
+ Fix dump graph bug.

* Apply some comments

* Remove unnecessary code.
---
 .../intel_gpu/runtime/debug_configuration.hpp |  2 +
 .../clDNN/runtime/debug_configuration.cpp     | 41 ++++++++++++++++++-
 .../graph_optimizer/graph_initializations.cpp |  5 ---
 .../thirdparty/clDNN/src/network.cpp          | 16 ++++++--
 .../thirdparty/clDNN/src/program.cpp          |  7 +++-
 .../intel_gpu/src/plugin/infer_request.cpp    |  7 ++++
 6 files changed, 68 insertions(+), 10 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp
index d96c151e5fe..600a322491b 100644
--- a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp
@@ -24,6 +24,7 @@ private:
     debug_configuration();
 public:
     static const char *prefix;
+    int help;                       // Print help messages
     int verbose;                    // Verbose execution
     int print_multi_kernel_perf;    // Print execution time of each kernel in multi-kernel primitimive
     int disable_usm;                // Disable usm usage
@@ -34,6 +35,7 @@ public:
     std::string dump_layers;        // Dump intermediate buffers of specified layers only, separated by space
     std::string dry_run_path;       // Dry run and serialize execution graph into the specified path
     int dump_layers_dst_only;       // Dump only output of layers
+    int dump_layers_limit_batch;    // Limit the size of batch to dump
     int base_batch_for_memory_estimation; // Base batch size to be used in memory estimation
     static const debug_configuration *get_instance();
 };
diff --git a/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp b/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
index 33ecf9625ba..2a387b39515 100644
--- a/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
@@ -3,7 +3,9 @@
 //
 
 #include "intel_gpu/runtime/debug_configuration.hpp"
+#include <algorithm>
 #include <iostream>
+#include <iomanip>
 #include <memory>
 #include <vector>
 #include <sstream>
@@ -97,10 +99,39 @@ void get_common_debug_env_var(const std::string &var, T &val) {
     return get_debug_env_var(var, val, allowed_option_prefixes);
 }
 
+static void print_help_messages() {
+    std::vector<std::pair<std::string, std::string>> message_list;
+    message_list.emplace_back("OV_GPU_Help", "Print help messages");
+    message_list.emplace_back("OV_GPU_Verbose", "Verbose execution");
+    message_list.emplace_back("OV_GPU_PrintMultiKernelPerf", "Print execution time of each kernel in multi-kernel primitimive");
+    message_list.emplace_back("OV_GPU_DisableUsm", "Disable usm usage");
+    message_list.emplace_back("OV_GPU_DisableOnednn", "Disable onednn for discrete GPU (no effect for integrated GPU)");
+    message_list.emplace_back("OV_GPU_DumpGraphs", "Dump optimized graph");
+    message_list.emplace_back("OV_GPU_DumpSources", "Dump opencl sources");
+    message_list.emplace_back("OV_GPU_DumpLayersPath", "Enable dumping intermediate buffers and set the dest path");
+    message_list.emplace_back("OV_GPU_DumpLayers", "Dump intermediate buffers of specified layers only, separated by space");
+    message_list.emplace_back("OV_GPU_DumpLayersDstOnly", "Dump only output of layers");
+    message_list.emplace_back("OV_GPU_DumpLayersLimitBatch", "Limit the size of batch to dump");
+    message_list.emplace_back("OV_GPU_DryRunPath", "Dry run and serialize execution graph into the specified path");
+    message_list.emplace_back("OV_GPU_BaseBatchForMemEstimation", "Base batch size to be used in memory estimation");
+
+    auto max_name_length_item = std::max_element(message_list.begin(), message_list.end(),
+        [](std::pair<std::string, std::string>& a, std::pair<std::string, std::string>& b){
+            return a.first.size() < b.first.size();
+    });
+    int name_width = static_cast<int>(max_name_length_item->first.size()) + 2;
+
+    GPU_DEBUG_COUT << "Supported environment variables for debugging" << std::endl;
+    for (auto& p : message_list) {
+        GPU_DEBUG_COUT << " - " << std::left << std::setw(name_width) << p.first + ": " << p.second << std::endl;
+    }
+}
+
 #endif
 
 debug_configuration::debug_configuration()
-        : verbose(0)
+        : help(0)
+        , verbose(0)
         , print_multi_kernel_perf(0)
         , disable_usm(0)
         , dump_graphs(std::string())
@@ -110,8 +141,10 @@ debug_configuration::debug_configuration()
         , dump_layers_dst_only(0)
         , dry_run_path(std::string())
         , disable_onednn(0)
+        , dump_layers_limit_batch(std::numeric_limits<int>::max())
         , base_batch_for_memory_estimation(-1) {
 #ifdef GPU_DEBUG_CONFIG
+    get_gpu_debug_env_var("Help", help);
     get_common_debug_env_var("Verbose", verbose);
     get_gpu_debug_env_var("PrintMultiKernelPerf", print_multi_kernel_perf);
     get_gpu_debug_env_var("DisableUsm", disable_usm);
@@ -120,10 +153,16 @@ debug_configuration::debug_configuration()
     get_gpu_debug_env_var("DumpLayersPath", dump_layers_path);
     get_gpu_debug_env_var("DumpLayers", dump_layers);
     get_gpu_debug_env_var("DumpLayersDstOnly", dump_layers_dst_only);
+    get_gpu_debug_env_var("DumpLayersLimitBatch", dump_layers_limit_batch);
     get_gpu_debug_env_var("DisableOnednn", disable_onednn);
     get_gpu_debug_env_var("DryRunPath", dry_run_path);
     get_gpu_debug_env_var("BaseBatchForMemEstimation", base_batch_for_memory_estimation);
 
+    if (help > 0) {
+        print_help_messages();
+        exit(0);
+    }
+
     if (dump_layers.length() > 0)
         dump_layers = " " + dump_layers + " "; // Insert delimiter for easier parsing when used
 #endif
diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/graph_initializations.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/graph_initializations.cpp
index f579819ac9f..0a1ba4156b7 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/graph_initializations.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/graph_initializations.cpp
@@ -436,10 +436,5 @@ void graph_initializations::run(program& p) {
     }
     set_outputs(p);
     p.get_processing_order().calc_processing_order(p);
-
-    for (auto& node : p.get_processing_order()) {
-        if (!node->is_type<data>())
-            node->get_output_layout();
-    }
 }
 }  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/network.cpp b/inference-engine/thirdparty/clDNN/src/network.cpp
index febc8d61bce..29210058d6b 100644
--- a/inference-engine/thirdparty/clDNN/src/network.cpp
+++ b/inference-engine/thirdparty/clDNN/src/network.cpp
@@ -110,8 +110,18 @@ template <class T>
 static void dump(memory::ptr mem, stream& stream, std::ofstream& file_stream) {
     auto&& size = mem->get_layout().size;
 
-    file_stream << "shape: " << size.to_string() << " ";
-    file_stream << "(count: " << size.count() << ", original format: " << cldnn::fmt_to_str(mem->get_layout().format) << ")" << std::endl;
+    GPU_DEBUG_GET_INSTANCE(debug_config);
+    auto batch_size = std::max(std::min(debug_config->dump_layers_limit_batch, size.batch[0]), 1);
+    tensor tmp_size(size);
+    tmp_size.batch[0] = batch_size;
+    if (tmp_size == size) {
+        file_stream << "shape: " << size.to_string() << " ";
+        file_stream << "(count: " << size.count() << ", original format: " << cldnn::fmt_to_str(mem->get_layout().format) << ")" << std::endl;
+    } else {
+        file_stream << "shape: " << tmp_size.to_string() << " ";
+        file_stream << "(count: " << tmp_size.count() << ", original format: " << cldnn::fmt_to_str(mem->get_layout().format)
+            << ", original shape: " << size.to_string() << ")" << std::endl;
+    }
 
     mem_lock<T, mem_lock_type::read> lock(mem, stream);
     auto mem_ptr = lock.data();
@@ -119,7 +129,7 @@ static void dump(memory::ptr mem, stream& stream, std::ofstream& file_stream) {
     std::stringstream buffer;
 
     for (cldnn::tensor::value_type g = 0; g < size.group[0]; ++g) {
-        for (cldnn::tensor::value_type b = 0; b < size.batch[0]; ++b) {
+        for (cldnn::tensor::value_type b = 0; b < batch_size; ++b) {
             for (cldnn::tensor::value_type f = 0; f < size.feature[0]; ++f) {
                 for (cldnn::tensor::value_type w = 0; w < size.spatial[3]; ++w) {
                     for (cldnn::tensor::value_type z = 0; z < size.spatial[2]; ++z) {
diff --git a/inference-engine/thirdparty/clDNN/src/program.cpp b/inference-engine/thirdparty/clDNN/src/program.cpp
index ebdc3f3920e..d4cb50f41c5 100644
--- a/inference-engine/thirdparty/clDNN/src/program.cpp
+++ b/inference-engine/thirdparty/clDNN/src/program.cpp
@@ -1225,8 +1225,13 @@ program::primitives_info program::get_current_stage_info() const {
 
 void program::save_pass_info(std::string pass_name) {
     // TODO: Directory path here can be probably changed to some bool flag
-    if (!options.get<build_option_type::graph_dumps_dir>()->directory_path.empty())
+    if (!options.get<build_option_type::graph_dumps_dir>()->directory_path.empty()) {
+        for (auto& node : this->get_processing_order()) {
+            if (!node->is_type<data>())
+                node->get_output_layout();
+        }
         optimizer_passes_info.emplace_back(pass_name, get_current_stage_info());
+    }
 }
 
 void program::add_optimized_primitive_info(primitive_id optimized_primitive_id,
diff --git a/src/plugins/intel_gpu/src/plugin/infer_request.cpp b/src/plugins/intel_gpu/src/plugin/infer_request.cpp
index 4801fb9a93f..2fc215403c3 100644
--- a/src/plugins/intel_gpu/src/plugin/infer_request.cpp
+++ b/src/plugins/intel_gpu/src/plugin/infer_request.cpp
@@ -557,6 +557,13 @@ void InferRequest::enqueue() {
 
     internal_outputs.clear();
     internal_outputs = m_graph->GetNetwork()->execute(dependencies);
+
+    // If dump layers path is set, only runs first inference.
+    GPU_DEBUG_GET_INSTANCE(debug_config);
+    GPU_DEBUG_IF(debug_config->dump_layers_path.length() > 0) {
+        GPU_DEBUG_COUT << "Only run first inference to dump layers." << std::endl;
+        exit(0);
+    }
 }
 
 void InferRequest::wait_notify() {

From 4564867a44ea936658dd4e9d068dd671e565d708 Mon Sep 17 00:00:00 2001
From: Liubov Talamanova <piccione-mail@yandex.ru>
Date: Mon, 20 Dec 2021 09:10:39 +0300
Subject: [PATCH 035/160] Update POT tests for OMZ location in
 openvino/thirdparty (#9171)

* Move OMZ to thirdparty/open_model_zoo

* Improve diagnostic of issues

* Update path to OMZ in the Python wheel setup.py

* Update POT tests for OMZ location in openvino/thirdparty

* fix mo path

Co-authored-by: azhogov <alexander.zhogov@intel.com>
---
 .gitmodules                                   |  2 +-
 .../thirdparty => thirdparty}/open_model_zoo  |  0
 tools/openvino_dev/setup.py                   |  8 +++-
 .../openvino/tools/pot/utils/ac_imports.py    | 39 +++++++------------
 tools/pot/setup.py                            |  1 -
 tools/pot/tests/utils/path.py                 |  4 +-
 tools/pot/thirdparty/__init__.py              |  2 -
 7 files changed, 23 insertions(+), 33 deletions(-)
 rename {tools/pot/thirdparty => thirdparty}/open_model_zoo (100%)
 delete mode 100644 tools/pot/thirdparty/__init__.py

diff --git a/.gitmodules b/.gitmodules
index 16915b5aa7a..f13e3c9db7e 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -57,7 +57,7 @@
 	path = thirdparty/onednn_gpu
 	url = https://github.com/oneapi-src/oneDNN.git
 [submodule "tools/pot/thirdparty/open_model_zoo"]
-	path = tools/pot/thirdparty/open_model_zoo
+	path = thirdparty/open_model_zoo
 	url = https://github.com/openvinotoolkit/open_model_zoo.git
 [submodule "thirdparty/json/nlohmann_json"]
 	path = thirdparty/json/nlohmann_json
diff --git a/tools/pot/thirdparty/open_model_zoo b/thirdparty/open_model_zoo
similarity index 100%
rename from tools/pot/thirdparty/open_model_zoo
rename to thirdparty/open_model_zoo
diff --git a/tools/openvino_dev/setup.py b/tools/openvino_dev/setup.py
index 44861d6a3ed..28982ea3b1c 100644
--- a/tools/openvino_dev/setup.py
+++ b/tools/openvino_dev/setup.py
@@ -42,13 +42,13 @@ PKG_INSTALL_CFG = {
         'extract_entry_points': True,
     },
     "accuracy_checker": {
-        'src_dir': OPENVINO_DIR / 'tools' / 'pot' / 'thirdparty' / 'open_model_zoo' / 'tools' / 'accuracy_checker',  # noqa:E501
+        'src_dir': OPENVINO_DIR / 'thirdparty' / 'open_model_zoo' / 'tools' / 'accuracy_checker',  # noqa:E501
         'black_list': ['*tests*'],
         'prefix': 'accuracy_checker',
         'extract_entry_points': True,
     },
     "omz_tools": {
-        'src_dir': OPENVINO_DIR / 'tools' / 'pot' / 'thirdparty' / 'open_model_zoo' / 'tools' / 'model_tools',  # noqa:E501
+        'src_dir': OPENVINO_DIR / 'thirdparty' / 'open_model_zoo' / 'tools' / 'model_tools',  # noqa:E501
         'black_list': [],
         'prefix': 'omz_tools',
         'extract_requirements': True,
@@ -80,6 +80,10 @@ class CustomBuild(build):
         self.announce('Installing packages', level=log.INFO)
         for cmp, cmp_data in PKG_INSTALL_CFG.items():
             self.announce(f'Processing package: {cmp}', level=log.INFO)
+            if not cmp_data['src_dir'].is_dir():
+                raise FileNotFoundError(
+                    f'The source directory was not found: {cmp_data["src_dir"]}'
+                )
             subprocess.call([sys.executable, 'setup.py', 'install',
                             '--root', str(SCRIPT_DIR),
                              '--prefix', str(cmp_data.get("prefix"))],
diff --git a/tools/pot/openvino/tools/pot/utils/ac_imports.py b/tools/pot/openvino/tools/pot/utils/ac_imports.py
index c6999b9bb26..7d870c2346e 100644
--- a/tools/pot/openvino/tools/pot/utils/ac_imports.py
+++ b/tools/pot/openvino/tools/pot/utils/ac_imports.py
@@ -1,28 +1,19 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-try:
-    from thirdparty.open_model_zoo.tools.accuracy_checker.\
-        openvino.tools.accuracy_checker.evaluators.quantization_model_evaluator import create_model_evaluator
-    from thirdparty.open_model_zoo.tools.accuracy_checker.openvino.tools.accuracy_checker.config import ConfigReader
-    from thirdparty.open_model_zoo.tools.accuracy_checker.openvino.tools.accuracy_checker.dataset import\
-        Dataset, DataProvider as DatasetWrapper
-    from thirdparty.open_model_zoo.tools.accuracy_checker.openvino.tools.accuracy_checker.logging\
-        import _DEFAULT_LOGGER_NAME
+import sys
+import importlib
+from pathlib import Path
 
-except ImportError:
-    try:
-        from openvino.tools.accuracy_checker.evaluators.quantization_model_evaluator import create_model_evaluator
-        from openvino.tools.accuracy_checker.config import ConfigReader
-        from openvino.tools.accuracy_checker.dataset import Dataset
-        from openvino.tools.accuracy_checker.logging import _DEFAULT_LOGGER_NAME
-        from openvino.tools.accuracy_checker.dataset import DataProvider as DatasetWrapper
-    except ImportError:
-        from accuracy_checker.evaluators.quantization_model_evaluator import create_model_evaluator
-        from accuracy_checker.config import ConfigReader
-        from accuracy_checker.dataset import Dataset
-        from accuracy_checker.logging import _DEFAULT_LOGGER_NAME
-        try:
-            from accuracy_checker.dataset import DataProvider as DatasetWrapper
-        except ImportError:
-            from accuracy_checker.dataset import DatasetWrapper
+import openvino
+
+LIBS_ROOT = Path(__file__).resolve().parents[6] / 'thirdparty'
+sys.path.append(str(LIBS_ROOT / 'open_model_zoo' / 'tools' / 'accuracy_checker'))
+# pylint: disable=E0611,C0413,C0411,E0401
+importlib.reload(openvino)
+
+from openvino.tools.accuracy_checker.evaluators.quantization_model_evaluator import create_model_evaluator
+from openvino.tools.accuracy_checker.config import ConfigReader
+from openvino.tools.accuracy_checker.dataset import Dataset
+from openvino.tools.accuracy_checker.logging import _DEFAULT_LOGGER_NAME
+from openvino.tools.accuracy_checker.dataset import DataProvider as DatasetWrapper
diff --git a/tools/pot/setup.py b/tools/pot/setup.py
index ee4615c6b29..12c86283f31 100644
--- a/tools/pot/setup.py
+++ b/tools/pot/setup.py
@@ -111,7 +111,6 @@ setup(
     long_description_content_type='text/markdown',
     url='https://software.intel.com/openvino-toolkit',
     packages=find_packages(exclude=["tests", "tests.*",
-                                    "thirdparty", "thirdparty.*",
                                     "tools", "tools.*"]),
     package_data={"openvino.tools.pot.configs.hardware": ['*.json'],
                   "openvino.tools.pot.api.samples": ['*.md', '*/*.md']},
diff --git a/tools/pot/tests/utils/path.py b/tools/pot/tests/utils/path.py
index 0b632253cfa..ffc44fc4cc7 100644
--- a/tools/pot/tests/utils/path.py
+++ b/tools/pot/tests/utils/path.py
@@ -7,10 +7,8 @@ from openvino.tools.mo import mo
 
 
 TEST_ROOT = Path(__file__).parent.parent.absolute()
-PROJECT_ROOT = TEST_ROOT.parent
-LIBS_ROOT = PROJECT_ROOT / 'thirdparty'
+LIBS_ROOT = Path(__file__).resolve().parents[4] / 'thirdparty'
 MO_PATH = Path(mo.__file__).parent
-AC_PATH = LIBS_ROOT / 'open_model_zoo' / 'tools' / 'accuracy_checker'
 
 MODELS_PATH = TEST_ROOT / 'data' / 'models'
 REFERENCE_MODELS_PATH = TEST_ROOT / 'data' / 'reference_models'
diff --git a/tools/pot/thirdparty/__init__.py b/tools/pot/thirdparty/__init__.py
deleted file mode 100644
index 6e02c1c9510..00000000000
--- a/tools/pot/thirdparty/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (C) 2020-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0

From fab4448ebddd2c257a286895d060d96fa2fde53a Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Mon, 20 Dec 2021 10:11:43 +0300
Subject: [PATCH 036/160] [dynamism][Transformations] Fusing transformations:
 dynamic batch support (#7351)

---
 .../include/transformations/utils/utils.hpp   |   2 +
 .../depth_to_space_fusion.cpp                 | 142 ++++++++++--------
 .../shuffle_channels_fusion.cpp               | 108 +++++++------
 .../src/transformations/utils/utils.cpp       |  14 +-
 .../depth_to_space_fusion_test.cpp            | 111 ++++++++++----
 .../shuffle_channels_fusion_test.cpp          |  96 ++++++------
 6 files changed, 285 insertions(+), 188 deletions(-)

diff --git a/src/common/transformations/include/transformations/utils/utils.hpp b/src/common/transformations/include/transformations/utils/utils.hpp
index 59180a83230..7d6da94f75c 100644
--- a/src/common/transformations/include/transformations/utils/utils.hpp
+++ b/src/common/transformations/include/transformations/utils/utils.hpp
@@ -178,6 +178,8 @@ TRANSFORMATIONS_API std::shared_ptr<Node> try_fold_unary_output(const std::share
 
 TRANSFORMATIONS_API std::shared_ptr<Node> clone_try_fold(const std::shared_ptr<Node>& node, const OutputVector& inputs);
 
+TRANSFORMATIONS_API bool shapes_equal_except_dynamic_expected_batch(const ngraph::PartialShape& expected, const ngraph::PartialShape& actual);
+
 template <typename T, typename... Args>
 std::shared_ptr<Node> make_try_fold(Args&&... args) {
     auto unary_output_node = std::make_shared<T>(std::forward<Args>(args)...);
diff --git a/src/common/transformations/src/transformations/common_optimizations/depth_to_space_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/depth_to_space_fusion.cpp
index c9c3025a881..d0418e10026 100644
--- a/src/common/transformations/src/transformations/common_optimizations/depth_to_space_fusion.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/depth_to_space_fusion.cpp
@@ -8,80 +8,103 @@
 #include <memory>
 #include <vector>
 
+#include <ngraph/pattern/op/wrap_type.hpp>
 #include <ngraph/opsets/opset3.hpp>
 #include <ngraph/rt_info.hpp>
 
-namespace {
+#include "transformations/utils/utils.hpp"
 
-bool check_block_first(const ngraph::Shape& shape_input, const ngraph::Shape& shape_reshape_before,
-                       const ngraph::AxisVector& permutation, const ngraph::Shape& shape_reshape_after,
+namespace {
+bool check_block_first(const ngraph::PartialShape& shape_input, const ngraph::PartialShape& shape_reshape_before,
+                       const ngraph::AxisVector& permutation, const ngraph::PartialShape& shape_reshape_after,
                        size_t& possible_block_size) {
-    bool is_transformation_valid = true;
-    uint64_t spatial_dims = shape_input.size() - 2;
-    possible_block_size = shape_reshape_before[1];
-    if (possible_block_size == 0)
+    const auto input_rank = shape_input.rank();
+    auto spatial_dims = input_rank.get_length() - 2;
+    if (shape_reshape_before[1].is_dynamic() || shape_reshape_before[1].get_length() == 0) {
         return false;
-    uint64_t c_dim = static_cast<uint64_t>(shape_input[1] / std::pow(possible_block_size, spatial_dims));
+    }
+
+    possible_block_size = shape_reshape_before[1].get_length();
+    ngraph::Dimension c_dim(shape_input[1].get_length() / std::pow(possible_block_size, spatial_dims));
 
     // x' = reshape(data, [N, block_size, block_size, ..., block_size, C / (block_size ^ K), D1, D2, ..., DK])
-    ngraph::Shape expected_shape = {shape_input[0]};
-    for (uint64_t i = 0; i < spatial_dims; ++i)
+    ngraph::PartialShape expected_shape = {shape_input[0]};
+    for (int i = 0; i < spatial_dims; ++i)
         expected_shape.push_back(possible_block_size);
     expected_shape.push_back(c_dim);
-    for (uint64_t i = 2; i < shape_input.size(); ++i)
+    for (int i = 2; i < input_rank.get_length(); ++i)
         expected_shape.push_back(shape_input[i]);
-    is_transformation_valid &= (expected_shape == shape_reshape_before);
+
+    if (!ngraph::op::util::shapes_equal_except_dynamic_expected_batch(expected_shape, shape_reshape_before)) {
+        return false;
+    }
 
     // x'' = transpose(x', [0,  K + 1,  K + 2, 1, K + 3, 2, K + 4, 3, ..., K + (K + 1), K])
     ngraph::AxisVector expected_permutation = {0, static_cast<size_t>(spatial_dims + 1)};
-    for (uint64_t i = 2; i < shape_input.size(); ++i) {
+    for (int i = 2; i < input_rank.get_length(); ++i) {
         expected_permutation.push_back(spatial_dims + i);
         expected_permutation.push_back(i - 1);
     }
-    is_transformation_valid &= (expected_permutation == permutation);
+
+    if (expected_permutation != permutation) {
+        return false;
+    }
 
     // y = reshape(x'', [N, C / (block_size ^ K), D1 * block_size, D2 * block_size, D3 * block_size, ..., DK * block_size])
-    expected_shape = {shape_input[0], static_cast<size_t>(c_dim)};
-    for (uint64_t i = 2; i < shape_input.size(); ++i)
+    expected_shape = {shape_input[0], c_dim};
+    for (int i = 2; i < input_rank.get_length(); ++i)
         expected_shape.push_back(shape_input[i] * possible_block_size);
-    is_transformation_valid &= (expected_shape == shape_reshape_after);
 
-    return is_transformation_valid;
+    if (!ngraph::op::util::shapes_equal_except_dynamic_expected_batch(expected_shape, shape_reshape_after)) {
+        return false;
+    }
+
+    return true;
 }
 
-bool check_depth_first(const ngraph::Shape& shape_input, const ngraph::Shape& shape_reshape_before,
-                       const ngraph::AxisVector& permutation, const ngraph::Shape& shape_reshape_after,
+bool check_depth_first(const ngraph::PartialShape& shape_input, const ngraph::PartialShape& shape_reshape_before,
+                       const ngraph::AxisVector& permutation, const ngraph::PartialShape& shape_reshape_after,
                        size_t& possible_block_size) {
-    bool is_transformation_valid = true;
-    uint64_t spatial_dims = shape_input.size() - 2;
-    possible_block_size = shape_reshape_before[2];
-    if (possible_block_size == 0)
+    const auto input_rank = shape_input.rank();
+    auto spatial_dims = input_rank.get_length() - 2;
+    if (shape_reshape_before[2].is_dynamic() || shape_reshape_before[2].get_length() == 0) {
         return false;
-    uint64_t c_dim = static_cast<uint64_t>(shape_input[1] / std::pow(possible_block_size, spatial_dims));
+    }
+
+    possible_block_size = shape_reshape_before[2].get_length();
+    ngraph::Dimension c_dim(shape_input[1].get_length() / std::pow(possible_block_size, spatial_dims));
 
     // x' = reshape(data, [N, C / (block_size ^ K), block_size, block_size, ..., block_size, D1, D2, ..., DK])
-    ngraph::Shape expected_shape = {shape_input[0], static_cast<size_t>(c_dim)};
-    for (uint64_t i = 0; i < spatial_dims; ++i)
+    ngraph::PartialShape expected_shape = {shape_input[0], c_dim};
+    for (int i = 0; i < spatial_dims; ++i)
         expected_shape.push_back(possible_block_size);
-    for (uint64_t i = 2; i < shape_input.size(); ++i)
+    for (int i = 2; i < input_rank.get_length(); ++i)
         expected_shape.push_back(shape_input[i]);
-    is_transformation_valid &= (expected_shape == shape_reshape_before);
+
+    if (!ngraph::op::util::shapes_equal_except_dynamic_expected_batch(expected_shape, shape_reshape_before)) {
+        return false;
+    }
 
     // x'' = transpose(x', [0,  1,  K + 2, 2, K + 3, 3, K + 4, 4, ..., K + (K + 1), K + 1])
     ngraph::AxisVector expected_permutation = {0, 1};
-    for (uint64_t i = 2; i < shape_input.size(); ++i) {
+    for (int i = 2; i < input_rank.get_length(); ++i) {
         expected_permutation.push_back(spatial_dims + i);
         expected_permutation.push_back(i);
     }
-    is_transformation_valid &= (expected_permutation == permutation);
+    if (expected_permutation != permutation) {
+        return false;
+    }
 
     // y = reshape(x'', [N, C / (block_size ^ K), D1 * block_size, D2 * block_size, D3 * block_size, ..., DK * block_size])
-    expected_shape = {shape_input[0], static_cast<size_t>(c_dim)};
-    for (uint64_t i = 2; i < shape_input.size(); ++i)
+    expected_shape = {shape_input[0], c_dim};
+    for (int i = 2; i < input_rank.get_length(); ++i)
         expected_shape.push_back(shape_input[i] * possible_block_size);
-    is_transformation_valid &= (expected_shape == shape_reshape_after);
 
-    return is_transformation_valid;
+    if (!ngraph::op::util::shapes_equal_except_dynamic_expected_batch(expected_shape, shape_reshape_after)) {
+        return false;
+    }
+
+    return true;
 }
 
 } // namespace
@@ -90,13 +113,13 @@ NGRAPH_RTTI_DEFINITION(ngraph::pass::DepthToSpaceFusion, "DepthToSpaceFusion", 0
 
 ngraph::pass::DepthToSpaceFusion::DepthToSpaceFusion() {
     MATCHER_SCOPE(DepthToSpaceFusion);
-    auto input0 = std::make_shared<pattern::op::Label>(element::f32, Shape{1, 1, 1, 1});
-    auto input1 = std::make_shared<pattern::op::Label>(element::i64, Shape{4});
-    auto input2 = std::make_shared<pattern::op::Label>(element::i64, Shape{4});
-    auto input3 = std::make_shared<pattern::op::Label>(element::i64, Shape{4});
-    auto reshape_before = std::make_shared<ngraph::opset3::Reshape> (input0, input1, false);
-    auto permute = std::make_shared<ngraph::opset3::Transpose> (reshape_before, input2);
-    auto reshape_after = std::make_shared<ngraph::opset3::Reshape> (permute, input3, false);
+    auto input0 = ngraph::pattern::any_input(pattern::rank_equals(4));
+    auto input1 = ngraph::pattern::any_input();
+    auto input2 = ngraph::pattern::any_input();
+    auto input3 = ngraph::pattern::any_input();
+    auto reshape_before = ngraph::pattern::wrap_type<ngraph::opset3::Reshape>({ input0, input1 }, pattern::consumers_count(1));
+    auto permute = ngraph::pattern::wrap_type<ngraph::opset3::Transpose>({ reshape_before, input2 }, pattern::consumers_count(1));
+    auto reshape_after = ngraph::pattern::wrap_type<ngraph::opset3::Reshape>({ permute, input3 });
 
     ngraph::matcher_pass_callback callback = [this](pattern::Matcher& m) {
         auto reshape_after = std::dynamic_pointer_cast<ngraph::opset3::Reshape>(m.get_match_root());
@@ -104,13 +127,13 @@ ngraph::pass::DepthToSpaceFusion::DepthToSpaceFusion() {
             return false;
         }
 
-        auto permute = std::dynamic_pointer_cast<ngraph::opset3::Transpose>(reshape_after->input_value(0).get_node_shared_ptr());
-        if (!permute || permute->get_output_target_inputs(0).size() != 1) {
+        auto permute = std::dynamic_pointer_cast<ngraph::opset3::Transpose>(reshape_after->get_input_node_shared_ptr(0));
+        if (!permute) {
             return false;
         }
 
-        auto reshape_before = std::dynamic_pointer_cast<ngraph::opset3::Reshape>(permute->input_value(0).get_node_shared_ptr());
-        if (!reshape_before || reshape_before->get_output_target_inputs(0).size() != 1) {
+        auto reshape_before = std::dynamic_pointer_cast<ngraph::opset3::Reshape>(permute->get_input_node_shared_ptr(0));
+        if (!reshape_before) {
             return false;
         }
 
@@ -119,29 +142,25 @@ ngraph::pass::DepthToSpaceFusion::DepthToSpaceFusion() {
         auto p_shape_permute = permute->get_output_partial_shape(0);
         auto p_shape_reshape_after = reshape_after->get_output_partial_shape(0);
 
-        if (p_shape_input.is_dynamic() || p_shape_reshape_before.is_dynamic() ||
-            p_shape_permute.is_dynamic() || p_shape_reshape_after.is_dynamic()) {
+        const auto input_rank = p_shape_input.rank();
+        if (input_rank.is_dynamic() || p_shape_input.rank().get_length() < 3) {
             return false;
         }
 
-        auto shape_input = p_shape_input.get_shape();
-        auto shape_reshape_before = p_shape_reshape_before.get_shape();
-        auto shape_permute = p_shape_permute.get_shape();
-        auto shape_reshape_after = p_shape_reshape_after.get_shape();
-
-        if (shape_input.size() < 3) {
+        // check that all dimensions except batch are static
+        if (std::any_of(p_shape_input.begin() + 1, p_shape_input.end(), [](const ngraph::Dimension& x) { return x.is_dynamic(); })) {
             return false;
         }
 
         // input shape: [ batch, C, spatial_dims], expected_shape = spatial_dims.size() * 2 + 2
-        size_t expected_shape_size = (shape_input.size() - 2) * 2 + 2;
-        if (shape_input.size() != shape_reshape_after.size() || shape_reshape_before.size() != expected_shape_size ||
-            shape_permute.size() != expected_shape_size) {
+        auto expected_shape_size = (input_rank.get_length() - 2) * 2 + 2;
+        if (input_rank != p_shape_reshape_after.rank().get_length() || p_shape_reshape_before.rank().get_length() != expected_shape_size ||
+            p_shape_permute.rank().get_length() != expected_shape_size) {
             return false;
         }
 
         ngraph::AxisVector permutation;
-        if (auto input_const = std::dynamic_pointer_cast<opset3::Constant>(permute->input_value(1).get_node_shared_ptr())) {
+        if (auto input_const = std::dynamic_pointer_cast<opset3::Constant>(permute->get_input_node_shared_ptr(1))) {
             permutation = input_const->get_axis_vector_val();
         } else {
             return false;
@@ -149,16 +168,15 @@ ngraph::pass::DepthToSpaceFusion::DepthToSpaceFusion() {
 
         ngraph::opset3::DepthToSpace::DepthToSpaceMode mode;
         size_t block_size;
-        if (check_depth_first(shape_input, shape_reshape_before, permutation, shape_reshape_after, block_size)) {
+        if (check_depth_first(p_shape_input, p_shape_reshape_before, permutation, p_shape_reshape_after, block_size)) {
             mode = ngraph::opset3::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST;
-        } else if (check_block_first(shape_input, shape_reshape_before, permutation, shape_reshape_after, block_size)) {
+        } else if (check_block_first(p_shape_input, p_shape_reshape_before, permutation, p_shape_reshape_after, block_size)) {
             mode = ngraph::opset3::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST;
         } else {
             return false;
         }
 
-        auto depth_to_space =
-                std::make_shared<ngraph::opset3::DepthToSpace>(reshape_before->input_value(0), mode, block_size);
+        auto depth_to_space = std::make_shared<ngraph::opset3::DepthToSpace>(reshape_before->input_value(0), mode, block_size);
         depth_to_space->set_friendly_name(reshape_after->get_friendly_name());
         ngraph::copy_runtime_info({reshape_before, permute, reshape_after}, depth_to_space);
         ngraph::replace_node(reshape_after, depth_to_space);
diff --git a/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp
index 515a8969e72..3318d5222eb 100644
--- a/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp
@@ -12,45 +12,70 @@
 #include <ngraph/pattern/op/wrap_type.hpp>
 #include <ngraph/rt_info.hpp>
 
-namespace {
+#include "transformations/utils/utils.hpp"
 
-bool check_shapes(const ngraph::Shape& shape_input, const ngraph::Shape& shape_reshape_before,
-                  const ngraph::AxisVector& transpose_constant_values, const ngraph::Shape& shape_reshape_after) {
+namespace {
+bool check_shapes(const ngraph::PartialShape& pshape_input, const ngraph::PartialShape& pshape_reshape_before,
+                  const ngraph::AxisVector& transpose_constant_values, const ngraph::PartialShape& pshape_reshape_after) {
     // x: [N, C, H, W]
-    bool is_transformation_valid = (shape_input.size() == 4);
+    const auto rank = pshape_input.rank();
+    if (rank.is_dynamic() || rank.get_length() != 4) {
+        return false;
+    }
+
+    // check that all dimensions except batch are static
+    if (std::any_of(pshape_input.begin() + 1, pshape_input.end(), [](const ngraph::Dimension& x) { return x.is_dynamic(); })) {
+        return false;
+    }
 
     // x'= reshape(x, [N, group, C / group, H * W]) or reshape(x, [N, group, C / group, H, W])
-    bool is_reshape_before_valid = (shape_reshape_before.size() == 4 || shape_reshape_before.size() == 5);
-    if (is_reshape_before_valid) {
-        size_t group = shape_reshape_before[1];
-        ngraph::Shape expected_reshape_before = { shape_input[0], group, shape_input[1] / group };
+    if (pshape_reshape_before.rank().get_length() != 4 && pshape_reshape_before.rank().get_length() != 5) {
+        return false;
+    }
 
-        if (shape_reshape_before.size() == 4) {
-            expected_reshape_before.push_back(shape_input[2] * shape_input[3]);
-        } else {
-            expected_reshape_before.push_back(shape_input[2]);
-            expected_reshape_before.push_back(shape_input[3]);
-        }
+    const auto group = pshape_reshape_before[1].get_length();
+    ngraph::PartialShape expected_reshape_before;
+    if (pshape_reshape_before.rank().get_length() == 4) {
+        expected_reshape_before = {
+            pshape_input[0],
+            group,
+            pshape_input[1].get_length() / group,
+            pshape_input[2].get_length() * pshape_input[3].get_length()
+        };
+    } else {
+        expected_reshape_before = {
+            pshape_input[0],
+            group,
+            pshape_input[1].get_length() / group,
+            pshape_input[2],
+            pshape_input[3]
+        };
+    }
 
-        is_reshape_before_valid &= (expected_reshape_before == shape_reshape_before);
+    if (!ngraph::op::util::shapes_equal_except_dynamic_expected_batch(expected_reshape_before, pshape_reshape_before)) {
+        return false;
     }
 
     // x''= transpose(x', [0, 2, 1, 3]) or transpose(x', [0, 2, 1, 3, 4])
-    bool is_transpose_valid = (transpose_constant_values.size() == 4 || transpose_constant_values.size() == 5);
-    if (is_transpose_valid) {
-        ngraph::AxisVector expected_transpose_values{ 0, 2, 1, 3 };
-        if (transpose_constant_values.size() == 5) {
-            expected_transpose_values.push_back(4);
-        }
+    if (transpose_constant_values.size() != 4 && transpose_constant_values.size() != 5) {
+        return false;
+    }
 
-        is_transpose_valid &= (expected_transpose_values == transpose_constant_values);
+    ngraph::AxisVector expected_transpose_values{ 0, 2, 1, 3 };
+    if (transpose_constant_values.size() == 5) {
+        expected_transpose_values.push_back(4);
+    }
+
+    if (expected_transpose_values != transpose_constant_values) {
+        return false;
     }
 
     // y = reshape(x'', [N, C, H, W])
-    bool is_reshape_after_valid = (shape_input == shape_reshape_after);
+    if (!ngraph::op::util::shapes_equal_except_dynamic_expected_batch(pshape_input, pshape_reshape_after)) {
+        return false;
+    }
 
-    is_transformation_valid &= is_reshape_before_valid & is_transpose_valid & is_reshape_after_valid;
-    return is_transformation_valid;
+    return true;
 }
 
 } // namespace
@@ -59,24 +84,16 @@ NGRAPH_RTTI_DEFINITION(ngraph::pass::ShuffleChannelsFusion, "ShuffleChannelsFusi
 
 ngraph::pass::ShuffleChannelsFusion::ShuffleChannelsFusion(const bool reshape_constants_check) {
     MATCHER_SCOPE(ShuffleChannelsFusion);
-    auto has_static_4d_shape = [](const Output<Node>& output) {
-        return pattern::has_static_shape()(output) && pattern::rank_equals(4)(output);
-    };
-
-    auto input = ngraph::pattern::any_input(has_static_4d_shape);
+    auto input = ngraph::pattern::any_input(pattern::rank_equals(4));
     auto reshape_before_const_pattern = ngraph::pattern::wrap_type<ngraph::opset6::Constant>();
     auto transpose_const_pattern = ngraph::pattern::wrap_type<ngraph::opset6::Constant>();
     auto reshape_after_const_pattern = ngraph::pattern::wrap_type<ngraph::opset6::Constant>();
 
-    auto has_static_shape_and_single_consumer = [](const Output<Node>& output) {
-        return pattern::has_static_shape()(output) && pattern::consumers_count(1)(output);
-    };
     auto reshape_before_pattern = ngraph::pattern::wrap_type<ngraph::opset6::Reshape>({input, reshape_before_const_pattern},
-                                                                                      has_static_shape_and_single_consumer);
+                                                                                      pattern::consumers_count(1));
     auto transpose_pattern = ngraph::pattern::wrap_type<ngraph::opset6::Transpose>({reshape_before_pattern, transpose_const_pattern},
-                                                                                   has_static_shape_and_single_consumer);
-    auto reshape_after_pattern = ngraph::pattern::wrap_type<ngraph::opset6::Reshape>({transpose_pattern, reshape_after_const_pattern},
-                                                                                     pattern::has_static_shape());
+                                                                                   pattern::consumers_count(1));
+    auto reshape_after_pattern = ngraph::pattern::wrap_type<ngraph::opset6::Reshape>({transpose_pattern, reshape_after_const_pattern});
 
     ngraph::matcher_pass_callback callback = [=](pattern::Matcher& m) {
         const auto& pattern_map = m.get_pattern_value_map();
@@ -85,8 +102,9 @@ ngraph::pass::ShuffleChannelsFusion::ShuffleChannelsFusion(const bool reshape_co
         auto reshape_before = std::dynamic_pointer_cast<ngraph::opset6::Reshape>(pattern_map.at(reshape_before_pattern).get_node_shared_ptr());
         auto transpose = std::dynamic_pointer_cast<ngraph::opset6::Transpose>(pattern_map.at(transpose_pattern).get_node_shared_ptr());
         auto reshape_after = std::dynamic_pointer_cast<ngraph::opset6::Reshape>(pattern_map.at(reshape_after_pattern).get_node_shared_ptr());
-        if (!reshape_after || !transpose || !reshape_after)
+        if (!reshape_after || !transpose || !reshape_after) {
             return false;
+        }
 
         if (reshape_constants_check) {
             auto reshape_before_constant = std::dynamic_pointer_cast<ngraph::opset6::Constant>(
@@ -94,8 +112,9 @@ ngraph::pass::ShuffleChannelsFusion::ShuffleChannelsFusion(const bool reshape_co
             auto reshape_after_constant = std::dynamic_pointer_cast<ngraph::opset6::Constant>(
                 pattern_map.at(reshape_after_const_pattern).get_node_shared_ptr());
 
-            if (!reshape_before_constant || !reshape_after_constant)
+            if (!reshape_before_constant || !reshape_after_constant) {
                 return false;
+            }
 
             const auto& reshape_before_values = reshape_before_constant->cast_vector<int64_t>();
             const auto& reshape_after_values = reshape_after_constant->cast_vector<int64_t>();
@@ -105,17 +124,18 @@ ngraph::pass::ShuffleChannelsFusion::ShuffleChannelsFusion(const bool reshape_co
             }
         }
 
-        auto shape_input = reshape_before->get_input_shape(0);
-        auto shape_reshape_before = reshape_before->get_output_shape(0);
-        auto shape_reshape_after = reshape_after->get_output_shape(0);
+        auto pshape_input = reshape_before->get_input_partial_shape(0);
+        auto pshape_reshape_before = reshape_before->get_output_partial_shape(0);
+        auto pshape_reshape_after = reshape_after->get_output_partial_shape(0);
 
         auto transpose_constant = std::dynamic_pointer_cast<ngraph::opset6::Constant>(pattern_map.at(transpose_const_pattern).get_node_shared_ptr());
         auto transpose_constant_values = transpose_constant->get_axis_vector_val();
-        if (!check_shapes(shape_input, shape_reshape_before, transpose_constant_values, shape_reshape_after))
+        if (!check_shapes(pshape_input, pshape_reshape_before, transpose_constant_values, pshape_reshape_after)) {
             return false;
+        }
 
         int64_t axis = 1ul;
-        int64_t group = shape_reshape_before[1];
+        int64_t group = pshape_reshape_before[1].get_length();
 
         auto shuffle_shannels = std::make_shared<ngraph::opset6::ShuffleChannels>(data, axis, group);
         shuffle_shannels->set_friendly_name(reshape_after->get_friendly_name());
diff --git a/src/common/transformations/src/transformations/utils/utils.cpp b/src/common/transformations/src/transformations/utils/utils.cpp
index 984e34dd47f..602cf06c0d2 100644
--- a/src/common/transformations/src/transformations/utils/utils.cpp
+++ b/src/common/transformations/src/transformations/utils/utils.cpp
@@ -162,11 +162,21 @@ std::shared_ptr<ngraph::Node> node_to_get_shape_value_of_indices_from_shape_node
 }
 
 std::shared_ptr<ngraph::Node> node_to_get_shape_value_of_indices_from_shape_source(const ngraph::Output<ngraph::Node>& shape_source,
-                                                                                   const std::vector<size_t>& indices) {
-    const auto & shape_node = make_try_fold<v3::ShapeOf>(shape_source);
+    const std::vector<size_t>& indices) {
+    const auto& shape_node = make_try_fold<v3::ShapeOf>(shape_source);
     return node_to_get_shape_value_of_indices_from_shape_node(shape_node, indices);
 }
 
+bool shapes_equal_except_dynamic_expected_batch(const ngraph::PartialShape& expected, const ngraph::PartialShape& actual) {
+    if (expected[0].is_static()) {
+        return actual == expected;
+    } else {
+        auto actual_with_dynamic_batch = actual;
+        actual_with_dynamic_batch[0] = expected[0];
+        return actual_with_dynamic_batch == expected;
+    }
+}
+
 }  // namespace util
 }  // namespace op
 }  // namespace ngraph
diff --git a/src/tests/functional/inference_engine/transformations/depth_to_space_fusion_test.cpp b/src/tests/functional/inference_engine/transformations/depth_to_space_fusion_test.cpp
index 99dc7bd254b..b023d1ba22d 100644
--- a/src/tests/functional/inference_engine/transformations/depth_to_space_fusion_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/depth_to_space_fusion_test.cpp
@@ -4,23 +4,19 @@
 
 #include <gtest/gtest.h>
 
-#include "common_test_utils/test_common.hpp"
 #include <string>
 #include <sstream>
-#include <fstream>
 #include <memory>
-#include <queue>
-#include <map>
 
 #include <ngraph/function.hpp>
 #include <ngraph/opsets/opset3.hpp>
-#include <ngraph/pass/constant_folding.hpp>
 #include <transformations/common_optimizations/depth_to_space_fusion.hpp>
 #include <transformations/utils/utils.hpp>
 #include <transformations/init_node_info.hpp>
 #include <ngraph/pass/manager.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
+#include "common_test_utils/test_common.hpp"
 
 using namespace testing;
 
@@ -55,6 +51,38 @@ TEST_F(TransformationTestsF, DepthToSpaceFusionDepthFirst) {
     }
 }
 
+TEST_F(TransformationTestsF, DepthToSpaceFusionDepthFirstDynamicBatch) {
+    {
+        auto input_pshape = ngraph::PartialShape{ ngraph::Dimension::dynamic(), 128, 720, 480 };
+        auto input = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, input_pshape);
+        auto shape_reshape_before = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 6 }, { 1, 32, 2, 2, 720, 480 });
+        auto permutation = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 6 }, { 0, 1, 4, 2, 5, 3 });
+        auto shape_reshape_after = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 1, 32, 1440, 960 });
+
+        auto reshape_before = std::make_shared<ngraph::opset3::Reshape>(input, shape_reshape_before, false);
+        auto permute = std::make_shared<ngraph::opset3::Transpose>(reshape_before, permutation);
+        auto reshape_after = std::make_shared<ngraph::opset3::Reshape>(permute, shape_reshape_after, false);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ reshape_after }, ngraph::ParameterVector{ input });
+
+        auto callback = [](const std::shared_ptr<const ngraph::Node>& node) -> bool {
+            return std::dynamic_pointer_cast<const ngraph::opset3::DepthToSpace>(node) != nullptr;
+        };
+
+        auto pass_config = manager.get_pass_config();
+        pass_config->set_callback<ngraph::pass::DepthToSpaceFusion>(callback);
+
+        manager.register_pass<ngraph::pass::DepthToSpaceFusion>();
+    }
+
+    {
+        auto input_pshape = ngraph::PartialShape{ ngraph::Dimension::dynamic(), 128, 720, 480 };
+        auto input = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, input_pshape);
+        auto depth_to_space = std::make_shared<ngraph::opset3::DepthToSpace>(input, ngraph::opset3::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST, 2);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ depth_to_space }, ngraph::ParameterVector{ input });
+    }
+}
+
 TEST_F(TransformationTestsF, DepthToSpaceFusionBlockFirst) {
     {
         auto input0 = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{1, 128, 720, 480});
@@ -86,6 +114,38 @@ TEST_F(TransformationTestsF, DepthToSpaceFusionBlockFirst) {
     }
 }
 
+TEST_F(TransformationTestsF, DepthToSpaceFusionBlockFirstDynamicBatch) {
+    {
+        auto input_pshape = ngraph::PartialShape{ ngraph::Dimension::dynamic(), 128, 720, 480 };
+        auto input = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, input_pshape);
+        auto shape_reshape_before = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 6 }, { 1, 2, 2, 32, 720, 480 });
+        auto permutation = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 6 }, { 0, 3, 4, 1, 5, 2 });
+        auto shape_reshape_after = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 1, 32, 1440, 960 });
+
+        auto reshape_before = std::make_shared<ngraph::opset3::Reshape>(input, shape_reshape_before, false);
+        auto permute = std::make_shared<ngraph::opset3::Transpose>(reshape_before, permutation);
+        auto reshape_after = std::make_shared<ngraph::opset3::Reshape>(permute, shape_reshape_after, false);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ reshape_after }, ngraph::ParameterVector{ input });
+
+        auto callback = [](const std::shared_ptr<const ngraph::Node>& node) -> bool {
+            return std::dynamic_pointer_cast<const ngraph::opset3::DepthToSpace>(node) != nullptr;
+        };
+
+        auto pass_config = manager.get_pass_config();
+        pass_config->set_callback<ngraph::pass::DepthToSpaceFusion>(callback);
+
+        manager.register_pass<ngraph::pass::DepthToSpaceFusion>();
+    }
+
+    {
+        auto input_pshape = ngraph::PartialShape{ ngraph::Dimension::dynamic(), 128, 720, 480 };
+        auto input = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, input_pshape);
+        auto depth_to_space = std::make_shared<ngraph::opset3::DepthToSpace>(input, ngraph::opset3::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST, 2);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ depth_to_space }, ngraph::ParameterVector{ input });
+    }
+}
+
 TEST_F(TransformationTestsF, DepthToSpaceFusionDynamicShape) {
     {
         auto input0 = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{1, 128, 720, 480});
@@ -124,8 +184,7 @@ TEST_F(TransformationTestsF, DepthToSpaceFusionDynamicShape) {
     }
 }
 
-TEST(TransformationTests, DepthToSpaceFusionSeveralConsumers) {
-    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+TEST_F(TransformationTestsF, DepthToSpaceFusionSeveralConsumers) {
     {
         auto input0 = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{1, 128, 720, 480});
         auto shape_reshape_before = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{6}, {1, 2, 2, 32, 720, 480});
@@ -135,44 +194,36 @@ TEST(TransformationTests, DepthToSpaceFusionSeveralConsumers) {
         auto reshape_before = std::make_shared<ngraph::opset3::Reshape> (input0, shape_reshape_before, false);
         auto permute = std::make_shared<ngraph::opset3::Transpose> (reshape_before, permutation);
         auto reshape_after = std::make_shared<ngraph::opset3::Reshape> (permute, shape_reshape_after, false);
+        auto result = std::make_shared<ngraph::opset3::Result>(reshape_after);
 
-        // additional consumers, not output of the function
-        auto result = std::make_shared<ngraph::opset3::Result> (reshape_before);
-        auto result_2 = std::make_shared<ngraph::opset3::Result> (permute);
-        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{reshape_after}, ngraph::ParameterVector{input0});
+        // additional consumer
+        auto additional_consumer = std::make_shared<ngraph::opset3::Result> (reshape_before);
 
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ result, additional_consumer }, ngraph::ParameterVector{ input0 });
         auto callback = [](const std::shared_ptr<const ngraph::Node> & node) -> bool {
             return std::dynamic_pointer_cast<const ngraph::opset3::DepthToSpace>(node) != nullptr;
         };
 
-        ngraph::pass::Manager manager;
-
         auto pass_config = manager.get_pass_config();
         pass_config->set_callback<ngraph::pass::DepthToSpaceFusion>(callback);
 
-        manager.register_pass<ngraph::pass::InitNodeInfo>();
         manager.register_pass<ngraph::pass::DepthToSpaceFusion>();
-        manager.run_passes(f);
-        ASSERT_NO_THROW(check_rt_info(f));
     }
 
     {
-        auto input0 = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{1, 128, 720, 480});
-        auto shape_reshape_before = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{6}, {1, 2, 2, 32, 720, 480});
-        auto permutation = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{6}, {0, 3, 4, 1, 5, 2});
-        auto shape_reshape_after = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1, 32, 1440, 960});
+        auto input0 = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 128, 720, 480 });
+        auto shape_reshape_before = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 6 }, { 1, 2, 2, 32, 720, 480 });
+        auto permutation = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 6 }, { 0, 3, 4, 1, 5, 2 });
+        auto shape_reshape_after = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 1, 32, 1440, 960 });
 
-        auto reshape_before = std::make_shared<ngraph::opset3::Reshape> (input0, shape_reshape_before, false);
-        auto permute = std::make_shared<ngraph::opset3::Transpose> (reshape_before, permutation);
-        auto reshape_after = std::make_shared<ngraph::opset3::Reshape> (permute, shape_reshape_after, false);
+        auto reshape_before = std::make_shared<ngraph::opset3::Reshape>(input0, shape_reshape_before, false);
+        auto permute = std::make_shared<ngraph::opset3::Transpose>(reshape_before, permutation);
+        auto reshape_after = std::make_shared<ngraph::opset3::Reshape>(permute, shape_reshape_after, false);
+        auto result = std::make_shared<ngraph::opset3::Result>(reshape_after);
 
-        // additional consumers, not output of the function
-        auto result = std::make_shared<ngraph::opset3::Result> (reshape_before);
-        auto result_2 = std::make_shared<ngraph::opset3::Result> (permute);
+        // additional consumer
+        auto additional_consumer = std::make_shared<ngraph::opset3::Result>(reshape_before);
 
-        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{reshape_after}, ngraph::ParameterVector{input0});
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ result, additional_consumer }, ngraph::ParameterVector{ input0 });
     }
-
-    auto res = compare_functions(f, f_ref);
-    ASSERT_TRUE(res.first) << res.second;
 }
diff --git a/src/tests/functional/inference_engine/transformations/shuffle_channels_fusion_test.cpp b/src/tests/functional/inference_engine/transformations/shuffle_channels_fusion_test.cpp
index b955c749bb2..56d75a28411 100644
--- a/src/tests/functional/inference_engine/transformations/shuffle_channels_fusion_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/shuffle_channels_fusion_test.cpp
@@ -15,34 +15,27 @@
 #include <ngraph/pass/manager.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
+#include "common_test_utils/common_utils.hpp"
 
 namespace {
 using namespace testing;
 using namespace ngraph;
 
+enum FuseHappened {
+    YES,
+    NO
+};
+
 class ShuffleChannelsFusionTestValues {
 public:
     ngraph::PartialShape inputPartialShape;
     std::vector<int64_t> reshape_before_val;
     std::vector<size_t> transpose_val;
     std::vector<int64_t> reshape_after_val;
-    bool check_reshape_values;
-    bool fuse_happened;
+    bool check_values;
+    FuseHappened fuse_happened;
 };
 
-template <typename T>
-inline std::ostream& operator<<(std::ostream& os, const std::vector<T>& values) {
-    os << "{ ";
-    for (size_t i = 0; i < values.size(); ++i) {
-        os << values[i];
-        if (i != (values.size() - 1ul)) {
-            os << ", ";
-        }
-    }
-    os << " }";
-    return os;
-}
-
 class ShuffleChannelsFusion : public ::testing::Test, public testing::WithParamInterface<ShuffleChannelsFusionTestValues> {
 public:
     void SetUp() override {
@@ -53,44 +46,40 @@ public:
             auto permutation = opset6::Constant::create(element::i64, Shape{ values.transpose_val.size() }, values.transpose_val);
             auto shape_reshape_after = opset6::Constant::create(element::i64, Shape{ values.reshape_after_val.size() }, values.reshape_after_val);
 
-            auto reshape_before = std::make_shared<ngraph::opset6::Reshape>(input0, shape_reshape_before, false);
+            auto reshape_before = std::make_shared<ngraph::opset6::Reshape>(input0, shape_reshape_before, true);
             auto permute = std::make_shared<ngraph::opset6::Transpose>(reshape_before, permutation);
-            auto reshape_after = std::make_shared<ngraph::opset6::Reshape>(permute, shape_reshape_after, false);
+            auto reshape_after = std::make_shared<ngraph::opset6::Reshape>(permute, shape_reshape_after, true);
             f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ reshape_after }, ngraph::ParameterVector{ input0 });
-
-            auto unh = std::make_shared<ngraph::pass::UniqueNamesHolder>();
-            ngraph::pass::Manager manager;
-            auto pass_config = manager.get_pass_config();
-            manager.register_pass<ngraph::pass::InitUniqueNames>(unh);
-            manager.register_pass<ngraph::pass::InitNodeInfo>();
-            manager.register_pass<ngraph::pass::ShuffleChannelsFusion>(values.check_reshape_values);
-            manager.register_pass<ngraph::pass::CheckUniqueNames>(unh);
-            manager.run_passes(f);
-            ASSERT_NO_THROW(check_rt_info(f));
         }
 
-        if (values.fuse_happened) {
+        if (values.fuse_happened == FuseHappened::YES) {
             auto input0 = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f32, values.inputPartialShape);
             auto shuffle_channels = std::make_shared<ngraph::opset6::ShuffleChannels>(input0, 1, values.reshape_before_val[1]);
             f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ shuffle_channels }, ngraph::ParameterVector{ input0 });
         } else {
             f_ref = f;
         }
+
+        auto unh = std::make_shared<ngraph::pass::UniqueNamesHolder>();
+        ngraph::pass::Manager manager;
+        auto pass_config = manager.get_pass_config();
+        manager.register_pass<ngraph::pass::InitUniqueNames>(unh);
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::ShuffleChannelsFusion>(values.check_values);
+        manager.register_pass<ngraph::pass::CheckUniqueNames>(unh);
+        manager.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
     }
 
     static std::string getTestCaseName(testing::TestParamInfo<ShuffleChannelsFusionTestValues> obj) {
         const ShuffleChannelsFusionTestValues testValues = obj.param;
 
         std::ostringstream result;
-        if (testValues.inputPartialShape.is_dynamic()) {
-            result << "_dynamic_shape_";
-        } else {
-            result << "_input_shape_" << testValues.inputPartialShape;
-        }
-
-        result << "_before_" << testValues.reshape_before_val
-               << "_transpose_" << testValues.transpose_val << "_after_" << testValues.reshape_after_val
-               << (testValues.check_reshape_values ? "check_reshape_values" : "");
+        result << "_input_shape_" << testValues.inputPartialShape
+               << "_before_" << CommonTestUtils::vec2str(testValues.reshape_before_val)
+               << "_transpose_" << CommonTestUtils::vec2str(testValues.transpose_val)
+               << "_after_" << CommonTestUtils::vec2str(testValues.reshape_after_val)
+               << (testValues.check_values ? "check_reshape_values" : "");
 
         return result.str();
     }
@@ -108,27 +97,34 @@ TEST_P(ShuffleChannelsFusion, CompareFunctions) {
 
 const std::vector<ShuffleChannelsFusionTestValues> testValues = {
     // dynamic shape
-    { ngraph::PartialShape::dynamic(), {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, 128, 720, 480}, false, false },
-    { ngraph::PartialShape::dynamic(), {1, 4, 32, 720 * 480}, {0, 2, 1, 3},  {1, 128, 720, 480}, false, false },
+    { PartialShape::dynamic(), {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, 128, 720, 480}, true, FuseHappened::NO},
+    { PartialShape::dynamic(), {1, 4, 32, 720 * 480}, {0, 2, 1, 3},  {1, 128, 720, 480}, false, FuseHappened::NO },
+
+    // 4D, dynamic batch
+    { PartialShape{Dimension::dynamic(), 128, 720, 480}, {0, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {0, 128, 720, 480}, true, FuseHappened::YES },
+    { PartialShape{Dimension::dynamic(), 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, 128, 720, 480}, true, FuseHappened::YES },
+    { PartialShape{Dimension::dynamic(), 128, 720, 480}, {4, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {4, 128, 720, 480}, true, FuseHappened::YES },
+    { PartialShape{Dimension::dynamic(), 128, 720, 480}, {0, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {0, -1, 720, 480}, false, FuseHappened::YES },
+    { PartialShape{Dimension::dynamic(), 128, 720, 480}, {4, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {4, -1, 720, 480}, false, FuseHappened::YES },
 
     // 4D, batch_size = 1, 4D reshape constant
-    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, 128, 720, 480}, false, true },
-    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, 128, 720, 480}, true, true },
-    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, -1, 720, 480}, false, true },
-    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, -1, 720, 480}, true, false },
+    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, 128, 720, 480}, false, FuseHappened::YES },
+    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, 128, 720, 480}, true, FuseHappened::YES },
+    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, -1, 720, 480}, false, FuseHappened::YES },
+    { {1, 128, 720, 480}, {1, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, -1, 720, 480}, true, FuseHappened::NO },
 
     // 4D, batch_size = 1, 3D reshape constant
-    { {1, 128, 720, 480}, {1, 4, 32, 720 * 480}, {0, 2, 1, 3},  {1, 128, 720, 480}, false, true },
-    { {1, 128, 720, 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, 128, 720, 480}, true, true },
-    { {1, 128, 720, 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, false, true },
-    { {1, 128, 720, 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, true, false },
+    { {1, 128, 720, 480}, {1, 4, 32, 720 * 480}, {0, 2, 1, 3},  {1, 128, 720, 480}, false, FuseHappened::YES },
+    { {1, 128, 720, 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, 128, 720, 480}, true, FuseHappened::YES },
+    { {1, 128, 720, 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, false, FuseHappened::YES },
+    { {1, 128, 720, 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, true, FuseHappened::NO },
 
     // 4D, batch_size = 4
-    { {4, 128, 720, 480}, {4, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, -1, 720, 480}, false, false },
-    { {4, 128, 720, 480}, {4, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, false, false },
+    { {4, 128, 720, 480}, {4, 2, 64, 720, 480}, {0, 2, 1, 3, 4},  {1, -1, 720, 480}, false, FuseHappened::NO },
+    { {4, 128, 720, 480}, {4, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, false, FuseHappened::NO },
 
     // 2D
-    { {128, 720 * 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, false, false },
+    { {128, 720 * 480}, {1, 2, 64, 720 * 480}, {0, 2, 1, 3},  {1, -1, 720, 480}, false, FuseHappened::NO },
 };
 
 INSTANTIATE_TEST_SUITE_P(

From abee3ea4d4ab2ca1ea1aa6c375de103da296590f Mon Sep 17 00:00:00 2001
From: Egor Duplensky <egor.duplenskii@intel.com>
Date: Mon, 20 Dec 2021 10:23:32 +0300
Subject: [PATCH 037/160] [CPU] Refactoring. Avoid using align arg when
 appending post ops (#9225)

Always align legacy scale shift post ops
---
 inference-engine/src/mkldnn_plugin/mkldnn_node.cpp       | 2 +-
 inference-engine/src/mkldnn_plugin/mkldnn_node.h         | 2 +-
 .../src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp     | 3 +--
 .../src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp         | 3 +--
 .../src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp       | 3 +--
 .../src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp      | 4 ++--
 .../src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h        | 2 +-
 .../mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp    | 9 +++++++--
 .../src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h  | 5 +----
 .../mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp   | 3 +--
 .../src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp  | 3 +--
 .../src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp          | 3 +--
 .../src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp    | 3 +--
 .../src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp       | 3 +--
 14 files changed, 21 insertions(+), 27 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
index 32e64b625ca..14fd8b886be 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
@@ -1102,7 +1102,7 @@ Layout MKLDNNNode::getWeightsLayoutByDims(SizeVector dims, bool isGrouped) {
     }
 }
 
-void MKLDNNNode::appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims, int align) {
+void MKLDNNNode::appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims) {
     IE_THROW() << "Fusing of " << this->getType() << " operation is not implemented";
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.h b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
index 85475733b13..48f497f9bb0 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
@@ -602,7 +602,7 @@ protected:
      * Seed node should call this routine and pass its post operations list as parameter.
      * @param ops List of fused post operations
      */
-    virtual void appendPostOps(mkldnn::post_ops& ops, const VectorDims& postOpDims, int align = -1);
+    virtual void appendPostOps(mkldnn::post_ops& ops, const VectorDims& postOpDims);
     virtual void appendBinPostOps(mkldnn::post_ops& ops, const VectorDims& postOpDims, std::vector<MKLDNNMemoryPtr>& binaryPostOpsMem);
 
     virtual std::shared_ptr<mkldnn::primitive_attr> initPrimitiveAttr() { return nullptr; }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp
index 079421635ec..7c0cfe2dc57 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp
@@ -1132,8 +1132,7 @@ void MKLDNNBinaryConvolutionNode::setPostOps(mkldnn::primitive_attr &attr) {
                 ops.append_sum(1.0);
             } else {
                 // TODO [DS]: change to shape from memory
-                constexpr int align = 16;
-                eltwiseNode->appendPostOps(ops, getOutputShapeAtPort(0).getStaticDims(), align);
+                eltwiseNode->appendPostOps(ops, getOutputShapeAtPort(0).getStaticDims());
             }
             continue;
         }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
index 6e46b42b4a8..c0ab2af45a1 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
@@ -352,8 +352,7 @@ void MKLDNNConvolutionNode::setPostOps(mkldnn::primitive_attr &attr, const Vecto
                 ops.append_sum(1.0, MKLDNNExtensionUtils::IEPrecisionToDataType(eltwisePrecision));
             } else {
                 if (useLegacyPostOps || eltwiseNode->getMKLDNNAlgorithm() != mkldnn::algorithm::undef) {
-                    constexpr int align = 16;
-                    eltwiseNode->appendPostOps(ops, dims, align);
+                    eltwiseNode->appendPostOps(ops, dims);
                 } else {
                     eltwiseNode->appendBinPostOps(ops, getBinPostOpShape(), binaryPostOpsArgs);
                 }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
index 19684324fbf..eb54a82cec8 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
@@ -365,9 +365,8 @@ void MKLDNNDeconvolutionNode::setPostOps(mkldnn::primitive_attr &attr, const Vec
     for (auto &node : fusedWith) {
         if (auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get())) {
             // TODO [DS]: change to shape from memory
-            constexpr int align = 16;
             // use legacy depthwise since backprop convolution does not support binary post ops
-            eltwiseNode->appendPostOps(ops, dims, align);
+            eltwiseNode->appendPostOps(ops, dims);
             continue;
         }
         if (auto* fakeQuantizeNode = dynamic_cast<MKLDNNFakeQuantizeNode *>(node.get())) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
index c046fb968f9..f02b8dd3b26 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
@@ -1744,7 +1744,7 @@ void MKLDNNEltwiseNode::fuseInto(MKLDNNNodePtr& parentNode) {
     MKLDNNNode::fuseInto(parentNode);
 }
 
-void MKLDNNEltwiseNode::appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims, int align) {
+void MKLDNNEltwiseNode::appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims) {
     const std::string errorPrefix = "Appending Eltwise node with name '" + getName() + "' ";
 
     if (getMKLDNNAlgorithm() != mkldnn::algorithm::undef) {
@@ -1775,11 +1775,11 @@ void MKLDNNEltwiseNode::appendPostOps(mkldnn::post_ops& ops, const VectorDims &p
         }
     } else {
         const size_t chIdx = postOpDims.size() > 1 ? getFusingAxis() : 0;
+        constexpr int align = 16; // always align for legacy scale/shift post ops
         scalesBuffer = makeAlignedBuffer(postOpDims[chIdx], scales, align);
         if (getAlgorithm() != EltwisePrelu) {
             shiftsBuffer = makeAlignedBuffer(postOpDims[chIdx], shifts, align);
         }
-
         /* @todo legacy depthwise post ops are kept for now
          * for performance reasons
          */
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h
index ce34a2096fc..b10d8b0a3c5 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h
@@ -75,7 +75,7 @@ public:
     bool created() const override;
     bool canBeInPlace() const override;
     bool canFuse(const MKLDNNNodePtr& node) const override;
-    void appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims, int align = -1) override;
+    void appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims) override;
     void appendBinPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims, std::vector<MKLDNNMemoryPtr>& binaryPostOpsMem) override;
     void fuseInto(MKLDNNNodePtr& parentNode) override;
     InferenceEngine::Precision getRuntimePrecision() const override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp
index b658f1b82ab..4b3a4c3aee1 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp
@@ -1706,8 +1706,13 @@ void MKLDNNFakeQuantizeNode::initializePostOpData(const VectorDims &dims, const
     isPostOpDataInitialized = true;
 }
 
-void MKLDNNFakeQuantizeNode::appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims, int align) {
-    initializePostOpData(postOpDims, align);
+void MKLDNNFakeQuantizeNode::appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims) {
+    // MKLDNN quantization_injectors assumes that quantization data memory is always aligned on 16
+    // by length of AVX512 vector register which is also enough for AVX2 and SSE42 implementations.
+    // Otherwise it can lead to buffer over-read and performance penalties due to denormals.
+    const size_t bufferAlignment = 16;
+
+    initializePostOpData(postOpDims, bufferAlignment);
 
     if (getAlgorithm() == FQBinarization) {
         ops.append_binarization(mkldnn::algorithm::binarization_depthwise, (const float*)&binarizationThresholds[0], (const float*)&binarizationOutputMask[0]);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h
index 8f5770d720f..1fd4c6c7a8e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h
@@ -120,10 +120,7 @@ public:
     InferenceEngine::Precision getInputPrecision() const { return inputPrecision; }
     InferenceEngine::Precision getOutputPrecision() const { return outputPrecision; }
 
-    // MKLDNN quantization_injectors assumes that quantization data memory is always aligned on 16
-    // by length of AVX512 vector register which is also enough for AVX2 and SSE42 implementations.
-    // Otherwise it can lead to buffer over-read and performance penalties due to denormals.
-    void appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims = {}, int align = 16) override;
+    void appendPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims = {}) override;
     void appendBinPostOps(mkldnn::post_ops& ops, const VectorDims &postOpDims, std::vector<MKLDNNMemoryPtr>& binaryPostOpsMem) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
index 97c509083a3..759507a2a6f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
@@ -198,9 +198,8 @@ void MKLDNNFullyConnectedNode::setPostOps(mkldnn::primitive_attr &attr, bool ini
 
         if (auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get())) {
             // TODO [DS]: change to shape from memory
-            constexpr int align = -1;
             if (eltwiseNode->getMKLDNNAlgorithm() != mkldnn::algorithm::undef) {
-                eltwiseNode->appendPostOps(ops, getOutputShapeAtPort(0).getStaticDims(), align);
+                eltwiseNode->appendPostOps(ops, getOutputShapeAtPort(0).getStaticDims());
             } else {
                 eltwiseNode->appendBinPostOps(ops, getBinPostOpShape(), binaryPostOpsArgs);
             }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
index 0f102e6dd28..9ccd5e86e98 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
@@ -2102,8 +2102,7 @@ void MKLDNNInterpolateNode::setPostOps(mkldnn::primitive_attr &attr, const Vecto
 
         auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
         if (eltwiseNode) {
-            constexpr int align = 16;
-            eltwiseNode->appendPostOps(ops, dims, align);
+            eltwiseNode->appendPostOps(ops, dims);
             continue;
         }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
index cb6496100eb..d7cc67f8a51 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
@@ -891,8 +891,7 @@ void MKLDNNMVNNode::setPostOps(mkldnn::primitive_attr &attr, bool initWeights) {
 
         auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
         if (eltwiseNode) {
-            constexpr int align = 16;
-            eltwiseNode->appendPostOps(ops, postOpDims, align);
+            eltwiseNode->appendPostOps(ops, postOpDims);
             continue;
         }
         IE_THROW() << "Fusing of " << NameFromType(node->getType()) << " operation to " << NameFromType(this->getType()) << " node is not implemented";
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
index 3e350b39613..3de521da034 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
@@ -813,8 +813,7 @@ void MKLDNNNormalizeL2Node::setPostOps(mkldnn::primitive_attr& kernel_attrs, con
 
         auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
         if (eltwiseNode) {
-            constexpr int align = 16;
-            eltwiseNode->appendPostOps(ops, dims, align);
+            eltwiseNode->appendPostOps(ops, dims);
             continue;
         }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
index aa78c2e9304..d236a9b45b5 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
@@ -2779,8 +2779,7 @@ void MKLDNNReduceNode::setPostOps(mkldnn::primitive_attr &attr, const VectorDims
 
         auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
         if (eltwiseNode) {
-            constexpr int align = 16;
-            eltwiseNode->appendPostOps(ops, postOpDims, align);
+            eltwiseNode->appendPostOps(ops, postOpDims);
             continue;
         }
         IE_THROW() << "Fusing of " << NameFromType(node->getType()) << " operation to " << NameFromType(this->getType()) << " node is not implemented";

From 37ad512d9807d335e3cf63804e8a6a1394626610 Mon Sep 17 00:00:00 2001
From: Edward Shogulin <edward.shogulin@intel.com>
Date: Mon, 20 Dec 2021 11:19:40 +0300
Subject: [PATCH 038/160] [CPU] Optimize quantization scheme for SPR/ATS
 (#7549)

* [CPU] Optimize quantization scheme for SPR/ATS

* [CPU] [LPT] plugin tests

* [GPU] [LPT] plugin tests

* [CPU] limitation was removed

* [CPU] optimization FP32 old way support

* [LPT] comment fix

* [LPT] Multiply plugin test improvement

* [LPT] Multiply support

* [LPT] GPU tests fix

* [LPT] test quick fix

* [LPT] new ppi fix

* look like spent time for tests refactoring
---
 .../mkldnn_plugin/mkldnn_graph_optimizer.cpp  |  34 ++++-
 .../src/multiply.cpp                          |  18 +--
 ...ntwise_branch_selection_transformation.cpp | 100 +++++++++++++++
 .../multiply_transformation.cpp               |  79 +++++++-----
 ...ntwise_branch_selection_transformation.cpp |  88 +++++++++++++
 .../multiply_transformation.cpp               |  81 ++++++------
 ...ntwise_branch_selection_transformation.hpp |  51 ++++++++
 .../multiply_transformation.hpp               |   8 +-
 ...ntwise_branch_selection_transformation.cpp | 120 ++++++++++++++++++
 .../multiply_transformation.cpp               |  45 +++++--
 .../base/layer_test_utils.hpp                 |   8 ++
 .../src/base/layer_test_utils.cpp             |  58 ++++++++-
 .../lpt_ngraph_functions/add_function.hpp     |   7 +-
 .../lpt_ngraph_functions/common/builders.hpp  |   5 +-
 .../common/convolution.hpp                    |  36 ++++++
 .../elementwise_function.hpp                  |  38 ++++++
 .../multiply_function.hpp                     |  11 +-
 .../lpt_ngraph_functions/src/add_function.cpp |   3 +-
 .../src/common/builders.cpp                   |  30 +++++
 .../src/common/convolution.cpp                |  29 +++++
 .../src/elementwise_function.cpp              | 119 +++++++++++++++++
 .../src/group_convolution_function.cpp        |   8 +-
 .../src/multiply_function.cpp                 |  34 ++++-
 23 files changed, 892 insertions(+), 118 deletions(-)
 create mode 100644 src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp
 create mode 100644 src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp
 create mode 100644 src/tests/functional/plugin/shared/include/low_precision_transformations/elementwise_branch_selection_transformation.hpp
 create mode 100644 src/tests/functional/plugin/shared/src/low_precision_transformations/elementwise_branch_selection_transformation.cpp
 create mode 100644 src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/convolution.hpp
 create mode 100644 src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/elementwise_function.hpp
 create mode 100644 src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/convolution.cpp
 create mode 100644 src/tests/ngraph_helpers/lpt_ngraph_functions/src/elementwise_function.cpp

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
index b789d1c01ff..3a6fa6c1df9 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
@@ -1134,8 +1134,38 @@ void MKLDNNGraphOptimizer::FuseConvolutionSumAndConvolutionSumActivation(MKLDNNG
         if (!isSuitableParent1 && !isSuitableParent2)
             continue;
 
-        auto mergedConv = isSuitableParent1 ? parent1 : parent2;
-        auto peerNode = isSuitableParent1 ? parent2 : parent1;
+        std::shared_ptr<MKLDNNNode> mergedConv;
+        std::shared_ptr<MKLDNNNode> peerNode;
+
+        if (isSuitableParent1 && isSuitableParent2) {
+            // not merged operation (peerNode) has to be in low precision
+            const auto isBranchQuantized = [](const MKLDNNNodePtr& branchParent) {
+                const auto& fused = branchParent->getFusedWith();
+                const auto branchPrecision = fused.empty() ?
+                        branchParent->getOriginalOutputPrecisionAtPort(0) :
+                        fused[fused.size() - 1]->getOriginalOutputPrecisionAtPort(0);
+                return (branchPrecision == Precision::I8) || (branchPrecision == Precision::U8);
+            };
+
+            const auto isBranch1Quantized = isBranchQuantized(graphNode->getParentEdgesAtPort(0)[0]->getParent());
+            const auto isBranch2Quantized = isBranchQuantized(graphNode->getParentEdgesAtPort(1)[0]->getParent());
+            if (isBranch1Quantized || isBranch2Quantized) {
+                // INT8
+                const auto parent1CanBeMerged = parent1->getChildEdges().size() == 1ul;
+
+                // if both branches are quantized, then parent1 is selected (result is not changed)
+                mergedConv = isBranch2Quantized && parent1CanBeMerged ? parent1 : parent2;
+                peerNode = isBranch2Quantized && parent1CanBeMerged ? parent2 : parent1;
+            } else {
+                // original FP32
+                mergedConv = isSuitableParent1 ? parent1 : parent2;
+                peerNode = isSuitableParent1 ? parent2 : parent1;
+            }
+        } else {
+            mergedConv = isSuitableParent1 ? parent1 : parent2;
+            peerNode = isSuitableParent1 ? parent2 : parent1;
+        }
+
         if (isSuitableParent1 && isSuitableParent2) {
             if ((peerNode->getType() == Convolution || peerNode->getType() == BinaryConvolution) &&
                 mergedConv->getChildEdges().size() != 1) {
diff --git a/src/common/low_precision_transformations/src/multiply.cpp b/src/common/low_precision_transformations/src/multiply.cpp
index 68792d2d3c7..021cd2cd569 100644
--- a/src/common/low_precision_transformations/src/multiply.cpp
+++ b/src/common/low_precision_transformations/src/multiply.cpp
@@ -97,18 +97,14 @@ bool MultiplyTransformation::transform(TransformationContext& context, ngraph::p
     } else {
         const int emptyPathIndex = fullPathIndex == 0 ? 1 : 0;
 
-        FakeQuantizeDequantization dequantizationEmptyPath = NetworkHelper::getDequantization(multiply, emptyPathIndex);
-        if ((updatePrecisions && !dequantizationEmptyPath.empty() && !dequantizationEmptyPath.isLowPrecision()) ||
-            (dequantizationEmptyPath.multiply == nullptr && dequantizationEmptyPath.subtract == nullptr)) {
-            return false;
+        if (updatePrecisions) {
+            const FakeQuantizeDequantization dequantizationEmptyPath = NetworkHelper::getDequantization(multiply, emptyPathIndex);
+            if (!dequantizationEmptyPath.empty() && !dequantizationEmptyPath.isLowPrecision()) {
+                return false;
+            }
         }
 
-        FakeQuantizeDequantization dequantizationFullPath = NetworkHelper::getDequantization(multiply, fullPathIndex);
-        if (updatePrecisions && !dequantizationFullPath.empty() && !dequantizationFullPath.isLowPrecision()) {
-            return false;
-        }
-
-        dequantizationEmptyPath = NetworkHelper::foldDequantization(multiply, emptyPathIndex);
+        FakeQuantizeDequantization dequantizationEmptyPath = NetworkHelper::foldDequantization(multiply, emptyPathIndex);
         std::shared_ptr<Node> subtractValuesEmptyPath;
         std::shared_ptr<Node> multiplyValuesEmptyPath;
         std::tie(subtractValuesEmptyPath, multiplyValuesEmptyPath) = NetworkHelper::createEmptyValues(dequantizationEmptyPath, deqPrecision);
@@ -118,7 +114,7 @@ bool MultiplyTransformation::transform(TransformationContext& context, ngraph::p
             return false;
         }
 
-        dequantizationFullPath = NetworkHelper::foldDequantization(multiply, fullPathIndex);
+        FakeQuantizeDequantization dequantizationFullPath = NetworkHelper::foldDequantization(multiply, fullPathIndex);
         std::shared_ptr<Node> subtractValuesFullPath;
         std::shared_ptr<Node> multiplyValuesFullPath;
         std::tie(subtractValuesFullPath, multiplyValuesFullPath) = NetworkHelper::createEmptyValues(dequantizationFullPath, deqPrecision);
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp
new file mode 100644
index 00000000000..4a92eb20391
--- /dev/null
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp
@@ -0,0 +1,100 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "low_precision_transformations/elementwise_branch_selection_transformation.hpp"
+#include "common_test_utils/test_constants.hpp"
+
+using namespace LayerTestsDefinitions;
+using namespace InferenceEngine::details;
+
+namespace {
+const std::vector<ngraph::element::Type> netPrecisions = {
+    ngraph::element::f32,
+};
+
+const std::vector<std::string> elementwiseTypes = {
+    "add",
+    "multiply"
+};
+
+const std::vector<LayerTestsDefinitions::ElementwiseBranchSelectionTestValues> params = {
+    {
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        },
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            {}
+        },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        {
+            {"Constant", "convolution1"},
+            {"Constant", "convolution2"},
+            {"fakeQuantizeBefore1", "convolution1"},
+            {"fakeQuantizeBefore2", "convolution2"},
+            {"maxPool", "result"}
+        },
+        {
+            {"convolution1", "U8"},
+            {"convolution2", "U8"},
+            {"eltwise", "U8"}
+        }
+    },
+    {
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            {}
+        },
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        {
+            {"Constant", "convolution1"},
+            {"Constant", "convolution2"},
+            {"fakeQuantizeBefore1", "convolution1"},
+            {"fakeQuantizeBefore2", "convolution2"},
+            {"maxPool", "result"}
+        },
+        {
+            {"convolution1", "U8"},
+            {"convolution2", "U8"},
+            {"eltwise", "U8"}
+        }
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LPT, ElementwiseBranchSelectionTransformation,
+    ::testing::Combine(
+        ::testing::ValuesIn(netPrecisions),
+        ::testing::Values(ngraph::PartialShape({ 1, 3, 16, 16 })),
+        ::testing::Values(CommonTestUtils::DEVICE_CPU),
+        ::testing::ValuesIn(params),
+        ::testing::ValuesIn(elementwiseTypes)),
+    ElementwiseBranchSelectionTransformation::getTestCaseName);
+}  // namespace
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp
index de85a663877..240a127894a 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp
@@ -17,54 +17,63 @@ const std::vector<ngraph::element::Type> netPrecisions = {
 
 const std::vector<LayerTestsDefinitions::MultiplyTestValues> params = {
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -128.f }, { 127.f } },
         false,
-        {ngraph::element::i8}, {ngraph::element::f32, ngraph::element::i8}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
         false,
-        {ngraph::element::i8}, {ngraph::element::f32, ngraph::element::f32}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        ngraph::element::i8
     },
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -128.f }, { 127.f } },
-        true,
-        {ngraph::element::i8}, {ngraph::element::f32, ngraph::element::f32}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        true,
-        {ngraph::element::i8}, {ngraph::element::i8, ngraph::element::f32}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -127.f }, { 128.f } },
         false,
-        {ngraph::element::u8}, {ngraph::element::f32, ngraph::element::f32}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
         false,
-        {ngraph::element::u8}, {ngraph::element::f32, ngraph::element::u8}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::u8
     },
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -127.f }, { 128.f } },
         true,
-        {ngraph::element::u8}, {ngraph::element::u8, ngraph::element::f32}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::u8
     },
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
         true,
-        {ngraph::element::u8}, {ngraph::element::f32, ngraph::element::f32}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        ngraph::element::i8
     },
-    { {}, {}, false }, { {}, {}, true },
+    {
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        true,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        ngraph::element::i8
+    },
+    {
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -128.f }, { 1.27f } },
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::u8
+    },
+    {
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        true,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.27f }, { 1.28f }, { -1.27f }, { 1.28f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::u8
+    },
+    { false, {}, false, {}, {}, ngraph::element::f32 },
+    { true, {}, true, {}, {}, ngraph::element::f32 },
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_LPT, MultiplyTransformation,
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp
new file mode 100644
index 00000000000..eb2eeba0a92
--- /dev/null
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/elementwise_branch_selection_transformation.cpp
@@ -0,0 +1,88 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "low_precision_transformations/elementwise_branch_selection_transformation.hpp"
+#include "common_test_utils/test_constants.hpp"
+
+using namespace LayerTestsDefinitions;
+using namespace InferenceEngine::details;
+
+namespace {
+const std::vector<ngraph::element::Type> netPrecisions = {
+    ngraph::element::f32,
+};
+
+const std::vector<std::string> elementwiseTypes = {
+    "add",
+    "multiply"
+};
+
+const std::vector<LayerTestsDefinitions::ElementwiseBranchSelectionTestValues> params = {
+    {
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        },
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            {}
+        },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        {}, // GPU doesn't returns Reorders in performance counters
+        {
+            {"convolution1", "U8"},
+            {"convolution2", "U8"},
+            {"eltwise", "U8"}
+        }
+    },
+    {
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            {}
+        },
+        {
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+            {
+                {},
+                { std::vector<float>(9, 1.f), ngraph::element::i8, {3, 3, 1, 1} },
+                { {ngraph::element::f32}, {}, {std::vector<float>(3, 1.f), ngraph::element::f32, {3, 1, 1, 1}} }
+            },
+            { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        {}, // GPU doesn't returns Reorders in performance counters
+        {
+            {"convolution1", "U8"},
+            {"convolution2", "U8"},
+            {"eltwise", "U8"}
+        }
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LPT, ElementwiseBranchSelectionTransformation,
+    ::testing::Combine(
+        ::testing::ValuesIn(netPrecisions),
+        ::testing::Values(ngraph::PartialShape({ 1, 3, 16, 16 })),
+        ::testing::Values(CommonTestUtils::DEVICE_GPU),
+        ::testing::ValuesIn(params),
+        ::testing::ValuesIn(elementwiseTypes)),
+    ElementwiseBranchSelectionTransformation::getTestCaseName);
+}  // namespace
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp
index 815f096e85c..614c5148ec6 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/multiply_transformation.cpp
@@ -12,59 +12,68 @@ using namespace LayerTestsDefinitions;
 namespace {
 const std::vector<ngraph::element::Type> netPrecisions = {
     ngraph::element::f32,
-    ngraph::element::f16
+    //ngraph::element::f16
 };
 
 const std::vector<LayerTestsDefinitions::MultiplyTestValues> params = {
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -128.f }, { 127.f } },
         false,
-        {ngraph::element::i8}, {ngraph::element::f32, ngraph::element::i8}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
         false,
-        {ngraph::element::i8}, {ngraph::element::f32, ngraph::element::f32}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        ngraph::element::undefined // ngraph::element::i8
     },
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -128.f }, { 127.f } },
-        true,
-        {ngraph::element::i8}, {ngraph::element::f32, ngraph::element::f32}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        true,
-        {ngraph::element::i8}, {ngraph::element::i8, ngraph::element::f32}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -127.f }, { 128.f } },
         false,
-        {ngraph::element::u8}, {ngraph::element::f32, ngraph::element::f32}
-    },
-    {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
         false,
-        {ngraph::element::u8}, {ngraph::element::f32, ngraph::element::u8}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::undefined // ngraph::element::u8
     },
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { -127.f }, { 128.f } },
         true,
-        {ngraph::element::u8}, {ngraph::element::u8, ngraph::element::f32}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::undefined //ngraph::element::u8
     },
     {
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -128.f }, { 127.f }, { -128.f }, { 127.f } },
-        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 255.f } },
         true,
-        {ngraph::element::u8}, {ngraph::element::f32, ngraph::element::f32}
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        ngraph::element::undefined // ngraph::element::i8
     },
-    { {}, {}, false }, { {}, {}, true },
+    {
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        true,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -1.28f }, { 1.27f } },
+        ngraph::element::undefined // ngraph::element::i8
+    },
+    {
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.28f }, { 1.27f }, { -128.f }, { 1.27f } },
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::undefined // ngraph::element::u8
+    },
+    {
+        false,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        true,
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { -1.27f }, { 1.28f }, { -1.27f }, { 1.28f } },
+        { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 2.55f }, { 0.f }, { 2.55f } },
+        ngraph::element::undefined // ngraph::element::u8
+    },
+    { false, {}, false, {}, {}, ngraph::element::undefined /* ngraph::element::f32 */ },
+    { true, {}, true, {}, {}, ngraph::element::undefined /* ngraph::element::f32 */ },
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_LPT, MultiplyTransformation,
diff --git a/src/tests/functional/plugin/shared/include/low_precision_transformations/elementwise_branch_selection_transformation.hpp b/src/tests/functional/plugin/shared/include/low_precision_transformations/elementwise_branch_selection_transformation.hpp
new file mode 100644
index 00000000000..06dbd4b63a2
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/low_precision_transformations/elementwise_branch_selection_transformation.hpp
@@ -0,0 +1,51 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <string>
+#include <memory>
+#include "shared_test_classes/base/low_precision_transformations/layer_transformation.hpp"
+#include "lpt_ngraph_functions/common/convolution.hpp"
+#include "lpt_ngraph_functions/common/fake_quantize_on_data.hpp"
+
+namespace LayerTestsDefinitions {
+
+class ElementwiseBranchSelectionTestValues{
+public:
+    class Branch {
+    public:
+        ngraph::builder::subgraph::FakeQuantizeOnData fakeQuantizeBefore;
+        ngraph::builder::subgraph::Convolution convolution;
+        ngraph::builder::subgraph::FakeQuantizeOnData fakeQuantizeAfter;
+    };
+
+    Branch branch1;
+    Branch branch2;
+    ngraph::builder::subgraph::FakeQuantizeOnData fakeQuantizeAfter;
+    std::vector<std::pair<std::string, std::string>> expectedReorders;
+    // expected operation name + expected operation precision
+    std::vector<std::pair<std::string, std::string>> expectedPrecisions;
+};
+
+typedef std::tuple<
+    ngraph::element::Type,
+    ngraph::PartialShape,
+    std::string,
+    ElementwiseBranchSelectionTestValues,
+    std::string
+> ElementwiseBranchSelectionTransformationParams;
+
+class ElementwiseBranchSelectionTransformation :
+    public testing::WithParamInterface<ElementwiseBranchSelectionTransformationParams>,
+    public LayerTestsUtils::LayerTransformation {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ElementwiseBranchSelectionTransformationParams>& obj);
+
+protected:
+    void SetUp() override;
+    void Run() override;
+};
+
+}  // namespace LayerTestsDefinitions
diff --git a/src/tests/functional/plugin/shared/include/low_precision_transformations/multiply_transformation.hpp b/src/tests/functional/plugin/shared/include/low_precision_transformations/multiply_transformation.hpp
index 81816916e63..67d484bb955 100644
--- a/src/tests/functional/plugin/shared/include/low_precision_transformations/multiply_transformation.hpp
+++ b/src/tests/functional/plugin/shared/include/low_precision_transformations/multiply_transformation.hpp
@@ -14,11 +14,12 @@ namespace LayerTestsDefinitions {
 
 class MultiplyTestValues {
 public:
+    bool broadcast1;
     ngraph::builder::subgraph::FakeQuantizeOnData fakeQuantize1;
+    bool broadcast2;
     ngraph::builder::subgraph::FakeQuantizeOnData fakeQuantize2;
-    bool broadcast;
-    std::vector<ngraph::element::Type> precisionOnActivations;
-    std::vector<ngraph::element::Type> expectedPrecisions;
+    ngraph::builder::subgraph::FakeQuantizeOnData fakeQuantizeAfter;
+    ngraph::element::Type expectedPrecisions;
 };
 
 typedef std::tuple<
@@ -36,6 +37,7 @@ public:
 
 protected:
     void SetUp() override;
+    void Run() override;
 };
 
 }  // namespace LayerTestsDefinitions
diff --git a/src/tests/functional/plugin/shared/src/low_precision_transformations/elementwise_branch_selection_transformation.cpp b/src/tests/functional/plugin/shared/src/low_precision_transformations/elementwise_branch_selection_transformation.cpp
new file mode 100644
index 00000000000..b6820698505
--- /dev/null
+++ b/src/tests/functional/plugin/shared/src/low_precision_transformations/elementwise_branch_selection_transformation.cpp
@@ -0,0 +1,120 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "low_precision_transformations/elementwise_branch_selection_transformation.hpp"
+
+#include <memory>
+#include <tuple>
+
+#include <transformations/init_node_info.hpp>
+#include "lpt_ngraph_functions/add_function.hpp"
+
+namespace LayerTestsDefinitions {
+
+std::string ElementwiseBranchSelectionTransformation::getTestCaseName(const testing::TestParamInfo<ElementwiseBranchSelectionTransformationParams>& obj) {
+    ngraph::element::Type netPrecision;
+    ngraph::PartialShape inputShapes;
+    std::string targetDevice;
+    auto params = LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParamsU8I8();
+    ElementwiseBranchSelectionTestValues param;
+    std::string elementwiseType;
+    std::tie(netPrecision, inputShapes, targetDevice, param, elementwiseType) = obj.param;
+
+    std::ostringstream result;
+    result << getTestCaseNameByParams(netPrecision, inputShapes, targetDevice, params) <<
+        "_elementwiseType_" << elementwiseType;
+
+    auto toString = [](const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnData) -> std::string {
+        if (fqOnData.empty()) {
+            return "";
+        }
+
+        std::stringstream ss;
+        ss << "_on_branch1_" <<
+            fqOnData.inputLowValues[0] << "_" <<
+            fqOnData.inputHighValues[0] << "_" <<
+            fqOnData.outputLowValues[0] << "_" <<
+            fqOnData.outputHighValues[0];
+        return ss.str();
+    };
+
+    result <<
+        "_on_branch1_" << toString(param.branch1.fakeQuantizeBefore) << toString(param.branch1.fakeQuantizeAfter) <<
+        "_on_branch1_" << toString(param.branch1.fakeQuantizeBefore) << toString(param.branch1.fakeQuantizeAfter) <<
+        "_" << toString(param.fakeQuantizeAfter);
+
+    return result.str();
+}
+
+void ElementwiseBranchSelectionTransformation::SetUp() {
+    ngraph::element::Type precision;
+    ngraph::PartialShape inputShape;
+    ElementwiseBranchSelectionTestValues param;
+    std::string elementwiseType;
+    std::tie(precision, inputShape, targetDevice, param, elementwiseType) = this->GetParam();
+
+    function = ngraph::builder::subgraph::AddFunction::getOriginalSubgraphWithConvolutions(
+        precision,
+        inputShape,
+        false,
+        elementwiseType,
+        param.branch1.fakeQuantizeBefore,
+        param.branch1.convolution,
+        param.branch1.fakeQuantizeAfter,
+        param.branch2.fakeQuantizeBefore,
+        param.branch2.convolution,
+        param.branch2.fakeQuantizeAfter,
+        param.fakeQuantizeAfter);
+
+    ngraph::pass::InitNodeInfo().run_on_function(function);
+}
+
+void ElementwiseBranchSelectionTransformation::Run() {
+    LayerTestsCommon::Run();
+
+    const auto params = std::get<3>(GetParam());
+    const auto elementwiseType = std::get<4>(GetParam());
+
+    std::vector<std::pair<std::string, std::string>> expectedReorders = params.expectedReorders;
+    if (!expectedReorders.empty()) {
+        auto rtInfo = LayerTestsCommon::getRuntimeInfo();
+        for (auto it : rtInfo) {
+            const auto& typeIt = it.second.find("layerType");
+            const auto type = typeIt->second.as<std::string>();
+            if (type == "Reorder") {
+                const auto name = it.first;
+                bool wasFound = false;
+                for (auto it = expectedReorders.begin(); it != expectedReorders.end(); ++it) {
+                    auto pair = *it;
+                    const std::string parent = name.substr(0, name.find("_"));
+                    const std::string child = name.substr(name.rfind("_") + 1, name.size() - name.rfind("_") - 1);
+                    if ((pair.first == parent) && (pair.second == child)) {
+                        expectedReorders.erase(it);
+                        wasFound = true;
+                        break;
+                    }
+                }
+
+                ASSERT_TRUE(wasFound) << it.first << " was not found in expected list";
+            } else if (type == "Convolution") {
+                const auto& precisionIt = it.second.find("runtimePrecision");
+                const auto precision = precisionIt->second.as<std::string>();
+                ASSERT_EQ("U8", precision);
+            }
+        }
+
+        ASSERT_TRUE(expectedReorders.empty()) << "Some Reorder operations were not found in execution graph";
+    }
+
+    for (auto it : params.expectedPrecisions) {
+        const auto actualPrecision = getRuntimePrecisionByFusedName(it.first == "eltwise" ? elementwiseType : it.first);
+        ASSERT_EQ(it.second, actualPrecision) << "actual precision for operation '" << it.first << "' is not correct";
+    }
+}
+
+TEST_P(ElementwiseBranchSelectionTransformation, CompareWithRefImpl) {
+    Run();
+};
+
+}  // namespace LayerTestsDefinitions
diff --git a/src/tests/functional/plugin/shared/src/low_precision_transformations/multiply_transformation.cpp b/src/tests/functional/plugin/shared/src/low_precision_transformations/multiply_transformation.cpp
index cf0c42c06e9..6bd05c2ca5c 100644
--- a/src/tests/functional/plugin/shared/src/low_precision_transformations/multiply_transformation.cpp
+++ b/src/tests/functional/plugin/shared/src/low_precision_transformations/multiply_transformation.cpp
@@ -27,14 +27,10 @@ std::string MultiplyTransformation::getTestCaseName(const testing::TestParamInfo
 
     std::ostringstream result;
     result << getTestCaseNameByParams(precision, inputShapes, targetDevice, params) <<
-        (param.broadcast ? "_broadcast" : "");
-    for (const auto& elem : param.precisionOnActivations) {
-        result << "_" << elem << "_";
-    }
-    result << "expected_precisions_";
-    for (const auto& elem : param.expectedPrecisions) {
-        result << "_" << elem << "_";
-    }
+        (param.broadcast1 ? "_broadcast1" : "") <<
+        (param.broadcast2 ? "_broadcast2" : "");
+
+    result << "_" << param.expectedPrecisions << "_";
 
     if (!param.fakeQuantize1.empty()) {
         result << "_on_branch1_" <<
@@ -62,13 +58,42 @@ void MultiplyTransformation::SetUp() {
     function = ngraph::builder::subgraph::MultiplyFunction::getOriginal(
         precision,
         inputShape,
-        param.broadcast,
+        param.broadcast1,
         param.fakeQuantize1,
-        param.fakeQuantize2);
+        param.broadcast2,
+        param.fakeQuantize2,
+        param.fakeQuantizeAfter);
 
     ngraph::pass::InitNodeInfo().run_on_function(function);
 }
 
+void MultiplyTransformation::Run() {
+    LayerTestsCommon::Run();
+
+    const auto params = std::get<3>(GetParam());
+
+    auto to_string = [](const ngraph::element::Type& precision) -> std::string {
+        switch (precision) {
+            case ngraph::element::f32: {
+                return "FP32";
+            }
+            case ngraph::element::i8: {
+                return "I8";
+            }
+            case ngraph::element::u8: {
+                return "U8";
+            }
+            default: {
+                return "";
+            }
+        }
+    };
+
+    const auto expectedFqPrecision = to_string(params.expectedPrecisions);
+    const auto actualFqPrecision = getRuntimePrecision("multiply");
+    EXPECT_EQ(expectedFqPrecision, actualFqPrecision);
+}
+
 TEST_P(MultiplyTransformation, CompareWithRefImpl) {
     Run();
 };
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp
index 92d12e3a8a6..35bfb91a492 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp
@@ -85,9 +85,17 @@ public:
 
     std::map<std::string, std::string>& GetConfiguration();
 
+    // get runtime precision by operation friendly name
     std::string getRuntimePrecision(const std::string& layerName);
+
+    // get runtime precision by operation type
     std::string getRuntimePrecisionByType(const std::string& layerType);
 
+    // get runtime precision by operation friendly name which can be fused
+    std::string getRuntimePrecisionByFusedName(const std::string& layerName);
+
+    std::map<std::string, ngraph::Node::RTMap> getRuntimeInfo();
+
 #ifndef NDEBUG
     void showRuntimePrecisions();
 #endif
diff --git a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
index 7292ae0eb78..d4b297c9420 100644
--- a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
+++ b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
@@ -516,6 +516,54 @@ std::string LayerTestsCommon::getRuntimePrecisionByType(const std::string& layer
     return "";
 }
 
+std::string LayerTestsCommon::getRuntimePrecisionByFusedName(const std::string& layerName) {
+    const auto execGraph = executableNetwork.GetExecGraphInfo();
+    const auto execFunction = execGraph.getFunction();
+
+    const auto parse = [](const std::string& originalLayersNames) -> std::set<std::string> {
+        std::set<std::string> names;
+
+        std::string tmp = originalLayersNames;
+        size_t beginPosition = 0ul;
+        size_t endPosition;
+        while ((endPosition = tmp.find(",", beginPosition)) != std::string::npos) {
+            names.insert(tmp.substr(beginPosition, endPosition - beginPosition));
+            beginPosition = endPosition + 1;
+        }
+
+        names.insert(tmp.substr(beginPosition, endPosition - beginPosition));
+        return names;
+    };
+
+    for (const auto& op : execFunction->get_ops()) {
+        const auto& rtInfo = op->get_rt_info();
+
+        const auto& nameIt = rtInfo.find("originalLayersNames");
+        IE_ASSERT(nameIt != rtInfo.end()) << "originalLayersNames is not found for node: " << layerName;
+        const auto fusedName = parse(nameIt->second.as<std::string>());
+        if (fusedName.find(layerName) == fusedName.end()) {
+            continue;
+        }
+
+        const auto& it = rtInfo.find("runtimePrecision");
+        IE_ASSERT(it != rtInfo.end()) << "runtimePrecision is not found for node: " << layerName;
+        const auto rtPrecisionPtr = it->second.as<std::string>();
+        return rtPrecisionPtr;
+    }
+
+    return "";
+}
+
+std::map<std::string, ngraph::Node::RTMap> LayerTestsCommon::getRuntimeInfo() {
+    const auto execGraph = executableNetwork.GetExecGraphInfo();
+    const auto function = execGraph.getFunction();
+    std::map<std::string, ngraph::Node::RTMap> runtimeInfo;
+    for (const auto& op : function->get_ops()) {
+        runtimeInfo[op->get_friendly_name()] = op->get_rt_info();
+    }
+    return runtimeInfo;
+}
+
 #ifndef NDEBUG
 void LayerTestsCommon::showRuntimePrecisions() {
     const auto execGraph = executableNetwork.GetExecGraphInfo();
@@ -523,13 +571,17 @@ void LayerTestsCommon::showRuntimePrecisions() {
 
     for (const auto& op : execFunction->get_ops()) {
         const auto& rtInfo = op->get_rt_info();
+
+        const auto& nameIt = rtInfo.find("originalLayersNames");
+        const auto name = nameIt->second.as<std::string>();
+
         const auto& typeIt = rtInfo.find("layerType");
-
         const auto type = typeIt->second.as<std::string>();
-        const auto& it = rtInfo.find("runtimePrecision");
 
+        const auto& it = rtInfo.find("runtimePrecision");
         const auto rtPrecisionPtr = it->second.as<std::string>();
-        std::cout << type << ": " << rtPrecisionPtr << std::endl;
+
+        std::cout << type << "(" << name << "): " << rtPrecisionPtr << std::endl;
     }
 }
 #endif
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/add_function.hpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/add_function.hpp
index b0b4e9ad693..3b5a8a86a24 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/add_function.hpp
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/add_function.hpp
@@ -8,8 +8,11 @@
 #include <ngraph/ngraph.hpp>
 #include <low_precision/layer_transformation.hpp>
 
-#include "lpt_ngraph_functions/common/dequantization_operations.hpp"
+#include "elementwise_function.hpp"
 #include "lpt_ngraph_functions/common/builders.hpp"
+#include "lpt_ngraph_functions/common/convolution.hpp"
+#include "lpt_ngraph_functions/common/dequantization_operations.hpp"
+#include "lpt_ngraph_functions/common/fake_quantize_on_data.hpp"
 
 namespace ngraph {
 namespace builder {
@@ -53,7 +56,7 @@ inline std::ostream& operator<<(std::ostream& out, const AddExpectedValues& valu
         "_mutliply" << values.mutliplyValuesAfter.size();
 }
 
-class AddFunction {
+class AddFunction : public ElementwiseFunction {
 public:
     static std::shared_ptr<ngraph::Function> getOriginal(
         const ngraph::element::Type precision,
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/builders.hpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/builders.hpp
index efed4204a3d..0ffab7b4a85 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/builders.hpp
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/builders.hpp
@@ -15,8 +15,9 @@
 #include "low_precision/network_helper.hpp"
 
 #include "lpt_ngraph_functions/common/add.hpp"
-#include "lpt_ngraph_functions/common/fake_quantize_on_data.hpp"
+#include "lpt_ngraph_functions/common/convolution.hpp"
 #include "lpt_ngraph_functions/common/dequantization_operations.hpp"
+#include "lpt_ngraph_functions/common/fake_quantize_on_data.hpp"
 #include "lpt_ngraph_functions/common/reshape.hpp"
 #include "lpt_ngraph_functions/common/transpose.hpp"
 
@@ -78,6 +79,8 @@ std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantize(
     const ngraph::element::Type precision,
     const FakeQuantizeOnData& fqOnData);
 
+std::shared_ptr<ngraph::opset1::Convolution> makeConvolution(const Output<Node>& output, const Convolution& convolution);
+
 std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantizeTypeRelaxed(
     const Output<ngraph::Node>& output,
     const ngraph::element::Type precision,
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/convolution.hpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/convolution.hpp
new file mode 100644
index 00000000000..907a46a162f
--- /dev/null
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/convolution.hpp
@@ -0,0 +1,36 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <vector>
+#include <ngraph/ngraph.hpp>
+
+#include "constant.hpp"
+#include "dequantization_operations.hpp"
+
+namespace ngraph {
+namespace builder {
+namespace subgraph {
+
+class Convolution {
+public:
+    Convolution();
+
+    Convolution(
+        const DequantizationOperations::Subtract zeroPointOnActivations,
+        const Constant& constantOnWeights,
+        const DequantizationOperations& dequantizationOnWeights);
+
+    bool empty() const;
+
+    DequantizationOperations::Subtract zeroPointOnActivations;
+    Constant constantOnWeights;
+    DequantizationOperations dequantizationOnWeights;
+};
+
+}  // namespace subgraph
+}  // namespace builder
+}  // namespace ngraph
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/elementwise_function.hpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/elementwise_function.hpp
new file mode 100644
index 00000000000..66e33ec307f
--- /dev/null
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/elementwise_function.hpp
@@ -0,0 +1,38 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <ngraph/ngraph.hpp>
+#include <low_precision/layer_transformation.hpp>
+
+#include "lpt_ngraph_functions/common/builders.hpp"
+#include "lpt_ngraph_functions/common/convolution.hpp"
+#include "lpt_ngraph_functions/common/dequantization_operations.hpp"
+#include "lpt_ngraph_functions/common/fake_quantize_on_data.hpp"
+
+namespace ngraph {
+namespace builder {
+namespace subgraph {
+
+class ElementwiseFunction {
+public:
+    static std::shared_ptr<ngraph::Function> getOriginalSubgraphWithConvolutions(
+        const ngraph::element::Type precision,
+        const ngraph::PartialShape& inputShape,
+        const bool broadcast,
+        const std::string& elementWiseType,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataBefore1,
+        const ngraph::builder::subgraph::Convolution& convolution1,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataAfter1,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataBefore2,
+        const ngraph::builder::subgraph::Convolution& convolution2,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataAfter2,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataAfter);
+};
+
+}  // namespace subgraph
+}  // namespace builder
+}  // namespace ngraph
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/multiply_function.hpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/multiply_function.hpp
index 96c1b387e34..870ba9b6116 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/multiply_function.hpp
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/multiply_function.hpp
@@ -7,6 +7,7 @@
 #include <memory>
 #include <ngraph/ngraph.hpp>
 
+#include "elementwise_function.hpp"
 #include "lpt_ngraph_functions/common/constant.hpp"
 #include "lpt_ngraph_functions/common/dequantization_operations.hpp"
 
@@ -37,7 +38,7 @@ inline std::ostream& operator<<(std::ostream& out, const MultiplyValues& values)
     return out << "_" << values.branch1 << "_" << values.branch2 << (values.isDequantization ? "_isDequantization" : "");
 }
 
-class MultiplyFunction {
+class MultiplyFunction : public ElementwiseFunction {
 public:
     static std::shared_ptr<ngraph::Function> get(
             const element::Type precision,
@@ -46,9 +47,11 @@ public:
     static std::shared_ptr<ngraph::Function> getOriginal(
         const ngraph::element::Type precision,
         const ngraph::PartialShape& inputShape,
-        const bool broadcast,
-        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnData1,
-        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnData2);
+        const bool broadcast1,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fq1,
+        const bool broadcast2,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fq2,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqAfter);
 };
 
 }  // namespace subgraph
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/add_function.cpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/add_function.cpp
index 634fc3bb962..b819c513b5b 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/add_function.cpp
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/add_function.cpp
@@ -2,6 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "lpt_ngraph_functions/add_function.hpp"
+
 #include "low_precision/network_helper.hpp"
 #include "low_precision/layer_transformation.hpp"
 
@@ -9,7 +11,6 @@
 
 #include "lpt_ngraph_functions/common/dequantization_operations.hpp"
 #include "ngraph_functions/subgraph_builders.hpp"
-#include "lpt_ngraph_functions/add_function.hpp"
 
 using namespace ngraph::pass::low_precision;
 
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/builders.cpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/builders.cpp
index 4500e739bd1..5ad53ea23ca 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/builders.cpp
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/builders.cpp
@@ -205,6 +205,36 @@ std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantize(
         fqOnData.outputHighValues));
 }
 
+std::shared_ptr<ngraph::opset1::Convolution> makeConvolution(const Output<Node>& output, const Convolution& convolution) {
+    auto parentOnActivations = output;
+    if (!convolution.zeroPointOnActivations.empty()) {
+        auto constant = std::make_shared<ngraph::opset1::Constant>(
+            convolution.zeroPointOnActivations.outPrecision,
+            convolution.zeroPointOnActivations.constantShape,
+            convolution.zeroPointOnActivations.values);
+        parentOnActivations = std::make_shared<ngraph::opset1::Subtract>(parentOnActivations, constant);
+    }
+
+    assert(!convolution.constantOnWeights.empty());
+
+    ngraph::Output<ngraph::Node> weights = std::make_shared<ngraph::opset1::Constant>(
+        convolution.constantOnWeights.outPrecision,
+        convolution.constantOnWeights.shape,
+        convolution.constantOnWeights.values);
+
+    if (!convolution.dequantizationOnWeights.empty()) {
+        weights = makeDequantization(weights, convolution.dequantizationOnWeights);
+    }
+
+    return std::make_shared<ngraph::opset1::Convolution>(
+        parentOnActivations,
+        weights,
+        ngraph::Strides{ 1, 1 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::Strides{ 1, 1 });
+}
+
 std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantizeTypeRelaxed(
     const Output<ngraph::Node>& output,
     const ngraph::element::Type precision,
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/convolution.cpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/convolution.cpp
new file mode 100644
index 00000000000..777a8350200
--- /dev/null
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/common/convolution.cpp
@@ -0,0 +1,29 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "lpt_ngraph_functions/common/convolution.hpp"
+
+namespace ngraph {
+namespace builder {
+namespace subgraph {
+
+Convolution::Convolution() {
+}
+
+Convolution::Convolution(
+    const DequantizationOperations::Subtract zeroPointOnActivations,
+    const Constant& constantOnWeights,
+    const DequantizationOperations& dequantizationOnWeights) :
+    zeroPointOnActivations(zeroPointOnActivations),
+    constantOnWeights(constantOnWeights),
+    dequantizationOnWeights(dequantizationOnWeights) {
+}
+
+bool Convolution::empty() const {
+    return zeroPointOnActivations.empty() && constantOnWeights.empty() && dequantizationOnWeights.empty();
+}
+
+}  // namespace subgraph
+}  // namespace builder
+}  // namespace ngraph
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/elementwise_function.cpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/elementwise_function.cpp
new file mode 100644
index 00000000000..de97ac89d46
--- /dev/null
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/elementwise_function.cpp
@@ -0,0 +1,119 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "lpt_ngraph_functions/elementwise_function.hpp"
+
+#include "low_precision/layer_transformation.hpp"
+#include "ngraph/opsets/opset1.hpp"
+#include "lpt_ngraph_functions/common/dequantization_operations.hpp"
+
+using namespace ngraph::pass::low_precision;
+
+namespace ngraph {
+namespace builder {
+namespace subgraph {
+
+namespace {
+
+std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantizeWithNames(
+        const Output<Node>& parent,
+        const ngraph::element::Type precision,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnData,
+        const std::string name) {
+    auto fq = ngraph::builder::subgraph::makeFakeQuantize(parent, precision, fqOnData);
+    fq->set_friendly_name(name);
+    fq->get_input_node_ptr(1)->set_friendly_name(name + "/inputLow");
+    fq->get_input_node_ptr(2)->set_friendly_name(name + "/inputHigh");
+    fq->get_input_node_ptr(3)->set_friendly_name(name + "/outputLow");
+    fq->get_input_node_ptr(4)->set_friendly_name(name + "/outputHigh");
+    return fq;
+}
+
+} // namespace
+
+std::shared_ptr<ngraph::Function> ElementwiseFunction::getOriginalSubgraphWithConvolutions(
+        const ngraph::element::Type precision,
+        const ngraph::PartialShape& inputShape,
+        const bool broadcast,
+        const std::string& elementWiseType,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataBefore1,
+        const ngraph::builder::subgraph::Convolution& convolution1,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataAfter1,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataBefore2,
+        const ngraph::builder::subgraph::Convolution& convolution2,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataAfter2,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataAfter) {
+    ngraph::PartialShape inputShape2 = inputShape;
+
+    if (broadcast) {
+        inputShape2[2] = 1;
+        inputShape2[3] = 1;
+    }
+
+    auto makeBranch = [&](
+        const ngraph::element::Type precision,
+        const ngraph::PartialShape& inputShape,
+        const size_t index,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataBefore,
+        const ngraph::builder::subgraph::Convolution& convolution,
+        const ngraph::builder::subgraph::FakeQuantizeOnData& fqOnDataAfter) ->
+            std::pair<std::shared_ptr<ngraph::opset1::Parameter>, std::shared_ptr<ngraph::Node>> {
+        const auto input = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape);
+        input->set_friendly_name("input" + std::to_string(index));
+        std::shared_ptr<ngraph::Node> parent = input;
+
+        if (!fqOnDataBefore.empty()) {
+            parent = makeFakeQuantizeWithNames(parent, precision, fqOnDataBefore, "fakeQuantizeBefore" + std::to_string(index));
+        }
+
+        if (!convolution.empty()) {
+            parent = makeConvolution(parent, convolution);
+            parent->set_friendly_name("convolution" + std::to_string(index));
+        }
+
+        if (!fqOnDataAfter.empty()) {
+            parent = makeFakeQuantizeWithNames(parent, precision, fqOnDataAfter, "fakeQuantizeAfter" + std::to_string(index));
+        }
+
+        return std::make_pair(input, parent);
+    };
+
+    const auto branch1 = makeBranch(precision, inputShape, 1, fqOnDataBefore1, convolution1, fqOnDataAfter1);
+    const auto branch2 = makeBranch(precision, inputShape, 2, fqOnDataBefore2, convolution2, fqOnDataAfter2);
+
+    std::shared_ptr<ngraph::Node> result;
+    if (elementWiseType == "add") {
+        result = std::make_shared<ngraph::opset1::Add>(branch1.second, branch2.second);
+        result->set_friendly_name("add");
+    } else if (elementWiseType == "multiply") {
+        result = std::make_shared<ngraph::opset1::Multiply>(branch1.second, branch2.second);
+        result->set_friendly_name("multiply");
+    } else {
+        THROW_TRANSFORMATION_EXCEPTION << "not supported element-wise operation type " << elementWiseType;
+    }
+
+    if (!fqOnDataAfter.empty()) {
+        result = makeFakeQuantizeWithNames(result, precision, fqOnDataAfter, "fakeQuantizeAfter");
+
+        // we need a some operation to move dequantization operations away from FakeQuantize to avoid cleanup fuse
+        result = std::make_shared<ngraph::opset1::MaxPool>(
+            result,
+            Strides{ 1, 1 },
+            Shape{ 1, 1 },
+            Shape{ 0, 0 },
+            Shape{ 2, 2 },
+            op::RoundingType::FLOOR);
+        result->set_friendly_name("maxPool");
+    }
+
+    result = std::make_shared<ngraph::opset1::Result>(result);
+    result->set_friendly_name("result");
+
+    ngraph::ResultVector results{ std::dynamic_pointer_cast<ngraph::opset1::Result>(result) };
+    return std::make_shared<ngraph::Function>(results, ngraph::ParameterVector{ branch1.first, branch2.first }, "AddTransformation");
+}
+
+}  // namespace subgraph
+}  // namespace builder
+}  // namespace ngraph
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp
index 335f6f44df4..6a2d7ff6f30 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp
@@ -169,25 +169,25 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::getOriginal(
     if (!fakeQuantizeOnData.empty()) {
         parent = std::make_shared<ngraph::opset1::FakeQuantize>(
             input,
-            std::make_shared<Constant>(
+            std::make_shared<ngraph::opset1::Constant>(
                 precision,
                 rankLength == 3 ?
                     Shape{ 1, fakeQuantizeOnData.inputLowValues.size(), 1 } :
                     Shape{ 1, fakeQuantizeOnData.inputLowValues.size(), 1, 1 },
                 fakeQuantizeOnData.inputLowValues),
-            std::make_shared<Constant>(
+            std::make_shared<ngraph::opset1::Constant>(
                 precision,
                 rankLength == 3 ?
                     Shape{ 1, fakeQuantizeOnData.inputHighValues.size(), 1 } :
                     Shape{ 1, fakeQuantizeOnData.inputHighValues.size(), 1, 1 },
                 fakeQuantizeOnData.inputHighValues),
-            std::make_shared<Constant>(
+            std::make_shared<ngraph::opset1::Constant>(
                 precision,
                 rankLength == 3 ?
                     Shape{ 1, fakeQuantizeOnData.outputLowValues.size(), 1 } :
                     Shape{ 1, fakeQuantizeOnData.outputLowValues.size(), 1, 1 },
                 fakeQuantizeOnData.outputLowValues),
-            std::make_shared<Constant>(
+            std::make_shared<ngraph::opset1::Constant>(
                 precision,
                 rankLength == 3 ?
                     Shape{ 1, fakeQuantizeOnData.outputHighValues.size(), 1 } :
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/multiply_function.cpp b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/multiply_function.cpp
index 714a7ab1cdf..29a4571756a 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/src/multiply_function.cpp
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/src/multiply_function.cpp
@@ -90,22 +90,32 @@ std::shared_ptr<ngraph::Function> MultiplyFunction::get(
 std::shared_ptr<ngraph::Function> MultiplyFunction::getOriginal(
     const ngraph::element::Type precision,
     const ngraph::PartialShape& inputShape,
-    const bool broadcast,
+    const bool broadcast1,
     const ngraph::builder::subgraph::FakeQuantizeOnData& fq1,
-    const ngraph::builder::subgraph::FakeQuantizeOnData& fq2) {
-    auto inputShape2 = inputShape;
+    const bool broadcast2,
+    const ngraph::builder::subgraph::FakeQuantizeOnData& fq2,
+    const ngraph::builder::subgraph::FakeQuantizeOnData& fqAfter) {
+    auto inputShape1 = inputShape;
+    if (broadcast1) {
+        inputShape1[2] = 1;
+        inputShape1[3] = 1;
+    }
 
-    if (broadcast) {
+    auto inputShape2 = inputShape;
+    if (broadcast2) {
         inputShape2[2] = 1;
         inputShape2[3] = 1;
     }
 
-    const auto input1 = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape);
+    const auto input1 = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape1);
     const auto fakeQuantize1 = fq1.empty() ?
         nullptr :
         ngraph::builder::makeFakeQuantize(
             input1, precision, fq1.quantizationLevel, fq1.constantShape,
             fq1.inputLowValues, fq1.inputHighValues, fq1.outputLowValues, fq1.outputHighValues);
+    if (fakeQuantize1 != nullptr) {
+        fakeQuantize1->set_friendly_name("fakeQuantize1");
+    }
 
     const auto input2 = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape2);
     const auto fakeQuantize2 = fq2.empty() ?
@@ -113,12 +123,24 @@ std::shared_ptr<ngraph::Function> MultiplyFunction::getOriginal(
         ngraph::builder::makeFakeQuantize(
             input2, precision, fq2.quantizationLevel, fq2.constantShape,
             fq2.inputLowValues, fq2.inputHighValues, fq2.outputLowValues, fq2.outputHighValues);
+    if (fakeQuantize2 != nullptr) {
+        fakeQuantize2->set_friendly_name("fakeQuantize2");
+    }
 
     const auto multiply = std::make_shared<ngraph::opset1::Multiply>(
         fq1.empty() ? input1 : fakeQuantize1,
         fq2.empty() ? input2 : fakeQuantize2);
+    multiply->set_friendly_name("multiply");
 
-    ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(multiply) };
+    auto const fakeQuantizeAfter = fqAfter.empty() ?
+        nullptr :
+        makeFakeQuantize(multiply, precision, fqAfter);
+    if (fakeQuantizeAfter != nullptr) {
+        fakeQuantizeAfter->set_friendly_name("fakeQuantizeAfter");
+    }
+
+    const std::shared_ptr<Node> result = fakeQuantizeAfter == nullptr ? std::dynamic_pointer_cast<Node>(multiply) : fakeQuantizeAfter;
+    ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(result) };
     std::shared_ptr<ngraph::Function> function = std::make_shared<ngraph::Function>(
         results,
         ngraph::ParameterVector{ input1, input2 },

From dbf8fcf556dbf8b670f695e9545197ead3ea9125 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Mon, 20 Dec 2021 11:59:08 +0300
Subject: [PATCH 039/160] Create ov_runtime library (#9199)

* Create ov_runtime library

* Added dev target

* Fixed SOURCE_DIR property for interface lib

* Fixed missed include path

* Fixed SOURCES for plugins and frontends

* Fixed Windows export

* Try to fix preproc build

* Fixed MKLDNNPlugin

* Fix C API

* Try to fix public CI

* Fixed comments

* Use TARGET_NAME variable

* Fixed static build

* Fixed target name in comfig

* Revert "Fixed target name in comfig"

This reverts commit f61c1e0c099bb1793ec783c2a1672d810eb569bf.

* Try to remove WA with include path

* Try to fix TBB dependency for old CMake

* Revert install to lib for legacy tests

* Try to fix public tests

* Add WA for v7 reader

* Fixed dev export name

Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
---
 .../frontends/frontends.cmake                 |   4 +-
 cmake/developer_package/plugins/plugins.cmake |   4 +-
 cmake/extra_modules.cmake                     |   3 +
 ...renceEngineDeveloperPackageConfig.cmake.in |   4 +-
 cmake/templates/OpenVINOConfig.cmake.in       |  16 +-
 cmake/templates/ngraphConfig.cmake.in         |  15 +-
 .../src/mkldnn_plugin/CMakeLists.txt          |   1 +
 src/CMakeLists.txt                            |  20 +--
 src/bindings/c/src/CMakeLists.txt             |   2 +-
 src/cmake/ov_runtime.cmake                    | 157 ++++++++++++++++++
 src/common/CMakeLists.txt                     |   4 +-
 src/common/legacy/CMakeLists.txt              |   2 +-
 .../CMakeLists.txt                            |  34 ++--
 .../include/low_precision/lpt_visibility.hpp  |  14 +-
 src/common/preprocessing/CMakeLists.txt       |   5 +-
 src/common/transformations/CMakeLists.txt     |  36 ++--
 .../include/transformations_visibility.hpp    |   2 +-
 src/core/CMakeLists.txt                       |  32 ++--
 .../include/openvino/core/core_visibility.hpp |   4 +-
 src/core/tests/CMakeLists.txt                 |   2 +-
 src/core/tests/frontend/frontend_manager.cpp  |   2 +-
 src/frontends/CMakeLists.txt                  |   3 -
 src/frontends/common/CMakeLists.txt           |  52 +++---
 .../include/openvino/frontend/visibility.hpp  |   2 +-
 src/inference/CMakeLists.txt                  | 124 +-------------
 .../inference_engine/CMakeLists.txt           |   2 +-
 .../behavior/vpu/CMakeLists.txt               |   2 +-
 .../readers/ir_reader_v7/CMakeLists.txt       |  11 +-
 28 files changed, 309 insertions(+), 250 deletions(-)
 create mode 100644 src/cmake/ov_runtime.cmake

diff --git a/cmake/developer_package/frontends/frontends.cmake b/cmake/developer_package/frontends/frontends.cmake
index 2474859dc77..43842a66114 100644
--- a/cmake/developer_package/frontends/frontends.cmake
+++ b/cmake/developer_package/frontends/frontends.cmake
@@ -35,7 +35,7 @@ function(ov_generate_frontends_hpp)
     endif()
 
     # add frontends to libraries including ov_frontends.hpp
-    ov_target_link_frontends(frontend_common)
+    ov_target_link_frontends(ov_runtime)
 
     set(ov_frontends_hpp "${CMAKE_BINARY_DIR}/src/frontends/common/src/ov_frontends.hpp")
     set(frontends_hpp_in "${IEDevScripts_DIR}/frontends/ov_frontends.hpp.in")
@@ -60,7 +60,7 @@ function(ov_generate_frontends_hpp)
     add_dependencies(frontend_common _ov_frontends_hpp)
 
     # add dependency for object files
-    get_target_property(sources frontend_common::static SOURCES)
+    get_target_property(sources frontend_common_obj SOURCES)
     foreach(source IN LISTS sources)
         if("${source}" MATCHES "\\$\\<TARGET_OBJECTS\\:([A-Za-z0-9_]*)\\>")
             # object library
diff --git a/cmake/developer_package/plugins/plugins.cmake b/cmake/developer_package/plugins/plugins.cmake
index 9f915610332..e1bdb66ce1d 100644
--- a/cmake/developer_package/plugins/plugins.cmake
+++ b/cmake/developer_package/plugins/plugins.cmake
@@ -314,7 +314,7 @@ function(ie_generate_plugins_hpp)
     endforeach()
 
     # add plugins to libraries including ie_plugins.hpp
-    ie_target_link_plugins(inference_engine)
+    ie_target_link_plugins(ov_runtime)
     if(TARGET inference_engine_s)
         ie_target_link_plugins(inference_engine_s)
     endif()
@@ -344,7 +344,7 @@ function(ie_generate_plugins_hpp)
     add_dependencies(inference_engine _ie_plugins_hpp)
 
     # add dependency for object files
-    get_target_property(sources inference_engine SOURCES)
+    get_target_property(sources inference_engine_obj SOURCES)
     foreach(source IN LISTS sources)
         if("${source}" MATCHES "\\$\\<TARGET_OBJECTS\\:([A-Za-z0-9_]*)\\>")
             # object library
diff --git a/cmake/extra_modules.cmake b/cmake/extra_modules.cmake
index c29c7a8ecdc..1fe5e4bd210 100644
--- a/cmake/extra_modules.cmake
+++ b/cmake/extra_modules.cmake
@@ -107,3 +107,6 @@ register_extra_modules()
 # for static libraries case we need to generate final ie_plugins.hpp
 # with all the information about plugins
 ie_generate_plugins_hpp()
+
+# used for static build
+ov_generate_frontends_hpp()
diff --git a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
index bf7f04d8f93..eb3f0579213 100644
--- a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
+++ b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
@@ -50,10 +50,10 @@ find_dependency(ngraph
                 NO_DEFAULT_PATH)
 
 # create targets with old names for compatibility
-if(TARGET openvino::core AND NOT TARGET IE::core)
+if(TARGET openvino::runtime AND NOT TARGET IE::core)
     add_library(IE::core INTERFACE IMPORTED)
     set_target_properties(IE::core PROPERTIES
-        INTERFACE_LINK_LIBRARIES openvino::core)
+        INTERFACE_LINK_LIBRARIES openvino::runtime)
 endif()
 
 if(TARGET openvino::runtime AND NOT TARGET IE::runtime)
diff --git a/cmake/templates/OpenVINOConfig.cmake.in b/cmake/templates/OpenVINOConfig.cmake.in
index cf002cd0bd9..fd6ad5d66a0 100644
--- a/cmake/templates/OpenVINOConfig.cmake.in
+++ b/cmake/templates/OpenVINOConfig.cmake.in
@@ -151,7 +151,7 @@ endfunction()
 set(_ov_package_prefix_dir "${PACKAGE_PREFIX_DIR}")
 
 set(THREADING "@THREADING@")
-if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO" AND NOT TBB_FOUND)
+if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND NOT TBB_FOUND)
     set_and_check(_tbb_dir "@PACKAGE_IE_TBB_DIR@")
     _ov_find_dependency(TBB
                         COMPONENTS tbb tbbmalloc
@@ -174,9 +174,16 @@ endif()
 
 _ov_find_dependency(Threads)
 
-if(NOT TARGET inference_engine)
+if(NOT TARGET ov_runtime)
     set(_ov_as_external_package ON)
     include("${CMAKE_CURRENT_LIST_DIR}/OpenVINOTargets.cmake")
+
+    # TODO: WA for cmake version < 3.16
+    if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND TBB_FOUND)
+        foreach (type RELEASE DEBUG RELWITHDEBINFO MINSIZEREL)
+            set_property(TARGET openvino::runtime APPEND PROPERTY IMPORTED_LINK_DEPENDENT_LIBRARIES_${type} "TBB::tbb;TBB::tbbmalloc")
+        endforeach()
+    endif()
 endif()
 
 #
@@ -204,9 +211,8 @@ endif()
 # Apply common functions
 #
 
-foreach(target openvino::runtime openvino::runtime::c openvino::core
-               openvino::frontend::common openvino::frontend::onnx
-               openvino::frontend::paddlepaddle openvino::frontend::tensorflow)
+foreach(target openvino::runtime openvino::runtime::c
+               openvino::frontend::onnx openvino::frontend::paddlepaddle openvino::frontend::tensorflow)
     if(TARGET ${target} AND _ov_as_external_package)
         _ov_target_no_deprecation_error(${target})
     endif()
diff --git a/cmake/templates/ngraphConfig.cmake.in b/cmake/templates/ngraphConfig.cmake.in
index f243dca140d..822c876a8b0 100644
--- a/cmake/templates/ngraphConfig.cmake.in
+++ b/cmake/templates/ngraphConfig.cmake.in
@@ -46,16 +46,23 @@ find_dependency(OpenVINO
                 NO_DEFAULT_PATH)
 
 # create targets with old names for compatibility
-if(TARGET openvino::core AND NOT TARGET ngraph::ngraph)
+if(TARGET openvino::runtime AND NOT TARGET ngraph::ngraph)
     add_library(ngraph::ngraph INTERFACE IMPORTED)
     set_target_properties(ngraph::ngraph PROPERTIES
-        INTERFACE_LINK_LIBRARIES openvino::core)
+        INTERFACE_LINK_LIBRARIES openvino::runtime)
 endif()
 
-if(TARGET openvino::frontend::common AND NOT TARGET ngraph::frontend_common)
+if(TARGET openvino::runtime AND NOT TARGET openvino::frontend::common)
+    add_library(openvino::frontend::common INTERFACE IMPORTED)
+    set_target_properties(openvino::frontend::common PROPERTIES
+        INTERFACE_LINK_LIBRARIES openvino::runtime)
+endif()
+
+
+if(TARGET openvino::runtime AND NOT TARGET ngraph::frontend_common)
     add_library(ngraph::frontend_common INTERFACE IMPORTED)
     set_target_properties(ngraph::frontend_common PROPERTIES
-        INTERFACE_LINK_LIBRARIES openvino::frontend::common)
+        INTERFACE_LINK_LIBRARIES openvino::runtime)
 endif()
 
 if(TARGET openvino::frontend::onnx AND NOT TARGET ngraph::ov_onnx_frontend)
diff --git a/inference-engine/src/mkldnn_plugin/CMakeLists.txt b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
index 007b2503d6f..24359ffe7f8 100644
--- a/inference-engine/src/mkldnn_plugin/CMakeLists.txt
+++ b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
@@ -71,6 +71,7 @@ link_system_libraries(${TARGET_NAME}_obj PUBLIC mkldnn)
 
 target_include_directories(${TARGET_NAME}_obj PRIVATE $<TARGET_PROPERTY:inference_engine_preproc_s,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>
+                                                      $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:openvino::itt,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:inference_engine_lp_transformations,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:ov_shape_inference,INTERFACE_INCLUDE_DIRECTORIES>
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 8c2e3f3be69..ed882c9c996 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -22,28 +22,10 @@ endif()
 
 add_subdirectory(plugins)
 add_subdirectory(inference)
+include(cmake/ov_runtime.cmake)
 add_subdirectory(common)
 
 if(ENABLE_TESTS)
     add_subdirectory(tests_deprecated)
     add_subdirectory(tests)
 endif()
-
-if(ENABLE_OV_IR_FRONTEND)
-    if(BUILD_SHARED_LIBS)
-        add_dependencies(ov_runtime_libraries ov_ir_frontend)
-    endif()
-    # use this one once CVS-69781 is fixed
-    # add_dependencies(inference_engine ov_ir_frontend)
-endif()
-if(ENABLE_OV_ONNX_FRONTEND)
-    add_dependencies(inference_engine ov_onnx_frontend)
-endif()
-
-if(ENABLE_OV_PDPD_FRONTEND)
-    add_dependencies(inference_engine ov_paddlepaddle_frontend)
-endif()
-
-if(ENABLE_OV_TF_FRONTEND)
-    add_dependencies(inference_engine ov_tensorflow_frontend)
-endif()
diff --git a/src/bindings/c/src/CMakeLists.txt b/src/bindings/c/src/CMakeLists.txt
index 811b91a7b45..97c9930c14c 100644
--- a/src/bindings/c/src/CMakeLists.txt
+++ b/src/bindings/c/src/CMakeLists.txt
@@ -12,7 +12,7 @@ file(GLOB HEADERS ${InferenceEngine_C_API_SOURCE_DIR}/include/*.h)
 add_library(${TARGET_NAME} ${HEADERS} ${SOURCES})
 add_library(openvino::runtime::c ALIAS ${TARGET_NAME})
 
-target_link_libraries(${TARGET_NAME} PRIVATE inference_engine)
+target_link_libraries(${TARGET_NAME} PRIVATE ov_runtime)
 
 target_include_directories(${TARGET_NAME} PUBLIC
     $<BUILD_INTERFACE:${InferenceEngine_C_API_SOURCE_DIR}/include>)
diff --git a/src/cmake/ov_runtime.cmake b/src/cmake/ov_runtime.cmake
new file mode 100644
index 00000000000..650b878a403
--- /dev/null
+++ b/src/cmake/ov_runtime.cmake
@@ -0,0 +1,157 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(TARGET_NAME ov_runtime)
+
+add_library(${TARGET_NAME} $<TARGET_OBJECTS:ngraph_obj>
+                           $<TARGET_OBJECTS:frontend_common_obj>
+                           $<TARGET_OBJECTS:inference_engine_obj>
+                           $<TARGET_OBJECTS:inference_engine_transformations_obj>
+                           $<TARGET_OBJECTS:inference_engine_lp_transformations_obj>
+    )
+
+add_library(openvino::runtime ALIAS ${TARGET_NAME})
+set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME runtime)
+ie_add_vs_version_file(NAME ${TARGET_NAME} FILEDESCRIPTION "OpenVINO runtime library")
+ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
+
+target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:$<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>>
+                                                 $<BUILD_INTERFACE:$<TARGET_PROPERTY:frontend_common,INTERFACE_INCLUDE_DIRECTORIES>>
+                                                 $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>>
+)
+
+target_link_libraries(${TARGET_NAME} PRIVATE ngraph_reference
+                                             ngraph_builders
+                                             ov_shape_inference
+                                             pugixml::static
+                                             ${CMAKE_DL_LIBS}
+                                             Threads::Threads)
+
+if (TBBBIND_2_5_FOUND)
+    target_link_libraries(${TARGET_NAME} PRIVATE ${TBBBIND_2_5_IMPORTED_TARGETS})
+endif()
+
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME} PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
+
+if(WIN32)
+    set_target_properties(${TARGET_NAME} PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME})
+endif()
+
+set_ie_threading_interface_for(${TARGET_NAME})
+ie_mark_target_as_cc(${TARGET_NAME})
+
+# LTO
+set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
+
+ie_register_plugins(MAIN_TARGET ${TARGET_NAME}
+                    POSSIBLE_PLUGINS ov_auto_plugin ov_hetero_plugin ov_intel_gpu_plugin ov_intel_gna_plugin MKLDNNPlugin myriadPlugin)
+
+# Export for build tree
+
+export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
+       APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
+
+install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
+        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
+        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
+        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core
+        INCLUDES DESTINATION runtime/include
+                             runtime/include/ie)
+
+# --------------- OpenVINO runtime library dev ------------------------------
+add_library(${TARGET_NAME}_dev INTERFACE)
+target_link_libraries(${TARGET_NAME}_dev INTERFACE inference_engine_plugin_api)
+target_include_directories(${TARGET_NAME}_dev INTERFACE $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>>
+                                                        $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine_lp_transformations,INTERFACE_INCLUDE_DIRECTORIES>>)
+add_library(openvino::runtime::dev ALIAS ${TARGET_NAME}_dev)
+set_ie_threading_interface_for(${TARGET_NAME}_dev)
+set_target_properties(${TARGET_NAME}_dev PROPERTIES EXPORT_NAME runtime::dev)
+
+openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME}_dev)
+
+# Install static libraries for case BUILD_SHARED_LIBS=OFF
+ov_install_static_lib(${TARGET_NAME}_dev core)
+# --------------- OpenVINO runtime library dev ------------------------------
+
+
+# Install OpenVINO runtime
+
+list(APPEND PATH_VARS "IE_INCLUDE_DIR" "OV_CORE_DIR"
+                      "IE_PARALLEL_CMAKE")
+
+ie_cpack_add_component(core REQUIRED DEPENDS ${core_components})
+ie_cpack_add_component(core_dev REQUIRED DEPENDS core ${core_dev_components})
+
+if(BUILD_SHARED_LIBS)
+    install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
+            DESTINATION ${IE_CPACK_RUNTIME_PATH}
+            COMPONENT core)
+
+    # for InferenceEngineUnitTest
+    # For public tests
+    install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
+        DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+    # For private tests
+    if (NOT WIN32)
+        install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
+                DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
+    endif()
+endif()
+
+# Install cmake scripts
+
+install(EXPORT OpenVINOTargets
+        FILE OpenVINOTargets.cmake
+        NAMESPACE openvino::
+        DESTINATION runtime/cmake
+        COMPONENT core_dev)
+
+set(OV_CORE_DIR "${CMAKE_BINARY_DIR}/src/core")
+set(PUBLIC_HEADERS_DIR "${OpenVINO_SOURCE_DIR}/src/inference/include")
+set(IE_INCLUDE_DIR "${PUBLIC_HEADERS_DIR}/ie")
+set(IE_PARALLEL_CMAKE "${OpenVINO_SOURCE_DIR}/src/cmake/ie_parallel.cmake")
+
+configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig.cmake.in"
+                              "${CMAKE_BINARY_DIR}/InferenceEngineConfig.cmake"
+                               INSTALL_DESTINATION "${CMAKE_INSTALL_PREFIX}"
+                               PATH_VARS ${PATH_VARS})
+
+configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig.cmake.in"
+                              "${CMAKE_BINARY_DIR}/OpenVINOConfig.cmake"
+                              INSTALL_DESTINATION "${CMAKE_INSTALL_PREFIX}"
+                              PATH_VARS ${PATH_VARS})
+
+set(IE_INCLUDE_DIR "include/ie")
+set(OV_CORE_DIR ".")
+set(IE_TBB_DIR "${IE_TBB_DIR_INSTALL}")
+set(IE_TBBBIND_DIR "${IE_TBBBIND_DIR_INSTALL}")
+set(IE_PARALLEL_CMAKE "cmake/ie_parallel.cmake")
+
+configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig.cmake.in"
+                              "${CMAKE_BINARY_DIR}/share/InferenceEngineConfig.cmake"
+                              INSTALL_DESTINATION cmake
+                              PATH_VARS ${PATH_VARS})
+
+configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig.cmake.in"
+                              "${CMAKE_BINARY_DIR}/share/OpenVINOConfig.cmake"
+                              INSTALL_DESTINATION share
+                              PATH_VARS ${PATH_VARS})
+
+configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig-version.cmake.in"
+               "${CMAKE_BINARY_DIR}/InferenceEngineConfig-version.cmake" @ONLY)
+configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig-version.cmake.in"
+               "${CMAKE_BINARY_DIR}/OpenVINOConfig-version.cmake" @ONLY)
+
+install(FILES "${CMAKE_BINARY_DIR}/share/InferenceEngineConfig.cmake"
+              "${CMAKE_BINARY_DIR}/InferenceEngineConfig-version.cmake"
+              "${OpenVINO_SOURCE_DIR}/src/cmake/ie_parallel.cmake"
+        DESTINATION runtime/cmake
+        COMPONENT core_dev)
+
+install(FILES "${CMAKE_BINARY_DIR}/share/OpenVINOConfig.cmake"
+              "${CMAKE_BINARY_DIR}/OpenVINOConfig-version.cmake"
+        DESTINATION runtime/cmake
+        COMPONENT core_dev)
diff --git a/src/common/CMakeLists.txt b/src/common/CMakeLists.txt
index 0631fdfc1e1..281cb21b1b2 100644
--- a/src/common/CMakeLists.txt
+++ b/src/common/CMakeLists.txt
@@ -13,6 +13,4 @@ add_subdirectory(transformations)
 add_subdirectory(offline_transformations)
 add_subdirectory(low_precision_transformations)
 
-add_dependencies(ov_runtime_libraries inference_engine_transformations
-                                      inference_engine_lp_transformations
-                                      inference_engine_preproc)
+add_dependencies(ov_runtime_libraries inference_engine_preproc)
diff --git a/src/common/legacy/CMakeLists.txt b/src/common/legacy/CMakeLists.txt
index 7b73f99c3fc..05587686b60 100644
--- a/src/common/legacy/CMakeLists.txt
+++ b/src/common/legacy/CMakeLists.txt
@@ -36,7 +36,7 @@ ie_faster_build(${TARGET_NAME}_obj
 target_include_directories(${TARGET_NAME}_obj PRIVATE
     ${PUBLIC_HEADERS_DIR}
     ${CMAKE_CURRENT_SOURCE_DIR}/src
-    $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src # For CNNNetworkNGraphImpl
+    $<TARGET_PROPERTY:inference_engine_obj,SOURCE_DIR>/src # For CNNNetworkNGraphImpl
     $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>
     $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
     $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>
diff --git a/src/common/low_precision_transformations/CMakeLists.txt b/src/common/low_precision_transformations/CMakeLists.txt
index 88e632c6cc2..528f13ff79a 100644
--- a/src/common/low_precision_transformations/CMakeLists.txt
+++ b/src/common/low_precision_transformations/CMakeLists.txt
@@ -17,28 +17,37 @@ source_group("include" FILES ${PUBLIC_HEADERS})
 
 # Create library
 
-add_library(${TARGET_NAME}
+add_library(${TARGET_NAME}_obj OBJECT
             ${LIBRARY_SRC}
             ${PUBLIC_HEADERS})
 
-ie_faster_build(${TARGET_NAME} UNITY)
+target_compile_definitions(${TARGET_NAME}_obj PRIVATE IMPLEMENT_OPENVINO_API)
 
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "Inference Engine LP transformations library")
+ie_faster_build(${TARGET_NAME}_obj UNITY)
 
-target_link_libraries(${TARGET_NAME} PUBLIC inference_engine_transformations
-                                     PRIVATE openvino::itt)
+target_link_libraries(${TARGET_NAME}_obj PRIVATE openvino::itt)
 
-target_include_directories(${TARGET_NAME} PUBLIC
-    $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>)
+target_include_directories(${TARGET_NAME}_obj PRIVATE $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>>
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>>)
 
-add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
-
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
+add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME}_obj)
 
 # LTO
 
-set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
+set_target_properties(${TARGET_NAME}_obj PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
+
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME}_obj PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
+
+add_library(${TARGET_NAME} INTERFACE)
+target_link_libraries(${TARGET_NAME} INTERFACE openvino::runtime)
+
+target_include_directories(${TARGET_NAME} INTERFACE
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>>
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>>)
 
 # developer package
 
@@ -46,6 +55,7 @@ openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
 
 # install
 
+# TODO: remove this
 if(BUILD_SHARED_LIBS)
     install(TARGETS ${TARGET_NAME}
             RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
diff --git a/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp b/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp
index 8540cb2868f..b000b5fc56f 100644
--- a/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp
+++ b/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp
@@ -12,11 +12,11 @@
  */
 
 #ifdef OPENVINO_STATIC_LIBRARY
-#define LP_TRANSFORMATIONS_API
+#    define LP_TRANSFORMATIONS_API
 #else
-#ifdef inference_engine_lp_transformations_EXPORTS
-#define LP_TRANSFORMATIONS_API OPENVINO_CORE_EXPORTS
-#else
-#define LP_TRANSFORMATIONS_API OPENVINO_CORE_IMPORTS
-#endif // inference_engine_lp_transformations_EXPORTS
-#endif // OPENVINO_STATIC_LIBRARY
+#    ifdef IMPLEMENT_OPENVINO_API
+#        define LP_TRANSFORMATIONS_API OPENVINO_CORE_EXPORTS
+#    else
+#        define LP_TRANSFORMATIONS_API OPENVINO_CORE_IMPORTS
+#    endif  // inference_engine_lp_transformations_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/common/preprocessing/CMakeLists.txt b/src/common/preprocessing/CMakeLists.txt
index 29382ebcc1e..a15fa60e039 100644
--- a/src/common/preprocessing/CMakeLists.txt
+++ b/src/common/preprocessing/CMakeLists.txt
@@ -142,7 +142,7 @@ if(ENABLE_GAPI_PREPROCESSING)
         target_link_libraries(${TARGET_NAME} PRIVATE inference_engine)
     else()
         # for static linkage the dependencies are in opposite order
-        target_link_libraries(inference_engine PRIVATE ${TARGET_NAME})
+        target_link_libraries(ov_runtime PRIVATE ${TARGET_NAME})
     endif()
 
     # Workaround to avoid warnings caused with bug in the avx512intrin.h of GCC5
@@ -178,8 +178,7 @@ if(WIN32)
     set_target_properties(${TARGET_NAME}_s PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}_s)
 endif()
 
-target_link_libraries(${TARGET_NAME}_s PRIVATE fluid openvino::itt
-                                       INTERFACE inference_engine_s)
+target_link_libraries(${TARGET_NAME}_s PRIVATE fluid openvino::itt inference_engine)
 
 target_compile_definitions(${TARGET_NAME}_s INTERFACE USE_STATIC_IE)
 
diff --git a/src/common/transformations/CMakeLists.txt b/src/common/transformations/CMakeLists.txt
index c32144a9181..16e25e9981d 100644
--- a/src/common/transformations/CMakeLists.txt
+++ b/src/common/transformations/CMakeLists.txt
@@ -17,31 +17,38 @@ source_group("include" FILES ${PUBLIC_HEADERS})
 
 # Create library
 
-add_library(${TARGET_NAME} ${LIBRARY_SRC} ${PUBLIC_HEADERS})
+add_library(${TARGET_NAME}_obj OBJECT ${LIBRARY_SRC} ${PUBLIC_HEADERS})
+target_compile_definitions(${TARGET_NAME}_obj PRIVATE IMPLEMENT_OPENVINO_API)
 
-ie_faster_build(${TARGET_NAME}
+ie_faster_build(${TARGET_NAME}_obj
     UNITY
     PCH PRIVATE "src/precomp.hpp"
 )
 
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "Inference Engine Transformations library")
+target_link_libraries(${TARGET_NAME}_obj PRIVATE ngraph_reference openvino::itt ngraph::builder)
 
-target_link_libraries(${TARGET_NAME} PUBLIC ngraph
-                                     PRIVATE ngraph_reference openvino::itt ngraph::builder)
+target_include_directories(${TARGET_NAME}_obj PRIVATE $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
+                                              "${CMAKE_CURRENT_SOURCE_DIR}/src")
 
-target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
-                                          PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src")
+add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME}_obj)
 
-add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
-
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
-
-ie_mark_target_as_cc(${TARGET_NAME})
+ie_mark_target_as_cc(${TARGET_NAME}_obj)
 
 # LTO
 
-set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
+set_target_properties(${TARGET_NAME}_obj PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
+
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME}_obj PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
+
+add_library(${TARGET_NAME} INTERFACE)
+
+target_include_directories(${TARGET_NAME} INTERFACE
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>>)
+
+target_link_libraries(${TARGET_NAME} INTERFACE openvino::runtime)
 
 # developer package
 
@@ -49,6 +56,7 @@ openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
 
 # install
 
+# TODO remove this
 install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
         ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
diff --git a/src/common/transformations/include/transformations_visibility.hpp b/src/common/transformations/include/transformations_visibility.hpp
index 3a9511783c7..77dc8f85f84 100644
--- a/src/common/transformations/include/transformations_visibility.hpp
+++ b/src/common/transformations/include/transformations_visibility.hpp
@@ -33,7 +33,7 @@
 #ifdef OPENVINO_STATIC_LIBRARY
 #define TRANSFORMATIONS_API
 #else
-#ifdef inference_engine_transformations_EXPORTS
+#ifdef IMPLEMENT_OPENVINO_API
 #define TRANSFORMATIONS_API OPENVINO_CORE_EXPORTS
 #else
 #define TRANSFORMATIONS_API OPENVINO_CORE_IMPORTS
diff --git a/src/core/CMakeLists.txt b/src/core/CMakeLists.txt
index b2d4b621326..a72e9a25f1e 100644
--- a/src/core/CMakeLists.txt
+++ b/src/core/CMakeLists.txt
@@ -33,7 +33,7 @@ set(MIXED_SRC ${IE_SHARED_SRCS}
 
 set_property(SOURCE ${MIXED_SRC}
     APPEND PROPERTY INCLUDE_DIRECTORIES
-        $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src
+        $<TARGET_PROPERTY:inference_engine_obj,SOURCE_DIR>/src
         $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>)
 
@@ -47,31 +47,27 @@ source_group("src" FILES ${LIBRARY_SRC})
 source_group("include" FILES ${PUBLIC_HEADERS})
 
 # Create static or shared library depending on BUILD_SHARED_LIBS
-add_library(ngraph ${LIBRARY_SRC} ${PUBLIC_HEADERS} ${IE_SHARED_SRCS})
+add_library(ngraph_obj OBJECT ${LIBRARY_SRC} ${PUBLIC_HEADERS} ${IE_SHARED_SRCS})
 
+target_compile_definitions(ngraph_obj PRIVATE IMPLEMENT_OPENVINO_API)
 
-ie_faster_build(ngraph
+ie_faster_build(ngraph_obj
     UNITY
     PCH PRIVATE "src/precomp.hpp")
 
-ie_add_api_validator_post_build_step(TARGET ngraph)
-
-ie_add_vs_version_file(NAME ngraph
-                       FILEDESCRIPTION "nGraph library")
-
 addVersionDefines(src/version.cpp CI_BUILD_NUMBER)
 
-target_link_libraries(ngraph PRIVATE ngraph::builder ngraph::reference openvino::util pugixml::static ov_shape_inference)
+target_link_libraries(ngraph_obj PRIVATE ngraph::builder ngraph::reference openvino::util pugixml::static ov_shape_inference)
 
-ie_mark_target_as_cc(ngraph)
+ie_mark_target_as_cc(ngraph_obj)
 
-ov_ncc_naming_style(FOR_TARGET ngraph
+ov_ncc_naming_style(FOR_TARGET ngraph_obj
                     INCLUDE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include")
 
-add_clang_format_target(ngraph_clang FOR_TARGETS ngraph)
+add_clang_format_target(ngraph_clang FOR_TARGETS ngraph_obj)
 
 if(NOT BUILD_SHARED_LIBS)
-    target_compile_definitions(ngraph PUBLIC OPENVINO_STATIC_LIBRARY)
+    target_compile_definitions(ngraph_obj PUBLIC OPENVINO_STATIC_LIBRARY)
 endif()
 
 if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
@@ -89,7 +85,7 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
         set(link_type PUBLIC)
     endif()
 
-    target_link_options(ngraph ${link_type} "/IGNORE:4217,4286")
+    target_link_options(ngraph_obj ${link_type} "/IGNORE:4217,4286")
 endif()
 
 # some sources are located in ngraph, while headers are in inference_engine_transformations
@@ -106,14 +102,16 @@ set_source_files_properties("${CMAKE_CURRENT_SOURCE_DIR}/src/pass/convert_precis
         PROPERTIES INCLUDE_DIRECTORIES $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>)
 
 # Defines macro in C++ to load backend plugin
-target_include_directories(ngraph PUBLIC $<BUILD_INTERFACE:${OV_CORE_INCLUDE_PATH}>
-                                  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
+target_include_directories(ngraph_obj PUBLIC $<BUILD_INTERFACE:${OV_CORE_INCLUDE_PATH}>
+                                      PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
 
+add_library(ngraph INTERFACE)
+target_link_libraries(ngraph INTERFACE openvino::runtime)
 # Add an alias so that library can be used inside the build tree, e.g. when testing
 add_library(ngraph::ngraph ALIAS ngraph)
 add_library(openvino::core ALIAS ngraph)
 
-target_link_libraries(ngraph PRIVATE ${CMAKE_DL_LIBS})
+target_include_directories(ngraph INTERFACE $<BUILD_INTERFACE:${OV_CORE_INCLUDE_PATH}>)
 
 #-----------------------------------------------------------------------------------------------
 # Export for build tree
diff --git a/src/core/include/openvino/core/core_visibility.hpp b/src/core/include/openvino/core/core_visibility.hpp
index dc96deaf606..8291ef764a4 100644
--- a/src/core/include/openvino/core/core_visibility.hpp
+++ b/src/core/include/openvino/core/core_visibility.hpp
@@ -20,11 +20,11 @@
 #    define OPENVINO_API
 #    define OPENVINO_API_C(...) __VA_ARGS__
 #else
-#    ifdef ngraph_EXPORTS  // defined if we are building the NGRAPH DLL (instead of using it)
+#    ifdef IMPLEMENT_OPENVINO_API  // defined if we are building the NGRAPH DLL (instead of using it)
 #        define OPENVINO_API        OPENVINO_CORE_EXPORTS
 #        define OPENVINO_API_C(...) OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS __VA_ARGS__ OPENVINO_CDECL
 #    else
 #        define OPENVINO_API        OPENVINO_CORE_IMPORTS
 #        define OPENVINO_API_C(...) OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS __VA_ARGS__ OPENVINO_CDECL
-#    endif  // ngraph_EXPORTS
+#    endif  // IMPLEMENT_OPENVINO_API
 #endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt
index 63c977f86fe..d47c2b65e0e 100644
--- a/src/core/tests/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -559,7 +559,7 @@ add_dependencies(ov_core_unit_tests template_extension)
 target_include_directories(ov_core_unit_tests PRIVATE ".")
 target_include_directories(ov_core_unit_tests PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/runtime)
 
-get_target_property(OV_CORE_SRC_DIR openvino::core SOURCE_DIR)
+get_target_property(OV_CORE_SRC_DIR ngraph_obj SOURCE_DIR)
 target_include_directories(ov_core_unit_tests PRIVATE ${OV_CORE_SRC_DIR}/src)
 
 target_compile_definitions(ov_core_unit_tests
diff --git a/src/core/tests/frontend/frontend_manager.cpp b/src/core/tests/frontend/frontend_manager.cpp
index b4255de3085..bbfa0c8a3e0 100644
--- a/src/core/tests/frontend/frontend_manager.cpp
+++ b/src/core/tests/frontend/frontend_manager.cpp
@@ -35,7 +35,7 @@ using namespace ov::frontend;
 
 static std::string find_my_pathname() {
 #ifdef _WIN32
-    HMODULE hModule = GetModuleHandleW(SHARED_LIB_PREFIX L"frontend_common" SHARED_LIB_SUFFIX);
+    HMODULE hModule = GetModuleHandleW(SHARED_LIB_PREFIX L"ov_runtime" SHARED_LIB_SUFFIX);
     WCHAR wpath[MAX_PATH];
     GetModuleFileNameW(hModule, wpath, MAX_PATH);
     std::wstring ws(wpath);
diff --git a/src/frontends/CMakeLists.txt b/src/frontends/CMakeLists.txt
index 3671fb39192..b60c8156aff 100644
--- a/src/frontends/CMakeLists.txt
+++ b/src/frontends/CMakeLists.txt
@@ -23,6 +23,3 @@ endif()
 if(ENABLE_OV_TF_FRONTEND)
     add_subdirectory(tensorflow)
 endif()
-
-# used for static build
-ov_generate_frontends_hpp()
diff --git a/src/frontends/common/CMakeLists.txt b/src/frontends/common/CMakeLists.txt
index 9cdf6bcd710..412d5f53e95 100644
--- a/src/frontends/common/CMakeLists.txt
+++ b/src/frontends/common/CMakeLists.txt
@@ -24,19 +24,25 @@ source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
 
 # create library
 
-add_library(${TARGET_NAME} ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
-add_library(openvino::frontend::common ALIAS ${TARGET_NAME})
+add_library(${TARGET_NAME}_obj OBJECT ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
 
-target_include_directories(${TARGET_NAME}
+target_include_directories(${TARGET_NAME}_obj
     PUBLIC
         $<BUILD_INTERFACE:${FRONTEND_INCLUDE_DIR}>
-        $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>
     PRIVATE
+        ${CMAKE_CURRENT_SOURCE_DIR}/src
+        $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>
         # for ov_frontends.hpp in static build
-        "${CMAKE_CURRENT_BINARY_DIR}/src")
+        ${CMAKE_CURRENT_BINARY_DIR}/src)
+
+target_compile_definitions(${TARGET_NAME}_obj PRIVATE IMPLEMENT_OPENVINO_API)
+
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME}_obj PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
+
+target_link_libraries(${TARGET_NAME}_obj PRIVATE openvino::util)
 
-target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
-target_link_libraries(${TARGET_NAME} PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC openvino::core)
 set_property(SOURCE
             ${CMAKE_CURRENT_SOURCE_DIR}/src/plugin_loader.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/manager.cpp
@@ -44,29 +50,21 @@ set_property(SOURCE
         FRONTEND_LIB_PREFIX="${CMAKE_SHARED_LIBRARY_PREFIX}${FRONTEND_NAME_PREFIX}"
         FRONTEND_LIB_SUFFIX="${FRONTEND_NAME_SUFFIX}${IE_BUILD_POSTFIX}${CMAKE_SHARED_LIBRARY_SUFFIX}")
 
-add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
+add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}_obj)
 
-ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
-                    INCLUDE_DIRECTORY "${FRONTEND_INCLUDE_DIR}")
+ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}_obj
+                    INCLUDE_DIRECTORY "${FRONTEND_INCLUDE_DIR}"
+                    ADDITIONAL_INCLUDE_DIRECTORIES
+                        $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>)
 
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "Manager of OpenVINO Frontends")
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
+add_library(${TARGET_NAME} INTERFACE)
+target_link_libraries(${TARGET_NAME} INTERFACE openvino::runtime)
+target_include_directories(${TARGET_NAME} INTERFACE $<BUILD_INTERFACE:${FRONTEND_INCLUDE_DIR}>
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>>)
 
-# create static library if needed
-
-if(BUILD_SHARED_LIBS)
-    add_library(${TARGET_NAME}_static STATIC ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
-    add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME}_static)
-    target_link_libraries(${TARGET_NAME}_static PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC openvino::core)
-    target_include_directories(${TARGET_NAME}_static PUBLIC
-        $<BUILD_INTERFACE:${FRONTEND_INCLUDE_DIR}>)
-    target_include_directories(${TARGET_NAME}_static PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
-    target_compile_definitions(${TARGET_NAME}_static PUBLIC USE_STATIC_FRONTEND_COMMON)
-    ov_install_static_lib(${TARGET_NAME}_static core)
-else()
-    add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME})
-endif()
+add_library(ngraph::${TARGET_NAME} ALIAS ${TARGET_NAME})
+add_library(openvino::frontend::common ALIAS ${TARGET_NAME})
+add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME})
 
 openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
 
diff --git a/src/frontends/common/include/openvino/frontend/visibility.hpp b/src/frontends/common/include/openvino/frontend/visibility.hpp
index b578a596673..c6069acd00b 100644
--- a/src/frontends/common/include/openvino/frontend/visibility.hpp
+++ b/src/frontends/common/include/openvino/frontend/visibility.hpp
@@ -14,7 +14,7 @@
 #    define FRONTEND_C_API
 #else
 // Defined if cmake is building the frontend_common DLL (instead of using it)
-#    ifdef frontend_common_EXPORTS
+#    ifdef IMPLEMENT_OPENVINO_API
 #        define FRONTEND_API   OPENVINO_CORE_EXPORTS
 #        define FRONTEND_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    else
diff --git a/src/inference/CMakeLists.txt b/src/inference/CMakeLists.txt
index 47fd6812b5b..02a4e1a922d 100644
--- a/src/inference/CMakeLists.txt
+++ b/src/inference/CMakeLists.txt
@@ -117,6 +117,8 @@ add_clang_format_target(${TARGET_NAME}_plugin_api_clang FOR_SOURCES ${plugin_api
 # Create object library
 
 add_library(${TARGET_NAME}_obj OBJECT
+            ${IE_STATIC_DEPENDENT_FILES}
+            ${LEGACY_LIBRARY_SHARED_SRCS}
             ${LIBRARY_SRC}
             ${LIBRARY_HEADERS}
             ${PUBLIC_HEADERS})
@@ -151,48 +153,17 @@ endif()
 
 # Create library file from object library
 
-add_library(${TARGET_NAME}
-            ${IE_STATIC_DEPENDENT_FILES}
-            ${LEGACY_LIBRARY_SHARED_SRCS}
-            ${vs_version_file}
-            $<TARGET_OBJECTS:${TARGET_NAME}_obj>)
-
-add_library(openvino::runtime ALIAS ${TARGET_NAME})
+add_library(${TARGET_NAME} INTERFACE)
+target_link_libraries(${TARGET_NAME} INTERFACE openvino::runtime)
+target_include_directories(${TARGET_NAME} INTERFACE $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
+                                                    $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}/ie>)
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_SOURCES ${IE_STATIC_DEPENDENT_FILES} ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${PUBLIC_HEADERS})
 
-ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
+ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}_obj
                     INCLUDE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include/openvino"
                     ADDITIONAL_INCLUDE_DIRECTORIES $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>)
 
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "Inference Engine Core Runtime library")
-
-set_ie_threading_interface_for(${TARGET_NAME})
-if (TBBBIND_2_5_FOUND)
-    target_link_libraries(${TARGET_NAME} PRIVATE ${TBBBIND_2_5_IMPORTED_TARGETS})
-endif()
-
-target_link_libraries(${TARGET_NAME} PRIVATE pugixml::static openvino::itt openvino::util ${CMAKE_DL_LIBS} Threads::Threads
-                                             frontend_common::static inference_engine_transformations
-                                     PUBLIC ngraph)
-
-target_include_directories(${TARGET_NAME} INTERFACE
-            $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
-            $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}/ie>
-    PRIVATE $<TARGET_PROPERTY:${TARGET_NAME}_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>)
-
-if(WIN32)
-    set_target_properties(${TARGET_NAME} PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME})
-endif()
-
-target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_ENGINE_API)
-
-ie_register_plugins(MAIN_TARGET ${TARGET_NAME}
-                    POSSIBLE_PLUGINS ov_auto_plugin ov_hetero_plugin ov_intel_gpu_plugin ov_intel_gna_plugin MKLDNNPlugin myriadPlugin)
-
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
-
 # Static library used for unit tests which are always built
 
 file(GLOB_RECURSE legacy_src "${LEGACY_SRC_ROOT}/*.cpp"
@@ -231,7 +202,7 @@ set_target_properties(${TARGET_NAME}_s PROPERTIES
 
 # LTO
 
-set_target_properties(${TARGET_NAME} ${TARGET_NAME}_obj
+set_target_properties(${TARGET_NAME}_obj
                       PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
 # Export for build tree
@@ -243,18 +214,10 @@ export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
 
 openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME}_plugin_api)
 
-list(APPEND PATH_VARS "IE_INCLUDE_DIR" "OV_CORE_DIR"
-                      "IE_PARALLEL_CMAKE")
-
-# Install Inference Engine
-
-ie_cpack_add_component(core REQUIRED DEPENDS ${core_components})
-ie_cpack_add_component(core_dev REQUIRED DEPENDS core ${core_dev_components})
-
 install(DIRECTORY "${PUBLIC_HEADERS_DIR}/" DESTINATION runtime/include
         COMPONENT core_dev)
 
-set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME runtime)
+# set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME runtime)
 install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
         ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
@@ -263,75 +226,6 @@ install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
                              # TODO: remove later once samples are updated
                              runtime/include/ie)
 
-if(BUILD_SHARED_LIBS)
-    install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
-            DESTINATION ${IE_CPACK_RUNTIME_PATH}
-            COMPONENT core)
-
-    # for InferenceEngineUnitTest
-    if(WIN32)
-        install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
-                DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
-    else()
-        install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
-                DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
-    endif()
-endif()
-
 # Install static libraries for case BUILD_SHARED_LIBS=OFF
 
 ov_install_static_lib(${TARGET_NAME}_plugin_api core)
-
-# Install cmake scripts
-
-install(EXPORT OpenVINOTargets
-        FILE OpenVINOTargets.cmake
-        NAMESPACE openvino::
-        DESTINATION runtime/cmake
-        COMPONENT core_dev)
-
-set(OV_CORE_DIR "${CMAKE_BINARY_DIR}/src/core")
-set(IE_INCLUDE_DIR "${PUBLIC_HEADERS_DIR}/ie")
-set(IE_PARALLEL_CMAKE "${OpenVINO_SOURCE_DIR}/src/cmake/ie_parallel.cmake")
-
-configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig.cmake.in"
-                              "${CMAKE_BINARY_DIR}/InferenceEngineConfig.cmake"
-                               INSTALL_DESTINATION "${CMAKE_INSTALL_PREFIX}"
-                               PATH_VARS ${PATH_VARS})
-
-configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig.cmake.in"
-                              "${CMAKE_BINARY_DIR}/OpenVINOConfig.cmake"
-                              INSTALL_DESTINATION "${CMAKE_INSTALL_PREFIX}"
-                              PATH_VARS ${PATH_VARS})
-
-set(IE_INCLUDE_DIR "include/ie")
-set(OV_CORE_DIR ".")
-set(IE_TBB_DIR "${IE_TBB_DIR_INSTALL}")
-set(IE_TBBBIND_DIR "${IE_TBBBIND_DIR_INSTALL}")
-set(IE_PARALLEL_CMAKE "cmake/ie_parallel.cmake")
-
-configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig.cmake.in"
-                              "${CMAKE_BINARY_DIR}/share/InferenceEngineConfig.cmake"
-                              INSTALL_DESTINATION cmake
-                              PATH_VARS ${PATH_VARS})
-
-configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig.cmake.in"
-                              "${CMAKE_BINARY_DIR}/share/OpenVINOConfig.cmake"
-                              INSTALL_DESTINATION share
-                              PATH_VARS ${PATH_VARS})
-
-configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig-version.cmake.in"
-               "${CMAKE_BINARY_DIR}/InferenceEngineConfig-version.cmake" @ONLY)
-configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig-version.cmake.in"
-               "${CMAKE_BINARY_DIR}/OpenVINOConfig-version.cmake" @ONLY)
-
-install(FILES "${CMAKE_BINARY_DIR}/share/InferenceEngineConfig.cmake"
-              "${CMAKE_BINARY_DIR}/InferenceEngineConfig-version.cmake"
-              "${OpenVINO_SOURCE_DIR}/src/cmake/ie_parallel.cmake"
-        DESTINATION runtime/cmake
-        COMPONENT core_dev)
-
-install(FILES "${CMAKE_BINARY_DIR}/share/OpenVINOConfig.cmake"
-              "${CMAKE_BINARY_DIR}/OpenVINOConfig-version.cmake"
-        DESTINATION runtime/cmake
-        COMPONENT core_dev)
diff --git a/src/tests/functional/inference_engine/CMakeLists.txt b/src/tests/functional/inference_engine/CMakeLists.txt
index cf17b0b78cc..05ef097efaa 100644
--- a/src/tests/functional/inference_engine/CMakeLists.txt
+++ b/src/tests/functional/inference_engine/CMakeLists.txt
@@ -4,7 +4,7 @@
 
 set(TARGET_NAME ieFuncTests)
 
-set(INCLUDES $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src)
+set(INCLUDES $<TARGET_PROPERTY:inference_engine_obj,SOURCE_DIR>/src)
 
 set(LINK_LIBRARIES
     gmock
diff --git a/src/tests_deprecated/behavior/vpu/CMakeLists.txt b/src/tests_deprecated/behavior/vpu/CMakeLists.txt
index 8bdadeb99c8..77f553062a2 100644
--- a/src/tests_deprecated/behavior/vpu/CMakeLists.txt
+++ b/src/tests_deprecated/behavior/vpu/CMakeLists.txt
@@ -58,7 +58,7 @@ function(enable_vpu TARGET_NAME FLAG_NAME PLUGIN_NAME)
         target_include_directories(${TARGET_NAME} PRIVATE
                 $<TARGET_PROPERTY:vpu_graph_transformer,INTERFACE_INCLUDE_DIRECTORIES>
                 ${CMAKE_CURRENT_SOURCE_DIR}/myriad_tests
-                $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src
+                $<TARGET_PROPERTY:inference_engine_obj,SOURCE_DIR>/src
                 ${XLINK_INCLUDE}
                 ${XLINK_PLATFORM_INCLUDE})
         target_link_libraries(${TARGET_NAME} PRIVATE mvnc)
diff --git a/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt b/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
index e69de57f879..6b9d7543a06 100644
--- a/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
+++ b/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
@@ -49,7 +49,7 @@ if(BUILD_SHARED_LIBS)
     target_link_libraries(${TARGET_NAME} PRIVATE inference_engine)
 else()
     # for static linkage the dependencies are in opposite order
-    target_link_libraries(inference_engine PRIVATE ${TARGET_NAME})
+    target_link_libraries(ov_runtime PRIVATE ${TARGET_NAME})
 endif()
 
 # code style
@@ -63,10 +63,11 @@ install(TARGETS ${TARGET_NAME}
         LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
 
 # for InferenceEngineUnitTest
-if(WIN32)
-    install(TARGETS ${TARGET_NAME}
-            LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
-else()
+# For public CI
+install(TARGETS ${TARGET_NAME}
+    LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+if(NOT WIN32)
+    # For private CI
     install(TARGETS ${TARGET_NAME}
             LIBRARY DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
 endif()

From 114d37b28c98f6259da1add6fb3e476cf35ddde1 Mon Sep 17 00:00:00 2001
From: Anton Pankratov <anton.pankratov@intel.com>
Date: Mon, 20 Dec 2021 12:11:32 +0300
Subject: [PATCH 040/160] Fixed release oreder (#9218)

---
 .../behavior/ov_plugin/life_time.cpp          |  19 +++
 src/core/include/openvino/core/any.hpp        |  40 ++++-
 .../include/openvino/runtime/allocator.hpp    |  29 +++-
 src/core/include/openvino/runtime/tensor.hpp  |  37 ++++-
 src/core/src/any.cpp                          |   7 +-
 src/core/src/runtime/allocator.cpp            |  10 +-
 src/core/src/runtime/ov_tensor.cpp            |   6 +-
 src/core/src/so_extension.hpp                 |  10 +-
 src/inference/dev_api/so_ptr.hpp              |  24 ++-
 .../include/ie/cpp/ie_executable_network.hpp  |  33 ++++-
 .../include/ie/cpp/ie_infer_request.hpp       |  33 ++++-
 .../include/ie/cpp/ie_memory_state.hpp        |  31 +++-
 .../openvino/runtime/compiled_model.hpp       |  13 +-
 .../openvino/runtime/infer_request.hpp        |  35 ++++-
 .../openvino/runtime/remote_context.hpp       |  37 ++++-
 .../openvino/runtime/variable_state.hpp       |  11 +-
 .../src/cpp/ie_executable_network.cpp         |  37 +++--
 src/inference/src/cpp/ie_infer_request.cpp    |  32 ++--
 src/inference/src/cpp/ie_plugin.hpp           |  58 ++++----
 src/inference/src/cpp/ie_remote_context.cpp   |  16 +-
 src/inference/src/cpp/ie_variable_state.cpp   |  22 ++-
 src/inference/src/ie_core.cpp                 |  38 ++---
 src/inference/src/ie_network_reader.cpp       |   2 +-
 src/inference/src/remote_tensor.cpp           |   2 +-
 src/plugins/auto/executable_network.cpp       |   2 +-
 src/plugins/hetero/infer_request.cpp          |   2 +-
 .../include/behavior/ov_plugin/life_time.hpp  |  42 ++++++
 .../src/behavior/ov_plugin/life_time.cpp      | 137 ++++++++++++++++++
 .../auto/auto_select_device_failed_test.cpp   |   2 +-
 .../unit/auto/exec_network_get_metrics.cpp    |   4 +-
 .../ie_infer_async_request_base_test.cpp      |   4 +-
 .../ie_memory_state_internal_test.cpp         |   2 +-
 .../ie_executable_network_test.cpp            |   2 +-
 33 files changed, 615 insertions(+), 164 deletions(-)
 create mode 100644 docs/template_plugin/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp
 create mode 100644 src/tests/functional/plugin/shared/include/behavior/ov_plugin/life_time.hpp
 create mode 100644 src/tests/functional/plugin/shared/src/behavior/ov_plugin/life_time.cpp

diff --git a/docs/template_plugin/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp
new file mode 100644
index 00000000000..58bcdda3278
--- /dev/null
+++ b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp
@@ -0,0 +1,19 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "behavior/ov_plugin/life_time.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVHoldersTest,
+        ::testing::Values(CommonTestUtils::DEVICE_TEMPLATE),
+        OVHoldersTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVHoldersTestOnImportedNetwork,
+        ::testing::Values(CommonTestUtils::DEVICE_TEMPLATE, "HETERO:TEMPLATE"),
+        OVHoldersTestOnImportedNetwork::getTestCaseName);
+
+}  // namespace
diff --git a/src/core/include/openvino/core/any.hpp b/src/core/include/openvino/core/any.hpp
index ce26f8d5e87..8fd41110023 100644
--- a/src/core/include/openvino/core/any.hpp
+++ b/src/core/include/openvino/core/any.hpp
@@ -85,6 +85,17 @@ class OPENVINO_API Any {
         constexpr static const bool value = sizeof(test<std::map<T...>>(nullptr)) == sizeof(char);
     };
 
+    template <typename... T>
+    struct EqualityComparable<std::vector<T...>> {
+        static void* conv(bool);
+        template <typename U>
+        static char test(decltype(conv(std::declval<typename U::value_type>() ==
+                                       std::declval<typename U::value_type>())));
+        template <typename U>
+        static long test(...);
+        constexpr static const bool value = sizeof(test<std::vector<T...>>(nullptr)) == sizeof(char);
+    };
+
     template <typename T>
     struct HasBaseMemberType {
         template <class U>
@@ -333,7 +344,7 @@ class OPENVINO_API Any {
     friend class ::ov::runtime::RemoteTensor;
     friend class ::ov::runtime::InferencePlugin;
 
-    Any(const std::shared_ptr<void>& so, const Any& other);
+    Any(const Any& other, const std::shared_ptr<void>& so);
 
     void impl_check() const;
 
@@ -342,11 +353,32 @@ class OPENVINO_API Any {
     Base::Ptr _impl;
 
 public:
-    /**
-     * @brief Default constructor
-     */
+    /// @brief Default constructor
     Any() = default;
 
+    /// @brief Default copy constructor
+    /// @param other other Any object
+    Any(const Any& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other Any object
+    /// @return reference to the current object
+    Any& operator=(const Any& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other Any object
+    Any(Any&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other Any object
+    /// @return reference to the current object
+    Any& operator=(Any&& other) = default;
+
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~Any();
+
     /**
      * @brief Constructor creates any with object
      *
diff --git a/src/core/include/openvino/runtime/allocator.hpp b/src/core/include/openvino/runtime/allocator.hpp
index 91ab1d901ee..0804acba5cd 100644
--- a/src/core/include/openvino/runtime/allocator.hpp
+++ b/src/core/include/openvino/runtime/allocator.hpp
@@ -64,25 +64,46 @@ class Tensor;
  *        And constructs default based on `new` `delete` c++ calls allocator if created without parameters
  */
 class OPENVINO_API Allocator {
-    std::shared_ptr<void> _so;
     AllocatorImpl::Ptr _impl;
+    std::shared_ptr<void> _so;
 
     /**
      * @brief Constructs Tensor from the initialized std::shared_ptr
+     * @param impl Initialized shared pointer
      * @param so Plugin to use. This is required to ensure that Allocator can work properly even if plugin object is
      * destroyed.
-     * @param impl Initialized shared pointer
      */
-    Allocator(const std::shared_ptr<void>& so, const AllocatorImpl::Ptr& impl);
+    Allocator(const AllocatorImpl::Ptr& impl, const std::shared_ptr<void>& so);
 
     friend class ov::runtime::Tensor;
 
 public:
     /**
-     * @brief Creates the default implementation of the OpenVINO allocator.
+     * @brief Destructor presereves unload order of implementation object and reference to library
      */
+    ~Allocator();
+
+    /// @brief Default constructor
     Allocator();
 
+    /// @brief Default copy constructor
+    /// @param other other Allocator object
+    Allocator(const Allocator& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other Allocator object
+    /// @return reference to the current object
+    Allocator& operator=(const Allocator& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other Allocator object
+    Allocator(Allocator&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other Allocator object
+    /// @return reference to the current object
+    Allocator& operator=(Allocator&& other) = default;
+
     /**
      * @brief Constructs Allocator from the initialized std::shared_ptr
      * @param impl Initialized shared pointer
diff --git a/src/core/include/openvino/runtime/tensor.hpp b/src/core/include/openvino/runtime/tensor.hpp
index 3de0e3c47d2..8dd2eb44a3b 100644
--- a/src/core/include/openvino/runtime/tensor.hpp
+++ b/src/core/include/openvino/runtime/tensor.hpp
@@ -36,16 +36,16 @@ class VariableState;
  */
 class OPENVINO_API Tensor {
 protected:
-    std::shared_ptr<void> _so;                     //!< Reference to dynamicly loaded library
     std::shared_ptr<InferenceEngine::Blob> _impl;  //!< Shared pointer to internal tensor representation
+    std::shared_ptr<void> _so;                     //!< Reference to dynamically loaded library
 
     /**
      * @brief Constructs Tensor from the initialized std::shared_ptr
+     * @param impl Initialized shared pointer
      * @param so Plugin to use. This is required to ensure that Tensor can work properly even if plugin object is
      * destroyed.
-     * @param impl Initialized shared pointer
      */
-    Tensor(const std::shared_ptr<void>& so, const std::shared_ptr<InferenceEngine::Blob>& impl);
+    Tensor(const std::shared_ptr<InferenceEngine::Blob>& impl, const std::shared_ptr<void>& so);
 
     friend class ov::runtime::Core;
     friend class ov::runtime::InferRequest;
@@ -53,6 +53,32 @@ protected:
     friend class ov::runtime::VariableState;
 
 public:
+    /// @brief Default constructor
+    Tensor() = default;
+
+    /// @brief Default copy constructor
+    /// @param other other Tensor object
+    Tensor(const Tensor& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other Tensor object
+    /// @return reference to the current object
+    Tensor& operator=(const Tensor& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other Tensor object
+    Tensor(Tensor&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other Tensor object
+    /// @return reference to the current object
+    Tensor& operator=(Tensor&& other) = default;
+
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~Tensor();
+
     /**
      * @brief Checks openvino tensor type
      * @param tensor a tensor which type will be checked
@@ -60,11 +86,6 @@ public:
      */
     static void type_check(const Tensor& tensor);
 
-    /**
-     * @brief Default constructor
-     */
-    Tensor() = default;
-
     /**
      * @brief Constructs Tensor using element type and shape. Allocate internal host storage using default allocator
      * @param type Tensor element type
diff --git a/src/core/src/any.cpp b/src/core/src/any.cpp
index 93036af1ba7..a3848466901 100644
--- a/src/core/src/any.cpp
+++ b/src/core/src/any.cpp
@@ -40,7 +40,12 @@ bool Any::Base::visit_attributes(AttributeVisitor& visitor) const {
     return const_cast<Any::Base*>(this)->visit_attributes(visitor);
 }
 
-Any::Any(const std::shared_ptr<void>& so, const Any& other) : _so{so}, _impl{other._impl} {}
+Any::~Any() {
+    _runtime_attribute_impl = {};
+    _impl = {};
+}
+
+Any::Any(const Any& other, const std::shared_ptr<void>& so) : _impl{other._impl}, _so{so} {}
 
 Any::Any(const char* str) : Any(std::string{str}) {}
 
diff --git a/src/core/src/runtime/allocator.cpp b/src/core/src/runtime/allocator.cpp
index 67c28917fe1..6a42b041f67 100644
--- a/src/core/src/runtime/allocator.cpp
+++ b/src/core/src/runtime/allocator.cpp
@@ -14,9 +14,13 @@ namespace runtime {
 
 Allocator::Allocator() : _impl{std::make_shared<BlobAllocator>()} {}
 
-Allocator::Allocator(const std::shared_ptr<void>& so, const std::shared_ptr<AllocatorImpl>& impl)
-    : _so{so},
-      _impl{impl} {
+Allocator::~Allocator() {
+    _impl = {};
+}
+
+Allocator::Allocator(const std::shared_ptr<AllocatorImpl>& impl, const std::shared_ptr<void>& so)
+    : _impl{impl},
+      _so{so} {
     OPENVINO_ASSERT(_impl != nullptr, "Allocator was not initialized.");
 }
 
diff --git a/src/core/src/runtime/ov_tensor.cpp b/src/core/src/runtime/ov_tensor.cpp
index 85333cb2eca..9321c6278b4 100644
--- a/src/core/src/runtime/ov_tensor.cpp
+++ b/src/core/src/runtime/ov_tensor.cpp
@@ -25,7 +25,11 @@ namespace runtime {
 
 void Tensor::type_check(const Tensor&) {}
 
-Tensor::Tensor(const std::shared_ptr<void>& so, const std::shared_ptr<ie::Blob>& impl) : _so{so}, _impl{impl} {
+Tensor::~Tensor() {
+    _impl = {};
+}
+
+Tensor::Tensor(const std::shared_ptr<ie::Blob>& impl, const std::shared_ptr<void>& so) : _impl{impl}, _so{so} {
     OPENVINO_ASSERT(_impl != nullptr, "Tensor was not initialized.");
 }
 
diff --git a/src/core/src/so_extension.hpp b/src/core/src/so_extension.hpp
index 7a7a892b229..cdac13bdfc1 100644
--- a/src/core/src/so_extension.hpp
+++ b/src/core/src/so_extension.hpp
@@ -13,15 +13,19 @@ namespace detail {
 
 class OPENVINO_API SOExtension : public Extension {
 public:
-    SOExtension(const std::shared_ptr<void>& so, const Extension::Ptr& ext) : m_so(so), m_ext(ext) {}
+    ~SOExtension() {
+        m_ext = {};
+    }
+
+    SOExtension(const Extension::Ptr& ext, const std::shared_ptr<void>& so) : m_ext(ext), m_so(so) {}
 
     const Extension::Ptr& extension() const;
 
     const std::shared_ptr<void> shared_object() const;
 
 private:
-    std::shared_ptr<void> m_so;
     Extension::Ptr m_ext;
+    std::shared_ptr<void> m_so;
 };
 
 inline std::vector<Extension::Ptr> load_extensions(const std::string& path) {
@@ -34,7 +38,7 @@ inline std::vector<Extension::Ptr> load_extensions(const std::string& path) {
     so_extensions.reserve(extensions.size());
 
     for (auto&& ex : extensions) {
-        so_extensions.emplace_back(std::make_shared<SOExtension>(so, ex));
+        so_extensions.emplace_back(std::make_shared<SOExtension>(ex, so));
     }
     return so_extensions;
 }
diff --git a/src/inference/dev_api/so_ptr.hpp b/src/inference/dev_api/so_ptr.hpp
index 0e23b68a891..e0edc46b797 100644
--- a/src/inference/dev_api/so_ptr.hpp
+++ b/src/inference/dev_api/so_ptr.hpp
@@ -31,10 +31,18 @@ struct SoPtr {
     SoPtr() = default;
 
     /**
-     * @brief Constructs an object with existing shared object reference and loaded pointer
-     * @param soLoader Existing pointer to a library loader
+     * @brief Destructor presereves unload order of implementation object and reference to library
      */
-    SoPtr(const std::shared_ptr<void>& so, const std::shared_ptr<T>& ptr) : _so{so}, _ptr{ptr} {}
+    ~SoPtr() {
+        _ptr = {};
+    }
+
+    /**
+     * @brief Constructs an object with existing shared object reference and loaded pointer
+     * @param ptr pointer to the loaded object
+     * @param so Existing reference to library
+     */
+    SoPtr(const std::shared_ptr<T>& ptr, const std::shared_ptr<void>& so) : _ptr{ptr}, _so{so} {}
 
     /**
      * @brief The copy-like constructor, can create So Pointer that dereferenced into child type if T is derived of U
@@ -71,15 +79,15 @@ struct SoPtr {
         return static_cast<bool>(ptr);
     }
 
-    /**
-     * @brief The shared object or dinamic loaded library
-     */
-    std::shared_ptr<void> _so;
-
     /**
      * @brief Gets a smart pointer to the custom object
      */
     std::shared_ptr<T> _ptr;
+
+    /**
+     * @brief The shared object or dinamic loaded library
+     */
+    std::shared_ptr<void> _so;
 };
 }  // namespace runtime
 }  // namespace ov
diff --git a/src/inference/include/ie/cpp/ie_executable_network.hpp b/src/inference/include/ie/cpp/ie_executable_network.hpp
index 614d1eced26..845f0eba2d0 100644
--- a/src/inference/include/ie/cpp/ie_executable_network.hpp
+++ b/src/inference/include/ie/cpp/ie_executable_network.hpp
@@ -35,25 +35,46 @@ class IExecutableNetworkInternal;
  * @brief This is an interface of an executable network
  */
 class INFERENCE_ENGINE_API_CLASS(ExecutableNetwork) {
-    std::shared_ptr<void> _so;
     std::shared_ptr<IExecutableNetworkInternal> _impl;
+    std::shared_ptr<void> _so;
 
     /**
      * @brief Constructs ExecutableNetwork from the initialized std::shared_ptr
+     * @param impl Initialized shared pointer
      * @param so Plugin to use. This is required to ensure that ExecutableNetwork can work properly even if plugin
      * object is destroyed.
-     * @param impl Initialized shared pointer
      */
-    ExecutableNetwork(const std::shared_ptr<void>& so, const std::shared_ptr<IExecutableNetworkInternal>& impl);
+    ExecutableNetwork(const std::shared_ptr<IExecutableNetworkInternal>& impl, const std::shared_ptr<void>& so);
     friend class Core;
     friend class ov::runtime::Core;
 
 public:
-    /**
-     * @brief A default constructor.
-     */
+    /// @brief Default constructor
     ExecutableNetwork() = default;
 
+    /// @brief Default copy constructor
+    /// @param other other ExecutableNetwork object
+    ExecutableNetwork(const ExecutableNetwork& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other ExecutableNetwork object
+    /// @return reference to the current object
+    ExecutableNetwork& operator=(const ExecutableNetwork& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other ExecutableNetwork object
+    ExecutableNetwork(ExecutableNetwork&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other ExecutableNetwork object
+    /// @return reference to the current object
+    ExecutableNetwork& operator=(ExecutableNetwork&& other) = default;
+
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~ExecutableNetwork();
+
     /**
      * @brief Gets the Executable network output Data node information.
      *
diff --git a/src/inference/include/ie/cpp/ie_infer_request.hpp b/src/inference/include/ie/cpp/ie_infer_request.hpp
index 658aaf4b493..d6e34d2c61b 100644
--- a/src/inference/include/ie/cpp/ie_infer_request.hpp
+++ b/src/inference/include/ie/cpp/ie_infer_request.hpp
@@ -32,16 +32,16 @@ class ICompletionCallbackWrapper;
  * It can throw exceptions safely for the application, where it is properly handled.
  */
 class INFERENCE_ENGINE_API_CLASS(InferRequest) {
-    std::shared_ptr<void> _so;
     std::shared_ptr<IInferRequestInternal> _impl;
+    std::shared_ptr<void> _so;
 
     /**
      * @brief Constructs InferRequest from the initialized std::shared_ptr
+     * @param impl Initialized shared pointer
      * @param so Plugin to use. This is required to ensure that InferRequest can work properly even if plugin object is
      * destroyed.
-     * @param impl Initialized shared pointer
      */
-    InferRequest(const std::shared_ptr<void>& so, const std::shared_ptr<IInferRequestInternal>& impl);
+    InferRequest(const std::shared_ptr<IInferRequestInternal>& impl, const std::shared_ptr<void>& so);
     friend class ExecutableNetwork;
 
 public:
@@ -61,11 +61,32 @@ public:
      */
     using Ptr = std::shared_ptr<InferRequest>;
 
-    /**
-     * @brief Default constructor
-     */
+    /// @brief Default constructor
     InferRequest() = default;
 
+    /// @brief Default copy constructor
+    /// @param other other InferRequest object
+    InferRequest(const InferRequest& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other InferRequest object
+    /// @return reference to the current object
+    InferRequest& operator=(const InferRequest& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other InferRequest object
+    InferRequest(InferRequest&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other InferRequest object
+    /// @return reference to the current object
+    InferRequest& operator=(InferRequest&& other) = default;
+
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~InferRequest();
+
     /**
      * @brief Sets input/output data to infer
      *
diff --git a/src/inference/include/ie/cpp/ie_memory_state.hpp b/src/inference/include/ie/cpp/ie_memory_state.hpp
index 7ef8437b95d..aff8d5733c5 100644
--- a/src/inference/include/ie/cpp/ie_memory_state.hpp
+++ b/src/inference/include/ie/cpp/ie_memory_state.hpp
@@ -24,8 +24,8 @@ class IVariableStateInternal;
  * @brief VariableState class
  */
 class INFERENCE_ENGINE_API_CLASS(VariableState) {
-    std::shared_ptr<void> _so;
     std::shared_ptr<IVariableStateInternal> _impl;
+    std::shared_ptr<void> _so;
 
     /**
      * @brief Constructs VariableState from the initialized std::shared_ptr
@@ -33,16 +33,37 @@ class INFERENCE_ENGINE_API_CLASS(VariableState) {
      * @param so Optional: Plugin to use. This is required to ensure that VariableState can work properly even if plugin
      * object is destroyed.
      */
-    VariableState(const std::shared_ptr<void>& so, const std::shared_ptr<IVariableStateInternal>& impl);
+    VariableState(const std::shared_ptr<IVariableStateInternal>& impl, const std::shared_ptr<void>& so);
     friend class InferRequest;
     friend class ExecutableNetwork;
 
 public:
-    /**
-     * @brief Default constructor
-     */
+    /// @brief Default constructor
     VariableState() = default;
 
+    /// @brief Default copy constructor
+    /// @param other other VariableState object
+    VariableState(const VariableState& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other VariableState object
+    /// @return reference to the current object
+    VariableState& operator=(const VariableState& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other VariableState object
+    VariableState(VariableState&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other VariableState object
+    /// @return reference to the current object
+    VariableState& operator=(VariableState&& other) = default;
+
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~VariableState();
+
     /**
      * @brief Reset internal variable state for relevant infer request,
      * to a value specified as default for according ReadValue node
diff --git a/src/inference/include/openvino/runtime/compiled_model.hpp b/src/inference/include/openvino/runtime/compiled_model.hpp
index 1f1f8febe14..b0921fa9edd 100644
--- a/src/inference/include/openvino/runtime/compiled_model.hpp
+++ b/src/inference/include/openvino/runtime/compiled_model.hpp
@@ -34,17 +34,17 @@ class InferRequest;
  * @brief This is an interface of an executable network
  */
 class OPENVINO_RUNTIME_API CompiledModel {
-    std::shared_ptr<void> _so;
     std::shared_ptr<InferenceEngine::IExecutableNetworkInternal> _impl;
+    std::shared_ptr<void> _so;
 
     /**
      * @brief Constructs CompiledModel from the initialized std::shared_ptr
+     * @param impl Initialized shared pointer
      * @param so Plugin to use. This is required to ensure that CompiledModel can work properly even if plugin
      * object is destroyed.
-     * @param impl Initialized shared pointer
      */
-    CompiledModel(const std::shared_ptr<void>& so,
-                  const std::shared_ptr<InferenceEngine::IExecutableNetworkInternal>& impl);
+    CompiledModel(const std::shared_ptr<InferenceEngine::IExecutableNetworkInternal>& impl,
+                  const std::shared_ptr<void>& so);
     friend class ov::runtime::Core;
     friend class ov::runtime::InferRequest;
 
@@ -54,6 +54,11 @@ public:
      */
     CompiledModel() = default;
 
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~CompiledModel();
+
     /**
      * @brief Get executable graph information from a device
      *
diff --git a/src/inference/include/openvino/runtime/infer_request.hpp b/src/inference/include/openvino/runtime/infer_request.hpp
index bef4de7037d..702a6dead8e 100644
--- a/src/inference/include/openvino/runtime/infer_request.hpp
+++ b/src/inference/include/openvino/runtime/infer_request.hpp
@@ -34,24 +34,47 @@ class CompiledModel;
  * It can throw exceptions safely for the application, where it is properly handled.
  */
 class OPENVINO_RUNTIME_API InferRequest {
-    std::shared_ptr<void> _so;
     std::shared_ptr<InferenceEngine::IInferRequestInternal> _impl;
+    std::shared_ptr<void> _so;
 
     /**
      * @brief Constructs InferRequest from the initialized std::shared_ptr
+     * @param impl Initialized shared pointer
      * @param so Plugin to use. This is required to ensure that InferRequest can work properly even if plugin object is
      * destroyed.
-     * @param impl Initialized shared pointer
      */
-    InferRequest(const std::shared_ptr<void>& so, const std::shared_ptr<InferenceEngine::IInferRequestInternal>& impl);
+    InferRequest(const std::shared_ptr<InferenceEngine::IInferRequestInternal>& impl, const std::shared_ptr<void>& so);
     friend class ov::runtime::CompiledModel;
 
 public:
-    /**
-     * @brief Default constructor
-     */
+    /// @brief Default constructor
     InferRequest() = default;
 
+    /// @brief Default copy constructor
+    /// @param other other InferRequest object
+    InferRequest(const InferRequest& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other InferRequest object
+    /// @return reference to the current object
+    InferRequest& operator=(const InferRequest& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other InferRequest object
+    InferRequest(InferRequest&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other InferRequest object
+    /// @return reference to the current object
+    InferRequest& operator=(InferRequest&& other) = default;
+
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     * @note To preserve destruction order inside default generated assignment operator we store `_impl` before `_so`.
+     *       And use destructor to remove implementation object before reference to library explicitly
+     */
+    ~InferRequest();
+
     /**
      * @brief Sets input/output data to infer
      *
diff --git a/src/inference/include/openvino/runtime/remote_context.hpp b/src/inference/include/openvino/runtime/remote_context.hpp
index 4a1353bfd30..9b2a0e9c9d8 100644
--- a/src/inference/include/openvino/runtime/remote_context.hpp
+++ b/src/inference/include/openvino/runtime/remote_context.hpp
@@ -37,20 +37,46 @@ class CompiledModel;
  */
 class OPENVINO_RUNTIME_API RemoteContext {
 protected:
-    std::shared_ptr<void> _so;                              //!< Reference to shared object that loaded implementation
     std::shared_ptr<InferenceEngine::RemoteContext> _impl;  //!< Pointer to remote context implementation
+    std::shared_ptr<void> _so;                              //!< Reference to shared object that loaded implementation
 
     /**
      * @brief Constructs RemoteContext from the initialized std::shared_ptr
+     * @param impl Initialized shared pointer
      * @param so Plugin to use. This is required to ensure that RemoteContext can work properly even if plugin
      * object is destroyed.
-     * @param impl Initialized shared pointer
      */
-    RemoteContext(const std::shared_ptr<void>& so, const std::shared_ptr<InferenceEngine::RemoteContext>& impl);
+    RemoteContext(const std::shared_ptr<InferenceEngine::RemoteContext>& impl, const std::shared_ptr<void>& so);
     friend class ov::runtime::Core;
     friend class ov::runtime::CompiledModel;
 
 public:
+    /// @brief Default constructor
+    RemoteContext() = default;
+
+    /// @brief Default copy constructor
+    /// @param other other RemoteContext object
+    RemoteContext(const RemoteContext& other) = default;
+
+    /// @brief Default copy assignment operator
+    /// @param other other RemoteContext object
+    /// @return reference to the current object
+    RemoteContext& operator=(const RemoteContext& other) = default;
+
+    /// @brief Default move constructor
+    /// @param other other RemoteContext object
+    RemoteContext(RemoteContext&& other) = default;
+
+    /// @brief Default move assignment operator
+    /// @param other other RemoteContext object
+    /// @return reference to current object
+    RemoteContext& operator=(RemoteContext&& other) = default;
+
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~RemoteContext();
+
     /**
      * @brief Checks openvino remote type
      * @param remote_context a remote context which type will be checked
@@ -60,11 +86,6 @@ public:
     static void type_check(const RemoteContext& remote_context,
                            const std::map<std::string, std::vector<std::string>>& type_info = {});
 
-    /**
-     * @brief Default constructor
-     */
-    RemoteContext() = default;
-
     /**
      * @brief Checks if the RemoteContext object can be cast to the type T
      *
diff --git a/src/inference/include/openvino/runtime/variable_state.hpp b/src/inference/include/openvino/runtime/variable_state.hpp
index 4cbe637cdb4..4b5efb6bccb 100644
--- a/src/inference/include/openvino/runtime/variable_state.hpp
+++ b/src/inference/include/openvino/runtime/variable_state.hpp
@@ -29,8 +29,8 @@ class InferRequest;
  * @brief VariableState class
  */
 class OPENVINO_RUNTIME_API VariableState {
-    std::shared_ptr<void> _so;
     std::shared_ptr<InferenceEngine::IVariableStateInternal> _impl;
+    std::shared_ptr<void> _so;
 
     /**
      * @brief Constructs VariableState from the initialized std::shared_ptr
@@ -38,8 +38,8 @@ class OPENVINO_RUNTIME_API VariableState {
      * @param so Optional: Plugin to use. This is required to ensure that VariableState can work properly even if plugin
      * object is destroyed.
      */
-    VariableState(const std::shared_ptr<void>& so,
-                  const std::shared_ptr<InferenceEngine::IVariableStateInternal>& impl);
+    VariableState(const std::shared_ptr<InferenceEngine::IVariableStateInternal>& impl,
+                  const std::shared_ptr<void>& so);
 
     friend class ov::runtime::InferRequest;
 
@@ -49,6 +49,11 @@ public:
      */
     VariableState() = default;
 
+    /**
+     * @brief Destructor presereves unload order of implementation object and reference to library
+     */
+    ~VariableState();
+
     /**
      * @brief Reset internal variable state for relevant infer request,
      * to a value specified as default for according ReadValue node
diff --git a/src/inference/src/cpp/ie_executable_network.cpp b/src/inference/src/cpp/ie_executable_network.cpp
index 4feb09c8123..be009101ab6 100644
--- a/src/inference/src/cpp/ie_executable_network.cpp
+++ b/src/inference/src/cpp/ie_executable_network.cpp
@@ -33,9 +33,13 @@ namespace InferenceEngine {
         OPENVINO_ASSERT(false, "Unexpected exception");                          \
     }
 
-ExecutableNetwork::ExecutableNetwork(const std::shared_ptr<void>& so, const IExecutableNetworkInternal::Ptr& impl)
-    : _so(so),
-      _impl(impl) {
+ExecutableNetwork::~ExecutableNetwork() {
+    _impl = {};
+}
+
+ExecutableNetwork::ExecutableNetwork(const IExecutableNetworkInternal::Ptr& impl, const std::shared_ptr<void>& so)
+    : _impl(impl),
+      _so(so) {
     IE_ASSERT(_impl != nullptr);
 }
 
@@ -66,7 +70,7 @@ ExecutableNetwork::operator IExecutableNetwork::Ptr() {
 }
 
 InferRequest ExecutableNetwork::CreateInferRequest() {
-    EXEC_NET_CALL_STATEMENT(return {_so, _impl->CreateInferRequest()});
+    EXEC_NET_CALL_STATEMENT(return {_impl->CreateInferRequest(), _so});
 }
 
 InferRequest::Ptr ExecutableNetwork::CreateInferRequestPtr() {
@@ -90,11 +94,11 @@ void ExecutableNetwork::SetConfig(const std::map<std::string, Parameter>& config
 }
 
 Parameter ExecutableNetwork::GetConfig(const std::string& name) const {
-    EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetConfig(name)});
+    EXEC_NET_CALL_STATEMENT(return {_impl->GetConfig(name), _so});
 }
 
 Parameter ExecutableNetwork::GetMetric(const std::string& name) const {
-    EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetMetric(name)});
+    EXEC_NET_CALL_STATEMENT(return {_impl->GetMetric(name), _so});
 }
 
 RemoteContext::Ptr ExecutableNetwork::GetContext() const {
@@ -112,10 +116,15 @@ ExecutableNetwork::operator bool() const noexcept {
 
 namespace ov {
 namespace runtime {
-CompiledModel::CompiledModel(const std::shared_ptr<void>& so,
-                             const std::shared_ptr<ie::IExecutableNetworkInternal>& impl)
-    : _so{so},
-      _impl{impl} {
+
+CompiledModel::~CompiledModel() {
+    _impl = {};
+}
+
+CompiledModel::CompiledModel(const std::shared_ptr<ie::IExecutableNetworkInternal>& impl,
+                             const std::shared_ptr<void>& so)
+    : _impl{impl},
+      _so{so} {
     OPENVINO_ASSERT(_impl != nullptr, "CompiledModel was not initialized.");
 }
 
@@ -191,7 +200,7 @@ ov::Output<const ov::Node> CompiledModel::output(const std::string& tensor_name)
 }
 
 InferRequest CompiledModel::create_infer_request() {
-    OV_EXEC_NET_CALL_STATEMENT(return {_so, _impl->CreateInferRequest()});
+    OV_EXEC_NET_CALL_STATEMENT(return {_impl->CreateInferRequest(), _so});
 }
 
 void CompiledModel::export_model(std::ostream& networkModel) {
@@ -203,15 +212,15 @@ void CompiledModel::set_config(const ie::ParamMap& config) {
 }
 
 ie::Parameter CompiledModel::get_config(const std::string& name) const {
-    OV_EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetConfig(name)});
+    OV_EXEC_NET_CALL_STATEMENT(return {_impl->GetConfig(name), _so});
 }
 
 ie::Parameter CompiledModel::get_metric(const std::string& name) const {
-    OV_EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetMetric(name)});
+    OV_EXEC_NET_CALL_STATEMENT(return {_impl->GetMetric(name), _so});
 }
 
 RemoteContext CompiledModel::get_context() const {
-    OV_EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetContext()});
+    OV_EXEC_NET_CALL_STATEMENT(return {_impl->GetContext(), _so});
 }
 
 bool CompiledModel::operator!() const noexcept {
diff --git a/src/inference/src/cpp/ie_infer_request.cpp b/src/inference/src/cpp/ie_infer_request.cpp
index 428888c6f19..3377afb6435 100644
--- a/src/inference/src/cpp/ie_infer_request.cpp
+++ b/src/inference/src/cpp/ie_infer_request.cpp
@@ -59,9 +59,13 @@ namespace InferenceEngine {
         OPENVINO_ASSERT(false, "Unexpected exception");                     \
     }
 
-InferRequest::InferRequest(const std::shared_ptr<void>& so, const IInferRequestInternal::Ptr& impl)
-    : _so(so),
-      _impl(impl) {
+InferRequest::~InferRequest() {
+    _impl = {};
+}
+
+InferRequest::InferRequest(const IInferRequestInternal::Ptr& impl, const std::shared_ptr<void>& so)
+    : _impl(impl),
+      _so(so) {
     IE_ASSERT(_impl != nullptr);
 }
 
@@ -152,7 +156,7 @@ void InferRequest::SetCompletionCallbackImpl(std::function<void()> callbackToSet
 void InferRequest::SetCompletionCallbackImpl(std::function<void(InferRequest, StatusCode)> callbackToSet) {
     INFER_REQ_CALL_STATEMENT(
         auto weakThis =
-            InferRequest{_so, std::shared_ptr<IInferRequestInternal>{_impl.get(), [](IInferRequestInternal*) {}}};
+            InferRequest{std::shared_ptr<IInferRequestInternal>{_impl.get(), [](IInferRequestInternal*) {}}, _so};
         _impl->SetCallback([callbackToSet, weakThis](std::exception_ptr exceptionPtr) {
             StatusCode statusCode = StatusCode::OK;
             if (exceptionPtr != nullptr) {
@@ -175,7 +179,7 @@ void InferRequest::SetCompletionCallbackImpl(std::function<void(InferRequest, St
 void InferRequest::SetCompletionCallbackImpl(IInferRequest::CompletionCallback callbackToSet) {
     INFER_REQ_CALL_STATEMENT(
         IInferRequest::Ptr weakThis =
-            InferRequest{_so, std::shared_ptr<IInferRequestInternal>{_impl.get(), [](IInferRequestInternal*) {}}};
+            InferRequest{std::shared_ptr<IInferRequestInternal>{_impl.get(), [](IInferRequestInternal*) {}}, _so};
         _impl->SetCallback([callbackToSet, weakThis](std::exception_ptr exceptionPtr) {
             StatusCode statusCode = StatusCode::OK;
             if (exceptionPtr != nullptr) {
@@ -203,7 +207,7 @@ std::vector<VariableState> InferRequest::QueryState() {
     std::vector<VariableState> controller;
     INFER_REQ_CALL_STATEMENT(for (auto&& state
                                   : _impl->QueryState()) {
-        controller.emplace_back(VariableState{_so, state});
+        controller.emplace_back(VariableState{state, _so});
     })
     return controller;
 }
@@ -241,9 +245,13 @@ std::string get_legacy_name_from_port(const ov::Output<const ov::Node>& port) {
 namespace ov {
 namespace runtime {
 
-InferRequest::InferRequest(const std::shared_ptr<void>& so, const ie::IInferRequestInternal::Ptr& impl)
-    : _so{so},
-      _impl{impl} {
+InferRequest::~InferRequest() {
+    _impl = {};
+}
+
+InferRequest::InferRequest(const ie::IInferRequestInternal::Ptr& impl, const std::shared_ptr<void>& so)
+    : _impl{impl},
+      _so{so} {
     OPENVINO_ASSERT(_impl != nullptr, "InferRequest was not initialized.");
 }
 
@@ -312,7 +320,7 @@ Tensor InferRequest::get_tensor(const ov::Output<const ov::Node>& port) {
     OV_INFER_REQ_CALL_STATEMENT({
         const auto& name = get_legacy_name_from_port(port);
         auto blob = _impl->GetBlob(name);
-        return {_so, blob};
+        return {blob, _so};
     });
 }
 
@@ -441,14 +449,14 @@ std::vector<VariableState> InferRequest::query_state() {
     std::vector<VariableState> variable_states;
     OV_INFER_REQ_CALL_STATEMENT({
         for (auto&& state : _impl->QueryState()) {
-            variable_states.emplace_back(VariableState{_so, state});
+            variable_states.emplace_back(VariableState{state, _so});
         }
     })
     return variable_states;
 }
 
 CompiledModel InferRequest::get_compiled_model() {
-    OV_INFER_REQ_CALL_STATEMENT(return {_so, _impl->getPointerToExecutableNetworkInternal()});
+    OV_INFER_REQ_CALL_STATEMENT(return {_impl->getPointerToExecutableNetworkInternal(), _so});
 }
 
 bool InferRequest::operator!() const noexcept {
diff --git a/src/inference/src/cpp/ie_plugin.hpp b/src/inference/src/cpp/ie_plugin.hpp
index 26a02391f74..c189615cded 100644
--- a/src/inference/src/cpp/ie_plugin.hpp
+++ b/src/inference/src/cpp/ie_plugin.hpp
@@ -38,8 +38,12 @@ namespace InferenceEngine {
  * It can throw exceptions safely for the application, where it is properly handled.
  */
 struct InferencePlugin {
-    std::shared_ptr<void> _so;
     std::shared_ptr<InferenceEngine::IInferencePlugin> _ptr;
+    std::shared_ptr<void> _so;
+
+    ~InferencePlugin() {
+        _ptr = {};
+    }
 
     void SetName(const std::string & deviceName) {
         PLUGIN_CALL_STATEMENT(_ptr->SetName(deviceName));
@@ -62,17 +66,17 @@ struct InferencePlugin {
     }
 
     ov::runtime::SoPtr<IExecutableNetworkInternal> LoadNetwork(const CNNNetwork& network, const std::map<std::string, std::string>& config) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->LoadNetwork(network, config)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->LoadNetwork(network, config), _so});
     }
 
     ov::runtime::SoPtr<IExecutableNetworkInternal> LoadNetwork(const CNNNetwork& network,
                                                                const std::shared_ptr<RemoteContext>& context,
                                                                const std::map<std::string, std::string>& config) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->LoadNetwork(network, config, context)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->LoadNetwork(network, config, context), _so});
     }
 
     ov::runtime::SoPtr<IExecutableNetworkInternal> LoadNetwork(const std::string& modelPath, const std::map<std::string, std::string>& config) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->LoadNetwork(modelPath, config)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->LoadNetwork(modelPath, config), _so});
     }
 
     QueryNetworkResult QueryNetwork(const CNNNetwork& network,
@@ -85,34 +89,34 @@ struct InferencePlugin {
 
     ov::runtime::SoPtr<IExecutableNetworkInternal> ImportNetwork(const std::string& modelFileName,
                                                                  const std::map<std::string, std::string>& config) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->ImportNetwork(modelFileName, config)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->ImportNetwork(modelFileName, config), _so});
     }
 
     ov::runtime::SoPtr<IExecutableNetworkInternal> ImportNetwork(std::istream& networkModel,
                                     const std::map<std::string, std::string>& config) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->ImportNetwork(networkModel, config)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->ImportNetwork(networkModel, config), _so});
     }
 
     ov::runtime::SoPtr<IExecutableNetworkInternal> ImportNetwork(std::istream& networkModel,
                                                                  const std::shared_ptr<RemoteContext>& context,
                                                                  const std::map<std::string, std::string>& config) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->ImportNetwork(networkModel, context, config)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->ImportNetwork(networkModel, context, config), _so});
     }
 
     Parameter GetMetric(const std::string& name, const std::map<std::string, Parameter>& options) const {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetMetric(name, options)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->GetMetric(name, options), _so});
     }
 
     ov::runtime::SoPtr<RemoteContext> CreateContext(const ParamMap& params) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->CreateContext(params)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->CreateContext(params), _so});
     }
 
     ov::runtime::SoPtr<RemoteContext> GetDefaultContext(const ParamMap& params) {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetDefaultContext(params)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->GetDefaultContext(params), _so});
     }
 
     Parameter GetConfig(const std::string& name, const std::map<std::string, Parameter>& options) const {
-        PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetConfig(name, options)});
+        PLUGIN_CALL_STATEMENT(return {_ptr->GetConfig(name, options), _so});
     }
 };
 }  // namespace InferenceEngine
@@ -139,15 +143,19 @@ namespace runtime {
  * It can throw exceptions safely for the application, where it is properly handled.
  */
 class InferencePlugin {
-    std::shared_ptr<void> _so;
     std::shared_ptr<ie::IInferencePlugin> _ptr;
+    std::shared_ptr<void> _so;
 
 public:
     InferencePlugin() = default;
 
-    InferencePlugin(const std::shared_ptr<void>& so, const std::shared_ptr<ie::IInferencePlugin>& impl) :
-        _so{so},
-        _ptr{impl} {
+    ~InferencePlugin() {
+        _ptr = {};
+    }
+
+    InferencePlugin(const std::shared_ptr<ie::IInferencePlugin>& ptr, const std::shared_ptr<void>& so) :
+        _ptr{ptr},
+        _so{so} {
         OPENVINO_ASSERT(_ptr != nullptr, "InferencePlugin was not initialized.");
     }
 
@@ -172,17 +180,17 @@ public:
     }
 
     SoPtr<ie::IExecutableNetworkInternal> compile_model(const ie::CNNNetwork& network, const ConfigMap& config) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->LoadNetwork(network, config)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->LoadNetwork(network, config), _so});
     }
 
     SoPtr<ie::IExecutableNetworkInternal> compile_model(const ie::CNNNetwork& network,
                                                         const std::shared_ptr<ie::RemoteContext>& context,
                                                         const ConfigMap& config) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->LoadNetwork(network, config, context)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->LoadNetwork(network, config, context), _so});
     }
 
     SoPtr<ie::IExecutableNetworkInternal> compile_model(const std::string& modelPath, const ConfigMap& config) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->LoadNetwork(modelPath, config)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->LoadNetwork(modelPath, config), _so});
     }
 
     ie::QueryNetworkResult query_model(const ie::CNNNetwork& network,
@@ -195,34 +203,34 @@ public:
 
     SoPtr<ie::IExecutableNetworkInternal> import_model(const std::string& modelFileName,
                                                        const ConfigMap& config) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->ImportNetwork(modelFileName, config)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->ImportNetwork(modelFileName, config), _so});
     }
 
     SoPtr<ie::IExecutableNetworkInternal> import_model(std::istream& networkModel,
                                     const ConfigMap& config) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->ImportNetwork(networkModel, config)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->ImportNetwork(networkModel, config), _so});
     }
 
     SoPtr<ie::IExecutableNetworkInternal> import_model(std::istream& networkModel,
                                                        const std::shared_ptr<ie::RemoteContext>& context,
                                                        const ConfigMap& config) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->ImportNetwork(networkModel, context, config)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->ImportNetwork(networkModel, context, config), _so});
     }
 
     ie::Parameter get_metric(const std::string& name, const ie::ParamMap& options) const {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetMetric(name, options)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->GetMetric(name, options), _so});
     }
 
     SoPtr<ie::RemoteContext> create_context(const ie::ParamMap& params) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->CreateContext(params)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->CreateContext(params), _so});
     }
 
     SoPtr<ie::RemoteContext> get_default_context(const ie::ParamMap& params) {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetDefaultContext(params)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->GetDefaultContext(params), _so});
     }
 
     ie::Parameter get_config(const std::string& name, const ie::ParamMap& options) const {
-        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetConfig(name, options)});
+        OV_PLUGIN_CALL_STATEMENT(return {_ptr->GetConfig(name, options), _so});
     }
 };
 
diff --git a/src/inference/src/cpp/ie_remote_context.cpp b/src/inference/src/cpp/ie_remote_context.cpp
index 0c873efa297..c1a63539e39 100644
--- a/src/inference/src/cpp/ie_remote_context.cpp
+++ b/src/inference/src/cpp/ie_remote_context.cpp
@@ -47,9 +47,13 @@ void RemoteContext::type_check(const RemoteContext& tensor,
     }
 }
 
-RemoteContext::RemoteContext(const std::shared_ptr<void>& so, const ie::RemoteContext::Ptr& impl)
-    : _so{so},
-      _impl{impl} {
+RemoteContext::~RemoteContext() {
+    _impl = {};
+}
+
+RemoteContext::RemoteContext(const ie::RemoteContext::Ptr& impl, const std::shared_ptr<void>& so)
+    : _impl{impl},
+      _so{so} {
     OPENVINO_ASSERT(_impl != nullptr, "RemoteContext was not initialized.");
 }
 
@@ -65,7 +69,7 @@ RemoteTensor RemoteContext::create_tensor(const element::Type& element_type,
             {ie::details::convertPrecision(element_type), shape, ie::TensorDesc::getLayoutByRank(shape.size())},
             params);
         blob->allocate();
-        return {_so, blob};
+        return {blob, _so};
     });
 }
 
@@ -74,7 +78,7 @@ Tensor RemoteContext::create_host_tensor(const element::Type element_type, const
         auto blob = _impl->CreateHostBlob(
             {ie::details::convertPrecision(element_type), shape, ie::TensorDesc::getLayoutByRank(shape.size())});
         blob->allocate();
-        return {_so, blob};
+        return {blob, _so};
     });
 }
 
@@ -82,7 +86,7 @@ ParamMap RemoteContext::get_params() const {
     ParamMap paramMap;
     OV_REMOTE_CONTEXT_STATEMENT({
         for (auto&& param : _impl->getParams()) {
-            paramMap.emplace(param.first, Any{_so, param.second});
+            paramMap.emplace(param.first, Any{param.second, _so});
         }
     });
     return paramMap;
diff --git a/src/inference/src/cpp/ie_variable_state.cpp b/src/inference/src/cpp/ie_variable_state.cpp
index e26d6df9409..fcec787f6e8 100644
--- a/src/inference/src/cpp/ie_variable_state.cpp
+++ b/src/inference/src/cpp/ie_variable_state.cpp
@@ -28,9 +28,13 @@
 
 namespace InferenceEngine {
 
-VariableState::VariableState(const std::shared_ptr<void>& so, const IVariableStateInternal::Ptr& impl)
-    : _so(so),
-      _impl(impl) {
+VariableState::~VariableState() {
+    _impl = {};
+}
+
+VariableState::VariableState(const IVariableStateInternal::Ptr& impl, const std::shared_ptr<void>& so)
+    : _impl(impl),
+      _so(so) {
     if (_impl == nullptr)
         IE_THROW() << "VariableState was not initialized.";
 }
@@ -58,9 +62,13 @@ void VariableState::SetState(Blob::Ptr state) {
 namespace ov {
 namespace runtime {
 
-VariableState::VariableState(const std::shared_ptr<void>& so, const ie::IVariableStateInternal::Ptr& impl)
-    : _so{so},
-      _impl{impl} {
+VariableState::~VariableState() {
+    _impl = {};
+}
+
+VariableState::VariableState(const ie::IVariableStateInternal::Ptr& impl, const std::shared_ptr<void>& so)
+    : _impl{impl},
+      _so{so} {
     OPENVINO_ASSERT(_impl != nullptr, "VariableState was not initialized.");
 }
 
@@ -73,7 +81,7 @@ std::string VariableState::get_name() const {
 }
 
 Tensor VariableState::get_state() const {
-    OV_VARIABLE_CALL_STATEMENT(return {_so, std::const_pointer_cast<ie::Blob>(_impl->GetState())});
+    OV_VARIABLE_CALL_STATEMENT(return {std::const_pointer_cast<ie::Blob>(_impl->GetState()), _so});
 }
 
 void VariableState::set_state(const Tensor& state) {
diff --git a/src/inference/src/ie_core.cpp b/src/inference/src/ie_core.cpp
index 979987e4f7c..fdddf89d344 100644
--- a/src/inference/src/ie_core.cpp
+++ b/src/inference/src/ie_core.cpp
@@ -525,7 +525,7 @@ public:
         } else {
             res = compile_model_impl(network, plugin, parsed._config, nullptr, {}, {}, forceDisableCache);
         }
-        return {{res._so}, res._ptr};
+        return {res._ptr, res._so};
     }
 
     ie::SoExecutableNetworkInternal LoadNetwork(const std::string& modelPath,
@@ -551,7 +551,7 @@ public:
             auto cnnNetwork = ReadNetwork(modelPath, std::string());
             res = compile_model_impl(cnnNetwork, plugin, parsed._config, nullptr, {}, modelPath);
         }
-        return {{res._so}, res._ptr};
+        return {res._ptr, res._so};
     }
 
     ie::SoExecutableNetworkInternal ImportNetwork(std::istream& networkModel,
@@ -607,7 +607,7 @@ public:
             //    not from ngraph. Plugins should use SetExeNetworkInfo
         }
 
-        return {{exec._so}, exec._ptr};
+        return {exec._ptr, exec._so};
     }
 
     ie::QueryNetworkResult QueryNetwork(const ie::CNNNetwork& network,
@@ -748,13 +748,13 @@ public:
                 if (desc.pluginCreateFunc) {  // static OpenVINO case
                     std::shared_ptr<ie::IInferencePlugin> plugin_impl;
                     desc.pluginCreateFunc(plugin_impl);
-                    plugin = InferencePlugin{nullptr, plugin_impl};
+                    plugin = InferencePlugin{plugin_impl, {}};
                 } else {
                     so = ov::util::load_shared_object(desc.libraryLocation.c_str());
                     std::shared_ptr<ie::IInferencePlugin> plugin_impl;
                     reinterpret_cast<InferenceEngine::CreatePluginEngineFunc*>(
                         ov::util::get_symbol(so, InferenceEngine::create_plugin_function))(plugin_impl);
-                    plugin = InferencePlugin{so, plugin_impl};
+                    plugin = InferencePlugin{plugin_impl, so};
                 }
 
                 {
@@ -1166,21 +1166,21 @@ ExecutableNetwork Core::LoadNetwork(const CNNNetwork& network,
                                     const std::string& deviceName,
                                     const std::map<std::string, std::string>& config) {
     auto exec = _impl->LoadNetwork(network, deviceName, config);
-    return {exec._so, exec._ptr};
+    return {exec._ptr, exec._so};
 }
 
 ExecutableNetwork Core::LoadNetwork(const CNNNetwork& network,
                                     RemoteContext::Ptr context,
                                     const std::map<std::string, std::string>& config) {
     auto exec = _impl->LoadNetwork(network, std::dynamic_pointer_cast<RemoteContext>(context), config);
-    return {{exec._so}, exec._ptr};
+    return {exec._ptr, exec._so};
 }
 
 ExecutableNetwork Core::LoadNetwork(const std::string& modelPath,
                                     const std::string& deviceName,
                                     const std::map<std::string, std::string>& config) {
     auto exec = _impl->LoadNetwork(modelPath, deviceName, config);
-    return {exec._so, exec._ptr};
+    return {exec._ptr, exec._so};
 }
 
 RemoteContext::Ptr Core::CreateContext(const std::string& deviceName, const ParamMap& params) {
@@ -1237,7 +1237,7 @@ ExecutableNetwork Core::ImportNetwork(const std::string& modelFileName,
     OV_ITT_SCOPED_TASK(ov::itt::domains::IE, "Core::ImportNetwork");
     auto parsed = ov::runtime::parseDeviceNameIntoConfig(deviceName, config);
     auto exec = _impl->GetCPPPluginByName(parsed._deviceName).import_model(modelFileName, parsed._config);
-    return {{exec._so}, exec._ptr};
+    return {exec._ptr, exec._so};
 }
 
 ExecutableNetwork Core::ImportNetwork(std::istream& networkModel,
@@ -1245,7 +1245,7 @@ ExecutableNetwork Core::ImportNetwork(std::istream& networkModel,
                                       const std::map<std::string, std::string>& config) {
     OV_ITT_SCOPED_TASK(ov::itt::domains::IE, "Core::ImportNetwork");
     auto exec = _impl->ImportNetwork(networkModel, deviceName, config);
-    return {exec._so, exec._ptr};
+    return {exec._ptr, exec._so};
 }
 
 ExecutableNetwork Core::ImportNetwork(std::istream& networkModel) {
@@ -1267,7 +1267,7 @@ ExecutableNetwork Core::ImportNetwork(std::istream& networkModel) {
     networkModel.seekg(currentPos, networkModel.beg);
 
     auto exec = _impl->GetCPPPluginByName(deviceName).import_model(networkModel, {});
-    return {{exec._so}, exec._ptr};
+    return {exec._ptr, exec._so};
 }
 
 ExecutableNetwork Core::ImportNetwork(std::istream& networkModel,
@@ -1286,7 +1286,7 @@ ExecutableNetwork Core::ImportNetwork(std::istream& networkModel,
     auto parsed = ov::runtime::parseDeviceNameIntoConfig(deviceName, config);
     auto exec = _impl->GetCPPPluginByName(deviceName)
                     .import_model(networkModel, std::dynamic_pointer_cast<RemoteContext>(context), parsed._config);
-    return {{exec._so}, exec._ptr};
+    return {exec._ptr, exec._so};
 }
 
 QueryNetworkResult Core::QueryNetwork(const CNNNetwork& network,
@@ -1446,7 +1446,7 @@ CompiledModel Core::compile_model(const std::shared_ptr<const ov::Model>& model,
                                   const ConfigMap& config) {
     OV_CORE_CALL_STATEMENT({
         auto exec = _impl->LoadNetwork(toCNN(model), deviceName, config);
-        return {exec._so, exec._ptr};
+        return {exec._ptr, exec._so};
     });
 }
 
@@ -1455,7 +1455,7 @@ CompiledModel Core::compile_model(const std::string& modelPath,
                                   const ConfigMap& config) {
     OV_CORE_CALL_STATEMENT({
         auto exec = _impl->LoadNetwork(modelPath, deviceName, config);
-        return {exec._so, exec._ptr};
+        return {exec._ptr, exec._so};
     });
 }
 
@@ -1464,7 +1464,7 @@ CompiledModel Core::compile_model(const std::shared_ptr<const ov::Model>& model,
                                   const ConfigMap& config) {
     OV_CORE_CALL_STATEMENT({
         auto exec = _impl->LoadNetwork(toCNN(model), context._impl, config);
-        return {exec._so, exec._ptr};
+        return {exec._ptr, exec._so};
     });
 }
 
@@ -1492,7 +1492,7 @@ CompiledModel Core::import_model(std::istream& modelStream, const std::string& d
     OV_ITT_SCOPED_TASK(ov::itt::domains::IE, "Core::import_model");
     OV_CORE_CALL_STATEMENT({
         auto exec = _impl->ImportNetwork(modelStream, deviceName, config);
-        return {exec._so, exec._ptr};
+        return {exec._ptr, exec._so};
     });
 }
 
@@ -1517,7 +1517,7 @@ CompiledModel Core::import_model(std::istream& modelStream, const RemoteContext&
 
     OV_CORE_CALL_STATEMENT({
         auto exec = _impl->GetCPPPluginByName(deviceName).import_model(modelStream, {});
-        return {exec._so, exec._ptr};
+        return {exec._ptr, exec._so};
     });
 }
 
@@ -1600,7 +1600,7 @@ RemoteContext Core::create_context(const std::string& deviceName, const ParamMap
     OV_CORE_CALL_STATEMENT({
         auto parsed = parseDeviceNameIntoConfig(deviceName, params);
         auto remoteContext = _impl->GetCPPPluginByName(parsed._deviceName).create_context(parsed._config);
-        return {remoteContext._so, remoteContext._ptr};
+        return {remoteContext._ptr, remoteContext._so};
     });
 }
 
@@ -1612,7 +1612,7 @@ RemoteContext Core::get_default_context(const std::string& deviceName) {
     OV_CORE_CALL_STATEMENT({
         auto parsed = parseDeviceNameIntoConfig(deviceName, ParamMap());
         auto remoteContext = _impl->GetCPPPluginByName(parsed._deviceName).get_default_context(parsed._config);
-        return {remoteContext._so, remoteContext._ptr};
+        return {remoteContext._ptr, remoteContext._so};
     });
 }
 
diff --git a/src/inference/src/ie_network_reader.cpp b/src/inference/src/ie_network_reader.cpp
index e7bc26c8e99..bd971a0dfb7 100644
--- a/src/inference/src/ie_network_reader.cpp
+++ b/src/inference/src/ie_network_reader.cpp
@@ -111,7 +111,7 @@ class Reader : public IReader {
             std::shared_ptr<IReader> plugin_impl;
             using createFunc = void(std::shared_ptr<IReader>&);
             reinterpret_cast<createFunc*>(ov::util::get_symbol(so, "CreateReader"))(plugin_impl);
-            ptr = {so, plugin_impl};
+            ptr = {plugin_impl, so};
 #    endif  // OPENVINO_STATIC_LIBRARY
         });
 
diff --git a/src/inference/src/remote_tensor.cpp b/src/inference/src/remote_tensor.cpp
index d1bf97474cd..a7b5901b3c8 100644
--- a/src/inference/src/remote_tensor.cpp
+++ b/src/inference/src/remote_tensor.cpp
@@ -40,7 +40,7 @@ ie::ParamMap RemoteTensor::get_params() const {
     try {
         ParamMap paramMap;
         for (auto&& param : remote_impl->getParams()) {
-            paramMap.emplace(param.first, Any{_so, param.second});
+            paramMap.emplace(param.first, Any{param.second, _so});
         }
         return paramMap;
     } catch (const std::exception& ex) {
diff --git a/src/plugins/auto/executable_network.cpp b/src/plugins/auto/executable_network.cpp
index fc23320f30d..9bcfb868038 100644
--- a/src/plugins/auto/executable_network.cpp
+++ b/src/plugins/auto/executable_network.cpp
@@ -119,7 +119,7 @@ void MultiDeviceExecutableNetwork::GenerateWorkers(const std::string& device, co
     auto* idleWorkerRequestsPtr = &(idleWorkerRequests);
     idleWorkerRequests.set_capacity(numRequests);
     for (auto&& workerRequest : workerRequests) {
-        workerRequest._inferRequest = { executableNetwork._so, executableNetwork->CreateInferRequest() };
+        workerRequest._inferRequest = {executableNetwork->CreateInferRequest(), executableNetwork._so};
         auto* workerRequestPtr = &workerRequest;
         IE_ASSERT(idleWorkerRequests.try_push(workerRequestPtr) == true);
         workerRequest._inferRequest->SetCallback(
diff --git a/src/plugins/hetero/infer_request.cpp b/src/plugins/hetero/infer_request.cpp
index 13a90b7e749..aaf2f3500a8 100644
--- a/src/plugins/hetero/infer_request.cpp
+++ b/src/plugins/hetero/infer_request.cpp
@@ -69,7 +69,7 @@ void HeteroInferRequest::CreateInferRequest(
 
     // go over all subnet and create requests
     for (auto&& desc : _inferRequests) {
-        desc._request = {desc._network._so, desc._network->CreateInferRequest()};
+        desc._request = {desc._network->CreateInferRequest(), desc._network._so};
         // go over all inputs and get blobs from subnet infer requests
         for (auto&& outputInfo : desc._network->GetOutputsInfo()) {
             requestBlob(outputInfo.first, desc._request, true);
diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_plugin/life_time.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_plugin/life_time.hpp
new file mode 100644
index 00000000000..d747460cdc0
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/behavior/ov_plugin/life_time.hpp
@@ -0,0 +1,42 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "base/ov_behavior_test_utils.hpp"
+
+namespace ov {
+namespace test {
+namespace behavior {
+
+class OVHoldersTest : public CommonTestUtils::TestsCommon,
+                      public ::testing::WithParamInterface<std::string> {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<std::string> obj);
+
+    void SetUp() override;
+
+    void TearDown() override;
+
+    std::string deathTestStyle;
+    std::shared_ptr<ngraph::Function> function;
+    std::string targetDevice;
+};
+
+class OVHoldersTestOnImportedNetwork : public CommonTestUtils::TestsCommon,
+                                       public ::testing::WithParamInterface<std::string> {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<std::string> obj);
+
+    void SetUp() override;
+    void TearDown() override;
+
+    std::shared_ptr<ngraph::Function> function;
+    std::string targetDevice;
+    std::string deathTestStyle;
+};
+
+}  // namespace behavior
+}  // namespace test
+}  // namespace ov
\ No newline at end of file
diff --git a/src/tests/functional/plugin/shared/src/behavior/ov_plugin/life_time.cpp b/src/tests/functional/plugin/shared/src/behavior/ov_plugin/life_time.cpp
new file mode 100644
index 00000000000..1b3564747b1
--- /dev/null
+++ b/src/tests/functional/plugin/shared/src/behavior/ov_plugin/life_time.cpp
@@ -0,0 +1,137 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <fstream>
+
+#include <ngraph_functions/subgraph_builders.hpp>
+#include <base/behavior_test_utils.hpp>
+#include "behavior/ov_plugin/life_time.hpp"
+
+namespace ov {
+namespace test {
+namespace behavior {
+std::string OVHoldersTest::getTestCaseName(testing::TestParamInfo<std::string> obj) {
+    return "targetDevice=" + obj.param;
+}
+
+void OVHoldersTest::SetUp() {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    targetDevice = this->GetParam();
+    deathTestStyle = ::testing::GTEST_FLAG(death_test_style);
+    if (deathTestStyle == "fast") {
+        ::testing::GTEST_FLAG(death_test_style) = "threadsafe";
+    }
+    function = ngraph::builder::subgraph::makeConvPoolRelu();
+}
+
+void OVHoldersTest::TearDown() {
+    ::testing::GTEST_FLAG(death_test_style) = deathTestStyle;
+}
+
+#define EXPECT_NO_CRASH(_statement) \
+EXPECT_EXIT(_statement; exit(0), testing::ExitedWithCode(0), "")
+
+static void release_order_test(std::vector<std::size_t> order, const std::string &deviceName,
+                               std::shared_ptr<ngraph::Function> function) {
+    ov::AnyVector objects;
+    {
+        runtime::Core core = createCoreWithTemplate();
+        auto compiled_model = core.compile_model(function, deviceName);
+        auto request = compiled_model.create_infer_request();
+
+        objects = {core, compiled_model, request};
+    }
+    for (auto&& i : order) {
+        objects.at(i) = {};
+    }
+}
+
+TEST_P(OVHoldersTest, Orders) {
+    std::vector<std::string> objects{ "core", "compiled_model", "request"};
+    std::vector<std::size_t> order(objects.size());
+    std::iota(order.begin(), order.end(), 0);
+    do {
+        std::stringstream order_str;
+        for (auto&& i : order) {
+            order_str << objects.at(i) << " ";
+        }
+        EXPECT_NO_CRASH(release_order_test(order, targetDevice, function)) << "for order: " << order_str.str();
+    } while (std::next_permutation(order.begin(), order.end()));
+}
+
+TEST_P(OVHoldersTest, LoadedState) {
+    std::vector<runtime::VariableState> states;
+    {
+        runtime::Core core = createCoreWithTemplate();
+        auto compiled_model = core.compile_model(function, targetDevice);
+        auto request = compiled_model.create_infer_request();
+        try {
+            states = request.query_state();
+        } catch(...) {}
+    }
+}
+
+TEST_P(OVHoldersTest, LoadedTensor) {
+    runtime::Tensor tensor;
+    {
+        runtime::Core core = createCoreWithTemplate();
+        auto compiled_model = core.compile_model(function, targetDevice);
+        auto request = compiled_model.create_infer_request();
+        tensor = request.get_input_tensor();
+    }
+}
+
+TEST_P(OVHoldersTest, LoadedAny) {
+    ov::Any any;
+    {
+        runtime::Core core = createCoreWithTemplate();
+        auto compiled_model = core.compile_model(function, targetDevice);
+        any = compiled_model.get_metric(METRIC_KEY(SUPPORTED_METRICS));
+    }
+}
+
+TEST_P(OVHoldersTest, LoadedRemoteContext) {
+    runtime::RemoteContext ctx;
+    {
+        runtime::Core core = createCoreWithTemplate();
+        auto compiled_model = core.compile_model(function, targetDevice);
+        try {
+            ctx = compiled_model.get_context();
+        } catch(...) {}
+    }
+}
+
+
+std::string OVHoldersTestOnImportedNetwork::getTestCaseName(testing::TestParamInfo<std::string> obj) {
+    return "targetDevice=" + obj.param;
+}
+
+void OVHoldersTestOnImportedNetwork::SetUp() {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    targetDevice = this->GetParam();
+    deathTestStyle = ::testing::GTEST_FLAG(death_test_style);
+    if (deathTestStyle == "fast") {
+        ::testing::GTEST_FLAG(death_test_style) = "threadsafe";
+    }
+    function = ngraph::builder::subgraph::makeConvPoolRelu();
+}
+
+void OVHoldersTestOnImportedNetwork::TearDown() {
+    ::testing::GTEST_FLAG(death_test_style) = deathTestStyle;
+}
+
+TEST_P(OVHoldersTestOnImportedNetwork, CreateRequestWithCoreRemoved) {
+    runtime::Core core = createCoreWithTemplate();
+    std::stringstream stream;
+    {
+        auto compiled_model = core.compile_model(function, targetDevice);
+        compiled_model.export_model(stream);
+    }
+    auto compiled_model = core.import_model(stream, targetDevice);
+    core = runtime::Core{};
+    auto request = compiled_model.create_infer_request();
+}
+}  // namespace behavior
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/unit/auto/auto_select_device_failed_test.cpp b/src/tests/unit/auto/auto_select_device_failed_test.cpp
index 51ade26194a..ff12afe42a7 100644
--- a/src/tests/unit/auto/auto_select_device_failed_test.cpp
+++ b/src/tests/unit/auto/auto_select_device_failed_test.cpp
@@ -129,7 +129,7 @@ public:
        mockIExeNet = std::make_shared<MockIExecutableNetworkInternal>();
        auto mockIPluginPtr = std::make_shared<MockIInferencePlugin>();
        ON_CALL(*mockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(mockIExeNet));
-       mockPlugin = InferenceEngine::InferencePlugin{{}, mockIPluginPtr};
+       mockPlugin = InferenceEngine::InferencePlugin{mockIPluginPtr, {}};
        // remove annoying ON CALL message
        EXPECT_CALL(*mockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).Times(1);
        mockExeNetwork = mockPlugin.LoadNetwork(CNNNetwork{}, {});
diff --git a/src/tests/unit/auto/exec_network_get_metrics.cpp b/src/tests/unit/auto/exec_network_get_metrics.cpp
index 6a29fd508c1..9c01e0fe7ab 100644
--- a/src/tests/unit/auto/exec_network_get_metrics.cpp
+++ b/src/tests/unit/auto/exec_network_get_metrics.cpp
@@ -117,7 +117,7 @@ public:
        cpuMockIExeNet = std::make_shared<MockIExecutableNetworkInternal>();
        auto cpuMockIPluginPtr = std::make_shared<MockIInferencePlugin>();
        ON_CALL(*cpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(cpuMockIExeNet));
-       cpuMockPlugin = InferenceEngine::InferencePlugin{{}, cpuMockIPluginPtr};
+       cpuMockPlugin = InferenceEngine::InferencePlugin{cpuMockIPluginPtr, {}};
        // remove annoying ON CALL message
        EXPECT_CALL(*cpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).Times(1);
        cpuMockExeNetwork = cpuMockPlugin.LoadNetwork(CNNNetwork{}, {});
@@ -126,7 +126,7 @@ public:
        gpuMockIExeNet = std::make_shared<MockIExecutableNetworkInternal>();
        auto gpuMockIPluginPtr = std::make_shared<MockIInferencePlugin>();
        ON_CALL(*gpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(gpuMockIExeNet));
-       gpuMockPlugin = InferenceEngine::InferencePlugin{{}, gpuMockIPluginPtr};
+       gpuMockPlugin = InferenceEngine::InferencePlugin{gpuMockIPluginPtr, {}};
        // remove annoying ON CALL message
        EXPECT_CALL(*gpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).Times(1);
        gpuMockExeNetwork = gpuMockPlugin.LoadNetwork(CNNNetwork{}, {});
diff --git a/src/tests/unit/inference_engine/cpp_interfaces/ie_infer_async_request_base_test.cpp b/src/tests/unit/inference_engine/cpp_interfaces/ie_infer_async_request_base_test.cpp
index bfb768f8b90..483bf9c797f 100644
--- a/src/tests/unit/inference_engine/cpp_interfaces/ie_infer_async_request_base_test.cpp
+++ b/src/tests/unit/inference_engine/cpp_interfaces/ie_infer_async_request_base_test.cpp
@@ -196,7 +196,7 @@ protected:
         ON_CALL(*mockIExeNet, CreateInferRequest()).WillByDefault(Return(mock_request));
         auto mockIPluginPtr = std::make_shared<MockIInferencePlugin>();
         ON_CALL(*mockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(mockIExeNet));
-        plugin = InferenceEngine::InferencePlugin{{}, mockIPluginPtr};
+        plugin = InferenceEngine::InferencePlugin{mockIPluginPtr, {}};
         exeNetwork = plugin.LoadNetwork(CNNNetwork{}, {});
         request = exeNetwork->CreateInferRequest();
         _incorrectName = "incorrect_name";
@@ -220,7 +220,7 @@ protected:
         ON_CALL(*mockIExeNet, CreateInferRequest()).WillByDefault(Return(mockInferRequestInternal));
         auto mockIPluginPtr = std::make_shared<MockIInferencePlugin>();
         ON_CALL(*mockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(mockIExeNet));
-        auto plugin = InferenceEngine::InferencePlugin{{}, mockIPluginPtr};
+        auto plugin = InferenceEngine::InferencePlugin{mockIPluginPtr, {}};
         auto exeNetwork = plugin.LoadNetwork(CNNNetwork{}, {});
         return exeNetwork->CreateInferRequest();
     }
diff --git a/src/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp b/src/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp
index 4460de98ac4..d0089d995d8 100644
--- a/src/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp
+++ b/src/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp
@@ -37,7 +37,7 @@ class InferRequestVariableStateTests : public ::testing::Test {
         ON_CALL(*mockExeNetworkInternal, CreateInferRequest()).WillByDefault(Return(mockInferRequestInternal));
         auto mockIPluginPtr = std::make_shared<MockIInferencePlugin>();
         ON_CALL(*mockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(mockExeNetworkInternal));
-        plugin = InferenceEngine::InferencePlugin{{}, mockIPluginPtr};
+        plugin = InferenceEngine::InferencePlugin{mockIPluginPtr, {}};
         net = plugin.LoadNetwork(CNNNetwork{}, {});
         req = net->CreateInferRequest();
     }
diff --git a/src/tests/unit/inference_engine/ie_executable_network_test.cpp b/src/tests/unit/inference_engine/ie_executable_network_test.cpp
index 9c1e54a5d60..de24b47d5ea 100644
--- a/src/tests/unit/inference_engine/ie_executable_network_test.cpp
+++ b/src/tests/unit/inference_engine/ie_executable_network_test.cpp
@@ -52,7 +52,7 @@ protected:
         mockIExeNet = std::make_shared<MockIExecutableNetworkInternal>();
         auto mockIPluginPtr = std::make_shared<MockIInferencePlugin>();
         ON_CALL(*mockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(mockIExeNet));
-        plugin = InferenceEngine::InferencePlugin{{}, mockIPluginPtr};
+        plugin = InferenceEngine::InferencePlugin{mockIPluginPtr, {}};
         exeNetwork = plugin.LoadNetwork(CNNNetwork{}, {});
     }
 };

From d56dc20956fa7ff6816e7f2116d3e36aaa677420 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Mon, 20 Dec 2021 12:14:32 +0300
Subject: [PATCH 041/160] [GPU] Moved GPU remote headers to intel_gpu folder
 and namespace (#9290)

---
 .../runtime/{gpu => intel_gpu}/ocl/dx.hpp     |  8 ++---
 .../runtime/{gpu => intel_gpu}/ocl/ocl.hpp    |  8 ++---
 .../{gpu => intel_gpu}/ocl/ocl_wrapper.hpp    |  0
 .../runtime/{gpu => intel_gpu}/ocl/va.hpp     |  8 ++---
 .../inference_engine/CMakeLists.txt           | 12 +++----
 .../gpu_remote_tensor_tests.cpp               | 34 +++++++++----------
 6 files changed, 35 insertions(+), 35 deletions(-)
 rename src/inference/include/openvino/runtime/{gpu => intel_gpu}/ocl/dx.hpp (98%)
 rename src/inference/include/openvino/runtime/{gpu => intel_gpu}/ocl/ocl.hpp (98%)
 rename src/inference/include/openvino/runtime/{gpu => intel_gpu}/ocl/ocl_wrapper.hpp (100%)
 rename src/inference/include/openvino/runtime/{gpu => intel_gpu}/ocl/va.hpp (97%)

diff --git a/src/inference/include/openvino/runtime/gpu/ocl/dx.hpp b/src/inference/include/openvino/runtime/intel_gpu/ocl/dx.hpp
similarity index 98%
rename from src/inference/include/openvino/runtime/gpu/ocl/dx.hpp
rename to src/inference/include/openvino/runtime/intel_gpu/ocl/dx.hpp
index e94ab133c6c..b920edd2988 100644
--- a/src/inference/include/openvino/runtime/gpu/ocl/dx.hpp
+++ b/src/inference/include/openvino/runtime/intel_gpu/ocl/dx.hpp
@@ -7,7 +7,7 @@
  * shared Video Acceleration device contexts
  * and shared memory tensors which contain Video Acceleration surfaces
  *
- * @file openvino/runtime/gpu/ocl/dx.hpp
+ * @file openvino/runtime/intel_gpu/ocl/dx.hpp
  */
 #pragma once
 
@@ -24,11 +24,11 @@
 #include <memory>
 #include <string>
 
-#include "openvino/runtime/gpu/ocl/ocl.hpp"
+#include "openvino/runtime/intel_gpu/ocl/ocl.hpp"
 
 namespace ov {
 namespace runtime {
-namespace gpu {
+namespace intel_gpu {
 namespace ocl {
 
 /**
@@ -193,6 +193,6 @@ public:
     }
 };
 }  // namespace ocl
-}  // namespace gpu
+}  // namespace intel_gpu
 }  // namespace runtime
 }  // namespace ov
diff --git a/src/inference/include/openvino/runtime/gpu/ocl/ocl.hpp b/src/inference/include/openvino/runtime/intel_gpu/ocl/ocl.hpp
similarity index 98%
rename from src/inference/include/openvino/runtime/gpu/ocl/ocl.hpp
rename to src/inference/include/openvino/runtime/intel_gpu/ocl/ocl.hpp
index 4477c87873d..42565232897 100644
--- a/src/inference/include/openvino/runtime/gpu/ocl/ocl.hpp
+++ b/src/inference/include/openvino/runtime/intel_gpu/ocl/ocl.hpp
@@ -6,7 +6,7 @@
  * @brief a header that defines wrappers for internal GPU plugin-specific
  * OpenCL context and OpenCL shared memory tensors
  *
- * @file openvino/runtime/gpu/ocl/ocl.hpp
+ * @file openvino/runtime/intel_gpu/ocl/ocl.hpp
  */
 #pragma once
 
@@ -15,13 +15,13 @@
 
 #include "gpu/gpu_params.hpp"
 #include "openvino/runtime/core.hpp"
-#include "openvino/runtime/gpu/ocl/ocl_wrapper.hpp"
+#include "openvino/runtime/intel_gpu/ocl/ocl_wrapper.hpp"
 #include "openvino/runtime/remote_context.hpp"
 #include "openvino/runtime/remote_tensor.hpp"
 
 namespace ov {
 namespace runtime {
-namespace gpu {
+namespace intel_gpu {
 namespace ocl {
 
 /**
@@ -320,6 +320,6 @@ public:
 };
 
 }  // namespace ocl
-}  // namespace gpu
+}  // namespace intel_gpu
 }  // namespace runtime
 }  // namespace ov
diff --git a/src/inference/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp b/src/inference/include/openvino/runtime/intel_gpu/ocl/ocl_wrapper.hpp
similarity index 100%
rename from src/inference/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp
rename to src/inference/include/openvino/runtime/intel_gpu/ocl/ocl_wrapper.hpp
diff --git a/src/inference/include/openvino/runtime/gpu/ocl/va.hpp b/src/inference/include/openvino/runtime/intel_gpu/ocl/va.hpp
similarity index 97%
rename from src/inference/include/openvino/runtime/gpu/ocl/va.hpp
rename to src/inference/include/openvino/runtime/intel_gpu/ocl/va.hpp
index 45e8611077c..ed846ce0bde 100644
--- a/src/inference/include/openvino/runtime/gpu/ocl/va.hpp
+++ b/src/inference/include/openvino/runtime/intel_gpu/ocl/va.hpp
@@ -7,7 +7,7 @@
  * shared Video Acceleration device contexts
  * and shared memory tensors which contain Video Acceleration surfaces
  *
- * @file openvino/runtime/gpu/ocl/va.hpp
+ * @file openvino/runtime/intel_gpu/ocl/va.hpp
  */
 #pragma once
 
@@ -18,7 +18,7 @@
 #include <memory>
 #include <string>
 
-#include "openvino/runtime/gpu/ocl/ocl.hpp"
+#include "openvino/runtime/intel_gpu/ocl/ocl.hpp"
 
 // clang-format off
 #include <va/va.h>
@@ -26,7 +26,7 @@
 
 namespace ov {
 namespace runtime {
-namespace gpu {
+namespace intel_gpu {
 namespace ocl {
 
 /**
@@ -147,6 +147,6 @@ public:
     }
 };
 }  // namespace ocl
-}  // namespace gpu
+}  // namespace intel_gpu
 }  // namespace runtime
 }  // namespace ov
diff --git a/src/tests/functional/inference_engine/CMakeLists.txt b/src/tests/functional/inference_engine/CMakeLists.txt
index 05ef097efaa..0cb94729dbc 100644
--- a/src/tests/functional/inference_engine/CMakeLists.txt
+++ b/src/tests/functional/inference_engine/CMakeLists.txt
@@ -148,18 +148,18 @@ function(ie_headers_compilation_with_custom_flags)
                                                 "gpu/gpu_context_api_va.hpp"
                                                 "gpu/gpu_context_api_dx.hpp"
                                                 "gpu/gpu_ocl_wrapper.hpp"
-                                                "openvino/runtime/gpu/ocl/ocl_wrapper.hpp"
-                                                "openvino/runtime/gpu/ocl/ocl.hpp"
-                                                "openvino/runtime/gpu/ocl/va.hpp"
-                                                "openvino/runtime/gpu/ocl/dx.hpp")
+                                                "openvino/runtime/intel_gpu/ocl/ocl_wrapper.hpp"
+                                                "openvino/runtime/intel_gpu/ocl/ocl.hpp"
+                                                "openvino/runtime/intel_gpu/ocl/va.hpp"
+                                                "openvino/runtime/intel_gpu/ocl/dx.hpp")
         endif()
         if(NOT WIN32)
             list(APPEND IE_TEST_HEADERS_TO_SKIP "gpu/gpu_context_api_dx.hpp"
-                                                "openvino/runtime/gpu/ocl/dx.hpp")
+                                                "openvino/runtime/intel_gpu/ocl/dx.hpp")
         endif()
         if(NOT LIBVA_FOUND)
             list(APPEND IE_TEST_HEADERS_TO_SKIP "gpu/gpu_context_api_va.hpp"
-                                                "openvino/runtime/gpu/ocl/va.hpp")
+                                                "openvino/runtime/intel_gpu/ocl/va.hpp")
         endif()
     endif()
 
diff --git a/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp b/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
index b8c736b965f..b4ae7c0ea9d 100644
--- a/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
+++ b/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
@@ -7,7 +7,7 @@
 #include <vector>
 #include <memory>
 
-#include "openvino/runtime/gpu/ocl/ocl.hpp"
+#include "openvino/runtime/intel_gpu/ocl/ocl.hpp"
 #include "openvino/runtime/core.hpp"
 
 #include <gpu/gpu_config.hpp>
@@ -98,7 +98,7 @@ TEST_P(OVRemoteTensorInputBlob_Test, smoke_canInputRemoteTensor) {
 
     // inference using remote tensor
     auto inf_req_shared = exec_net.create_infer_request();
-    auto cldnn_context = exec_net.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+    auto cldnn_context = exec_net.get_context().as<ov::runtime::intel_gpu::ocl::ClContext>();
     cl_context ctx = cldnn_context;
     auto ocl_instance = std::make_shared<OpenCL>(ctx);
     cl_int err;
@@ -159,8 +159,8 @@ TEST_P(OVRemoteTensorInputBlob_Test, smoke_canInputRemoteTensor) {
         }
         case RemoteTensorSharingType::PLUGIN_CL_TENSOR: {
             auto cldnn_tensor = cldnn_context.create_tensor(input->get_element_type(), input->get_shape());
-            ASSERT_TRUE(cldnn_tensor.is<ov::runtime::gpu::ocl::ClBufferTensor>());
-            auto cl_tensor = cldnn_tensor.as<ov::runtime::gpu::ocl::ClBufferTensor>();
+            ASSERT_TRUE(cldnn_tensor.is<ov::runtime::intel_gpu::ocl::ClBufferTensor>());
+            auto cl_tensor = cldnn_tensor.as<ov::runtime::intel_gpu::ocl::ClBufferTensor>();
             {
                 cl::Buffer shared_buffer = cl_tensor;
                 void* buffer = fakeImageData.data();
@@ -175,9 +175,9 @@ TEST_P(OVRemoteTensorInputBlob_Test, smoke_canInputRemoteTensor) {
                 GTEST_SKIP();
 
             auto cldnn_tensor = cldnn_context.create_usm_host_tensor(input->get_element_type(), input->get_shape());
-            ASSERT_TRUE(cldnn_tensor.is<ov::runtime::gpu::ocl::USMTensor>());
+            ASSERT_TRUE(cldnn_tensor.is<ov::runtime::intel_gpu::ocl::USMTensor>());
             {
-                auto cl_tensor = cldnn_tensor.as<ov::runtime::gpu::ocl::USMTensor>();
+                auto cl_tensor = cldnn_tensor.as<ov::runtime::intel_gpu::ocl::USMTensor>();
                 void* shared_buffer = cl_tensor.get();
                 ASSERT_EQ(ocl_instance->get_allocation_type(shared_buffer), CL_MEM_TYPE_HOST_INTEL);
                 void* buffer = fakeImageData.data();
@@ -194,9 +194,9 @@ TEST_P(OVRemoteTensorInputBlob_Test, smoke_canInputRemoteTensor) {
                 GTEST_SKIP();
 
             auto cldnn_tensor = cldnn_context.create_usm_device_tensor(input->get_element_type(), input->get_shape());
-            ASSERT_TRUE(cldnn_tensor.is<ov::runtime::gpu::ocl::USMTensor>());
+            ASSERT_TRUE(cldnn_tensor.is<ov::runtime::intel_gpu::ocl::USMTensor>());
             {
-                auto cl_tensor = cldnn_tensor.as<ov::runtime::gpu::ocl::USMTensor>();
+                auto cl_tensor = cldnn_tensor.as<ov::runtime::intel_gpu::ocl::USMTensor>();
                 void* shared_buffer = cl_tensor.get();
                 ASSERT_EQ(ocl_instance->get_allocation_type(shared_buffer), CL_MEM_TYPE_DEVICE_INTEL);
                 void* buffer = fakeImageData.data();
@@ -277,7 +277,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContext) {
     // inference using remote tensor
     auto ocl_instance = std::make_shared<OpenCL>();
 
-    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_context.get());
+    auto remote_context = ov::runtime::intel_gpu::ocl::ClContext(ie, ocl_instance->_context.get());
     auto exec_net_shared = ie.compile_model(function, remote_context);
     auto inf_req_shared = exec_net_shared.create_infer_request();
     inf_req_shared.set_tensor(input, fakeImageData);
@@ -323,7 +323,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContextWithMultipleDevices) {
     cl::Context multi_device_ctx({ocl_instance_tmp->_device, ocl_instance_tmp->_device});
     auto ocl_instance = std::make_shared<OpenCL>(multi_device_ctx.get());
 
-    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_context.get(), 1);
+    auto remote_context = ov::runtime::intel_gpu::ocl::ClContext(ie, ocl_instance->_context.get(), 1);
 
     ASSERT_EQ(remote_context.get_device_name(), "GPU.0");
     auto exec_net_shared = ie.compile_model(function, remote_context);
@@ -376,9 +376,9 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_out_of_order) {
     cl::Buffer shared_input_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, in_size, NULL, &err);
     cl::Buffer shared_output_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, out_size, NULL, &err);
 
-    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
+    auto remote_context = ov::runtime::intel_gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
     auto exec_net_shared = ie.compile_model(function, remote_context);
-    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::intel_gpu::ocl::ClContext>();
 
     auto gpu_in_tensor = gpu_context.create_tensor(input->get_output_element_type(0), input->get_output_shape(0), shared_input_buffer);
     auto gpu_out_tensor = gpu_context.create_tensor(output->get_output_element_type(0), output->get_output_shape(0), shared_output_buffer);
@@ -456,9 +456,9 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_in_order) {
     cl::Buffer shared_input_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, in_size, NULL, &err);
     cl::Buffer shared_output_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, out_size, NULL, &err);
 
-    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
+    auto remote_context = ov::runtime::intel_gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
     auto exec_net_shared = ie.compile_model(function, remote_context);
-    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::intel_gpu::ocl::ClContext>();
 
     auto gpu_in_tensor = gpu_context.create_tensor(input->get_output_element_type(0), input->get_output_shape(0), shared_input_buffer);
     auto gpu_out_tensor = gpu_context.create_tensor(output->get_output_element_type(0), output->get_output_shape(0), shared_output_buffer);
@@ -531,7 +531,7 @@ TEST_F(OVRemoteTensor_Test, NV12toBGR_image) {
     auto exec_net_b = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
     auto inf_req_remote = exec_net_b.create_infer_request();
 
-    auto cldnn_context = exec_net_b.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+    auto cldnn_context = exec_net_b.get_context().as<ov::runtime::intel_gpu::ocl::ClContext>();
     cl_context ctx = cldnn_context.get();
     auto ocl_instance = std::make_shared<OpenCL>(ctx);
     cl_int err;
@@ -650,9 +650,9 @@ TEST_F(OVRemoteTensor_Test, NV12toBGR_buffer) {
     cl::Buffer shared_input_uv_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, in_size_uv, NULL, &err);
     cl::Buffer shared_output_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, out_size, NULL, &err);
 
-    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
+    auto remote_context = ov::runtime::intel_gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
     auto exec_net_shared = ie.compile_model(function, remote_context);
-    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::intel_gpu::ocl::ClContext>();
 
     auto gpu_in_y_tensor = gpu_context.create_tensor(param_input_y->get_output_element_type(0), fake_image_data_y.get_shape(), shared_input_y_buffer);
     auto gpu_in_uv_tensor = gpu_context.create_tensor(param_input_uv->get_output_element_type(0), fake_image_data_uv.get_shape(), shared_input_uv_buffer);

From ca7600eddeed56e1e76927f3c92a0a5feeb8484d Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Mon, 20 Dec 2021 12:49:39 +0300
Subject: [PATCH 042/160] [dynamism][Transformations] Sequences conversions:
 dynamic shapes support (#7766)

* [Transformations] Sequences conversion: refactoring & dynamic shapes support

* tests moved
---
 .../src/mkldnn_plugin/mkldnn_plugin.cpp       |   8 +-
 .../convert_to_cpu_specific_opset.hpp         |   4 +-
 .../rnn_sequences_optimization.cpp            |  96 ++-
 .../rnn_sequences_optimization.hpp            |   6 +
 .../convert_sequences_to_tensor_iterator.hpp  |   9 +-
 .../convert_ti_to_sequences.hpp               |   9 +-
 .../convert_sequences_to_tensor_iterator.cpp  | 647 ++++++-----------
 .../convert_ti_to_sequences.cpp               | 679 ++++++++----------
 src/core/src/op/tensor_iterator.cpp           |   9 +-
 .../src/plugin/transformations_pipeline.cpp   |  12 +-
 .../convert_sequences_to_ti_test.cpp          | 540 ++++++++++++++
 .../convert_ti_to_sequences_test.cpp          | 319 ++++++--
 .../cpu/optimize_sequence_transposes_test.cpp | 445 ++++++++++++
 13 files changed, 1860 insertions(+), 923 deletions(-)
 create mode 100644 src/tests/functional/inference_engine/transformations/convert_sequences_to_ti_test.cpp
 create mode 100644 src/tests/unit/cpu/optimize_sequence_transposes_test.cpp

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index a6bda152e23..6f8af6141d9 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -173,14 +173,10 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
     manager.register_pass<ngraph::pass::CommonOptimizations>();
     manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
     manager.register_pass<ngraph::pass::TransposeSinking>();
-    manager.register_pass<ngraph::pass::ConvertRNNSequenceToTensorIterator>();
-    manager.register_pass<ngraph::pass::ConvertGRUSequenceToTensorIterator>();
-    manager.register_pass<ngraph::pass::ConvertLSTMSequenceToTensorIterator>();
+    manager.register_pass<ngraph::pass::ConvertSequenceToTensorIterator>();
     manager.register_pass<ngraph::pass::ConvertOpSet3ToOpSet2>();
     manager.register_pass<ngraph::pass::ConvertOpSet2ToOpSet1>();
-    manager.register_pass<ngraph::pass::ConvertTensorIteratorToGRUSequence>();
-    manager.register_pass<ngraph::pass::ConvertTensorIteratorToLSTMSequence>();
-    manager.register_pass<ngraph::pass::ConvertTensorIteratorToRNNSequence>();
+    manager.register_pass<ngraph::pass::ConvertTensorIteratorToSequence>();
     manager.register_pass<ngraph::pass::LSTMCellDecomposition>();
     manager.register_pass<ngraph::pass::GRUCellDecomposition>();
     manager.register_pass<ngraph::pass::RNNCellDecomposition>();
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
index 078fb75c14d..121247c702c 100644
--- a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
@@ -32,9 +32,7 @@ inline void ConvertToCPUSpecificOpset(std::shared_ptr<ngraph::Function> &nGraphF
     manager.register_pass<ConvertToLeakyRelu>();
     manager.register_pass<ReshapePRelu>();
     manager.register_pass<ConvertToSwishCPU>();
-    manager.register_pass<OptimizeGRUSequenceTransposes>();
-    manager.register_pass<OptimizeLSTMSequenceTransposes>();
-    manager.register_pass<OptimizeRNNSequenceTransposes>();
+    manager.register_pass<OptimizeSequenceTransposes>();
     if (!ngraph::op::util::has_op_with_type<ngraph::op::FakeQuantize>(nGraphFunc)) {
         manager.register_pass<ReshapeFullyConnectedFusion>();
     }
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.cpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.cpp
index 869df73be6d..9be3be78020 100644
--- a/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.cpp
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.cpp
@@ -3,14 +3,17 @@
 //
 
 #include "rnn_sequences_optimization.hpp"
+
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset5.hpp>
 #include <ngraph/pattern/op/wrap_type.hpp>
-#include <ngraph/pattern/op/or.hpp>
 #include <transformations/utils/utils.hpp>
 #include <ngraph/variant.hpp>
 
 NGRAPH_RTTI_DEFINITION(MKLDNNPlugin::OptimizeGRUSequenceTransposes, "OptimizeGRUSequenceTransposes", 0);
 NGRAPH_RTTI_DEFINITION(MKLDNNPlugin::OptimizeLSTMSequenceTransposes, "OptimizeLSTMSequenceTransposes", 0);
 NGRAPH_RTTI_DEFINITION(MKLDNNPlugin::OptimizeRNNSequenceTransposes, "OptimizeRNNSequenceTransposes", 0);
+NGRAPH_RTTI_DEFINITION(MKLDNNPlugin::OptimizeSequenceTransposes, "OptimizeSequenceTransposes", 0);
 
 namespace {
     int64_t getSeqAxis(const std::shared_ptr<ngraph::Node>& sequenceOp) {
@@ -23,16 +26,16 @@ namespace {
         // of the attribute to plug-ins.
         // todo: specify seqAxis attribute for Sequence ops.
         int64_t seqAxis = 1; // default
-        const auto& target_inputs = sequenceOp->output(0).get_target_inputs();
+        const auto& target_inputs = sequenceOp->get_output_target_inputs(0);
         if (target_inputs.size() == 1) {
-            const auto& transpose_before = std::dynamic_pointer_cast<ngraph::op::v1::Transpose>(sequenceOp->input_value(0).get_node_shared_ptr());
-            const auto& transpose_after = std::dynamic_pointer_cast<ngraph::op::v1::Transpose>(target_inputs.begin()->get_node()->shared_from_this());
-            if (transpose_after != nullptr && transpose_before != nullptr) {
-                auto order_before = std::dynamic_pointer_cast<ngraph::op::v0::Constant>(
-                        transpose_before->input_value(1).get_node_shared_ptr());
-                auto order_after = std::dynamic_pointer_cast<ngraph::op::v0::Constant>(
-                        transpose_after->input_value(1).get_node_shared_ptr());
-                if (order_before != nullptr && order_after != nullptr) {
+            const auto transpose_before = ngraph::as_type_ptr<ngraph::opset1::Transpose>(sequenceOp->get_input_node_shared_ptr(0));
+            const auto transpose_after = ngraph::as_type_ptr<ngraph::opset1::Transpose>(target_inputs.begin()->get_node()->shared_from_this());
+
+            if (transpose_after && transpose_before) {
+                auto order_before = ngraph::as_type_ptr<ngraph::opset1::Constant>(transpose_before->get_input_node_shared_ptr(1));
+                auto order_after = ngraph::as_type_ptr<ngraph::opset1::Constant>(transpose_after->get_input_node_shared_ptr(1));
+
+                if (order_before && order_after) {
                     auto order_before_values = order_before->cast_vector<int64_t>();
                     auto order_after_values = order_after->cast_vector<int64_t>();
                     std::vector<int64_t> order_ref_before = {1, 0, 2};
@@ -50,21 +53,30 @@ namespace {
         // Detect pattern: Transpose_before -> Seq -> Transpose_after
         auto seqAxis = getSeqAxis(sequenceOp);
         if (seqAxis == 0) {
-            ngraph::Output<ngraph::Node> in_0 = sequenceOp->get_input_source_output(0).get_node_shared_ptr()->get_input_source_output(0);
+            ngraph::Output<ngraph::Node> in_0 = sequenceOp->get_input_node_shared_ptr(0)->input_value(0);
 
-            auto newInShape = ngraph::op::v0::Constant::create(ngraph::element::i32, ngraph::Shape{3}, sequenceOp->get_input_shape(0));
-            auto reshape1 = std::make_shared<ngraph::op::v1::Reshape>(in_0, newInShape, false);
-            ngraph::replace_node(sequenceOp->get_input_node_shared_ptr(0), {reshape1->output(0)});
+            auto shapeBeforeTranspose = ngraph::op::util::make_try_fold<ngraph::opset1::ShapeOf>(in_0);
+            auto newInShape = ngraph::op::util::make_try_fold<ngraph::opset1::Gather>(shapeBeforeTranspose,
+                ngraph::opset1::Constant::create(ngraph::element::i32, { 3 }, { 1, 0, 2 }),
+                ngraph::opset1::Constant::create(ngraph::element::i32, {}, { 0 }));
+            auto reshape1 = std::make_shared<ngraph::opset1::Reshape>(in_0, newInShape, false);
+            ngraph::copy_runtime_info(sequenceOp->get_input_node_shared_ptr(0), reshape1);
+            ngraph::replace_node(sequenceOp->get_input_node_shared_ptr(0), reshape1);
 
-            const auto &seqTargetInputs = sequenceOp->output(0).get_target_inputs();
+            const auto &seqTargetInputs = sequenceOp->get_output_target_inputs(0);
             if (seqTargetInputs.empty())
                 return false;
             auto transposeAfter = seqTargetInputs.begin()->get_node()->shared_from_this();
 
-            auto newOutShape = ngraph::op::v0::Constant::create(ngraph::element::i32, ngraph::Shape{4}, transposeAfter->get_output_shape(0));
-            auto reshape2 = std::make_shared<ngraph::op::v1::Reshape>(sequenceOp->output(0), newOutShape, false);
+            auto lstmOutShape = ngraph::op::util::make_try_fold<ngraph::opset1::ShapeOf>(sequenceOp->output(0));
+            auto newOutShape = ngraph::op::util::make_try_fold<ngraph::opset1::Gather>(lstmOutShape,
+                ngraph::opset1::Constant::create(ngraph::element::i32, { 4 }, { 2, 1, 0, 3 }),
+                ngraph::opset1::Constant::create(ngraph::element::i32, {}, { 0 }));
+
+            auto reshape2 = std::make_shared<ngraph::opset1::Reshape>(sequenceOp->output(0), newOutShape, false);
             reshape2->set_friendly_name(transposeAfter->get_friendly_name());
-            ngraph::replace_node(transposeAfter, {reshape2->output(0)});
+            ngraph::copy_runtime_info(transposeAfter, reshape2);
+            ngraph::replace_node(transposeAfter, reshape2);
         }
 
         sequenceOp->get_rt_info()["seqAxis"] = seqAxis;
@@ -74,8 +86,10 @@ namespace {
 } // namespace
 
 MKLDNNPlugin::OptimizeGRUSequenceTransposes::OptimizeGRUSequenceTransposes() {
+    auto gruSequenceNgraph = ngraph::pattern::wrap_type<ngraph::opset5::GRUSequence>();
+
     ngraph::matcher_pass_callback callback = [](ngraph::pattern::Matcher &m) {
-        auto gruSequence = std::dynamic_pointer_cast<ngraph::op::v5::GRUSequence>(m.get_match_root());
+        auto gruSequence = ngraph::as_type_ptr<ngraph::opset5::GRUSequence>(m.get_match_root());
         if (!gruSequence) {
             return false;
         }
@@ -86,15 +100,15 @@ MKLDNNPlugin::OptimizeGRUSequenceTransposes::OptimizeGRUSequenceTransposes() {
         return transform(gruSequence);
     };
 
-    auto gruSequenceNgraph = ngraph::pattern::wrap_type<ngraph::op::v5::GRUSequence>();
-
     auto m = std::make_shared<ngraph::pattern::Matcher>(gruSequenceNgraph, "OptimizeGRUSequenceTransposes");
     this->register_matcher(m, callback);
 }
 
 MKLDNNPlugin::OptimizeRNNSequenceTransposes::OptimizeRNNSequenceTransposes() {
+    auto rnnSequenceNgraph = ngraph::pattern::wrap_type<ngraph::opset5::RNNSequence>();
+
     ngraph::matcher_pass_callback callback = [](ngraph::pattern::Matcher &m) {
-        auto rnnSequence = std::dynamic_pointer_cast<ngraph::op::v5::RNNSequence>(m.get_match_root());
+        auto rnnSequence = ngraph::as_type_ptr<ngraph::opset5::RNNSequence>(m.get_match_root());
         if (!rnnSequence) {
             return false;
         }
@@ -105,30 +119,34 @@ MKLDNNPlugin::OptimizeRNNSequenceTransposes::OptimizeRNNSequenceTransposes() {
         return transform(rnnSequence);
     };
 
-    auto rnnSequenceNgraph = ngraph::pattern::wrap_type<ngraph::op::v5::RNNSequence>();
-
     auto m = std::make_shared<ngraph::pattern::Matcher>(rnnSequenceNgraph, "OptimizeRNNSequenceTransposes");
     this->register_matcher(m, callback);
 }
 
 MKLDNNPlugin::OptimizeLSTMSequenceTransposes::OptimizeLSTMSequenceTransposes() {
-    ngraph::matcher_pass_callback callback = [](ngraph::pattern::Matcher &m) {
-        auto lstmSequence = std::dynamic_pointer_cast<ngraph::op::v5::LSTMSequence>(m.get_match_root());
-        if (!lstmSequence) {
-            return false;
-        }
-        // Bidirectional cases are not supported
-        if (lstmSequence->get_direction() == ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL)
-            return false;
+    auto lstmSequenceNgraph = ngraph::pattern::wrap_type<ngraph::opset1::LSTMSequence, ngraph::opset5::LSTMSequence>();
 
-        return transform(lstmSequence);
+    ngraph::matcher_pass_callback callback = [](ngraph::pattern::Matcher &m) {
+        auto checkSequence = [](const std::shared_ptr<ngraph::Node>& node) {
+            if (auto lstm5 = ngraph::as_type_ptr<ngraph::opset5::LSTMSequence>(node)) {
+                return lstm5->get_direction() != ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL;
+            } else if (auto lstm1 = ngraph::as_type_ptr<ngraph::opset1::LSTMSequence>(node)) {
+                return lstm1->get_direction() != ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL;
+            } else {
+                return false;
+            }
+        };
+
+        std::shared_ptr<ngraph::Node> lstmSequence = m.get_match_root();
+        return checkSequence(lstmSequence) ? transform(lstmSequence) : false;
     };
 
-    auto lstmSequenceNgraph_0 = ngraph::pattern::wrap_type<ngraph::op::v0::LSTMSequence>();
-    auto lstmSequenceNgraph_5 = ngraph::pattern::wrap_type<ngraph::op::v5::LSTMSequence>();
-    const auto lstmSeqInputs = std::make_shared<ngraph::pattern::op::Or>(ngraph::OutputVector{lstmSequenceNgraph_0, lstmSequenceNgraph_5});
-
-    auto m = std::make_shared<ngraph::pattern::Matcher>(lstmSeqInputs, "OptimizeLSTMSequenceTransposes");
-
+    auto m = std::make_shared<ngraph::pattern::Matcher>(lstmSequenceNgraph, "OptimizeLSTMSequenceTransposes");
     this->register_matcher(m, callback);
 }
+
+MKLDNNPlugin::OptimizeSequenceTransposes::OptimizeSequenceTransposes() {
+    add_matcher<OptimizeLSTMSequenceTransposes>();
+    add_matcher<OptimizeRNNSequenceTransposes>();
+    add_matcher<OptimizeGRUSequenceTransposes>();
+}
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.hpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.hpp
index 14cf1a585af..2b83d790390 100644
--- a/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.hpp
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.hpp
@@ -26,4 +26,10 @@ public:
     OptimizeRNNSequenceTransposes();
 };
 
+class OptimizeSequenceTransposes : public ngraph::pass::GraphRewrite {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    OptimizeSequenceTransposes();
+};
+
 }  // namespace MKLDNNPlugin
diff --git a/src/common/transformations/include/transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp b/src/common/transformations/include/transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp
index be6df96815f..49ea97b52fb 100644
--- a/src/common/transformations/include/transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp
+++ b/src/common/transformations/include/transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp
@@ -17,6 +17,7 @@ namespace pass {
 class TRANSFORMATIONS_API ConvertRNNSequenceToTensorIterator;
 class TRANSFORMATIONS_API ConvertGRUSequenceToTensorIterator;
 class TRANSFORMATIONS_API ConvertLSTMSequenceToTensorIterator;
+class TRANSFORMATIONS_API ConvertSequenceToTensorIterator;
 
 }  // namespace pass
 }  // namespace ngraph
@@ -55,4 +56,10 @@ class ngraph::pass::ConvertLSTMSequenceToTensorIterator: public ngraph::pass::Ma
 public:
     NGRAPH_RTTI_DECLARATION;
     ConvertLSTMSequenceToTensorIterator();
-};
\ No newline at end of file
+};
+
+class ngraph::pass::ConvertSequenceToTensorIterator : public GraphRewrite {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    ConvertSequenceToTensorIterator();
+};
diff --git a/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp b/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp
index 67e41fd85ef..cb3c8c1e1aa 100644
--- a/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp
+++ b/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp
@@ -20,6 +20,7 @@ namespace pass {
 class TRANSFORMATIONS_API ConvertTensorIteratorToLSTMSequence;
 class TRANSFORMATIONS_API ConvertTensorIteratorToRNNSequence;
 class TRANSFORMATIONS_API ConvertTensorIteratorToGRUSequence;
+class TRANSFORMATIONS_API ConvertTensorIteratorToSequence;
 
 }  // namespace pass
 }  // namespace ngraph
@@ -58,4 +59,10 @@ class ngraph::pass::ConvertTensorIteratorToGRUSequence: public ngraph::pass::Mat
 public:
     NGRAPH_RTTI_DECLARATION;
     ConvertTensorIteratorToGRUSequence();
-};
\ No newline at end of file
+};
+
+class ngraph::pass::ConvertTensorIteratorToSequence : public GraphRewrite {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    ConvertTensorIteratorToSequence();
+};
diff --git a/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp b/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp
index f4d2cca0c53..c10a55a8d91 100644
--- a/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp
+++ b/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp
@@ -17,6 +17,7 @@
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertRNNSequenceToTensorIterator, "ConvertRNNSequenceToTensorIterator", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertGRUSequenceToTensorIterator, "ConvertGRUSequenceToTensorIterator", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertLSTMSequenceToTensorIterator, "ConvertLSTMSequenceToTensorIterator", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertSequenceToTensorIterator, "ConvertSequenceToTensorIterator", 0);
 
 namespace {
     ngraph::Output<ngraph::Node> get_current_iter(ngraph::ParameterVector &body_params,
@@ -25,8 +26,7 @@ namespace {
         auto curr_iter_body_param = std::make_shared<ngraph::opset5::Parameter>(seq_lengths.get_element_type(),
                                                                                 ngraph::Shape{1});
         // increment current iteration
-        auto one = std::make_shared<ngraph::opset5::Constant>(seq_lengths.get_element_type(), ngraph::Shape{1},
-                                                              std::vector<int64_t>{1});
+        auto one = ngraph::opset5::Constant::create(seq_lengths.get_element_type(), ngraph::Shape{1}, {1});
         auto add = std::make_shared<ngraph::opset5::Add>(curr_iter_body_param, one);
         auto curr_iter_result = std::make_shared<ngraph::opset5::Result>(add);
         body_params.push_back(curr_iter_body_param);
@@ -40,19 +40,14 @@ namespace {
                                                   const ngraph::Output<ngraph::Node> &current_iter,
                                                   const ngraph::Output<ngraph::Node> &data,
                                                   const ngraph::Output<ngraph::Node> &seq_lengths) {
-        const auto &data_type = data.get_element_type();
-        const auto &data_shape = data.get_shape();
-        const auto &data_shape_size = ngraph::shape_size(data_shape);
-
         // body parameters
-        auto aggregated_Y_h_body_param = std::make_shared<ngraph::opset5::Parameter>(data_type, data_shape);
+        auto aggregated_Y_h_body_param = std::make_shared<ngraph::opset5::Parameter>(data.get_element_type(), data.get_partial_shape());
 
         body_params.push_back(aggregated_Y_h_body_param);
 
         // Create mask node deciding whether or not to mask batch data.
-        ngraph::Output<ngraph::Node> batch_seq_length = ngraph::builder::opset1::legacy_broadcast_for_binary_operation(
-                data, seq_lengths, 0);
-        auto mask_value = std::make_shared<ngraph::opset5::Constant>(data_type, data_shape, std::vector<float>(data_shape_size, 0.f));
+        auto batch_seq_length = ngraph::builder::opset1::legacy_broadcast_for_binary_operation(data, seq_lengths, 0);
+
         auto mask_condition = std::make_shared<ngraph::opset5::Greater>(current_iter, batch_seq_length);
         auto mask_Y_h = std::make_shared<ngraph::opset5::Equal>(current_iter, batch_seq_length);
 
@@ -61,101 +56,136 @@ namespace {
         auto select_aggregated_H = std::make_shared<ngraph::opset5::Select>(mask_Y_h, data, aggregated_Y_h_body_param);
         auto aggregated_result = std::make_shared<ngraph::opset5::Result>(select_aggregated_H);
         body_results.push_back(aggregated_result);
-        return std::make_shared<ngraph::opset5::Select>(mask_condition, mask_value, data);
+
+        auto scalar_mask_value = ngraph::opset5::Constant::create(data.get_element_type(), {}, { 0.f });
+        auto data_shape = ngraph::op::util::make_try_fold<ngraph::opset5::ShapeOf>(data);
+        auto mask_value = ngraph::op::util::make_try_fold<ngraph::opset5::Broadcast>(scalar_mask_value, data_shape);
+        return ngraph::op::util::make_try_fold<ngraph::opset5::Select>(mask_condition, mask_value, data);
     }
 
-    ngraph::NodeVector squeeze_nodes(const ngraph::OutputVector &nodes_to_squeeze, const ngraph::OutputVector &axes) {
-        ngraph::NodeVector squeezed_nodes(nodes_to_squeeze.size());
-        for (size_t i = 0; i < nodes_to_squeeze.size(); ++i) {
-            squeezed_nodes[i] = std::make_shared<ngraph::opset5::Squeeze>(nodes_to_squeeze[i], axes[i]);
-        }
-        return squeezed_nodes;
-    }
-} // namespace
-
-ngraph::pass::ConvertRNNSequenceToTensorIterator::ConvertRNNSequenceToTensorIterator() {
-    MATCHER_SCOPE(ConvertRNNSequenceToTensorIterator);
-    // X, H, seq_lengths - static, W,R,B - any
-    auto rnn_seq = ngraph::pattern::wrap_type<opset5::RNNSequence>({pattern::any_input(pattern::has_static_shape()),
-                                                                    pattern::any_input(pattern::has_static_shape()),
-                                                                    pattern::any_input(pattern::has_static_shape()),
-                                                                    pattern::any_input(),
-                                                                    pattern::any_input(),
-                                                                    pattern::any_input()});
-    ngraph::matcher_pass_callback callback = [this](ngraph::pattern::Matcher &m) {
-        auto sequence = std::dynamic_pointer_cast<ngraph::opset5::RNNSequence>(m.get_match_root());
-
-        // Bidirectional Sequence op should be decomposed to Reverse + Forward
-        // (e.g. apply BidirectionalRNNSequenceDecomposition transformation before this one)
-        if (!sequence || sequence->get_direction() == ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL || transformation_callback(sequence)) {
+    bool convert_sequence_to_ti(
+        const std::shared_ptr<ngraph::Node>& sequence,
+        const ngraph::Output<ngraph::Node>& X,
+        const ngraph::Output<ngraph::Node>& H_t,
+        const ngraph::Output<ngraph::Node>& C_t,
+        const ngraph::Output<ngraph::Node>& seq_lengths,
+        const ngraph::Output<ngraph::Node>& W,
+        const ngraph::Output<ngraph::Node>& R,
+        const ngraph::Output<ngraph::Node>& B,
+        const ngraph::op::RecurrentSequenceDirection& direction) {
+        auto X_pshape = X.get_partial_shape();
+        if (X_pshape.size() < 2 || X_pshape[1].is_dynamic()) {
             return false;
         }
 
-        NodeVector new_nodes;
-        const auto &X = sequence->input_value(0); // split
-        const auto &H_t = sequence->input_value(1); // merged (init value + back edge)
-        const auto &seq_lengths = sequence->input_value(2); // invariant
-        const auto &W = sequence->input_value(3); // const in the body
-        const auto &R = sequence->input_value(4); // const in the body
-        const auto &B = sequence->input_value(5); // const in the body
-        bool is_reverse = sequence->get_direction() == ngraph::op::RecurrentSequenceDirection::REVERSE;
-
-        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
-        auto max_seq_len = X.get_shape().at(1);
+        auto max_seq_len = X_pshape[1].get_length();
         bool enable_mask = ngraph::op::util::is_seq_len_provided(seq_lengths.get_node_shared_ptr(), max_seq_len);
 
-        std::shared_ptr<Node> reverse_seq_before;
+        const bool is_reverse = direction == ngraph::op::RecurrentSequenceDirection::REVERSE;
+        std::shared_ptr<ngraph::Node> reverse_seq_before;
         if (is_reverse && enable_mask) {
-            reverse_seq_before = std::make_shared<opset5::ReverseSequence>(X, seq_lengths, 0, 1);
+            reverse_seq_before = std::make_shared<ngraph::opset5::ReverseSequence>(X, seq_lengths, 0, 1);
         }
 
         // TensorIterator Body: begin
-        Shape X_param_shape = X.get_shape();
-        X_param_shape.at(1) = 1; // split by seq_lengths dimension
-        auto X_body_param = std::make_shared<opset5::Parameter>(X.get_element_type(), X_param_shape);
-        auto H_body_param = std::make_shared<opset5::Parameter>(H_t.get_element_type(),
-                                                                H_t.get_shape());
-        auto seq_body_param = std::make_shared<opset5::Parameter>(seq_lengths.get_element_type(),
-                                                                  seq_lengths.get_partial_shape());
+        auto X_param_pshape = X_pshape;
+        X_param_pshape[1] = 1; // split by seq_lengths dimension
+        auto X_body_param = std::make_shared<ngraph::opset5::Parameter>(X.get_element_type(), X_param_pshape);
+        auto H_body_param = std::make_shared<ngraph::opset5::Parameter>(H_t.get_element_type(), H_t.get_partial_shape());
+        auto seq_body_param = std::make_shared<ngraph::opset5::Parameter>(seq_lengths.get_element_type(), seq_lengths.get_partial_shape());
 
-        auto axis_0 = std::make_shared<opset5::Constant>(element::i64, Shape{1}, std::vector<int64_t>{0});
-        auto axis_1 = std::make_shared<opset5::Constant>(element::i64, Shape{1}, std::vector<int64_t>{1});
+        // LSTM sequence case
+        const bool cell_state_defined = C_t.get_node_shared_ptr() != nullptr;
+        std::shared_ptr<ngraph::opset5::Parameter> C_body_param = cell_state_defined ?
+            std::make_shared<ngraph::opset5::Parameter>(C_t.get_element_type(), C_t.get_partial_shape()) :
+            nullptr;
 
-        const auto& ins = squeeze_nodes({X_body_param, H_body_param, W, R, B}, {axis_1, axis_1, axis_0, axis_0, axis_0});
-        auto cell = std::make_shared<opset5::RNNCell>(ins[0],
-                                                      ins[1],
-                                                      ins[2],
-                                                      ins[3],
-                                                      ins[4],
-                                                      sequence->get_hidden_size(),
-                                                      sequence->get_activations(),
-                                                      sequence->get_activations_alpha(),
-                                                      sequence->get_activations_beta(),
-                                                      sequence->get_clip());
+        auto axis_0 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 0 });
+        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
 
-        ParameterVector body_params;
-        ResultVector body_results;
-        auto unsqueeze_dum_dir = std::make_shared<opset5::Unsqueeze>(cell->output(0), axis_1);
-        Output<Node> h_node_to_result = unsqueeze_dum_dir;
-        if (enable_mask) {
-            auto current_iter = get_current_iter(body_params, body_results, seq_body_param);
-            h_node_to_result = get_masked_value(tensor_iterator, body_params, body_results, current_iter,
-                                                unsqueeze_dum_dir, seq_body_param);
+        const auto squeezed_x = ngraph::op::util::make_try_fold<ngraph::opset5::Squeeze>(X_body_param, axis_1);
+        const auto squeezed_h = ngraph::op::util::make_try_fold<ngraph::opset5::Squeeze>(H_body_param, axis_1);
+        const auto squeezed_w = ngraph::op::util::make_try_fold<ngraph::opset5::Squeeze>(W, axis_0);
+        const auto squeezed_r = ngraph::op::util::make_try_fold<ngraph::opset5::Squeeze>(R, axis_0);
+        const auto squeezed_b = ngraph::op::util::make_try_fold<ngraph::opset5::Squeeze>(B, axis_0);
+
+        std::shared_ptr<ngraph::Node> cell;
+        if (const auto lstm_sequence = ngraph::as_type_ptr<ngraph::opset5::LSTMSequence>(sequence)) {
+            const auto squeezed_c = ngraph::op::util::make_try_fold<ngraph::opset5::Squeeze>(C_body_param, axis_1);
+            cell = std::make_shared<ngraph::opset5::LSTMCell>(
+                squeezed_x,
+                squeezed_h,
+                squeezed_c,
+                squeezed_w,
+                squeezed_r,
+                squeezed_b,
+                lstm_sequence->get_hidden_size(),
+                lstm_sequence->get_activations(),
+                lstm_sequence->get_activations_alpha(),
+                lstm_sequence->get_activations_beta(),
+                lstm_sequence->get_clip());
+        } else if (const auto rnn_sequence = ngraph::as_type_ptr<ngraph::opset5::RNNSequence>(sequence)) {
+            cell = std::make_shared<ngraph::opset5::RNNCell>(
+                squeezed_x,
+                squeezed_h,
+                squeezed_w,
+                squeezed_r,
+                squeezed_b,
+                rnn_sequence->get_hidden_size(),
+                rnn_sequence->get_activations(),
+                rnn_sequence->get_activations_alpha(),
+                rnn_sequence->get_activations_beta(),
+                rnn_sequence->get_clip());
+        } else if (const auto gnn_sequence = ngraph::as_type_ptr<ngraph::opset5::GRUSequence>(sequence)) {
+            cell = std::make_shared<ngraph::opset5::GRUCell>(
+                squeezed_x,
+                squeezed_h,
+                squeezed_w,
+                squeezed_r,
+                squeezed_b,
+                gnn_sequence->get_hidden_size(),
+                gnn_sequence->get_activations(),
+                gnn_sequence->get_activations_alpha(),
+                gnn_sequence->get_activations_beta(),
+                gnn_sequence->get_clip(),
+                gnn_sequence->get_linear_before_reset());
         }
 
-        auto H_res = std::make_shared<opset5::Result>(h_node_to_result);
-        auto unsqueeze_seq_len = std::make_shared<opset5::Unsqueeze>(h_node_to_result, axis_1);
-        auto concat_res = std::make_shared<opset5::Result>(unsqueeze_seq_len);
+        ngraph::ParameterVector body_params;
+        ngraph::ResultVector body_results;
+        auto unsqueeze_dum_dir_h = std::make_shared<ngraph::opset5::Unsqueeze>(cell->output(0), axis_1);
+        ngraph::Output<ngraph::Node> h_node_to_result = unsqueeze_dum_dir_h;
+
+        auto unsqueeze_dum_dir_c = cell_state_defined ? std::make_shared<ngraph::opset5::Unsqueeze>(cell->output(1), axis_1) : nullptr;
+        ngraph::Output<ngraph::Node> c_node_to_result = cell_state_defined ? unsqueeze_dum_dir_c : ngraph::Output<ngraph::Node>();
+
+        auto tensor_iterator = std::make_shared<ngraph::opset5::TensorIterator>();
+        if (enable_mask) {
+            const auto current_iter = get_current_iter(body_params, body_results, seq_body_param);
+            h_node_to_result = get_masked_value(tensor_iterator, body_params, body_results, current_iter, unsqueeze_dum_dir_h, seq_body_param);
+            if (cell_state_defined)
+                c_node_to_result = get_masked_value(tensor_iterator, body_params, body_results, current_iter, unsqueeze_dum_dir_c, seq_body_param);
+        }
+
+        auto H_res = std::make_shared<ngraph::opset5::Result>(h_node_to_result);
+        auto C_res = cell_state_defined ? std::make_shared<ngraph::opset5::Result>(c_node_to_result) : nullptr;
+        auto unsqueeze_seq_len = std::make_shared<ngraph::opset5::Unsqueeze>(h_node_to_result, axis_1);
+        auto concat_res = std::make_shared<ngraph::opset5::Result>(unsqueeze_seq_len);
+
         body_params.push_back(X_body_param);
         body_params.push_back(H_body_param);
+        if (cell_state_defined)
+            body_params.push_back(C_body_param);
         body_params.push_back(seq_body_param);
+
         body_results.push_back(concat_res);
         body_results.push_back(H_res);
+        if (cell_state_defined)
+            body_results.push_back(C_res);
 
         auto body = std::make_shared<ngraph::Function>(body_results, body_params);
         tensor_iterator->set_function(body);
         // TensorIterator Body: end
-
         if (is_reverse) {
             if (!enable_mask) {
                 // Reversed order, stride -1
@@ -171,31 +201,49 @@ ngraph::pass::ConvertRNNSequenceToTensorIterator::ConvertRNNSequenceToTensorIter
             tensor_iterator->set_sliced_input(X_body_param, X, 0, 1, 1, -1, 1);
             tensor_iterator->get_concatenated_slices(concat_res, 0, 1, 1, -1, 2);
         }
+
         tensor_iterator->set_merged_input(H_body_param, H_t, H_res);
+        if (cell_state_defined)
+            tensor_iterator->set_merged_input(C_body_param, C_t, C_res);
         tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
-        Output<Node> H_out = H_res;
+
+        ngraph::Output<ngraph::Node> H_out = H_res;
+        ngraph::Output<ngraph::Node> C_out = C_res;
         if (enable_mask) {
             // create initial values for body_parameters in outer graph
             // aggregated Y_h - concatenation of the last non-zero values for each batch
-            auto aggregated_Y_h = std::make_shared<ngraph::opset5::Constant>(H_body_param->get_element_type(),
-                                                                             H_body_param->get_shape(),
-                                                                             std::vector<float>(shape_size(H_body_param->get_shape()),
-                                                                                                0.f));
-            auto init_val_curr_iter = std::make_shared<ngraph::opset5::Constant>(seq_lengths.get_element_type(),
-                                                                                 ngraph::Shape{1},
-                                                                                 std::vector<int64_t>{1});
-            ngraph::copy_runtime_info(sequence, {aggregated_Y_h, init_val_curr_iter});
+            auto H_body_param_shape = ngraph::op::util::make_try_fold<ngraph::opset5::ShapeOf>(H_body_param);
+            auto aggregated_Y_h_scalar = ngraph::opset5::Constant::create(H_body_param->get_element_type(), {}, { 0.f });
+            auto aggregated_Y_h = ngraph::op::util::make_try_fold<ngraph::opset5::Broadcast>(aggregated_Y_h_scalar, H_body_param_shape);
+
+            auto init_val_curr_iter = ngraph::opset5::Constant::create(seq_lengths.get_element_type(), ngraph::Shape{ 1 }, { 1 });
+            ngraph::copy_runtime_info(sequence, { aggregated_Y_h, init_val_curr_iter });
 
             // set initial value and back edge for current iteration
             tensor_iterator->set_merged_input(body_params.at(0), init_val_curr_iter, body_results.at(0));
             // set initial value and back edge for aggregated H
             tensor_iterator->set_merged_input(body_params.at(1), aggregated_Y_h, body_results.at(1));
+
             H_out = tensor_iterator->get_function()->get_results()[1];
+
+            if (cell_state_defined) {
+                auto C_body_param_shape = ngraph::op::util::make_try_fold<ngraph::opset5::ShapeOf>(C_body_param);
+                auto aggregated_Y_c_scalar = ngraph::opset5::Constant::create(C_body_param->get_element_type(), {}, { 0.f });
+                auto aggregated_Y_c = ngraph::op::util::make_try_fold<ngraph::opset5::Broadcast>(aggregated_Y_c_scalar, C_body_param_shape);
+                ngraph::copy_runtime_info(sequence, aggregated_Y_c);
+
+                // set initial value and back edge for aggregated C
+                tensor_iterator->set_merged_input(body_params.at(2), aggregated_Y_c, body_results.at(2));
+                C_out = tensor_iterator->get_function()->get_results()[2];
+            }
         }
+
         tensor_iterator->get_iter_value(H_out);
+        if (cell_state_defined)
+            tensor_iterator->get_iter_value(C_out);
         tensor_iterator->set_friendly_name(sequence->get_friendly_name());
         if (enable_mask && is_reverse) {
-            auto reverse_seq_after = std::make_shared<opset5::ReverseSequence>(tensor_iterator->output(0), seq_lengths, 0, 2);
+            auto reverse_seq_after = std::make_shared<ngraph::opset5::ReverseSequence>(tensor_iterator->output(0), seq_lengths, 0, 2);
             // Resolve a collision of names data nodes in CNN Network in Reverse case with mask.
             /*
              *   Before transformation (no collisions)
@@ -214,18 +262,65 @@ ngraph::pass::ConvertRNNSequenceToTensorIterator::ConvertRNNSequenceToTensorIter
              *   TI [other_name] -- (data_node: other_name.0) --> RevSequence [rnn_name.0] -- (data_node: rnn_name.0) -> Result1
              *                   -- (data_node: other_name.1) --> Identity(rnn_name.1) -- (data_node: rnn_name.1) -> Result2
              */
-            auto identity_1 = std::make_shared<opset5::Unsqueeze>(tensor_iterator->output(1), axis_1);
-            auto identity_2 = std::make_shared<opset5::Squeeze>(identity_1, axis_1);
-            ngraph::copy_runtime_info(sequence, {reverse_seq_after, tensor_iterator, identity_1, identity_2, reverse_seq_before});
-            ngraph::replace_node(sequence, {reverse_seq_after, identity_2});
+            auto identity_1_h = std::make_shared<ngraph::opset5::Unsqueeze>(tensor_iterator->output(1), axis_1);
+            auto identity_2_h = std::make_shared<ngraph::opset5::Squeeze>(identity_1_h, axis_1);
+
+            ngraph::NodeVector new_nodes{ reverse_seq_after, tensor_iterator, reverse_seq_before, identity_1_h, identity_2_h };
+            ngraph::OutputVector nodes_to_replace{ reverse_seq_after, identity_2_h };
+
+            if (cell_state_defined) {
+                auto identity_1_c = std::make_shared<ngraph::opset5::Unsqueeze>(tensor_iterator->output(2), axis_1);
+                auto identity_2_c = std::make_shared<ngraph::opset5::Squeeze>(identity_1_c, axis_1);
+
+                new_nodes.emplace_back(identity_1_c);
+                new_nodes.emplace_back(identity_2_c);
+                nodes_to_replace.emplace_back(identity_2_c);
+
+                identity_2_c->set_friendly_name(sequence->get_friendly_name() + ".2");
+            }
+
+            ngraph::copy_runtime_info(sequence, new_nodes);
+            ngraph::replace_node(sequence, nodes_to_replace);
+
             tensor_iterator->set_friendly_name(sequence->get_friendly_name() + "/tensor_iterator");
             reverse_seq_after->set_friendly_name(sequence->get_friendly_name() + ".0");
-            identity_2->set_friendly_name(sequence->get_friendly_name() + ".1");
+            identity_2_h->set_friendly_name(sequence->get_friendly_name() + ".1");
         } else {
             ngraph::copy_runtime_info(sequence, tensor_iterator);
             ngraph::replace_node(sequence, tensor_iterator);
         }
         return true;
+    }
+} // namespace
+
+ngraph::pass::ConvertRNNSequenceToTensorIterator::ConvertRNNSequenceToTensorIterator() {
+    MATCHER_SCOPE(ConvertRNNSequenceToTensorIterator);
+    auto X_m = pattern::any_input(pattern::has_static_rank());
+    auto H_t_m = pattern::any_input();
+    auto seq_lengths_m = pattern::any_input();
+    auto W_m = pattern::any_input();
+    auto R_m = pattern::any_input();
+    auto B_m = pattern::any_input();
+    auto rnn_seq = ngraph::pattern::wrap_type<opset5::RNNSequence>({X_m, H_t_m, seq_lengths_m, W_m, R_m, B_m});
+
+    ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher &m) {
+        auto sequence = ngraph::as_type_ptr<ngraph::opset5::RNNSequence>(m.get_match_root());
+
+        // Bidirectional Sequence op should be decomposed to Reverse + Forward
+        // (e.g. apply BidirectionalRNNSequenceDecomposition transformation before this one)
+        if (!sequence || sequence->get_direction() == ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL || transformation_callback(sequence)) {
+            return false;
+        }
+
+        const auto& pattern_map = m.get_pattern_value_map();
+        const auto& X = pattern_map.at(X_m);                     // split
+        const auto& H_t = pattern_map.at(H_t_m);                 // merged (init value + back edge)
+        const auto& seq_lengths = pattern_map.at(seq_lengths_m); // invariant
+        const auto& W = pattern_map.at(W_m);                     // const in the body
+        const auto& R = pattern_map.at(R_m);                     // const in the body
+        const auto& B = pattern_map.at(B_m);                     // const in the body
+
+        return convert_sequence_to_ti(sequence, X, H_t, Output<Node>(), seq_lengths, W, R, B, sequence->get_direction());
     };
 
     auto m = std::make_shared<ngraph::pattern::Matcher>(rnn_seq, matcher_name);
@@ -234,15 +329,16 @@ ngraph::pass::ConvertRNNSequenceToTensorIterator::ConvertRNNSequenceToTensorIter
 
 ngraph::pass::ConvertGRUSequenceToTensorIterator::ConvertGRUSequenceToTensorIterator() {
     MATCHER_SCOPE(ConvertGRUSequenceToTensorIterator);
-    // X, H, seq_lengths - static, W,R,B - any
-    auto rnn_seq = ngraph::pattern::wrap_type<opset5::GRUSequence>({pattern::any_input(pattern::has_static_shape()),
-                                                                    pattern::any_input(pattern::has_static_shape()),
-                                                                    pattern::any_input(pattern::has_static_shape()),
-                                                                    pattern::any_input(),
-                                                                    pattern::any_input(),
-                                                                    pattern::any_input()});
-    ngraph::matcher_pass_callback callback = [this](ngraph::pattern::Matcher &m) {
-        auto sequence = std::dynamic_pointer_cast<ngraph::opset5::GRUSequence>(m.get_match_root());
+    auto X_m = pattern::any_input(pattern::has_static_rank());
+    auto H_t_m = pattern::any_input();
+    auto seq_lengths_m = pattern::any_input();
+    auto W_m = pattern::any_input();
+    auto R_m = pattern::any_input();
+    auto B_m = pattern::any_input();
+    auto gru_seq = ngraph::pattern::wrap_type<opset5::GRUSequence>({X_m, H_t_m, seq_lengths_m, W_m, R_m, B_m});
+
+    ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher &m) {
+        auto sequence = ngraph::as_type_ptr<ngraph::opset5::GRUSequence>(m.get_match_root());
 
         // Bidirectional Sequence op should be decomposed to Reverse + Forward
         // (e.g. apply BidirectionalRNNSequenceDecomposition transformation before this one)
@@ -250,159 +346,34 @@ ngraph::pass::ConvertGRUSequenceToTensorIterator::ConvertGRUSequenceToTensorIter
             return false;
         }
 
-        NodeVector new_nodes;
-        const auto &X = sequence->input_value(0); // split
-        const auto &H_t = sequence->input_value(1); // merged (init value + back edge)
-        const auto &seq_lengths = sequence->input_value(2); // invariant
-        const auto &W = sequence->input_value(3); // const in the body
-        const auto &R = sequence->input_value(4); // const in the body
-        const auto &B = sequence->input_value(5); // const in the body
-        bool is_reverse = sequence->get_direction() == ngraph::op::RecurrentSequenceDirection::REVERSE;
+        const auto& pattern_map = m.get_pattern_value_map();
+        const auto& X = pattern_map.at(X_m);                      // split
+        const auto& H_t = pattern_map.at(H_t_m);                  // merged (init value + back edge)
+        const auto& seq_lengths = pattern_map.at(seq_lengths_m);  // invariant
+        const auto& W = pattern_map.at(W_m);                      // const in the body
+        const auto& R = pattern_map.at(R_m);                      // const in the body
+        const auto& B = pattern_map.at(B_m);                      // const in the body
 
-        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
-        auto max_seq_len = X.get_shape().at(1);
-        bool enable_mask = ngraph::op::util::is_seq_len_provided(seq_lengths.get_node_shared_ptr(), max_seq_len);
-
-        std::shared_ptr<Node> reverse_seq_before;
-        if (is_reverse && enable_mask) {
-            reverse_seq_before = std::make_shared<opset5::ReverseSequence>(X, seq_lengths, 0, 1);
-        }
-        // TensorIterator Body: begin
-        Shape X_param_shape = X.get_shape();
-        X_param_shape.at(1) = 1; // split by seq_lengths dimension
-        auto X_body_param = std::make_shared<opset5::Parameter>(X.get_element_type(), X_param_shape);
-        auto H_body_param = std::make_shared<opset5::Parameter>(H_t.get_element_type(),
-                                                                H_t.get_shape());
-        auto seq_body_param = std::make_shared<opset5::Parameter>(seq_lengths.get_element_type(),
-                                                                  seq_lengths.get_partial_shape());
-
-        auto axis_0 = std::make_shared<opset5::Constant>(element::i64, Shape{1}, std::vector<int64_t>{0});
-        auto axis_1 = std::make_shared<opset5::Constant>(element::i64, Shape{1}, std::vector<int64_t>{1});
-
-        const auto& ins = squeeze_nodes({X_body_param, H_body_param, W, R, B}, {axis_1, axis_1, axis_0, axis_0, axis_0});
-        auto cell = std::make_shared<opset5::GRUCell>(ins[0],
-                                                      ins[1],
-                                                      ins[2],
-                                                      ins[3],
-                                                      ins[4],
-                                                      sequence->get_hidden_size(),
-                                                      sequence->get_activations(),
-                                                      sequence->get_activations_alpha(),
-                                                      sequence->get_activations_beta(),
-                                                      sequence->get_clip(),
-                                                      sequence->get_linear_before_reset());
-
-        ParameterVector body_params;
-        ResultVector body_results;
-        auto unsqueeze_dum_dir = std::make_shared<opset5::Unsqueeze>(cell->output(0), axis_1);
-        Output<Node> h_node_to_result = unsqueeze_dum_dir;
-        if (enable_mask) {
-            auto current_iter = get_current_iter(body_params, body_results, seq_body_param);
-            h_node_to_result = get_masked_value(tensor_iterator, body_params, body_results, current_iter,
-                                                unsqueeze_dum_dir, seq_body_param);
-        }
-
-        auto H_res = std::make_shared<opset5::Result>(h_node_to_result);
-        auto unsqueeze_seq_len = std::make_shared<opset5::Unsqueeze>(h_node_to_result, axis_1);
-        auto concat_res = std::make_shared<opset5::Result>(unsqueeze_seq_len);
-        body_params.push_back(X_body_param);
-        body_params.push_back(H_body_param);
-        body_params.push_back(seq_body_param);
-        body_results.push_back(concat_res);
-        body_results.push_back(H_res);
-
-        auto body = std::make_shared<ngraph::Function>(body_results, body_params);
-        tensor_iterator->set_function(body);
-        // TensorIterator Body: end
-
-        if (is_reverse) {
-            if (!enable_mask) {
-                // Reversed order, stride -1
-                tensor_iterator->set_sliced_input(X_body_param, X, -1, -1, 1, 0, 1);
-                tensor_iterator->get_concatenated_slices(concat_res, -1, -1, 1, 0, 2);
-            } else {
-                // use ReverseSequence as initializer
-                tensor_iterator->set_sliced_input(X_body_param, reverse_seq_before, 0, 1, 1, -1, 1);
-                tensor_iterator->get_concatenated_slices(concat_res, 0, 1, 1, -1, 2);
-            }
-        } else {
-            // forward order
-            tensor_iterator->set_sliced_input(X_body_param, X, 0, 1, 1, -1, 1);
-            tensor_iterator->get_concatenated_slices(concat_res, 0, 1, 1, -1, 2);
-        }
-        tensor_iterator->set_merged_input(H_body_param, H_t, H_res);
-        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
-        Output<Node> H_out = H_res;
-        if (enable_mask) {
-            // create initial values for body_parameters in outer graph
-            // aggregated Y_h - concatenation of the last non-zero values for each batch
-            auto aggregated_Y_h = std::make_shared<ngraph::opset5::Constant>(H_body_param->get_element_type(),
-                                                                             H_body_param->get_shape(),
-                                                                             std::vector<float>(shape_size(H_body_param->get_shape()),
-                                                                                                0.f));
-            auto init_val_curr_iter = std::make_shared<ngraph::opset5::Constant>(seq_lengths.get_element_type(),
-                                                                                 ngraph::Shape{1},
-                                                                                 std::vector<int64_t>{1});
-            ngraph::copy_runtime_info(sequence, {aggregated_Y_h, init_val_curr_iter});
-
-            // set initial value and back edge for current iteration
-            tensor_iterator->set_merged_input(body_params.at(0), init_val_curr_iter, body_results.at(0));
-            // set initial value and back edge for aggregated H
-            tensor_iterator->set_merged_input(body_params.at(1), aggregated_Y_h, body_results.at(1));
-            H_out = tensor_iterator->get_function()->get_results()[1];
-        }
-        tensor_iterator->get_iter_value(H_out);
-        tensor_iterator->set_friendly_name(sequence->get_friendly_name());
-        if (enable_mask && is_reverse) {
-            auto reverse_seq_after = std::make_shared<opset5::ReverseSequence>(tensor_iterator->output(0), seq_lengths, 0, 2);
-            // Resolve a collision of names data nodes in CNN Network in Reverse case with mask.
-            /*
-             *   Before transformation (no collisions)
-             *   RNN/LSTM/GRU Sequence [rnn_name] -- (data_node: rnn_name.0) - > Result1
-             *                                    -- (data_node: rnn_name.1) - > Result2
-             *
-             *
-             *   After transformation (without identity, there are collisions):
-             *   We need to set rnn_name.0 to RevSequence to store result name.
-             *   TI [rnn_name] -- (DATA_NODE: rnn_name.0) --> RevSequence [rnn_name.0] -- (DATA_NODE: rnn_name.0) -> Result1
-             *                 -- (data_node: rnn_name.1) --> Result2
-             *
-             *
-             *   After transformation (with identity, no collisions):
-             *   TI has other_name, but it doesn't affect result names due TI is not connected to Results directly.
-             *   TI [other_name] -- (data_node: other_name.0) --> RevSequence [rnn_name.0] -- (data_node: rnn_name.0) -> Result1
-             *                   -- (data_node: other_name.1) --> Identity(rnn_name.1) -- (data_node: rnn_name.1) -> Result2
-             */
-            auto identity_1 = std::make_shared<opset5::Unsqueeze>(tensor_iterator->output(1), axis_1);
-            auto identity_2 = std::make_shared<opset5::Squeeze>(identity_1, axis_1);
-            ngraph::copy_runtime_info(sequence, {reverse_seq_after, tensor_iterator, reverse_seq_before, identity_2, identity_1});
-            ngraph::replace_node(sequence, {reverse_seq_after, identity_2});
-            tensor_iterator->set_friendly_name(sequence->get_friendly_name() + "/tensor_iterator");
-            reverse_seq_after->set_friendly_name(sequence->get_friendly_name() + ".0");
-            identity_2->set_friendly_name(sequence->get_friendly_name() + ".1");
-        } else {
-            ngraph::copy_runtime_info(sequence, tensor_iterator);
-            ngraph::replace_node(sequence, tensor_iterator);
-        }
-        return true;
+        return convert_sequence_to_ti(sequence, X, H_t, Output<Node>(), seq_lengths, W, R, B, sequence->get_direction());
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(rnn_seq, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(gru_seq, matcher_name);
     register_matcher(m, callback);
 }
 
 ngraph::pass::ConvertLSTMSequenceToTensorIterator::ConvertLSTMSequenceToTensorIterator() {
     MATCHER_SCOPE(ConvertLSTMSequenceToTensorIterator);
-    // X, H, C, seq_lengths - static, W,R,B - any
-    auto rnn_seq = ngraph::pattern::wrap_type<opset5::LSTMSequence>({pattern::any_input(pattern::has_static_shape()),
-                                                                     pattern::any_input(pattern::has_static_shape()),
-                                                                     pattern::any_input(pattern::has_static_shape()),
-                                                                     pattern::any_input(pattern::has_static_shape()),
-                                                                     pattern::any_input(),
-                                                                     pattern::any_input(),
-                                                                     pattern::any_input()});
-    ngraph::matcher_pass_callback callback = [this](ngraph::pattern::Matcher &m) {
-        auto sequence = std::dynamic_pointer_cast<ngraph::opset5::LSTMSequence>(m.get_match_root());
+    auto X_m = pattern::any_input(pattern::has_static_rank());
+    auto H_t_m = pattern::any_input();
+    auto C_t_m = pattern::any_input();
+    auto seq_lengths_m = pattern::any_input();
+    auto W_m = pattern::any_input();
+    auto R_m = pattern::any_input();
+    auto B_m = pattern::any_input();
+    auto lstm_seq = ngraph::pattern::wrap_type<opset5::LSTMSequence>({X_m, H_t_m, C_t_m, seq_lengths_m, W_m, R_m, B_m});
+
+    ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
+        auto sequence = ngraph::as_type_ptr<ngraph::opset5::LSTMSequence>(m.get_match_root());
 
         // Bidirectional Sequence op should be decomposed to Reverse + Forward
         // (e.g. apply BidirectionalRNNSequenceDecomposition transformation before this one)
@@ -410,170 +381,24 @@ ngraph::pass::ConvertLSTMSequenceToTensorIterator::ConvertLSTMSequenceToTensorIt
             return false;
         }
 
-        NodeVector new_nodes;
-        const auto &X = sequence->input_value(0); // split
-        const auto &H_t = sequence->input_value(1); // merged (init value + back edge)
-        const auto &C_t = sequence->input_value(2); // merged (init value + back edge)
-        const auto &seq_lengths = sequence->input_value(3); // invariant
-        const auto &W = sequence->input_value(4); // const in the body
-        const auto &R = sequence->input_value(5); // const in the body
-        const auto &B = sequence->input_value(6); // const in the body
-        bool is_reverse = sequence->get_direction() == ngraph::op::RecurrentSequenceDirection::REVERSE;
+        const auto& pattern_map = m.get_pattern_value_map();
+        const auto& X = pattern_map.at(X_m);                      // split
+        const auto& H_t = pattern_map.at(H_t_m);                  // merged (init value + back edge)
+        const auto& C_t = pattern_map.at(C_t_m);                  // merged (init value + back edge)
+        const auto& seq_lengths = pattern_map.at(seq_lengths_m);  // invariant
+        const auto& W = pattern_map.at(W_m);                      // const in the body
+        const auto& R = pattern_map.at(R_m);                      // const in the body
+        const auto& B = pattern_map.at(B_m);                      // const in the body
 
-        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
-        auto max_seq_len = X.get_shape().at(1);
-        bool enable_mask = ngraph::op::util::is_seq_len_provided(seq_lengths.get_node_shared_ptr(), max_seq_len);
-
-        std::shared_ptr<Node> reverse_seq_before;
-        if (is_reverse && enable_mask) {
-            reverse_seq_before = std::make_shared<opset5::ReverseSequence>(X, seq_lengths, 0, 1);
-        }
-
-        // TensorIterator Body: begin
-        Shape X_param_shape = X.get_shape();
-        X_param_shape.at(1) = 1; // split by seq_lengths dimension
-        auto X_body_param = std::make_shared<opset5::Parameter>(X.get_element_type(), X_param_shape);
-        auto H_body_param = std::make_shared<opset5::Parameter>(H_t.get_element_type(),
-                                                                H_t.get_shape());
-        auto C_body_param = std::make_shared<opset5::Parameter>(C_t.get_element_type(),
-                                                                C_t.get_partial_shape());
-        auto seq_body_param = std::make_shared<opset5::Parameter>(seq_lengths.get_element_type(),
-                                                                  seq_lengths.get_partial_shape());
-
-        auto axis_0 = std::make_shared<opset5::Constant>(element::i64, Shape{1}, std::vector<int64_t>{0});
-        auto axis_1 = std::make_shared<opset5::Constant>(element::i64, Shape{1}, std::vector<int64_t>{1});
-
-        const auto& ins = squeeze_nodes({X_body_param, H_body_param, C_body_param, W, R, B},
-                                        {axis_1, axis_1, axis_1, axis_0, axis_0, axis_0});
-        auto cell = std::make_shared<opset5::LSTMCell>(ins[0],
-                                                       ins[1],
-                                                       ins[2],
-                                                       ins[3],
-                                                       ins[4],
-                                                       ins[5],
-                                                       sequence->get_hidden_size(),
-                                                       sequence->get_activations(),
-                                                       sequence->get_activations_alpha(),
-                                                       sequence->get_activations_beta(),
-                                                       sequence->get_clip());
-
-        ParameterVector body_params;
-        ResultVector body_results;
-        auto unsqueeze_dum_dir_h = std::make_shared<opset5::Unsqueeze>(cell->output(0), axis_1);
-        auto unsqueeze_dum_dir_c = std::make_shared<opset5::Unsqueeze>(cell->output(1), axis_1);
-        Output<Node> h_node_to_result = unsqueeze_dum_dir_h;
-        Output<Node> c_node_to_result = unsqueeze_dum_dir_c;
-        if (enable_mask) {
-            auto current_iter = get_current_iter(body_params, body_results, seq_body_param);
-            h_node_to_result = get_masked_value(tensor_iterator, body_params, body_results, current_iter,
-                                                unsqueeze_dum_dir_h, seq_body_param);
-            c_node_to_result = get_masked_value(tensor_iterator, body_params, body_results, current_iter,
-                                                unsqueeze_dum_dir_c, seq_body_param);
-        }
-
-        auto H_res = std::make_shared<opset5::Result>(h_node_to_result);
-        auto C_res = std::make_shared<opset5::Result>(c_node_to_result);
-        auto unsqueeze_seq_len = std::make_shared<opset5::Unsqueeze>(h_node_to_result, axis_1);
-        auto concat_res = std::make_shared<opset5::Result>(unsqueeze_seq_len);
-        body_params.push_back(X_body_param);
-        body_params.push_back(H_body_param);
-        body_params.push_back(C_body_param);
-        body_params.push_back(seq_body_param);
-        body_results.push_back(concat_res);
-        body_results.push_back(H_res);
-        body_results.push_back(C_res);
-
-        auto body = std::make_shared<ngraph::Function>(body_results, body_params);
-        tensor_iterator->set_function(body);
-        // TensorIterator Body: end
-        if (is_reverse) {
-            if (!enable_mask) {
-                // Reversed order, stride -1
-                tensor_iterator->set_sliced_input(X_body_param, X, -1, -1, 1, 0, 1);
-                tensor_iterator->get_concatenated_slices(concat_res, -1, -1, 1, 0, 2);
-            } else {
-                // use ReverseSequence as initializer
-                tensor_iterator->set_sliced_input(X_body_param, reverse_seq_before, 0, 1, 1, -1, 1);
-                tensor_iterator->get_concatenated_slices(concat_res, 0, 1, 1, -1, 2);
-            }
-        } else {
-            // forward order
-            tensor_iterator->set_sliced_input(X_body_param, X, 0, 1, 1, -1, 1);
-            tensor_iterator->get_concatenated_slices(concat_res, 0, 1, 1, -1, 2);
-        }
-        tensor_iterator->set_merged_input(H_body_param, H_t, H_res);
-        tensor_iterator->set_merged_input(C_body_param, C_t, C_res);
-        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
-        Output<Node> H_out = H_res;
-        Output<Node> C_out = C_res;
-        if (enable_mask) {
-            // create initial values for body_parameters in outer graph
-            // aggregated Y_h - concatenation of the last non-zero values for each batch
-            auto aggregated_Y_h = std::make_shared<ngraph::opset5::Constant>(H_body_param->get_element_type(),
-                                                                             H_body_param->get_shape(),
-                                                                             std::vector<float>(shape_size(H_body_param->get_shape()),
-                                                                                                0.f));
-            auto aggregated_Y_c = std::make_shared<ngraph::opset5::Constant>(C_body_param->get_element_type(),
-                                                                             C_body_param->get_shape(),
-                                                                             std::vector<float>(shape_size(C_body_param->get_shape()),
-                                                                                                0.f));
-            auto init_val_curr_iter = std::make_shared<ngraph::opset5::Constant>(seq_lengths.get_element_type(),
-                                                                                 ngraph::Shape{1},
-                                                                                 std::vector<int64_t>{1});
-            ngraph::copy_runtime_info(sequence, {aggregated_Y_h, init_val_curr_iter, aggregated_Y_c});
-
-            // set initial value and back edge for current iteration
-            tensor_iterator->set_merged_input(body_params.at(0), init_val_curr_iter, body_results.at(0));
-            // set initial value and back edge for aggregated H
-            tensor_iterator->set_merged_input(body_params.at(1), aggregated_Y_h, body_results.at(1));
-            // set initial value and back edge for aggregated H
-            tensor_iterator->set_merged_input(body_params.at(2), aggregated_Y_c, body_results.at(2));
-            H_out = tensor_iterator->get_function()->get_results()[1];
-            C_out = tensor_iterator->get_function()->get_results()[2];
-        }
-        tensor_iterator->get_iter_value(H_out);
-        tensor_iterator->get_iter_value(C_out);
-        tensor_iterator->set_friendly_name(sequence->get_friendly_name());
-        if (enable_mask && is_reverse) {
-            auto reverse_seq_after = std::make_shared<opset5::ReverseSequence>(tensor_iterator->output(0), seq_lengths, 0, 2);
-            // Resolve a collision of names data nodes in CNN Network in Reverse case with mask.
-            /*
-             *   Before transformation (no collisions)
-             *   RNN/LSTM/GRU Sequence [rnn_name] -- (data_node: rnn_name.0) - > Result1
-             *                                    -- (data_node: rnn_name.1) - > Result2
-             *
-             *
-             *   After transformation (without identity, there are collisions):
-             *   We need to set rnn_name.0 to RevSequence to store result name.
-             *   TI [rnn_name] -- (DATA_NODE: rnn_name.0) --> RevSequence [rnn_name.0] -- (DATA_NODE: rnn_name.0) -> Result1
-             *                 -- (data_node: rnn_name.1) --> Result2
-             *
-             *
-             *   After transformation (with identity, no collisions):
-             *   TI has other_name, but it doesn't affect result names due TI is not connected to Results directly.
-             *   TI [other_name] -- (data_node: other_name.0) --> RevSequence [rnn_name.0] -- (data_node: rnn_name.0) -> Result1
-             *                   -- (data_node: other_name.1) --> Identity(rnn_name.1) -- (data_node: rnn_name.1) -> Result2
-             */
-            auto identity_1_h = std::make_shared<opset5::Unsqueeze>(tensor_iterator->output(1), axis_1);
-            auto identity_2_h = std::make_shared<opset5::Squeeze>(identity_1_h, axis_1);
-
-            auto identity_1_c = std::make_shared<opset5::Unsqueeze>(tensor_iterator->output(2), axis_1);
-            auto identity_2_c = std::make_shared<opset5::Squeeze>(identity_1_c, axis_1);
-
-            ngraph::copy_runtime_info(sequence, {reverse_seq_after, tensor_iterator, reverse_seq_before, identity_2_c, identity_1_c,
-                                                 identity_1_h, identity_2_h});
-            ngraph::replace_node(sequence, {reverse_seq_after, identity_2_h, identity_2_c});
-            tensor_iterator->set_friendly_name(sequence->get_friendly_name() + "/tensor_iterator");
-            reverse_seq_after->set_friendly_name(sequence->get_friendly_name() + ".0");
-            identity_2_h->set_friendly_name(sequence->get_friendly_name() + ".1");
-            identity_2_c->set_friendly_name(sequence->get_friendly_name() + ".2");
-        } else {
-            ngraph::copy_runtime_info(sequence, tensor_iterator);
-            ngraph::replace_node(sequence, tensor_iterator);
-        }
-        return true;
+        return convert_sequence_to_ti(sequence, X, H_t, C_t, seq_lengths, W, R, B, sequence->get_direction());
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(rnn_seq, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(lstm_seq, matcher_name);
     register_matcher(m, callback);
 }
+
+ngraph::pass::ConvertSequenceToTensorIterator::ConvertSequenceToTensorIterator() {
+    add_matcher<ConvertLSTMSequenceToTensorIterator>();
+    add_matcher<ConvertRNNSequenceToTensorIterator>();
+    add_matcher<ConvertGRUSequenceToTensorIterator>();
+}
diff --git a/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp b/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp
index 35da32feed3..4213385562b 100644
--- a/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp
+++ b/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp
@@ -20,6 +20,211 @@
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertTensorIteratorToLSTMSequence, "ConvertTensorIteratorToLSTMSequence", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertTensorIteratorToRNNSequence, "ConvertTensorIteratorToRNNSequence", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertTensorIteratorToGRUSequence, "ConvertTensorIteratorToGRUSequence", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertTensorIteratorToSequence, "ConvertTensorIteratorToSequence", 0);
+
+namespace {
+bool convertTensorIteratorToSequence(
+    const std::shared_ptr<ngraph::opset5::TensorIterator>& ti,
+    const std::shared_ptr<ngraph::op::util::RNNCellBase>& found_cell,
+    const ngraph::Output<ngraph::Node>& data,
+    const ngraph::Output<ngraph::Node>& h_pattern,
+    const ngraph::Output<ngraph::Node>& c_pattern,
+    const ngraph::Output<ngraph::Node>& w_pattern,
+    const ngraph::Output<ngraph::Node>& r_pattern,
+    const ngraph::Output<ngraph::Node>& b_pattern,
+    const ngraph::Output<ngraph::Node>& unsqueeze_after_cell) {
+    const auto& func = ti->get_function();
+    const auto& params = func->get_parameters();
+
+    std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::InputDescription>> ordered_in_descs(3);
+    int64_t stride = 0, slice_axis = 0;
+
+    // Remember the order of the X and initial_hidden_state (+ initial_cell_state in case of LSTM) in the TensorIterator params
+    for (const auto& input_desc : ti->get_input_descriptions()) {
+        auto param = params[input_desc->m_body_parameter_index];
+        if (param == data.get_node_shared_ptr()) {
+            auto slice_input = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::SliceInputDescription>(input_desc);
+            if (!slice_input)
+                return false;
+
+            stride = slice_input->m_stride;
+            slice_axis = slice_input->m_axis;
+
+            if (!(slice_axis == 0 || slice_axis == 1)) {
+                return false;
+            }
+            ordered_in_descs[0] = input_desc;
+        } else if (param == h_pattern.get_node_shared_ptr()) {
+            ordered_in_descs[1] = input_desc;
+        } else if (param == c_pattern.get_node_shared_ptr()) {
+            ordered_in_descs[2] = input_desc;
+        } else {
+            return false;
+        }
+    }
+
+    const auto& results = func->get_results();
+    std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::OutputDescription>> ordered_out_descs(3);
+
+    // Remember the order of cell outputs in the TensorIterator
+    for (const auto& output_desc : ti->get_output_descriptions()) {
+        std::shared_ptr<ngraph::opset5::Result> res = results[output_desc->m_body_value_index];
+        if (res->input_value(0) == unsqueeze_after_cell) {
+            auto concat_output = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::ConcatOutputDescription>(output_desc);
+            if (!concat_output)
+                return false;
+
+            stride = concat_output->m_stride;
+            ordered_out_descs[0] = output_desc;
+        } else if (res->input_value(0) == found_cell->output(0)) {
+            ordered_out_descs[1] = output_desc;
+        } else if (found_cell->get_output_size() == 3 && res->input_value(0) == found_cell->output(1)) {
+            ordered_out_descs[2] = output_desc;
+        } else {
+            return false;
+        }
+    }
+
+    const auto ti_inputs = ti->input_values();
+    auto X = ti_inputs[ordered_in_descs[0]->m_input_index];
+    if (slice_axis == 0) {
+        auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
+        X = std::make_shared<ngraph::opset5::Transpose>(ti_inputs[ordered_in_descs[0]->m_input_index], order);
+    }
+
+    // We must prepare cell inputs to sequence creation: insert num_directions elem via unsqueeze where needed (please, see specification)
+    auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+    auto initial_hidden_state = std::make_shared<ngraph::opset5::Unsqueeze>(ti_inputs[ordered_in_descs[1]->m_input_index], axis_1);
+
+    // LSTM case
+    std::shared_ptr<ngraph::Node> initial_cell_state = c_pattern.get_node_shared_ptr() == nullptr ?
+        nullptr :
+        std::make_shared<ngraph::opset5::Unsqueeze>(ti_inputs[ordered_in_descs[2]->m_input_index], axis_1);
+
+    const size_t batch_dim = slice_axis == 0 ? 1 : 0;
+    //TODO: replace to ngraph::op::util::node_to_get_shape_value_of_indices_from_shape_node(target_param, { batch_dimension });
+    auto shape_node = ngraph::op::util::make_try_fold<ngraph::opset5::ShapeOf>(ti_inputs[ordered_in_descs[0]->m_input_index]);
+    auto batch_dimension = ngraph::op::util::make_try_fold<ngraph::opset5::Gather>(
+        shape_node,
+        ngraph::opset5::Constant::create(ngraph::element::i64, { 1 }, { batch_dim }),
+        ngraph::opset5::Constant::create(ngraph::element::i64, {}, { 0 }));
+
+    auto seq_lengths_scalar = ngraph::opset5::Constant::create(ngraph::element::i32, {}, { ti->get_num_iterations() });
+    auto seq_lengths = ngraph::op::util::make_try_fold<ngraph::opset5::Broadcast>(seq_lengths_scalar, batch_dimension);
+
+    auto axis_0 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
+    auto W = ngraph::op::util::make_try_fold<ngraph::opset5::Unsqueeze>(w_pattern, axis_0);
+    auto R = ngraph::op::util::make_try_fold<ngraph::opset5::Unsqueeze>(r_pattern, axis_0);
+    auto B = ngraph::op::util::make_try_fold<ngraph::opset5::Unsqueeze>(b_pattern, axis_0);
+
+    std::shared_ptr<ngraph::Node> sequence;
+    if (ngraph::is_type<ngraph::opset5::LSTMCell>(found_cell) || ngraph::is_type<ngraph::opset1::LSTMCell>(found_cell)) {
+        sequence = std::make_shared<ngraph::opset5::LSTMSequence>(
+            X,
+            initial_hidden_state,
+            initial_cell_state,
+            seq_lengths,
+            W,
+            R,
+            B,
+            found_cell->get_hidden_size(),
+            stride > 0 ? ngraph::op::RecurrentSequenceDirection::FORWARD : ngraph::op::RecurrentSequenceDirection::REVERSE,
+            found_cell->get_activations_alpha(),
+            found_cell->get_activations_beta(),
+            found_cell->get_activations(),
+            found_cell->get_clip());
+    } else if (ngraph::is_type<ngraph::opset5::RNNCell>(found_cell)) {
+        sequence = std::make_shared<ngraph::opset5::RNNSequence>(
+            X,
+            initial_hidden_state,
+            seq_lengths,
+            W,
+            R,
+            B,
+            found_cell->get_hidden_size(),
+            stride > 0 ? ngraph::op::RecurrentSequenceDirection::FORWARD : ngraph::op::RecurrentSequenceDirection::REVERSE,
+            found_cell->get_activations(),
+            found_cell->get_activations_alpha(),
+            found_cell->get_activations_beta(),
+            found_cell->get_clip());
+    } else if (ngraph::is_type<ngraph::opset5::GRUCell>(found_cell)) {
+        const auto gru_cell = ngraph::as_type_ptr<ngraph::opset5::GRUCell>(found_cell);
+        sequence = std::make_shared<ngraph::opset5::GRUSequence>(
+            X,
+            initial_hidden_state,
+            seq_lengths,
+            W,
+            R,
+            B,
+            gru_cell->get_hidden_size(),
+            stride > 0 ? ngraph::op::RecurrentSequenceDirection::FORWARD : ngraph::op::RecurrentSequenceDirection::REVERSE,
+            gru_cell->get_activations(),
+            gru_cell->get_activations_alpha(),
+            gru_cell->get_activations_beta(),
+            gru_cell->get_clip(),
+            gru_cell->get_linear_before_reset());
+    } else {
+        throw ngraph::ngraph_error("Unsupported sequence type");
+    }
+
+    ngraph::Output<ngraph::Node> out = sequence->output(0);
+    if (slice_axis == 0) {
+        auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {2, 1, 0, 3});
+        out = std::make_shared<ngraph::opset5::Transpose>(out, order);
+    }
+
+    ngraph::NodeVector outputs;
+    // We must remove num_directions dimension that was added before sequence creation
+    auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+    auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(out, axis_out);
+    auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(1), axis_out);
+    out_0->set_friendly_name(ti->get_friendly_name() + ".0");
+    out_1->set_friendly_name(ti->get_friendly_name() + ".1");
+    outputs.emplace_back(out_0);
+    outputs.emplace_back(out_1);
+
+    if (sequence->get_output_size() == 3) {
+        auto out_2 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(2), axis_out);
+        out_2->set_friendly_name(ti->get_friendly_name() + ".2");
+        outputs.emplace_back(out_2);
+    }
+
+    for (size_t i = 0; i < ordered_out_descs.size(); ++i) {
+        if (ordered_out_descs[i]) {
+            for (const auto &input : ti->output(ordered_out_descs[i]->m_output_index).get_target_inputs()) {
+                input.replace_source_output(outputs[i]->output(0));
+            }
+            NGRAPH_SUPPRESS_DEPRECATED_START
+            outputs[i]->get_output_tensor(0).set_name(ngraph::op::util::create_ie_output_name(ti->output(ordered_out_descs[i]->m_output_index)));
+            NGRAPH_SUPPRESS_DEPRECATED_END
+        }
+    }
+
+    ngraph::NodeVector new_nodes = outputs;
+    new_nodes.emplace_back(initial_hidden_state);
+    new_nodes.emplace_back(W);
+    new_nodes.emplace_back(R);
+    new_nodes.emplace_back(B);
+    new_nodes.emplace_back(sequence);
+
+    if (c_pattern.get_node_shared_ptr()) {
+        new_nodes.emplace_back(initial_cell_state);
+    }
+    if (!std::dynamic_pointer_cast<ngraph::opset5::Constant>(seq_lengths)) {
+        new_nodes.emplace_back(shape_node);
+        new_nodes.emplace_back(batch_dimension);
+        new_nodes.emplace_back(seq_lengths_scalar);
+        new_nodes.emplace_back(seq_lengths);
+    }
+    if (slice_axis == 0) {
+        new_nodes.emplace_back(out.get_node_shared_ptr());
+        new_nodes.emplace_back(X.get_node_shared_ptr());
+    }
+
+    copy_runtime_info(ti, new_nodes);
+    return true;
+}
+} // namespace
 
 ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSequence() {
     MATCHER_SCOPE(ConvertTensorIteratorToLSTMSequence);
@@ -30,20 +235,22 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
         if (!ti || transformation_callback(ti))
             return false;
 
-        // create pattern
-        auto data = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
-        auto pattern_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 1});
-        auto squeeze = std::make_shared<ngraph::opset5::Reshape>(data, pattern_1, false);
-        auto input_H_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
-        auto input_C_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
-        auto input_W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{4, 1});
-        auto input_R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{4, 1});
-        auto input_B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{4});
+        // create a pattern for the TensorIterator body
+        auto data = ngraph::pattern::wrap_type<ngraph::opset5::Parameter>(ngraph::pattern::rank_equals(3));
+        auto pattern_1 = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
+        auto squeeze = ngraph::pattern::wrap_type<ngraph::opset5::Reshape>({ data, pattern_1 });
 
-        auto cell = std::make_shared<ngraph::opset5::LSTMCell>(squeeze, input_H_state, input_C_state,
-                                                               input_W, input_R, input_B, 1);
-        auto pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 1, 1});
-        auto unsqueeze = std::make_shared<ngraph::opset5::Reshape>(cell, pattern_2, false);
+        auto input_H_state = ngraph::pattern::wrap_type<ngraph::opset5::Parameter>(ngraph::pattern::rank_equals(2));
+        auto input_C_state = ngraph::pattern::wrap_type<ngraph::opset5::Parameter>(ngraph::pattern::rank_equals(2));
+        auto input_W = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(2));
+        auto input_R = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(2));
+        auto input_B = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
+
+        ngraph::OutputVector cell_inputs{ squeeze, input_H_state, input_C_state, input_W, input_R, input_B };
+        auto cell = ngraph::pattern::wrap_type<ngraph::opset1::LSTMCell, ngraph::opset5::LSTMCell>(cell_inputs);
+
+        auto pattern_2 = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
+        auto unsqueeze = ngraph::pattern::wrap_type<ngraph::opset5::Reshape>({ cell, pattern_2 });
         ngraph::pattern::Matcher matcher(unsqueeze);
 
         bool match = false;
@@ -54,147 +261,24 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
                 break;
         }
 
-        // support for opset1::LSTMCell
-        auto cell_v1 = std::make_shared<ngraph::opset1::LSTMCell>(squeeze, input_H_state, input_C_state,
-                                                                 input_W, input_R, input_B, 1);
-        if (!match) {
-            unsqueeze = std::make_shared<ngraph::opset5::Reshape>(cell_v1, pattern_2, false);
-            matcher.clear_state();
-            matcher.m_pattern_node = unsqueeze;
-            for (const auto& res : func->get_results()) {
-                match = matcher.match((res->get_input_source_output(0)));
-                if (match)
-                    break;
-            }
-        }
-
         // All nodes are in the TI body should be matched in pattern
         if (!match || (matcher.get_matched_nodes().size() + func->get_results().size()) != func->get_ops().size())
             return false;
 
-        auto pattern_map = matcher.get_pattern_map();
-        std::shared_ptr<Node>& found_cell = pattern_map[cell];
-        if (!found_cell)
-            found_cell = pattern_map[cell_v1];
-
-        auto params = func->get_parameters();
-        std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::InputDescription>> ordered_in_descs(3);
-        int64_t stride = 0, slice_axis = 0;
-        size_t batch_size = 0;
-        for (const auto& input_desc : ti->get_input_descriptions()) {
-            auto param = params[input_desc->m_body_parameter_index];
-            if (param == pattern_map[data]) {
-                // to get batch size value
-                if (param->get_partial_shape().is_dynamic()) {
-                    return false;
-                }
-                auto slice_input
-                        = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::SliceInputDescription>(input_desc);
-                if (!slice_input)
-                    return false;
-
-                stride = slice_input->m_stride;
-                slice_axis = slice_input->m_axis;
-
-                if (!(slice_axis == 0 || slice_axis == 1)) {
-                    return false;
-                }
-                batch_size = param->get_shape()[slice_axis == 0 ? 1 : 0];
-                ordered_in_descs[0] = input_desc;
-            } else if (param == pattern_map[input_H_state]) {
-                ordered_in_descs[1] = input_desc;
-            } else if (param == pattern_map[input_C_state]) {
-                ordered_in_descs[2] = input_desc;
-            } else {
-                return false;
-            }
-        }
-
-        auto results = func->get_results();
-        std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::OutputDescription>> ordered_out_descs(3);
-        for (const auto& output_desc : ti->get_output_descriptions()) {
-            std::shared_ptr<opset5::Result> res = results[output_desc->m_body_value_index];
-            if (res->get_input_source_output(0) == pattern_map[unsqueeze]) {
-                auto concat_output
-                        = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::ConcatOutputDescription>(output_desc);
-                if (!concat_output)
-                    return false;
-
-                stride = concat_output->m_stride;
-                ordered_out_descs[0] = output_desc;
-            } else if (res->get_input_source_output(0) == found_cell->output(0)) {
-                ordered_out_descs[1] = output_desc;
-            } else if (res->get_input_source_output(0) == found_cell->output(1)) {
-                ordered_out_descs[2] = output_desc;
-            } else {
-                return false;
-            }
-        }
-
-        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
-        const auto& lstm_cell = std::dynamic_pointer_cast<ngraph::op::util::RNNCellBase>(found_cell);
+        const auto& pattern_map = matcher.get_pattern_value_map();
+        std::shared_ptr<Node> found_cell = pattern_map.at(cell).get_node_shared_ptr();
+        const auto lstm_cell = std::dynamic_pointer_cast<ngraph::op::util::RNNCellBase>(found_cell);
         if (lstm_cell == nullptr)
             return false;
-        auto in_0 = ti->input_values()[ordered_in_descs[0]->m_input_index];
-        if (slice_axis == 0) {
-            auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
-            in_0 = std::make_shared<ngraph::opset5::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
-        }
-        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
-        auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
-        auto in_2 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[2]->m_input_index], axis_1);
 
-        auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
-        auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
-        auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
-        auto in_6 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
-        auto sequence = std::make_shared<opset5::LSTMSequence>(
-                in_0,
-                in_1,
-                in_2,
-                seq_lengths,
-                in_4,
-                in_5,
-                in_6,
-                lstm_cell->get_hidden_size(),
-                stride > 0 ? ngraph::op::RecurrentSequenceDirection::FORWARD: ngraph::op::RecurrentSequenceDirection::REVERSE,
-                lstm_cell->get_activations_alpha(),
-                lstm_cell->get_activations_beta(),
-                lstm_cell->get_activations(),
-                lstm_cell->get_clip());
-
-        auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
-        Output<Node> out = sequence->output(0);
-        if (slice_axis == 0) {
-            auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {2, 1, 0, 3});
-            out = std::make_shared<ngraph::opset5::Transpose>(out, order);
-        }
-        auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(out, axis_out);
-        out_0->set_friendly_name(ti->get_friendly_name() + ".0");
-        auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(1), axis_out);
-        out_1->set_friendly_name(ti->get_friendly_name() + ".1");
-        auto out_2 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(2), axis_out);
-        out_2->set_friendly_name(ti->get_friendly_name() + ".2");
-
-        ngraph::NodeVector outputs = {out_0, out_1, out_2};
-        for (size_t i = 0; i < ordered_out_descs.size(); ++i) {
-            if (ordered_out_descs[i]) {
-                for (const auto &input : ti->output(ordered_out_descs[i]->m_output_index).get_target_inputs()) {
-                    input.replace_source_output(outputs[i]->output(0));
-                }
-                NGRAPH_SUPPRESS_DEPRECATED_START
-                outputs[i]->get_output_tensor(0).set_name(op::util::create_ie_output_name(ti->output(ordered_out_descs[i]->m_output_index)));
-                NGRAPH_SUPPRESS_DEPRECATED_END
-            }
-        }
-
-        ngraph::OutputVector new_nodes = {in_1, in_2, in_4, in_5, in_6, sequence->output(0), out_0, out_1, out_2};
-        if (slice_axis == 0) {
-            new_nodes.push_back(out);
-            new_nodes.push_back(in_0.get_node_shared_ptr());
-        }
-        copy_runtime_info(ti, as_node_vector(new_nodes));
-        return true;
+        return convertTensorIteratorToSequence(ti, lstm_cell,
+            pattern_map.at(data),
+            pattern_map.at(input_H_state),
+            pattern_map.at(input_C_state),
+            pattern_map.at(input_W),
+            pattern_map.at(input_R),
+            pattern_map.at(input_B),
+            pattern_map.at(unsqueeze));
     };
 
     auto m = std::make_shared<ngraph::pattern::Matcher>(tensor_iterator, matcher_name);
@@ -210,20 +294,21 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
         if (!ti || transformation_callback(ti))
             return false;
 
-        // create pattern
-        auto data = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
-        auto pattern_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 1});
-        auto squeeze = std::make_shared<ngraph::opset5::Reshape>(data, pattern_1, false);
+        // create a pattern for the TensorIterator body
+        auto data = ngraph::pattern::wrap_type<ngraph::opset5::Parameter>(ngraph::pattern::rank_equals(3));
+        auto pattern_1 = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
+        auto squeeze = ngraph::pattern::wrap_type<ngraph::opset5::Reshape>({ data, pattern_1 });
 
-        auto input_H_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
-        auto input_W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{1, 1});
-        auto input_R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{1, 1});
-        auto input_B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{1});
+        auto input_H_state = ngraph::pattern::wrap_type<ngraph::opset5::Parameter>(ngraph::pattern::rank_equals(2));
+        auto input_W = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(2));
+        auto input_R = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(2));
+        auto input_B = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
 
-        auto cell = std::make_shared<ngraph::opset5::RNNCell>(squeeze, input_H_state, input_W, input_R, input_B, 1);
+        ngraph::OutputVector cell_inputs{ squeeze, input_H_state, input_W, input_R, input_B };
+        auto cell = ngraph::pattern::wrap_type<ngraph::opset5::RNNCell>(cell_inputs);
 
-        auto pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 1, 1});
-        auto unsqueeze = std::make_shared<ngraph::opset5::Reshape>(cell, pattern_2, false);
+        auto pattern_2 = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
+        auto unsqueeze = ngraph::pattern::wrap_type<ngraph::opset5::Reshape>({ cell, pattern_2 });
         ngraph::pattern::Matcher matcher(unsqueeze);
 
         bool match = false;
@@ -238,121 +323,19 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
         if (!match || (matcher.get_matched_nodes().size() + func->get_results().size()) != func->get_ops().size())
             return false;
 
-        auto pattern_map = matcher.get_pattern_map();
-
-        auto params = func->get_parameters();
-        std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::InputDescription>> ordered_in_descs(3);
-        int64_t stride = 0, slice_axis = 0;
-        size_t batch_size = 0;
-        for (const auto& input_desc : ti->get_input_descriptions()) {
-            auto param = params[input_desc->m_body_parameter_index];
-            if (param == pattern_map[data]) {
-                // to get batch size value
-                if (param->get_partial_shape().is_dynamic()) {
-                    return false;
-                }
-                auto slice_input
-                        = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::SliceInputDescription>(input_desc);
-                if (!slice_input)
-                    return false;
-
-                stride = slice_input->m_stride;
-                slice_axis = slice_input->m_axis;
-                if (!(slice_axis == 0 || slice_axis == 1)) {
-                    return false;
-                }
-                batch_size = param->get_shape()[slice_axis == 0 ? 1 : 0];
-                ordered_in_descs[0] = input_desc;
-            } else if (param == pattern_map[input_H_state]) {
-                ordered_in_descs[1] = input_desc;
-            } else {
-                return false;
-            }
-        }
-
-        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
-
-        auto results = func->get_results();
-        std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::OutputDescription>> ordered_out_descs(2);
-        for (const auto& output_desc : ti->get_output_descriptions()) {
-            std::shared_ptr<opset5::Result> res = results[output_desc->m_body_value_index];
-            if (res->get_input_source_output(0) == pattern_map[unsqueeze]) {
-                auto concat_output
-                        = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::ConcatOutputDescription>(output_desc);
-                if (!concat_output)
-                    return false;
-
-                stride = concat_output->m_stride;
-                ordered_out_descs[0] = output_desc;
-            } else if (res->get_input_source_output(0) == pattern_map[cell]->output(0)) {
-                ordered_out_descs[1] = output_desc;
-            } else {
-                return false;
-            }
-        }
-
-        const auto& rnn_cell = std::dynamic_pointer_cast<ngraph::opset5::RNNCell>(pattern_map[cell]);
+        const auto& pattern_map = matcher.get_pattern_value_map();
+        const auto& rnn_cell = std::dynamic_pointer_cast<ngraph::opset5::RNNCell>(pattern_map.at(cell).get_node_shared_ptr());
         if (rnn_cell == nullptr)
             return false;
 
-        auto in_0 = ti->input_values()[ordered_in_descs[0]->m_input_index];
-        if (slice_axis == 0) {
-            auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
-            in_0 = std::make_shared<ngraph::opset5::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
-        }
-
-        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
-        auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
-
-        auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
-        auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
-        auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
-        auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
-        auto sequence = std::make_shared<opset5::RNNSequence>(
-                in_0,
-                in_1,
-                seq_lengths,
-                in_3,
-                in_4,
-                in_5,
-                rnn_cell->get_hidden_size(),
-                stride > 0 ? ngraph::op::RecurrentSequenceDirection::FORWARD: ngraph::op::RecurrentSequenceDirection::REVERSE,
-                rnn_cell->get_activations(),
-                rnn_cell->get_activations_alpha(),
-                rnn_cell->get_activations_beta(),
-                rnn_cell->get_clip());
-
-        auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
-        auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(1), axis_out);
-        out_1->set_friendly_name(ti->get_friendly_name() + ".1");
-
-        Output<Node> out = sequence->output(0);
-        if (slice_axis == 0) {
-            auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {2, 1, 0, 3});
-            out = std::make_shared<ngraph::opset5::Transpose>(out, order);
-        }
-        auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(out, axis_out);
-        out_0->set_friendly_name(ti->get_friendly_name() + ".0");
-
-        ngraph::NodeVector outputs = {out_0, out_1};
-        for (size_t i = 0; i < ordered_out_descs.size(); ++i) {
-            if (ordered_out_descs[i]) {
-                for (const auto &input : ti->output(ordered_out_descs[i]->m_output_index).get_target_inputs()) {
-                    input.replace_source_output(outputs[i]->output(0));
-                }
-                NGRAPH_SUPPRESS_DEPRECATED_START
-                outputs[i]->get_output_tensor(0).set_name(op::util::create_ie_output_name(ti->output(ordered_out_descs[i]->m_output_index)));
-                NGRAPH_SUPPRESS_DEPRECATED_END
-            }
-        }
-
-        ngraph::OutputVector new_nodes = {in_1, in_3, in_4, in_5, sequence, out_0, out_1};
-        if (slice_axis == 0) {
-            new_nodes.push_back(out);
-            new_nodes.push_back(in_0);
-        }
-        copy_runtime_info(ti, as_node_vector(new_nodes));
-        return true;
+        return convertTensorIteratorToSequence(ti, rnn_cell,
+            pattern_map.at(data),
+            pattern_map.at(input_H_state),
+            ngraph::Output<ngraph::Node>(),
+            pattern_map.at(input_W),
+            pattern_map.at(input_R),
+            pattern_map.at(input_B),
+            pattern_map.at(unsqueeze));
     };
 
     auto m = std::make_shared<ngraph::pattern::Matcher>(tensor_iterator, matcher_name);
@@ -363,25 +346,26 @@ ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequ
     MATCHER_SCOPE(ConvertTensorIteratorToGRUSequence);
     auto tensor_iterator = pattern::wrap_type<ngraph::opset5::TensorIterator>();
 
-    ngraph::matcher_pass_callback callback = [this](pattern::Matcher &m) {
+    ngraph::matcher_pass_callback callback = [this](pattern::Matcher& m) {
         auto ti = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator>(m.get_match_root());
         if (!ti || transformation_callback(ti))
             return false;
 
-        // create pattern
-        auto data = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
-        auto pattern_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 1});
-        auto squeeze = std::make_shared<ngraph::opset5::Reshape>(data, pattern_1, false);
+        // create a pattern for the TensorIterator body
+        auto data = ngraph::pattern::wrap_type<ngraph::opset5::Parameter>(ngraph::pattern::rank_equals(3));
+        auto pattern_1 = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
+        auto squeeze = ngraph::pattern::wrap_type<ngraph::opset5::Reshape>({ data, pattern_1 });
 
-        auto input_H_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
-        auto input_W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{3, 1});
-        auto input_R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{3, 1});
-        auto input_B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{3});
+        auto input_H_state = ngraph::pattern::wrap_type<ngraph::opset5::Parameter>(ngraph::pattern::rank_equals(2));
+        auto input_W = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(2));
+        auto input_R = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(2));
+        auto input_B = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
 
-        auto cell = std::make_shared<ngraph::opset5::GRUCell>(squeeze, input_H_state, input_W, input_R, input_B, 1);
+        ngraph::OutputVector cell_inputs{ squeeze, input_H_state, input_W, input_R, input_B };
+        auto cell = ngraph::pattern::wrap_type<ngraph::opset5::GRUCell>(cell_inputs);
 
-        auto pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 1, 1});
-        auto unsqueeze = std::make_shared<ngraph::opset5::Reshape>(cell, pattern_2, false);
+        auto pattern_2 = ngraph::pattern::wrap_type<ngraph::opset5::Constant>(ngraph::pattern::rank_equals(1));
+        auto unsqueeze = ngraph::pattern::wrap_type<ngraph::opset5::Reshape>({ cell, pattern_2 });
         ngraph::pattern::Matcher matcher(unsqueeze);
 
         bool match = false;
@@ -396,124 +380,27 @@ ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequ
         if (!match || (matcher.get_matched_nodes().size() + func->get_results().size()) != func->get_ops().size())
             return false;
 
-        auto pattern_map = matcher.get_pattern_map();
-
-        auto params = func->get_parameters();
-        std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::InputDescription>> ordered_in_descs(3);
-        int64_t stride = 0, slice_axis = 0;
-        size_t batch_size = 0;
-        for (const auto& input_desc : ti->get_input_descriptions()) {
-            auto param = params[input_desc->m_body_parameter_index];
-            if (param == pattern_map[data]) {
-                // to get batch size value
-                if (param->get_partial_shape().is_dynamic()) {
-                    return false;
-                }
-                auto slice_input
-                        = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::SliceInputDescription>(input_desc);
-                if (!slice_input)
-                    return false;
-
-                stride = slice_input->m_stride;
-                slice_axis = slice_input->m_axis;
-                if (!(slice_axis == 0 || slice_axis == 1)) {
-                    return false;
-                }
-                batch_size = param->get_shape()[slice_axis == 0 ? 1 : 0];
-                ordered_in_descs[0] = input_desc;
-            } else if (param == pattern_map[input_H_state]) {
-                ordered_in_descs[1] = input_desc;
-            } else {
-                return false;
-            }
-        }
-
-        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
-
-        auto results = func->get_results();
-        std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::OutputDescription>> ordered_out_descs(2);
-        for (const auto& output_desc : ti->get_output_descriptions()) {
-            std::shared_ptr<opset5::Result> res = results[output_desc->m_body_value_index];
-            if (res->get_input_source_output(0) == pattern_map[unsqueeze]) {
-                auto concat_output
-                        = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::ConcatOutputDescription>(output_desc);
-                if (!concat_output)
-                    return false;
-
-                stride = concat_output->m_stride;
-                ordered_out_descs[0] = output_desc;
-            } else if (res->get_input_source_output(0) == pattern_map[cell]->output(0)) {
-                ordered_out_descs[1] = output_desc;
-            } else {
-                return false;
-            }
-        }
-
-        const auto& rnn_cell = std::dynamic_pointer_cast<ngraph::opset5::GRUCell>(pattern_map[cell]);
-        if (rnn_cell == nullptr)
+        const auto& pattern_map = matcher.get_pattern_value_map();
+        const auto& gru_cell = std::dynamic_pointer_cast<ngraph::opset5::GRUCell>(pattern_map.at(cell).get_node_shared_ptr());
+        if (gru_cell == nullptr)
             return false;
 
-        auto in_0 = ti->input_values()[ordered_in_descs[0]->m_input_index];
-        if (slice_axis == 0) {
-            auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
-            in_0 = std::make_shared<ngraph::opset5::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
-        }
-
-        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
-        auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
-
-        auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
-        auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
-        auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
-        auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
-        auto sequence = std::make_shared<opset5::GRUSequence>(
-                in_0,
-                in_1,
-                seq_lengths,
-                in_3,
-                in_4,
-                in_5,
-                rnn_cell->get_hidden_size(),
-                stride > 0 ? ngraph::op::RecurrentSequenceDirection::FORWARD: ngraph::op::RecurrentSequenceDirection::REVERSE,
-                rnn_cell->get_activations(),
-                rnn_cell->get_activations_alpha(),
-                rnn_cell->get_activations_beta(),
-                rnn_cell->get_clip(),
-                rnn_cell->get_linear_before_reset());
-
-        auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
-        auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(1), axis_out);
-        out_1->set_friendly_name(ti->get_friendly_name() + ".1");
-
-        Output<Node> out = sequence->output(0);
-        if (slice_axis == 0) {
-            auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {2, 1, 0, 3});
-            out = std::make_shared<ngraph::opset5::Transpose>(out, order);
-        }
-        auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(out, axis_out);
-        out_0->set_friendly_name(ti->get_friendly_name() + ".0");
-
-        ngraph::NodeVector outputs = {out_0, out_1};
-        for (size_t i = 0; i < ordered_out_descs.size(); ++i) {
-            if (ordered_out_descs[i]) {
-                for (const auto &input : ti->output(ordered_out_descs[i]->m_output_index).get_target_inputs()) {
-                    input.replace_source_output(outputs[i]->output(0));
-                }
-                NGRAPH_SUPPRESS_DEPRECATED_START
-                outputs[i]->get_output_tensor(0).set_name(op::util::create_ie_output_name(ti->output(ordered_out_descs[i]->m_output_index)));
-                NGRAPH_SUPPRESS_DEPRECATED_END
-            }
-        }
-
-        ngraph::OutputVector new_nodes = {in_1, in_3, in_4, in_5, sequence, out_0, out_1};
-        if (slice_axis == 0) {
-            new_nodes.push_back(out);
-            new_nodes.push_back(in_0);
-        }
-        copy_runtime_info(ti, as_node_vector(new_nodes));
-        return true;
+        return convertTensorIteratorToSequence(ti, gru_cell,
+            pattern_map.at(data),
+            pattern_map.at(input_H_state),
+            ngraph::Output<ngraph::Node>(),
+            pattern_map.at(input_W),
+            pattern_map.at(input_R),
+            pattern_map.at(input_B),
+            pattern_map.at(unsqueeze));
     };
 
     auto m = std::make_shared<ngraph::pattern::Matcher>(tensor_iterator, matcher_name);
     register_matcher(m, callback);
 }
+
+ngraph::pass::ConvertTensorIteratorToSequence::ConvertTensorIteratorToSequence() {
+    add_matcher<ConvertTensorIteratorToLSTMSequence>();
+    add_matcher<ConvertTensorIteratorToRNNSequence>();
+    add_matcher<ConvertTensorIteratorToGRUSequence>();
+}
diff --git a/src/core/src/op/tensor_iterator.cpp b/src/core/src/op/tensor_iterator.cpp
index dfc5fca2d72..3ee69126cd9 100644
--- a/src/core/src/op/tensor_iterator.cpp
+++ b/src/core/src/op/tensor_iterator.cpp
@@ -87,19 +87,18 @@ void op::v0::TensorIterator::validate_and_infer_types() {
         if (auto slice_input_description = ov::as_type_ptr<SliceInputDescription>(input_description)) {
             auto body_parameter = body->get_parameters().at(slice_input_description->m_body_parameter_index);
             auto input_partial_shape = inputs().at(index).get_source_output().get_partial_shape();
-            if (input_partial_shape.is_static()) {
-                auto input_shape = input_partial_shape.to_shape();
-                auto axis = slice_input_description->m_axis;
+            auto axis = slice_input_description->m_axis;
+            if (input_partial_shape.rank().is_static() && input_partial_shape[axis].is_static()) {
                 auto part_size = slice_input_description->m_part_size;
 
-                auto dim_size = input_shape[axis];
+                auto dim_size = input_partial_shape[axis].get_length();
                 auto start = make_positive(slice_input_description->m_start, dim_size);
                 auto end = make_positive(slice_input_description->m_end, dim_size);
 
                 // +1 because the left and right borders are included [start, end]
                 m_num_iterations = (abs(end - start) + 1) / part_size;
                 // infer type for m_body_parameter
-                ov::Shape out_shape{input_shape};
+                ov::PartialShape out_shape{input_partial_shape};
                 out_shape[axis] = part_size;
                 body_parameter->set_partial_shape(out_shape);
             } else {
diff --git a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
index b2543cbfeaf..392510e079a 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
@@ -134,15 +134,11 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Model> func) {
             manager.register_pass<ngraph::pass::BidirectionalRNNSequenceDecomposition>();
         }
 
-        manager.register_pass<ngraph::pass::ConvertRNNSequenceToTensorIterator>();
-        manager.register_pass<ngraph::pass::ConvertGRUSequenceToTensorIterator>();
-        manager.register_pass<ngraph::pass::ConvertLSTMSequenceToTensorIterator>();
+        manager.register_pass<ngraph::pass::ConvertSequenceToTensorIterator>();
         manager.register_pass<ngraph::pass::ConvertOpSet3ToOpSet2>();
         manager.register_pass<ngraph::pass::ConvertOpSet2ToOpSet1>();
 
-        manager.register_pass<ngraph::pass::ConvertTensorIteratorToGRUSequence>();
-        manager.register_pass<ngraph::pass::ConvertTensorIteratorToLSTMSequence>();
-        manager.register_pass<ngraph::pass::ConvertTensorIteratorToRNNSequence>();
+        manager.register_pass<ngraph::pass::ConvertTensorIteratorToSequence>();
         manager.register_pass<ngraph::pass::LSTMCellDecomposition>();
         manager.register_pass<ngraph::pass::GRUCellDecomposition>();
         manager.register_pass<ngraph::pass::RNNCellDecomposition>();
@@ -317,9 +313,7 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Model> func) {
         pass_config->enable<ngraph::pass::ConvertGather8ToGather7>();
 
         if (!config.enable_loop_unrolling) {
-            pass_config->disable<ngraph::pass::ConvertTensorIteratorToRNNSequence>();
-            pass_config->disable<ngraph::pass::ConvertTensorIteratorToLSTMSequence>();
-            pass_config->disable<ngraph::pass::ConvertTensorIteratorToGRUSequence>();
+            pass_config->disable<ngraph::pass::ConvertTensorIteratorToSequence>();
         }
 
         pass_config->enable<ngraph::pass::ConvertInterpolate1ToInterpolate4>();
diff --git a/src/tests/functional/inference_engine/transformations/convert_sequences_to_ti_test.cpp b/src/tests/functional/inference_engine/transformations/convert_sequences_to_ti_test.cpp
new file mode 100644
index 00000000000..48b060d725b
--- /dev/null
+++ b/src/tests/functional/inference_engine/transformations/convert_sequences_to_ti_test.cpp
@@ -0,0 +1,540 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "common_test_utils/test_common.hpp"
+#include <string>
+#include <memory>
+
+#include <ngraph/pass/manager.hpp>
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset5.hpp>
+#include <transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp>
+#include <transformations/utils/utils.hpp>
+#include <transformations/init_node_info.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+using namespace ngraph;
+
+TEST(TransformationTests, ConvertLSTMSequenceToTensorIterator) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 2, 16 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512 }, b_val);
+
+        auto rnn_sequence = std::make_shared<opset5::LSTMSequence>(X, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto Y_out = std::make_shared<opset5::Result>(rnn_sequence->output(0));
+        auto Ho = std::make_shared<opset5::Result>(rnn_sequence->output(1));
+        auto Co = std::make_shared<opset5::Result>(rnn_sequence->output(2));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+        Co->set_friendly_name("Co");
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ Y_out, Ho, Co }, ngraph::ParameterVector{ X, Y, Z });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertLSTMSequenceToTensorIterator>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 2, 16 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 16 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto Zi = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto seq_body_param = std::make_shared<opset5::Parameter>(element::i32, PartialShape{ 1 });
+
+        // Body
+        auto squeeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto squeeze_x = std::make_shared<opset5::Squeeze>(Xi, squeeze_pattern);
+        auto squeeze_y = std::make_shared<opset5::Squeeze>(Yi, squeeze_pattern);
+        auto squeeze_z = std::make_shared<opset5::Squeeze>(Zi, squeeze_pattern);
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512 }, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::LSTMCell>(squeeze_x, squeeze_y, squeeze_z, W, R, B, 128);
+
+        auto unsqueeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto unsqueeze1 = std::make_shared<opset5::Unsqueeze>(rnn_cell->output(0), unsqueeze_pattern);
+        auto Ho = std::make_shared<opset5::Result>(unsqueeze1);
+
+        auto unsqueeze2 = std::make_shared<opset5::Unsqueeze>(unsqueeze1, unsqueeze_pattern);
+        auto Y_out = std::make_shared<opset5::Result>(unsqueeze2);
+
+        auto unsqueeze3 = std::make_shared<opset5::Unsqueeze>(rnn_cell->output(1), unsqueeze_pattern);
+        auto Co = std::make_shared<opset5::Result>(unsqueeze3);
+
+        auto body = std::make_shared<Function>(OutputVector{ Y_out, Ho, Co }, ParameterVector{ Xi, Yi, Zi, seq_body_param });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->get_concatenated_slices(Y_out, 0, 1, 1, -1, 2);
+
+        tensor_iterator->set_merged_input(Yi, Y, Ho);
+        tensor_iterator->set_merged_input(Zi, Z, Co);
+
+        auto seq_lengths = opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
+
+        tensor_iterator->get_iter_value(Ho);
+        tensor_iterator->get_iter_value(Co);
+
+        auto res_ti_Y = std::make_shared<opset5::Result>(tensor_iterator->output(0));
+        auto res_ti_H = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        auto res_ti_C = std::make_shared<opset5::Result>(tensor_iterator->output(2));
+        res_ti_Y->set_friendly_name("Y_out");
+        res_ti_H->set_friendly_name("Ho");
+        res_ti_C->set_friendly_name("Co");
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_Y, res_ti_H, res_ti_C }, ngraph::ParameterVector{X, Y, Z});
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, ConvertLSTMSequenceToTensorIteratorDynamic) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512 }, b_val);
+
+        auto rnn_sequence = std::make_shared<opset5::LSTMSequence>(X, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto Y_out = std::make_shared<opset5::Result>(rnn_sequence->output(0));
+        auto Ho = std::make_shared<opset5::Result>(rnn_sequence->output(1));
+        auto Co = std::make_shared<opset5::Result>(rnn_sequence->output(2));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+        Co->set_friendly_name("Co");
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ Y_out, Ho, Co }, ngraph::ParameterVector{ X, Y, Z });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertLSTMSequenceToTensorIterator>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 1, -1 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto Zi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto seq_body_param = std::make_shared<opset5::Parameter>(element::i32, PartialShape{ 1 });
+
+        // Body
+        auto squeeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto squeeze_x = std::make_shared<opset5::Squeeze>(Xi, squeeze_pattern);
+        auto squeeze_y = std::make_shared<opset5::Squeeze>(Yi, squeeze_pattern);
+        auto squeeze_z = std::make_shared<opset5::Squeeze>(Zi, squeeze_pattern);
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512 }, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::LSTMCell>(squeeze_x, squeeze_y, squeeze_z, W, R, B, 128);
+
+        auto unsqueeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto unsqueeze1 = std::make_shared<opset5::Unsqueeze>(rnn_cell->output(0), unsqueeze_pattern);
+        auto Ho = std::make_shared<opset5::Result>(unsqueeze1);
+
+        auto unsqueeze2 = std::make_shared<opset5::Unsqueeze>(unsqueeze1, unsqueeze_pattern);
+        auto Y_out = std::make_shared<opset5::Result>(unsqueeze2);
+
+        auto unsqueeze3 = std::make_shared<opset5::Unsqueeze>(rnn_cell->output(1), unsqueeze_pattern);
+        auto Co = std::make_shared<opset5::Result>(unsqueeze3);
+
+        auto body = std::make_shared<Function>(OutputVector{ Y_out, Ho, Co }, ParameterVector{ Xi, Yi, Zi, seq_body_param });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->get_concatenated_slices(Y_out, 0, 1, 1, -1, 2);
+
+        tensor_iterator->set_merged_input(Yi, Y, Ho);
+        tensor_iterator->set_merged_input(Zi, Z, Co);
+
+        auto seq_lengths = opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
+
+        tensor_iterator->get_iter_value(Ho);
+        tensor_iterator->get_iter_value(Co);
+
+        auto res_ti_Y = std::make_shared<opset5::Result>(tensor_iterator->output(0));
+        auto res_ti_H = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        auto res_ti_C = std::make_shared<opset5::Result>(tensor_iterator->output(2));
+        res_ti_Y->set_friendly_name("Y_out");
+        res_ti_H->set_friendly_name("Ho");
+        res_ti_C->set_friendly_name("Co");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_Y, res_ti_H, res_ti_C }, ngraph::ParameterVector{ X, Y, Z });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, ConvertRNNSequenceToTensorIterator) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 128});
+        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 128, 16}, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 128, 128}, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 128}, b_val);
+
+        auto rnn_sequence = std::make_shared<opset5::RNNSequence>(X, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto Y_out = std::make_shared<opset5::Result>(rnn_sequence->output(0));
+        auto Ho = std::make_shared<opset5::Result>(rnn_sequence->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ Y_out, Ho }, ngraph::ParameterVector{X, Y});
+
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertRNNSequenceToTensorIterator>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 2, 16 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 16 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto seq_body_param = std::make_shared<opset5::Parameter>(element::i32, PartialShape{ 1 });
+
+        // Body
+        auto squeeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto squeeze_x = std::make_shared<opset5::Squeeze>(Xi, squeeze_pattern);
+        auto squeeze_y = std::make_shared<opset5::Squeeze>(Yi, squeeze_pattern);
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128 }, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::RNNCell>(squeeze_x, squeeze_y, W, R, B, 128);
+        auto unsqueeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto unsqueeze1 = std::make_shared<opset5::Unsqueeze>(rnn_cell, unsqueeze_pattern);
+        auto Ho = std::make_shared<opset5::Result>(unsqueeze1);
+        auto unsqueeze2 = std::make_shared<opset5::Unsqueeze>(unsqueeze1, unsqueeze_pattern);
+        auto Y_out = std::make_shared<opset5::Result>(unsqueeze2);
+        auto body = std::make_shared<Function>(OutputVector{ Y_out, Ho }, ParameterVector{ Xi, Yi, seq_body_param });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->get_concatenated_slices(Y_out, 0, 1, 1, -1, 2);
+
+        tensor_iterator->set_merged_input(Yi, Y, Ho);
+        auto seq_lengths = opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
+
+        tensor_iterator->get_iter_value(Ho);
+
+        auto res_ti_Y = std::make_shared<opset5::Result>(tensor_iterator->output(0));
+        auto res_ti_H = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        res_ti_Y->set_friendly_name("Y_out");
+        res_ti_H->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_Y, res_ti_H }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, ConvertRNNSequenceToTensorIteratorDynamic) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128 }, b_val);
+
+        auto rnn_sequence = std::make_shared<opset5::RNNSequence>(X, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto Y_out = std::make_shared<opset5::Result>(rnn_sequence->output(0));
+        auto Ho = std::make_shared<opset5::Result>(rnn_sequence->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertRNNSequenceToTensorIterator>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 1, -1 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto seq_body_param = std::make_shared<opset5::Parameter>(element::i32, PartialShape{ 1 });
+
+        // Body
+        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto squeeze_x = std::make_shared<opset5::Squeeze>(Xi, axis_1);
+        auto squeeze_y = std::make_shared<opset5::Squeeze>(Yi, axis_1);
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128 }, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::RNNCell>(squeeze_x, squeeze_y, W, R, B, 128);
+        auto unsqueeze1 = std::make_shared<opset5::Unsqueeze>(rnn_cell, axis_1);
+        auto Ho = std::make_shared<opset5::Result>(unsqueeze1);
+        auto unsqueeze2 = std::make_shared<opset5::Unsqueeze>(unsqueeze1, axis_1);
+        auto Y_out = std::make_shared<opset5::Result>(unsqueeze2);
+        auto body = std::make_shared<Function>(OutputVector{ Y_out, Ho }, ParameterVector{ Xi, Yi, seq_body_param });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->get_concatenated_slices(Y_out, 0, 1, 1, -1, 2);
+
+        tensor_iterator->set_merged_input(Yi, Y, Ho);
+        auto seq_lengths = opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
+
+        tensor_iterator->get_iter_value(Ho);
+
+        auto res_ti_Y = std::make_shared<opset5::Result>(tensor_iterator->output(0));
+        auto res_ti_H = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        res_ti_Y->set_friendly_name("Y_out");
+        res_ti_H->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_Y, res_ti_H }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, ConvertGRUSequenceToTensorIterator) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 2, 16 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
+
+        auto rnn_sequence = std::make_shared<opset5::GRUSequence>(X, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto Y_out = std::make_shared<opset5::Result>(rnn_sequence->output(0));
+        auto Ho = std::make_shared<opset5::Result>(rnn_sequence->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertGRUSequenceToTensorIterator>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 2, 16 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 16 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, Shape{ 1, 1, 128 });
+        auto seq_body_param = std::make_shared<opset5::Parameter>(element::i32, PartialShape{ 1 });
+
+        // Body
+        auto squeeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto squeeze_x = std::make_shared<opset5::Squeeze>(Xi, squeeze_pattern);
+        auto squeeze_y = std::make_shared<opset5::Squeeze>(Yi, squeeze_pattern);
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384 }, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::GRUCell>(squeeze_x, squeeze_y, W, R, B, 128);
+        auto unsqueeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto unsqueeze1 = std::make_shared<opset5::Unsqueeze>(rnn_cell, unsqueeze_pattern);
+        auto Ho = std::make_shared<opset5::Result>(unsqueeze1);
+        auto unsqueeze2 = std::make_shared<opset5::Unsqueeze>(unsqueeze1, unsqueeze_pattern);
+        auto Y_out = std::make_shared<opset5::Result>(unsqueeze2);
+        auto body = std::make_shared<Function>(OutputVector{ Y_out, Ho }, ParameterVector{ Xi, Yi, seq_body_param });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->get_concatenated_slices(Y_out, 0, 1, 1, -1, 2);
+
+        tensor_iterator->set_merged_input(Yi, Y, Ho);
+        auto seq_lengths = opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
+
+        tensor_iterator->get_iter_value(Ho);
+
+        auto res_ti_Y = std::make_shared<opset5::Result>(tensor_iterator->output(0));
+        auto res_ti_H = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        res_ti_Y->set_friendly_name("Y_out");
+        res_ti_H->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_Y, res_ti_H }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, ConvertGRUSequenceToTensorIteratorDynamic) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
+
+        auto rnn_sequence = std::make_shared<opset5::GRUSequence>(X, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto Y_out = std::make_shared<opset5::Result>(rnn_sequence->output(0));
+        auto Ho = std::make_shared<opset5::Result>(rnn_sequence->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertGRUSequenceToTensorIterator>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 1, -1 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ 1, 1, 128 });
+        auto seq_body_param = std::make_shared<opset5::Parameter>(element::i32, PartialShape{ 1 });
+
+        // Body
+        auto squeeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto squeeze_x = std::make_shared<opset5::Squeeze>(Xi, squeeze_pattern);
+        auto squeeze_y = std::make_shared<opset5::Squeeze>(Yi, squeeze_pattern);
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384 }, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::GRUCell>(squeeze_x, squeeze_y, W, R, B, 128);
+        auto unsqueeze_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto unsqueeze1 = std::make_shared<opset5::Unsqueeze>(rnn_cell, unsqueeze_pattern);
+        auto Ho = std::make_shared<opset5::Result>(unsqueeze1);
+        auto unsqueeze2 = std::make_shared<opset5::Unsqueeze>(unsqueeze1, unsqueeze_pattern);
+        auto Y_out = std::make_shared<opset5::Result>(unsqueeze2);
+        auto body = std::make_shared<Function>(OutputVector{ Y_out, Ho }, ParameterVector{ Xi, Yi, seq_body_param });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->get_concatenated_slices(Y_out, 0, 1, 1, -1, 2);
+
+        tensor_iterator->set_merged_input(Yi, Y, Ho);
+        auto seq_lengths = opset5::Constant::create(element::i32, Shape{ 1 }, { 2 });
+        tensor_iterator->set_invariant_input(seq_body_param, seq_lengths);
+
+        tensor_iterator->get_iter_value(Ho);
+
+        auto res_ti_Y = std::make_shared<opset5::Result>(tensor_iterator->output(0));
+        auto res_ti_H = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        res_ti_Y->set_friendly_name("Y_out");
+        res_ti_H->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_Y, res_ti_H }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
diff --git a/src/tests/functional/inference_engine/transformations/convert_ti_to_sequences_test.cpp b/src/tests/functional/inference_engine/transformations/convert_ti_to_sequences_test.cpp
index 3d9135b32e0..c8048c09a65 100644
--- a/src/tests/functional/inference_engine/transformations/convert_ti_to_sequences_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/convert_ti_to_sequences_test.cpp
@@ -7,7 +7,6 @@
 #include "common_test_utils/test_common.hpp"
 #include <string>
 #include <memory>
-#include <queue>
 
 #include <ngraph/pass/manager.hpp>
 #include <ngraph/function.hpp>
@@ -48,8 +47,7 @@ TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
         auto reshape_pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 1, 128});
         auto unsqueeze = std::make_shared<opset5::Reshape>(lstm_cell, reshape_pattern_2, false);
         auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
-        auto body = std::make_shared<Function>(OutputVector{res_1, res_2},
-                                               ParameterVector{Xi, Yi, Zi});
+        auto body = std::make_shared<Function>(OutputVector{res_1, res_2}, ParameterVector{Xi, Yi, Zi});
 
         auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
         tensor_iterator->set_body(body);
@@ -62,9 +60,7 @@ TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
         auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
 
         auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
-        //auto res_ti_2 = std::make_shared<opset5::Result>(tensor_iterator->output(0));
-        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1},
-                                               ngraph::ParameterVector{X, Y, Z});
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1}, ngraph::ParameterVector{X, Y, Z});
 
         ngraph::pass::Manager m;
         m.register_pass<ngraph::pass::InitNodeInfo>();
@@ -81,26 +77,18 @@ TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
         auto w_val = std::vector<float>(512 * 16, 0);
         auto r_val = std::vector<float>(512 * 128, 0);
         auto b_val = std::vector<float>(512, 0);
-        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512, 16}, w_val);
-        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512, 128}, r_val);
-        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512}, b_val);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 512, 16}, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 512, 128}, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 512}, b_val);
 
         auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
         auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
         auto in_2 = std::make_shared<ngraph::opset5::Unsqueeze>(Z, axis_1);
 
-        auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
-        auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(W, axis_2);
-        auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(R, axis_2);
-        auto in_6 = std::make_shared<ngraph::opset5::Unsqueeze>(B, axis_2);
-
         auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{1}, {2});
-        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(X, in_1, in_2, seq_lengths, in_4, in_5, in_6,
-                                                               128, ngraph::op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(X, in_1, in_2, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
         auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
         auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(0), axis_out);
-        auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(1), axis_out);
-        auto out_2 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(1), axis_out);
         auto res_ti_1 = std::make_shared<opset5::Result>(out_0);
         f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1}, ngraph::ParameterVector{X, Y, Z});
     }
@@ -109,6 +97,93 @@ TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
     ASSERT_TRUE(res.first) << res.second;
 }
 
+TEST(TransformationTests, ConvertTensorIteratorToLSTMSequenceDynamic) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 1, -1 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+        auto Zi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        // Body
+        auto reshape_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 2 }, { 1, -1 });
+        auto squeeze = std::make_shared<opset5::Reshape>(Xi, reshape_pattern, false);
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 512 }, b_val);
+
+        auto lstm_cell = std::make_shared<opset5::LSTMCell>(squeeze, Yi, Zi, W, R, B, 128);
+
+        auto res_1 = std::make_shared<opset5::Result>(lstm_cell);
+        auto reshape_pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 1, 128 });
+        auto unsqueeze = std::make_shared<opset5::Reshape>(lstm_cell, reshape_pattern_2, false);
+        auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
+        auto body = std::make_shared<Function>(OutputVector{ res_1, res_2 }, ParameterVector{ Xi, Yi, Zi });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_invariant_input(Zi, Z);
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->set_merged_input(Yi, Y, res_1);
+
+        auto out0 = tensor_iterator->get_iter_value(res_1, -1);
+        auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
+
+        auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_1 }, ngraph::ParameterVector{ X, Y, Z });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertTensorIteratorToLSTMSequence>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+        auto Z = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
+        auto in_2 = std::make_shared<ngraph::opset5::Unsqueeze>(Z, axis_1);
+
+        auto shape_of = std::make_shared<opset5::ShapeOf>(X);
+        auto batch_dimension = ngraph::op::util::make_try_fold<ngraph::opset5::Gather>(
+            shape_of,
+            ngraph::opset5::Constant::create(ngraph::element::i64, { 1 }, { 0 }),
+            ngraph::opset5::Constant::create(ngraph::element::i64, {}, { 0 }));
+        auto seq_lengths = std::make_shared<opset5::Broadcast>(ngraph::opset5::Constant::create(element::i32, Shape{}, { 2 }), batch_dimension);
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512 }, b_val);
+
+        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(X, in_1, in_2, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(0), axis_out);
+        auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(1), axis_out);
+        auto out_2 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(2), axis_out);
+        auto res_ti_1 = std::make_shared<opset5::Result>(out_0);
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_1 }, ngraph::ParameterVector{ X, Y, Z });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
 TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
@@ -134,8 +209,7 @@ TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
         auto reshape_pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 1, 128});
         auto unsqueeze = std::make_shared<opset5::Reshape>(rnn_cell, reshape_pattern_2, false);
         auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
-        auto body = std::make_shared<Function>(OutputVector{res_1, res_2},
-                                               ParameterVector{Xi, Yi});
+        auto body = std::make_shared<Function>(OutputVector{res_1, res_2}, ParameterVector{Xi, Yi});
 
         auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
         tensor_iterator->set_body(body);
@@ -147,9 +221,7 @@ TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
         auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
 
         auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
-        //auto res_ti_2 = std::make_shared<opset5::Result>(tensor_iterator->output(0));
-        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1},
-                                               ngraph::ParameterVector{X, Y});
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1}, ngraph::ParameterVector{X, Y});
 
         ngraph::pass::Manager m;
         m.register_pass<ngraph::pass::InitNodeInfo>();
@@ -165,21 +237,15 @@ TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
         auto w_val = std::vector<float>(128 * 16, 0);
         auto r_val = std::vector<float>(128 * 128, 0);
         auto b_val = std::vector<float>(128, 0);
-        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128, 16}, w_val);
-        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128, 128}, r_val);
-        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128}, b_val);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 128, 16}, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 128, 128}, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{1, 128}, b_val);
 
         auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
         auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
 
-        auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
-        auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(W, axis_2);
-        auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(R, axis_2);
-        auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(B, axis_2);
-
         auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{1}, {2});
-        auto rnn_sequence = std::make_shared<opset5::RNNSequence>(X, in_1, seq_lengths, in_3, in_4, in_5,
-                                                                  128, ngraph::op::RecurrentSequenceDirection::FORWARD);
+        auto rnn_sequence = std::make_shared<opset5::RNNSequence>(X, in_1, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
         auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
         auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->output(0), axis_out);
         auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->output(1), axis_out);
@@ -191,6 +257,85 @@ TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
     ASSERT_TRUE(res.first) << res.second;
 }
 
+TEST(TransformationTests, ConvertTensorIteratorToRNNSequenceDynamic) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 1, -1 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        // Body
+        auto reshape_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 2 }, { 1, -1 });
+        auto squeeze = std::make_shared<opset5::Reshape>(Xi, reshape_pattern, false);
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 128 }, b_val);
+
+        auto rnn_cell = std::make_shared<opset5::RNNCell>(squeeze, Yi, W, R, B, 128);
+        auto res_1 = std::make_shared<opset5::Result>(rnn_cell);
+        auto reshape_pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 1, -1 });
+        auto unsqueeze = std::make_shared<opset5::Reshape>(rnn_cell, reshape_pattern_2, false);
+        auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
+        auto body = std::make_shared<Function>(OutputVector{ res_1, res_2 }, ParameterVector{ Xi, Yi });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->set_merged_input(Yi, Y, res_1);
+
+        auto out0 = tensor_iterator->get_iter_value(res_1, -1);
+        auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
+
+        auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_1 }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertTensorIteratorToRNNSequence>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128 }, b_val);
+
+        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
+
+        auto shape_of = std::make_shared<opset5::ShapeOf>(X);
+        auto batch_dimension = ngraph::op::util::make_try_fold<ngraph::opset5::Gather>(
+            shape_of,
+            ngraph::opset5::Constant::create(ngraph::element::i64, { 1 }, { 0 }),
+            ngraph::opset5::Constant::create(ngraph::element::i64, {}, { 0 }));
+        auto seq_lengths = std::make_shared<opset5::Broadcast>(ngraph::opset5::Constant::create(element::i32, Shape{}, { 2 }), batch_dimension);
+
+        auto rnn_sequence = std::make_shared<opset5::RNNSequence>(X, in_1, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->output(0), axis_out);
+        auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->output(1), axis_out);
+        auto res_ti_1 = std::make_shared<opset5::Result>(out_0);
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_1 }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
 TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
@@ -216,8 +361,7 @@ TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
         auto reshape_pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 1, 128});
         auto unsqueeze = std::make_shared<opset5::Reshape>(gru_cell, reshape_pattern_2, false);
         auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
-        auto body = std::make_shared<Function>(OutputVector{res_1, res_2},
-                                               ParameterVector{Xi, Yi});
+        auto body = std::make_shared<Function>(OutputVector{res_1, res_2}, ParameterVector{Xi, Yi});
 
         auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
         tensor_iterator->set_body(body);
@@ -229,9 +373,7 @@ TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
         auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
 
         auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
-        //auto res_tRNNCelli_2 = std::make_shared<opset5::Result>(tensor_iterator->output(0));
-        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1},
-                                               ngraph::ParameterVector{X, Y});
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1}, ngraph::ParameterVector{X, Y});
 
         ngraph::pass::Manager m;
         m.register_pass<ngraph::pass::InitNodeInfo>();
@@ -244,24 +386,18 @@ TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
         auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
         auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
 
-        auto w_val = std::vector<float>(384 * 16, 0);
-        auto r_val = std::vector<float>(384 * 128, 0);
-        auto b_val = std::vector<float>(384, 0);
-        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384, 16}, w_val);
-        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384, 128}, r_val);
-        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384}, b_val);
-
         auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
         auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
 
-        auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
-        auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(W, axis_2);
-        auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(R, axis_2);
-        auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(B, axis_2);
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
 
         auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{1}, {2});
-        auto gru_sequence = std::make_shared<opset5::GRUSequence>(X, in_1, seq_lengths, in_3, in_4, in_5,
-                                                                  128, ngraph::op::RecurrentSequenceDirection::FORWARD);
+        auto gru_sequence = std::make_shared<opset5::GRUSequence>(X, in_1, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
         auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
         auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->output(0), axis_out);
         auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->output(1), axis_out);
@@ -271,4 +407,83 @@ TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
 
     auto res = compare_functions(f, f_ref);
     ASSERT_TRUE(res.first) << res.second;
-}
\ No newline at end of file
+}
+
+TEST(TransformationTests, ConvertTensorIteratorToGRUSequenceDynamic) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        auto Xi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 1, -1 });
+        auto Yi = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        // Body
+        auto reshape_pattern = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 2 }, { 1, -1 });
+        auto squeeze = std::make_shared<opset5::Reshape>(Xi, reshape_pattern, false);
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 384 }, b_val);
+
+        auto gru_cell = std::make_shared<opset5::GRUCell>(squeeze, Yi, W, R, B, 128);
+        auto res_1 = std::make_shared<opset5::Result>(gru_cell);
+        auto reshape_pattern_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 1, 128 });
+        auto unsqueeze = std::make_shared<opset5::Reshape>(gru_cell, reshape_pattern_2, false);
+        auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
+        auto body = std::make_shared<Function>(OutputVector{ res_1, res_2 }, ParameterVector{ Xi, Yi });
+
+        auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
+        tensor_iterator->set_body(body);
+
+        tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
+        tensor_iterator->set_merged_input(Yi, Y, res_1);
+
+        auto out0 = tensor_iterator->get_iter_value(res_1, -1);
+        auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
+
+        auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_1 }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::ConvertTensorIteratorToGRUSequence>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto X = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, 2, -1 });
+        auto Y = std::make_shared<opset5::Parameter>(element::f32, PartialShape{ -1, -1 });
+
+        auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
+
+        auto shape_of = std::make_shared<opset5::ShapeOf>(X);
+        auto batch_dimension = ngraph::op::util::make_try_fold<ngraph::opset5::Gather>(
+            shape_of,
+            ngraph::opset5::Constant::create(ngraph::element::i64, { 1 }, { 0 }),
+            ngraph::opset5::Constant::create(ngraph::element::i64, {}, { 0 }));
+        auto seq_lengths = std::make_shared<opset5::Broadcast>(ngraph::opset5::Constant::create(element::i32, Shape{}, { 2 }), batch_dimension);
+
+        auto gru_sequence = std::make_shared<opset5::GRUSequence>(X, in_1, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{ 1 }, { 1 });
+        auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->output(0), axis_out);
+        auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->output(1), axis_out);
+        auto res_ti_1 = std::make_shared<opset5::Result>(out_0);
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ res_ti_1 }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
diff --git a/src/tests/unit/cpu/optimize_sequence_transposes_test.cpp b/src/tests/unit/cpu/optimize_sequence_transposes_test.cpp
new file mode 100644
index 00000000000..2fa0db074da
--- /dev/null
+++ b/src/tests/unit/cpu/optimize_sequence_transposes_test.cpp
@@ -0,0 +1,445 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset5.hpp>
+#include <ngraph_transformations/rnn_sequences_optimization.hpp>
+#include <transformations/init_node_info.hpp>
+#include <transformations/utils/utils.hpp>
+#include <ngraph_ops/type_relaxed.hpp>
+#include <ngraph/pass/manager.hpp>
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+using namespace MKLDNNPlugin;
+using namespace ngraph;
+
+TEST(TransformationTests, OptimizeLSTMSequenceTransposesTest) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512 }, b_val);
+
+        auto transpose_before_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 3 }, { 1, 0, 2 });
+        auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(transpose_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
+        auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
+
+        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+        Co->set_friendly_name("Co");
+
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho, Co }, ngraph::ParameterVector{ X, Y, Z });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<OptimizeLSTMSequenceTransposes>();
+        m.run_passes(f);
+    }
+
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512 }, b_val);
+
+        auto reshape_before_const  = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 2, 16 });
+        auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_const, false);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(reshape_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
+        auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
+
+        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+        Co->set_friendly_name("Co");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho, Co }, ngraph::ParameterVector{ X, Y, Z });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, OptimizeLSTMSequenceTransposesDynamicTest) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512 }, b_val);
+
+        auto transpose_before_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 3 }, { 1, 0, 2 });
+        auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(transpose_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
+        auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
+
+        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+        Co->set_friendly_name("Co");
+
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho, Co }, ngraph::ParameterVector{ X, Y, Z });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<OptimizeLSTMSequenceTransposes>();
+        m.run_passes(f);
+    }
+
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
+        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(512 * 16, 0);
+        auto r_val = std::vector<float>(512 * 128, 0);
+        auto b_val = std::vector<float>(512, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 512 }, b_val);
+
+        auto data = std::make_shared<ngraph::opset1::ShapeOf>(X);
+        auto reshape_before_pattern = std::make_shared<ngraph::opset1::Gather>(data,
+            ngraph::opset1::Constant::create(ngraph::element::i32, { 3 }, { 1, 0, 2 }),
+            ngraph::opset1::Constant::create(ngraph::element::i32, {}, { 0 }));
+        auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_pattern, false);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(reshape_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
+        auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
+
+        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+        Co->set_friendly_name("Co");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho, Co }, ngraph::ParameterVector{ X, Y, Z });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, OptimizeRNNSequenceTransposesTest) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128 }, b_val);
+
+        auto transpose_before_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 3 }, { 1, 0, 2 });
+        auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::RNNSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
+        auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
+
+        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<OptimizeRNNSequenceTransposes>();
+        m.run_passes(f);
+    }
+
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128 }, b_val);
+
+        auto reshape_before_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 2, 16 });
+        auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_const, false);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::RNNSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
+        auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
+
+        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, OptimizeRNNSequenceTransposesDynamicTest) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128 }, b_val);
+
+        auto transpose_before_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 3 }, { 1, 0, 2 });
+        auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::RNNSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
+        auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
+
+        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<OptimizeRNNSequenceTransposes>();
+        m.run_passes(f);
+    }
+
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(128 * 16, 0);
+        auto r_val = std::vector<float>(128 * 128, 0);
+        auto b_val = std::vector<float>(128, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 128 }, b_val);
+
+        auto data = std::make_shared<ngraph::opset1::ShapeOf>(X);
+        auto reshape_before_pattern = std::make_shared<ngraph::opset1::Gather>(data,
+            ngraph::opset1::Constant::create(ngraph::element::i32, { 3 }, { 1, 0, 2 }),
+            ngraph::opset1::Constant::create(ngraph::element::i32, {}, { 0 }));
+        auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_pattern, false);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::RNNSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
+        auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
+
+        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, OptimizeGRUSequenceTransposesTest) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
+
+        auto transpose_before_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 3 }, { 1, 0, 2 });
+        auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::GRUSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
+        auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
+
+        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<OptimizeGRUSequenceTransposes>();
+        m.run_passes(f);
+    }
+
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
+
+        auto reshape_before_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 2, 16 });
+        auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_const, false);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::GRUSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
+        auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
+
+        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, OptimizeGRUSequenceTransposesDynamicTest) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
+
+        auto transpose_before_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 3 }, { 1, 0, 2 });
+        auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::GRUSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
+        auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
+
+        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<OptimizeGRUSequenceTransposes>();
+        m.run_passes(f);
+    }
+
+    {
+        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+
+        auto w_val = std::vector<float>(384 * 16, 0);
+        auto r_val = std::vector<float>(384 * 128, 0);
+        auto b_val = std::vector<float>(384, 0);
+        auto W = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 16 }, w_val);
+        auto R = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384, 128 }, r_val);
+        auto B = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{ 1, 384 }, b_val);
+
+        auto data = std::make_shared<ngraph::opset1::ShapeOf>(X);
+        auto reshape_before_pattern = std::make_shared<ngraph::opset1::Gather>(data,
+            ngraph::opset1::Constant::create(ngraph::element::i32, { 3 }, { 1, 0, 2 }),
+            ngraph::opset1::Constant::create(ngraph::element::i32, {}, { 0 }));
+        auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_pattern, false);
+
+        auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
+        auto lstm_seq = std::make_shared<opset5::GRUSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+
+        auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
+        auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
+
+        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        Y_out->set_friendly_name("Y_out");
+        Ho->set_friendly_name("Ho");
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::ResultVector{ Y_out, Ho }, ngraph::ParameterVector{ X, Y });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}

From a847e1f7fa93891d9fc1a07a507ccf0b62fe5e75 Mon Sep 17 00:00:00 2001
From: Liubov Talamanova <piccione-mail@yandex.ru>
Date: Mon, 20 Dec 2021 13:09:22 +0300
Subject: [PATCH 043/160] [POT] Added printing IE, MO and POT versions during
 POT launch (#8679)

* Add POT version print

* Changed version in setup.py

* Add version to setup.py

* Update imports
---
 tools/CMakeLists.txt                          |  2 ++
 tools/pot/openvino/tools/pot/graph/utils.py   |  4 +--
 .../openvino/tools/pot/pipeline/pipeline.py   |  7 +++++
 .../pot/openvino/tools/pot/utils/telemetry.py |  6 ++--
 tools/pot/openvino/tools/pot/utils/utils.py   | 12 ++++++++
 tools/pot/openvino/tools/pot/version.py       | 26 +++++++++++++++-
 tools/pot/setup.py                            | 30 +++++++++++--------
 7 files changed, 69 insertions(+), 18 deletions(-)

diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 416d624f841..5d5862d4cd3 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -83,6 +83,8 @@ endif()
 
 add_subdirectory(mo)
 
+file(WRITE ${CMAKE_CURRENT_SOURCE_DIR}/pot/version.txt ${CI_BUILD_NUMBER})
+
 # build and install openvino-dev wheel
 
 if(ENABLE_WHEEL)
diff --git a/tools/pot/openvino/tools/pot/graph/utils.py b/tools/pot/openvino/tools/pot/graph/utils.py
index 0067b56d693..3a051c8cdfe 100644
--- a/tools/pot/openvino/tools/pot/graph/utils.py
+++ b/tools/pot/openvino/tools/pot/graph/utils.py
@@ -7,7 +7,7 @@ import json
 
 import numpy as np
 
-import openvino.tools.pot.version
+from openvino.tools.pot.version import get_version
 from .cpu_patterns import get_cpu_ignored_patterns
 from .gpu_patterns import get_gpu_ignored_patterns
 from .vpu_patterns import get_vpu_ignored_patterns
@@ -126,7 +126,7 @@ def create_quantization_info_for_mo(config):
     config_info = {key: config[key] for key in ['compression', 'engine']}
     quantization_section['config'] = json.dumps(config_info, indent='\t',
                                                 cls=PathEncoder).replace('"', "'").replace('\n', '\n\t')
-    quantization_section['version'] = openvino.tools.pot.version.__version__
+    quantization_section['version'] = get_version()
     return quantization_section
 
 
diff --git a/tools/pot/openvino/tools/pot/pipeline/pipeline.py b/tools/pot/openvino/tools/pot/pipeline/pipeline.py
index 71b034b7f1e..0dc3a123be7 100644
--- a/tools/pot/openvino/tools/pot/pipeline/pipeline.py
+++ b/tools/pot/openvino/tools/pot/pipeline/pipeline.py
@@ -1,9 +1,13 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+from openvino.tools.mo.utils.version import get_version as get_mo_version
+
 from ..algorithms.algorithm import Algorithm
 from ..statistics.collector import collect_statistics
 from ..utils.logger import get_logger
+from ..utils.utils import get_ie_version
+from ..version import get_version as get_pot_version
 
 logger = get_logger(__name__)
 
@@ -31,6 +35,9 @@ class Pipeline:
         :param model: initial model
         :return model after applying all added algorithms
         """
+        logger.info(f'Inference Engine version:                {get_ie_version()}')
+        logger.info(f'Model Optimizer version:                 {get_mo_version()}')
+        logger.info(f'Post-Training Optimization Tool version: {get_pot_version()}')
         current_algo_seq = []
         self.set_max_algorithms_steps(model)
         logger.reset_progress_total(self._algorithms_steps, self.default_steps_size)
diff --git a/tools/pot/openvino/tools/pot/utils/telemetry.py b/tools/pot/openvino/tools/pot/utils/telemetry.py
index 6268cfa3b9d..b36cf08f169 100644
--- a/tools/pot/openvino/tools/pot/utils/telemetry.py
+++ b/tools/pot/openvino/tools/pot/utils/telemetry.py
@@ -1,10 +1,10 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from ..version import __version__ as pot_version
 from ..engines.ac_engine import ACEngine
 from ..engines.simplified_engine import SimplifiedEngine
 from .logger import get_logger
+from ..version import get_version as pot_version
 try:
     import openvino_telemetry as tm
 except ImportError:
@@ -17,7 +17,9 @@ def get_tid_telemetry():
     return 'UA-17808594-29'
 
 
-def send_event(action, label, telemetry=tm.Telemetry(tid=get_tid_telemetry(), app_name='pot', app_version=pot_version)):
+def send_event(action,
+               label,
+               telemetry=tm.Telemetry(tid=get_tid_telemetry(), app_name='pot', app_version=pot_version())):
     try:
         telemetry.send_event('pot', action, label)
     except Exception as e: # pylint: disable=broad-except
diff --git a/tools/pot/openvino/tools/pot/utils/utils.py b/tools/pot/openvino/tools/pot/utils/utils.py
index 3bec41279e9..ad6e5776cdc 100644
--- a/tools/pot/openvino/tools/pot/utils/utils.py
+++ b/tools/pot/openvino/tools/pot/utils/utils.py
@@ -45,3 +45,15 @@ class Environment:
     def __exit__(self, *args):
         if not self.was_set:
             del os.environ[self.variable]
+
+
+def get_ie_version():
+    try:
+        from openvino.runtime import get_version  # pylint: disable=import-error,no-name-in-module,import-outside-toplevel
+        return get_version()
+    except ImportError:
+        try:
+            from openvino.inference_engine import get_version  # pylint: disable=import-error,no-name-in-module,import-outside-toplevel
+            return get_version()
+        except ImportError:
+            return None
diff --git a/tools/pot/openvino/tools/pot/version.py b/tools/pot/openvino/tools/pot/version.py
index 5ea7974c594..4ed39f922e4 100644
--- a/tools/pot/openvino/tools/pot/version.py
+++ b/tools/pot/openvino/tools/pot/version.py
@@ -1,4 +1,28 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = '1.0'
+import os
+import subprocess
+
+
+def generate_pot_version():
+    """
+    Function generates version like in cmake
+    custom_{branch_name}_{commit_hash}
+    """
+    try:
+        branch_name = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).strip().decode()
+        commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip().decode()
+        return "custom_{}_{}".format(branch_name, commit_hash)
+    except subprocess.CalledProcessError:
+        return "unknown version"
+
+
+def get_version():
+    version = generate_pot_version()
+    if version == "unknown version":
+        version_txt = os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir, "version.txt")
+        if os.path.isfile(version_txt):
+            with open(version_txt) as f:
+                version = f.readline().replace('\n', '')
+    return version
diff --git a/tools/pot/setup.py b/tools/pot/setup.py
index 12c86283f31..b3114abc090 100644
--- a/tools/pot/setup.py
+++ b/tools/pot/setup.py
@@ -2,10 +2,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import os
+import subprocess
 import sys
-import re
 
-import codecs
 from setuptools import setup, find_packages
 
 
@@ -15,18 +14,23 @@ with open(os.path.join(here, 'README.md'), 'r') as fh:
     long_description = fh.read()
 
 
-def read(*parts):
-    with codecs.open(os.path.join(here, *parts), 'r') as fp:
-        return fp.read()
+def generate_pot_version():
+    try:
+        branch_name = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).strip().decode()
+        commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip().decode()
+        return "custom_{}_{}".format(branch_name, commit_hash)
+    except subprocess.CalledProcessError:
+        return "unknown version"
 
 
-def find_version(*file_paths):
-    version_file = read(*file_paths)
-    version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]",
-                              version_file, re.M)
-    if version_match:
-        return version_match.group(1)
-    raise RuntimeError('Unable to find version string.')
+def get_version():
+    version = generate_pot_version()
+    if version == "unknown version":
+        version_txt = os.path.join(os.path.dirname(os.path.realpath(__file__)), "version.txt")
+        if os.path.isfile(version_txt):
+            with open(version_txt) as f:
+                version = f.readline().replace('\n', '')
+    return version
 
 
 INSTALL_EXTRAS = False
@@ -103,7 +107,7 @@ DEPENDENCY_LINKS = [torch_source_url]
 
 setup(
     name='pot',
-    version=find_version(os.path.join(here, 'openvino/tools/pot/version.py')),
+    version=get_version(),
     author='Intel',
     author_email='alexander.kozlov@intel.com',
     description='Post-training Optimization Toolkit',

From f3dd39e3d2498dcf3ba854195270a3f54d158b39 Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Mon, 20 Dec 2021 13:25:09 +0300
Subject: [PATCH 044/160] DetectionOutput8ToDetectionOutput1 test disabled
 (#9307)

---
 src/tests/functional/inference_engine/skip_tests_config.cpp   | 2 ++
 .../transformations/detection_output_downgrade_test.cpp       | 4 ++++
 2 files changed, 6 insertions(+)

diff --git a/src/tests/functional/inference_engine/skip_tests_config.cpp b/src/tests/functional/inference_engine/skip_tests_config.cpp
index 124d02ba9d0..6af60bfd426 100644
--- a/src/tests/functional/inference_engine/skip_tests_config.cpp
+++ b/src/tests/functional/inference_engine/skip_tests_config.cpp
@@ -13,5 +13,7 @@ std::vector<std::string> disabledTestPatterns() {
         ".*TransformationTests\\.ConstFoldingPriorBox.*",
         // azure is failing after #6199
         ".*/NmsLayerTest.*",
+        // Issue 74365: azure is failing after #7351
+        ".*DetectionOutput8ToDetectionOutput1",
     };
 }
diff --git a/src/tests/functional/inference_engine/transformations/detection_output_downgrade_test.cpp b/src/tests/functional/inference_engine/transformations/detection_output_downgrade_test.cpp
index bcbc40a0135..a2c32b462b6 100644
--- a/src/tests/functional/inference_engine/transformations/detection_output_downgrade_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/detection_output_downgrade_test.cpp
@@ -18,6 +18,8 @@
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "transformations/init_node_info.hpp"
 
+#include "functional_test_utils/skip_tests_config.hpp"
+
 using namespace ngraph;
 using namespace testing;
 
@@ -50,6 +52,7 @@ void create_attributes_vectors(std::vector<opset1::DetectionOutput::Attributes>&
 }  // namespace
 
 TEST(TransformationTests, DetectionOutput8ToDetectionOutput1) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
     std::vector<opset1::DetectionOutput::Attributes> attrs_v1_vector;
     std::vector<opset8::DetectionOutput::Attributes> attrs_v8_vector;
     Dimension N = 5;
@@ -112,6 +115,7 @@ TEST(TransformationTests, DetectionOutput8ToDetectionOutput1) {
 }
 
 TEST(TransformationTests, DetectionOutput8ToDetectionOutput1FiveArguments) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
     // In this case num_classes attribute value is deduced using inputs shapes
     std::vector<opset1::DetectionOutput::Attributes> attrs_v1_vector;
     std::vector<opset8::DetectionOutput::Attributes> attrs_v8_vector;

From ff330ea41e0426f4c762633a648d64dc6ed3e3c9 Mon Sep 17 00:00:00 2001
From: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>
Date: Mon, 20 Dec 2021 14:01:17 +0300
Subject: [PATCH 045/160] Fetch requirements directly from openvino tools
 (#9154)

* Fetch requirements directly from accuracy_checker

* get requirements from pot & benchmark tool directly
---
 tools/benchmark_tool/setup.py       | 13 +++++++++----
 tools/openvino_dev/requirements.txt | 28 ----------------------------
 tools/openvino_dev/setup.py         |  3 +++
 3 files changed, 12 insertions(+), 32 deletions(-)

diff --git a/tools/benchmark_tool/setup.py b/tools/benchmark_tool/setup.py
index c696a999459..fc02defda8b 100644
--- a/tools/benchmark_tool/setup.py
+++ b/tools/benchmark_tool/setup.py
@@ -8,14 +8,19 @@ Use this script to create a wheel with OpenVINO™ Python* tools:
 
 $ python setup.py sdist bdist_wheel
 """
+import pkg_resources
 from setuptools import setup, find_packages
 
 with open('README.md', 'r', encoding='utf-8') as f:
     long_description = f.read()
 
-with open('requirements.txt') as f:
-    required = f.read().splitlines()
-required.extend(['openvino'])
+with open('requirements.txt') as requirements_txt:
+    reqs = [
+        str(requirement)
+        for requirement
+        in pkg_resources.parse_requirements(requirements_txt)
+    ]
+
 
 setup(
     name='benchmark_tool',
@@ -37,6 +42,6 @@ setup(
         'Operating System :: OS Independent',
     ],
     packages=find_packages(),
-    install_requires=required,
+    install_requires=reqs,
     python_requires='>=3.6',
 )
diff --git a/tools/openvino_dev/requirements.txt b/tools/openvino_dev/requirements.txt
index ccc569a0194..e69de29bb2d 100644
--- a/tools/openvino_dev/requirements.txt
+++ b/tools/openvino_dev/requirements.txt
@@ -1,28 +0,0 @@
-defusedxml>=0.7.1
-scipy~=1.5.4
-jstyleson~=0.0.2
-numpy>=1.16.6,<1.20
-addict>=2.4.0
-pandas~=1.1.5
-hyperopt~=0.1.2
-networkx~=2.5
-tqdm>=4.54.1
-texttable~=1.6.3
-py-cpuinfo>=7.0.0
-PyYAML>=5.4.1
-pillow>=8.1.2
-scikit-image>=0.17.2
-scikit-learn>=0.24.1
-yamlloader>=0.5
-shapely>=1.7.1
-nibabel>=3.2.1
-pydicom>=2.1.2
-sentencepiece>=0.1.95
-tokenizers>=0.10.1
-editdistance>=0.5.3
-parasail>=1.2.4
-fast-ctc-decode>=0.2.5
-rawpy>=0.16.0
-nltk>=3.5
-opencv-python==4.5.*
-progress>=1.5
diff --git a/tools/openvino_dev/setup.py b/tools/openvino_dev/setup.py
index 28982ea3b1c..0fffaa4b750 100644
--- a/tools/openvino_dev/setup.py
+++ b/tools/openvino_dev/setup.py
@@ -40,12 +40,14 @@ PKG_INSTALL_CFG = {
         'black_list': [],
         'prefix': 'benchmark_tool',
         'extract_entry_points': True,
+        'extract_requirements': True,
     },
     "accuracy_checker": {
         'src_dir': OPENVINO_DIR / 'thirdparty' / 'open_model_zoo' / 'tools' / 'accuracy_checker',  # noqa:E501
         'black_list': ['*tests*'],
         'prefix': 'accuracy_checker',
         'extract_entry_points': True,
+        'extract_requirements': True,
     },
     "omz_tools": {
         'src_dir': OPENVINO_DIR / 'thirdparty' / 'open_model_zoo' / 'tools' / 'model_tools',  # noqa:E501
@@ -60,6 +62,7 @@ PKG_INSTALL_CFG = {
         'black_list': ['*tests*'],
         'prefix': 'pot',
         'extract_entry_points': True,
+        'extract_requirements': True,
     },
 }
 

From 191b15a228cc61b186006d012017053a381368a2 Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Mon, 20 Dec 2021 14:58:04 +0300
Subject: [PATCH 046/160] build fix (#9311)

---
 .../optimize_sequence_transposes_test.cpp     | 149 ++++++++++--------
 1 file changed, 80 insertions(+), 69 deletions(-)
 rename src/tests/unit/cpu/{ => ngraph_transformations}/optimize_sequence_transposes_test.cpp (71%)

diff --git a/src/tests/unit/cpu/optimize_sequence_transposes_test.cpp b/src/tests/unit/cpu/ngraph_transformations/optimize_sequence_transposes_test.cpp
similarity index 71%
rename from src/tests/unit/cpu/optimize_sequence_transposes_test.cpp
rename to src/tests/unit/cpu/ngraph_transformations/optimize_sequence_transposes_test.cpp
index 2fa0db074da..69a44f23ec2 100644
--- a/src/tests/unit/cpu/optimize_sequence_transposes_test.cpp
+++ b/src/tests/unit/cpu/ngraph_transformations/optimize_sequence_transposes_test.cpp
@@ -19,14 +19,13 @@
 
 using namespace testing;
 using namespace MKLDNNPlugin;
-using namespace ngraph;
 
 TEST(TransformationTests, OptimizeLSTMSequenceTransposesTest) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
-        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto Z = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(512 * 16, 0);
         auto r_val = std::vector<float>(512 * 128, 0);
@@ -39,14 +38,15 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesTest) {
         auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(transpose_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::LSTMSequence>(transpose_before, Y, Z, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
         auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
 
-        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
-        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(2));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
         Co->set_friendly_name("Co");
@@ -60,9 +60,9 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesTest) {
     }
 
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
-        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto Z = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(512 * 16, 0);
         auto r_val = std::vector<float>(512 * 128, 0);
@@ -75,14 +75,15 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesTest) {
         auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_const, false);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(reshape_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::LSTMSequence>(reshape_before, Y, Z, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
         auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
 
-        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
-        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(2));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
         Co->set_friendly_name("Co");
@@ -97,9 +98,9 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesTest) {
 TEST(TransformationTests, OptimizeLSTMSequenceTransposesDynamicTest) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
-        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 1, 1, 128 });
+        auto Z = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(512 * 16, 0);
         auto r_val = std::vector<float>(512 * 128, 0);
@@ -112,14 +113,15 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesDynamicTest) {
         auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(transpose_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::LSTMSequence>(transpose_before, Y, Z, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
         auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
 
-        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
-        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(2));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
         Co->set_friendly_name("Co");
@@ -133,9 +135,9 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesDynamicTest) {
     }
 
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
-        auto Z = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 1, 1, 128 });
+        auto Z = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(512 * 16, 0);
         auto r_val = std::vector<float>(512 * 128, 0);
@@ -151,14 +153,15 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesDynamicTest) {
         auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_pattern, false);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::LSTMSequence>(reshape_before, Y, Z, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::LSTMSequence>(reshape_before, Y, Z, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
         auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
 
-        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
-        const auto Co = std::make_shared<opset1::Result>(lstm_seq->output(2));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
+        const auto Co = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(2));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
         Co->set_friendly_name("Co");
@@ -173,8 +176,8 @@ TEST(TransformationTests, OptimizeLSTMSequenceTransposesDynamicTest) {
 TEST(TransformationTests, OptimizeRNNSequenceTransposesTest) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(128 * 16, 0);
         auto r_val = std::vector<float>(128 * 128, 0);
@@ -187,13 +190,14 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesTest) {
         auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::RNNSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::RNNSequence>(transpose_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
         auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
 
-        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 
@@ -206,8 +210,8 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesTest) {
     }
 
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(128 * 16, 0);
         auto r_val = std::vector<float>(128 * 128, 0);
@@ -220,13 +224,14 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesTest) {
         auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_const, false);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::RNNSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::RNNSequence>(reshape_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
         auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
 
-        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 
@@ -240,8 +245,8 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesTest) {
 TEST(TransformationTests, OptimizeRNNSequenceTransposesDynamicTest) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(128 * 16, 0);
         auto r_val = std::vector<float>(128 * 128, 0);
@@ -254,13 +259,14 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesDynamicTest) {
         auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::RNNSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::RNNSequence>(transpose_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
         auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
 
-        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 
@@ -273,8 +279,8 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesDynamicTest) {
     }
 
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(128 * 16, 0);
         auto r_val = std::vector<float>(128 * 128, 0);
@@ -290,13 +296,14 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesDynamicTest) {
         auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_pattern, false);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::RNNSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::RNNSequence>(reshape_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
         auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
 
-        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 
@@ -310,8 +317,8 @@ TEST(TransformationTests, OptimizeRNNSequenceTransposesDynamicTest) {
 TEST(TransformationTests, OptimizeGRUSequenceTransposesTest) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(384 * 16, 0);
         auto r_val = std::vector<float>(384 * 128, 0);
@@ -324,13 +331,14 @@ TEST(TransformationTests, OptimizeGRUSequenceTransposesTest) {
         auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::GRUSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::GRUSequence>(transpose_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
         auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
 
-        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 
@@ -343,8 +351,8 @@ TEST(TransformationTests, OptimizeGRUSequenceTransposesTest) {
     }
 
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 2, 1, 16 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(384 * 16, 0);
         auto r_val = std::vector<float>(384 * 128, 0);
@@ -357,13 +365,14 @@ TEST(TransformationTests, OptimizeGRUSequenceTransposesTest) {
         auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_const, false);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::GRUSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::GRUSequence>(reshape_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
         auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
 
-        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 
@@ -377,8 +386,8 @@ TEST(TransformationTests, OptimizeGRUSequenceTransposesTest) {
 TEST(TransformationTests, OptimizeGRUSequenceTransposesDynamicTest) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(384 * 16, 0);
         auto r_val = std::vector<float>(384 * 128, 0);
@@ -391,13 +400,14 @@ TEST(TransformationTests, OptimizeGRUSequenceTransposesDynamicTest) {
         auto transpose_before = std::make_shared<ngraph::opset1::Transpose>(X, transpose_before_const);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::GRUSequence>(transpose_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::GRUSequence>(transpose_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto transpose_after_const = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 4 }, { 2, 1, 0, 3 });
         auto transpose_after = std::make_shared<ngraph::opset1::Transpose>(lstm_seq->output(0), transpose_after_const);
 
-        const auto Y_out = std::make_shared<opset1::Result>(transpose_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(transpose_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 
@@ -410,8 +420,8 @@ TEST(TransformationTests, OptimizeGRUSequenceTransposesDynamicTest) {
     }
 
     {
-        auto X = std::make_shared<opset1::Parameter>(ngraph::element::f32, PartialShape{ 2, -1, -1 });
-        auto Y = std::make_shared<opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
+        auto X = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::PartialShape{ 2, -1, -1 });
+        auto Y = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 1, 128 });
 
         auto w_val = std::vector<float>(384 * 16, 0);
         auto r_val = std::vector<float>(384 * 128, 0);
@@ -427,13 +437,14 @@ TEST(TransformationTests, OptimizeGRUSequenceTransposesDynamicTest) {
         auto reshape_before = std::make_shared<ngraph::opset1::Reshape>(X, reshape_before_pattern, false);
 
         auto seq_lengths = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{ 1 }, { 2 });
-        auto lstm_seq = std::make_shared<opset5::GRUSequence>(reshape_before, Y, seq_lengths, W, R, B, 128, op::RecurrentSequenceDirection::FORWARD);
+        auto lstm_seq = std::make_shared<ngraph::opset5::GRUSequence>(reshape_before, Y, seq_lengths, W, R, B, 128,
+            ngraph::op::RecurrentSequenceDirection::FORWARD);
 
         auto reshape_after_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 2, 1, 1, 128 });
         auto reshape_after = std::make_shared<ngraph::opset1::Reshape>(lstm_seq->output(0), reshape_after_const, false);
 
-        const auto Y_out = std::make_shared<opset1::Result>(reshape_after);
-        const auto Ho = std::make_shared<opset1::Result>(lstm_seq->output(1));
+        const auto Y_out = std::make_shared<ngraph::opset1::Result>(reshape_after);
+        const auto Ho = std::make_shared<ngraph::opset1::Result>(lstm_seq->output(1));
         Y_out->set_friendly_name("Y_out");
         Ho->set_friendly_name("Ho");
 

From 249e1266fbee2fd53e944a05aa0effd2f513512e Mon Sep 17 00:00:00 2001
From: Gleb Kazantaev <gleb.kazantaev@intel.com>
Date: Mon, 20 Dec 2021 15:55:50 +0300
Subject: [PATCH 047/160] MO: --transform Pruning option (#9304)

---
 tools/mo/openvino/tools/mo/back/offline_transformations.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tools/mo/openvino/tools/mo/back/offline_transformations.py b/tools/mo/openvino/tools/mo/back/offline_transformations.py
index 19970a0f1f2..7714fd1c60c 100644
--- a/tools/mo/openvino/tools/mo/back/offline_transformations.py
+++ b/tools/mo/openvino/tools/mo/back/offline_transformations.py
@@ -9,10 +9,13 @@ from openvino.tools.mo.utils.cli_parser import parse_transform
 
 def get_available_transformations():
     try:
-        from openvino.offline_transformations_pybind import apply_low_latency_transformation, apply_make_stateful_transformation # pylint: disable=import-error,no-name-in-module
+        from openvino.offline_transformations_pybind import apply_low_latency_transformation # pylint: disable=import-error,no-name-in-module
+        from openvino.offline_transformations_pybind import apply_make_stateful_transformation # pylint: disable=import-error,no-name-in-module
+        from openvino.offline_transformations_pybind import apply_pruning_transformation # pylint: disable=import-error,no-name-in-module
         return {
             'MakeStateful': apply_make_stateful_transformation,
             'LowLatency2': apply_low_latency_transformation,
+            'Pruning': apply_pruning_transformation,
         }
     except Exception as e:
         return {}

From 513867e1686a7ea7148e0be8a438ed73225d239d Mon Sep 17 00:00:00 2001
From: Piotr Szmelczynski <piotr.szmelczynski@intel.com>
Date: Mon, 20 Dec 2021 14:31:48 +0100
Subject: [PATCH 048/160] [PYTHON] Rename arg names python api (#9209)

---
 .../src/pyopenvino/core/compiled_model.cpp    |   6 +-
 .../python/src/pyopenvino/core/core.cpp       |   2 +-
 .../src/pyopenvino/core/infer_request.cpp     |   8 +-
 .../src/pyopenvino/frontend/inputmodel.cpp    |  52 +--
 .../python/src/pyopenvino/frontend/place.cpp  |  48 +--
 .../python/src/pyopenvino/graph/dimension.cpp |  12 +-
 .../python/src/pyopenvino/graph/function.cpp  |  28 +-
 .../python/src/pyopenvino/graph/node.cpp      |  24 +-
 .../src/pyopenvino/graph/partial_shape.cpp    |  18 +-
 .../test_frontend_onnx_editor.py              | 319 +++++++++---------
 .../python/tests/test_ngraph/test_core.py     |   2 +-
 11 files changed, 262 insertions(+), 257 deletions(-)

diff --git a/src/bindings/python/src/pyopenvino/core/compiled_model.cpp b/src/bindings/python/src/pyopenvino/core/compiled_model.cpp
index 472b7b070db..a01799b3557 100644
--- a/src/bindings/python/src/pyopenvino/core/compiled_model.cpp
+++ b/src/bindings/python/src/pyopenvino/core/compiled_model.cpp
@@ -38,7 +38,7 @@ void regclass_CompiledModel(py::module m) {
         },
         py::arg("inputs"));
 
-    cls.def("export_model", &ov::runtime::CompiledModel::export_model, py::arg("network_model"));
+    cls.def("export_model", &ov::runtime::CompiledModel::export_model, py::arg("model_stream"));
 
     cls.def(
         "get_config",
@@ -64,7 +64,7 @@ void regclass_CompiledModel(py::module m) {
     cls.def(
         "input",
         (ov::Output<const ov::Node>(ov::runtime::CompiledModel::*)(size_t) const) & ov::runtime::CompiledModel::input,
-        py::arg("i"));
+        py::arg("index"));
 
     cls.def("input",
             (ov::Output<const ov::Node>(ov::runtime::CompiledModel::*)(const std::string&) const) &
@@ -79,7 +79,7 @@ void regclass_CompiledModel(py::module m) {
     cls.def(
         "output",
         (ov::Output<const ov::Node>(ov::runtime::CompiledModel::*)(size_t) const) & ov::runtime::CompiledModel::output,
-        py::arg("i"));
+        py::arg("index"));
 
     cls.def("output",
             (ov::Output<const ov::Node>(ov::runtime::CompiledModel::*)(const std::string&) const) &
diff --git a/src/bindings/python/src/pyopenvino/core/core.cpp b/src/bindings/python/src/pyopenvino/core/core.cpp
index 481268e08bc..2201ea00aad 100644
--- a/src/bindings/python/src/pyopenvino/core/core.cpp
+++ b/src/bindings/python/src/pyopenvino/core/core.cpp
@@ -48,7 +48,7 @@ void regclass_Core(py::module m) {
         py::arg("device_name"),
         py::arg("config") = py::dict());
 
-    cls.def("get_versions", &ov::runtime::Core::get_versions);
+    cls.def("get_versions", &ov::runtime::Core::get_versions, py::arg("device_name"));
 
     cls.def(
         "read_model",
diff --git a/src/bindings/python/src/pyopenvino/core/infer_request.cpp b/src/bindings/python/src/pyopenvino/core/infer_request.cpp
index 6bb7186c586..ccaade841f4 100644
--- a/src/bindings/python/src/pyopenvino/core/infer_request.cpp
+++ b/src/bindings/python/src/pyopenvino/core/infer_request.cpp
@@ -153,7 +153,7 @@ void regclass_InferRequest(py::module m) {
         [](InferRequestWrapper& self, size_t idx) {
             return self._request.get_input_tensor(idx);
         },
-        py::arg("idx"));
+        py::arg("index"));
 
     cls.def("get_input_tensor", [](InferRequestWrapper& self) {
         return self._request.get_input_tensor();
@@ -164,7 +164,7 @@ void regclass_InferRequest(py::module m) {
         [](InferRequestWrapper& self, size_t idx) {
             return self._request.get_output_tensor(idx);
         },
-        py::arg("idx"));
+        py::arg("index"));
 
     cls.def("get_output_tensor", [](InferRequestWrapper& self) {
         return self._request.get_output_tensor();
@@ -199,7 +199,7 @@ void regclass_InferRequest(py::module m) {
         [](InferRequestWrapper& self, size_t idx, const ov::runtime::Tensor& tensor) {
             self._request.set_input_tensor(idx, tensor);
         },
-        py::arg("idx"),
+        py::arg("index"),
         py::arg("tensor"));
 
     cls.def(
@@ -214,7 +214,7 @@ void regclass_InferRequest(py::module m) {
         [](InferRequestWrapper& self, size_t idx, const ov::runtime::Tensor& tensor) {
             self._request.set_output_tensor(idx, tensor);
         },
-        py::arg("idx"),
+        py::arg("index"),
         py::arg("tensor"));
 
     cls.def(
diff --git a/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp b/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
index db6c9c55c74..951bdf7c5c4 100644
--- a/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
@@ -19,14 +19,14 @@ void regclass_frontend_InputModel(py::module m) {
 
     im.def("get_place_by_tensor_name",
            &ov::frontend::InputModel::get_place_by_tensor_name,
-           py::arg("tensorName"),
+           py::arg("tensor_name"),
            R"(
                 Returns a tensor place by a tensor name following framework conventions, or
                 nullptr if a tensor with this name doesn't exist.
 
                 Parameters
                 ----------
-                tensorName : str
+                tensor_name : str
                     Name of tensor.
 
                 Returns
@@ -37,14 +37,14 @@ void regclass_frontend_InputModel(py::module m) {
 
     im.def("get_place_by_operation_name",
            &ov::frontend::InputModel::get_place_by_operation_name,
-           py::arg("operationName"),
+           py::arg("operation_name"),
            R"(
                 Returns an operation place by an operation name following framework conventions, or
                 nullptr if an operation with this name doesn't exist.
 
                 Parameters
                 ----------
-                operationName : str
+                operation_name : str
                     Name of operation.
 
                 Returns
@@ -55,17 +55,17 @@ void regclass_frontend_InputModel(py::module m) {
 
     im.def("get_place_by_operation_name_and_input_port",
            &ov::frontend::InputModel::get_place_by_operation_name_and_input_port,
-           py::arg("operationName"),
-           py::arg("inputPortIndex"),
+           py::arg("operation_name"),
+           py::arg("input_port_index"),
            R"(
                 Returns an input port place by operation name and appropriate port index.
 
                 Parameters
                 ----------
-                operationName : str
+                operation_name : str
                     Name of operation.
 
-                inputPortIndex : int
+                input_port_index : int
                     Index of input port for this operation.
 
                 Returns
@@ -76,17 +76,17 @@ void regclass_frontend_InputModel(py::module m) {
 
     im.def("get_place_by_operation_name_and_output_port",
            &ov::frontend::InputModel::get_place_by_operation_name_and_output_port,
-           py::arg("operationName"),
-           py::arg("outputPortIndex"),
+           py::arg("operation_name"),
+           py::arg("output_port_index"),
            R"(
                 Returns an output port place by operation name and appropriate port index.
 
                 Parameters
                 ----------
-                operationName : str
+                operation_name : str
                     Name of operation.
 
-                outputPortIndex : int
+                output_port_index : int
                     Index of output port for this operation.
 
                 Returns
@@ -98,7 +98,7 @@ void regclass_frontend_InputModel(py::module m) {
     im.def("set_name_for_tensor",
            &ov::frontend::InputModel::set_name_for_tensor,
            py::arg("tensor"),
-           py::arg("newName"),
+           py::arg("new_name"),
            R"(
                 Sets name for tensor. Overwrites existing names of this place.
 
@@ -107,14 +107,14 @@ void regclass_frontend_InputModel(py::module m) {
                 tensor : Place
                     Tensor place.
 
-                newName : str
+                new_name : str
                     New name for this tensor.
             )");
 
     im.def("add_name_for_tensor",
            &ov::frontend::InputModel::add_name_for_tensor,
            py::arg("tensor"),
-           py::arg("newName"),
+           py::arg("new_name"),
            R"(
                 Adds new name for tensor
 
@@ -123,14 +123,14 @@ void regclass_frontend_InputModel(py::module m) {
                 tensor : Place
                     Tensor place.
 
-                newName : str
+                new_name : str
                     New name to be added to this place.
             )");
 
     im.def("set_name_for_operation",
            &ov::frontend::InputModel::set_name_for_operation,
            py::arg("operation"),
-           py::arg("newName"),
+           py::arg("new_name"),
            R"(
                 Adds new name for tensor.
 
@@ -139,7 +139,7 @@ void regclass_frontend_InputModel(py::module m) {
                 operation : Place
                     Operation place.
 
-                newName : str
+                new_name : str
                     New name for this operation.
             )");
 
@@ -170,8 +170,8 @@ void regclass_frontend_InputModel(py::module m) {
     im.def("set_name_for_dimension",
            &ov::frontend::InputModel::set_name_for_dimension,
            py::arg("place"),
-           py::arg("dimIndex"),
-           py::arg("dimName"),
+           py::arg("dim_index"),
+           py::arg("dim_name"),
            R"(
                 Set name for a particular dimension of a place (e.g. batch dimension).
 
@@ -180,17 +180,17 @@ void regclass_frontend_InputModel(py::module m) {
                 place : Place
                     Model's place.
 
-                dimIndex : int
+                dim_index : int
                     Dimension index.
 
-                dimName : str
+                dim_name : str
                     Name to assign on this dimension.
             )");
 
     im.def("cut_and_add_new_input",
            &ov::frontend::InputModel::cut_and_add_new_input,
            py::arg("place"),
-           py::arg("newName") = std::string(),
+           py::arg("new_name") = std::string(),
            R"(
                 Cut immediately before this place and assign this place as new input; prune
                 all nodes that don't contribute to any output.
@@ -200,14 +200,14 @@ void regclass_frontend_InputModel(py::module m) {
                 place : Place
                     New place to be assigned as input.
 
-                newNameOptional : str
+                new_name_optional : str
                     Optional new name assigned to this input place.
             )");
 
     im.def("cut_and_add_new_output",
            &ov::frontend::InputModel::cut_and_add_new_output,
            py::arg("place"),
-           py::arg("newName") = std::string(),
+           py::arg("new_name") = std::string(),
            R"(
                 Cut immediately before this place and assign this place as new output; prune
                 all nodes that don't contribute to any output.
@@ -217,7 +217,7 @@ void regclass_frontend_InputModel(py::module m) {
                 place : Place
                     New place to be assigned as output.
 
-                newNameOptional : str
+                new_name_optional : str
                     Optional new name assigned to this output place.
             )");
 
diff --git a/src/bindings/python/src/pyopenvino/frontend/place.cpp b/src/bindings/python/src/pyopenvino/frontend/place.cpp
index 69744140a2a..b5937deba81 100644
--- a/src/bindings/python/src/pyopenvino/frontend/place.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/place.cpp
@@ -104,17 +104,17 @@ void regclass_frontend_Place(py::module m) {
                 }
             }
         },
-        py::arg("outputName") = py::none(),
-        py::arg("outputPortIndex") = py::none(),
+        py::arg("output_name") = py::none(),
+        py::arg("output_port_index") = py::none(),
         R"(
                 Returns references to all operation nodes that consume data from this place for specified output port.
                 Note: It can be called for any kind of graph place searching for the first consuming operations.
 
                 Parameters
                 ----------
-                outputName : str
+                output_name : str
                     Name of output port group. May not be set if node has one output port group.
-                outputPortIndex : int
+                output_port_index : int
                     If place is an operational node it specifies which output port should be considered
                     May not be set if node has only one output port.
 
@@ -141,17 +141,17 @@ void regclass_frontend_Place(py::module m) {
                 }
             }
         },
-        py::arg("outputName") = py::none(),
-        py::arg("outputPortIndex") = py::none(),
+        py::arg("output_name") = py::none(),
+        py::arg("output_port_index") = py::none(),
         R"(
                 Returns a tensor place that gets data from this place; applicable for operations,
                 output ports and output edges.
 
                 Parameters
                 ----------
-                outputName : str
+                output_name : str
                     Name of output port group. May not be set if node has one output port group.
-                outputPortIndex : int
+                output_port_index : int
                     Output port index if the current place is an operation node and has multiple output ports.
                     May not be set if place has only one output port.
 
@@ -179,16 +179,16 @@ void regclass_frontend_Place(py::module m) {
                 }
             }
         },
-        py::arg("inputName") = py::none(),
-        py::arg("inputPortIndex") = py::none(),
+        py::arg("input_name") = py::none(),
+        py::arg("input_port_index") = py::none(),
         R"(
                 Get an operation node place that immediately produces data for this place.
 
                 Parameters
                 ----------
-                inputName : str
+                input_name : str
                     Name of port group. May not be set if node has one input port group.
-                inputPortIndex : int
+                input_port_index : int
                     If a given place is itself an operation node, this specifies a port index.
                     May not be set if place has only one input port.
 
@@ -226,17 +226,17 @@ void regclass_frontend_Place(py::module m) {
                 }
             }
         },
-        py::arg("inputName") = py::none(),
-        py::arg("inputPortIndex") = py::none(),
+        py::arg("input_name") = py::none(),
+        py::arg("input_port_index") = py::none(),
         R"(
                 For operation node returns reference to an input port with specified name and index.
 
                 Parameters
                 ----------
-                inputName : str
+                input_name : str
                     Name of port group. May not be set if node has one input port group.
 
-                inputPortIndex : int
+                input_port_index : int
                     Input port index in a group. May not be set if node has one input port in a group.
 
                 Returns
@@ -262,17 +262,17 @@ void regclass_frontend_Place(py::module m) {
                 }
             }
         },
-        py::arg("outputName") = py::none(),
-        py::arg("outputPortIndex") = py::none(),
+        py::arg("output_name") = py::none(),
+        py::arg("output_port_index") = py::none(),
         R"(
                 For operation node returns reference to an output port with specified name and index.
 
                 Parameters
                 ----------
-                outputName : str
+                output_name : str
                     Name of output port group. May not be set if node has one output port group.
 
-                outputPortIndex : int
+                output_port_index : int
                     Output port index. May not be set if node has one output port in a group.
 
                 Returns
@@ -309,17 +309,17 @@ void regclass_frontend_Place(py::module m) {
                 }
             }
         },
-        py::arg("inputName") = py::none(),
-        py::arg("inputPortIndex") = py::none(),
+        py::arg("input_name") = py::none(),
+        py::arg("input_port_index") = py::none(),
         R"(
                 Returns a tensor place that supplies data for this place; applicable for operations,
                 input ports and input edges.
 
                 Parameters
                 ----------
-                inputName : str
+                input_name : str
                     Name of port group. May not be set if node has one input port group.
-                inputPortIndex : int
+                input_port_index : int
                     Input port index for operational node. May not be specified if place has only one input port.
 
                 Returns
diff --git a/src/bindings/python/src/pyopenvino/graph/dimension.cpp b/src/bindings/python/src/pyopenvino/graph/dimension.cpp
index 878f222f0ab..93b024025cd 100644
--- a/src/bindings/python/src/pyopenvino/graph/dimension.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/dimension.cpp
@@ -137,14 +137,14 @@ void regclass_graph_Dimension(py::module m) {
             )");
     dim.def("compatible",
             &ov::Dimension::compatible,
-            py::arg("d"),
+            py::arg("dim"),
             R"(
                 Check whether this dimension is capable of being merged
                 with the argument dimension.
 
                 Parameters
                 ----------
-                d : Dimension
+                dim : Dimension
                     The dimension to compare this dimension with.
 
                 Returns
@@ -154,7 +154,7 @@ void regclass_graph_Dimension(py::module m) {
             )");
     dim.def("relaxes",
             &ov::Dimension::relaxes,
-            py::arg("d"),
+            py::arg("dim"),
             R"(
                 Check whether this dimension is a relaxation of the argument.
                 This dimension relaxes (or is a relaxation of) d if:
@@ -166,7 +166,7 @@ void regclass_graph_Dimension(py::module m) {
 
                 Parameters
                 ----------
-                d : Dimension
+                dim : Dimension
                     The dimension to compare this dimension with.
 
                 Returns
@@ -176,7 +176,7 @@ void regclass_graph_Dimension(py::module m) {
             )");
     dim.def("refines",
             &ov::Dimension::refines,
-            py::arg("d"),
+            py::arg("dim"),
             R"(
                 Check whether this dimension is a refinement of the argument.
                 This dimension refines (or is a refinement of) d if:
@@ -188,7 +188,7 @@ void regclass_graph_Dimension(py::module m) {
 
                 Parameters
                 ----------
-                d : Dimension
+                dim : Dimension
                     The dimension to compare this dimension with.
 
                 Returns
diff --git a/src/bindings/python/src/pyopenvino/graph/function.cpp b/src/bindings/python/src/pyopenvino/graph/function.cpp
index cce137b160d..bb6314ed5b8 100644
--- a/src/bindings/python/src/pyopenvino/graph/function.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/function.cpp
@@ -374,13 +374,13 @@ void regclass_graph_Model(py::module m) {
                  )");
     function.def("get_output_op",
                  &ov::Model::get_output_op,
-                 py::arg("i"),
+                 py::arg("index"),
                  R"(
                     Return the op that generates output i
 
                     Parameters
                     ----------
-                    i : int
+                    index : int
                         output index
 
                     Returns
@@ -390,13 +390,13 @@ void regclass_graph_Model(py::module m) {
                 )");
     function.def("get_output_element_type",
                  &ov::Model::get_output_element_type,
-                 py::arg("i"),
+                 py::arg("index"),
                  R"(
                     Return the element type of output i
 
                     Parameters
                     ----------
-                    i : int
+                    index : int
                         output index
 
                     Returns
@@ -406,13 +406,13 @@ void regclass_graph_Model(py::module m) {
                  )");
     function.def("get_output_shape",
                  &ov::Model::get_output_shape,
-                 py::arg("i"),
+                 py::arg("index"),
                  R"(
                     Return the shape of element i
 
                     Parameters
                     ----------
-                    i : int
+                    index : int
                         element index
 
                     Returns
@@ -422,13 +422,13 @@ void regclass_graph_Model(py::module m) {
                  )");
     function.def("get_output_partial_shape",
                  &ov::Model::get_output_partial_shape,
-                 py::arg("i"),
+                 py::arg("index"),
                  R"(
                     Return the partial shape of element i
 
                     Parameters
                     ----------
-                    i : int
+                    index : int
                         element index
 
                     Returns
@@ -551,7 +551,7 @@ void regclass_graph_Model(py::module m) {
                  )");
     function.def("input", (ov::Output<ov::Node>(ov::Model::*)()) & ov::Model::input);
 
-    function.def("input", (ov::Output<ov::Node>(ov::Model::*)(size_t)) & ov::Model::input, py::arg("i"));
+    function.def("input", (ov::Output<ov::Node>(ov::Model::*)(size_t)) & ov::Model::input, py::arg("index"));
 
     function.def("input",
                  (ov::Output<ov::Node>(ov::Model::*)(const std::string&)) & ov::Model::input,
@@ -559,7 +559,9 @@ void regclass_graph_Model(py::module m) {
 
     function.def("input", (ov::Output<const ov::Node>(ov::Model::*)() const) & ov::Model::input);
 
-    function.def("input", (ov::Output<const ov::Node>(ov::Model::*)(size_t) const) & ov::Model::input, py::arg("i"));
+    function.def("input",
+                 (ov::Output<const ov::Node>(ov::Model::*)(size_t) const) & ov::Model::input,
+                 py::arg("index"));
 
     function.def("input",
                  (ov::Output<const ov::Node>(ov::Model::*)(const std::string&) const) & ov::Model::input,
@@ -567,7 +569,7 @@ void regclass_graph_Model(py::module m) {
 
     function.def("output", (ov::Output<ov::Node>(ov::Model::*)()) & ov::Model::output);
 
-    function.def("output", (ov::Output<ov::Node>(ov::Model::*)(size_t)) & ov::Model::output, py::arg("i"));
+    function.def("output", (ov::Output<ov::Node>(ov::Model::*)(size_t)) & ov::Model::output, py::arg("index"));
 
     function.def("output",
                  (ov::Output<ov::Node>(ov::Model::*)(const std::string&)) & ov::Model::output,
@@ -575,7 +577,9 @@ void regclass_graph_Model(py::module m) {
 
     function.def("output", (ov::Output<const ov::Node>(ov::Model::*)() const) & ov::Model::output);
 
-    function.def("output", (ov::Output<const ov::Node>(ov::Model::*)(size_t) const) & ov::Model::output, py::arg("i"));
+    function.def("output",
+                 (ov::Output<const ov::Node>(ov::Model::*)(size_t) const) & ov::Model::output,
+                 py::arg("index"));
 
     function.def("output",
                  (ov::Output<const ov::Node>(ov::Model::*)(const std::string&) const) & ov::Model::output,
diff --git a/src/bindings/python/src/pyopenvino/graph/node.cpp b/src/bindings/python/src/pyopenvino/graph/node.cpp
index 950f0035743..57ee00bb68d 100644
--- a/src/bindings/python/src/pyopenvino/graph/node.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/node.cpp
@@ -129,14 +129,14 @@ void regclass_graph_Node(py::module m) {
              )");
     node.def("get_input_tensor",
              &ov::Node::get_input_tensor,
-             py::arg("i"),
+             py::arg("index"),
              py::return_value_policy::reference_internal,
              R"(
                 Returns the tensor for the node's input with index i
 
                 Parameters
                 ----------
-                i : int
+                index : int
                     Index of Input.
 
                 Returns
@@ -166,13 +166,13 @@ void regclass_graph_Node(py::module m) {
              )");
     node.def("input_value",
              &ov::Node::input_value,
-             py::arg("i"),
+             py::arg("index"),
              R"(
                 Returns input of the node with index i
 
                 Parameters
                 ----------
-                i : int
+                index : int
                     Index of Input.
 
                 Returns
@@ -202,13 +202,13 @@ void regclass_graph_Node(py::module m) {
              )");
     node.def("get_output_element_type",
              &ov::Node::get_output_element_type,
-             py::arg("i"),
+             py::arg("index"),
              R"(
                 Returns the element type for output i
 
                 Parameters
                 ----------
-                i : int
+                index : int
                     Index of the output.
 
                 Returns
@@ -218,13 +218,13 @@ void regclass_graph_Node(py::module m) {
              )");
     node.def("get_output_shape",
              &ov::Node::get_output_shape,
-             py::arg("i"),
+             py::arg("index"),
              R"(
                 Returns the shape for output i
 
                 Parameters
                 ----------
-                i : int
+                index : int
                     Index of the output.
 
                 Returns
@@ -234,13 +234,13 @@ void regclass_graph_Node(py::module m) {
              )");
     node.def("get_output_partial_shape",
              &ov::Node::get_output_partial_shape,
-             py::arg("i"),
+             py::arg("index"),
              R"(
                 Returns the partial shape for output i
 
                 Parameters
                 ----------
-                i : int
+                index : int
                     Index of the output.
 
                 Returns
@@ -250,14 +250,14 @@ void regclass_graph_Node(py::module m) {
              )");
     node.def("get_output_tensor",
              &ov::Node::get_output_tensor,
-             py::arg("i"),
+             py::arg("index"),
              py::return_value_policy::reference_internal,
              R"(
                 Returns the tensor for output i
 
                 Parameters
                 ----------
-                i : int
+                index : int
                     Index of the output.
 
                 Returns
diff --git a/src/bindings/python/src/pyopenvino/graph/partial_shape.cpp b/src/bindings/python/src/pyopenvino/graph/partial_shape.cpp
index 3fa5ecb4b81..f4451ff7204 100644
--- a/src/bindings/python/src/pyopenvino/graph/partial_shape.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/partial_shape.cpp
@@ -33,7 +33,7 @@ void regclass_graph_PartialShape(py::module m) {
     shape.def(py::init<const ov::Shape&>());
     shape.def(py::init<const ov::PartialShape&>());
 
-    shape.def_static("dynamic", &ov::PartialShape::dynamic, py::arg("r") = ov::Dimension());
+    shape.def_static("dynamic", &ov::PartialShape::dynamic, py::arg("rank") = ov::Dimension());
 
     shape.def_property_readonly("is_dynamic",
                                 &ov::PartialShape::is_dynamic,
@@ -63,14 +63,14 @@ void regclass_graph_PartialShape(py::module m) {
 
     shape.def("compatible",
               &ov::PartialShape::compatible,
-              py::arg("s"),
+              py::arg("shape"),
               R"(
                 Check whether this shape is compatible with the argument, i.e.,
                 whether it is possible to merge them.
 
                 Parameters
                 ----------
-                s : PartialShape
+                shape : PartialShape
                     The shape to be checked for compatibility with this shape.
 
 
@@ -81,13 +81,13 @@ void regclass_graph_PartialShape(py::module m) {
               )");
     shape.def("refines",
               &ov::PartialShape::refines,
-              py::arg("s"),
+              py::arg("shape"),
               R"(
                 Check whether this shape is a refinement of the argument.
 
                 Parameters
                 ----------
-                s : PartialShape
+                shape : PartialShape
                     The shape which is being compared against this shape.
 
                 Returns
@@ -97,13 +97,13 @@ void regclass_graph_PartialShape(py::module m) {
               )");
     shape.def("relaxes",
               &ov::PartialShape::relaxes,
-              py::arg("s"),
+              py::arg("shape"),
               R"(
                 Check whether this shape is a relaxation of the argument.
 
                 Parameters
                 ----------
-                s : PartialShape
+                shape : PartialShape
                     The shape which is being compared against this shape.
 
                 Returns
@@ -113,13 +113,13 @@ void regclass_graph_PartialShape(py::module m) {
               )");
     shape.def("same_scheme",
               &ov::PartialShape::same_scheme,
-              py::arg("s"),
+              py::arg("shape"),
               R"(
                 Check whether this shape represents the same scheme as the argument.
 
                 Parameters
                 ----------
-                s : PartialShape
+                shape : PartialShape
                     The shape which is being compared against this shape.
 
                 Returns
diff --git a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
index 24ba146aa06..6eff1d885f7 100644
--- a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
+++ b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
@@ -331,9 +331,9 @@ def test_extract_subgraph():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="add_out").get_input_port(inputPortIndex=0)  # in1
-    place2 = model.get_place_by_tensor_name(tensorName="add_out").get_input_port(inputPortIndex=1)  # in2
-    place3 = model.get_place_by_tensor_name(tensorName="add_out")
+    place1 = model.get_place_by_tensor_name(tensor_name="add_out").get_input_port(input_port_index=0)  # in1
+    place2 = model.get_place_by_tensor_name(tensor_name="add_out").get_input_port(input_port_index=1)  # in2
+    place3 = model.get_place_by_tensor_name(tensor_name="add_out")
     model.extract_subgraph(inputs=[place1, place2], outputs=[place3])
     result_func = fe.convert(model)
 
@@ -352,8 +352,8 @@ def test_extract_subgraph_2():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="add_out")
-    place2 = model.get_place_by_tensor_name(tensorName="out3")
+    place1 = model.get_place_by_tensor_name(tensor_name="add_out")
+    place2 = model.get_place_by_tensor_name(tensor_name="out3")
     model.extract_subgraph(inputs=[], outputs=[place1, place2])
     result_func = fe.convert(model)
 
@@ -372,9 +372,9 @@ def test_extract_subgraph_3():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_operation_name_and_input_port(operationName="split1", inputPortIndex=0)
-    place2 = model.get_place_by_tensor_name(tensorName="out1")
-    place3 = model.get_place_by_tensor_name(tensorName="out2")
+    place1 = model.get_place_by_operation_name_and_input_port(operation_name="split1", input_port_index=0)
+    place2 = model.get_place_by_tensor_name(tensor_name="out1")
+    place3 = model.get_place_by_tensor_name(tensor_name="out2")
     model.extract_subgraph(inputs=[place1], outputs=[place2, place3])
     result_func = fe.convert(model)
 
@@ -393,13 +393,13 @@ def test_extract_subgraph_4():
     model = fe.load("input_model.onnx")
     assert model
 
-    out4_tensor = model.get_place_by_tensor_name(tensorName="out4")
-    place1 = model.get_place_by_operation_name_and_input_port(operationName="split1", inputPortIndex=0)
-    place2 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
-    place3 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=1)
-    place4 = model.get_place_by_tensor_name(tensorName="out1")
-    place5 = model.get_place_by_tensor_name(tensorName="out2")
-    place6 = model.get_place_by_tensor_name(tensorName="out4")
+    out4_tensor = model.get_place_by_tensor_name(tensor_name="out4")
+    place1 = model.get_place_by_operation_name_and_input_port(operation_name="split1", input_port_index=0)
+    place2 = out4_tensor.get_producing_operation().get_input_port(input_port_index=0)
+    place3 = out4_tensor.get_producing_operation().get_input_port(input_port_index=1)
+    place4 = model.get_place_by_tensor_name(tensor_name="out1")
+    place5 = model.get_place_by_tensor_name(tensor_name="out2")
+    place6 = model.get_place_by_tensor_name(tensor_name="out4")
     model.extract_subgraph(inputs=[place1, place2, place3], outputs=[place4, place5, place6])
     result_func = fe.convert(model)
 
@@ -418,11 +418,11 @@ def test_extract_subgraph_by_op_place_as_input():
     model = fe.load("input_model.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split1")
-    out4 = model.get_place_by_tensor_name(tensorName="out4")
+    split_op = model.get_place_by_operation_name(operation_name="split1")
+    out4 = model.get_place_by_tensor_name(tensor_name="out4")
     mul_op = out4.get_producing_operation()
-    out1 = model.get_place_by_tensor_name(tensorName="out1")
-    out2 = model.get_place_by_tensor_name(tensorName="out2")
+    out1 = model.get_place_by_tensor_name(tensor_name="out1")
+    out2 = model.get_place_by_tensor_name(tensor_name="out2")
 
     model.extract_subgraph(inputs=[split_op, mul_op], outputs=[out1, out2, out4])
     result_func = fe.convert(model)
@@ -442,9 +442,9 @@ def test_extract_subgraph_by_op_place_as_output():
     model = fe.load("input_model.onnx")
     assert model
 
-    in1_tensor = model.get_place_by_tensor_name(tensorName="in1")
-    in2_tensor = model.get_place_by_tensor_name(tensorName="in2")
-    add_out_tensor = model.get_place_by_tensor_name(tensorName="add_out")
+    in1_tensor = model.get_place_by_tensor_name(tensor_name="in1")
+    in2_tensor = model.get_place_by_tensor_name(tensor_name="in2")
+    add_out_tensor = model.get_place_by_tensor_name(tensor_name="add_out")
     add_op = add_out_tensor.get_producing_operation()
 
     model.extract_subgraph(inputs=[in1_tensor, in2_tensor], outputs=[add_op])
@@ -465,8 +465,8 @@ def test_extract_subgraph_by_op_place_as_output_2():
     model = fe.load("input_model.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split1")
-    out4 = model.get_place_by_tensor_name(tensorName="out4")
+    split_op = model.get_place_by_operation_name(operation_name="split1")
+    out4 = model.get_place_by_tensor_name(tensor_name="out4")
     mul_op = out4.get_producing_operation()
 
     model.extract_subgraph(inputs=[split_op, mul_op], outputs=[])
@@ -487,11 +487,11 @@ def test_extract_subgraph_by_port_place_as_output():
     model = fe.load("input_model.onnx")
     assert model
 
-    add_out_tensor = model.get_place_by_tensor_name(tensorName="add_out")
+    add_out_tensor = model.get_place_by_tensor_name(tensor_name="add_out")
     add_op = add_out_tensor.get_producing_operation()
-    add_op_out_port = add_op.get_output_port(outputPortIndex=0)
-    in1_tensor = model.get_place_by_tensor_name(tensorName="in1")
-    in2_tensor = model.get_place_by_tensor_name(tensorName="in2")
+    add_op_out_port = add_op.get_output_port(output_port_index=0)
+    in1_tensor = model.get_place_by_tensor_name(tensor_name="in1")
+    in2_tensor = model.get_place_by_tensor_name(tensor_name="in2")
 
     model.extract_subgraph(inputs=[in1_tensor, in2_tensor], outputs=[add_op_out_port])
     result_func = fe.convert(model)
@@ -511,8 +511,8 @@ def test_override_all_outputs():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="add_out")
-    place2 = model.get_place_by_tensor_name(tensorName="out3")
+    place1 = model.get_place_by_tensor_name(tensor_name="add_out")
+    place2 = model.get_place_by_tensor_name(tensor_name="out3")
     model.override_all_outputs(outputs=[place1, place2])
     result_func = fe.convert(model)
 
@@ -531,7 +531,7 @@ def test_override_all_outputs_2():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="out4")
+    place1 = model.get_place_by_tensor_name(tensor_name="out4")
     model.override_all_outputs(outputs=[place1])
     result_func = fe.convert(model)
 
@@ -551,11 +551,11 @@ def test_override_all_inputs():
     assert model
 
     place1 = model.get_place_by_operation_name_and_input_port(
-        operationName="split1", inputPortIndex=0)
-    out4_tensor = model.get_place_by_tensor_name(tensorName="out4")
-    place2 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
-    place3 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=1)
-    place4 = model.get_place_by_tensor_name(tensorName="in3")
+        operation_name="split1", input_port_index=0)
+    out4_tensor = model.get_place_by_tensor_name(tensor_name="out4")
+    place2 = out4_tensor.get_producing_operation().get_input_port(input_port_index=0)
+    place3 = out4_tensor.get_producing_operation().get_input_port(input_port_index=1)
+    place4 = model.get_place_by_tensor_name(tensor_name="in3")
     model.override_all_inputs(inputs=[place1, place2, place3, place4])
     result_func = fe.convert(model)
 
@@ -574,10 +574,10 @@ def test_override_all_inputs_exceptions():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="in1")
-    place2 = model.get_place_by_tensor_name(tensorName="in2")
-    place3 = model.get_place_by_operation_name_and_input_port(operationName="split1", inputPortIndex=0)
-    place4 = model.get_place_by_tensor_name(tensorName="in3")
+    place1 = model.get_place_by_tensor_name(tensor_name="in1")
+    place2 = model.get_place_by_tensor_name(tensor_name="in2")
+    place3 = model.get_place_by_operation_name_and_input_port(operation_name="split1", input_port_index=0)
+    place4 = model.get_place_by_tensor_name(tensor_name="in3")
 
     with pytest.raises(Exception) as e:
         model.override_all_inputs(inputs=[place1, place2])
@@ -596,24 +596,24 @@ def test_is_input_output():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="in2")
+    place1 = model.get_place_by_tensor_name(tensor_name="in2")
     assert place1.is_input()
     assert not place1.is_output()
 
-    place2 = model.get_place_by_tensor_name(tensorName="out2")
+    place2 = model.get_place_by_tensor_name(tensor_name="out2")
     assert not place2.is_input()
     assert place2.is_output()
 
-    place3 = model.get_place_by_tensor_name(tensorName="add_out")
+    place3 = model.get_place_by_tensor_name(tensor_name="add_out")
     assert not place3.is_input()
     assert not place3.is_output()
 
     place4 = model.get_place_by_operation_name_and_input_port(
-        operationName="split1", inputPortIndex=0)
+        operation_name="split1", input_port_index=0)
     assert not place4.is_input()
     assert not place4.is_output()
 
-    place5 = model.get_place_by_operation_name(operationName="split1")
+    place5 = model.get_place_by_operation_name(operation_name="split1")
     assert not place5.is_input()
     assert not place5.is_output()
 
@@ -626,11 +626,11 @@ def test_set_partial_shape():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="in1")
+    place1 = model.get_place_by_tensor_name(tensor_name="in1")
     model.set_partial_shape(place1, PartialShape([8, 16]))
-    place2 = model.get_place_by_tensor_name(tensorName="in2")
+    place2 = model.get_place_by_tensor_name(tensor_name="in2")
     model.set_partial_shape(place2, PartialShape([8, 16]))
-    place3 = model.get_place_by_tensor_name(tensorName="in3")
+    place3 = model.get_place_by_tensor_name(tensor_name="in3")
     model.set_partial_shape(place3, PartialShape([4, 6]))
     result_func = fe.convert(model)
 
@@ -649,16 +649,16 @@ def test_get_partial_shape():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="in1")
+    place1 = model.get_place_by_tensor_name(tensor_name="in1")
     assert model.get_partial_shape(place1) == PartialShape([2, 2])
 
-    place2 = model.get_place_by_tensor_name(tensorName="out1")
+    place2 = model.get_place_by_tensor_name(tensor_name="out1")
     assert model.get_partial_shape(place2) == PartialShape([1, 2])
 
-    place3 = model.get_place_by_tensor_name(tensorName="add_out")
+    place3 = model.get_place_by_tensor_name(tensor_name="add_out")
     assert model.get_partial_shape(place3) == PartialShape([2, 2])
 
-    place4 = model.get_place_by_tensor_name(tensorName="in3")
+    place4 = model.get_place_by_tensor_name(tensor_name="in3")
     model.set_partial_shape(place4, PartialShape([4, 6]))
     assert model.get_partial_shape(place4) == PartialShape([4, 6])
     assert model.get_partial_shape(place2) == PartialShape([1, 2])
@@ -694,33 +694,33 @@ def test_is_equal():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="in1")
+    place1 = model.get_place_by_tensor_name(tensor_name="in1")
     assert place1.is_equal(place1)
 
-    place2 = model.get_place_by_tensor_name(tensorName="out2")
+    place2 = model.get_place_by_tensor_name(tensor_name="out2")
     assert place2.is_equal(place2)
 
-    out4_tensor = model.get_place_by_tensor_name(tensorName="out4")
-    place3 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
-    place4 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
+    out4_tensor = model.get_place_by_tensor_name(tensor_name="out4")
+    place3 = out4_tensor.get_producing_operation().get_input_port(input_port_index=0)
+    place4 = out4_tensor.get_producing_operation().get_input_port(input_port_index=0)
     assert place3.is_equal(place4)
 
-    out1_tensor = model.get_place_by_tensor_name(tensorName="out1")
-    place5 = model.get_place_by_operation_name_and_input_port(operationName="split1", inputPortIndex=0)
-    place6 = out1_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
+    out1_tensor = model.get_place_by_tensor_name(tensor_name="out1")
+    place5 = model.get_place_by_operation_name_and_input_port(operation_name="split1", input_port_index=0)
+    place6 = out1_tensor.get_producing_operation().get_input_port(input_port_index=0)
     assert place5.is_equal(place6)
 
-    place7 = model.get_place_by_tensor_name(tensorName="out4").get_producing_port()
+    place7 = model.get_place_by_tensor_name(tensor_name="out4").get_producing_port()
     assert place7.is_equal(place7)
 
-    place8 = model.get_place_by_tensor_name(tensorName="add_out")
+    place8 = model.get_place_by_tensor_name(tensor_name="add_out")
     assert place8.is_equal(place8)
 
     assert not place1.is_equal(place2)
     assert not place6.is_equal(place7)
     assert not place8.is_equal(place2)
 
-    place9 = model.get_place_by_operation_name(operationName="split1")
+    place9 = model.get_place_by_operation_name(operation_name="split1")
     assert place2.get_producing_operation().is_equal(place9)
     assert not place9.is_equal(place2)
 
@@ -733,32 +733,32 @@ def test_is_equal_data():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="in1")
+    place1 = model.get_place_by_tensor_name(tensor_name="in1")
     assert place1.is_equal_data(place1)
 
-    place2 = model.get_place_by_tensor_name(tensorName="add_out")
+    place2 = model.get_place_by_tensor_name(tensor_name="add_out")
     assert place2.is_equal_data(place2)
 
-    place3 = model.get_place_by_tensor_name(tensorName="in2")
+    place3 = model.get_place_by_tensor_name(tensor_name="in2")
     assert not place1.is_equal_data(place3)
     assert not place2.is_equal_data(place1)
 
     place4 = place2.get_producing_port()
     assert place2.is_equal_data(place4)
 
-    out4_tensor = model.get_place_by_tensor_name(tensorName="out4")
-    place5 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
+    out4_tensor = model.get_place_by_tensor_name(tensor_name="out4")
+    place5 = out4_tensor.get_producing_operation().get_input_port(input_port_index=0)
     assert place2.is_equal_data(place5)
     assert place4.is_equal_data(place5)
 
-    place6 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=1)
+    place6 = out4_tensor.get_producing_operation().get_input_port(input_port_index=1)
     assert place6.is_equal_data(place5)
 
-    place7 = model.get_place_by_operation_name_and_input_port(operationName="split1", inputPortIndex=0)
+    place7 = model.get_place_by_operation_name_and_input_port(operation_name="split1", input_port_index=0)
     assert place7.is_equal_data(place7)
 
-    place8 = model.get_place_by_tensor_name(tensorName="out1")
-    place9 = model.get_place_by_tensor_name(tensorName="out2")
+    place8 = model.get_place_by_tensor_name(tensor_name="out1")
+    place9 = model.get_place_by_tensor_name(tensor_name="out2")
     place10 = place8.get_producing_port()
     assert not place8.is_equal_data(place9)
     assert not place9.is_equal_data(place10)
@@ -773,16 +773,16 @@ def test_get_place_by_tensor_name():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="out2")
+    place1 = model.get_place_by_tensor_name(tensor_name="out2")
     assert place1
 
-    place2 = model.get_place_by_tensor_name(tensorName="add_out")
+    place2 = model.get_place_by_tensor_name(tensor_name="add_out")
     assert place2
 
-    place3 = model.get_place_by_tensor_name(tensorName="in1")
+    place3 = model.get_place_by_tensor_name(tensor_name="in1")
     assert place3
 
-    assert not model.get_place_by_tensor_name(tensorName="0:add_out")
+    assert not model.get_place_by_tensor_name(tensor_name="0:add_out")
 
 
 def test_get_place_by_operation_name():
@@ -793,10 +793,10 @@ def test_get_place_by_operation_name():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_operation_name(operationName="split1")
+    place1 = model.get_place_by_operation_name(operation_name="split1")
     assert place1
 
-    place2 = model.get_place_by_operation_name(operationName="not_existed")
+    place2 = model.get_place_by_operation_name(operation_name="not_existed")
     assert not place2
 
 
@@ -807,16 +807,16 @@ def test_get_output_port():
     model = fe.load("input_model.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split1")
-    place1 = split_op.get_output_port(outputPortIndex=0)
-    place2 = split_op.get_output_port(outputName="out2")
+    split_op = model.get_place_by_operation_name(operation_name="split1")
+    place1 = split_op.get_output_port(output_port_index=0)
+    place2 = split_op.get_output_port(output_name="out2")
 
     assert place1.get_target_tensor().get_names()[0] == "out1"
     assert place2.get_target_tensor().get_names()[0] == "out2"
 
     assert not split_op.get_output_port()
-    assert not split_op.get_output_port(outputPortIndex=3)
-    assert not split_op.get_output_port(outputName="not_existed")
+    assert not split_op.get_output_port(output_port_index=3)
+    assert not split_op.get_output_port(output_name="not_existed")
 
 
 def test_get_input_port():
@@ -826,15 +826,15 @@ def test_get_input_port():
     model = fe.load("input_model.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split1")
-    place1 = split_op.get_input_port(inputPortIndex=0)
+    split_op = model.get_place_by_operation_name(operation_name="split1")
+    place1 = split_op.get_input_port(input_port_index=0)
     assert place1.get_source_tensor().get_names()[0] == "add_out"
 
     place2 = split_op.get_input_port()
     assert place1.is_equal(place2)
 
-    assert not split_op.get_input_port(inputPortIndex=1)
-    assert not split_op.get_input_port(inputName="not_existed")
+    assert not split_op.get_input_port(input_port_index=1)
+    assert not split_op.get_input_port(input_name="not_existed")
 
 
 def test_get_consuming_ports():
@@ -844,15 +844,15 @@ def test_get_consuming_ports():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_tensor_name(tensorName="add_out")
+    place1 = model.get_place_by_tensor_name(tensor_name="add_out")
     add_tensor_consuming_ports = place1.get_consuming_ports()
     assert len(add_tensor_consuming_ports) == 3
-    place2 = model.get_place_by_operation_name_and_input_port(operationName="split1", inputPortIndex=0)
+    place2 = model.get_place_by_operation_name_and_input_port(operation_name="split1", input_port_index=0)
     assert add_tensor_consuming_ports[0].is_equal(place2)
-    out4_tensor = model.get_place_by_tensor_name(tensorName="out4")
-    place3 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
+    out4_tensor = model.get_place_by_tensor_name(tensor_name="out4")
+    place3 = out4_tensor.get_producing_operation().get_input_port(input_port_index=0)
     assert add_tensor_consuming_ports[1].is_equal(place3)
-    place4 = out4_tensor.get_producing_operation().get_input_port(inputPortIndex=1)
+    place4 = out4_tensor.get_producing_operation().get_input_port(input_port_index=1)
     assert add_tensor_consuming_ports[2].is_equal(place4)
 
     add_op_consuming_ports = place1.get_producing_operation().get_consuming_ports()
@@ -868,16 +868,17 @@ def test_get_consuming_ports_2():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split2")
+    split_op = model.get_place_by_operation_name(operation_name="split2")
     split_op_consuming_ports = split_op.get_consuming_ports()
     assert len(split_op_consuming_ports) == 2
-    abs_input_port = model.get_place_by_operation_name(operationName="abs1").get_input_port(inputPortIndex=0)
+    abs_input_port = model.get_place_by_operation_name(operation_name="abs1") \
+                          .get_input_port(input_port_index=0)
     assert split_op_consuming_ports[0].is_equal(abs_input_port)
-    out2_tensor = model.get_place_by_tensor_name(tensorName="out2")
-    sin_input_port = out2_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
+    out2_tensor = model.get_place_by_tensor_name(tensor_name="out2")
+    sin_input_port = out2_tensor.get_producing_operation().get_input_port(input_port_index=0)
     assert split_op_consuming_ports[1].is_equal(sin_input_port)
 
-    split_out_port_0 = split_op.get_output_port(outputPortIndex=0)
+    split_out_port_0 = split_op.get_output_port(output_port_index=0)
     split_out_port_0_consuming_ports = split_out_port_0.get_consuming_ports()
     assert len(split_out_port_0_consuming_ports) == 1
     assert split_out_port_0_consuming_ports[0].is_equal(abs_input_port)
@@ -890,12 +891,12 @@ def test_get_producing_operation():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    split_tensor_out_2 = model.get_place_by_tensor_name(tensorName="sp_out2")
-    split_op = model.get_place_by_operation_name(operationName="split2")
+    split_tensor_out_2 = model.get_place_by_tensor_name(tensor_name="sp_out2")
+    split_op = model.get_place_by_operation_name(operation_name="split2")
     assert split_tensor_out_2.get_producing_operation().is_equal(split_op)
 
-    split_op = model.get_place_by_operation_name(operationName="split2")
-    split_out_port_2 = split_op.get_output_port(outputPortIndex=1)
+    split_op = model.get_place_by_operation_name(operation_name="split2")
+    split_out_port_2 = split_op.get_output_port(output_port_index=1)
     assert split_out_port_2.get_producing_operation().is_equal(split_op)
 
 
@@ -906,22 +907,22 @@ def test_get_producing_operation_2():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    abs_op = model.get_place_by_operation_name(operationName="abs1")
+    abs_op = model.get_place_by_operation_name(operation_name="abs1")
     abs_port_0 = abs_op.get_input_port()
-    split_op = model.get_place_by_operation_name(operationName="split2")
+    split_op = model.get_place_by_operation_name(operation_name="split2")
     assert abs_port_0.get_producing_operation().is_equal(split_op)
     assert abs_op.get_producing_operation().is_equal(split_op)
 
-    add_out_tensor = model.get_place_by_tensor_name(tensorName="add_out")
+    add_out_tensor = model.get_place_by_tensor_name(tensor_name="add_out")
     add_op = add_out_tensor.get_producing_operation()
     assert not add_op.get_producing_operation()
 
-    split_op_producing_op = split_op.get_producing_operation(inputName="add_out")
+    split_op_producing_op = split_op.get_producing_operation(input_name="add_out")
     assert split_op_producing_op.is_equal(add_op)
 
-    out2_tensor = model.get_place_by_tensor_name(tensorName="out2")
+    out2_tensor = model.get_place_by_tensor_name(tensor_name="out2")
     sin_op = out2_tensor.get_producing_operation()
-    assert sin_op.get_producing_operation(inputPortIndex=0).is_equal(split_op)
+    assert sin_op.get_producing_operation(input_port_index=0).is_equal(split_op)
 
 
 def test_get_consuming_operations():
@@ -931,40 +932,40 @@ def test_get_consuming_operations():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split2")
+    split_op = model.get_place_by_operation_name(operation_name="split2")
     split_op_consuming_ops = split_op.get_consuming_operations()
-    abs_op = model.get_place_by_operation_name(operationName="abs1")
-    sin_op = model.get_place_by_tensor_name(tensorName="out2").get_producing_operation()
+    abs_op = model.get_place_by_operation_name(operation_name="abs1")
+    sin_op = model.get_place_by_tensor_name(tensor_name="out2").get_producing_operation()
 
     assert len(split_op_consuming_ops) == 2
     assert split_op_consuming_ops[0].is_equal(abs_op)
     assert split_op_consuming_ops[1].is_equal(sin_op)
 
-    split_op_port = split_op.get_input_port(inputPortIndex=0)
+    split_op_port = split_op.get_input_port(input_port_index=0)
     split_op_port_consuming_ops = split_op_port.get_consuming_operations()
 
     assert len(split_op_port_consuming_ops) == 1
     assert split_op_port_consuming_ops[0].is_equal(split_op)
 
-    add_out_port = model.get_place_by_tensor_name(tensorName="add_out").get_producing_port()
+    add_out_port = model.get_place_by_tensor_name(tensor_name="add_out").get_producing_port()
     add_out_port_consuming_ops = add_out_port.get_consuming_operations()
     assert len(add_out_port_consuming_ops) == 1
     assert add_out_port_consuming_ops[0].is_equal(split_op)
 
-    sp_out2_tensor = model.get_place_by_tensor_name(tensorName="sp_out2")
+    sp_out2_tensor = model.get_place_by_tensor_name(tensor_name="sp_out2")
     sp_out2_tensor_consuming_ops = sp_out2_tensor.get_consuming_operations()
     assert len(sp_out2_tensor_consuming_ops) == 1
     assert sp_out2_tensor_consuming_ops[0].is_equal(sin_op)
 
-    out2_tensor = model.get_place_by_tensor_name(tensorName="out2")
+    out2_tensor = model.get_place_by_tensor_name(tensor_name="out2")
     out2_tensor_consuming_ops = out2_tensor.get_consuming_operations()
     assert len(out2_tensor_consuming_ops) == 0
     out2_port_consuming_ops = out2_tensor.get_producing_port().get_consuming_operations()
     assert len(out2_port_consuming_ops) == 0
 
-    split_out_1_consuming_ops = split_op.get_consuming_operations(outputPortIndex=1)
+    split_out_1_consuming_ops = split_op.get_consuming_operations(output_port_index=1)
     assert len(split_out_1_consuming_ops) == 1
-    split_out_sp_out_2_consuming_ops = split_op.get_consuming_operations(outputName="sp_out2")
+    split_out_sp_out_2_consuming_ops = split_op.get_consuming_operations(output_name="sp_out2")
     assert len(split_out_sp_out_2_consuming_ops) == 1
     assert split_out_1_consuming_ops[0].is_equal(split_out_sp_out_2_consuming_ops[0])
     assert split_out_1_consuming_ops[0].is_equal(sin_op)
@@ -977,18 +978,18 @@ def test_get_target_tensor():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split2")
+    split_op = model.get_place_by_operation_name(operation_name="split2")
     assert not split_op.get_target_tensor()
 
-    split_op_tensor_1 = split_op.get_target_tensor(outputPortIndex=1)
-    sp_out2_tensor = model.get_place_by_tensor_name(tensorName="sp_out2")
+    split_op_tensor_1 = split_op.get_target_tensor(output_port_index=1)
+    sp_out2_tensor = model.get_place_by_tensor_name(tensor_name="sp_out2")
     assert split_op_tensor_1.is_equal(sp_out2_tensor)
 
-    split_tensor_sp_out2 = split_op.get_target_tensor(outputName="sp_out2")
+    split_tensor_sp_out2 = split_op.get_target_tensor(output_name="sp_out2")
     assert split_tensor_sp_out2.is_equal(split_op_tensor_1)
 
-    abs_op = model.get_place_by_operation_name(operationName="abs1")
-    out1_tensor = model.get_place_by_tensor_name(tensorName="out1")
+    abs_op = model.get_place_by_operation_name(operation_name="abs1")
+    out1_tensor = model.get_place_by_tensor_name(tensor_name="out1")
     assert abs_op.get_target_tensor().is_equal(out1_tensor)
 
 
@@ -999,18 +1000,18 @@ def test_get_source_tensor():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    add_out_tensor = model.get_place_by_tensor_name(tensorName="add_out")
+    add_out_tensor = model.get_place_by_tensor_name(tensor_name="add_out")
     add_op = add_out_tensor.get_producing_operation()
     assert not add_op.get_source_tensor()
 
-    add_op_in_tensor_1 = add_op.get_source_tensor(inputPortIndex=1)
-    in2_tensor = model.get_place_by_tensor_name(tensorName="in2")
+    add_op_in_tensor_1 = add_op.get_source_tensor(input_port_index=1)
+    in2_tensor = model.get_place_by_tensor_name(tensor_name="in2")
     assert add_op_in_tensor_1.is_equal(in2_tensor)
 
-    add_op_in_tensor_in2 = add_op.get_source_tensor(inputName="in2")
+    add_op_in_tensor_in2 = add_op.get_source_tensor(input_name="in2")
     assert add_op_in_tensor_in2.is_equal(in2_tensor)
 
-    split_op = model.get_place_by_operation_name(operationName="split2")
+    split_op = model.get_place_by_operation_name(operation_name="split2")
     assert split_op.get_source_tensor().is_equal(add_out_tensor)
 
 
@@ -1021,11 +1022,11 @@ def test_get_producing_port():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    split_op = model.get_place_by_operation_name(operationName="split2")
+    split_op = model.get_place_by_operation_name(operation_name="split2")
     split_op_in_port = split_op.get_input_port()
     split_op_in_port_prod_port = split_op_in_port.get_producing_port()
 
-    add_out_tensor = model.get_place_by_tensor_name(tensorName="add_out")
+    add_out_tensor = model.get_place_by_tensor_name(tensor_name="add_out")
     add_op = add_out_tensor.get_producing_operation()
     add_op_out_port = add_op.get_output_port()
 
@@ -1040,9 +1041,9 @@ def test_get_place_by_operation_name_and_input_port():
     model = fe.load("input_model.onnx")
     assert model
 
-    place1 = model.get_place_by_operation_name_and_input_port(operationName="split1", inputPortIndex=0)
+    place1 = model.get_place_by_operation_name_and_input_port(operation_name="split1", input_port_index=0)
     sp_out1_tensor = model.get_place_by_tensor_name("out2")
-    place2 = sp_out1_tensor.get_producing_operation().get_input_port(inputPortIndex=0)
+    place2 = sp_out1_tensor.get_producing_operation().get_input_port(input_port_index=0)
 
     assert place1.is_equal(place2)
 
@@ -1055,9 +1056,9 @@ def test_get_place_by_operation_name_and_output_port():
     model = fe.load("input_model_2.onnx")
     assert model
 
-    place1 = model.get_place_by_operation_name_and_output_port(operationName="split2", outputPortIndex=0)
+    place1 = model.get_place_by_operation_name_and_output_port(operation_name="split2", output_port_index=0)
     sp_out1_tensor = model.get_place_by_tensor_name("sp_out1")
-    place2 = sp_out1_tensor.get_producing_operation().get_output_port(outputPortIndex=0)
+    place2 = sp_out1_tensor.get_producing_operation().get_output_port(output_port_index=0)
 
     assert place1.is_equal(place2)
 
@@ -1066,10 +1067,10 @@ def test_not_supported_methods():
     skip_if_onnx_frontend_is_disabled()
     fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
     model = fe.load("test_place_names.onnx")
-    tensor = model.get_place_by_tensor_name(tensorName="add_out")
+    tensor = model.get_place_by_tensor_name(tensor_name="add_out")
 
     with pytest.raises(Exception) as e:
-        model.add_name_for_tensor(tensor=tensor, newName="new_name")
+        model.add_name_for_tensor(tensor=tensor, new_name="new_name")
     assert "not applicable for ONNX model" in str(e)
 
     with pytest.raises(Exception) as e:
@@ -1084,34 +1085,34 @@ def test_set_name_for_tensor():
     old_name = "add_out"
     new_name = "add_out_new"
 
-    tensor = model.get_place_by_tensor_name(tensorName=old_name)
+    tensor = model.get_place_by_tensor_name(tensor_name=old_name)
 
     # ignore rename to own name (expect no exception)
-    model.set_name_for_tensor(tensor=tensor, newName=old_name)
+    model.set_name_for_tensor(tensor=tensor, new_name=old_name)
 
     with pytest.raises(Exception) as e:
-        model.set_name_for_tensor(tensor=tensor, newName="")
+        model.set_name_for_tensor(tensor=tensor, new_name="")
     assert "name must not be empty" in str(e)
 
     # ONNX model stores tensor info separately for inputs, outputs and between nodes tensors
     with pytest.raises(Exception) as e:
-        model.set_name_for_tensor(tensor=tensor, newName="in1")
+        model.set_name_for_tensor(tensor=tensor, new_name="in1")
     assert "already used by another tensor" in str(e)
     with pytest.raises(Exception) as e:
-        model.set_name_for_tensor(tensor=tensor, newName="out1")
+        model.set_name_for_tensor(tensor=tensor, new_name="out1")
     assert "already used by another tensor" in str(e)
     with pytest.raises(Exception) as e:
-        model.set_name_for_tensor(tensor=tensor, newName="sub_out")
+        model.set_name_for_tensor(tensor=tensor, new_name="sub_out")
     assert "already used by another tensor" in str(e)
 
     # actual rename
-    model.set_name_for_tensor(tensor=tensor, newName=new_name)
+    model.set_name_for_tensor(tensor=tensor, new_name=new_name)
 
-    new_tensor = model.get_place_by_tensor_name(tensorName=new_name)
+    new_tensor = model.get_place_by_tensor_name(tensor_name=new_name)
     assert new_tensor
     assert new_tensor.is_equal(tensor)  # previous Place object holds the handle
 
-    old_tensor = model.get_place_by_tensor_name(tensorName=old_name)
+    old_tensor = model.get_place_by_tensor_name(tensor_name=old_name)
     assert old_tensor is None
 
 
@@ -1122,21 +1123,21 @@ def test_set_name_for_operation_with_name():
     old_name = "split1"
     new_name = "split1_new"
 
-    operation = model.get_place_by_operation_name(operationName=old_name)
+    operation = model.get_place_by_operation_name(operation_name=old_name)
 
     # ignore rename to own name (expect no exception)
-    model.set_name_for_operation(operation=operation, newName=old_name)
+    model.set_name_for_operation(operation=operation, new_name=old_name)
 
     # actual rename
-    model.set_name_for_operation(operation=operation, newName=new_name)
+    model.set_name_for_operation(operation=operation, new_name=new_name)
 
-    new_operation = model.get_place_by_operation_name(operationName=new_name)
+    new_operation = model.get_place_by_operation_name(operation_name=new_name)
     assert new_operation
     assert new_operation.is_equal(operation)  # previous Place object holds the handle
 
     # Below test passes for models with unique operation names, what is not required by ONNX standard
     # If there were more that one nodes with "split1" name, this test would fail.
-    old_operation = model.get_place_by_operation_name(operationName=old_name)
+    old_operation = model.get_place_by_operation_name(operation_name=old_name)
     assert old_operation is None
 
 
@@ -1147,14 +1148,14 @@ def test_set_name_for_operation_without_name():
     output_name = "add_out"
     new_name = "Add_new"
 
-    operation = model.get_place_by_tensor_name(tensorName=output_name).get_producing_operation()
+    operation = model.get_place_by_tensor_name(tensor_name=output_name).get_producing_operation()
     # assure the test is performed on node with empty name
     assert not operation.get_names() or len(operation.get_names()) == 0 or not operation.get_names()[0]
 
     # actual rename
-    model.set_name_for_operation(operation=operation, newName=new_name)
+    model.set_name_for_operation(operation=operation, new_name=new_name)
 
-    new_operation = model.get_place_by_tensor_name(tensorName=output_name).get_producing_operation()
+    new_operation = model.get_place_by_tensor_name(tensor_name=output_name).get_producing_operation()
     assert new_operation
     assert new_operation.is_equal(operation)  # previous Place object holds the handle
 
@@ -1168,13 +1169,13 @@ def test_free_name_for_operation():
     # assure non existent names are ignored (expect no exception)
     model.free_name_for_operation("non existent name")
 
-    split1 = model.get_place_by_operation_name(operationName=name)
+    split1 = model.get_place_by_operation_name(operation_name=name)
     assert split1
     model.free_name_for_operation(name)
-    operation = model.get_place_by_operation_name(operationName=name)
+    operation = model.get_place_by_operation_name(operation_name=name)
     assert not operation
 
-    new_split1 = model.get_place_by_tensor_name(tensorName="out1").get_producing_operation()
+    new_split1 = model.get_place_by_tensor_name(tensor_name="out1").get_producing_operation()
     assert split1.is_equal(new_split1)
 
 
@@ -1184,16 +1185,16 @@ def test_set_name_for_dimension():
     model = fe.load("test_place_names.onnx")
     dim_name = "batch_size"
 
-    input1 = model.get_place_by_tensor_name(tensorName="in1")
+    input1 = model.get_place_by_tensor_name(tensor_name="in1")
     model.set_name_for_dimension(input1, 0, dim_name)
     assert model.get_partial_shape(input1) == PartialShape([-1, 2])
 
-    output1 = model.get_place_by_tensor_name(tensorName="out1")
+    output1 = model.get_place_by_tensor_name(tensor_name="out1")
     model.set_name_for_dimension(output1, 1, dim_name)
     assert model.get_partial_shape(output1) == PartialShape([1, -1])
 
     # sub_output rank is 2 so setting dim_name at index 3 extends its rank to 4
-    sub_output = model.get_place_by_tensor_name(tensorName="sub_out")
+    sub_output = model.get_place_by_tensor_name(tensor_name="sub_out")
     model.set_name_for_dimension(sub_output, 3, dim_name)
     assert model.get_partial_shape(sub_output) == PartialShape([2, 2, -1, -1])
 
@@ -1201,7 +1202,7 @@ def test_set_name_for_dimension():
         model.set_name_for_dimension(input1, 0, "")
     assert "name must not be empty" in str(e)
 
-    one_const = model.get_place_by_tensor_name(tensorName="one_const")
+    one_const = model.get_place_by_tensor_name(tensor_name="one_const")
     with pytest.raises(Exception) as e:
         model.set_name_for_dimension(one_const, 0, dim_name)
     assert "ONNX initializer shape dimension cannot be dynamic." in str(e)
diff --git a/src/bindings/python/tests/test_ngraph/test_core.py b/src/bindings/python/tests/test_ngraph/test_core.py
index 9908df30df4..111d89d3781 100644
--- a/src/bindings/python/tests/test_ngraph/test_core.py
+++ b/src/bindings/python/tests/test_ngraph/test_core.py
@@ -131,7 +131,7 @@ def test_partial_shape():
     assert list(ps.get_max_shape()) == []
     assert repr(ps) == "<PartialShape: ...>"
 
-    ps = PartialShape.dynamic(r=Dimension(2))
+    ps = PartialShape.dynamic(rank=Dimension(2))
     assert not ps.is_static
     assert ps.is_dynamic
     assert ps.rank == 2

From cf92c54a12c4698cb16aa916babe1c893e8d78ea Mon Sep 17 00:00:00 2001
From: Yuan Hu <yuan2.hu@intel.com>
Date: Mon, 20 Dec 2021 22:32:00 +0800
Subject: [PATCH 049/160] [Auto plugin] select logic need to check CPU
 precision (#9259)

* cpu also need to check precision

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>

* fix test case failed issue

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
---
 src/plugins/auto/plugin.cpp                    |  3 +--
 .../unit/auto/key_network_priority_test.cpp    | 18 +++++++++---------
 src/tests/unit/auto/select_device_test.cpp     |  7 ++-----
 3 files changed, 12 insertions(+), 16 deletions(-)

diff --git a/src/plugins/auto/plugin.cpp b/src/plugins/auto/plugin.cpp
index 7e388cc7ff9..80994021001 100644
--- a/src/plugins/auto/plugin.cpp
+++ b/src/plugins/auto/plugin.cpp
@@ -445,6 +445,7 @@ DeviceInformation MultiDeviceInferencePlugin::SelectDevice(const std::vector<Dev
     devices.splice(devices.end(), VPUX);
     devices.splice(devices.end(), iGPU);
     devices.splice(devices.end(), MYRIAD);
+    devices.splice(devices.end(), CPU);
 
     std::list<DeviceInformation> validDevices;
 
@@ -466,8 +467,6 @@ DeviceInformation MultiDeviceInferencePlugin::SelectDevice(const std::vector<Dev
        const std::string f16 = "FP16";
        selectSupportDev(f16);
     }
-    // add cpu devices if exist.
-    validDevices.splice(validDevices.end(), CPU);
 
     if (validDevices.empty()) {
          IE_THROW() << "Cannot select any device";
diff --git a/src/tests/unit/auto/key_network_priority_test.cpp b/src/tests/unit/auto/key_network_priority_test.cpp
index 2410ba52d72..487ba5bb123 100644
--- a/src/tests/unit/auto/key_network_priority_test.cpp
+++ b/src/tests/unit/auto/key_network_priority_test.cpp
@@ -162,12 +162,12 @@ TEST_P(KeyNetworkPriorityTest, MultiThreadsSelectDevice) {
 const std::vector<ConfigParams> testConfigs = {
                                                ConfigParams {"FP32", {PriorityParams {0, "dGPU_01"},
                                                                       PriorityParams {1, "iGPU_01"},
-                                                                      PriorityParams {2, "MYRIAD_01"},
-                                                                      PriorityParams {2, "MYRIAD_01"}}},
+                                                                      PriorityParams {2, "CPU_01"},
+                                                                      PriorityParams {2, "CPU_01"}}},
                                                ConfigParams {"FP32", {PriorityParams {2, "dGPU_01"},
                                                                       PriorityParams {3, "iGPU_01"},
-                                                                      PriorityParams {4, "MYRIAD_01"},
-                                                                      PriorityParams {5, "CPU_01"}}},
+                                                                      PriorityParams {4, "CPU_01"},
+                                                                      PriorityParams {5, "MYRIAD_01"}}},
                                                ConfigParams {"FP32", {PriorityParams {2, "dGPU_01"},
                                                                       PriorityParams {0, "dGPU_01"},
                                                                       PriorityParams {2, "iGPU_01"},
@@ -175,15 +175,15 @@ const std::vector<ConfigParams> testConfigs = {
                                                ConfigParams {"FP32", {PriorityParams {2, "dGPU_01"},
                                                                       PriorityParams {0, "dGPU_01"},
                                                                       PriorityParams {2, "iGPU_01"},
-                                                                      PriorityParams {3, "MYRIAD_01"}}},
+                                                                      PriorityParams {3, "CPU_01"}}},
                                                ConfigParams {"FP32", {PriorityParams {0, "dGPU_01"},
                                                                       PriorityParams {1, "iGPU_01"},
-                                                                      PriorityParams {2, "MYRIAD_01"},
-                                                                      PriorityParams {3, "CPU_01"},
+                                                                      PriorityParams {2, "CPU_01"},
+                                                                      PriorityParams {3, "MYRIAD_01"},
                                                                       PriorityParams {0, "dGPU_01"},
                                                                       PriorityParams {1, "iGPU_01"},
-                                                                      PriorityParams {2, "MYRIAD_01"},
-                                                                      PriorityParams {3, "CPU_01"}}},
+                                                                      PriorityParams {2, "CPU_01"},
+                                                                      PriorityParams {3, "MYRIAD_01"}}},
                                                ConfigParams {"INT8", {PriorityParams {0, "VPUX_01"},
                                                                       PriorityParams {1, "CPU_01"},
                                                                       PriorityParams {2, "CPU_01"},
diff --git a/src/tests/unit/auto/select_device_test.cpp b/src/tests/unit/auto/select_device_test.cpp
index 83d4cba279f..ac019375b81 100644
--- a/src/tests/unit/auto/select_device_test.cpp
+++ b/src/tests/unit/auto/select_device_test.cpp
@@ -46,14 +46,11 @@ const DeviceInformation IGPU_INFO = {CommonTestUtils::DEVICE_GPU, {}, 2, "01", "
 const DeviceInformation DGPU_INFO = {CommonTestUtils::DEVICE_GPU, {}, 2, "01", "dGPU_01"};
 const DeviceInformation MYRIAD_INFO = {CommonTestUtils::DEVICE_MYRIAD, {}, 2, "01", "MYRIAD_01" };
 const DeviceInformation KEEMBAY_INFO = {CommonTestUtils::DEVICE_KEEMBAY, {}, 2, "01", "VPUX_01" };
-const std::vector<DeviceInformation>  fp32DeviceVector = {DGPU_INFO, IGPU_INFO, MYRIAD_INFO, CPU_INFO};
+const std::vector<DeviceInformation>  fp32DeviceVector = {DGPU_INFO, IGPU_INFO, CPU_INFO, MYRIAD_INFO};
 const std::vector<DeviceInformation>  fp16DeviceVector = {DGPU_INFO, IGPU_INFO, MYRIAD_INFO, CPU_INFO};
 const std::vector<DeviceInformation>  int8DeviceVector = {KEEMBAY_INFO, CPU_INFO};
 const std::vector<DeviceInformation>  binDeviceVector = {DGPU_INFO, IGPU_INFO, CPU_INFO};
-// if CPU support BATCHED_BLOB ?
-// currently if there are CPU in metaDevices, the selectDevice will select CPU if on other device support
-//const std::vector<DeviceInformation>  batchedblobDeviceVector = {DGPU_INFO, IGPU_INFO};
-const std::vector<DeviceInformation>  batchedblobDeviceVector = {DGPU_INFO, IGPU_INFO, CPU_INFO};
+const std::vector<DeviceInformation>  batchedblobDeviceVector = {DGPU_INFO, IGPU_INFO};
 std::map<std::string, const std::vector<DeviceInformation>> devicesMap = {{"FP32", fp32DeviceVector},
                                                                            {"FP16", fp16DeviceVector},
                                                                            {"INT8", int8DeviceVector},

From 1901f33bd3cee794725e9a062b3f46d1949a30ef Mon Sep 17 00:00:00 2001
From: "song, bell" <bell.song@intel.com>
Date: Mon, 20 Dec 2021 10:06:50 -0500
Subject: [PATCH 050/160] auto dynamic shape (#8731)

* initial commit

Signed-off-by: fishbell <bell.song@intel.com>

* overwrite GetBlob/SetBlob to support dynamic shape when needed

Signed-off-by: fishbell <bell.song@intel.com>

* extend workers

Signed-off-by: fishbell <bell.song@intel.com>

* clean up code

Signed-off-by: fishbell <bell.song@intel.com>

* fix lock and add case for CPU,GPU

Signed-off-by: fishbell <bell.song@intel.com>

* fix mem leak

Signed-off-by: fishbell <bell.song@intel.com>

* clean up allocated worker

Signed-off-by: fishbell <bell.song@intel.com>

* fix test case failure

Signed-off-by: fishbell <bell.song@intel.com>

* enable case for chaining

Signed-off-by: fishbell <bell.song@intel.com>

* fix arm compile issue

Signed-off-by: fishbell <bell.song@intel.com>

* clang format, fix potential threading issue

Signed-off-by: fishbell <bell.song@intel.com>

* clean up idle request queue if infer request destroyed

Signed-off-by: fishbell <bell.song@intel.com>

* keep multi legacy behavior

Signed-off-by: fishbell <bell.song@intel.com>

* elegant way to handle recycling

Signed-off-by: fishbell <bell.song@intel.com>

* avoid the exception with set output blob in auto dynamic case

Signed-off-by: fishbell <bell.song@intel.com>

* remove increase workers logic

Signed-off-by: fishbell <bell.song@intel.com>

* formatting

Signed-off-by: fishbell <bell.song@intel.com>

* remove the irrelevant code to dynamic shape

Signed-off-by: fishbell <bell.song@intel.com>

* filter target device to CPU in the case of dynamic shape

Signed-off-by: fishbell <bell.song@intel.com>

* fix logic in selectdevice, add case for GPU

Signed-off-by: fishbell <bell.song@intel.com>

* add case for AUTO:GPU

Signed-off-by: fishbell <bell.song@intel.com>

* fix build error

Signed-off-by: fishbell <bell.song@intel.com>

* add comment for skipped cases

Signed-off-by: fishbell <bell.song@intel.com>

* fix the case failure caused by naming conflict

Signed-off-by: fishbell <bell.song@intel.com>

* move file to new folder

Signed-off-by: fishbell <bell.song@intel.com>

* change to 2.0 API in plugin

Signed-off-by: fishbell <bell.song@intel.com>

* refactor mkldnn code change

Signed-off-by: fishbell <bell.song@intel.com>

* formatting

Signed-off-by: fishbell <bell.song@intel.com>
---
 .../mkldnn_plugin/mkldnn_infer_request.cpp    | 14 ++--
 .../interface/ie_iinfer_request_internal.cpp  | 22 ++++--
 src/plugins/auto/plugin.cpp                   | 22 +++++-
 src/plugins/auto/plugin.hpp                   |  2 +
 .../infer_request_dynamic.cpp                 | 14 ++++
 .../ov_infer_request/inference_chaining.cpp   |  9 +++
 .../skip_tests_config.cpp                     |  4 +-
 .../infer_request_dynamic.cpp                 | 70 +++++++++++++++++++
 .../skip_tests_config.cpp                     |  4 ++
 .../gpu/subgraph_tests/shared_constant.cpp    |  3 +
 .../infer_request_dynamic.hpp                 |  1 +
 .../infer_request_dynamic.cpp                 | 14 ++++
 12 files changed, 166 insertions(+), 13 deletions(-)
 create mode 100644 src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
index 6179f52394a..fb41fbc281d 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
@@ -468,14 +468,16 @@ void MKLDNNPlugin::MKLDNNInferRequest::SetBlob(const std::string& name, const In
             IE_THROW(ParameterMismatch) << "Failed to set output blob with precision: "
                                << blobDesc.getPrecision() << ", if CNNNetwork output blob precision is: " << foundOutput->getPrecision();
         }
-        size_t outputSize = foundOutput->getTensorDesc().getLayout() != InferenceEngine::Layout::SCALAR
-            ? InferenceEngine::details::product(foundOutput->getDims())
-            : 1;
 
         const bool isDynamic = outputNode->isDynamicNode();
-        if (!isDynamic && dataSize != outputSize) {
-            IE_THROW() << "Output blob size is not equal network output size ("
-                               << dataSize << "!=" << outputSize << ").";
+        if (!isDynamic) {
+            size_t outputSize = foundOutput->getTensorDesc().getLayout() != InferenceEngine::Layout::SCALAR
+                ? InferenceEngine::details::product(foundOutput->getDims())
+                : 1;
+            if (dataSize != outputSize) {
+                IE_THROW() << "Output blob size is not equal network output size ("
+                                   << dataSize << "!=" << outputSize << ").";
+            }
         }
         if (!isDynamic && foundOutput->getTensorDesc().getDims() != blobDesc.getDims()) {
             IE_THROW(ParameterMismatch) << "Failed to set output Blob. Dimensions mismatch.";
diff --git a/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
index da3299b2302..62ba6521932 100644
--- a/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
+++ b/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
@@ -103,6 +103,8 @@ void IInferRequestInternal::SetBlob(const std::string& name, const Blob::Ptr& us
         !((foundInput && foundInput->getInputData()->isDynamic()) || (foundOutput && foundOutput->isDynamic()))) {
         IE_THROW() << "Input data is empty. Input name: \'" << name << "\'";
     }
+    const bool isInputDynamic = foundInput && foundInput->getInputData()->isDynamic();
+    const bool isOutputDynamic = foundOutput && foundOutput->isDynamic();
     IE_SUPPRESS_DEPRECATED_END
 
     size_t dataSize = userBlob->size();
@@ -126,7 +128,7 @@ void IInferRequestInternal::SetBlob(const std::string& name, const Blob::Ptr& us
             size_t inputSize = foundInput->getTensorDesc().getLayout() != InferenceEngine::Layout::SCALAR
                                    ? InferenceEngine::details::product(foundInput->getTensorDesc().getDims())
                                    : 1;
-            if (dataSize != inputSize) {
+            if (!isInputDynamic && dataSize != inputSize) {
                 IE_THROW() << "Input blob size is not equal network input size (" << dataSize << "!=" << inputSize
                            << ").";
             }
@@ -140,7 +142,7 @@ void IInferRequestInternal::SetBlob(const std::string& name, const Blob::Ptr& us
         size_t outputSize = foundOutput->getTensorDesc().getLayout() != InferenceEngine::Layout::SCALAR
                                 ? details::product(foundOutput->getTensorDesc().getDims())
                                 : 1;
-        if (dataSize != outputSize) {
+        if (!isOutputDynamic && dataSize != outputSize) {
             IE_THROW() << "Output blob size is not equal network output size (" << dataSize << "!=" << outputSize
                        << ").";
         }
@@ -164,6 +166,9 @@ Blob::Ptr IInferRequestInternal::GetBlob(const std::string& name) {
     DataPtr foundOutput;
     const SizeVector oneVector = {1};
     if (findInputAndOutputBlobByName(name, foundInput, foundOutput)) {
+        IE_SUPPRESS_DEPRECATED_START
+        const bool isInputDynamic = foundInput && foundInput->getInputData()->isDynamic();
+        IE_SUPPRESS_DEPRECATED_END
         // ROI blob is returned only if it was set previously. Otherwise default blob is returned.
         auto it = _preProcData.find(name);
         if (it != _preProcData.end()) {
@@ -171,7 +176,10 @@ Blob::Ptr IInferRequestInternal::GetBlob(const std::string& name) {
         } else {
             data = _inputs[name];
             const auto& dims = foundInput->getTensorDesc().getDims();
-            checkBlob(data, name, true, foundInput->getTensorDesc().getLayout() != SCALAR ? dims : oneVector);
+            if (isInputDynamic)
+                checkBlob(data, name, true);
+            else
+                checkBlob(data, name, true, foundInput->getTensorDesc().getLayout() != SCALAR ? dims : oneVector);
 
             auto& devBlob = _deviceInputs[name];
             if (preProcessingRequired(foundInput, data, devBlob)) {
@@ -180,9 +188,15 @@ Blob::Ptr IInferRequestInternal::GetBlob(const std::string& name) {
             }
         }
     } else {
+        IE_SUPPRESS_DEPRECATED_START
+        const bool isOutputDynamic = foundOutput && foundOutput->isDynamic();
+        IE_SUPPRESS_DEPRECATED_END
         data = _outputs[name];
         const auto& dims = foundOutput->getTensorDesc().getDims();
-        checkBlob(data, name, false, foundOutput->getTensorDesc().getLayout() != SCALAR ? dims : oneVector);
+        if (isOutputDynamic)
+            checkBlob(data, name, false);
+        else
+            checkBlob(data, name, false, foundOutput->getTensorDesc().getLayout() != SCALAR ? dims : oneVector);
     }
     return data;
 }
diff --git a/src/plugins/auto/plugin.cpp b/src/plugins/auto/plugin.cpp
index 80994021001..c1bdb8953c6 100644
--- a/src/plugins/auto/plugin.cpp
+++ b/src/plugins/auto/plugin.cpp
@@ -269,10 +269,11 @@ IExecutableNetworkInternal::Ptr MultiDeviceInferencePlugin::LoadNetworkImpl(cons
         // filter the device that supports filter configure
         auto strDevices = GetDeviceList(fullConfig);
         auto metaDevices = ParseMetaDevices(strDevices, fullConfig);
-        auto supportDevices = FilterDevice(metaDevices, filterConfig);
-        if (supportDevices.size() == 0) {
+        auto supportDevicesByConfig = FilterDevice(metaDevices, filterConfig);
+        if (supportDevicesByConfig.size() == 0) {
              IE_THROW() << "there is no device support the configure";
         }
+        auto supportDevices = FilterDeviceByNetwork(supportDevicesByConfig, network);
         // replace the configure with configure that auto want to pass to device
         // and reset the strDevices to support devices
         auto validConfigKey = PerfHintsConfig::SupportedKeys();
@@ -636,4 +637,21 @@ std::vector<DeviceInformation> MultiDeviceInferencePlugin::FilterDevice(const st
     }
     return filterDevice;
 }
+std::vector<DeviceInformation> MultiDeviceInferencePlugin::FilterDeviceByNetwork(const std::vector<DeviceInformation>& metaDevices,
+                                                InferenceEngine::CNNNetwork network) {
+    std::vector<DeviceInformation> filterDevice;
+    auto model = network.getFunction();
+    if (model->is_dynamic()) {
+        for (auto& iter : metaDevices) {
+            if (iter.deviceName.find("CPU") != std::string::npos) {
+                filterDevice.push_back(iter);
+                break;
+            }
+        }
+        if (filterDevice.size() == 0)
+            IE_THROW(NotFound) << "No available device for dynamic shape network !";
+        return filterDevice;
+    }
+    return metaDevices;
+}
 }  // namespace MultiDevicePlugin
diff --git a/src/plugins/auto/plugin.hpp b/src/plugins/auto/plugin.hpp
index 0329ec050f9..7ca3307e5e3 100644
--- a/src/plugins/auto/plugin.hpp
+++ b/src/plugins/auto/plugin.hpp
@@ -64,6 +64,8 @@ private:
                             std::map<std::string, std::string>& filterConfig);
     std::vector<DeviceInformation> FilterDevice(const std::vector<DeviceInformation>& metaDevices,
                                                 const std::map<std::string, std::string>& config);
+    std::vector<DeviceInformation> FilterDeviceByNetwork(const std::vector<DeviceInformation>& metaDevices,
+                                                InferenceEngine::CNNNetwork network);
     static std::mutex _mtx;
     static std::map<unsigned int, std::list<std::string>> _priorityMap;
 };
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
index 8330d9ef26c..2f6c5f8a761 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
@@ -18,6 +18,10 @@ const std::vector<std::map<std::string, std::string>> HeteroConfigs = {
     {{"TARGET_FALLBACK", CommonTestUtils::DEVICE_CPU}}
 };
 
+const std::vector<std::map<std::string, std::string>> AutoConfigs = {
+    {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, CommonTestUtils::DEVICE_CPU}}
+};
+
 std::shared_ptr<ngraph::Function> getFunction1() {
     const std::vector<size_t> inputShape = {1, 4, 20, 20};
     const ngraph::element::Type_t ngPrc = ngraph::element::Type_t::f32;
@@ -90,4 +94,14 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVInferRequestDynamicTests,
                                 ::testing::ValuesIn(HeteroConfigs)),
                         OVInferRequestDynamicTests::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestDynamicTests,
+                        ::testing::Combine(
+                                ::testing::Values(getFunction2()),
+                                ::testing::Values(std::vector<std::pair<std::vector<size_t>, std::vector<size_t>>>{
+                                    {{1, 4, 20, 20}, {1, 2, 20, 40}},
+                                    {{2, 4, 20, 20}, {2, 2, 20, 40}}}),
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(AutoConfigs)),
+                        OVInferRequestDynamicTests::getTestCaseName);
+
 }  // namespace
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/inference_chaining.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/inference_chaining.cpp
index 007fc174531..21745a327b6 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/inference_chaining.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_infer_request/inference_chaining.cpp
@@ -17,6 +17,10 @@ const std::vector<std::map<std::string, std::string>> HeteroConfigs = {
     {{"TARGET_FALLBACK", CommonTestUtils::DEVICE_CPU}}
 };
 
+const std::vector<std::map<std::string, std::string>> AutoConfigs = {
+    {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, CommonTestUtils::DEVICE_CPU}}
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferenceChaining,
                         ::testing::Combine(
                                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
@@ -29,4 +33,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVInferenceChaining,
                                 ::testing::ValuesIn(HeteroConfigs)),
                         OVInferenceChaining::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferenceChaining,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(AutoConfigs)),
+                        OVInferenceChaining::getTestCaseName);
 }  // namespace
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index cfdb50a9208..1ff8c21a217 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -109,11 +109,13 @@ std::vector<std::string> disabledTestPatterns() {
         // Issue: CVS-66778
         R"(.*smoke_BehaviorTests.*InferFullyDynamicNetworkWith(S|G)etTensor.*)",
         R"(.*smoke_Hetero_BehaviorTests.*InferFullyDynamicNetworkWith(S|G)etTensor.*)",
+        R"(.*smoke_Auto_BehaviorTests.*InferFullyDynamicNetworkWith(S|G)etTensor.*)",
         R"(.*smoke_BehaviorTests.*DynamicOutputToDynamicInput.*)",
         R"(.*smoke_BehaviorTests.*DynamicInputToDynamicOutput.*)",
         R"(.*smoke_Hetero_BehaviorTests.*DynamicOutputToDynamicInput.*)",
         R"(.*smoke_Hetero_BehaviorTests.*DynamicInputToDynamicOutput.*)",
-
+        R"(.*smoke_Auto_BehaviorTests.*DynamicOutputToDynamicInput.*)",
+        R"(.*smoke_Auto_BehaviorTests.*DynamicInputToDynamicOutput.*)",
         // CPU dynamism: empty tensor returns size() == 1. Looks like layout is SCALAR
         // Issue: CVS-66780
         R"(.*smoke_BehaviorTests.*InferUpperBoundNetworkWithGetTensor.*)",
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
new file mode 100644
index 00000000000..281b1b0682d
--- /dev/null
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
@@ -0,0 +1,70 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "behavior/ov_infer_request/infer_request_dynamic.hpp"
+#include "behavior/ov_infer_request/inference_chaining.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+
+const std::vector<std::map<std::string, std::string>> AutoConfigs = {
+    {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES,
+    CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU}},
+    {}
+};
+const std::vector<std::map<std::string, std::string>> AutoNotSupportConfigs = {
+    {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, CommonTestUtils::DEVICE_GPU}}
+};
+
+std::shared_ptr<ngraph::Function> getFunction2() {
+    const std::vector<size_t> inputShape = {1, 4, 20, 20};
+    const ngraph::element::Type_t ngPrc = ngraph::element::Type_t::f32;
+
+    auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+    params.front()->set_friendly_name("Param_1");
+    params.front()->get_output_tensor(0).set_names({"input_tensor"});
+    auto split = ngraph::builder::makeSplit(params[0], ngPrc, 2, 1);
+
+    auto in2add = ngraph::builder::makeConstant(ngPrc, {1, 2, 1, 1}, std::vector<float>{}, true);
+    auto add = ngraph::builder::makeEltwise(split->output(0), in2add, ngraph::helpers::EltwiseTypes::ADD);
+    auto relu1 = std::make_shared<ngraph::opset1::Relu>(add);
+
+    auto in2mult = ngraph::builder::makeConstant(ngPrc, {1, 2, 1, 1}, std::vector<float>{}, true);
+    auto mult = ngraph::builder::makeEltwise(split->output(1), in2mult, ngraph::helpers::EltwiseTypes::MULTIPLY);
+    auto relu2 = std::make_shared<ngraph::opset1::Relu>(mult);
+
+    auto concat = std::make_shared<ngraph::opset1::Concat>(ngraph::OutputVector{relu1->output(0), relu2->output(0)}, 3);
+    concat->get_output_tensor(0).set_names({"concat"});
+
+    return std::make_shared<ngraph::Function>(concat, params, "SplitAddConcat");
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestDynamicTests,
+                        ::testing::Combine(
+                                ::testing::Values(getFunction2()),
+                                ::testing::Values(std::vector<std::pair<std::vector<size_t>, std::vector<size_t>>>{
+                                    {{1, 4, 20, 20}, {1, 2, 20, 40}},
+                                    {{2, 4, 20, 20}, {2, 2, 20, 40}}}),
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(AutoConfigs)),
+                        OVInferRequestDynamicTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferenceChaining,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(AutoConfigs)),
+                        OVInferenceChaining::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVNotSupportRequestDynamicTests,
+                        ::testing::Combine(
+                                ::testing::Values(getFunction2()),
+                                ::testing::Values(std::vector<std::pair<std::vector<size_t>, std::vector<size_t>>>{
+                                    {{1, 4, 20, 20}, {1, 2, 20, 40}},
+                                    {{2, 4, 20, 20}, {2, 2, 20, 40}}}),
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(AutoNotSupportConfigs)),
+                        OVInferRequestDynamicTests::getTestCaseName);
+}  // namespace
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
index eeb729c5bdd..9a0e8f3f144 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
@@ -91,5 +91,9 @@ std::vector<std::string> disabledTestPatterns() {
             R"(smoke_Behavior/InferRequestSetBlobByType.setInputBlobsByType/BlobType=Batched_Device=GPU_Config=().*)",
             // TODO: Issue 72624
             R"(smoke_PrePostProcess.*resize_dynamic.*)",
+            // Issue: CVS-66778
+            R"(.*smoke_Auto_BehaviorTests.*DynamicOutputToDynamicInput.*)",
+            R"(.*smoke_Auto_BehaviorTests.*DynamicInputToDynamicOutput.*)",
+            R"(.*smoke_Auto_BehaviorTests.*InferFullyDynamicNetworkWith(S|G)etTensor.*)",
     };
 }
diff --git a/src/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp b/src/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp
index f106acae24f..7f47d99ab07 100644
--- a/src/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp
+++ b/src/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp
@@ -33,6 +33,9 @@ protected:
         auto mul = std::make_shared<opset8::Multiply>(input4, constant);
         auto input5 = std::make_shared<opset8::Parameter>(type, constGroupConvBackpropShape);
         auto mul2 = std::make_shared<opset8::Multiply>(input5, constantGroupConv);
+        // explicitly set the output name, to avoid global conflict
+        mul2->set_friendly_name("Multiply_0");
+        mul->set_friendly_name("Multiply_1");
         function = std::make_shared<ngraph::Function>(NodeVector{convBprop, conv, groupConvBprop, mul2, mul},
                 ParameterVector{input1, input2, input3, input4, input5});
     }
diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/infer_request_dynamic.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/infer_request_dynamic.hpp
index 4acb0c1f651..5a2987a1d7c 100644
--- a/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/infer_request_dynamic.hpp
+++ b/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/infer_request_dynamic.hpp
@@ -57,6 +57,7 @@ protected:
     std::map<std::string, std::string> configuration;
     std::vector<std::pair<std::vector<size_t>, std::vector<size_t>>> inOutShapes;
 };
+using OVNotSupportRequestDynamicTests = OVInferRequestDynamicTests;
 }  // namespace behavior
 }  // namespace test
 }  // namespace ov
diff --git a/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp b/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp
index c9f0d6d2d38..346d37a0ec7 100644
--- a/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp
+++ b/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp
@@ -359,6 +359,20 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetworkWithSetTensor2times) {
     ASSERT_EQ(tensor.get_shape(), refOutShape2);
 }
 
+TEST_P(OVNotSupportRequestDynamicTests, InferDynamicNotSupported) {
+    const std::string tensor_name = "input_tensor";
+    const ov::Shape refShape = inOutShapes[0].first;
+    const ov::Shape refShape2 = inOutShapes[1].first;
+    const ov::Shape refOutShape = inOutShapes[0].second;
+    const ov::Shape refOutShape2 = inOutShapes[1].second;
+    std::map<std::string, ov::PartialShape> shapes;
+    shapes[tensor_name] = {ov::Dimension::dynamic(), 4, 20, 20};
+    OV_ASSERT_NO_THROW(function->reshape(shapes));
+    const std::string outputName = function->outputs().back().get_any_name();
+    // Load ov::Function to target plugins
+    ov::runtime::CompiledModel execNet;
+    ASSERT_THROW((execNet = ie->compile_model(function, targetDevice, configuration)), ov::Exception);
+}
 }  // namespace behavior
 }  // namespace test
 }  // namespace ov

From d4fdbba7d8dde679dc3f23857d87222d8feec809 Mon Sep 17 00:00:00 2001
From: Elizaveta Lobanova <elizaveta.lobanova@intel.com>
Date: Mon, 20 Dec 2021 18:12:56 +0300
Subject: [PATCH 051/160] [GNA] Fixed a bug with channel multiplier for int8
 weights quantization (#9234)

---
 .../convert_matmul_to_fc_or_gemm.cpp          |  18 ++-
 .../intel_gna/frontend/quantization.cpp       |   3 +
 .../perchannel_quant_test.cpp                 | 140 ++++++++++++++++++
 3 files changed, 157 insertions(+), 4 deletions(-)
 create mode 100644 src/tests/functional/plugin/gna/scale_factors_tests/perchannel_quant_test.cpp

diff --git a/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp
index a75b1c9d1ff..2063eebfc9c 100644
--- a/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp
+++ b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp
@@ -111,9 +111,9 @@ ngraph::pass::ConvertMatMulToFC::ConvertMatMulToFC() {
         // Check that if second inputs is Constant operation and it's shape without ones dimensions has length <= 2
         // we replace MatMul with FullyConnected operation.
         // Otherwise we replace MatMul with Gemm.
-        if ((std::dynamic_pointer_cast<opset1::Constant>    (fc_input_b.get_node_shared_ptr())  ||
-             std::dynamic_pointer_cast<opset1::FakeQuantize>(fc_input_b.get_node_shared_ptr())) &&
-            std::count_if(shape_b.begin(), shape_b.end(), [](size_t x) {
+        auto fq_after_const = std::dynamic_pointer_cast<opset1::FakeQuantize>(fc_input_b.get_node_shared_ptr());
+        if ((std::dynamic_pointer_cast<opset1::Constant>    (fc_input_b.get_node_shared_ptr())  || fq_after_const) &&
+             std::count_if(shape_b.begin(), shape_b.end(), [](size_t x) {
                 return x != 1;
             }) <= 2) {
             Shape shape_a_aligned, shape_b_aligned;
@@ -131,8 +131,18 @@ ngraph::pass::ConvertMatMulToFC::ConvertMatMulToFC() {
 
             // Weights normalization
             if (!matmul->get_transpose_b()) {
-                fc_input_b = create_transpose(fc_input_b, matmul->get_friendly_name() + "/transpose_b");
+                Output<ov::Node> constant = fc_input_b;
+                // transpose the constant itself, not FQ output, to allow constant folding to apply this transpose
+                if (fq_after_const) {
+                    constant = fc_input_b.get_node_shared_ptr()->input_value(0);
+                }
+                fc_input_b = create_transpose(constant, matmul->get_friendly_name() + "/transpose_b");
                 new_ops.push_back(fc_input_b.get_node_shared_ptr());
+                if (fq_after_const) {
+                    fc_input_b = fq_after_const->clone_with_new_inputs(OutputVector{fc_input_b, fq_after_const->input_value(1),
+                        fq_after_const->input_value(2), fq_after_const->input_value(3), fq_after_const->input_value(4)});
+                    new_ops.push_back(fc_input_b.get_node_shared_ptr());
+                }
             }
 
             if (shape_b.size() != 2) {
diff --git a/src/plugins/intel_gna/frontend/quantization.cpp b/src/plugins/intel_gna/frontend/quantization.cpp
index f9a4b9018c8..f7209d8b532 100644
--- a/src/plugins/intel_gna/frontend/quantization.cpp
+++ b/src/plugins/intel_gna/frontend/quantization.cpp
@@ -258,6 +258,9 @@ void QuantizationCallback<int8_t, gna_compound_bias_t>::runFakeQuantize() const
             channel_multiplier = scaled_row_max / static_cast<float>(MAX_VAL_1B_WEIGHT);
         }
 
+        // channel multiplier shouldn't be 0
+        channel_multiplier = channel_multiplier == 0 ? 1 : channel_multiplier;
+
         ptr_int_biases[i].multiplier = static_cast<uint8_t> (channel_multiplier + 0.5f);
         if (channel_multiplier > MAX_OUT_MULTIPLIER) {
             THROW_GNA_EXCEPTION << "invalid channel multiplier: " << channel_multiplier;
diff --git a/src/tests/functional/plugin/gna/scale_factors_tests/perchannel_quant_test.cpp b/src/tests/functional/plugin/gna/scale_factors_tests/perchannel_quant_test.cpp
new file mode 100644
index 00000000000..4403b3317d5
--- /dev/null
+++ b/src/tests/functional/plugin/gna/scale_factors_tests/perchannel_quant_test.cpp
@@ -0,0 +1,140 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+#include <memory>
+#include <tuple>
+#include <string>
+
+#include <ie_core.hpp>
+
+#include "common_test_utils/common_utils.hpp"
+#include "functional_test_utils/plugin_cache.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "functional_test_utils/blob_utils.hpp"
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "ngraph_functions/builders.hpp"
+
+#include "ngraph_functions/pass/convert_prc.hpp"
+
+typedef std::tuple<
+    InferenceEngine::Precision,         // Network Precision
+    std::string,                        // Target Device
+    std::map<std::string, std::string>, // Configuration
+    std::pair<float, float>,            // Weights values
+    std::vector<size_t>                 // Input shapes
+> matmulParams;
+
+namespace LayerTestsDefinitions {
+
+class PerchannelQuantTest : public testing::WithParamInterface<matmulParams>,
+    public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<matmulParams> obj) {
+        InferenceEngine::Precision netPrecision;
+        std::string targetDevice;
+        std::map<std::string, std::string> configuration;
+        std::pair<float, float> weightsValues;
+        std::vector<size_t> inputShape;
+        std::tie(netPrecision, targetDevice, configuration, weightsValues, inputShape) = obj.param;
+
+        std::ostringstream result;
+        result << "netPRC=" << netPrecision.name() << "_";
+        result << "targetDevice=" << targetDevice << "_";
+        for (auto const& configItem : configuration) {
+            result << "_configItem=" << configItem.first << "_" << configItem.second;
+        }
+        result << "_range=(" << weightsValues.first << ", " << weightsValues.second << ")";
+        result << "_IS=(" << CommonTestUtils::vec2str(inputShape) << ")";
+
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        InferenceEngine::Precision netPrecision;
+        std::pair<float, float> weightsValues;
+        std::vector<size_t> inputShape;
+        std::tie(netPrecision, targetDevice, configuration, weightsValues, inputShape) = this->GetParam();
+        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+
+        const ngraph::Shape constShape = {inputShape.back(), inputShape.back()};
+        auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+
+        std::vector<float> weights;
+        std::vector<float> weightsMin, weightsMax;
+        for (int i = 0; i < constShape.front(); ++i) {
+            // multiplier to increase weights ranges difference between different channels
+            float mul = (i % 2 ? 1.0 : 0.001);
+            float rowMin = weightsValues.first * mul;
+            float rowMax = weightsValues.second * mul;
+            auto rowWeights = CommonTestUtils::generate_float_numbers(constShape.back(), rowMin, rowMax);
+            weights.insert(std::end(weights), std::begin(rowWeights), std::end(rowWeights));
+            weightsMin.push_back(rowMin);
+            weightsMax.push_back(rowMax);
+        }
+
+        auto constant = ngraph::builder::makeConstant<float>(ngPrc, constShape, weights);
+        auto wLowNode = ngraph::builder::makeConstant<float>(ngPrc, {constShape.front()}, { weightsMin });
+        auto wHighNode = ngraph::builder::makeConstant<float>(ngPrc, {constShape.front()}, { weightsMax });
+        auto wFq = std::make_shared<ngraph::opset8::FakeQuantize>(constant, wLowNode, wHighNode, wLowNode, wHighNode,
+            std::numeric_limits<uint8_t>::max() - 1);
+        auto matmul = std::make_shared<ngraph::opset8::MatMul>(params[0], wFq, false, true);
+
+        ngraph::ResultVector results{std::make_shared<ngraph::opset8::Result>(matmul)};
+        function = std::make_shared<ngraph::Function>(results, params, "PerchannelQuantTest");
+    }
+};
+
+TEST_P(PerchannelQuantTest, CompareWithRefImpl) {
+    LoadNetwork();
+    GenerateInputs();
+    Infer();
+    auto results = GetOutputs();
+    size_t size = results.front()->size();
+    auto memory = InferenceEngine::as<InferenceEngine::MemoryBlob>(results.front());
+    IE_ASSERT(memory);
+    const auto lockedMemory = memory->wmap();
+    const auto actualBuffer = lockedMemory.as<const float*>();
+
+    // check that outputs haven't been zero out by a channel multilplier
+    for (size_t i = 0; i < size; ++i) {
+        if (actualBuffer[i] == 0.0) {
+            IE_THROW() << "Unexpected 0 output value";
+        }
+    }
+};
+
+const std::vector<InferenceEngine::Precision> netPrecisions = {
+    InferenceEngine::Precision::FP32,
+    InferenceEngine::Precision::FP16
+};
+
+const std::vector<std::map<std::string, std::string>> configs = {
+    {
+        {"GNA_DEVICE_MODE", "GNA_SW_EXACT"}
+    }
+};
+
+const std::vector<std::pair<float, float>> weightsValues = {
+    {-0.1, 0.1},
+    {-1.0, 1.0},
+    {-10.0, 10.0}
+};
+
+const std::vector<std::vector<size_t>> inputShapes = {
+    {1, 128},
+    {1, 38},
+    {1, 8}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_base, PerchannelQuantTest,
+    ::testing::Combine(
+        ::testing::ValuesIn(netPrecisions),
+        ::testing::Values(CommonTestUtils::DEVICE_GNA),
+        ::testing::ValuesIn(configs),
+        ::testing::ValuesIn(weightsValues),
+        ::testing::ValuesIn(inputShapes)),
+    PerchannelQuantTest::getTestCaseName);
+} // namespace LayerTestsDefinitions
\ No newline at end of file

From 808d6a3dc149f37499cec69c42d8b5d9d4ebee4a Mon Sep 17 00:00:00 2001
From: "Wang, Yang" <yang4.wang@intel.com>
Date: Tue, 21 Dec 2021 00:48:54 +0800
Subject: [PATCH 052/160] Add default device value for ie.LoadNetwork (#8956)

* [Auto Plugin] Change LoadNetwork api with default deviceName "AUTO" and add corrresponding test case.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Remove the changes for ImportNetwork API.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Update LoadNetwork API.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Update compile_network API for OV2.0.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Modify the visable scope of global value 'DEFAULT_DEVICE_NAME'.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Update the test case for compile_model wit the default device.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Replace the pointer with the string object as the default name of device.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* update.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Add a new test case for compile_model to defatul device.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Add a new test case for compile_model loading network to defatul device.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Add a new test case for compile_model loading network to defatul device.

Signed-off-by: ywang2 <yang4.wang@intel.com>

* Update tese case for LoadNetwork and compile_model.

Signed-off-by: ywang2 <yang4.wang@intel.com>
---
 src/inference/include/ie/ie_core.hpp          | 29 +++++++++++++++++++
 .../include/openvino/runtime/core.hpp         | 27 +++++++++++++++++
 src/inference/src/ie_core.cpp                 | 27 ++++++++++++++++-
 .../exec_network_base.hpp                     | 19 +++++++-----
 .../behavior/plugin/core_integration.hpp      |  8 +++++
 5 files changed, 101 insertions(+), 9 deletions(-)

diff --git a/src/inference/include/ie/ie_core.hpp b/src/inference/include/ie/ie_core.hpp
index 0ed7c91ddd7..3bcd321e9cc 100644
--- a/src/inference/include/ie/ie_core.hpp
+++ b/src/inference/include/ie/ie_core.hpp
@@ -94,6 +94,20 @@ public:
      */
     CNNNetwork ReadNetwork(const std::string& model, const Blob::CPtr& weights) const;
 
+    /**
+     * @brief Creates an executable network from a network object and uses AUTO plugin as the default device to load
+     * executable network.
+     *
+     * Users can create as many networks as they need and use
+     *        them simultaneously (up to the limitation of the hardware resources)
+     *
+     * @param network CNNNetwork object acquired from Core::ReadNetwork
+     * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load
+     * operation
+     * @return An executable network reference
+     */
+    ExecutableNetwork LoadNetwork(const CNNNetwork& network, const std::map<std::string, std::string>& config = {});
+
     /**
      * @brief Creates an executable network from a network object.
      *
@@ -110,6 +124,21 @@ public:
                                   const std::string& deviceName,
                                   const std::map<std::string, std::string>& config = {});
 
+    /**
+     * @brief Reads model and creates an executable network from IR or ONNX file and uses AUTO plugin as the default
+     * device to load executable network.
+     *
+     * This can be more efficient than using ReadNetwork + LoadNetwork(CNNNetwork) flow
+     *        especially for cases when caching is enabled and cached model is available
+     *
+     * @param modelPath path to model
+     * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load
+     * operation/
+     *
+     * @return An executable network reference
+     */
+    ExecutableNetwork LoadNetwork(const std::string& modelPath, const std::map<std::string, std::string>& config = {});
+
     /**
      * @brief Reads model and creates an executable network from IR or ONNX file
      *
diff --git a/src/inference/include/openvino/runtime/core.hpp b/src/inference/include/openvino/runtime/core.hpp
index e3629cfa3a8..df0d78e4400 100644
--- a/src/inference/include/openvino/runtime/core.hpp
+++ b/src/inference/include/openvino/runtime/core.hpp
@@ -101,6 +101,19 @@ public:
      */
     std::shared_ptr<ov::Model> read_model(const std::string& model, const Tensor& weights) const;
 
+    /**
+     * @brief Creates an executable network from a model object and loads model to default device.
+     *
+     * Users can create as many executable networks as they need and use
+     * them simultaneously (up to the limitation of the hardware resources)
+     *
+     * @param model Model object acquired from Core::read_model
+     * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load
+     * operation
+     * @return An executable network reference
+     */
+    CompiledModel compile_model(const std::shared_ptr<const ov::Model>& model, const ConfigMap& config = {});
+
     /**
      * @brief Creates an executable network from a model object.
      *
@@ -117,6 +130,20 @@ public:
                                 const std::string& device_name,
                                 const ConfigMap& config = {});
 
+    /**
+     * @brief Reads model and creates an executable network from IR or ONNX file and load model to default device.
+     *
+     * This can be more efficient than using read_model + compile_model(Model) flow
+     * especially for cases when caching is enabled and cached model is available
+     *
+     * @param model_path path to model
+     * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load
+     * operation/
+     *
+     * @return An executable network reference
+     */
+    CompiledModel compile_model(const std::string& model_path, const ConfigMap& config = {});
+
     /**
      * @brief Reads model and creates an executable network from IR or ONNX file
      *
diff --git a/src/inference/src/ie_core.cpp b/src/inference/src/ie_core.cpp
index fdddf89d344..ed56b123dac 100644
--- a/src/inference/src/ie_core.cpp
+++ b/src/inference/src/ie_core.cpp
@@ -49,6 +49,10 @@ using namespace InferenceEngine::PluginConfigParams;
 using namespace std::placeholders;
 
 namespace ov {
+
+// Specify the default device when no device name is provided.
+const std::string DEFAULT_DEVICE_NAME = "DEFAULT_DEVICE";
+
 namespace runtime {
 
 namespace {
@@ -732,9 +736,14 @@ public:
 
         std::lock_guard<std::mutex> lock(pluginsMutex);
         auto deviceName = pluginName;
+        if (deviceName == ov::DEFAULT_DEVICE_NAME)
+            deviceName = "AUTO";
         auto it = pluginRegistry.find(deviceName);
         if (it == pluginRegistry.end()) {
-            IE_THROW() << "Device with \"" << deviceName << "\" name is not registered in the InferenceEngine";
+            if (pluginName == ov::DEFAULT_DEVICE_NAME)
+                IE_THROW() << "No device is provided, so AUTO device is used by default, which failed loading.";
+            else
+                IE_THROW() << "Device with \"" << deviceName << "\" name is not registered in the InferenceEngine";
         }
 
         // Plugin is in registry, but not created, let's create
@@ -1162,6 +1171,10 @@ CNNNetwork Core::ReadNetwork(const std::string& model, const Blob::CPtr& weights
     return _impl->ReadNetwork(model, weights);
 }
 
+ExecutableNetwork Core::LoadNetwork(const CNNNetwork& network, const std::map<std::string, std::string>& config) {
+    return LoadNetwork(network, ov::DEFAULT_DEVICE_NAME, config);
+}
+
 ExecutableNetwork Core::LoadNetwork(const CNNNetwork& network,
                                     const std::string& deviceName,
                                     const std::map<std::string, std::string>& config) {
@@ -1183,6 +1196,10 @@ ExecutableNetwork Core::LoadNetwork(const std::string& modelPath,
     return {exec._ptr, exec._so};
 }
 
+ExecutableNetwork Core::LoadNetwork(const std::string& modelPath, const std::map<std::string, std::string>& config) {
+    return LoadNetwork(modelPath, ov::DEFAULT_DEVICE_NAME, config);
+}
+
 RemoteContext::Ptr Core::CreateContext(const std::string& deviceName, const ParamMap& params) {
     if (deviceName.find("HETERO") == 0) {
         IE_THROW() << "HETERO device does not support remote context";
@@ -1441,6 +1458,10 @@ ie::CNNNetwork toCNN(const std::shared_ptr<const ngraph::Function>& model) {
 
 }  // namespace
 
+CompiledModel Core::compile_model(const std::shared_ptr<const ov::Model>& model, const ConfigMap& config) {
+    return compile_model(model, ov::DEFAULT_DEVICE_NAME, config);
+}
+
 CompiledModel Core::compile_model(const std::shared_ptr<const ov::Model>& model,
                                   const std::string& deviceName,
                                   const ConfigMap& config) {
@@ -1450,6 +1471,10 @@ CompiledModel Core::compile_model(const std::shared_ptr<const ov::Model>& model,
     });
 }
 
+CompiledModel Core::compile_model(const std::string& modelPath, const ConfigMap& config) {
+    return compile_model(modelPath, ov::DEFAULT_DEVICE_NAME, config);
+}
+
 CompiledModel Core::compile_model(const std::string& modelPath,
                                   const std::string& deviceName,
                                   const ConfigMap& config) {
diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_network_base.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_network_base.hpp
index 611325ceb6b..85b5aeb182d 100644
--- a/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_network_base.hpp
+++ b/src/tests/functional/plugin/shared/include/behavior/ov_executable_network/exec_network_base.hpp
@@ -61,12 +61,9 @@ public:
             // Remote tensor
             data2 = nullptr;
         }
-        return t1.get_element_type() == t2.get_element_type() &&
-            t1.get_shape() == t2.get_shape() &&
-            t1.get_byte_size() == t2.get_byte_size() &&
-            t1.get_size() == t2.get_size() &&
-            t1.get_strides() == t2.get_strides() &&
-            data1 == data2;
+        return t1.get_element_type() == t2.get_element_type() && t1.get_shape() == t2.get_shape() &&
+               t1.get_byte_size() == t2.get_byte_size() && t1.get_size() == t2.get_size() &&
+               t1.get_strides() == t2.get_strides() && data1 == data2;
     }
 
 protected:
@@ -80,6 +77,12 @@ TEST_P(OVExecutableNetworkBaseTest, canLoadCorrectNetworkToGetExecutable) {
     EXPECT_NO_THROW(auto execNet = core->compile_model(function, targetDevice, configuration));
 }
 
+TEST(OVExecutableNetworkBaseTest, smoke_LoadNetworkToDefaultDeviceNoThrow) {
+    std::shared_ptr<ov::runtime::Core> core = utils::PluginCache::get().core();
+    std::shared_ptr<ov::Model> function = ngraph::builder::subgraph::makeConvPoolRelu();
+    EXPECT_NO_THROW(auto execNet = core->compile_model(function));
+}
+
 TEST_P(OVExecutableNetworkBaseTest, canLoadCorrectNetworkToGetExecutableWithIncorrectConfig) {
     std::map<std::string, std::string> incorrectConfig = {{"abc", "def"}};
     EXPECT_ANY_THROW(auto execNet = core->compile_model(function, targetDevice, incorrectConfig));
@@ -542,8 +545,8 @@ TEST_P(OVExecutableNetworkBaseTest, getOutputsFromSplitFunctionWithSeveralOutput
         result1->set_friendly_name("result1");
         auto result2 = std::make_shared<ov::opset8::Result>(split->output(1));
         result2->set_friendly_name("result2");
-        function = std::make_shared<ngraph::Function>(ngraph::ResultVector{result1, result2},
-                                                      ngraph::ParameterVector{param1});
+        function =
+            std::make_shared<ngraph::Function>(ngraph::ResultVector{result1, result2}, ngraph::ParameterVector{param1});
         function->set_friendly_name("SingleSplit");
     }
     execNet = core->compile_model(function, targetDevice, configuration);
diff --git a/src/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp b/src/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp
index 42bab2d5299..69deeb32e9c 100644
--- a/src/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp
+++ b/src/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp
@@ -886,6 +886,14 @@ TEST_P(IEClassQueryNetworkTest, QueryNetworkHETEROWithBigDeviceIDThrows) {
 // LoadNetwork
 //
 
+TEST(IEClassBasicTest, smoke_LoadNetworkToDefaultDeviceNoThrow) {
+  InferenceEngine::CNNNetwork actualCnnNetwork;
+  std::shared_ptr<ngraph::Function> actualNetwork = ngraph::builder::subgraph::makeSplitConvConcat();
+  ASSERT_NO_THROW(actualCnnNetwork = InferenceEngine::CNNNetwork(actualNetwork));
+  InferenceEngine::Core  ie = BehaviorTestsUtils::createIECoreWithTemplate();
+  ASSERT_NO_THROW(ie.LoadNetwork(actualCnnNetwork));
+}
+
 TEST_P(IEClassNetworkTestP, LoadNetworkActualNoThrow) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
     InferenceEngine::Core  ie = BehaviorTestsUtils::createIECoreWithTemplate();

From 46c9755b6e73bd680b587af06b5c10740033704d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomasz=20Do=C5=82bniak?= <tomasz.dolbniak@intel.com>
Date: Mon, 20 Dec 2021 18:46:15 +0100
Subject: [PATCH 053/160] ONNX FE tests: fix after a conflicting merge of 2 PRs
 (#9320)

* Fix after a conflicting merge of 2 PRs

* Even more alignment
---
 .../test_frontend_onnx_editor.py              | 10 +--
 .../test_frontend/test_frontendmanager.py     | 88 +++++++++----------
 .../tools/mo/moc_frontend/extractor.py        |  4 +-
 3 files changed, 51 insertions(+), 51 deletions(-)

diff --git a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
index 6eff1d885f7..7463a0bd0c9 100644
--- a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
+++ b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
@@ -1214,9 +1214,9 @@ def test_set_input_partial_shape_using_input_edge():
     model = fe.load("input_model.onnx")
 
     add_operator = model.get_place_by_operation_name("onnx_add_op")
-    add_input_edge = add_operator.get_input_port(inputPortIndex=0)
+    add_input_edge = add_operator.get_input_port(input_port_index=0)
     model.set_partial_shape(add_input_edge, PartialShape([10, 10]))
-    add_input_edge = add_operator.get_input_port(inputPortIndex=1)
+    add_input_edge = add_operator.get_input_port(input_port_index=1)
     model.set_partial_shape(add_input_edge, PartialShape([1]))
 
     ov_model = fe.convert(model)
@@ -1232,7 +1232,7 @@ def test_get_partial_shape_using_input_edge():
     model = fe.load("input_model.onnx")
 
     add_operator = model.get_place_by_operation_name("onnx_add_op")
-    add_input_edge = add_operator.get_input_port(inputPortIndex=0)
+    add_input_edge = add_operator.get_input_port(input_port_index=0)
 
     pshape = model.get_partial_shape(add_input_edge)
     assert pshape == PartialShape([2, 2])
@@ -1244,10 +1244,10 @@ def test_get_partial_shape_using_output_edge():
     model = fe.load("input_model.onnx")
 
     add_operator = model.get_place_by_operation_name("onnx_add_op")
-    add_output_edge = add_operator.get_output_port(outputPortIndex=0)
+    add_output_edge = add_operator.get_output_port(output_port_index=0)
 
     assert model.get_partial_shape(add_output_edge) == PartialShape([2, 2])
 
     split_operator = model.get_place_by_tensor_name("out1").get_producing_operation()
-    out2_edge = split_operator.get_output_port(outputPortIndex=1)
+    out2_edge = split_operator.get_output_port(output_port_index=1)
     assert model.get_partial_shape(out2_edge) == PartialShape([1, 2])
diff --git a/src/bindings/python/tests/test_frontend/test_frontendmanager.py b/src/bindings/python/tests/test_frontend/test_frontendmanager.py
index 4d8994514d2..6a5dee350d0 100644
--- a/src/bindings/python/tests/test_frontend/test_frontendmanager.py
+++ b/src/bindings/python/tests/test_frontend/test_frontendmanager.py
@@ -141,7 +141,7 @@ def test_model_get_place_by_tensor_name():
     model = init_model()
     for i in range(1, 10):
         name = str(i)
-        model.get_place_by_tensor_name(tensorName=name)
+        model.get_place_by_tensor_name(tensor_name=name)
         stat = get_mdl_stat(model)
         assert stat.get_place_by_tensor_name == i
         assert stat.lastArgString == name
@@ -152,7 +152,7 @@ def test_model_get_place_by_operation_name():
     model = init_model()
     for i in range(1, 10):
         name = str(i)
-        model.get_place_by_operation_name(operationName=name)
+        model.get_place_by_operation_name(operation_name=name)
         stat = get_mdl_stat(model)
         assert stat.get_place_by_operation_name == i
         assert stat.lastArgString == name
@@ -163,7 +163,7 @@ def test_model_get_place_by_operation_name_and_input_port():
     model = init_model()
     for i in range(1, 10):
         name = str(i)
-        model.get_place_by_operation_name_and_input_port(operationName=name, inputPortIndex=i * 2)
+        model.get_place_by_operation_name_and_input_port(operation_name=name, input_port_index=i * 2)
         stat = get_mdl_stat(model)
         assert stat.get_place_by_operation_and_input_port == i
         assert stat.lastArgString == name
@@ -175,7 +175,7 @@ def test_model_get_place_by_operation_name_and_output_port():
     model = init_model()
     for i in range(1, 10):
         name = str(i)
-        model.get_place_by_operation_name_and_output_port(operationName=name, outputPortIndex=i * 2)
+        model.get_place_by_operation_name_and_output_port(operation_name=name, output_port_index=i * 2)
         stat = get_mdl_stat(model)
         assert stat.get_place_by_operation_and_output_port == i
         assert stat.lastArgString == name
@@ -185,8 +185,8 @@ def test_model_get_place_by_operation_name_and_output_port():
 @mock_needed
 def test_model_set_name_for_tensor():
     model = init_model()
-    place = model.get_place_by_tensor_name(tensorName="")
-    model.set_name_for_tensor(tensor=place, newName="1234")
+    place = model.get_place_by_tensor_name(tensor_name="")
+    model.set_name_for_tensor(tensor=place, new_name="1234")
     stat = get_mdl_stat(model)
     assert stat.set_name_for_tensor == 1
     assert stat.lastArgString == "1234"
@@ -196,8 +196,8 @@ def test_model_set_name_for_tensor():
 @mock_needed
 def test_model_add_name_for_tensor():
     model = init_model()
-    place = model.get_place_by_tensor_name(tensorName="")
-    model.add_name_for_tensor(tensor=place, newName="1234")
+    place = model.get_place_by_tensor_name(tensor_name="")
+    model.add_name_for_tensor(tensor=place, new_name="1234")
     stat = get_mdl_stat(model)
     assert stat.add_name_for_tensor == 1
     assert stat.lastArgString == "1234"
@@ -207,8 +207,8 @@ def test_model_add_name_for_tensor():
 @mock_needed
 def test_model_set_name_for_operation():
     model = init_model()
-    place = model.get_place_by_operation_name(operationName="")
-    model.set_name_for_operation(operation=place, newName="1111")
+    place = model.get_place_by_operation_name(operation_name="")
+    model.set_name_for_operation(operation=place, new_name="1111")
     stat = get_mdl_stat(model)
     assert stat.set_name_for_operation == 1
     assert stat.lastArgString == "1111"
@@ -236,8 +236,8 @@ def test_model_free_name_for_operation():
 @mock_needed
 def test_model_set_name_for_dimension():
     model = init_model()
-    place = model.get_place_by_operation_name(operationName="")
-    model.set_name_for_dimension(place=place, dimIndex=123, dimName="4444")
+    place = model.get_place_by_operation_name(operation_name="")
+    model.set_name_for_dimension(place=place, dim_index=123, dim_name="4444")
     stat = get_mdl_stat(model)
     assert stat.set_name_for_dimension == 1
     assert stat.lastArgString == "4444"
@@ -249,7 +249,7 @@ def test_model_set_name_for_dimension():
 def test_model_cut_and_add_new_input():
     model = init_model()
     place = model.get_place_by_operation_name("")
-    model.cut_and_add_new_input(place=place, newName="5555")
+    model.cut_and_add_new_input(place=place, new_name="5555")
     stat = get_mdl_stat(model)
     assert stat.cut_and_add_new_input == 1
     assert stat.lastArgString == "5555"
@@ -265,7 +265,7 @@ def test_model_cut_and_add_new_input():
 def test_model_cut_and_add_new_output():
     model = init_model()
     place = model.get_place_by_operation_name("")
-    model.cut_and_add_new_output(place=place, newName="5555")
+    model.cut_and_add_new_output(place=place, new_name="5555")
     stat = get_mdl_stat(model)
     assert stat.cut_and_add_new_output == 1
     assert stat.lastArgString == "5555"
@@ -301,7 +301,7 @@ def test_model_remove_output():
 @mock_needed
 def test_model_set_partial_shape():
     model = init_model()
-    place = model.get_place_by_tensor_name(tensorName="")
+    place = model.get_place_by_tensor_name(tensor_name="")
     test_shape = PartialShape([1, 2, 3, 4])
     model.set_partial_shape(place=place, shape=test_shape)
     stat = get_mdl_stat(model)
@@ -313,7 +313,7 @@ def test_model_set_partial_shape():
 @mock_needed
 def test_model_get_partial_shape():
     model = init_model()
-    place = model.get_place_by_tensor_name(tensorName="")
+    place = model.get_place_by_tensor_name(tensor_name="")
     shape = model.get_partial_shape(place=place)
     assert shape is not None
     stat = get_mdl_stat(model)
@@ -324,8 +324,8 @@ def test_model_get_partial_shape():
 @mock_needed
 def test_model_override_all_inputs():
     model = init_model()
-    place1 = model.get_place_by_tensor_name(tensorName="p1")
-    place2 = model.get_place_by_tensor_name(tensorName="p2")
+    place1 = model.get_place_by_tensor_name(tensor_name="p1")
+    place2 = model.get_place_by_tensor_name(tensor_name="p2")
     model.override_all_inputs(inputs=[place1, place2])
     stat = get_mdl_stat(model)
     assert stat.override_all_inputs == 1
@@ -337,8 +337,8 @@ def test_model_override_all_inputs():
 @mock_needed
 def test_model_override_all_outputs():
     model = init_model()
-    place1 = model.get_place_by_tensor_name(tensorName="p1")
-    place2 = model.get_place_by_tensor_name(tensorName="p2")
+    place1 = model.get_place_by_tensor_name(tensor_name="p1")
+    place2 = model.get_place_by_tensor_name(tensor_name="p2")
     model.override_all_outputs(outputs=[place1, place2])
     stat = get_mdl_stat(model)
     assert stat.override_all_outputs == 1
@@ -350,10 +350,10 @@ def test_model_override_all_outputs():
 @mock_needed
 def test_model_extract_subgraph():
     model = init_model()
-    place1 = model.get_place_by_tensor_name(tensorName="p1")
-    place2 = model.get_place_by_tensor_name(tensorName="p2")
-    place3 = model.get_place_by_tensor_name(tensorName="p3")
-    place4 = model.get_place_by_tensor_name(tensorName="p4")
+    place1 = model.get_place_by_tensor_name(tensor_name="p1")
+    place2 = model.get_place_by_tensor_name(tensor_name="p2")
+    place3 = model.get_place_by_tensor_name(tensor_name="p3")
+    place4 = model.get_place_by_tensor_name(tensor_name="p4")
     model.extract_subgraph(inputs=[place1, place2], outputs=[place3, place4])
     stat = get_mdl_stat(model)
     assert stat.extract_subgraph == 1
@@ -368,7 +368,7 @@ def test_model_extract_subgraph():
 @mock_needed
 def test_model_set_element_type():
     model = init_model()
-    place = model.get_place_by_tensor_name(tensorName="")
+    place = model.get_place_by_tensor_name(tensor_name="")
     model.set_element_type(place=place, type=get_element_type(np.int32))
     stat = get_mdl_stat(model)
     assert stat.set_element_type == 1
@@ -381,7 +381,7 @@ def test_model_set_element_type():
 def init_place():
     fe = fem.load_by_framework(framework="mock_py")
     model = fe.load(path="")
-    place = model.get_place_by_tensor_name(tensorName="")
+    place = model.get_place_by_tensor_name(tensor_name="")
     return model, place
 
 
@@ -432,7 +432,7 @@ def test_place_is_equal_data():
 @mock_needed
 def test_place_get_consuming_operations():
     _, place = init_place()
-    assert place.get_consuming_operations(outputPortIndex=22) is not None
+    assert place.get_consuming_operations(output_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_consuming_operations == 1
     assert stat.lastArgInt == 22
@@ -440,12 +440,12 @@ def test_place_get_consuming_operations():
     stat = get_place_stat(place)
     assert stat.get_consuming_operations == 2
     assert stat.lastArgInt == -1
-    assert place.get_consuming_operations(outputName="2") is not None
+    assert place.get_consuming_operations(output_name="2") is not None
     stat = get_place_stat(place)
     assert stat.get_consuming_operations == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
-    assert place.get_consuming_operations(outputName="3", outputPortIndex=33) is not None
+    assert place.get_consuming_operations(output_name="3", output_port_index=33) is not None
     stat = get_place_stat(place)
     assert stat.get_consuming_operations == 4
     assert stat.lastArgInt == 33
@@ -455,7 +455,7 @@ def test_place_get_consuming_operations():
 @mock_needed
 def test_place_get_target_tensor():
     _, place = init_place()
-    assert place.get_target_tensor(outputPortIndex=22) is not None
+    assert place.get_target_tensor(output_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_target_tensor == 1
     assert stat.lastArgInt == 22
@@ -463,12 +463,12 @@ def test_place_get_target_tensor():
     stat = get_place_stat(place)
     assert stat.get_target_tensor == 2
     assert stat.lastArgInt == -1
-    assert place.get_target_tensor(outputName="2") is not None
+    assert place.get_target_tensor(output_name="2") is not None
     stat = get_place_stat(place)
     assert stat.get_target_tensor == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
-    assert place.get_target_tensor(outputName="3", outputPortIndex=33) is not None
+    assert place.get_target_tensor(output_name="3", output_port_index=33) is not None
     stat = get_place_stat(place)
     assert stat.get_target_tensor == 4
     assert stat.lastArgInt == 33
@@ -478,7 +478,7 @@ def test_place_get_target_tensor():
 @mock_needed
 def test_place_get_producing_operation():
     _, place = init_place()
-    assert place.get_producing_operation(inputPortIndex=22) is not None
+    assert place.get_producing_operation(input_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_producing_operation == 1
     assert stat.lastArgInt == 22
@@ -486,12 +486,12 @@ def test_place_get_producing_operation():
     stat = get_place_stat(place)
     assert stat.get_producing_operation == 2
     assert stat.lastArgInt == -1
-    assert place.get_producing_operation(inputName="2") is not None
+    assert place.get_producing_operation(input_name="2") is not None
     stat = get_place_stat(place)
     assert stat.get_producing_operation == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
-    assert place.get_producing_operation(inputName="3", inputPortIndex=33) is not None
+    assert place.get_producing_operation(input_name="3", input_port_index=33) is not None
     stat = get_place_stat(place)
     assert stat.get_producing_operation == 4
     assert stat.lastArgInt == 33
@@ -513,7 +513,7 @@ def test_place_get_input_port():
     stat = get_place_stat(place)
     assert stat.get_input_port == 1
     assert stat.lastArgInt == -1
-    assert place.get_input_port(inputPortIndex=22) is not None
+    assert place.get_input_port(input_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_input_port == 2
     assert stat.lastArgInt == 22
@@ -522,12 +522,12 @@ def test_place_get_input_port():
 @mock_needed
 def test_place_get_input_port2():
     _, place = init_place()
-    assert place.get_input_port(inputName="abc") is not None
+    assert place.get_input_port(input_name="abc") is not None
     stat = get_place_stat(place)
     assert stat.get_input_port == 1
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "abc"
-    assert place.get_input_port(inputName="abcd", inputPortIndex=22) is not None
+    assert place.get_input_port(input_name="abcd", input_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_input_port == 2
     assert stat.lastArgInt == 22
@@ -541,7 +541,7 @@ def test_place_get_output_port():
     stat = get_place_stat(place)
     assert stat.get_output_port == 1
     assert stat.lastArgInt == -1
-    assert place.get_output_port(outputPortIndex=22) is not None
+    assert place.get_output_port(output_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_output_port == 2
     assert stat.lastArgInt == 22
@@ -550,12 +550,12 @@ def test_place_get_output_port():
 @mock_needed
 def test_place_get_output_port2():
     _, place = init_place()
-    assert place.get_output_port(outputName="abc") is not None
+    assert place.get_output_port(output_name="abc") is not None
     stat = get_place_stat(place)
     assert stat.get_output_port == 1
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "abc"
-    assert place.get_output_port(outputName="abcd", outputPortIndex=22) is not None
+    assert place.get_output_port(output_name="abcd", output_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_output_port == 2
     assert stat.lastArgInt == 22
@@ -577,16 +577,16 @@ def test_place_get_source_tensor():
     stat = get_place_stat(place)
     assert stat.get_source_tensor == 1
     assert stat.lastArgInt == -1
-    assert place.get_source_tensor(inputPortIndex=22) is not None
+    assert place.get_source_tensor(input_port_index=22) is not None
     stat = get_place_stat(place)
     assert stat.get_source_tensor == 2
     assert stat.lastArgInt == 22
-    assert place.get_source_tensor(inputName="2") is not None
+    assert place.get_source_tensor(input_name="2") is not None
     stat = get_place_stat(place)
     assert stat.get_source_tensor == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
-    assert place.get_source_tensor(inputName="3", inputPortIndex=33) is not None
+    assert place.get_source_tensor(input_name="3", input_port_index=33) is not None
     stat = get_place_stat(place)
     assert stat.get_source_tensor == 4
     assert stat.lastArgInt == 33
diff --git a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
index 8f69c123b6d..e24dfa3661b 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
@@ -32,7 +32,7 @@ def decode_name_with_port(input_model: InputModel, node_name: str):
     if match_post:
         node_post = input_model.get_place_by_operation_name(match_post.group(1))
         if node_post:
-            node_post = node_post.get_output_port(outputPortIndex=int(match_post.group(2)))
+            node_post = node_post.get_output_port(output_port_index=int(match_post.group(2)))
             if node_post:
                 found_node_names.append(match_post.group(1))
                 found_nodes.append(node_post)
@@ -42,7 +42,7 @@ def decode_name_with_port(input_model: InputModel, node_name: str):
     if match_pre:
         node_pre = input_model.get_place_by_operation_name(match_pre.group(2))
         if node_pre:
-            node_pre = node_pre.get_input_port(inputPortIndex=int(match_pre.group(1)))
+            node_pre = node_pre.get_input_port(input_port_index=int(match_pre.group(1)))
             if node_pre:
                 found_node_names.append(match_pre.group(2))
                 found_nodes.append(node_pre)

From c10a27f60162a8839ef281f15128656a6b075c9e Mon Sep 17 00:00:00 2001
From: Alexander Zhogov <alexander.zhogov@intel.com>
Date: Mon, 20 Dec 2021 21:04:28 +0300
Subject: [PATCH 054/160] github_org_control: Update to the latest version
 (#9322)

---
 .../__init__.py                               |   0
 .github/github_org_control/check_org.py       | 139 +++++++
 .../check_pr.py                               |   8 +-
 .../config.json                               |  10 +-
 .../configs.py                                |  55 +--
 .../dev_emails-test.txt                       |   0
 .github/github_org_control/github_api.py      | 384 ++++++++++++++++++
 .../ldap_api.py                               | 189 +++++----
 .../github_org_control/requirements-dev.txt   |   1 +
 .github/github_org_control/requirements.txt   |   2 +
 .github/org_control/check_org.py              |  94 -----
 .github/org_control/github_api.py             | 282 -------------
 .github/org_control/requirements-dev.txt      |   1 -
 .github/org_control/requirements.txt          |   2 -
 .github/workflows/check_pr_commits.yml        |   4 +-
 15 files changed, 672 insertions(+), 499 deletions(-)
 rename .github/{org_control => github_org_control}/__init__.py (100%)
 create mode 100644 .github/github_org_control/check_org.py
 rename .github/{org_control => github_org_control}/check_pr.py (98%)
 rename .github/{org_control => github_org_control}/config.json (82%)
 rename .github/{org_control => github_org_control}/configs.py (60%)
 rename .github/{org_control => github_org_control}/dev_emails-test.txt (100%)
 create mode 100644 .github/github_org_control/github_api.py
 rename .github/{org_control => github_org_control}/ldap_api.py (50%)
 create mode 100644 .github/github_org_control/requirements-dev.txt
 create mode 100644 .github/github_org_control/requirements.txt
 delete mode 100644 .github/org_control/check_org.py
 delete mode 100644 .github/org_control/github_api.py
 delete mode 100644 .github/org_control/requirements-dev.txt
 delete mode 100644 .github/org_control/requirements.txt

diff --git a/.github/org_control/__init__.py b/.github/github_org_control/__init__.py
similarity index 100%
rename from .github/org_control/__init__.py
rename to .github/github_org_control/__init__.py
diff --git a/.github/github_org_control/check_org.py b/.github/github_org_control/check_org.py
new file mode 100644
index 00000000000..ab59d05066a
--- /dev/null
+++ b/.github/github_org_control/check_org.py
@@ -0,0 +1,139 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Check GitHub organization and invite members
+"""
+
+# pylint: disable=fixme,no-member,too-many-locals
+
+import sys
+from pathlib import Path
+from argparse import ArgumentParser
+
+sys.path.append(str(Path(__file__).resolve().parents[1]))
+from github_org_control.configs import Config
+from github_org_control.github_api import GithubOrgApi, get_dev_emails, print_users
+from github_org_control.ldap_api import LdapApi, print_user_info, InfoLevel
+
+
+def remove_members(gh_api, cfg_emails, org_emails, dev_emails, org_emails_no_in_ldap):
+    """Checks and remove members"""
+    print(
+        f"\n{'=' * 10} Check accounts below and remove from the GitHub organization or "
+        f"configuration {'=' * 10}"
+    )
+
+    cfg_emails_no_in_org = sorted(cfg_emails.difference(org_emails))
+    print(
+        f"\nCfg developer emails - absent in GitHub organization {len(cfg_emails_no_in_org)}:",
+        "; ".join(cfg_emails_no_in_org),
+    )
+
+    non_member_ignored_logins = set(Config().IGNORE_LOGINS).difference(
+        set(gh_api.org_members_by_login.keys())
+    )
+    print(
+        f"\nIgnored logins - absent in GitHub organization {len(non_member_ignored_logins)}:\n",
+        "\n".join(non_member_ignored_logins),
+    )
+
+    org_emails_no_in_dev = sorted(org_emails.difference(dev_emails))
+    print(
+        f"\nOrg member emails - absent in cfg and LDAP PDLs {len(org_emails_no_in_dev)}:",
+        "; ".join(org_emails_no_in_dev),
+    )
+
+    print(
+        f"\nOrg member emails - absent in LDAP at all {len(org_emails_no_in_ldap)}:",
+        "; ".join(sorted(org_emails_no_in_ldap)),
+    )
+
+    print("\nOrg members - no real name:")
+    members_to_fix_name = sorted(gh_api.members_to_fix_name, key=lambda member: member.email)
+    print_users(members_to_fix_name)
+    print(
+        "\nOrg member emails - no real name:",
+        "; ".join([member.email.lower() for member in members_to_fix_name]),
+    )
+
+    print("\nOrg members - no Intel emails:")
+    print_users(gh_api.members_to_remove)
+
+    gh_api.remove_users(org_emails_no_in_ldap | gh_api.members_to_remove)
+
+
+def main():
+    """The main entry point function"""
+    arg_parser = ArgumentParser()
+    arg_parser.add_argument(
+        "--cfg-file",
+        metavar="PATH",
+        default=Config.default_cfg_path,
+        help=f"Path to json configuration file, e.g. {Config.default_cfg_path}",
+    )
+    arg_parser.add_argument("--teams", action="store_true", help="Check GitHub teams")
+    arg_parser.add_argument("--no-ldap", action="store_true", help="Don't use LDAP info")
+    args, unknown_args = arg_parser.parse_known_args()
+
+    Config(args.cfg_file, unknown_args)
+    gh_api = GithubOrgApi()
+
+    if args.teams:
+        gh_api.get_org_teams()
+        return
+
+    cfg_emails = get_dev_emails()
+    print(f"\nCfg developer emails {len(cfg_emails)}:", "; ".join(sorted(cfg_emails)))
+
+    dev_emails = set()
+    dev_emails.update(cfg_emails)
+
+    if not args.no_ldap:
+        ldap_api = LdapApi()
+        ldap_emails = ldap_api.get_user_emails()
+        dev_emails.update(ldap_emails)
+        print(f"\nLDAP developer emails {len(ldap_emails)}:", "; ".join(sorted(ldap_emails)))
+
+        cfg_emails_no_in_ldap = ldap_api.get_absent_emails(cfg_emails)
+        print(
+            f"\nCfg developer emails - absent in LDAP at all {len(cfg_emails_no_in_ldap)}:",
+            "; ".join(sorted(cfg_emails_no_in_ldap)),
+        )
+
+        cfg_ldap_inters = cfg_emails.intersection(ldap_emails)
+        print(
+            f"\nCfg developer emails - present in LDAP developers {len(cfg_ldap_inters)}:",
+            "; ".join(sorted(cfg_ldap_inters)),
+        )
+
+    org_emails = gh_api.get_org_emails()
+    print(f"\nOrg emails {len(org_emails)}:", "; ".join(sorted(org_emails)))
+
+    org_emails_no_in_ldap = set()
+    if not args.no_ldap:
+        org_ldap_diff = org_emails.difference(ldap_emails)
+        print(
+            f"\nOrg member emails - absent in LDAP developers {len(org_ldap_diff)}:",
+            "; ".join(sorted(org_ldap_diff)),
+        )
+
+        for email in org_ldap_diff:
+            user_info = ldap_api.get_user_info_by_email(email)
+            if user_info:
+                print_user_info(user_info, InfoLevel.PDL)
+            else:
+                org_emails_no_in_ldap.add(email)
+
+    org_pendig_invitation_emails = gh_api.get_org_invitation_emails()
+    invite_emails = dev_emails.difference(org_emails).difference(org_pendig_invitation_emails)
+    print(f"\nInvite emails {len(invite_emails)}:", "; ".join(sorted(invite_emails)))
+
+    valid_github_users = gh_api.get_valid_github_users(invite_emails)
+    gh_api.invite_users(valid_github_users)
+
+    remove_members(gh_api, cfg_emails, org_emails, dev_emails, org_emails_no_in_ldap)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/.github/org_control/check_pr.py b/.github/github_org_control/check_pr.py
similarity index 98%
rename from .github/org_control/check_pr.py
rename to .github/github_org_control/check_pr.py
index 0e23251e157..08cf6f4b4db 100644
--- a/.github/org_control/check_pr.py
+++ b/.github/github_org_control/check_pr.py
@@ -10,11 +10,13 @@ Check GitHub PRs and set labels by type and categories, e.g. 'ExternalPR', 'cate
 import re
 import sys
 import datetime
-from argparse import ArgumentParser
 from enum import Enum
+from pathlib import Path
+from argparse import ArgumentParser
 
-import github_api
-from configs import Config
+sys.path.append(str(Path(__file__).resolve().parents[1]))
+from github_org_control import github_api
+from github_org_control.configs import Config
 
 
 class PrType(Enum):
diff --git a/.github/org_control/config.json b/.github/github_org_control/config.json
similarity index 82%
rename from .github/org_control/config.json
rename to .github/github_org_control/config.json
index e013ab30923..66c7de60085 100644
--- a/.github/org_control/config.json
+++ b/.github/github_org_control/config.json
@@ -8,8 +8,10 @@
         "lab-nerval",
         "lab-nerval-onnx-ci",
         "onnx-watchdog-agent",
-        "dependabot"
+        "workbench-ci-bot",
+        "openvino-pot-ci"
     ],
+    "MAX_MEMBERS_TO_REMOVE": 15,
     "EMAILS_FILE_PATH": "dev_emails-test.txt",
     "PROXIES": {
         "HTTP_PROXY": null,
@@ -28,12 +30,16 @@
         "openvino-ie-lpt-maintainers": "category: LP transformations",
         "openvino-ie-multi-maintainers": "category: MULTI",
         "openvino-ie-python-api-maintainers": "category: python api",
+        "openvino-ie-template-maintainers": "category: TEMPLATE",
         "openvino-ie-tests-maintainers": "category: IE Tests",
         "openvino-ie-vpu-maintainers": "category: VPU",
         "openvino-mo-maintainers": "category: MO",
         "openvino-ngraph-maintainers": "category: nGraph",
+        "openvino-scripts-maintainers": "category: build",
         "openvino-tests-maintainers": "category: IE Tests",
         "openvino-tools-maintainers": "category: tools",
-        "openvino-configuration-mgmt": "category: dependency_changes"
+        "openvino-pot-maintainers": "category: POT",
+        "openvino-configuration-mgmt": "category: dependency_changes",
+        "openvino-samples-maintainers": "category: samples"
     }
 }
diff --git a/.github/org_control/configs.py b/.github/github_org_control/configs.py
similarity index 60%
rename from .github/org_control/configs.py
rename to .github/github_org_control/configs.py
index 79c96c90abd..042d2b7304c 100644
--- a/.github/org_control/configs.py
+++ b/.github/github_org_control/configs.py
@@ -15,7 +15,7 @@ from pathlib import Path
 
 
 if sys.hexversion < 0x3060000:
-    raise Exception('Python version must be >= 3.6')
+    raise Exception("Python version must be >= 3.6")
 
 
 class ConfigException(Exception):
@@ -24,9 +24,11 @@ class ConfigException(Exception):
 
 class Config:
     """Configuration wrapper"""
+
     _instance = None
-    properties = None
-    default_cfg_path = Path(__file__).resolve().parent / 'config.json'
+    _properties = None
+
+    default_cfg_path = Path(__file__).resolve().parent / "config.json"
 
     def __new__(cls, *_args, **_kwargs):
         if not Config._instance:
@@ -41,7 +43,7 @@ class Config:
         :param args: List of argparse arguments with patterns: 'name=value' or 'name'
         :type args: list
         """
-        if Config.properties:
+        if Config._properties:
             return
 
         self._file_path = file_path or Config.default_cfg_path
@@ -53,10 +55,10 @@ class Config:
         self._load_cfg()
         self._parse_cli_args()
 
-        Config.properties = {}
+        Config._properties = {}
         for name, value in self._json_cfg.items():
             if hasattr(self, name):
-                raise ConfigException(f'Duplicating prosperity: {name}')
+                raise ConfigException(f"Duplicating prosperity: {name}")
             property_value = self._args.get(name) or os.getenv(name)
             if property_value:
                 # Try to set prosperity_value as Python literal structures, e.g. DRY_RUN=False
@@ -65,49 +67,54 @@ class Config:
                 except Exception:
                     pass
                 if not isinstance(property_value, type(value)):
-                    raise ConfigException(f'Python type of {name} parameter must be {type(value)}')
+                    raise ConfigException(f"Python type of {name} parameter must be {type(value)}")
             else:
                 property_value = value
-            setattr(self, name, property_value)
-            Config.properties[name] = property_value
+            Config._properties[name] = property_value
 
         self.set_proxy()
 
+    def __getattr__(self, attr_name):
+        if attr_name in self._properties:
+            return self._properties.get(attr_name)
+        raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{attr_name}'")
+
     def _load_cfg(self):
         """Load the json configuration file"""
         try:
-            with open(self._file_path) as conf:
+            with open(self._file_path, encoding="utf-8") as conf:
                 self._json_cfg = json.load(conf)
-        except Exception:
-            print('Failed to load configuration from:', self._file_path)
-            raise
+        except Exception as exc:
+            raise ConfigException("Failed to load configuration from:", self._file_path) from exc
 
     def _parse_cli_args(self):
         """Parse argparse arguments with patterns: 'name=value' or 'name'"""
         for cli_arg in self._cli_args:
-            arg = cli_arg.split('=')
+            arg = cli_arg.split("=")
             if arg[0] not in self._json_cfg:
-                raise ConfigException(f'Unsupported argument: {arg}')
-            self._args[arg[0]] = True if len(arg) == 1 else '='.join(arg[1:])
+                raise ConfigException(f"Unsupported argument: {arg}")
+            self._args[arg[0]] = True if len(arg) == 1 else "=".join(arg[1:])
 
-    def get_properties(self):
+    @property
+    def properties(self):
         """Get all properties as Dict"""
-        return self.properties
+        return self._properties
 
     def set_proxy(self):
         """Set proxies"""
-        for proxy_name, url in self.properties['PROXIES'].items():
+        for proxy_name, url in self._properties["PROXIES"].items():
             if url is not None:
-                print(f'Set proxy: {proxy_name}={url}')
+                print(f"Set proxy: {proxy_name}={url}")
                 os.environ[proxy_name] = url
 
 
 def _test():
     """Test and debug"""
-    print('Config.default_cfg_path:', Config.default_cfg_path)
-    cfg = Config(cli_args=['DRY_RUN', 'PROXIES={"NO_PROXY": "localhost"}'])
-    print('Config.properties:', cfg.get_properties())
+    print("Config.default_cfg_path:", Config.default_cfg_path)
+    cfg = Config(cli_args=["DRY_RUN", 'PROXIES={"NO_PROXY": "localhost"}'])
+    print("Config.properties:", cfg.properties)
+    print("cfg.PROXIES:", cfg.PROXIES)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     _test()
diff --git a/.github/org_control/dev_emails-test.txt b/.github/github_org_control/dev_emails-test.txt
similarity index 100%
rename from .github/org_control/dev_emails-test.txt
rename to .github/github_org_control/dev_emails-test.txt
diff --git a/.github/github_org_control/github_api.py b/.github/github_org_control/github_api.py
new file mode 100644
index 00000000000..bc90f19d547
--- /dev/null
+++ b/.github/github_org_control/github_api.py
@@ -0,0 +1,384 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+GitHub API for controlling organization
+"""
+
+# pylint: disable=fixme,no-member
+
+import re
+import sys
+import time
+import typing
+from pathlib import Path
+
+from github import Github, GithubException, RateLimitExceededException, IncompletableObject
+from github.PaginatedList import PaginatedList
+
+sys.path.append(str(Path(__file__).resolve().parents[1]))
+from github_org_control.configs import Config
+
+
+class GithubApiException(Exception):
+    """Base GitHub API exception"""
+
+
+def is_valid_user(user):
+    """Checks that user is valid github.Github object"""
+    try:
+        return user and user.login
+    except IncompletableObject:
+        return False
+
+
+def is_user_ignored(user):
+    """Checks that user should be ignored"""
+    if is_valid_user(user) and user.login.lower() not in Config().IGNORE_LOGINS:
+        return False
+    return True
+
+
+def is_valid_name(name):
+    """Checks that GitHub user's name is valid"""
+    return name and len(name) >= 3 and " " in name
+
+
+def is_intel_email(email):
+    """Checks that email is valid Intel email"""
+    return email and len(email) > 10 and " " not in email and email.lower().endswith("@intel.com")
+
+
+def is_intel_company(company):
+    """Checks that company contains intel"""
+    return company and "intel" in company.lower()
+
+
+def is_valid_intel_user(user):
+    """Checks that user is valid GitHub and Intel user"""
+    try:
+        return is_valid_user(user) and is_valid_name(user.name) and is_intel_email(user.email)
+    except IncompletableObject:
+        return False
+
+
+def print_users(users):
+    """Print list of users in different formats: list, set, PaginatedList"""
+    if isinstance(users, (list, set, PaginatedList)):
+        users_count = users.totalCount if isinstance(users, PaginatedList) else len(users)
+        print(f"GitHub users {users_count} (login - name - company - email - valid):")
+    else:
+        users = [users]
+    for user in users:
+        if not is_valid_user(user):
+            print("WRONG GitHub user: ???")
+            continue
+
+        try:
+            name = user.name
+        except IncompletableObject:
+            name = "???"
+
+        try:
+            company = user.company
+        except IncompletableObject:
+            company = "???"
+
+        try:
+            email = user.email
+        except IncompletableObject:
+            email = "???"
+
+        valid_check = "OK" if is_valid_intel_user(user) else "FIX"
+        if not is_intel_email(email):
+            valid_check += " email"
+        if not is_valid_name(name):
+            valid_check += " name"
+        print(f'{user.login} - "{name}" - "{company}" - {email} - {valid_check}')
+
+
+def get_dev_emails():
+    """
+    Read a file with developer emails. Supported email formats
+    first_name.last_name@intel.com
+    Import from Outlook: Last_name, First_name <first_name.last_name@intel.com>
+    """
+    re_email = re.compile(r".+<(.+)>")
+    emails = set()
+    cfg = Config()
+    with open(cfg.properties["EMAILS_FILE_PATH"]) as file_obj:
+        for line in file_obj:
+            line = line.strip().lower()
+            if not line or line.startswith("#"):
+                continue
+            re_outlook_email = re_email.match(line)
+            if re_outlook_email:
+                line = re_outlook_email.group(1).strip()
+            if not is_intel_email(line):
+                print(f'Wrong email in {cfg.properties["EMAILS_FILE_PATH"]}: {line}')
+                continue
+            emails.add(line)
+    return emails
+
+
+class GithubOrgApi:
+    """Common API for GitHub organization"""
+
+    def __init__(self):
+        self._cfg = Config()
+        self.github = Github(self._cfg.GITHUB_TOKEN)
+        self.github_org = self.github.get_organization(self._cfg.GITHUB_ORGANIZATION)
+        self.repo = self.github.get_repo(f"{self._cfg.GITHUB_ORGANIZATION}/{self._cfg.GITHUB_REPO}")
+        self.github_users_by_email = {}
+        self.org_members_by_login = {}
+        self.members_to_remove = set()
+        self.members_to_fix_name = set()
+
+    def is_org_user(self, user):
+        """Checks that user is a member of GitHub organization"""
+        if is_valid_user(user):
+            # user.get_organization_membership(self.github_org) doesn't work with org members
+            # permissions, GITHUB_TOKEN must be org owner now
+            return self.github_org.has_in_members(user)
+        return False
+
+    def get_org_emails(self):
+        """Gets and prints emails of all GitHub organization members"""
+        org_members = self.github_org.get_members()
+        org_emails = set()
+
+        print(f"\nOrg members {org_members.totalCount} (login - name - company - email - valid):")
+        for org_member in org_members:
+            self.org_members_by_login[org_member.login.lower()] = org_member
+            print_users(org_member)
+            if is_intel_email(org_member.email):
+                email = org_member.email.lower()
+                org_emails.add(email)
+                self.github_users_by_email[email] = org_member
+                if not is_valid_name(org_member.name):
+                    self.members_to_fix_name.add(org_member)
+            elif not is_user_ignored(org_member):
+                self.members_to_remove.add(org_member)
+
+        print("\nOrg members - no Intel emails:")
+        print_users(self.members_to_remove)
+
+        print("\nOrg members - no real name:")
+        print_users(self.members_to_fix_name)
+        print(
+            "\nOrg member emails - no real name:",
+            "; ".join([member.email.lower() for member in self.members_to_fix_name]),
+        )
+
+        return org_emails
+
+    def get_org_invitation_emails(self):
+        """Gets GitHub organization teams prints info"""
+        org_invitations = self.github_org.invitations()
+        org_invitation_emails = set()
+
+        print(
+            f"\nOrg invitations {org_invitations.totalCount} "
+            "(login - name - company - email - valid):"
+        )
+        for org_invitation in org_invitations:
+            print_users(org_invitation)
+            if is_user_ignored(org_invitation):
+                continue
+            if is_intel_email(org_invitation.email):
+                org_invitation_emails.add(org_invitation.email.lower())
+            else:
+                print("Strange org invitation:", org_invitation)
+
+        print(
+            f"\nOrg invitation emails {len(org_invitation_emails)}:",
+            "; ".join(org_invitation_emails),
+        )
+        return org_invitation_emails
+
+    def get_org_teams(self):
+        """Gets GitHub organization teams prints info"""
+        teams = []
+        org_teams = self.github_org.get_teams()
+        print("\nOrg teams count:", org_teams.totalCount)
+        for team in org_teams:
+            teams.append(team.name)
+            print(f"\nTeam: {team.name} - parent: {team.parent}")
+
+            repos = team.get_repos()
+            print("Repos:")
+            for repo in repos:
+                print(f"    {repo.name} -", team.get_repo_permission(repo))
+
+            team_maintainers = team.get_members(role="maintainer")
+            team_maintainer_logins = set()
+            for maintainer in team_maintainers:
+                team_maintainer_logins.add(maintainer.login)
+            team_members = team.get_members(role="member")
+            team_member_logins = set()
+            for member in team_members:
+                team_member_logins.add(member.login)
+            members = team.get_members(role="all")
+            member_emails = []
+            print("Members (role - login - name - company - email - valid):")
+            for user in members:
+                if user.login in team_maintainer_logins:
+                    print("    Maintainer - ", end="")
+                elif user.login in team_member_logins:
+                    print("    Member - ", end="")
+                else:
+                    # It is not possible to check child teams members
+                    print("    ??? - ", end="")
+                print_users(user)
+                if is_intel_email(user.email) and not is_user_ignored(user):
+                    member_emails.append(user.email.lower())
+            print(f"Intel emails {len(member_emails)}:", "; ".join(member_emails))
+        return teams
+
+    def get_github_user_by_email(self, email):
+        """Gets GitHub user by email"""
+        if email in self.github_users_by_email:
+            return self.github_users_by_email.get(email)
+
+        def search_users():
+            paginated_users = self.github.search_users(f"{email} in:email")
+            # Minimize the GitHub Rate Limit
+            users = []
+            for user in paginated_users:
+                users.append(user)
+            if len(users) == 1:
+                return users[0]
+            if len(users) == 0:
+                return None
+            raise GithubApiException(
+                f"ERROR: Found {len(users)} GitHub accounts with the same email {email}"
+            )
+
+        try:
+            user = search_users()
+        except RateLimitExceededException:
+            print("WARNING: RateLimitExceededException")
+            time.sleep(30)
+            user = search_users()
+        self.github_users_by_email[email] = user
+
+        return user
+
+    def get_valid_github_users(self, emails):
+        """Gets valid GitHub users by email and prints status"""
+        valid_users = set()
+        wrong_emails = set()
+        no_account_emails = set()
+        no_account_names = set()
+        print(f"\nGitHub users from {len(emails)} invite emails (email - status):")
+        for email in emails:
+            if not is_intel_email(email):
+                print(f"{email} - Non Intel email")
+                wrong_emails.add(email)
+                continue
+
+            # You can make up to 30 requests per minute; https://developer.github.com/v3/search/
+            time.sleep(2)
+            user = self.get_github_user_by_email(email)
+
+            if not user:
+                print(f"{email} - No valid GitHub account")
+                no_account_emails.add(email)
+                continue
+
+            if user.email and user.email.lower() == email:
+                if is_valid_name(user.name):
+                    print(f"{email} - OK")
+                    valid_users.add(user)
+                else:
+                    print(f"{email} - No valid name in GitHub account: ", end="")
+                    print_users(user)
+                    no_account_names.add(email)
+            else:
+                print(f"{email} - Non public or wrong email in GitHub account: ", end="")
+                print_users(user)
+                no_account_emails.add(email)
+
+        print("\nValid users:")
+        print_users(valid_users)
+
+        print(f"\nWrong emails {len(wrong_emails)}:", "; ".join(wrong_emails))
+
+        print(
+            f"\nIntel emails - No valid GitHub account {len(no_account_emails)}:",
+            "; ".join(no_account_emails),
+        )
+
+        print(
+            f"\nIntel emails - No valid name in GitHub account {len(no_account_names)}:",
+            "; ".join(no_account_names),
+        )
+        return valid_users
+
+    def invite_users(self, users):
+        """Invites users to GitHub organization and prints status"""
+        if not isinstance(users, typing.Iterable):
+            users = [users]
+        print(f"\nInvite {len(users)} users:")
+
+        for user in users:
+            if isinstance(user, str):
+                print(f"Email: {user}")
+                self.github_org.invite_user(email=user)
+            else:
+                print(f'{user.login} - "{user.name}" - {user.email} - ', end="")
+                try:
+                    if is_user_ignored(user):
+                        print("Ignored")
+                        continue
+                    if self._cfg.DRY_RUN:
+                        print("Dry run")
+                        continue
+                    self.github_org.invite_user(user=user)
+                    print("OK")
+                except GithubException as exc:
+                    print(f'FAIL: {exc.data["errors"][0]["message"]}')
+
+    def remove_users(self, users):
+        """Removes users from GitHub organization"""
+        if not isinstance(users, typing.Iterable):
+            users = [users]
+        print(f"\nRemove {len(users)} users:")
+
+        dry_run = self._cfg.DRY_RUN
+        if not dry_run and len(users) > self._cfg.MAX_MEMBERS_TO_REMOVE:
+            print(
+                "WARNING: Review is required for removing members more than "
+                f"{self._cfg.MAX_MEMBERS_TO_REMOVE}"
+            )
+            # TODO: Add notification
+            dry_run = True
+
+        for user in users:
+            member = self.get_github_user_by_email(user) if isinstance(user, str) else user
+            print(f'{member.login} - "{member.name}" - {member.email} - ', end="")
+            try:
+                if is_user_ignored(member):
+                    print("Ignored")
+                    continue
+                if dry_run:
+                    print("Dry run")
+                    continue
+                self.github_org.remove_from_membership(member)
+                print("OK")
+            except GithubException as exc:
+                print(f'FAIL: {exc.data["errors"][0]["message"]}')
+
+
+def _test():
+    """Test and debug"""
+    Config(cli_args=["DRY_RUN=True"])
+    dev_emails = get_dev_emails()
+    print("dev_emails:", dev_emails)
+
+    gh_api = GithubOrgApi()
+    gh_api.get_org_emails()
+
+
+if __name__ == "__main__":
+    _test()
diff --git a/.github/org_control/ldap_api.py b/.github/github_org_control/ldap_api.py
similarity index 50%
rename from .github/org_control/ldap_api.py
rename to .github/github_org_control/ldap_api.py
index 44a0eacf379..c0f7e2c1811 100644
--- a/.github/org_control/ldap_api.py
+++ b/.github/github_org_control/ldap_api.py
@@ -7,11 +7,14 @@ Gets info about users and groups via LDAP
 
 # pylint: disable=fixme,no-member
 
+import sys
 from enum import Enum
+from pathlib import Path
 
 from ldap3 import Server, Connection, ALL, SUBTREE
 
-from configs import Config
+sys.path.append(str(Path(__file__).resolve().parents[1]))
+from github_org_control.configs import Config
 
 
 class LdapApiException(Exception):
@@ -20,49 +23,53 @@ class LdapApiException(Exception):
 
 class InfoLevel(Enum):
     """Constants for printing user info from LDAP"""
-    PDL = 'PDL' # Public Distribution List (group of e-mail addresses)
-    FULL = 'Full'
+
+    PDL = "PDL"  # Public Distribution List (group of e-mail addresses)
+    FULL = "Full"
 
 
 def print_user_info(info, info_level=None):
     """Pretty-print of a user info data structure (dict). info_level is the InfoLevel Enum"""
-    if not info or not info.get('mail'):
-        raise LdapApiException('ERROR: No info or absent mail')
+    if not info or not info.get("mail"):
+        raise LdapApiException("ERROR: No info or absent mail")
 
     def get_membership():
         if info_level == InfoLevel.PDL:
-            membership_info = '  PDLs:'
+            membership_info = "  PDLs:"
         elif info_level == InfoLevel.FULL:
-            membership_info = '  memberOf :'
+            membership_info = "  memberOf :"
         else:
-            return ''
+            return ""
         # Grouping groups by purpose
         if info_level == InfoLevel.PDL:
-            sort_key = lambda i: i.split(',', 1)[0].lower()
+            sort_key = lambda i: i.split(",", 1)[0].lower()
         else:
-            sort_key = lambda i: i.split(',', 1)[1] + i.split(',', 1)[0].lower()
-        for item in sorted(info['memberOf'], key=sort_key):
-            if info_level == InfoLevel.PDL and 'OU=Delegated' not in item:
+            sort_key = lambda i: i.split(",", 1)[1] + i.split(",", 1)[0].lower()
+        for item in sorted(info["memberOf"], key=sort_key):
+            if info_level == InfoLevel.PDL and "OU=Delegated" not in item:
                 continue
-            membership_info += f'\n    {item}'
+            membership_info += f"\n    {item}"
         return membership_info
 
     try:
-        text_info = \
-            f'\n{info["cn"]} <{info["mail"]}>; {info["sAMAccountName"]}; {info["employeeID"]}' \
-            f'\n  Org group: {info["intelSuperGroupDescr"]} ({info["intelSuperGroupShortName"]}) /'\
-            f' {info["intelGroupDescr"]} ({info["intelGroupShortName"]}) /' \
-            f' {info["intelDivisionDescr"]} ({info["intelDivisionShortName"]}) /' \
-            f' {info["intelOrgUnitDescr"]}' \
-            f'\n  Manager: {info["manager"]}' \
-            f'\n  Location: {info["intelRegionCode"]} / {info["co"]} / {info["intelSiteCode"]} /' \
-            f' {info["intelBldgCode"]} ({info["intelSiteName"]}) /' \
-            f' {info["physicalDeliveryOfficeName"]}' \
-            f'\n  Other: {info["employeeType"]} | {info["intelExportCountryGroup"]} |' \
+        text_info = (
+            f'\n{info["cn"]} <{info["mail"]}>; {info["sAMAccountName"]}; {info["employeeID"]}'
+            f'\n  Org group: {info["intelSuperGroupDescr"]} ({info["intelSuperGroupShortName"]}) /'
+            f' {info["intelGroupDescr"]} ({info["intelGroupShortName"]}) /'
+            f' {info["intelDivisionDescr"]} ({info["intelDivisionShortName"]}) /'
+            f' {info["intelOrgUnitDescr"]}'
+            f'\n  Manager: {info.get("manager")}'
+            f'\n  Location: {info["intelRegionCode"]} / {info["co"]} / {info["intelSiteCode"]} /'
+            f' {info["intelBldgCode"]} ({info.get("intelSiteName")}) /'
+            f' {info["physicalDeliveryOfficeName"]}'
+            f'\n  Other: {info["employeeType"]} | {info["intelExportCountryGroup"]} |'
             f' {info["whenCreated"]} | {info["intelCostCenterDescr"]} | {info["jobDescription"]}'
+        )
     except Exception as exc:
-        raise LdapApiException(f'ERROR: Failed to get info about "{info["mail"]}". ' \
-                               f'Exception occurred:\n{repr(exc)}') from exc
+        raise LdapApiException(
+            f'ERROR: Failed to get info about "{info["mail"]}". '
+            f"Exception occurred:\n{repr(exc)}"
+        ) from exc
     print(text_info)
 
     membership = get_membership()
@@ -71,140 +78,144 @@ def print_user_info(info, info_level=None):
     elif info_level == InfoLevel.FULL:
         for key in sorted(info):
             if isinstance(info[key], list):
-                if key == 'memberOf':
+                if key == "memberOf":
                     print(membership)
                 else:
-                    print(f'  {key} :')
+                    print(f"  {key} :")
                     for item in info[key]:
-                        print('   ', item)
+                        print("   ", item)
             else:
-                print(f'  {key} : {info[key]}')
+                print(f"  {key} : {info[key]}")
 
 
 class LdapApi:
     """LDAP API for getting user info and emails"""
 
-    _binary_blobs = ['thumbnailPhoto', 'msExchUMSpokenName', 'msExchBlockedSendersHash']
+    _binary_blobs = ["thumbnailPhoto", "msExchUMSpokenName", "msExchBlockedSendersHash"]
     _check_existing = [
-        'intelExportCountryGroup',
-        'physicalDeliveryOfficeName',
-        'intelSuperGroupShortName',
-        'intelGroupShortName',
-        'intelDivisionShortName',
+        "intelExportCountryGroup",
+        "physicalDeliveryOfficeName",
+        "intelSuperGroupShortName",
+        "intelGroupShortName",
+        "intelDivisionShortName",
     ]
 
-    null = '<null>'
+    null = "<null>"
 
     def __init__(self):
         self._cfg = Config()
         self.server = Server(self._cfg.LDAP_SERVER, get_info=ALL)
-        self.connection = Connection(self.server,
-                                     user=self._cfg.LDAP_USER,
-                                     password=self._cfg.LDAP_PASSWORD,
-                                     auto_bind=True)
+        self.connection = Connection(
+            self.server, user=self._cfg.LDAP_USER, password=self._cfg.LDAP_PASSWORD, auto_bind=True
+        )
         self.connection.bind()
 
     def get_user_emails(self, groups=None):
         """Gets emails of LDAP groups and sub-groups"""
-        print('\nGet emails from LDAP groups:')
+        print("\nGet emails from LDAP groups:")
         processed_ldap_members = {}
 
         def process_group_members(member, parent_group):
             if member in processed_ldap_members:
-                processed_ldap_members[member]['parent_groups'].append(parent_group)
-                print('\nWARNING: Ignore LDAP member to avoid duplication and recursive cycling '
-                      f'of PDLs: {member}\n    '
-                      f'email: {processed_ldap_members[member].get("email")}\n    parent_groups:')
-                for group in processed_ldap_members[member].get('parent_groups', []):
-                    print(7 * ' ', group)
+                processed_ldap_members[member]["parent_groups"].append(parent_group)
+                print(
+                    "\nWARNING: Ignore LDAP member to avoid duplication and recursive cycling "
+                    f"of PDLs: {member}\n    "
+                    f'email: {processed_ldap_members[member].get("email")}\n    parent_groups:'
+                )
+                for group in processed_ldap_members[member].get("parent_groups", []):
+                    print(7 * " ", group)
 
                 return
-            processed_ldap_members[member] = {'email': None, 'parent_groups': [parent_group]}
+            processed_ldap_members[member] = {"email": None, "parent_groups": [parent_group]}
 
             # AD moves terminated users to the boneyard OU in case the user returns,
             # so it can be reactivated with little effort.
             # After 30 days it is removed and the unix personality becomes unlinked.
-            if 'OU=Boneyard' in member:
+            if "OU=Boneyard" in member:
                 return
-            self.connection.search(member, r'(objectClass=*)', SUBTREE,
-                                   attributes=['cn', 'member', 'mail'])
+            self.connection.search(
+                member, r"(objectClass=*)", SUBTREE, attributes=["cn", "member", "mail"]
+            )
 
-            #print(self.connection.entries)
+            # print(self.connection.entries)
             if not self.connection.response:
-                raise LdapApiException(f'ERROR: empty response. LDAP member: {member}')
+                raise LdapApiException(f"ERROR: empty response. LDAP member: {member}")
 
             # Check that the member is worker.
             # The response can contain several items, but the first item is valid only
-            if 'OU=Workers' in member:
-                if self.connection.response[0]['attributes']['mail']:
-                    processed_ldap_members[member]['email'] = \
-                        self.connection.response[0]['attributes']['mail'].lower()
+            if "OU=Workers" in member:
+                if self.connection.response[0]["attributes"]["mail"]:
+                    processed_ldap_members[member]["email"] = self.connection.response[0][
+                        "attributes"
+                    ]["mail"].lower()
                     return
-                raise LdapApiException(f'ERROR: no mail. LDAP worker: {member}\n'
-                                       f'{self.connection.entries}')
+                raise LdapApiException(
+                    f"ERROR: no mail. LDAP worker: {member}\n" f"{self.connection.entries}"
+                )
 
             if len(self.connection.response) > 1:
-                raise LdapApiException(f'ERROR: multiple responses for {member}: '
-                                       f'{len(self.connection.response)}\n'
-                                       f'{self.connection.entries}')
+                raise LdapApiException(
+                    f"ERROR: multiple responses for {member}: "
+                    f"{len(self.connection.response)}\n"
+                    f"{self.connection.entries}"
+                )
 
-            if self.connection.response[0]['attributes']['member']:
-                for group_member in self.connection.response[0]['attributes']['member']:
+            if self.connection.response[0]["attributes"]["member"]:
+                for group_member in self.connection.response[0]["attributes"]["member"]:
                     process_group_members(group_member, member)
             else:
-                print(f'\nERROR: no members in LDAP group: {member}\n{self.connection.entries}')
+                print(f"\nERROR: no members in LDAP group: {member}\n{self.connection.entries}")
 
         for group in groups or self._cfg.LDAP_PDLs:
-            print('\nProcess ROOT LDAP group:', group)
-            process_group_members(group, 'ROOT')
+            print("\nProcess ROOT LDAP group:", group)
+            process_group_members(group, "ROOT")
         return {
-            member.get('email') for member in processed_ldap_members.values() if member.get('email')
+            member.get("email") for member in processed_ldap_members.values() if member.get("email")
         }
 
-
     def _get_user_info(self, query):
         """Gets user info from LDAP as dict matching key and values pairs from query"""
-        query_filter = ''.join(f'({key}={value})' for key, value in query.items())
+        query_filter = "".join(f"({key}={value})" for key, value in query.items())
 
         for domain in self._cfg.LDAP_DOMAINS:
-            search_base = f'OU=Workers,DC={domain},DC=corp,DC=intel,DC=com'
+            search_base = f"OU=Workers,DC={domain},DC=corp,DC=intel,DC=com"
             self.connection.search(
                 search_base,
-                f'(&(objectcategory=person)(objectclass=user)(intelflags=1){query_filter})',
+                f"(&(objectcategory=person)(objectclass=user)(intelflags=1){query_filter})",
                 SUBTREE,
-                attributes=['*'])
+                attributes=["*"],
+            )
 
             if self.connection.response:
                 if len(self.connection.response) > 1:
-                    raise LdapApiException(f'ERROR: multiple responses for {query_filter}: '
-                                           f'{len(self.connection.response)}\n'
-                                           f'{self.connection.entries}')
-                info = self.connection.response[0]['attributes']
+                    raise LdapApiException(
+                        f"ERROR: multiple responses for {query_filter}: "
+                        f"{len(self.connection.response)}\n"
+                        f"{self.connection.entries}"
+                    )
+                info = self.connection.response[0]["attributes"]
 
                 # remove long binary blobs
                 for blob in LdapApi._binary_blobs:
-                    info[blob] = b''
+                    info[blob] = b""
                 for key in LdapApi._check_existing:
                     if not info.get(key):
                         info[key] = LdapApi.null
                 return info
         return {}
 
-
     def get_user_info_by_idsid(self, idsid):
         """Gets user info from LDAP as dict using account name for searching"""
-        return self._get_user_info({'sAMAccountName': idsid})
-
+        return self._get_user_info({"sAMAccountName": idsid})
 
     def get_user_info_by_name(self, name):
         """Gets user info from LDAP as dict using common name for searching"""
-        return self._get_user_info({'cn': name})
-
+        return self._get_user_info({"cn": name})
 
     def get_user_info_by_email(self, email):
         """Gets user info from LDAP as dict using emails for searching"""
-        return self._get_user_info({'mail': email})
-
+        return self._get_user_info({"mail": email})
 
     def get_absent_emails(self, emails):
         """Checks users by email in LDAP and returns absent emails"""
@@ -222,15 +233,15 @@ def _test():
     emails = ldap.get_user_emails()
     print(f'\nLDAP emails count: {len(emails)}\n{"; ".join(emails)}')
 
-    emails = ['foo@intel.com']
+    emails = ["foo@intel.com"]
 
     for email in emails:
         info = ldap.get_user_info_by_email(email)
         if info:
             print_user_info(info, InfoLevel.PDL)
         else:
-            print(f'\n{email} - not found')
+            print(f"\n{email} - not found")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     _test()
diff --git a/.github/github_org_control/requirements-dev.txt b/.github/github_org_control/requirements-dev.txt
new file mode 100644
index 00000000000..96f4ec95e53
--- /dev/null
+++ b/.github/github_org_control/requirements-dev.txt
@@ -0,0 +1 @@
+pylint==2.11.1
diff --git a/.github/github_org_control/requirements.txt b/.github/github_org_control/requirements.txt
new file mode 100644
index 00000000000..6ec73e8ca71
--- /dev/null
+++ b/.github/github_org_control/requirements.txt
@@ -0,0 +1,2 @@
+PyGithub==1.55
+ldap3==2.7
diff --git a/.github/org_control/check_org.py b/.github/org_control/check_org.py
deleted file mode 100644
index 60ec464b621..00000000000
--- a/.github/org_control/check_org.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Check GitHub organization and invite members
-"""
-
-# pylint: disable=fixme,no-member,too-many-locals
-
-from argparse import ArgumentParser
-
-from configs import Config
-from github_api import GithubOrgApi, get_dev_emails
-from ldap_api import LdapApi, print_user_info, InfoLevel
-
-
-def main():
-    """The main entry point function"""
-    arg_parser = ArgumentParser()
-    arg_parser.add_argument("--cfg-file", metavar="PATH", default=Config.default_cfg_path,
-                            help=f"Path to json configuration file, e.g. {Config.default_cfg_path}")
-    arg_parser.add_argument("--teams", action="store_true", help="Check GitHub teams")
-    arg_parser.add_argument("--no-ldap", action="store_true", help="Don't use LDAP info")
-    args, unknown_args = arg_parser.parse_known_args()
-
-    Config(args.cfg_file, unknown_args)
-    gh_api = GithubOrgApi()
-
-    if args.teams:
-        gh_api.get_org_teams()
-        return
-
-    cfg_emails = get_dev_emails()
-    print(f'\nCfg developer emails {len(cfg_emails)}:', '; '.join(sorted(cfg_emails)))
-
-    dev_emails = set()
-    dev_emails.update(cfg_emails)
-
-    if not args.no_ldap:
-        ldap_api = LdapApi()
-        ldap_emails = ldap_api.get_user_emails()
-        dev_emails.update(ldap_emails)
-        print(f'\nLDAP developer emails {len(ldap_emails)}:', '; '.join(sorted(ldap_emails)))
-
-        cfg_emails_no_in_ldap = ldap_api.get_absent_emails(cfg_emails)
-        print(f'\nCfg developer emails - absent in LDAP at all {len(cfg_emails_no_in_ldap)}:',
-              '; '.join(sorted(cfg_emails_no_in_ldap)))
-
-        cfg_ldap_inters = cfg_emails.intersection(ldap_emails)
-        print(f'\nCfg developer emails - present in LDAP developers {len(cfg_ldap_inters)}:',
-              '; '.join(sorted(cfg_ldap_inters)))
-
-    org_emails, org_logins_no_intel_email = gh_api.get_org_emails()
-    print(f'\nOrg emails {len(org_emails)}:', '; '.join(sorted(org_emails)))
-
-    org_emails_no_in_ldap = set()
-    if not args.no_ldap:
-        org_ldap_diff = org_emails.difference(ldap_emails)
-        print(f'\nOrg member emails - absent in LDAP developers {len(org_ldap_diff)}:',
-              '; '.join(sorted(org_ldap_diff)))
-
-        for email in org_ldap_diff:
-            user_info = ldap_api.get_user_info_by_email(email)
-            if user_info:
-                print_user_info(user_info, InfoLevel.PDL)
-            else:
-                org_emails_no_in_ldap.add(email)
-
-    org_pendig_invitation_emails = gh_api.get_org_invitation_emails()
-    invite_emails = dev_emails.difference(org_emails).difference(org_pendig_invitation_emails)
-    print(f'\nInvite emails {len(invite_emails)}:', '; '.join(sorted(invite_emails)))
-
-    valid_github_users = gh_api.get_valid_github_users(invite_emails)
-    gh_api.invite_users(valid_github_users)
-
-    print('\nCheck accounts below and remove from the GitHub organization and cfg list')
-
-    cfg_emails_no_in_org = sorted(cfg_emails.difference(org_emails))
-    print(f'\nCfg developer emails - absent in GitHub organization {len(cfg_emails_no_in_org)}:',
-          '; '.join(cfg_emails_no_in_org))
-
-    org_emails_no_in_dev = sorted(org_emails.difference(dev_emails))
-    print(f'\nOrg member emails - absent in cfg and LDAP developers {len(org_emails_no_in_dev)}:',
-          '; '.join(org_emails_no_in_dev))
-
-    print(f'\nOrg member emails - absent in LDAP at all {len(org_emails_no_in_ldap)}:',
-          '; '.join(sorted(org_emails_no_in_ldap)))
-
-    print(f'\nOrg member logins - absent Intel email {len(org_logins_no_intel_email)}:',
-          '; '.join(sorted(org_logins_no_intel_email)))
-
-
-if __name__ == '__main__':
-    main()
diff --git a/.github/org_control/github_api.py b/.github/org_control/github_api.py
deleted file mode 100644
index dcea9d9f2b2..00000000000
--- a/.github/org_control/github_api.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-GitHub API for controlling organization
-"""
-
-# pylint: disable=fixme,no-member
-
-import re
-import time
-
-from github import Github, GithubException, RateLimitExceededException, IncompletableObject
-from github.PaginatedList import PaginatedList
-
-from configs import Config
-
-
-def is_valid_user(user):
-    """Checks that user is valid github.Github object"""
-    try:
-        return user and user.login
-    except IncompletableObject:
-        return False
-
-
-def is_user_ignored(user):
-    """Checks that user should be ignored"""
-    cfg = Config()
-    if is_valid_user(user) and user.login.lower() not in cfg.properties['IGNORE_LOGINS']:
-        return False
-    return True
-
-
-def is_valid_name(name):
-    """Checks that GitHub user's name is valid"""
-    return name and len(name) >= 3 and ' ' in name
-
-
-def is_intel_email(email):
-    """Checks that email is valid Intel email"""
-    return email and len(email) > 10 and ' ' not in email and email.lower().endswith('@intel.com')
-
-
-def is_intel_company(company):
-    """Checks that company contains intel"""
-    return company and 'intel' in company.lower()
-
-
-def is_valid_intel_user(user):
-    """Checks that user is valid GitHub and Intel user"""
-    return is_valid_user(user) and (is_valid_name(user.name) and is_intel_email(user.email) or
-                                    is_user_ignored(user))
-
-
-def print_users(users):
-    """Print list of users in different formats: list, set, PaginatedList"""
-    if isinstance(users, (list, set, PaginatedList)):
-        users_count = users.totalCount if isinstance(users, PaginatedList) else len(users)
-        print(f'\nGitHub users {users_count} (login - name - company - email - valid):')
-    else:
-        users = [users]
-    for user in users:
-        if not is_valid_user(user):
-            print('WRONG GitHub user: ???')
-            continue
-        valid_check = 'OK' if is_valid_intel_user(user) else 'FIX'
-        if not is_intel_email(user.email):
-            valid_check += ' email'
-        if not is_valid_name(user.name):
-            valid_check += ' name'
-        print(f'{user.login} - "{user.name}" - "{user.company}" - {user.email} - {valid_check}')
-
-
-def get_dev_emails():
-    """
-    Read a file with developer emails. Supported email formats
-    first_name.last_name@intel.com
-    Import from Outlook: Last_name, First_name <first_name.last_name@intel.com>
-    """
-    re_email = re.compile(r'.+<(.+)>')
-    emails = set()
-    cfg = Config()
-    with open(cfg.properties['EMAILS_FILE_PATH']) as file_obj:
-        for line in file_obj:
-            line = line.strip().lower()
-            if not line or line.startswith('#'):
-                continue
-            re_outlook_email = re_email.match(line)
-            if re_outlook_email:
-                line = re_outlook_email.group(1).strip()
-            if not is_intel_email(line):
-                print(f'Wrong email in {cfg.properties["EMAILS_FILE_PATH"]}: {line}')
-                continue
-            emails.add(line)
-    return emails
-
-
-class GithubOrgApi:
-    """Common API for GitHub organization"""
-
-    def __init__(self):
-        self._cfg = Config()
-        self.github = Github(self._cfg.GITHUB_TOKEN)
-        self.github_org = self.github.get_organization(self._cfg.GITHUB_ORGANIZATION)
-        self.repo = self.github.get_repo(f'{self._cfg.GITHUB_ORGANIZATION}/'
-                                         f'{self._cfg.GITHUB_REPO}')
-
-    def is_org_user(self, user):
-        """Checks that user is a member of GitHub organization"""
-        if is_valid_user(user):
-            # user.get_organization_membership(self.github_org) doesn't work with org members
-            # permissions, GITHUB_TOKEN must be org owner now
-            return self.github_org.has_in_members(user)
-        return False
-
-    def get_org_emails(self):
-        """Gets and prints emails of all GitHub organization members"""
-        org_members = self.github_org.get_members()
-        org_emails = set()
-        org_members_fix = set()
-        org_emails_fix_name = set()
-        org_logins_fix_intel_email = set()
-
-        print(f'\nOrg members {org_members.totalCount} (login - name - company - email - valid):')
-        for org_member in org_members:
-            print_users(org_member)
-            if is_user_ignored(org_member):
-                continue
-            if is_intel_email(org_member.email):
-                org_emails.add(org_member.email.lower())
-                if not is_valid_name(org_member.name):
-                    org_members_fix.add(org_member)
-                    org_emails_fix_name.add(org_member.email.lower())
-            else:
-                org_members_fix.add(org_member)
-                org_logins_fix_intel_email.add(org_member.login.lower())
-
-        print_users(org_members_fix)
-        print(f'\nOrg members - no Intel emails {len(org_logins_fix_intel_email)}:',
-              '; '.join(org_logins_fix_intel_email))
-        print(f'\nOrg members - no real name {len(org_emails_fix_name)}:',
-              '; '.join(org_emails_fix_name))
-        return (org_emails, org_logins_fix_intel_email)
-
-    def get_org_invitation_emails(self):
-        """Gets GitHub organization teams prints info"""
-        org_invitations = self.github_org.invitations()
-        org_invitation_emails = set()
-
-        print(f'\nOrg invitations {org_invitations.totalCount} (login - name - email - valid):')
-        for org_invitation in org_invitations:
-            # TODO: investigate GithubException while access to user name and enable print_users()
-            # github.GithubException.IncompletableObject: 400 "Returned object contains no URL"
-            #print_users(org_invitation)
-            print(f'{org_invitation.login} - ??? - {org_invitation.email} - ???')
-            if is_user_ignored(org_invitation):
-                continue
-            if is_intel_email(org_invitation.email):
-                org_invitation_emails.add(org_invitation.email.lower())
-            else:
-                print('Strange org invitation:', org_invitation)
-
-        print(f'\nOrg invitation emails {len(org_invitation_emails)}:',
-              '; '.join(org_invitation_emails))
-        return org_invitation_emails
-
-    def get_org_teams(self):
-        """Gets GitHub organization teams prints info"""
-        teams = []
-        org_teams = self.github_org.get_teams()
-        print('\nOrg teams count:', org_teams.totalCount)
-        for team in org_teams:
-            teams.append(team.name)
-            print(f'\nTeam: {team.name} - parent: {team.parent}')
-
-            repos = team.get_repos()
-            print('Repos:')
-            for repo in repos:
-                print(f'    {repo.name} -', team.get_repo_permission(repo))
-
-            team_maintainers = team.get_members(role='maintainer')
-            team_maintainer_logins = set()
-            for maintainer in team_maintainers:
-                team_maintainer_logins.add(maintainer.login)
-            team_members = team.get_members(role='member')
-            team_member_logins = set()
-            for member in team_members:
-                team_member_logins.add(member.login)
-            members = team.get_members(role='all')
-            member_emails = []
-            print('Members (role - login - name - company - email - valid):')
-            for user in members:
-                if user.login in team_maintainer_logins:
-                    print('    Maintainer - ', end='')
-                elif user.login in team_member_logins:
-                    print('    Member - ', end='')
-                else:
-                    # It is not possible to check child teams members
-                    print('    ??? - ', end='')
-                print_users(user)
-                if is_intel_email(user.email) and not is_user_ignored(user):
-                    member_emails.append(user.email.lower())
-            print(f'Intel emails {len(member_emails)}:', '; '.join(member_emails))
-        return teams
-
-    def get_valid_github_users(self, emails):
-        """Gets valid GitHub users by email and prints status"""
-        valid_users = set()
-        no_account_emails = set()
-        print(f'\nGitHub users from {len(emails)} invite emails (email - status):')
-        for email in emails:
-            if not is_intel_email(email):
-                print(f'{email} - Non Intel email')
-                continue
-
-            # You can make up to 30 requests per minute; https://developer.github.com/v3/search/
-            # Sleep 2.4 sec is about 25 requests per minute
-            time.sleep(2.4)
-            try:
-                users = self.github.search_users(f'{email} in:email')
-            except RateLimitExceededException:
-                time.sleep(5)
-                users = self.github.search_users(f'{email} in:email')
-
-            if users.totalCount == 0:
-                print(f'{email} - No valid GitHub account')
-                no_account_emails.add(email)
-                continue
-            if users.totalCount > 1:
-                print(f'{email} - Found {users.totalCount} GitHub accounts')
-            for user in users:
-                if user.email and user.email.lower() == email:
-                    print(f'{email} - OK')
-                    valid_users.add(user)
-                else:
-                    print(f'{email} - Non public or wrong email - login: {user.login} - '
-                          f'email: {user.email}')
-        print('Valid users count:', len(valid_users))
-        print_users(valid_users)
-        print(f'\nIntel emails - No valid GitHub account {len(no_account_emails)}:',
-              '; '.join(no_account_emails))
-        return valid_users
-
-    def invite_users(self, users):
-        """Invites users and prints status"""
-        if isinstance(users, (list, set)):
-            print(f'\nInvite {len(users)} users:')
-        else:
-            users = [users]
-
-        for user in users:
-            if isinstance(user, str):
-                print(f'Email: {user}')
-                self.github_org.invite_user(email=user)
-            else:
-                print(f'{user.login} - "{user.name}" - {user.email} - ', end='')
-                try:
-                    if is_user_ignored(user):
-                        print('Ignored')
-                        continue
-                    if not self._cfg.DRY_RUN:
-                        self.github_org.invite_user(user=user)
-                        print('OK')
-                    else:
-                        print('Dry run')
-                except GithubException as exc:
-                    print(f'FAIL: {exc.data["errors"][0]["message"]}')
-
-
-def _test():
-    """Test and debug"""
-    Config(cli_args=['DRY_RUN=True'])
-    dev_emails = get_dev_emails()
-    print('dev_emails:', dev_emails)
-
-    gh_api = GithubOrgApi()
-    gh_api.get_org_emails()
-
-
-if __name__ == '__main__':
-    _test()
diff --git a/.github/org_control/requirements-dev.txt b/.github/org_control/requirements-dev.txt
deleted file mode 100644
index 36d944e3a50..00000000000
--- a/.github/org_control/requirements-dev.txt
+++ /dev/null
@@ -1 +0,0 @@
-pylint==2.5.3
diff --git a/.github/org_control/requirements.txt b/.github/org_control/requirements.txt
deleted file mode 100644
index 57c70e74bd3..00000000000
--- a/.github/org_control/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-PyGithub==1.51
-ldap3==2.7
diff --git a/.github/workflows/check_pr_commits.yml b/.github/workflows/check_pr_commits.yml
index fc2dceaeb3f..8ba58b7fbb7 100644
--- a/.github/workflows/check_pr_commits.yml
+++ b/.github/workflows/check_pr_commits.yml
@@ -9,9 +9,9 @@ jobs:
         uses: actions/checkout@v2
 
       - name: Install dependencies
-        run: python3 -m pip install -r ./.github/org_control/requirements.txt
+        run: python3 -m pip install -r ./.github/github_org_control/requirements.txt
 
       - name: PR commits
-        run: python3 ./.github/org_control/check_pr.py --pr=${{ github.event.number }} --check-commits DRY_RUN
+        run: python3 ./.github/github_org_control/check_pr.py --pr=${{ github.event.number }} --check-commits DRY_RUN
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

From fa9434494faf6ca4435f697242c2ffb432bacea9 Mon Sep 17 00:00:00 2001
From: Ivan Novoselov <ivan.novoselov@intel.com>
Date: Mon, 20 Dec 2021 22:45:01 +0300
Subject: [PATCH 055/160] [Snippets][CPU] Eltwise subgraphs code generation
 (#7936)

---
 .../src/mkldnn_plugin/CMakeLists.txt          |   4 +-
 inference-engine/src/mkldnn_plugin/config.cpp |   3 +
 inference-engine/src/mkldnn_plugin/config.h   |   1 +
 .../src/mkldnn_plugin/cpu_types.cpp           |   3 +
 .../src/mkldnn_plugin/cpu_types.h             |   3 +-
 .../mkldnn_plugin/emitters/cpu_generator.cpp  | 139 ++++
 .../mkldnn_plugin/emitters/cpu_generator.hpp  |  33 +
 .../emitters/jit_eltwise_emitters.cpp         |  20 +-
 .../emitters/jit_eltwise_emitters.hpp         |   3 +
 .../mkldnn_plugin/emitters/jit_emitter.cpp    |   6 +-
 .../mkldnn_plugin/emitters/jit_emitter.hpp    |  16 +-
 .../emitters/jit_mkldnn_ext_emitters.hpp      | 144 ++++
 .../emitters/jit_snippets_emitters.hpp        | 641 ++++++++++++++++++
 .../mkldnn_plugin/mkldnn_nodes_factory.cpp    |   2 +
 .../src/mkldnn_plugin/mkldnn_plugin.cpp       |  56 +-
 .../convert_to_cpu_specific_opset.hpp         |   2 -
 .../snippets_mark_skipped.cpp                 | 352 ++++++++++
 .../snippets_mark_skipped.hpp                 |  39 ++
 .../src/mkldnn_plugin/nodes/subgraph.cpp      | 548 +++++++++++++++
 .../src/mkldnn_plugin/nodes/subgraph.h        |  91 +++
 .../python/tests/test_onnx/test_zoo_models.py |   3 +-
 src/common/snippets/CMakeLists.txt            |  10 +-
 .../snippets/include/snippets/generator.hpp   |   6 +-
 .../{src => include/snippets}/itt.hpp         |   2 +-
 .../snippets/include/snippets/op/kernel.hpp   |   3 +-
 .../include/snippets/op/powerstatic.hpp       |  44 ++
 .../include/snippets/op/staticpower.hpp       |  42 --
 .../snippets/include/snippets/op/subgraph.hpp |  34 +-
 .../snippets/include/snippets/op/tile.hpp     |   1 +
 .../snippets/pass/assign_registers.hpp        |   2 +-
 .../snippets/pass/collapse_subgraph.hpp       |  40 +-
 .../snippets/pass/insert_load_store.hpp       |   4 +-
 .../snippets/pass/insert_movebroadcast.hpp    |   2 +-
 .../load_movebroadcast_to_broadcastload.hpp   |   2 +-
 .../snippets/pass/vector_to_scalar.hpp        |   8 +-
 .../include/snippets/snippets_isa.hpp         |   4 +-
 src/common/snippets/src/generator.cpp         |  76 ++-
 src/common/snippets/src/op/broadcastload.cpp  |   2 +-
 src/common/snippets/src/op/broadcastmove.cpp  |   2 +-
 src/common/snippets/src/op/load.cpp           |   2 +-
 src/common/snippets/src/op/store.cpp          |   2 +-
 src/common/snippets/src/op/subgraph.cpp       | 107 +--
 .../snippets/src/pass/assign_registers.cpp    |   7 +-
 .../snippets/src/pass/collapse_subgraph.cpp   | 602 ++++++++--------
 .../snippets/src/pass/insert_load_store.cpp   |   4 +-
 .../src/pass/insert_movebroadcast.cpp         |   9 +-
 .../load_movebroadcast_to_broadcastload.cpp   |   7 +-
 .../snippets/src/pass/vector_to_scalar.cpp    |   4 +-
 src/common/snippets/src/remarks.hpp           |   4 +-
 .../inference_engine/skip_tests_config.cpp    |   4 +
 .../snippets/tokenization.cpp                 | 291 +++++---
 src/tests/unit/cpu/CMakeLists.txt             |   7 +
 src/tests/unit/cpu/generate_add.cpp           | 427 ++++++++++++
 53 files changed, 3265 insertions(+), 605 deletions(-)
 create mode 100644 inference-engine/src/mkldnn_plugin/emitters/cpu_generator.cpp
 create mode 100644 inference-engine/src/mkldnn_plugin/emitters/cpu_generator.hpp
 create mode 100644 inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_ext_emitters.hpp
 create mode 100644 inference-engine/src/mkldnn_plugin/emitters/jit_snippets_emitters.hpp
 create mode 100644 inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.cpp
 create mode 100644 inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.hpp
 create mode 100644 inference-engine/src/mkldnn_plugin/nodes/subgraph.cpp
 create mode 100644 inference-engine/src/mkldnn_plugin/nodes/subgraph.h
 rename src/common/snippets/{src => include/snippets}/itt.hpp (96%)
 create mode 100644 src/common/snippets/include/snippets/op/powerstatic.hpp
 delete mode 100644 src/common/snippets/include/snippets/op/staticpower.hpp
 create mode 100644 src/tests/unit/cpu/generate_add.cpp

diff --git a/inference-engine/src/mkldnn_plugin/CMakeLists.txt b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
index 24359ffe7f8..e6397b6a77c 100644
--- a/inference-engine/src/mkldnn_plugin/CMakeLists.txt
+++ b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
@@ -44,7 +44,8 @@ target_link_libraries(${TARGET_NAME} PRIVATE mkldnn
                                              inference_engine
                                              inference_engine_transformations
                                              inference_engine_lp_transformations
-                                             ov_shape_inference)
+                                             ov_shape_inference
+                                             inference_engine_snippets)
 
 target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_EXTENSION_API)
 target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
@@ -75,6 +76,7 @@ target_include_directories(${TARGET_NAME}_obj PRIVATE $<TARGET_PROPERTY:inferenc
                                                       $<TARGET_PROPERTY:openvino::itt,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:inference_engine_lp_transformations,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:ov_shape_inference,INTERFACE_INCLUDE_DIRECTORIES>
+                                                      $<TARGET_PROPERTY:inference_engine_snippets,INTERFACE_INCLUDE_DIRECTORIES>
                                               PUBLIC  ${CMAKE_CURRENT_SOURCE_DIR}
                                                       $<TARGET_PROPERTY:openvino::conditional_compilation,INTERFACE_INCLUDE_DIRECTORIES>)
 
diff --git a/inference-engine/src/mkldnn_plugin/config.cpp b/inference-engine/src/mkldnn_plugin/config.cpp
index bec2c5ee0b6..02d12b349e2 100644
--- a/inference-engine/src/mkldnn_plugin/config.cpp
+++ b/inference-engine/src/mkldnn_plugin/config.cpp
@@ -115,6 +115,8 @@ void Config::readProperties(const std::map<std::string, std::string> &prop) {
                 IE_THROW() << "Wrong value for property key " << PluginConfigParams::KEY_ENFORCE_BF16
                     << ". Expected only YES/NO";
             }
+        } else if (key == PluginConfigParams::KEY_CACHE_DIR) {
+            cache_dir = val;
         } else {
             IE_THROW(NotFound) << "Unsupported property " << key << " by CPU plugin";
         }
@@ -167,6 +169,7 @@ void Config::updateProperties() {
         _config.insert({ PluginConfigParams::KEY_PERFORMANCE_HINT, perfHintsConfig.ovPerfHint });
         _config.insert({ PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS,
                          std::to_string(perfHintsConfig.ovPerfHintNumRequests) });
+        _config.insert({PluginConfigParams::KEY_CACHE_DIR, cache_dir});
     }
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/config.h b/inference-engine/src/mkldnn_plugin/config.h
index c1edd197639..6a1e785866c 100644
--- a/inference-engine/src/mkldnn_plugin/config.h
+++ b/inference-engine/src/mkldnn_plugin/config.h
@@ -41,6 +41,7 @@ struct Config {
 #ifdef CPU_DEBUG_CAPS
     DebugCaps::Config debugCaps;
 #endif
+    std::string cache_dir{};
 
     void readProperties(const std::map<std::string, std::string> &config);
     void updateProperties();
diff --git a/inference-engine/src/mkldnn_plugin/cpu_types.cpp b/inference-engine/src/mkldnn_plugin/cpu_types.cpp
index 9836b1a5f32..9facd0d07e8 100644
--- a/inference-engine/src/mkldnn_plugin/cpu_types.cpp
+++ b/inference-engine/src/mkldnn_plugin/cpu_types.cpp
@@ -183,6 +183,7 @@ const InferenceEngine::details::caseless_unordered_map<std::string, Type> type_t
         { "MatrixNms", MatrixNms},
         { "MulticlassNms", MulticlassNms},
         { "Reference", Reference},
+        { "Subgraph", Subgraph}
 };
 
 Type TypeFromName(const std::string& type) {
@@ -364,6 +365,8 @@ std::string NameFromType(const Type type) {
             return "MulticlassNms";
         case Reference:
             return "Reference";
+        case Subgraph:
+            return "Subgraph";
         default:
             return "Unknown";
     }
diff --git a/inference-engine/src/mkldnn_plugin/cpu_types.h b/inference-engine/src/mkldnn_plugin/cpu_types.h
index 8052b73fe9d..9299febc116 100644
--- a/inference-engine/src/mkldnn_plugin/cpu_types.h
+++ b/inference-engine/src/mkldnn_plugin/cpu_types.h
@@ -99,7 +99,8 @@ enum Type {
     ExtractImagePatches,
     NonMaxSuppression,
     MatrixNms,
-    MulticlassNms
+    MulticlassNms,
+    Subgraph
 };
 
 enum Algorithm {
diff --git a/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.cpp b/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.cpp
new file mode 100644
index 00000000000..a5117b9dcf9
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.cpp
@@ -0,0 +1,139 @@
+// Copyright (C) 2020-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "snippets/snippets_isa.hpp"
+
+#include <string>
+#include <iostream>
+#include <array>
+
+#include "cpu_generator.hpp"
+#include "jit_snippets_emitters.hpp"
+#include "jit_eltwise_emitters.hpp"
+#include "jit_mkldnn_emitters.hpp"
+#include "jit_mkldnn_ext_emitters.hpp"
+
+using namespace std;
+using namespace ngraph::snippets;
+
+#define CREATE_EMITTER(e_type) [this](const std::shared_ptr<ngraph::Node>& n) \
+    -> std::shared_ptr<ngraph::snippets::Emitter> {return std::make_shared<e_type>(h.get(), isa, n);};
+
+class jit_snippet : public dnnl::impl::cpu::x64::jit_generator {
+public:
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_snippet)
+
+    ~jit_snippet() = default;
+
+    jit_snippet() : jit_generator() {
+    }
+
+    void generate() override {
+    }
+};
+
+MKLDNNPlugin::CPUTargetMachine::CPUTargetMachine(dnnl::impl::cpu::x64::cpu_isa_t host_isa)
+    : TargetMachine(), h(new jit_snippet()), isa(host_isa) {
+    // data movement
+    jitters[ngraph::opset1::Parameter::get_type_info_static()] = CREATE_EMITTER(NopEmitter);
+    jitters[ngraph::snippets::op::BlockedParameter::get_type_info_static()] = CREATE_EMITTER(NopEmitter);
+    jitters[ngraph::opset1::Result::get_type_info_static()] = CREATE_EMITTER(NopEmitter);
+    // jitters[ngraph::opset1::Constant::get_type_info_static()] = CREATE_EMITTER(); // Not supported
+
+    jitters[ngraph::snippets::op::Load::get_type_info_static()] = CREATE_EMITTER(LoadEmitter);
+    jitters[ngraph::snippets::op::VectorLoad::get_type_info_static()] = CREATE_EMITTER(LoadEmitter);
+    jitters[ngraph::snippets::op::ScalarLoad::get_type_info_static()] = CREATE_EMITTER(ScalarLoadEmitter);
+    jitters[ngraph::snippets::op::BroadcastLoad::get_type_info_static()] = CREATE_EMITTER(BroadcastLoadEmitter);
+
+    jitters[ngraph::snippets::op::Store::get_type_info_static()] = CREATE_EMITTER(StoreEmitter);
+    jitters[ngraph::snippets::op::VectorStore::get_type_info_static()] = CREATE_EMITTER(StoreEmitter);
+    jitters[ngraph::snippets::op::ScalarStore::get_type_info_static()] = CREATE_EMITTER(ScalarStoreEmitter);
+
+    jitters[ngraph::snippets::op::Scalar::get_type_info_static()] = CREATE_EMITTER(ScalarEmitter);
+    jitters[ngraph::snippets::op::BroadcastMove::get_type_info_static()] = CREATE_EMITTER(FakeBroadcastEmitter);
+    // jitters[ngraph::snippets::op::Nop::get_type_info_static()] = CREATE_EMITTER(NopEmitter); // Not supported
+    // jitters[ngraph::opset1::Broadcast::get_type_info_static()] = CREATE_EMITTER(); // Not supported
+
+    // jitters[ngraph::opset1::Convert::get_type_info_static()] = CREATE_EMITTER(); // Not supported
+    // jitters[ngraph::opset1::FakeQuantize::get_type_info_static()] = CREATE_EMITTER(); // not supported
+
+    // binary
+    jitters[ngraph::opset1::Add::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_add_emitter);
+    jitters[ngraph::opset1::Divide::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_divide_emitter);
+    jitters[ngraph::opset1::Equal::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_equal_emitter);
+    jitters[ngraph::opset1::FloorMod::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_floor_mod_emitter);
+    jitters[ngraph::opset1::Greater::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_greater_emitter);
+    jitters[ngraph::opset1::GreaterEqual::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_greater_equal_emitter);
+    jitters[ngraph::opset1::Less::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_less_emitter);
+    jitters[ngraph::opset1::LessEqual::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_less_equal_emitter);
+    jitters[ngraph::opset1::LogicalAnd::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_logical_and_emitter);
+    jitters[ngraph::opset1::LogicalOr::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_logical_or_emitter);
+    jitters[ngraph::opset1::LogicalXor::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_logical_xor_emitter);
+    jitters[ngraph::opset1::Maximum::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_maximum_emitter);
+    jitters[ngraph::opset1::Minimum::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_minimum_emitter);
+    jitters[ngraph::opset1::Mod::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_mod_emitter);
+    jitters[ngraph::opset1::Multiply::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_multiply_emitter);
+    jitters[ngraph::opset1::NotEqual::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_not_equal_emitter);
+    jitters[ngraph::snippets::op::PowerStatic::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_power_static_emitter);
+    jitters[ngraph::opset1::Power::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_power_dynamic_emitter);
+    jitters[ngraph::opset1::PRelu::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_prelu_emitter);
+    jitters[ngraph::opset1::SquaredDifference::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_squared_difference_emitter);
+    jitters[ngraph::opset1::Subtract::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_subtract_emitter);
+    jitters[ngraph::opset1::Xor::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_logical_xor_emitter);
+
+    // unary
+    jitters[ngraph::opset1::Abs::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_abs_emitter);
+    // jitters[ngraph::opset1::Acos::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    // jitters[ngraph::opset1::Asin::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    // jitters[ngraph::opset1::Atan::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    // jitters[ngraph::opset1::Ceiling::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    jitters[ngraph::opset1::Clamp::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_clamp_emitter);
+    // jitters[ngraph::opset1::Cos::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    // jitters[ngraph::opset1::Cosh::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    jitters[ngraph::opset1::Elu::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_elu_emitter);
+    jitters[ngraph::opset1::Erf::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_erf_emitter);
+    jitters[ngraph::opset1::Exp::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_exp_emitter);
+    // jitters[ngraph::opset1::Floor::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    // jitters[ngraph::opset1::Log::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    jitters[ngraph::opset1::LogicalNot::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_logical_not_emitter);
+    jitters[ngraph::opset1::Negative::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_negative_emitter);
+    jitters[ngraph::opset1::Relu::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_relu_emitter);
+    // jitters[ngraph::opset1::Sign::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    jitters[ngraph::opset1::Sigmoid::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_sigmoid_emitter);
+    // jitters[ngraph::opset1::Sin::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    // jitters[ngraph::opset1::Sinh::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    jitters[ngraph::opset1::Sqrt::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_sqrt_emitter);
+    // jitters[ngraph::opset1::Tan::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    jitters[ngraph::opset1::Tanh::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_tanh_emitter);
+
+    jitters[ngraph::op::v4::HSwish::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_hswish_emitter);
+    // jitters[ngraph::opset1::HardSigmoid::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    // jitters[ngraph::opset1::Selu::get_type_info_static()] = CREATE_EMITTER(); // not supported
+    jitters[ngraph::op::v0::Gelu::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_gelu_v0_emitter);
+    jitters[ngraph::op::v7::Gelu::get_type_info_static()] = CREATE_EMITTER(MKLDNNPlugin::jit_gelu_v7_emitter);
+
+    jitters[ngraph::snippets::op::Kernel::get_type_info_static()] = CREATE_EMITTER(KernelEmitter);
+    jitters[ngraph::snippets::op::Tile::get_type_info_static()] = CREATE_EMITTER(TileEmitter);
+}
+
+size_t MKLDNNPlugin::CPUTargetMachine::get_lanes() const {
+    switch (isa) {
+        case dnnl::impl::cpu::x64::avx2 : return dnnl::impl::cpu::x64::cpu_isa_traits<dnnl::impl::cpu::x64::avx2>::vlen / sizeof(float);
+        case dnnl::impl::cpu::x64::sse41 : return dnnl::impl::cpu::x64::cpu_isa_traits<dnnl::impl::cpu::x64::sse41>::vlen / sizeof(float);
+        case dnnl::impl::cpu::x64::avx512_common : return dnnl::impl::cpu::x64::cpu_isa_traits<dnnl::impl::cpu::x64::avx512_common>::vlen / sizeof(float);
+        default : IE_THROW() << "unknown isa " << isa;
+    }
+}
+
+bool MKLDNNPlugin::CPUTargetMachine::is_supported() const {
+    return dnnl::impl::cpu::x64::mayiuse(isa);
+}
+
+code MKLDNNPlugin::CPUTargetMachine::get_snippet() const {
+    h->create_kernel();
+    return h->jit_ker();
+}
+
+MKLDNNPlugin::CPUGenerator::CPUGenerator(dnnl::impl::cpu::x64::cpu_isa_t isa_) : Generator(std::make_shared<CPUTargetMachine>(isa_)) {
+}
diff --git a/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.hpp b/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.hpp
new file mode 100644
index 00000000000..3b2b94cb204
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.hpp
@@ -0,0 +1,33 @@
+// Copyright (C) 2020-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <transformations_visibility.hpp>
+#include <cpu/x64/jit_generator.hpp>
+
+#include "snippets/generator.hpp"
+
+namespace MKLDNNPlugin {
+
+class CPUTargetMachine : public ngraph::snippets::TargetMachine {
+public:
+    CPUTargetMachine(dnnl::impl::cpu::x64::cpu_isa_t host_isa);
+
+    bool is_supported() const override;
+    ngraph::snippets::code get_snippet() const override;
+    size_t get_lanes() const override;
+
+private:
+    std::unique_ptr<dnnl::impl::cpu::x64::jit_generator> h;
+    dnnl::impl::cpu::x64::cpu_isa_t isa;
+};
+
+class CPUGenerator : public ngraph::snippets::Generator {
+public:
+    CPUGenerator(dnnl::impl::cpu::x64::cpu_isa_t isa);
+    ~CPUGenerator() = default;
+};
+
+} // namespace MKLDNNPlugin
\ No newline at end of file
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.cpp b/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.cpp
index 03eb2fdca44..c801082c01f 100644
--- a/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.cpp
+++ b/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.cpp
@@ -1286,21 +1286,14 @@ size_t jit_logical_not_emitter::aux_vecs_count() const {
 /// POWER_STATIC ///
 jit_power_static_emitter::jit_power_static_emitter(jit_generator *host, cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& node, Precision exec_prc)
 : jit_emitter(host, host_isa, node, exec_prc) {
-    auto parent = node->input(1).get_source_output().get_node_shared_ptr();
-    if (!std::dynamic_pointer_cast<ngraph::op::Constant>(parent)) {
-        throw ngraph::ngraph_error("unsupported non constant power");
+    auto powerStaticNode = ov::as_type_ptr<ngraph::snippets::op::PowerStatic>(node);
+    if (powerStaticNode == nullptr) {
+        IE_THROW() << "Can't cast to snippets::op::PowerStatic";
     }
 
-    if (!(node->input(1).get_shape() == ngraph::Shape() || ngraph::shape_size(node->input(1).get_shape()) == 1)) {
-        throw ngraph::ngraph_error("unsupported non scalar power");
-    }
-    power = ngraph::as_type_ptr<ngraph::op::Constant>(parent)->get_data_ptr<float>()[0];
+    power = powerStaticNode->get_power();
     scale = 1.f;
     shift = 0.f;
-    push_arg_entry_of("power", cpu::x64::float2int(power), true);
-    push_arg_entry_of("scale", 0x3f800000, true);
-    push_arg_entry_of("shift", 0x00000000, true);
-    push_arg_entry_of("one",   0x3f800000, true);
 
     prepare_table();
 }
@@ -1611,6 +1604,11 @@ jit_erf_emitter::jit_erf_emitter(jit_generator *host, cpu_isa_t host_isa, const
     prepare_table();
 }
 
+jit_erf_emitter::jit_erf_emitter(jit_generator *host, cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& node, Precision exec_prc)
+: jit_emitter(host, host_isa, node, exec_prc) {
+    prepare_table();
+}
+
 size_t jit_erf_emitter::get_inputs_num() const { return 1; }
 
 void jit_erf_emitter::emit_impl(
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.hpp b/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.hpp
index 50ddb57bad6..e03d32de9c1 100644
--- a/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.hpp
+++ b/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.hpp
@@ -531,6 +531,9 @@ public:
     jit_erf_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const MKLDNNNode* node,
         InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32);
 
+    jit_erf_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                    InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32);
+
     size_t get_inputs_num() const override;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.cpp b/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.cpp
index 49e853a24c9..236f6e873f0 100644
--- a/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.cpp
+++ b/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.cpp
@@ -101,7 +101,8 @@ void jit_emitter::emitter_preamble(const std::vector<size_t> &in_idxs, const std
         aux_vec_idxs.push_back(idx);
         preserved_vec_idxs.push_back(idx);
     }
-    assert(aux_vec_idxs.size() >= aux_vecs_count());
+    if (aux_vec_idxs.size() < aux_vecs_count())
+        IE_THROW() << "Failed to allocate required number of vector registers";
 
     // Same logic but to allocate gprs
     for (auto idx : pool_gpr_idxs)
@@ -124,7 +125,8 @@ void jit_emitter::emitter_preamble(const std::vector<size_t> &in_idxs, const std
         aux_gpr_idxs.push_back(_idx);
         preserved_gpr_idxs.push_back(_idx);
     }
-    assert(aux_gpr_idxs.size() == aux_gprs_count());
+    if (aux_gpr_idxs.size() < aux_gprs_count())
+        IE_THROW() << "Failed to allocate required number of general-purpose registers";
 
     if (!entry_map_.empty()) {
         // last aux_gpr_idx is for p_table, we can use aux_gpr_idxs from idx 0 for other purpose
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.hpp b/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.hpp
index 026936caa32..12f422b7d7f 100644
--- a/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.hpp
+++ b/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.hpp
@@ -7,6 +7,8 @@
 #include <ie_common.h>
 #include <cpu/x64/jit_generator.hpp>
 
+#include "snippets/snippets_isa.hpp"
+#include "snippets/generator.hpp"
 #include "mkldnn_node.h"
 
 #include <set>
@@ -24,23 +26,23 @@ struct emitter_context {
     virtual ~emitter_context() = default;
 };
 
-class jit_emitter {
+class jit_emitter : public ngraph::snippets::Emitter {
 public:
     jit_emitter(dnnl::impl::cpu::x64::jit_generator* host, dnnl::impl::cpu::x64::cpu_isa_t host_isa, const MKLDNNNode* node,
                 InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32, emitter_in_out_map in_out_type = emitter_in_out_map::vec_to_vec)
-        : h(host), host_isa_(host_isa), exec_prc_(exec_prc), in_out_type_(in_out_type), l_table (new Xbyak::Label()) {
+        : Emitter(nullptr), h(host), host_isa_(host_isa), exec_prc_(exec_prc), in_out_type_(in_out_type), l_table (new Xbyak::Label()) {
         k_mask = Xbyak::Opmask(1); // FIXME: in general case we need preserve k_mask state as well
     }
 
     jit_emitter(dnnl::impl::cpu::x64::jit_generator* host, dnnl::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
                 InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32, emitter_in_out_map in_out_type = emitter_in_out_map::vec_to_vec)
-        : h(host), host_isa_(host_isa), exec_prc_(exec_prc), in_out_type_(in_out_type), l_table (new Xbyak::Label()) {
+        : Emitter(n), h(host), host_isa_(host_isa), exec_prc_(exec_prc), in_out_type_(in_out_type), l_table (new Xbyak::Label()) {
         k_mask = Xbyak::Opmask(1); // FIXME: in general case we need preserve k_mask state as well
     }
 
-    virtual void emit_code(const std::vector<size_t> &in_idxs, const std::vector<size_t> &out_idxs,
-                   const std::vector<size_t> &pool_vec_idxs = {}, const std::vector<size_t> &pool_gpr_idxs = {}) const;
-    virtual void emit_data() const;
+    void emit_code(const std::vector<size_t> &in_idxs, const std::vector<size_t> &out_idxs,
+                   const std::vector<size_t> &pool_vec_idxs = {}, const std::vector<size_t> &pool_gpr_idxs = {}) const override;
+    void emit_data() const override;
 
     virtual void emit_code(const std::vector<size_t> &in_idxs, const std::vector<size_t> &out_idxs,
                       const std::shared_ptr<const emitter_context> &emit_context,
@@ -147,6 +149,8 @@ private:
         const auto scale = te.bcast ? get_vec_length() : sizeof(table_entry_val_t);
         return te.off + key_off_val_shift * scale;
     }
+    virtual void validate_arguments(const std::vector<size_t>&, const std::vector<size_t>&,
+                                    const std::vector<size_t>&, const std::vector<size_t> &) const {}
 };
 
 } // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_ext_emitters.hpp b/inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_ext_emitters.hpp
new file mode 100644
index 00000000000..15c2dc98563
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_ext_emitters.hpp
@@ -0,0 +1,144 @@
+// Copyright (C) 2020-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "jit_mkldnn_emitters.hpp"
+
+namespace MKLDNNPlugin {
+
+class jit_relu_emitter : public jit_mkldnn_emitter {
+public:
+    jit_relu_emitter(dnnl::impl::cpu::x64::jit_generator *host, dnnl::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                       InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+        : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+            kind = mkldnn_eltwise_relu;
+            alpha = 0.f;
+            beta = 0.f;
+
+            set_injector();
+        }
+};
+
+class jit_sigmoid_emitter : public jit_mkldnn_emitter {
+public:
+    jit_sigmoid_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                       InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+        : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+            kind = mkldnn_eltwise_logistic;
+            alpha = 0.f;
+            beta = 0.f;
+
+            set_injector();
+        }
+};
+
+class jit_tanh_emitter : public jit_mkldnn_emitter {
+public:
+    jit_tanh_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                       InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+        : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+            kind = mkldnn_eltwise_tanh;
+            alpha = 0.f;
+            beta = 0.f;
+
+            set_injector();
+        }
+};
+
+class jit_elu_emitter : public jit_mkldnn_emitter {
+public:
+    jit_elu_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                       InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+        : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+            kind = mkldnn_eltwise_elu;
+            alpha = ngraph::as_type_ptr<ngraph::opset1::Elu>(n)->get_alpha();
+            beta = 0.f;
+
+            set_injector();
+        }
+};
+
+class jit_exp_emitter : public jit_mkldnn_emitter {
+public:
+    jit_exp_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                       InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+        : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+            kind = mkldnn_eltwise_exp;
+            alpha = 0.f;
+            beta = 0.f;
+
+            set_injector();
+        }
+};
+
+class jit_abs_emitter : public jit_mkldnn_emitter {
+public:
+    jit_abs_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                       InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+        : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+            kind = mkldnn_eltwise_abs;
+            alpha = 0.f;
+            beta = 0.f;
+
+            set_injector();
+        }
+};
+
+class jit_clamp_emitter : public jit_mkldnn_emitter {
+public:
+    jit_clamp_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                       InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+        : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+            kind = dnnl_eltwise_clip;
+            auto op = ngraph::as_type_ptr<ngraph::opset1::Clamp>(n);
+            alpha = op->get_min();
+            beta = op->get_max();
+
+            set_injector();
+        }
+};
+
+class jit_hswish_emitter : public jit_mkldnn_emitter {
+public:
+    jit_hswish_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                        InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+            : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+        kind = dnnl_eltwise_hardswish;
+        alpha = 0.f;
+        beta = 0.f;
+
+        set_injector();
+    }
+};
+class jit_gelu_v0_emitter : public jit_mkldnn_emitter {
+public:
+    jit_gelu_v0_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                        InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+            : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+        kind = dnnl_eltwise_gelu_erf;
+
+        set_injector();
+    }
+};
+
+class jit_gelu_v7_emitter : public jit_mkldnn_emitter {
+public:
+    jit_gelu_v7_emitter(mkldnn::impl::cpu::x64::jit_generator *host, mkldnn::impl::cpu::x64::cpu_isa_t host_isa, const std::shared_ptr<ngraph::Node>& n,
+                        InferenceEngine::Precision exec_prc = InferenceEngine::Precision::FP32)
+            : jit_mkldnn_emitter(host, host_isa, n, exec_prc) {
+        auto gelu = getNgraphOpAs<ngraph::op::v7::Gelu>(n);
+        ngraph::op::GeluApproximationMode approximationMode = gelu->get_approximation_mode();
+        if (approximationMode == ngraph::op::GeluApproximationMode::ERF)
+            kind = dnnl_eltwise_gelu_erf;
+        else if (approximationMode == ngraph::op::GeluApproximationMode::TANH)
+            kind = dnnl_eltwise_gelu_tanh;
+        else
+            IE_THROW(NotImplemented) << "Subgraph node doesn't support ngraph operation Gelu with approximation mode: " << approximationMode;
+
+        set_injector();
+    }
+};
+
+} // namespace MKLDNNPlugin
\ No newline at end of file
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_snippets_emitters.hpp b/inference-engine/src/mkldnn_plugin/emitters/jit_snippets_emitters.hpp
new file mode 100644
index 00000000000..e2f131b9baf
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/emitters/jit_snippets_emitters.hpp
@@ -0,0 +1,641 @@
+// Copyright (C) 2020-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/rt_info.hpp>
+#include <ngraph/variant.hpp>
+
+#include "jit_emitter.hpp"
+using namespace Xbyak;
+namespace MKLDNNPlugin {
+
+#define SNIPPETS_MAX_SNIPPETS_DIMS 7
+#define SNIPPETS_MAX_HARNESS_DIMS 5
+#define SNIPPETS_MAX_TILE_RANK 2
+#define GET_OFF(field) offsetof(jit_snippets_call_args, field)
+struct jit_snippets_call_args {
+    const void *src_ptrs[SNIPPETS_MAX_SNIPPETS_DIMS] = {};
+    void *dst_ptrs[SNIPPETS_MAX_SNIPPETS_DIMS] = {};
+};
+
+struct jit_snippets_compile_args {
+    int64_t scheduler_dims[SNIPPETS_MAX_TILE_RANK] = {};
+    int64_t scheduler_offsets[SNIPPETS_MAX_SNIPPETS_DIMS] = {};
+    int64_t data_offsets[SNIPPETS_MAX_SNIPPETS_DIMS * SNIPPETS_MAX_HARNESS_DIMS] = {};
+    std::vector<int64_t> output_dims = {};
+};
+///
+/// \brief    Kernel is the only entry point to Codogen Jit compilation. Kernel calculates appropriate data offsets,
+/// and invokes enclosed outer Tiles. Only 2d Tiles are currently supported, so the emitters should
+/// be organized in the following way:
+/// KernelEmitter {          /* entry point */
+///     TileEmitter {        /* outer tile */
+///         TileEmitter {    /* inner vector tile */
+///             ...          /* All the necessary Load/Strore/elementwise emitters */
+///         }
+///         TileEmitter {    /* inner scalar tile for tail processing */
+///             ...          /* All the necessary Load/Strore/elementwise emitters */
+///         }
+///     }
+/// }
+/// Note that Kernel params are passed directly to the emit_code(). The vector of inputs should contain 2 arguments, the
+/// output vector should be empty. Input parameters
+///
+/// \param      in[0]       The number of the node inputs
+/// \param      in[1]      The number of the node outputs
+///
+// Todo: Scheduler dims and offsets are currently calculated in MKLDNN Subgraph node and passed to the KernelEmitter.
+//  However, it seems more natural to calculate all the offsets right in the Kernel op, because the calculation is
+//  not device-specific. It is based only on input/output dims (which we already know) and harness num dims
+//  (which we should pass from the plugin). It seems also better to wrap the enclosed emitters in tiles in the Kernel op
+//  and avoid creating empty tiles.
+class KernelEmitter : public jit_emitter {
+public:
+    KernelEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa,
+    const std::shared_ptr<ov::Node>& n)
+    : jit_emitter(h, isa, n) {
+        const auto kernel = ov::as_type_ptr<ngraph::snippets::op::Kernel>(n);
+        if (!kernel)
+            IE_THROW() << "KernelEmitter invoked with invalid op argument";
+        if (!kernel->compile_params)
+            IE_THROW() << "KernelEmitter invoked without compile_params";
+        code = kernel->region;
+        jcp = *reinterpret_cast<const jit_snippets_compile_args*>(kernel->compile_params);
+    }
+
+    size_t get_inputs_num() const override {return 0;}
+
+    void emit_code(const std::vector<size_t> &in, const std::vector<size_t> &out,
+              const std::vector<size_t> &pool = {}, const std::vector<size_t> &gpr = {}) const override {
+        validate_arguments(in, out, pool, gpr);
+        emit_impl(in, out, pool, gpr, nullptr);
+    }
+
+private:
+    void validate_arguments(const std::vector<size_t> &in, const std::vector<size_t> &out,
+                            const std::vector<size_t> &pool = {}, const std::vector<size_t> &gpr = {}) const override {
+        if (in.size() != 2)
+            IE_THROW() << "KernelEmitter got invalid number of inputs. Expected 2, got " << in.size();
+        if (out.size() != 0)
+            IE_THROW() << "KernelEmitter got unexpected output arguments.";
+        const size_t num_params = in[0] + in[1];
+        if (num_params > SNIPPETS_MAX_SNIPPETS_DIMS)
+            IE_THROW() << "KernelEmitter supports only up to " << SNIPPETS_MAX_SNIPPETS_DIMS <<
+                       " parameters, got " << num_params;
+        const int64_t harness_num_dims = jcp.output_dims.size() - 1;
+        if (harness_num_dims > SNIPPETS_MAX_HARNESS_DIMS)
+            IE_THROW() << "KernelEmitter supports harness with up to " << SNIPPETS_MAX_HARNESS_DIMS <<
+                       " dims, got " << harness_num_dims;
+    }
+
+    void emit_impl(const std::vector<size_t>& in,
+                   const std::vector<size_t>& out,
+                   const std::vector<size_t>& pool,
+                   const std::vector<size_t>& gpr,
+                   const MKLDNNPlugin::emitter_context *emit_context) const override {
+        const size_t num_inputs = in[0];
+        const size_t num_outputs = in[1];
+        const size_t num_params = num_inputs + num_outputs;
+        int reg64_tmp_start { 8 }; // R8, R9, R10, R11, R12, R13, R14, R15 inputs+outputs+1
+        const int64_t harness_num_dims = jcp.output_dims.size() - 1;
+
+        Reg64 reg_indexes   { dnnl::impl::cpu::x64::abi_param1 };
+        Reg64 reg_const_params { dnnl::impl::cpu::x64::abi_param2 };
+        Xbyak::Reg64 reg_tmp_64 { dnnl::impl::cpu::x64::abi_not_param1};
+
+        h->preamble();
+
+        std::vector<Reg64> regs(num_params);
+        auto init_ptrs_with_offsets = [&](Reg64 pointer, const int64_t *offsets) {
+            for (int j = 0; j < harness_num_dims; j++) {
+                if (jcp.output_dims[j] != 1 && offsets[j] != 0) {
+                    h->mov(reg_tmp_64, offsets[j]);
+                    h->imul(reg_tmp_64, h->ptr[reg_indexes + j * sizeof(size_t)]);
+                    h->add(pointer, reg_tmp_64);
+                }
+            }
+        };
+        for (auto i = 0; i < num_params; i++) {
+            regs[i] = Reg64(reg64_tmp_start + i);
+            if (i < num_inputs)
+                h->mov(regs[i], h->ptr[reg_const_params + GET_OFF(src_ptrs) + i * sizeof(void*)]);
+            else
+                h->mov(regs[i], h->ptr[reg_const_params + GET_OFF(dst_ptrs) + (i - num_inputs) * sizeof(void*)]);
+            init_ptrs_with_offsets(regs[i], &jcp.data_offsets[i * harness_num_dims]);
+        }
+
+        for (auto& c : code) {
+            c.first->emit_code(c.second.first, c.second.second, pool, gpr);
+        }
+
+        h->postamble();
+    }
+
+    jit_snippets_compile_args jcp;
+    std::vector<std::pair<std::shared_ptr<Emitter>, ngraph::snippets::RegInfo>> code;
+};
+///
+/// \brief    Tile is designed to organize loop over the input and output data. It is essentially a for(...) loop:
+/// it calculates the total number of iterations, performs operations specified by enclosed emitters, advances iteration counters
+/// and breaks when necessary.
+///
+/// \param      in[0]    The number of input entities (or scheduler counts) processed during one iteration of the tile.
+/// It is expected to be 1 for outer or scalar tiles and vlen for vector tiles.
+/// \param      in[1]    Increment of the previous Tile in current dimension. Must be 0 if this is the first Tile.
+/// So previous_inc is zero for outer and vector tiles (the are the first in dim) and vlen for scalar tiles (they usually go after vector Tiles).
+/// \param      in[2]    sum number inputs and number of outputs of the node.
+/// \param      in[3]    dimension of the tile. Note that only 2d Tile are currently supported, so dim is 0 for outer tiles, 1 for inner tiles.
+///
+// Todo: Inner and outer tiles have different semantics. For example, outer tile always has the increment == 1, and it can contain only
+//  tile emitters (one outer or two inner). So it seems better to create different classes for inner and outer tiles.
+// Todo: Currently data pointers incremented after each read/write in Load/Store emitters, so we have to decrement them here
+//  if the same data needs to be read twice. Better to move all the pointer increments to TileEmitter and avoid the increments if necessary.
+class TileEmitter : public jit_emitter {
+public:
+    TileEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa,
+    const std::shared_ptr<ov::Node>& n)
+    : jit_emitter(h, isa, n) {
+        const auto tile = ov::as_type_ptr<ngraph::snippets::op::Tile>(n);
+        if (!tile)
+            IE_THROW() << "TileEmitter invoked with invalid op argument";
+        if (!tile->compile_params)
+            IE_THROW() << "TileEmitter invoked without compile_params";
+        code = tile->region;
+        jcp = *reinterpret_cast<const jit_snippets_compile_args*>(tile->compile_params);
+    }
+
+    size_t get_inputs_num() const override {return 0;}
+
+    void emit_code(const std::vector<size_t> &in, const std::vector<size_t> &out,
+              const std::vector<size_t> &pool = {}, const std::vector<size_t> &gpr = {}) const override {
+        validate_arguments(in, out, pool, gpr);
+        emit_impl(in, out, pool, gpr, nullptr);
+    }
+
+private:
+    void validate_arguments(const std::vector<size_t> &in, const std::vector<size_t> &out,
+                            const std::vector<size_t> &pool = {}, const std::vector<size_t> &gpr = {}) const override {
+        if (in.size() != 4)
+            IE_THROW() << "TileEmitter got invalid number of inputs. Expected 4, got " << in.size();
+        if (out.size() != 0)
+            IE_THROW() << "TileEmitter got unexpected output arguments.";
+        const size_t num_params = in[2];
+        if (num_params > SNIPPETS_MAX_SNIPPETS_DIMS)
+            IE_THROW() << "TileEmitter supports only up to " << SNIPPETS_MAX_SNIPPETS_DIMS <<
+                       " parameters, got " << num_params;
+        const size_t dim = in[3];
+        if (dim >= SNIPPETS_MAX_TILE_RANK)
+            IE_THROW() << "TileEmitter supports tile ranks up to " << SNIPPETS_MAX_TILE_RANK <<
+                       " got " << dim;
+    }
+
+    void emit_impl(const std::vector<size_t>& in,
+                   const std::vector<size_t>& out,
+                   const std::vector<size_t>& pool,
+                   const std::vector<size_t>& gpr,
+                   const MKLDNNPlugin::emitter_context *emit_context) const override {
+        const size_t inc = in[0];
+        const size_t previous_inc = in[1]; // increment of a previous tile in the same dim (0 if the first tile in the dim)
+        const size_t num_params = in[2];
+        const size_t dim = in[3]; // tile dimension: 0 - outer, 1 - inner
+        const int reg64_tmp_start { 8 }; // R8, R9, R10, R11, R12, R13, R14, R15 inputs+outputs+1
+        Reg64 amount = Reg64(reg64_tmp_start + num_params); // amount
+        std::array<Label, 2> for_body;
+
+        // If R15 is not used, reserve it for use in scalar to avoid redundant push-pop's.
+        // todo: Do we need explicitly check that code contains ScalarEmitter?
+        std::vector<size_t> local_gpr = reg64_tmp_start + num_params < 15 ? std::vector<size_t>{15} : std::vector<size_t>{};
+        std::vector<Reg64> regs(num_params);
+        for (auto i = 0; dim == 0 && i < num_params; i++)
+            regs[i] = Reg64(reg64_tmp_start + i);
+        // Loop processing could be simplified in some cases
+        if (inc > jcp.scheduler_dims[dim]) {
+            return;
+        } else if (inc == jcp.scheduler_dims[dim]) {
+            for (auto& c : code) {
+                c.first->emit_code(c.second.first, c.second.second, pool, local_gpr);
+            }
+        } else {
+            // The previous tile has done nothing, all the work is ours
+            if (previous_inc == 0 || previous_inc > jcp.scheduler_dims[dim]) {
+                h->mov(amount, jcp.scheduler_dims[dim]);
+            // The previous tile has done all the work
+            } else if (jcp.scheduler_dims[dim] % previous_inc == 0) {
+                return;
+            }// else: the previous tile has already set a proper work amount
+            h->cmp(amount, inc);
+            h->jl(for_body[0], CodeGenerator::T_NEAR);
+
+            h->L(for_body[1]);
+            {
+                h->push(amount);
+                for (auto& c : code) {
+                    c.first->emit_code(c.second.first, c.second.second, pool, local_gpr);
+                }
+                h->pop(amount);
+                // Todo: Load and Store emitters are currently implemented so they ALWAYS increment appropriate pointers
+                //   after reading/writing. This might be a problem if we need to read the same data multiple times (broadcasting shapes).
+                //   To overcome this limitation, we add appropriate negative offsets if necessary.
+                for (auto i = 0; dim == 0 && i < num_params; i++) {
+                    if (jcp.scheduler_offsets[i] != 0) {
+                        h->add(regs[i], jcp.scheduler_offsets[i]);
+                    }
+                }
+                    h->sub(amount, inc);
+                    h->cmp(amount, inc);
+                    h->jge(for_body[1], CodeGenerator::T_NEAR);
+            }
+
+            h->L(for_body[0]);
+        }
+    }
+
+    // A = <42, 17>
+    // B = < 1, 17>
+    // for (auto k = 0; k < dom_0; k++) { // 42
+    //   for (auto n = 0; n < dom_1; n++) { // 17
+    //     auto a = *ptr0; ptr0 += vlan; // vector/scalar load
+    //     auto b = *ptr1; ptr1 += vlan; // vector/scalar load
+    //   }
+    //   ptr0 -= 0*dom_1;
+    //   ptr1 -= 1*dom_1;
+    // }
+
+    // broadcast by MVD is extra case
+    // A = <42, 17>
+    // B = <42,  1>
+    // for (auto k = 0; k < dom_0; k++) { // 42
+    //   for (auto n = 0; n < dom_1; n++) { // 17
+    //     auto a = *ptr0; ptr0 += vlan; // vector/scalar load
+    //     auto b = *ptr1;  // broadcast load
+    //   }
+    //   ptr0 -= 0*dom_1;
+    //   ptr1 += sizeof(ptr1[0]); //ptr1 -= -sizeof(ptr1[0]);
+    // }
+
+    // A = <42, 17, 31>
+    // B = < 1, 17, 31>
+    // for (auto k = 0; k < dom_0; k++) { // 42
+    //   for (auto n = 0; n < dom_1; n++) { // 17
+    //     for (auto m = 0; m < dom_2; m++) { // 31
+    //       auto a = *ptr0; ptr0 += vlan; // vector/scalar load
+    //       auto b = *ptr1; ptr1 += vlan; // vector/scalar load
+    //     }
+    //   }
+    //   ptr0 -= 0*dom_1*dom2;
+    //   ptr1 -= 1*dom_1*dom2;
+    // }
+    jit_snippets_compile_args jcp;
+    std::vector<std::pair<std::shared_ptr<Emitter>, ngraph::snippets::RegInfo>> code;
+};
+
+class NopEmitter : public jit_emitter {
+public:
+    NopEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : jit_emitter(h, isa, n) {
+    }
+
+    size_t get_inputs_num() const override {return 0;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+                   const std::vector<size_t>& out,
+                   const std::vector<size_t>& pool,
+                   const std::vector<size_t>& gpr,
+                   const MKLDNNPlugin::emitter_context *emit_context) const override {
+    }
+};
+
+class FakeBroadcastEmitter : public jit_emitter {
+public:
+    FakeBroadcastEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : jit_emitter(h, isa, n) {
+        if (n->get_input_shape(0).empty())
+            use_broadcast = true;
+        else if (*n->get_input_shape(0).rbegin() != *n->get_output_shape(0).rbegin())
+            use_broadcast = true;
+        else
+            use_broadcast = false;
+    }
+    size_t get_inputs_num() const override {return 1;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+              const std::vector<size_t>& out,
+              const std::vector<size_t>& pool,
+              const std::vector<size_t>& gpr,
+              const MKLDNNPlugin::emitter_context *emit_context) const override {
+        if (host_isa_ == dnnl::impl::cpu::x64::sse41) {
+            emit_isa<dnnl::impl::cpu::x64::sse41>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx2) {
+            emit_isa<dnnl::impl::cpu::x64::avx2>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx512_common) {
+            emit_isa<dnnl::impl::cpu::x64::avx512_common>(in, out);
+        } else {
+            IE_THROW() << host_isa_;
+            assert(!"unsupported isa");
+        }
+    }
+
+    template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in, const std::vector<size_t> &out) const {
+        using Vmm = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::sse41,
+                                    Xmm, isa == dnnl::impl::cpu::x64::avx2, Ymm, Zmm>::type;
+        Vmm vmm_src0 = Vmm(in[0]);
+        Vmm vmm_dst  = Vmm(out[0]);
+
+        if (use_broadcast) {
+            h->uni_vbroadcastss(vmm_dst, Xmm(in[0]));
+        } else {
+            h->uni_vmovups(vmm_dst, vmm_src0);
+        }
+    }
+
+private:
+    bool use_broadcast;
+};
+
+class ScalarEmitter : public jit_emitter {
+public:
+    ScalarEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : jit_emitter(h, isa, n) {
+        auto out_pshape = n->output(0).get_tensor().get_partial_shape();
+        if (out_pshape.is_dynamic())
+            IE_THROW() << "ScalarEmitter supports only static input shapes";
+        if ( out_pshape.get_shape() != ov::Shape() && ov::shape_size(out_pshape.get_shape()) != 1)
+            IE_THROW() << "ScalarEmitter got invalid shape";
+        value = mkldnn::impl::cpu::x64::float2int(ov::as_type_ptr<ngraph::snippets::op::Scalar>(n)->cast_vector<float>()[0]);
+
+        push_arg_entry_of("scalar", value, true);
+        prepare_table();
+    }
+
+    size_t get_inputs_num() const override {return 0;}
+
+protected:
+    size_t aux_gprs_count() const override {return 1;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+              const std::vector<size_t>& out,
+              const std::vector<size_t>& pool,
+              const std::vector<size_t>& gpr,
+              const MKLDNNPlugin::emitter_context *emit_context) const override {
+        if (host_isa_ == dnnl::impl::cpu::x64::sse41) {
+            emit_isa<dnnl::impl::cpu::x64::sse41>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx2) {
+            emit_isa<dnnl::impl::cpu::x64::avx2>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx512_common) {
+            emit_isa<dnnl::impl::cpu::x64::avx512_common>(in, out);
+        } else {
+            IE_THROW() << host_isa_;
+            assert(!"unsupported isa");
+        }
+    }
+
+    template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in, const std::vector<size_t> &out) const {
+        using Vmm = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::sse41,
+                                    Xmm, isa == dnnl::impl::cpu::x64::avx2, Ymm, Zmm>::type;
+        Vmm vmm_dst  = Vmm(out[0]);
+        h->uni_vbroadcastss(vmm_dst, table_val("scalar"));
+    }
+
+private:
+    int32_t value;
+};
+
+///
+/// Memory emitters:
+///
+/// *Note*: post increment is embedded into Load/Store operation which means that
+/// it's illigal to load/store to the same address multiple times
+/// Typical application can be if Load and BroadcastLoad are performed from the same pointer.
+/// If Load goes before BroadcastLoad topologicaly the resilt will be incorrect
+/// For scalar loads we can use different tiles. Tiling indeed can be arbitrary and post increment should be somehow coded into ISA.
+/// Blocked parameter to tell if input is actually blocked. Broadcast means broadcast by W in other cases no need to substitute load.
+class MemoryEmitter : public jit_emitter  {
+public:
+    MemoryEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : jit_emitter(h, isa, n), ea(getEA(n)) {
+    }
+
+    size_t get_inputs_num() const override {return 1;}
+
+protected:
+    static auto getEA(const std::shared_ptr<ov::Node>& n) -> size_t {
+        auto& rt = n->get_rt_info();
+        size_t ea = 0;
+        auto it = rt.find("effectiveAddress");
+        if (it != rt.end()) {
+            ea = it->second.as<int64_t>();
+        } else {
+            throw ov::Exception("effective address for Load generation cannot be determined");
+        }
+        return ea;
+    }
+
+    size_t ea;
+};
+
+class StoreEmitter : public MemoryEmitter  {
+public:
+    StoreEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : MemoryEmitter(h, isa, n) {
+    }
+
+    size_t get_inputs_num() const override {return 1;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+              const std::vector<size_t>& out,
+              const std::vector<size_t>& pool,
+              const std::vector<size_t>& gpr,
+              const MKLDNNPlugin::emitter_context *emit_context) const override {
+        if (host_isa_ == dnnl::impl::cpu::x64::sse41) {
+            emit_isa<dnnl::impl::cpu::x64::sse41>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx2) {
+            emit_isa<dnnl::impl::cpu::x64::avx2>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx512_common) {
+            emit_isa<dnnl::impl::cpu::x64::avx512_common>(in, out);
+        } else {
+            IE_THROW() << host_isa_;
+            assert(!"unsupported isa");
+        }
+    }
+
+    template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in, const std::vector<size_t> &out) const {
+        using Vmm = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::sse41,
+                                    Xmm, isa == dnnl::impl::cpu::x64::avx2, Ymm, Zmm>::type;
+        Reg64 out_reg(ea);
+        Vmm vmm_src0 = Vmm(in[0]);
+        h->uni_vmovups(h->ptr[out_reg], vmm_src0);
+        h->add(out_reg, mkldnn::impl::cpu::x64::cpu_isa_traits<isa>::vlen);
+    }
+};
+
+class ScalarStoreEmitter : public MemoryEmitter {
+public:
+    ScalarStoreEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : MemoryEmitter(h, isa, n) {
+    }
+
+    size_t get_inputs_num() const override {return 1;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+              const std::vector<size_t>& out,
+              const std::vector<size_t>& pool,
+              const std::vector<size_t>& gpr,
+              const MKLDNNPlugin::emitter_context *emit_context) const override {
+        if (host_isa_ == dnnl::impl::cpu::x64::sse41) {
+            emit_isa<dnnl::impl::cpu::x64::sse41>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx2) {
+            emit_isa<dnnl::impl::cpu::x64::avx2>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx512_common) {
+            emit_isa<dnnl::impl::cpu::x64::avx512_common>(in, out);
+        } else {
+            IE_THROW() << host_isa_;
+            assert(!"unsupported isa");
+        }
+    }
+
+    template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in, const std::vector<size_t> &out) const {
+        using Vmm = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::sse41,
+                                        Xmm, isa == dnnl::impl::cpu::x64::avx2, Ymm, Zmm>::type;
+        Reg64 out_reg(ea);
+        Xmm vmm_src0 = Xmm(in[0]);
+        h->uni_vmovss(h->ptr[out_reg], vmm_src0);
+        h->add(out_reg, sizeof(float));
+    }
+};
+
+class LoadEmitter : public MemoryEmitter {
+public:
+    LoadEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : MemoryEmitter(h, isa, n), shouldPostIncrement(*n->get_input_shape(0).rbegin() != 1) {
+    }
+
+    size_t get_inputs_num() const override {return 0;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+              const std::vector<size_t>& out,
+              const std::vector<size_t>& pool,
+              const std::vector<size_t>& gpr,
+              const MKLDNNPlugin::emitter_context *emit_context) const override {
+        if (host_isa_ == dnnl::impl::cpu::x64::sse41) {
+            emit_isa<dnnl::impl::cpu::x64::sse41>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx2) {
+            emit_isa<dnnl::impl::cpu::x64::avx2>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx512_common) {
+            emit_isa<dnnl::impl::cpu::x64::avx512_common>(in, out);
+        } else {
+            IE_THROW() << host_isa_;
+            assert(!"unsupported isa");
+        }
+    }
+
+    template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in, const std::vector<size_t> &out) const {
+        using Vmm = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::sse41,
+                                            Xmm, isa == dnnl::impl::cpu::x64::avx2, Ymm, Zmm>::type;
+        Reg64 in_reg(ea);
+        Vmm vmm_src0 = Vmm(out[0]);
+        h->uni_vmovups(vmm_src0, h->ptr[in_reg]);
+
+        if (shouldPostIncrement) {
+            h->add(in_reg, mkldnn::impl::cpu::x64::cpu_isa_traits<isa>::vlen);
+        }
+    }
+
+private:
+    bool shouldPostIncrement;
+};
+
+class BroadcastLoadEmitter : public MemoryEmitter {
+public:
+    BroadcastLoadEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : MemoryEmitter(h, isa, n) {
+    }
+    size_t get_inputs_num() const override {return 0;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+              const std::vector<size_t>& out,
+              const std::vector<size_t>& pool,
+              const std::vector<size_t>& gpr,
+              const MKLDNNPlugin::emitter_context *emit_context) const override {
+        if (host_isa_ == dnnl::impl::cpu::x64::sse41) {
+            emit_isa<dnnl::impl::cpu::x64::sse41>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx2) {
+            emit_isa<dnnl::impl::cpu::x64::avx2>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx512_common) {
+            emit_isa<dnnl::impl::cpu::x64::avx512_common>(in, out);
+        } else {
+            IE_THROW() << host_isa_;
+            assert(!"unsupported isa");
+        }
+    }
+
+    template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in, const std::vector<size_t> &out) const {
+        using Vmm = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::sse41,
+                                            Xmm, isa == dnnl::impl::cpu::x64::avx2, Ymm, Zmm>::type;
+        Reg64 in_reg(ea);
+        Vmm vmm_src0 = Vmm(out[0]);
+
+        // In doesn't really matter if we broadcast or `movss` for vector tails so keep only one version for `BroadcastLoad`,
+        // key point here is not to add post-increment, it might be fixed by some other approach in future
+        h->uni_vbroadcastss(vmm_src0, h->ptr[in_reg]);
+    }
+};
+
+class ScalarLoadEmitter : public MemoryEmitter {
+public:
+    ScalarLoadEmitter(mkldnn::impl::cpu::x64::jit_generator* h, mkldnn::impl::cpu::x64::cpu_isa_t isa, const std::shared_ptr<ov::Node>& n)
+    : MemoryEmitter(h, isa, n), shouldPostIncrement(*n->get_input_shape(0).rbegin() != 1) {
+    }
+    size_t get_inputs_num() const override {return 0;}
+
+private:
+    void emit_impl(const std::vector<size_t>& in,
+              const std::vector<size_t>& out,
+              const std::vector<size_t>& pool,
+              const std::vector<size_t>& gpr,
+              const MKLDNNPlugin::emitter_context *emit_context) const override {
+        if (host_isa_ == dnnl::impl::cpu::x64::sse41) {
+            emit_isa<dnnl::impl::cpu::x64::sse41>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx2) {
+            emit_isa<dnnl::impl::cpu::x64::avx2>(in, out);
+        } else if (host_isa_ == dnnl::impl::cpu::x64::avx512_common) {
+            emit_isa<dnnl::impl::cpu::x64::avx512_common>(in, out);
+        } else {
+            IE_THROW() << host_isa_;
+            assert(!"unsupported isa");
+        }
+    }
+
+    template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in, const std::vector<size_t> &out) const {
+        using Vmm = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::sse41,
+                                            Xmm, isa == dnnl::impl::cpu::x64::avx2, Ymm, Zmm>::type;
+        Reg64 in_reg(ea);
+        Xmm vmm_src0 = Xmm(out[0]);
+        h->uni_vmovss(vmm_src0, h->ptr[in_reg]);
+
+        // Doesn't work if the same pointer comes with multiple load operations
+        if (shouldPostIncrement) {
+            h->add(in_reg, sizeof(float));
+        }
+    }
+
+private:
+    bool shouldPostIncrement;
+};
+
+} // namespace MKLDNNPlugin
\ No newline at end of file
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
index f3ff2beba9a..85df2cbbad1 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
@@ -81,6 +81,7 @@
 #include "nodes/mkldnn_if_node.h"
 #include "nodes/mkldnn_ctc_greedy_decoder_node.h"
 #include "nodes/mkldnn_non_zero.h"
+#include "nodes/subgraph.h"
 
 #define MKLDNN_NODE(__prim, __type) \
     registerNodeIfRequired(MKLDNNPlugin, __prim, __type, MKLDNNNodeImpl<__prim>)
@@ -170,4 +171,5 @@ MKLDNNPlugin::MKLDNNNode::NodesFactory::NodesFactory()
     MKLDNN_NODE(MKLDNNStridedSliceNode, StridedSlice);
     MKLDNN_NODE(MKLDNNGRNNode, GRN);
     MKLDNN_NODE(MKLDNNNonZeroNode, NonZero);
+    MKLDNN_NODE(MKLDNNSnippetNode, Subgraph);
 }
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index 6f8af6141d9..7d948150c1e 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -80,6 +80,8 @@
 #include <transformations/rt_info/fused_names_attribute.hpp>
 #include <transformations/op_conversions/fq_decomposition.hpp>
 #include <transformations/utils/utils.hpp>
+#include <snippets/pass/collapse_subgraph.hpp>
+#include "ngraph_transformations/snippets_mark_skipped.hpp"
 
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/opsets/opset2.hpp>
@@ -125,6 +127,7 @@
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
+
 Engine::Engine() {
     _pluginName = "CPU";
     extensionManager->AddExtension(std::make_shared<MKLDNNPlugin::MKLDNNExtension>());
@@ -136,7 +139,8 @@ Engine::~Engine() {
     ExecutorManager::getInstance()->clear("CPUCallbackExecutor");
 }
 
-static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function> nGraphFunc, const bool _enableLPT) {
+static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function> nGraphFunc, const bool _enableLPT,
+                                               const bool _enableSnippets) {
     ngraph::pass::Manager manager;
     manager.set_per_pass_validation(false);
     manager.register_pass<ngraph::pass::InitNodeInfo>();
@@ -468,6 +472,7 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
     ngraph::pass::Manager postLPTPassManager;
     postLPTPassManager.register_pass<ngraph::pass::FakeQuantizeDecomposition>();
     postLPTPassManager.register_pass<ngraph::pass::UnrollTensorIterator>();
+    postLPTPassManager.register_pass<ReshapePRelu>();
 
     postLPTPassManager.get_pass_config()->set_callback<ngraph::pass::FakeQuantizeDecomposition>([](const_node_ptr &node) -> bool {
         std::string errMsg;
@@ -478,6 +483,7 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
         return node->get_rt_info().count("UNROLL_TI") == 0;
     });
 
+
     postLPTPassManager.register_pass<MoveEltwiseUpThroughDataMov>();
     postLPTPassManager.get_pass_config()->set_callback<MoveEltwiseUpThroughDataMov>([](const std::shared_ptr<const ngraph::Node>& node) -> bool {
         if (node->get_input_size() >= 2) {
@@ -486,12 +492,41 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
         return false;
     });
 
+    postLPTPassManager.register_pass<ngraph::pass::ConstantFolding>();
     postLPTPassManager.run_passes(nGraphFunc);
+
+    if (!useLpt && _enableSnippets && with_cpu_x86_avx2()) {
+        ngraph::pass::Manager tokenization_manager;
+        tokenization_manager.register_pass<SnippetsMarkSkipped>();
+        tokenization_manager.register_pass<ngraph::snippets::pass::EnumerateNodes>();
+        tokenization_manager.register_pass<ngraph::snippets::pass::TokenizeSnippets>();
+        tokenization_manager.get_pass_config()->set_callback<ngraph::snippets::pass::TokenizeSnippets>(
+                [](const std::shared_ptr<const ov::Node>& n) -> bool {
+                    const auto& inputs = n->inputs();
+                    // todo: clarify whether we can evaluate snippets on const paths
+                    const bool has_only_const_inputs = std::all_of(inputs.begin(), inputs.end(),
+                                [](const ov::Input<const ov::Node> &in) {
+                                        return ov::is_type<ov::op::v0::Constant>(in.get_source_output().get_node_shared_ptr());
+                                      });
+                    // todo: clarify whether we can evaluate snippets on inputs with larger ranks
+                    auto rank_is_too_large = [](const ov::descriptor::Tensor& t ) {
+                        // callback is called has_supported_in_out(), so it's safe to assume that the shapes are static
+                        return t.get_partial_shape().rank().get_length() > 6;
+                    };
+                    const bool bad_input_rank = std::any_of(inputs.begin(), inputs.end(),
+                                                            [&](const ov::Input<const ov::Node>& in) {return  rank_is_too_large(in.get_tensor());});
+                    const auto& outputs = n->outputs();
+                    const bool bad_output_rank = std::any_of(outputs.begin(), outputs.end(),
+                                                             [&](const ov::Output<const ov::Node>& out) {return  rank_is_too_large(out.get_tensor());});
+                    return has_only_const_inputs || bad_input_rank || bad_output_rank;
+                });
+        tokenization_manager.run_passes(nGraphFunc);
+    }
 }
 
-static void Transformation(CNNNetwork& clonedNetwork, const bool _enableLPT) {
+static void Transformation(CNNNetwork& clonedNetwork, const bool _enableLPT, const bool _enableSnippets) {
     auto nGraphFunc = clonedNetwork.getFunction();
-    TransformationUpToCPUSpecificOpSet(nGraphFunc, _enableLPT);
+    TransformationUpToCPUSpecificOpSet(nGraphFunc, _enableLPT, _enableSnippets);
     ConvertToCPUSpecificOpset(nGraphFunc);
 }
 
@@ -526,8 +561,18 @@ Engine::LoadExeNetworkImpl(const InferenceEngine::CNNNetwork &network, const std
     const auto& lptProp = config.find(InferenceEngine::PluginConfigInternalParams::KEY_LP_TRANSFORMS_MODE);
     const bool enableLPT = (lptProp != config.end() && lptProp->second == PluginConfigParams::YES) /* enabled in the orig_config*/
             || Config::LPTransformsMode::On == engConfig.lpTransformsMode /* or already enabled for the plugin */;
+    const auto& BF16Prop = config.find(InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16);
+    const bool enableBF16 = ((BF16Prop != config.end() && BF16Prop->second == PluginConfigParams::YES)
+            || engConfig.enforceBF16) && with_cpu_x86_avx512_core();
+    const auto& modelCacheProp = config.find(InferenceEngine::PluginConfigParams::KEY_CACHE_DIR);
+    const bool enableModelCache = (modelCacheProp != config.end() && !modelCacheProp->second.empty())
+            || !engConfig.cache_dir.empty();
+    const auto& dynamicBatchProp = config.find(InferenceEngine::PluginConfigParams::KEY_DYN_BATCH_ENABLED);
+    const bool enableDynamicBatch = (dynamicBatchProp != config.end() && dynamicBatchProp->second == PluginConfigParams::YES)
+            || engConfig.enableDynamicBatch;
+    const bool enableSnippets = !(enableModelCache || enableDynamicBatch || enableBF16);
     auto nGraphFunc = clonedNetwork.getFunction();
-    TransformationUpToCPUSpecificOpSet(nGraphFunc, enableLPT);
+    TransformationUpToCPUSpecificOpSet(nGraphFunc, enableLPT, enableSnippets);
 
     // Here the OV perf modes are turned into specific settings (as we need the network for better params selection)
     const auto& mode = config.find(PluginConfigParams::KEY_PERFORMANCE_HINT);
@@ -736,7 +781,8 @@ QueryNetworkResult Engine::QueryNetwork(const CNNNetwork& network, const std::ma
         const auto& lptProp = config.find(InferenceEngine::PluginConfigInternalParams::KEY_LP_TRANSFORMS_MODE);
         const bool enableLPT = (lptProp != config.end() && lptProp->second == PluginConfigParams::YES) /* enabled in the orig_config*/
                                || Config::LPTransformsMode::On == engConfig.lpTransformsMode /* or already enabled */;
-        Transformation(clonedNetwork, enableLPT);
+        const bool enableSnippets = !(conf.cache_dir.empty() || conf.enableDynamicBatch || (conf.enforceBF16 && with_cpu_x86_avx512_core()));
+        Transformation(clonedNetwork, enableLPT, enableSnippets);
         auto ops = clonedNetwork.getFunction()->get_ordered_ops();
         std::unordered_set<std::string> supported;
         std::unordered_set<std::string> unsupported;
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
index 121247c702c..9702ebf0d89 100644
--- a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
@@ -23,14 +23,12 @@ namespace MKLDNNPlugin {
 
 inline void ConvertToCPUSpecificOpset(std::shared_ptr<ngraph::Function> &nGraphFunc) {
     ngraph::pass::Manager manager;
-    manager.register_pass<ngraph::pass::ConstantFolding>();
     manager.register_pass<ConvertMatMulToFC>();
     manager.register_pass<AlignMatMulInputRanks>();
     manager.register_pass<ConvertTileToSeqTiles>();
     manager.register_pass<FullyConnectedBiasFusion>();
     manager.register_pass<ConvertToPowerStatic>();
     manager.register_pass<ConvertToLeakyRelu>();
-    manager.register_pass<ReshapePRelu>();
     manager.register_pass<ConvertToSwishCPU>();
     manager.register_pass<OptimizeSequenceTransposes>();
     if (!ngraph::op::util::has_op_with_type<ngraph::op::FakeQuantize>(nGraphFunc)) {
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.cpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.cpp
new file mode 100644
index 00000000000..7a284e3a89f
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.cpp
@@ -0,0 +1,352 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "snippets_mark_skipped.hpp"
+#include <snippets/pass/collapse_subgraph.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <utils/general_utils.h>
+#include <utils/cpu_utils.hpp>
+
+NGRAPH_RTTI_DEFINITION(MKLDNNPlugin::SnippetsMarkSkipped, "SnippetsMarkSkipped", 0);
+
+using namespace ngraph;
+namespace MKLDNNPlugin {
+namespace {
+NodeFusingType GetNodeFusingType(const std::shared_ptr<const Node> &node) {
+    auto &rt = node->get_rt_info();
+    const auto rinfo = rt.find("MayBeFusedInPlugin");
+    if (rinfo == rt.end())
+        return NodeFusingType::NotSet;
+    return rinfo->second.as<NodeFusingType>();
+}
+void SetNodeFusingType(const std::shared_ptr<Node> &node, NodeFusingType nodeType) {
+    auto &rt = node->get_rt_info();
+    rt["MayBeFusedInPlugin"] = nodeType;
+}
+std::vector<NodeFusingType> getContinuableChains(const std::shared_ptr<const Node> &node) {
+    std::vector<NodeFusingType> result;
+    for (const auto& input : node->inputs()) {
+        const auto parent = input.get_source_output().get_node_shared_ptr();
+        const auto snt = GetNodeFusingType(parent);
+        if (snt > NodeFusingType::FusedTerminator) {
+            result.push_back(snt);
+        }
+    }
+    return result;
+}
+int getNumNonConstInputs(const std::shared_ptr<const Node> &node) {
+    int num_non_const_inputs = 0;
+    for (const auto &parent_out : node->input_values()) {
+        const auto parent = parent_out.get_node_shared_ptr();
+        if (ov::is_type<ngraph::op::v1::Reshape>(parent)) {
+            for (const auto &grandparent_out : parent->input_values()) {
+                const auto grandparent = grandparent_out.get_node_shared_ptr();
+                if (!ngraph::op::is_constant(grandparent))
+                    num_non_const_inputs++;
+            }
+        } else if (!ngraph::op::is_constant(parent)) {
+            num_non_const_inputs++;
+        }
+    }
+    return num_non_const_inputs;
+}
+bool SupportsFusingWithConvolution_SumActivation(const std::shared_ptr<const Node> &node) {
+    // todo: Do all PReLUs are fused? Not sure about round and softRelu
+    // EltwiseRoundHalfToEven, EltwiseRoundHalfAwayFromZero, EltwiseSoftRelu
+    return  ov::is_type<ngraph::op::Relu>(node) ||
+            ov::is_type<ngraph::op::PRelu>(node) ||
+            ov::is_type<ngraph::op::Elu>(node) ||
+            ov::is_type<ngraph::op::Sigmoid>(node) ||
+            ov::is_type<ngraph::op::v5::HSigmoid>(node) ||
+            ov::is_type<ngraph::op::Clamp>(node) ||
+            ov::is_type<ngraph::op::v4::Swish>(node) ||
+            ov::is_type<ngraph::op::v4::HSwish>(node) ||
+            ov::is_type<ngraph::op::v4::Mish>(node) ||
+            ov::is_type<ngraph::op::v5::Round>(node);
+}
+
+bool canBePerformedAsScaleShift(const std::shared_ptr<const Node> &node, const size_t channelAxis) {
+    size_t fusingPort = 0;
+    size_t numNonConstInputs = 0;
+    ov::PartialShape dataShape;
+    for (size_t i = 0; i < node->get_input_size(); i++) {
+        const auto parent = node->get_input_node_shared_ptr(i);
+        if (!ngraph::op::is_constant(parent)) {
+            fusingPort = i;
+            dataShape = node->get_input_partial_shape(i);
+            // only one non-const parent is allowed
+            if (dataShape.is_dynamic() || ++numNonConstInputs != 1)
+                return false;
+        } else {
+            // every const parent must have exactly one child
+            const auto out = parent->outputs();
+            const bool has_only_child = (out.size() == 1) && (out[0].get_target_inputs().size() == 1);
+            if (!has_only_child)
+                return false;
+        }
+    }
+
+    const auto isBroadcastableToDataInput = [&]() {
+        for (size_t i = 0; i < node->get_input_size(); i++) {
+            if (i == fusingPort)
+                continue;
+            const ov::PartialShape weightShape = node->get_input_partial_shape(i);
+            if (weightShape.is_dynamic() ||
+                !isPerTensorOrPerChannelBroadcastable(dataShape.get_shape(), weightShape.get_shape(), channelAxis, true))
+                return false;
+        }
+        return true;
+    };
+
+    // Prelu and MulAdd are still ignored
+    // isConvertablePowerStatic() is ignored
+    return (ov::is_type<ngraph::opset1::Add>(node) ||
+            ov::is_type<ngraph::opset1::Multiply>(node) ||
+            ov::is_type<ngraph::opset1::Subtract>(node) ||
+            ov::is_type<ngraph::opset1::Divide>(node)) &&
+           isBroadcastableToDataInput();
+}
+
+bool SupportsFusingWithConvolution_Simple(const std::shared_ptr<const Node> &node, const size_t channelAxis = 1) {
+    return SupportsFusingWithConvolution_SumActivation(node) ||
+           ov::is_type<ngraph::op::Tanh>(node) ||
+           ov::is_type<ngraph::op::v0::Gelu>(node) ||
+           ov::is_type<ngraph::op::v7::Gelu>(node) ||
+           ov::is_type<ngraph::op::Abs>(node) ||
+           ov::is_type<ngraph::op::Sqrt>(node) ||
+           canBePerformedAsScaleShift(node, channelAxis);
+}
+// Convolution is a special case, since it supports peculiar fusings
+bool isSuitableConvolutionParent(const std::shared_ptr<const Node> &node) {
+    const bool is_suitable_node = ov::is_type<ngraph::op::v1::Convolution>(node) ||
+                                  ov::is_type<ngraph::op::v1::GroupConvolution>(node);
+    // has a single output, connected to a single child
+    const auto out = node->outputs();
+    const bool has_only_child = (out.size() == 1) && (out[0].get_target_inputs().size() == 1);
+    return is_suitable_node && has_only_child;
+}
+bool isSuitableBinaryConvolutionParent(const std::shared_ptr<const Node> &node) {
+    const bool is_suitable_node = ov::is_type<ngraph::op::v1::BinaryConvolution>(node);
+    // has a single output, connected to a single child
+    const auto out = node->outputs();
+    const bool has_only_child = (out.size() == 1) && (out[0].get_target_inputs().size() == 1);
+    return is_suitable_node && has_only_child;
+}
+bool isSuitableMiscParent(const std::shared_ptr<const Node> &node) {
+    const bool is_suitable_node = ov::is_type<ngraph::op::v0::MVN>(node) ||
+                                  ov::is_type<ngraph::op::v6::MVN>(node) ||
+                                  ov::is_type<ngraph::op::v0::NormalizeL2>(node) ||
+                                  ov::is_type<ngraph::op::v0::Interpolate>(node) ||
+                                  ov::is_type<ngraph::op::v4::Interpolate>(node) ||
+                                  ov::is_type<ngraph::op::v0::LSTMCell>(node) ||
+                                  ov::is_type<ngraph::op::v4::LSTMCell>(node) ||
+                                  ov::is_type<ngraph::opset1::ConvolutionBackpropData>(node) ||
+                                  ov::is_type<ngraph::op::util::ArithmeticReductionKeepDims>(node) ||
+                                  ov::is_type<ngraph::op::util::LogicalReductionKeepDims>(node) ||
+                                  ov::is_type<ngraph::opset1::GroupConvolutionBackpropData>(node);
+    // has a single output, connected to a single child
+    const auto out = node->outputs();
+    const bool has_only_child = (out.size() == 1) && (out[0].get_target_inputs().size() == 1);
+    return is_suitable_node && has_only_child;
+}
+// Matmul is a special case, since it supports simple + bias fusings
+bool isSuitableMatMulParent(const std::shared_ptr<const Node> &node) {
+    const bool is_suitable_node = ov::is_type<ngraph::op::MatMul>(node);
+    // has a single output, connected to a single child
+    const auto out = node->outputs();
+    const bool has_only_child = (out.size() == 1) && (out[0].get_target_inputs().size() == 1);
+    return is_suitable_node && has_only_child;
+}
+bool isSuitablePoolChild(const std::shared_ptr<const Node> &node) {
+    const bool is_suitable_node = ov::is_type<ngraph::op::v1::MaxPool>(node);
+    // has a single output, connected to a single child
+    const auto out = node->outputs();
+    const bool has_only_child = (out.size() == 1) && (out[0].get_target_inputs().size() == 1);
+    return is_suitable_node && has_only_child;
+}
+bool isSuitableChildForFusingSimple(const std::shared_ptr<const Node> &node, const size_t channelAxis = 1) {
+    // Note: Fusing child is allowed to have several users, but that must be the end of the chain
+    return SupportsFusingWithConvolution_Simple(node, channelAxis) && getNumNonConstInputs(node) == 1;
+}
+bool isSuitableChildForFusingMatMul(const std::shared_ptr<const Node> &node, NodeFusingType &updatedChainType) {
+    int num_non_const_inputs = 0;
+    bool can_be_converted_to_FC = false;
+    ov::Shape bias_shape;
+    ov::Shape matmul_shape;
+    for (const auto &parent_out : node->input_values()) {
+        const auto parent = parent_out.get_node_shared_ptr();
+        if (ngraph::op::is_constant(parent)) {
+            bias_shape = parent_out.get_shape();
+            num_non_const_inputs++;
+        } else {
+            const auto pshape = parent_out.get_partial_shape();
+            if (pshape.is_dynamic() || pshape.get_shape().empty())
+                return false;
+            matmul_shape = pshape.get_shape();
+            const auto& grandparents = parent->input_values();
+            // first check that weights are constant and both activations and weights have static shape
+            if (grandparents.size() == 2 &&
+                grandparents[0].get_partial_shape().is_static() &&
+                grandparents[1].get_partial_shape().is_static() &&
+                ov::is_type<ov::op::v0::Constant>(grandparents[1].get_node_shared_ptr())) {
+                auto rank_a = grandparents[0].get_partial_shape().rank().get_length();
+                auto rank_w = grandparents[1].get_partial_shape().rank().get_length();
+                if (rank_a != 1 && rank_w != 1 && rank_a <= 3 && rank_w <= 3)
+                    can_be_converted_to_FC = true;
+            }
+        }
+    }
+    if (num_non_const_inputs != 1)
+        return false;
+
+    // FuseMatMulAndSimpleOperation or FuseFullyConnectedAndSimpleOperation
+    // Invoke SupportsFusingWithConvolution_Simple directly instead of isSuitableChildForFusingSimple to
+    // eliminate getNumNonConstInputs() check
+    size_t fusingAxis;
+    if (can_be_converted_to_FC)
+        fusingAxis = matmul_shape.size() == 3 ? 2 : 1;
+    else
+        fusingAxis = matmul_shape.size() - 1;
+    if (SupportsFusingWithConvolution_Simple(node, fusingAxis)) {
+        updatedChainType = NodeFusingType::FusedWithMisc;
+        return true;
+    }
+    //    FullyConnectedBiasFusion
+    if (!(can_be_converted_to_FC && ov::is_type<ngraph::opset1::Add>(node) &&
+        bias_shape.back() == matmul_shape.back() &&
+        bias_shape.back() == shape_size(bias_shape))) {
+        return false;
+    }
+    // Fusing chain must be interrupted after the node, since reshape will be inserted
+    if (bias_shape.size() >= 2)
+        updatedChainType = NodeFusingType::FusedTerminator;
+    return true;
+}
+bool isSuitableParentForFusingSumActivation(const std::shared_ptr<const Node> &node) {
+    if (!ov::is_type<ngraph::op::v1::Add>(node))
+        return false;
+    auto isFusedBiasNode = [](std::shared_ptr<Node> n){
+        if (!(ov::is_type<ngraph::op::v1::Add>(n) &&
+              GetNodeFusingType(n) ==  NodeFusingType::FusedWithConvolution))
+            return false;
+        const auto conv = n->get_input_source_output(0);
+        const auto bias = n->get_input_source_output(1);
+        if (!(ngraph::op::is_constant(bias.get_node_shared_ptr()) && isSuitableConvolutionParent(conv.get_node_shared_ptr())))
+            return false;
+        const auto conv_shape = conv.get_partial_shape();
+        const auto bias_shape = bias.get_partial_shape();
+        if  (bias_shape.is_dynamic() || conv_shape.is_dynamic() || bias_shape.size() > conv_shape.size())
+            return false;
+        auto getNormalizedDims = [](const ov::Shape &dims, size_t ndims) -> std::vector<size_t>{
+            std::vector<size_t> normalizedDims = dims;
+            for (size_t i = 0; i < (ndims - dims.size()); i++) {
+                normalizedDims.insert(normalizedDims.begin(), 1);
+            }
+            return normalizedDims;
+        };
+        const auto bias_norm_dims = getNormalizedDims(bias_shape.get_shape(), conv_shape.size());
+        if (bias_norm_dims.size() < 2 || bias_norm_dims[0] != 1 || conv_shape[1] != bias_norm_dims[1])
+            return false;
+        for (size_t i = 2; i < bias_norm_dims.size(); i++) {
+            if (bias_norm_dims[i] != 1)
+                return false;
+        }
+        return true;
+    };
+    int num_conv_parents = 0;
+    for (size_t i = 0; i < node->get_input_size(); i++) {
+        const auto n = node->get_input_node_shared_ptr(i);
+        //BinaryConvolution allows other ops to be fused before the Add, while Convolution doesn't
+        num_conv_parents += (isSuitableConvolutionParent(n) || isFusedBiasNode(n) ||
+                             GetNodeFusingType(n) == NodeFusingType::FusedWithBinaryConvolution);
+    }
+    return getNumNonConstInputs(node) == 2 && num_conv_parents >=1;
+}
+bool isSuitableChildForFusingSumActivation(const std::shared_ptr<const Node> &node) {
+    return SupportsFusingWithConvolution_SumActivation(node);
+}
+// Continue fusing chain of the passed type if the node has one child
+// Otherwise mark node as FusedTerminator (Fused, but fusing chain is interrupted)
+void PropagateIfHasOnlyChild(const std::shared_ptr<Node> &node, NodeFusingType nodeType) {
+    const auto out = node->outputs();
+    const bool has_only_child = out.size() == 1 && out[0].get_target_inputs().size() == 1;
+    SetNodeFusingType(node, has_only_child ? nodeType : NodeFusingType::FusedTerminator);
+}
+// todo: Skipping MultiSubGraphOp such as TensorIterator, Loop and If. Snippets might tokenize their bodies in the future.
+//  Note that the function is recurrent, since there might be multi-level MultiSubGraphOp, if(){if(){}}else{} for example.
+void MarkSubgraphOpAsSkipped(const std::shared_ptr<Node> &node) {
+    if (ov::is_type<ov::op::util::MultiSubGraphOp>(node)) {
+        std::vector<std::shared_ptr<ov::Model>> models{};
+        // Covers TensorIterator and Loop
+        if (auto s = ov::as_type_ptr<ov::op::util::SubGraphOp>(node)) {
+            models.push_back(s->get_function());
+        // Add new multi-body subgraph op here
+        } else if (auto if_op  = ov::as_type_ptr<ov::op::v8::If>(node)) {
+            models.push_back(if_op->get_then_body());
+            models.push_back(if_op->get_else_body());
+        }
+        for (auto& m : models) {
+            for (auto& n : m->get_ops()) {
+                snippets::pass::SetSnippetsNodeType(n, snippets::pass::SnippetsNodeType::SkippedByPlugin);
+                MarkSubgraphOpAsSkipped(n);
+            }
+        }
+    }
+}
+} // namespace
+
+bool SnippetsMarkSkipped::run_on_model(const std::shared_ptr<ov::Model> &m) {
+    for (auto &node : m->get_ordered_ops()) {
+        if (ngraph::op::is_constant(node))
+            continue;
+        if (ngraph::op::is_parameter(node)) {
+            SetNodeFusingType(node, NodeFusingType::IgnoredAfterInputs);
+            continue;
+        } else if (isSuitableConvolutionParent(node)) {
+            // Initiate fusing chain
+            SetNodeFusingType(node, NodeFusingType::FusedWithConvolution);
+            continue;
+        } else if (isSuitableBinaryConvolutionParent(node)) {
+            SetNodeFusingType(node, NodeFusingType::FusedWithBinaryConvolution);
+            continue;
+        } else if (isSuitableMiscParent(node)) {
+            SetNodeFusingType(node, NodeFusingType::FusedWithMisc);
+            continue;
+        } else if (isSuitableMatMulParent(node)) {
+            SetNodeFusingType(node, NodeFusingType::FusedWithMatMul);
+            continue;
+        }
+        for (const auto fusingChainType : getContinuableChains(node)) {
+            if (isSuitableChildForFusingSimple(node)) {
+                PropagateIfHasOnlyChild(node, fusingChainType);
+            } else if (fusingChainType == NodeFusingType::FusedWithConvolution ||
+                       fusingChainType == NodeFusingType::FusedWithBinaryConvolution) {
+                if (isSuitableParentForFusingSumActivation(node)) {
+                    PropagateIfHasOnlyChild(node, NodeFusingType::FusedWithConvolutionSumActivation);
+                    // Mimic FuseConvolutionAndSimpleOperationThroughMaxPool
+                } else if (isSuitablePoolChild(node)) {
+                    PropagateIfHasOnlyChild(node, fusingChainType);
+                }
+            } else if (fusingChainType == NodeFusingType::FusedWithConvolutionSumActivation &&
+                       isSuitableChildForFusingSumActivation(node)) {
+                // Todo: Chain could be converted from FusedWithBinaryConvolution to FusedWithConvolution at this point
+                // Set FusedWithConvolution, so the fusing chain could be propagated
+                PropagateIfHasOnlyChild(node, NodeFusingType::FusedWithConvolution);
+            } else if (fusingChainType == NodeFusingType::FusedWithMatMul) {
+                // Handle fusings for both MatMul and FullyConnected
+                NodeFusingType updatedChainType = fusingChainType;
+                if (isSuitableChildForFusingMatMul(node, updatedChainType))
+                    PropagateIfHasOnlyChild(node, updatedChainType);
+            } else if (fusingChainType == NodeFusingType::IgnoredAfterInputs && snippets::pass::AppropriateForSubgraph(node)) {
+                SetNodeFusingType(node, NodeFusingType::IgnoredAfterInputs);
+            }
+        }
+        if (GetNodeFusingType(node) != NodeFusingType::NotSet) {
+            SetSnippetsNodeType(node, snippets::pass::SnippetsNodeType::SkippedByPlugin);
+        } else {
+            MarkSubgraphOpAsSkipped(node);
+        }
+    }
+    return true;
+}
+}  // namespace MKLDNNPlugin
\ No newline at end of file
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.hpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.hpp
new file mode 100644
index 00000000000..0dce74c8bf6
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.hpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/pass/graph_rewrite.hpp>
+
+namespace MKLDNNPlugin {
+/**
+ * @interface SnippetsMarkSkipped
+ * @brief Mark operations that should be ignored by snippets on tokenization stage. A typical example is eltwise operations
+ * that will be fused into convolutions on plugin side.
+ */
+class SnippetsMarkSkipped : public ov::pass::ModelPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    SnippetsMarkSkipped() : ModelPass() {}
+    bool run_on_model(const std::shared_ptr<ov::Model> &) override;
+};
+
+/*
+NotSet - not part of a fusing chain
+FusedTerminator - the node is fused, but the chain can't be continued
+FusedWithConvolution, FusedWithConvolutionSumActivation, FusedWithMisc - fusing chains with different continuation rules
+IgnoredAfterInputs - node must be skipped, since can't be handled properly at this time. Also a continuable fusing chain.
+Order of SnippetsNodeType is important!:
+* SnippetsNodeType >= FusedTerminator is a Fused chain
+* SnippetsNodeType > FusedTerminator is a Fused chain that may be continued
+*/
+// Todo: Snippets currently support only FP32 precision, however network inputs could be converted to another precision by plugin
+//  (in other words after tokenization). To handle this behavior, eltwise chains that start at input are marked as IgnoredAfterInputs.
+//  Tis is not a real plugin-side fusing, but rather a workaround to guarantee that snippets always executed in FP32.
+enum class NodeFusingType : int64_t {
+    NotSet,
+    FusedTerminator,
+    FusedWithConvolution,  FusedWithBinaryConvolution, FusedWithConvolutionSumActivation,
+    FusedWithMatMul, FusedWithMisc, IgnoredAfterInputs};
+}  // namespace MKLDNNPlugin
\ No newline at end of file
diff --git a/inference-engine/src/mkldnn_plugin/nodes/subgraph.cpp b/inference-engine/src/mkldnn_plugin/nodes/subgraph.cpp
new file mode 100644
index 00000000000..f7341d48fea
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/subgraph.cpp
@@ -0,0 +1,548 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "subgraph.h"
+
+#include <ie_parallel.hpp>
+
+#include <vector>
+#include <algorithm>
+#include <array>
+#include <tuple>
+
+#include <mkldnn.hpp>
+#include <mkldnn_debug.h>
+#include <mkldnn_types.h>
+#include <mkldnn_extension_utils.h>
+
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/pass/visualize_tree.hpp>
+#include <ngraph/rt_info.hpp>
+#include <ie_ngraph_utils.hpp>
+
+#include <snippets/op/subgraph.hpp>
+#include "emitters/cpu_generator.hpp"
+
+using namespace MKLDNNPlugin;
+using namespace InferenceEngine;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::cpu;
+using namespace mkldnn::impl::cpu::x64;
+using namespace Xbyak;
+
+MKLDNNSnippetNode::MKLDNNSnippetNode(const std::shared_ptr<ngraph::Node>& op, const dnnl::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
+        : MKLDNNNode(op, eng, cache) {
+    host_isa = dnnl::impl::cpu::x64::mayiuse(dnnl::impl::cpu::x64::avx512_common) ?
+        dnnl::impl::cpu::x64::avx512_common : dnnl::impl::cpu::x64::avx2;
+
+    // Create a deep local copy of the input snippet to perform canonicalization & code generation
+    // Todo: Probably better to implement a proper copy constructor
+    if (const auto tmp_snippet =  ov::as_type_ptr<ngraph::snippets::op::Subgraph>(op)) {
+        ngraph::OutputVector subgraph_node_inputs;
+        for (const auto &input : tmp_snippet->input_values()) {
+            auto new_input = std::make_shared<ngraph::opset1::Parameter>(input.get_element_type(), input.get_partial_shape());
+            subgraph_node_inputs.push_back(new_input);
+        }
+        auto new_body = ov::clone_model(*tmp_snippet->get_body().get());
+        snippet = std::make_shared<ngraph::snippets::op::Subgraph>(subgraph_node_inputs, new_body);
+        ngraph::copy_runtime_info(tmp_snippet, snippet);
+        snippet->set_friendly_name(tmp_snippet->get_friendly_name());
+        snippet->set_generator(std::make_shared<CPUGenerator>(host_isa));
+    } else {
+        IE_THROW(NotImplemented) << "Node is not an instance of snippets::op::Subgraph";
+    }
+}
+
+void MKLDNNSnippetNode::initSupportedPrimitiveDescriptors() {
+    if (!supportedPrimitiveDescriptors.empty())
+        return;
+
+    auto hasBroadcastByC = [this]() -> bool {
+        for (auto op : ngraph::as_type_ptr<ngraph::snippets::op::Subgraph>(snippet)->get_body()->get_ops()) {
+            if (ngraph::op::supports_auto_broadcast(op)) {
+                auto shape = op->get_input_shape(0);
+                // Filter out scalar empty shape Shape{}
+                if (ngraph::shape_size(shape) != 1) {
+                    for (const auto& input : op->inputs()) {
+                        if (input.get_shape().size() > 1 && shape[1] != input.get_shape()[1] && ngraph::shape_size(input.get_shape()) != 1) {
+                            return true;
+                        }
+                    }
+                } else {
+                    return false;
+                }
+            }
+        }
+        return false;
+    };
+
+    const Precision supportedPrecision = Precision::FP32;
+
+    bool dimRanksAreEqual = true;
+    for (size_t i = 0; dimRanksAreEqual && i < inputShapes.size(); i++) {
+        for (size_t j = 0; dimRanksAreEqual && j < outputShapes.size(); j++) {
+            if (inputShapes[i].getRank() != outputShapes[j].getRank())
+                dimRanksAreEqual = false;
+        }
+    }
+
+    const size_t ndims = outputShapes[0].getRank();
+    const bool isChannelsFirstApplicable = dnnl::impl::utils::one_of(ndims, 1, 2, 4, 5) && dimRanksAreEqual;
+    // Todo: Snippets currently don't support per-channel broadcasting of Blocked descriptors because
+    //  canonicalization can't distinguish between <N, C, H, W, c> and <N, C, D, H, W> cases. So we need to pass an
+    //  additional parameter to canonicalization, see snippets::op::Subgraph::canonicalize for details.
+    const bool isBlockedApplicable = dnnl::impl::utils::one_of(ndims,  4, 5) && dimRanksAreEqual && !hasBroadcastByC();
+    enum LayoutType {
+        Planar,
+        ChannelsFirst,
+        Blocked
+    };
+    auto initDesc = [&] (LayoutType lt) -> NodeDesc {
+        auto createMemoryDesc = [lt](const Shape &shape, Precision prc, size_t offset) -> std::shared_ptr<CpuBlockedMemoryDesc> {
+            const auto &dims = shape.getDims();
+            if (lt == ChannelsFirst && shape.getRank() != 1) {
+                auto ndims = shape.getRank();
+                VectorDims order(ndims);
+                std::iota(order.begin(), order.end(), 0);
+                if (ndims > 1) {
+                    order.erase(order.begin() + 1);
+                    order.push_back(1);
+                }
+
+                VectorDims blocks(ndims);
+                for (size_t i = 0; i < order.size(); i++) {
+                    blocks[i] = dims[order[i]];
+                }
+
+                return std::make_shared<CpuBlockedMemoryDesc>(prc, shape, blocks, order, offset);
+            } else if (lt == Blocked && shape.getRank() != 1 && (shape.getMinDims()[1] != Shape::UNDEFINED_DIM && shape.getMinDims()[1] > 1)) {
+                size_t blockSize = mayiuse(dnnl::impl::cpu::x64::avx512_common) ? 16 : 8;
+
+                VectorDims blocks = dims;
+                VectorDims order(blocks.size());
+                std::iota(order.begin(), order.end(), 0);
+
+                blocks[1] = dims[1] != Shape::UNDEFINED_DIM ? div_up(blocks[1], blockSize) : Shape::UNDEFINED_DIM;
+                blocks.push_back(blockSize);
+                order.push_back(1);
+
+                return std::make_shared<CpuBlockedMemoryDesc>(prc, shape, blocks, order, offset);
+            } else {
+                VectorDims blocks = dims;
+                VectorDims order(blocks.size());
+                std::iota(order.begin(), order.end(), 0);
+
+                return std::make_shared<CpuBlockedMemoryDesc>(prc, shape, blocks, order, offset);
+            }
+        };
+
+        size_t offset = std::numeric_limits<size_t>::max();
+        NodeConfig config;
+        config.dynBatchSupport = false;
+        config.inConfs.resize(inputShapes.size());
+        for (size_t i = 0; i < inputShapes.size(); i++) {
+            PortConfig portConfig;
+            portConfig.inPlace = (!i && canBeInPlace()) ? 0 : -1;
+            portConfig.constant = false;
+            portConfig.desc = createMemoryDesc(inputShapes[i], supportedPrecision, offset);
+            if (inputShapes[i].getDims()[0] == 1) {
+                const auto denseDesc = portConfig.desc->as<BlockedMemoryDesc>();
+                auto strides = denseDesc->getStrides();
+                strides[0] = Shape::UNDEFINED_DIM;
+                portConfig.desc = std::make_shared<CpuBlockedMemoryDesc>(denseDesc->getPrecision(),
+                                                                         denseDesc->getShape(),
+                                                                         denseDesc->getBlockDims(),
+                                                                         denseDesc->getOrder(),
+                                                                         denseDesc->getOffsetPadding(),
+                                                                         denseDesc->getOffsetPaddingToData(),
+                                                                         strides);
+            }
+            config.inConfs[i] = portConfig;
+        }
+        config.outConfs.resize(outputShapes.size());
+        for (size_t i = 0; i < outputShapes.size(); i++) {
+            PortConfig portConfig;
+            portConfig.inPlace = -1;
+            portConfig.constant = false;
+            portConfig.desc = createMemoryDesc(outputShapes[i], supportedPrecision, offset);
+            if (outputShapes[i].getDims()[0] == 1) {
+                const auto denseDesc = portConfig.desc->as<BlockedMemoryDesc>();
+                auto strides = denseDesc->getStrides();
+                strides[0] = Shape::UNDEFINED_DIM;
+                portConfig.desc = std::make_shared<CpuBlockedMemoryDesc>(denseDesc->getPrecision(),
+                                                                         denseDesc->getShape(),
+                                                                         denseDesc->getBlockDims(),
+                                                                         denseDesc->getOrder(),
+                                                                         denseDesc->getOffsetPadding(),
+                                                                         denseDesc->getOffsetPaddingToData(),
+                                                                         strides);
+            }
+            config.outConfs[i] = portConfig;
+        }
+
+        impl_desc_type impl_type = impl_desc_type::unknown;
+        if (mayiuse(x64::avx512_common)) {
+            impl_type = impl_desc_type::jit_avx512;
+        } else if (mayiuse(x64::avx2)) {
+            impl_type = impl_desc_type::jit_avx2;
+        }
+        return {config, impl_type};
+    };
+
+    if (isChannelsFirstApplicable)
+        supportedPrimitiveDescriptors.emplace_back(initDesc(ChannelsFirst));
+    if (isBlockedApplicable)
+        supportedPrimitiveDescriptors.emplace_back(initDesc(Blocked));
+    supportedPrimitiveDescriptors.emplace_back(initDesc(Planar));
+}
+
+void MKLDNNSnippetNode::selectOptimalPrimitiveDescriptor() {
+    selectPreferPrimitiveDescriptor(getPrimitivesPriority(), true);
+}
+
+void MKLDNNSnippetNode::createPrimitive() {
+    // schedule definition part
+    // it defines offsets, strides and sizes for snippet kernel scheduling
+    define_schedule();
+
+    // code generation part
+    // it might be worth to generate explicitly for scheduler work amount for now,
+    // but in future some interface should be defined in order to communicate schedule for a kernel
+    // or generate schedule for a kernel.
+    // Here kernel is generated for most warying dimension by default.
+    generate();
+}
+
+void MKLDNNSnippetNode::execute(dnnl::stream strm) {
+    if (schedule.ptr == nullptr || !canUseOptimizedImpl) {
+        IE_THROW() << "MKLDNNSnippetNode can't use Optimized implementation and can't fallback to reference";
+    }
+    jit_snippets_call_args call_args;
+    for (size_t i = 0; i < srcMemPtrs.size(); i++)
+        call_args.src_ptrs[i] = reinterpret_cast<const uint8_t*>(srcMemPtrs[i]->GetData()) + start_offset_in[i];
+
+    for (size_t i = 0; i < dstMemPtrs.size(); i++)
+        call_args.dst_ptrs[i] = reinterpret_cast<uint8_t*>(dstMemPtrs[i]->GetData()) + start_offset_out[i];
+
+    if (tensorRank == rank6D) {
+        schedule_6d(call_args);
+    } else {
+        schedule_nt(call_args);
+    }
+}
+
+bool MKLDNNSnippetNode::created() const {
+    return getType() == Subgraph;
+}
+
+// internal interface for subgraph execution
+
+static size_t argmax_rank(const std::vector<MKLDNNEdgeWeakPtr> &childEdges) {
+    auto getOutBlockedDims = [childEdges](int i) {
+            if ( auto childEdge =  childEdges[i].lock() )
+                return childEdge->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
+            else
+                IE_THROW() << "Unable to lock childEdge weak_ptr";
+            return VectorDims{};
+    };
+    auto getOutRank = [getOutBlockedDims](int i) {
+        return getOutBlockedDims(i).size();
+    };
+    size_t max_rank_idx = 0;
+    size_t max_rank_val = getOutRank(0);
+    for (size_t i = 1; i < childEdges.size(); i++) {
+        const auto i_rank_val = getOutRank(i);
+        if (max_rank_val < i_rank_val) {
+            max_rank_idx = i;
+            max_rank_val = i_rank_val;
+        } else if (max_rank_val == i_rank_val) {
+            const auto max_rank_dims = getOutBlockedDims(max_rank_idx);
+            const auto i_dims = getOutBlockedDims(i);
+            for (size_t j = 0; j < max_rank_val; j++) {
+                if (i_dims[j] > max_rank_dims[j]) {
+                    max_rank_idx = i;
+                    max_rank_val =  i_rank_val;
+                    break;
+                }
+            }
+        }
+    }
+    return max_rank_idx;
+}
+
+static auto offset_calculation(std::vector<int64_t>& offset, const std::vector<int64_t>& dims_in, const std::vector<int64_t>& dims_out) -> void {
+    int k = 1;
+    for (int i = offset.size() - 1; i >= 0; i--) {
+        offset[i] = (dims_in[i] == dims_out[i]) ? k : 0;
+        k *= dims_in[i];
+    }
+}
+
+static auto collapseLastDims(std::vector<int64_t>& dims, int dimsToCollapse) -> void {
+    for (int i = dims.size() - 2; i > dims.size() - dimsToCollapse - 2; i--) {
+        dims[dims.size() - 1] *= dims[i];
+    }
+
+    for (int i = dims.size() - 2; i >= dimsToCollapse; i--) {
+        dims[i] = dims[i - dimsToCollapse];
+    }
+
+    for (int i = dimsToCollapse - 1; i >= 0; i--) {
+        dims[i] = 1;
+    }
+}
+
+void MKLDNNSnippetNode::define_schedule() {
+    const auto config = getSelectedPrimitiveDescriptor()->getConfig();
+    const auto dataSize = config.inConfs[0].desc->getPrecision().size();
+    // store to use as an execution domain
+    max_rank_out_desc_idx = argmax_rank(getChildEdges());
+    const auto outBlockingDesc_maxRank = getChildEdgeAt(max_rank_out_desc_idx)->getMemory().GetDescWithType<BlockedMemoryDesc>();
+    // initialize by maximum output dimension. Dimensions of outputs should be broadcastable
+    tensorRank = std::max(static_cast<size_t>(rank6D), outBlockingDesc_maxRank->getBlockDims().size());
+
+    auto initDims = [this, config, &outBlockingDesc_maxRank](size_t tensorRank) {
+        // assume all input sizes are even
+        const size_t inputNum = getParentEdges().size();
+
+        dims_in.resize(inputNum);
+        for (size_t i = 0; i < inputNum; i++) {
+            dims_in[i].resize(tensorRank, 1);
+        }
+
+        const auto outOrder = outBlockingDesc_maxRank->getOrder();
+        for (size_t i = 0; i < inputNum; i++) {
+            auto inBlockingDesc = getParentEdgeAt(i)->getMemory().GetDescWithType<BlockedMemoryDesc>();
+            size_t rank = inBlockingDesc->getBlockDims().size();
+
+            // WA to normalize blocked and planar layouts
+            // not actual thought, since [§] doesn't support mixed layouts yet
+            auto inOrder = inBlockingDesc->getOrder();
+            size_t startOff = outOrder.size() != outBlockingDesc_maxRank->getShape().getRank() &&
+                              outOrder.back() != inOrder.back() ? 1 : 0;
+            for (size_t j = 0; j < rank; j++) {
+                dims_in[i][dims_in[i].size() - 1 - j - startOff] = inBlockingDesc->getBlockDims()[rank - 1 - j];
+            }
+        }
+
+        // assume all output sizes are even
+        const size_t outputNum = config.outConfs.size();
+
+        dims_out.resize(outputNum);
+        for (size_t i = 0; i < outputNum; i++) {
+            dims_out[i].resize(tensorRank, 1);
+        }
+
+        for (size_t i = 0; i < outputNum; i++) {
+            auto outBlockingDesc = getChildEdgeAt(i)->getMemory().GetDescWithType<BlockedMemoryDesc>();
+            size_t rank = outBlockingDesc->getBlockDims().size();
+
+            for (size_t j = 0; j < rank; j++) {
+                dims_out[i][dims_out[i].size() - 1 - j] = outBlockingDesc->getBlockDims()[rank - 1 - j];
+            }
+        }
+    };
+
+    auto initOffsets = [this, config, dataSize](size_t tensorRank) {
+        // find max rank input among all outputs
+        const size_t inputNum = getParentEdges().size();
+        offsets_in.resize(inputNum);
+        for (size_t i = 0; i < inputNum; i++) {
+            offsets_in[i].resize(tensorRank, 1);
+            offset_calculation(offsets_in[i], dims_in[i], dims_out[max_rank_out_desc_idx]);
+            for (size_t j = 0; j < tensorRank; j++) {
+                offsets_in[i][j] *= dataSize;
+            }
+        }
+
+        start_offset_in.resize(inputNum);
+        srcMemPtrs.resize(inputNum);
+        for (size_t i = 0; i < inputNum; i++) {
+            const auto memPtr = getParentEdgeAt(i)->getMemoryPtr();
+            srcMemPtrs[i] = memPtr;
+            start_offset_in[i] =  memPtr->GetDescWithType<BlockedMemoryDesc>()->getOffsetPadding() * dataSize;
+        }
+
+        const size_t outputNum = config.outConfs.size();
+        offsets_out.resize(outputNum);
+        for (size_t i = 0; i < outputNum; i++) {
+            offsets_out[i].resize(tensorRank, 1);
+            offset_calculation(offsets_out[i], dims_out[i], dims_out[max_rank_out_desc_idx]);
+            for (size_t j = 0; j < tensorRank; j++) {
+                offsets_out[i][j] *= dataSize;
+            }
+        }
+
+        start_offset_out.resize(outputNum);
+        dstMemPtrs.resize(outputNum);
+        for (size_t i = 0; i < outputNum; i++) {
+            const auto memPtr = getChildEdgeAt(i)->getMemoryPtr();
+            dstMemPtrs[i] = memPtr;
+            start_offset_out[i] = memPtr->GetDescWithType<BlockedMemoryDesc>()->getOffsetPadding() * dataSize;
+        }
+    };
+
+    auto find_dims_to_collapse = [this, config, &outBlockingDesc_maxRank]() -> int {
+        int collapsedDims = 0;
+        size_t minimalConcurrency = parallel_get_max_threads();
+        size_t minimalJitWorkAmount = 256;
+        size_t currentJitWorkAmount = dims_out[max_rank_out_desc_idx].back();
+        while (currentJitWorkAmount < minimalJitWorkAmount && currentJitWorkAmount < fullWorkAmount) {
+            if (dims_out[max_rank_out_desc_idx].size() - collapsedDims - 2 < 0)
+                break;
+
+            bool canCollapse = true;
+            for (size_t i = 0; i < dims_in.size(); i++) {
+                if ((dims_in[i][dims_in[i].size() - 2] != 1 && dims_in[i][dims_in[i].size() - 1] == 1) ||
+                    (dims_in[i][dims_in[i].size() - 2] == 1 && dims_in[i][dims_in[i].size() - 1] != 1)) {
+                    canCollapse = false;
+                    break;
+                }
+            }
+
+            size_t nextJitWorkAmount = currentJitWorkAmount * dims_out[max_rank_out_desc_idx][dims_out[max_rank_out_desc_idx].size() - 2];
+            if (fullWorkAmount / nextJitWorkAmount >= minimalConcurrency) {
+                currentJitWorkAmount = nextJitWorkAmount;
+                // if we cannot use dim collapsing we should use tile2D
+                if (!canCollapse) {
+                    if (tileRank < maxTileRank) {
+                        tileRank++;
+                        continue;
+                    }
+
+                    break;
+                }
+
+                collapsedDims++;
+                for (size_t i = 0; i < dims_in.size(); i++) {
+                    collapseLastDims(dims_in[i], 1);
+                }
+
+                for (size_t i = 0; i < dims_out.size(); i++) {
+                    collapseLastDims(dims_out[i], 1);
+                }
+            } else {
+                break;
+            }
+        }
+        return collapsedDims;
+    };
+
+    auto initSchedulingInfo = [this, dataSize](const size_t tensorRank) -> void {
+        // initialize scheduling information
+        sch_offsets_in.resize(offsets_in.size(), 0);
+        sch_offsets_out.resize(offsets_out.size(), 0);
+        sch_dims.resize(maxTileRank, 1);
+        sch_dims[maxTileRank-1] = dims_out[max_rank_out_desc_idx].back();
+        schedulerWorkAmount = fullWorkAmount / dims_out[max_rank_out_desc_idx].back();
+        if (tileRank > 1) {
+            sch_dims[maxTileRank - tileRank] = dims_out[max_rank_out_desc_idx][tensorRank - 2];
+            schedulerWorkAmount /= dims_out[max_rank_out_desc_idx][tensorRank - 2];
+            dims_out[max_rank_out_desc_idx][tensorRank - 2] = 1;
+
+            // update offsets for tile 2D because loaders have ptr shifts in some cases and stores have always ptrs shifts
+            for (size_t i = 0; i < offsets_in.size(); i++) {
+                int64_t offset = offsets_in[i][tensorRank - 2];
+                if ((offset > dataSize) || (offset == 0 && dims_in[i].back() != 1)) {
+                    sch_offsets_in[i] = offset - dims_out[max_rank_out_desc_idx].back() * dataSize;
+                } else if (offset == dataSize) {
+                    sch_offsets_in[i] = offset;
+                }
+            }
+
+            for (size_t i = 0; i < offsets_out.size(); i++) {
+                int64_t offset = offsets_out[i][tensorRank - 2];
+                sch_offsets_out[i] = offset - dims_out[max_rank_out_desc_idx].back() * dataSize;
+            }
+        }
+    };
+
+    initDims(tensorRank);
+
+    fullWorkAmount = 1;
+    for (size_t i = 0; i < dims_out[max_rank_out_desc_idx].size(); i++) {
+        fullWorkAmount *= dims_out[max_rank_out_desc_idx][i];
+    }
+
+    const int collapsedDims = find_dims_to_collapse();
+    batchDimIdx = tensorRank - outBlockingDesc_maxRank->getBlockDims().size() + collapsedDims;
+
+    initOffsets(tensorRank);
+    initSchedulingInfo(tensorRank);
+}
+
+void MKLDNNSnippetNode::generate() {
+    std::vector<MKLDNNEdgePtr> input_first_row;
+    for (size_t i = 0; i < inputShapes.size(); i++)
+        input_first_row.push_back(getParentEdgesAtPort(i)[0]);
+
+    auto edgeToBlockedShape = [](const MKLDNNEdgePtr& edge) -> ngraph::snippets::op::Subgraph::BlockedShape {
+        const auto blockedDesc = edge->getMemory().GetDescWithType<BlockedMemoryDesc>();
+        ngraph::Shape shape(blockedDesc->getBlockDims());
+        ngraph::AxisVector blocking(blockedDesc->getOrder());
+        ngraph::element::Type precision = InferenceEngine::details::convertPrecision(blockedDesc->getPrecision());
+        return std::make_tuple(shape, blocking, precision);
+    };
+    ngraph::snippets::op::Subgraph::BlockedShapeVector input_blocked_shapes;
+    std::transform(input_first_row.begin(), input_first_row.end(), std::back_inserter(input_blocked_shapes), edgeToBlockedShape);
+
+    std::vector<MKLDNNEdgePtr> output_first_row;
+    for (size_t i = 0; i < outputShapes.size(); i++)
+        // Can it go with difference shape or precision to different edges? I assume no.
+        output_first_row.push_back(getChildEdgesAtPort(i)[0]);
+
+    ngraph::snippets::op::Subgraph::BlockedShapeVector output_blocked_shapes;
+    std::transform(output_first_row.begin(), output_first_row.end(), std::back_inserter(output_blocked_shapes), edgeToBlockedShape);
+    jit_snippets_compile_args jcp;
+    jcp.output_dims = dims_out[max_rank_out_desc_idx];
+    std::copy(sch_dims.begin(), sch_dims.end(), jcp.scheduler_dims);
+    std::copy(sch_offsets_in.begin(), sch_offsets_in.end(), jcp.scheduler_offsets);
+    std::copy(sch_offsets_out.begin(), sch_offsets_out.end(), &jcp.scheduler_offsets[sch_offsets_in.size()]);
+    size_t harness_num_dims = jcp.output_dims.size() - 1;
+    if (harness_num_dims > SNIPPETS_MAX_HARNESS_DIMS) {
+        canUseOptimizedImpl = false;
+        harness_num_dims = SNIPPETS_MAX_HARNESS_DIMS;
+    }
+    for (size_t i = 0; i < inputShapes.size(); i++) {
+        auto b = offsets_in[i].begin();
+        std::copy(b, b + harness_num_dims, &jcp.data_offsets[i * harness_num_dims]);
+    }
+    for (size_t i = 0; i < outputShapes.size(); i++) {
+        auto b = offsets_out[i].begin();
+        std::copy(b, b + harness_num_dims, &jcp.data_offsets[(inputShapes.size() + i) * harness_num_dims]);
+    }
+    schedule = snippet->generate(output_blocked_shapes, input_blocked_shapes, reinterpret_cast<void*>(&jcp));
+}
+
+void MKLDNNSnippetNode::schedule_6d(const jit_snippets_call_args& call_args) const {
+    const auto& dom = dims_out[max_rank_out_desc_idx];
+    // < N, C, H, W > < 1, 1, N, C*H*W>
+    parallel_for5d(dom[0], dom[1], dom[2], dom[3], dom[4],
+        [&](int64_t d0, int64_t d1, int64_t d2, int64_t d3, int64_t d4) {
+            int64_t indexes[] = {d0, d1, d2, d3, d4};
+            schedule.get_callable<kernel>()(indexes, &call_args);
+        });
+}
+
+void MKLDNNSnippetNode::schedule_nt(const jit_snippets_call_args& call_args) const {
+    const auto& work_size = dims_out[max_rank_out_desc_idx];
+    parallel_nt(0, [&](const int ithr, const int nthr) {
+        size_t start = 0, end = 0;
+        splitter(schedulerWorkAmount, nthr, ithr, start, end);
+
+        std::vector<int64_t> indexes(work_size.size() - 1, 0);
+        for (size_t iwork = start; iwork < end; ++iwork) {
+            size_t tmp = iwork;
+            for (ptrdiff_t j = work_size.size() - 2; j >= 0; j--) {
+                indexes[j] = tmp % work_size[j];
+                tmp /= work_size[j];
+            }
+
+            schedule.get_callable<kernel>()(indexes.data(), &call_args);
+        }
+    });
+}
+
+REG_MKLDNN_PRIM_FOR(MKLDNNSnippetNode, Subgraph);
+
diff --git a/inference-engine/src/mkldnn_plugin/nodes/subgraph.h b/inference-engine/src/mkldnn_plugin/nodes/subgraph.h
new file mode 100644
index 00000000000..a434c80d0f6
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/subgraph.h
@@ -0,0 +1,91 @@
+// Copyright (C) 2020-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ie_common.h>
+
+#include <mkldnn.hpp>
+#include <cpu/x64/jit_generator.hpp>
+#include "emitters/jit_snippets_emitters.hpp"
+
+#include "mkldnn_node.h"
+#include "snippets/op/subgraph.hpp"
+
+#include <array>
+
+namespace MKLDNNPlugin {
+
+/// MKLDNNSnippetNode represents subgraph node in MKLDNN plugin
+/// potentially, snippet can be placed as a postop to any support operation while it doesn't support postops itself
+/// precision: fp32
+class MKLDNNSnippetNode : public MKLDNNNode {
+public:
+    MKLDNNSnippetNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
+    ~MKLDNNSnippetNode() override = default;
+
+    void getSupportedDescriptors() override {};
+    void initSupportedPrimitiveDescriptors() override;
+    void selectOptimalPrimitiveDescriptor() override;
+
+    // Here we convert to canonical for & jit everything
+    void createPrimitive() override;
+
+    bool created() const override;
+
+    // if generator is set, it would execute generated code otherwise it would fallback to nGraph reference
+    void execute(mkldnn::stream strm) override;
+
+private:
+    static const size_t rank6D {6};
+
+    typedef void (*kernel)(const void *, const void *);
+
+    void define_schedule();
+
+    void generate();
+
+    // Evaluates generated snippet using parallel backend
+    void schedule_6d(const jit_snippets_call_args& const_args) const;
+    void schedule_nt(const jit_snippets_call_args& const_args) const;
+
+    // Local copy of subgraph node for canonization & code generation
+    std::shared_ptr<ngraph::snippets::op::Subgraph> snippet;
+
+    // Holds generated snippet with information about how to schedule it
+    ngraph::snippets::Schedule schedule;
+
+    // Holds ISA version used is codeGeneration target
+    dnnl::impl::cpu::x64::cpu_isa_t host_isa;
+
+    // Holds index of output used as in execution domain
+    // it should be compatible with a schedule's work size
+    size_t max_rank_out_desc_idx = 0;
+
+    /// scheduling info
+    size_t batchDimIdx = 0;
+    size_t tensorRank = 0;
+    size_t tileRank = 1;
+    size_t fullWorkAmount = 0;
+    size_t schedulerWorkAmount = 0;
+    const size_t maxTileRank = 2;
+
+    std::vector<MKLDNNMemoryPtr> srcMemPtrs = {};
+    std::vector<MKLDNNMemoryPtr> dstMemPtrs = {};
+
+    std::vector<std::vector<int64_t>> dims_in = {};
+    std::vector<std::vector<int64_t>> offsets_in = {};
+    std::vector<ptrdiff_t> start_offset_in = {};
+    std::vector<ptrdiff_t> start_offset_out = {};
+
+    std::vector<std::vector<int64_t>> dims_out = {};
+    std::vector<std::vector<int64_t>> offsets_out = {};
+
+    std::vector<int64_t> sch_dims = {};
+    std::vector<int64_t> sch_offsets_in = {};
+    std::vector<int64_t> sch_offsets_out = {};
+    bool canUseOptimizedImpl = true;
+};
+
+}  // namespace MKLDNNPlugin
diff --git a/src/bindings/python/tests/test_onnx/test_zoo_models.py b/src/bindings/python/tests/test_onnx/test_zoo_models.py
index 9530e73be2e..d012e9ced3a 100644
--- a/src/bindings/python/tests/test_onnx/test_zoo_models.py
+++ b/src/bindings/python/tests/test_onnx/test_zoo_models.py
@@ -54,6 +54,7 @@ tolerance_map = {
     "fp16_inception_v1": {"atol": 0.001, "rtol": 0.001},
     "mobilenet_opset7": {"atol": 0.001, "rtol": 0.001},
     "resnet50_v2_opset7": {"atol": 0.001, "rtol": 0.001},
+    "resnet50-v2-7": {"atol": 0.001, "rtol": 0.001},
     "test_mobilenetv2-1.0": {"atol": 0.001, "rtol": 0.001},
     "test_resnet101v2": {"atol": 0.001, "rtol": 0.001},
     "test_resnet18v2": {"atol": 0.001, "rtol": 0.001},
@@ -97,7 +98,7 @@ tolerance_map = {
     "tiny_yolov2": {"atol": 1e-05, "rtol": 0.001},
     "mobilenetv2-1": {"atol": 1e-04, "rtol": 0.001},
     "resnet101v1": {"atol": 1e-04, "rtol": 0.001},
-    "resnet101v2": {"atol": 1e-06, "rtol": 0.001},
+    "resnet101v2": {"atol": 1e-5, "rtol": 0.001},
     "resnet152v2": {"atol": 1e-05, "rtol": 0.001},
     "resnet18v2": {"atol": 1e-05, "rtol": 0.001},
     "resnet34v2": {"atol": 1e-05, "rtol": 0.001},
diff --git a/src/common/snippets/CMakeLists.txt b/src/common/snippets/CMakeLists.txt
index e9105c9044f..de6b5dd3992 100644
--- a/src/common/snippets/CMakeLists.txt
+++ b/src/common/snippets/CMakeLists.txt
@@ -25,10 +25,10 @@ ie_faster_build(${TARGET_NAME}
     UNITY
 )
 
-target_link_libraries(${TARGET_NAME} PUBLIC ngraph
+target_link_libraries(${TARGET_NAME} PUBLIC ngraph inference_engine_transformations
                                      PRIVATE ngraph_reference)
 
-target_include_directories(${TARGET_NAME} PUBLIC ${PUBLIC_HEADERS_DIR}
+target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
                                           PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
@@ -36,10 +36,8 @@ add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
 ie_mark_target_as_cc(${TARGET_NAME})
 
 # LTO
-
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
-# install
 
-# TODO: uncomment once snippets are integrated into CPU plugin
-# ov_install_static_lib(${TARGET_NAME} core)
+# install
+ov_install_static_lib(${TARGET_NAME} core)
diff --git a/src/common/snippets/include/snippets/generator.hpp b/src/common/snippets/include/snippets/generator.hpp
index 9943b60a644..a8a79c91c19 100644
--- a/src/common/snippets/include/snippets/generator.hpp
+++ b/src/common/snippets/include/snippets/generator.hpp
@@ -3,7 +3,7 @@
 //
 
 /**
- * @brief A file contains public interface for target indepenent code generator.
+ * @brief A file contains public interface for target independent code generator.
  * @file generator.hpp
  */
 #pragma once
@@ -112,10 +112,10 @@ public:
     virtual ~Generator() = default;
     /**
      * @brief virtual method any specific implementation should implement
-     * @param f runction in canonical for for table-based code generation
+     * @param m model in canonical for for table-based code generation
      * @return pointer to generated code
      */
-    code generate(std::shared_ptr<Function>& f) const;
+    code generate(std::shared_ptr<ov::Model>& m, const void* compile_params = nullptr) const;
 
 protected:
     std::shared_ptr<TargetMachine> target;
diff --git a/src/common/snippets/src/itt.hpp b/src/common/snippets/include/snippets/itt.hpp
similarity index 96%
rename from src/common/snippets/src/itt.hpp
rename to src/common/snippets/include/snippets/itt.hpp
index cc4589a9008..cc974f7ed44 100644
--- a/src/common/snippets/src/itt.hpp
+++ b/src/common/snippets/include/snippets/itt.hpp
@@ -15,7 +15,7 @@ namespace ngraph {
 namespace pass {
 namespace itt {
 namespace domains {
-    OV_ITT_DOMAIN(IETransform);
+    OV_ITT_DOMAIN(SnippetsTransform);
 }   // namespace domains
 }   // namespace itt
 }   // namespace pass
diff --git a/src/common/snippets/include/snippets/op/kernel.hpp b/src/common/snippets/include/snippets/op/kernel.hpp
index e10e73c97d3..9b604c83e50 100644
--- a/src/common/snippets/include/snippets/op/kernel.hpp
+++ b/src/common/snippets/include/snippets/op/kernel.hpp
@@ -13,7 +13,7 @@ namespace op {
 
 /**
  * @interface Kernel
- * @brief Generated by Canonicalization and represents compute kernel legal for sheduling
+ * @brief Generated by Canonicalization and represents compute kernel legal for scheduling
  * @ingroup snippets
  */
 class Kernel : public ngraph::op::Op {
@@ -28,6 +28,7 @@ public:
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
         return std::make_shared<Kernel>(region);
     }
+    const void *compile_params = nullptr;
 };
 
 } // namespace op
diff --git a/src/common/snippets/include/snippets/op/powerstatic.hpp b/src/common/snippets/include/snippets/op/powerstatic.hpp
new file mode 100644
index 00000000000..e67ff979f03
--- /dev/null
+++ b/src/common/snippets/include/snippets/op/powerstatic.hpp
@@ -0,0 +1,44 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/op/op.hpp>
+#include <ngraph/op/power.hpp>
+#include <snippets/snippets_isa.hpp>
+
+namespace ngraph {
+namespace snippets {
+namespace op {
+
+/**
+ * @interface PowerStatic
+ * @brief Generated by Canonicalization for a spasical case of power innstruction which has constant power value
+ * @ingroup snippets
+ */
+class PowerStatic : public ov::op::util::UnaryElementwiseArithmetic {
+public:
+    OPENVINO_OP("PowerStatic", "SnippetsOpset", ov::op::util::UnaryElementwiseArithmetic);
+
+    PowerStatic() = default;
+    PowerStatic(const Output <Node> &arg, float power) : UnaryElementwiseArithmetic(arg), power(power) {
+        constructor_validate_and_infer_types();
+    }
+
+    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector &new_args) const override {
+        check_new_args_count(this, new_args);
+        return std::make_shared<PowerStatic>(new_args.at(0), power);
+    }
+    bool visit_attributes(AttributeVisitor &visitor) override {
+        visitor.on_attribute("power", power);
+        return true;
+    }
+    float get_power() const {  return power; }
+
+private:
+    float power = 0;
+};
+} // namespace op
+} // namespace snippets
+} // namespace ngraph
\ No newline at end of file
diff --git a/src/common/snippets/include/snippets/op/staticpower.hpp b/src/common/snippets/include/snippets/op/staticpower.hpp
deleted file mode 100644
index b1d6452b6e1..00000000000
--- a/src/common/snippets/include/snippets/op/staticpower.hpp
+++ /dev/null
@@ -1,42 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <ngraph/op/op.hpp>
-#include <ngraph/op/power.hpp>
-#include <snippets/snippets_isa.hpp>
-
-namespace ngraph {
-namespace snippets {
-namespace op {
-
-/**
- * @interface PowerStatic
- * @brief Generated by Canonicalization for a spasical case of power innstruction which has constant power value
- * @ingroup snippets
- */
-class PowerStatic : public ngraph::op::v1::Power {
-public:
-    OPENVINO_OP("PowerStatic", "SnippetsOpset", ngraph::op::v1::Power);
-
-    PowerStatic() : Power() {
-    }
-
-    PowerStatic(const Output<Node>& arg0,
-            const Output<Node>& arg1,
-            const ngraph::op::AutoBroadcastSpec& auto_broadcast =
-                ngraph::op::AutoBroadcastSpec(ngraph::op::AutoBroadcastType::NUMPY)) : Power(arg0, arg1, auto_broadcast) {
-        NGRAPH_CHECK(!!std::dynamic_pointer_cast<ngraph::snippets::op::Scalar>(arg1.get_node_shared_ptr()), "second argument must be scalar constant.");
-    }
-
-    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override {
-        check_new_args_count(this, new_args);
-        return std::make_shared<PowerStatic>(new_args.at(0), new_args.at(1), this->get_autob());
-    }
-};
-
-} // namespace op
-} // namespace snippets
-} // namespace ngraph
\ No newline at end of file
diff --git a/src/common/snippets/include/snippets/op/subgraph.hpp b/src/common/snippets/include/snippets/op/subgraph.hpp
index 92c10f9b972..262b86ad8c3 100644
--- a/src/common/snippets/include/snippets/op/subgraph.hpp
+++ b/src/common/snippets/include/snippets/op/subgraph.hpp
@@ -6,7 +6,7 @@
 
 #include <memory>
 
-#include <ngraph/function.hpp>
+#include <openvino/core/model.hpp>
 #include <ngraph/op/op.hpp>
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pass/manager.hpp>
@@ -19,7 +19,7 @@ namespace op {
 
 /**
  * @interface Subgraph
- * @brief An operation that is implemented by a function
+ * @brief An operation that is implemented by a model
  * @ingroup snippets
  */
 class Subgraph : public ngraph::op::Op {
@@ -70,9 +70,9 @@ public:
     using BlockedShape = std::tuple<ngraph::Shape, ngraph::AxisVector, ngraph::element::Type>;
     using BlockedShapeVector = std::vector<BlockedShape>;
 
-    Subgraph(const OutputVector& args, std::shared_ptr<Function> body);
+    Subgraph(const OutputVector& args, std::shared_ptr<ov::Model> body);
 
-    Subgraph(const NodeVector& args, std::shared_ptr<Function> body);
+    Subgraph(const NodeVector& args, std::shared_ptr<ov::Model> body);
 
     bool visit_attributes(AttributeVisitor& visitor) override;
 
@@ -80,7 +80,7 @@ public:
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override;
 
-    std::shared_ptr<Function> get_body() const {
+    std::shared_ptr<ov::Model> get_body() const {
         return m_body;
     }
 
@@ -91,13 +91,11 @@ public:
     std::shared_ptr<Subgraph> make_canonical_from_this();
 
     snippets::Schedule generate(const BlockedShapeVector& output_shapes, const BlockedShapeVector& input_shapes,
-                                ngraph::pass::Manager opt = ngraph::pass::Manager());
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& output_values, const HostTensorVector& input_values) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
-
+                                ngraph::pass::Manager opt = ngraph::pass::Manager(), const void* compile_params = nullptr);
+    snippets::Schedule generate(const BlockedShapeVector& output_shapes, const BlockedShapeVector& input_shapes,
+                                const void* compile_params = nullptr);
     /// Set a new body for the op; body needs to satisfy requirements on inputs/outputs
-    void set_body(std::shared_ptr<Function> body);
+    void set_body(std::shared_ptr<ov::Model> body);
 
     // plugin sets generator for a snippet to some specific generator.
     // it's going to be replaced with Jitters table later
@@ -114,7 +112,7 @@ private:
     void canonicalize(const BlockedShapeVector& output_shapes, const BlockedShapeVector& input_shapes);
     void convert_to_snippet_dialect();
 
-    std::shared_ptr<Function> m_body;
+    std::shared_ptr<ov::Model> m_body;
     std::shared_ptr<ngraph::snippets::Generator> m_generator;
 };
 
@@ -124,21 +122,21 @@ static inline std::ostream& operator<<(std::ostream& os, const op::Subgraph::Blo
 }
 
 static inline auto is_scalar_constant(const std::shared_ptr<ngraph::Node>& source_output_node) -> bool {
-    return !!ngraph::as_type_ptr<ngraph::opset1::Constant>(source_output_node) &&
-        (source_output_node->get_shape() == ngraph::Shape() || ngraph::shape_size(source_output_node->get_shape()) == 1);
+    return ngraph::is_type<ngraph::opset1::Constant>(source_output_node) && ngraph::shape_size(source_output_node->get_shape()) == 1;
 };
 
 static inline auto create_body(std::string name, const ngraph::ResultVector& results, const ngraph::ParameterVector& parameters) ->
-    std::shared_ptr<ngraph::Function> {
-    auto body = std::make_shared<ngraph::Function>(results, parameters, name);
+    std::shared_ptr<ov::Model> {
+    auto body = std::make_shared<ov::Model>(results, parameters, name);
     return body;
 };
 
-static inline auto build_subgraph(const std::shared_ptr<ngraph::Node>& node, const ngraph::OutputVector& inputs, const std::shared_ptr<ngraph::Function>& body)
+static inline auto build_subgraph(const std::shared_ptr<ngraph::Node>& node, const ngraph::OutputVector& inputs,
+                                  const std::shared_ptr<ov::Model>& body, const std::string name = "")
     -> std::shared_ptr<Subgraph>{
     auto subgraph = std::make_shared<Subgraph>(inputs, body);
     copy_runtime_info(node, subgraph);
-    subgraph->set_friendly_name(node->get_friendly_name());
+    subgraph->set_friendly_name(name.empty() ? node->get_friendly_name() : name);
     return subgraph;
 };
 
diff --git a/src/common/snippets/include/snippets/op/tile.hpp b/src/common/snippets/include/snippets/op/tile.hpp
index 15656ba764e..3aa477e710b 100644
--- a/src/common/snippets/include/snippets/op/tile.hpp
+++ b/src/common/snippets/include/snippets/op/tile.hpp
@@ -27,6 +27,7 @@ public:
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
         return std::make_shared<Tile>(region);
     }
+    const void *compile_params;
 };
 
 } // namespace op
diff --git a/src/common/snippets/include/snippets/pass/assign_registers.hpp b/src/common/snippets/include/snippets/pass/assign_registers.hpp
index da61cd2eacb..362936256e5 100644
--- a/src/common/snippets/include/snippets/pass/assign_registers.hpp
+++ b/src/common/snippets/include/snippets/pass/assign_registers.hpp
@@ -21,7 +21,7 @@ public:
     AssignRegisters() {
         set_property(ngraph::pass::PassProperty::REQUIRE_STATIC_SHAPE, true);
     }
-    bool run_on_model(const std::shared_ptr<ngraph::Function>& m) override;
+    bool run_on_model(const std::shared_ptr<ov::Model>& m) override;
 };
 
 }  // namespace pass
diff --git a/src/common/snippets/include/snippets/pass/collapse_subgraph.hpp b/src/common/snippets/include/snippets/pass/collapse_subgraph.hpp
index e26e28b00b8..dd41f0af411 100644
--- a/src/common/snippets/include/snippets/pass/collapse_subgraph.hpp
+++ b/src/common/snippets/include/snippets/pass/collapse_subgraph.hpp
@@ -12,32 +12,32 @@
 namespace ngraph {
 namespace snippets {
 namespace pass {
+/*
+ NotSet - default value returned by GetSnippetsNodeType(...) if the node wasn't marked
+ SkippedByPlugin - indicate that snippets can't include this node in subgraph. Can be set by Plugin via SetSnippetsNodeType(...).
+ */
+enum class SnippetsNodeType : int64_t {NotSet, SkippedByPlugin};
+void SetSnippetsNodeType(const std::shared_ptr<Node>&, SnippetsNodeType);
+SnippetsNodeType GetSnippetsNodeType(const std::shared_ptr<const Node>&);
+void SetTopologicalOrder(const std::shared_ptr<Node>&, int64_t);
+int64_t GetTopologicalOrder(const std::shared_ptr<const Node>&);
+bool AppropriateForSubgraph(const std::shared_ptr<const Node>&);
 
 /**
- * @interface StartSubgraph
- * @brief Matches multiple output loyout-oblivious operations to start a new subgraph
+ * @interface EnumerateNodes
+ * @brief  Snippets rely on topological order to avoid creating cyclic dependencies. This transformation sets the order.
  * @ingroup snippets
  */
-class StartSubgraph: public ngraph::pass::MatcherPass {
+class EnumerateNodes : public ov::pass::ModelPass {
 public:
     NGRAPH_RTTI_DECLARATION;
-    explicit StartSubgraph(bool tokenize_by_node = false);
-};
-
-/**
- * @interface AttachToSubgraph
- * @brief Matches loyout-oblivious operations with subgraph operation as an input to attech this node into it
- * @ingroup snippets
- */
-class AttachToSubgraph: public ngraph::pass::MatcherPass {
-public:
-    NGRAPH_RTTI_DECLARATION;
-    explicit AttachToSubgraph(bool tokenize_by_node = false);
+    EnumerateNodes() : ModelPass() {}
+    bool run_on_model(const std::shared_ptr<ov::Model>&) override;
 };
 
 /**
  * @interface TokenizeSnippets
- * @brief Splits function to subgraphs if possible using rules above
+ * @brief Splits model to subgraphs if possible using rules above
  * This pass tokenizes topology graph into subgraphs.
  * Those subgraphs consists of unary or binary layout-oblivious (LO) opetations found in subset 1.
  * Non-layout-oblivious (NLO) operations operations (called also support in this context) are ignored and become a fullstop in tokenization routine
@@ -51,20 +51,16 @@ public:
  *       1. outputs are collected subgraph (outputs consumed by some other node & subgraph outputs consumed by the node to be merged)
  *       1. finally current node is replaced with the new subgraph. We cannot use replace_node because multiple nodes are replaced so
  *       make the replacement manually by redirecting ports
- * Input subgraph is prefented from visiting twice if more than one output of it consumed by currently considered node
  * New subgraph is introduced, if there is a loop introduced
  * New subgraph is introduced, if number of inputs and outputs exceeds 7 due to scheduling limitation
  * New subgraph is introduced, if multiple outputs of merged nodes are not broadcastable to each other (equality of all outputs is too much on the other hand)
  * Scalar constants are placed as is into subgraph due to optimization purpose
  * @ingroup snippets
  */
-class TokenizeSnippets: public ngraph::pass::GraphRewrite {
+class TokenizeSnippets: public ngraph::pass::MatcherPass {
 public:
     NGRAPH_RTTI_DECLARATION;
-    TokenizeSnippets(bool tokenize_by_node = false) {
-        add_matcher<ngraph::snippets::pass::StartSubgraph>(tokenize_by_node);
-        add_matcher<ngraph::snippets::pass::AttachToSubgraph>(tokenize_by_node);
-    }
+    explicit TokenizeSnippets();
 };
 
 }  // namespace pass
diff --git a/src/common/snippets/include/snippets/pass/insert_load_store.hpp b/src/common/snippets/include/snippets/pass/insert_load_store.hpp
index c01726fd880..d32c5e90e78 100644
--- a/src/common/snippets/include/snippets/pass/insert_load_store.hpp
+++ b/src/common/snippets/include/snippets/pass/insert_load_store.hpp
@@ -14,7 +14,7 @@ namespace pass {
 /**
  * @interface InsertLoad
  * @brief Inserts explicit load instruction after each parameter.
- * The pass is used to convert function to a canonical form for code generation
+ * The pass is used to convert model to a canonical form for code generation
  * @ingroup snippets
  */
 class InsertLoad: public ngraph::pass::MatcherPass {
@@ -25,7 +25,7 @@ public:
 /**
  * @interface InsertStore
  * @brief Inserts explicit store instruction before each result.
- * The pass is used to convert function to a canonical form for code generation
+ * The pass is used to convert model to a canonical form for code generation
  * @ingroup snippets
  */
 class InsertStore: public ngraph::pass::MatcherPass {
diff --git a/src/common/snippets/include/snippets/pass/insert_movebroadcast.hpp b/src/common/snippets/include/snippets/pass/insert_movebroadcast.hpp
index 5364781ab6e..325b5024a49 100644
--- a/src/common/snippets/include/snippets/pass/insert_movebroadcast.hpp
+++ b/src/common/snippets/include/snippets/pass/insert_movebroadcast.hpp
@@ -14,7 +14,7 @@ namespace pass {
 /**
  * @interface InsertMoveBroadcast
  * @brief Inserts explicit MoveBroadcast instruction if broadcasting by most warying dimension is needed.
- * The pass is used to convert function to a canonical form for code generation
+ * The pass is used to convert model to a canonical form for code generation
  * @ingroup snippets
  */
 class InsertMoveBroadcast: public ngraph::pass::MatcherPass {
diff --git a/src/common/snippets/include/snippets/pass/load_movebroadcast_to_broadcastload.hpp b/src/common/snippets/include/snippets/pass/load_movebroadcast_to_broadcastload.hpp
index 32abbcbdab9..bddc2452e78 100644
--- a/src/common/snippets/include/snippets/pass/load_movebroadcast_to_broadcastload.hpp
+++ b/src/common/snippets/include/snippets/pass/load_movebroadcast_to_broadcastload.hpp
@@ -14,7 +14,7 @@ namespace pass {
 /**
  * @interface LoadMoveBroadcastToBroadcastLoad
  * @brief Fuses consecutive Load and MoveBroadcast into a single load insctruction.
- * The pass is used to convert function to a canonical form for code generation
+ * The pass is used to convert model to a canonical form for code generation
  * @ingroup snippets
  */
 class LoadMoveBroadcastToBroadcastLoad: public ngraph::pass::MatcherPass {
diff --git a/src/common/snippets/include/snippets/pass/vector_to_scalar.hpp b/src/common/snippets/include/snippets/pass/vector_to_scalar.hpp
index e928d5154c2..6ee7c1bda86 100644
--- a/src/common/snippets/include/snippets/pass/vector_to_scalar.hpp
+++ b/src/common/snippets/include/snippets/pass/vector_to_scalar.hpp
@@ -13,8 +13,8 @@ namespace pass {
 
 /**
  * @interface ReplaceLoadsWithScalarLoads
- * @brief Replases vector loads with scalar versions.
- * The pass is used to cange alement type of function in a canonical form vector to scalar.
+ * @brief Replaces vector loads with scalar versions.
+ * The pass is used to cange element type of function in a canonical form vector to scalar.
  * Used for tail generation
  * @ingroup snippets
  */
@@ -25,8 +25,8 @@ public:
 
 /**
  * @interface ReplaceStoresWithScalarStores
- * @brief Replases vector stores with scalar versions.
- * The pass is used to cange alement type of function in a canonical form vector to scalar.
+ * @brief Replaces vector stores with scalar versions.
+ * The pass is used to cange element type of model in a canonical form vector to scalar.
  * Used for tail generation
  * @ingroup snippets
  */
diff --git a/src/common/snippets/include/snippets/snippets_isa.hpp b/src/common/snippets/include/snippets/snippets_isa.hpp
index 9f4f1cd663d..be00e5e9b2a 100644
--- a/src/common/snippets/include/snippets/snippets_isa.hpp
+++ b/src/common/snippets/include/snippets/snippets_isa.hpp
@@ -11,13 +11,15 @@
 #include "op/blockedparameter.hpp"
 #include "op/broadcastload.hpp"
 #include "op/broadcastmove.hpp"
+#include "op/kernel.hpp"
 #include "op/load.hpp"
 #include "op/nop.hpp"
 #include "op/scalar.hpp"
 #include "op/scalarload.hpp"
 #include "op/scalarstore.hpp"
-#include "op/staticpower.hpp"
+#include "op/powerstatic.hpp"
 #include "op/store.hpp"
+#include "op/tile.hpp"
 #include "op/vectorload.hpp"
 #include "op/vectorstore.hpp"
 
diff --git a/src/common/snippets/src/generator.cpp b/src/common/snippets/src/generator.cpp
index 13659752a81..1489afa89aa 100644
--- a/src/common/snippets/src/generator.cpp
+++ b/src/common/snippets/src/generator.cpp
@@ -8,10 +8,12 @@
 #include "snippets/pass/insert_load_store.hpp"
 #include "snippets/op/tile.hpp"
 #include "snippets/op/kernel.hpp"
+#include <snippets/itt.hpp>
 
 #include <ngraph/pass/manager.hpp>
 
 auto ngraph::snippets::getRegisters(std::shared_ptr<ngraph::Node>& n) -> ngraph::snippets::RegInfo {
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::getRegisters")
     auto rt = n->get_rt_info();
 
     // ToDo: change to reg_t
@@ -27,7 +29,7 @@ auto ngraph::snippets::getRegisters(std::shared_ptr<ngraph::Node>& n) -> ngraph:
     for (auto input : n->inputs()) {
         auto rt = input.get_source_output().get_node_shared_ptr()->get_rt_info();
         auto it_rt = rt.find("reginfo");
-        if (it_rt == rt.end()) {
+        if (it_rt != rt.end()) {
             for (auto reg : it_rt->second.as<std::vector<size_t>>()) {
                 rin.push_back(reg);
             }
@@ -36,53 +38,69 @@ auto ngraph::snippets::getRegisters(std::shared_ptr<ngraph::Node>& n) -> ngraph:
     return std::make_pair(rin, rout);
 }
 
-ngraph::snippets::code ngraph::snippets::Generator::generate(std::shared_ptr<ngraph::Function>& f) const {
+ngraph::snippets::code ngraph::snippets::Generator::generate(std::shared_ptr<ov::Model>& m,
+                                                             const void* compile_params) const {
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::Generator::generate")
     if (!target->is_supported())
         throw ngraph_error("unsupported architecture for code genration");
 
-    auto params = f->get_parameters();
-    auto results = f->get_results();
-    auto nptrs = results.size() + params.size();
-
-    if (nptrs > 7) {
-        throw ngraph_error("snippet signature should not exceed 7 arguments. got " + std::to_string(nptrs));
-    }
+    auto params = m->get_parameters();
+    auto results = m->get_results();
+    auto in = params.size();
+    auto out = results.size();
+    auto nptrs = in + out;
 
+    OV_ITT_TASK_CHAIN(GENERATE, ngraph::pass::itt::domains::SnippetsTransform, "Snippets::Generator", "::VectorTile")
     // vector tile
     std::vector<std::pair<std::shared_ptr<ngraph::snippets::Emitter>, ngraph::snippets::RegInfo>> lowered;
-    for (auto n : f->get_ordered_ops()) {
+    for (auto n : m->get_ordered_ops()) {
         lowered.push_back(std::make_pair(target->get(n->get_type_info())(n), ngraph::snippets::getRegisters(n)));
     }
+    OV_ITT_TASK_NEXT(GENERATE, "::ScalarTile")
 
     // scalar tile
-    auto f_scalar = ngraph::clone_function(*f.get());
-    ngraph::pass::Manager m;
-    m.register_pass<ngraph::snippets::pass::ReplaceLoadsWithScalarLoads>();
-    m.register_pass<ngraph::snippets::pass::ReplaceStoresWithScalarStores>();
-    m.run_passes(f_scalar);
-
+    auto m_scalar = ov::clone_model(*m.get());
+    ngraph::pass::Manager mng;
+    mng.register_pass<ngraph::snippets::pass::ReplaceLoadsWithScalarLoads>();
+    mng.register_pass<ngraph::snippets::pass::ReplaceStoresWithScalarStores>();
+    mng.run_passes(m_scalar);
+    OV_ITT_TASK_NEXT(GENERATE, "::ScalarTile_get")
     std::vector<std::pair<std::shared_ptr<Emitter>, RegInfo>> scalar_lowered;
-    for (auto n : f_scalar->get_ordered_ops()) {
+    for (auto n : m_scalar->get_ordered_ops()) {
         scalar_lowered.push_back(std::make_pair(target->get(n->get_type_info())(n), ngraph::snippets::getRegisters(n)));
     }
+    OV_ITT_TASK_NEXT(GENERATE, "::Tiles1D")
 
-    // wrapping into tiles
-    std::vector<std::pair<std::shared_ptr<Emitter>, RegInfo>> tiles;
-    tiles.push_back(std::make_pair(target->get(ngraph::snippets::op::Tile::get_type_info_static())(
-                        std::make_shared<ngraph::snippets::op::Tile>(lowered)),
-                    std::make_pair(std::vector<size_t>({target->get_lanes(), nptrs}), std::vector<size_t>{})));
-    tiles.push_back(std::make_pair(target->get(ngraph::snippets::op::Tile::get_type_info_static())(
-                        std::make_shared<ngraph::snippets::op::Tile>(scalar_lowered)),
-                    std::make_pair(std::vector<size_t>{{1, nptrs}}, std::vector<size_t>{})));
+    // wrapping into tiles1D
+    std::vector<std::pair<std::shared_ptr<Emitter>, RegInfo>> tiles1D;
+    auto tile = std::make_shared<ngraph::snippets::op::Tile>(lowered);
+    tile->compile_params = compile_params;
+    tiles1D.push_back(std::make_pair(target->get(ngraph::snippets::op::Tile::get_type_info_static())(tile),
+                                   std::make_pair(std::vector<size_t>({target->get_lanes(), 0, nptrs, 1}), std::vector<size_t>{})));
+    tile = std::make_shared<ngraph::snippets::op::Tile>(scalar_lowered);
+    tile->compile_params = compile_params;
+    tiles1D.push_back(std::make_pair(target->get(ngraph::snippets::op::Tile::get_type_info_static())(tile),
+                    std::make_pair(std::vector<size_t>{{1, target->get_lanes(), nptrs, 1}}, std::vector<size_t>{})));
 
+    OV_ITT_TASK_NEXT(GENERATE, "::Tiles2D")
+    // wrapping into tiles2D
+    std::vector<std::pair<std::shared_ptr<Emitter>, RegInfo>> tiles2D;
+    tile = std::make_shared<ngraph::snippets::op::Tile>(tiles1D);
+    tile->compile_params = compile_params;
+    tiles2D.push_back(std::make_pair(target->get(ngraph::snippets::op::Tile::get_type_info_static())(tile),
+                                     std::make_pair(std::vector<size_t>({1, 0, nptrs, 0}), std::vector<size_t>{})));
+
+    OV_ITT_TASK_NEXT(GENERATE, "::EmitCode")
     // emission
-    std::shared_ptr<Emitter> kernel = target->get(ngraph::snippets::op::Kernel::get_type_info_static())(std::make_shared<ngraph::snippets::op::Kernel>(tiles));
-    kernel->emit_code({params.size(), results.size()}, {});
-
+    auto tiles2DKernel = std::make_shared<ngraph::snippets::op::Kernel>(tiles2D);
+    tiles2DKernel->compile_params = compile_params;
+    std::shared_ptr<Emitter> kernel = target->get(ngraph::snippets::op::Kernel::get_type_info_static())(tiles2DKernel);
+    kernel->emit_code({in, out}, {});
+    OV_ITT_TASK_NEXT(GENERATE, "::EmitData")
     lowered.insert(lowered.end(), scalar_lowered.begin(), scalar_lowered.end());
     for (auto& op : lowered) {
         op.first->emit_data();
     }
-
+    OV_ITT_TASK_NEXT(GENERATE, "::GetSnippet")
     return target->get_snippet();
 }
diff --git a/src/common/snippets/src/op/broadcastload.cpp b/src/common/snippets/src/op/broadcastload.cpp
index 0b29b21214e..89ba270f23b 100644
--- a/src/common/snippets/src/op/broadcastload.cpp
+++ b/src/common/snippets/src/op/broadcastload.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/op/broadcastload.hpp"
 
diff --git a/src/common/snippets/src/op/broadcastmove.cpp b/src/common/snippets/src/op/broadcastmove.cpp
index 5a1fd4bddca..d14f53c7e82 100644
--- a/src/common/snippets/src/op/broadcastmove.cpp
+++ b/src/common/snippets/src/op/broadcastmove.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/op/broadcastmove.hpp"
 
diff --git a/src/common/snippets/src/op/load.cpp b/src/common/snippets/src/op/load.cpp
index 8ad943ebdd4..837d64dd9b0 100644
--- a/src/common/snippets/src/op/load.cpp
+++ b/src/common/snippets/src/op/load.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/op/load.hpp"
 
diff --git a/src/common/snippets/src/op/store.cpp b/src/common/snippets/src/op/store.cpp
index 99b86b164ce..066bcf7f8fd 100644
--- a/src/common/snippets/src/op/store.cpp
+++ b/src/common/snippets/src/op/store.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/op/scalarstore.hpp"
 
diff --git a/src/common/snippets/src/op/subgraph.cpp b/src/common/snippets/src/op/subgraph.cpp
index 1270edd3e3b..d9a2fc34301 100644
--- a/src/common/snippets/src/op/subgraph.cpp
+++ b/src/common/snippets/src/op/subgraph.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 #include "remarks.hpp"
 
 #include "snippets/op/subgraph.hpp"
@@ -25,21 +25,22 @@ void snippets::op::Subgraph::set_generator(std::shared_ptr<ngraph::snippets::Gen
     m_generator = generator;
 }
 
-snippets::op::Subgraph::Subgraph(const OutputVector& args, std::shared_ptr<Function> body)
+snippets::op::Subgraph::Subgraph(const OutputVector& args, std::shared_ptr<ov::Model> body)
     : Op(args), m_body(body), m_generator(nullptr) {
     constructor_validate_and_infer_types();
 }
 
-snippets::op::Subgraph::Subgraph(const NodeVector& args, std::shared_ptr<Function> body)
+snippets::op::Subgraph::Subgraph(const NodeVector& args, std::shared_ptr<ov::Model> body)
     : Subgraph(as_output_vector(args), body) {}
 
 std::shared_ptr<Node> snippets::op::Subgraph::clone_with_new_inputs(const OutputVector& inputs) const {
     INTERNAL_OP_SCOPE(Subgraph);
-    return make_shared<Subgraph>(inputs, ngraph::clone_function(*m_body.get()));
+    return make_shared<Subgraph>(inputs, ov::clone_model(*m_body.get()));
 }
 
 void snippets::op::Subgraph::validate_and_infer_types() {
     INTERNAL_OP_SCOPE(Subgraph);
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::validate_and_infer_types")
     ngraph::ParameterVector old_parameters;
     for (auto op : m_body->get_parameters()) {
         old_parameters.push_back(op);
@@ -65,24 +66,24 @@ bool snippets::op::Subgraph::visit_attributes(AttributeVisitor& visitor) {
     return true;
 }
 
-auto snippets::op::Subgraph::wrap_node_as_subgraph(const std::shared_ptr<ngraph::Node>& node) -> std::shared_ptr<op::Subgraph> {
+auto snippets::op::Subgraph::wrap_node_as_subgraph(const std::shared_ptr<ov::Node>& node) -> std::shared_ptr<op::Subgraph> {
     INTERNAL_OP_SCOPE(Subgraph);
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::wrap_node_as_subgraph")
     ngraph::ParameterVector body_parameters;
     ngraph::OutputVector body_inputs;
 
     ngraph::OutputVector subgraph_inputs;
 
-    for (auto input : node->inputs()) {
-        auto source_output = input.get_source_output();
-        if (is_scalar_constant(source_output.get_node_shared_ptr())) {
-            body_inputs.push_back(source_output);
+    for (const auto& input : node->input_values()) {
+        if (is_scalar_constant(input.get_node_shared_ptr())) {
+            body_inputs.push_back(input);
         } else {
             auto parameter = std::make_shared<ngraph::opset1::Parameter>(input.get_element_type(), input.get_partial_shape());
             body_parameters.push_back(parameter);
-            body_parameters.back()->set_friendly_name(source_output.get_node()->get_friendly_name());
+            body_parameters.back()->set_friendly_name(input.get_node()->get_friendly_name());
             body_inputs.push_back(parameter->output(0));
 
-            subgraph_inputs.push_back(source_output);
+            subgraph_inputs.push_back(input);
         }
     }
 
@@ -93,6 +94,9 @@ auto snippets::op::Subgraph::wrap_node_as_subgraph(const std::shared_ptr<ngraph:
         throw ngraph::ngraph_error("original node outputs size and extracted subgraph node outputs size doesn't much");
     }
 
+    // Clear the node dependencies so graph::topological_sort will not find any extra ops in get_ordered_ops()
+    //  This is needed so the model body will be created correctly
+    body_node->clear_control_dependencies();
     ngraph::ResultVector body_results;
     for (auto output : node->outputs()) {
         body_results.push_back(std::make_shared<ngraph::opset1::Result>(body_node->output(output.get_index())));
@@ -118,7 +122,7 @@ std::shared_ptr<snippets::op::Subgraph> snippets::op::Subgraph::make_canonical_f
     for (auto input : this->input_values()) {
         subgraph_node_inputs.push_back(input);
     }
-    auto new_body = ngraph::clone_function(*this->get_body().get());
+    auto new_body = ov::clone_model(*this->get_body().get());
     auto snippet = std::make_shared<op::Subgraph>(subgraph_node_inputs, new_body);
     ngraph::copy_runtime_info(this->shared_from_this(), snippet);
     snippet->set_friendly_name(this->get_friendly_name());
@@ -129,15 +133,16 @@ std::shared_ptr<snippets::op::Subgraph> snippets::op::Subgraph::make_canonical_f
 
 // We also can think of canonization as of pass to copy original subgraph and transforming it to canonical form suitable for code generation
 // pass actual parameters and results shapes to generate for as well as channel mapping,
-// we need to distinguish between 5d tensors that represents <N, C, H, W, c> and <N, C, D, H, W> somehow like locked dimensions
-// ngraph::AxisVector to code
+// Todo: we need to distinguish between 5d tensors that represents <N, C, H, W, c> and <N, C, D, H, W> somehow like locked dimensions
+//  ngraph::AxisVector to code
 void snippets::op::Subgraph::canonicalize(const BlockedShapeVector& output_shapes, const BlockedShapeVector& input_shapes) {
     INTERNAL_OP_SCOPE(Subgraph);
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::canonicalize")
     NODE_VALIDATION_CHECK(this, input_shapes.size() == m_body->get_parameters().size(),
-        "Number of parameters for snippet doesn't much passed to generate method: ", input_shapes.size(), " vs ", m_body->get_parameters().size(), ".");
+        "Number of parameters for snippet doesn't match passed to generate method: ", input_shapes.size(), " vs ", m_body->get_parameters().size(), ".");
 
     NODE_VALIDATION_CHECK(this, output_shapes.size() == m_body->get_results().size(),
-        "number of results for snippet doesn't much passed to generate method: ", output_shapes.size(), " vs ", m_body->get_results().size(), ".");
+        "number of results for snippet doesn't match passed to generate method: ", output_shapes.size(), " vs ", m_body->get_results().size(), ".");
 
     // replace only constants which are actually should be represented as scalars during code generation and probably move this step a bit later
     for (auto op : m_body->get_ordered_ops()) {
@@ -149,18 +154,6 @@ void snippets::op::Subgraph::canonicalize(const BlockedShapeVector& output_shape
         }
     }
 
-    // repalace power with power static
-    for (auto op : m_body->get_ordered_ops()) {
-        if (auto power = ngraph::as_type_ptr<opset1::Power>(op)) {
-            if (ngraph::as_type_ptr<snippets::op::Scalar>(power->input(1).get_node()->shared_from_this())) {
-                auto power_static = std::make_shared<snippets::op::PowerStatic>(
-                    power->input(0).get_source_output(), power->input(1).get_source_output(), power->get_autob());
-                power_static->set_friendly_name(power->get_friendly_name());
-                ngraph::copy_runtime_info(power, power_static);
-                ngraph::replace_node(power, power_static);
-            }
-        }
-    }
 
 
     // it should be in subgraph node to be aligned with internal and external parameter list, but adding this for testing
@@ -193,6 +186,7 @@ void snippets::op::Subgraph::canonicalize(const BlockedShapeVector& output_shape
 
 void snippets::op::Subgraph::convert_to_snippet_dialect() {
     INTERNAL_OP_SCOPE(Subgraph);
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::convert_to_snippet_dialect")
     ngraph::pass::Manager manager;
     manager.register_pass<snippets::pass::InsertLoad>();
     manager.register_pass<snippets::pass::InsertStore>();
@@ -201,24 +195,54 @@ void snippets::op::Subgraph::convert_to_snippet_dialect() {
     manager.run_passes(m_body);
 }
 
-snippets::Schedule snippets::op::Subgraph::generate(const BlockedShapeVector& output_shapes, const BlockedShapeVector& input_shapes,
-                                                    ngraph::pass::Manager opt) {
+snippets::Schedule snippets::op::Subgraph::generate(const BlockedShapeVector& output_shapes,
+                                                    const BlockedShapeVector& input_shapes,
+                                                    const void* compile_params) {
+    return generate(output_shapes, input_shapes, ngraph::pass::Manager(), compile_params);
+}
+
+snippets::Schedule snippets::op::Subgraph::generate(const BlockedShapeVector& output_shapes,
+                                                    const BlockedShapeVector& input_shapes,
+                                                    ngraph::pass::Manager opt,
+                                                    const void* compile_params) {
     INTERNAL_OP_SCOPE(Subgraph);
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::generate")
     NGRAPH_CHECK(m_generator != nullptr, "generate is called while generator is not set");
 
     canonicalize(output_shapes, input_shapes);
+
+    // Todo: ngraph::pass::Manager introduces appreciable overheads, especially while used on small graphs.
+    // So don't wrap this transformation as a MatcherPass, but rewrite convert_to_snippet_dialect() as a
+    // for loop to improve first-inference time.
+    // replace power with power static
+
+    for (auto op : m_body->get_ordered_ops()) {
+        if (ov::is_type<opset1::Power>(op) &&
+            ov::is_type<snippets::op::Scalar>(op->get_input_node_shared_ptr(1)) &&
+            ov::shape_size(op->get_input_shape(1)) == 1) {
+            auto power = ov::as_type_ptr<opset1::Power>(op);
+            auto scalar = ov::as_type_ptr<snippets::op::Scalar>(op->get_input_node_shared_ptr(1));
+            auto value = scalar->cast_vector<float>()[0];;
+            auto power_static = std::make_shared<snippets::op::PowerStatic>(power->input(0).get_source_output(), value);
+            power_static->set_friendly_name(power->get_friendly_name());
+            ngraph::copy_runtime_info(power, power_static);
+            ngraph::replace_node(power, power_static);
+        }
+    }
+
+
     convert_to_snippet_dialect();
     opt.run_passes(m_body);
 
     // generation flow
     snippets::pass::AssignRegisters().run_on_model(m_body);
 
-    // shedule generation should go here and be target agnostic
+    // schedule generation should go here and be target agnostic
 
     // actual code emission
-    ngraph::snippets::code ptr = m_generator->generate(m_body);
+    ngraph::snippets::code ptr = m_generator->generate(m_body, compile_params);
 
-    // chack that body doesnt have constants for scheduling
+    // check that body doesn't have constants for scheduling
     std::vector<std::shared_ptr<opset1::Constant>> constants;
     for (auto op : m_body->get_ordered_ops()) {
         if (auto constant = ov::as_type_ptr<opset1::Constant>(op)) {
@@ -227,7 +251,7 @@ snippets::Schedule snippets::op::Subgraph::generate(const BlockedShapeVector& ou
             }
         }
     }
-    NGRAPH_CHECK(!constants.size(), "External constants detected. Snippet is illigal for sheduling");
+    NGRAPH_CHECK(!constants.size(), "External constants detected. Snippet is illigal for scheduling");
 
     // check resulting shapes are broadcastable to each other so can be scheduled
     Shape work_size = m_body->output(0).get_shape();
@@ -252,13 +276,6 @@ snippets::Schedule snippets::op::Subgraph::generate(const BlockedShapeVector& ou
     return {work_size, false /*canBeLinearized*/, ptr};
 }
 
-bool snippets::op::Subgraph::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
-    INTERNAL_OP_SCOPE(Subgraph);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    return m_body->evaluate(outputs, inputs);
-    OPENVINO_SUPPRESS_DEPRECATED_END
-}
-
 void snippets::op::Subgraph::print() const {
     INTERNAL_OP_SCOPE(Subgraph);
     remark(13) << "subgraph " << this->get_friendly_name() << " "
@@ -285,7 +302,7 @@ void snippets::op::Subgraph::print() const {
 
 void snippets::op::Subgraph::print_statistics(bool verbose) {
     INTERNAL_OP_SCOPE(Subgraph);
-    auto getNodeInventory = [](std::shared_ptr<ngraph::Node> n) -> size_t {
+    auto getNodeInventory = [](std::shared_ptr<ov::Node> n) -> size_t {
         size_t total = 0;
 
         for (auto input : n->inputs()) {
@@ -307,7 +324,7 @@ void snippets::op::Subgraph::print_statistics(bool verbose) {
         return total;
     };
 
-    auto getFunctionInventory = [getNodeInventory](std::shared_ptr<ngraph::Function> f) -> size_t {
+    auto getModelInventory = [getNodeInventory](std::shared_ptr<ov::Model> f) -> size_t {
         size_t total = 0;
         for (auto op : f->get_ordered_ops()) {
             // Results and parameters are artificially introduced,
@@ -322,7 +339,7 @@ void snippets::op::Subgraph::print_statistics(bool verbose) {
         return total;
     };
 
-    auto countConstants = [](std::shared_ptr<ngraph::Function> f) -> size_t {
+    auto countConstants = [](std::shared_ptr<ov::Model> f) -> size_t {
         size_t count = 0;
         for (auto op : f->get_ordered_ops()) {
             count += !!ngraph::as_type_ptr<ngraph::opset1::Constant>(op) ? 1 : 0;
@@ -338,7 +355,7 @@ void snippets::op::Subgraph::print_statistics(bool verbose) {
                 << ";" << body->get_parameters().size()
                 << ";" << body->get_results().size()
                 << ";" << countConstants(body)
-                << ";" << getFunctionInventory(body)
+                << ";" << getModelInventory(body)
                 << ";" << getNodeInventory(this->shared_from_this()) << std::endl;
 
     if (verbose) {
diff --git a/src/common/snippets/src/pass/assign_registers.cpp b/src/common/snippets/src/pass/assign_registers.cpp
index e71122e860d..1ed965913f2 100644
--- a/src/common/snippets/src/pass/assign_registers.cpp
+++ b/src/common/snippets/src/pass/assign_registers.cpp
@@ -3,7 +3,7 @@
 //
 
 // #include <openvino/cc/selective_build.h>
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 #include "remarks.hpp"
 
 #include "snippets/pass/assign_registers.hpp"
@@ -13,8 +13,9 @@
 
 #include <iterator>
 
-bool ngraph::snippets::pass::AssignRegisters::run_on_model(const std::shared_ptr<Function>& f) {
+bool ngraph::snippets::pass::AssignRegisters::run_on_model(const std::shared_ptr<ov::Model>& f) {
     RUN_ON_FUNCTION_SCOPE(AssignRegisters);
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::AssignRegisters")
     int reg64_tmp_start { 8 }; // R8, R9, R10, R11, R12, R13, R14, R15 inputs+outputs+1
     using Reg = size_t;
     auto ops = f->get_ordered_ops();
@@ -143,7 +144,7 @@ bool ngraph::snippets::pass::AssignRegisters::run_on_model(const std::shared_ptr
 
     for (auto n : f->get_ordered_ops()) {
         auto& rt = n->get_rt_info();
-        // nothing to do for function signature
+        // nothing to do for model signature
         if (std::dynamic_pointer_cast<opset1::Parameter>(n) || std::dynamic_pointer_cast<opset1::Result>(n)) {
             continue;
         }
diff --git a/src/common/snippets/src/pass/collapse_subgraph.cpp b/src/common/snippets/src/pass/collapse_subgraph.cpp
index afa847b80f5..14a38a0e2c6 100644
--- a/src/common/snippets/src/pass/collapse_subgraph.cpp
+++ b/src/common/snippets/src/pass/collapse_subgraph.cpp
@@ -3,7 +3,7 @@
 //
 
 #include "remarks.hpp"
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/pass/collapse_subgraph.hpp"
 #include "snippets/op/subgraph.hpp"
@@ -11,6 +11,7 @@
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/rt_info.hpp>
 #include <ngraph/op/loop.hpp>
+#include "transformations/utils/utils.hpp"
 
 #include <memory>
 #include <vector>
@@ -18,27 +19,29 @@
 #include <queue>
 #include <string>
 #include <numeric>
+#include <climits>
 
-NGRAPH_RTTI_DEFINITION(ngraph::snippets::pass::StartSubgraph, "CollapseSubgraph", 0);
-NGRAPH_RTTI_DEFINITION(ngraph::snippets::pass::AttachToSubgraph, "CollapseSubgraph", 0);
-NGRAPH_RTTI_DEFINITION(ngraph::snippets::pass::TokenizeSnippets, "CollapseSubgraph", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::snippets::pass::TokenizeSnippets, "Snippets::TokenizeSnippets", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::snippets::pass::EnumerateNodes, "Snippets::EnumerateNodes", 0);
+
+namespace ngraph {
+namespace snippets {
+namespace pass {
 
-using namespace ngraph;
-using namespace snippets;
 
 namespace {
 
-auto outputs_are_not_broadcastable(const std::shared_ptr<ngraph::Node>& node) -> bool {
+auto outputs_are_not_broadcastable(const std::shared_ptr<const Node>& node) -> bool {
     auto outputs = node->outputs();
-    auto find_smallest_output_shape = [](const std::vector<ngraph::Output<ngraph::Node>>& outputs) -> ngraph::Shape {
+    auto find_smallest_output_shape = [](const std::vector<Output<const Node>>& outputs) -> Shape {
         return std::accumulate(std::begin(outputs), std::end(outputs), ngraph::Shape(outputs.begin()->get_shape()),
-            [](ngraph::Shape other_shape, ngraph::Output<ngraph::Node> output){
-                return ngraph::shape_size(output.get_shape()) < ngraph::shape_size(other_shape) ? output.get_shape() : other_shape;
+            [](Shape& other_shape, const Output<const Node>& output){
+                return shape_size(output.get_shape()) < shape_size(other_shape) ? output.get_shape() : other_shape;
             });
     };
     auto ref_shape = find_smallest_output_shape(outputs);
 
-    auto check_shapes_broadcastable = [ref_shape](const ngraph::Output<ngraph::Node>& output) -> bool {
+    auto check_shapes_broadcastable = [ref_shape](const Output<const Node>& output) -> bool {
         auto other_shape = output.get_shape();
 
         if (other_shape.size() != ref_shape.size()) {
@@ -46,300 +49,316 @@ auto outputs_are_not_broadcastable(const std::shared_ptr<ngraph::Node>& node) ->
         }
 
         return std::inner_product(std::begin(other_shape), std::end(other_shape), std::begin(ref_shape), true,
-                            std::logical_and<bool>(), [](ngraph::Shape::value_type lsh, ngraph::Shape::value_type rsh){
+                            std::logical_and<bool>(), [](Shape::value_type lsh, Shape::value_type rsh){
                                 return rsh == 1 || lsh == rsh;
                             });
     };
 
     return std::find_if_not(std::begin(outputs), std::end(outputs), check_shapes_broadcastable) != std::end(outputs);
-};
+}
 
-auto has_cycles_of_dependencies(const std::vector<std::set<ngraph::Input<ngraph::Node>>>& results,
-                                const std::vector<ngraph::Input<ngraph::Node>>& inputs) -> bool {
-    auto BFS_from_to = [](ngraph::Node* from, ngraph::Node* to) -> bool {
-        std::unordered_set<ngraph::Node*> visited;
-        std::queue<ngraph::Node*> stack;
-        stack.push(from);
-
-        while (stack.size() > 0) {
-            ngraph::Node* curr = stack.front();
-            visited.insert(curr);
-
-            stack.pop();
-
-            if (curr != to) {
-                for (const auto& next : curr->get_users()) {
-                    if (visited.count(next.get()) == 0) {
-                        stack.push(next.get());
-                    }
-                }
-            } else {
-                return true;
-            }
-        }
-        return false;
+auto is_layout_oblivious(const std::shared_ptr<const Node> &n) -> bool {
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::is_layout_oblivious")
+    auto is_layout_oblivious_binary = [](const std::shared_ptr<const Node> &n) -> bool {
+        return ov::is_type<opset1::Add>(n)
+            || ov::is_type<opset1::Divide>(n)
+            || ov::is_type<opset1::Equal>(n)
+            || ov::is_type<opset1::FloorMod>(n)
+            || ov::is_type<opset1::Greater>(n)
+            || ov::is_type<opset1::GreaterEqual>(n)
+            || ov::is_type<opset1::Less>(n)
+            || ov::is_type<opset1::LessEqual>(n)
+            || ov::is_type<opset1::LogicalAnd>(n)
+            || ov::is_type<opset1::LogicalOr>(n)
+            || ov::is_type<opset1::LogicalXor>(n)
+            || ov::is_type<opset1::Maximum>(n)
+            || ov::is_type<opset1::Minimum>(n)
+            || ov::is_type<opset1::Mod>(n)
+            || ov::is_type<opset1::Multiply>(n)
+            || ov::is_type<opset1::NotEqual>(n)
+            || ov::is_type<opset1::PRelu>(n)
+            || ov::is_type<opset1::Power>(n)
+            || ov::is_type<opset1::SquaredDifference>(n)
+            || ov::is_type<opset1::Subtract>(n)
+            || ov::is_type<opset1::Xor>(n);
     };
 
-    for (auto& result : results) {
-        for (auto& user : result) {
-            for (auto& input : inputs) {
-                auto source = input.get_source_output().get_node();
-                auto containsLoop = BFS_from_to(user.get_node(), source);
+    auto is_layout_oblivious_unary = [](const std::shared_ptr<const Node> &n) -> bool {
+        return ov::is_type<opset1::Abs>(n)
+            || ov::is_type<opset1::Clamp>(n)
+            || ov::is_type<opset1::Elu>(n)
+            || ov::is_type<opset1::Erf>(n)
+            || ov::is_type<opset1::Exp>(n)
+            || ov::is_type<opset1::LogicalNot>(n)
+            || ov::is_type<opset1::Negative>(n)
+            || ov::is_type<opset1::Relu>(n)
+            || ov::is_type<opset1::Sigmoid>(n)
+            || ov::is_type<opset1::Sqrt>(n)
+            || ov::is_type<opset1::Tanh>(n)
+            || ov::is_type<ngraph::op::v0::Gelu>(n)
+            || ov::is_type<ngraph::op::v7::Gelu>(n)
+            || ov::is_type<ngraph::op::v4::HSwish>(n);
+    };
+    return is_layout_oblivious_unary(n) || is_layout_oblivious_binary(n);
+}
 
-                remark(1) <<  "checking path from "
-                        << user.get_node()->get_friendly_name()
-                        << " to " << source->get_friendly_name()
-                        << " resulted in " << containsLoop << std::endl;
-
-                if (containsLoop) {
-                    return true;
-                }
+auto has_supported_in_out(const std::shared_ptr<const Node> &n) -> bool {
+    auto supported = [](descriptor::Tensor& t) -> bool {
+        return t.get_element_type() == ngraph::element::f32 &&
+               t.get_partial_shape().is_static();
+    };
+    const auto & inputs = n->inputs();
+    const auto & outputs = n->outputs();
+    // todo: Is this check necessary? Remove if not
+    for (const auto& out : outputs) {
+        for (const auto &in_out : out.get_target_inputs()) {
+            if (ov::is_type<ngraph::op::v5::Loop>(in_out.get_node()->shared_from_this())) {
+                return false;
             }
         }
     }
-    return false;
+    return std::all_of(inputs.begin(), inputs.end(), [&](const Input<const Node>& in) {return  supported(in.get_tensor());}) &&
+           std::all_of(outputs.begin(), outputs.end(), [&](const Output<const Node>& out) {return  supported(out.get_tensor());});
 }
 
-auto has_subgraph_as_input(std::shared_ptr<Node> node) -> bool {
-    auto inputs = node->inputs();
-    for (auto input : inputs) {
-        auto parent = input.get_source_output().get_node_shared_ptr();
-        if (!!ov::as_type_ptr<snippets::op::Subgraph>(parent)) {
+auto has_result_child(const std::shared_ptr<const Node> &node) -> bool {
+    for (const auto &child : node->get_users()) {
+        if (ov::is_type<ngraph::opset1::Result>(child)) {
             return true;
         }
     }
     return false;
-};
-
-auto is_lo(std::shared_ptr<Node> n) -> bool {
-    auto is_lob = [](std::shared_ptr<Node> n) -> bool {
-        using ngraph::as_type_ptr;
-        return !!ov::as_type_ptr<opset1::Add>(n)
-            || !!ov::as_type_ptr<opset1::Divide>(n)
-            || !!ov::as_type_ptr<opset1::Equal>(n)
-            || !!ov::as_type_ptr<opset1::FloorMod>(n)
-            || !!ov::as_type_ptr<opset1::Greater>(n)
-            || !!ov::as_type_ptr<opset1::GreaterEqual>(n)
-            || !!ov::as_type_ptr<opset1::Less>(n)
-            || !!ov::as_type_ptr<opset1::LessEqual>(n)
-            || !!ov::as_type_ptr<opset1::LogicalAnd>(n)
-            || !!ov::as_type_ptr<opset1::LogicalOr>(n)
-            || !!ov::as_type_ptr<opset1::LogicalXor>(n)
-            || !!ov::as_type_ptr<opset1::Maximum>(n)
-            || !!ov::as_type_ptr<opset1::Minimum>(n)
-            || !!ov::as_type_ptr<opset1::Mod>(n)
-            || !!ov::as_type_ptr<opset1::Multiply>(n)
-            || !!ov::as_type_ptr<opset1::NotEqual>(n)
-            || !!ov::as_type_ptr<opset1::PRelu>(n)
-            || !!ov::as_type_ptr<opset1::Power>(n)
-            || !!ov::as_type_ptr<opset1::SquaredDifference>(n)
-            || !!ov::as_type_ptr<opset1::Subtract>(n)
-            || !!ov::as_type_ptr<opset1::Xor>(n);
-    };
-
-    auto is_lou = [](std::shared_ptr<Node> n) -> bool {
-        using ngraph::as_type_ptr;
-        return !!ov::as_type_ptr<opset1::Abs>(n)
-            // || !!ov::as_type_ptr<opset1::Acos>(n)
-            // || !!ov::as_type_ptr<opset1::Asin>(n)
-            // || !!ov::as_type_ptr<opset1::Atan>(n)
-            // || !!ov::as_type_ptr<opset1::Ceiling>(n) ?
-            || !!ov::as_type_ptr<opset1::Clamp>(n)
-            // || !!ov::as_type_ptr<opset1::Cos>(n)
-            // || !!ov::as_type_ptr<opset1::Cosh>(n)
-            || !!ov::as_type_ptr<opset1::Elu>(n)
-            || !!ov::as_type_ptr<opset1::Erf>(n)
-            || !!ov::as_type_ptr<opset1::Exp>(n)
-            // || !!ov::as_type_ptr<opset1::Floor>(n) ?
-            // || !!ov::as_type_ptr<opset1::Log>(n) ?
-            || !!ov::as_type_ptr<opset1::LogicalNot>(n)
-            || !!ov::as_type_ptr<opset1::Negative>(n)
-            || !!ov::as_type_ptr<opset1::Relu>(n)
-            // || !!ov::as_type_ptr<opset1::Sign>(n) ?
-            || !!ov::as_type_ptr<opset1::Sigmoid>(n)
-            // || !!ov::as_type_ptr<opset1::Sin>(n)
-            // || !!ov::as_type_ptr<opset1::Sinh>(n)
-            || !!ov::as_type_ptr<opset1::Sqrt>(n)
-            // || !!ov::as_type_ptr<opset1::Tan>(n)
-            || !!ov::as_type_ptr<opset1::Tanh>(n);
-    };
-
-    auto is_lot = [](std::shared_ptr<Node> n) -> bool {
-        using ngraph::as_type_ptr;
-        return false;
-        // return !!ov::as_type_ptr<opset1::HardSigmoid>(n) // ternary with 2 constants
-            // || !!ov::as_type_ptr<opset1::Selu>(n); // ternary with 2 constants / or DW
-    };
-
-    auto is_fq = [](std::shared_ptr<Node> n) -> bool {
-        using ngraph::as_type_ptr;
-        return false;//!!ov::as_type_ptr<opset1::FakeQuantize>(n); // 4->1
-    };
-
-    return is_lou(n) || is_lob(n) ||is_lot(n) || is_fq(n);
 }
 
-auto has_supported_in_out(std::shared_ptr<Node> n) -> bool {
-    for (auto in : n->inputs()) {
-        if (in.get_tensor().get_element_type() != ngraph::element::f32) {
-            return false;
-        }
-
-        if (in.get_partial_shape().is_dynamic()) {
-            return false;
-        }
-
-        if (in.get_partial_shape().is_static() && in.get_shape().size() > 6) {
-            return false;
+auto get_num_result_children(const std::shared_ptr<const Node> &node) -> size_t {
+    size_t result = 0;
+    for (const auto &child : node->get_users()) {
+        if (ov::is_type<ngraph::opset1::Result>(child)) {
+            result++;
         }
     }
-
-    for (auto out : n->outputs()) {
-        if (out.get_tensor().get_element_type() != ngraph::element::f32) {
-            return false;
-        }
-
-        if (out.get_partial_shape().is_dynamic()) {
-            return false;
-        }
-
-        if (out.get_partial_shape().is_static() && out.get_shape().size() > 6) {
-            return false;
-        }
-
-        for (auto in_out : out.get_target_inputs()) {
-            if (!!ov::as_type_ptr<ngraph::op::v5::Loop>(in_out.get_node()->shared_from_this())) {
-                return false;
-            }
-
-            if (!!ov::as_type_ptr<ngraph::op::v0::Result>(in_out.get_node()->shared_from_this())) {
-                return false;
+    return result;
+}
+// Need to update tensor name manually, since MKLDNNGraph::Replicate() looks at input.get_tensor().get_name();
+// If subgraph->get_output_size() == 1, then the name will be restored correctly from the node name
+auto update_out_tensor_name(std::shared_ptr<ngraph::snippets::op::Subgraph> &subgraph) -> void {
+    bool not_set = true;
+    for (unsigned int i = 0; i < subgraph->get_output_size() && not_set; i++) {
+        for (const auto &in : subgraph->get_output_target_inputs(i)) {
+            if (ov::is_type<opset1::Result>(in.get_node())) {
+                auto out_tensor = subgraph->output(i).get_tensor_ptr();
+                NGRAPH_SUPPRESS_DEPRECATED_START
+                if (out_tensor->get_name().empty()) {
+                    const auto& body_result = subgraph->get_body()->get_output_op(i);
+                    const std::string newTensorName = ngraph::op::util::get_ie_output_name(
+                            body_result->get_input_source_output(0));
+                    out_tensor->set_name(newTensorName);
+                }
+                NGRAPH_SUPPRESS_DEPRECATED_END
+                not_set = false;
+                break;
             }
         }
     }
-
-    return true;
-};
-
+}
 } // namespace
 
-ngraph::snippets::pass::StartSubgraph::StartSubgraph(bool tokenize_by_node) : MatcherPass() {
-    MATCHER_SCOPE(StartSubgraph);
-
-    auto has_multiple_output_edges = [](std::shared_ptr<Node> n) -> bool {
-        for (auto out : n->outputs()) {
-            if (out.get_target_inputs().size() != 1) return true;
-        }
-
-        return false;
-    };
-
-    register_matcher(std::make_shared<pattern::Matcher>(
-        std::make_shared<pattern::op::Label>(pattern::any_input(),
-        [tokenize_by_node, has_multiple_output_edges](std::shared_ptr<Node> n) {
-            return is_lo(n) &&
-                   has_supported_in_out(n) &&
-                   (tokenize_by_node || !has_subgraph_as_input(n)) &&
-                   has_multiple_output_edges(n);
-        })),
-        [](ngraph::pattern::Matcher &m) -> bool {
-        auto node = m.get_match_root();
-
-        remark(1) << "Match root"
-                  << node->get_friendly_name()
-                  << " " << node
-                  << " Creating new snippet - no input subgraphs found" << std::endl;
-
-        auto subgraph = op::Subgraph::wrap_node_as_subgraph(node);
-        ngraph::replace_node(node, subgraph);
-
-        remark(1) << "Replacement (new) done for: "
-                  << subgraph->get_friendly_name()
-                  << " with " << subgraph->inputs().size()
-                  << " inputs and " << subgraph->outputs().size()
-                  << " outputs and " << subgraph->get_body()->get_ops().size() << " ops total\n";
-        return true;
-    });
+bool AppropriateForSubgraph(const std::shared_ptr<const Node> &node) {
+    return is_layout_oblivious(node) && has_supported_in_out(node);
 }
 
-ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node) : MatcherPass() {
-    MATCHER_SCOPE(AttachToSubgraph);
+void SetSnippetsNodeType(const std::shared_ptr<Node> &node, SnippetsNodeType nodeType) {
+    auto &rt = node->get_rt_info();
+    rt["SnippetsNodeType"] = nodeType;
+}
+
+SnippetsNodeType GetSnippetsNodeType(const std::shared_ptr<const Node> &node) {
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::GetSnippetsNodeType")
+    auto &rt = node->get_rt_info();
+    const auto rinfo = rt.find("SnippetsNodeType");
+    if (rinfo == rt.end())
+        return SnippetsNodeType::NotSet;
+    return rinfo->second.as<SnippetsNodeType>();
+}
+
+void SetTopologicalOrder(const std::shared_ptr<Node> &node, int64_t order) {
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::SetTopologicalOrder")
+    auto &rt = node->get_rt_info();
+    rt["TopologicalOrder"] = order;
+}
+
+int64_t GetTopologicalOrder(const std::shared_ptr<const Node> &node) {
+    auto &rt = node->get_rt_info();
+    const auto rinfo = rt.find("TopologicalOrder");
+    if (rinfo == rt.end())
+        throw ngraph_error("Topological order is required, but not set.");
+    return rinfo->second.as<int64_t>();
+}
+
+bool EnumerateNodes::run_on_model(const std::shared_ptr<ov::Model> &m) {
+    OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::EnumerateNodes")
+    int64_t order = 0;
+    // Todo: We don't really have to set order for every node, just for subgraph parents and children would be enough
+    for (auto &node : m->get_ordered_ops()) {
+        SetTopologicalOrder(node, order++);
+    }
+    return true;
+}
+TokenizeSnippets::TokenizeSnippets() {
+    MATCHER_SCOPE(TokenizeSnippets);
     enum continuation_strategy {
         reset,
         abort
     };
 
-    continuation_strategy strategy = continuation_strategy::abort;
-
-    ngraph::graph_rewrite_callback continuation_callback = [strategy](ngraph::pattern::Matcher &m) -> bool {
+    continuation_strategy strategy = continuation_strategy::reset;
+    auto label = std::make_shared<pattern::op::Label>(pattern::any_input(),
+        [](const std::shared_ptr<const Node> &n) {
+            return GetSnippetsNodeType(n) != SnippetsNodeType::SkippedByPlugin && AppropriateForSubgraph(n);
+        });
+    ngraph::graph_rewrite_callback callback = [&, strategy](ngraph::pattern::Matcher &m) -> bool {
+        OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::CreateSubgraph_callback")
         auto node = m.get_match_root();
+        if (transformation_callback(node)) {
+            return false;
+        }
 
-        remark(1) << "Match root " << node->get_friendly_name() << " " << node << std::endl;
+        remark(1) << "Match root: " << node->get_friendly_name() << " " << node << std::endl;
 
-        // inputs that are already subgraphs
-        std::unordered_set<std::shared_ptr<Node>> input_subgraphs;
-        // clone bodies because we need a rollback if loop is found
-        std::map<std::shared_ptr<Node>, std::shared_ptr<ngraph::Function>> clones;
+        const auto getFusedNames = [](const std::shared_ptr<Node>& n) -> std::string {
+            auto rt_info = n->get_rt_info();
+            auto it = rt_info.find("originalLayersNames");
+            if (it != rt_info.end()) {
+                return it->second.as<std::string>() + ",";
+            }
+            return "";
+        };
 
-        ParameterVector body_parameters;
-        OutputVector external_inputs;
-        OutputVector internal_inputs;
+        auto create_single_node_subgraph = [&](const std::shared_ptr<Node> &node) {
+            auto subgraph = op::Subgraph::wrap_node_as_subgraph(node);
+            subgraph->get_rt_info()["originalLayersNames"] = getFusedNames(node) + node->get_friendly_name();
+            ngraph::replace_node(node, subgraph);
+            update_out_tensor_name(subgraph);
+        };
 
-        auto inputs = node->inputs();
-
-        auto is_recurrent = [inputs](const ngraph::Output<ngraph::Node>& to_find) -> bool {
-            for (auto in : inputs) {
-                if (in.get_source_output().get_node_shared_ptr() == to_find.get_node_shared_ptr()) {
-                    return true;
+        auto abort_with_strategy = [&](const std::string& message_reset,
+                                                     const std::string& message_abort = "", int priority = 3) {
+            if (strategy == continuation_strategy::reset) {
+                create_single_node_subgraph(node);
+                return true;
+            } else if (strategy == continuation_strategy::abort) {
+                if (!message_abort.empty()) {
+                    remark(priority) << message_abort << std::endl;
                 }
             }
             return false;
         };
+        // inputs that are already subgraphs
+        std::unordered_set<std::shared_ptr<Node>> input_subgraphs;
+        // clone bodies because we need a rollback if loop is found
+        std::map<std::shared_ptr<Node>, std::shared_ptr<ov::Model>> clones;
 
-        auto get_input_index = [](const Output<Node>& found) -> size_t {
-            for (auto& input : found.get_target_inputs()) {
-                remark(13) << input.get_node() << " " << input.get_source_output() << " vs "
-                    << found << found.get_node() << " : " << input.get_index() << " " << found.get_index() << std::endl;
-            }
+        ParameterVector body_parameters;
+        // inputs to merged subgraph
+        OutputVector external_inputs;
+        // inputs to the node before merge to subgraph
+        OutputVector internal_inputs;
 
-            for (auto& input : found.get_target_inputs()) {
-                remark(13) << input.get_node() << " " << input.get_source_output() << " vs "
-                    << found << " : " << input.get_index() << " " << found.get_index() << std::endl;
-                if (ov::as_type_ptr<op::Subgraph>(input.get_node()->shared_from_this()) != nullptr && input.get_source_output() == found) {
-                    return input.get_index();
-                }
+        auto input_values = node->input_values();
+        /* 
+        * Called with subgraph->input_value(i) arg and used to 
+        * Check that the attached node input subgraph has the same input as the node itself.
+        * If true, then ternary merge is initiated.
+        *        input
+        *        /   \
+        *  subgraph--node
+        */
+        auto is_recurrent = [&input_values](const ngraph::Output<ngraph::Node>& to_find) -> bool {
+            return std::any_of(input_values.begin(), input_values.end(),
+                        [&](const ov::Output<ov::Node> &in) {return in == to_find;});
+        };
+        /*
+         * Checks if the passed node introduces loop dependency for given topological bounds (pair of maxParentOrder, minChildOrder).
+         * The bounds are presumed to be without dependency. The bounds are updated if no dependency is introduced by the node.
+        */
+        const auto cyclicDependencyIsIntoduced = [&node](const std::shared_ptr<Node>& nodeToExamine, std::pair<int64_t, int64_t>& currentBounds) -> bool {
+            assert(currentBounds.first < currentBounds.second && "Invalid currentBounds passed");
+            const auto &parentNodes = ngraph::as_node_vector(nodeToExamine->input_values());
+            const int64_t maxParentOrder = std::accumulate(parentNodes.begin(), parentNodes.end(), currentBounds.first,
+                                                            [](int64_t maxOrder, std::shared_ptr<Node> n){
+                                                                if (ngraph::op::is_constant(n) || ngraph::op::is_parameter(n))
+                                                                    return maxOrder;
+                                                                return std::max(maxOrder, GetTopologicalOrder(n));
+                                                            });
+            const auto &childNodes = nodeToExamine->get_users();
+            // Skip the node being attached, since it will be a part of subgraph and can't introduce loop dependency
+            const int64_t minChildOrder = std::accumulate(childNodes.begin(), childNodes.end(), currentBounds.second,
+                                                            [&node](int64_t minOrder, std::shared_ptr<Node> n){
+                                                                if (ngraph::op::is_constant(n) || ngraph::op::is_parameter(n) || n == node)
+                                                                    return minOrder;
+                                                                return std::min(minOrder, GetTopologicalOrder(n));
+                                                            });
+            if (maxParentOrder < minChildOrder) {
+                currentBounds = std::pair<int64_t, int64_t>(maxParentOrder, minChildOrder);
+                return false;
             }
-            return 0;
+            return true;
         };
 
-        for (auto input : inputs) {
-            auto input_node = input.get_source_output().get_node_shared_ptr();
-
+        for (const auto &input_node : ngraph::as_node_vector(input_values)) {
             if (auto subgraph = ov::as_type_ptr<op::Subgraph>(input_node)) {
                 if (!clones.count(input_node)) {
-                    auto f = ngraph::clone_function(*subgraph->get_body().get());
+                    auto f = ov::clone_model(*subgraph->get_body().get());
                     f->set_friendly_name(subgraph->get_body()->get_friendly_name());
                     clones[input_node] = f;
                 }
             }
         }
-
-        for (auto input : inputs) {
-            auto input_node = input.get_source_output().get_node_shared_ptr();
-
-            if (auto subgraph = ov::as_type_ptr<op::Subgraph>(input_node)) {
+        //  If there are no input subgraphs no need to go further, just create a new one.
+        if (clones.empty()) {
+            create_single_node_subgraph(node);
+            remark(1) << "Starting subgraph at: "  << node->get_friendly_name()
+                      << " with " << node->inputs().size() << " inputs and " << node->outputs().size()
+                      << " outputs" << std::endl;
+            return true;
+        }
+        std::string newSubgraphName{};
+        std::string fusedNames{};
+        size_t num_result_children = 0;
+        std::pair<int64_t, int64_t> currentTopoBounds {-1, LONG_MAX};
+        cyclicDependencyIsIntoduced(node, currentTopoBounds);
+        assert(!cyclicDependencyIsIntoduced(node, currentTopoBounds) && "Cyclic dependency is introduced by the node itself");
+        for (const auto& input_value : input_values) {
+            auto input_node = input_value.get_node_shared_ptr();
+            if (ov::is_type<op::Subgraph>(input_node) &&
+                !cyclicDependencyIsIntoduced(input_node, currentTopoBounds)) {
+                auto subgraph = std::static_pointer_cast<op::Subgraph>(input_node);
                 if (!input_subgraphs.count(input_node)) {
                     input_subgraphs.insert(input_node);
 
+                    fusedNames += getFusedNames(subgraph);
+                    if (newSubgraphName.empty())
+                            newSubgraphName = subgraph->get_friendly_name();
+                    num_result_children += has_result_child(subgraph);
                     auto f = clones[input_node];
                     const auto& input_body_parameters = f->get_parameters();
-
+                    // Todo:
+                    //  Some of the input subgraphs might have common parents, so some of the input_parameters might already be
+                    //  in external_inputs and hence in body_parameters. Here we handle this case and remove repeated body_parameters.
+                    //  Would it be better to incorporate all inputs first and then remove repeated params.
                     for (size_t i = 0; i < input_body_parameters.size(); ++i) {
                         auto found = std::find(external_inputs.begin(), external_inputs.end(), subgraph->input_value(i));
                         if (found != external_inputs.end()) {
-                            auto current_input_index = get_input_index(*found);
-                            // Handling the case if multiple inputs referencing the same parameter comes from one subgraph => it's not introduced by SS.
-                            // It might be better to keep track if body parameter relationship rather than that
+                            // Todo: here we rely on friendly_name uniqueness. Propose a different algorithm.
+                            size_t current_input_index = body_parameters.size();
+                            for (size_t p_ind = 0; p_ind <  body_parameters.size(); p_ind++) {
+                                const auto & p = body_parameters[p_ind];
+                                if (p->get_friendly_name() == found->get_node_shared_ptr()->get_friendly_name()) {
+                                    current_input_index = p_ind;
+                                    break;
+                                }
+                            }
+
                             if (current_input_index < body_parameters.size()) {
                                 remark(13) << "replacing " << *found << " " << current_input_index << " with "
                                           << body_parameters[current_input_index] << std::endl;
@@ -354,16 +373,19 @@ ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node
                             auto internal = input_body_parameters[i];
                             auto internal_consumers = internal->outputs();
 
-                            for (auto output : internal->outputs()) {
-                                for (auto consumer : output.get_target_inputs()) {
-                                    if (auto to_replace_with = ov::as_type_ptr<op::Subgraph>(subgraph->input_value(i).get_node_shared_ptr())) {
-                                        auto other_body = clones[subgraph->input_value(i).get_node_shared_ptr()];
-                                        auto other_body_result = other_body->get_results()[consumer.get_source_output().get_index()];
-                                        auto result_producer = other_body_result->input(0).get_source_output();
+                            if (auto to_replace_with = ov::as_type_ptr<op::Subgraph>(subgraph->get_input_node_shared_ptr(i))) {
+                                 for (auto output : internal_consumers) {
+                                     for (auto consumer : output.get_target_inputs()) {
+                                         auto other_body = clones[subgraph->get_input_node_shared_ptr(i)];
+                                         auto other_body_result = other_body->get_results()[consumer.get_source_output().get_index()];
+                                         auto result_producer = other_body_result->input(0).get_source_output();
 
-                                        consumer.replace_source_output(result_producer.get_node_shared_ptr());
-                                    }
-                                }
+                                         consumer.replace_source_output(result_producer.get_node_shared_ptr());
+                                     }
+                                 }
+                            } else {
+                                external_inputs.push_back(subgraph->input_value(i));
+                                body_parameters.push_back(input_body_parameters[i]);
                             }
                         } else {
                             external_inputs.push_back(subgraph->input_value(i));
@@ -375,7 +397,7 @@ ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node
                 // this is there stitching happens, get result of a copy of a body of currently processed input and put it to the new inputs
                 // internal output index == external output index
                 auto& input_body = clones[input_node];
-                size_t source_output_index = input.get_source_output().get_index();
+                size_t source_output_index = input_value.get_index();
                 auto source_result = input_body->get_results()[source_output_index];
                 // Result op has a single input
                 internal_inputs.push_back(source_result->input_value(0));
@@ -383,15 +405,20 @@ ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node
                 if (op::is_scalar_constant(input_node)) {
                     internal_inputs.push_back(input_node->output(0));
                 } else {
-                    external_inputs.push_back(input.get_source_output());
-                    auto new_parameter = std::make_shared<opset1::Parameter>(input.get_element_type(), input.get_partial_shape());
-                    new_parameter->set_friendly_name(input.get_source_output().get_node()->get_friendly_name());
+                    external_inputs.push_back(input_value);
+                    auto new_parameter = std::make_shared<opset1::Parameter>(input_value.get_element_type(), input_value.get_partial_shape());
+                    new_parameter->set_friendly_name(input_node->get_friendly_name());
                     body_parameters.push_back(new_parameter);
-                    body_parameters.back()->set_friendly_name(input.get_source_output().get_node()->get_friendly_name());
                     internal_inputs.push_back(new_parameter->output(0));
                 }
             }
         }
+        fusedNames += node->get_friendly_name();
+        num_result_children += get_num_result_children(node);
+        if (num_result_children > 1)
+            return abort_with_strategy("New subgraph is created since too many Result children are detected");
+        if (newSubgraphName.empty())
+            newSubgraphName = node->get_friendly_name();
 
         auto body_node = node->copy_with_new_inputs(internal_inputs);
         body_node->set_friendly_name(node->get_friendly_name());
@@ -444,26 +471,20 @@ ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node
         if (body_results.size() != subgraph_result_inputs.size()) {
             throw ngraph_error("body results and node results size mismatch during subgraph collaps");
         }
-
+        // todo: move this plugin-specific constraint to the plugin callback
         if (body_parameters.size() + body_results.size() > 7) {
-            if (strategy == continuation_strategy::reset) {
-                remark(13) << "new subgraph is created. Impossible to schedule subgraph with "
-                        << body_parameters.size() << " inputs and " << body_results.size() << " outputs." << std::endl;
-
-                auto single_node_subgraph = op::Subgraph::wrap_node_as_subgraph(node);
-                ngraph::replace_node(node, single_node_subgraph);
-                return true;
-            } else {
-                return false;
-            }
+            const std::string message_reset = "new subgraph is created. Impossible to schedule subgraph with " +
+            std::to_string(body_parameters.size()) + " inputs and " + std::to_string(body_results.size()) + " outputs.";
+            const std::string message_abort = "failed to continue subgraph. Impossible to schedule subgraph with " +
+            std::to_string(body_parameters.size()) + " inputs and " + std::to_string(body_results.size()) + " outputs.";
+            return abort_with_strategy(message_reset, message_abort);
         }
 
-        auto body = op::create_body(node->get_friendly_name(), body_results, body_parameters);
+        auto body = op::create_body(newSubgraphName, body_results, body_parameters);
         for (size_t i = 0; i < body->get_parameters().size(); i++) {
             body->get_parameters()[i]->set_friendly_name(body_parameters[i]->get_friendly_name());
         }
-
-        auto subgraph = op::build_subgraph(node, external_inputs, body);
+        auto subgraph = op::build_subgraph(node, external_inputs, body, newSubgraphName);
         auto act_body = subgraph->get_body();
         for (size_t i = 0; i < act_body->get_parameters().size(); i++) {
             act_body->get_parameters()[i]->set_friendly_name(body_parameters[i]->get_friendly_name());
@@ -473,37 +494,15 @@ ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node
             throw ngraph_error("newly create subgraph doesn't much number of results");
         }
 
-        if (outputs_are_not_broadcastable(subgraph)) {
-            if (strategy == continuation_strategy::reset) {
-                remark(13) << "New subgraph is created due to outputs of a subgraph not broadcastable." << std::endl;
-
-                auto single_node_subgraph = op::Subgraph::wrap_node_as_subgraph(node);
-                single_node_subgraph->validate_and_infer_types();
-                ngraph::replace_node(node, single_node_subgraph);
-                return true;
-            } else {
-                return false;
-            }
-        }
-
-        if (has_cycles_of_dependencies(subgraph_result_inputs, subgraph->inputs())) {
-            if (strategy == continuation_strategy::reset) {
-                remark(13) << "New subgraph is created due to loop dependency introduced by one of input subgraphs." << std::endl;
-
-                auto single_node_subgraph = op::Subgraph::wrap_node_as_subgraph(node);
-                single_node_subgraph->validate_and_infer_types();
-                ngraph::replace_node(node, single_node_subgraph);
-                return true;
-            } else {
-                return false;
-            }
-        }
+        if (outputs_are_not_broadcastable(subgraph))
+            return abort_with_strategy("New subgraph is created due to outputs of a subgraph not broadcastable.");
 
         for (size_t i = 0; i < subgraph->get_output_size(); ++i) {
             for (auto target_input : subgraph_result_inputs[i]) {
                 target_input.replace_source_output(subgraph->output(i));
             }
         }
+        update_out_tensor_name(subgraph);
 
         subgraph->validate_and_infer_types();
 
@@ -511,6 +510,7 @@ ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node
         for (size_t i = 0; i < act_body1->get_parameters().size(); i++) {
             act_body1->get_parameters()[i]->set_friendly_name(body_parameters[i]->get_friendly_name());
         }
+        subgraph->get_rt_info()["originalLayersNames"] = node->get_friendly_name();
 
         remark(1) << "Replacement (merge) done for: "
                     << subgraph->get_friendly_name()
@@ -520,11 +520,9 @@ ngraph::snippets::pass::AttachToSubgraph::AttachToSubgraph(bool tokenize_by_node
 
         return true;
     };
-
-    register_matcher(std::make_shared<pattern::Matcher>(
-        std::make_shared<pattern::op::Label>(pattern::any_input(),
-        [](std::shared_ptr<Node> n) {
-            return is_lo(n) && has_supported_in_out(n) && has_subgraph_as_input(n);
-        })),
-        continuation_callback);
+    auto matcher = std::make_shared<ngraph::pattern::Matcher>(label);
+    register_matcher(matcher, callback);
 }
+} // namespace pass
+} // namespace snippets
+} // namespace ngraph
\ No newline at end of file
diff --git a/src/common/snippets/src/pass/insert_load_store.cpp b/src/common/snippets/src/pass/insert_load_store.cpp
index c8130a92ae2..abc3bd5b919 100644
--- a/src/common/snippets/src/pass/insert_load_store.cpp
+++ b/src/common/snippets/src/pass/insert_load_store.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 #include "remarks.hpp"
 
 #include "snippets/pass/insert_load_store.hpp"
@@ -17,6 +17,7 @@ ngraph::snippets::pass::InsertLoad::InsertLoad() {
     register_matcher(std::make_shared<ngraph::pattern::Matcher>(
         ngraph::pattern::wrap_type<ngraph::opset1::Parameter>()),
             [this](ngraph::pattern::Matcher &m) {
+            OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::InsertLoad")
             auto root = m.get_match_root();
 
             // check if already has Load as an output
@@ -50,6 +51,7 @@ ngraph::snippets::pass::InsertStore::InsertStore() {
     register_matcher(std::make_shared<ngraph::pattern::Matcher>(
         ngraph::pattern::wrap_type<ngraph::opset1::Result>()),
             [this](ngraph::pattern::Matcher &m) {
+            OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::InsertStore")
             auto root = m.get_match_root();
 
             // check if already has Store as an input
diff --git a/src/common/snippets/src/pass/insert_movebroadcast.cpp b/src/common/snippets/src/pass/insert_movebroadcast.cpp
index 93640195ab4..727046a54c9 100644
--- a/src/common/snippets/src/pass/insert_movebroadcast.cpp
+++ b/src/common/snippets/src/pass/insert_movebroadcast.cpp
@@ -3,7 +3,7 @@
 //
 
 #include "remarks.hpp"
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/pass/insert_movebroadcast.hpp"
 #include "snippets/snippets_isa.hpp"
@@ -138,6 +138,7 @@ auto reset_broacast_config(const std::shared_ptr<ngraph::Node>& op) -> void {
 ngraph::snippets::pass::InsertMoveBroadcast::InsertMoveBroadcast() {
     MATCHER_SCOPE(InsertMoveBroadcast);
     ngraph::graph_rewrite_callback callback = [this](ngraph::pattern::Matcher &m) {
+        OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::InsertMoveBroadcast")
         auto root = m.get_match_root();
         const auto& values = root->input_values();
         if (values.empty()) {
@@ -173,8 +174,8 @@ ngraph::snippets::pass::InsertMoveBroadcast::InsertMoveBroadcast() {
     auto any = std::make_shared<pattern::op::Label>(pattern::any_input(),
         [](std::shared_ptr<Node> n) {
             // should add supports_auto_broadcast to SquaredDifference
-            return (ngraph::op::supports_auto_broadcast(n) || !!as_type_ptr<opset1::SquaredDifference>(n) || !!as_type_ptr<opset1::Mod>(n))
-                && n->get_autob().m_type == ngraph::op::AutoBroadcastType::NUMPY; });
+            return ((ngraph::op::supports_auto_broadcast(n) || is_type<opset1::SquaredDifference>(n) || is_type<opset1::Mod>(n)) &&
+                 n->get_autob().m_type == ngraph::op::AutoBroadcastType::NUMPY) || is_type<opset1::PRelu>(n); });
 
     register_matcher(std::make_shared<ngraph::pattern::Matcher>(any), callback);
-}
\ No newline at end of file
+}
diff --git a/src/common/snippets/src/pass/load_movebroadcast_to_broadcastload.cpp b/src/common/snippets/src/pass/load_movebroadcast_to_broadcastload.cpp
index 8fa9a604ddc..bb64309f8b1 100644
--- a/src/common/snippets/src/pass/load_movebroadcast_to_broadcastload.cpp
+++ b/src/common/snippets/src/pass/load_movebroadcast_to_broadcastload.cpp
@@ -3,7 +3,7 @@
 //
 
 #include "remarks.hpp"
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/pass/load_movebroadcast_to_broadcastload.hpp"
 #include "snippets/snippets_isa.hpp"
@@ -20,6 +20,7 @@ ngraph::snippets::pass::LoadMoveBroadcastToBroadcastLoad::LoadMoveBroadcastToBro
 
     register_matcher(std::make_shared<ngraph::pattern::Matcher>(fbn),
         [load_pattern, param_pattern](ngraph::pattern::Matcher &m) {
+            OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::LoadMoveBroadcastToBroadcastLoad")
             auto root = m.get_match_root();
 
             const auto &pm = m.get_pattern_value_map();
@@ -44,9 +45,9 @@ ngraph::snippets::pass::LoadMoveBroadcastToBroadcastLoad::LoadMoveBroadcastToBro
                     bct[k] = 1;
                 }
             }
-
+            // Todo: consider refactoring BroadcastLoad, it seems we don't need broadcast_info at this point.
             broadcastload->set_broadcast_info(bct);
-            if (broadcastload->is_broadcast(outshape.size()-1)) {
+            if (inshape.back() == 1 && outshape.back() != 1) {
                 ngraph::copy_runtime_info(root, broadcastload);
                 ngraph::replace_node(root, broadcastload);
                 return true;
diff --git a/src/common/snippets/src/pass/vector_to_scalar.cpp b/src/common/snippets/src/pass/vector_to_scalar.cpp
index 6bf17fe2f1f..a54186bf488 100644
--- a/src/common/snippets/src/pass/vector_to_scalar.cpp
+++ b/src/common/snippets/src/pass/vector_to_scalar.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "itt.hpp"
+#include <snippets/itt.hpp>
 
 #include "snippets/pass/vector_to_scalar.hpp"
 #include "snippets/snippets_isa.hpp"
@@ -16,6 +16,7 @@ ngraph::snippets::pass::ReplaceLoadsWithScalarLoads::ReplaceLoadsWithScalarLoads
     register_matcher(std::make_shared<ngraph::pattern::Matcher>(
         ngraph::pattern::wrap_type<ngraph::snippets::op::Load>()),
             [this](ngraph::pattern::Matcher &m) {
+            OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::ReplaceLoadsWithScalarLoads_callback")
             auto root = m.get_match_root();
             auto load = std::make_shared<ngraph::snippets::op::ScalarLoad> (root->input_value(0));
             load->set_friendly_name(root->get_friendly_name());
@@ -30,6 +31,7 @@ ngraph::snippets::pass::ReplaceStoresWithScalarStores::ReplaceStoresWithScalarSt
     register_matcher(std::make_shared<ngraph::pattern::Matcher>(
         ngraph::pattern::wrap_type<ngraph::snippets::op::Store>()),
             [this](ngraph::pattern::Matcher &m) {
+            OV_ITT_SCOPED_TASK(ngraph::pass::itt::domains::SnippetsTransform, "Snippets::op::ReplaceStoresWithScalarStores_callback")
             auto root = m.get_match_root();
             auto store = std::make_shared<ngraph::snippets::op::ScalarStore> (root->input_value(0));
             store->set_friendly_name(root->get_friendly_name());
diff --git a/src/common/snippets/src/remarks.hpp b/src/common/snippets/src/remarks.hpp
index bd40ae83650..3addf90cb7e 100644
--- a/src/common/snippets/src/remarks.hpp
+++ b/src/common/snippets/src/remarks.hpp
@@ -5,10 +5,10 @@
 #pragma once
 
 #include <iostream>
-
+// Todo: Replace remarks with DEBUG_CAPS
 class logstreambuf: public std::streambuf {
 public:
-    static const int threshold {5};
+    static const int threshold {50};
 };
 
 template <typename T>
diff --git a/src/tests/functional/inference_engine/skip_tests_config.cpp b/src/tests/functional/inference_engine/skip_tests_config.cpp
index 6af60bfd426..bd418b65c19 100644
--- a/src/tests/functional/inference_engine/skip_tests_config.cpp
+++ b/src/tests/functional/inference_engine/skip_tests_config.cpp
@@ -15,5 +15,9 @@ std::vector<std::string> disabledTestPatterns() {
         ".*/NmsLayerTest.*",
         // Issue 74365: azure is failing after #7351
         ".*DetectionOutput8ToDetectionOutput1",
+        // TODO: Snpippets rewrite this test
+        ".*TransformationTests\\.DontStartSubgraphSingleOutput.*",
+        // TODO: Move this test to CPU-specific
+        ".*TransformationTests\\.DoNotStartSubgraphAfterInputs.*"
     };
 }
diff --git a/src/tests/functional/inference_engine/snippets/tokenization.cpp b/src/tests/functional/inference_engine/snippets/tokenization.cpp
index b6be642784a..f2a7aaad5c6 100644
--- a/src/tests/functional/inference_engine/snippets/tokenization.cpp
+++ b/src/tests/functional/inference_engine/snippets/tokenization.cpp
@@ -14,67 +14,187 @@
 #include <transformations/init_node_info.hpp>
 
 #include "common_test_utils/ngraph_test_utils.hpp"
+#include "functional_test_utils/skip_tests_config.hpp"
 
 using namespace testing;
-using namespace ngraph;
+using namespace ov;
+using ngraph::snippets::op::Subgraph;
+using ngraph::pass::InitNodeInfo;
+using ngraph::snippets::pass::EnumerateNodes;
+using ngraph::snippets::pass::TokenizeSnippets;
+//using ngraph::snippets::pass::CreateSubgraph;
 
-TEST(TransformationTests, StartSubgraphMultipleOutputs) {
-    std::shared_ptr<Function> f(nullptr), f_ref(nullptr);
+// Todo: Move this test to CPU-specific
+TEST(TransformationTests, DoNotStartSubgraphAfterInputs) {
+    // Do not start Subgraph after input parameters to avoid U8->FP32 and FP32->U8 conversion pairs
+    // Todo: Remove this test when U8 support is enabled in SnippetS and StartSubgraph logics is updated
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    std::shared_ptr<Model> f(nullptr), f_ref(nullptr);
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto add = std::make_shared<opset1::Add>(data0, data1);
-        auto sub = std::make_shared<opset1::Subtract>(add, data1);
-        auto mul = std::make_shared<opset1::Multiply>(add, sub);
-        f = std::make_shared<Function>(NodeVector{mul}, ParameterVector{data0, data1});
+        auto data0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        const std::vector<float> const_values{3, 2, 10};
+        auto const_data = std::make_shared<op::v0::Constant>(element::f32, Shape{1, 3}, const_values);
+        auto add = std::make_shared<op::v1::Add>(data0, data1);
+        auto sub = std::make_shared<op::v1::Subtract>(add, const_data);
+        auto mul = std::make_shared<op::v1::Multiply>(add, sub);
+        f = std::make_shared<Model>(NodeVector{mul}, ParameterVector{data0, data1});
 
         pass::Manager m;
-        m.register_pass<pass::InitNodeInfo>();
-        m.register_pass<snippets::pass::StartSubgraph>();
+        m.register_pass<InitNodeInfo>();
+        // Todo: When moved to CPU-specific tests, uncomment the markup transformation below.
+        //  m.register_pass<MKLDNNPlugin::SnippetsMarkFused>();
+        m.register_pass<EnumerateNodes>();
+        m.register_pass<TokenizeSnippets>();
         m.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
+    ASSERT_EQ(count_ops_of_type<Subgraph>(f), 0);
+}
 
+TEST(TransformationTests, StartSubgraphMultipleOutputs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    std::shared_ptr<Model> f(nullptr), f_ref(nullptr);
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto indata0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto indata1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto add = std::make_shared<snippets::op::Subgraph>(NodeVector{data0, data1},
-            std::make_shared<Function>(NodeVector{std::make_shared<opset1::Add>(indata0, indata1)}, ParameterVector{indata0, indata1}));
-        auto sub = std::make_shared<opset1::Subtract>(add, data1);
-        auto mul = std::make_shared<opset1::Multiply>(add, sub);
-        f_ref = std::make_shared<Function>(NodeVector{mul}, ParameterVector{data0, data1});
+        auto data0 = std::make_shared<op::v0::Parameter>(element::i32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::i32, Shape{1, 3});
+        auto convert0 = std::make_shared<op::v0::Convert>(data0, element::f32);
+        auto convert1 = std::make_shared<op::v0::Convert>(data1, element::f32);
+        const std::vector<float> const_values{3, 2, 10};
+        auto const_data = std::make_shared<op::v0::Constant>(element::f32, Shape{1, 3}, const_values);
+        auto add = std::make_shared<op::v1::Add>(convert0, convert1);
+        auto sub = std::make_shared<op::v1::Subtract>(add, const_data);
+        auto mul = std::make_shared<op::v1::Multiply>(add, sub);
+        f = std::make_shared<Model>(NodeVector{mul}, ParameterVector{data0, data1});
+        pass::Manager m;
+        m.register_pass<InitNodeInfo>();
+        m.register_pass<EnumerateNodes>();
+        m.register_pass<TokenizeSnippets>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+    {
+        auto data0 = std::make_shared<op::v0::Parameter>(element::i32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::i32, Shape{1, 3});
+        auto convert0 = std::make_shared<op::v0::Convert>(data0, element::f32);
+        auto convert1 = std::make_shared<op::v0::Convert>(data1, element::f32);
+        const std::vector<float> const_values{3, 2, 10};
+        auto const_data = std::make_shared<op::v0::Constant>(element::f32, Shape{1, 3}, const_values);
+        auto indata0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto indata1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto indata2 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto add = std::make_shared<op::v1::Add>(indata0, indata1);
+        auto sub = std::make_shared<op::v1::Subtract>(add, const_data);
+        auto mul = std::make_shared<Subgraph>(NodeVector{convert0, convert1, const_data},
+       std::make_shared<Model>(NodeVector{std::make_shared<op::v1::Multiply>(add, sub)},
+                                  ParameterVector{indata0, indata1, indata2}));
+        f_ref = std::make_shared<Model>(NodeVector{mul}, ParameterVector{data0, data1});
     }
 
     auto res = compare_functions(f, f_ref);
     ASSERT_TRUE(res.first) << res.second;
 }
 
-TEST(TransformationTests, DontStartSubgraphSingleOuptut) {
-    std::shared_ptr<Function> f(nullptr), f_ref(nullptr);
+TEST(TransformationTests, TokenizeMulAddSubgraph) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    std::shared_ptr<Model> f(nullptr), f_ref(nullptr);
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto add = std::make_shared<opset1::Add>(data0, data1);
-        auto sub = std::make_shared<opset1::Subtract>(add, data1);
-        auto mul = std::make_shared<opset1::Multiply>(data0, sub);
-        f = std::make_shared<Function>(NodeVector{mul}, ParameterVector{data0, data1});
+        auto data_1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 4, 4 });
+        auto data_2 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 4, 4 });
+        auto non_snippet_op = std::make_shared<op::v0::MatMul>(data_1, data_2);
+
+        auto mul_const_1 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 4.f });
+        auto mul_1 = std::make_shared<op::v1::Multiply>(non_snippet_op, mul_const_1);
+        auto add_const_1 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 16.4f });
+        auto add_1 = std::make_shared<op::v1::Add>(mul_1, add_const_1);
+        auto elu = std::make_shared<op::v0::Elu>(add_1, 0.01);
+
+        auto mul_const_2 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 4.f });
+        auto mul_2 = std::make_shared<op::v1::Multiply>(non_snippet_op, mul_const_2);
+        auto add_const_2 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 16.4f });
+        auto add_2 = std::make_shared<op::v1::Add>(mul_2, add_const_2);
+        auto relu = std::make_shared<op::v0::Relu>(add_2);
+
+        auto add = std::make_shared<op::v1::Add>(elu, relu);
+        auto result = std::make_shared<op::v0::Result>(add);
+
+        f = std::make_shared<Model>(ResultVector{ result }, ParameterVector{ data_1, data_2 });
 
         pass::Manager m;
-        m.register_pass<pass::InitNodeInfo>();
-        m.register_pass<snippets::pass::StartSubgraph>();
+        m.register_pass<InitNodeInfo>();
+        m.register_pass<EnumerateNodes>();
+        m.register_pass<TokenizeSnippets>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+    {
+        auto data_1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 4, 4 });
+        auto data_2 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 4, 4 });
+
+        // snippet inputs
+        auto non_snippet_op = std::make_shared<op::v0::MatMul>(data_1, data_2);
+        auto mul_const_1 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 4.f });
+        auto add_const_1 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 16.4f });
+        auto mul_const_2 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 4.f });
+        auto add_const_2 = op::v0::Constant::create(element::f32, { 1, 3, 1, 1 }, { 16.4f });
+
+        // snippet function
+        auto snippet_input = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 4, 4 });
+
+        auto sn_mul_const_1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 1, 1 });
+        auto mul_1 = std::make_shared<op::v1::Multiply>(snippet_input, sn_mul_const_1);
+        auto sn_add_const_1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 1, 1 });
+        auto add_1 = std::make_shared<op::v1::Add>(mul_1, sn_add_const_1);
+        auto elu = std::make_shared<op::v0::Elu>(add_1, 0.01);
+
+        auto sn_mul_const_2 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 1, 1 });
+        auto mul_2 = std::make_shared<op::v1::Multiply>(snippet_input, sn_mul_const_2);
+        auto sn_add_const_2 = std::make_shared<op::v0::Parameter>(element::f32, Shape{ 1, 3, 1, 1 });
+        auto add_2 = std::make_shared<op::v1::Add>(mul_2, sn_add_const_2);
+        auto relu = std::make_shared<op::v0::Relu>(add_2);
+
+        auto add = std::make_shared<op::v1::Add>(elu, relu);
+        ParameterVector subgraph_params{ snippet_input, sn_mul_const_1, sn_add_const_1, sn_mul_const_2, sn_add_const_2 };
+        auto snippet_function = std::make_shared<Model>(NodeVector{ add }, subgraph_params);
+
+        ngraph::NodeVector snippet_inputs{ non_snippet_op, mul_const_1, add_const_1, mul_const_2, add_const_2 };
+        auto snippet = std::make_shared<Subgraph>(snippet_inputs, snippet_function);
+        auto result = std::make_shared<op::v0::Result>(snippet);
+
+        f_ref = std::make_shared<Model>(NodeVector{ result }, ParameterVector{ data_1, data_2 });
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+
+TEST(TransformationTests, DontStartSubgraphSingleOutput) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    std::shared_ptr<Model> f(nullptr), f_ref(nullptr);
+    {
+        auto data0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto add = std::make_shared<op::v1::Add>(data0, data1);
+        auto sub = std::make_shared<op::v1::Subtract>(add, data1);
+        auto mul = std::make_shared<op::v1::Multiply>(data0, sub);
+        f = std::make_shared<Model>(NodeVector{mul}, ParameterVector{data0, data1});
+
+        pass::Manager m;
+        m.register_pass<InitNodeInfo>();
+        m.register_pass<EnumerateNodes>();
+        m.register_pass<TokenizeSnippets>();
         m.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
 
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto add = std::make_shared<opset1::Add>(data0, data1);
-        auto sub = std::make_shared<opset1::Subtract>(add, data1);
-        auto mul = std::make_shared<opset1::Multiply>(data0, sub);
-        f_ref = std::make_shared<Function>(NodeVector{mul}, ParameterVector{data0, data1});
+        auto data0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto add = std::make_shared<op::v1::Add>(data0, data1);
+        auto sub = std::make_shared<op::v1::Subtract>(add, data1);
+        auto mul = std::make_shared<op::v1::Multiply>(data0, sub);
+        f_ref = std::make_shared<Model>(NodeVector{mul}, ParameterVector{data0, data1});
     }
 
     auto res = compare_functions(f, f_ref);
@@ -82,35 +202,37 @@ TEST(TransformationTests, DontStartSubgraphSingleOuptut) {
 }
 
 TEST(TransformationTests, AttachToSubgraph) {
-    std::shared_ptr<Function> f(nullptr), f_ref(nullptr);
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    std::shared_ptr<Model> f(nullptr), f_ref(nullptr);
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto indata0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto indata1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto add = std::make_shared<snippets::op::Subgraph>(NodeVector{data0, data1},
-            std::make_shared<Function>(NodeVector{std::make_shared<opset1::Add>(indata0, indata1)}, ParameterVector{indata0, indata1}));
-        auto neg = std::make_shared<opset1::Negative>(add);
-        auto concat = std::make_shared<opset1::Concat>(NodeVector{add, neg}, 0);
-        f = std::make_shared<Function>(NodeVector{concat}, ParameterVector{data0, data1});
+        auto data0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto indata0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto indata1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto add = std::make_shared<Subgraph>(NodeVector{data0, data1},
+            std::make_shared<Model>(NodeVector{std::make_shared<op::v1::Add>(indata0, indata1)}, ParameterVector{indata0, indata1}));
+        auto neg = std::make_shared<op::v0::Negative>(add);
+        auto concat = std::make_shared<op::v0::Concat>(NodeVector{add, neg}, 0);
+        f = std::make_shared<Model>(NodeVector{concat}, ParameterVector{data0, data1});
 
         pass::Manager m;
-        m.register_pass<pass::InitNodeInfo>();
-        m.register_pass<snippets::pass::AttachToSubgraph>();
+        m.register_pass<InitNodeInfo>();
+        m.register_pass<EnumerateNodes>();
+        m.register_pass<TokenizeSnippets>();
         m.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
 
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto indata0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto indata1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto inner = std::make_shared<opset1::Add>(indata0, indata1);
-        auto add = std::make_shared<snippets::op::Subgraph>(NodeVector{data0, data1},
-            std::make_shared<Function>(NodeVector{std::make_shared<opset1::Negative>(inner), inner}, ParameterVector{indata0, indata1}));
-        auto concat = std::make_shared<opset1::Concat>(OutputVector{add->output(0), add->output(1)}, 0);
-        f_ref = std::make_shared<Function>(NodeVector{concat}, ParameterVector{data0, data1});
+        auto data0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto indata0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto indata1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto inner = std::make_shared<op::v1::Add>(indata0, indata1);
+        auto add = std::make_shared<Subgraph>(NodeVector{data0, data1},
+            std::make_shared<Model>(NodeVector{std::make_shared<op::v0::Negative>(inner), inner}, ParameterVector{indata0, indata1}));
+        auto concat = std::make_shared<op::v0::Concat>(OutputVector{add->output(0), add->output(1)}, 0);
+        f_ref = std::make_shared<Model>(NodeVector{concat}, ParameterVector{data0, data1});
     }
 
     auto res = compare_functions(f, f_ref);
@@ -118,37 +240,48 @@ TEST(TransformationTests, AttachToSubgraph) {
 }
 
 TEST(TransformationTests, DontAttachToSubgraphIfLoop) {
-    std::shared_ptr<Function> f(nullptr), f_ref(nullptr);
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    std::shared_ptr<Model> f(nullptr), f_ref(nullptr);
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto indata0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto indata1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto add = std::make_shared<snippets::op::Subgraph>(NodeVector{data0, data1},
-            std::make_shared<Function>(NodeVector{std::make_shared<opset1::Add>(indata0, indata1)}, ParameterVector{indata0, indata1}));
-        auto log = std::make_shared<opset1::Log>(add);
-        auto mul = std::make_shared<opset1::Multiply>(add, log);
-        f = std::make_shared<Function>(NodeVector{mul}, ParameterVector{data0, data1});
+        auto data0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto indata0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto indata1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto add = std::make_shared<Subgraph>(NodeVector{data0, data1},
+            std::make_shared<Model>(NodeVector{std::make_shared<op::v1::Add>(indata0, indata1)}, ParameterVector{indata0, indata1}));
+        auto log = std::make_shared<op::v0::Log>(add);
+        auto mul = std::make_shared<op::v1::Multiply>(add, log);
+        f = std::make_shared<Model>(NodeVector{mul}, ParameterVector{data0, data1});
 
         pass::Manager m;
-        m.register_pass<pass::InitNodeInfo>();
-        m.register_pass<snippets::pass::AttachToSubgraph>();
+        m.register_pass<InitNodeInfo>();
+        m.register_pass<EnumerateNodes>();
+        m.register_pass<TokenizeSnippets>();
         m.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
 
     {
-        auto data0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto data1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto indata0 = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 3});
-        auto indata1 = std::make_shared<opset1::Parameter>(element::f32, Shape{1, 3});
-        auto add = std::make_shared<snippets::op::Subgraph>(NodeVector{data0, data1},
-            std::make_shared<Function>(NodeVector{std::make_shared<opset1::Add>(indata0, indata1)}, ParameterVector{indata0, indata1}));
-        auto log = std::make_shared<opset1::Log>(add);
-        auto mul = std::make_shared<opset1::Multiply>(add, log);
-        f_ref = std::make_shared<Function>(NodeVector{mul}, ParameterVector{data0, data1});
+        auto data0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto data1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto indata0 = std::make_shared<op::v0::Parameter>(element::f32, Shape{2, 3});
+        auto indata1 = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, 3});
+        auto add = std::make_shared<Subgraph>(NodeVector{data0, data1},
+            std::make_shared<Model>(NodeVector{std::make_shared<op::v1::Add>(indata0, indata1)}, ParameterVector{indata0, indata1}));
+        auto log = std::make_shared<op::v0::Log>(add);
+        /*
+         * Note that log is not currently supported by snippets, so it won't be converted to subgraph.
+         * Mul will be converted for the "reset" continuation strategy, (present case)
+         * or left as-is for the "abort" continuation strategy
+        */
+        auto add_param = std::make_shared<op::v0::Parameter>(element::f32, add->get_output_shape(0));
+        auto log_param = std::make_shared<op::v0::Parameter>(element::f32, log->get_output_shape(0));
+        auto mul = std::make_shared<Subgraph>(NodeVector{add, log},
+                   std::make_shared<Model>(NodeVector{std::make_shared<op::v1::Multiply>(add_param, log_param)},
+                                                ParameterVector{add_param, log_param}));
+        f_ref = std::make_shared<Model>(NodeVector{mul}, ParameterVector{data0, data1});
     }
 
     auto res = compare_functions(f, f_ref);
     ASSERT_TRUE(res.first) << res.second;
-}
\ No newline at end of file
+}
diff --git a/src/tests/unit/cpu/CMakeLists.txt b/src/tests/unit/cpu/CMakeLists.txt
index d44d2584212..7f66c147d95 100644
--- a/src/tests/unit/cpu/CMakeLists.txt
+++ b/src/tests/unit/cpu/CMakeLists.txt
@@ -23,6 +23,8 @@ addIeTargetTest(
             ov_shape_inference
             inference_engine_s
             unitTestUtils
+            inference_engine_snippets
+            ngraphFunctions
         ADD_CPPLINT
         LABELS
             CPU
@@ -31,3 +33,8 @@ addIeTargetTest(
 ie_faster_build(${TARGET_NAME}
     UNITY
 )
+
+target_include_directories(${TARGET_NAME} SYSTEM PRIVATE
+        ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/src/common
+        ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/src/cpu
+        ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/include)
diff --git a/src/tests/unit/cpu/generate_add.cpp b/src/tests/unit/cpu/generate_add.cpp
new file mode 100644
index 00000000000..a1069d58957
--- /dev/null
+++ b/src/tests/unit/cpu/generate_add.cpp
@@ -0,0 +1,427 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <sstream>
+#include <fstream>
+#include <memory>
+#include <queue>
+#include <map>
+#include <array>
+
+#include <snippets/generator.hpp>
+
+#include <ngraph/function.hpp>
+
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset2.hpp>
+
+#include <ngraph/pass/constant_folding.hpp>
+#include <ngraph/pass/visualize_tree.hpp>
+
+#include <snippets/op/subgraph.hpp>
+
+#include <transformations/init_node_info.hpp>
+#include <transformations/utils/utils.hpp>
+
+#include <ngraph_functions/utils/ngraph_helpers.hpp>
+
+using namespace testing;
+
+auto gen_inputs(const ngraph::Shape& shape, size_t n = 2) -> std::vector<std::vector<std::uint8_t>>{
+    std::vector<std::vector<std::uint8_t>> referenceInputs(n);
+    for (size_t k = 0; k < n; k++) {
+        referenceInputs[k].resize(ngraph::shape_size(shape)*sizeof(float));
+        float* in0 = reinterpret_cast<float*>(&referenceInputs[k][0]);
+
+        for (size_t i = 0; i < ngraph::shape_size(shape); i++) {
+            if (k % 3 == 0) {
+                in0[i] = i / 2048.f;
+            } else if (k % 3 == 1) {
+                in0[i] = 1 - i / 2048.f;
+            } else {
+                in0[i] = i / 1024.f;
+            }
+        }
+    }
+    return referenceInputs;
+}
+
+auto compare(std::shared_ptr<ngraph::Function>& s, std::shared_ptr<ngraph::Function>& f, std::vector<std::vector<std::uint8_t>>& in) -> bool{
+    auto act = ngraph::helpers::interpreterFunction(s, in);
+    auto exp = ngraph::helpers::interpreterFunction(f, in);
+
+    const float* pexp = reinterpret_cast<float*>(&exp[0].second[0]);
+    const float* pact = reinterpret_cast<float*>(&act[0].second[0]);
+
+    bool isCorrect = true;
+    for (int i = 0; i < ngraph::shape_size(f->get_result()->get_shape()); i++) {
+        if (std::abs(pexp[i]-pact[i]) > std::numeric_limits<float>::epsilon()
+            || std::isnan(pexp[i]) != std::isnan(pact[i])) {
+            isCorrect = false;
+            std::cout << i << " expected " << pexp[i] << " actual " << pact[i] << " diff " << std::abs(pexp[i]-pact[i]) << std::endl;
+        }
+    }
+    return isCorrect;
+}
+
+auto wrapAsSnippet(std::shared_ptr<ngraph::Function>& f, const ngraph::Shape& shape0, const ngraph::Shape& shape1)
+-> std::shared_ptr<ngraph::Function>{
+    auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape0);
+    auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+    auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input0, input1}, ngraph::clone_function(*f.get()));
+    return std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input0, input1});
+}
+
+auto wrapAsSnippet(std::shared_ptr<ngraph::Function>& f, const ngraph::Shape& shape0)
+-> std::shared_ptr<ngraph::Function>{
+    auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape0);
+    auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input0}, ngraph::clone_function(*f.get()));
+    return std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input0});
+}
+
+// Todo: Reimplement Snippets tests, so they won't require evaluate method for OP Subgraph
+//  In more detail, we can't use interpreter backend for op::Subgraph evaluation, since it will
+//  depend on the snippets lib in this (which is not allowed).
+TEST(SnippetsTests, GenerateAddParams) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+
+    auto f = ([] (const ngraph::Shape& shape) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto add    = std::make_shared<ngraph::opset1::Add>(input0, input1);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{input0, input1});
+    })(shape);
+
+    auto s = wrapAsSnippet(f, shape, shape);
+    auto referenceInputs = gen_inputs(shape, 2);
+    bool isCorrect = compare(s, f, referenceInputs);
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddConstant) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+
+    auto f = ([] (const ngraph::Shape& shape) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+
+        std::vector<float> vals(ngraph::shape_size(shape));
+        for (int i = 0; i < ngraph::shape_size(shape); i++) {
+            vals[i] = 1-i/2048.f;
+        }
+        auto input1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::f32, shape, vals);
+        auto add    = std::make_shared<ngraph::opset1::Add>(input0, input1);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{input0});
+    })(shape);
+
+    auto s = wrapAsSnippet(f, shape);
+    auto referenceInputs = gen_inputs(shape, 1);
+    bool isCorrect = compare(s, f, referenceInputs);
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddConstantScalar) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+
+    auto f = ([] (const ngraph::Shape& shape) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto input1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>({42.f}));
+        auto add    = std::make_shared<ngraph::opset1::Add>(input0, input1);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{input0});
+    })(shape);
+
+    auto s = wrapAsSnippet(f, shape);
+    auto referenceInputs = gen_inputs(shape, 1);
+    bool isCorrect = compare(s, f, referenceInputs);
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddConstantScalarEmptySize) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+
+    auto f = ([] (const ngraph::Shape& shape) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto input1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::f32, ngraph::Shape(), std::vector<float>({42.f}));
+        auto add    = std::make_shared<ngraph::opset1::Add>(input0, input1);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{input0});
+    })(shape);
+
+    auto s = wrapAsSnippet(f, shape);
+    auto referenceInputs = gen_inputs(shape, 1);
+    bool isCorrect = compare(s, f, referenceInputs);
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddBroadcastX2Edges) {
+    GTEST_SKIP();
+    auto shape0 = ngraph::Shape{1, 4, 16, 31};
+    auto shape1 = ngraph::Shape{1, 4, 16, 1};
+
+    auto f = ([] (const ngraph::Shape& shape0, const ngraph::Shape& shape1) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape0);
+        auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+        auto input2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+
+        auto add    = std::make_shared<ngraph::opset1::Add>(input0, input1);
+        auto mul    = std::make_shared<ngraph::opset1::Add>(input1, input2);
+
+        auto sub = std::make_shared<ngraph::opset1::Add>(add, mul);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{sub}, ngraph::ParameterVector{input0, input1, input2});
+    })(shape0, shape1);
+
+    auto s = ([f] (const ngraph::Shape& shape0, const ngraph::Shape& shape1) -> std::shared_ptr<ngraph::Function>{
+        auto input2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape0);
+        auto input3 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+        auto input4 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+        auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input2, input3, input4}, ngraph::clone_function(*f.get()));
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input2, input3, input4});
+    })(shape0, shape1);
+
+    std::vector<std::vector<std::uint8_t>> referenceInputs(3);
+    referenceInputs[0].resize(ngraph::shape_size(shape0)*sizeof(float));
+    referenceInputs[1].resize(ngraph::shape_size(shape1)*sizeof(float));
+    referenceInputs[2].resize(ngraph::shape_size(shape1)*sizeof(float));
+
+    float* in0 = reinterpret_cast<float*>(&referenceInputs[0][0]);
+    float* in1 = reinterpret_cast<float*>(&referenceInputs[1][0]);
+    float* in2 = reinterpret_cast<float*>(&referenceInputs[2][0]);
+    for (int i = 0; i < ngraph::shape_size(shape0); i++) {
+        in0[i] = i/2048.f;
+    }
+
+    in1[0] = 1.f;
+    in2[0] = 0.42f;
+
+    auto act = ngraph::helpers::interpreterFunction(s, referenceInputs);
+    auto exp = ngraph::helpers::interpreterFunction(f, referenceInputs);
+
+    const float* pexp = reinterpret_cast<float*>(&exp[0].second[0]);
+    const float* pact = reinterpret_cast<float*>(&act[0].second[0]);
+
+    bool isCorrect = true;
+    for (int i = 0; i < ngraph::shape_size(shape0); i++) {
+        if (pexp[i] != pact[i]) {
+            isCorrect = false;
+            std::cout << i << " expected " << pexp[i] << " actual " << pact[i] << std::endl;
+        }
+    }
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddBroadcastY) {
+    GTEST_SKIP();
+    auto shape0 = ngraph::Shape{1, 4, 16, 31};
+    auto shape1 = ngraph::Shape{1, 4,  1, 31};
+
+    auto f = ([] (const ngraph::Shape& shape0, const ngraph::Shape& shape1) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape0);
+        auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+        auto add    = std::make_shared<ngraph::opset1::Add>(input0, input1);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{input0, input1});
+    })(shape0, shape1);
+
+    auto s = wrapAsSnippet(f, shape0, shape1);
+
+    std::vector<std::vector<std::uint8_t>> referenceInputs(2);
+    referenceInputs[0].resize(ngraph::shape_size(shape0)*sizeof(float));
+    referenceInputs[1].resize(ngraph::shape_size(shape1)*sizeof(float));
+
+    float* in0 = reinterpret_cast<float*>(&referenceInputs[0][0]);
+    float* in1 = reinterpret_cast<float*>(&referenceInputs[1][0]);
+    for (size_t i = 0; i < ngraph::shape_size(shape0); i++) {
+        in0[i] = i / 2048.f;
+    }
+    for (size_t i = 0; i < ngraph::shape_size(shape1); i++) {
+        in1[i] = 1 - i / 2048.f;
+    }
+
+    auto act = ngraph::helpers::interpreterFunction(s, referenceInputs);
+    auto exp = ngraph::helpers::interpreterFunction(f, referenceInputs);
+
+    const float* pexp = reinterpret_cast<float*>(&exp[0].second[0]);
+    const float* pact = reinterpret_cast<float*>(&act[0].second[0]);
+
+    bool isCorrect = true;
+    for (size_t i = 0; i < ngraph::shape_size(shape0); i++) {
+        if (pexp[i] != pact[i]) {
+            isCorrect = false;
+            std::cout << i << " expected " << pexp[i] << " actual " << pact[i] << std::endl;
+        }
+    }
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddNegate) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+
+    auto f = ([] (const ngraph::Shape& shape) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto add    = std::make_shared<ngraph::opset1::Add>(input0, input1);
+        auto nagate = std::make_shared<ngraph::opset1::Negative>(add);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{nagate}, ngraph::ParameterVector{input0, input1});
+    })(shape);
+
+    auto s = ([f] (const ngraph::Shape& shape) -> std::shared_ptr<ngraph::Function>{
+        auto input2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto input3 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+        auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input2, input3}, ngraph::clone_function(*f.get()));
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input2, input3});
+    })(shape);
+
+    auto referenceInputs = gen_inputs(shape, 2);
+    bool isCorrect = compare(s, f, referenceInputs);
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddNegateAdd) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+    auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto input2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto add = std::make_shared<ngraph::opset1::Add>(input1, input2);
+    auto nagate = std::make_shared<ngraph::opset1::Negative>(add);
+    auto input3 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto add2 = std::make_shared<ngraph::opset1::Add>(nagate, input3);
+    std::shared_ptr<ngraph::Function> f = std::make_shared<ngraph::Function>(ngraph::NodeVector{add2}, ngraph::ParameterVector{input1, input2, input3});
+
+    auto input11 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto input21 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto input31 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input11, input21, input31}, ngraph::clone_function(*f.get()));
+    std::shared_ptr<ngraph::Function>  s = std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input11, input21, input31});
+
+    auto referenceInputs = gen_inputs(shape, 3);
+    bool isCorrect = compare(s, f, referenceInputs);
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddNegateAddMultiEdge) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+    auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto input2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto add    = std::make_shared<ngraph::opset1::Add>(input1, input2);
+    auto nagate = std::make_shared<ngraph::opset1::Negative>(add);
+    auto add2 = std::make_shared<ngraph::opset1::Add>(nagate, input1);
+    std::shared_ptr<ngraph::Function> f = std::make_shared<ngraph::Function>(ngraph::NodeVector{add2}, ngraph::ParameterVector{input1, input2});
+
+    auto s = wrapAsSnippet(f, shape, shape);
+    auto referenceInputs = gen_inputs(shape, 2);
+    bool isCorrect = compare(s, f, referenceInputs);
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateAddNegateAddMultiEdgeConst) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+    auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto input2 = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{}, {0.42});
+    auto add = std::make_shared<ngraph::opset1::Add>(input1, input2);
+    auto nagate = std::make_shared<ngraph::opset1::Negative>(add);
+    auto add2 = std::make_shared<ngraph::opset1::Add>(nagate, input1);
+    std::shared_ptr<ngraph::Function> f = std::make_shared<ngraph::Function>(ngraph::NodeVector{add2}, ngraph::ParameterVector{input1});
+
+    auto input11 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input11}, ngraph::clone_function(*f.get()));
+    std::shared_ptr<ngraph::Function>  s = std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input11});
+
+    auto referenceInputs = gen_inputs(shape, 1);
+    bool isCorrect = compare(s, f, referenceInputs);
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+TEST(SnippetsTests, GenerateErf) {
+    GTEST_SKIP();
+    auto shape = ngraph::Shape{1, 4, 16, 31};
+
+    auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto gelu   = std::make_shared<ngraph::opset1::Erf>(input1);
+    std::shared_ptr<ngraph::Function> f = std::make_shared<ngraph::Function>(ngraph::NodeVector{gelu}, ngraph::ParameterVector{input1});
+
+    auto input11 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape);
+    auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input11}, ngraph::clone_function(*f.get()));
+    std::shared_ptr<ngraph::Function> s = std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input11});
+
+    auto referenceInputs = gen_inputs(shape, 1);
+    bool isCorrect = compare(s, f, referenceInputs);
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}
+
+// ToDO: implement tile selection logic & broadcast emission to make it working. Broadcast substitution works
+TEST(SnippetsTests, GenerateAddBroadcastAutomatic) {
+    GTEST_SKIP();
+    std::array<ngraph::Shape, 3> shapes {
+        ngraph::Shape{1, 4, 16, 31},
+        ngraph::Shape{1, 4, 16, 1},
+        ngraph::Shape{1, 4, 16, 1}
+    };
+
+    auto f = ([] (const ngraph::Shape& shape0, const ngraph::Shape& shape1, const ngraph::Shape& shape2) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape0);
+        auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+        auto input2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape2);
+
+        auto add = std::make_shared<ngraph::opset1::Add>(input0, input1);
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(input1, input2);
+        auto sub = std::make_shared<ngraph::opset1::Subtract>(add, mul);
+
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{sub}, ngraph::ParameterVector{input0, input1, input2});
+    })(shapes[0], shapes[1], shapes[2]);
+
+    auto s = ([f] (const ngraph::Shape& shape0, const ngraph::Shape& shape1, const ngraph::Shape& shape2) -> std::shared_ptr<ngraph::Function>{
+        auto input0 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape0);
+        auto input1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape1);
+        auto input2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, shape2);
+        auto snippet = std::make_shared<ngraph::snippets::op::Subgraph>(ngraph::OutputVector{input0, input1, input2}, ngraph::clone_function(*f.get()));
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{snippet}, ngraph::ParameterVector{input0, input1, input2});
+    })(shapes[0], shapes[1], shapes[2]);
+
+    std::vector<std::vector<std::uint8_t>> referenceInputs(3);
+    for (int k = 0; k < referenceInputs.size(); k++) {
+        referenceInputs[k].resize(ngraph::shape_size(shapes[k]) * sizeof(float));
+
+        auto in0 = reinterpret_cast<float*>(&referenceInputs[k][0]);
+        for (int i = 0; i < ngraph::shape_size(shapes[k]); i++) {
+            in0[i] = k == 0 ? i/2048.f : (k == 1 ? 1.f : 0.42f);
+        }
+    }
+
+    auto exp = ngraph::helpers::interpreterFunction(f, referenceInputs);
+    auto act = ngraph::helpers::interpreterFunction(s, referenceInputs);
+
+    const float* pexp = reinterpret_cast<float*>(&exp[0].second[0]);
+    const float* pact = reinterpret_cast<float*>(&act[0].second[0]);
+
+    bool isCorrect = true;
+    for (size_t i = 0; i < ngraph::shape_size(shapes[0]); i++) {
+        if (pexp[i] != pact[i]) {
+            isCorrect = false;
+            std::cout << i << " expected " << pexp[i] << " actual " << pact[i] << std::endl;
+        }
+    }
+
+    ASSERT_TRUE(isCorrect) << "snippet and native implementation differs";
+}

From be0e77304f925d41aee1e00d4c9a066988ba3c81 Mon Sep 17 00:00:00 2001
From: Vladimir Zinoviev <vladimir.zinoviev@intel.com>
Date: Mon, 20 Dec 2021 22:51:44 +0300
Subject: [PATCH 056/160] [LPT] getDequantization: INT4 input support to avoid
 ConvertSubtract transformation (#9288)

---
 .../src/network_helper.cpp                    |  1 +
 .../convolution_qdq_transformation.cpp        | 20 +++++++++++++++++++
 2 files changed, 21 insertions(+)

diff --git a/src/common/low_precision_transformations/src/network_helper.cpp b/src/common/low_precision_transformations/src/network_helper.cpp
index c796c1ed90d..171f841f4cc 100644
--- a/src/common/low_precision_transformations/src/network_helper.cpp
+++ b/src/common/low_precision_transformations/src/network_helper.cpp
@@ -1360,6 +1360,7 @@ FakeQuantizeDequantization NetworkHelper::getDequantization(const std::shared_pt
     const std::shared_ptr<opset1::Convert> convert = ov::as_type_ptr<opset1::Convert>(dataNode.get_node_shared_ptr());
     if (convert != nullptr) {
         if ((convert->input(0).get_element_type() != element::i8) && (convert->input(0).get_element_type() != element::u8) &&
+            (convert->input(0).get_element_type() != element::i4) && (convert->input(0).get_element_type() != element::u4) &&
             (convert->output(0).get_element_type() != element::f32)) {
             return FakeQuantizeDequantization(dataNode, nullptr, subtract, subtractConvert, subtractConstant, multiply, multiplyConstant);
         }
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_qdq_transformation.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_qdq_transformation.cpp
index 36a57944048..fd566d3da1c 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_qdq_transformation.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_qdq_transformation.cpp
@@ -231,6 +231,26 @@ const std::vector<LayerTestsDefinitions::ConvolutionQDqTransformationParam> para
         "Convolution",
         "U8"
     },
+
+    {
+        { 16ul, {{ 1, 1, 1, 1 }}, { -0.8f }, { 0.f }, { 0.f }, { 15.f }, ngraph::element::f32 },
+        { ngraph::element::u8, false },
+        {
+                { ngraph::element::f32, false },
+                { {128.f}, ngraph::element::f32, {}, false, 1ul, ngraph::element::u8, true },
+                { {0.1f}, ngraph::element::f32, {}, false }
+        },
+        {{0.5f}, ngraph::element::i8},
+        {},
+        {},
+        {
+                { ngraph::element::f32, false },
+                {},
+                { {0.2f}, ngraph::element::f32, {}, false }
+        },
+        "Convolution",
+        "U8"
+    },
 };
 
 const std::vector<ngraph::PartialShape> shapes = {

From 29eb83d9041217c471f1922a059281afb46bc6ac Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomasz=20Do=C5=82bniak?= <tomasz.dolbniak@intel.com>
Date: Mon, 20 Dec 2021 21:23:03 +0100
Subject: [PATCH 057/160] MO: adding extra tensor names during the graph
 modification (#9292)

---
 .../test_frontend_onnx_editor.py              | 109 +++++++++++++++++-
 .../onnx/frontend/src/input_model.cpp         |  48 +++++++-
 .../onnx/frontend/src/input_model.hpp         |   5 +-
 .../tools/mo/moc_frontend/extractor.py        |   4 +-
 .../tools/mo/moc_frontend/pipeline.py         |  27 ++++-
 5 files changed, 177 insertions(+), 16 deletions(-)

diff --git a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
index 7463a0bd0c9..1649845dd82 100644
--- a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
+++ b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
@@ -1067,11 +1067,6 @@ def test_not_supported_methods():
     skip_if_onnx_frontend_is_disabled()
     fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
     model = fe.load("test_place_names.onnx")
-    tensor = model.get_place_by_tensor_name(tensor_name="add_out")
-
-    with pytest.raises(Exception) as e:
-        model.add_name_for_tensor(tensor=tensor, new_name="new_name")
-    assert "not applicable for ONNX model" in str(e)
 
     with pytest.raises(Exception) as e:
         model.free_name_for_tensor("add_out")
@@ -1251,3 +1246,107 @@ def test_get_partial_shape_using_output_edge():
     split_operator = model.get_place_by_tensor_name("out1").get_producing_operation()
     out2_edge = split_operator.get_output_port(output_port_index=1)
     assert model.get_partial_shape(out2_edge) == PartialShape([1, 2])
+
+
+def test_add_name_for_tensor():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    tensor = model.get_place_by_tensor_name(tensor_name="in2")
+    model.add_name_for_tensor(tensor, "extra_name")
+
+    ov_model = fe.convert(model)
+
+    add_input = ov_model.input(1)
+    add_input_tensor_names = add_input.get_names()
+    assert "in2" in add_input_tensor_names
+    assert "extra_name" in add_input_tensor_names
+
+
+def test_add_two_names_for_tensor():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    tensor = model.get_place_by_tensor_name(tensor_name="in2")
+    model.add_name_for_tensor(tensor, "extra_name1")
+    model.add_name_for_tensor(tensor, "extra_name2")
+
+    ov_model = fe.convert(model)
+
+    add_input = ov_model.input(1)
+    add_input_tensor_names = add_input.get_names()
+    assert len(add_input_tensor_names) == 3
+    assert "extra_name1" in add_input_tensor_names
+    assert "extra_name2" in add_input_tensor_names
+
+
+def test_add_the_same_name_to_tensor_twice():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    tensor = model.get_place_by_tensor_name(tensor_name="in2")
+    model.add_name_for_tensor(tensor, "extra_name")
+    model.add_name_for_tensor(tensor, "extra_name")
+
+    ov_model = fe.convert(model)
+
+    add_input = ov_model.input(1)
+    add_input_tensor_names = add_input.get_names()
+    assert len(add_input_tensor_names) == 2
+    assert "extra_name" in add_input_tensor_names
+
+
+def test_add_name_for_tensor_and_cut_it_off():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model_2.onnx")
+
+    tensor = model.get_place_by_tensor_name(tensor_name="in2")
+    model.add_name_for_tensor(tensor, "extra_name")
+
+    split_in = model.get_place_by_operation_name("split2").get_input_port(input_port_index=0)
+    model.extract_subgraph(inputs=[split_in], outputs=[])
+
+    ov_model = fe.convert(model)
+
+    model_input = ov_model.input(0)
+    input_tensor_names = model_input.get_names()
+    assert "extra_name" not in input_tensor_names
+
+
+def test_add_name_for_tensor_and_override_all_inputs():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model_2.onnx")
+
+    # test with an InputEdge type of Place
+    split_in = model.get_place_by_operation_name("split2").get_input_port(input_port_index=0)
+    model.add_name_for_tensor(split_in, "extra_name")
+    model.override_all_inputs([split_in])
+
+    ov_model = fe.convert(model)
+
+    model_input = ov_model.input(0)
+    input_tensor_names = model_input.get_names()
+    assert "extra_name" in input_tensor_names
+
+
+def test_add_name_for_tensor_and_rename_it():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model_2.onnx")
+
+    tensor = model.get_place_by_tensor_name(tensor_name="in2")
+    model.add_name_for_tensor(tensor, "extra_name")
+    model.set_name_for_tensor(tensor, "renamed_input")
+
+    ov_model = fe.convert(model)
+
+    model_input = ov_model.input(1)
+    input_tensor_names = model_input.get_names()
+    assert "renamed_input" in input_tensor_names
+    assert "extra_name" in input_tensor_names
+    assert "in2" not in input_tensor_names
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index d35dfd2b090..23096a23fb2 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -93,7 +93,13 @@ ov::frontend::Place::Ptr InputModel::get_place_by_operation_name_and_output_port
 void InputModel::set_name_for_tensor(const ov::frontend::Place::Ptr& tensor, const std::string& new_name) {
     const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor);
     FRONT_END_GENERAL_CHECK(onnx_tensor, __FUNCTION__, " expects a pointer to place of ONNX tensor type.");
+    const auto original_name = onnx_tensor->get_names().at(0);
     onnx_tensor->set_name(new_name);
+
+    if (m_additional_tensor_names.count(original_name) > 0) {
+        m_additional_tensor_names[new_name] = m_additional_tensor_names[original_name];
+        m_additional_tensor_names.erase(original_name);
+    }
 }
 
 void InputModel::set_name_for_operation(const ov::frontend::Place::Ptr& operation, const std::string& new_name) {
@@ -114,8 +120,22 @@ void InputModel::set_name_for_dimension(const ov::frontend::Place::Ptr& tensor,
     onnx_tensor->set_name_for_dimension(shape_dim_index, dim_name);
 }
 
-void InputModel::add_name_for_tensor(const ov::frontend::Place::Ptr&, const std::string&) {
-    FRONT_END_THROW("Method add_name_for_tensor is not applicable for ONNX model. ONNX tensor has just one name.");
+void InputModel::add_name_for_tensor(const ov::frontend::Place::Ptr& tensor, const std::string& new_name) {
+    FRONT_END_GENERAL_CHECK(!new_name.empty(), "The additional tensor name cannot be empty.");
+
+    ov::frontend::Place::Ptr tensor_place = tensor;
+    const auto input_edge = std::dynamic_pointer_cast<PlaceInputEdge>(tensor);
+    if (input_edge) {
+        tensor_place = input_edge->get_source_tensor();
+    }
+
+    const auto onnx_tensor = std::dynamic_pointer_cast<PlaceTensor>(tensor_place);
+    FRONT_END_GENERAL_CHECK(onnx_tensor != nullptr,
+                            "Incorrect Place passed to add_name_for_tensor. This method expects a PlaceTensor object "
+                            "pointing to the ONNX tensor.");
+
+    auto& names_to_add = m_additional_tensor_names[onnx_tensor->get_names().at(0)];
+    names_to_add.insert(new_name);
 }
 
 void InputModel::free_name_for_tensor(const std::string&) {
@@ -171,7 +191,9 @@ std::shared_ptr<Model> InputModel::decode() {
 }
 
 std::shared_ptr<Model> InputModel::convert() {
-    return m_editor->get_function();
+    auto converted_model = m_editor->get_function();
+    add_tensor_names(converted_model);
+    return converted_model;
 }
 
 // Editor features
@@ -254,3 +276,23 @@ void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& i
     }
     m_editor->extract_subgraph(onnx_inputs, onnx_outputs);
 }
+
+void InputModel::add_tensor_names(std::shared_ptr<Model>& model) {
+    auto model_inputs = model->inputs();
+    const auto find_input_by_tensor_name = [&model_inputs](const std::string& name) {
+        return std::find_if(std::begin(model_inputs),
+                            std::end(model_inputs),
+                            [&name](const OutputVector::value_type& input) {
+                                return input.get_names().count(name) > 0;
+                            });
+    };
+
+    for (auto& tensor_names : m_additional_tensor_names) {
+        auto it = find_input_by_tensor_name(tensor_names.first);
+        // add names only to the tensors which still exist in the converted model
+        // multiple graph cuts might have removed some parts of the model which initially required additional names
+        if (it != model_inputs.end()) {
+            it->add_names(tensor_names.second);
+        }
+    }
+}
diff --git a/src/frontends/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
index 53a2d81ac24..24b16e78318 100644
--- a/src/frontends/onnx/frontend/src/input_model.hpp
+++ b/src/frontends/onnx/frontend/src/input_model.hpp
@@ -46,8 +46,8 @@ public:
                                 size_t shape_dim_index,
                                 const std::string& dim_name) override;
 
-    /// \brief  Not applicable for ONNX model. Throws immediately
     void add_name_for_tensor(const ov::frontend::Place::Ptr& tensor, const std::string& new_name) override;
+
     /// \brief  Not applicable for ONNX model. Throws immediately
     void free_name_for_tensor(const std::string& name) override;
 
@@ -66,6 +66,9 @@ public:
 
 private:
     std::shared_ptr<ov::onnx_editor::ONNXModelEditor> m_editor;
+
+    std::unordered_map<std::string, std::unordered_set<std::string>> m_additional_tensor_names;
+    void add_tensor_names(std::shared_ptr<Model>& model);
 };
 
 }  // namespace onnx
diff --git a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
index e24dfa3661b..92b5df2ecac 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
@@ -113,9 +113,9 @@ def fe_input_user_data_repack(input_model: InputModel, input_user_shapes: [None,
             shape = None if isinstance(input_user_shapes, list) else input_user_shapes[input_name]
             if input_user_data_types.get(input_name) is not None:
                 data_type = input_user_data_types[input_name]
-                _input_shapes.append({'node': node, 'shape': shape, 'data_type': data_type})
+                _input_shapes.append({'node': node, 'shape': shape, 'data_type': data_type, 'input_name': input_name})
             else:
-                _input_shapes.append({'node': node, 'shape': shape})
+                _input_shapes.append({'node': node, 'shape': shape, 'input_name': input_name})
     elif isinstance(input_user_shapes, tuple):
         model_inputs = input_model.get_inputs()
         assert len(model_inputs) == 1
diff --git a/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py b/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
index 0e3c816c92c..a393fd8742a 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
@@ -9,7 +9,7 @@ from openvino.tools.mo.moc_frontend.extractor import fe_user_data_repack
 from openvino.tools.mo.middle.passes.infer import validate_batch_in_shape
 
 from openvino.runtime import Dimension, PartialShape        # pylint: disable=no-name-in-module,import-error
-from openvino.frontend import FrontEnd, Place       # pylint: disable=no-name-in-module,import-error
+from openvino.frontend import FrontEnd, InputModel, NotImplementedFailure, Place # pylint: disable=no-name-in-module,import-error
 from openvino.runtime.utils.types import get_element_type   # pylint: disable=no-name-in-module,import-error
 
 import numpy as np
@@ -39,6 +39,21 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
             [item for item in places_original if any(
                 [item.is_equal(item2['node']) for item2 in places_new])]) == len(places_original)
 
+    def add_names_to_tensors(model: InputModel, places: List[Place]):
+        """
+        Adds additional names to some model input tensors. This helper should be used
+        when a model modification is going to happen.
+        :param model The input model loaded by a given frontend
+        :param places An object containing Places and names that will be used for model modification
+        """
+        for new_input in places:
+            try:
+                model.add_name_for_tensor(new_input['node'], new_input['input_name'])
+            except NotImplementedFailure as e:
+                # some frontends might not implement this method
+                log.warn('Could not add an additional name to a tensor pointed to by \'{}\'. Details: {}'.format(
+                    new_input['input_name'], str(e)))
+
     inputs_equal = True
     if user_shapes:
         inputs_equal = check_places_are_same(input_model.get_inputs(), user_shapes)
@@ -50,10 +65,10 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
         inputs_equal, outputs_equal))
 
     if not inputs_equal and not outputs_equal:
-        # Use ExtractSubgraph
+        log.debug('Using extract subgraph')
         new_input_places = [x['node'] for x in user_shapes]
         new_output_places = [x['node'] for x in outputs]
-        log.debug('Using extract subgraph')
+        add_names_to_tensors(input_model, user_shapes)
         input_model.extract_subgraph(new_input_places, new_output_places)
         # invalidation of existing Place objects could have happened in the operation above
         if user_shapes:
@@ -61,8 +76,9 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
                 input_model, argv.placeholder_shapes, argv.placeholder_data_types,
                 argv.output, argv.freeze_placeholder_with_value)
     elif not inputs_equal:
-        new_input_places = [x['node'] for x in user_shapes]
         log.debug('Using override_all_inputs')
+        add_names_to_tensors(input_model, user_shapes)
+        new_input_places = [x['node'] for x in user_shapes]
         input_model.override_all_inputs(new_input_places)
         # invalidation of existing Place objects could have happened in the operation above
         if user_shapes:
@@ -70,8 +86,9 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
                 input_model, argv.placeholder_shapes, argv.placeholder_data_types,
                 argv.output, argv.freeze_placeholder_with_value)
     elif not outputs_equal:
-        new_output_places = [x['node'] for x in outputs]
         log.debug('Using override_all_outputs')
+        add_names_to_tensors(input_model, user_shapes)
+        new_output_places = [x['node'] for x in outputs]
         input_model.override_all_outputs(new_output_places)
 
     if user_shapes:

From b7ffbe54593dba94f898213ed4365da4a552aeef Mon Sep 17 00:00:00 2001
From: Mikhail Nosov <mikhail.nosov@intel.com>
Date: Mon, 20 Dec 2021 23:32:14 +0300
Subject: [PATCH 058/160] Leftover: Added tests for fix for PR #8546 (#9313)

Added more preprocessing unit tests for fix of PR #8546
Testing: revert changes for the fix. Verify that new tests are failed
---
 src/core/tests/preprocess.cpp | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/src/core/tests/preprocess.cpp b/src/core/tests/preprocess.cpp
index 532f1427de7..5a9fa25d9bb 100644
--- a/src/core/tests/preprocess.cpp
+++ b/src/core/tests/preprocess.cpp
@@ -1285,6 +1285,39 @@ TEST(pre_post_process, postprocess_convert_layout_invalid_dims_dyn_shape) {
                  p.build(), ov::AssertFailure);
 }
 
+TEST(pre_post_process, postprocess_keep_friendly_names_compatibility) {
+    auto f = create_simple_function(element::f32, Shape{1, 3, 10, 10});
+    auto result_fr_name = f->get_results()[0]->get_friendly_name();
+    auto node_before_result_old = f->get_results()[0]->get_input_source_output(0).get_node_shared_ptr();
+    auto node_name = node_before_result_old->get_friendly_name();
+    auto p = PrePostProcessor(f);
+    p.output().postprocess().convert_element_type(element::u8);
+    f = p.build();
+    EXPECT_EQ(f->get_results()[0]->get_friendly_name(), result_fr_name);
+    auto node_before_result_new = f->get_results()[0]->get_input_source_output(0).get_node_shared_ptr();
+    // Compatibility check: verify that old name is assigned to new 'output' node
+    EXPECT_EQ(node_before_result_new->get_friendly_name(), node_name);
+    // Compatibility check: Verify that old name is not set for old 'output' node anymore
+    EXPECT_NE(node_before_result_old->get_friendly_name(), node_name);
+}
+
+TEST(pre_post_process, postprocess_keep_friendly_names_compatibility_implicit) {
+    auto f = create_simple_function(element::f32, Shape{1, 3, 10, 10});
+    auto result_fr_name = f->get_results()[0]->get_friendly_name();
+    auto node_before_result_old = f->get_results()[0]->get_input_source_output(0).get_node_shared_ptr();
+    auto node_name = node_before_result_old->get_friendly_name();
+    auto p = PrePostProcessor(f);
+    p.output().model().set_layout("NCHW");
+    p.output().tensor().set_layout("NHWC");
+    f = p.build();
+    EXPECT_EQ(f->get_results()[0]->get_friendly_name(), result_fr_name);
+    auto node_before_result_new = f->get_results()[0]->get_input_source_output(0).get_node_shared_ptr();
+    // Compatibility check: verify that old name is assigned to new 'output' node
+    EXPECT_EQ(node_before_result_new->get_friendly_name(), node_name);
+    // Compatibility check: Verify that old name is not set for old 'output' node anymore
+    EXPECT_NE(node_before_result_old->get_friendly_name(), node_name);
+}
+
 // Postprocessing - other
 
 TEST(pre_post_process, postprocess_preserve_rt_info) {

From 4f9d908c1e62037e798a99111c81e0d39a6f9a77 Mon Sep 17 00:00:00 2001
From: Zlobin Vladimir <vladimir.zlobin@intel.com>
Date: Tue, 21 Dec 2021 00:44:47 +0300
Subject: [PATCH 059/160] Catch up
 https://github.com/openvinotoolkit/open_model_zoo/pull/3002 (#9324)

---
 thirdparty/open_model_zoo | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/thirdparty/open_model_zoo b/thirdparty/open_model_zoo
index a04512d8553..f18c014ad41 160000
--- a/thirdparty/open_model_zoo
+++ b/thirdparty/open_model_zoo
@@ -1 +1 @@
-Subproject commit a04512d8553aed4b7bde5032141f28bfd26f6f5c
+Subproject commit f18c014ad4173babaf5554aa7930a66701f80bf6

From 3c93c3e76652d1b33b65f2b4a10f8af618919cbe Mon Sep 17 00:00:00 2001
From: Maxim Gordeev <maxim.gordeev@intel.com>
Date: Tue, 21 Dec 2021 01:33:12 +0300
Subject: [PATCH 060/160] Hello classification, classification_async,
 hello_reshape_ssd python samples with API 2.0 (#9091)

* Hello classification python with API 2.0

* Update samples/python/hello_classification/hello_classification.py

Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>

* Update samples/python/hello_classification/hello_classification.py

Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>

* changed linters processing

* Update samples/python/hello_classification/hello_classification.py

Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com>

* Update samples/python/hello_classification/hello_classification.py

Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com>

* Update samples/python/hello_classification/hello_classification.py

Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com>

* Update samples/python/hello_classification/hello_classification.py

Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com>

* Update samples/python/hello_classification/hello_classification.py

Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com>

* updated import

* Moved classification_sample_async to new API 2.0

* moved hello_reshape_ssd sample to new API 2.0

* [classification_sample_async] refactoring

* [hello_classification] refactoring

* [hello_reshape_ssd] refactoring

Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>
Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com>
Co-authored-by: Dmitry Pigasin <dmitry.pigasin@intel.com>
---
 .../classification_sample_async.py            | 197 +++++++-----------
 .../hello_classification.py                   | 152 +++++++-------
 .../hello_reshape_ssd/hello_reshape_ssd.py    | 148 ++++++-------
 3 files changed, 211 insertions(+), 286 deletions(-)

diff --git a/samples/python/classification_sample_async/classification_sample_async.py b/samples/python/classification_sample_async/classification_sample_async.py
index 6c6b89ba609..94d1302a6b4 100755
--- a/samples/python/classification_sample_async/classification_sample_async.py
+++ b/samples/python/classification_sample_async/classification_sample_async.py
@@ -2,13 +2,15 @@
 # -*- coding: utf-8 -*-
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
+
 import argparse
 import logging as log
 import sys
 
 import cv2
 import numpy as np
-from openvino.inference_engine import IECore, StatusCode
+from openvino.preprocess import PrePostProcessor
+from openvino.runtime import AsyncInferQueue, Core, InferRequest, Layout, Type
 
 
 def parse_args() -> argparse.Namespace:
@@ -16,150 +18,111 @@ def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(add_help=False)
     args = parser.add_argument_group('Options')
     # fmt: off
-    args.add_argument('-h', '--help', action='help', help='Show this help message and exit.')
-    args.add_argument('-m', '--model', required=True, type=str,
+    args.add_argument('-h', '--help', action='help',
+                      help='Show this help message and exit.')
+    args.add_argument('-m', '--model', type=str, required=True,
                       help='Required. Path to an .xml or .onnx file with a trained model.')
-    args.add_argument('-i', '--input', required=True, type=str, nargs='+', help='Required. Path to an image file(s).')
-    args.add_argument('-l', '--extension', type=str, default=None,
-                      help='Optional. Required by the CPU Plugin for executing the custom operation on a CPU. '
-                      'Absolute path to a shared library with the kernels implementations.')
-    args.add_argument('-c', '--config', type=str, default=None,
-                      help='Optional. Required by GPU or VPU Plugins for the custom operation kernel. '
-                      'Absolute path to operation description file (.xml).')
-    args.add_argument('-d', '--device', default='CPU', type=str,
+    args.add_argument('-i', '--input', type=str, required=True, nargs='+',
+                      help='Required. Path to an image file(s).')
+    args.add_argument('-d', '--device', type=str, default='CPU',
                       help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
                       'is acceptable. The sample will look for a suitable plugin for device specified. '
                       'Default value is CPU.')
-    args.add_argument('--labels', default=None, type=str, help='Optional. Path to a labels mapping file.')
-    args.add_argument('-nt', '--number_top', default=10, type=int, help='Optional. Number of top results.')
     # fmt: on
     return parser.parse_args()
 
 
-def main():
+def completion_callback(infer_request: InferRequest, image_path: str) -> None:
+    predictions = next(iter(infer_request.results.values()))
+
+    # Change a shape of a numpy.ndarray with results to get another one with one dimension
+    probs = predictions.reshape(-1)
+
+    # Get an array of 10 class IDs in descending order of probability
+    top_10 = np.argsort(probs)[-10:][::-1]
+
+    header = 'class_id probability'
+
+    log.info(f'Image path: {image_path}')
+    log.info('Top 10 results: ')
+    log.info(header)
+    log.info('-' * len(header))
+
+    for class_id in top_10:
+        probability_indent = ' ' * (len('class_id') - len(str(class_id)) + 1)
+        log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}')
+
+    log.info('')
+
+
+def main() -> int:
     log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
     args = parse_args()
 
-    # ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
-    log.info('Creating Inference Engine')
-    ie = IECore()
+# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
+    log.info('Creating OpenVINO Runtime Core')
+    core = Core()
 
-    if args.extension and args.device == 'CPU':
-        log.info(f'Loading the {args.device} extension: {args.extension}')
-        ie.add_extension(args.extension, args.device)
-
-    if args.config and args.device in ('GPU', 'MYRIAD', 'HDDL'):
-        log.info(f'Loading the {args.device} configuration: {args.config}')
-        ie.set_config({'CONFIG_FILE': args.config}, args.device)
-
-    # ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation or ONNX format---------------
+# --------------------------- Step 2. Read a model --------------------------------------------------------------------
     log.info(f'Reading the network: {args.model}')
     # (.xml and .bin files) or (.onnx file)
-    net = ie.read_network(model=args.model)
+    model = core.read_model(args.model)
 
-    if len(net.input_info) != 1:
+    if len(model.inputs) != 1:
         log.error('Sample supports only single input topologies')
         return -1
-    if len(net.outputs) != 1:
+
+    if len(model.outputs) != 1:
         log.error('Sample supports only single output topologies')
         return -1
 
-    # ---------------------------Step 3. Configure input & output----------------------------------------------------------
-    log.info('Configuring input and output blobs')
-    # Get names of input and output blobs
-    input_blob = next(iter(net.input_info))
-    out_blob = next(iter(net.outputs))
+# --------------------------- Step 3. Set up input --------------------------------------------------------------------
+    # Read input images
+    images = [cv2.imread(image_path) for image_path in args.input]
 
-    # Set input and output precision manually
-    net.input_info[input_blob].precision = 'U8'
-    net.outputs[out_blob].precision = 'FP32'
+    # Resize images to model input dims
+    _, _, h, w = model.input().shape
+    resized_images = [cv2.resize(image, (w, h)) for image in images]
 
-    # Get a number of input images
-    num_of_input = len(args.input)
-    # Get a number of classes recognized by a model
-    num_of_classes = max(net.outputs[out_blob].shape)
+    # Add N dimension
+    input_tensors = [np.expand_dims(image, 0) for image in resized_images]
 
-    # ---------------------------Step 4. Loading model to the device-------------------------------------------------------
+# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
+    ppp = PrePostProcessor(model)
+
+    # 1) Set input tensor information:
+    # - input() provides information about a single model input
+    # - precision of tensor is supposed to be 'u8'
+    # - layout of data is 'NHWC'
+    ppp.input().tensor() \
+        .set_element_type(Type.u8) \
+        .set_layout(Layout('NHWC'))  # noqa: N400
+
+    # 2) Here we suppose model has 'NCHW' layout for input
+    ppp.input().model().set_layout(Layout('NCHW'))
+
+    # 3) Set output tensor information:
+    # - precision of tensor is supposed to be 'f32'
+    ppp.output().tensor().set_element_type(Type.f32)
+
+    # 4) Apply preprocessing modifing the original 'model'
+    model = ppp.build()
+
+# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
     log.info('Loading the model to the plugin')
-    exec_net = ie.load_network(network=net, device_name=args.device, num_requests=num_of_input)
+    compiled_model = core.compile_model(model, args.device)
 
-    # ---------------------------Step 5. Create infer request--------------------------------------------------------------
-    # load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
-    # instance which stores infer requests. So you already created Infer requests in the previous step.
-
-    # ---------------------------Step 6. Prepare input---------------------------------------------------------------------
-    input_data = []
-    _, _, h, w = net.input_info[input_blob].input_data.shape
-
-    for i in range(num_of_input):
-        image = cv2.imread(args.input[i])
-
-        if image.shape[:-1] != (h, w):
-            log.warning(f'Image {args.input[i]} is resized from {image.shape[:-1]} to {(h, w)}')
-            image = cv2.resize(image, (w, h))
-
-        # Change data layout from HWC to CHW
-        image = image.transpose((2, 0, 1))
-        # Add N dimension to transform to NCHW
-        image = np.expand_dims(image, axis=0)
-
-        input_data.append(image)
-
-    # ---------------------------Step 7. Do inference----------------------------------------------------------------------
+# --------------------------- Step 6. Create infer request queue ------------------------------------------------------
     log.info('Starting inference in asynchronous mode')
-    for i in range(num_of_input):
-        exec_net.requests[i].async_infer({input_blob: input_data[i]})
+    infer_queue = AsyncInferQueue(compiled_model, len(input_tensors))
+    infer_queue.set_callback(completion_callback)
 
-    # ---------------------------Step 8. Process output--------------------------------------------------------------------
-    # Generate a label list
-    if args.labels:
-        with open(args.labels, 'r') as f:
-            labels = [line.split(',')[0].strip() for line in f]
+# --------------------------- Step 7. Do inference --------------------------------------------------------------------
+    for i, input_tensor in enumerate(input_tensors):
+        infer_queue.start_async({0: input_tensor}, args.input[i])
 
-    # Create a list to control a order of output
-    output_queue = list(range(num_of_input))
-
-    while True:
-        for i in output_queue:
-            # Immediately returns a inference status without blocking or interrupting
-            infer_status = exec_net.requests[i].wait(0)
-
-            if infer_status == StatusCode.RESULT_NOT_READY:
-                continue
-
-            log.info(f'Infer request {i} returned {infer_status}')
-
-            if infer_status != StatusCode.OK:
-                return -2
-
-            # Read infer request results from buffer
-            res = exec_net.requests[i].output_blobs[out_blob].buffer
-            # Change a shape of a numpy.ndarray with results to get another one with one dimension
-            probs = res.reshape(num_of_classes)
-            # Get an array of args.number_top class IDs in descending order of probability
-            top_n_idexes = np.argsort(probs)[-args.number_top :][::-1]
-
-            header = 'classid probability'
-            header = header + ' label' if args.labels else header
-
-            log.info(f'Image path: {args.input[i]}')
-            log.info(f'Top {args.number_top} results: ')
-            log.info(header)
-            log.info('-' * len(header))
-
-            for class_id in top_n_idexes:
-                probability_indent = ' ' * (len('classid') - len(str(class_id)) + 1)
-                label_indent = ' ' * (len('probability') - 8) if args.labels else ''
-                label = labels[class_id] if args.labels else ''
-                log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}{label_indent}{label}')
-            log.info('')
-
-            output_queue.remove(i)
-
-        if len(output_queue) == 0:
-            break
-
-    # ----------------------------------------------------------------------------------------------------------------------
+    infer_queue.wait_all()
+# ----------------------------------------------------------------------------------------------------------------------
     log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
     return 0
 
diff --git a/samples/python/hello_classification/hello_classification.py b/samples/python/hello_classification/hello_classification.py
index a7d0f8c5e66..364f43d955d 100755
--- a/samples/python/hello_classification/hello_classification.py
+++ b/samples/python/hello_classification/hello_classification.py
@@ -2,121 +2,111 @@
 # -*- coding: utf-8 -*-
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-import argparse
+
 import logging as log
 import sys
 
 import cv2
 import numpy as np
-from openvino.inference_engine import IECore
-
-
-def parse_args() -> argparse.Namespace:
-    """Parse and return command line arguments"""
-    parser = argparse.ArgumentParser(add_help=False)
-    args = parser.add_argument_group('Options')
-    # fmt: off
-    args.add_argument('-h', '--help', action='help', help='Show this help message and exit.')
-    args.add_argument('-m', '--model', required=True, type=str,
-                      help='Required. Path to an .xml or .onnx file with a trained model.')
-    args.add_argument('-i', '--input', required=True, type=str, help='Required. Path to an image file.')
-    args.add_argument('-d', '--device', default='CPU', type=str,
-                      help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
-                      'is acceptable. The sample will look for a suitable plugin for device specified. '
-                      'Default value is CPU.')
-    args.add_argument('--labels', default=None, type=str, help='Optional. Path to a labels mapping file.')
-    args.add_argument('-nt', '--number_top', default=10, type=int, help='Optional. Number of top results.')
-    # fmt: on
-    return parser.parse_args()
+from openvino.preprocess import PrePostProcessor, ResizeAlgorithm
+from openvino.runtime import Core, Layout, Type
 
 
 def main():
     log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
-    args = parse_args()
 
-    # ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
-    log.info('Creating Inference Engine')
-    ie = IECore()
+    # Parsing and validation of input arguments
+    if len(sys.argv) != 4:
+        log.info('Usage: <path_to_model> <path_to_image> <device_name>')
+        return 1
 
-    # ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation or ONNX format---------------
-    log.info(f'Reading the network: {args.model}')
+    model_path = sys.argv[1]
+    image_path = sys.argv[2]
+    device_name = sys.argv[3]
+
+# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
+    log.info('Creating OpenVINO Runtime Core')
+    core = Core()
+
+# --------------------------- Step 2. Read a model --------------------------------------------------------------------
+    log.info(f'Reading the network: {model_path}')
     # (.xml and .bin files) or (.onnx file)
-    net = ie.read_network(model=args.model)
+    model = core.read_model(model_path)
 
-    if len(net.input_info) != 1:
+    if len(model.inputs) != 1:
         log.error('Sample supports only single input topologies')
         return -1
-    if len(net.outputs) != 1:
+
+    if len(model.outputs) != 1:
         log.error('Sample supports only single output topologies')
         return -1
 
-    # ---------------------------Step 3. Configure input & output----------------------------------------------------------
-    log.info('Configuring input and output blobs')
-    # Get names of input and output blobs
-    input_blob = next(iter(net.input_info))
-    out_blob = next(iter(net.outputs))
+# --------------------------- Step 3. Set up input --------------------------------------------------------------------
+    # Read input image
+    image = cv2.imread(image_path)
+    # Add N dimension
+    input_tensor = np.expand_dims(image, 0)
 
-    # Set input and output precision manually
-    net.input_info[input_blob].precision = 'U8'
-    net.outputs[out_blob].precision = 'FP32'
+# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
+    ppp = PrePostProcessor(model)
 
-    # Get a number of classes recognized by a model
-    num_of_classes = max(net.outputs[out_blob].shape)
+    _, h, w, _ = input_tensor.shape
 
-    # ---------------------------Step 4. Loading model to the device-------------------------------------------------------
+    # 1) Set input tensor information:
+    # - input() provides information about a single model input
+    # - precision of tensor is supposed to be 'u8'
+    # - layout of data is 'NHWC'
+    # - set static spatial dimensions to input tensor to resize from
+    ppp.input().tensor() \
+        .set_element_type(Type.u8) \
+        .set_layout(Layout('NHWC')) \
+        .set_spatial_static_shape(h, w)  # noqa: ECE001, N400
+
+    # 2) Adding explicit preprocessing steps:
+    # - apply linear resize from tensor spatial dims to model spatial dims
+    ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
+
+    # 3) Here we suppose model has 'NCHW' layout for input
+    ppp.input().model().set_layout(Layout('NCHW'))
+
+    # 4) Set output tensor information:
+    # - precision of tensor is supposed to be 'f32'
+    ppp.output().tensor().set_element_type(Type.f32)
+
+    # 5) Apply preprocessing modifing the original 'model'
+    model = ppp.build()
+
+# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
     log.info('Loading the model to the plugin')
-    exec_net = ie.load_network(network=net, device_name=args.device)
+    compiled_model = core.compile_model(model, device_name)
 
-    # ---------------------------Step 5. Create infer request--------------------------------------------------------------
-    # load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
-    # instance which stores infer requests. So you already created Infer requests in the previous step.
-
-    # ---------------------------Step 6. Prepare input---------------------------------------------------------------------
-    original_image = cv2.imread(args.input)
-    image = original_image.copy()
-    _, _, h, w = net.input_info[input_blob].input_data.shape
-
-    if image.shape[:-1] != (h, w):
-        log.warning(f'Image {args.input} is resized from {image.shape[:-1]} to {(h, w)}')
-        image = cv2.resize(image, (w, h))
-
-    # Change data layout from HWC to CHW
-    image = image.transpose((2, 0, 1))
-    # Add N dimension to transform to NCHW
-    image = np.expand_dims(image, axis=0)
-
-    # ---------------------------Step 7. Do inference----------------------------------------------------------------------
+# --------------------------- Step 6. Create infer request and do inference synchronously -----------------------------
     log.info('Starting inference in synchronous mode')
-    res = exec_net.infer(inputs={input_blob: image})
+    results = compiled_model.infer_new_request({0: input_tensor})
 
-    # ---------------------------Step 8. Process output--------------------------------------------------------------------
-    # Generate a label list
-    if args.labels:
-        with open(args.labels, 'r') as f:
-            labels = [line.split(',')[0].strip() for line in f]
+# --------------------------- Step 7. Process output ------------------------------------------------------------------
+    predictions = next(iter(results.values()))
 
-    res = res[out_blob]
     # Change a shape of a numpy.ndarray with results to get another one with one dimension
-    probs = res.reshape(num_of_classes)
-    # Get an array of args.number_top class IDs in descending order of probability
-    top_n_idexes = np.argsort(probs)[-args.number_top :][::-1]
+    probs = predictions.reshape(-1)
 
-    header = 'classid probability'
-    header = header + ' label' if args.labels else header
+    # Get an array of 10 class IDs in descending order of probability
+    top_10 = np.argsort(probs)[-10:][::-1]
 
-    log.info(f'Image path: {args.input}')
-    log.info(f'Top {args.number_top} results: ')
+    header = 'class_id probability'
+
+    log.info(f'Image path: {image_path}')
+    log.info('Top 10 results: ')
     log.info(header)
     log.info('-' * len(header))
 
-    for class_id in top_n_idexes:
-        probability_indent = ' ' * (len('classid') - len(str(class_id)) + 1)
-        label_indent = ' ' * (len('probability') - 8) if args.labels else ''
-        label = labels[class_id] if args.labels else ''
-        log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}{label_indent}{label}')
+    for class_id in top_10:
+        probability_indent = ' ' * (len('class_id') - len(str(class_id)) + 1)
+        log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}')
+
     log.info('')
 
-    # ----------------------------------------------------------------------------------------------------------------------
+# ----------------------------------------------------------------------------------------------------------------------
     log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
     return 0
 
diff --git a/samples/python/hello_reshape_ssd/hello_reshape_ssd.py b/samples/python/hello_reshape_ssd/hello_reshape_ssd.py
index f0dab4bc384..3d12df0f560 100755
--- a/samples/python/hello_reshape_ssd/hello_reshape_ssd.py
+++ b/samples/python/hello_reshape_ssd/hello_reshape_ssd.py
@@ -2,144 +2,116 @@
 # -*- coding: utf-8 -*-
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-import argparse
+
 import logging as log
 import os
 import sys
 
 import cv2
 import numpy as np
-from openvino.inference_engine import IECore
-
-
-def parse_args() -> argparse.Namespace:
-    """Parse and return command line arguments"""
-    parser = argparse.ArgumentParser(add_help=False)
-    args = parser.add_argument_group('Options')
-    # fmt: off
-    args.add_argument('-h', '--help', action='help', help='Show this help message and exit.')
-    args.add_argument('-m', '--model', required=True, type=str,
-                      help='Required. Path to an .xml or .onnx file with a trained model.')
-    args.add_argument('-i', '--input', required=True, type=str, help='Required. Path to an image file.')
-    args.add_argument('-l', '--extension', type=str, default=None,
-                      help='Optional. Required by the CPU Plugin for executing the custom operation on a CPU. '
-                      'Absolute path to a shared library with the kernels implementations.')
-    args.add_argument('-c', '--config', type=str, default=None,
-                      help='Optional. Required by GPU or VPU Plugins for the custom operation kernel. '
-                      'Absolute path to operation description file (.xml).')
-    args.add_argument('-d', '--device', default='CPU', type=str,
-                      help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
-                      'is acceptable. The sample will look for a suitable plugin for device specified. '
-                      'Default value is CPU.')
-    args.add_argument('--labels', default=None, type=str, help='Optional. Path to a labels mapping file.')
-    # fmt: on
-    return parser.parse_args()
+from openvino.preprocess import PrePostProcessor
+from openvino.runtime import Core, Layout, PartialShape, Type
 
 
 def main():
     log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
-    args = parse_args()
 
-    # ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
-    log.info('Creating Inference Engine')
-    ie = IECore()
+    # Parsing and validation of input arguments
+    if len(sys.argv) != 4:
+        log.info('Usage: <path_to_model> <path_to_image> <device_name>')
+        return 1
 
-    if args.extension and args.device == 'CPU':
-        log.info(f'Loading the {args.device} extension: {args.extension}')
-        ie.add_extension(args.extension, args.device)
+    model_path = sys.argv[1]
+    image_path = sys.argv[2]
+    device_name = sys.argv[3]
 
-    if args.config and args.device in ('GPU', 'MYRIAD', 'HDDL'):
-        log.info(f'Loading the {args.device} configuration: {args.config}')
-        ie.set_config({'CONFIG_FILE': args.config}, args.device)
+# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
+    log.info('Creating OpenVINO Runtime Core')
+    core = Core()
 
-    # ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation or ONNX format---------------
-    log.info(f'Reading the network: {args.model}')
+# --------------------------- Step 2. Read a model --------------------------------------------------------------------
+    log.info(f'Reading the network: {model_path}')
     # (.xml and .bin files) or (.onnx file)
-    net = ie.read_network(model=args.model)
+    model = core.read_model(model_path)
 
-    if len(net.input_info) != 1:
+    if len(model.inputs) != 1:
         log.error('Sample supports only single input topologies')
         return -1
-    if len(net.outputs) != 1:
+
+    if len(model.outputs) != 1:
         log.error('Sample supports only single output topologies')
         return -1
 
-    # ---------------------------Step 3. Configure input & output----------------------------------------------------------
-    log.info('Configuring input and output blobs')
-    # Get names of input and output blobs
-    input_blob = next(iter(net.input_info))
-    out_blob = next(iter(net.outputs))
-
-    # Set input and output precision manually
-    net.input_info[input_blob].precision = 'U8'
-    net.outputs[out_blob].precision = 'FP32'
-
-    original_image = cv2.imread(args.input)
-    image = original_image.copy()
-
-    # Change data layout from HWC to CHW
-    image = image.transpose((2, 0, 1))
-    # Add N dimension to transform to NCHW
-    image = np.expand_dims(image, axis=0)
+# --------------------------- Step 3. Set up input --------------------------------------------------------------------
+    # Read input image
+    image = cv2.imread(image_path)
+    # Add N dimension
+    input_tensor = np.expand_dims(image, 0)
 
     log.info('Reshaping the network to the height and width of the input image')
-    log.info(f'Input shape before reshape: {net.input_info[input_blob].input_data.shape}')
-    net.reshape({input_blob: image.shape})
-    log.info(f'Input shape after reshape: {net.input_info[input_blob].input_data.shape}')
+    n, h, w, c = input_tensor.shape
+    model.reshape({model.input().get_any_name(): PartialShape((n, c, h, w))})
 
-    # ---------------------------Step 4. Loading model to the device-------------------------------------------------------
+# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
+    ppp = PrePostProcessor(model)
+
+    # 1) Set input tensor information:
+    # - input() provides information about a single model input
+    # - precision of tensor is supposed to be 'u8'
+    # - layout of data is 'NHWC'
+    ppp.input().tensor() \
+        .set_element_type(Type.u8) \
+        .set_layout(Layout('NHWC'))  # noqa: N400
+
+    # 2) Here we suppose model has 'NCHW' layout for input
+    ppp.input().model().set_layout(Layout('NCHW'))
+
+    # 3) Set output tensor information:
+    # - precision of tensor is supposed to be 'f32'
+    ppp.output().tensor().set_element_type(Type.f32)
+
+    # 4) Apply preprocessing modifing the original 'model'
+    model = ppp.build()
+
+# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
     log.info('Loading the model to the plugin')
-    exec_net = ie.load_network(network=net, device_name=args.device)
+    compiled_model = core.compile_model(model, device_name)
 
-    # ---------------------------Step 5. Create infer request--------------------------------------------------------------
-    # load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
-    # instance which stores infer requests. So you already created Infer requests in the previous step.
-
-    # ---------------------------Step 6. Prepare input---------------------------------------------------------------------
-    # This sample changes a network input layer shape instead of a image shape. See Step 4.
-
-    # ---------------------------Step 7. Do inference----------------------------------------------------------------------
+# --------------------------- Step 6. Create infer request and do inference synchronously -----------------------------
     log.info('Starting inference in synchronous mode')
-    res = exec_net.infer(inputs={input_blob: image})
+    results = compiled_model.infer_new_request({0: input_tensor})
 
-    # ---------------------------Step 8. Process output--------------------------------------------------------------------
-    # Generate a label list
-    if args.labels:
-        with open(args.labels, 'r') as f:
-            labels = [line.split(',')[0].strip() for line in f]
-
-    res = res[out_blob]
-
-    output_image = original_image.copy()
-    h, w, _ = output_image.shape
+# ---------------------------Step 6. Process output--------------------------------------------------------------------
+    predictions = next(iter(results.values()))
 
     # Change a shape of a numpy.ndarray with results ([1, 1, N, 7]) to get another one ([N, 7]),
     # where N is the number of detected bounding boxes
-    detections = res.reshape(-1, 7)
+    detections = predictions.reshape(-1, 7)
 
     for detection in detections:
         confidence = detection[2]
+
         if confidence > 0.5:
             class_id = int(detection[1])
-            label = labels[class_id] if args.labels else class_id
 
             xmin = int(detection[3] * w)
             ymin = int(detection[4] * h)
             xmax = int(detection[5] * w)
             ymax = int(detection[6] * h)
 
-            log.info(f'Found: label = {label}, confidence = {confidence:.2f}, ' f'coords = ({xmin}, {ymin}), ({xmax}, {ymax})')
+            log.info(f'Found: class_id = {class_id}, confidence = {confidence:.2f}, ' f'coords = ({xmin}, {ymin}), ({xmax}, {ymax})')
 
             # Draw a bounding box on a output image
-            cv2.rectangle(output_image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+
+    cv2.imwrite('out.bmp', image)
 
-    cv2.imwrite('out.bmp', output_image)
     if os.path.exists('out.bmp'):
         log.info('Image out.bmp was created!')
     else:
         log.error('Image out.bmp was not created. Check your permissions.')
 
-    # ----------------------------------------------------------------------------------------------------------------------
+# ----------------------------------------------------------------------------------------------------------------------
     log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
     return 0
 

From b5238e55e155f782602f0d78a986f66c3049a4ac Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Tue, 21 Dec 2021 05:27:53 +0300
Subject: [PATCH 061/160] Removed inference_engine, ngraph, transformations
 libraries (#9309)

---
 .../Deep_Learning_Inference_Engine_DevGuide.md     | 10 +++++-----
 .../Integrate_with_customer_application_new_API.md |  2 +-
 docs/IE_DG/inference_engine_intro.md               | 10 +++++-----
 docs/doxygen/ie_plugin_api.config                  |  2 +-
 docs/nGraph_DG/nGraphTransformation.md             |  3 +--
 src/bindings/python/tests/test_ngraph/__init__.py  |  2 +-
 .../tests_compatibility/test_ngraph/__init__.py    |  2 +-
 .../include/low_precision/lpt_visibility.hpp       |  2 +-
 .../include/transformations_visibility.hpp         | 14 +++++++-------
 tests/conditional_compilation/test_cc.py           |  2 +-
 tests/fuzz/README.md                               |  2 +-
 tests/utils/path_utils.py                          |  9 +++------
 tools/deployment_manager/configs/darwin.json       |  6 +-----
 tools/deployment_manager/configs/linux.json        |  7 +------
 tools/deployment_manager/configs/windows.json      |  7 +------
 15 files changed, 31 insertions(+), 49 deletions(-)

diff --git a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
index 8297a16d1f5..9381122313d 100644
--- a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
+++ b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
@@ -23,11 +23,11 @@ Inference Engine uses a plugin architecture. Inference Engine plugin is a softwa
 
 Your application must link to the core Inference Engine libraries:
 * Linux* OS:
-    - `libinference_engine.so`, which depends on `libinference_engine_transformations.so`, `libtbb.so`, `libtbbmalloc.so` and `libngraph.so`
+    - `libov_runtime.so`, which depends on `libtbb.so`, `libtbbmalloc.so`
 * Windows* OS:
-    - `inference_engine.dll`, which depends on `inference_engine_transformations.dll`, `tbb.dll`, `tbbmalloc.dll` and `ngraph.dll`
+    - `ov_runtime.dll`, which depends on `tbb.dll`, `tbbmalloc.dll`
 * macOS*:
-    - `libinference_engine.dylib`, which depends on `libinference_engine_transformations.dylib`, `libtbb.dylib`, `libtbbmalloc.dylib` and `libngraph.dylib`
+    - `libov_runtime.dylib`, which depends on `libtbb.dylib`, `libtbbmalloc.dylib`
 
 The required C++ header files are located in the `include` directory.
 
@@ -65,8 +65,8 @@ The table below shows the plugin libraries and additional dependencies for Linux
 
 | Plugin | Library name for Linux      | Dependency libraries for Linux                              | Library name for Windows | Dependency libraries for Windows                                                                       | Library name for macOS       | Dependency libraries for macOS              |
 |--------|-----------------------------|-------------------------------------------------------------|--------------------------|--------------------------------------------------------------------------------------------------------|------------------------------|---------------------------------------------|
-| CPU    | `libMKLDNNPlugin.so`        | `libinference_engine_lp_transformations.so`                 | `MKLDNNPlugin.dll`       | `inference_engine_lp_transformations.dll`                                                              | `libMKLDNNPlugin.so`      | `inference_engine_lp_transformations.dylib` |
-| GPU    | `libov_intel_gpu_plugin.so`         | `libinference_engine_lp_transformations.so`, `libOpenCL.so` | `ov_intel_gpu_plugin.dll`        | `OpenCL.dll`, `inference_engine_lp_transformations.dll`                                                |  Is not supported            |  -                                          |
+| CPU    | `libMKLDNNPlugin.so`        | | `MKLDNNPlugin.dll`       | | `libMKLDNNPlugin.so`      | |
+| GPU    | `libov_intel_gpu_plugin.so`         | `libOpenCL.so` | `ov_intel_gpu_plugin.dll`        | `OpenCL.dll` |  Is not supported            |  -                                          |
 | MYRIAD | `libmyriadPlugin.so`        | `libusb.so`,                                                | `myriadPlugin.dll`       | `usb.dll`                                                                                              | `libmyriadPlugin.so`      | `libusb.dylib`                              |
 | HDDL   | `libHDDLPlugin.so`          | `libbsl.so`, `libhddlapi.so`, `libmvnc-hddl.so`             | `HDDLPlugin.dll`         | `bsl.dll`, `hddlapi.dll`, `json-c.dll`, `libcrypto-1_1-x64.dll`, `libssl-1_1-x64.dll`, `mvnc-hddl.dll` |  Is not supported            |  -                                          |
 | GNA    | `libov_intel_gna_plugin.so`           | `libgna.so`,                                                | `ov_intel_gna_plugin.dll`          | `gna.dll`                                                                                              |  Is not supported            |  -                                          |
diff --git a/docs/IE_DG/Integrate_with_customer_application_new_API.md b/docs/IE_DG/Integrate_with_customer_application_new_API.md
index 4d543a9e891..b7d3b69735b 100644
--- a/docs/IE_DG/Integrate_with_customer_application_new_API.md
+++ b/docs/IE_DG/Integrate_with_customer_application_new_API.md
@@ -7,7 +7,7 @@ for example of using the Inference Engine in applications.
 
 ## Use the Inference Engine API in Your Code
 
-The core `libinference_engine.so` library implements loading and parsing a model Intermediate Representation (IR), and triggers inference using a specified device. The core library has the following API:
+The core `libov_runtime.so` library implements loading and parsing a model Intermediate Representation (IR), and triggers inference using a specified device. The core library has the following API:
 
 * `InferenceEngine::Core`
 * `InferenceEngine::Blob`, `InferenceEngine::TBlob`,
diff --git a/docs/IE_DG/inference_engine_intro.md b/docs/IE_DG/inference_engine_intro.md
index f0fae908d51..cc6ae5254c0 100644
--- a/docs/IE_DG/inference_engine_intro.md
+++ b/docs/IE_DG/inference_engine_intro.md
@@ -28,11 +28,11 @@ Modules in the Inference Engine component
 
 Your application must link to the core Inference Engine libraries:
 * Linux* OS:
-    - `libinference_engine.so`, which depends on `libinference_engine_transformations.so`, `libtbb.so`, `libtbbmalloc.so` and `libngraph.so`
+    - `libov_runtime.so`, which depends on `libtbb.so`, `libtbbmalloc.so`
 * Windows* OS:
-    - `inference_engine.dll`, which depends on `inference_engine_transformations.dll`, `tbb.dll`, `tbbmalloc.dll` and `ngraph.dll`
+    - `ov_runtime.dll`, which depends on `tbb.dll`, `tbbmalloc.dll`
 * macOS*:
-    - `libinference_engine.dylib`, which depends on `libinference_engine_transformations.dylib`, `libtbb.dylib`, `libtbbmalloc.dylib` and `libngraph.dylib`
+    - `libov_runtime.dylib`, which depends on `libtbb.dylib`, `libtbbmalloc.dylib`
 
 The required C++ header files are located in the `include` directory.
 
@@ -70,8 +70,8 @@ The table below shows the plugin libraries and additional dependencies for Linux
 
 | Plugin | Library name for Linux      | Dependency libraries for Linux                              | Library name for Windows | Dependency libraries for Windows                                                                       | Library name for macOS       | Dependency libraries for macOS              |
 |--------|-----------------------------|-------------------------------------------------------------|--------------------------|--------------------------------------------------------------------------------------------------------|------------------------------|---------------------------------------------|
-| CPU    | `libMKLDNNPlugin.so`        | `libinference_engine_lp_transformations.so`                 | `MKLDNNPlugin.dll`       | `inference_engine_lp_transformations.dll`                                                              | `libMKLDNNPlugin.so`      | `inference_engine_lp_transformations.dylib` |
-| GPU    | `libov_intel_gpu_plugin.so`         | `libinference_engine_lp_transformations.so`, `libOpenCL.so` | `ov_intel_gpu_plugin.dll`        | `OpenCL.dll`, `inference_engine_lp_transformations.dll`                                                |  Is not supported            |  -                                          |
+| CPU    | `libMKLDNNPlugin.so`        | | `MKLDNNPlugin.dll`       | | `libMKLDNNPlugin.so`      | |
+| GPU    | `libov_intel_gpu_plugin.so`         | `libOpenCL.so` | `ov_intel_gpu_plugin.dll`        | `OpenCL.dll` |  Is not supported            |  -                                          |
 | MYRIAD | `libmyriadPlugin.so`        | `libusb.so`,                                                | `myriadPlugin.dll`       | `usb.dll`                                                                                              | `libmyriadPlugin.so`      | `libusb.dylib`                              |
 | HDDL   | `libHDDLPlugin.so`          | `libbsl.so`, `libhddlapi.so`, `libmvnc-hddl.so`             | `HDDLPlugin.dll`         | `bsl.dll`, `hddlapi.dll`, `json-c.dll`, `libcrypto-1_1-x64.dll`, `libssl-1_1-x64.dll`, `mvnc-hddl.dll` |  Is not supported            |  -                                          |
 | GNA    | `libov_intel_gna_plugin.so`           | `libgna.so`,                                                | `ov_intel_gna_plugin.dll`          | `gna.dll`                                                                                              |  Is not supported            |  -                                          |
diff --git a/docs/doxygen/ie_plugin_api.config b/docs/doxygen/ie_plugin_api.config
index b6c72a139e6..4304ffaa9a4 100644
--- a/docs/doxygen/ie_plugin_api.config
+++ b/docs/doxygen/ie_plugin_api.config
@@ -69,7 +69,7 @@ PREDEFINED             = "INFERENCE_ENGINE_API=" \
                          "OPENVINO_CORE_EXPORTS=" \
                          "INFERENCE_ENGINE_DEPRECATED=" \
                          "OPENVINO_DEPRECATED=" \
-                         "inference_engine_transformations_EXPORTS" \
+                         "IMPLEMENT_OPENVINO_API" \
                          "TRANSFORMATIONS_API=" \
                          "NGRAPH_HELPER_DLL_EXPORT=" \
                          "NGRAPH_HELPER_DLL_IMPORT=" \
diff --git a/docs/nGraph_DG/nGraphTransformation.md b/docs/nGraph_DG/nGraphTransformation.md
index b27a2876b98..2dd3e0d73ad 100644
--- a/docs/nGraph_DG/nGraphTransformation.md
+++ b/docs/nGraph_DG/nGraphTransformation.md
@@ -10,8 +10,7 @@ Before creating a transformation, do the following:
 * Understand where to put your transformation code
 
 ### Transformation Library Structure
-Transformation library is independent from Inference Engine target library named as `inference_engine_transformations`
-and is located in the `src/common/transformations` directory.
+OpenVINO transformations are located in the `src/common/transformations` directory.
 
 Transformations root directory contains two folders:
 * `ngraph_ops` - Contains internal opset operations that are common for plugins.
diff --git a/src/bindings/python/tests/test_ngraph/__init__.py b/src/bindings/python/tests/test_ngraph/__init__.py
index 4417c13d097..127e415364b 100644
--- a/src/bindings/python/tests/test_ngraph/__init__.py
+++ b/src/bindings/python/tests/test_ngraph/__init__.py
@@ -1,6 +1,6 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-# ngraph.dll directory path visibility is needed to use _pyngraph module
+# ov_runtime.dll directory path visibility is needed to use _pyngraph module
 # import below causes adding this path to os.environ["PATH"]
 import openvino  # noqa: F401 'imported but unused'
diff --git a/src/bindings/python/tests_compatibility/test_ngraph/__init__.py b/src/bindings/python/tests_compatibility/test_ngraph/__init__.py
index b274453fb17..e5dacbda431 100644
--- a/src/bindings/python/tests_compatibility/test_ngraph/__init__.py
+++ b/src/bindings/python/tests_compatibility/test_ngraph/__init__.py
@@ -1,6 +1,6 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-# ngraph.dll directory path visibility is needed to use _pyngraph module
+# ov_runtime.dll directory path visibility is needed to use _pyngraph module
 # import below causes adding this path to os.environ["PATH"]
 import ngraph  # noqa: F401 'imported but unused'
diff --git a/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp b/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp
index b000b5fc56f..913f9b06548 100644
--- a/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp
+++ b/src/common/low_precision_transformations/include/low_precision/lpt_visibility.hpp
@@ -18,5 +18,5 @@
 #        define LP_TRANSFORMATIONS_API OPENVINO_CORE_EXPORTS
 #    else
 #        define LP_TRANSFORMATIONS_API OPENVINO_CORE_IMPORTS
-#    endif  // inference_engine_lp_transformations_EXPORTS
+#    endif  // IMPLEMENT_OPENVINO_API
 #endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/common/transformations/include/transformations_visibility.hpp b/src/common/transformations/include/transformations_visibility.hpp
index 77dc8f85f84..692447a19d4 100644
--- a/src/common/transformations/include/transformations_visibility.hpp
+++ b/src/common/transformations/include/transformations_visibility.hpp
@@ -31,11 +31,11 @@
  */
 
 #ifdef OPENVINO_STATIC_LIBRARY
-#define TRANSFORMATIONS_API
+#    define TRANSFORMATIONS_API
 #else
-#ifdef IMPLEMENT_OPENVINO_API
-#define TRANSFORMATIONS_API OPENVINO_CORE_EXPORTS
-#else
-#define TRANSFORMATIONS_API OPENVINO_CORE_IMPORTS
-#endif // inference_engine_transformations_EXPORTS
-#endif // OPENVINO_STATIC_LIBRARY
+#    ifdef IMPLEMENT_OPENVINO_API
+#        define TRANSFORMATIONS_API OPENVINO_CORE_EXPORTS
+#    else
+#        define TRANSFORMATIONS_API OPENVINO_CORE_IMPORTS
+#    endif  // IMPLEMENT_OPENVINO_API
+#endif      // OPENVINO_STATIC_LIBRARY
diff --git a/tests/conditional_compilation/test_cc.py b/tests/conditional_compilation/test_cc.py
index f42a4a1a877..48ebc7c9294 100644
--- a/tests/conditional_compilation/test_cc.py
+++ b/tests/conditional_compilation/test_cc.py
@@ -125,7 +125,7 @@ def test_verify(test_id, prepared_models, openvino_ref, artifacts, tolerance=1e-
 @pytest.mark.dependency(depends=["cc_collect", "minimized_pkg"])
 def test_libs_size(test_id, models, openvino_ref, artifacts):  # pylint: disable=unused-argument
     """Test if libraries haven't increased in size after conditional compilation."""
-    libraries = ["inference_engine_transformations", "MKLDNNPlugin", "ngraph"]
+    libraries = ["ov_runtime", "MKLDNNPlugin"]
     minimized_pkg = artifacts / test_id / "install_pkg"
     ref_libs_size = get_lib_sizes(openvino_ref, libraries)
     lib_sizes = get_lib_sizes(minimized_pkg, libraries)
diff --git a/tests/fuzz/README.md b/tests/fuzz/README.md
index 97a3bcca621..ad70db95d47 100644
--- a/tests/fuzz/README.md
+++ b/tests/fuzz/README.md
@@ -75,7 +75,7 @@ To build coverage report after fuzz test execution run:
 
 ```
 llvm-profdata merge -sparse *.profraw -o default.profdata && \
-llvm-cov show ./read_network-fuzzer -object=lib/libinference_engine.so -instr-profile=default.profdata -format=html -output-dir=read_network-coverage
+llvm-cov show ./read_network-fuzzer -object=lib/libov_runtime.so -instr-profile=default.profdata -format=html -output-dir=read_network-coverage
 ```
 
 ## Reproducing findings
diff --git a/tests/utils/path_utils.py b/tests/utils/path_utils.py
index 8e9864059ad..bd678681de5 100644
--- a/tests/utils/path_utils.py
+++ b/tests/utils/path_utils.py
@@ -36,15 +36,12 @@ def get_lib_path(lib_name):
     """Function for getting absolute path in OpenVINO directory to specific lib"""
     os_name = get_os_name()
     all_libs = {
-        'inference_engine_transformations': {
-            'Windows': Path('runtime/bin/intel64/Release/inference_engine_transformations.dll'),
-            'Linux': Path('runtime/lib/intel64/libinference_engine_transformations.so')},
         'MKLDNNPlugin': {
             'Windows': Path('runtime/bin/intel64/Release/MKLDNNPlugin.dll'),
             'Linux': Path('runtime/lib/intel64/libMKLDNNPlugin.so')},
-        'ngraph': {
-            'Windows': Path('runtime/bin/intel64/Release/ngraph.dll'),
-            'Linux': Path('runtime/lib/intel64/libngraph.so')}
+        'ov_runtime': {
+            'Windows': Path('runtime/bin/intel64/Release/ov_runtime.dll'),
+            'Linux': Path('runtime/lib/intel64/libov_runtime.so')}
                 }
     return all_libs[lib_name][os_name]
 
diff --git a/tools/deployment_manager/configs/darwin.json b/tools/deployment_manager/configs/darwin.json
index 98e1c3c6b67..a8b07728099 100644
--- a/tools/deployment_manager/configs/darwin.json
+++ b/tools/deployment_manager/configs/darwin.json
@@ -16,14 +16,11 @@
     "ie_core": {
       "group": ["ie"],
       "files": [
-        "runtime/lib/intel64/libinference_engine.dylib",
-        "runtime/lib/intel64/libinference_engine_transformations.dylib",
+        "runtime/lib/intel64/libov_runtime.dylib",
         "runtime/lib/intel64/libinference_engine_preproc.so",
         "runtime/lib/intel64/libinference_engine_c_api.dylib",
         "runtime/lib/intel64/libov_hetero_plugin.so",
         "runtime/lib/intel64/libov_auto_plugin.so",
-        "runtime/lib/intel64/libngraph.dylib",
-        "runtime/lib/intel64/libfrontend_common.dylib",
         "runtime/lib/intel64/libov_ir_frontend.dylib",
         "runtime/lib/intel64/libov_onnx_frontend.dylib",
         "runtime/lib/intel64/libov_paddlepaddle_frontend.dylib",
@@ -36,7 +33,6 @@
       "group": ["ie"],
       "dependencies" : ["ie_core"],
       "files": [
-        "runtime/lib/intel64/libinference_engine_lp_transformations.dylib",
         "runtime/lib/intel64/libMKLDNNPlugin.so"
       ]
     },
diff --git a/tools/deployment_manager/configs/linux.json b/tools/deployment_manager/configs/linux.json
index 5e71b7153e9..2d17f1a788f 100644
--- a/tools/deployment_manager/configs/linux.json
+++ b/tools/deployment_manager/configs/linux.json
@@ -22,14 +22,11 @@
     "ie_core": {
       "group": ["ie"],
       "files": [
-        "runtime/lib/intel64/libinference_engine.so",
-        "runtime/lib/intel64/libinference_engine_transformations.so",
+        "runtime/lib/intel64/libov_runtime.so",
         "runtime/lib/intel64/libinference_engine_preproc.so",
         "runtime/lib/intel64/libinference_engine_c_api.so",
         "runtime/lib/intel64/libov_hetero_plugin.so",
         "runtime/lib/intel64/libov_auto_plugin.so",
-        "runtime/lib/intel64/libngraph.so",
-        "runtime/lib/intel64/libfrontend_common.so",
         "runtime/lib/intel64/libov_ir_frontend.so",
         "runtime/lib/intel64/libov_onnx_frontend.so",
         "runtime/lib/intel64/libov_paddlepaddle_frontend.so",
@@ -42,7 +39,6 @@
       "group": ["ie"],
       "dependencies" : ["ie_core"],
       "files": [
-        "runtime/lib/intel64/libinference_engine_lp_transformations.so",
         "runtime/lib/intel64/libMKLDNNPlugin.so"
       ]
     },
@@ -53,7 +49,6 @@
       "files": [
         "runtime/lib/intel64/cache.json",
         "runtime/lib/intel64/libov_intel_gpu_plugin.so",
-        "runtime/lib/intel64/libinference_engine_lp_transformations.so",
         "install_dependencies/install_NEO_OCL_driver.sh"
       ]
     },
diff --git a/tools/deployment_manager/configs/windows.json b/tools/deployment_manager/configs/windows.json
index 776a666755a..9cb8d68b379 100644
--- a/tools/deployment_manager/configs/windows.json
+++ b/tools/deployment_manager/configs/windows.json
@@ -16,14 +16,11 @@
     "ie_core": {
       "group": ["ie"],
       "files": [
-        "runtime/bin/intel64/Release/inference_engine.dll",
-        "runtime/bin/intel64/Release/inference_engine_transformations.dll",
+        "runtime/bin/intel64/Release/ov_runtime.dll",
         "runtime/bin/intel64/Release/inference_engine_preproc.dll",
         "runtime/bin/intel64/Release/inference_engine_c_api.dll",
         "runtime/bin/intel64/Release/ov_hetero_plugin.dll",
         "runtime/bin/intel64/Release/ov_auto_plugin.dll",
-        "runtime/bin/intel64/Release/ngraph.dll",
-        "runtime/bin/intel64/Release/frontend_common.dll",
         "runtime/bin/intel64/Release/ov_ir_frontend.dll",
         "runtime/bin/intel64/Release/ov_onnx_frontend.dll",
         "runtime/bin/intel64/Release/ov_paddlepaddle_frontend.dll",
@@ -36,7 +33,6 @@
       "group": ["ie"],
       "dependencies" : ["ie_core"],
       "files": [
-        "runtime/bin/intel64/Release/inference_engine_lp_transformations.dll",
         "runtime/bin/intel64/Release/MKLDNNPlugin.dll"
       ]
     },
@@ -46,7 +42,6 @@
       "dependencies" : ["ie_core"],
       "files": [
         "runtime/bin/intel64/Release/cache.json",
-        "runtime/bin/intel64/Release/inference_engine_lp_transformations.dll",
         "runtime/bin/intel64/Release/ov_intel_gpu_plugin.dll"
       ]
     },

From cf15cbd301cabe4ab640efa7dee40b2863084fc3 Mon Sep 17 00:00:00 2001
From: Vitaliy Urusovskij <vitaliy.urusovskij@intel.com>
Date: Tue, 21 Dec 2021 06:45:14 +0300
Subject: [PATCH 062/160] Save Parameters order during IR deserialization
 (#9286)

* Save Parameters order during IR deserialization

* Add test to check inputs/outputs in IR
---
 src/frontends/ir/src/ir_deserializer.cpp      | 23 +++---
 .../ir_serialization/deterministicity.cpp     | 77 +++++++++++++++++++
 2 files changed, 91 insertions(+), 9 deletions(-)

diff --git a/src/frontends/ir/src/ir_deserializer.cpp b/src/frontends/ir/src/ir_deserializer.cpp
index 166c9a92808..d319a5690aa 100644
--- a/src/frontends/ir/src/ir_deserializer.cpp
+++ b/src/frontends/ir/src/ir_deserializer.cpp
@@ -423,6 +423,9 @@ std::shared_ptr<ngraph::Function> XmlDeserializer::parse_function(
     std::vector<size_t /*layer-id*/> outputs;
     std::unordered_set<std::string> opName;
 
+    std::vector<size_t> order;
+    std::set<size_t> dfs_used_nodes;
+    std::map<size_t /*to-layer-id*/, std::vector<edge>> edges;
     // Read all layers and store their parameters in params map
     FOREACH_CHILD (node, root.child("layers"), "layer") {
         auto node_param = parseGenericParams(node);
@@ -433,11 +436,15 @@ std::shared_ptr<ngraph::Function> XmlDeserializer::parse_function(
         if (node_param.type == "Result" || node_param.type == "Assign") {
             outputs.push_back(node_param.layerId);
         }
+        if (node_param.type == "Parameter") {
+            // Save Parameters order according to order in XML.
+            // To do so, handle nodes manually and ignore during DFS
+            dfs_used_nodes.insert(node_param.layerId);
+            order.push_back(node_param.layerId);
+            edges[node_param.layerId] = {};
+        }
     }
 
-    std::map<size_t /*to-layer-id*/, std::vector<edge>> edges;
-    std::map<size_t, std::shared_ptr<ngraph::Node>> id_to_node;
-
     // Read all edges and store them for further usage
     FOREACH_CHILD (_ec, root.child("edges"), "edge") {
         size_t fromLayer = XMLParseUtils::GetUIntAttr(_ec, "from-layer");
@@ -448,12 +455,10 @@ std::shared_ptr<ngraph::Function> XmlDeserializer::parse_function(
     }
 
     // Run DFS starting from outputs to get nodes topological order
-    std::set<size_t> used;
-    std::vector<size_t> order;
-    std::function<void(size_t)> dfs = [&edges, &order, &used, &dfs](const size_t id) {
-        if (used.count(id))
+    std::function<void(size_t)> dfs = [&edges, &order, &dfs_used_nodes, &dfs](const size_t id) {
+        if (dfs_used_nodes.count(id))
             return;
-        used.insert(id);
+        dfs_used_nodes.insert(id);
         for (auto& edge : edges[id]) {
             dfs(edge.fromLayerId);
         }
@@ -464,7 +469,7 @@ std::shared_ptr<ngraph::Function> XmlDeserializer::parse_function(
     // OV_ITT_SCOPE_NEXT(FIRST_INFERENCE, taskChain, "ConstructNgraphNodes");
 
     FunctionNodes func_nodes;
-
+    std::map<size_t, std::shared_ptr<ngraph::Node>> id_to_node;
     std::map<std::string, std::shared_ptr<ngraph::Node>> variable_id_to_read_value;
 
     //  Following topological order create nGraph operations
diff --git a/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp b/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp
index c22afcac38d..d97ef097677 100644
--- a/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp
@@ -9,6 +9,12 @@
 #include "gtest/gtest.h"
 #include "ie_core.hpp"
 
+#include <openvino/runtime/runtime.hpp>
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset5.hpp>
+
+using namespace ngraph;
+
 #ifndef IR_SERIALIZATION_MODELS_PATH  // should be already defined by cmake
 #    error "IR_SERIALIZATION_MODELS_PATH is not defined"
 #endif
@@ -92,3 +98,74 @@ TEST_F(SerializationDeterministicityTest, SerializeToBlob) {
     ASSERT_TRUE(expected.layerCount() == result.layerCount());
     ASSERT_TRUE(expected.getInputShapes() == result.getInputShapes());
 }
+
+class DeterministicityTest : public ::testing::Test {
+protected:
+    std::string test_name = ::testing::UnitTest::GetInstance()->current_test_info()->name();
+    std::string m_out_xml_path = test_name + ".xml";
+    std::string m_out_bin_path = test_name + ".bin";
+
+    void TearDown() override {
+        std::remove(m_out_xml_path.c_str());
+        std::remove(m_out_bin_path.c_str());
+    }
+};
+
+TEST_F(DeterministicityTest, IRInputsOrder) {
+    const std::vector<std::string> friend_names = {"A", "B", "C"};
+
+    auto a = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
+    auto b = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
+    auto add = std::make_shared<opset5::Add>(a, b);
+    auto c = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
+    auto relu = std::make_shared<opset5::Relu>(c);
+    auto add2 = std::make_shared<opset5::Add>(add, relu);
+    auto res = std::make_shared<opset5::Result>(add2);
+
+    a->set_friendly_name(friend_names[0]);
+    b->set_friendly_name(friend_names[1]);
+    c->set_friendly_name(friend_names[2]);
+
+    auto model = std::make_shared<ov::Model>(NodeVector{res}, ParameterVector{a, b, c});
+
+    ov::runtime::Core core;
+    auto serialize = ov::pass::Serialize(m_out_xml_path, m_out_bin_path);
+    serialize.run_on_function(model);
+    auto serialized_func = core.read_model(m_out_xml_path);
+
+    for (size_t i = 0; i < friend_names.size(); i++) {
+        auto param = serialized_func->get_parameters()[i];
+        ASSERT_EQ(i, serialized_func->get_parameter_index(param));
+        ASSERT_STREQ(friend_names[i].c_str(), param->get_friendly_name().c_str());
+    }
+}
+
+TEST_F(DeterministicityTest, IROutputsOrder) {
+    const std::vector<std::string> friend_names = {"D", "E", "F"};
+
+    auto a = std::make_shared<opset5::Parameter>(element::f32, Shape{4, 4});
+    auto axis_1 = ngraph::opset5::Constant::create(element::i64, Shape{}, {1});
+    auto split1 = std::make_shared<opset5::Split>(a, axis_1, 2);
+    auto res1 = std::make_shared<opset5::Result>(split1->output(0));
+    auto relu = std::make_shared<opset5::Relu>(split1->output(1));
+    auto split2 = std::make_shared<opset5::Split>(relu, axis_1, 2);
+    auto res2 = std::make_shared<opset5::Result>(split2->output(0));
+    auto res3 = std::make_shared<opset5::Result>(split2->output(1));
+
+    res1->set_friendly_name(friend_names[0]);
+    res2->set_friendly_name(friend_names[1]);
+    res3->set_friendly_name(friend_names[2]);
+
+    auto model = std::make_shared<ov::Model>(NodeVector{res1, res2, res3}, ParameterVector{a});
+
+    ov::runtime::Core core;
+    auto serialize = ov::pass::Serialize(m_out_xml_path, m_out_bin_path);
+    serialize.run_on_function(model);
+    auto serialized_func = core.read_model(m_out_xml_path);
+
+    for (size_t i = 0; i < friend_names.size(); i++) {
+        auto out = serialized_func->get_results()[i];
+        ASSERT_EQ(i, serialized_func->get_result_index(out));
+        ASSERT_STREQ(friend_names[i].c_str(), out->get_friendly_name().c_str());
+    }
+}
\ No newline at end of file

From 6c92ce48c1e5e3c73c5ffb88fcfa7ea11f0c367f Mon Sep 17 00:00:00 2001
From: Krzysztof Bruniecki <krzysztof.bruniecki@intel.com>
Date: Tue, 21 Dec 2021 07:44:06 +0100
Subject: [PATCH 063/160] Remove non official code (#9315)

- feature to export for embedded GNA3 which is not in fact supported.
    - SW emulation modes like SSE etc, which are not useful and are undocumented.
---
 samples/cpp/speech_sample/main.cpp            |  4 --
 samples/cpp/speech_sample/speech_sample.hpp   | 45 +++++++++----------
 src/inference/include/ie/gna/gna_config.hpp   | 13 ------
 src/plugins/intel_gna/gna_device.cpp          | 22 ---------
 src/plugins/intel_gna/gna_device.hpp          |  3 --
 src/plugins/intel_gna/gna_plugin.cpp          | 32 ++++---------
 src/plugins/intel_gna/gna_plugin_config.cpp   | 19 --------
 src/tests/unit/gna/gna_plugin_config_test.cpp |  6 ---
 8 files changed, 30 insertions(+), 114 deletions(-)

diff --git a/samples/cpp/speech_sample/main.cpp b/samples/cpp/speech_sample/main.cpp
index 0db982f5676..b035c724d56 100644
--- a/samples/cpp/speech_sample/main.cpp
+++ b/samples/cpp/speech_sample/main.cpp
@@ -177,7 +177,6 @@ int main(int argc, char* argv[]) {
         // Embedded GNA model dumping (for Intel(R) Speech Enabling Developer Kit)
         if (!FLAGS_we.empty()) {
             gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_FIRMWARE_MODEL_IMAGE] = FLAGS_we;
-            gnaPluginConfig[InferenceEngine::GNAConfigParams::KEY_GNA_FIRMWARE_MODEL_IMAGE_GENERATION] = FLAGS_we_gen;
         }
         // -----------------------------------------------------------------------------------------------------
         // --------------------------- Step 2. Loading model to the device ------------------------------------------
@@ -232,9 +231,6 @@ int main(int argc, char* argv[]) {
         }
         if (!FLAGS_we.empty()) {
             slog::info << "Exported GNA embedded model to file " << FLAGS_we << slog::endl;
-            if (!FLAGS_we_gen.empty()) {
-                slog::info << "GNA embedded model export done for GNA generation: " << FLAGS_we_gen << slog::endl;
-            }
             return 0;
         }
         // ---------------------------------------------------------------------------------------------------------
diff --git a/samples/cpp/speech_sample/speech_sample.hpp b/samples/cpp/speech_sample/speech_sample.hpp
index e93d6efd817..7d53fcd07af 100644
--- a/samples/cpp/speech_sample/speech_sample.hpp
+++ b/samples/cpp/speech_sample/speech_sample.hpp
@@ -162,9 +162,6 @@ DEFINE_string(wg, "", write_gna_model_message);
 /// @brief Write GNA embedded model to file (model.bin)
 DEFINE_string(we, "", write_embedded_model_message);
 
-/// @brief Optional GNA embedded device generation (default GNA1 aka Sue Creek) - hide option
-DEFINE_string(we_gen, "GNA1", write_embedded_model_generation_message);
-
 /// @brief Input quantization mode (default static)
 DEFINE_string(q, "static", quantization_message);
 
@@ -203,26 +200,28 @@ static void showUsage() {
     std::cout << "speech_sample [OPTION]" << std::endl;
     std::cout << "Options:" << std::endl;
     std::cout << std::endl;
-    std::cout << "    -h                      " << help_message << std::endl;
-    std::cout << "    -i \"<path>\"             " << input_message << std::endl;
-    std::cout << "    -m \"<path>\"             " << model_message << std::endl;
-    std::cout << "    -o \"<path>\"             " << output_message << std::endl;
-    std::cout << "    -d \"<device>\"           " << target_device_message << std::endl;
-    std::cout << "    -pc                     " << performance_counter_message << std::endl;
-    std::cout << "    -q \"<mode>\"             " << quantization_message << std::endl;
-    std::cout << "    -qb \"<integer>\"         " << quantization_bits_message << std::endl;
-    std::cout << "    -sf \"<double>\"          " << scale_factor_message << std::endl;
-    std::cout << "    -bs \"<integer>\"         " << batch_size_message << std::endl;
-    std::cout << "    -r \"<path>\"             " << reference_score_message << std::endl;
-    std::cout << "    -rg \"<path>\"            " << read_gna_model_message << std::endl;
-    std::cout << "    -wg \"<path>\"            " << write_gna_model_message << std::endl;
-    std::cout << "    -we \"<path>\"            " << write_embedded_model_message << std::endl;
-    std::cout << "    -nthreads \"<integer>\"   " << infer_num_threads_message << std::endl;
-    std::cout << "    -cw_l \"<integer>\"       " << context_window_message_l << std::endl;
-    std::cout << "    -cw_r \"<integer>\"       " << context_window_message_r << std::endl;
-    std::cout << "    -oname \"<string>\"       " << output_layer_names_message << std::endl;
-    std::cout << "    -iname \"<string>\"       " << input_layer_names_message << std::endl;
-    std::cout << "    -pwl_me \"<double>\"      " << pwl_max_error_percent_message << std::endl;
+    std::cout << "    -h                         " << help_message << std::endl;
+    std::cout << "    -i \"<path>\"                " << input_message << std::endl;
+    std::cout << "    -m \"<path>\"                " << model_message << std::endl;
+    std::cout << "    -o \"<path>\"                " << output_message << std::endl;
+    std::cout << "    -d \"<device>\"              " << target_device_message << std::endl;
+    std::cout << "    -pc                        " << performance_counter_message << std::endl;
+    std::cout << "    -q \"<mode>\"                " << quantization_message << std::endl;
+    std::cout << "    -qb \"<integer>\"            " << quantization_bits_message << std::endl;
+    std::cout << "    -sf \"<double>\"             " << scale_factor_message << std::endl;
+    std::cout << "    -bs \"<integer>\"            " << batch_size_message << std::endl;
+    std::cout << "    -r \"<path>\"                " << reference_score_message << std::endl;
+    std::cout << "    -rg \"<path>\"               " << read_gna_model_message << std::endl;
+    std::cout << "    -wg \"<path>\"               " << write_gna_model_message << std::endl;
+    std::cout << "    -we \"<path>\"               " << write_embedded_model_message << std::endl;
+    std::cout << "    -nthreads \"<integer>\"      " << infer_num_threads_message << std::endl;
+    std::cout << "    -cw_l \"<integer>\"          " << context_window_message_l << std::endl;
+    std::cout << "    -cw_r \"<integer>\"          " << context_window_message_r << std::endl;
+    std::cout << "    -oname \"<string>\"          " << output_layer_names_message << std::endl;
+    std::cout << "    -iname \"<string>\"          " << input_layer_names_message << std::endl;
+    std::cout << "    -pwl_me \"<double>\"         " << pwl_max_error_percent_message << std::endl;
+    std::cout << "    -exec_target \"<string>\"    " << execution_target_message << std::endl;
+    std::cout << "    -compile_target \"<string>\" " << compile_target_message << std::endl;
 }
 
 /**
diff --git a/src/inference/include/ie/gna/gna_config.hpp b/src/inference/include/ie/gna/gna_config.hpp
index ab281b07591..963a0413271 100644
--- a/src/inference/include/ie/gna/gna_config.hpp
+++ b/src/inference/include/ie/gna/gna_config.hpp
@@ -53,11 +53,6 @@ DECLARE_GNA_CONFIG_KEY(PRECISION);
  */
 DECLARE_GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE);
 
-/**
- * @brief information on GNA generation chosen for firmware model dump, can be overridden by GNA3
- */
-DECLARE_GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE_GENERATION);
-
 /**
  * @brief GNA proc_type setting that should be one of GNA_AUTO, GNA_HW, GNA_HW_WITH_SW_FBACK, GNA_SW, GNA_SW_EXACT
  */
@@ -69,14 +64,6 @@ DECLARE_GNA_CONFIG_VALUE(HW_WITH_SW_FBACK);
 DECLARE_GNA_CONFIG_VALUE(SW);
 DECLARE_GNA_CONFIG_VALUE(SW_EXACT);
 DECLARE_GNA_CONFIG_VALUE(SW_FP32);
-DECLARE_GNA_CONFIG_VALUE(GEN);
-DECLARE_GNA_CONFIG_VALUE(GEN_EXACT);
-DECLARE_GNA_CONFIG_VALUE(SSE);
-DECLARE_GNA_CONFIG_VALUE(SSE_EXACT);
-DECLARE_GNA_CONFIG_VALUE(AVX1);
-DECLARE_GNA_CONFIG_VALUE(AVX1_EXACT);
-DECLARE_GNA_CONFIG_VALUE(AVX2);
-DECLARE_GNA_CONFIG_VALUE(AVX2_EXACT);
 
 /**
  * @brief The option to override the GNA HW execution target. May be one of GNA_TARGET_2_0, GNA_TARGET_3_0.
diff --git a/src/plugins/intel_gna/gna_device.cpp b/src/plugins/intel_gna/gna_device.cpp
index 023799790da..67a731849c3 100644
--- a/src/plugins/intel_gna/gna_device.cpp
+++ b/src/plugins/intel_gna/gna_device.cpp
@@ -473,28 +473,6 @@ GNADeviceHelper::DumpResult GNADeviceHelper::dumpXnn(const uint32_t modelId) {
 
 #if GNA_LIB_VER == 2
 
-void GNADeviceHelper::dumpXnnForDeviceVersion(
-    const uint32_t modelId,
-    std::ostream & outStream,
-    const Gna2DeviceVersion targetDeviceVersion) {
-
-    Gna2ModelSueCreekHeader sueHeader;
-    auto ptr = ExportSueLegacyUsingGnaApi2(modelId, nGnaDeviceIndex, &sueHeader);
-    gnaUserFree(ptr);
-
-    ExportGnaDescriptorPartiallyFilled(sueHeader.NumberOfLayers, outStream);
-
-    ExportLdForDeviceVersion(modelId, outStream, targetDeviceVersion);
-    if (dumpXNNROPtr == nullptr) {
-        THROW_GNA_EXCEPTION << "Bad RO pointer (nullptr)";
-    }
-    outStream.write(static_cast<const char*>(dumpXNNROPtr), dumpXNNROSize);
-
-    // TODO: GNA2: remove
-    outStream.write("Gna2ModelSueCreekHeader", 24);
-    outStream.write(reinterpret_cast<const char*>(&sueHeader), sizeof(sueHeader));
-}
-
 void GNADeviceHelper::createVirtualDevice(Gna2DeviceVersion devVersion, std::string purpose) {
     const auto status = Gna2DeviceCreateForExport(devVersion, &nGnaDeviceIndex);
     GNADeviceHelper::checkGna2Status(status, "Gna2DeviceCreateForExport(" + std::to_string(devVersion) + ")" + purpose);
diff --git a/src/plugins/intel_gna/gna_device.hpp b/src/plugins/intel_gna/gna_device.hpp
index 4024b3c1807..2955a084eba 100644
--- a/src/plugins/intel_gna/gna_device.hpp
+++ b/src/plugins/intel_gna/gna_device.hpp
@@ -176,9 +176,6 @@ public:
 #else
 
     DumpResult dumpXnn(const uint32_t modelId);
-    void dumpXnnForDeviceVersion(const uint32_t modelId,
-        std::ostream & outStream,
-        Gna2DeviceVersion targetDeviceVersion);
 #endif
     void free(void * ptr);
 
diff --git a/src/plugins/intel_gna/gna_plugin.cpp b/src/plugins/intel_gna/gna_plugin.cpp
index f406cdce23a..6fae93ffac7 100644
--- a/src/plugins/intel_gna/gna_plugin.cpp
+++ b/src/plugins/intel_gna/gna_plugin.cpp
@@ -1164,17 +1164,8 @@ void GNAPlugin::createRequestConfigsForGnaModels() {
 #endif
 
 int GNAPlugin::GetDeviceVersionFromString(const std::string deviceString) {
-    constexpr uint32_t embeddedSuffix = 0xE;
     if (deviceString.empty())
-        return 0x100 + embeddedSuffix;
-    if (deviceString.size() == 4 && deviceString.substr(0, 3) == "GNA") {
-        int version = deviceString[3] - '0';
-            if (version > 0) {
-            version <<= 8;
-            version += embeddedSuffix;
-            return version;
-        }
-    }
+        return static_cast<int>(Gna2DeviceVersionEmbedded1_0);
     THROW_GNA_EXCEPTION << "Wrong GNA generation for embedded model dump: " << deviceString;
 }
 
@@ -1185,8 +1176,6 @@ void GNAPlugin::DumpXNNToFile() const {
         return;
     }
 
-    const auto versionInt = GetDeviceVersionFromString(config.dumpXNNGeneration);
-
     if (!gnadevice) {
         THROW_GNA_EXCEPTION << "Cannot generate XNNDump for float network";
     }
@@ -1202,18 +1191,13 @@ void GNAPlugin::DumpXNNToFile() const {
     dumpStream.write(reinterpret_cast<char*>(dump.model.get()), dump.header.model_size);
 #else
     auto const modelId = gnadevice->createModel(std::get<0>(gnaModels.front())->obj);
-    if (versionInt == Gna2DeviceVersionEmbedded1_0) {
-        auto dump = gnadevice->dumpXnn(modelId);
-        dump.header.RwRegionSize = gnamem->getRWBytes();
-        dump.header.InputScalingFactor = inputsDesc->inputScaleFactors.front();
-        dump.header.OutputScalingFactor = outputsDesc.front().scale_factor;
-        dumpStream.write(reinterpret_cast<char*>(&dump.header), sizeof(Gna2ModelSueCreekHeader));
-        dumpStream.write(reinterpret_cast<char*>(dump.model.get()), dump.header.ModelSize);
-    } else {
-        static_assert(sizeof(versionInt) >= sizeof(Gna2DeviceVersion), "");
-        gnadevice->dumpXnnForDeviceVersion(modelId, dumpStream,
-            *reinterpret_cast<const Gna2DeviceVersion*>(&versionInt));
-    }
+    auto dump = gnadevice->dumpXnn(modelId);
+    dump.header.RwRegionSize = gnamem->getRWBytes();
+    dump.header.InputScalingFactor = inputsDesc->inputScaleFactors.front();
+    dump.header.OutputScalingFactor = outputsDesc.front().scale_factor;
+    dumpStream.write(reinterpret_cast<char*>(&dump.header), sizeof(Gna2ModelSueCreekHeader));
+    dumpStream.write(reinterpret_cast<char*>(dump.model.get()), dump.header.ModelSize);
+
     gnadevice->releaseModel(modelId);
 #endif
 }
diff --git a/src/plugins/intel_gna/gna_plugin_config.cpp b/src/plugins/intel_gna/gna_plugin_config.cpp
index 3495eb1d935..4f2fca2089a 100644
--- a/src/plugins/intel_gna/gna_plugin_config.cpp
+++ b/src/plugins/intel_gna/gna_plugin_config.cpp
@@ -24,14 +24,6 @@ static const caseless_unordered_map<std::string, uint32_t> supported_values = {
 };
 static const  std::vector<std::string> supported_values_on_gna2 = {
         GNAConfigParams::GNA_HW_WITH_SW_FBACK,
-        GNAConfigParams::GNA_GEN,
-        GNAConfigParams::GNA_GEN_EXACT,
-        GNAConfigParams::GNA_SSE,
-        GNAConfigParams::GNA_SSE_EXACT,
-        GNAConfigParams::GNA_AVX1,
-        GNAConfigParams::GNA_AVX1_EXACT,
-        GNAConfigParams::GNA_AVX2,
-        GNAConfigParams::GNA_AVX2_EXACT
 };
 #else
 static const caseless_unordered_map <std::string, std::pair<Gna2AccelerationMode, bool>> supported_values = {
@@ -40,14 +32,6 @@ static const caseless_unordered_map <std::string, std::pair<Gna2AccelerationMode
                 {GNAConfigParams::GNA_HW_WITH_SW_FBACK, {Gna2AccelerationModeHardwareWithSoftwareFallback, false}},
                 {GNAConfigParams::GNA_SW,               {Gna2AccelerationModeSoftware,                     false}},
                 {GNAConfigParams::GNA_SW_EXACT,         {Gna2AccelerationModeSoftware,                     true}},
-                {GNAConfigParams::GNA_GEN,              {Gna2AccelerationModeGeneric,                      false}},
-                {GNAConfigParams::GNA_GEN_EXACT,        {Gna2AccelerationModeGeneric,                      true}},
-                {GNAConfigParams::GNA_SSE,              {Gna2AccelerationModeSse4x2,                       false}},
-                {GNAConfigParams::GNA_SSE_EXACT,        {Gna2AccelerationModeSse4x2,                       true}},
-                {GNAConfigParams::GNA_AVX1,             {Gna2AccelerationModeAvx1,                         false}},
-                {GNAConfigParams::GNA_AVX1_EXACT,       {Gna2AccelerationModeAvx1,                         true}},
-                {GNAConfigParams::GNA_AVX2,             {Gna2AccelerationModeAvx2,                         false}},
-                {GNAConfigParams::GNA_AVX2_EXACT,       {Gna2AccelerationModeAvx2,                         true}},
         };
 #endif
 
@@ -100,8 +84,6 @@ void Config::UpdateFromMap(const std::map<std::string, std::string>& config) {
             inputScaleFactors[input_index] = InferenceEngine::CNNLayer::ie_parse_float(value);
         } else if (key == GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE)) {
             dumpXNNPath = value;
-        } else if (key == GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE_GENERATION)) {
-            dumpXNNGeneration = value;
         } else if (key == GNA_CONFIG_KEY(DEVICE_MODE)) {
             auto procType = supported_values.find(value);
             if (procType == supported_values.end()) {
@@ -267,7 +249,6 @@ void Config::AdjustKeyMapValues() {
                 std::to_string(inputScaleFactors[n]);
     }
     keyConfigMap[GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE)] = dumpXNNPath;
-    keyConfigMap[GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE_GENERATION)] = dumpXNNGeneration;
 
     std::string device_mode;
     if (gnaFlags.sw_fp32) {
diff --git a/src/tests/unit/gna/gna_plugin_config_test.cpp b/src/tests/unit/gna/gna_plugin_config_test.cpp
index 1b061c8bf1f..6d0a7c23413 100644
--- a/src/tests/unit/gna/gna_plugin_config_test.cpp
+++ b/src/tests/unit/gna/gna_plugin_config_test.cpp
@@ -15,7 +15,6 @@ const std::map<std::string, std::string>  supportedConfigKeysWithDefaults = {
     {GNA_CONFIG_KEY(SCALE_FACTOR), "1.000000"},
     {GNA_CONFIG_KEY(SCALE_FACTOR) + std::string("_0"), "1.000000"},
     {GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE), ""},
-    {GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE_GENERATION), ""},
     {GNA_CONFIG_KEY(EXEC_TARGET), ""},
     {GNA_CONFIG_KEY(COMPILE_TARGET), ""},
     {GNA_CONFIG_KEY(DEVICE_MODE), GNAConfigParams::GNA_SW_EXACT},
@@ -96,11 +95,6 @@ TEST_F(GNAPluginConfigTest, GnaConfigFirmwareModelImageTest) {
     EXPECT_EQ(config.dumpXNNPath, "abc");
 }
 
-TEST_F(GNAPluginConfigTest, GnaConfigFirmwareModelImageGeneratorTest) {
-    SetAndCompare(GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE_GENERATION), "def");
-    EXPECT_EQ(config.dumpXNNGeneration, "def");
-}
-
 TEST_F(GNAPluginConfigTest, GnaConfigDeviceModeTest) {
     SetAndCompare(GNA_CONFIG_KEY(DEVICE_MODE), GNAConfigParams::GNA_HW);
 #if GNA_LIB_VER == 1

From 0262d05a77f496e45f47ed9acadf9397f0dded68 Mon Sep 17 00:00:00 2001
From: Milana Shhanukova <milana.shhanukova@intel.com>
Date: Tue, 21 Dec 2021 10:58:20 +0300
Subject: [PATCH 064/160] [POT] Ports in input nodes in BC (#8643)

* Make ports visible in names in BC algo

* mistypes

* updates in regards to lstm

* lstm update

* delete code dublication

* mistype in get_node_data_type
---
 .../quantization/bias_correction/algorithm.py | 24 ++++++++++++-------
 .../openvino/tools/pot/graph/node_utils.py    | 20 ++++++++++++----
 2 files changed, 30 insertions(+), 14 deletions(-)

diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
index d36f8602fa5..26a7c0532b7 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
@@ -173,6 +173,7 @@ class BiasCorrection(Algorithm):
             assigns = ge.get_nodes_by_type(main_node.graph, ['Assign'], recursively=False)
             for node_name in checked_input_names:
                 node = ge.get_node_by_name(main_node.graph, node_name, recursively=False)
+
                 if node.type == 'ReadValue':
                     output_nodes.extend(nu.get_lstm_ends(node, assigns, checked_input_names))
 
@@ -183,13 +184,15 @@ class BiasCorrection(Algorithm):
                 # Jump over Split nodes
                 if node_input_0.type in self._split_types:
                     node_input_0 = nu.get_node_input(node_input_0, 0)
+
+            node_input_0_name = nu.create_node_name(node_input_0)
             if node.type in self._types_with_bias \
                     and (nu.node_with_quantized_weights(node) and not self._apply_for_all_nodes):
                 if node_input_0.fullname not in checked_stat_names:
                     checked_stat_names.append(node_input_0.fullname)
                     checked_input_names.append(node_input_0.fullname)
                     stats_nodes.append(node_input_0)
-                    self._collected_stat_inputs.append(node_input_0.fullname)
+                    self._collected_stat_inputs.append(node_input_0_name)
             elif is_this_branch_node and len(node_parents) > 1:
                 return
             else:
@@ -254,12 +257,13 @@ class BiasCorrection(Algorithm):
 
     @staticmethod
     def _create_parameters_for_input_nodes(input_nodes):
-        outputs_shapes = {n.name: nu.get_output_shape(n, 0).copy() for n in input_nodes}
+        outputs_shapes = {nu.create_node_name(n): nu.get_output_shape(n, 0).copy() for n in input_nodes}
         inputs_data = []
         for input_node in input_nodes:
-            c_input_shape = outputs_shapes[input_node.name]
+            input_node_name = nu.create_node_name(input_node)
+            c_input_shape = outputs_shapes[input_node_name]
             c_input_shape[0] = 1
-            parameter_name = input_node.name + '/parameter'
+            parameter_name = input_node_name + '/parameter'
             param_node = ge.create_node(input_node.graph, parameter_name, 'Parameter',
                                         {'shape': c_input_shape})
             for _, port in input_node.out_ports().items():
@@ -270,7 +274,7 @@ class BiasCorrection(Algorithm):
             inputs_data.append({
                 'param_name': param_node.name,
                 'param_shape': tuple(c_input_shape),
-                'input_name': input_node.name
+                'input_name': input_node_name
             })
 
         return inputs_data
@@ -278,7 +282,7 @@ class BiasCorrection(Algorithm):
     def _create_results_after_nodes(self, output_nodes):
         outputs_data = []
         for output_node in output_nodes:
-            output_name = output_node.name
+            output_name = nu.create_node_name(output_node, str)
             result_name = output_name + '/result'
             result_node = ge.create_node(output_node.graph, result_name, 'Result', {})
             result_node.in_port(0).connect(output_node.out_port(0))
@@ -405,8 +409,9 @@ class BiasCorrection(Algorithm):
                 add_node_name = nu.reset_node_fullname(add_node_name, add_node['orig_node_name'])
             axis = OPERATIONS_CHANNEL_AXIS[node.type]
             self._channel_axis[add_node_name] = axis
-            if node.fullname in biased_after_param_nodes:
-                input_name = biased_after_param_nodes[node.fullname]
+            node_name = node.fullname
+            if node_name in biased_after_param_nodes:
+                input_name = biased_after_param_nodes[node_name]
                 statistics_layout[input_name] = {'batch_mean_param_in': agf.batch_mean}
                 self._collected_stat_inputs.append(input_name)
             statistics_layout[add_node_name] =\
@@ -447,7 +452,8 @@ class BiasCorrection(Algorithm):
             node_children = self.get_node_children(node)
             if node.type in self._types_with_bias:
                 node_input = nu.get_node_input(node, 0)
-                biased_after_param_nodes[node.fullname] = node_input.fullname
+                node_input_name = nu.create_node_name(node_input)
+                biased_after_param_nodes[node.fullname] = node_input_name
                 return
             for node_child in node_children:
                 walk_to_children(node_child, parameter_name)
diff --git a/tools/pot/openvino/tools/pot/graph/node_utils.py b/tools/pot/openvino/tools/pot/graph/node_utils.py
index 39157a7eab1..7b8fa99f252 100644
--- a/tools/pot/openvino/tools/pot/graph/node_utils.py
+++ b/tools/pot/openvino/tools/pot/graph/node_utils.py
@@ -192,11 +192,8 @@ def get_quantized_input_key(quantized_node):
     Otherwise, key is tuple (fq_input name, output port number)
     """
     quantized_input = get_node_input(quantized_node, 0)
-    key = quantized_input.fullname
-    if len(quantized_input.out_ports()) > 1:
-        port_number = quantized_node.in_port(0).get_source().out
-        key = (quantized_input.fullname, port_number)
-    return key
+    quantized_key = create_node_name(quantized_input)
+    return quantized_key
 
 
 def node_with_quantized_weights(node):
@@ -267,6 +264,19 @@ def get_lstm_ends(read_value, assigns, ignore_nodes):
     return lstm_outputs
 
 
+def create_node_name(input_node, mode=tuple):
+    """
+    Returns key for node input.
+    If input node has one output port -> key is name of input node.
+    Otherwise, key is tuple (input name, output port number)
+    """
+    key = input_node.fullname
+    if len(input_node.out_ports()) > 1:
+        port_number = input_node.in_port(0).get_source().out
+        key = (input_node.fullname, port_number) if mode == tuple else f"{input_node.fullname}.{port_number}"
+    return key
+
+
 def get_node_data_type(node, port_id=0):
     if node.type != 'Const' and node.in_port(port_id).get_source() is not None \
             and node.in_port(port_id).get_source().is_data_type_defined():

From 3032f20e03000aced22ea5abd9fee90d4198ca05 Mon Sep 17 00:00:00 2001
From: Maxim Shevtsov <maxim.y.shevtsov@intel.com>
Date: Tue, 21 Dec 2021 12:07:59 +0300
Subject: [PATCH 065/160] brushed the #streams logic when #requests is limited
 (#9184)

---
 inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index 7d948150c1e..193df08ec59 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -635,12 +635,14 @@ Engine::LoadExeNetworkImpl(const InferenceEngine::CNNNetwork &network, const std
                     num_streams = std::max(default_num_streams, num_streams_less_aggressive);
                 }
                 auto num_requests = config.find(PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS);
-                if (engConfig.perfHintsConfig.ovPerfHintNumRequests)  // set thru SetConfig to the plugin
-                    num_streams = std::min(engConfig.perfHintsConfig.ovPerfHintNumRequests,
-                            engConfig.perfHintsConfig.ovPerfHintNumRequests);
-                if (num_requests != config.end())   // arrived with config to the LoadNetwork (and thus higher pri)
+                if (num_requests != config.end()) {  // arrived with config to the LoadNetwork (and thus higher pri)
+                    auto val = PerfHintsConfig::CheckPerformanceHintRequestValue(num_requests->second);
+                    if (val > 0)
+                        num_streams = std::min(num_streams, val);
+                } else if (engConfig.perfHintsConfig.ovPerfHintNumRequests) {  //set thru SetConfig to the plugin, 2nd priority
                     num_streams = std::min(num_streams,
-                            PerfHintsConfig::CheckPerformanceHintRequestValue(num_requests->second));
+                                           engConfig.perfHintsConfig.ovPerfHintNumRequests);
+                }
                 config[PluginConfigParams::KEY_CPU_THROUGHPUT_STREAMS] = std::to_string(num_streams);
            }
         }

From ec858fcffd6ba091a72581ba0d6c2dd68bb1c341 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Tue, 21 Dec 2021 12:25:07 +0300
Subject: [PATCH 066/160] Add OV developer config (#9298)

* Introduced OV developer package config

* Remove new targets from old configs

* Fixed python build

* Removed OpenVINO_SOURCE_DIR from developer config
---
 cmake/extra_modules.cmake                     | 28 +++++++
 ...renceEngineDeveloperPackageConfig.cmake.in |  7 --
 .../OpenVINODeveloperPackageConfig.cmake.in   | 76 +++++++++++++++++++
 cmake/templates/ngraphConfig.cmake.in         | 15 ----
 .../src/compatibility/pyngraph/CMakeLists.txt |  2 +-
 .../python/src/pyopenvino/CMakeLists.txt      |  2 +-
 .../CMakeLists.txt                            |  2 +-
 src/common/transformations/CMakeLists.txt     |  2 +-
 src/core/CMakeLists.txt                       |  1 -
 src/frontends/common/CMakeLists.txt           |  2 +-
 src/inference/CMakeLists.txt                  |  2 +-
 11 files changed, 110 insertions(+), 29 deletions(-)
 create mode 100644 cmake/templates/OpenVINODeveloperPackageConfig.cmake.in

diff --git a/cmake/extra_modules.cmake b/cmake/extra_modules.cmake
index 1fe5e4bd210..b9dc0d91337 100644
--- a/cmake/extra_modules.cmake
+++ b/cmake/extra_modules.cmake
@@ -26,6 +26,33 @@ function(ie_generate_dev_package_config)
                    @ONLY)
 endfunction()
 
+function(ov_generate_dev_package_config)
+    # dummy check that OpenCV is here
+    find_package(OpenCV QUIET)
+
+    set(all_dev_targets gflags ov_runtime_libraries)
+    foreach(component IN LISTS openvino_export_components)
+        string(FIND "${component}" "_legacy" index)
+        if (index EQUAL -1)
+            # export all targets with prefix and use them during extra modules build
+            export(TARGETS ${${component}} NAMESPACE openvino::
+                APPEND FILE "${CMAKE_BINARY_DIR}/ov_${component}_dev_targets.cmake")
+            list(APPEND all_dev_targets ${${component}})
+        endif()
+    endforeach()
+    add_custom_target(ov_dev_targets DEPENDS ${all_dev_targets})
+
+    configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in"
+                                  "${CMAKE_BINARY_DIR}/OpenVINODeveloperPackageConfig.cmake"
+                                  INSTALL_DESTINATION share # not used
+                                  PATH_VARS "OpenVINO_SOURCE_DIR;gflags_BINARY_DIR"
+                                  NO_CHECK_REQUIRED_COMPONENTS_MACRO)
+
+    configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig-version.cmake.in"
+                   "${CMAKE_BINARY_DIR}/OpenVINODeveloperPackageConfig-version.cmake" 
+                   @ONLY)
+endfunction()
+
 #
 # Add extra modules
 #
@@ -97,6 +124,7 @@ endfunction()
 # during extra modules build since it's generated after modules
 # are configured
 ie_generate_dev_package_config()
+ov_generate_dev_package_config()
 
 # extra modules must be registered after inference_engine library
 # and all other IE common libraries (ov_runtime_libraries) are creared
diff --git a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
index eb3f0579213..ef7d2beec02 100644
--- a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
+++ b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
@@ -49,13 +49,6 @@ find_dependency(ngraph
                 NO_CMAKE_FIND_ROOT_PATH
                 NO_DEFAULT_PATH)
 
-# create targets with old names for compatibility
-if(TARGET openvino::runtime AND NOT TARGET IE::core)
-    add_library(IE::core INTERFACE IMPORTED)
-    set_target_properties(IE::core PROPERTIES
-        INTERFACE_LINK_LIBRARIES openvino::runtime)
-endif()
-
 if(TARGET openvino::runtime AND NOT TARGET IE::runtime)
     add_library(IE::runtime INTERFACE IMPORTED)
     set_target_properties(IE::runtime PROPERTIES
diff --git a/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
new file mode 100644
index 00000000000..da3a7b0427b
--- /dev/null
+++ b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
@@ -0,0 +1,76 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+@PACKAGE_INIT@
+
+include(CMakeFindDependencyMacro)
+
+# Variables to export in plugin's projects
+
+set(ie_options "@IE_OPTIONS@;CMAKE_BUILD_TYPE;CMAKE_SKIP_RPATH")
+list(APPEND ie_options CMAKE_CXX_COMPILER_LAUNCHER CMAKE_C_COMPILER_LAUNCHER)
+file(TO_CMAKE_PATH "${CMAKE_CURRENT_LIST_DIR}" cache_path)
+
+message(STATUS "The following CMake options are exported from Inference Engine Developer package")
+message("")
+foreach(option IN LISTS ie_options)
+    if(NOT DEFINED "${option}")
+        load_cache("${cache_path}" READ_WITH_PREFIX "" ${option})
+    endif()
+    message("    ${option}: ${${option}}")
+endforeach()
+message("")
+
+# for samples in 3rd party projects
+set_and_check(gflags_DIR "@gflags_BINARY_DIR@")
+
+#
+# Content
+#
+
+find_dependency(IEDevScripts
+                PATHS "@OpenVINO_SOURCE_DIR@/cmake/developer_package"
+                NO_CMAKE_FIND_ROOT_PATH
+                NO_DEFAULT_PATH)
+
+find_dependency(OpenVINO
+                PATHS "${CMAKE_CURRENT_LIST_DIR}"
+                NO_CMAKE_FIND_ROOT_PATH
+                NO_DEFAULT_PATH)
+
+foreach(component @openvino_export_components@)
+    string(FIND "${component}" "_legacy" index)
+    if (index EQUAL -1)
+        include("${CMAKE_CURRENT_LIST_DIR}/ov_${component}_dev_targets.cmake")
+    endif()
+endforeach()
+
+if(ENABLE_SYSTEM_PUGIXML)
+    find_dependency(PugiXML)
+    set_property(TARGET pugixml PROPERTY IMPORTED_GLOBAL TRUE)
+    add_library(IE::pugixml ALIAS pugixml)
+endif()
+
+# inherit OpenCV from main IE project if enabled
+if ("@OpenCV_FOUND@")
+    load_cache("${cache_path}" READ_WITH_PREFIX "" OpenCV_DIR)
+    find_dependency(OpenCV)
+endif()
+
+#
+# Extra Compile Flags
+#
+
+if(NOT MSVC)
+    ie_add_compiler_flags(-Wno-error=unused-variable)
+    if(CMAKE_COMPILER_IS_GNUCXX)
+        ie_add_compiler_flags(-Wno-error=unused-but-set-variable)
+        if(SUGGEST_OVERRIDE_SUPPORTED)
+            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
+        endif()
+    endif()
+endif()
+
+# Don't threat deprecated API warnings as errors in 3rd party apps
+ie_deprecated_no_errors()
diff --git a/cmake/templates/ngraphConfig.cmake.in b/cmake/templates/ngraphConfig.cmake.in
index 822c876a8b0..a67c3f3c473 100644
--- a/cmake/templates/ngraphConfig.cmake.in
+++ b/cmake/templates/ngraphConfig.cmake.in
@@ -26,8 +26,6 @@
 #
 # Frontends:
 #
-#   ngraph::common                         - nGraph frontend common
-#
 #   ngraph_ov_onnx_frontend_FOUND          - True if the system has ov_onnx_frontend library
 #   ngraph::ov_onnx_frontend               - ONNX FrontEnd target (optional)
 #
@@ -52,19 +50,6 @@ if(TARGET openvino::runtime AND NOT TARGET ngraph::ngraph)
         INTERFACE_LINK_LIBRARIES openvino::runtime)
 endif()
 
-if(TARGET openvino::runtime AND NOT TARGET openvino::frontend::common)
-    add_library(openvino::frontend::common INTERFACE IMPORTED)
-    set_target_properties(openvino::frontend::common PROPERTIES
-        INTERFACE_LINK_LIBRARIES openvino::runtime)
-endif()
-
-
-if(TARGET openvino::runtime AND NOT TARGET ngraph::frontend_common)
-    add_library(ngraph::frontend_common INTERFACE IMPORTED)
-    set_target_properties(ngraph::frontend_common PROPERTIES
-        INTERFACE_LINK_LIBRARIES openvino::runtime)
-endif()
-
 if(TARGET openvino::frontend::onnx AND NOT TARGET ngraph::ov_onnx_frontend)
     add_library(ngraph::ov_onnx_frontend INTERFACE IMPORTED)
     set_target_properties(ngraph::ov_onnx_frontend PROPERTIES
diff --git a/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt b/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
index a0870de63f8..7c8fd86ebd1 100644
--- a/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
+++ b/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
@@ -54,7 +54,7 @@ pybind11_add_module(_${PROJECT_NAME} MODULE ${SOURCES})
 
 target_include_directories(_${PROJECT_NAME} PRIVATE "../")
 
-target_link_libraries(_${PROJECT_NAME} PRIVATE openvino::core)
+target_link_libraries(_${PROJECT_NAME} PRIVATE openvino::runtime)
 
 # perform copy
 if(OpenVINO_SOURCE_DIR)
diff --git a/src/bindings/python/src/pyopenvino/CMakeLists.txt b/src/bindings/python/src/pyopenvino/CMakeLists.txt
index 73142ab988f..1a5d31bfcfa 100644
--- a/src/bindings/python/src/pyopenvino/CMakeLists.txt
+++ b/src/bindings/python/src/pyopenvino/CMakeLists.txt
@@ -61,7 +61,7 @@ else()
 endif()
 
 target_include_directories(${PROJECT_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/..")
-target_link_libraries(${PROJECT_NAME} PRIVATE openvino::runtime ${OFFLINE_TRANSFORMATIONS_LIB} openvino::frontend::common)
+target_link_libraries(${PROJECT_NAME} PRIVATE openvino::runtime ${OFFLINE_TRANSFORMATIONS_LIB})
 
 # perform copy
 if(OpenVINO_SOURCE_DIR)
diff --git a/src/common/low_precision_transformations/CMakeLists.txt b/src/common/low_precision_transformations/CMakeLists.txt
index 528f13ff79a..d0bdc4a11a2 100644
--- a/src/common/low_precision_transformations/CMakeLists.txt
+++ b/src/common/low_precision_transformations/CMakeLists.txt
@@ -51,7 +51,7 @@ target_include_directories(${TARGET_NAME} INTERFACE
 
 # developer package
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
+openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME})
 
 # install
 
diff --git a/src/common/transformations/CMakeLists.txt b/src/common/transformations/CMakeLists.txt
index 16e25e9981d..97deaaa294f 100644
--- a/src/common/transformations/CMakeLists.txt
+++ b/src/common/transformations/CMakeLists.txt
@@ -52,7 +52,7 @@ target_link_libraries(${TARGET_NAME} INTERFACE openvino::runtime)
 
 # developer package
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
+openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME})
 
 # install
 
diff --git a/src/core/CMakeLists.txt b/src/core/CMakeLists.txt
index a72e9a25f1e..df7360a2a94 100644
--- a/src/core/CMakeLists.txt
+++ b/src/core/CMakeLists.txt
@@ -117,7 +117,6 @@ target_include_directories(ngraph INTERFACE $<BUILD_INTERFACE:${OV_CORE_INCLUDE_
 # Export for build tree
 #-----------------------------------------------------------------------------------------------
 
-set_target_properties(ngraph PROPERTIES EXPORT_NAME core)
 export(TARGETS ngraph NAMESPACE openvino::
        APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
 
diff --git a/src/frontends/common/CMakeLists.txt b/src/frontends/common/CMakeLists.txt
index 412d5f53e95..0402c98e6bf 100644
--- a/src/frontends/common/CMakeLists.txt
+++ b/src/frontends/common/CMakeLists.txt
@@ -66,7 +66,7 @@ add_library(ngraph::${TARGET_NAME} ALIAS ${TARGET_NAME})
 add_library(openvino::frontend::common ALIAS ${TARGET_NAME})
 add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME})
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
+openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME})
 
 # Installation rules for shared version only
 
diff --git a/src/inference/CMakeLists.txt b/src/inference/CMakeLists.txt
index 02a4e1a922d..85b58bcba12 100644
--- a/src/inference/CMakeLists.txt
+++ b/src/inference/CMakeLists.txt
@@ -212,7 +212,7 @@ export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
 
 # Export for developer package
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME}_plugin_api)
+openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME}_plugin_api)
 
 install(DIRECTORY "${PUBLIC_HEADERS_DIR}/" DESTINATION runtime/include
         COMPONENT core_dev)

From 4b0a2c912754eb08708e4014e41463d494dacdc5 Mon Sep 17 00:00:00 2001
From: Aleksandr Korolev <aleksandr.korolev@intel.com>
Date: Tue, 21 Dec 2021 12:58:05 +0300
Subject: [PATCH 067/160] [VPU] Coverity fixes (#9214)

Some coverity fixes
---
 .../hw/conv_tiling/hw_convolution_tiler.hpp   |  2 +-
 .../hw/pooling_tiling/hw_pooling_tiler.hpp    |  2 +-
 .../hw/conv_tiling/hw_convolution_tiler.cpp   |  2 +-
 .../hw/pooling_tiling/hw_pooling_tiler.cpp    |  2 +-
 .../XLink/shared/src/XLinkDispatcher.c        | 10 ++++++++--
 .../XLink/shared/src/XLinkDispatcherImpl.c    | 20 +++++++++----------
 .../XLink/shared/src/XLinkPrivateDefines.c    |  4 ++--
 .../movidius/XLink/shared/src/XLinkStream.c   |  2 +-
 .../thirdparty/movidius/mvnc/src/mvnc_api.c   |  3 ++-
 9 files changed, 27 insertions(+), 20 deletions(-)

diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/conv_tiling/hw_convolution_tiler.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/conv_tiling/hw_convolution_tiler.hpp
index 4815a815eba..16811b2e248 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/conv_tiling/hw_convolution_tiler.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/conv_tiling/hw_convolution_tiler.hpp
@@ -173,7 +173,7 @@ class HWConvolutionTiler final {
 public:
     HWConvolutionTiler() = delete;
     HWConvolutionTiler(const HWConvolutionTiler&) = default;
-    HWConvolutionTiler(ConvolutionOptions convolutionOptions, const Direction& direction, std::size_t maxTilingOptions);
+    HWConvolutionTiler(const ConvolutionOptions& convolutionOptions, const Direction& direction, std::size_t maxTilingOptions);
 
 
     bool isTilingPossible() const {
diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/pooling_tiling/hw_pooling_tiler.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/pooling_tiling/hw_pooling_tiler.hpp
index d23a5734a74..66cc8c2434e 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/pooling_tiling/hw_pooling_tiler.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/hw/pooling_tiling/hw_pooling_tiler.hpp
@@ -83,7 +83,7 @@ public:
     HWPoolingTiler() = delete;
 
     HWPoolingTiler(const HWPoolingTiler&) = default;
-    HWPoolingTiler(ConvolutionOptions convolutionOptions, const Direction& direction, std::size_t maxTilingOptions);
+    HWPoolingTiler(const ConvolutionOptions& convolutionOptions, const Direction& direction, std::size_t maxTilingOptions);
 
     bool isTilingPossible() const {
         return _tilingPossible;
diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/hw/conv_tiling/hw_convolution_tiler.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/hw/conv_tiling/hw_convolution_tiler.cpp
index 6b7f9c4e610..18bbda73f7e 100644
--- a/inference-engine/src/vpu/graph_transformer/src/middleend/hw/conv_tiling/hw_convolution_tiler.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/middleend/hw/conv_tiling/hw_convolution_tiler.cpp
@@ -257,7 +257,7 @@ public:
     }
 };
 
-HWConvolutionTiler::HWConvolutionTiler(ConvolutionOptions convolutionOptions, const Direction& direction,
+HWConvolutionTiler::HWConvolutionTiler(const ConvolutionOptions& convolutionOptions, const Direction& direction,
                                        std::size_t maxTilingOptions) :
     _convolutionOptions(std::move(convolutionOptions)),
     _searcher(_convolutionOptions, direction, maxTilingOptions) {
diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/hw/pooling_tiling/hw_pooling_tiler.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/hw/pooling_tiling/hw_pooling_tiler.cpp
index cb5129f9e3c..55bb37e15ea 100644
--- a/inference-engine/src/vpu/graph_transformer/src/middleend/hw/pooling_tiling/hw_pooling_tiler.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/middleend/hw/pooling_tiling/hw_pooling_tiler.cpp
@@ -247,7 +247,7 @@ private:
 };
 
 
-HWPoolingTiler::HWPoolingTiler(ConvolutionOptions convolutionOptions, const Direction& direction,
+HWPoolingTiler::HWPoolingTiler(const ConvolutionOptions& convolutionOptions, const Direction& direction,
                                std::size_t maxTilingOptions) :
     _convolutionOptions(std::move(convolutionOptions)),
     _searcher(_convolutionOptions, direction, maxTilingOptions) {
diff --git a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcher.c b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcher.c
index 1b6ca953281..7e942b4040f 100644
--- a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcher.c
+++ b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcher.c
@@ -977,7 +977,7 @@ static int dispatcherClean(xLinkSchedulerState_t* curr)
     XLINK_RET_ERR_IF(pthread_mutex_lock(&clean_mutex), 1);
     if (curr->schedulerId == -1) {
         mvLog(MVLOG_WARN,"Scheduler has already been reset or cleaned");
-        if(pthread_mutex_unlock(&clean_mutex) != 0) {
+        if (pthread_mutex_unlock(&clean_mutex) != 0) {
             mvLog(MVLOG_ERROR, "Failed to unlock clean_mutex");
         }
 
@@ -996,7 +996,13 @@ static int dispatcherClean(xLinkSchedulerState_t* curr)
 
         XLINK_RET_ERR_IF(pthread_mutex_lock(&(curr->queueMutex)) != 0, 1);
         postAndMarkEventServed(event);
-        XLINK_RET_ERR_IF(pthread_mutex_unlock(&(curr->queueMutex)) != 0, 1);
+        if (pthread_mutex_unlock(&(curr->queueMutex)) != 0) {
+            if (pthread_mutex_unlock(&clean_mutex) != 0) {
+                mvLog(MVLOG_ERROR, "Failed to unlock clean_mutex after failing to unlock curr->queueMutex");
+            }
+            mvLog(MVLOG_ERROR, "Failed to unlock curr->queueMutex");
+            return 1;
+        }
         event = dispatcherGetNextEvent(curr);
     }
 
diff --git a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcherImpl.c b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcherImpl.c
index 76e830c2962..2e06a7e5e1f 100644
--- a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcherImpl.c
+++ b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkDispatcherImpl.c
@@ -44,7 +44,7 @@ static int handleIncomingEvent(xLinkEvent_t* event);
 //adds a new event with parameters and returns event id
 int dispatcherEventSend(xLinkEvent_t *event)
 {
-    mvLog(MVLOG_DEBUG, "Send event: %s, size %lu, streamId %lu.\n",
+    mvLog(MVLOG_DEBUG, "Send event: %s, size %u, streamId %u.\n",
         TypeToStr(event->header.type), event->header.size, event->header.streamId);
 
     int rc = XLinkPlatformWrite(&event->deviceHandle,
@@ -124,17 +124,17 @@ int dispatcherLocalEventGetResponse(xLinkEvent_t* event, xLinkEvent_t* response)
             XLINK_EVENT_ACKNOWLEDGE(event);
             event->header.flags.bitField.localServe = 0;
 
-            if(!isStreamSpaceEnoughFor(stream, event->header.size)){
+            if (!isStreamSpaceEnoughFor(stream, event->header.size)) {
                 mvLog(MVLOG_DEBUG,"local NACK RTS. stream '%s' is full (event %d)\n", stream->name, event->header.id);
                 event->header.flags.bitField.block = 1;
                 event->header.flags.bitField.localServe = 1;
                 // TODO: easy to implement non-blocking read here, just return nack
                 mvLog(MVLOG_WARN, "Blocked event would cause dispatching thread to wait on semaphore infinitely\n");
-            }else{
+            } else {
                 event->header.flags.bitField.block = 0;
                 stream->remoteFillLevel += event->header.size;
                 stream->remoteFillPacketLevel++;
-                mvLog(MVLOG_DEBUG,"S%lu: Got local write of %lu , remote fill level %lu out of %lu %lu\n",
+                mvLog(MVLOG_DEBUG,"S%u: Got local write of %u , remote fill level %u out of %u %u\n",
                       event->header.streamId, event->header.size, stream->remoteFillLevel, stream->writeSize, stream->readSize);
             }
             releaseStream(stream);
@@ -295,7 +295,7 @@ int dispatcherRemoteEventGetResponse(xLinkEvent_t* event, xLinkEvent_t* response
             stream->remoteFillLevel -= event->header.size;
             stream->remoteFillPacketLevel--;
 
-            mvLog(MVLOG_DEBUG,"S%lu: Got remote release of %lu, remote fill level %lu out of %lu %lu\n",
+            mvLog(MVLOG_DEBUG,"S%u: Got remote release of %u, remote fill level %u out of %u %u\n",
                   event->header.streamId, event->header.size, stream->remoteFillLevel, stream->writeSize, stream->readSize);
             releaseStream(stream);
 
@@ -321,7 +321,7 @@ int dispatcherRemoteEventGetResponse(xLinkEvent_t* event, xLinkEvent_t* response
             stream->remoteFillLevel -= event->header.size;
             stream->remoteFillPacketLevel--;
 
-            mvLog(MVLOG_DEBUG,"S%lu: Got remote release of %lu, remote fill level %lu out of %lu %lu\n",
+            mvLog(MVLOG_DEBUG,"S%u: Got remote release of %u, remote fill level %u out of %u %u\n",
                   event->header.streamId, event->header.size, stream->remoteFillLevel, stream->writeSize, stream->readSize);
             releaseStream(stream);
 
@@ -537,7 +537,7 @@ int isStreamSpaceEnoughFor(streamDesc_t* stream, uint32_t size)
 {
     if(stream->remoteFillPacketLevel >= XLINK_MAX_PACKETS_PER_STREAM ||
        stream->remoteFillLevel + size > stream->writeSize){
-        mvLog(MVLOG_DEBUG, "S%lu: Not enough space in stream '%s' for %lu: PKT %lu, FILL %lu SIZE %lu\n",
+        mvLog(MVLOG_DEBUG, "S%u: Not enough space in stream '%s' for %u: PKT %u, FILL %u SIZE %u\n",
               stream->id, stream->name, size, stream->remoteFillPacketLevel, stream->remoteFillLevel, stream->writeSize);
         return 0;
     }
@@ -568,7 +568,7 @@ int releasePacketFromStream(streamDesc_t* stream, uint32_t* releasedSize)
     }
 
     stream->localFillLevel -= currPack->length;
-    mvLog(MVLOG_DEBUG, "S%lu: Got release of %lu , current local fill level is %lu out of %lu %lu\n",
+    mvLog(MVLOG_DEBUG, "S%u: Got release of %u , current local fill level is %u out of %u %u\n",
           stream->id, currPack->length, stream->localFillLevel, stream->readSize, stream->writeSize);
 
     XLinkPlatformDeallocateData(currPack->data,
@@ -606,7 +606,7 @@ int releaseSpecificPacketFromStream(streamDesc_t* stream, uint32_t* releasedSize
 
     stream->localFillLevel -= currPack->length;
 
-  mvLog(MVLOG_DEBUG, "S%lu: Got release of %lu , current local fill level is %lu out of %lu %lu\n",
+  mvLog(MVLOG_DEBUG, "S%u: Got release of %u , current local fill level is %u out of %u %u\n",
           stream->id, currPack->length, stream->localFillLevel, stream->readSize, stream->writeSize);
     XLinkPlatformDeallocateData(currPack->data,
                                 ALIGN_UP_INT32((int32_t) currPack->length, __CACHE_LINE_SIZE), __CACHE_LINE_SIZE);
@@ -665,7 +665,7 @@ int handleIncomingEvent(xLinkEvent_t* event) {
     ASSERT_XLINK(stream);
 
     stream->localFillLevel += event->header.size;
-    mvLog(MVLOG_DEBUG,"S%lu: Got write of %lu, current local fill level is %lu out of %lu %lu\n",
+    mvLog(MVLOG_DEBUG,"S%u: Got write of %u, current local fill level is %u out of %u %u\n",
           event->header.streamId, event->header.size, stream->localFillLevel, stream->readSize, stream->writeSize);
 
     void* buffer = XLinkPlatformAllocateData(ALIGN_UP(event->header.size, __CACHE_LINE_SIZE), __CACHE_LINE_SIZE);
diff --git a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkPrivateDefines.c b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkPrivateDefines.c
index 718d804ea97..8d9ebda4da7 100644
--- a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkPrivateDefines.c
+++ b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkPrivateDefines.c
@@ -37,7 +37,7 @@ static streamId_t getNextStreamUniqueId(xLinkDesc_t *link);
 streamId_t XLinkAddOrUpdateStream(void *fd, const char *name,
     uint32_t writeSize, uint32_t readSize, streamId_t forcedId)
 {
-    mvLog(MVLOG_DEBUG, "name: %s, writeSize: %lu, readSize: %lu, forcedId: %lu\n",
+    mvLog(MVLOG_DEBUG, "name: %s, writeSize: %u, readSize: %u, forcedId: %u\n",
         name, writeSize, readSize, forcedId);
 
     streamId_t retStreamId = INVALID_STREAM_ID;
@@ -50,7 +50,7 @@ streamId_t XLinkAddOrUpdateStream(void *fd, const char *name,
         int streamAlreadyExists = (writeSize > stream->writeSize && stream->writeSize != 0)
             || (readSize > stream->readSize && stream->readSize != 0);
         XLINK_OUT_WITH_LOG_IF(streamAlreadyExists,
-            mvLog(MVLOG_ERROR, "Stream with name:%s already exists: id=%lu\n", name, stream->id));
+            mvLog(MVLOG_ERROR, "Stream with name:%s already exists: id=%u\n", name, stream->id));
     } else {
         streamId_t nextStreamId = forcedId == INVALID_STREAM_ID ?
                                   getNextStreamUniqueId(link) : forcedId;
diff --git a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkStream.c b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkStream.c
index 6d5ececd0d7..20b5bf813a7 100644
--- a/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkStream.c
+++ b/inference-engine/thirdparty/movidius/XLink/shared/src/XLinkStream.c
@@ -17,7 +17,7 @@
 
 XLinkError_t XLinkStreamInitialize(
     streamDesc_t* stream, streamId_t id, const char* name) {
-    mvLog(MVLOG_DEBUG, "name: %s, id: %lu\n", name, id);
+    mvLog(MVLOG_DEBUG, "name: %s, id: %u\n", name, id);
     ASSERT_XLINK(stream);
 
     memset(stream, 0, sizeof(*stream));
diff --git a/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c b/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c
index ece30e7e906..2aa97dcd42f 100644
--- a/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c
+++ b/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c
@@ -1483,8 +1483,9 @@ static void printfOverXLinkOpen(struct _devicePrivate_t *d) {
 
     int portNum = 7788;
     int connfd = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP);
-    if (connfd != 0) {
+    if (connfd == -1) {
         fprintf(stderr,"ERROR in socket function, return value is : %d\n", connfd);
+        close(connfd);
         return;
     }
     bzero((char *) &serv_addr, sizeof(serv_addr));

From f9ece370f310514df16270bc148dd6a68f2e827f Mon Sep 17 00:00:00 2001
From: tgubanova-lohika <93532421+tgubanova-lohika@users.noreply.github.com>
Date: Tue, 21 Dec 2021 12:24:07 +0200
Subject: [PATCH 068/160] [GPU] Implement RandomUniform operation (#9043)

* [GPU] Implement Random Uniform kernel

* [GPU] Add random uniform kernel selector

* [GPU] Add random uniform primitive

* [GPU] Add random uniform instantiation helpers

* [GPU] Implement unit test for the random uniform operation

* [GPU] Add random uniform operation builder in cldnn_engine plugin

* [GPU] Add single layer tests for the random uniform operation

* [GPU] Add conversion for Random Uniform output type

* [GPU] Add RTTI macroses to RandomUniform operation
---
 .../intel_gpu/primitives/random_uniform.hpp   |  52 +++++
 .../kernel_selector/common/common_types.h     |   3 +-
 .../random_uniform_kernel_ref.cpp             | 109 +++++++++++
 .../random_uniform_kernel_ref.h               |  56 ++++++
 .../random_uniform_kernel_selector.cpp        |  16 ++
 .../random_uniform_kernel_selector.h          |  27 +++
 .../core/cl_kernels/random_uniform_ref.cl     | 142 ++++++++++++++
 .../clDNN/src/impls/ocl/random_uniform.cpp    |  66 +++++++
 .../clDNN/src/impls/ocl/register.cpp          |   1 +
 .../clDNN/src/impls/ocl/register.hpp          |   2 +
 .../clDNN/src/include/random_uniform_inst.h   |  39 ++++
 .../thirdparty/clDNN/src/random_uniform.cpp   |  41 ++++
 .../test_cases/random_uniform_gpu_test.cpp    | 184 ++++++++++++++++++
 .../include/openvino/op/random_uniform.hpp    |   1 +
 src/core/src/op/random_uniform.cpp            |   2 +
 src/core/src/pass/convert_precision.cpp       |  15 +-
 .../intel_gpu/plugin/primitives_list.hpp      |   1 +
 .../src/plugin/ops/random_uniform.cpp         |  39 ++++
 .../single_layer_tests/random_uniform.cpp     |  40 ++++
 .../single_layer_tests/random_uniform.hpp     |  15 ++
 .../base/layer_test_utils.hpp                 |   4 +
 .../single_layer/random_uniform.hpp           |  42 ++++
 .../src/base/layer_test_utils.cpp             |   5 +-
 .../src/single_layer/random_uniform.cpp       |  89 +++++++++
 24 files changed, 988 insertions(+), 3 deletions(-)
 create mode 100644 inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/random_uniform.hpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.h
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/random_uniform_ref.cl
 create mode 100644 inference-engine/thirdparty/clDNN/src/impls/ocl/random_uniform.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/src/include/random_uniform_inst.h
 create mode 100644 inference-engine/thirdparty/clDNN/src/random_uniform.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/tests/test_cases/random_uniform_gpu_test.cpp
 create mode 100644 src/plugins/intel_gpu/src/plugin/ops/random_uniform.cpp
 create mode 100644 src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/random_uniform.cpp
 create mode 100644 src/tests/functional/plugin/shared/include/single_layer_tests/random_uniform.hpp
 create mode 100644 src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/random_uniform.hpp
 create mode 100644 src/tests/functional/shared_test_classes/src/single_layer/random_uniform.cpp

diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/random_uniform.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/random_uniform.hpp
new file mode 100644
index 00000000000..b5b3385ad76
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/random_uniform.hpp
@@ -0,0 +1,52 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <vector>
+
+#include "primitive.hpp"
+
+namespace cldnn {
+
+/// @addtogroup cpp_api C++ API
+/// @{
+/// @addtogroup cpp_topology Network Topology
+/// @{
+/// @addtogroup cpp_primitives Primitives
+/// @{
+
+/// @brief RandomUniform-8 primitive
+/// @details
+struct random_uniform : public primitive_base<random_uniform> {
+    CLDNN_DECLARE_PRIMITIVE(random_uniform)
+
+    /**
+     * Construct Random Uniform privitive.
+     * @param id primitive id
+     * @param inputs inputs parameters ids
+     * @param data_type output values data type
+     * @param global_seed, op_seed random uniform seed attributes
+     * @param output_shape output data shape
+     * @param output_format output data shape format
+     */
+    random_uniform(const primitive_id &id, const std::vector<primitive_id> &inputs,
+                   const data_types &data_type, const uint64_t global_seed,
+                   const uint64_t op_seed, const tensor output_shape,
+                   const format output_format,
+                   const padding &output_padding = padding())
+            : primitive_base(id, inputs, "", output_padding,
+                             optional_data_type{data_type}),
+              global_seed(global_seed),
+              op_seed(op_seed),
+              output_shape(output_shape),
+              output_format(output_format) {}
+
+    const uint64_t global_seed;
+    const uint64_t op_seed;
+    const tensor output_shape;
+    const format output_format;
+};
+
+}  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
index 775dc355661..480ec8cc868 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
@@ -75,7 +75,8 @@ enum class KernelType {
     NON_MAX_SUPPRESSION,
     DETECTION_OUTPUT,
     EXPERIMENTAL_DETECTRON_ROI_FEATURE_EXTRACTOR,
-    CONVERT_COLOR
+    CONVERT_COLOR,
+    RANDOM_UNIFORM
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.cpp
new file mode 100644
index 00000000000..247f85af4cc
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.cpp
@@ -0,0 +1,109 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "random_uniform_kernel_ref.h"
+
+#include <kernel_selector_utils.h>
+#include <random>
+
+
+namespace kernel_selector {
+
+namespace {
+
+int getStep(const random_uniform_params &params) {
+    return BytesPerElement(params.output.GetDType()) > 4 ? 2 : 4;
+}
+
+size_t GetGwsSize(const random_uniform_params &params) {
+    size_t shapeSize = params.output.LogicalSize();
+    int step = getStep(params);
+    return CeilDiv(shapeSize, step);
+}
+
+CommonDispatchData SetDefault(const random_uniform_params &params, const optional_params &) {
+    CommonDispatchData dispatchData;
+    dispatchData.gws = {GetGwsSize(params), 1, 1};
+    dispatchData.lws = GetOptimalLocalWorkGroupSizes(dispatchData.gws, params.engineInfo);
+    return dispatchData;
+}
+
+}  // namespace
+
+JitConstants RandomUniformKernelRef::GetJitConstants(const random_uniform_params &params) const {
+    JitConstants jit = MakeBaseParamsJitConstants(params);
+    // When both seed values are equal to zero RandomUniform should generate non-deterministic sequence.
+    if (params.global_seed == 0 && params.op_seed == 0) {
+        jit.AddConstant(MakeJitConstant("GLOBAL_SEED", std::random_device {}()));
+    } else {
+        jit.AddConstant(MakeJitConstant("GLOBAL_SEED", params.global_seed));
+    }
+
+    jit.AddConstant(MakeJitConstant("OP_SEED", params.op_seed));
+    jit.AddConstant(MakeJitConstant("OUTPUT_STEP", getStep(params)));
+    return jit;
+}
+
+
+KernelsData RandomUniformKernelRef::GetKernelsData(const Params &params, const optional_params &options) const {
+    if (!Validate(params, options)) {
+        return {};
+    }
+
+    KernelData kernel_data = KernelData::Default<random_uniform_params>(params);
+    const random_uniform_params &new_params = dynamic_cast<const random_uniform_params &>(*kernel_data.params.get());
+
+    auto dispatch_data = SetDefault(new_params, options);
+    auto entry_point = GetEntryPoint(kernelName, new_params.layerID, params, options);
+
+    auto random_uniform_specific_jit = GetJitConstants(new_params);
+    auto jit = CreateJit(kernelName, random_uniform_specific_jit, entry_point);
+
+    FillCLKernelData(kernel_data.kernels[0], dispatch_data, params.engineInfo, kernelName, jit, entry_point, "", false,
+                     false, 3);
+
+    KernelsData kernelsData;
+    kernelsData.push_back(std::move(kernel_data));
+    return kernelsData;
+}
+
+KernelsPriority RandomUniformKernelRef::GetKernelsPriority(const Params & /*params*/,
+                                                           const optional_params & /*options*/) const {
+    return FORCE_PRIORITY_1;
+}
+
+ParamsKey RandomUniformKernelRef::GetSupportedKey() const {
+    ParamsKey k;
+    k.EnableInputDataType(Datatype::INT32);
+    k.EnableInputDataType(Datatype::INT64);
+    k.EnableInputDataType(Datatype::F16);
+    k.EnableInputDataType(Datatype::F32);
+
+    k.EnableOutputDataType(Datatype::INT32);
+    k.EnableOutputDataType(Datatype::INT64);
+    k.EnableOutputDataType(Datatype::F16);
+    k.EnableOutputDataType(Datatype::F32);
+    k.EnableDifferentTypes();
+    k.EnableAllInputLayout();
+    k.EnableAllOutputLayout();
+    k.EnableBatching();
+    return k;
+}
+
+bool RandomUniformKernelRef::Validate(const Params &params, const optional_params &optionalParams) const {
+    if (params.GetType() != KernelType::RANDOM_UNIFORM || optionalParams.GetType() != KernelType::RANDOM_UNIFORM) {
+        return false;
+    }
+
+    // output shape, min value, max value
+    constexpr uint32_t number_of_inputs = 3;
+    auto &randomUniformParams = dynamic_cast<const random_uniform_params &>(params);
+    if (randomUniformParams.inputs.size() != number_of_inputs) {
+        return false;
+    }
+
+    return true;
+}
+
+}  // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h
new file mode 100644
index 00000000000..489f4b5a786
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h
@@ -0,0 +1,56 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <vector>
+
+#include "kernel_base_opencl.h"
+
+namespace kernel_selector {
+
+/**
+ * Random Uniform kernel params. All the needed inputs are in base input, output shape is static and
+ * presented in output.
+ */
+struct random_uniform_params: public base_params {
+    random_uniform_params() :
+        base_params{KernelType::RANDOM_UNIFORM} {
+    }
+
+    // operation attributes
+    uint64_t global_seed;
+    uint64_t op_seed;
+};
+
+/**
+ * Specific optional params is not defined for RandomUniform operation.
+ */
+struct random_uniform_optional_params: optional_params {
+    random_uniform_optional_params() :
+        optional_params{KernelType::RANDOM_UNIFORM} {
+    }
+};
+
+/**
+ * Reference GPU kernel for the RandomUniform-8 operation.
+ */
+class RandomUniformKernelRef: public KernelBaseOpenCL {
+public:
+    RandomUniformKernelRef() :
+        KernelBaseOpenCL{"random_uniform_ref"} {
+    }
+private:
+    KernelsData GetKernelsData(const Params &params, const optional_params &options) const override;
+
+    KernelsPriority GetKernelsPriority(const Params &params, const optional_params &options) const override;
+
+    ParamsKey GetSupportedKey() const override;
+
+    bool Validate(const Params &params, const optional_params &optionalParams) const override;
+
+    JitConstants GetJitConstants(const random_uniform_params &params) const;
+};
+
+} /* namespace kernel_selector */
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.cpp
new file mode 100644
index 00000000000..1a8b00472c8
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.cpp
@@ -0,0 +1,16 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "random_uniform_kernel_selector.h"
+#include "random_uniform_kernel_ref.h"
+
+namespace kernel_selector {
+
+random_uniform_kernel_selector::random_uniform_kernel_selector() {
+    Attach<RandomUniformKernelRef>();
+}
+
+KernelsData random_uniform_kernel_selector::GetBestKernels(const Params &params, const optional_params &options) const {
+    return GetNaiveBestKernel(params, options, KernelType::RANDOM_UNIFORM);
+}
+} // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.h
new file mode 100644
index 00000000000..151eead48da
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_selector.h
@@ -0,0 +1,27 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "kernel_selector.h"
+
+#pragma once
+
+namespace kernel_selector {
+
+/**
+ * GPU kernel selector for the RandomUniform-8 operation
+ */
+class random_uniform_kernel_selector: public kernel_selector_base {
+public:
+    static random_uniform_kernel_selector& Instance() {
+        static random_uniform_kernel_selector instance_;
+        return instance_;
+    }
+
+    KernelsData GetBestKernels(const Params &params, const optional_params &options) const override;
+
+private:
+    random_uniform_kernel_selector();
+};
+
+}  // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/random_uniform_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/random_uniform_ref.cl
new file mode 100644
index 00000000000..b1dba325cb9
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/random_uniform_ref.cl
@@ -0,0 +1,142 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "include/batch_headers/fetch_data.cl"
+#include "include/batch_headers/data_types.cl"
+
+#define N_ROUNDS 10
+#define STATISTIC_MAXIMIZING_MULTIPLIER_N 0xD2511F53UL
+#define STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER 0xCD9E8D57UL
+#define CRUSH_RESISTANCE_CONST_LOWER_VALUE 0x9E3779B9U
+#define CRUSH_RESISTANCE_CONST_UPPER_VALUE 0xBB67AE85U
+
+#define FNAME(type) fill_##type
+#define FUNC_NAME(type) FNAME(type)
+#define FILL_FUNC(func, result, min_value, max_value, output, output_index) FUNC_CALL(func)(result, min_value, max_value, output, output_index)
+
+inline ulong FUNC(unite_high_low)(uint high, uint low) {
+    return ((ulong)high << 32) + low;
+}
+
+// Runs single "round" of Philox algorithm.
+inline ulong2 FUNC(calculate_round)(ulong key, ulong2 counter_n) {
+    uint2 counter_lr = as_uint2(counter_n[0]);
+    uint2 key_lr = as_uint2(key);
+    uint2 n_lr = as_uint2(counter_n[1]);
+
+    uint2 prod0 = as_uint2(STATISTIC_MAXIMIZING_MULTIPLIER_N * n_lr[0]);
+    uint2 prod1 = as_uint2(STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER * counter_lr[0]);
+
+    n_lr[0] = prod1[1] ^ n_lr[1] ^ key_lr[0];
+    n_lr[1] = prod1[0];
+    counter_lr[0] = prod0[1] ^ counter_lr[1] ^ key_lr[1];
+    counter_lr[1] = prod0[0];
+
+    return (ulong2)(FUNC_CALL(unite_high_low)(counter_lr[1], counter_lr[0]),  FUNC_CALL(unite_high_low)(n_lr[1], n_lr[0]));
+}
+
+inline ulong FUNC(raise_key)(uint2 key_lr) {
+    key_lr[0] += CRUSH_RESISTANCE_CONST_LOWER_VALUE;
+    key_lr[1] += CRUSH_RESISTANCE_CONST_UPPER_VALUE;
+    return FUNC_CALL(unite_high_low)(key_lr[1], key_lr[0]);
+}
+
+inline uint4 FUNC(run_philox)(ulong n) {
+    ulong key = GLOBAL_SEED;
+    ulong counter = OP_SEED;
+    ulong2 counter_n = {counter, n};
+    for (size_t i = 0; i < N_ROUNDS; i++) {
+        counter_n = FUNC_CALL(calculate_round)(key, counter_n);
+        key = FUNC_CALL(raise_key)(as_uint2(key));
+    }
+    uint2 res1 = as_uint2(counter_n[1]);
+    uint2 res2 = as_uint2(counter_n[0]);
+    uint4 res;
+    res[0] = res1[0];
+    res[1] = res1[1];
+    res[2] = res2[0];
+    res[3] = res2[1];
+    return res;
+}
+
+
+inline float FUNC(uint32_to_float)(uint x) {
+    uint out_val = (127u << 23) | (x & 0x7fffffu);
+    float float_val = as_float(out_val);
+    return float_val - 1.0f;
+}
+
+inline void FUNC(fill_float)(const uint4 res,
+                float min_val,
+                float max_val,
+                __global float *output,
+                uint output_index) {
+    float diff = max_val - min_val;
+    for (uint i = 0; i < 4; ++i) {
+        if (output_index + i < OUTPUT_LENGTH) {
+            output[output_index + i] = FUNC_CALL(uint32_to_float)(res[i]) * diff + min_val;
+        }
+    }
+}
+
+inline void FUNC(fill_int)(const uint4 res,
+              int min_val,
+              int max_val,
+              __global int *output,
+              uint output_index) {
+    int diff = max_val - min_val;
+    for (uint i = 0; i < 4; ++i) {
+        if (output_index + i < OUTPUT_LENGTH) {
+            output[output_index + i] = (int) (res[i] % diff + min_val);
+        }
+    }
+}
+
+inline void FUNC(fill_long)(const uint4 res,
+               long min_val,
+               long max_val,
+               __global long *output,
+               uint output_index) {
+    long diff = max_val - min_val;
+    output[output_index] = (long)(FUNC_CALL(unite_high_low)(res[1], res[0]) % diff + min_val);
+    if (output_index + 1 < OUTPUT_LENGTH) {
+        output[output_index + 1] = (long)(FUNC_CALL(unite_high_low)(res[3], res[2]) % diff + min_val);
+    }
+}
+
+inline half FUNC(uint32_to_float16)(uint x) {
+    ushort x_uint16 = (ushort) x;
+    ushort out_val = (ushort)(15 << 10) | (x_uint16 & 0x3ffu);
+    return as_half(out_val) - 1;
+}
+
+inline void FUNC(fill_half)(const uint4 res,
+               half min_val,
+               half max_val,
+               __global half *output,
+               uint output_index) {
+    half diff = max_val - min_val;
+    for (uint i = 0; i < 4; ++i) {
+        if (output_index + i < OUTPUT_LENGTH) {
+            output[output_index + i] = FUNC_CALL(uint32_to_float16)(res[i]) * diff + min_val;
+        }
+    }
+}
+
+KERNEL(random_uniform_ref)(const __global INPUT0_TYPE* shape, const __global INPUT1_TYPE *min_val,
+                            const __global INPUT2_TYPE* max_val, __global OUTPUT_TYPE *output) {
+    const uint plain_index = get_global_id(0);
+    uint4 result = FUNC_CALL(run_philox)(plain_index);
+    FILL_FUNC(FUNC_NAME(OUTPUT_TYPE), result, *min_val, *max_val, output, plain_index * OUTPUT_STEP);
+}
+
+#undef FILL_FUNC
+#undef FUNC_NAME
+#undef FNAME
+
+#undef STATISTIC_MAXIMIZING_MULTIPLIER_N
+#undef STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER
+#undef CRUSH_RESISTANCE_CONST_LOWER_VALUE
+#undef CRUSH_RESISTANCE_CONST_UPPER_VALUE
+#undef N_ROUNDS
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/random_uniform.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/random_uniform.cpp
new file mode 100644
index 00000000000..982d1b4a612
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/random_uniform.cpp
@@ -0,0 +1,66 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <random_uniform_inst.h>
+#include <random_uniform/random_uniform_kernel_ref.h>
+#include <api/intel_gpu/runtime/error_handler.hpp>
+#include <impls/implementation_map.hpp>
+#include <random_uniform/random_uniform_kernel_selector.h>
+#include "primitive_base.hpp"
+#include <vector>
+
+namespace cldnn {
+namespace ocl {
+
+struct random_uniform_impl : typed_primitive_impl_ocl<random_uniform> {
+    using parent = typed_primitive_impl_ocl<random_uniform>;
+    using parent::parent;
+
+    std::unique_ptr<primitive_impl> clone() const override {
+        return make_unique<random_uniform_impl>(*this);
+    }
+
+    static primitive_impl *create(const random_uniform_node &arg) {
+        auto params = get_default_params<kernel_selector::random_uniform_params>(
+                arg);
+        auto &random_uniform_kernel_selector =
+                kernel_selector::random_uniform_kernel_selector::Instance();
+        const auto &primitive = arg.get_primitive();
+        params.global_seed = primitive->global_seed;
+        params.op_seed = primitive->op_seed;
+        params.inputs.push_back(convert_data_tensor(arg.input(1).get_output_layout()));
+        params.inputs.push_back(convert_data_tensor(arg.input(2).get_output_layout()));
+        auto best_kernels = random_uniform_kernel_selector.GetBestKernels(params,
+                                                                          kernel_selector::random_uniform_optional_params());
+        CLDNN_ERROR_BOOL(arg.id(),
+                         "Best_kernel.empty()",
+                         best_kernels.empty(),
+                         "Cannot find a proper kernel with this arguments");
+        return new random_uniform_impl(arg, best_kernels[0]);
+    }
+};
+
+namespace detail {
+
+attach_random_uniform_impl::attach_random_uniform_impl() {
+    implementation_map<random_uniform>::add(impl_types::ocl, random_uniform_impl::create, {
+            std::make_tuple(data_types::f16, format::bfyx),
+            std::make_tuple(data_types::f16, format::bfzyx),
+            std::make_tuple(data_types::f16, format::bfwzyx),
+            std::make_tuple(data_types::f32, format::bfyx),
+            std::make_tuple(data_types::f32, format::bfzyx),
+            std::make_tuple(data_types::f32, format::bfwzyx),
+            std::make_tuple(data_types::i32, format::bfyx),
+            std::make_tuple(data_types::i32, format::bfzyx),
+            std::make_tuple(data_types::i32, format::bfwzyx),
+            std::make_tuple(data_types::i64, format::bfyx),
+            std::make_tuple(data_types::i64, format::bfzyx),
+            std::make_tuple(data_types::i64, format::bfwzyx),
+    });
+}
+
+}  // namespace detail
+
+} // namespace ocl
+} // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
index b07d40e4134..365dafc3ff5 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
@@ -48,6 +48,7 @@ void register_implementations() {
     REGISTER_OCL(pooling);
     REGISTER_OCL(pyramid_roi_align);
     REGISTER_OCL(quantize);
+    REGISTER_OCL(random_uniform);
     REGISTER_OCL(reduce);
     REGISTER_OCL(region_yolo);
     REGISTER_OCL(reorder);
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
index 6232a937ccf..26e52c8e3ee 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
@@ -39,6 +39,7 @@
 #include "intel_gpu/primitives/pooling.hpp"
 #include "intel_gpu/primitives/pyramid_roi_align.hpp"
 #include "intel_gpu/primitives/quantize.hpp"
+#include "intel_gpu/primitives/random_uniform.hpp"
 #include "intel_gpu/primitives/reduce.hpp"
 #include "intel_gpu/primitives/region_yolo.hpp"
 #include "intel_gpu/primitives/reorder.hpp"
@@ -117,6 +118,7 @@ REGISTER_OCL(permute);
 REGISTER_OCL(pooling);
 REGISTER_OCL(pyramid_roi_align);
 REGISTER_OCL(quantize);
+REGISTER_OCL(random_uniform);
 REGISTER_OCL(reduce);
 REGISTER_OCL(region_yolo);
 REGISTER_OCL(reorder);
diff --git a/inference-engine/thirdparty/clDNN/src/include/random_uniform_inst.h b/inference-engine/thirdparty/clDNN/src/include/random_uniform_inst.h
new file mode 100644
index 00000000000..c48897832c3
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/include/random_uniform_inst.h
@@ -0,0 +1,39 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <api/intel_gpu/primitives/random_uniform.hpp>
+#include "primitive_inst.h"
+#include <api/intel_gpu/runtime/error_handler.hpp>
+
+namespace cldnn {
+
+template<>
+struct typed_program_node<random_uniform> : public typed_program_node_base<random_uniform> {
+    using parent = typed_program_node_base<random_uniform>;
+public:
+    using parent::parent;
+
+    const program_node &input(std::size_t index = 0) const { return get_dependency(index); }
+};
+
+using random_uniform_node = typed_program_node<random_uniform>;
+
+template<>
+class typed_primitive_inst<random_uniform> : public typed_primitive_inst_base<random_uniform> {
+    using parent = typed_primitive_inst_base<random_uniform>;
+
+public:
+    static layout calc_output_layout(random_uniform_node const &node);
+
+    static std::string to_string(random_uniform_node const &node);
+
+public:
+    typed_primitive_inst(network &network, random_uniform_node const &desc);
+};
+
+using random_uniform_inst = typed_primitive_inst<random_uniform>;
+
+} // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/random_uniform.cpp b/inference-engine/thirdparty/clDNN/src/random_uniform.cpp
new file mode 100644
index 00000000000..8fd474a39df
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/random_uniform.cpp
@@ -0,0 +1,41 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <random_uniform_inst.h>
+#include "primitive_type_base.h"
+#include <sstream>
+#include <json_object.h>
+#include <data_inst.h>
+
+namespace cldnn {
+
+primitive_type_id random_uniform::type_id() {
+    static primitive_type_base<random_uniform> instance;
+    return &instance;
+}
+
+random_uniform_inst::typed_primitive_inst(network& network, random_uniform_node const &node)
+: parent(network, node) {
+}
+
+layout random_uniform_inst::calc_output_layout(random_uniform_node const &node) {
+    auto primitive = node.get_primitive();
+    return {*primitive->output_data_type, primitive->output_format, primitive->output_shape};
+}
+
+std::string random_uniform_inst::to_string(random_uniform_node const &node) {
+    auto node_info = node.desc_to_json();
+    json_composite random_uniform_info;
+    random_uniform_info.add("input id", node.input().id());
+    random_uniform_info.add("min_value id", node.input(1).id());
+    random_uniform_info.add("max_value  id", node.input(2).id());
+    random_uniform_info.add("global_seed", node.get_primitive()->global_seed);
+    random_uniform_info.add("op_seed", node.get_primitive()->op_seed);
+    node_info->add("random uniform info", random_uniform_info);
+    std::stringstream primitive_description;
+    node_info->dump(primitive_description);
+    return primitive_description.str();
+}
+
+} // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/random_uniform_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/random_uniform_gpu_test.cpp
new file mode 100644
index 00000000000..67e0d0bfe02
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/random_uniform_gpu_test.cpp
@@ -0,0 +1,184 @@
+#include "test_utils.h"
+
+#include <intel_gpu/primitives/input_layout.hpp>
+#include <intel_gpu/primitives/random_uniform.hpp>
+#include <string>
+
+#include <cstddef>
+
+using namespace cldnn;
+using namespace ::tests;
+
+/**
+ * Specific Random Uniform params to define the tests. Input and output should be the same type
+ */
+template<typename T>
+struct RandomUniformParams {
+    tensor output_tensor;
+    format f;
+    T min_val;
+    T max_val;
+    uint64_t global_seed;
+    uint64_t op_seed;
+    std::vector<T> expected_out;
+};
+
+template<typename T>
+struct random_uniform_gpu_test : public ::testing::TestWithParam<RandomUniformParams<T> > {
+public:
+    void test() {
+
+        auto data_type = type_to_data_type<T>::value;
+        RandomUniformParams<T> params = testing::TestWithParam<RandomUniformParams<T> >::GetParam();
+        auto &engine = get_test_engine();
+
+        auto shape = engine.allocate_memory(
+                {data_type, params.f, {1, 1, static_cast<int32_t >(params.output_tensor.sizes().size()), 1}});
+        auto min_val = engine.allocate_memory(layout(data_type, format::bfyx, {1, 1, 1, 1}));
+        auto max_val = engine.allocate_memory(layout(data_type, format::bfyx, {1, 1, 1, 1}));
+
+        set_values(shape, params.output_tensor.sizes());
+        set_values(min_val, {params.min_val});
+        set_values(max_val, {params.max_val});
+
+        topology topology;
+        topology.add(
+                random_uniform("random_uniform", {"shape", "min_val", "max_val"}, data_type, params.global_seed,
+                               params.op_seed, params.output_tensor,
+                               params.f));
+        topology.add(input_layout("shape", shape->get_layout()));
+        topology.add(input_layout("min_val", min_val->get_layout()));
+        topology.add(input_layout("max_val", max_val->get_layout()));
+        build_options bo;
+        bo.set_option(build_option::optimize_data(true));
+
+        cldnn::network net{engine, topology, bo};
+
+        net.set_input_data("shape", shape);
+        net.set_input_data("min_val", min_val);
+        net.set_input_data("max_val", max_val);
+
+        auto result = net.execute();
+
+        auto out_mem = result.at("random_uniform").get_memory();
+        cldnn::mem_lock<T> out_ptr(out_mem, get_test_stream());
+
+        ASSERT_EQ(params.expected_out.size(), out_ptr.size());
+        for (size_t i = 0; i < params.expected_out.size(); ++i) {
+            EXPECT_NEAR(params.expected_out[i], out_ptr[i], 0.0001) << "at i = " << i;
+        }
+    }
+};
+
+struct PrintToStringParamName {
+    template<class T>
+    std::string operator()(const testing::TestParamInfo<RandomUniformParams<T> > &param) {
+        std::stringstream buf;
+        buf << " output tensor" << param.param.output_tensor.to_string()
+            << " min_value " << param.param.min_val
+            << " max_value " << param.param.max_val
+            << " global_seed " << param.param.global_seed
+            << " op_seed " << param.param.op_seed;
+        return buf.str();
+    }
+
+};
+
+template<>
+std::string PrintToStringParamName::operator()(const testing::TestParamInfo<RandomUniformParams<half_t> > &param) {
+    std::stringstream buf;
+    buf << " output tensor" << param.param.output_tensor.to_string()
+        << " min_value " << static_cast<float>(param.param.min_val)
+        << " max_value " << static_cast<float>(param.param.max_val)
+        << " global_seed " << param.param.global_seed
+        << " op_seed " << param.param.op_seed;
+    return buf.str();
+}
+
+using random_uniform_gpu_test_i32 = random_uniform_gpu_test<int32_t>;
+using random_uniform_gpu_test_i64 = random_uniform_gpu_test<int64_t>;
+using random_uniform_gpu_test_f32 = random_uniform_gpu_test<float>;
+using random_uniform_gpu_test_f16 = random_uniform_gpu_test<half_t>;
+
+TEST_P(random_uniform_gpu_test_i32, random_int32) {
+    ASSERT_NO_FATAL_FAILURE(test());
+}
+
+TEST_P(random_uniform_gpu_test_i64, random_int64) {
+    ASSERT_NO_FATAL_FAILURE(test());
+}
+
+
+TEST_P(random_uniform_gpu_test_f32, random_f32) {
+    ASSERT_NO_FATAL_FAILURE(test());
+}
+
+TEST_P(random_uniform_gpu_test_f16, random_f16) {
+    ASSERT_NO_FATAL_FAILURE(test());
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_random_uniform_int32,
+                         random_uniform_gpu_test_i32,
+                         ::testing::Values(
+                                 RandomUniformParams<int32_t>{tensor(1, 1, 2, 3), format::bfyx, 50, 100, 80, 100,
+                                                              std::vector<int32_t>{
+                                                                      65, 70, 56,
+                                                                      59, 82, 92
+                                                              }}
+                         ),
+                         PrintToStringParamName());
+
+INSTANTIATE_TEST_SUITE_P(smoke_random_uniform_int64,
+                         random_uniform_gpu_test_i64,
+                         ::testing::Values(
+                                 RandomUniformParams<int64_t>{tensor(1, 1, 5, 4, 3), format::bfzyx, -2600, 3700, 755,
+                                                              951,
+                                                              {
+                                                                      2116L, -1581L, 2559L, -339L, -1660L, 519L, 90L,
+                                                                      2027L, -210L, 3330L, 1831L, -1737L,
+                                                                      2683L, 2661L, 3473L, 1220L, 3534L, -2384L, 2199L,
+                                                                      1935L, 499L, 2861L, 2743L, 3223L,
+                                                                      -531L, -836L, -65L, 3435L, 632L, 1765L, 2613L,
+                                                                      1891L, 1698L, 3069L, 169L, -792L,
+                                                                      -32L, 2976L, -1552L, -2588L, 3327L, -1756L, 2637L,
+                                                                      -1084L, 3567L, -778L, -1465L, 2967L,
+                                                                      1242L, 2672L, -1585L, -2271L, 3536L, -1502L, 400L,
+                                                                      2241L, 3126L, 908L, 1073L, -2110L}}
+                         ),
+                         PrintToStringParamName());
+
+
+INSTANTIATE_TEST_SUITE_P(smoke_random_uniform_f32,
+                         random_uniform_gpu_test_f32,
+                         ::testing::Values(
+                                 RandomUniformParams<float>{tensor(1, 1, 3, 3), format::bfyx, 0.0, 1.0, 150, 10,
+                                                            {
+                                                                    0.7011236, 0.30539632, 0.93931055,
+                                                                    0.9456035, 0.11694777, 0.50770056,
+                                                                    0.5197197, 0.22727466, 0.991374
+                                                            }
+                                 }
+                         ),
+                         PrintToStringParamName());
+
+
+INSTANTIATE_TEST_SUITE_P(smoke_random_uniform_f16,
+                         random_uniform_gpu_test_f16,
+                         ::testing::Values(
+                                 RandomUniformParams<half_t>{tensor(1, 1, 3, 2, 4), format::bfzyx, half_t(-1.5),
+                                                             half_t(-1.0), 150, 10,
+                                                             {half_t(-1.19726562), half_t(-1.09667969),
+                                                              half_t(-1.08398438), half_t(-1.30859375),
+                                                              half_t(-1.48242188), half_t(-1.45898438),
+                                                              half_t(-1.22851562), half_t(-1.08300781),
+                                                              half_t(-1.33203125), half_t(-1.14062500),
+                                                              half_t(-1.42285156), half_t(-1.43554688),
+                                                              half_t(-1.32617188), half_t(-1.06542969),
+                                                              half_t(-1.29296875), half_t(-1.21386719),
+                                                              half_t(-1.21289062), half_t(-1.03027344),
+                                                              half_t(-1.17187500), half_t(-1.08886719),
+                                                              half_t(-1.08789062), half_t(-1.43359375),
+                                                              half_t(-1.17773438), half_t(-1.16992188)}
+                                 }
+                         ),
+                         PrintToStringParamName());
diff --git a/src/core/include/openvino/op/random_uniform.hpp b/src/core/include/openvino/op/random_uniform.hpp
index c6eb9cfdc3f..79975e17a40 100644
--- a/src/core/include/openvino/op/random_uniform.hpp
+++ b/src/core/include/openvino/op/random_uniform.hpp
@@ -13,6 +13,7 @@ namespace v8 {
 class OPENVINO_API RandomUniform : public Op {
 public:
     OPENVINO_OP("RandomUniform", "opset8");
+    BWDCMP_RTTI_DECLARATION;
 
     RandomUniform() = default;
 
diff --git a/src/core/src/op/random_uniform.cpp b/src/core/src/op/random_uniform.cpp
index 55d4beb7e24..606b0a5fcb1 100644
--- a/src/core/src/op/random_uniform.cpp
+++ b/src/core/src/op/random_uniform.cpp
@@ -12,6 +12,8 @@
 using namespace std;
 using namespace ngraph;
 
+BWDCMP_RTTI_DEFINITION(ov::op::v8::RandomUniform);
+
 op::v8::RandomUniform::RandomUniform(const Output<Node>& out_shape,
                                      const Output<Node>& min_val,
                                      const Output<Node>& max_val,
diff --git a/src/core/src/pass/convert_precision.cpp b/src/core/src/pass/convert_precision.cpp
index a393d3e3a73..7cb3a8adf13 100644
--- a/src/core/src/pass/convert_precision.cpp
+++ b/src/core/src/pass/convert_precision.cpp
@@ -40,6 +40,8 @@ bool fuse_type_to_ctc_greedy_decoder_seq_len(const std::shared_ptr<ngraph::Node>
                                              ngraph::element::Type to,
                                              size_t idx);
 
+bool fuse_type_to_random_uniform_v8(const std::shared_ptr<ngraph::Node>& node, element::Type to, size_t idx);
+
 bool extend_select_type(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 
 template <typename T>
@@ -275,7 +277,8 @@ bool ngraph::pass::ConvertPrecision::run_on_model(const std::shared_ptr<ngraph::
         {opset4::ReduceLogicalAnd::get_type_info_static(), fuse_type_to_reduce_logical<opset4::ReduceLogicalAnd>},
         {opset4::ReduceLogicalOr::get_type_info_static(), fuse_type_to_reduce_logical<opset4::ReduceLogicalOr>},
         {opset1::ShapeOf::get_type_info_static(), fuse_type_to_shapeof_v0},
-        {opset4::Range::get_type_info_static(), fuse_type_to_range_v4}};
+        {opset4::Range::get_type_info_static(), fuse_type_to_range_v4},
+        {opset8::RandomUniform::get_type_info_static(), fuse_type_to_random_uniform_v8}};
 
     type_to_fuse.insert(m_additional_type_to_fuse_map.begin(), m_additional_type_to_fuse_map.end());
 
@@ -321,6 +324,16 @@ bool fuse_type_to_range_v4(const std::shared_ptr<ngraph::Node>& node, element::T
     return false;
 }
 
+bool fuse_type_to_random_uniform_v8(const std::shared_ptr<ngraph::Node>& node, element::Type to, size_t idx) {
+    if (auto random_uniform = ov::as_type_ptr<opset8::RandomUniform>(node)) {
+        if (to.is_integral_number() || to.is_real()) {
+            random_uniform->set_out_type(to);
+            return true;
+        }
+    }
+    return false;
+}
+
 bool fuse_type_to_parameter(const std::shared_ptr<ngraph::Node>& node, element::Type to, size_t idx) {
     if (auto param = ov::as_type_ptr<opset4::Parameter>(node)) {
         param->set_element_type(to);
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
index 3962ffa1b23..0963273d9d1 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
@@ -219,6 +219,7 @@ REGISTER_FACTORY(v8, NV12toRGB);
 REGISTER_FACTORY(v8, NV12toBGR);
 REGISTER_FACTORY(v8, I420toRGB);
 REGISTER_FACTORY(v8, I420toBGR);
+REGISTER_FACTORY(v8, RandomUniform)
 
 // --------------------------- Supported internal ops --------------------------- //
 REGISTER_FACTORY(internal, NonMaxSuppressionIEInternal);
diff --git a/src/plugins/intel_gpu/src/plugin/ops/random_uniform.cpp b/src/plugins/intel_gpu/src/plugin/ops/random_uniform.cpp
new file mode 100644
index 00000000000..a9243694ee4
--- /dev/null
+++ b/src/plugins/intel_gpu/src/plugin/ops/random_uniform.cpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "intel_gpu/plugin/program.hpp"
+#include "intel_gpu/plugin/common_utils.hpp"
+#include "ngraph/op/random_uniform.hpp"
+#include "api/intel_gpu/primitives/random_uniform.hpp"
+
+
+namespace ov {
+namespace runtime {
+namespace intel_gpu {
+
+namespace {
+
+void CreateRandomUniformOp(Program &p, const std::shared_ptr<ngraph::op::v8::RandomUniform> &op) {
+    auto input_primitives = p.GetInputPrimitiveIDs(op);
+    auto output_shape = op->get_output_shape(0);
+    cldnn::format outputFormat = DefaultFormatForDims(output_shape.size());
+
+    auto random_uniform_prim = cldnn::random_uniform(layer_type_name_ID(op),
+                                                     input_primitives,
+                                                     DataTypeFromPrecision(op->get_out_type()),
+                                                     op->get_global_seed(),
+                                                     op->get_op_seed(),
+                                                     tensor_from_dims(output_shape),
+                                                     outputFormat);
+    p.AddPrimitive(random_uniform_prim);
+    p.AddPrimitiveToProfiler(op);
+}
+
+} // namespace
+
+REGISTER_FACTORY_IMPL(v8, RandomUniform);
+
+} // namespace intel_gpu
+} // namespace runtime
+} // namespace ov
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/random_uniform.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/random_uniform.cpp
new file mode 100644
index 00000000000..6c73fcdf394
--- /dev/null
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/random_uniform.cpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+#include <ie_precision.hpp>
+#include "common_test_utils/test_constants.hpp"
+#include "single_layer_tests/random_uniform.hpp"
+
+using namespace LayerTestsDefinitions;
+
+namespace {
+
+const std::vector<RandomUniformTypeSpecificParams> random_uniform_type_specific_params = {
+        {InferenceEngine::Precision::I32, -100, 100},
+        {InferenceEngine::Precision::FP32, 0.0f, 1.0f},
+        {InferenceEngine::Precision::FP16, -10.0, 10.0}
+};
+
+
+const std::vector<int64_t> global_seeds = {10, 100, 500};
+const std::vector<int64_t> op_seeds = {10, 50};
+
+const std::vector<ov::Shape> output_shapes = {
+        {1, 3, 3,  3},
+        {1, 1, 5,  5},
+        {2, 1, 10, 10}
+};
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_BasicRandomUniform, RandomUniformLayerTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(output_shapes),
+                ::testing::ValuesIn(random_uniform_type_specific_params),
+                ::testing::ValuesIn(global_seeds),
+                ::testing::ValuesIn(op_seeds),
+                ::testing::Values(CommonTestUtils::DEVICE_GPU)),
+        RandomUniformLayerTest::getTestCaseName);
+
+}  // namespace
diff --git a/src/tests/functional/plugin/shared/include/single_layer_tests/random_uniform.hpp b/src/tests/functional/plugin/shared/include/single_layer_tests/random_uniform.hpp
new file mode 100644
index 00000000000..894eed27319
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/single_layer_tests/random_uniform.hpp
@@ -0,0 +1,15 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/single_layer/random_uniform.hpp"
+
+namespace LayerTestsDefinitions {
+
+TEST_P(RandomUniformLayerTest, CompareWithRefs) {
+    Run();
+};
+
+}  // namespace LayerTestsDefinitions
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp
index 35bfb91a492..615ba6db724 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/layer_test_utils.hpp
@@ -171,6 +171,10 @@ protected:
 
     virtual std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>> CalculateRefs();
 
+    /// default method to convert parameters for reference operation. Used before reference implementation execution
+    /// can be overridden by specific operation test
+    virtual void ConvertRefsParams();
+
     virtual std::vector<InferenceEngine::Blob::Ptr> GetOutputs();
 
     InferenceEngine::InferRequest inferRequest;
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/random_uniform.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/random_uniform.hpp
new file mode 100644
index 00000000000..ba1bd11d434
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/random_uniform.hpp
@@ -0,0 +1,42 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <tuple>
+#include <string>
+#include <vector>
+#include <memory>
+
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "constant.hpp"
+
+namespace LayerTestsDefinitions {
+
+struct RandomUniformTypeSpecificParams {
+    InferenceEngine::Precision precision;  // Output data precision
+    double min_value; // min value constant, will be cast to the needed precision
+    double max_value; // max value constant, will be cast to the needed precision
+};
+
+using RandomUniformParamsTuple = typename std::tuple<
+        ov::Shape, // output shape
+        RandomUniformTypeSpecificParams, // parameters which depends on output type
+        int64_t, // global seed
+        int64_t, // operation seed
+        std::string>; // Device name
+
+class RandomUniformLayerTest : public testing::WithParamInterface<RandomUniformParamsTuple>,
+                               virtual public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<RandomUniformParamsTuple> &obj);
+
+protected:
+    void SetUp() override;
+
+    void ConvertRefsParams() override;
+};
+
+}  // namespace LayerTestsDefinitions
+
diff --git a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
index d4b297c9420..cb3d90f46d6 100644
--- a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
+++ b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
@@ -394,10 +394,13 @@ void LayerTestsCommon::Infer() {
     inferRequest.Infer();
 }
 
-std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>> LayerTestsCommon::CalculateRefs() {
+void LayerTestsCommon::ConvertRefsParams() {
     ngraph::pass::ConvertPrecision<ngraph::element::Type_t::f16, ngraph::element::Type_t::f32>().run_on_function(functionRefs);
     ngraph::pass::ConvertPrecision<ngraph::element::Type_t::bf16, ngraph::element::Type_t::f32>().run_on_function(functionRefs);
+}
 
+std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>> LayerTestsCommon::CalculateRefs() {
+    ConvertRefsParams();
     functionRefs->validate_nodes_and_infer_types();
 
     auto referenceInputs = std::vector<std::vector<uint8_t>>(inputs.size());
diff --git a/src/tests/functional/shared_test_classes/src/single_layer/random_uniform.cpp b/src/tests/functional/shared_test_classes/src/single_layer/random_uniform.cpp
new file mode 100644
index 00000000000..ed7d120f232
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/src/single_layer/random_uniform.cpp
@@ -0,0 +1,89 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <ngraph/op/parameter.hpp>
+#include "shared_test_classes/single_layer/random_uniform.hpp"
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+
+namespace LayerTestsDefinitions {
+
+
+std::string RandomUniformLayerTest::getTestCaseName(
+        const testing::TestParamInfo<RandomUniformParamsTuple> &obj) {
+    RandomUniformTypeSpecificParams randomUniformTypeSpecificParams;
+    ov::Shape output_shape;
+    int64_t global_seed;
+    int64_t op_seed;
+    std::string targetName;
+    std::tie(output_shape, randomUniformTypeSpecificParams, global_seed, op_seed, targetName) = obj.param;
+
+    std::ostringstream result;
+    result << "outputShape=" << CommonTestUtils::vec2str(output_shape) << "_";
+    result << "global_seed=" << global_seed << "_";
+    result << "op_seed=" << op_seed << "_";
+    result << "outputType=" << randomUniformTypeSpecificParams.precision.name() << "_";
+    result << "min_val=" << randomUniformTypeSpecificParams.min_value << "_";
+    result << "max_val=" << randomUniformTypeSpecificParams.max_value;
+    return result.str();
+}
+
+namespace {
+
+template<InferenceEngine::Precision::ePrecision p>
+std::shared_ptr<ov::op::v0::Constant>
+createRangeConst(const typename InferenceEngine::PrecisionTrait<p>::value_type &value) {
+    return std::make_shared<ov::op::v0::Constant>(FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(p), ov::Shape{1},
+                                                  std::vector<typename InferenceEngine::PrecisionTrait<p>::value_type>{
+                                                          value});
+}
+
+std::shared_ptr<ov::op::v0::Constant> createConstant(InferenceEngine::Precision p, double value) {
+    using namespace InferenceEngine;
+    switch (p) {
+        case Precision::FP32:
+            return createRangeConst<InferenceEngine::Precision::FP32>(
+                    static_cast<PrecisionTrait<Precision::FP32>::value_type>(value));
+        case Precision::FP16:
+            return createRangeConst<InferenceEngine::Precision::FP16>(
+                    static_cast<PrecisionTrait<Precision::FP16>::value_type>(value));
+        default:
+            return createRangeConst<InferenceEngine::Precision::I32>(
+                    static_cast<PrecisionTrait<Precision::I32>::value_type>(value));
+    }
+}
+
+} // unnamed namespace
+
+void RandomUniformLayerTest::SetUp() {
+    RandomUniformTypeSpecificParams randomUniformParams;
+    int64_t global_seed;
+    int64_t op_seed;
+    ov::Shape output_shape;
+    std::string targetName;
+    std::tie(output_shape, randomUniformParams, global_seed, op_seed, targetDevice) = this->GetParam();
+    const auto precision = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(randomUniformParams.precision);
+    auto out_shape_ = std::make_shared<ov::opset8::Constant>(ov::element::i64,
+                                                             ov::Shape{output_shape.size()},
+                                                             output_shape);
+
+    auto min_value = createConstant(randomUniformParams.precision, randomUniformParams.min_value);
+    auto max_value = createConstant(randomUniformParams.precision, randomUniformParams.max_value);
+    auto random_uniform = std::make_shared<ngraph::op::v8::RandomUniform>(out_shape_,
+                                                                          min_value,
+                                                                          max_value,
+                                                                          precision,
+                                                                          global_seed,
+                                                                          op_seed);
+    ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(random_uniform)};
+
+    function = std::make_shared<ngraph::Function>(results, ngraph::ParameterVector{}, "random_uniform");
+}
+
+void RandomUniformLayerTest::ConvertRefsParams() {
+    // we shouldn't use default conversion from f16 to f32
+    ngraph::pass::ConvertPrecision<ngraph::element::Type_t::bf16, ngraph::element::Type_t::f32>().run_on_function(
+            functionRefs);
+}
+
+}  // namespace LayerTestsDefinitions

From 5f53e6f697038d454f29777c81afe05020cdbec1 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Tue, 21 Dec 2021 13:49:15 +0300
Subject: [PATCH 069/160] [GPU] Try disabling onednn primitive cache (#9289)

---
 thirdparty/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/thirdparty/CMakeLists.txt b/thirdparty/CMakeLists.txt
index 8737df02196..2e34ec79e53 100644
--- a/thirdparty/CMakeLists.txt
+++ b/thirdparty/CMakeLists.txt
@@ -164,7 +164,7 @@ if(ENABLE_ONEDNN_FOR_GPU)
                 "-DCMAKE_INSTALL_LIBDIR=lib/$<CONFIG>"
                 "-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}"
                 "-DDNNL_ENABLE_CONCURRENT_EXEC=ON"
-                "-DDNNL_ENABLE_PRIMITIVE_CACHE=ON"
+                "-DDNNL_ENABLE_PRIMITIVE_CACHE=OFF"
                 "-DDNNL_ENABLE_JIT_PROFILING=${BUILD_SHARED_LIBS}"
                 "-DDNNL_ENABLE_ITT_TASKS=${BUILD_SHARED_LIBS}"
                 "-DDNNL_BUILD_TESTS=OFF"

From 0100060cd9aa46de560b2a2a1b18c22f0c5c7f93 Mon Sep 17 00:00:00 2001
From: Indira Salyahova <indira.salyahova@intel.com>
Date: Tue, 21 Dec 2021 14:16:41 +0300
Subject: [PATCH 070/160] Update README.md (#9337)

---
 tools/pot/openvino/tools/pot/api/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/pot/openvino/tools/pot/api/README.md b/tools/pot/openvino/tools/pot/api/README.md
index 913c71bead6..0a73d707558 100644
--- a/tools/pot/openvino/tools/pot/api/README.md
+++ b/tools/pot/openvino/tools/pot/api/README.md
@@ -306,7 +306,7 @@ given `batch_size`. Returns a list of data items.
 ## Pipeline
 
 ```
-class compression.pipline.pipeline.Pipeline(engine)
+class compression.pipeline.pipeline.Pipeline(engine)
 ```
 Pipeline class represents the optimization pipeline.
 
@@ -319,7 +319,7 @@ The pipeline can be applied to the DL model by calling `run(model)` method where
 
 The POT Python* API provides the utility function to create and configure the pipeline:
 ```
-compression.pipline.initializer.create_pipeline(algo_config, engine)
+compression.pipeline.initializer.create_pipeline(algo_config, engine)
 ```
 *Parameters* 
 - `algo_config` - a list defining optimization algorithms and their parameters included in the optimization pipeline. 

From 9334f34ff07a8a139f4dfc90abac3e17d710c1cc Mon Sep 17 00:00:00 2001
From: Ivan Tikhonov <ivan.tikhonov@intel.com>
Date: Tue, 21 Dec 2021 14:35:58 +0300
Subject: [PATCH 071/160] TensorFlow FrontEnd Refactoring (#9173)

* Move tensorflow fe to openvino subfolder; renaming; refactoring

* codestyle

* delete redundant file

* fix Win build

* fix missprint
---
 .../frontend/tensorflow/transpose_sinking.cpp |  26 ++--
 .../frontend/tensorflow}/decoder.hpp          |  10 +-
 .../frontend/tensorflow}/frontend.hpp         |  18 ++-
 .../frontend/tensorflow}/graph_iterator.hpp   |  10 +-
 .../frontend/tensorflow/visibility.hpp}       |  12 +-
 .../tensorflow/src/decoder_proto.cpp          |  20 +--
 .../tensorflow/src/decoder_proto.hpp          |  10 +-
 src/frontends/tensorflow/src/exceptions.cpp   |   6 +-
 src/frontends/tensorflow/src/exceptions.hpp   |  16 +--
 src/frontends/tensorflow/src/frontend.cpp     |  88 ++++++-------
 .../tensorflow/src/graph_iterator_proto.hpp   |  13 +-
 .../src/{model.cpp => input_model.cpp}        | 115 ++++++++---------
 .../src/{model.hpp => input_model.hpp}        |  21 ++--
 src/frontends/tensorflow/src/node_context.hpp |   6 +-
 src/frontends/tensorflow/src/op/addN.cpp      |   4 +-
 .../tensorflow/src/op/arg_min_max.cpp         |   8 +-
 src/frontends/tensorflow/src/op/avg_pool.cpp  |  10 +-
 src/frontends/tensorflow/src/op/bias_add.cpp  |  12 +-
 src/frontends/tensorflow/src/op/binary_op.cpp |   4 +-
 src/frontends/tensorflow/src/op/cast.cpp      |   4 +-
 src/frontends/tensorflow/src/op/concat.cpp    |   8 +-
 src/frontends/tensorflow/src/op/const.cpp     |   6 +-
 src/frontends/tensorflow/src/op/conv_2d.cpp   |  16 +--
 .../tensorflow/src/op/conv_2d_backprop.cpp    |  10 +-
 src/frontends/tensorflow/src/op/conv_3d.cpp   |  10 +-
 .../tensorflow/src/op/crop_and_resize.cpp     |  18 +--
 src/frontends/tensorflow/src/op/cumsum.cpp    |   4 +-
 .../tensorflow/src/op/depth_to_space.cpp      |  10 +-
 .../tensorflow/src/op/depthwise_conv_2d.cpp   |  10 +-
 src/frontends/tensorflow/src/op/elu.cpp       |   4 +-
 .../tensorflow/src/op/expand_dims.cpp         |   4 +-
 .../src/op/fake_quant_min_max_vars.cpp        |   4 +-
 src/frontends/tensorflow/src/op/fill.cpp      |   4 +-
 .../tensorflow/src/op/fused_batch_norm.cpp    |   6 +-
 src/frontends/tensorflow/src/op/gather.cpp    |   4 +-
 src/frontends/tensorflow/src/op/identity.cpp  |   4 +-
 .../tensorflow/src/op/interpolate.cpp         |   4 +-
 src/frontends/tensorflow/src/op/is_finite.cpp |   4 +-
 src/frontends/tensorflow/src/op/l2_loss.cpp   |   4 +-
 .../tensorflow/src/op/leaky_relu.cpp          |   4 +-
 src/frontends/tensorflow/src/op/log1p.cpp     |   4 +-
 .../tensorflow/src/op/log_softmax.cpp         |   4 +-
 src/frontends/tensorflow/src/op/lrn.cpp       |   4 +-
 src/frontends/tensorflow/src/op/matmul.cpp    |   4 +-
 src/frontends/tensorflow/src/op/max_pool.cpp  |   6 +-
 src/frontends/tensorflow/src/op/no_op.cpp     |  10 +-
 .../tensorflow/src/op/non_max_suppression.cpp |   6 +-
 src/frontends/tensorflow/src/op/one_hot.cpp   |   4 +-
 src/frontends/tensorflow/src/op/pack.cpp      |   4 +-
 src/frontends/tensorflow/src/op/pad.cpp       |  16 +--
 .../tensorflow/src/op/placeholder.cpp         |   4 +-
 .../tensorflow/src/op/random_uniform.cpp      |   4 +-
 src/frontends/tensorflow/src/op/range.cpp     |   4 +-
 src/frontends/tensorflow/src/op/rank.cpp      |   4 +-
 .../tensorflow/src/op/reciprocal.cpp          |   4 +-
 src/frontends/tensorflow/src/op/reduce.cpp    |  12 +-
 src/frontends/tensorflow/src/op/relu_6.cpp    |   4 +-
 src/frontends/tensorflow/src/op/reshape.cpp   |   4 +-
 src/frontends/tensorflow/src/op/reverse.cpp   |  10 +-
 src/frontends/tensorflow/src/op/roll.cpp      |   6 +-
 src/frontends/tensorflow/src/op/round.cpp     |   4 +-
 src/frontends/tensorflow/src/op/rsqrt.cpp     |   4 +-
 src/frontends/tensorflow/src/op/select.cpp    |   6 +-
 src/frontends/tensorflow/src/op/shape.cpp     |   4 +-
 src/frontends/tensorflow/src/op/size.cpp      |   4 +-
 src/frontends/tensorflow/src/op/slice.cpp     |   4 +-
 src/frontends/tensorflow/src/op/softmax.cpp   |   6 +-
 .../tensorflow/src/op/space_to_batch_nd.cpp   |   8 +-
 .../tensorflow/src/op/space_to_depth.cpp      |   6 +-
 src/frontends/tensorflow/src/op/split.cpp     |   4 +-
 src/frontends/tensorflow/src/op/sqrt.cpp      |   4 +-
 src/frontends/tensorflow/src/op/square.cpp    |   4 +-
 src/frontends/tensorflow/src/op/squeeze.cpp   |   4 +-
 .../tensorflow/src/op/strided_slice.cpp       |   4 +-
 src/frontends/tensorflow/src/op/tile.cpp      |   4 +-
 src/frontends/tensorflow/src/op/top_k.cpp     |  12 +-
 src/frontends/tensorflow/src/op/transpose.cpp |   4 +-
 src/frontends/tensorflow/src/op/unary_op.cpp  |   4 +-
 src/frontends/tensorflow/src/op/unpack.cpp    |   4 +-
 src/frontends/tensorflow/src/op/where.cpp     |   4 +-
 src/frontends/tensorflow/src/op/xdivy.cpp     |   4 +-
 .../tensorflow/src/op/zeros_like.cpp          |   4 +-
 src/frontends/tensorflow/src/op_table.cpp     |   6 +-
 src/frontends/tensorflow/src/op_table.hpp     |   6 +-
 .../tensorflow/src/openvino_conversions.cpp   |   4 +-
 .../tensorflow/src/openvino_conversions.hpp   |   5 +-
 .../tensorflow/src/pass/transpose_sinking.cpp |   2 +-
 .../tensorflow/src/pass/transpose_sinking.hpp |  12 +-
 src/frontends/tensorflow/src/place.cpp        | 118 +++++++++---------
 src/frontends/tensorflow/src/place.hpp        |  83 ++++++------
 src/frontends/tensorflow/src/tensorflow.cpp   |   9 +-
 .../tensorflow/src/tf_framework_node.cpp      |   6 +-
 .../tensorflow/src/tf_framework_node.hpp      |  16 +--
 src/frontends/tensorflow/src/utils.cpp        |   7 +-
 src/frontends/tensorflow/src/utils.hpp        |  28 ++---
 95 files changed, 549 insertions(+), 528 deletions(-)
 rename src/frontends/tensorflow/include/{tensorflow_frontend => openvino/frontend/tensorflow}/decoder.hpp (88%)
 rename src/frontends/tensorflow/include/{tensorflow_frontend => openvino/frontend/tensorflow}/frontend.hpp (92%)
 rename src/frontends/tensorflow/include/{tensorflow_frontend => openvino/frontend/tensorflow}/graph_iterator.hpp (82%)
 rename src/frontends/tensorflow/include/{tensorflow_frontend/utility.hpp => openvino/frontend/tensorflow/visibility.hpp} (50%)
 rename src/frontends/tensorflow/src/{model.cpp => input_model.cpp} (73%)
 rename src/frontends/tensorflow/src/{model.hpp => input_model.hpp} (73%)

diff --git a/src/core/tests/frontend/tensorflow/transpose_sinking.cpp b/src/core/tests/frontend/tensorflow/transpose_sinking.cpp
index 44f50d2c80b..40d5765a2ee 100644
--- a/src/core/tests/frontend/tensorflow/transpose_sinking.cpp
+++ b/src/core/tests/frontend/tensorflow/transpose_sinking.cpp
@@ -15,7 +15,7 @@
 using namespace std;
 using namespace ov;
 using namespace opset8;
-using namespace frontend::tf::pass;
+using namespace frontend::tensorflow::pass;
 
 template <class T>
 int64_t count_ops_of_type(const shared_ptr<Model>& f) {
@@ -27,7 +27,7 @@ int64_t count_ops_of_type(const shared_ptr<Model>& f) {
 }
 
 TEST(TransposeSinkingTest, PassProperty) {
-    auto pass = std::make_shared<TransposeSinkingOVTF>();
+    auto pass = std::make_shared<TransposeSinking>();
     ASSERT_TRUE(pass->get_property(ov::pass::PassProperty::REQUIRE_STATIC_SHAPE));
     ASSERT_FALSE(pass->get_property(ov::pass::PassProperty::CHANGE_DYNAMIC_STATE));
 }
@@ -51,7 +51,7 @@ TEST(TransposeSinkingTest, EdgeSplitting) {
     size_t before_count = count_ops_of_type<Transpose>(func);
 
     ov::pass::Manager pass_manager;
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     ASSERT_EQ(before_count, 1);
@@ -107,7 +107,7 @@ TEST(TransposeSinkingTest, PoolAdd1) {
 
     ov::pass::Manager pass_manager;
     size_t before_count = count_ops_of_type<Transpose>(func);
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t after_count = count_ops_of_type<Transpose>(func);
@@ -159,7 +159,7 @@ TEST(TransposeSinkingTest, PoolAdd2) {
 
     ov::pass::Manager pass_manager;
     size_t before_count = count_ops_of_type<Transpose>(func);  // 3
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t after_count = count_ops_of_type<Transpose>(func);  // 4
@@ -171,7 +171,7 @@ TEST(TransposeSinkingTest, PoolAdd2) {
     ASSERT_EQ(new_transpose->get_output_shape(0), (ngraph::Shape{1, 3, 3, 1}));
 }
 
-// Different rank constant input to Add1. After TransposeSinkingOVTF the const
+// Different rank constant input to Add1. After TransposeSinking the const
 // would need a Reshape to have the same order as the other input to
 // Add1.
 TEST(TransposeSinkingTest, PoolAdd3) {
@@ -203,7 +203,7 @@ TEST(TransposeSinkingTest, PoolAdd3) {
 
     ov::pass::Manager pass_manager;
     size_t before_count = count_ops_of_type<Transpose>(func);
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t after_count = count_ops_of_type<Transpose>(func);
@@ -228,7 +228,7 @@ TEST(TransposeSinkingTest, Concat) {
     auto func = make_shared<ngraph::Function>(ngraph::OutputVector{c}, ngraph::ParameterVector{a, b});
 
     ov::pass::Manager pass_manager;
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t transpose_count = count_ops_of_type<Transpose>(func);
@@ -260,7 +260,7 @@ TEST(TransposeSinkingTest, Concat_DummyShape) {
     auto func = make_shared<ngraph::Function>(ngraph::OutputVector{out}, ngraph::ParameterVector{a1, a2, a3, a4});
 
     ov::pass::Manager pass_manager;
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t transpose_count = count_ops_of_type<Transpose>(func);  // 1
@@ -306,7 +306,7 @@ TEST(TransposeSinkingTest, Pad) {
 
     ov::pass::Manager pass_manager;
     size_t before_count = count_ops_of_type<Transpose>(func);  // 2
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t after_count = count_ops_of_type<Transpose>(func);  // 2
@@ -337,7 +337,7 @@ TEST(TransposeSinkingTest, SimpleUnary) {
     size_t before_count = count_ops_of_type<Transpose>(func);  // 2
 
     ov::pass::Manager pass_manager;
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t after_count = count_ops_of_type<Transpose>(func);  // 0
@@ -396,7 +396,7 @@ TEST(TransposeSinkingTest, MultiOutput) {
 
     ov::pass::Manager pass_manager;
     size_t before_count = count_ops_of_type<Transpose>(func);  // 3
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t after_count = count_ops_of_type<Transpose>(func);  // 4
@@ -503,7 +503,7 @@ TEST(TransposeSinkingTest, AlexnetPattern) {
 
     ov::pass::Manager pass_manager;
     size_t before_count = count_ops_of_type<Transpose>(func);
-    pass_manager.register_pass<TransposeSinkingOVTF>();
+    pass_manager.register_pass<TransposeSinking>();
     pass_manager.run_passes(func);
 
     size_t after_count = count_ops_of_type<Transpose>(func);
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/decoder.hpp b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/decoder.hpp
similarity index 88%
rename from src/frontends/tensorflow/include/tensorflow_frontend/decoder.hpp
rename to src/frontends/tensorflow/include/openvino/frontend/tensorflow/decoder.hpp
index 423f030934c..7377f551ad5 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/decoder.hpp
+++ b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/decoder.hpp
@@ -4,14 +4,14 @@
 
 #pragma once
 
-#include <openvino/core/any.hpp>
-
-#include "tensorflow_frontend/utility.hpp"
+#include "openvino/core/any.hpp"
+#include "openvino/frontend/tensorflow/visibility.hpp"
 
 namespace ov {
 namespace frontend {
+namespace tensorflow {
 
-class TF_API DecoderBase {
+class TENSORFLOW_API DecoderBase {
 public:
     /// \brief Get attribute value by name and requested type
     ///
@@ -41,5 +41,7 @@ public:
     /// \brief Destructor
     virtual ~DecoderBase() = default;
 };
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/frontend.hpp
similarity index 92%
rename from src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
rename to src/frontends/tensorflow/include/openvino/frontend/tensorflow/frontend.hpp
index 274f47bd041..d1d9d50ee36 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/frontend.hpp
+++ b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/frontend.hpp
@@ -13,28 +13,24 @@
 #include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/frontend.hpp"
 #include "openvino/frontend/input_model.hpp"
-#include "tensorflow_frontend/utility.hpp"
+#include "openvino/frontend/tensorflow/visibility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
+
 class NodeContext;
-}
-}  // namespace frontend
-}  // namespace ov
 
-namespace ov {
-namespace frontend {
-class TF_API FrontEndTF : public ov::frontend::FrontEnd {
+class TENSORFLOW_API FrontEnd : public ov::frontend::FrontEnd {
 public:
-    using CreatorFunction = std::function<::ov::OutputVector(const ::ov::frontend::tf::NodeContext&)>;
+    using CreatorFunction = std::function<::ov::OutputVector(const ::ov::frontend::tensorflow::NodeContext&)>;
     using TranslatorDictionaryType = std::map<const std::string, const CreatorFunction>;
 
 private:
     TranslatorDictionaryType m_op_translators;
 
 public:
-    FrontEndTF();
+    FrontEnd();
 
     /// \brief Completely convert the model
     /// \return fully converted ov Model
@@ -86,5 +82,7 @@ private:
     std::shared_ptr<TelemetryExtension> m_telemetry;
     std::vector<std::shared_ptr<DecoderTransformationExtension>> m_transformation_extensions;
 };
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/graph_iterator.hpp b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/graph_iterator.hpp
similarity index 82%
rename from src/frontends/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
rename to src/frontends/tensorflow/include/openvino/frontend/tensorflow/graph_iterator.hpp
index de8a548fd14..0c2f0628fb4 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
+++ b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/graph_iterator.hpp
@@ -5,13 +5,15 @@
 #pragma once
 
 #include "openvino/core/any.hpp"
-#include "tensorflow_frontend/decoder.hpp"
-#include "tensorflow_frontend/utility.hpp"
+#include "openvino/frontend/tensorflow/decoder.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 namespace ov {
 namespace frontend {
+namespace tensorflow {
+
 /// Abstract representation for an input model graph that gives nodes in topologically sorted order
-class TF_API GraphIterator : ov::RuntimeAttribute {
+class TENSORFLOW_API GraphIterator : ov::RuntimeAttribute {
 public:
     OPENVINO_RTTI("Variant::GraphIterator");
 
@@ -35,5 +37,7 @@ public:
     /// \brief Destructor
     virtual ~GraphIterator() = default;
 };
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/visibility.hpp
similarity index 50%
rename from src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
rename to src/frontends/tensorflow/include/openvino/frontend/tensorflow/visibility.hpp
index ed07e8fe4a2..578e7b77480 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
+++ b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/visibility.hpp
@@ -7,14 +7,14 @@
 #include "openvino/frontend/exception.hpp"
 
 #ifdef OPENVINO_STATIC_LIBRARY
-#    define TF_API
-#    define TF_C_API
+#    define TENSORFLOW_API
+#    define TENSORFLOW_C_API
 #else
 #    ifdef ov_tensorflow_frontend_EXPORTS
-#        define TF_API   OPENVINO_CORE_EXPORTS
-#        define TF_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#        define TENSORFLOW_API   OPENVINO_CORE_EXPORTS
+#        define TENSORFLOW_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    else
-#        define TF_API   OPENVINO_CORE_IMPORTS
-#        define TF_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
+#        define TENSORFLOW_API   OPENVINO_CORE_IMPORTS
+#        define TENSORFLOW_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
 #    endif  // ov_tensorflow_frontend_EXPORTS
 #endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/tensorflow/src/decoder_proto.cpp b/src/frontends/tensorflow/src/decoder_proto.cpp
index 5a7a26c4356..c51c21382db 100644
--- a/src/frontends/tensorflow/src/decoder_proto.cpp
+++ b/src/frontends/tensorflow/src/decoder_proto.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 
 namespace {
 const std::map<::tensorflow::DataType, ov::element::Type>& TYPE_MAP() {
@@ -27,7 +27,7 @@ const std::map<::tensorflow::DataType, ov::element::Type>& TYPE_MAP() {
 }
 }  // namespace
 
-ov::Any DecoderTFProto::get_attribute(const std::string& name, const std::type_info& type_info) const {
+ov::Any DecoderProto::get_attribute(const std::string& name, const std::type_info& type_info) const {
     auto attrs = decode_attribute_helper(name);
     if (attrs.empty()) {
         return {};
@@ -85,13 +85,13 @@ ov::Any DecoderTFProto::get_attribute(const std::string& name, const std::type_i
     return {};
 }
 
-size_t DecoderTFProto::get_input_size() const {
+size_t DecoderProto::get_input_size() const {
     return m_node_def->input_size();
 }
 
-void DecoderTFProto::get_input_node(size_t input_port_idx,
-                                    std::string& producer_name,
-                                    size_t& producer_output_port_index) const {
+void DecoderProto::get_input_node(size_t input_port_idx,
+                                  std::string& producer_name,
+                                  size_t& producer_output_port_index) const {
     // TODO: handle body graph nodes with a couple of columns
     std::string producer_port_name = m_node_def->input(input_port_idx);
     auto delim_pos = producer_port_name.find(':');
@@ -104,15 +104,15 @@ void DecoderTFProto::get_input_node(size_t input_port_idx,
     producer_output_port_index = 0;
 }
 
-const std::string& DecoderTFProto::get_op_type() const {
+const std::string& DecoderProto::get_op_type() const {
     return m_node_def->op();
 }
 
-const std::string& DecoderTFProto::get_op_name() const {
+const std::string& DecoderProto::get_op_name() const {
     return m_node_def->name();
 }
 
-std::vector<::tensorflow::AttrValue> DecoderTFProto::decode_attribute_helper(const std::string& name) const {
+std::vector<::tensorflow::AttrValue> DecoderProto::decode_attribute_helper(const std::string& name) const {
     auto attr_map = m_node_def->attr();
     FRONT_END_GENERAL_CHECK(attr_map.contains(name),
                             "An error occurred while parsing the ",
@@ -123,6 +123,6 @@ std::vector<::tensorflow::AttrValue> DecoderTFProto::decode_attribute_helper(con
     auto value = m_node_def->attr().at(name);
     return {value};
 }
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/decoder_proto.hpp b/src/frontends/tensorflow/src/decoder_proto.hpp
index a0871b20aa7..f5e664185a9 100644
--- a/src/frontends/tensorflow/src/decoder_proto.hpp
+++ b/src/frontends/tensorflow/src/decoder_proto.hpp
@@ -9,16 +9,16 @@
 
 #include "attr_value.pb.h"
 #include "node_def.pb.h"
-#include "tensorflow_frontend/decoder.hpp"
+#include "openvino/frontend/tensorflow/decoder.hpp"
 #include "types.pb.h"
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 
-class DecoderTFProto : public DecoderBase {
+class DecoderProto : public ov::frontend::tensorflow::DecoderBase {
 public:
-    explicit DecoderTFProto(const ::tensorflow::NodeDef* node_def) : m_node_def(node_def) {}
+    explicit DecoderProto(const ::tensorflow::NodeDef* node_def) : m_node_def(node_def) {}
 
     ov::Any get_attribute(const std::string& name, const std::type_info& type_info) const override;
 
@@ -36,6 +36,6 @@ private:
     std::vector<::tensorflow::AttrValue> decode_attribute_helper(const std::string& name) const;
     const ::tensorflow::NodeDef* m_node_def;
 };
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/exceptions.cpp b/src/frontends/tensorflow/src/exceptions.cpp
index 83b7c5716db..1b786df7aad 100644
--- a/src/frontends/tensorflow/src/exceptions.cpp
+++ b/src/frontends/tensorflow/src/exceptions.cpp
@@ -8,12 +8,12 @@
 
 namespace ov {
 namespace frontend {
-namespace tf {
-std::string OpValidationFailureTF::get_error_msg_prefix_tf(const tf::NodeContext& node) {
+namespace tensorflow {
+std::string OpValidationFailure::get_error_msg_prefix_tf(const tensorflow::NodeContext& node) {
     std::stringstream ss;
     ss << "While validating node '" << node.get_op_type() << '\'';
     return ss.str();
 }
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/exceptions.hpp b/src/frontends/tensorflow/src/exceptions.hpp
index d43995a765d..e44b4229057 100644
--- a/src/frontends/tensorflow/src/exceptions.hpp
+++ b/src/frontends/tensorflow/src/exceptions.hpp
@@ -7,19 +7,19 @@
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 
 class NodeContext;
 
-class OpValidationFailureTF : public ov::frontend::OpValidationFailure {
+class OpValidationFailure : public ov::frontend::OpValidationFailure {
 public:
-    OpValidationFailureTF(const CheckLocInfo& check_loc_info, const NodeContext& node, const std::string& explanation)
-        : OpValidationFailure(check_loc_info, get_error_msg_prefix_tf(node), explanation) {}
+    OpValidationFailure(const CheckLocInfo& check_loc_info, const NodeContext& node, const std::string& explanation)
+        : ov::frontend::OpValidationFailure(check_loc_info, get_error_msg_prefix_tf(node), explanation) {}
 
 private:
     static std::string get_error_msg_prefix_tf(const NodeContext& node);
 };
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 
 /// \brief Macro to check whether a boolean condition holds.
@@ -28,7 +28,7 @@ private:
 /// \param ... Additional error message info to be added to the error message via the `<<`
 ///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
 ///            i.e., only if the `cond` evalutes to `false`.
-/// \throws ::ov::OpValidationFailureTF if `cond` is false.
-#define TF_OP_VALIDATION_CHECK(node_context, ...) \
-    OPENVINO_ASSERT_HELPER(::ov::frontend::tf::OpValidationFailureTF, (node_context), __VA_ARGS__)
+/// \throws ::ov::OpValidationFailure if `cond` is false.
+#define TENSORFLOW_OP_VALIDATION(node_context, ...) \
+    OPENVINO_ASSERT_HELPER(::ov::frontend::tensorflow::OpValidationFailure, (node_context), __VA_ARGS__)
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/frontend.cpp b/src/frontends/tensorflow/src/frontend.cpp
index 51ee27fcc12..604f87523b2 100644
--- a/src/frontends/tensorflow/src/frontend.cpp
+++ b/src/frontends/tensorflow/src/frontend.cpp
@@ -2,23 +2,22 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "tensorflow_frontend/frontend.hpp"
+#include "openvino/frontend/tensorflow/frontend.hpp"
 
-#include "model.hpp"
+#include "input_model.hpp"
 #include "op_table.hpp"
+#include "openvino/frontend/tensorflow/graph_iterator.hpp"
 #include "openvino/pass/manager.hpp"
 #include "openvino/util/common_util.hpp"
 #include "pass/transpose_sinking.hpp"
-#include "tensorflow_frontend/graph_iterator.hpp"
 #include "tf_framework_node.hpp"
 #include "utils.hpp"
 
-using namespace ::ov::frontend;
-using namespace ::ov::frontend::tf;
+using namespace ov::frontend::tensorflow;
 
 namespace {
-void translate_framework_node(const std::shared_ptr<TFFrameworkNode>& node,
-                              const FrontEndTF::TranslatorDictionaryType& op_translators) {
+void translate_framework_node(const std::shared_ptr<FrameworkNode>& node,
+                              const FrontEnd::TranslatorDictionaryType& op_translators) {
     auto type = node->get_op_type();
 
     const auto& TRANSLATE_OP_MAP = op_translators;
@@ -46,19 +45,19 @@ void translate_framework_node(const std::shared_ptr<TFFrameworkNode>& node,
 }
 }  // namespace
 
-FrontEndTF::FrontEndTF() : m_op_translators(tf::op::get_supported_ops()) {}
+FrontEnd::FrontEnd() : m_op_translators(tensorflow::op::get_supported_ops()) {}
 
-void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
-                                 const std::string& model_name,
-                                 bool fail_fast,
-                                 bool no_conversion,
-                                 std::shared_ptr<ov::Model>& ng_function) const {
+void FrontEnd::translate_graph(const ov::frontend::InputModel::Ptr& model,
+                               const std::string& model_name,
+                               bool fail_fast,
+                               bool no_conversion,
+                               std::shared_ptr<ov::Model>& ng_function) const {
     // a map from operation names to generated OV Output<TFNodeDecoder>
-    tf::OpMap ng_op_map;
+    tensorflow::OpMap ng_op_map;
 
     ov::ParameterVector params;
     ov::ResultVector results;
-    const auto& model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
+    const auto& model_tf = std::dynamic_pointer_cast<InputModel>(model);
     FRONT_END_GENERAL_CHECK(model_tf, "nullptr for InputModel is given for translation into OV function");
     const auto& operation_places = model_tf->get_op_places();
     const auto& model_inputs = model_tf->get_inputs();
@@ -92,7 +91,7 @@ void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
             // probably this input is frozen
             continue;
         }
-        const auto& input_tensor_place = std::dynamic_pointer_cast<TensorPlaceTF>(input_place);
+        const auto& input_tensor_place = std::dynamic_pointer_cast<TensorPlace>(input_place);
         auto input_shape = input_tensor_place->get_partial_shape();
         auto input_type = input_tensor_place->get_element_type();
 
@@ -113,7 +112,7 @@ void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
 
         // prepare a list of OV node inputs for each node
         ov::OutputVector ng_inputs;
-        ::ov::frontend::tf::NamedInputs named_inputs;
+        ::ov::frontend::tensorflow::NamedInputs named_inputs;
         for (size_t input_port_idx = 0; input_port_idx < operation_decoder->get_input_size(); ++input_port_idx) {
             std::string producer_name;
             size_t producer_port_idx;
@@ -166,7 +165,7 @@ void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
             auto op_fun = &(translate_map[operation_decoder->get_op_type()]);
             // NodeContext node_context(ng_inputs, operation_decoder, model_inputs);
             // TODO: Check why NodeContextNew doesn't have ngOutputVector ng_inputs input in constructor
-            ::ov::frontend::tf::NodeContext node_context(*operation_decoder, named_inputs);
+            ::ov::frontend::tensorflow::NodeContext node_context(*operation_decoder, named_inputs);
             // generate OV node output vector using translator for given operation type
             ng_outputs = (*op_fun)(node_context);
         } catch (...) {
@@ -174,9 +173,9 @@ void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
                 // re-throw any exception
                 throw;
             } else {
-                auto ng_node = std::make_shared<TFFrameworkNode>(operation_decoder,
-                                                                 ng_inputs,
-                                                                 operation_place->get_output_ports().size());
+                auto ng_node = std::make_shared<FrameworkNode>(operation_decoder,
+                                                               ng_inputs,
+                                                               operation_place->get_output_ports().size());
                 set_node_name(operation_name, ng_node);
                 ng_outputs = ng_node->outputs();
             }
@@ -199,12 +198,15 @@ void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
 
     // create Result nodes for all model outputs
     for (const auto& model_output : model_outputs) {
-        auto model_output_tensor_place = std::dynamic_pointer_cast<TensorPlaceTF>(model_output);
+        auto model_output_tensor_place = std::dynamic_pointer_cast<TensorPlace>(model_output);
         auto model_output_name = model_output_tensor_place->get_names()[0];
         std::string operation_name;
         std::string port_type;
         size_t port_index;
-        ov::frontend::tf::extract_operation_name_and_port(model_output_name, operation_name, port_index, port_type);
+        ov::frontend::tensorflow::extract_operation_name_and_port(model_output_name,
+                                                                  operation_name,
+                                                                  port_index,
+                                                                  port_type);
 
         if (port_type == "none") {
             for (const auto& node_output : ng_op_map[operation_name]) {
@@ -218,7 +220,7 @@ void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
             results.push_back(std::make_shared<ov::opset8::Result>(node_outputs[port_index]));
         } else if (port_type == "in") {
             // TODO: avoid this traversing by having a map for OpPlace objects, for example
-            std::shared_ptr<OpPlaceTF> operation_place = nullptr;
+            std::shared_ptr<OpPlace> operation_place = nullptr;
             for (const auto& op_place : operation_places) {
                 FRONT_END_GENERAL_CHECK(!op_place->get_names().empty(), "No names for OpPlace found.");
                 if (op_place->get_names()[0] == operation_name) {
@@ -268,7 +270,7 @@ void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
 }
 
 /// \brief Check if FrontEndTensorflow can recognize model from given parts
-bool FrontEndTF::supported_impl(const std::vector<ov::Any>& variants) const {
+bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
     // TODO: Support other TensorFlow formats: SavedModel, .meta, checkpoint, pbtxt
     if (variants.size() != 1)
         return false;
@@ -286,7 +288,7 @@ bool FrontEndTF::supported_impl(const std::vector<ov::Any>& variants) const {
     return false;
 }
 
-ov::frontend::InputModel::Ptr FrontEndTF::load_impl(const std::vector<ov::Any>& variants) const {
+ov::frontend::InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const {
     // TODO: Support other TensorFlow formats: SavedModel, .meta, checkpoint, pbtxt
     if (variants.size() == 1) {
         // a case when binary protobuf format is provided
@@ -294,26 +296,26 @@ ov::frontend::InputModel::Ptr FrontEndTF::load_impl(const std::vector<ov::Any>&
             std::string suffix = ".pb";
             std::string model_path = variants[0].as<std::string>();
             if (ov::util::ends_with(model_path, suffix.c_str())) {
-                return std::make_shared<InputModelTF>(
-                    std::make_shared<::ov::frontend::tf::GraphIteratorProto>(model_path),
+                return std::make_shared<InputModel>(
+                    std::make_shared<::ov::frontend::tensorflow::GraphIteratorProto>(model_path),
                     m_telemetry);
             }
         } else if (variants[0].is<GraphIterator::Ptr>()) {
             auto graph_iterator = variants[0].as<GraphIterator::Ptr>();
-            return std::make_shared<InputModelTF>(graph_iterator, m_telemetry);
+            return std::make_shared<InputModel>(graph_iterator, m_telemetry);
         }
     }
     return nullptr;
 }
 
-std::shared_ptr<ov::Model> FrontEndTF::convert(const ov::frontend::InputModel::Ptr& model) const {
-    auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
+std::shared_ptr<ov::Model> FrontEnd::convert(const ov::frontend::InputModel::Ptr& model) const {
+    auto model_tf = std::dynamic_pointer_cast<InputModel>(model);
     FRONT_END_GENERAL_CHECK(model_tf != nullptr, "Invalid input model");
 
     if (!m_transformation_extensions.empty()) {
         auto function = decode(model);
 
-        pass::Manager manager;
+        ov::pass::Manager manager;
         for (const auto& transformation : m_transformation_extensions) {
             transformation->register_pass(manager);
         }
@@ -330,14 +332,14 @@ std::shared_ptr<ov::Model> FrontEndTF::convert(const ov::frontend::InputModel::P
     return f;
 }
 
-std::shared_ptr<ov::Model> FrontEndTF::convert_partially(const ov::frontend::InputModel::Ptr& model) const {
-    auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
+std::shared_ptr<ov::Model> FrontEnd::convert_partially(const ov::frontend::InputModel::Ptr& model) const {
+    auto model_tf = std::dynamic_pointer_cast<InputModel>(model);
     FRONT_END_GENERAL_CHECK(model_tf != nullptr, "Invalid input model");
 
     if (!m_transformation_extensions.empty()) {
         auto function = decode(model);
 
-        pass::Manager manager;
+        ov::pass::Manager manager;
         for (const auto& transformation : m_transformation_extensions) {
             transformation->register_pass(manager);
         }
@@ -352,17 +354,17 @@ std::shared_ptr<ov::Model> FrontEndTF::convert_partially(const ov::frontend::Inp
     return f;
 }
 
-std::shared_ptr<ov::Model> FrontEndTF::decode(const ov::frontend::InputModel::Ptr& model) const {
-    auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
+std::shared_ptr<ov::Model> FrontEnd::decode(const ov::frontend::InputModel::Ptr& model) const {
+    auto model_tf = std::dynamic_pointer_cast<InputModel>(model);
     std::shared_ptr<ov::Model> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", false, true, f);
     return f;
 }
 
-void FrontEndTF::convert(const std::shared_ptr<ov::Model>& partiallyConverted) const {
+void FrontEnd::convert(const std::shared_ptr<ov::Model>& partiallyConverted) const {
     for (const auto& node : partiallyConverted->get_ordered_ops()) {
-        if (ov::is_type<TFFrameworkNode>(node)) {
-            translate_framework_node(std::dynamic_pointer_cast<TFFrameworkNode>(node), m_op_translators);
+        if (ov::is_type<FrameworkNode>(node)) {
+            translate_framework_node(std::dynamic_pointer_cast<FrameworkNode>(node), m_op_translators);
         }
     }
     for (const auto& result : partiallyConverted->get_results()) {
@@ -372,13 +374,13 @@ void FrontEndTF::convert(const std::shared_ptr<ov::Model>& partiallyConverted) c
     normalize(partiallyConverted);
 }
 
-void FrontEndTF::normalize(const std::shared_ptr<ov::Model>& function) const {
+void FrontEnd::normalize(const std::shared_ptr<ov::Model>& function) const {
     ov::pass::Manager manager;
-    manager.register_pass<ov::frontend::tf::pass::TransposeSinkingOVTF>();
+    manager.register_pass<ov::frontend::tensorflow::pass::TransposeSinking>();
     manager.run_passes(function);
 }
 
-void FrontEndTF::add_extension(const std::shared_ptr<ov::Extension>& extension) {
+void FrontEnd::add_extension(const std::shared_ptr<ov::Extension>& extension) {
     if (auto telemetry = std::dynamic_pointer_cast<TelemetryExtension>(extension)) {
         m_telemetry = telemetry;
     } else if (auto transformation = std::dynamic_pointer_cast<DecoderTransformationExtension>(extension)) {
diff --git a/src/frontends/tensorflow/src/graph_iterator_proto.hpp b/src/frontends/tensorflow/src/graph_iterator_proto.hpp
index 50b0796ff6a..e1390265cb3 100644
--- a/src/frontends/tensorflow/src/graph_iterator_proto.hpp
+++ b/src/frontends/tensorflow/src/graph_iterator_proto.hpp
@@ -9,12 +9,14 @@
 #include "decoder_proto.hpp"
 #include "graph.pb.h"
 #include "node_def.pb.h"
-#include "tensorflow_frontend/decoder.hpp"
-#include "tensorflow_frontend/graph_iterator.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/tensorflow/decoder.hpp"
+#include "openvino/frontend/tensorflow/graph_iterator.hpp"
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
+
 class GraphIteratorProto : public GraphIterator {
     std::vector<const ::tensorflow::NodeDef*> m_nodes;
     size_t node_index = 0;
@@ -53,9 +55,10 @@ public:
 
     /// Return NodeContext for the current node that iterator points to
     std::shared_ptr<DecoderBase> get_decoder() const override {
-        return std::make_shared<DecoderTFProto>(m_nodes[node_index]);
+        return std::make_shared<DecoderProto>(m_nodes[node_index]);
     }
 };
-}  // namespace tf
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/model.cpp b/src/frontends/tensorflow/src/input_model.cpp
similarity index 73%
rename from src/frontends/tensorflow/src/model.cpp
rename to src/frontends/tensorflow/src/input_model.cpp
index 4c502eef5e6..964392aea5c 100644
--- a/src/frontends/tensorflow/src/model.cpp
+++ b/src/frontends/tensorflow/src/input_model.cpp
@@ -2,23 +2,23 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "model.hpp"
+#include "input_model.hpp"
 
 #include <fstream>
 #include <queue>
 
 #include "node_context.hpp"
 #include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/tensorflow/graph_iterator.hpp"
 #include "openvino/opsets/opset7.hpp"
 #include "place.hpp"
-#include "tensorflow_frontend/graph_iterator.hpp"
 #include "utils.hpp"
 
 using namespace google;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 void extract_operation_name_and_port(const std::string& port_name,
                                      std::string& operation_name,
                                      size_t& port_index,
@@ -49,8 +49,8 @@ void extract_operation_name_and_port(const std::string& port_name,
         FRONT_END_GENERAL_CHECK(false, "Incorrect port name specified: " + port_name);
     }
 }
-}  // namespace tf
-class InputModelTF::InputModelTFImpl {
+
+class InputModel::InputModelTFImpl {
 public:
     InputModelTFImpl(const GraphIterator::Ptr& graph_iterator, const ov::frontend::InputModel& input_model);
     InputModelTFImpl(const GraphIterator::Ptr& graph_iterator,
@@ -68,8 +68,8 @@ public:
     void setElementType(ov::frontend::Place::Ptr place, const ov::element::Type&);
     void setTensorValue(ov::frontend::Place::Ptr place, const void* value);
 
-    std::vector<std::shared_ptr<OpPlaceTF>> get_op_places() const;
-    std::map<std::string, std::shared_ptr<TensorPlaceTF>> get_tensor_places() const {
+    std::vector<std::shared_ptr<OpPlace>> get_op_places() const;
+    std::map<std::string, std::shared_ptr<TensorPlace>> get_tensor_places() const {
         return m_tensor_places;
     }
     std::map<std::string, Output<Node>> get_tensor_values() const {
@@ -78,11 +78,11 @@ public:
 
 private:
     void loadPlaces();
-    std::vector<std::shared_ptr<OpPlaceTF>> determine_cut_nodes() const;
+    std::vector<std::shared_ptr<OpPlace>> determine_cut_nodes() const;
 
-    std::vector<std::shared_ptr<OpPlaceTF>> m_op_places;
-    std::map<std::string, std::shared_ptr<OpPlaceTF>> m_op_places_map;
-    mutable std::map<std::string, std::shared_ptr<TensorPlaceTF>> m_tensor_places;
+    std::vector<std::shared_ptr<OpPlace>> m_op_places;
+    std::map<std::string, std::shared_ptr<OpPlace>> m_op_places_map;
+    mutable std::map<std::string, std::shared_ptr<TensorPlace>> m_tensor_places;
     std::vector<ov::frontend::Place::Ptr> m_inputs;
     std::vector<ov::frontend::Place::Ptr> m_outputs;
     std::map<std::string, Output<Node>> m_tensor_values;
@@ -96,7 +96,7 @@ private:
     bool m_graph_changed = false;
 };
 
-void InputModelTF::InputModelTFImpl::loadPlaces() {
+void InputModel::InputModelTFImpl::loadPlaces() {
     std::set<std::string> all_op_names;
     std::set<std::string> op_names_with_consumers;
     std::map<std::string, uint64_t> op_statistics;
@@ -111,7 +111,7 @@ void InputModelTF::InputModelTFImpl::loadPlaces() {
             op_statistics[op_type]++;
         }
 
-        auto op_place = std::make_shared<OpPlaceTF>(m_input_model, node_decoder);
+        auto op_place = std::make_shared<OpPlace>(m_input_model, node_decoder);
         all_op_names.insert(op_name);
         m_op_places.push_back(op_place);
         m_op_places_map[op_name] = op_place;
@@ -119,7 +119,7 @@ void InputModelTF::InputModelTFImpl::loadPlaces() {
             auto pshape = node_decoder->get_attribute("shape", typeid(ov::PartialShape)).as<ov::PartialShape>();
             auto type = node_decoder->get_attribute("dtype", typeid(ov::element::Type)).as<ov::element::Type>();
             std::vector<std::string> names = {op_name};
-            auto tensor_place = std::make_shared<TensorPlaceTF>(m_input_model, pshape, type, names);
+            auto tensor_place = std::make_shared<TensorPlace>(m_input_model, pshape, type, names);
             m_tensor_places[op_name] = tensor_place;
             m_inputs.push_back(tensor_place);
         }
@@ -156,30 +156,30 @@ void InputModelTF::InputModelTFImpl::loadPlaces() {
     for (auto& output_name : op_names_without_consumers) {
         std::vector<std::string> output_names = {output_name};
         auto output_place =
-            std::make_shared<TensorPlaceTF>(m_input_model, ov::PartialShape({}), ov::element::undefined, output_names);
+            std::make_shared<TensorPlace>(m_input_model, ov::PartialShape({}), ov::element::undefined, output_names);
         m_tensor_places[output_name] = output_place;
         m_outputs.push_back(output_place);
     }
 }
 
-std::vector<std::shared_ptr<OpPlaceTF>> InputModelTF::InputModelTFImpl::get_op_places() const {
+std::vector<std::shared_ptr<OpPlace>> InputModel::InputModelTFImpl::get_op_places() const {
     if (m_graph_changed) {
         return determine_cut_nodes();
     }
     return m_op_places;
 }
 
-std::vector<std::shared_ptr<OpPlaceTF>> InputModelTF::InputModelTFImpl::determine_cut_nodes() const {
+std::vector<std::shared_ptr<OpPlace>> InputModel::InputModelTFImpl::determine_cut_nodes() const {
     std::queue<std::shared_ptr<DecoderBase>> decoders_queue;
     std::unordered_set<std::string> visited;
-    std::vector<std::shared_ptr<OpPlaceTF>> new_ops;
+    std::vector<std::shared_ptr<OpPlace>> new_ops;
     for (const auto& output_place : m_outputs) {
         FRONT_END_GENERAL_CHECK(output_place->get_names().size() > 0, "TensorPlace must have at least one name.");
         auto output_place_name = output_place->get_names()[0];
         std::string operation_name;
         size_t port_idx;
         std::string port_type;
-        tf::extract_operation_name_and_port(output_place_name, operation_name, port_idx, port_type);
+        tensorflow::extract_operation_name_and_port(output_place_name, operation_name, port_idx, port_type);
         if (!visited.count(operation_name)) {
             visited.insert(operation_name);
             FRONT_END_GENERAL_CHECK(m_op_places_map.count(operation_name),
@@ -248,17 +248,17 @@ std::vector<std::shared_ptr<OpPlaceTF>> InputModelTF::InputModelTFImpl::determin
     return new_ops;
 }
 
-InputModelTF::InputModelTFImpl::InputModelTFImpl(const GraphIterator::Ptr& graph_iterator,
-                                                 const ov::frontend::InputModel& input_model)
+InputModel::InputModelTFImpl::InputModelTFImpl(const GraphIterator::Ptr& graph_iterator,
+                                               const ov::frontend::InputModel& input_model)
     : m_input_model(input_model),
       m_graph_iterator(graph_iterator) {
     FRONT_END_GENERAL_CHECK(m_graph_iterator, "Null pointer specified for GraphIterator");
     loadPlaces();
 }
 
-InputModelTF::InputModelTFImpl::InputModelTFImpl(const GraphIterator::Ptr& graph_iterator,
-                                                 const InputModel& input_model,
-                                                 const std::shared_ptr<TelemetryExtension>& telemetry)
+InputModel::InputModelTFImpl::InputModelTFImpl(const GraphIterator::Ptr& graph_iterator,
+                                               const ov::frontend::InputModel& input_model,
+                                               const std::shared_ptr<TelemetryExtension>& telemetry)
     : m_input_model(input_model),
       m_graph_iterator(graph_iterator),
       m_telemetry(telemetry) {
@@ -266,15 +266,15 @@ InputModelTF::InputModelTFImpl::InputModelTFImpl(const GraphIterator::Ptr& graph
     loadPlaces();
 }
 
-std::vector<ov::frontend::Place::Ptr> InputModelTF::InputModelTFImpl::getInputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModel::InputModelTFImpl::getInputs() const {
     return m_inputs;
 }
 
-std::vector<ov::frontend::Place::Ptr> InputModelTF::InputModelTFImpl::getOutputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModel::InputModelTFImpl::getOutputs() const {
     return m_outputs;
 }
 
-ov::frontend::Place::Ptr InputModelTF::InputModelTFImpl::getPlaceByTensorName(const std::string& tensorName) const {
+ov::frontend::Place::Ptr InputModel::InputModelTFImpl::getPlaceByTensorName(const std::string& tensorName) const {
     if (m_tensor_places.find(tensorName) != m_tensor_places.end())
         return m_tensor_places.at(tensorName);
 
@@ -282,11 +282,11 @@ ov::frontend::Place::Ptr InputModelTF::InputModelTFImpl::getPlaceByTensorName(co
     std::string operation_name;
     size_t port_idx;
     std::string port_type;
-    tf::extract_operation_name_and_port(tensorName, operation_name, port_idx, port_type);
+    tensorflow::extract_operation_name_and_port(tensorName, operation_name, port_idx, port_type);
     if (m_op_places_map.find(operation_name) != m_op_places_map.end()) {
         std::vector<std::string> names = {tensorName};
         auto m_var_place =
-            std::make_shared<TensorPlaceTF>(m_input_model, ov::PartialShape(), ov::element::undefined, names);
+            std::make_shared<TensorPlace>(m_input_model, ov::PartialShape(), ov::element::undefined, names);
         m_tensor_places[tensorName] = m_var_place;
         return m_var_place;
     }
@@ -294,18 +294,18 @@ ov::frontend::Place::Ptr InputModelTF::InputModelTFImpl::getPlaceByTensorName(co
     return nullptr;
 }
 
-std::shared_ptr<TensorPlaceTF> castToTensorPlace(const ov::frontend::Place::Ptr& place) {
-    if (auto var_place = std::dynamic_pointer_cast<TensorPlaceTF>(place)) {
+std::shared_ptr<TensorPlace> castToTensorPlace(const ov::frontend::Place::Ptr& place) {
+    if (auto var_place = std::dynamic_pointer_cast<TensorPlace>(place)) {
         return var_place;
-    } else if (auto in_port_place = std::dynamic_pointer_cast<InPortPlaceTF>(place)) {
+    } else if (auto in_port_place = std::dynamic_pointer_cast<InPortPlace>(place)) {
         return in_port_place->get_source_tensor_tf();
-    } else if (auto out_port_place = std::dynamic_pointer_cast<OutPortPlaceTF>(place)) {
+    } else if (auto out_port_place = std::dynamic_pointer_cast<OutPortPlace>(place)) {
         return out_port_place->get_target_tensor_tf();
     }
     FRONT_END_GENERAL_CHECK(false, "Cannot cast this Place to TensorPlaceTF.");
 }
 
-void InputModelTF::InputModelTFImpl::overrideAllInputs(const std::vector<ov::frontend::Place::Ptr>& inputs) {
+void InputModel::InputModelTFImpl::overrideAllInputs(const std::vector<ov::frontend::Place::Ptr>& inputs) {
     m_graph_changed = true;
     m_inputs.clear();
     for (const auto& input_place : inputs) {
@@ -313,7 +313,7 @@ void InputModelTF::InputModelTFImpl::overrideAllInputs(const std::vector<ov::fro
     }
 }
 
-void InputModelTF::InputModelTFImpl::overrideAllOutputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
+void InputModel::InputModelTFImpl::overrideAllOutputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
     m_graph_changed = true;
     m_outputs.clear();
     for (const auto& output_place : outputs) {
@@ -321,26 +321,26 @@ void InputModelTF::InputModelTFImpl::overrideAllOutputs(const std::vector<ov::fr
     }
 }
 
-void InputModelTF::InputModelTFImpl::extractSubgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
-                                                     const std::vector<ov::frontend::Place::Ptr>& outputs) {
+void InputModel::InputModelTFImpl::extractSubgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                                                   const std::vector<ov::frontend::Place::Ptr>& outputs) {
     m_graph_changed = true;
     overrideAllInputs(inputs);
     overrideAllOutputs(outputs);
 }
 
-void InputModelTF::InputModelTFImpl::setPartialShape(ov::frontend::Place::Ptr place, const ov::PartialShape& p_shape) {
+void InputModel::InputModelTFImpl::setPartialShape(ov::frontend::Place::Ptr place, const ov::PartialShape& p_shape) {
     castToTensorPlace(place)->set_partial_shape(p_shape);
 }
 
-ov::PartialShape InputModelTF::InputModelTFImpl::getPartialShape(ov::frontend::Place::Ptr place) const {
+ov::PartialShape InputModel::InputModelTFImpl::getPartialShape(ov::frontend::Place::Ptr place) const {
     return castToTensorPlace(place)->get_partial_shape();
 }
 
-void InputModelTF::InputModelTFImpl::setElementType(ov::frontend::Place::Ptr place, const ov::element::Type& type) {
+void InputModel::InputModelTFImpl::setElementType(ov::frontend::Place::Ptr place, const ov::element::Type& type) {
     castToTensorPlace(place)->set_element_type(type);
 }
 
-void InputModelTF::InputModelTFImpl::setTensorValue(ov::frontend::Place::Ptr place, const void* value) {
+void InputModel::InputModelTFImpl::setTensorValue(ov::frontend::Place::Ptr place, const void* value) {
     m_graph_changed = true;
     auto tensor_place = castToTensorPlace(place);
     auto p_shape = tensor_place->get_partial_shape();
@@ -351,61 +351,62 @@ void InputModelTF::InputModelTFImpl::setTensorValue(ov::frontend::Place::Ptr pla
     m_tensor_values[name] = constant;
 }
 
-InputModelTF::InputModelTF(const GraphIterator::Ptr& graph_iterator,
-                           const std::shared_ptr<TelemetryExtension>& telemetry)
+InputModel::InputModel(const GraphIterator::Ptr& graph_iterator, const std::shared_ptr<TelemetryExtension>& telemetry)
     : _impl{std::make_shared<InputModelTFImpl>(graph_iterator, *this, telemetry)} {}
 
-std::vector<std::shared_ptr<OpPlaceTF>> InputModelTF::get_op_places() const {
+std::vector<std::shared_ptr<OpPlace>> InputModel::get_op_places() const {
     return _impl->get_op_places();
 }
 
-std::map<std::string, std::shared_ptr<TensorPlaceTF>> InputModelTF::get_tensor_places() const {
+std::map<std::string, std::shared_ptr<TensorPlace>> InputModel::get_tensor_places() const {
     return _impl->get_tensor_places();
 }
 
-std::map<std::string, Output<Node>> InputModelTF::get_tensor_values() const {
+std::map<std::string, Output<Node>> InputModel::get_tensor_values() const {
     return _impl->get_tensor_values();
 }
 
-std::vector<ov::frontend::Place::Ptr> InputModelTF::get_inputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModel::get_inputs() const {
     return _impl->getInputs();
 }
 
-std::vector<ov::frontend::Place::Ptr> InputModelTF::get_outputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModel::get_outputs() const {
     return _impl->getOutputs();
 }
 
-ov::frontend::Place::Ptr InputModelTF::get_place_by_tensor_name(const std::string& tensorName) const {
+ov::frontend::Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensorName) const {
     return _impl->getPlaceByTensorName(tensorName);
 }
 
-void InputModelTF::override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
+void InputModel::override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
     _impl->overrideAllOutputs(outputs);
 }
 
-void InputModelTF::override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) {
+void InputModel::override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) {
     _impl->overrideAllInputs(inputs);
 }
 
-void InputModelTF::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
-                                    const std::vector<ov::frontend::Place::Ptr>& outputs) {
+void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                                  const std::vector<ov::frontend::Place::Ptr>& outputs) {
     _impl->extractSubgraph(inputs, outputs);
 }
 
-void InputModelTF::set_partial_shape(const ov::frontend::Place::Ptr& place, const ov::PartialShape& p_shape) {
+void InputModel::set_partial_shape(const ov::frontend::Place::Ptr& place, const ov::PartialShape& p_shape) {
     _impl->setPartialShape(place, p_shape);
 }
 
-ov::PartialShape InputModelTF::get_partial_shape(const ov::frontend::Place::Ptr& place) const {
+ov::PartialShape InputModel::get_partial_shape(const ov::frontend::Place::Ptr& place) const {
     return _impl->getPartialShape(place);
 }
 
-void InputModelTF::set_element_type(const ov::frontend::Place::Ptr& place, const ov::element::Type& type) {
+void InputModel::set_element_type(const ov::frontend::Place::Ptr& place, const ov::element::Type& type) {
     _impl->setElementType(place, type);
 }
 
-void InputModelTF::set_tensor_value(const ov::frontend::Place::Ptr& place, const void* value) {
+void InputModel::set_tensor_value(const ov::frontend::Place::Ptr& place, const void* value) {
     _impl->setTensorValue(place, value);
 }
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/model.hpp b/src/frontends/tensorflow/src/input_model.hpp
similarity index 73%
rename from src/frontends/tensorflow/src/model.hpp
rename to src/frontends/tensorflow/src/input_model.hpp
index 2ff9d73706c..1e4b4a4d452 100644
--- a/src/frontends/tensorflow/src/model.hpp
+++ b/src/frontends/tensorflow/src/input_model.hpp
@@ -7,26 +7,27 @@
 #include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/input_model.hpp"
 #include "openvino/frontend/place.hpp"
-#include "tensorflow_frontend/graph_iterator.hpp"
+#include "openvino/frontend/tensorflow/graph_iterator.hpp"
 
 namespace ov {
 namespace frontend {
+namespace tensorflow {
 
-class OpPlaceTF;
-class TensorPlaceTF;
+class OpPlace;
+class TensorPlace;
 
-class InputModelTF : public ov::frontend::InputModel {
-    friend class FrontEndTF;
+class InputModel : public ov::frontend::InputModel {
+    friend class FrontEnd;
     class InputModelTFImpl;
     std::shared_ptr<InputModelTFImpl> _impl;
 
-    std::vector<std::shared_ptr<OpPlaceTF>> get_op_places() const;
-    std::map<std::string, std::shared_ptr<TensorPlaceTF>> get_tensor_places() const;
+    std::vector<std::shared_ptr<OpPlace>> get_op_places() const;
+    std::map<std::string, std::shared_ptr<TensorPlace>> get_tensor_places() const;
     std::map<std::string, Output<Node>> get_tensor_values() const;
 
 public:
-    explicit InputModelTF(const GraphIterator::Ptr& graph_iterator,
-                          const std::shared_ptr<TelemetryExtension>& telemetry = {});
+    explicit InputModel(const GraphIterator::Ptr& graph_iterator,
+                        const std::shared_ptr<TelemetryExtension>& telemetry = {});
 
     std::vector<ov::frontend::Place::Ptr> get_inputs() const override;
     std::vector<ov::frontend::Place::Ptr> get_outputs() const override;
@@ -40,5 +41,7 @@ public:
     void set_element_type(const ov::frontend::Place::Ptr& place, const ov::element::Type&) override;
     void set_tensor_value(const ov::frontend::Place::Ptr& place, const void* value) override;
 };
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/node_context.hpp b/src/frontends/tensorflow/src/node_context.hpp
index 2913a2fa799..b34cb068aa0 100644
--- a/src/frontends/tensorflow/src/node_context.hpp
+++ b/src/frontends/tensorflow/src/node_context.hpp
@@ -8,12 +8,12 @@
 #include "exceptions.hpp"
 #include "place.hpp"
 #include "tensor.pb.h"
-#include "tensorflow_frontend/utility.hpp"
 #include "types.pb.h"
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
+
 using InPortName = size_t;
 using OutPortName = size_t;
 using NamedOutputs = std::map<OutPortName, OutputVector>;
@@ -103,6 +103,6 @@ public:
     }
 };
 
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/addN.cpp b/src/frontends/tensorflow/src/op/addN.cpp
index 7b08abde177..3a613d335d0 100644
--- a/src/frontends/tensorflow/src/op/addN.cpp
+++ b/src/frontends/tensorflow/src/op/addN.cpp
@@ -12,7 +12,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_add_n_op(const NodeContext& node) {
@@ -27,6 +27,6 @@ OutputVector translate_add_n_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/arg_min_max.cpp b/src/frontends/tensorflow/src/op/arg_min_max.cpp
index ed88729ea8a..b8b11b093a3 100644
--- a/src/frontends/tensorflow/src/op/arg_min_max.cpp
+++ b/src/frontends/tensorflow/src/op/arg_min_max.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_arg_min_max(const NodeContext& node, std::string mode) {
@@ -22,7 +22,9 @@ OutputVector translate_arg_min_max(const NodeContext& node, std::string mode) {
     Shape input_shape = ng_input.get_shape();
     size_t input_rank = input_shape.size();
 
-    TF_OP_VALIDATION_CHECK(node, tf_dim.size() == 1, "ArgMax Op: dimension must be scalar, operates on a single axis");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_dim.size() == 1,
+                             "ArgMax Op: dimension must be scalar, operates on a single axis");
 
     // If input dimension is negative, make it positive
     if (tf_dim[0] < 0) {
@@ -54,6 +56,6 @@ OutputVector translate_arg_min_op(const NodeContext& node) {
     return (translate_arg_min_max(node, "min"));
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/avg_pool.cpp b/src/frontends/tensorflow/src/op/avg_pool.cpp
index 9430f6d90cb..395d409e176 100644
--- a/src/frontends/tensorflow/src/op/avg_pool.cpp
+++ b/src/frontends/tensorflow/src/op/avg_pool.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_avg_pool_op(const NodeContext& node) {
@@ -21,9 +21,9 @@ OutputVector translate_avg_pool_op(const NodeContext& node) {
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
-                           "AvgPool data format is neither NHWC nor NCHW");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                             "AvgPool data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
@@ -65,6 +65,6 @@ OutputVector translate_avg_pool_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/bias_add.cpp b/src/frontends/tensorflow/src/op/bias_add.cpp
index 518116c86c8..e4f1aeadc09 100644
--- a/src/frontends/tensorflow/src/op/bias_add.cpp
+++ b/src/frontends/tensorflow/src/op/bias_add.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_bias_add_op(const NodeContext& node) {
@@ -18,13 +18,13 @@ OutputVector translate_bias_add_op(const NodeContext& node) {
 
     std::string tf_data_format = node.get_attribute<std::string>("data_format", "NHWC");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
-                           "BiasAdd data format is neither NHWC nor NCHW");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                             "BiasAdd data format is neither NHWC nor NCHW");
 
     auto ng_input_shape = ng_input.get_shape();
     auto ng_bias_shape = ng_bias.get_shape();
-    TF_OP_VALIDATION_CHECK(node, ng_bias_shape.size() == 1, "Bias argument to BiasAdd does not have one dimension");
+    TENSORFLOW_OP_VALIDATION(node, ng_bias_shape.size() == 1, "Bias argument to BiasAdd does not have one dimension");
 
     // We'll choose reshape over broadcast
     // Reshape the bias to (1, C, 1, ...) if input is channels-first.
@@ -48,6 +48,6 @@ OutputVector translate_bias_add_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/binary_op.cpp b/src/frontends/tensorflow/src/op/binary_op.cpp
index 97c824aac50..5e260b45728 100644
--- a/src/frontends/tensorflow/src/op/binary_op.cpp
+++ b/src/frontends/tensorflow/src/op/binary_op.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_binary_op(const NodeContext& node,
@@ -57,6 +57,6 @@ template OutputVector translate_binary_op<SquaredDifference>(const NodeContext&
 template OutputVector translate_binary_op<Subtract>(const NodeContext& node);
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/cast.cpp b/src/frontends/tensorflow/src/op/cast.cpp
index 9fe2b177c0f..ceefab1c4f0 100644
--- a/src/frontends/tensorflow/src/op/cast.cpp
+++ b/src/frontends/tensorflow/src/op/cast.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_cast_op(const NodeContext& node) {
@@ -23,6 +23,6 @@ OutputVector translate_cast_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/concat.cpp b/src/frontends/tensorflow/src/op/concat.cpp
index 4e2bbe26039..8324a801f26 100644
--- a/src/frontends/tensorflow/src/op/concat.cpp
+++ b/src/frontends/tensorflow/src/op/concat.cpp
@@ -9,11 +9,11 @@ using namespace std;
 using namespace ov;
 using namespace opset8;
 using namespace ov::frontend;
-using namespace frontend::tf::detail;
+using namespace frontend::tensorflow::detail;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_concat_op(const NodeContext& node) {
@@ -27,7 +27,7 @@ OutputVector translate_concat_op(const NodeContext& node) {
         concat_idx_start = 1;
         concat_idx_stop = node.get_input_size();
     } else {
-        TF_OP_VALIDATION_CHECK(node, false, "Incorrect operation type.");
+        TENSORFLOW_OP_VALIDATION(node, false, "Incorrect operation type.");
     }
 
     std::vector<int64_t> tf_concat_axis_vec;
@@ -45,6 +45,6 @@ OutputVector translate_concat_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/const.cpp b/src/frontends/tensorflow/src/op/const.cpp
index 054f2e44eb8..ea0b9af4bb9 100644
--- a/src/frontends/tensorflow/src/op/const.cpp
+++ b/src/frontends/tensorflow/src/op/const.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 namespace {
@@ -49,12 +49,12 @@ OutputVector translate_const_op(const NodeContext& node) {
         const auto& func_param = TF_OPENVINO_CONST_MAP().at(dt);
         func_param.first(node, func_param.second, res);
     } catch (const std::out_of_range&) {
-        TF_OP_VALIDATION_CHECK(node, false, "Failed to translate Constant with target OV type:" + dt.get_type_name());
+        TENSORFLOW_OP_VALIDATION(node, false, "Failed to translate Constant with target OV type:" + dt.get_type_name());
     }
     set_node_name(node.get_name(), res.get_node_shared_ptr());
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/conv_2d.cpp b/src/frontends/tensorflow/src/op/conv_2d.cpp
index 53f132415b0..d85af6e86be 100644
--- a/src/frontends/tensorflow/src/op/conv_2d.cpp
+++ b/src/frontends/tensorflow/src/op/conv_2d.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_conv_2d_op(const NodeContext& node) {
@@ -21,18 +21,18 @@ OutputVector translate_conv_2d_op(const NodeContext& node) {
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
-                           "Conv2D data format is neither NHWC nor NCHW");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                             "Conv2D data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
     // TF Kernel Test Checks
     // Strides in the batch and depth dimension is not supported
     if (tf_strides[0] != 1 || tf_strides[is_nhwc ? 3 : 1] != 1) {
-        TF_OP_VALIDATION_CHECK(node,
-                               false,
-                               "Strides in batch and depth dimensions is not supported: " + node.get_op_type());
+        TENSORFLOW_OP_VALIDATION(node,
+                                 false,
+                                 "Strides in batch and depth dimensions is not supported: " + node.get_op_type());
     }
 
     Strides ng_strides(2);
@@ -68,6 +68,6 @@ OutputVector translate_conv_2d_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/conv_2d_backprop.cpp b/src/frontends/tensorflow/src/op/conv_2d_backprop.cpp
index 59ba2346bea..129d2f9840d 100644
--- a/src/frontends/tensorflow/src/op/conv_2d_backprop.cpp
+++ b/src/frontends/tensorflow/src/op/conv_2d_backprop.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_conv_2d_backprop_input_op(const NodeContext& node) {
@@ -22,9 +22,9 @@ OutputVector translate_conv_2d_backprop_input_op(const NodeContext& node) {
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
-                           "Conv2DBackpropInput data format is neither NHWC nor NCHW");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                             "Conv2DBackpropInput data format is neither NHWC nor NCHW");
 
     std::vector<int64_t> tf_input_sizes;
     get_const_input(node, 0, &tf_input_sizes);
@@ -92,6 +92,6 @@ OutputVector translate_conv_2d_backprop_input_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/conv_3d.cpp b/src/frontends/tensorflow/src/op/conv_3d.cpp
index a8c008e2abb..41901d10591 100644
--- a/src/frontends/tensorflow/src/op/conv_3d.cpp
+++ b/src/frontends/tensorflow/src/op/conv_3d.cpp
@@ -11,7 +11,7 @@ using namespace ov::opset8;
 // Translate Conv3D Op
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_conv_3d_op(const NodeContext& node) {
@@ -22,9 +22,9 @@ OutputVector translate_conv_3d_op(const NodeContext& node) {
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
-                           "Conv3D data format is neither NHWC nor NCHW");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                             "Conv3D data format is neither NHWC nor NCHW");
 
     bool is_ndhwc = (tf_data_format == "NDHWC");
 
@@ -72,6 +72,6 @@ OutputVector translate_conv_3d_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/crop_and_resize.cpp b/src/frontends/tensorflow/src/op/crop_and_resize.cpp
index 57578f3f7ea..94125428531 100644
--- a/src/frontends/tensorflow/src/op/crop_and_resize.cpp
+++ b/src/frontends/tensorflow/src/op/crop_and_resize.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_crop_and_resize_op(const NodeContext& node) {
@@ -33,10 +33,10 @@ OutputVector translate_crop_and_resize_op(const NodeContext& node) {
 
     auto resize_method = node.get_attribute<string>("method");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           ng_input.get_partial_shape().is_static() && ng_boxes.get_partial_shape().is_static() &&
-                               ng_box_ind.get_partial_shape().is_static() && ng_size.get_partial_shape().is_static(),
-                           "Dynamic shapes are not supported.");
+    TENSORFLOW_OP_VALIDATION(node,
+                             ng_input.get_partial_shape().is_static() && ng_boxes.get_partial_shape().is_static() &&
+                                 ng_box_ind.get_partial_shape().is_static() && ng_size.get_partial_shape().is_static(),
+                             "Dynamic shapes are not supported.");
 
     auto spatial_shape = ng_input.get_shape();
     auto image_height = spatial_shape[1];
@@ -47,9 +47,9 @@ OutputVector translate_crop_and_resize_op(const NodeContext& node) {
     auto const_box_ind = dynamic_pointer_cast<Constant>(ng_boxes.get_node_shared_ptr());
     auto const_crop_size = dynamic_pointer_cast<Constant>(ng_boxes.get_node_shared_ptr());
 
-    TF_OP_VALIDATION_CHECK(node,
-                           const_boxes && const_box_ind && const_crop_size,
-                           "Boxes, BoxIndexes, CropSize inputs must be constant.");
+    TENSORFLOW_OP_VALIDATION(node,
+                             const_boxes && const_box_ind && const_crop_size,
+                             "Boxes, BoxIndexes, CropSize inputs must be constant.");
 
     auto boxes = const_boxes->cast_vector<float>();
     auto box_ind = const_box_ind->cast_vector<int64_t>();
@@ -137,6 +137,6 @@ OutputVector translate_crop_and_resize_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/cumsum.cpp b/src/frontends/tensorflow/src/op/cumsum.cpp
index 92738c0ef24..b2106354980 100644
--- a/src/frontends/tensorflow/src/op/cumsum.cpp
+++ b/src/frontends/tensorflow/src/op/cumsum.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_cumsum_op(const NodeContext& node) {
@@ -24,6 +24,6 @@ OutputVector translate_cumsum_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/depth_to_space.cpp b/src/frontends/tensorflow/src/op/depth_to_space.cpp
index 5a5757ea0c7..3b2c0e1c0ce 100644
--- a/src/frontends/tensorflow/src/op/depth_to_space.cpp
+++ b/src/frontends/tensorflow/src/op/depth_to_space.cpp
@@ -11,7 +11,7 @@ using namespace ov::opset8;
 // Translate DepthToSpace op
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_depth_to_space_op(const NodeContext& node) {
@@ -21,9 +21,9 @@ OutputVector translate_depth_to_space_op(const NodeContext& node) {
     auto block_size = node.get_attribute<int64_t>("block_size");
     std::string tf_data_format = node.get_attribute<std::string>("data_format");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
-                           "DepthToSpace data format is neither NHWC nor NCHW");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                             "DepthToSpace data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
@@ -35,6 +35,6 @@ OutputVector translate_depth_to_space_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/depthwise_conv_2d.cpp b/src/frontends/tensorflow/src/op/depthwise_conv_2d.cpp
index 6506b3c5ff8..c951010abed 100644
--- a/src/frontends/tensorflow/src/op/depthwise_conv_2d.cpp
+++ b/src/frontends/tensorflow/src/op/depthwise_conv_2d.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_depthwise_conv_2d_native_op(const NodeContext& node) {
@@ -22,9 +22,9 @@ OutputVector translate_depthwise_conv_2d_native_op(const NodeContext& node) {
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    TF_OP_VALIDATION_CHECK(node,
-                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
-                           "DepthwiseConv2D data format is neither NHWC nor NCHW");
+    TENSORFLOW_OP_VALIDATION(node,
+                             tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                             "DepthwiseConv2D data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
@@ -76,6 +76,6 @@ OutputVector translate_depthwise_conv_2d_native_op(const NodeContext& node) {
     return {ng_conv};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/elu.cpp b/src/frontends/tensorflow/src/op/elu.cpp
index 7241c1549f1..971a54c5c05 100644
--- a/src/frontends/tensorflow/src/op/elu.cpp
+++ b/src/frontends/tensorflow/src/op/elu.cpp
@@ -12,7 +12,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_elu_op(const NodeContext& node) {
@@ -23,6 +23,6 @@ OutputVector translate_elu_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/expand_dims.cpp b/src/frontends/tensorflow/src/op/expand_dims.cpp
index 11d1a0efd1a..7156a650208 100644
--- a/src/frontends/tensorflow/src/op/expand_dims.cpp
+++ b/src/frontends/tensorflow/src/op/expand_dims.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_expand_dims_op(const NodeContext& node) {
@@ -21,6 +21,6 @@ OutputVector translate_expand_dims_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/fake_quant_min_max_vars.cpp b/src/frontends/tensorflow/src/op/fake_quant_min_max_vars.cpp
index 526a6fd1722..53fb62ef038 100644
--- a/src/frontends/tensorflow/src/op/fake_quant_min_max_vars.cpp
+++ b/src/frontends/tensorflow/src/op/fake_quant_min_max_vars.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_fake_quant_op(const NodeContext& node) {
@@ -61,6 +61,6 @@ OutputVector translate_fake_quant_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/fill.cpp b/src/frontends/tensorflow/src/op/fill.cpp
index 28e9ed65cf3..a58f306bbb6 100644
--- a/src/frontends/tensorflow/src/op/fill.cpp
+++ b/src/frontends/tensorflow/src/op/fill.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_fill_op(const NodeContext& node) {
@@ -21,6 +21,6 @@ OutputVector translate_fill_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/fused_batch_norm.cpp b/src/frontends/tensorflow/src/op/fused_batch_norm.cpp
index b5a7b6016d4..fe94545dee2 100644
--- a/src/frontends/tensorflow/src/op/fused_batch_norm.cpp
+++ b/src/frontends/tensorflow/src/op/fused_batch_norm.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_fused_batch_norm_op(const NodeContext& node) {
@@ -23,7 +23,7 @@ OutputVector translate_fused_batch_norm_op(const NodeContext& node) {
     bool is_v3 = node.get_op_type() == "FusedBatchNormV3";
 
     auto data_format = node.get_attribute<std::string>("data_format");
-    TF_OP_VALIDATION_CHECK(node, data_format == "NHWC" || data_format == "NCHW", "Unsupported data format");
+    TENSORFLOW_OP_VALIDATION(node, data_format == "NHWC" || data_format == "NCHW", "Unsupported data format");
 
     bool is_nhwc = (data_format == "NHWC");
 
@@ -50,6 +50,6 @@ OutputVector translate_fused_batch_norm_op(const NodeContext& node) {
     return result;
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/gather.cpp b/src/frontends/tensorflow/src/op/gather.cpp
index c26cef15c08..b791915f3b4 100644
--- a/src/frontends/tensorflow/src/op/gather.cpp
+++ b/src/frontends/tensorflow/src/op/gather.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_gather_op(const NodeContext& node) {
@@ -42,6 +42,6 @@ OutputVector translate_gather_nd_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/identity.cpp b/src/frontends/tensorflow/src/op/identity.cpp
index 285f9da8db5..ecc92644c3b 100644
--- a/src/frontends/tensorflow/src/op/identity.cpp
+++ b/src/frontends/tensorflow/src/op/identity.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_identity_op(const NodeContext& node) {
@@ -27,6 +27,6 @@ OutputVector translate_identity_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/interpolate.cpp b/src/frontends/tensorflow/src/op/interpolate.cpp
index cf6bf93e85d..011dbb17991 100644
--- a/src/frontends/tensorflow/src/op/interpolate.cpp
+++ b/src/frontends/tensorflow/src/op/interpolate.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 ov::OutputVector translate_interpolate_op(const NodeContext& node) {
     auto input = node.get_input(0);
@@ -43,6 +43,6 @@ ov::OutputVector translate_interpolate_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/is_finite.cpp b/src/frontends/tensorflow/src/op/is_finite.cpp
index 626d3aec9bc..ebd96fa2b95 100644
--- a/src/frontends/tensorflow/src/op/is_finite.cpp
+++ b/src/frontends/tensorflow/src/op/is_finite.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_is_finite_op(const NodeContext& node) {
@@ -35,6 +35,6 @@ OutputVector translate_is_finite_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/l2_loss.cpp b/src/frontends/tensorflow/src/op/l2_loss.cpp
index 8cad1e58958..371ea117e93 100644
--- a/src/frontends/tensorflow/src/op/l2_loss.cpp
+++ b/src/frontends/tensorflow/src/op/l2_loss.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_l2_loss_op(const NodeContext& node) {
@@ -42,6 +42,6 @@ OutputVector translate_l2_loss_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/leaky_relu.cpp b/src/frontends/tensorflow/src/op/leaky_relu.cpp
index ecba626e855..36752fe5469 100644
--- a/src/frontends/tensorflow/src/op/leaky_relu.cpp
+++ b/src/frontends/tensorflow/src/op/leaky_relu.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 ov::OutputVector translate_leaky_relu_op(const NodeContext& node) {
     auto in = node.get_input(0);
@@ -21,6 +21,6 @@ ov::OutputVector translate_leaky_relu_op(const NodeContext& node) {
     return leaky_relu->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/log1p.cpp b/src/frontends/tensorflow/src/op/log1p.cpp
index 84d15a4d45e..ca270a7c889 100644
--- a/src/frontends/tensorflow/src/op/log1p.cpp
+++ b/src/frontends/tensorflow/src/op/log1p.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_log_1p_op(const NodeContext& node) {
@@ -22,6 +22,6 @@ OutputVector translate_log_1p_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/log_softmax.cpp b/src/frontends/tensorflow/src/op/log_softmax.cpp
index eaf6ed06869..52e04cd2f5a 100644
--- a/src/frontends/tensorflow/src/op/log_softmax.cpp
+++ b/src/frontends/tensorflow/src/op/log_softmax.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_log_softmax_op(const NodeContext& node) {
@@ -20,6 +20,6 @@ OutputVector translate_log_softmax_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/lrn.cpp b/src/frontends/tensorflow/src/op/lrn.cpp
index ea4005e761e..ba3cd959153 100644
--- a/src/frontends/tensorflow/src/op/lrn.cpp
+++ b/src/frontends/tensorflow/src/op/lrn.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_lrn_op(const NodeContext& node) {
@@ -34,6 +34,6 @@ OutputVector translate_lrn_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/matmul.cpp b/src/frontends/tensorflow/src/op/matmul.cpp
index f05c95d64b4..f1fea472ccc 100644
--- a/src/frontends/tensorflow/src/op/matmul.cpp
+++ b/src/frontends/tensorflow/src/op/matmul.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_mat_mul_op(const NodeContext& node) {
@@ -24,6 +24,6 @@ OutputVector translate_mat_mul_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/max_pool.cpp b/src/frontends/tensorflow/src/op/max_pool.cpp
index 54585f1af0a..a945a003ec5 100644
--- a/src/frontends/tensorflow/src/op/max_pool.cpp
+++ b/src/frontends/tensorflow/src/op/max_pool.cpp
@@ -8,11 +8,11 @@
 
 using namespace std;
 using namespace ov;
-using namespace ov::frontend::tf::detail;
+using namespace ov::frontend::tensorflow::detail;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_max_pool_op(const NodeContext& node) {
@@ -68,6 +68,6 @@ OutputVector translate_max_pool_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/no_op.cpp b/src/frontends/tensorflow/src/op/no_op.cpp
index 135b7c3a323..ff2593eafe4 100644
--- a/src/frontends/tensorflow/src/op/no_op.cpp
+++ b/src/frontends/tensorflow/src/op/no_op.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_no_op(const NodeContext& node) {
@@ -18,9 +18,9 @@ OutputVector translate_no_op(const NodeContext& node) {
         return OutputVector{};
     }
 
-    TF_OP_VALIDATION_CHECK(node,
-                           node.get_input_size() == 1,
-                           "NoOp has " + to_string(node.get_input_size()) + " inputs, should have 1");
+    TENSORFLOW_OP_VALIDATION(node,
+                             node.get_input_size() == 1,
+                             "NoOp has " + to_string(node.get_input_size()) + " inputs, should have 1");
 
     auto input = node.get_input(0);
     set_out_name(node.get_name(), input);
@@ -28,6 +28,6 @@ OutputVector translate_no_op(const NodeContext& node) {
     return {input};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/non_max_suppression.cpp b/src/frontends/tensorflow/src/op/non_max_suppression.cpp
index 4f527cce2a9..7d7626ab16a 100644
--- a/src/frontends/tensorflow/src/op/non_max_suppression.cpp
+++ b/src/frontends/tensorflow/src/op/non_max_suppression.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_non_max_suppression_op(const NodeContext& node) {
@@ -77,9 +77,9 @@ OutputVector translate_non_max_suppression_op(const NodeContext& node) {
         set_node_name(node.get_name(), res);
         return {res->output(0)};
     }
-    TF_OP_VALIDATION_CHECK(node, false, "No translator found.");
+    TENSORFLOW_OP_VALIDATION(node, false, "No translator found.");
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/one_hot.cpp b/src/frontends/tensorflow/src/op/one_hot.cpp
index 79f48069414..fe7f70c2933 100644
--- a/src/frontends/tensorflow/src/op/one_hot.cpp
+++ b/src/frontends/tensorflow/src/op/one_hot.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_one_hot_op(const NodeContext& node) {
@@ -25,6 +25,6 @@ OutputVector translate_one_hot_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/pack.cpp b/src/frontends/tensorflow/src/op/pack.cpp
index 16624d8efea..559e94e71ac 100644
--- a/src/frontends/tensorflow/src/op/pack.cpp
+++ b/src/frontends/tensorflow/src/op/pack.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_pack_op(const NodeContext& node) {
@@ -28,6 +28,6 @@ OutputVector translate_pack_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/pad.cpp b/src/frontends/tensorflow/src/op/pad.cpp
index fec2fbd3854..399c9421efb 100644
--- a/src/frontends/tensorflow/src/op/pad.cpp
+++ b/src/frontends/tensorflow/src/op/pad.cpp
@@ -14,7 +14,7 @@ using namespace ov::opset8;
 // See https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/mirror-pad
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_pad_op(const NodeContext& node) {
@@ -28,7 +28,7 @@ OutputVector translate_pad_op(const NodeContext& node) {
     } else if (op_type == "PadV2") {
         pad_val_op = node.get_input(2);
     } else {
-        TF_OP_VALIDATION_CHECK(node, false, "Incorrect TF Pad OpType: " + node.get_op_type());
+        TENSORFLOW_OP_VALIDATION(node, false, "Incorrect TF Pad OpType: " + node.get_op_type());
     }
 
     // Set pad_mode
@@ -40,7 +40,7 @@ OutputVector translate_pad_op(const NodeContext& node) {
         } else if (pad_mode_str == "SYMMETRIC") {
             pad_mode = ov::op::PadMode::SYMMETRIC;
         } else {
-            TF_OP_VALIDATION_CHECK(node, false, pad_mode_str + " is not an allowed padding mode.");
+            TENSORFLOW_OP_VALIDATION(node, false, pad_mode_str + " is not an allowed padding mode.");
         }
     }
 
@@ -48,10 +48,10 @@ OutputVector translate_pad_op(const NodeContext& node) {
     std::vector<int64_t> paddings;
     get_const_input(node, 1, &paddings);
     if (paddings.size() % 2 != 0) {
-        TF_OP_VALIDATION_CHECK(node,
-                               false,
-                               "Constant node for paddings does not have an even number of "
-                               "elements");
+        TENSORFLOW_OP_VALIDATION(node,
+                                 false,
+                                 "Constant node for paddings does not have an even number of "
+                                 "elements");
     }
     std::vector<int64_t> pad_begin(paddings.size() / 2);
     std::vector<int64_t> pad_end(paddings.size() / 2);
@@ -68,6 +68,6 @@ OutputVector translate_pad_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/placeholder.cpp b/src/frontends/tensorflow/src/op/placeholder.cpp
index 0277d5e7e3a..bb0e3a8305d 100644
--- a/src/frontends/tensorflow/src/op/placeholder.cpp
+++ b/src/frontends/tensorflow/src/op/placeholder.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_placeholder_op(const NodeContext& node) {
@@ -22,6 +22,6 @@ OutputVector translate_placeholder_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/random_uniform.cpp b/src/frontends/tensorflow/src/op/random_uniform.cpp
index 9c1a050a411..71aaeae0bba 100644
--- a/src/frontends/tensorflow/src/op/random_uniform.cpp
+++ b/src/frontends/tensorflow/src/op/random_uniform.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 ov::OutputVector translate_random_uniform_op(const NodeContext& node) {
     auto shape = node.get_input(0);
@@ -36,6 +36,6 @@ ov::OutputVector translate_random_uniform_int_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/range.cpp b/src/frontends/tensorflow/src/op/range.cpp
index 544af501e0d..b7fd9c8d6e4 100644
--- a/src/frontends/tensorflow/src/op/range.cpp
+++ b/src/frontends/tensorflow/src/op/range.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_range_op(const NodeContext& node) {
@@ -25,6 +25,6 @@ OutputVector translate_range_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/rank.cpp b/src/frontends/tensorflow/src/op/rank.cpp
index e4fc655706c..aae9c12c53b 100644
--- a/src/frontends/tensorflow/src/op/rank.cpp
+++ b/src/frontends/tensorflow/src/op/rank.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 ov::OutputVector translate_rank_op(const NodeContext& node) {
@@ -22,6 +22,6 @@ ov::OutputVector translate_rank_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/reciprocal.cpp b/src/frontends/tensorflow/src/op/reciprocal.cpp
index 017bbddbb4c..ad6d0cc18f1 100644
--- a/src/frontends/tensorflow/src/op/reciprocal.cpp
+++ b/src/frontends/tensorflow/src/op/reciprocal.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_reciprocal_op(const NodeContext& node) {
@@ -21,6 +21,6 @@ OutputVector translate_reciprocal_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/reduce.cpp b/src/frontends/tensorflow/src/op/reduce.cpp
index 11c9e7f6c1b..f00d1612364 100644
--- a/src/frontends/tensorflow/src/op/reduce.cpp
+++ b/src/frontends/tensorflow/src/op/reduce.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector TranslateReduceOp(const NodeContext& node,
@@ -28,10 +28,10 @@ OutputVector translate_direct_reduce_op(const NodeContext& node) {
     // ensure its either an arithmetic or a logical reduction
     if (!(std::is_base_of<ov::op::util::ArithmeticReduction, T>::value ||
           std::is_base_of<ov::op::util::LogicalReduction, T>::value)) {
-        TF_OP_VALIDATION_CHECK(node,
-                               false,
-                               "Expected node to be either a valid logical or arithmetic reduction "
-                               "type");
+        TENSORFLOW_OP_VALIDATION(node,
+                                 false,
+                                 "Expected node to be either a valid logical or arithmetic reduction "
+                                 "type");
     }
     return TranslateReduceOp(node, [](Output<Node> ng_input, Output<Node> ng_reduction_axes, const bool keep_dims) {
         return make_shared<T>(ng_input, ng_reduction_axes, keep_dims);
@@ -46,6 +46,6 @@ template OutputVector translate_direct_reduce_op<ReduceMin>(const NodeContext& n
 template OutputVector translate_direct_reduce_op<ReduceProd>(const NodeContext& node);
 template OutputVector translate_direct_reduce_op<ReduceSum>(const NodeContext& node);
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/relu_6.cpp b/src/frontends/tensorflow/src/op/relu_6.cpp
index 83692a56312..64bf4cea004 100644
--- a/src/frontends/tensorflow/src/op/relu_6.cpp
+++ b/src/frontends/tensorflow/src/op/relu_6.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 ov::OutputVector translate_relu_6_op(const NodeContext& node) {
     auto data = node.get_input(0);
@@ -19,6 +19,6 @@ ov::OutputVector translate_relu_6_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/reshape.cpp b/src/frontends/tensorflow/src/op/reshape.cpp
index b14c02b6879..6f7ad327e85 100644
--- a/src/frontends/tensorflow/src/op/reshape.cpp
+++ b/src/frontends/tensorflow/src/op/reshape.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_reshape_op(const NodeContext& node) {
@@ -21,6 +21,6 @@ OutputVector translate_reshape_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/reverse.cpp b/src/frontends/tensorflow/src/op/reverse.cpp
index 768e1ad8f27..fd511fd2b2d 100644
--- a/src/frontends/tensorflow/src/op/reverse.cpp
+++ b/src/frontends/tensorflow/src/op/reverse.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_reverse_op(const NodeContext& node) {
@@ -18,9 +18,9 @@ OutputVector translate_reverse_op(const NodeContext& node) {
     auto axes = node.get_input(1);
 
     auto axes_const = dynamic_pointer_cast<Constant>(axes.get_node_shared_ptr());
-    TF_OP_VALIDATION_CHECK(node, axes_const != nullptr, "Axes input must be constant.");
-    TF_OP_VALIDATION_CHECK(node, axes_const->get_shape().size() == 1, "Axes input must be 1D.");
-    TF_OP_VALIDATION_CHECK(node, axes_const->get_shape()[0] == 1, "Axes input must have only one value.");
+    TENSORFLOW_OP_VALIDATION(node, axes_const != nullptr, "Axes input must be constant.");
+    TENSORFLOW_OP_VALIDATION(node, axes_const->get_shape().size() == 1, "Axes input must be 1D.");
+    TENSORFLOW_OP_VALIDATION(node, axes_const->get_shape()[0] == 1, "Axes input must have only one value.");
     auto seq_axis = axes_const->cast_vector<int64_t>().at(0);
     int64_t batch_axis = !seq_axis;
 
@@ -46,6 +46,6 @@ OutputVector translate_reverse_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/roll.cpp b/src/frontends/tensorflow/src/op/roll.cpp
index a004436b9af..1be6e9a716e 100644
--- a/src/frontends/tensorflow/src/op/roll.cpp
+++ b/src/frontends/tensorflow/src/op/roll.cpp
@@ -8,11 +8,11 @@
 using namespace std;
 using namespace ov;
 using namespace ov::opset8;
-using namespace ov::frontend::tf;
+using namespace ov::frontend::tensorflow;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 ov::OutputVector translate_roll_op(const NodeContext& node) {
     auto data = node.get_input(0);
@@ -23,6 +23,6 @@ ov::OutputVector translate_roll_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/round.cpp b/src/frontends/tensorflow/src/op/round.cpp
index 8c2d64bc951..f0b41d74a0e 100644
--- a/src/frontends/tensorflow/src/op/round.cpp
+++ b/src/frontends/tensorflow/src/op/round.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_round_op(const NodeContext& node) {
@@ -23,6 +23,6 @@ OutputVector translate_round_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/rsqrt.cpp b/src/frontends/tensorflow/src/op/rsqrt.cpp
index a67fa255767..6161d5f9fa3 100644
--- a/src/frontends/tensorflow/src/op/rsqrt.cpp
+++ b/src/frontends/tensorflow/src/op/rsqrt.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_rsqrt_op(const NodeContext& node) {
@@ -21,6 +21,6 @@ OutputVector translate_rsqrt_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/select.cpp b/src/frontends/tensorflow/src/op/select.cpp
index 9f3d9771ff0..7a314114c8a 100644
--- a/src/frontends/tensorflow/src/op/select.cpp
+++ b/src/frontends/tensorflow/src/op/select.cpp
@@ -10,10 +10,10 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 OutputVector translate_select_op(const NodeContext& node) {
-    TF_OP_VALIDATION_CHECK(node, node.get_all_inputs().size() == 3, "Select op cannot be converted");
+    TENSORFLOW_OP_VALIDATION(node, node.get_all_inputs().size() == 3, "Select op cannot be converted");
     auto in_1 = node.get_input(0);
     auto in_2 = node.get_input(1);
     auto in_3 = node.get_input(2);
@@ -34,6 +34,6 @@ OutputVector translate_select_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/shape.cpp b/src/frontends/tensorflow/src/op/shape.cpp
index 511bb65bbab..c39be021e92 100644
--- a/src/frontends/tensorflow/src/op/shape.cpp
+++ b/src/frontends/tensorflow/src/op/shape.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 ov::OutputVector translate_shape_op(const NodeContext& node) {
@@ -22,6 +22,6 @@ ov::OutputVector translate_shape_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/size.cpp b/src/frontends/tensorflow/src/op/size.cpp
index 79855e09913..675230584b5 100644
--- a/src/frontends/tensorflow/src/op/size.cpp
+++ b/src/frontends/tensorflow/src/op/size.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 ov::OutputVector translate_size_op(const NodeContext& node) {
@@ -24,6 +24,6 @@ ov::OutputVector translate_size_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/slice.cpp b/src/frontends/tensorflow/src/op/slice.cpp
index 89376e9955e..b26ec01d072 100644
--- a/src/frontends/tensorflow/src/op/slice.cpp
+++ b/src/frontends/tensorflow/src/op/slice.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_slice_op(const NodeContext& node) {
@@ -29,6 +29,6 @@ OutputVector translate_slice_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/softmax.cpp b/src/frontends/tensorflow/src/op/softmax.cpp
index 24fc5620858..9a520be0293 100644
--- a/src/frontends/tensorflow/src/op/softmax.cpp
+++ b/src/frontends/tensorflow/src/op/softmax.cpp
@@ -11,12 +11,12 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 OutputVector translate_softmax_op(const NodeContext& node) {
     auto ng_inp = node.get_input(0);
     // todo: switch to opset8::Softmax when is ready and delete Dynamic rank limitation
-    TF_OP_VALIDATION_CHECK(node, ng_inp.get_partial_shape().rank().is_static(), "Dynamic rank is not supported.");
+    TENSORFLOW_OP_VALIDATION(node, ng_inp.get_partial_shape().rank().is_static(), "Dynamic rank is not supported.");
     size_t axis = ng_inp.get_partial_shape().rank().get_length() - 1;
     auto res = make_shared<opset7::Softmax>(ng_inp, axis);
     set_node_name(node.get_name(), res);
@@ -24,6 +24,6 @@ OutputVector translate_softmax_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/space_to_batch_nd.cpp b/src/frontends/tensorflow/src/op/space_to_batch_nd.cpp
index 940cc344677..902f1926213 100644
--- a/src/frontends/tensorflow/src/op/space_to_batch_nd.cpp
+++ b/src/frontends/tensorflow/src/op/space_to_batch_nd.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 OutputVector translate_batch_nd_and_space_nd_op(const NodeContext& node) {
     auto input = node.get_input(0);
@@ -32,7 +32,7 @@ OutputVector translate_batch_nd_and_space_nd_op(const NodeContext& node) {
             return {input};
     } else {
         // TODO: support dynamic rank
-        TF_OP_VALIDATION_CHECK(node, false, "Dynamic rank is not supported.");
+        TENSORFLOW_OP_VALIDATION(node, false, "Dynamic rank is not supported.");
     }
 
     auto N = input_pshape.rank().get_length();
@@ -72,10 +72,10 @@ OutputVector translate_batch_nd_and_space_nd_op(const NodeContext& node) {
         set_node_name(node.get_name(), res);
         return res->outputs();
     }
-    TF_OP_VALIDATION_CHECK(node, false, "No translator found.");
+    TENSORFLOW_OP_VALIDATION(node, false, "No translator found.");
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/space_to_depth.cpp b/src/frontends/tensorflow/src/op/space_to_depth.cpp
index ac173f6120c..9ed6926dcb9 100644
--- a/src/frontends/tensorflow/src/op/space_to_depth.cpp
+++ b/src/frontends/tensorflow/src/op/space_to_depth.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_space_to_depth_op(const NodeContext& node) {
@@ -19,7 +19,7 @@ OutputVector translate_space_to_depth_op(const NodeContext& node) {
     auto block_size = node.get_attribute<int64_t>("block_size");
     auto data_format = node.get_attribute<string>("data_format");
 
-    TF_OP_VALIDATION_CHECK(node, data_format == "NHWC" || data_format == "NCHW", "Unsupported data format.");
+    TENSORFLOW_OP_VALIDATION(node, data_format == "NHWC" || data_format == "NCHW", "Unsupported data format.");
 
     bool is_nhwc = (data_format == "NHWC");
     convert_nhwc_to_nchw(node.get_name(), is_nhwc, input);
@@ -30,6 +30,6 @@ OutputVector translate_space_to_depth_op(const NodeContext& node) {
     return {res};
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/split.cpp b/src/frontends/tensorflow/src/op/split.cpp
index 20ea1ad789e..cf6462040b7 100644
--- a/src/frontends/tensorflow/src/op/split.cpp
+++ b/src/frontends/tensorflow/src/op/split.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_split_op(const NodeContext& node) {
@@ -34,6 +34,6 @@ OutputVector translate_split_v_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/sqrt.cpp b/src/frontends/tensorflow/src/op/sqrt.cpp
index 97490a4bc3c..74dd14474f6 100644
--- a/src/frontends/tensorflow/src/op/sqrt.cpp
+++ b/src/frontends/tensorflow/src/op/sqrt.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_sqrt_op(const NodeContext& node) {
@@ -21,6 +21,6 @@ OutputVector translate_sqrt_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/square.cpp b/src/frontends/tensorflow/src/op/square.cpp
index 9a74550f1f8..7e34cf858aa 100644
--- a/src/frontends/tensorflow/src/op/square.cpp
+++ b/src/frontends/tensorflow/src/op/square.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_square_op(const NodeContext& node) {
@@ -21,6 +21,6 @@ OutputVector translate_square_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/squeeze.cpp b/src/frontends/tensorflow/src/op/squeeze.cpp
index 1af6b5272b6..b55d1b3b572 100644
--- a/src/frontends/tensorflow/src/op/squeeze.cpp
+++ b/src/frontends/tensorflow/src/op/squeeze.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_squeeze_op(const NodeContext& node) {
@@ -23,6 +23,6 @@ OutputVector translate_squeeze_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/strided_slice.cpp b/src/frontends/tensorflow/src/op/strided_slice.cpp
index 31cc7f63d91..ef5273f1f2c 100644
--- a/src/frontends/tensorflow/src/op/strided_slice.cpp
+++ b/src/frontends/tensorflow/src/op/strided_slice.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_strided_slice_op(const NodeContext& node) {
@@ -53,6 +53,6 @@ OutputVector translate_strided_slice_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/tile.cpp b/src/frontends/tensorflow/src/op/tile.cpp
index b3c63ddad27..373b27a7c54 100644
--- a/src/frontends/tensorflow/src/op/tile.cpp
+++ b/src/frontends/tensorflow/src/op/tile.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_tile_op(const NodeContext& node) {
@@ -23,6 +23,6 @@ OutputVector translate_tile_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/top_k.cpp b/src/frontends/tensorflow/src/op/top_k.cpp
index 4826a8efbcd..1eed8caac06 100644
--- a/src/frontends/tensorflow/src/op/top_k.cpp
+++ b/src/frontends/tensorflow/src/op/top_k.cpp
@@ -10,17 +10,17 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_top_k_v2_op(const NodeContext& node) {
     auto input = node.get_input(0);
     auto k = node.get_input(1);
 
-    TF_OP_VALIDATION_CHECK(node, input.get_partial_shape().rank().is_static(), "Input rank must be static.");
-    TF_OP_VALIDATION_CHECK(node,
-                           input.get_partial_shape().rank().get_length() >= 1,
-                           "Input rank must be greater than 0.");
+    TENSORFLOW_OP_VALIDATION(node, input.get_partial_shape().rank().is_static(), "Input rank must be static.");
+    TENSORFLOW_OP_VALIDATION(node,
+                             input.get_partial_shape().rank().get_length() >= 1,
+                             "Input rank must be greater than 0.");
     // axis along which to compute top k indices
     int64_t k_axis = input.get_partial_shape().rank().get_length() - 1;
     bool sorted = node.get_attribute<bool>("sorted", true);
@@ -34,6 +34,6 @@ OutputVector translate_top_k_v2_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/transpose.cpp b/src/frontends/tensorflow/src/op/transpose.cpp
index 0a07c8e43c3..5ed01994d49 100644
--- a/src/frontends/tensorflow/src/op/transpose.cpp
+++ b/src/frontends/tensorflow/src/op/transpose.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_transpose_op(const NodeContext& node) {
@@ -22,6 +22,6 @@ OutputVector translate_transpose_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/unary_op.cpp b/src/frontends/tensorflow/src/op/unary_op.cpp
index e1d89402762..d00f45bf52e 100644
--- a/src/frontends/tensorflow/src/op/unary_op.cpp
+++ b/src/frontends/tensorflow/src/op/unary_op.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_unary_op(const NodeContext& op,
@@ -54,6 +54,6 @@ template OutputVector translate_unary_op<Tanh>(const NodeContext& node);
 template OutputVector translate_unary_op<Swish>(const NodeContext& node);
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/unpack.cpp b/src/frontends/tensorflow/src/op/unpack.cpp
index 772ee493bae..8c27d374331 100644
--- a/src/frontends/tensorflow/src/op/unpack.cpp
+++ b/src/frontends/tensorflow/src/op/unpack.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_unpack_op(const NodeContext& node) {
@@ -24,6 +24,6 @@ OutputVector translate_unpack_op(const NodeContext& node) {
     return res->outputs();
 }
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/where.cpp b/src/frontends/tensorflow/src/op/where.cpp
index cdddb82f5dc..8209b52d3d5 100644
--- a/src/frontends/tensorflow/src/op/where.cpp
+++ b/src/frontends/tensorflow/src/op/where.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_where_op(const NodeContext& node) {
@@ -23,6 +23,6 @@ OutputVector translate_where_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op/xdivy.cpp b/src/frontends/tensorflow/src/op/xdivy.cpp
index 121efbc58ee..da87ef80e12 100644
--- a/src/frontends/tensorflow/src/op/xdivy.cpp
+++ b/src/frontends/tensorflow/src/op/xdivy.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_x_div_y_op(const NodeContext& node) {
@@ -27,6 +27,6 @@ OutputVector translate_x_div_y_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op/zeros_like.cpp b/src/frontends/tensorflow/src/op/zeros_like.cpp
index 0ece26c16c4..d4bcfd2aea3 100644
--- a/src/frontends/tensorflow/src/op/zeros_like.cpp
+++ b/src/frontends/tensorflow/src/op/zeros_like.cpp
@@ -10,7 +10,7 @@ using namespace ov::opset8;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 
 OutputVector translate_zeros_like_op(const NodeContext& node) {
@@ -23,6 +23,6 @@ OutputVector translate_zeros_like_op(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op_table.cpp b/src/frontends/tensorflow/src/op_table.cpp
index 2220fd7b1d8..14ca5bd8973 100644
--- a/src/frontends/tensorflow/src/op_table.cpp
+++ b/src/frontends/tensorflow/src/op_table.cpp
@@ -6,11 +6,11 @@
 
 using namespace std;
 using namespace ov;
-using namespace ov::frontend::tf;
+using namespace ov::frontend::tensorflow;
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
 #define OP_CONVERTER(op) OutputVector op(const NodeContext& node)
 #define OP_T_CONVERTER(op) \
@@ -244,6 +244,6 @@ const std::map<const std::string, const CreatorFunction> get_supported_ops() {
     };
 };
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/tensorflow/src/op_table.hpp b/src/frontends/tensorflow/src/op_table.hpp
index f1785cd9956..9f9ee4fe826 100644
--- a/src/frontends/tensorflow/src/op_table.hpp
+++ b/src/frontends/tensorflow/src/op_table.hpp
@@ -15,12 +15,12 @@
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace op {
-using CreatorFunction = std::function<::ov::OutputVector(const ::ov::frontend::tf::NodeContext&)>;
+using CreatorFunction = std::function<::ov::OutputVector(const ::ov::frontend::tensorflow::NodeContext&)>;
 
 const std::map<const std::string, const CreatorFunction> get_supported_ops();
 }  // namespace op
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/openvino_conversions.cpp b/src/frontends/tensorflow/src/openvino_conversions.cpp
index 6f98ae472a7..39f9ca05107 100644
--- a/src/frontends/tensorflow/src/openvino_conversions.cpp
+++ b/src/frontends/tensorflow/src/openvino_conversions.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 
 void convert_nhwc_to_nchw(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& node) {
     if (need_convert) {
@@ -32,6 +32,6 @@ void convert_nchw_to_nhwc(const std::string& op_name, bool need_convert, ov::Out
     }
 }
 
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/openvino_conversions.hpp b/src/frontends/tensorflow/src/openvino_conversions.hpp
index bf8d6d589a0..d56971530bf 100644
--- a/src/frontends/tensorflow/src/openvino_conversions.hpp
+++ b/src/frontends/tensorflow/src/openvino_conversions.hpp
@@ -8,12 +8,11 @@
 
 #include "graph.pb.h"
 #include "openvino/opsets/opset8.hpp"
-#include "tensorflow_frontend/utility.hpp"
 #include "types.pb.h"
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 
 using ::tensorflow::DataType;
 
@@ -85,6 +84,6 @@ void convert_nhwc_to_hw(bool is_nhwc, const std::vector<T>& src, std::vector<siz
     }
 }
 
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/pass/transpose_sinking.cpp b/src/frontends/tensorflow/src/pass/transpose_sinking.cpp
index aaa70351fa2..7543057c5f0 100644
--- a/src/frontends/tensorflow/src/pass/transpose_sinking.cpp
+++ b/src/frontends/tensorflow/src/pass/transpose_sinking.cpp
@@ -362,7 +362,7 @@ static void sink_concat(const shared_ptr<Concat>& n,
 // For each op type we support we can either combine
 // two transposes by replacing the existing Transpose,
 // materialize pending transposes if they can't be propagated through op
-bool ov::frontend::tf::pass::TransposeSinkingOVTF::run_on_model(const shared_ptr<Model>& f) {
+bool ov::frontend::tensorflow::pass::TransposeSinking::run_on_model(const shared_ptr<Model>& f) {
     TransposeMap reorders;
     set<shared_ptr<Node>> transposes_to_delete;
     unordered_map<std::string, Shape> orig_result_out_shape;
diff --git a/src/frontends/tensorflow/src/pass/transpose_sinking.hpp b/src/frontends/tensorflow/src/pass/transpose_sinking.hpp
index c23bcf888e8..4d88adcbd14 100644
--- a/src/frontends/tensorflow/src/pass/transpose_sinking.hpp
+++ b/src/frontends/tensorflow/src/pass/transpose_sinking.hpp
@@ -4,24 +4,24 @@
 
 #pragma once
 
+#include "openvino/frontend/tensorflow/visibility.hpp"
 #include "openvino/pass/pass.hpp"
-#include "tensorflow_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 namespace pass {
 
-class TF_API TransposeSinkingOVTF : public ov::pass::ModelPass {
+class TENSORFLOW_API TransposeSinking : public ov::pass::ModelPass {
 public:
-    OPENVINO_RTTI("ov::frontend::tf::pass::TransposeSinkingOVTF");
-    TransposeSinkingOVTF() {
+    OPENVINO_RTTI("ov::frontend::tensorflow::pass::TransposeSinking");
+    TransposeSinking() {
         set_property(ov::pass::PassProperty::REQUIRE_STATIC_SHAPE, true);
     }
     bool run_on_model(const std::shared_ptr<ov::Model>& function) override;
 };
 
 }  // namespace pass
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/place.cpp b/src/frontends/tensorflow/src/place.cpp
index 5a933f14320..43c0e54aa26 100644
--- a/src/frontends/tensorflow/src/place.cpp
+++ b/src/frontends/tensorflow/src/place.cpp
@@ -12,8 +12,8 @@
 
 namespace ov {
 namespace frontend {
-
-bool PlaceTF::is_input() const {
+namespace tensorflow {
+bool Place::is_input() const {
     const auto& model_ins = m_input_model.get_inputs();
 
     const auto cmp = [this](const ov::frontend::Place::Ptr& p) {
@@ -22,7 +22,7 @@ bool PlaceTF::is_input() const {
     return std::find_if(model_ins.begin(), model_ins.end(), cmp) != model_ins.end();
 }
 
-bool PlaceTF::is_output() const {
+bool Place::is_output() const {
     const auto& model_outs = m_input_model.get_outputs();
     const auto cmp = [this](const ov::frontend::Place::Ptr& p) {
         return p.get() == this;
@@ -30,65 +30,65 @@ bool PlaceTF::is_output() const {
     return std::find_if(model_outs.begin(), model_outs.end(), cmp) != model_outs.end();
 }
 
-OpPlaceTF::OpPlaceTF(const ov::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder)
-    : PlaceTF(input_model, {op_decoder->get_op_name()}),
+OpPlace::OpPlace(const ov::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder)
+    : Place(input_model, {op_decoder->get_op_name()}),
       m_op_decoder(op_decoder) {}
 
-const std::vector<std::shared_ptr<OutPortPlaceTF>>& OpPlaceTF::get_output_ports() const {
+const std::vector<std::shared_ptr<OutPortPlace>>& OpPlace::get_output_ports() const {
     return m_output_ports;
 }
 
-const std::map<std::string, std::vector<std::shared_ptr<InPortPlaceTF>>>& OpPlaceTF::get_input_ports() const {
+const std::map<std::string, std::vector<std::shared_ptr<InPortPlace>>>& OpPlace::get_input_ports() const {
     return m_input_ports;
 }
 
-std::shared_ptr<InPortPlaceTF> OpPlaceTF::get_input_port_tf(const std::string& inputName, int inputPortIndex) const {
+std::shared_ptr<InPortPlace> OpPlace::get_input_port_tf(const std::string& inputName, int inputPortIndex) const {
     FRONT_END_GENERAL_CHECK(inputPortIndex <= m_input_ports.at(inputName).size(), "inputPortIndex is out of bounds.");
     return m_input_ports.at(inputName)[inputPortIndex];
 }
 
-std::shared_ptr<DecoderBase> OpPlaceTF::get_decoder() const {
+std::shared_ptr<DecoderBase> OpPlace::get_decoder() const {
     return m_op_decoder;
 }
 
-void OpPlaceTF::add_out_port(const std::shared_ptr<OutPortPlaceTF>& output, int idx) {
+void OpPlace::add_out_port(const std::shared_ptr<OutPortPlace>& output, int idx) {
     while (idx >= m_output_ports.size()) {
-        m_output_ports.push_back(std::shared_ptr<OutPortPlaceTF>());
+        m_output_ports.push_back(std::shared_ptr<OutPortPlace>());
     }
     m_output_ports[idx] = output;
 }
 
-void OpPlaceTF::add_in_port(const std::shared_ptr<InPortPlaceTF>& input, const std::string& name) {
+void OpPlace::add_in_port(const std::shared_ptr<InPortPlace>& input, const std::string& name) {
     m_input_ports[name].push_back(input);
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_input_port(const std::string& name) const {
+ov::frontend::Place::Ptr OpPlace::get_input_port(const std::string& name) const {
     FRONT_END_GENERAL_CHECK(m_input_ports.at(name).size() == 1, "Only one input port should exist.");
     return m_input_ports.at(name)[0];
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_input_port(int outputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_input_port(int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(m_input_ports.size() == 1, "Only one named input port should exist.");
     return m_input_ports.begin()->second[outputPortIndex];
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_output_port(int outputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_output_port(int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(m_output_ports.size() > outputPortIndex, "No port with index: ", outputPortIndex);
     return m_output_ports[outputPortIndex];
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_output_port() const {
+ov::frontend::Place::Ptr OpPlace::get_output_port() const {
     FRONT_END_GENERAL_CHECK(m_output_ports.size() == 1, "Only one output port should exist.");
     return m_output_ports[0];
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_input_port() const {
+ov::frontend::Place::Ptr OpPlace::get_input_port() const {
     FRONT_END_GENERAL_CHECK(m_input_ports.size() == 1 && m_input_ports.begin()->second.size() == 1,
                             "Only one input port should exist.");
     return m_input_ports.begin()->second[0];
 }
 
-std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations() const {
+std::vector<ov::frontend::Place::Ptr> OpPlace::get_consuming_operations() const {
     std::vector<ov::frontend::Place::Ptr> consuming_ops;
     for (const auto& out_port : m_output_ports) {
         auto consuming_ops_out = out_port->get_consuming_operations();
@@ -97,11 +97,11 @@ std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations() cons
     return consuming_ops;
 }
 
-std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations(int outputPortIndex) const {
+std::vector<ov::frontend::Place::Ptr> OpPlace::get_consuming_operations(int outputPortIndex) const {
     return get_output_port(outputPortIndex)->get_consuming_operations();
 }
 
-std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_ports() const {
+std::vector<ov::frontend::Place::Ptr> OpPlace::get_consuming_ports() const {
     std::vector<ov::frontend::Place::Ptr> consuming_ports;
     for (const auto& out_port : m_output_ports) {
         auto consuming_ops_out = out_port->get_consuming_ports();
@@ -110,60 +110,60 @@ std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_ports() const {
     return consuming_ports;
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_input_port(const std::string& inputName, int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_input_port(const std::string& inputName, int inputPortIndex) const {
     FRONT_END_GENERAL_CHECK(inputPortIndex <= m_input_ports.at(inputName).size(), "inputPortIndex is out of bounds.");
     return m_input_ports.at(inputName)[inputPortIndex];
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor() const {
+ov::frontend::Place::Ptr OpPlace::get_source_tensor() const {
     return get_input_port()->get_source_tensor();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor(const std::string& inputName) const {
+ov::frontend::Place::Ptr OpPlace::get_source_tensor(const std::string& inputName) const {
     return get_input_port(inputName)->get_source_tensor();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor(int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_source_tensor(int inputPortIndex) const {
     return get_input_port(inputPortIndex)->get_source_tensor();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
     return get_input_port(inputName, inputPortIndex)->get_source_tensor();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_target_tensor() const {
+ov::frontend::Place::Ptr OpPlace::get_target_tensor() const {
     return get_output_port()->get_target_tensor();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation(const std::string& inputName) const {
+ov::frontend::Place::Ptr OpPlace::get_producing_operation(const std::string& inputName) const {
     return get_input_port(inputName)->get_producing_operation();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation(const std::string& inputName, int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_producing_operation(const std::string& inputName, int inputPortIndex) const {
     return get_input_port(inputName, inputPortIndex)->get_producing_operation();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr OpPlace::get_producing_operation() const {
     return get_input_port()->get_producing_operation();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation(int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_producing_operation(int inputPortIndex) const {
     return get_input_port(inputPortIndex)->get_producing_operation();
 }
 
-ov::frontend::Place::Ptr OpPlaceTF::get_target_tensor(int outputPortIndex) const {
+ov::frontend::Place::Ptr OpPlace::get_target_tensor(int outputPortIndex) const {
     return get_output_port(outputPortIndex)->get_target_tensor();
 }
 
-TensorPlaceTF::TensorPlaceTF(const ov::frontend::InputModel& input_model,
-                             const ov::PartialShape& pshape,
-                             ov::element::Type type,
-                             const std::vector<std::string>& names)
-    : PlaceTF(input_model, names),
+TensorPlace::TensorPlace(const ov::frontend::InputModel& input_model,
+                         const ov::PartialShape& pshape,
+                         ov::element::Type type,
+                         const std::vector<std::string>& names)
+    : Place(input_model, names),
       m_pshape(pshape),
       m_type(type) {}
 
-std::vector<ov::frontend::Place::Ptr> TensorPlaceTF::get_consuming_ports() const {
+std::vector<ov::frontend::Place::Ptr> TensorPlace::get_consuming_ports() const {
     std::vector<ov::frontend::Place::Ptr> consuming_ports;
     for (const auto& consuming_port : m_consuming_ports) {
         if (const auto& locked = consuming_port.lock()) {
@@ -175,7 +175,7 @@ std::vector<ov::frontend::Place::Ptr> TensorPlaceTF::get_consuming_ports() const
     return consuming_ports;
 }
 
-ov::frontend::Place::Ptr TensorPlaceTF::get_producing_port() const {
+ov::frontend::Place::Ptr TensorPlace::get_producing_port() const {
     FRONT_END_GENERAL_CHECK(m_producing_ports.size() == 1, "Only one producing port is supported.");
     if (const auto& producing_port = m_producing_ports[0].lock()) {
         return producing_port;
@@ -183,15 +183,15 @@ ov::frontend::Place::Ptr TensorPlaceTF::get_producing_port() const {
     FRONT_END_THROW("Producing Port has expired.");
 }
 
-void TensorPlaceTF::add_producing_port(const std::shared_ptr<OutPortPlaceTF>& out_port) {
+void TensorPlace::add_producing_port(const std::shared_ptr<OutPortPlace>& out_port) {
     m_producing_ports.push_back(out_port);
 }
 
-void TensorPlaceTF::add_consuming_port(const std::shared_ptr<InPortPlaceTF>& in_port) {
+void TensorPlace::add_consuming_port(const std::shared_ptr<InPortPlace>& in_port) {
     m_consuming_ports.push_back(in_port);
 }
 
-std::vector<ov::frontend::Place::Ptr> TensorPlaceTF::get_consuming_operations() const {
+std::vector<ov::frontend::Place::Ptr> TensorPlace::get_consuming_operations() const {
     std::vector<ov::frontend::Place::Ptr> consuming_ops;
     for (const auto& consuming_port : m_consuming_ports) {
         if (auto port_ptr = consuming_port.lock()) {
@@ -204,7 +204,7 @@ std::vector<ov::frontend::Place::Ptr> TensorPlaceTF::get_consuming_operations()
     return consuming_ops;
 }
 
-bool TensorPlaceTF::is_equal_data(const ov::frontend::Place::Ptr& another) const {
+bool TensorPlace::is_equal_data(const ov::frontend::Place::Ptr& another) const {
     auto consuming_ports = get_consuming_ports();
     bool eq_to_consuming_port =
         std::any_of(consuming_ports.begin(), consuming_ports.end(), [&another](const Ptr& place) {
@@ -213,95 +213,97 @@ bool TensorPlaceTF::is_equal_data(const ov::frontend::Place::Ptr& another) const
     return is_equal(another) || get_producing_port()->is_equal(another) || eq_to_consuming_port;
 }
 
-ov::frontend::Place::Ptr TensorPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr TensorPlace::get_producing_operation() const {
     return get_producing_port()->get_producing_operation();
 }
 
-std::shared_ptr<TensorPlaceTF> InPortPlaceTF::get_source_tensor_tf() const {
+std::shared_ptr<TensorPlace> InPortPlace::get_source_tensor_tf() const {
     if (const auto& tensor = m_source_tensor.lock()) {
         return tensor;
     }
     FRONT_END_THROW("Source Tensor has expired.");
 }
 
-std::shared_ptr<OpPlaceTF> InPortPlaceTF::get_op() {
+std::shared_ptr<OpPlace> InPortPlace::get_op() {
     if (const auto& op = m_op.lock()) {
         return op;
     }
     FRONT_END_THROW("Operation has expired.");
 }
 
-void InPortPlaceTF::set_source_tensor(const std::weak_ptr<TensorPlaceTF>& source_tensor) {
+void InPortPlace::set_source_tensor(const std::weak_ptr<TensorPlace>& source_tensor) {
     m_source_tensor = source_tensor;
 }
 
-std::vector<ov::frontend::Place::Ptr> InPortPlaceTF::get_consuming_operations() const {
+std::vector<ov::frontend::Place::Ptr> InPortPlace::get_consuming_operations() const {
     if (const auto& consuming_op = m_op.lock()) {
         return {consuming_op};
     }
     FRONT_END_THROW("Operation has expired.");
 }
 
-ov::frontend::Place::Ptr InPortPlaceTF::get_source_tensor() const {
+ov::frontend::Place::Ptr InPortPlace::get_source_tensor() const {
     if (const auto& tensor = m_source_tensor.lock()) {
         return tensor;
     }
     FRONT_END_THROW("Source Tensor has expired.");
 }
 
-ov::frontend::Place::Ptr InPortPlaceTF::get_producing_port() const {
+ov::frontend::Place::Ptr InPortPlace::get_producing_port() const {
     return get_source_tensor()->get_producing_port();
 }
 
-bool InPortPlaceTF::is_equal_data(const ov::frontend::Place::Ptr& another) const {
+bool InPortPlace::is_equal_data(const ov::frontend::Place::Ptr& another) const {
     return get_source_tensor()->is_equal_data(another);
 }
 
-ov::frontend::Place::Ptr InPortPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr InPortPlace::get_producing_operation() const {
     return get_producing_port()->get_producing_operation();
 }
 
-std::shared_ptr<TensorPlaceTF> OutPortPlaceTF::get_target_tensor_tf() const {
+std::shared_ptr<TensorPlace> OutPortPlace::get_target_tensor_tf() const {
     if (const auto& target_tensor = m_target_tensor.lock()) {
         return target_tensor;
     }
     FRONT_END_THROW("Target Tensor has expired.");
 }
 
-std::vector<ov::frontend::Place::Ptr> OutPortPlaceTF::get_consuming_operations() const {
+std::vector<ov::frontend::Place::Ptr> OutPortPlace::get_consuming_operations() const {
     if (auto tensor_ptr = m_target_tensor.lock()) {
         return tensor_ptr->get_consuming_operations();
     }
     FRONT_END_THROW("Tensor has expired.");
 }
 
-void OutPortPlaceTF::set_target_tensor(const std::weak_ptr<TensorPlaceTF>& target_tensor) {
+void OutPortPlace::set_target_tensor(const std::weak_ptr<TensorPlace>& target_tensor) {
     m_target_tensor = target_tensor;
 }
 
-std::vector<ov::frontend::Place::Ptr> OutPortPlaceTF::get_consuming_ports() const {
+std::vector<ov::frontend::Place::Ptr> OutPortPlace::get_consuming_ports() const {
     if (auto tensor_ptr = m_target_tensor.lock()) {
         return tensor_ptr->get_consuming_ports();
     }
     FRONT_END_THROW("Tensor has expired.");
 }
 
-bool OutPortPlaceTF::is_equal_data(const ov::frontend::Place::Ptr& another) const {
+bool OutPortPlace::is_equal_data(const ov::frontend::Place::Ptr& another) const {
     return get_target_tensor()->is_equal_data(another);
 }
 
-ov::frontend::Place::Ptr OutPortPlaceTF::get_target_tensor() const {
+ov::frontend::Place::Ptr OutPortPlace::get_target_tensor() const {
     if (const auto& target_tensor = m_target_tensor.lock()) {
         return target_tensor;
     }
     FRONT_END_THROW("Target Tensor has expired.");
 }
 
-ov::frontend::Place::Ptr OutPortPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr OutPortPlace::get_producing_operation() const {
     if (auto op = m_op.lock()) {
         return op;
     }
     FRONT_END_THROW("Operation has expired.");
 }
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/place.hpp b/src/frontends/tensorflow/src/place.hpp
index 0366342b9d8..639cd02a36e 100644
--- a/src/frontends/tensorflow/src/place.hpp
+++ b/src/frontends/tensorflow/src/place.hpp
@@ -5,23 +5,24 @@
 #pragma once
 
 #include "openvino/frontend/frontend.hpp"
-#include "tensorflow_frontend/decoder.hpp"
+#include "openvino/frontend/tensorflow/decoder.hpp"
 
 namespace ov {
 namespace frontend {
+namespace tensorflow {
 
-class TensorPlaceTF;
-class OpPlaceTF;
+class TensorPlace;
+class OpPlace;
 
-class PlaceTF : public ov::frontend::Place {
+class Place : public ov::frontend::Place {
 public:
-    PlaceTF(const ov::frontend::InputModel& input_model, const std::vector<std::string>& names)
+    Place(const ov::frontend::InputModel& input_model, const std::vector<std::string>& names)
         : m_input_model(input_model),
           m_names(names) {}
 
-    explicit PlaceTF(const ov::frontend::InputModel& input_model) : PlaceTF(input_model, std::vector<std::string>{}) {}
+    explicit Place(const ov::frontend::InputModel& input_model) : Place(input_model, std::vector<std::string>{}) {}
 
-    ~PlaceTF() override = default;
+    ~Place() override = default;
 
     bool is_input() const override;
     bool is_output() const override;
@@ -38,18 +39,18 @@ private:
     std::vector<std::string> m_names;
 };
 
-class InPortPlaceTF : public PlaceTF {
+class InPortPlace : public Place {
 public:
-    explicit InPortPlaceTF(const ov::frontend::InputModel& input_model) : PlaceTF(input_model) {}
+    explicit InPortPlace(const ov::frontend::InputModel& input_model) : Place(input_model) {}
 
-    void set_op(const std::weak_ptr<OpPlaceTF>& op) {
+    void set_op(const std::weak_ptr<OpPlace>& op) {
         m_op = op;
     }
-    void set_source_tensor(const std::weak_ptr<TensorPlaceTF>& source_tensor);
+    void set_source_tensor(const std::weak_ptr<TensorPlace>& source_tensor);
 
     // Internal usage
-    std::shared_ptr<TensorPlaceTF> get_source_tensor_tf() const;
-    std::shared_ptr<OpPlaceTF> get_op();
+    std::shared_ptr<TensorPlace> get_source_tensor_tf() const;
+    std::shared_ptr<OpPlace> get_op();
 
     // External usage
     std::vector<Ptr> get_consuming_operations() const override;
@@ -60,20 +61,20 @@ public:
     bool is_equal_data(const Ptr& another) const override;
 
 private:
-    std::weak_ptr<TensorPlaceTF> m_source_tensor;
-    std::weak_ptr<OpPlaceTF> m_op;
+    std::weak_ptr<TensorPlace> m_source_tensor;
+    std::weak_ptr<OpPlace> m_op;
 };
 
-class OutPortPlaceTF : public PlaceTF {
+class OutPortPlace : public Place {
 public:
-    explicit OutPortPlaceTF(const ov::frontend::InputModel& input_model) : PlaceTF(input_model) {}
+    explicit OutPortPlace(const ov::frontend::InputModel& input_model) : Place(input_model) {}
 
-    void set_op(const std::weak_ptr<OpPlaceTF>& op) {
+    void set_op(const std::weak_ptr<OpPlace>& op) {
         m_op = op;
     }
-    void set_target_tensor(const std::weak_ptr<TensorPlaceTF>& target_tensor);
+    void set_target_tensor(const std::weak_ptr<TensorPlace>& target_tensor);
 
-    std::shared_ptr<TensorPlaceTF> get_target_tensor_tf() const;
+    std::shared_ptr<TensorPlace> get_target_tensor_tf() const;
 
     // External usage
     std::vector<Ptr> get_consuming_operations() const override;
@@ -83,21 +84,21 @@ public:
     bool is_equal_data(const Ptr& another) const override;
 
 private:
-    std::weak_ptr<OpPlaceTF> m_op;
-    std::weak_ptr<TensorPlaceTF> m_target_tensor;
+    std::weak_ptr<OpPlace> m_op;
+    std::weak_ptr<TensorPlace> m_target_tensor;
 };
 
-class OpPlaceTF : public PlaceTF {
+class OpPlace : public Place {
 public:
-    OpPlaceTF(const ov::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder);
+    OpPlace(const ov::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder);
 
-    void add_in_port(const std::shared_ptr<InPortPlaceTF>& input, const std::string& name);
-    void add_out_port(const std::shared_ptr<OutPortPlaceTF>& output, int idx);
+    void add_in_port(const std::shared_ptr<InPortPlace>& input, const std::string& name);
+    void add_out_port(const std::shared_ptr<OutPortPlace>& output, int idx);
 
     // Internal usage
-    const std::vector<std::shared_ptr<OutPortPlaceTF>>& get_output_ports() const;
-    const std::map<std::string, std::vector<std::shared_ptr<InPortPlaceTF>>>& get_input_ports() const;
-    std::shared_ptr<InPortPlaceTF> get_input_port_tf(const std::string& inputName, int inputPortIndex) const;
+    const std::vector<std::shared_ptr<OutPortPlace>>& get_output_ports() const;
+    const std::map<std::string, std::vector<std::shared_ptr<InPortPlace>>>& get_input_ports() const;
+    std::shared_ptr<InPortPlace> get_input_port_tf(const std::string& inputName, int inputPortIndex) const;
     std::shared_ptr<DecoderBase> get_decoder() const;
 
     // External API methods
@@ -129,19 +130,19 @@ public:
 
 private:
     std::shared_ptr<DecoderBase> m_op_decoder;
-    std::map<std::string, std::vector<std::shared_ptr<InPortPlaceTF>>> m_input_ports;
-    std::vector<std::shared_ptr<OutPortPlaceTF>> m_output_ports;
+    std::map<std::string, std::vector<std::shared_ptr<InPortPlace>>> m_input_ports;
+    std::vector<std::shared_ptr<OutPortPlace>> m_output_ports;
 };
 
-class TensorPlaceTF : public PlaceTF {
+class TensorPlace : public Place {
 public:
-    TensorPlaceTF(const ov::frontend::InputModel& input_model,
-                  const ov::PartialShape& pshape,
-                  ov::element::Type type,
-                  const std::vector<std::string>& names);
+    TensorPlace(const ov::frontend::InputModel& input_model,
+                const ov::PartialShape& pshape,
+                ov::element::Type type,
+                const std::vector<std::string>& names);
 
-    void add_producing_port(const std::shared_ptr<OutPortPlaceTF>& out_port);
-    void add_consuming_port(const std::shared_ptr<InPortPlaceTF>& in_port);
+    void add_producing_port(const std::shared_ptr<OutPortPlace>& out_port);
+    void add_consuming_port(const std::shared_ptr<InPortPlace>& in_port);
 
     // Internal usage
     const PartialShape& get_partial_shape() const {
@@ -168,8 +169,10 @@ private:
     PartialShape m_pshape;
     element::Type m_type;
 
-    std::vector<std::weak_ptr<OutPortPlaceTF>> m_producing_ports;
-    std::vector<std::weak_ptr<InPortPlaceTF>> m_consuming_ports;
+    std::vector<std::weak_ptr<OutPortPlace>> m_producing_ports;
+    std::vector<std::weak_ptr<InPortPlace>> m_consuming_ports;
 };
+
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/tensorflow.cpp b/src/frontends/tensorflow/src/tensorflow.cpp
index e462ef7b92e..1e826f9c590 100644
--- a/src/frontends/tensorflow/src/tensorflow.cpp
+++ b/src/frontends/tensorflow/src/tensorflow.cpp
@@ -3,17 +3,18 @@
 //
 
 #include "openvino/frontend/manager.hpp"
-#include "tensorflow_frontend/frontend.hpp"
+#include "openvino/frontend/tensorflow/frontend.hpp"
+#include "openvino/frontend/tensorflow/visibility.hpp"
 
-TF_C_API ov::frontend::FrontEndVersion GetAPIVersion() {
+TENSORFLOW_C_API ov::frontend::FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
-TF_C_API void* GetFrontEndData() {
+TENSORFLOW_C_API void* GetFrontEndData() {
     auto res = new ov::frontend::FrontEndPluginInfo();
     res->m_name = "tf";
     res->m_creator = []() {
-        return std::make_shared<ov::frontend::FrontEndTF>();
+        return std::make_shared<ov::frontend::tensorflow::FrontEnd>();
     };
     return res;
 }
diff --git a/src/frontends/tensorflow/src/tf_framework_node.cpp b/src/frontends/tensorflow/src/tf_framework_node.cpp
index 995eda18aaf..fd78a8afbbe 100644
--- a/src/frontends/tensorflow/src/tf_framework_node.cpp
+++ b/src/frontends/tensorflow/src/tf_framework_node.cpp
@@ -6,13 +6,13 @@
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 
-void TFFrameworkNode::validate_and_infer_types() {
+void FrameworkNode::validate_and_infer_types() {
     for (size_t i = 0; i < get_output_size(); ++i) {
         set_output_type(i, ov::element::dynamic, PartialShape::dynamic());
     }
 }
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/tf_framework_node.hpp b/src/frontends/tensorflow/src/tf_framework_node.hpp
index 1ed553c299f..25c060956b0 100644
--- a/src/frontends/tensorflow/src/tf_framework_node.hpp
+++ b/src/frontends/tensorflow/src/tf_framework_node.hpp
@@ -6,19 +6,19 @@
 
 #include <algorithm>
 
+#include "openvino/frontend/tensorflow/decoder.hpp"
 #include "openvino/op/util/framework_node.hpp"
-#include "tensorflow_frontend/decoder.hpp"
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 
-class TFFrameworkNode : public ::ov::op::util::FrameworkNode {
+class FrameworkNode : public ov::op::util::FrameworkNode {
 public:
-    OPENVINO_OP("TFFrameworkNode", "util", ::ov::op::util::FrameworkNode);
+    OPENVINO_OP("FrameworkNode", "util", ::ov::op::util::FrameworkNode);
 
-    TFFrameworkNode(const std::shared_ptr<DecoderBase>& decoder, const OutputVector& inputs, size_t num_outputs)
-        : FrameworkNode(inputs, std::max(num_outputs, size_t(1))),
+    FrameworkNode(const std::shared_ptr<DecoderBase>& decoder, const OutputVector& inputs, size_t num_outputs)
+        : ov::op::util::FrameworkNode(inputs, std::max(num_outputs, size_t(1))),
           m_decoder(decoder) {
         ov::op::util::FrameworkNodeAttrs attrs;
         attrs.set_type_name(m_decoder->get_op_type());
@@ -30,7 +30,7 @@ public:
     void validate_and_infer_types() override;
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
-        return std::make_shared<TFFrameworkNode>(m_decoder, inputs, get_output_size());
+        return std::make_shared<FrameworkNode>(m_decoder, inputs, get_output_size());
     }
 
     std::string get_op_type() const {
@@ -44,6 +44,6 @@ public:
 private:
     std::shared_ptr<DecoderBase> m_decoder;
 };
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/utils.cpp b/src/frontends/tensorflow/src/utils.cpp
index b8557330e78..02488d0d979 100644
--- a/src/frontends/tensorflow/src/utils.cpp
+++ b/src/frontends/tensorflow/src/utils.cpp
@@ -4,7 +4,8 @@
 
 #include "utils.hpp"
 
-void ov::frontend::tf::tf_shape_to_ov_shape(const tensorflow::TensorShapeProto& tf_shape, ov::PartialShape* ng_shape) {
+void ov::frontend::tensorflow::tf_shape_to_ov_shape(const ::tensorflow::TensorShapeProto& tf_shape,
+                                                    ov::PartialShape* ng_shape) {
     std::vector<ov::Dimension> dims;
     for (int i = 0; i < tf_shape.dim_size(); i++) {
         dims.emplace_back(tf_shape.dim(i).size());
@@ -12,7 +13,7 @@ void ov::frontend::tf::tf_shape_to_ov_shape(const tensorflow::TensorShapeProto&
     *ng_shape = ov::PartialShape(dims);
 }
 
-void ov::frontend::tf::set_node_name(const std::string& node_name, const std::shared_ptr<Node>& node) {
+void ov::frontend::tensorflow::set_node_name(const std::string& node_name, const std::shared_ptr<Node>& node) {
     const auto& outputs = node->outputs();
     node->set_friendly_name(node_name);
     if (outputs.size() == 1) {
@@ -23,6 +24,6 @@ void ov::frontend::tf::set_node_name(const std::string& node_name, const std::sh
     }
 }
 
-void ov::frontend::tf::set_out_name(const std::string& out_name, const ov::Output<ov::Node>& output) {
+void ov::frontend::tensorflow::set_out_name(const std::string& out_name, const ov::Output<ov::Node>& output) {
     output.get_tensor().add_names({out_name});
 }
diff --git a/src/frontends/tensorflow/src/utils.hpp b/src/frontends/tensorflow/src/utils.hpp
index 6de7e432ee2..352444099f2 100644
--- a/src/frontends/tensorflow/src/utils.hpp
+++ b/src/frontends/tensorflow/src/utils.hpp
@@ -29,7 +29,7 @@
 
 namespace ov {
 namespace frontend {
-namespace tf {
+namespace tensorflow {
 using OpMap = std::unordered_map<std::string, std::vector<ov::Output<ov::Node>>>;
 
 void extract_operation_name_and_port(const std::string& port_name,
@@ -90,14 +90,14 @@ void get_const_input(const NodeContext& node, int64_t input_index, std::vector<T
 // compatibility with OpenVINO).
 template <typename T, typename VecT = T>
 void values_from_const_node(const NodeContext& node, ov::Shape* const_tensor_shape, std::vector<VecT>* values) {
-    TF_OP_VALIDATION_CHECK(node, node.get_op_type() == "Const", "Node is expected to be Constant.");
+    TENSORFLOW_OP_VALIDATION(node, node.get_op_type() == "Const", "Node is expected to be Constant.");
     auto dt = node.get_attribute<::tensorflow::DataType>("dtype");
     auto tensor_proto = node.get_attribute<::tensorflow::TensorProto>("value");
-    const tensorflow::TensorShapeProto& shape = tensor_proto.tensor_shape();
+    const ::tensorflow::TensorShapeProto& shape = tensor_proto.tensor_shape();
     ov::PartialShape pshape;
     tf_shape_to_ov_shape(shape, &pshape);
     *const_tensor_shape = pshape.get_shape();
-    TF_OP_VALIDATION_CHECK(node, pshape.is_static(), "Dynamic shapes are not supported in Constant conversion.");
+    TENSORFLOW_OP_VALIDATION(node, pshape.is_static(), "Dynamic shapes are not supported in Constant conversion.");
     auto tensor_content = tensor_proto.tensor_content();
     std::vector<char> tensor_values_plain(tensor_content.begin(), tensor_content.end());
     const T* tensor_values = reinterpret_cast<const T*>(tensor_values_plain.data());
@@ -122,9 +122,9 @@ void values_from_const_node(const NodeContext& node, ov::Shape* const_tensor_sha
     if (tensor_content_size == 0) {
         int64_t n_elements = 1;
         for (auto i = 0; i < shape.dim_size(); i++) {
-            TF_OP_VALIDATION_CHECK(node,
-                                   shape.dim(i).size() >= 0,
-                                   "Const node has empty tensor and an unknown dimension size");
+            TENSORFLOW_OP_VALIDATION(node,
+                                     shape.dim(i).size() >= 0,
+                                     "Const node has empty tensor and an unknown dimension size");
             n_elements *= shape.dim(i).size();
         }
         values->resize(n_elements);
@@ -136,27 +136,27 @@ void values_from_const_node(const NodeContext& node, ov::Shape* const_tensor_sha
             switch (dt) {
             // TODO: there are more element types to support
             // here
-            case tensorflow::DT_INT32:
+            case ::tensorflow::DT_INT32:
                 val_size = tensor_proto.int_val_size();
                 if (val_size > 0)
                     val_i = tensor_proto.int_val()[i];
                 break;
-            case tensorflow::DT_INT64:
+            case ::tensorflow::DT_INT64:
                 val_size = tensor_proto.int64_val_size();
                 if (val_size > 0)
                     val_i = tensor_proto.int64_val()[i];
                 break;
-            case tensorflow::DT_FLOAT:
+            case ::tensorflow::DT_FLOAT:
                 val_size = tensor_proto.float_val_size();
                 if (val_size > 0)
                     val_i = tensor_proto.float_val()[i];
                 break;
-            case tensorflow::DT_BOOL:
+            case ::tensorflow::DT_BOOL:
                 val_size = tensor_proto.bool_val_size();
                 if (val_size > 0)
                     val_i = tensor_proto.bool_val()[i];
                 break;
-            case tensorflow::DT_DOUBLE:
+            case ::tensorflow::DT_DOUBLE:
                 val_size = tensor_proto.double_val_size();
                 if (val_size > 0)
                     val_i = tensor_proto.double_val()[i];
@@ -166,7 +166,7 @@ void values_from_const_node(const NodeContext& node, ov::Shape* const_tensor_sha
                                   "handle this element type";
                 FRONT_END_THROW("Encountered unknown element type " + DataType_Name(dt) + " on an empty tensor_proto");
             }
-            TF_OP_VALIDATION_CHECK(node, val_size != 0, "Empty values vector");
+            TENSORFLOW_OP_VALIDATION(node, val_size != 0, "Empty values vector");
 
             if (i < val_size) {
                 (*values)[i] = val_i;
@@ -188,6 +188,6 @@ void make_const_op(const NodeContext& node, element::Type et, ov::Output<ov::Nod
     values_from_const_node<T, VecT>(node, &ng_shape, &const_values);
     ng_node = std::make_shared<ov::opset8::Constant>(et, ng_shape, const_values);
 };
-}  // namespace tf
+}  // namespace tensorflow
 }  // namespace frontend
 }  // namespace ov

From 2d73eb61573bde9884cd6c7ad520d70bc78a5430 Mon Sep 17 00:00:00 2001
From: Gorokhov Dmitriy <dmitry.gorokhov@intel.com>
Date: Tue, 21 Dec 2021 14:45:44 +0300
Subject: [PATCH 072/160] [CPU] Migration on oneDNN v2.4.4 (#9326)

Co-authored-by: Anton Voronov <anton.voronov@intel.com>
---
 inference-engine/src/mkldnn_plugin/utils/verbose.cpp | 7 +++++--
 inference-engine/thirdparty/mkl-dnn                  | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/utils/verbose.cpp b/inference-engine/src/mkldnn_plugin/utils/verbose.cpp
index 29ddb6524e4..185c9535931 100644
--- a/inference-engine/src/mkldnn_plugin/utils/verbose.cpp
+++ b/inference-engine/src/mkldnn_plugin/utils/verbose.cpp
@@ -10,6 +10,7 @@
 
 #include "dnnl_types.h"
 #include "dnnl_debug.h"
+#include "../src/common/verbose.hpp"
 
 #include <string>
 #include <cstdlib>
@@ -103,11 +104,13 @@ void Verbose::printInfo() {
         shift(written);
         written = snprintf(portsInfo + written_total, CPU_VERBOSE_DAT_LEN - written_total, "%s", prefix.c_str());
         shift(written);
-        written = dnnl_md2fmt_str(portsInfo + written_total, CPU_VERBOSE_DAT_LEN - written_total, &desc);
+        std::string fmt_str = dnnl::impl::md2fmt_str(&desc);
+        written = snprintf(portsInfo + written_total, CPU_VERBOSE_DAT_LEN - written_total, "%s", fmt_str.c_str());
         shift(written);
         written = snprintf(portsInfo + written_total, CPU_VERBOSE_DAT_LEN - written_total, ":");
         shift(written);
-        written = dnnl_md2dim_str(portsInfo + written_total, CPU_VERBOSE_DAT_LEN - written_total, &desc);
+        std::string dim_str = dnnl::impl::md2dim_str(&desc);
+        written = snprintf(portsInfo + written_total, CPU_VERBOSE_DAT_LEN - written_total, "%s", dim_str.c_str());
         shift(written);
     };
 
diff --git a/inference-engine/thirdparty/mkl-dnn b/inference-engine/thirdparty/mkl-dnn
index acee807d849..7cd4218a3d4 160000
--- a/inference-engine/thirdparty/mkl-dnn
+++ b/inference-engine/thirdparty/mkl-dnn
@@ -1 +1 @@
-Subproject commit acee807d84944008df6741677ab52e01d790d58a
+Subproject commit 7cd4218a3d45c2c7e7b321dc825c3635a723b60e

From de980b8089f4450de8513c6a0e380b2cb20b6d16 Mon Sep 17 00:00:00 2001
From: Vitaliy Urusovskij <vitaliy.urusovskij@intel.com>
Date: Tue, 21 Dec 2021 15:31:43 +0300
Subject: [PATCH 073/160] Actualize DeterministicityTest: update headers and
 used opset (#9335)

---
 .../ir_serialization/deterministicity.cpp     | 47 +++++++++----------
 1 file changed, 23 insertions(+), 24 deletions(-)

diff --git a/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp b/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp
index d97ef097677..d7e98027610 100644
--- a/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/deterministicity.cpp
@@ -10,10 +10,9 @@
 #include "ie_core.hpp"
 
 #include <openvino/runtime/runtime.hpp>
-#include <ngraph/function.hpp>
-#include <ngraph/opsets/opset5.hpp>
+#include <openvino/opsets/opset8.hpp>
 
-using namespace ngraph;
+using namespace ov;
 
 #ifndef IR_SERIALIZATION_MODELS_PATH  // should be already defined by cmake
 #    error "IR_SERIALIZATION_MODELS_PATH is not defined"
@@ -114,22 +113,22 @@ protected:
 TEST_F(DeterministicityTest, IRInputsOrder) {
     const std::vector<std::string> friend_names = {"A", "B", "C"};
 
-    auto a = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
-    auto b = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
-    auto add = std::make_shared<opset5::Add>(a, b);
-    auto c = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
-    auto relu = std::make_shared<opset5::Relu>(c);
-    auto add2 = std::make_shared<opset5::Add>(add, relu);
-    auto res = std::make_shared<opset5::Result>(add2);
+    auto a = std::make_shared<opset8::Parameter>(element::f32, Shape{2, 2});
+    auto b = std::make_shared<opset8::Parameter>(element::f32, Shape{2, 2});
+    auto add = std::make_shared<opset8::Add>(a, b);
+    auto c = std::make_shared<opset8::Parameter>(element::f32, Shape{2, 2});
+    auto relu = std::make_shared<opset8::Relu>(c);
+    auto add2 = std::make_shared<opset8::Add>(add, relu);
+    auto res = std::make_shared<opset8::Result>(add2);
 
     a->set_friendly_name(friend_names[0]);
     b->set_friendly_name(friend_names[1]);
     c->set_friendly_name(friend_names[2]);
 
-    auto model = std::make_shared<ov::Model>(NodeVector{res}, ParameterVector{a, b, c});
+    auto model = std::make_shared<Model>(NodeVector{res}, ParameterVector{a, b, c});
 
-    ov::runtime::Core core;
-    auto serialize = ov::pass::Serialize(m_out_xml_path, m_out_bin_path);
+    runtime::Core core;
+    auto serialize = pass::Serialize(m_out_xml_path, m_out_bin_path);
     serialize.run_on_function(model);
     auto serialized_func = core.read_model(m_out_xml_path);
 
@@ -143,23 +142,23 @@ TEST_F(DeterministicityTest, IRInputsOrder) {
 TEST_F(DeterministicityTest, IROutputsOrder) {
     const std::vector<std::string> friend_names = {"D", "E", "F"};
 
-    auto a = std::make_shared<opset5::Parameter>(element::f32, Shape{4, 4});
-    auto axis_1 = ngraph::opset5::Constant::create(element::i64, Shape{}, {1});
-    auto split1 = std::make_shared<opset5::Split>(a, axis_1, 2);
-    auto res1 = std::make_shared<opset5::Result>(split1->output(0));
-    auto relu = std::make_shared<opset5::Relu>(split1->output(1));
-    auto split2 = std::make_shared<opset5::Split>(relu, axis_1, 2);
-    auto res2 = std::make_shared<opset5::Result>(split2->output(0));
-    auto res3 = std::make_shared<opset5::Result>(split2->output(1));
+    auto a = std::make_shared<opset8::Parameter>(element::f32, Shape{4, 4});
+    auto axis_1 = opset8::Constant::create(element::i64, Shape{}, {1});
+    auto split1 = std::make_shared<opset8::Split>(a, axis_1, 2);
+    auto res1 = std::make_shared<opset8::Result>(split1->output(0));
+    auto relu = std::make_shared<opset8::Relu>(split1->output(1));
+    auto split2 = std::make_shared<opset8::Split>(relu, axis_1, 2);
+    auto res2 = std::make_shared<opset8::Result>(split2->output(0));
+    auto res3 = std::make_shared<opset8::Result>(split2->output(1));
 
     res1->set_friendly_name(friend_names[0]);
     res2->set_friendly_name(friend_names[1]);
     res3->set_friendly_name(friend_names[2]);
 
-    auto model = std::make_shared<ov::Model>(NodeVector{res1, res2, res3}, ParameterVector{a});
+    auto model = std::make_shared<Model>(NodeVector{res1, res2, res3}, ParameterVector{a});
 
-    ov::runtime::Core core;
-    auto serialize = ov::pass::Serialize(m_out_xml_path, m_out_bin_path);
+    runtime::Core core;
+    auto serialize = pass::Serialize(m_out_xml_path, m_out_bin_path);
     serialize.run_on_function(model);
     auto serialized_func = core.read_model(m_out_xml_path);
 

From 132b755a469af94213485fea508b4a8e7db1ef60 Mon Sep 17 00:00:00 2001
From: Katarzyna Mitrus <katarzyna.mitrus@intel.com>
Date: Tue, 21 Dec 2021 13:57:04 +0100
Subject: [PATCH 074/160] Enable Slice-8 dynamic lower bounds serialization
 tests (#9323)

---
 .../serialization/single_layer/slice.cpp               | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/src/tests/functional/inference_engine/serialization/single_layer/slice.cpp b/src/tests/functional/inference_engine/serialization/single_layer/slice.cpp
index a842ae2e08b..34e15cb3dfc 100644
--- a/src/tests/functional/inference_engine/serialization/single_layer/slice.cpp
+++ b/src/tests/functional/inference_engine/serialization/single_layer/slice.cpp
@@ -111,12 +111,10 @@ std::vector<Slice8SpecificParams> dynamicParams = {
                 std::numeric_limits<std::int64_t>::max(),
                 std::numeric_limits<std::int64_t>::max(),
                 std::numeric_limits<std::int64_t>::max() }, { 1, 1, 1, 1, 16 }, {} },
-
-        // Shapes mismatch because of missing lower bounds serialization support (ticket: 69092)
-        // Slice8SpecificParams{ {{{ov::Dimension(1, 5), ov::Dimension(1, 7), ov::Dimension(1, 35), ov::Dimension(1, 35)},
-        //     {{ 1, 5, 32, 32 }, { 2, 5, 32, 20 }, { 2, 5, 32, 32 }}}}, { 0, 2, 5, 4 }, { 1, 4, 28, 27 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3 } },
-        // Slice8SpecificParams{ {{{ov::Dimension(1, 5), ov::Dimension(10, 20), ov::Dimension(20, 30), 16, ov::Dimension(30, 40)},
-        //     {{ 4, 15, 30, 16, 39 }}}}, { 0, 2, 10, 0, 35 }, { 1, 8, 25, 16, 40 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3, 4 } }
+        Slice8SpecificParams{ {{{ov::Dimension(1, 5), ov::Dimension(1, 7), ov::Dimension(1, 35), ov::Dimension(1, 35)},
+            {{ 1, 5, 32, 32 }, { 2, 5, 32, 20 }, { 2, 5, 32, 32 }}}}, { 0, 2, 5, 4 }, { 1, 4, 28, 27 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3 } },
+        Slice8SpecificParams{ {{{ov::Dimension(1, 5), ov::Dimension(10, 20), ov::Dimension(20, 30), 16, ov::Dimension(30, 40)},
+            {{ 4, 15, 30, 16, 39 }}}}, { 0, 2, 10, 0, 35 }, { 1, 8, 25, 16, 40 }, { 1, 1, 1, 1, 1 }, { 0, 1, 2, 3, 4 } }
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_Slice8Serialization_dynamic, Slice8LayerTest,

From 6f437fc1bd17a3cde2bc48887b7fe19909e13908 Mon Sep 17 00:00:00 2001
From: Andrew Kwangwoong Park <andrew.kwangwoong.park@intel.com>
Date: Wed, 22 Dec 2021 00:03:38 +0900
Subject: [PATCH 075/160] [GPU] Exclude peak memory usage from the result of
 memory statistics (#9332)

Signed-off-by: Andrew Kwangwoong Park <andrew.kwangwoong.park@intel.com>
---
 .../thirdparty/clDNN/api/intel_gpu/runtime/engine.hpp      | 2 +-
 inference-engine/thirdparty/clDNN/runtime/engine.cpp       | 7 +------
 src/inference/include/ie/gpu/gpu_config.hpp                | 2 +-
 3 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/engine.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/engine.hpp
index acc57d689ea..014692b68df 100644
--- a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/engine.hpp
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/engine.hpp
@@ -110,7 +110,7 @@ public:
     uint64_t get_used_device_memory(allocation_type type) const;
 
     /// Returns statistics of GPU memory allocated by engine in current process for all allocation types.
-    /// @note It contains information about both current and peak memory usage
+    /// @note It contains information about current memory usage
     std::map<std::string, uint64_t> get_memory_statistics() const;
 
     /// Adds @p bytes count to currently used memory size of the specified allocation @p type
diff --git a/inference-engine/thirdparty/clDNN/runtime/engine.cpp b/inference-engine/thirdparty/clDNN/runtime/engine.cpp
index e4671f206ca..cefb78ecaf2 100644
--- a/inference-engine/thirdparty/clDNN/runtime/engine.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/engine.cpp
@@ -164,12 +164,7 @@ std::map<std::string, uint64_t> engine::get_memory_statistics() const {
     std::map<std::string, uint64_t> statistics;
     for (auto const& m : _memory_usage_map) {
         std::ostringstream oss;
-        oss << m.first << "_current";
-        statistics[oss.str()] = m.second.load();
-    }
-    for (auto const& m : _peak_memory_usage_map) {
-        std::ostringstream oss;
-        oss << m.first << "_peak";
+        oss << m.first;
         statistics[oss.str()] = m.second.load();
     }
     return statistics;
diff --git a/src/inference/include/ie/gpu/gpu_config.hpp b/src/inference/include/ie/gpu/gpu_config.hpp
index 186509467be..0534f211941 100644
--- a/src/inference/include/ie/gpu/gpu_config.hpp
+++ b/src/inference/include/ie/gpu/gpu_config.hpp
@@ -47,7 +47,7 @@ DECLARE_GPU_METRIC_KEY(EXECUTION_UNITS_COUNT, int);
 
 /**
  * @brief Metric to get statistics of GPU memory allocated by engine for each allocation type
- * It contains information about both current and peak memory usage
+ * It contains information about current memory usage
  */
 DECLARE_GPU_METRIC_KEY(MEMORY_STATISTICS, std::map<std::string, uint64_t>);
 

From dcfaf424a0c9c59706703e0c3f1e78a77ae3795c Mon Sep 17 00:00:00 2001
From: Tomasz Jankowski <tomasz1.jankowski@intel.com>
Date: Tue, 21 Dec 2021 16:38:15 +0100
Subject: [PATCH 076/160] [ONNX FE] Add support for partial shapes with
 boundaries (#9321)

---
 .../test_frontend_onnx_editor.py              | 46 ++++++++++++++++++-
 .../onnx/frontend/src/input_model.cpp         | 27 +++++++++++
 .../onnx/frontend/src/input_model.hpp         |  3 ++
 3 files changed, 75 insertions(+), 1 deletion(-)

diff --git a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
index 1649845dd82..90430c357b0 100644
--- a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
+++ b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
@@ -5,7 +5,7 @@ import os
 import onnx
 import pytest
 from onnx.helper import make_graph, make_model, make_tensor_value_info
-from openvino.runtime import PartialShape
+from openvino.runtime import Dimension, PartialShape
 from openvino.frontend import FrontEndManager
 
 
@@ -1221,6 +1221,50 @@ def test_set_input_partial_shape_using_input_edge():
     assert ov_model.output("out4").get_partial_shape() == PartialShape([10, 10])
 
 
+def test_set_partial_shape_with_range():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    input1 = model.get_place_by_tensor_name("in1")
+    ranged_shape = PartialShape([Dimension(1, 4), Dimension(2)])
+    model.set_partial_shape(input1, ranged_shape)
+
+    ov_model = fe.convert(model)
+    assert ov_model.input("in1").get_partial_shape() == ranged_shape
+
+
+def test_set_partial_shape_with_range_and_cut_it_off():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    input1 = model.get_place_by_tensor_name("in1")
+    ranged_shape = PartialShape([Dimension(1, 4), Dimension(2)])
+    model.set_partial_shape(input1, ranged_shape)
+
+    add_out = model.get_place_by_tensor_name("add_out")
+    model.extract_subgraph(inputs=[add_out], outputs=[])
+
+    ov_model = fe.convert(model)
+    for input in ov_model.inputs:
+        assert input.get_partial_shape() != ranged_shape
+
+
+def test_set_partial_shape_with_range_and_rename_it():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    model = fe.load("input_model.onnx")
+
+    input1 = model.get_place_by_tensor_name("in1")
+    ranged_shape = PartialShape([Dimension(1, 4), Dimension(2)])
+    model.set_partial_shape(input1, ranged_shape)
+    model.set_name_for_tensor(input1, "new_in1")
+
+    ov_model = fe.convert(model)
+    assert ov_model.input("new_in1").get_partial_shape() == ranged_shape
+
+
 def test_get_partial_shape_using_input_edge():
     skip_if_onnx_frontend_is_disabled()
     fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index 23096a23fb2..8ff8c67b4e8 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -100,6 +100,11 @@ void InputModel::set_name_for_tensor(const ov::frontend::Place::Ptr& tensor, con
         m_additional_tensor_names[new_name] = m_additional_tensor_names[original_name];
         m_additional_tensor_names.erase(original_name);
     }
+
+    if (m_inputs_to_reshape.count(original_name) > 0) {
+        m_inputs_to_reshape[new_name] = m_inputs_to_reshape[original_name];
+        m_inputs_to_reshape.erase(original_name);
+    }
 }
 
 void InputModel::set_name_for_operation(const ov::frontend::Place::Ptr& operation, const std::string& new_name) {
@@ -157,6 +162,9 @@ void InputModel::set_partial_shape(const ov::frontend::Place::Ptr& place, const
     }
 
     m_editor->set_input_shapes({{input_name, shape}});
+
+    if (shape.get_min_shape() != shape.get_max_shape())
+        m_inputs_to_reshape[input_name] = shape;
 }
 
 ngraph::PartialShape InputModel::get_partial_shape(const ov::frontend::Place::Ptr& place) const {
@@ -193,6 +201,7 @@ std::shared_ptr<Model> InputModel::decode() {
 std::shared_ptr<Model> InputModel::convert() {
     auto converted_model = m_editor->get_function();
     add_tensor_names(converted_model);
+    reshape_model_inputs(converted_model);
     return converted_model;
 }
 
@@ -296,3 +305,21 @@ void InputModel::add_tensor_names(std::shared_ptr<Model>& model) {
         }
     }
 }
+
+void InputModel::reshape_model_inputs(std::shared_ptr<Model>& model) {
+    const auto& inputs = model->inputs();
+    const auto is_input_name = [&inputs](const std::string& name) {
+        return std::find_if(std::begin(inputs), std::end(inputs), [&name](const OutputVector::value_type& input) {
+                   return input.get_names().count(name) > 0;
+               }) != std::end(inputs);
+    };
+
+    // assure that names actually refer to model's inputs
+    std::map<std::string, ov::PartialShape> actual_inputs_to_reshape;
+    for (const auto& in : m_inputs_to_reshape)
+        if (is_input_name(in.first))
+            actual_inputs_to_reshape.insert(in);
+
+    if (!actual_inputs_to_reshape.empty())
+        model->reshape(actual_inputs_to_reshape);
+}
diff --git a/src/frontends/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
index 24b16e78318..d8f87c5d2ae 100644
--- a/src/frontends/onnx/frontend/src/input_model.hpp
+++ b/src/frontends/onnx/frontend/src/input_model.hpp
@@ -69,6 +69,9 @@ private:
 
     std::unordered_map<std::string, std::unordered_set<std::string>> m_additional_tensor_names;
     void add_tensor_names(std::shared_ptr<Model>& model);
+
+    std::unordered_map<std::string, ov::PartialShape> m_inputs_to_reshape;
+    void reshape_model_inputs(std::shared_ptr<Model>& model);
 };
 
 }  // namespace onnx

From 0c7089acc6597e13aadef19a2713160d76f5fd24 Mon Sep 17 00:00:00 2001
From: Yuan Hu <yuan2.hu@intel.com>
Date: Tue, 21 Dec 2021 23:40:35 +0800
Subject: [PATCH 077/160] [Auto plugin] remove thread_utils.hpp (#9279)

* remove thread_utils.hpp

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>

* disable using windows in log utils test

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
---
 src/plugins/auto/utils/log.hpp          |   2 -
 src/plugins/auto/utils/thread_utils.cpp | 107 ------------------------
 src/plugins/auto/utils/thread_utils.hpp |  63 --------------
 src/tests/unit/auto/log_utils_test.cpp  |  10 ++-
 4 files changed, 8 insertions(+), 174 deletions(-)
 delete mode 100644 src/plugins/auto/utils/thread_utils.cpp
 delete mode 100644 src/plugins/auto/utils/thread_utils.hpp

diff --git a/src/plugins/auto/utils/log.hpp b/src/plugins/auto/utils/log.hpp
index 02c0f1e2bc2..7f7c8eec2c9 100644
--- a/src/plugins/auto/utils/log.hpp
+++ b/src/plugins/auto/utils/log.hpp
@@ -14,7 +14,6 @@
 
 #include "singleton.hpp"
 #include "time_utils.hpp"
-#include "thread_utils.hpp"
 
 #ifdef  MULTIUNITTEST
 #define MOCKTESTMACRO virtual
@@ -185,7 +184,6 @@ inline void Log::doLog(bool on, bool isTraceCallStack, LogLevel level, const cha
     std::stringstream stream;
     stream << colorBegin(level) << prefix << '[' << TimeUtils::getCurrentTime() << ']';
 
-    stream << '[' << ThreadUtils::getThreadId() << ']';
     if (level < LogLevel::ERROR) {
         stream << levelStr[0];
     } else {
diff --git a/src/plugins/auto/utils/thread_utils.cpp b/src/plugins/auto/utils/thread_utils.cpp
deleted file mode 100644
index 970decb28b8..00000000000
--- a/src/plugins/auto/utils/thread_utils.cpp
+++ /dev/null
@@ -1,107 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-#include <fstream>
-#include <iomanip>
-
-#include "thread_utils.hpp"
-#include <mutex>
-
-namespace MultiDevicePlugin {
-namespace ThreadUtils {
-void setName(const std::string& name) {
-    setName(name.c_str());
-}
-
-std::string getName() {
-    char threadName[32] = { '\0' };
-    getName(threadName, 32);
-    return threadName;
-}
-
-void saveThreadInfo(const std::string& threadName, const std::string& saveFile) {
-    static std::mutex mutex;
-    static uint32_t threadCount = 0;
-
-    std::lock_guard<std::mutex> autoLock(mutex);
-
-    std::ofstream stream;
-    if (!threadCount) {
-        stream.open(saveFile, std::ios::out | std::ios::trunc);
-    } else {
-        stream.open(saveFile, std::ios::out | std::ios::app);
-    }
-
-    stream << "[" << std::setw(2) << std::setfill('0') << threadCount++ << "]"
-           << " threadId=" << getThreadId() << " threadName=" << threadName << std::endl;
-}
-
-#if (defined(_WIN32) || defined(_WIN64))
-DWORD getThreadId() {
-    return GetCurrentThreadId();
-}
-
-static void setThreadName(DWORD dwThreadID, const char* threadName) {
-    HANDLE handle = OpenThread(THREAD_SET_LIMITED_INFORMATION, true, dwThreadID);
-    auto len = MultiByteToWideChar(CP_ACP, 0, threadName, static_cast<int>(strlen(threadName)), NULL, 0);
-    PWSTR threadDesc = new WCHAR[len + 1];
-    MultiByteToWideChar(CP_ACP, 0, threadName, static_cast<int>(strlen(threadName)), threadDesc, len);
-    threadDesc[len] = '\0';
-    SetThreadDescription(handle, threadDesc);
-    delete[] threadDesc;
-    CloseHandle(handle);
-}
-
-void setName(const char* pname) {
-    setThreadName(GetCurrentThreadId(), pname);
-}
-
-void getName(char* name, size_t size) {
-    HANDLE handle = OpenThread(THREAD_QUERY_LIMITED_INFORMATION, true, GetCurrentThreadId());
-    PWSTR threadDesc;
-    if (SUCCEEDED(GetThreadDescription(handle, &threadDesc))) {
-        auto len = WideCharToMultiByte(CP_ACP, 0, threadDesc, static_cast<int>(wcslen(threadDesc)), NULL, 0, NULL, NULL);
-        if (len < size) {
-            WideCharToMultiByte(CP_ACP, 0, threadDesc, static_cast<int>(wcslen(threadDesc)), name, len, NULL, NULL);
-            name[len] = '\0';
-        }
-        LocalFree(threadDesc);
-    }
-    CloseHandle(handle);
-}
-#elif defined(__linux__)
-long getThreadId() {
-    return syscall(SYS_gettid);
-}
-
-void setName(const char* pname) {
-    prctl(PR_SET_NAME, pname);
-}
-
-void getName(char* name, size_t size) {
-    if (name && size >= 16) {
-        prctl(PR_GET_NAME, name);
-    }
-}
-
-#elif defined(__APPLE__)
-long getThreadId() {
-    return (long)pthread_self();
-}
-
-void setName(const char* pname) {
-    pthread_setname_np(pname);
-}
-
-void getName(char* name, size_t size) {
-    pthread_getname_np(pthread_self(), name, size);
-}
-
-#else
-#endif
-} // namespace ThreadUtils
-} // namespace MultiDevicePlugin
-
diff --git a/src/plugins/auto/utils/thread_utils.hpp b/src/plugins/auto/utils/thread_utils.hpp
deleted file mode 100644
index e22fa974015..00000000000
--- a/src/plugins/auto/utils/thread_utils.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-#ifndef MULTIDEVICEPLUGIN_THREADUTILS_H
-#define MULTIDEVICEPLUGIN_THREADUTILS_H
-
-#include <cstdint>
-#include <string>
-
-#ifdef  MULTIUNITTEST
-#define MOCKTESTMACRO virtual
-#define MultiDevicePlugin MockMultiDevicePlugin
-#else
-#define MOCKTESTMACRO
-#endif
-
-#if (defined(_WIN32) || defined(_WIN64))
-#include <windows.h>
-#include <winsock.h>
-#include <intrin.h>
-namespace MultiDevicePlugin {
-namespace ThreadUtils {
-DWORD getThreadId();
-} // namespace ThreadUtils
-#elif defined(__linux__)
-#include <sys/types.h>
-#include <sys/prctl.h>
-#include <sys/syscall.h>
-#include <unistd.h>
-namespace MultiDevicePlugin {
-namespace ThreadUtils {
-long getThreadId();
-} // namespace ThreadUtils
-#elif defined(__APPLE__)
-#include <pthread.h>
-#include <sys/proc_info.h>
-namespace MultiDevicePlugin {
-namespace ThreadUtils {
-long getThreadId();
-} // namespace ThreadUtils
-#else
-namespace MultiDevicePlugin {
-namespace ThreadUtils {
-long getThreadId();
-} // namespace ThreadUtils
-#endif
-
-
-
-namespace ThreadUtils {
-uint64_t getRdtsc();
-std::string getName();
-
-void setName(const char* name);
-void setName(const std::string& name);
-void getName(char* name, size_t size);
-void saveThreadInfo(const std::string& threadName, const std::string& saveFile = "threads.info");
-} // namespace ThreadUtils
-} // namespace MultiDevicePlugin
-
-#endif //MULTIDEVICEPLUGIN_THREADUTILS_H
diff --git a/src/tests/unit/auto/log_utils_test.cpp b/src/tests/unit/auto/log_utils_test.cpp
index 5c694b30d99..c6733a6cf0f 100644
--- a/src/tests/unit/auto/log_utils_test.cpp
+++ b/src/tests/unit/auto/log_utils_test.cpp
@@ -9,14 +9,17 @@
 #include <future>
 using ::testing::_;
 using namespace MockMultiDevice;
+// disable using windows.h
+#if 0
 #if (defined(_WIN32) || defined(_WIN64))
-#include <winbase.h>
+#include <windows.h>
 #elif defined(__linux__)
 #include <stdlib.h>
 #elif defined(__APPLE__)
 #include <stdlib.h>
 #else
 #endif
+#endif
 
 MockLog* MockLog::_mockLog = NULL;
 using ConfigParams = std::tuple<
@@ -42,6 +45,7 @@ public:
         return result.str();
     }
 
+#if 0
     void SetTestEnv(std::string key, std::string value) {
 #ifdef WIN32
         SetEnvironmentVariable(key.c_str(), value.c_str());
@@ -52,7 +56,7 @@ public:
 #else
 #endif
     }
-
+#endif
     void SetUp() override {
         std::tie(_logLevel, _envLogLevel, _expectCallNum) = this->GetParam();
     }
@@ -81,12 +85,14 @@ TEST_P(LogUtilsTest, setLogLevel) {
     printLog();
 }
 
+#if 0
 TEST_P(LogUtilsTest, setEnvNotAffectSetLogLevel) {
     EXPECT_CALL(*(HLogger), print(_)).Times(_expectCallNum);
     setLogLevel(_logLevel);
     SetTestEnv("OPENVINO_LOG_LEVEL", "3");
     printLog();
 }
+#endif
 
 //can not test ENV case. because of the ENV variable is readed at the
 //beginning of test application and modify it in runtime is not valid

From 4ae6258bed333b02c7ca3bd170a318eb319e07ec Mon Sep 17 00:00:00 2001
From: Andrey Zaytsev <andrey.zaytsev@intel.com>
Date: Tue, 21 Dec 2021 20:26:37 +0300
Subject: [PATCH 078/160] Feature/azaytsev/from 2021 4 (#9247)

* Added info on DockerHub CI Framework

* Feature/azaytsev/change layout (#3295)

* Changes according to feedback comments

* Replaced @ref's with html links

* Fixed links, added a title page for installing from repos and images, fixed formatting issues

* Added links

* minor fix

* Added DL Streamer to the list of components installed by default

* Link fixes

* Link fixes

* ovms doc fix (#2988)

* added OpenVINO Model Server

* ovms doc fixes

Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>

* Updated openvino_docs.xml

* Updated the link to software license agreements

* Revert "Updated the link to software license agreements"

This reverts commit 706dac500e764bd7534f7005ac6197f827d68cb5.

* Docs to Sphinx (#8151)

* docs to sphinx

* Update GPU.md

* Update CPU.md

* Update AUTO.md

* Update performance_int8_vs_fp32.md

* update

* update md

* updates

* disable doc ci

* disable ci

* fix index.rst

Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
# Conflicts:
#	.gitignore
#	docs/CMakeLists.txt
#	docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
#	docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
#	docs/IE_DG/Extensibility_DG/VPU_Kernel.md
#	docs/IE_DG/InferenceEngine_QueryAPI.md
#	docs/IE_DG/Int8Inference.md
#	docs/IE_DG/Integrate_with_customer_application_new_API.md
#	docs/IE_DG/Model_caching_overview.md
#	docs/IE_DG/supported_plugins/GPU_RemoteBlob_API.md
#	docs/IE_DG/supported_plugins/HETERO.md
#	docs/IE_DG/supported_plugins/MULTI.md
#	docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md
#	docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md
#	docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md
#	docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md
#	docs/MO_DG/prepare_model/convert_model/Converting_Model.md
#	docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md
#	docs/MO_DG/prepare_model/convert_model/Cutting_Model.md
#	docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md
#	docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md
#	docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_WideAndDeep_Family_Models.md
#	docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md
#	docs/doxygen/Doxyfile.config
#	docs/doxygen/ie_docs.xml
#	docs/doxygen/ie_plugin_api.config
#	docs/doxygen/ngraph_cpp_api.config
#	docs/doxygen/openvino_docs.xml
#	docs/get_started/get_started_macos.md
#	docs/get_started/get_started_raspbian.md
#	docs/get_started/get_started_windows.md
#	docs/img/cpu_int8_flow.png
#	docs/index.md
#	docs/install_guides/VisionAcceleratorFPGA_Configure.md
#	docs/install_guides/VisionAcceleratorFPGA_Configure_Windows.md
#	docs/install_guides/deployment-manager-tool.md
#	docs/install_guides/installing-openvino-linux.md
#	docs/install_guides/installing-openvino-macos.md
#	docs/install_guides/installing-openvino-windows.md
#	docs/optimization_guide/dldt_optimization_guide.md
#	inference-engine/ie_bridges/c/include/c_api/ie_c_api.h
#	inference-engine/ie_bridges/python/docs/api_overview.md
#	inference-engine/ie_bridges/python/sample/ngraph_function_creation_sample/README.md
#	inference-engine/ie_bridges/python/sample/speech_sample/README.md
#	inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
#	inference-engine/include/ie_api.h
#	inference-engine/include/ie_core.hpp
#	inference-engine/include/ie_version.hpp
#	inference-engine/samples/benchmark_app/README.md
#	inference-engine/samples/speech_sample/README.md
#	inference-engine/src/plugin_api/exec_graph_info.hpp
#	inference-engine/src/plugin_api/file_utils.h
#	inference-engine/src/transformations/include/transformations_visibility.hpp
#	inference-engine/tools/benchmark_tool/README.md
#	ngraph/core/include/ngraph/ngraph.hpp
#	ngraph/frontend/onnx_common/include/onnx_common/parser.hpp
#	ngraph/python/src/ngraph/utils/node_factory.py
#	openvino/itt/include/openvino/itt.hpp
#	thirdparty/ade
#	tools/benchmark/README.md

* Cherry-picked remove font-family (#8211)

* Cherry-picked: Update get_started_scripts.md (#8338)

* doc updates (#8268)

* Various doc changes

* theme changes

* remove font-family (#8211)

* fix  css

* Update uninstalling-openvino.md

* fix css

* fix

* Fixes for Installation Guides

Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
Co-authored-by: kblaszczak-intel <karol.blaszczak@intel.com>
# Conflicts:
#	docs/IE_DG/Bfloat16Inference.md
#	docs/IE_DG/InferenceEngine_QueryAPI.md
#	docs/IE_DG/OnnxImporterTutorial.md
#	docs/IE_DG/supported_plugins/AUTO.md
#	docs/IE_DG/supported_plugins/HETERO.md
#	docs/IE_DG/supported_plugins/MULTI.md
#	docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md
#	docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md
#	docs/install_guides/installing-openvino-macos.md
#	docs/install_guides/installing-openvino-windows.md
#	docs/ops/opset.md
#	inference-engine/samples/benchmark_app/README.md
#	inference-engine/tools/benchmark_tool/README.md
#	thirdparty/ade

* Cherry-picked: doc script changes (#8568)

* fix openvino-sphinx-theme

* add linkcheck target

* fix

* change version

* add doxygen-xfail.txt

* fix

* AA

* fix

* fix

* fix

* fix

* fix
# Conflicts:
#	thirdparty/ade

* Cherry-pick: Feature/azaytsev/doc updates gna 2021 4 2 (#8567)

* Various doc changes

* Reformatted C++/Pythob sections. Updated with info from PR8490

* additional fix

* Gemini Lake replaced with Elkhart Lake

* Fixed links in IGs, Added 12th Gen
# Conflicts:
#	docs/IE_DG/supported_plugins/GNA.md
#	thirdparty/ade

* Cherry-pick: Feature/azaytsev/doc fixes (#8897)

* Various doc changes

* Removed the empty Learning path topic

* Restored the Gemini Lake CPIU list
# Conflicts:
#	docs/IE_DG/supported_plugins/GNA.md
#	thirdparty/ade

* Cherry-pick: sphinx copybutton doxyrest code blocks (#8992)

# Conflicts:
#	thirdparty/ade

* Cherry-pick: iframe video enable fullscreen (#9041)

# Conflicts:
#	thirdparty/ade

* Cherry-pick: fix untitled titles (#9213)

# Conflicts:
#	thirdparty/ade

* Cherry-pick: perf bench graph animation (#9045)

* animation

* fix
# Conflicts:
#	thirdparty/ade

* Cherry-pick: doc pytest (#8888)

* docs pytest

* fixes
# Conflicts:
#	docs/doxygen/doxygen-ignore.txt
#	docs/scripts/ie_docs.xml
#	thirdparty/ade

* Cherry-pick: restore deleted files (#9215)

* Added new operations to the doc structure (from removed ie_docs.xml)

* Additional fixes

* Update docs/IE_DG/InferenceEngine_QueryAPI.md

Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>

* Update docs/IE_DG/Int8Inference.md

Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>

* Update Custom_Layers_Guide.md

* Changes according to review  comments

* doc scripts fixes

* Update docs/IE_DG/Int8Inference.md

Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>

* Update Int8Inference.md

* update xfail

* clang format

* updated xfail

Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>
Co-authored-by: Nikolay Tyukaev <nikolay.tyukaev@intel.com>
Co-authored-by: kblaszczak-intel <karol.blaszczak@intel.com>
Co-authored-by: Yury Gorbachev <yury.gorbachev@intel.com>
Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>
---
 .github/workflows/build_doc.yml               |   46 +-
 .gitignore                                    |    2 +
 docs/CMakeLists.txt                           |  412 ++---
 .../ie_docs.config => Doxyfile.config}        |  147 +-
 docs/HOWTO/Custom_Layers_Guide.md             |   51 +-
 docs/IE_DG/Bfloat16Inference.md               |  192 ++-
 ...Deep_Learning_Inference_Engine_DevGuide.md |  146 +-
 docs/IE_DG/DynamicBatching.md                 |  126 +-
 .../IE_DG/Extensibility_DG/AddingNGraphOps.md |   17 +-
 docs/IE_DG/Extensibility_DG/Building.md       |    4 +-
 docs/IE_DG/Extensibility_DG/CPU_Kernel.md     |   16 +-
 .../IE_DG/Extensibility_DG/Custom_ONNX_Ops.md |   15 +-
 docs/IE_DG/Extensibility_DG/Extension.md      |    5 +-
 docs/IE_DG/Extensibility_DG/GPU_Kernel.md     |   21 +-
 docs/IE_DG/Extensibility_DG/Intro.md          |   38 +-
 docs/IE_DG/Extensibility_DG/VPU_Kernel.md     |   42 +-
 docs/IE_DG/InferenceEngine_QueryAPI.md        |  209 ++-
 docs/IE_DG/Int8Inference.md                   |   72 +-
 ...grate_with_customer_application_new_API.md |  520 ++++--
 docs/IE_DG/Intro_to_Performance.md            |    2 +-
 docs/IE_DG/Introduction.md                    |    2 +-
 docs/IE_DG/Known_Issues_Limitations.md        |   32 +-
 docs/IE_DG/Memory_primitives.md               |   23 +-
 docs/IE_DG/Model_Downloader.md                |  446 +++++
 docs/IE_DG/Model_caching_overview.md          |  123 +-
 docs/IE_DG/ONNX_Support.md                    |   97 +-
 docs/IE_DG/Operations_specifications.md       |  194 +++
 docs/IE_DG/Samples_Overview.md                |   33 +-
 docs/IE_DG/ShapeInference.md                  |  212 ++-
 docs/IE_DG/img/BASIC_FLOW_IE_C.svg            |   32 +
 docs/IE_DG/img/BASIC_FLOW_MO.svg              |   32 +
 docs/IE_DG/img/BASIC_FLOW_MO_simplified.svg   |   19 +
 docs/IE_DG/img/BASIC_IE_API_workflow_Cpp.svg  |   15 +
 .../img/BASIC_IE_API_workflow_Python.svg      |   10 +
 docs/IE_DG/img/DEVELOPMENT_FLOW_V3_crunch.svg |   63 +
 .../img/IMPLEMENT_PIPELINE_with_API_C.svg     |   29 +
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 docs/IE_DG/inference_engine_intro.md          |    4 +-
 ...egrate_with_customer_application_python.md |  152 ++
 docs/IE_DG/network_state_intro.md             |   10 +-
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 docs/IE_PLUGIN_DG/Building.md                 |    4 +-
 docs/IE_PLUGIN_DG/ExecutableNetwork.md        |   10 +-
 docs/IE_PLUGIN_DG/InferRequest.md             |    6 +-
 docs/IE_PLUGIN_DG/Intro.md                    |   44 +-
 .../LowPrecisionModelRepresentation.md        |    2 +-
 docs/IE_PLUGIN_DG/Plugin.md                   |   16 +-
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 docs/IE_PLUGIN_DG/QuantizedNetworks.md        |    4 +-
 .../Deep_Learning_Model_Optimizer_DevGuide.md |  681 +++++++-
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 .../prepare_model/Additional_Optimizations.md |   15 +
 .../prepare_model/Config_Model_Optimizer.md   |  353 +++-
 .../Default_Model_Optimizer_Optimizations.md  |   11 +
 .../Getting_performance_numbers.md            |  133 ++
 .../prepare_model/Model_Optimizer_FAQ.md      |   13 -
 .../convert_model/Convert_Model_From_Caffe.md |   69 +-
 .../convert_model/Convert_Model_From_Kaldi.md |   49 +-
 .../convert_model/Convert_Model_From_MxNet.md |   25 +-
 .../convert_model/Convert_Model_From_ONNX.md  |   22 +-
 .../Convert_Model_From_PyTorch.md             |   13 +
 .../Convert_Model_From_TensorFlow.md          |   44 +-
 .../convert_model/Converting_Model.md         |  260 ++-
 .../convert_model/Converting_Model_General.md |  238 ---
 .../convert_model/Cutting_Model.md            |   51 +-
 .../IR_suitable_for_INT8_inference.md         |    2 +-
 .../Legacy_IR_Layers_Catalog_Spec.md          |    2 +-
 .../Convert_Style_Transfer_From_MXNet.md      |    6 +-
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 .../pytorch_specific/Convert_Bert_ner.md      |    2 +-
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 .../pytorch_specific/Convert_RNNT.md          |    4 +-
 .../pytorch_specific/Convert_YOLACT.md        |    9 +-
 .../Convert_EfficientDet_Models.md            |    4 +-
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 .../Convert_RetinaNet_From_Tensorflow.md      |    2 +-
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 docs/_static/css/media/union-down.svg         |    3 +
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 docs/_static/logo.svg                         |   14 +
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 docs/benchmarks/performance_benchmarks.md     |   12 +
 docs/benchmarks/performance_benchmarks_faq.md |   13 -
 .../performance_benchmarks_openvino.md        |  245 ++-
 .../benchmarks/performance_benchmarks_ovms.md |  794 ++++-----
 docs/benchmarks/performance_int8_vs_fp32.md   |  871 +++++-----
 docs/conf.py                                  |  150 ++
 docs/documentation.md                         |  106 ++
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 docs/doxygen/ngraph_py_api.xml                |  215 ---
 docs/doxygen/openvino_docs.xml                |  373 ----
 docs/doxygen/pyx_filter.py                    |  132 --
 docs/doxyrest-config.lua                      |  464 +++++
 .../conf/doxyrest-docutils.conf.in            |    9 +
 docs/doxyrest-sphinx/cpplexer.py              |  315 ++++
 .../doxyrest-sphinx/css/doxyrest-pygments.css |   21 +
 .../css/doxyrest-sphinx_rtd_theme.css         |   51 +
 .../css/doxyrest-sphinxdoc.css                |   36 +
 docs/doxyrest-sphinx/doxyrest.py              |  494 ++++++
 docs/doxyrest-sphinx/js/target-highlight.js   |   68 +
 docs/doxyrest-sphinx/sty/doxyrest.sty         |   26 +
 docs/doxyrest/frame/cfamily/class.rst.in      |   48 +
 docs/doxyrest/frame/cfamily/compound.rst.in   |  144 ++
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 .../frame/cfamily/overview_compound.rst.in    |  104 ++
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 .../frame/cfamily/overview_members.rst.in     |  108 ++
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 .../frame/cfamily/overview_typedefs.rst.in    |   23 +
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 docs/doxyrest/frame/common/doc.lua            |  592 +++++++
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 docs/doxyrest/frame/common/string.lua         |  106 ++
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 docs/doxyrest/frame/common/toc.lua            |  219 +++
 docs/get_started.md                           |  120 ++
 docs/get_started/get_started_demos.md         |  874 ++++++++++
 docs/get_started/get_started_dl_workbench.md  |   49 +-
 docs/get_started/get_started_instructions.md  |   20 +
 docs/get_started/get_started_linux.md         |    2 +-
 docs/get_started/get_started_macos.md         |  296 +++-
 docs/get_started/get_started_raspbian.md      |   25 +-
 docs/get_started/get_started_scripts.md       |  357 ++++
 docs/get_started/get_started_windows.md       |  308 +++-
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 docs/img/BASIC_IE_API_workflow_Python.svg     |   10 +
 docs/img/BASIC_IE_API_workflow_С.svg          |   15 +
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 docs/img/OpenVINO-diagram.png                 |    4 +-
 docs/img/THROUGHPUT.svg                       |   93 +
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 create mode 100644 docs/scripts/create_mapping.py
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 rename docs/{doxygen => scripts}/log.py (100%)
 create mode 100644 docs/scripts/prepare_xml.py
 create mode 100644 docs/scripts/remove_xml.py
 create mode 100644 docs/scripts/tests/conftest.py
 create mode 100644 docs/scripts/tests/test_docs.py
 create mode 100644 docs/scripts/tests/utils/__init__.py
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diff --git a/.github/workflows/build_doc.yml b/.github/workflows/build_doc.yml
index 92b88967845..2c9f44735d4 100644
--- a/.github/workflows/build_doc.yml
+++ b/.github/workflows/build_doc.yml
@@ -14,15 +14,25 @@ jobs:
 
       - name: Install dependencies
         run: |
+          set -e
+          # install doc dependencies
           sudo apt update
           sudo apt --assume-yes install libusb-1.0-0-dev graphviz texlive
-          python3 -m pip install lxml
+          cd docs
+          python -m pip install -r requirements.txt --user
+          cd openvino_sphinx_theme
+          python setup.py install --user
+          cd ../..
+          # install doxyrest
+          wget https://github.com/vovkos/doxyrest/releases/download/doxyrest-2.1.3/doxyrest-2.1.3-linux-amd64.tar.xz
+          tar -xf doxyrest-2.1.3-linux-amd64.tar.xz
+          echo "$(pwd)/doxyrest-2.1.3-linux-amd64/bin/" >> $GITHUB_PATH
           # install doxygen
           mkdir doxygen
           cd doxygen
           git clone https://github.com/doxygen/doxygen.git
           cd doxygen
-          git checkout Release_1_9_1
+          git checkout Release_1_9_2
           mkdir build
           cd build
           cmake ..
@@ -33,15 +43,37 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake -DENABLE_DOCS=ON ..
+          cmake -DENABLE_DOCS=ON -DENABLE_PYTHON=ON -DNGRAPH_PYTHON_BUILD_ENABLE=ON -DCMAKE_BUILD_TYPE=Release ..
 
       - name: Build doc
-        run: cmake --build . --target openvino_docs
+        run: |
+          cmake --build . --target sphinx_docs
         working-directory: build
+      
+      - name: Archive HTML
+        run: |
+          zip -r openvino_html.zip _build
+        working-directory: build/docs
+      
+      - name: Run Pytest
+        run: |
+          pytest --doxygen="./build/docs/doxygen.log" \
+                 --confcutdir="./docs/scripts/tests/" \
+                 --html="./build/docs/_artifacts/doc-generation.html" \
+                 --doxygen-strip="$(pwd)" \
+                 --doxygen-xfail="./docs/doxygen-xfail.txt" \
+                 --self-contained-html ./docs/scripts/tests/test_docs.py
 
-      - name: 'Upload doc'
+      - name: 'Upload test results'
+        if: always()
+        uses: actions/upload-artifact@v2
+        with:
+          name: openvino_doc_pytest
+          path: build/docs/_artifacts/
+
+      - name: 'Upload html'
         if: github.event_name == 'push'
         uses: actions/upload-artifact@v2
         with:
-          name: openvino_doc
-          path: build/docs/html/
+          name: openvino_html
+          path: build/docs/openvino_html.zip
diff --git a/.gitignore b/.gitignore
index b422239d45a..01cb8775871 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,8 @@ _*
 # but ensure we don't skip __init__.py and __main__.py
 !__init__.py
 !__main__.py
+# and sphinx documentation folders
+!docs/_*
 
 # developer tools
 *.idea
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 0eb202372e3..41b1402eee4 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -46,335 +46,189 @@ if(NOT ENABLE_DOCKER)
     endforeach()
 endif()
 
-set(OMZ_DOCS_DIR "" CACHE PATH "Path to open_model_zoo documentation")
-set(WORKBENCH_DOCS_DIR "" CACHE PATH "Path to workbench documentation")
-set(POT_DOCS_DIR "" CACHE PATH "Path to post-training-compression-tool documentation")
-set(GST_DOCS_DIR "" CACHE PATH "Path to gst-video-analytics documentation")
+set(LINKCHECKER_PY "" CACHE FILEPATH "Path to linkchecker.py for documentation check dir.")
+set(OMZ_DOCS_DIR "" CACHE PATH "Path to open_model_zoo documentation dir.")
+set(WORKBENCH_DOCS_DIR "" CACHE PATH "Path to workbench documentation dir.")
+set(POT_DOCS_DIR "" CACHE PATH "Path to post-training-compression-tool documentation dir.")
+set(OVMS_DOCS_DIR "" CACHE PATH "Path to model server documentation dir.")
+set(GST_DOCS_DIR "" CACHE PATH "Path to gst-video-analytics documentation dir.")
+set(GITHUB_API_TOKEN "" CACHE PATH "Path to file containing github api token.")
+set(GRAPH_CSV_DIR "" CACHE PATH "Path to the folder containing csv data for rendering graphs.")
 
 function(build_docs)
     find_package(Doxygen REQUIRED dot)
     find_package(LATEX REQUIRED)
 
-    ie_check_pip_package(lxml FATAL_ERROR)
-    ie_check_pip_package(LinkChecker WARNING)
-
-    find_host_program(LINKCHECKER_PY
-                      NAMES linkchecker
-                      DOC "linkchecker tools for documentation check")
+    find_program(DOXYREST_EXECUTABLE NAMES doxyrest)
+    if (NOT DOXYREST_EXECUTABLE)
+        message(FATAL_ERROR "No doxyrest found. Documentation output is not available")
+    endif()
 
     set(DOCS_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}")
-    set(DOXYGEN_DIR "${OpenVINO_SOURCE_DIR}/docs/doxygen")
-    set(IE_SOURCE_DIR "${OpenVINO_SOURCE_DIR}/inference-engine")
-    set(OV_INFERENCE_DIR "${OpenVINO_SOURCE_DIR}/src/inference")
-    set(PYTHON_API_IN "${IE_SOURCE_DIR}/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx")
-    set(PYTHON_API_OUT "${DOCS_BUILD_DIR}/python_api/ie_api.pyx")
-    set(C_API "${OpenVINO_SOURCE_DIR}/bindings/c/include")
-    set(PLUGIN_API_DIR "${DOCS_BUILD_DIR}/IE_PLUGIN_DG")
-    set(CORE_DIR "${OpenVINO_SOURCE_DIR}/src/core")
-    set(FRONTENDS_DIR "${OpenVINO_SOURCE_DIR}/src/frontends")
-    set(NGRAPH_PY_DIR "${OpenVINO_SOURCE_DIR}/src/bindings/python/src/compatibility/ngraph/")
+    set(DOCS_SOURCE_DIR "${OpenVINO_SOURCE_DIR}/docs")
+    set(SCRIPTS_DIR "${DOCS_SOURCE_DIR}/scripts")
+
+    # markdown docs
+    set(MARKDOWN_INPUT "${DOCS_BUILD_DIR}")
 
     # Preprocessing scripts
-    set(DOXY_MD_FILTER "${DOXYGEN_DIR}/doxy_md_filter.py")
-    set(DOXY_LAYOUT_SCRIPT "${DOXYGEN_DIR}/build_main_layout.py")
-    set(DOXY_LOG_SCRIPT "${DOXYGEN_DIR}/log.py")
-    set(PYX_FILTER "${DOXYGEN_DIR}/pyx_filter.py")
-
-    # assets dir
-    set(ASSETS_DIR "${DOXYGEN_DIR}/assets")
-    
-    # header and footer
-    set(HEADER_SOURCE "${DOXYGEN_DIR}/header.html.in")
-    set(FOOTER_SOURCE "${DOXYGEN_DIR}/footer.html.in")
-    set(HEADER_BUILD "${DOCS_BUILD_DIR}/header.html")
-    set(FOOTER_BUILD "${DOCS_BUILD_DIR}/footer.html")
-
-    configure_file(${HEADER_SOURCE} ${HEADER_BUILD} @ONLY)
-    configure_file(${FOOTER_SOURCE} ${FOOTER_BUILD} @ONLY)
-
-    file(GLOB_RECURSE doc_source_files
-        LIST_DIRECTORIES true RELATIVE ${OpenVINO_SOURCE_DIR}
-        "${OpenVINO_SOURCE_DIR}/docs/*.md"
-        "${OpenVINO_SOURCE_DIR}/docs/*.png"
-        "${OpenVINO_SOURCE_DIR}/docs/*.gif"
-        "${OpenVINO_SOURCE_DIR}/docs/*.jpg"
-        "${OpenVINO_SOURCE_DIR}/docs/*.svg"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.md"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.png"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.gif"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.jpg"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.svg")
-
-    configure_file(${PYTHON_API_IN} ${PYTHON_API_OUT} @ONLY)
-
-    set(NGRAPH_CPP_CONFIG_SOURCE "${DOXYGEN_DIR}/ngraph_cpp_api.config")
-    set(NGRAPH_PY_CONFIG_SOURCE "${DOXYGEN_DIR}/ngraph_py_api.config")
-    set(IE_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_docs.config")
-    set(C_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_c_api.config")
-    set(PY_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_py_api.config")
-    set(PLUGIN_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_plugin_api.config")
-
-    set(NGRAPH_CPP_CONFIG_BUILD "${DOCS_BUILD_DIR}/ngraph_cpp_api.config")
-    set(NGRAPH_PY_CONFIG_BUILD "${DOCS_BUILD_DIR}/ngraph_py_api.config")
-    set(IE_CONFIG_BUILD "${DOCS_BUILD_DIR}/ie_docs.config")
-    set(C_CONFIG_BUILD "${DOCS_BUILD_DIR}/ie_c_api.config")
-    set(PY_CONFIG_BUILD "${DOCS_BUILD_DIR}/ie_py_api.config")
-    set(PLUGIN_CONFIG_BUILD "${DOCS_BUILD_DIR}/ie_plugin_api.config")
-
-    set(NGRAPH_CPP_LAYOUT_SOURCE "${DOXYGEN_DIR}/ngraph_cpp_api.xml")
-    set(NGRAPH_PY_LAYOUT_SOURCE "${DOXYGEN_DIR}/ngraph_py_api.xml")
-    set(IE_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_docs.xml")
-    set(OPENVINO_LAYOUT_SOURCE "${DOXYGEN_DIR}/openvino_docs.xml")
-    set(C_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_c_api.xml")
-    set(PY_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_py_api.xml")
-    set(PLUGIN_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_plugin_api.xml")
-
-    set(NGRAPH_CPP_LAYOUT_BUILD "${DOCS_BUILD_DIR}/ngraph_cpp_api.xml")
-    set(NGRAPH_PY_LAYOUT_BUILD "${DOCS_BUILD_DIR}/ngraph_py_api.xml")
-    set(IE_LAYOUT_BUILD "${DOCS_BUILD_DIR}/ie_docs.xml")
-    set(OPENVINO_LAYOUT_BUILD "${DOCS_BUILD_DIR}/openvino_docs.xml")
-    set(C_LAYOUT_BUILD "${DOCS_BUILD_DIR}/ie_c_api.xml")
-    set(PY_LAYOUT_BUILD "${DOCS_BUILD_DIR}/ie_py_api.xml")
-    set(PLUGIN_LAYOUT_BUILD "${DOCS_BUILD_DIR}/ie_plugin_api.xml")
+    set(DOXY_MD_FILTER "${SCRIPTS_DIR}/doxy_md_filter.py")
+    set(PYNGRAPH_REF_SCRIPT "${SCRIPTS_DIR}/pyngraph_ref.py")
+    set(DOXY_LOG_SCRIPT "${SCRIPTS_DIR}/log.py")
+    set(PYX_FILTER "${SCRIPTS_DIR}/pyx_filter.py")
+    set(PREPARE_XML_SCRIPT "${SCRIPTS_DIR}/prepare_xml.py")
+    set(REMOVE_XML_SCRIPT "${SCRIPTS_DIR}/remove_xml.py")
+    set(COPY_IMAGES_SCRIPT "${SCRIPTS_DIR}/copy_images.py")
+    set(DOC_TEST_DIR "${SCRIPTS_DIR}/tests")
+    set(DOXYGEN_MAPPING_SCRIPT "${SCRIPTS_DIR}/create_mapping.py")
+    set(DOXYGEN_MAPPING_FILE "${DOCS_BUILD_DIR}/mapping.json")
 
     # out dirs
-    set(OUTPUT_DIRECTORY "${DOCS_BUILD_DIR}/html")
-    set(IE_OUTPUT "${OUTPUT_DIRECTORY}")
-    set(C_OUTPUT "${OUTPUT_DIRECTORY}/ie_c_api")
-    set(PY_OUTPUT "${OUTPUT_DIRECTORY}/ie_python_api")
-    set(PLUGIN_OUTPUT "${OUTPUT_DIRECTORY}/ie_plugin_api")
-    set(NGRAPH_CPP_OUTPUT "${OUTPUT_DIRECTORY}/ngraph_cpp_api")
-    set(NGRAPH_PY_OUTPUT "${OUTPUT_DIRECTORY}/ngraph_python_api")
+    set(XML_OUTPUT "${DOCS_BUILD_DIR}/xml")
+    set(RST_OUTPUT "${DOCS_BUILD_DIR}/rst")
+    set(SPHINX_OUTPUT "${DOCS_BUILD_DIR}/_build")
 
-    # Tables of contents
-    configure_file(${NGRAPH_CPP_LAYOUT_SOURCE} ${NGRAPH_CPP_LAYOUT_BUILD} @ONLY)
-    configure_file(${NGRAPH_PY_LAYOUT_SOURCE} ${NGRAPH_PY_LAYOUT_BUILD} @ONLY)
-    configure_file(${IE_LAYOUT_SOURCE} ${IE_LAYOUT_BUILD} @ONLY)
-    configure_file(${OPENVINO_LAYOUT_SOURCE} ${OPENVINO_LAYOUT_BUILD} @ONLY)
-    configure_file(${C_LAYOUT_SOURCE} ${C_LAYOUT_BUILD} @ONLY)
-    configure_file(${PY_LAYOUT_SOURCE} ${PY_LAYOUT_BUILD} @ONLY)
-    configure_file(${PLUGIN_LAYOUT_SOURCE} ${PLUGIN_LAYOUT_BUILD} @ONLY)
+    # Sphinx folders, doxyrest templates and config
+    set(SPHINX_CONF_IN "${DOCS_SOURCE_DIR}/conf.py")
+    set(SPHINX_CONF_OUT "${RST_OUTPUT}/conf.py")
+    set(SPHINX_STATIC_IN "${DOCS_SOURCE_DIR}/_static")
+    set(SPHINX_STATIC_OUT "${RST_OUTPUT}/_static")
+    set(SPHINX_INDEX_IN "${DOCS_SOURCE_DIR}/index.rst")
+    set(SPHINX_INDEX_OUT "${RST_OUTPUT}/index.rst")
+    set(API_DOCS_IN "${DOCS_SOURCE_DIR}/api")
+    set(API_DOCS_OUT "${RST_OUTPUT}/api")
+    set(DOXYREST_IN "${DOCS_SOURCE_DIR}/doxyrest")
+    set(DOXYREST_OUT "${DOCS_BUILD_DIR}/doxyrest")
+    set(DOXYREST_SPHINX_IN "${DOCS_SOURCE_DIR}/doxyrest-sphinx")
+    set(DOXYREST_SPHINX_OUT "${RST_OUTPUT}/doxyrest-sphinx")
+    set(DOXYREST_CONFIG_IN "${DOCS_SOURCE_DIR}/doxyrest-config.lua")
+    set(DOXYREST_CONFIG_OUT "${DOCS_BUILD_DIR}/doxyrest-config.lua")
+    configure_file(${DOXYREST_CONFIG_IN} ${DOXYREST_CONFIG_OUT} @ONLY)
+    configure_file(${SPHINX_CONF_IN} ${SPHINX_CONF_OUT} @ONLY)
 
-    # Doxygen config files
-    configure_file(${NGRAPH_CPP_CONFIG_SOURCE} ${NGRAPH_CPP_CONFIG_BUILD} @ONLY)
-    configure_file(${NGRAPH_PY_CONFIG_SOURCE} ${NGRAPH_PY_CONFIG_BUILD} @ONLY)
-    configure_file(${IE_CONFIG_SOURCE} ${IE_CONFIG_BUILD} @ONLY)
-    configure_file(${C_CONFIG_SOURCE} ${C_CONFIG_BUILD} @ONLY)
-    configure_file(${PY_CONFIG_SOURCE} ${PY_CONFIG_BUILD} @ONLY)
-    configure_file(${PLUGIN_CONFIG_SOURCE} ${PLUGIN_CONFIG_BUILD} @ONLY)
+    # Doxygen config
+    set(DOXYFILE_SOURCE "${DOCS_SOURCE_DIR}/Doxyfile.config")
+    set(DOXYFILE_BUILD "${DOCS_BUILD_DIR}/Doxyfile.config")
+    configure_file(${DOXYFILE_SOURCE} ${DOXYFILE_BUILD} @ONLY)
 
-    # Preprocessing scripts
-    set(DOXY_MD_FILTER "${DOXYGEN_DIR}/doxy_md_filter.py")
-    set(PYX_FILTER "${DOXYGEN_DIR}/pyx_filter.py")
+    list(APPEND commands COMMAND ${PYTHON_EXECUTABLE} ${DOXY_MD_FILTER}
+        --input_dir=${OpenVINO_SOURCE_DIR}
+        --output_dir=${DOCS_BUILD_DIR}/openvino
+        --exclude_dir=${DOCS_BUILD_DIR})
 
-    # nGraph C++ API
+    # include additional repositories
 
-    add_custom_target(ngraph_cpp_api
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory ${ASSETS_DIR} ${NGRAPH_CPP_OUTPUT}/assets
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${NGRAPH_CPP_CONFIG_BUILD}
-                      WORKING_DIRECTORY ${DOCS_BUILD_DIR}
-                      VERBATIM)
+    # openvino notebooks dir
+    if(GITHUB_API_TOKEN)
+        set(NBDOC_SCRIPT "${DOCS_SOURCE_DIR}/nbdoc/nbdoc.py")
+        list(APPEND commands
+            COMMAND ${PYTHON_EXECUTABLE} "${NBDOC_SCRIPT}" "${GITHUB_API_TOKEN}" "${RST_OUTPUT}/notebooks"
+        )
+    endif()
 
-    # nGraph Python API
-
-    add_custom_target(ngraph_py_api
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory ${ASSETS_DIR} ${NGRAPH_PY_OUTPUT}/assets
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${NGRAPH_PY_CONFIG_BUILD}
-                      WORKING_DIRECTORY ${DOCS_BUILD_DIR}
-                      VERBATIM)
-
-    # C API
-
-    add_custom_target(c_api
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory ${ASSETS_DIR} ${C_OUTPUT}/assets
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${C_CONFIG_BUILD}
-                      WORKING_DIRECTORY ${DOCS_BUILD_DIR}
-                      COMMENT "Generating C API Reference"
-                      VERBATIM)
-
-    # Python API
-
-    add_custom_target(py_api
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory ${ASSETS_DIR} ${PY_OUTPUT}/assets
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${PY_CONFIG_BUILD}
-                      WORKING_DIRECTORY ${DOCS_BUILD_DIR}
-                      COMMENT "Generating Python API Reference"
-                      VERBATIM)
-
-    add_custom_command(TARGET py_api
-                       PRE_BUILD
-                       COMMAND ${PYTHON_EXECUTABLE} ${PYX_FILTER} ${PYTHON_API_OUT}
-                       COMMENT "Pre-process Python API")
-
-    # Preprocess docs
-
-    add_custom_target(preprocess_docs
-                      COMMENT "Pre-process docs"
-                      VERBATIM)
-
-    # ovino doc files
-    file(GLOB_RECURSE ovino_doc_files
-        LIST_DIRECTORIES true RELATIVE ${OpenVINO_SOURCE_DIR}
-        "${OpenVINO_SOURCE_DIR}/docs/*.md"
-        "${OpenVINO_SOURCE_DIR}/docs/*.png"
-        "${OpenVINO_SOURCE_DIR}/docs/*.gif"
-        "${OpenVINO_SOURCE_DIR}/docs/*.jpg"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.md"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.png"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.gif"
-        "${OpenVINO_SOURCE_DIR}/inference-engine/*.jpg"
-        "${OpenVINO_SOURCE_DIR}/src/*.md"
-        "${OpenVINO_SOURCE_DIR}/src/*.png"
-        "${OpenVINO_SOURCE_DIR}/src/*.gif"
-        "${OpenVINO_SOURCE_DIR}/src/*.jpg"
-        "${OpenVINO_SOURCE_DIR}/samples/*.md"
-        "${OpenVINO_SOURCE_DIR}/samples/*.png"
-        "${OpenVINO_SOURCE_DIR}/samples/*.gif"
-        "${OpenVINO_SOURCE_DIR}/samples/*.jpg"
-        "${OpenVINO_SOURCE_DIR}/tools/*.md"
-        "${OpenVINO_SOURCE_DIR}/tools/*.png"
-        "${OpenVINO_SOURCE_DIR}/tools/*.gif"
-        "${OpenVINO_SOURCE_DIR}/tools/*.jpg")
-
-    foreach(source_file ${ovino_doc_files})
-        list(APPEND commands COMMAND ${CMAKE_COMMAND} -E copy
-            "${OpenVINO_SOURCE_DIR}/${source_file}" "${DOCS_BUILD_DIR}/openvino/${source_file}")
-    endforeach()
+    if(GRAPH_CSV_DIR)
+        set(GRAPH_CSV_DIR_OUT "${RST_OUTPUT}/csv")
+    list(APPEND commands
+        COMMAND ${CMAKE_COMMAND} -E copy_directory "${GRAPH_CSV_DIR}" "${GRAPH_CSV_DIR_OUT}"
+    )
+    endif()
 
     # omz doc files
     if(EXISTS "${OMZ_DOCS_DIR}")
         get_filename_component(OMZ_DOCS_DIR "${OMZ_DOCS_DIR}" ABSOLUTE)
-
-        file(GLOB_RECURSE omz_doc_files
-            LIST_DIRECTORIES true RELATIVE ${OMZ_DOCS_DIR}
-            "${OMZ_DOCS_DIR}/*.md"
-            "${OMZ_DOCS_DIR}/*.png"
-            "${OMZ_DOCS_DIR}/*.gif"
-            "${OMZ_DOCS_DIR}/*.jpg")
-
-        foreach(source_file ${omz_doc_files})
-            list(APPEND commands COMMAND ${CMAKE_COMMAND} -E copy
-                "${OMZ_DOCS_DIR}/${source_file}" "${DOCS_BUILD_DIR}/omz/${source_file}")
-        endforeach()
-        configure_file("${OMZ_DOCS_DIR}/omz_docs.xml" "${DOCS_BUILD_DIR}/omz_docs.xml" @ONLY)
+        list(APPEND commands
+        COMMAND ${PYTHON_EXECUTABLE} ${OMZ_DOCS_DIR}/ci/prepare-documentation.py ${CMAKE_BINARY_DIR}/open_model_zoo)
+        list(APPEND commands COMMAND ${PYTHON_EXECUTABLE} ${DOXY_MD_FILTER}
+            --input_dir=${CMAKE_BINARY_DIR}/open_model_zoo
+            --output_dir=${DOCS_BUILD_DIR}/open_model_zoo)
     endif()
 
     # workbench doc files
     if(EXISTS "${WORKBENCH_DOCS_DIR}")
         get_filename_component(WORKBENCH_DOCS_DIR "${WORKBENCH_DOCS_DIR}" ABSOLUTE)
 
-        file(GLOB_RECURSE workbench_doc_files
-            LIST_DIRECTORIES true RELATIVE ${WORKBENCH_DOCS_DIR}
-            "${WORKBENCH_DOCS_DIR}/*.md"
-            "${WORKBENCH_DOCS_DIR}/*.png"
-            "${WORKBENCH_DOCS_DIR}/*.gif"
-            "${WORKBENCH_DOCS_DIR}/*.jpg")
-
-        foreach(source_file ${workbench_doc_files})
-            list(APPEND commands COMMAND ${CMAKE_COMMAND} -E copy
-                "${WORKBENCH_DOCS_DIR}/${source_file}" "${DOCS_BUILD_DIR}/workbench/${source_file}")
-        endforeach()
-        configure_file("${WORKBENCH_DOCS_DIR}/docs/Workbench_DG/workbench_docs.xml" "${DOCS_BUILD_DIR}/workbench_docs.xml" @ONLY)
+        list(APPEND commands COMMAND ${PYTHON_EXECUTABLE} ${DOXY_MD_FILTER}
+            --input_dir=${WORKBENCH_DOCS_DIR}
+            --output_dir=${DOCS_BUILD_DIR}/workbench)
     endif()
 
     # pot doc files
     if(EXISTS "${POT_DOCS_DIR}")
         get_filename_component(POT_DOCS_DIR "${POT_DOCS_DIR}" ABSOLUTE)
 
-        file(GLOB_RECURSE pot_doc_files
-            LIST_DIRECTORIES true RELATIVE ${POT_DOCS_DIR}
-            "${POT_DOCS_DIR}/*.md"
-            "${POT_DOCS_DIR}/*.png"
-            "${POT_DOCS_DIR}/*.gif"
-            "${POT_DOCS_DIR}/*.jpg")
+        list(APPEND commands COMMAND ${PYTHON_EXECUTABLE} ${DOXY_MD_FILTER}
+        --input_dir=${POT_DOCS_DIR}
+        --output_dir=${DOCS_BUILD_DIR}/pot)
+    endif()
 
-        foreach(source_file ${pot_doc_files})
-            list(APPEND commands COMMAND ${CMAKE_COMMAND} -E copy
-                "${POT_DOCS_DIR}/${source_file}" "${DOCS_BUILD_DIR}/pot/${source_file}")
-        endforeach()
-        configure_file("${POT_DOCS_DIR}/docs/pot_docs.xml" "${DOCS_BUILD_DIR}/pot_docs.xml" @ONLY)
+    # ovms doc files
+    if(EXISTS "${OVMS_DOCS_DIR}")
+        get_filename_component(OVMS_DOCS_DIR "${OVMS_DOCS_DIR}" ABSOLUTE)
+
+        list(APPEND commands COMMAND ${PYTHON_EXECUTABLE} ${DOXY_MD_FILTER}
+        --input_dir=${OVMS_DOCS_DIR}
+        --output_dir=${DOCS_BUILD_DIR}/ovms)
     endif()
 
     # gst doc files
     if(EXISTS "${GST_DOCS_DIR}")
         get_filename_component(GST_DOCS_DIR "${GST_DOCS_DIR}" ABSOLUTE)
-
-        file(GLOB_RECURSE gst_doc_files
-            LIST_DIRECTORIES true RELATIVE ${GST_DOCS_DIR}
-            "${GST_DOCS_DIR}/*.md"
-            "${GST_DOCS_DIR}/*.png"
-            "${GST_DOCS_DIR}/*.gif"
-            "${GST_DOCS_DIR}/*.jpg")
-
-        foreach(source_file ${gst_doc_files})
-            list(APPEND commands COMMAND ${CMAKE_COMMAND} -E copy
-                "${GST_DOCS_DIR}/${source_file}" "${DOCS_BUILD_DIR}/gst/${source_file}")
-        endforeach()
+        list(APPEND commands COMMAND ${PYTHON_EXECUTABLE} ${DOXY_MD_FILTER}
+        --input_dir=${GST_DOCS_DIR}
+        --output_dir=${DOCS_BUILD_DIR}/gst)
     endif()
 
+    add_custom_target(preprocess_docs
+                     COMMENT "Preprocess documentation"
+                     VERBATIM)
+
+    # Preprocess docs
     add_custom_command(TARGET preprocess_docs
-                       PRE_BUILD
-                       ${commands}
-                       COMMAND ${PYTHON_EXECUTABLE} ${DOXY_LAYOUT_SCRIPT} --openvino ${OPENVINO_LAYOUT_BUILD}
-                       COMMAND ${PYTHON_EXECUTABLE} ${DOXY_MD_FILTER} ${DOCS_BUILD_DIR}
-                       COMMENT "Pre-process markdown and image links")
-
-    # IE dev guide and C++ API
-
-    add_custom_target(ie_docs
-                      DEPENDS ngraph_cpp_api preprocess_docs
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory ${ASSETS_DIR} ${IE_OUTPUT}/assets
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${IE_CONFIG_BUILD}
-                      WORKING_DIRECTORY ${DOCS_BUILD_DIR}
-                      VERBATIM)
-
-    # Plugin API
-
-    add_custom_target(plugin_api
-                      DEPENDS ngraph_cpp_api ie_docs
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory ${ASSETS_DIR} ${PLUGIN_OUTPUT}/assets
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${PLUGIN_CONFIG_BUILD}
-                      WORKING_DIRECTORY ${DOCS_BUILD_DIR}
-                      COMMENT "Generating Plugin API Reference"
-                      VERBATIM)
-
-    # Umbrella OpenVINO target
-
-    add_custom_target(openvino_docs
-                      DEPENDS ngraph_cpp_api ngraph_py_api c_api py_api ie_docs plugin_api
-                      COMMENT "Generating OpenVINO documentation"
-                      VERBATIM)
-
-    set_target_properties(openvino_docs ie_docs c_api py_api preprocess_docs plugin_api
-                          ngraph_py_api ngraph_cpp_api
-                          PROPERTIES FOLDER docs)
-
-    add_custom_command(TARGET openvino_docs
                        POST_BUILD
-                       COMMAND ${PYTHON_EXECUTABLE} ${DOXY_LOG_SCRIPT} --log "${DOCS_BUILD_DIR}/ie_docs.log"
-                                                                        --include_omz $<BOOL:${OMZ_DOCS_DIR}>
-                                                                        --include_wb $<BOOL:${WORKBENCH_DOCS_DIR}>
-                                                                        --include_pot $<BOOL:${POT_DOCS_DIR}>
-                                                                        --include_gst $<BOOL:${GST_DOCS_DIR}>
-                       COMMENT "Parse doxygen log to find errors."
+                       ${commands}
+                       WORKING_DIRECTORY ${DOCS_BUILD_DIR}
+                       COMMENT "Preprocess documentation"
                        VERBATIM)
 
-    # added linkcheker
+    add_custom_target(doxygen_xml
+                      DEPENDS preprocess_docs
+                      COMMAND ${PYTHON_EXECUTABLE} ${REMOVE_XML_SCRIPT} ${XML_OUTPUT}
+                      COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYFILE_BUILD}
+                      WORKING_DIRECTORY ${DOCS_BUILD_DIR}
+                      COMMENT "Generate doxygen XML output"
+                      VERBATIM)
+
+    # Post-process docs
+    add_custom_command(TARGET doxygen_xml
+                       POST_BUILD
+                       COMMAND ${PYTHON_EXECUTABLE} ${PREPARE_XML_SCRIPT} ${XML_OUTPUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${DOXYREST_IN} ${DOXYREST_OUT}
+                       COMMAND ${DOXYREST_EXECUTABLE} -c ${DOXYREST_CONFIG_OUT}
+                       COMMAND ${PYTHON_EXECUTABLE} ${COPY_IMAGES_SCRIPT} ${XML_OUTPUT} ${RST_OUTPUT}
+                       COMMAND ${PYTHON_EXECUTABLE} ${DOXYGEN_MAPPING_SCRIPT} ${XML_OUTPUT} ${DOCS_BUILD_DIR} ${OpenVINO_SOURCE_DIR}/../
+                       COMMAND ${CMAKE_COMMAND} -E copy ${SPHINX_INDEX_IN} ${SPHINX_INDEX_OUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${API_DOCS_IN} ${API_DOCS_OUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${DOXYREST_IN} ${DOXYREST_OUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${DOXYREST_SPHINX_IN} ${DOXYREST_SPHINX_OUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${SPHINX_STATIC_IN} ${SPHINX_STATIC_OUT}
+                       COMMENT "Prepare xml"
+                       VERBATIM)
+
+    add_custom_target(sphinx_docs
+                      DEPENDS doxygen_xml
+                      COMMAND sphinx-build -b html ${RST_OUTPUT} ${SPHINX_OUTPUT}
+                      WORKING_DIRECTORY ${RST_OUTPUT}
+                      VERBATIM)
+
+    set_target_properties(doxygen_xml sphinx_docs
+                          PROPERTIES FOLDER docs)
 
-    if(EXISTS "${LINKCHECKER_PY}")
-        add_custom_target(docs_check
-                            COMMAND "${LINKCHECKER_PY}" -v "${DOCS_BUILD_DIR}/html/"
-                            COMMENT "Check links in generated documentation"
-                            WORKING_DIRECTORY "${DOCS_BUILD_DIR}"
-                            VERBATIM)
-        set_target_properties(docs_check PROPERTIES FOLDER docs)
-    endif()
 
     find_program(browser NAMES xdg-open)
     if(browser)
         add_custom_target(ie_docs_open
-                          COMMAND ${browser} "${OpenVINO_SOURCE_DIR}/docs/html/index.html"
-                          DEPENDS ie_docs
+                          COMMAND ${browser} "${SPHINX_OUTPUT}/index.html"
+                          DEPENDS sphinx_docs
                           COMMENT "Open OpenVINO documentation"
                           VERBATIM)
         set_target_properties(ie_docs_open PROPERTIES FOLDER docs)
diff --git a/docs/doxygen/ie_docs.config b/docs/Doxyfile.config
similarity index 94%
rename from docs/doxygen/ie_docs.config
rename to docs/Doxyfile.config
index fd5ca445ceb..527c0cdd4c4 100644
--- a/docs/doxygen/ie_docs.config
+++ b/docs/Doxyfile.config
@@ -58,7 +58,7 @@ PROJECT_LOGO           =
 # entered, it will be relative to the location where doxygen was started. If
 # left blank the current directory will be used.
 
-OUTPUT_DIRECTORY       = "@OUTPUT_DIRECTORY@"
+OUTPUT_DIRECTORY       = "@DOCS_BUILD_DIR@"
 
 # If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
 # directories (in 2 levels) under the output directory of each output format and
@@ -262,6 +262,8 @@ TAB_SIZE               = 4
 # a double escape (\\{ and \\})
 
 ALIASES                = "ref_ie{1}=@ref InferenceEngine::\1 \"\1\""
+ALIASES                += sphinxdirective="\n\xmlonly<sphinxdirective>"
+ALIASES                += endsphinxdirective="</sphinxdirective>\endxmlonly"
 
 # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
 # only. Doxygen will then generate output that is more tailored for C. For
@@ -317,7 +319,7 @@ OPTIMIZE_OUTPUT_SLICE  = NO
 # Note that for custom extensions you also need to set FILE_PATTERNS otherwise
 # the files are not read by doxygen.
 
-EXTENSION_MAPPING      =
+EXTENSION_MAPPING      = pyx=Python
 
 # If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
 # according to the Markdown format, which allows for more readable
@@ -461,7 +463,7 @@ LOOKUP_CACHE_SIZE      = 0
 # normally produced when WARNINGS is set to YES.
 # The default value is: NO.
 
-EXTRACT_ALL            = NO
+EXTRACT_ALL            = YES
 
 # If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
 # be included in the documentation.
@@ -556,7 +558,7 @@ INTERNAL_DOCS          = NO
 # (including Cygwin) ands Mac users are advised to set this option to NO.
 # The default value is: system dependent.
 
-CASE_SENSE_NAMES       = YES
+CASE_SENSE_NAMES       = NO
 
 # If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
 # their full class and namespace scopes in the documentation. If set to YES, the
@@ -811,7 +813,7 @@ WARN_FORMAT            = "$file:$line: $text"
 # messages should be written. If left blank the output is written to standard
 # error (stderr).
 
-WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ie_docs.log"
+WARN_LOGFILE           = "@DOCS_BUILD_DIR@/doxygen.log"
 
 #---------------------------------------------------------------------------
 # Configuration options related to the input files
@@ -823,8 +825,27 @@ WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ie_docs.log"
 # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
 # Note: If this tag is empty the current directory is searched.
 
-INPUT                  = "@DOCS_BUILD_DIR@" \
-                         "@OV_INFERENCE_DIR@/include"
+INPUT                  = "@MARKDOWN_INPUT@" \
+                         "@OpenVINO_SOURCE_DIR@/src/frontends/common/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/conditional_compilation/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/itt/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/legacy/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/legacy/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/low_precision_transformations/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/low_precision_transformations/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/offline_transformations/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/preprocessing/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/snippets/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/transformations/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/util/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/core/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/core/builder/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/core/reference/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/core/shape_inference/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/frontends/common/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/inference/dev_api" \
+                         "@OpenVINO_SOURCE_DIR@/src/inference/include"
+
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
@@ -855,7 +876,8 @@ FILE_PATTERNS          = *.md \
                          *.cpp \
                          *.c \
                          *.hpp \
-                         *.h
+                         *.h \
+                         *.c++ \
 
 # The RECURSIVE tag can be used to specify whether or not subdirectories should
 # be searched for input files as well.
@@ -891,7 +913,11 @@ EXCLUDE_PATTERNS       = */temp/* \
                          */tests/* \
                          */openvx/* \
                          */thirdparty/* \
-                         */IE_PLUGIN_DG/*
+                         "@DOXYREST_OUT@" \
+                         "@XML_OUTPUT@" \
+                         "@RST_OUTPUT@" \
+                         "@SPHINX_OUTPUT@" \
+                         */build/open_model_zoo/*
 
 # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
 # (namespaces, classes, functions, etc.) that should be excluded from the
@@ -953,20 +979,61 @@ EXCLUDE_SYMBOLS        = InferenceEngine::details \
                          TBB_PREVIEW_NUMA_SUPPORT \
                          TBB_PREVIEW_TASK_ARENA_CONSTRAINTS_EXTENSION \
                          _TBB_REDUCE_FUNC \
-                         IE_THREAD_*
+                         IE_THREAD_* \
+                         INFERENCE_ENGINE_C_API_EXTERN \
+                         INFERENCE_ENGINE_C_API \
+                         INFERENCE_ENGINE_C_API_CALLBACK \
+                         IE_NODISCARD \
+                         InferenceEngine::details \
+                         ie_api::BlobBuffer \
+                         *impl* \
+                         *device_name* \
+                         *num_requests* \
+                         *exec_net* \
+                         *c_config* \
+                         *ie_core_impl* \
+                         *plugin_impl* \
+                         *extension_str* \
+                         *buffer* \
+                         *__cinit__* \
+                         ngraph::utils \
+                         ie_core_version \
+                         ie_core_versions \
+                         ie_available_devices \
+                         ie_core_versions \
+                         input_shapes \
+                         colorformat_e \
+                         layout_e \
+                         dimensions \
+                         ie_param_config \
+                         struct_desc \
+                         ie_param \
+                         ie_complete_call_back \
+                         IEStatusCode \
+                         input_shape \
+                         struct_desc
 
 # The EXAMPLE_PATH tag can be used to specify one or more files or directories
 # that contain example code fragments that are included (see the \include
 # command).
 
-EXAMPLE_PATH           = "@CMAKE_CURRENT_SOURCE_DIR@"
+EXAMPLE_PATH           = "@CMAKE_CURRENT_SOURCE_DIR@" \
+                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/src" \
+                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/include" \
+                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/src/CMakeLists.txt" \
+                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/tests/functional/CMakeLists.txt" \
+                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/tests/functional/transformations" \
+                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/tests/functional/shared_tests_instances/" \
+                         "@CMAKE_CURRENT_SOURCE_DIR@/snippets"
+                         "@IE_SOURCE_DIR@/tests/functional/plugin/shared/include"
 
 # If the value of the EXAMPLE_PATH tag contains directories, you can use the
 # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
 # *.h) to filter out the source-files in the directories. If left blank all
 # files are included.
 
-EXAMPLE_PATTERNS       =
+EXAMPLE_PATTERNS       = *.cpp \
+                         *.hpp
 
 # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
 # searched for input files to be used with the \include or \dontinclude commands
@@ -979,7 +1046,7 @@ EXAMPLE_RECURSIVE      = YES
 # that contain images that are to be included in the documentation (see the
 # \image command).
 
-IMAGE_PATH             = .
+IMAGE_PATH             = "@DOCS_BUILD_DIR@"
 
 # The INPUT_FILTER tag can be used to specify a program that doxygen should
 # invoke to filter for each input file. Doxygen will invoke the filter program
@@ -1185,7 +1252,7 @@ IGNORE_PREFIX          =
 # If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
 # The default value is: YES.
 
-GENERATE_HTML          = YES
+GENERATE_HTML          = NO
 
 # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
 # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
@@ -1193,7 +1260,7 @@ GENERATE_HTML          = YES
 # The default directory is: html.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_OUTPUT            = @OUTPUT_DIRECTORY@
+HTML_OUTPUT            = html
 
 # The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
 # generated HTML page (for example: .htm, .php, .asp).
@@ -1220,7 +1287,7 @@ HTML_FILE_EXTENSION    = .html
 # of the possible markers and block names see the documentation.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_HEADER            = @HEADER_BUILD@
+HTML_HEADER            =
 
 # The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
 # generated HTML page. If the tag is left blank doxygen will generate a standard
@@ -1230,7 +1297,7 @@ HTML_HEADER            = @HEADER_BUILD@
 # that doxygen normally uses.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_FOOTER            = @FOOTER_BUILD@
+HTML_FOOTER            =
 
 # The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
 # sheet that is used by each HTML page. It can be used to fine-tune the look of
@@ -2059,7 +2126,7 @@ MAN_LINKS              = NO
 # captures the structure of the code including all documentation.
 # The default value is: NO.
 
-GENERATE_XML           = NO
+GENERATE_XML           = YES
 
 # The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
 # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
@@ -2067,7 +2134,7 @@ GENERATE_XML           = NO
 # The default directory is: xml.
 # This tag requires that the tag GENERATE_XML is set to YES.
 
-XML_OUTPUT             = xml
+XML_OUTPUT             = "@XML_OUTPUT@"
 
 # If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
 # listings (including syntax highlighting and cross-referencing information) to
@@ -2076,7 +2143,7 @@ XML_OUTPUT             = xml
 # The default value is: YES.
 # This tag requires that the tag GENERATE_XML is set to YES.
 
-XML_PROGRAMLISTING     = YES
+XML_PROGRAMLISTING     = NO
 
 # If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
 # namespace members in file scope as well, matching the HTML output.
@@ -2235,7 +2302,45 @@ PREDEFINED             = "INFERENCE_ENGINE_API_CLASS=" \
                          "OPENVINO_PLUGIN_API=" \
                          "IE_SUPPRESS_DEPRECATED_END=" \
                          "_IE_SUPPRESS_DEPRECATED_START_GCC=" \
-                         "_IE_SUPPRESS_DEPRECATED_END_GCC="
+                         "_IE_SUPPRESS_DEPRECATED_END_GCC=" \
+                         "INFERENCE_ENGINE_NN_BUILDER_API_CLASS=" \
+                         "INFERENCE_ENGINE_NN_BUILDER_DEPRECATED(x)=" \
+                         "INFERENCE_ENGINE_INTERNAL(x)=" \
+                         "INFERENCE_ENGINE_INTERNAL_CNNLAYER_CLASS(x)=" \
+                         "__attribute__(x)=" \
+                         "__VA_ARGS__=" \
+                         "INFERENCE_ENGINE_C_API_EXTERN=" \
+                         "INFERENCE_ENGINE_C_API_CALLBACK=" \
+                         "INFERENCE_ENGINE_C_API=" \
+                         "IE_NODISCARD=" \
+                         "__cdecl=" \
+                         "__declspec(x)=" \
+                         "_WIN32" \
+                         "INFERENCE_ENGINE_API=" \
+                         "INFERENCE_ENGINE_API_CPP=" \
+                         "INFERENCE_ENGINE_API_CLASS=" \
+                         "INFERENCE_ENGINE_DEPRECATED=" \
+                         "IMPLEMENT_OPENVINO_API" \
+                         "TRANSFORMATIONS_API=" \
+                         "NGRAPH_HELPER_DLL_EXPORT=" \
+                         "NGRAPH_HELPER_DLL_IMPORT=" \
+                         "IE_SUPPRESS_DEPRECATED_START=" \
+                         "IE_SUPPRESS_DEPRECATED_END=" \
+                         "_IE_SUPPRESS_DEPRECATED_START_MSVC=" \
+                         "_IE_SUPPRESS_DEPRECATED_END_MSVC=" \
+                         "_IE_SUPPRESS_DEPRECATED_START_GCC=" \
+                         "_IE_SUPPRESS_DEPRECATED_END_GCC=" \
+                         "IE_THREAD=IE_THREAD_TBB" \
+                         "NGRAPH_RTTI_DECLARATION=" \
+                         "__attribute__(x)=" \
+                         "__VA_ARGS__=" \
+                         "INFERENCE_ENGINE_C_API_EXTERN=" \
+                         "INFERENCE_ENGINE_C_API=" \
+                         "IE_NODISCARD=" \
+                         "__cdecl=" \
+                         "__declspec(x)=" \
+                         "__GNUC__=" \
+                         "_WIN32"
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The
diff --git a/docs/HOWTO/Custom_Layers_Guide.md b/docs/HOWTO/Custom_Layers_Guide.md
index bddd6d37dd7..ef2124d491e 100644
--- a/docs/HOWTO/Custom_Layers_Guide.md
+++ b/docs/HOWTO/Custom_Layers_Guide.md
@@ -5,28 +5,22 @@ TensorFlow*, Caffe*, MXNet*, Kaldi* and ONNX* file format. The list of supported
 each of the supported frameworks. To see the operations supported by your framework, refer to
 [Supported Framework Layers](../MO_DG/prepare_model/Supported_Frameworks_Layers.md).
 
-Custom operations are operations that are not included in the list of known operations. If your model contains any
-operation that is not in the list of known operations, the Model Optimizer is not able to generate an Intermediate
-Representation (IR) for this model.
+Custom operations, that is those not included in the list, are not recognized by Model Optimizer out-of-the-box. Therefore, creating Intermediate Representation (IR) for a model using them requires additional steps. This guide illustrates the workflow for running inference on topologies featuring custom operations, allowing you to plug in your own implementation for existing or completely new operations.
 
-This guide illustrates the workflow for running inference on topologies featuring custom operations, allowing you to
-plug in your own implementation for existing or completely new operation.
-
-> **NOTE:** *Layer* — The legacy term for an *operation* which came from Caffe\* framework. Currently it is not used.
+> **NOTE**: *Layer* is a legacy term for *operation* which came from Caffe\* framework. Currently it is not used.
 > Refer to the [Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™](../MO_DG/IR_and_opsets.md)
 > for more information on the topic.
 
 ## Terms Used in This Guide
 
-- *Intermediate Representation (IR)* — Neural Network used only by the Inference Engine in OpenVINO abstracting the
-  different frameworks and describing the model topology, operations parameters and weights.
+- *Intermediate Representation (IR)* — OpenVINO's Neural Network format used by Inference Engine. It abstracts different frameworks and describs model topology, operations parameters, and weights.
 
-- *Operation* — The abstract concept of a math function that is selected for a specific purpose. Operations supported by
+- *Operation* — an abstract concept of a math function selected for a specific purpose. Operations supported by
   OpenVINO™ are listed in the supported operation set provided in the [Available Operations Sets](../ops/opset.md).
   Examples of the operations are: [ReLU](../ops/activation/ReLU_1.md), [Convolution](../ops/convolution/Convolution_1.md),
   [Add](../ops/arithmetic/Add_1.md), etc.
 
-- *Kernel* — The implementation of a operation function in the OpenVINO™ plugin, in this case, the math programmed (in
+- *Kernel* — The implementation of an operation function in the OpenVINO™ plugin, in this case, the math programmed (in
   C++ and OpenCL) to perform the operation for a target hardware (CPU or GPU).
 
 - *Inference Engine Extension* — Device-specific module implementing custom operations (a set of kernels).
@@ -44,7 +38,7 @@ plugins to support inference of this operation using a particular target hardwar
 To see the operations that are supported by each device plugin for the Inference Engine, refer to the
 [Supported Devices](../IE_DG/supported_plugins/Supported_Devices.md).
 
-> **NOTE:** If a device doesn't support a particular operation, an alternative to creating a new operation is to target
+> **NOTE**: If a device doesn't support a particular operation, an alternative to creating a new operation is to target
 > an additional device using the HETERO plugin. The [Heterogeneous Plugin](../IE_DG/supported_plugins/HETERO.md) may be
 > used to run an inference model on multiple devices allowing the unsupported operations on one device to "fallback" to
 > run on another device (e.g., CPU) that does support those operations.
@@ -56,18 +50,18 @@ Model Optimizer model conversion pipeline is described in detail in "Model Conve
 Model Optimizer provides an extensions mechanism to support new operations and implement custom model transformations to generate optimized IR. This mechanism is described in the "Model Optimizer Extensions" section of 
 [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md).
 
-Two types of the Model Optimizer extensions should be implemented to support custom operations, at a minimum:
+Two types of Model Optimizer extensions should be implemented to support custom operations, at a minimum:
 1. Operation class for a new operation. This class stores information about the operation, its attributes, shape inference function, attributes to be saved to an IR and some others internally used attributes. Refer to the "Model Optimizer Operation" section of [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md) for detailed instructions on how to implement it.
 2. Operation attributes extractor. The extractor is responsible for parsing framework-specific representation of the
 operation and uses corresponding operation class to update graph node attributes with necessary attributes of the
 operation. Refer to the "Operation Extractor" section of
 [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md) for detailed instructions on how to implement it.
 
-> **NOTE:** In some cases you may need to implement some transformation to support the operation. This topic is covered in the "Graph Transformation Extensions" section of [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md).
+> **NOTE**: In some cases you may need to implement some transformation to support the operation. This topic is covered in the "Graph Transformation Extensions" section of [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md).
 
 ## Custom Operations Extensions for the Inference Engine
 
-Inference Engine provides extensions mechanism to support new operations. This mechanism is described in [Inference Engine Extensibility Mechanism](../IE_DG/Extensibility_DG/Intro.md).
+Inference Engine provides an extension mechanism to support new operations. This mechanism is described in [Inference Engine Extensibility Mechanism](../IE_DG/Extensibility_DG/Intro.md).
 
 Each device plugin includes a library of optimized implementations to execute known operations which must be extended to execute a custom operation. The custom operation extension is implemented according to the target device:
 
@@ -127,19 +121,18 @@ As a result the TensorFlow\* frozen model file "wnet_20.pb" is generated.
 
 ### Convert the Frozen TensorFlow\* Model to Intermediate Representation
 
-Firstly, open the model in the TensorBoard or other TensorFlow* model visualization tool. The model supports dynamic
+Firstly, open the model in TensorBoard or other TensorFlow* model visualization tool. The model supports dynamic
 batch dimension because the value for the batch dimension is not hardcoded in the model. Model Optimizer need to set all
 dynamic dimensions to some specific value to create the IR, therefore specify the command line parameter `-b 1` to set
 the batch dimension equal to 1. The actual batch size dimension can be changed at runtime using the Inference Engine API
-described in the [Using Shape Inference](../IE_DG/ShapeInference.md). Also refer to
-[Converting a Model Using General Conversion Parameters](../MO_DG/prepare_model/convert_model/Converting_Model_General.md)
-and [Convert Your TensorFlow* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
+described in the [Using Shape Inference](../IE_DG/ShapeInference.md). Also refer to the General Conversion Parameters section in [Converting a Model to Intermediate Representation (IR)](../MO_DG/prepare_model/convert_model/Converting_Model.md) and [Convert Your TensorFlow* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
 for more details and command line parameters used for the model conversion.
 
-```bash
+```sh
 mo --input_model <PATH_TO_MODEL>/wnet_20.pb -b 1
 ```
-> **NOTE:** This conversion guide is applicable for the 2021.3 release of OpenVINO and that starting from 2021.4
+
+> **NOTE**: This conversion guide is applicable for the 2021.3 release of OpenVINO and that starting from 2021.4
 > the OpenVINO supports this model out of the box.
 
 Model Optimizer produces the following error:
@@ -221,7 +214,7 @@ following snippet provides two extractors: one for "IFFT2D", another one for "FF
 
 @snippet FFT_ext.py fft_ext:extractor
 
-> **NOTE:** The graph is in inconsistent state after extracting node attributes because according to original operation
+> **NOTE**: The graph is in inconsistent state after extracting node attributes because according to original operation
 > "IFFT2D" semantic it should have an input consuming a tensor of complex numbers, but the extractor instantiated an
 > operation "FFT" which expects a real tensor with specific layout. But the inconsistency will be resolved during
 > applying front phase transformations discussed below.
@@ -239,7 +232,7 @@ information on how this type of transformation works. The code snippet should be
 
 @snippet Complex.py complex:transformation
 
-> **NOTE:** The graph is in inconsistent state because the "ComplexAbs" operation consumes complex value tensor but
+> **NOTE**: The graph is in inconsistent state because the "ComplexAbs" operation consumes complex value tensor but
 >  "FFT" produces real value tensor.
 
 Now lets implement a transformation which replace a "ComplexAbs" operation with a sub-graph of primitive operations
@@ -257,15 +250,17 @@ The implementation should be saved to the file `mo_extensions/front/tf/ComplexAb
 @snippet ComplexAbs.py complex_abs:transformation
 
 Now it is possible to convert the model using the following command line:
-```bash
+```sh
 mo --input_model <PATH_TO_MODEL>/wnet_20.pb -b 1 --extensions mo_extensions/
 ```
 
+@endsphinxdirective
+
 The sub-graph corresponding to the originally non-supported one is depicted in the image below:
 
 ![Converted sub-graph](img/converted_subgraph.png)
 
-> **NOTE:** Model Optimizer performed conversion of the model from NHWC to NCHW layout that is why the dimension with
+> **NOTE**: Model Optimizer performed conversion of the model from NHWC to NCHW layout that is why the dimension with
 > the value 2 moved to another position.
 
 ### Inference Engine Extension Implementation
@@ -279,7 +274,7 @@ file is the following:
 @snippet ../template_extension/CMakeLists.txt cmake:extension
 
 The CPU FFT kernel implementation uses OpenCV to perform the FFT that is why the extension library is linked with
-"opencv_core" which comes with the OpenVINO.
+`opencv_core` which comes with the OpenVINO.
 
 #### Custom nGraph Operation "FFT" Implementation
 The next step is to create the nGraph operation FFT. The header file "fft_op.hpp" has the following content:
@@ -287,7 +282,7 @@ The next step is to create the nGraph operation FFT. The header file "fft_op.hpp
 @snippet ../template_extension/fft_op.hpp fft_op:header
 
 The operation has just one boolean attribute `inverse`. Implementation of the necessary nGraph operation functions are
-in the "fft_op.cpp" file with the following content:
+in the `fft_op.cpp` file with the following content:
 
 @snippet ../template_extension/fft_op.cpp fft_op:implementation
 
@@ -350,7 +345,7 @@ python3 mri_reconstruction_demo.py \
 ## Converting Models:
 
 - [Convert Your Caffe* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md)
-- [Convert Your Kaldi* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
 - [Convert Your TensorFlow* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
 - [Convert Your MXNet* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md)
+- [Convert Your Kaldi* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
 - [Convert Your ONNX* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
diff --git a/docs/IE_DG/Bfloat16Inference.md b/docs/IE_DG/Bfloat16Inference.md
index dcca07a47eb..8b0e8ff6779 100644
--- a/docs/IE_DG/Bfloat16Inference.md
+++ b/docs/IE_DG/Bfloat16Inference.md
@@ -1,50 +1,57 @@
 # Bfloat16 Inference {#openvino_docs_IE_DG_Bfloat16Inference}
 
-## Disclaimer
+## Bfloat16 Inference Usage (C++)
 
-Inference Engine with the bfloat16 inference implemented on CPU must support the native `avx512_bf16` instruction and therefore the bfloat16 data format.
-It is possible to use bfloat16 inference in simulation mode on platforms with Intel® Advanced Vector Extensions 512 (Intel® AVX-512), but it leads to significant performance degradation in comparison with FP32 or native `avx512_bf16` instruction usage.
+@sphinxdirective
+.. raw:: html
 
-## Introduction
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
 
+### Disclaimer
+
+Inference Engine with the bfloat16 inference implemented on CPU must support the native *avx512_bf16* instruction and therefore the bfloat16 data format. It is possible to use bfloat16 inference in simulation mode on platforms with Intel® Advanced Vector Extensions 512 (Intel® AVX-512), but it leads to significant performance degradation in comparison with FP32 or native *avx512_bf16* instruction usage.
+
+### Introduction
 Bfloat16 computations (referred to as BF16) is the Brain Floating-Point format with 16 bits. This is a truncated 16-bit version of the 32-bit IEEE 754 single-precision floating-point format FP32. BF16 preserves 8 exponent bits as FP32 but reduces precision of the sign and mantissa from 24 bits to 8 bits.
 
 ![bf16_format]
 
-Preserving the exponent bits keeps BF16 to the same range as the FP32 (~1e-38 to ~3e38). This simplifies conversion between two data types: you just need to skip or flush to zero 16 low bits.
-Truncated mantissa leads to occasionally less precision, but according to [investigations](https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus), neural networks are more sensitive to the size of the exponent than the mantissa size. Also, in lots of models, precision is needed close to zero but not so much at the maximum range.
-Another useful feature of BF16 is possibility to encode INT8 in BF16 without loss of accuracy, because INT8 range completely fits in BF16 mantissa field. It reduces data flow in conversion from INT8 input image data to BF16 directly without intermediate representation in FP32, or in combination of [INT8 inference](Int8Inference.md) and BF16 layers.
+Preserving the exponent bits keeps BF16 to the same range as the FP32 (~1e-38 to ~3e38). This simplifies conversion between two data types: you just need to skip or flush to zero 16 low bits. Truncated mantissa leads to occasionally less precision, but according to [investigations](https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus), neural networks are more sensitive to the size of the exponent than the mantissa size. Also, in lots of models, precision is needed close to zero but not so much at the maximum range. Another useful feature of BF16 is possibility to encode INT8 in BF16 without loss of accuracy, because INT8 range completely fits in BF16 mantissa field. It reduces data flow in conversion from INT8 input image data to BF16 directly without intermediate representation in FP32, or in combination of [INT8 inference](Int8Inference.md) and BF16 layers.
 
-See the ["BFLOAT16 – Hardware Numerics Definition" white paper"](https://software.intel.com/sites/default/files/managed/40/8b/bf16-hardware-numerics-definition-white-paper.pdf) for more bfloat16 format details.
+See the [BFLOAT16 – Hardware Numerics Definition white paper](https://software.intel.com/content/dam/develop/external/us/en/documents/bf16-hardware-numerics-definition-white-paper.pdf) for more bfloat16 format details.
 
 There are two ways to check if CPU device can support bfloat16 computations for models:
-1. Query the instruction set via system `lscpu | grep avx512_bf16` or `cat /proc/cpuinfo | grep avx512_bf16`.
-2. Use [Query API](InferenceEngine_QueryAPI.md) with `METRIC_KEY(OPTIMIZATION_CAPABILITIES)`, which should return `BF16` in the list of CPU optimization options:
+
+1. Query the instruction set using one of these system commands:
+   * `lscpu | grep avx512_bf16`
+   * `cat /proc/cpuinfo | grep avx512_bf16`
+2. Use the [Query API](InferenceEngine_QueryAPI.md) with `METRIC_KEY(OPTIMIZATION_CAPABILITIES)`, which should return `BF16` in the list of CPU optimization options:
 
 @snippet snippets/Bfloat16Inference0.cpp part0
 
-Current Inference Engine solution for bfloat16 inference uses Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) and supports inference of the significant number of layers in BF16 computation mode.
+The current Inference Engine solution for bfloat16 inference uses the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) and supports inference of the significant number of layers in BF16 computation mode.
 
-## Lowering Inference Precision
+### Lowering Inference Precision
 
-Lowering precision to increase performance is [widely used](https://software.intel.com/content/www/us/en/develop/articles/lower-numerical-precision-deep-learning-inference-and-training.html) for optimization of inference. The bfloat16 data type usage on CPU for the first time opens the possibility of default optimization approach.
-The embodiment of this approach is to use the optimization capabilities of the current platform to achieve maximum performance while maintaining the accuracy of calculations within the acceptable range.
+Lowering precision to increase performance is [widely used](https://software.intel.com/content/www/us/en/develop/articles/lower-numerical-precision-deep-learning-inference-and-training.html) for optimization of inference. The bfloat16 data type usage on CPU for the first time opens the possibility of default optimization approach. The embodiment of this approach is to use the optimization capabilities of the current platform to achieve maximum performance while maintaining the accuracy of calculations within the acceptable range.
+
+Using Bfloat16 precision provides the following performance benefits:
 
-Bfloat16 data usage provides the following benefits that increase performance:
 1. Faster multiplication of two BF16 numbers because of shorter mantissa of bfloat16 data.
 2. No need to support denormals and handling exceptions as this is a performance optimization.
 3. Fast conversion of float32 to bfloat16 and vice versa.
 4. Reduced size of data in memory, as a result, larger models fit in the same memory bounds.
 5. Reduced amount of data that must be transferred, as a result, reduced data transition time.
 
-For default optimization on CPU, source model is converted from FP32 or FP16 to BF16 and executed internally on platforms with native BF16 support. In this case, `KEY_ENFORCE_BF16` is set to `YES`.
-The code below demonstrates how to check if the key is set:
+For default optimization on CPU, the source model is converted from FP32 or FP16 to BF16 and executed internally on platforms with native BF16 support. In this case, `KEY_ENFORCE_BF16` is set to `YES` in the `PluginConfigParams` for `GetConfig()`. The code below demonstrates how to check if the key is set:
 
 @snippet snippets/Bfloat16Inference1.cpp part1
 
-To disable BF16 internal transformations, set the `KEY_ENFORCE_BF16` to `NO`. In this case, the model infers as is without modifications with precisions that were set on each layer edge.
+To disable BF16 internal transformations in C++ API, set the `KEY_ENFORCE_BF16` to `NO`. In this case, the model infers as is without modifications with precisions that were set on each layer edge.
 
 @snippet snippets/Bfloat16Inference2.cpp part2
+
 To disable BF16 in C API:
 
 ```
@@ -52,15 +59,16 @@ ie_config_t config = { "ENFORCE_BF16", "NO", NULL};
 ie_core_load_network(core, network, device_name, &config, &exe_network);
 ```
 
-An exception with message `Platform doesn't support BF16 format` is formed in case of setting `KEY_ENFORCE_BF16` to `YES` on CPU without native BF16 support or BF16 simulation mode.
+An exception with the message `Platform doesn't support BF16 format` is formed in case of setting `KEY_ENFORCE_BF16` to `YES` on CPU without native BF16 support or BF16 simulation mode.
 
-Low-Precision 8-bit integer models cannot be converted to BF16, even if bfloat16 optimization is set by default.         
+Low-Precision 8-bit integer models cannot be converted to BF16, even if bfloat16 optimization is set by default.
 
-## Bfloat16 Simulation Mode
+### Bfloat16 Simulation Mode
 
-Bfloat16 simulation mode is available on CPU and Intel® AVX-512 platforms that do not support the native `avx512_bf16` instruction. The simulator does not guarantee an adequate performance.
-To enable Bfloat16 simulator:
-* In [Benchmark App](../../samples/cpp/benchmark_app/README.md), add the `-enforcebf16=true` option
+Bfloat16 simulation mode is available on CPU and Intel® AVX-512 platforms that do not support the native `avx512_bf16` instruction. The simulator does not guarantee good performance. Note that the CPU must still support the AVX-512 extensions.
+
+To enable the simulation of Bfloat16:
+* In the [Benchmark App](../../samples/cpp/benchmark_app/README.md), add the `-enforcebf16=true` option
 * In C++ API, set `KEY_ENFORCE_BF16` to `YES`
 * In C API:
 ```
@@ -68,25 +76,139 @@ ie_config_t config = { "ENFORCE_BF16", "YES", NULL};
 ie_core_load_network(core, network, device_name, &config, &exe_network);
 ```
 
-## Performance Counters
+### Performance Counters
+
+Information about layer precision is stored in the performance counters that are available from the Inference Engine API. The layers have the following marks:
 
-Information about layer precision is stored in the performance counters that are
-available from the Inference Engine API. The layers have the following marks:
 * Suffix `BF16` for layers that had bfloat16 data type input and were computed in BF16 precision
 * Suffix `FP32` for layers computed in 32-bit precision
 
 For example, the performance counters table for the Inception model can look as follows:
 
 ```
-pool5                         EXECUTED       layerType: Pooling            realTime: 143       cpu: 143             execType: jit_avx512_BF16
-fc6                           EXECUTED       layerType: FullyConnected     realTime: 47723     cpu: 47723           execType: jit_gemm_BF16
-relu6                         NOT_RUN        layerType: ReLU               realTime: 0         cpu: 0               execType: undef
-fc7                           EXECUTED       layerType: FullyConnected     realTime: 7558      cpu: 7558            execType: jit_gemm_BF16
-relu7                         NOT_RUN        layerType: ReLU               realTime: 0         cpu: 0               execType: undef
-fc8                           EXECUTED       layerType: FullyConnected     realTime: 2193      cpu: 2193            execType: jit_gemm_BF16
-prob                          EXECUTED       layerType: SoftMax            realTime: 68        cpu: 68              execType: jit_avx512_FP32
+pool5     EXECUTED       layerType: Pooling            realTime: 143       cpu: 143        execType: jit_avx512_BF16
+fc6       EXECUTED       layerType: FullyConnected     realTime: 47723     cpu: 47723      execType: jit_gemm_BF16
+relu6     NOT_RUN        layerType: ReLU               realTime: 0         cpu: 0          execType: undef
+fc7       EXECUTED       layerType: FullyConnected     realTime: 7558      cpu: 7558       execType: jit_gemm_BF16
+relu7     NOT_RUN        layerType: ReLU               realTime: 0         cpu: 0          execType: undef
+fc8       EXECUTED       layerType: FullyConnected     realTime: 2193      cpu: 2193       execType: jit_gemm_BF16
+prob      EXECUTED       layerType: SoftMax            realTime: 68        cpu: 68         execType: jit_avx512_FP32
 ```
 
-The `execType` column of the table includes inference primitives with specific suffixes.
+The **execType** column of the table includes inference primitives with specific suffixes.
+
+## Bfloat16 Inference Usage (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+### Disclaimer
+
+Inference Engine with the bfloat16 inference implemented on CPU must support the native *avx512_bf16* instruction and therefore the bfloat16 data format. It is possible to use bfloat16 inference in simulation mode on platforms with Intel® Advanced Vector Extensions 512 (Intel® AVX-512), but it leads to significant performance degradation in comparison with FP32 or native *avx512_bf16* instruction usage.
+
+### Introduction
+Bfloat16 computations (referred to as BF16) is the Brain Floating-Point format with 16 bits. This is a truncated 16-bit version of the 32-bit IEEE 754 single-precision floating-point format FP32. BF16 preserves 8 exponent bits as FP32 but reduces precision of the sign and mantissa from 24 bits to 8 bits.
+
+![bf16_format]
+
+Preserving the exponent bits keeps BF16 to the same range as the FP32 (~1e-38 to ~3e38). This simplifies conversion between two data types: you just need to skip or flush to zero 16 low bits. Truncated mantissa leads to occasionally less precision, but according to investigations, neural networks are more sensitive to the size of the exponent than the mantissa size. Also, in lots of models, precision is needed close to zero but not so much at the maximum range. Another useful feature of BF16 is possibility to encode INT8 in BF16 without loss of accuracy, because INT8 range completely fits in BF16 mantissa field. It reduces data flow in conversion from INT8 input image data to BF16 directly without intermediate representation in FP32, or in combination of [INT8 inference](Int8Inference.md) and BF16 layers.
+
+See the [BFLOAT16 – Hardware Numerics Definition white paper](https://software.intel.com/content/dam/develop/external/us/en/documents/bf16-hardware-numerics-definition-white-paper.pdf) for more bfloat16 format details.
+
+There are two ways to check if CPU device can support bfloat16 computations for models:
+
+1. Query the instruction set using one of these system commands:
+   * `lscpu | grep avx512_bf16`
+   * `cat /proc/cpuinfo | grep avx512_bf16`
+2. Use the Query API with METRIC_KEY(OPTIMIZATION_CAPABILITIES), which should return BF16 in the list of CPU optimization options:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(path_to_xml_file)
+cpu_caps = ie.get_metric(metric_name="OPTIMIZATION_CAPABILITIES", device_name="CPU")
+```
+
+The current Inference Engine solution for bfloat16 inference uses the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) and supports inference of the significant number of layers in BF16 computation mode.
+
+### Lowering Inference Precision
+
+Lowering precision to increase performance is widely used for optimization of inference. The bfloat16 data type usage on CPU for the first time opens the possibility of default optimization approach. The embodiment of this approach is to use the optimization capabilities of the current platform to achieve maximum performance while maintaining the accuracy of calculations within the acceptable range.
+
+Using Bfloat16 precision provides the following performance benefits:
+
+1. Faster multiplication of two BF16 numbers because of shorter mantissa of bfloat16 data.
+2. No need to support denormals and handling exceptions as this is a performance optimization.
+3. Fast conversion of float32 to bfloat16 and vice versa.
+4. Reduced size of data in memory, as a result, larger models fit in the same memory bounds.
+5. Reduced amount of data that must be transferred, as a result, reduced data transition time.
+
+For default optimization on CPU, the source model is converted from FP32 or FP16 to BF16 and executed internally on platforms with native BF16 support. In this case, ENFORCE_BF16 is set to YES. The code below demonstrates how to check if the key is set:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(path_to_xml_file)
+exec_net = ie.load_network(network=net, device_name="CPU")
+exec_net.get_config("ENFORCE_BF16")
+```
+
+To enable BF16 internal transformations, set the key "ENFORCE_BF16" to "YES" in the ExecutableNetwork configuration.
+
+```python
+bf16_config = {"ENFORCE_BF16" : "YES"}
+exec_net = ie.load_network(network=net, device_name="CPU", config = bf16_config)
+```
+
+To disable BF16 internal transformations, set the key "ENFORCE_BF16" to "NO". In this case, the model infers as is without modifications with precisions that were set on each layer edge.
+
+An exception with the message `Platform doesn't support BF16 format` is formed in case of setting "ENFORCE_BF16" to "YES"on CPU without native BF16 support or BF16 simulation mode.
+
+Low-Precision 8-bit integer models cannot be converted to BF16, even if bfloat16 optimization is set by default.
+
+### Bfloat16 Simulation Mode
+
+Bfloat16 simulation mode is available on CPU and Intel® AVX-512 platforms that do not support the native avx512_bf16 instruction. The simulator does not guarantee good performance. Note that the CPU must still support the AVX-512 extensions.
+
+#### To Enable the simulation of Bfloat16:
+
+* In the Benchmark App, add the -enforcebf16=true option
+* In Python, use the following code as an example:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(path_to_xml_file)
+bf16_config = {"ENFORCE_BF16" : "YES"}
+exec_net = ie.load_network(network=net, device_name="CPU", config=bf16_config)
+```
+
+### Performance Counters
+
+Information about layer precision is stored in the performance counters that are available from the Inference Engine API. The layers have the following marks:
+
+* Suffix *BF16* for layers that had bfloat16 data type input and were computed in BF16 precision
+* Suffix *FP32* for layers computed in 32-bit precision
+
+For example, the performance counters table for the Inception model can look as follows:
+
+```
+pool5     EXECUTED       layerType: Pooling            realTime: 143       cpu: 143        execType: jit_avx512_BF16
+fc6       EXECUTED       layerType: FullyConnected     realTime: 47723     cpu: 47723      execType: jit_gemm_BF16
+relu6     NOT_RUN        layerType: ReLU               realTime: 0         cpu: 0          execType: undef
+fc7       EXECUTED       layerType: FullyConnected     realTime: 7558      cpu: 7558       execType: jit_gemm_BF16
+relu7     NOT_RUN        layerType: ReLU               realTime: 0         cpu: 0          execType: undef
+fc8       EXECUTED       layerType: FullyConnected     realTime: 2193      cpu: 2193       execType: jit_gemm_BF16
+prob      EXECUTED       layerType: SoftMax            realTime: 68        cpu: 68         execType: jit_avx512_FP32
+```
+
+
+The **execType** column of the table includes inference primitives with specific suffixes.
 
 [bf16_format]: img/bf16_format.png
diff --git a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
index 9381122313d..04c1894bf76 100644
--- a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
+++ b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
@@ -1,120 +1,52 @@
 # Inference Engine Developer Guide {#openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide}
 
-This Guide provides an overview of the Inference Engine describing the typical workflow for performing inference of a pre-trained and optimized deep learning model and a set of sample applications.
+@sphinxdirective
 
-> **NOTE:** Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer or built directly in runtime using nGraph API. To learn about how to use Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to [Pre-Trained Models](@ref omz_models_group_intel).
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_IE_DG_Integrate_with_customer_application_new_API
+   openvino_docs_deployment_optimization_guide_dldt_optimization_guide
+   openvino_docs_IE_DG_Device_Plugins
+   Direct ONNX Format Support <openvino_docs_IE_DG_ONNX_Support>
+   openvino_docs_IE_DG_Int8Inference
+   openvino_docs_IE_DG_Bfloat16Inference
+   openvino_docs_IE_DG_DynamicBatching
+   openvino_docs_IE_DG_ShapeInference
+   openvino_docs_IE_DG_Model_caching_overview
+   openvino_docs_IE_DG_Extensibility_DG_Intro
+   openvino_docs_IE_DG_Memory_primitives
+   openvino_docs_IE_DG_network_state_intro   
+   openvino_docs_IE_DG_API_Changes
+   openvino_docs_IE_DG_Known_Issues_Limitations
+   openvino_docs_IE_DG_Glossary
+      
+@endsphinxdirective
 
-After you have used the Model Optimizer to create an Intermediate Representation (IR), use the Inference Engine to infer the result for a given input data.
+## Introduction
+Inference Engine is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice. Use the Inference Engine API to read the Intermediate Representation (IR), ONNX and execute the model on devices.
 
-Inference Engine is a set of C++ libraries providing a common API to deliver inference solutions on the platform of your choice: CPU, GPU, or VPU. Use the Inference Engine API to read the Intermediate Representation, set the input and output formats, and execute the model on devices. While the C++ libraries is the primary implementation, C libraries and Python bindings are also available.
+Inference Engine uses a plugin architecture. Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel® hardware device: CPU, GPU, VPU, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.
+ 
+The scheme below illustrates the typical workflow for deploying a trained deep learning model: 
 
-For Intel® Distribution of OpenVINO™ toolkit, Inference Engine binaries are delivered within release packages.
+![](img/BASIC_FLOW_IE_C.svg)
 
-The open source version is available in the [OpenVINO™ toolkit GitHub repository](https://github.com/openvinotoolkit/openvino) and can be built for supported platforms using the <a href="https://github.com/openvinotoolkit/openvino/wiki/BuildingCode">Inference Engine Build Instructions</a>.
+\\* _nGraph_ is the internal graph representation in the OpenVINO™ toolkit. Use it to [build a model from source code](https://docs.openvinotoolkit.org/latest/openvino_docs_nGraph_DG_build_function.html).
 
-To learn about how to use the Inference Engine API for your application, see the [Integrating Inference Engine in Your Application](Integrate_with_customer_application_new_API.md) documentation.
 
-For complete API Reference, see the [Inference Engine API References](./api_references.html) section.
+## Video
 
-Inference Engine uses a plugin architecture. Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel&reg; hardware device: CPU, GPU, VPU, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.
+@sphinxdirective
 
-## Modules in the Inference Engine component
-### Core Inference Engine Libraries
+.. list-table::
 
-Your application must link to the core Inference Engine libraries:
-* Linux* OS:
-    - `libov_runtime.so`, which depends on `libtbb.so`, `libtbbmalloc.so`
-* Windows* OS:
-    - `ov_runtime.dll`, which depends on `tbb.dll`, `tbbmalloc.dll`
-* macOS*:
-    - `libov_runtime.dylib`, which depends on `libtbb.dylib`, `libtbbmalloc.dylib`
+   * - .. raw:: html
 
-The required C++ header files are located in the `include` directory.
-
-This library contains the classes to:
-* Create Inference Engine Core object to work with devices and read network (InferenceEngine::Core)
-* Manipulate network information (InferenceEngine::CNNNetwork)
-* Execute and pass inputs and outputs (InferenceEngine::ExecutableNetwork and InferenceEngine::InferRequest)
-
-### Plugin Libraries to Read a Network Object
-
-Starting from 2022.1 release, OpenVINO Runtime introduced a concept of frontend plugins. Such plugins can be automatically dynamically loaded by OpenVINO Runtime dynamically depending on file format:
-* Linux* OS:
-    - `libov_ir_frontend.so` to read a network from IR
-    - `libov_paddlepaddle_frontend.so` to read a network from PaddlePaddle model format
-    - `libov_onnx_frontend.so` to read a network from ONNX model format
-* Windows* OS:
-    - `ov_ir_frontend.dll` to read a network from IR
-    - `ov_paddlepaddle_frontend.dll` to read a network from PaddlePaddle model format
-    - `ov_onnx_frontend.dll` to read a network from ONNX model format
-
-### Device-Specific Plugin Libraries
-
-For each supported target device, Inference Engine provides a plugin — a DLL/shared library that contains complete implementation for inference on this particular device. The following plugins are available:
-
-| Plugin  | Device Type                   |
-| ------- | ----------------------------- |
-|CPU      |	Intel® Xeon® with Intel® AVX2 and AVX512, Intel® Core™ Processors with Intel® AVX2, Intel® Atom® Processors with Intel® SSE |
-|GPU      | Intel® Processor Graphics, including Intel® HD Graphics and Intel® Iris® Graphics |
-|MYRIAD   |	Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X |
-|GNA      |	Intel&reg; Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel&reg; Pentium&reg; Silver J5005 Processor, Intel&reg; Pentium&reg; Silver N5000 Processor, Intel&reg; Celeron&reg; J4005 Processor, Intel&reg; Celeron&reg; J4105 Processor, Intel&reg; Celeron&reg; Processor N4100, Intel&reg; Celeron&reg; Processor N4000, Intel&reg; Core&trade; i3-8121U Processor, Intel&reg; Core&trade; i7-1065G7 Processor, Intel&reg; Core&trade; i7-1060G7 Processor, Intel&reg; Core&trade; i5-1035G4 Processor, Intel&reg; Core&trade; i5-1035G7 Processor, Intel&reg; Core&trade; i5-1035G1 Processor, Intel&reg; Core&trade; i5-1030G7 Processor, Intel&reg; Core&trade; i5-1030G4 Processor, Intel&reg; Core&trade; i3-1005G1 Processor, Intel&reg; Core&trade; i3-1000G1 Processor, Intel&reg; Core&trade; i3-1000G4 Processor |
-|HETERO   | Automatic splitting of a network inference between several devices (for example if a device doesn't support certain layers|
-|MULTI    | Simultaneous inference of the same network on several devices in parallel|
-
-The table below shows the plugin libraries and additional dependencies for Linux, Windows and macOS platforms.
-
-| Plugin | Library name for Linux      | Dependency libraries for Linux                              | Library name for Windows | Dependency libraries for Windows                                                                       | Library name for macOS       | Dependency libraries for macOS              |
-|--------|-----------------------------|-------------------------------------------------------------|--------------------------|--------------------------------------------------------------------------------------------------------|------------------------------|---------------------------------------------|
-| CPU    | `libMKLDNNPlugin.so`        | | `MKLDNNPlugin.dll`       | | `libMKLDNNPlugin.so`      | |
-| GPU    | `libov_intel_gpu_plugin.so`         | `libOpenCL.so` | `ov_intel_gpu_plugin.dll`        | `OpenCL.dll` |  Is not supported            |  -                                          |
-| MYRIAD | `libmyriadPlugin.so`        | `libusb.so`,                                                | `myriadPlugin.dll`       | `usb.dll`                                                                                              | `libmyriadPlugin.so`      | `libusb.dylib`                              |
-| HDDL   | `libHDDLPlugin.so`          | `libbsl.so`, `libhddlapi.so`, `libmvnc-hddl.so`             | `HDDLPlugin.dll`         | `bsl.dll`, `hddlapi.dll`, `json-c.dll`, `libcrypto-1_1-x64.dll`, `libssl-1_1-x64.dll`, `mvnc-hddl.dll` |  Is not supported            |  -                                          |
-| GNA    | `libov_intel_gna_plugin.so`           | `libgna.so`,                                                | `ov_intel_gna_plugin.dll`          | `gna.dll`                                                                                              |  Is not supported            |  -                                          |
-| HETERO | `libov_hetero_plugin.so`        | Same as for selected plugins                                | `ov_hetero_plugin.dll`       | Same as for selected plugins                                                                           | `libov_hetero_plugin.so`      |  Same as for selected plugins               |
-| MULTI  | `libov_auto_plugin.so`   | Same as for selected plugins                                | `ov_auto_plugin.dll`  | Same as for selected plugins                                                                           | `libov_auto_plugin.so` |  Same as for selected plugins               |
-| AUTO | `libov_auto_plugin.so`   | Same as for selected plugins                                | `ov_auto_plugin.dll`  | Same as for selected plugins                                                                           | `libov_auto_plugin.so` |  Same as for selected plugins               |
-
-> **NOTE**: All plugin libraries also depend on core Inference Engine libraries.
-
-Make sure those libraries are in your computer's path or in the place you pointed to in the plugin loader. Make sure each plugin's related dependencies are in the:
-
-* Linux: `LD_LIBRARY_PATH`
-* Windows: `PATH`
-* macOS: `DYLD_LIBRARY_PATH`
-
-On Linux and macOS, use the script `setupvars.sh` to set the environment variables.
-
-On Windows, run the `setupvars.bat` batch file to set the environment variables.
-
-To learn more about supported devices and corresponding plugins, see the [Supported Devices](supported_plugins/Supported_Devices.md) chapter.
-
-## Common Workflow for Using the Inference Engine API
-
-The common workflow contains the following steps:
-
-1. **Create Inference Engine Core object** - Create an `InferenceEngine::Core` object to work with different devices, all device plugins are managed internally by the `Core` object. Register extensions with custom nGraph operations (`InferenceEngine::Core::AddExtension`).
-
-2. **Read the Intermediate Representation** - Using the `InferenceEngine::Core` class, read an Intermediate Representation file into an object of the `InferenceEngine::CNNNetwork` class. This class represents the network in the host memory.
-
-3. **Prepare inputs and outputs format** - After loading the network, specify input and output precision and the layout on the network. For these specification, use the `InferenceEngine::CNNNetwork::getInputsInfo()` and `InferenceEngine::CNNNetwork::getOutputsInfo()`.
-
-4. Pass per device loading configurations specific to this device (`InferenceEngine::Core::SetConfig`), and register extensions to this device (`InferenceEngine::Core::AddExtension`).
-
-5. **Compile and Load Network to device** - Use the `InferenceEngine::Core::LoadNetwork()` method with specific device (e.g. `CPU`, `GPU`, etc.) to compile and load the network on the device. Pass in the per-target load configuration for this compilation and load operation.
-
-6. **Set input data** - With the network loaded, you have an `InferenceEngine::ExecutableNetwork` object. Use this object to create an `InferenceEngine::InferRequest` in which you signal the input buffers to use for input and output. Specify a device-allocated memory and copy it into the device memory directly, or tell the device to use your application memory to save a copy.
-
-7. **Execute** - With the input and output memory now defined, choose your execution mode:
-
-    * Synchronously - `InferenceEngine::InferRequest::Infer()` method. Blocks until inference is completed.
-    * Asynchronously - `InferenceEngine::InferRequest::StartAsync()` method. Check status with the `InferenceEngine::InferRequest::Wait()` method (0 timeout), wait, or specify a completion callback.
-
-8. **Get the output** - After inference is completed, get the output memory or read the memory you provided earlier. Do this with the `InferenceEngine::IInferRequest::GetBlob()` method.
-
-## Video: Inference Engine Concept
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/e6R13V8nbak" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-
-## Further Reading
-
-For more details on the Inference Engine API, refer to the [Integrating Inference Engine in Your Application](Integrate_with_customer_application_new_API.md) documentation.
+           <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen height="315" width="100%"
+           src="https://www.youtube.com/embed/e6R13V8nbak">
+           </iframe>
+   * - **Inference Engine Concept**. Duration: 3:43
+     
+@endsphinxdirective
diff --git a/docs/IE_DG/DynamicBatching.md b/docs/IE_DG/DynamicBatching.md
index 67475f0b83f..d962aa2a664 100644
--- a/docs/IE_DG/DynamicBatching.md
+++ b/docs/IE_DG/DynamicBatching.md
@@ -1,52 +1,106 @@
-Using Dynamic Batching {#openvino_docs_IE_DG_DynamicBatching}
-======================
+# Using Dynamic Batching {#openvino_docs_IE_DG_DynamicBatching}
 
-Dynamic Batching feature allows you to dynamically change batch size for inference calls
-within preset batch size limit.
-This feature might be useful when batch size is unknown beforehand, and using extra large batch size is
-undesired or impossible due to resource limitations.
-For example, face detection with person age, gender, or mood recognition is a typical usage scenario.
+## Using Dynamic Batching (C++)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
+
+The Dynamic Batching feature allows you to dynamically change batch size for inference calls
+within a preset batch size limit. This feature might be useful when batch size is unknown beforehand and using an extra-large batch size is undesirable or impossible due to resource limitations. For example, applying face detection and then mood labeling to a video, you won't know in advance how many frames will contain a face when you pass inferencing results to a secondary model.
 
 
-## Usage
-
-You can activate Dynamic Batching by setting <code>KEY_DYN_BATCH_ENABLED</code> flag to <code>YES</code> in a configuration map that is
+You can activate Dynamic Batching by setting `KEY_DYN_BATCH_ENABLED` flag to `YES` in a configuration map that is
 passed to the plugin while loading a network.
-This configuration creates an <code>ExecutableNetwork</code> object that will allow setting batch size
-dynamically in all of its infer requests using <code>SetBatch()</code> method.
-The batch size that was set in passed <code>CNNNetwork</code> object will be used as a maximum batch size limit.
+This configuration creates an `ExecutableNetwork` object that will allow setting batch size
+dynamically in all of its infer requests using `SetBatch()` method.
+The batch size that was set in the passed `CNNNetwork` object will be used as a maximum batch size limit.
 
 Here is a code example:
 
 @snippet snippets/DynamicBatching.cpp part0
 
 
-## Limitations
+### Limitations
 
-Currently, certain limitations for using Dynamic Batching exist:
+Currently, there are certain limitations for the use of Dynamic Batching exist:
 
 * Use Dynamic Batching with CPU and GPU plugins only.
-
 * Use Dynamic Batching on topologies that consist of certain layers only:
+   * Convolution
+   * Deconvolution
+   * Activation
+   * LRN
+   * Pooling
+   * FullyConnected
+   * SoftMax
+   * Split
+   * Concatenation
+   * Power
+   * Eltwise
+   * Crop
+   * BatchNormalization
+   * Copy
 
-	* Convolution
-	* Deconvolution
-	* Activation
-	* LRN
-	* Pooling
-	* FullyConnected
-	* SoftMax
-	* Split
-	* Concatenation
-	* Power
-	* Eltwise
-	* Crop
-	* BatchNormalization
-	* Copy
-	
-Do not use layers that might arbitrary change tensor shape (such as Flatten, Permute, Reshape),
-layers specific to object detection topologies (ROIPooling, ProirBox, DetectionOutput), and
-custom layers.
-Topology analysis is performed during the process of loading a network into plugin, and if topology is
-not applicable, an exception is generated.
+The following types of layers are not supported:
 
+* Layers that might arbitrary change tensor shape (such as Flatten, Permute, Reshape)
+* Layers specific to object detection topologies (ROIPooling, ProirBox, DetectionOutput)
+* Custom layers
+
+Topology analysis is performed during the process of loading a network into plugin, and if the topology is not supported, an exception is generated.
+
+## Using Dynamic Batching (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+Dynamic Batching is a feature that allows you to dynamically change batch size for inference calls within a preset batch size limit. This feature might be useful when batch size is unknown beforehand, and using extra large batch size is not desired or impossible due to resource limitations. For example, face detection with person age, gender, or mood recognition is a typical usage scenario.
+
+You can activate Dynamic Batching by setting the "DYN_BATCH_ENABLED" flag to "YES" in a configuration map that is passed to the plugin while loading a network. This configuration creates an `ExecutableNetwork` object that will allow setting batch size dynamically in all of its infer requests using the  [ie_api.batch_size](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.batch_size) method. The batch size that was set in the passed CNNNetwork object will be used as a maximum batch size limit.
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+dyn_config = {"DYN_BATCH_ENABLED": "YES"}
+ie.set_config(config=dyn_config, device_name=device)
+# Read a network in IR or ONNX format
+net = ie.read_network(path_to_model)
+net.batch_size = 32  # set the maximum batch size to 32
+exec_net = ie.load_network(network=net, device_name=device)
+```
+
+### Limitations
+
+Currently, certain limitations for the use of Dynamic Batching exist:
+
+* Use Dynamic Batching with CPU and GPU plugins only.
+* Use Dynamic Batching on topologies that consist of certain layers only:
+   * Convolution
+   * Deconvolution
+   * Activation
+   * LRN
+   * Pooling
+   * FullyConnected
+   * SoftMax
+   * Split
+   * Concatenation
+   * Power
+   * Eltwise
+   * Crop
+   * BatchNormalization
+   * Copy
+
+The following types of layers are not supported:
+
+* Layers that might arbitrary change tensor shape (such as Flatten, Permute, Reshape)
+* Layers specific to object detection topologies (ROIPooling, ProirBox, DetectionOutput)
+* Custom layers
+
+Topology analysis is performed during the process of loading a network into plugin, and if the topology is not supported, an exception is generated.
\ No newline at end of file
diff --git a/docs/IE_DG/Extensibility_DG/AddingNGraphOps.md b/docs/IE_DG/Extensibility_DG/AddingNGraphOps.md
index 8b3d50f6660..79ab802a5a1 100644
--- a/docs/IE_DG/Extensibility_DG/AddingNGraphOps.md
+++ b/docs/IE_DG/Extensibility_DG/AddingNGraphOps.md
@@ -1,6 +1,8 @@
-# Custom nGraph Operation {#openvino_docs_IE_DG_Extensibility_DG_AddingNGraphOps}
+# Custom nGraph Operations {#openvino_docs_IE_DG_Extensibility_DG_AddingNGraphOps}
 
-The Inference Engine Extension API allows you to register operation sets (opsets) with custom nGraph operations to support models with operations that OpenVINO™ does not support out-of-the-box.
+Inference Engine Extension API allows you to register operation sets (opsets) with custom nGraph operations to support models with operations which OpenVINO™ does not support out-of-the-box.
+
+Besides creating custom nGraph operations, to [support custom operations](../../HOWTO/Custom_Layers_Guide.md) in your model you must also create a Model Optimizer extension for the custom operations and an Inference Engine device plugin extension for the device you will use for inference.
 
 ## Operation Class
 
@@ -26,8 +28,8 @@ Based on that, declaration of an operation class can look as follows:
 
 The provided implementation has several fields:
 
- * `add` of type `int64_t` is an attribute of a custom operation.
- * `type_info` of type `ngraph::NodeTypeInfo` defines the type and version of an operation.
+ * `add` of type `int64_t` is an attribute of a custom operation
+ * `type_info` of type `ngraph::NodeTypeInfo` defines type and version of an operation
 
 ### Operation Constructors
 
@@ -67,14 +69,13 @@ To add custom operations to the [Extension](Extension.md) class, create an opera
 
 @snippet template_extension/old/extension.cpp extension:getOpSets
 
-This method returns a map of opsets that exist in the extension library.
-
-nGraph provides an opset mechanism to group operations into clusters. S. Different opsets distinguish between different versions of one operation.
+This method returns a map of opsets that exist in the [extension library](Extension.md). 
+nGraph provides an opset mechanism to group operations into clusters. Different opsets distinguish between different versions of one operation.
 
 When specifying opset names, follow the rules below:
 * Use unique opset names.
 * Do not use the following built-in opset names: `extension`, `experimental`, `opset1`, `opset2`, `opset3`, ... , `opsetN`.
-* Make sure that the Model Optimizer and your extension use the same opset names.
+* [Make sure that the Model Optimizer](../../HOWTO/Custom_Layers_Guide.md) and your extension use the same opset names.
 * IR v10 operations have the mandatory `version` attribute specifying the opset.
 Operations from the default opset cannot be redefined.
 
diff --git a/docs/IE_DG/Extensibility_DG/Building.md b/docs/IE_DG/Extensibility_DG/Building.md
index b1435914ccc..b4cf5a1f84d 100644
--- a/docs/IE_DG/Extensibility_DG/Building.md
+++ b/docs/IE_DG/Extensibility_DG/Building.md
@@ -2,13 +2,13 @@
 
 Inference Engine build infrastructure provides the Inference Engine Package for application development.
 
-To build an extension library, use the following CMake script:
+To configure the build of your extension library, use the following CMake script:
 
 @snippet template_extension/old/CMakeLists.txt cmake:extension
 
 This CMake script finds the Inference Engine and nGraph using the `find_package` CMake command.
 
-To build an extension library, run the commands below:
+To build the extension library, run the commands below:
 
 ```sh
 $ cd template_extension/old
diff --git a/docs/IE_DG/Extensibility_DG/CPU_Kernel.md b/docs/IE_DG/Extensibility_DG/CPU_Kernel.md
index 09f1838ca88..a5e4929b4f2 100644
--- a/docs/IE_DG/Extensibility_DG/CPU_Kernel.md
+++ b/docs/IE_DG/Extensibility_DG/CPU_Kernel.md
@@ -1,6 +1,8 @@
-# How to Implement Custom CPU Operations {#openvino_docs_IE_DG_Extensibility_DG_CPU_Kernel}
+# CPU Kernel Custom Operations {#openvino_docs_IE_DG_Extensibility_DG_CPU_Kernel}
 
-The primary means of the performance of the CPU codepath in the Inference Engine is the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN), and new CPU kernels extend the Inference Engine plugin for the Intel MKL-DNN. Implementing the InferenceEngine::ILayerExecImpl defines a general CPU-side extension. There are no Intel MKL-DNN specifics in the way you need to implement a kernel.
+To enable operations not supported by OpenVINO™ out of the box, you need a custom extension for Model Optimizer, a custom nGraph operation set, and a custom kernel for the device you will target. This page describes custom kernel support for the CPU device.
+
+The primary means of the performance of the CPU codepath in the Inference Engine is the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN), and new CPU kernels extend the Inference Engine plugin for the Intel MKL-DNN. Implementing the InferenceEngine::ILayerExecImpl API call defines a general CPU-side extension. There are no Intel MKL-DNN specifics in the way you need to implement a kernel.
 
 ## Implementation Class
 
@@ -20,31 +22,32 @@ The provided implementation has several fields:
 
 ### Constructor of Implementation
 
-An implementation constructor checks parameters of an nGraph operation, stores required attributes, and stores an error message in the case of an error.
+An implementation constructor checks parameters of an nGraph operation, stores required attributes, and stores an error message in case of an error.
 
 @snippet template_extension/old/cpu_kernel.cpp cpu_implementation:ctor
 
 ### `getSupportedConfigurations`
 
-InferenceEngine::ILayerExecImpl::getSupportedConfigurations method returns all supported configuration formats (input/output tensor layouts) for your implementation. To specify formats of data, use InferenceEngine::TensorDesc. Refer to the [Memory Primitives](../Memory_primitives.md) section for instructions.
+The InferenceEngine::ILayerExecImpl::getSupportedConfigurations method returns all supported configuration formats (input/output tensor layouts) for your implementation. To specify formats of data, use InferenceEngine::TensorDesc. Refer to the [Memory Primitives](../Memory_primitives.md) section for instructions.
 
 @snippet template_extension/old/cpu_kernel.cpp cpu_implementation:getSupportedConfigurations
 
 ### `init`
 
-InferenceEngine::ILayerExecImpl::init method gets a runtime-selected configuration from a vector that is populated from the `getSupportedConfigurations` method and checks the parameters:
+The InferenceEngine::ILayerExecImpl::init method gets a runtime-selected configuration from a vector that is populated from the `getSupportedConfigurations` method and checks the parameters:
 
 @snippet template_extension/old/cpu_kernel.cpp cpu_implementation:init
 
 ### `execute`
 
-InferenceEngine::ILayerExecImpl::execute method accepts and processes the actual tenors as input/output blobs:
+The InferenceEngine::ILayerExecImpl::execute method accepts and processes the actual tensors as input/output blobs:
 
 @snippet template_extension/old/cpu_kernel.cpp cpu_implementation:execute
 
 ## Register Implementation in `Extension` Class
 
 To register custom kernel implementation in the [Extension](Extension.md) class, implement the following methods:
+
 * <a href="#getImpTypes">getImplTypes</a>
 * <a href="#getImplementation">getImplementation</a>
 
@@ -66,4 +69,3 @@ InferenceEngine::IExtension::getImplementation returns the kernel implementation
 Use the `AddExtension` method of the general plugin interface to load your primitives:
 
 @snippet snippets/CPU_Kernel.cpp part0
-
diff --git a/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md b/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
index 3d276ede298..086f23045fd 100644
--- a/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
+++ b/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
@@ -1,7 +1,7 @@
 # Custom ONNX* Operators {#openvino_docs_IE_DG_Extensibility_DG_Custom_ONNX_Ops}
 
 The ONNX\* importer provides a mechanism to register custom ONNX operators based on predefined or custom nGraph operations.
-The function responsible for registering a new operator is called `ngraph::onnx_import::register_operator` and is defined in `onnx_import/onnx_utils.hpp`.
+The function responsible for registering a new operator is called `ngraph::onnx_import::register_operator` and defined in the `onnx_import/onnx_utils.hpp` file.
 
 ## Register Custom ONNX Operator Based on Predefined nGraph Operations
 
@@ -14,19 +14,25 @@ x <  0 => f(x) = x * beta
 where `alpha` and `beta` are float constants.
 
 1. Include headers:
+
 @snippet onnx_custom_op/onnx_custom_op.cpp onnx_custom_op:headers
 
 2. Register the CustomRelu operator in the ONNX importer:
+
 @snippet onnx_custom_op/onnx_custom_op.cpp onnx_custom_op:register_operator
+
 The `register_operator` function takes four arguments: op_type, opset version, domain, and a function object.
 The function object is a user-defined function that takes `ngraph::onnx_import::Node` as an input and based on that, returns a graph with nGraph operations.
-The `ngraph::onnx_import::Node` class represents a node in an ONNX model. It provides functions to fetch input node(s) using `get_ng_inputs`, attribute value using `get_attribute_value`, and many more. See `onnx_import/core/node.hpp` for full class declaration.
+The `ngraph::onnx_import::Node` class represents a node in an ONNX model. It provides functions to fetch input node(s) using `get_ng_inputs`, attribute value using `get_attribute_value`, and many more. See the `onnx_import/core/node.hpp` file for the full class declaration.
 
 New operator registration must happen before an ONNX model is read. For example, if an model uses the `CustomRelu` operator, call `register_operator("CustomRelu", ...)` before InferenceEngine::Core::ReadNetwork.
 Reregistering ONNX operators within the same process is supported. If you register an existing operator, you get a warning.
 
 The example below demonstrates an exemplary model that requires a previously created `CustomRelu` operator:
+```
 @include onnx_custom_op/custom_relu_model.prototxt
+```
+
 This model is in text format, so before it can be passed to Inference Engine, it has to be converted to binary using:
 ```py
 from google.protobuf import text_format
@@ -44,12 +50,14 @@ To create a graph with nGraph operations, visit [Custom nGraph Operations](Addin
 For a complete list of predefined nGraph operators, visit [Available Operations Sets](../../ops/opset.md).
 
 If you do not need an operator anymore, unregister it by calling `unregister_operator`. The function takes three arguments: `op_type`, `version`, and `domain`.
+
 @snippet onnx_custom_op/onnx_custom_op.cpp onnx_custom_op:unregister_operator
 
 ## Register Custom ONNX Operator Based on Custom nGraph Operations
 
 The same principles apply when registering a custom ONNX operator based on custom nGraph operations.
-This example shows how to register a custom ONNX operator based on `Operation` presented in [this tutorial](AddingNGraphOps.md), which is used in [TemplateExtension](Extension.md).
+This example shows how to register a custom ONNX operator based on `Operation` presented in [this tutorial](AddingNGraphOps.md), which is used in [TemplateExtension](Extension.md):
+
 @snippet template_extension/old/extension.cpp extension:ctor
 
 Here, the `register_operator` function is called in the constructor of Extension. The constructor makes sure that the function is called before InferenceEngine::Core::ReadNetwork, because InferenceEngine::Core::AddExtension must be called before a model with a custom operator is read.
@@ -66,4 +74,5 @@ The `ov_onnx_frontend` is a component of the `OpenVINO` package , so `find_packa
 Those libraries need to be passed to the `target_link_libraries` command in the CMakeLists.txt file.
 
 See CMakeLists.txt below for reference:
+
 @snippet onnx_custom_op/CMakeLists.txt cmake:onnx_custom_op
diff --git a/docs/IE_DG/Extensibility_DG/Extension.md b/docs/IE_DG/Extensibility_DG/Extension.md
index 189f5aaffc6..1925ce9e25e 100644
--- a/docs/IE_DG/Extensibility_DG/Extension.md
+++ b/docs/IE_DG/Extensibility_DG/Extension.md
@@ -25,6 +25,5 @@ Also, an `Extension` object should implement the following methods:
 Implement the InferenceEngine::IExtension::getOpSets method if the extension contains custom layers. 
 Read [Custom nGraph Operation](AddingNGraphOps.md) for more information.
 
-To understand how to integrate execution kernels to the extension library, read the [documentation about development of custom CPU kernels](CPU_Kernel.md).
-
-To understand how to register custom ONNX operator to the extension library, read the [documentation about custom ONNX operators](Custom_ONNX_Ops.md).
+To integrate execution kernels to the extension library, read [How to Implement Custom CPU Operations](CPU_Kernel.md).
+To register a custom ONNX\* operator to the extension library, read [Custom ONNX Operators](Custom_ONNX_Ops.md).
diff --git a/docs/IE_DG/Extensibility_DG/GPU_Kernel.md b/docs/IE_DG/Extensibility_DG/GPU_Kernel.md
index f206c2c0bcb..3305c00085b 100644
--- a/docs/IE_DG/Extensibility_DG/GPU_Kernel.md
+++ b/docs/IE_DG/Extensibility_DG/GPU_Kernel.md
@@ -1,15 +1,17 @@
 # How to Implement Custom GPU Operations {#openvino_docs_IE_DG_Extensibility_DG_GPU_Kernel}
 
-The GPU codepath abstracts many details about OpenCL\*. You need to provide the kernel code in OpenCL C and the configuration file that connects the kernel and its parameters to the parameters of the operation.
+To enable operations not supported by OpenVINO™ out of the box, you need a custom extension for Model Optimizer, a custom nGraph operation set, and a custom kernel for the device you will target. This page describes custom kernel support for the GPU device.
 
-There are two options of using the custom operation configuration file:
+The GPU codepath abstracts many details about OpenCL\*. You need to provide the kernel code in OpenCL C and an XML configuration file that connects the kernel and its parameters to the parameters of the operation.
+
+There are two options for using the custom operation configuration file:
 
 * Include a section with your kernels into the global automatically-loaded `cldnn_global_custom_kernels/cldnn_global_custom_kernels.xml` file, which is hosted in the `<INSTALL_DIR>/runtime/bin` folder
 * Call the `InferenceEngine::Core::SetConfig()` method from your application with the `InferenceEngine::PluginConfigParams::KEY_CONFIG_FILE` key and the configuration file name as a value before loading the network that uses custom operations to the plugin:
 
 @snippet snippets/GPU_Kernel.cpp part0
 
-All Inference Engine samples, except the trivial `hello_classification`,
+All Inference Engine samples, except the trivial `hello_classification`, and most Open Model Zoo demos 
 feature a dedicated command-line option `-c` to load custom kernels. For example, to load custom operations for the classification sample, run the command below:
 ```sh
 $ ./classification_sample -m <path_to_model>/bvlc_alexnet_fp16.xml -i ./validation_set/daily/227x227/apron.bmp -d GPU
@@ -132,8 +134,8 @@ queuing an OpenCL program for execution.
 
 ## Example Configuration File
 
-The following code sample provides an example configuration file in the
-`.xml` format. For information on the configuration file structure, see
+The following code sample provides an example configuration file in XML 
+format. For information on the configuration file structure, see
 [Configuration File Format](#config-file-format).
 ```xml
 <CustomLayer name="ReLU" type="SimpleGPU" version="1">
@@ -208,12 +210,12 @@ __kernel void example_relu_kernel(
 }
 ```
 
-> **NOTE:** As described in the previous section, all things like
+> **NOTE**: As described in the previous section, all items like
 > `INPUT0_TYPE` are actually defined as OpenCL (pre-)compiler inputs by
 > the Inference Engine for efficiency reasons. See [Debugging
 > Tips](#debugging-tips) for information on debugging the results.
 
-> **NOTE**: Several GPU-targeted kernels are also added to the binaries upon samples compilation
+> **NOTE**: Several GPU-targeted kernels are also added to the binaries upon compilation of samples
 > so that the sample application can easy load them.
 > Refer to the `cldnn_global_custom_kernels` folder in the GPU plugin installation directory.
 
@@ -221,10 +223,11 @@ __kernel void example_relu_kernel(
 
 * **Using `printf` in the OpenCL™ Kernels**.
 To debug the specific values, you can use `printf` in your kernels.
-However, be careful: for instance, do not output excessively
-as it would generate too much data. The `printf` output is typical, so
+However, be careful not to output excessively, which
+could generate too much data. The `printf` output is typical, so
 your output can be truncated to fit the buffer. Also, because of
 buffering, you actually get an entire buffer of output when the
 execution ends.<br>
+
 For more information, refer to the [printf
 Function](https://www.khronos.org/registry/OpenCL/sdk/1.2/docs/man/xhtml/printfFunction.html).
diff --git a/docs/IE_DG/Extensibility_DG/Intro.md b/docs/IE_DG/Extensibility_DG/Intro.md
index 852a20324df..ca3217a26ce 100644
--- a/docs/IE_DG/Extensibility_DG/Intro.md
+++ b/docs/IE_DG/Extensibility_DG/Intro.md
@@ -1,27 +1,39 @@
 # Inference Engine Extensibility Mechanism {#openvino_docs_IE_DG_Extensibility_DG_Intro}
 
-Inference Engine Extensibility API enables you to add support of custom operations to the Inference Engine.
-Extension should contain operation sets with custom operations and execution kernels for custom operations.
-Physically, an extension library can be represented as a dynamic library exporting the single function
-that creates a new extension instance.
+@sphinxdirective
 
-To load the Extensibility library to the `InferenceEngine::Core` object, use the
-`InferenceEngine::Core::AddExtension` method.
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_IE_DG_Extensibility_DG_AddingNGraphOps
+   openvino_docs_IE_DG_Extensibility_DG_Custom_ONNX_Ops
+   CPU Kernels Extensibility <openvino_docs_IE_DG_Extensibility_DG_CPU_Kernel>
+   GPU Kernels Extensibility <openvino_docs_IE_DG_Extensibility_DG_GPU_Kernel>
+   VPU Kernels Extensibility <openvino_docs_IE_DG_Extensibility_DG_VPU_Kernel>
+   openvino_docs_IE_DG_Extensibility_DG_Extension
+   openvino_docs_IE_DG_Extensibility_DG_Building
+
+@endsphinxdirective
+
+If your model contains operations not normally supported by OpenVINO, the Inference Engine Extensibility API lets you add support for those custom operations in a library containing custom nGraph operation sets, corresponding extensions to the Model Optimizer, and a device plugin extension. See the overview in the [Custom Operations Guide](../../HOWTO/Custom_Layers_Guide.md) to learn how these work together.
+
+To load the Extensibility library to the `InferenceEngine::Core` object, use the `InferenceEngine::Core::AddExtension` method.
 
 ## Inference Engine Extension Library
 
-Inference Engine Extension dynamic library contains the following components:
+An Inference Engine Extension dynamic library contains the following components:
 
  * [Extension Library](Extension.md):
-    - Contains custom operation sets.
-    - Provides CPU implementations for custom operations.
+    - Contains custom operation sets
+    - Provides CPU implementations for custom operations
  * [Custom nGraph Operation](AddingNGraphOps.md):
     - Enables the use of `InferenceEngine::Core::ReadNetwork` to read Intermediate Representation (IR) with unsupported
-    operations.
-    - Enables the creation of `ngraph::Function` with unsupported operations.
-    - Provides a shape inference mechanism for custom operations.
+    operations
+    - Enables the creation of `ngraph::Function` with unsupported operations
+    - Provides a shape inference mechanism for custom operations
 
-> **NOTE**: This documentation is written based on the `Template extension`, which demonstrates extension development details. Find the complete code of the `Template extension`, which is fully compilable and up-to-date, at `<dldt source tree>/docs/template_extension`.
+> **NOTE**: This documentation is written based on the [Template extension](https://github.com/openvinotoolkit/openvino/tree/master/docs/template_extension), which demonstrates extension development details. You can review the complete code, which is fully compilable and up-to-date, to see how it works.
 
 ## Execution Kernels
 
diff --git a/docs/IE_DG/Extensibility_DG/VPU_Kernel.md b/docs/IE_DG/Extensibility_DG/VPU_Kernel.md
index 4dca14ce502..86b2ad092bc 100644
--- a/docs/IE_DG/Extensibility_DG/VPU_Kernel.md
+++ b/docs/IE_DG/Extensibility_DG/VPU_Kernel.md
@@ -1,10 +1,12 @@
 # How to Implement Custom Layers for VPU (Intel® Neural Compute Stick 2) {#openvino_docs_IE_DG_Extensibility_DG_VPU_Kernel}
 
+To enable operations not supported by OpenVINO™ out of the box, you need a custom extension for Model Optimizer, a custom nGraph operation set, and a custom kernel for the device you will target. This page describes custom kernel support for one the VPU, the Intel® Neural Compute Stick 2 device, which uses the MYRIAD device plugin.
+
 > **NOTES:** 
 > * OpenCL\* custom layer support is available in the preview mode.
 > * This section assumes you are familiar with developing kernels using OpenCL.
 
-To customize your topology with an OpenCL layer, follow the steps below:
+To customize your topology with an OpenCL layer, carry out the tasks described on this page:
 
 1. Write and compile your OpenCL code with the standalone offline OpenCL compiler (`clc`).
 2. Write a configuration file to bind the OpenCL kernel to the topology file (`.xml`) of the model IR.
@@ -12,12 +14,12 @@ To customize your topology with an OpenCL layer, follow the steps below:
 
 ## Compile OpenCL code for VPU (Intel® Neural Compute Stick 2)
 
-> **NOTE:** OpenCL compiler, targeting Intel® Neural Compute Stick 2 for the SHAVE* processor only, is redistributed with OpenVINO.
-OpenCL support is provided by ComputeAorta*, and is distributed under a license agreement between Intel® and Codeplay* Software Ltd.
+> **NOTE**: OpenCL compiler, targeting Intel® Neural Compute Stick 2 for the SHAVE* processor only, is redistributed with OpenVINO.
+OpenCL support is provided by ComputeAorta* and is distributed under a license agreement between Intel® and Codeplay* Software Ltd.
 
 The OpenCL toolchain for the Intel® Neural Compute Stick 2 supports offline compilation only, so first compile OpenCL C code using the standalone `clc` compiler. You can find the compiler binary at `<INSTALL_DIR>/tools/cl_compiler`.
 
-> **NOTE:** By design, custom OpenCL layers support any OpenCL kernels written with 1.2 version assumed. It also supports half float extension and is optimized for this type, because it is a native type for Intel® Movidius™ VPUs.
+> **NOTE**: By design, custom OpenCL layers support any OpenCL kernels written assuming OpenCL version 1.2. It also supports half float extension and is optimized for this type, because it is a native type for Intel® Movidius™ VPUs.
 
 1. Prior to running a compilation, make sure that the following variables are set:
    * `SHAVE_MA2X8XLIBS_DIR=<INSTALL_DIR>/tools/cl_compiler/lib/`
@@ -25,19 +27,19 @@ The OpenCL toolchain for the Intel® Neural Compute Stick 2 supports offline com
    * `SHAVE_MYRIAD_LD_DIR=<INSTALL_DIR>/tools/cl_compiler/bin/`
    * `SHAVE_MOVIASM_DIR=<INSTALL_DIR>/tools/cl_compiler/bin/`
 2. Run the compilation with the command below. You should use `--strip-binary-header` to make an OpenCL runtime-agnostic binary runnable with the Inference Engine.
-```bash
-cd <INSTALL_DIR>/tools/cl_compiler/bin
-./clc --strip-binary-header custom_layer.cl -o custom_layer.bin
-```
+   ```bash
+   cd <INSTALL_DIR>/tools/cl_compiler/bin
+   ./clc --strip-binary-header custom_layer.cl -o custom_layer.bin
+   ```
 
 ## Write a Configuration File
 
 To tie the topology IR for a layer you customize, prepare a configuration file, so that the Inference Engine can find parameters for your kernel and the execution work grid is described.
-For example, given the following OpenCL kernel signature:
+For example, consider the following OpenCL kernel signature:
 ```cpp
 __kernel void reorg_nhwc(__global const half *src, __global half *out, int w, int h, int c, int stride);
 ```
-Configuration file for this kernel might be the following:
+A configuration file for this kernel might be the following:
 ```xml
 <CustomLayer name="ReorgYolo" type="MVCL" version="1">
    <Kernel entry="reorg_nhwc">
@@ -62,7 +64,7 @@ Each custom layer is described with the `CustomLayer` node. It has the following
   - Sub-node `Kernel` must contain the following attributes:
     - `entry` – The name of your kernel function as you defined it in a source file. In the example above, it is `reorg_nhwc`.
     - Node `Source` must contain the following attributes:
-      - `filename` – The path to a compiled binary relative to the `.xml` binding file.
+      - `filename` – The path to a compiled binary relative to the XML configuration file.
   - Sub-node `Parameters` – Describes parameters bindings. For more information, see the description below.
   - Sub-node `WorkSizes` – Describes local and global work group sizes and the source for dimension deduction as a pair `direction,port`. In the example above, the work group is described relatively to the dimension of the input tensor that comes through port 0 in the IR. `global` and `local` work group configurations support any simple math expressions with +,-,\*,/, and () from `B`(batch), `Y`(height), `X`(width) and `F`(channels).
   - Sub-node `Where` – Allows to customize bindings with the `key="value"` attribute. For example, to substitute only 3x3 convolutions, write `<Where kernel="3,3"/>` in the binding xml.
@@ -70,8 +72,8 @@ Each custom layer is described with the `CustomLayer` node. It has the following
   Parameter description supports `Tensor` of one of tensor types such as `input`, `output`, `input_buffer`, `output_buffer` or `data`, `Scalar`, or `Data` nodes and has the following format:
   - Each `Tensor` node of `input` or `output` type must contain the following attributes:
     - `arg-name` – The name of a kernel parameter in the kernel signature.
-    - `type` – Node type: `input` or `output` as in the IR.
-    - `port-index` – A number of input/output ports as in the IR.
+    - `type` – Node type: `input` or `output` as specified in the IR.
+    - `port-index` – A number of input/output ports as specified in the IR.
     - `format` – The channel order in the tensor. Optional conversion layers are generated if the custom layer format is not compatible with formats of neighboring layers. `BFXY`, `BYXF`, and `ANY` formats are supported currently.
   - Each `Tensor` node of `input_buffer` or `output_buffer` type must contain the following attributes:
     - `arg-name` – The name of a kernel parameter in the kernel signature.
@@ -417,7 +419,7 @@ This decreases the execution time up to 40% against the best performing vectoriz
 stalls completely on memory access without any prefetch. The same recommendation is applicable for scalar load/store
 from/to a `__blobal` pointer since work-group copying could be done in a vector fashion.
 
-10. Use a manual DMA extension. Local (on-chip) memory throughput is up to 24x higher than DDR throughput. Starting from OpenVINO™ 2020.1, VPU OpenCL features manual-DMA kernel extension to copy sub-tensor used by work group into local memory and performing compute without DDR evolved. Here is the simple GRN kernel implementation that runs over DDR. Local size is equal to (width of the input tensor, 1, 1) to define a large enough work group to get code automatically vectorized and unrolled, while global size is (width of the input tensor, height of the input tensor, 1):
+10. Use a manual DMA extension. Local (on-chip) memory throughput is up to 24x higher than DDR throughput. Starting from OpenVINO™ 2020.1, VPU OpenCL features manual-DMA kernel extension to copy sub-tensor used by work group into local memory and performing compute without DDR evolved. Here is the simple GRN kernel implementation that runs over DDR. Local size is in the form (width of the input tensor, 1, 1) to define a large enough work group to get code automatically vectorized and unrolled, while global size is (width of the input tensor, height of the input tensor, 1):
    ```cpp
    __kernel void grn_NCHW(
      __global const half* restrict src_data,
@@ -444,7 +446,9 @@ from/to a `__blobal` pointer since work-group copying could be done in a vector
      }
    }
    ```
+   
 This kernel can be rewritten to introduce special data binding `__dma_preload` and `__dma_postwrite intrinsics`. This means that instead of one kernel, a group of three kernels should be implemented: `kernelName`, `__dma_preload_kernelName`, and `__dma_postwrite_kernelName`.  `__dma_preload_kernelName` for a particular work group `n` is guaranteed to be executed before the `n`-th work group itself, while `__dma_postwrite_kernelName` is guaranteed to be executed after a corresponding work group. You can define one of those functions that are intended to be used to copy data from-to `__global` and `__local` memory. The syntactics requires exact functional signature match. The example below illustrates how to prepare your kernel for manual-DMA.
+
    ```cpp
    __kernel void __dma_preload_grn_NCHW(
      __global const half* restrict src,
@@ -453,7 +457,7 @@ This kernel can be rewritten to introduce special data binding `__dma_preload` a
      __local        half* restrict local_dst,
      int C,
      float bias)
-   {
+     {
      // ToDO: copy required piece of src tensor into local_src
    }
    
@@ -478,9 +482,9 @@ This kernel can be rewritten to introduce special data binding `__dma_preload` a
    {
      // same as the example above
    }
-   ```
-GRN kernel operates on channel-major tensors to compute average over full channel range and then normalizes input elements to produce the output.
-As a part of manual DMA extension, a group of work group copy functions are introduced in addition to `async_work_group_copy`, which is also mapped to DMA call.
+   ``` 
+The GRN kernel operates on channel-major tensors to compute average over full channel range and then normalizes input elements to produce the output.
+As a part of the manual DMA extension, a group of work group copy functions are introduced in addition to `async_work_group_copy`, which is also mapped to a DMA call.
 
 Here is the list of supported functions:
 ```cpp
@@ -613,7 +617,7 @@ __kernel void grn_NCHW(
 
 Note the `get_local_size` and `get_local_id` usage inside the kernel. 21x speedup is expected for a kernel on enet-curbs setup because it was completely limited by memory usage.
 
-An alternative method of using DMA is to use work item copy extension. Those functions are executed inside a kernel and requires work groups equal to single work item.
+An alternative method to using DMA is to use work item copy extension. Those functions are executed inside a kernel and requires work groups equal to single work item.
 
 Here is the list of supported work item functions:
 ```cpp
diff --git a/docs/IE_DG/InferenceEngine_QueryAPI.md b/docs/IE_DG/InferenceEngine_QueryAPI.md
index 34579924bad..904e4c20053 100644
--- a/docs/IE_DG/InferenceEngine_QueryAPI.md
+++ b/docs/IE_DG/InferenceEngine_QueryAPI.md
@@ -1,55 +1,62 @@
-Introduction to Inference Engine Device Query API {#openvino_docs_IE_DG_InferenceEngine_QueryAPI}
-===============================
+# Introduction to Inference Engine Device Query API {#openvino_docs_IE_DG_InferenceEngine_QueryAPI}
 
-This section provides a high-level description of the process of querying of different device properties and configuration values.
-Refer to the [Hello Query Device Sample](../../samples/cpp/hello_query_device/README.md) sources and [Multi-Device Plugin guide](supported_plugins/MULTI.md) for example of using the Inference Engine Query API in user applications.
+## Inference Engine Query API (C++)
 
-## Using the Inference Engine Query API in Your Code
+@sphinxdirective
+.. raw:: html
 
-The Inference Engine `Core` class provides the following API to query device information, set or get different device configuration properties:
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
 
-* <code>InferenceEngine::Core::GetAvailableDevices</code> - Provides a list of available devices. If there are more than one instance of a specific device, the devices are enumerated with `.suffix` where `suffix` is a unique string identifier. The device name can be passed to all methods of the `InferenceEngine::Core` class that work with devices, for example `InferenceEngine::Core::LoadNetwork`.
-* <code>InferenceEngine::Core::GetMetric</code> - Provides information about specific device.
-  <code>InferenceEngine::Core::GetConfig</code> - Gets the current value of a specific configuration key.
-* <code>InferenceEngine::Core::SetConfig</code> - Sets a new value for the configuration key.
+The OpenVINO™ toolkit supports inferencing with several types of devices (processors or accelerators). 
+This section provides a high-level description of the process of querying of different device properties and configuration values at runtime. Refer to the [Hello Query Device С++ Sample](../../samples/cpp/hello_query_device/README.md) sources and the [Multi-Device Plugin documentation](supported_plugins/MULTI.md) for examples of using the Inference Engine Query API in user applications.
+
+### Using the Inference Engine Query API in Your Code
+
+The `InferenceEngine::Core` class provides the following API to query device information, set or get different device configuration properties:
+
+* `InferenceEngine::Core::GetAvailableDevices` - Provides a list of available devices. If there are more than one instance of a specific device, the devices are enumerated with `.suffix` where `suffix` is a unique string identifier. The device name can be passed to all methods of the `InferenceEngine::Core` class that work with devices, for example `InferenceEngine::Core::LoadNetwork`.
+* `InferenceEngine::Core::GetMetric` - Provides information about specific device.
+  `InferenceEngine::Core::GetConfig` - Gets the current value of a specific configuration key.
+* `InferenceEngine::Core::SetConfig` - Sets a new value for the configuration key.
 
 The `InferenceEngine::ExecutableNetwork` class is also extended to support the Query API:
 
-* <code>InferenceEngine::ExecutableNetwork::GetMetric</code>
-* <code>InferenceEngine::ExecutableNetwork::GetConfig</code>
-* <code>InferenceEngine::ExecutableNetwork::SetConfig</code>
+* `InferenceEngine::ExecutableNetwork::GetMetric`
+* `InferenceEngine::ExecutableNetwork::GetConfig`
+* `InferenceEngine::ExecutableNetwork::SetConfig`
 
-## Query API in the Core Class
+### Query API in the Core Class
 
-### GetAvailableDevices
+#### GetAvailableDevices
 
 @snippet snippets/InferenceEngine_QueryAPI0.cpp part0
 
-The function returns list of available devices, for example:
+The function returns a list of available devices, for example:
+
 ```
 MYRIAD.1.2-ma2480
 MYRIAD.1.4-ma2480
 CPU
 GPU.0
 GPU.1
-...
 ```
 
 Each device name can then be passed to:
 
 * `InferenceEngine::Core::LoadNetwork` to load the network to a specific device.
 * `InferenceEngine::Core::GetMetric` to get common or device specific metrics.
-* All other methods of the `Core` class that accept `deviceName`.
+* All other methods of the `InferenceEngine::Core` class that accept `deviceName`.
 
-### GetConfig()
+#### GetConfig()
 
-The code below demonstrates how to understand whether `HETERO` device dumps `.dot` files with split graphs during the split stage:
+The code below demonstrates how to understand whether the `HETERO` device dumps GraphViz `.dot` files with split graphs during the split stage:
 
 @snippet snippets/InferenceEngine_QueryAPI1.cpp part1
 
 For documentation about common configuration keys, refer to `ie_plugin_config.hpp`. Device specific configuration keys can be found in corresponding plugin folders.
 
-### GetMetric()
+#### GetMetric()
 
 * To extract device properties such as available device, device name, supported configuration keys, and others, use the `InferenceEngine::Core::GetMetric` method:
 
@@ -57,26 +64,172 @@ For documentation about common configuration keys, refer to `ie_plugin_config.hp
 
 A returned value appears as follows: `Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz`.
 
-> **NOTE**: All metrics have specific type, which is specified during metric instantiation. The list of common device-agnostic metrics can be found in `ie_plugin_config.hpp`. Device specific metrics (for example, for `HDDL`, `MYRIAD` devices) can be found in corresponding plugin folders.
+> **NOTE**: All metrics have a type, which is specified during metric instantiation. The list of common device-agnostic metrics can be found in `ie_plugin_config.hpp`. Device specific metrics (for example, for HDDL or MYRIAD devices) can be found in corresponding plugin folders.
 
-## Query API in the ExecutableNetwork Class
+### Query API in the ExecutableNetwork Class
 
-### GetMetric()
+#### GetMetric()
 
-The method is used to get executable network specific metric such as `METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)`:
+The method is used to get an executable network specific metric such as `METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)`:
 
 @snippet snippets/InferenceEngine_QueryAPI3.cpp part3
 
-Or the current temperature of `MYRIAD` device:
+Or the current temperature of the `MYRIAD` device:
 
 @snippet snippets/InferenceEngine_QueryAPI4.cpp part4
 
-### GetConfig()
+#### GetConfig()
 
 The method is used to get information about configuration values the executable network has been created with:
 
 @snippet snippets/InferenceEngine_QueryAPI5.cpp part5
 
-### SetConfig()
+#### SetConfig()
 
 The only device that supports this method is [Multi-Device](supported_plugins/MULTI.md).
+
+## Inference Engine Query API (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+This section provides a high-level description of the process of querying of different device properties and configuration values. Refer to the [Hello Query Device Python Sample](../../inference-engine/ie_bridges/python/sample/hello_query_device/README.md) sources and the [Multi-Device Plugin documentation](supported_plugins/MULTI.md) for examples of using the Inference Engine Query API in user applications.
+
+### Using the Inference Engine Query API in Your Code
+
+The Inference Engine [Core](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino-inference-engine-iecore) class provides the following API to query device information, set or get different device configuration properties:
+
+* [ie_api.IECore.available_devices](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.available_devices) - Provides a list of available devices. If there are more than one instance of a specific device, the devices are enumerated with .suffix where suffix is a unique string identifier. The device name can be passed to all methods of the IECore class that work with devices, for example [ie_api.IECore.load_network](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.load_network).
+* [ie_api.ieCore.get_metric](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.get_metric) - Provides information about specific device.
+* [ie_api.IECore.get_config](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.get_config) - Gets the current value of a specific configuration key.
+* [ie_api.IECore.set_config](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.set_config)  - Sets a new value for the configuration key.
+
+The [ie_api.ExecutableNetwork](api/ie_python_api/_autosummary/openvino.inference_engine.ExecutableNetwork.html) class is also extended to support the Query API:
+* [ie_api.ExecutableNetwork.get_metric](api/ie_python_api/_autosummary/openvino.inference_engine.ExecutableNetwork.html#openvino.inference_engine.ExecutableNetwork.get_metric)
+* [ie_api.ExecutableNetwork.get_config](latest/api/ie_python_api/_autosummary/openvino.inference_engine.ExecutableNetwork.html#openvino.inference_engine.ExecutableNetwork.get_config)
+* There is no method to call for set_config, but the equivalent action is described below.
+
+### Query API in the IECore Class
+
+#### Get Available Devices
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+print(ie.available_devices)
+```
+
+This code prints a list of available devices, for example:
+
+```
+MYRIAD.1.2-ma2480
+MYRIAD.1.4-ma2480
+FPGA.0
+FPGA.1
+CPU
+GPU.0
+GPU.1
+```
+
+Each device name can then be passed to:
+
+* `IECore.load_network` to load the network to a specific device.
+* `IECore.get_metric` to get common or device specific metrics.
+* All other methods of the `IECore` class that accept a device name.
+
+#### Get Metric
+
+To extract device properties such as available device, device name, supported configuration keys, and others, use the [IECore.get_metric](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.get_metric) method:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+ie.get_metric(device_name="CPU", metric_name="FULL_DEVICE_NAME")
+```
+
+A returned value appears as follows: `Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz`.
+
+To list all supported metrics for a device:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+ie.get_metric(device_name="GPU", metric_name="SUPPORTED_METRICS")
+```
+
+#### Get Configuration
+
+The code below uses the [IECore.get_config](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.get_config) method and demonstrates how to understand whether the HETERO device dumps .dot files with split graphs during the split stage:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+ie.get_config(device_name="HETERO", config_name="HETERO_DUMP_GRAPH_DOT")
+```
+
+To list all supported configuration keys for a device:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+ie.get_metric(device_name=device, metric_name="SUPPORTED_CONFIG_KEYS")
+```
+
+For documentation about common configuration keys, refer to `ie_plugin_config.hpp`. Device specific configuration keys can be found in corresponding plugin folders.
+
+
+### Query API in the ExecutableNetwork Class
+
+#### Get Metric
+
+To get the name of the loaded network:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_xml_file)
+exec_net = ie.load_network(network=net, device_name=device)
+exec_net.get_metric("NETWORK_NAME")
+```
+
+Use `exec_net.get_metric("SUPPORTED_METRICS")` to list all supported metrics for an ExecutableNetwork instance.
+
+
+#### Get Configuration
+
+The [IECore.get_config](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.get_config) method is used to get information about configuration values the executable network has been created with:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_xml_file)
+exec_net = ie.load_network(network=net, device_name="CPU")
+exec_net.get_config("CPU_THREADS_NUM")
+```
+
+Or the current temperature of MYRIAD device:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_xml_file)
+exec_net = ie.load_network(network=net, device_name="MYRIAD")
+exec_net.get_config("DEVICE_THERMAL")
+```
+
+Use `exec_net.get_metric("SUPPORTED_CONFIG_KEYS")`  to list all supported configuration keys.
+
+#### Set Configuration
+
+The only device that supports this method in the ExecutableNetwork class is the [Multi-Device](supported_plugins/MULTI.md), where you can change the priorities of the devices for the Multi plugin in real time: `exec_net.set_config({{"MULTI_DEVICE_PRIORITIES", "GPU,CPU"}})`. See the Multi-Device documentation for more details.
\ No newline at end of file
diff --git a/docs/IE_DG/Int8Inference.md b/docs/IE_DG/Int8Inference.md
index 32d410b09ef..20f002b2a29 100644
--- a/docs/IE_DG/Int8Inference.md
+++ b/docs/IE_DG/Int8Inference.md
@@ -1,6 +1,6 @@
 # Low-Precision 8-bit Integer Inference {#openvino_docs_IE_DG_Int8Inference}
 
-## Supported devices
+## Disclaimer
 
 Low-precision 8-bit inference is optimized for:
 - Intel® architecture processors with the following instruction set architecture extensions:  
@@ -11,43 +11,57 @@ Low-precision 8-bit inference is optimized for:
 - Intel® processor graphics:
   - Intel® Iris® Xe Graphics
   - Intel® Iris® Xe MAX Graphics
-- A model must be quantized. You can use a quantized model from [OpenVINO™ Toolkit Intel's Pre-Trained Models](@ref omz_models_group_intel) or quantize a model yourself. For quantization, you can use the:
-  - [Post-Training Optimization Tool](@ref pot_docs_LowPrecisionOptimizationGuide) delivered with the Intel® Distribution of OpenVINO™ toolkit release package.
-  - [Neural Network Compression Framework](https://www.intel.com/content/www/us/en/artificial-intelligence/posts/openvino-nncf.html) available on GitHub: https://github.com/openvinotoolkit/nncf
 
-## Low-Precision 8-bit Integer Inference Workflow
+## Introduction
 
-8-bit computations (referred to as `int8`) offer better performance compared to the results of inference in higher precision (for example, `fp32`), because they allow loading more data into a single processor instruction. Usually the cost for significant boost is reduced accuracy. However, it is proved that an accuracy drop can be negligible and depends on task requirements, so that the application engineer can set up the maximum accuracy drop that is acceptable.
+For 8-bit integer computation, a model must be quantized. You can use a quantized model from [OpenVINO™ Toolkit Intel's Pre-Trained Models](@ref omz_models_group_intel) or quantize a model yourself. For quantization, you can use the following:
+- [Post-Training Optimization Tool](@ref pot_docs_LowPrecisionOptimizationGuide) delivered with the Intel® Distribution of OpenVINO™ toolkit release package
+- [Neural Network Compression Framework](https://www.intel.com/content/www/us/en/artificial-intelligence/posts/openvino-nncf.html) available on GitHub: https://github.com/openvinotoolkit/nncf
 
-For 8-bit integer computations, a model must be quantized. Quantized models can be downloaded from [Overview of OpenVINO™ Toolkit Intel's Pre-Trained Models](@ref omz_models_group_intel). If the model is not quantized, you can use the [Post-Training Optimization Tool](@ref pot_README) to quantize the model. The quantization process adds [FakeQuantize](../ops/quantization/FakeQuantize_1.md) layers on activations and weights for most layers. Read more about mathematical computations in the [Uniform Quantization with Fine-Tuning](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md).
+The quantization process adds [FakeQuantize](../ops/quantization/FakeQuantize_1.md) layers on activations and weights for most layers. Read more about mathematical computations in the [Uniform Quantization with Fine-Tuning](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md).
 
-When you pass the quantized IR to the OpenVINO™ plugin, the plugin automatically recognizes it as a quantized model and performs 8-bit inference. Note, if you pass a quantized model to another plugin that does not support 8-bit inference but supports all operations from the model, the model is inferred in precision that this plugin supports.
+When you pass the quantized IR to the OpenVINO™ plugin, the plugin automatically recognizes it as a quantized model and performs 8-bit inference. Note that if you pass a quantized model to another plugin that does not support 8-bit inference but supports all operations from the model, the model is inferred in precision that this plugin supports.
 
-In *Runtime stage*, the quantized model is loaded to the plugin. The plugin uses the `Low Precision Transformation` component to update the model to infer it in low precision:
-   - Update `FakeQuantize` layers to have quantized output tensors in a low precision range and add dequantization layers to compensate the update. Dequantization layers are pushed through as many layers as possible to have more layers in low precision. After that, most layers quantized input tensors in the low precision range and can be inferred in low precision. Ideally, dequantization layers should be fused in the next `FakeQuantize` layer.
-   - Quantize weights and store them in `Constant` layers. 
+At runtime, the quantized model is loaded to the plugin. The plugin uses the `Low Precision Transformation` component to update the model to infer it in low precision:
+   - Update `FakeQuantize` layers to have quantized output tensors in low-precision range and add dequantization layers to compensate for the update. Dequantization layers are pushed through as many layers as possible to have more layers in low precision. After that, most layers have quantized input tensors in low-precision range and can be inferred in low precision. Ideally, dequantization layers should be fused in the next `FakeQuantize` layer.
+   - Weights are quantized and stored in `Constant` layers. 
 
 ## Prerequisites
 
-Let's explore the quantized [TensorFlow* implementation of ResNet-50](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf) model. Use the [Model Downloader](@ref omz_tools_downloader) tool to download the `fp16` model from [OpenVINO™ Toolkit - Open Model Zoo repository](https://github.com/openvinotoolkit/open_model_zoo):
+Let's explore quantized [TensorFlow* implementation of the ResNet-50](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf) model. Use [Model Downloader](@ref omz_tools_downloader) to download the `FP16` model from [OpenVINO™ Toolkit - Open Model Zoo repository](https://github.com/openvinotoolkit/open_model_zoo):
+
+> **NOTE**: If you installed OpenVINO with pip, use `omz_downloader` and `omz_quantizer` instead of `download.py` and `quantize.py`. See [Open Model Zoo documentation](https://github.com/openvinotoolkit/open_model_zoo/tree/master/tools/model_tools#model-downloader-usage). Replace `./benchmark_app` with `benchmark_app`.
+
 ```sh
-cd $INTEL_OPENVINO_DIR/deployment_tools/tools/model_downloader
-./downloader.py --name resnet-50-tf --precisions FP16-INT8 --output_dir <your_model_directory>
+<omz_dir>/tools/downloader/downloader.py --name resnet-50-tf --precisions FP16-INT8
 ```
-After that, you should quantize the model by the [Model Quantizer](@ref omz_tools_downloader) tool. For the dataset, you can choose to download the ImageNet dataset from [here](https://www.image-net.org/download.php).
+After that you should quantize the model with the [Model Quantizer](@ref omz_tools_downloader) tool.
 ```sh
-./quantizer.py --model_dir --name public/resnet-50-tf --dataset_dir <DATASET_DIR> --precisions=FP16-INT8
+<omz_dir>/tools/downloader/quantizer.py --model_dir public/resnet-50-tf --dataset_dir <DATASET_DIR> --precisions=FP16-INT8
 ```
 
-## Inference
-
-The simplest way to infer the model and collect performance counters is the [C++ Benchmark Application](../../samples/cpp/benchmark_app/README.md). 
+The simplest way to infer the model and collect performance counters is the [Benchmark Application](../../samples/cpp/benchmark_app/README.md): 
 ```sh
 ./benchmark_app -m resnet-50-tf.xml -d CPU -niter 1 -api sync -report_type average_counters  -report_folder pc_report_dir
 ```
-If you infer the model with the Inference Engine CPU plugin and collect performance counters, all operations (except the last non-quantized SoftMax) are executed in INT8 precision.  
+If you infer the model with the OpenVINO™ CPU plugin and collect performance counters, all operations (except the last non-quantized SoftMax) are executed in INT8 precision.  
 
-## Results analysis
+## Low-Precision 8-bit Integer Inference Workflow
+
+For 8-bit integer computations, a model must be quantized. Quantized models can be downloaded from [Overview of OpenVINO™ Toolkit Intel's Pre-Trained Models](@ref omz_models_group_intel). If the model is not quantized, you can use the [Post-Training Optimization Tool](@ref pot_README) to quantize the model. The quantization process adds [FakeQuantize](../ops/quantization/FakeQuantize_1.md) layers on activations and weights for most layers. Read more about mathematical computations in the [Uniform Quantization with Fine-Tuning](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md).
+
+8-bit inference pipeline includes two stages (also refer to the figure below):
+1. *Offline stage*, or *model quantization*. During this stage, [FakeQuantize](../ops/quantization/FakeQuantize_1.md) layers are added before most layers to have quantized tensors before layers in a way that low-precision accuracy drop for 8-bit integer inference satisfies the specified threshold. The output of this stage is a quantized model. Quantized model precision is not changed, quantized tensors are in original precision range (`fp32`). `FakeQuantize` layer has `levels` attribute which defines quants count. Quants count defines precision which is used during inference. For `int8` range `levels` attribute value has to be 255 or 256. To quantize the model, you can use the [Post-Training Optimization Tool](@ref pot_README) delivered with the Intel® Distribution of OpenVINO™ toolkit release package.
+
+   When you pass the quantized IR to the OpenVINO™ plugin, the plugin automatically recognizes it as a quantized model and performs 8-bit inference. Note, if you pass a quantized model to another plugin that does not support 8-bit inference but supports all operations from the model, the model is inferred in precision that this plugin supports.
+
+2. *Runtime stage*. This stage is an internal procedure of the OpenVINO™ plugin. During this stage, the quantized model is loaded to the plugin. The plugin uses `Low Precision Transformation` component to update the model to infer it in low precision:
+   - Update `FakeQuantize` layers to have quantized output tensors in low precision range and add dequantization layers to compensate the update. Dequantization layers are pushed through as many layers as possible to have more layers in low precision. After that, most layers have quantized input tensors in low precision range and can be inferred in low precision. Ideally, dequantization layers should be fused in the next `FakeQuantize` layer.
+   - Weights are quantized and stored in `Constant` layers. 
+
+![int8_flow]
+
+## Performance Counters
 
 Information about layer precision is stored in the performance counters that are
 available from the Inference Engine API. For example, the part of performance counters table for quantized [TensorFlow* implementation of ResNet-50](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf) model inference on [CPU Plugin](supported_plugins/CPU.md) looks as follows:
@@ -65,12 +79,12 @@ available from the Inference Engine API. For example, the part of performance co
 | resnet\_model/add\_5/fq\_input\_1                         | NOT\_RUN   | FakeQuantize | undef                | 0             | 0            |
 
 
-> The `exeStatus` column of the table includes possible values:
-> - `EXECUTED` - layer was executed by standalone primitive,
-> - `NOT_RUN` - layer was not executed by standalone primitive or was fused with another operation and executed in another layer primitive.  
->
-> The `execType` column of the table includes inference primitives with specific suffixes. The layers have the following marks:
-> * Suffix `I8` for layers that had 8-bit data type input and were computed in 8-bit precision
-> * Suffix `FP32` for layers computed in 32-bit precision 
+   The `exeStatus` column of the table includes possible values:
+   - `EXECUTED` - layer was executed by standalone primitive,
+   - `NOT_RUN` - layer was not executed by standalone primitive or was fused with another operation and executed in another layer primitive.  
+   
+   The `execType` column of the table includes inference primitives with specific suffixes. The layers have the following marks:
+   * Suffix `I8` for layers that had 8-bit data type input and were computed in 8-bit precision
+   * Suffix `FP32` for layers computed in 32-bit precision 
 
-All `Convolution` layers are executed in int8 precision. Rest layers are fused into Convolutions using post operations optimization technique, which is described in [Internal CPU Plugin Optimizations](supported_plugins/CPU.md).
+   All `Convolution` layers are executed in int8 precision. Rest layers are fused into Convolutions using post operations optimization technique, which is described in [Internal CPU Plugin Optimizations](supported_plugins/CPU.md).
diff --git a/docs/IE_DG/Integrate_with_customer_application_new_API.md b/docs/IE_DG/Integrate_with_customer_application_new_API.md
index b7d3b69735b..b3c16cd6e38 100644
--- a/docs/IE_DG/Integrate_with_customer_application_new_API.md
+++ b/docs/IE_DG/Integrate_with_customer_application_new_API.md
@@ -1,131 +1,252 @@
-Integrate the Inference Engine with Your Application {#openvino_docs_IE_DG_Integrate_with_customer_application_new_API}
-===============================
+# Integrate Inference Engine {#openvino_docs_IE_DG_Integrate_with_customer_application_new_API}
 
-This section provides a high-level description of the process of integrating the Inference Engine into your application.
-Refer to the [Hello Classification Sample](../../samples/cpp/hello_classification/README.md) sources
-for example of using the Inference Engine in applications.
+## Integrate Inference Engine with Your C++ Application
 
-## Use the Inference Engine API in Your Code
+@sphinxdirective
+.. raw:: html
 
-The core `libov_runtime.so` library implements loading and parsing a model Intermediate Representation (IR), and triggers inference using a specified device. The core library has the following API:
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
 
-* `InferenceEngine::Core`
-* `InferenceEngine::Blob`, `InferenceEngine::TBlob`,
-  `InferenceEngine::NV12Blob`
-* `InferenceEngine::BlobMap`
-* `InferenceEngine::InputsDataMap`, `InferenceEngine::InputInfo`,
-* `InferenceEngine::OutputsDataMap`
+The following diagram illustrates the typical Inference Engine С++ API workflow:
 
-C++ Inference Engine API wraps the capabilities of core library:
+![ie_api_flow_cpp]
 
-* `InferenceEngine::CNNNetwork`
-* `InferenceEngine::ExecutableNetwork`
-* `InferenceEngine::InferRequest`
+Read the sections below to learn about each item.
 
-## Integration Steps
+> **NOTE**: Before start using Inference Engine, make sure you set all environment variables during the installation. If you did not, follow the instructions from the _Set the Environment Variables_ section in the installation guides:
+> * [For Windows* 10](../install_guides/installing-openvino-windows.md)
+> * [For Linux*](../install_guides/installing-openvino-linux.md)
+> * [For macOS*](../install_guides/installing-openvino-macos.md)
+> * To build an open source version, use the [Inference Engine Build Instructions](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
 
-Integration process includes the following steps:
-![integration_process]
+### Link with Inference Library
 
-1) **Create Inference Engine Core** to manage available devices and read network objects:
+1. **Create a structure** for the project:
+   ``` sh
+   project/
+       ├── CMakeLists.txt  - CMake file to build
+       ├── ...             - Additional folders like includes/
+       └── src/            - source folder
+           └── main.cpp
+   build/                  - build directory
+       ...      
+   ```
+
+2. **Include Inference Engine, nGraph and OpenCV libraries** in `project/CMakeLists.txt`  
+[OpenCV](https://docs.opencv.org/master/db/df5/tutorial_linux_gcc_cmake.html) integration is needed mostly for pre-processing input data and nGraph for more complex applications using [nGraph API](../nGraph_DG/nGraph_dg.md).
+   ``` cmake
+   cmake_minimum_required(VERSION 3.0.0)
+   project(project_name)
+   find_package(ngraph REQUIRED)
+   find_package(InferenceEngine REQUIRED)
+   find_package(OpenCV REQUIRED)
+   add_executable(${PROJECT_NAME} src/main.cpp)
+   target_link_libraries(${PROJECT_NAME} PRIVATE ${InferenceEngine_LIBRARIES} ${OpenCV_LIBS}    ${NGRAPH_LIBRARIES})
+   ```
+
+### Use Inference Engine API to Implement Inference Pipeline
+
+This section provides step-by-step instructions to implement a typical inference pipeline with the Inference Engine C++ API:   
+
+![ie_api_use_cpp]
+#### Step 1. Create Inference Engine Core 
+
+Use the following code to create Inference Engine Core to manage available devices and read network objects:
 
 @snippet snippets/Integrate_with_customer_application_new_API.cpp part0
 
-2) **Read a model IR** created by the Model Optimizer (.xml is supported format):
+#### Step 2 (Optional). Configure Input and Output of the Model
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part1
+@sphinxdirective
+.. raw:: html
 
-**Or read the model from ONNX format**. You can find more information about the ONNX format support in the document [ONNX format support in the OpenVINO™](./ONNX_Support.md).
+    <div class="collapsible-section">
+@endsphinxdirective
+    
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part2
+Optionally, configure input and output of the model using the steps below:
 
-3) **Configure input and output**. Request input and output information using `InferenceEngine::CNNNetwork::getInputsInfo()`, and `InferenceEngine::CNNNetwork::getOutputsInfo()`
-methods:
+1. Load a model to a Core object:
+   @sphinxdirective
+   
+   .. tab:: IR
+   
+      .. code-block:: c
+   
+         auto network  = core.ReadNetwork("model.xml");
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part3
+   .. tab:: ONNX
+      
+      .. code-block:: c
+         
+         auto network = core.ReadNetwork("model.onnx");
 
-  Optionally, set the number format (precision) and memory layout for inputs and outputs. Refer to the
-  [Supported configurations](supported_plugins/Supported_Devices.md) chapter to choose the relevant configuration.
+      You can find more information about the ONNX format support in the document `ONNX format support in the OpenVINO™ <https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_ONNX_Support.html>`_   
+   
+   .. tab:: nGraph
+      
+      .. code-block:: c
+         
+         std::shared_ptr<Function> createNetwork() {
+            // To construct a network, please follow 
+            // https://docs.openvinotoolkit.org/latest/openvino_docs_nGraph_DG_build_function.html
+         }
+         auto network = CNNNetwork(createNetwork());
 
-  You can also allow input of any size. To do this, mark each input as resizable by setting a desired resize algorithm (e.g. `BILINEAR`) inside of the appropriate input info.
+   @endsphinxdirective
 
-  Basic color format conversions are supported as well. By default, the Inference Engine assumes
-  that the input color format is `BGR` and color format conversions are disabled. The Inference
-  Engine supports the following color format conversions:
-  * `RGB->BGR`
-  * `RGBX->BGR`
-  * `BGRX->BGR`
-  * `NV12->BGR`
+2. Request input and output information using `InferenceEngine::CNNNetwork::getInputsInfo()`, and `InferenceEngine::CNNNetwork::getOutputsInfo()` methods:
+   ```cpp
+   /** Take information about all topology inputs **/
+   InferenceEngine::InputsDataMap input_info = network.getInputsInfo();
+   /** Iterate over all input info**/
+   for (auto &item : input_info) {
+       auto input_data = item.second;
+           // Add your input configuration steps here
+   }
+   
+   /** Take information about all topology outputs **/
+   InferenceEngine::OutputsDataMap output_info = network.getOutputsInfo();
+   /** Iterate over all output info**/
+   for (auto &item : output_info) {
+       auto output_data = item.second;
+           // Add your output configuration steps here
+   }
+   ```
+   Configuring options:
+   1. **Set precision** (number format): FP16, FP32, INT8, etc. Refer to the Supported Configurations section on the [Supported Devices](supported_plugins/Supported_Devices.md) page to choose the relevant configuration.<br>
+   For input (*iterate over all input info*):
+   ```cpp
+   input_data->setPrecision(InferenceEngine::Precision::U8);
+   ```
+   For output  (*iterate over all output info*):
+   ```cpp
+   output_data->setPrecision(InferenceEngine::Precision::FP32);
+   ```
+   **By default**, the input and output precision is set to `Precision::FP32`.
 
-  where `X` is a channel that will be ignored during inference. To enable the conversions, set a
-  desired color format (for example, `RGB`) for each input inside of the appropriate input info.
+   2. **Set layout** (NCHW, ).<br>
+   For input (*iterate over all input info*):
+   ```cpp
+   input_data->setLayout(InferenceEngine::Layout::NCHW);
+   ```
+   **By default**, the input layout is set to `Layout::NCHW`.<br>
+   For output (*iterate over all output info*):
+   ```cpp
+   output_data->setLayout(InferenceEngine::Layout::NC);
+   ```
+      **By default**, the output layout depends on a number of its dimensions:<br>
+      |Number of dimensions |  5    |  4   |   3 |  2 |  1 |
+      |:--------------------|-------|------|-----|----|----|
+      |Layout               | NCDHW | NCHW | CHW | NC | C  |
+   3. **Set resize algorithm for inputs** (Bilinear). You can allow input of any size. To do this, mark each input as resizable by setting a desired resize algorithm (e.g. `BILINEAR`) inside of the appropriate input info (*Iterate over all input info*):
+   ```cpp
+   input_data->getPreProcess().setResizeAlgorithm(InferenceEngine::RESIZE_BILINEAR);
+   ```
+   **By default**, no resize algorithm is set for inputs.
 
-  If you want to run inference for multiple images at once, you can use the built-in batch
-  pre-processing functionality.
+   4. **Set color format** (BGR, RGB, NV12). Basic color format conversions are supported as well. **By default**, the Inference Engine assumes that the input color format is BGR and color format conversions are disabled. Set `ColorFormat::RAW` input color format if the input does not need color conversions. The Inference Engine supports the following color format conversions:
+      * RGB->BGR
+      * RGBX->BGR
+      * BGRX->BGR
+      * NV12->BGR
+      where X is a channel that will be ignored during inference. To enable the conversions, set a desired color format (for example, RGB) for each input inside of the appropriate input info (*iterate over all input info*):
+   ```cpp
+   input_data->getPreProcess().setColorFormat(InferenceEngine::ColorFormat::RGB);
+   ```
+   > **NOTE**: NV12 input color format pre-processing differs from other color conversions. In case of NV12, Inference Engine expects two separate image planes (Y and UV). You must use a specific `InferenceEngine::NV12Blob` object instead of default blob object and set this blob to the Inference Engine Infer Request using `InferenceEngine::InferRequest::SetBlob()`. Refer to [Hello NV12 Input Classification C++ Sample](../../samples/cpp/hello_nv12_input_classification/README.md) for more details.
+   
+   5. **Run on multiple images** with setting batch. If you want to run inference for multiple images at once, you can use the built-in batch pre-processing functionality.
+   
+      **NOTE** : Batch pre-processing is not supported if input color format is set to `ColorFormat::NV12`.
 
-> **NOTE**: Batch pre-processing is not supported if input color format is set to `ColorFormat::NV12`.
+@sphinxdirective
+.. raw:: html
 
-  You can use the following code snippet to configure input and output:
+    </div>
+@endsphinxdirective
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part4
+#### Step 3. Load the Model to the Device
 
-> **NOTE**: NV12 input color format pre-processing differs from other color conversions. In case of NV12,
->  Inference Engine expects two separate image planes (Y and UV). You must use a specific
->  `InferenceEngine::NV12Blob` object instead of default blob object and set this blob to
->  the Inference Engine Infer Request using `InferenceEngine::InferRequest::SetBlob()`.
->  Refer to [Hello NV12 Input Classification C++ Sample](../../samples/cpp/hello_nv12_input_classification/README.md)
->  for more details.
+Load the model to the device using `InferenceEngine::Core::LoadNetwork()`:
 
-  If you skip this step, the default values are set:
 
-  * no resize algorithm is set for inputs
-  * input color format - `ColorFormat::RAW` meaning that input does not need color
-    conversions
-  * input and output precision - `Precision::FP32`
-  * input layout - `Layout::NCHW`
-  * output layout depends on number of its dimensions:
+@sphinxdirective
+   
+.. tab:: IR
 
-|Number of dimensions |  5    |  4   |   3 |  2 |  1 |
-|:--------------------|-------|------|-----|----|----|
-|Layout               | NCDHW | NCHW | CHW | NC | C  |
+   .. code-block:: c
 
-4) **Load the model** to the device using `InferenceEngine::Core::LoadNetwork()`:
+      executable_network = core.LoadNetwork("model.xml", "CPU");
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part5
+.. tab:: ONNX
 
-    It creates an executable network from a network object. The executable network is associated with single hardware device.
-    It is possible to create as many networks as needed and to use them simultaneously (up to the limitation of the hardware resources).
-    Third parameter is a configuration for plugin. It is map of pairs: (parameter name, parameter value). Choose device from
-     [Supported devices](supported_plugins/Supported_Devices.md) page for more details about supported configuration parameters.
+   .. code-block:: c
+
+      executable_network = core.LoadNetwork("model.onnx", "CPU");
+
+.. tab:: nGraph
+
+   .. code-block:: c
+
+      std::shared_ptr<Function> createNetwork() {
+         // To construct a network, please follow 
+         // https://docs.openvinotoolkit.org/latest/openvino_docs_nGraph_DG_build_function.html
+      }
+      auto network = CNNNetwork(createNetwork());
+      executable_network = core.LoadNetwork(network, "CPU");
+
+.. tab:: Model From Step 2
+   
+   Follow this step only if you went through optional "Step 2 (Optional). Configure Input and Output of the Model", otherwise use another tab for your model type: IR (OpenVINO Intermediate Representation), ONNX or nGraph.
+   
+   .. code-block:: c
+
+      executable_network = core.LoadNetwork(network, "CPU");
+
+@endsphinxdirective
+
+
+It creates an executable network from a network object. The executable network is associated with single hardware device.
+It is possible to create as many networks as needed and to use them simultaneously (up to the limitation of the hardware resources).
+
+Third parameter is a configuration for plugin. It is map of pairs: (parameter name, parameter value). Choose device from
+[Supported devices](supported_plugins/Supported_Devices.md) page for more details about supported configuration parameters.
 
 @snippet snippets/Integrate_with_customer_application_new_API.cpp part6
 
-5) **Create an infer request**:
+#### Step 4. Create an Inference Request
+
+Create an infer request using the following code:
 
 @snippet snippets/Integrate_with_customer_application_new_API.cpp part7
 
-6) **Prepare input**. You can use one of the following options to prepare input:
-    * **Optimal way for a single network.** Get blobs allocated by an infer request using `InferenceEngine::InferRequest::GetBlob()` and feed an image and the input data to the blobs. In this case, input data must be aligned (resized manually) with a given blob size and have a correct color format.
+#### Step 5. Prepare Input 
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part8
+You can use one of the following options to prepare input:
 
-    * **Optimal way for a cascade of networks (output of one network is input for another).** Get output blob from the first request using `InferenceEngine::InferRequest::GetBlob()` and set it as input for the second request using `InferenceEngine::InferRequest::SetBlob()`.
+* **Optimal way for a single network.** Get blobs allocated by an infer request using `InferenceEngine::InferRequest::GetBlob()` and feed an image and the input data to the blobs. In this case, input data must be aligned (resized manually) with a given blob size and have a correct color format.
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part9
+   @snippet snippets/Integrate_with_customer_application_new_API.cpp part8
 
-    * **Optimal way to handle ROI (a ROI object located inside of input of one network is input for another).** It is possible to re-use shared input by several networks. You do not need to allocate separate input blob for a network if it processes a ROI object located inside of already allocated input of a previous network. For instance, when first network detects objects on a video frame (stored as input blob) and second network accepts detected bounding boxes (ROI inside of the frame) as input. In this case, it is allowed to re-use pre-allocated input blob (used by first network) by second network and just crop ROI without allocation of new memory using `InferenceEngine::make_shared_blob()` with passing of `InferenceEngine::Blob::Ptr` and `InferenceEngine::ROI` as parameters.
+* **Optimal way for a cascade of networks (output of one network is input for another).** Get output blob from the first request using `InferenceEngine::InferRequest::GetBlob()` and set it as input for the second request using `InferenceEngine::InferRequest::SetBlob()`.
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part10
+   @snippet snippets/Integrate_with_customer_application_new_API.cpp part9
 
-Make sure that shared input is kept valid during execution of each network. Otherwise, ROI blob may be corrupted if the original input blob (that ROI is cropped from) has already been rewritten.
+* **Optimal way to handle ROI (a ROI object located inside of input of one network is input for another).** It is possible to re-use shared input by several networks. You do not need to allocate separate input blob for a network if it processes a ROI object located inside of already allocated input of a previous network. For instance, when first network detects objects on a video frame (stored as input blob) and second network accepts detected bounding boxes (ROI inside of the frame) as input. In this case, it is allowed to re-use pre-allocated input blob (used by first network) by second network and just crop ROI without allocation of new memory using `InferenceEngine::make_shared_blob()` with passing of `InferenceEngine::Blob::Ptr` and `InferenceEngine::ROI` as parameters.
 
-    * Allocate input blobs of the appropriate types and sizes, feed an image and the input data to the blobs, and call `InferenceEngine::InferRequest::SetBlob()` to set these blobs for an infer request:
+   @snippet snippets/Integrate_with_customer_application_new_API.cpp part10
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part11
+   Make sure that shared input is kept valid during execution of each network. Otherwise, ROI blob may be corrupted if the original input blob (that ROI is cropped from) has already been rewritten.
 
-      A blob can be filled before and after `SetBlob()`.
+* Allocate input blobs of the appropriate types and sizes, feed an image and the input data to the blobs, and call `InferenceEngine::InferRequest::SetBlob()` to set these blobs for an infer request:
 
-> **NOTE:**
+   @snippet snippets/Integrate_with_customer_application_new_API.cpp part11
+
+A blob can be filled before and after `SetBlob()`.
+
+> **NOTE**:
 >
 > * The `SetBlob()` method compares precision and layout of an input blob with the ones defined in step 3 and
 > throws an exception if they do not match. It also compares a size of the input blob with input
@@ -141,22 +262,28 @@ Make sure that shared input is kept valid during execution of each network. Othe
 > corresponding values of the read network. No pre-processing will happen for this blob. If you
 > call `GetBlob()` after `SetBlob()`, you will get the blob you set in `SetBlob()`.
 
-7) **Do inference** by calling the `InferenceEngine::InferRequest::StartAsync` and `InferenceEngine::InferRequest::Wait` methods for asynchronous request:
+#### Step 6. Start Inference
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part12
+Start inference in asynchronous or synchronous mode. Async API usage can improve overall frame-rate of the application, because rather than wait for inference to complete, the app can continue doing things on the host, while accelerator is busy.
 
-or by calling the `InferenceEngine::InferRequest::Infer` method for synchronous request:
+* For synchronous inference request:
+   ```cpp
+   infer_request.Infer();
+   ```
 
-@snippet snippets/Integrate_with_customer_application_new_API.cpp part13
+* For asynchronous inference request: 
+  ```cpp
+  infer_request.StartAsync();
+  infer_request.Wait(InferenceEngine::InferRequest::WaitMode::RESULT_READY);
+  ```
+  `StartAsync` returns immediately and starts inference without blocking main thread, `Infer` blocks main thread and returns when inference is completed. Call `Wait` for waiting result to become available for asynchronous request.
 
-`StartAsync` returns immediately and starts inference without blocking main thread, `Infer` blocks
- main thread and returns when inference is completed. Call `Wait` for waiting result to become available for asynchronous request.
-
-There are three ways to use it:
-* specify maximum duration in milliseconds to block for. The method is blocked until the specified timeout has elapsed, or the result becomes available, whichever comes first.
-* `InferenceEngine::InferRequest::WaitMode::RESULT_READY` - waits until inference result becomes available
-* `InferenceEngine::InferRequest::WaitMode::STATUS_ONLY` - immediately returns request status.It does not
-block or interrupts current thread.
+  There are three ways to use it:
+      * specify maximum duration in milliseconds to block for. The method is blocked until the specified timeout has elapsed, or the result becomes available, whichever comes first.
+      * `InferenceEngine::InferRequest::WaitMode::RESULT_READY` - waits until inference result becomes available
+      * `InferenceEngine::InferRequest::WaitMode::STATUS_ONLY` - immediately returns request status.It does not
+      block or interrupts current thread.
+   
 
 Both requests are thread-safe: can be called from different threads without fearing corruption and failures.
 
@@ -165,41 +292,21 @@ Multiple requests for single `ExecutableNetwork` are executed sequentially one b
 While request is ongoing, all its methods except `InferenceEngine::InferRequest::Wait` would throw an
 exception.
 
-8) Go over the output blobs and **process the results**.
-Note that casting `Blob` to `TBlob` via `std::dynamic_pointer_cast` is not the recommended way. It's better to access data via the `buffer()` and `as()` methods as follows:
+#### Step 7. Process the Inference Results 
+
+Go over the output blobs and process the inference results. Note that casting `Blob` to `TBlob` via `std::dynamic_pointer_cast` is not the recommended way. It's better to access data via the `buffer()` and `as()` methods as follows:
 
 @snippet snippets/Integrate_with_customer_application_new_API.cpp part14
 
-## Build Your Application
+### Build Your Application
 
 For details about building your application, refer to the CMake files for the sample applications.
 All samples source code is located in the `<INSTALL_DIR>/samples` directory, where `INSTALL_DIR` is the OpenVINO™ installation directory.
 
-### CMake project creation
+To build your project using CMake with the default build tools currently available on your machine, execute the following commands:
 
-1. **Create a structure** for the project:
-``` sh
-project/
-    ├── CMakeLists.txt  - CMake file to build
-    ├── ...             - Additional folders like includes/
-    └── src/            - source folder
-        └── main.cpp
-build/                  - build directory
-    ...      
-```
+> **NOTE**: Make sure you set environment variables first by running `<INSTALL_DIR>/setupvars.sh` (or `setupvars.bat` for Windows). Otherwise the `InferenceEngine_DIR` and `OpenCV_DIR` variables won't be configured properly to pass `find_package` calls.
 
-2. **Include Inference Engine, nGraph and OpenCV libraries** in `project/CMakeLists.txt`  
-[OpenCV](https://docs.opencv.org/master/db/df5/tutorial_linux_gcc_cmake.html) integration is needed mostly for pre-processing input data and ngraph for more complex applications using [ngraph API](../nGraph_DG/nGraph_dg.md).
-``` cmake
-cmake_minimum_required(VERSION 3.0.0)
-project(project_name)
-find_package(OpenVINO REQUIRED)
-find_package(OpenCV REQUIRED)
-add_executable(${PROJECT_NAME} src/main.cpp)
-target_link_libraries(${PROJECT_NAME} PRIVATE openvino::runtime ${OpenCV_LIBS})
-```
-3. **To build your project** using CMake with the default build tools currently available on your machine, execute the following commands:
-> **NOTE**: Make sure you set environment variables first by running `<INSTALL_DIR>/setupvars.sh` (or setupvars.bat for Windows)`. Otherwise the `InferenceEngine_DIR` and `OpenCV_DIR` variables won't be configured properly to pass `find_package` calls.
 ```sh
 cd build/
 cmake ../project
@@ -209,6 +316,179 @@ It's allowed to specify additional build options (e.g. to build CMake project on
 
 ### Run Your Application
 
-Before running, make sure you completed **Set the Environment Variables** section in [OpenVINO Installation](../../samples/cpp/hello_nv12_input_classification/README.md) document so that the application can find the libraries.
+> **NOTE**: Before running, make sure you completed **Set the Environment Variables** section in [OpenVINO Installation](../../samples/cpp/hello_nv12_input_classification/README.md) document so that the application can find the libraries.
 
-[integration_process]: img/integration_process.png
+To run compiled applications on Microsoft* Windows* OS, make sure that Microsoft* Visual C++ 2017
+Redistributable and Intel® C++ Compiler 2017 Redistributable packages are installed and
+`<INSTALL_DIR>/bin/intel64/Release/*.dll` files are placed to the
+application folder or accessible via `%PATH%` environment variable.
+
+## Integrate Inference Engine with Your Python Application
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+This document explains how to integrate and use the Inference Engine API with your Python application.   
+
+The following diagram illustrates the typical Inference Engine Python API workflow:
+![ie_api_flow_python] 
+
+Read the sections below to learn about each item.
+
+### Import Inference Module
+
+To make use of the Inference Engine functionality, import IECore to your application: 
+
+```py
+from openvino.inference_engine import IECore
+``` 
+ 
+### Use Inference Engine API 
+
+This section provides step-by-step instructions to implement a typical inference pipeline with the Inference Engine API:   
+
+![ie_api_use_python]
+
+#### Step 1. Create Inference Engine Core
+
+Use the following code to create Inference Engine Core to manage available devices and read network objects: 
+```py
+ie = IECore()
+``` 
+#### Step 2 (Optional). Read model. Configure Input and Output of the Model
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="collapsible-section">
+@endsphinxdirective
+
+Optionally, configure input and output of the model using the steps below: 
+
+1. Read model 
+   @sphinxdirective
+      
+   .. tab:: IR
+   
+      .. code-block:: python
+   
+         net = ie.read_network(model="model.xml")
+   
+   .. tab:: ONNX
+      
+      .. code-block:: python
+         
+         net = ie.read_network(model="model.onnx")
+   
+   .. tab:: nGraph
+      
+      .. code-block:: python
+         
+         #Basic example of nGraph model creation
+         param = Parameter(Type.f32, Shape([1, 3, 22, 22]))
+         relu = ng.relu(param)
+         func = Function([relu], [param], 'test')
+         caps = Function.to_capsule(func)
+         net = IENetwork(caps)
+   
+   @endsphinxdirective
+
+2. Request input and output information using input_info, outputs 
+   ```py
+   inputs = net.input_info 
+   input_name = next(iter(net.input_info))  
+
+   outputs = net.outputs 
+   output_name = next(iter(net.outputs)) 
+   ``` 
+   Information for this input layer is stored in input_info. The next cell prints the input layout, precision and shape. 
+   ```py
+   print("Inputs:")
+   for name, info in net.input_info.items():
+       print("\tname: {}".format(name))
+       print("\tshape: {}".format(info.tensor_desc.dims))
+       print("\tlayout: {}".format(info.layout))
+       print("\tprecision: {}\n".format(info.precision))
+   ```
+   This cell output tells us that the model expects inputs with a shape of [1,3,224,224], and that this is in NCHW layout. This means that the model expects input data with a batch size (N) of 1, 3 channels (C), and images of a height (H) and width (W) of 224. The input data is expected to be of FP32 (floating point) precision. 
+    
+   Getting the output layout, precision and shape is similar to getting the input layout, precision and shape. 
+   ```py
+   print("Outputs:")
+   for name, info in net.outputs.items():
+       print("\tname: {}".format(name))
+       print("\tshape: {}".format(info.shape))
+       print("\tlayout: {}".format(info.layout))
+       print("\tprecision: {}\n".format(info.precision))
+   ```
+   This cell output shows that the model returns outputs with a shape of [1, 1001], where 1 is the batch size (N) and 1001 the number of classes (C). The output is returned as 32-bit floating point. 
+
+@sphinxdirective
+.. raw:: html
+
+    </div>
+@endsphinxdirective 
+
+#### Step 3. Load model to the Device 
+
+Load the model to the device using `load_network()`:
+
+@sphinxdirective
+   
+.. tab:: IR
+
+   .. code-block:: python
+
+      exec_net = ie.load_network(network= "model.xml", device_name="CPU") 
+.. tab:: ONNX
+   
+   .. code-block:: python
+      
+      exec_net = ie.load_network(network= "model.onnx", device_name="CPU") 
+
+.. tab:: Model from step 2
+   
+   .. code-block:: python
+   
+      exec_net = ie.load_network(network=net, device_name="CPU")
+
+@endsphinxdirective
+
+This example is designed for CPU device, refer to the [Supported Devices](../IE_DG/supported_plugins/Supported_Devices.md) page to read about more devices. 
+
+#### Step 4. Prepare input 
+```py
+import cv2 
+import numpy as np 
+
+image = cv2.imread("image.png") 
+
+# Resize with OpenCV your image if needed to match with net input shape 
+# N, C, H, W = net.input_info[input_name].tensor_desc.dims
+# image = cv2.resize(src=image, dsize=(W, H)) 
+
+# Converting image to NCHW format with FP32 type 
+input_data = np.expand_dims(np.transpose(image, (2, 0, 1)), 0).astype(np.float32) 
+```
+
+#### Step 5. Start Inference
+```py
+result = exec_net.infer({input_name: input_data}) 
+``` 
+
+#### Step 6. Process the Inference Results 
+```py
+output = result[output_name] 
+```
+
+### Run Your Application
+
+Congratulations, you have made your first Python application with OpenVINO™ toolkit, now you may run it.
+
+[ie_api_flow_cpp]: img/BASIC_IE_API_workflow_Cpp.svg
+[ie_api_use_cpp]: img/IMPLEMENT_PIPELINE_with_API_C.svg
+[ie_api_flow_python]: img/BASIC_IE_API_workflow_Python.svg
+[ie_api_use_python]: img/IMPLEMENT_PIPELINE_with_API_Python.svg
\ No newline at end of file
diff --git a/docs/IE_DG/Intro_to_Performance.md b/docs/IE_DG/Intro_to_Performance.md
index 5a714240470..21f857707e6 100644
--- a/docs/IE_DG/Intro_to_Performance.md
+++ b/docs/IE_DG/Intro_to_Performance.md
@@ -1,4 +1,4 @@
-# Introduction to the Performance Topics {#openvino_docs_IE_DG_Intro_to_Performance}
+#  Runtime Optimization Guide {#openvino_docs_IE_DG_Intro_to_Performance}
 
 This section is a shorter version of the
 [Optimization Guide](../optimization_guide/dldt_optimization_guide.md) for the Intel® Distribution of OpenVINO™ Toolkit.
diff --git a/docs/IE_DG/Introduction.md b/docs/IE_DG/Introduction.md
index 1682ec7466e..b104357fb5e 100644
--- a/docs/IE_DG/Introduction.md
+++ b/docs/IE_DG/Introduction.md
@@ -72,8 +72,8 @@ For the list of supported models refer to the framework or format specific page:
 * [Supported Caffe* models](../MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md)
 * [Supported TensorFlow* models](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
 * [Supported MXNet* models](../MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md)
-* [Supported ONNX* models](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
 * [Supported Kaldi* models](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
+* [Supported ONNX* models](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
 
 
 ## Intermediate Representation
diff --git a/docs/IE_DG/Known_Issues_Limitations.md b/docs/IE_DG/Known_Issues_Limitations.md
index ec3e4ffd8e2..89723f26153 100644
--- a/docs/IE_DG/Known_Issues_Limitations.md
+++ b/docs/IE_DG/Known_Issues_Limitations.md
@@ -3,7 +3,8 @@
 ## Multiple OpenMP Loadings
 
 If the application uses the Inference Engine with third-party components that depend on Intel OpenMP, multiple loadings of the libiomp library may occur and cause OpenMP runtime initialization conflicts. This may happen, for example, if the application uses Intel® Math Kernel Library (Intel® MKL) through the “Single Dynamic Library” (<code>libmkl_rt.so</code>) mechanism and calls Intel MKL after loading the Inference Engine plugin.
-The error log looks as follows:
+The error log looks like this:
+
 ```sh
 OMP: Error #15: Initializing libiomp5.so, but found libiomp5.so already initialized.
 OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
@@ -12,20 +13,20 @@ OMP: Hint: This means that multiple copies of the OpenMP runtime have been linke
 Possible workarounds:
 
 *  Preload the OpenMP runtime using the <code>LD_PRELOAD</code> variable:
-```sh
-LD_PRELOAD=<path_to_libiomp5.so> <path_to your_executable>
-```
+   ```sh
+   LD_PRELOAD=<path_to_libiomp5.so> <path_to your_executable>
+   ```
    This eliminates multiple loadings of libiomp, and makes all the components use this specific version of OpenMP.
 
-*  Alternatively, you can set <code>KMP_DUPLICATE_LIB_OK=TRUE</code>. However, performance degradation or results incorrectness may occur in this case.
+*  Alternatively, you can set <code>KMP_DUPLICATE_LIB_OK=TRUE</code>. However, performance degradation or incorrect results may occur in this case.
 
 
 ## Old proto compiler breaks protobuf library
 
-With python protobuf library version 3.5.1 the following incompatibility can happen.
-The known case is for Cent OS 7.4
+With python protobuf library version 3.5.1, the following incompatibility can happen.
+The known case is for Cent OS 7.4.
 
-The error log looks as follows:
+The error log looks like this:
 
 ```sh
 File "../lib64/python3.5/site-packages/google/protobuf/descriptor.py", line 829, in _new_
@@ -33,25 +34,24 @@ return _message.default_pool.AddSerializedFile(serialized_pb)
 TypeError: expected bytes, str found
 ```
 
-Possible workaround is to upgrade default protobuf compiler (libprotoc 2.5.0) to newer version, for example
-libprotoc 2.6.1.
+A possible workaround is to upgrade default protobuf compiler (libprotoc 2.5.0) to newer version, for example libprotoc 2.6.1.
 
 [protobuf_issue]: https://github.com/google/protobuf/issues/4272
 
 ## Dynamic batching
-Refer to the **Limitations** section of [Dynamic batching page](DynamicBatching.md)
+Refer to the **Limitations** section of the [Dynamic batching page](DynamicBatching.md).
 
 ## Static Shape Infer
-Refer to the **Limitations** section of [Static Shape Infer page](ShapeInference.md)
+Refer to the **Limitations** section of the [Static Shape Infer page](ShapeInference.md).
 
 
 ## Image Pre-Processing Performance Optimization Issue
 
-As described in [documentation for new API](Integrate_with_customer_application_new_API.md), you can set an image blob of any size to an
-infer request using resizable input. Resize is executed during inference using configured resize algorithm.
+As described in [documentation for the new API](Integrate_with_customer_application_new_API.md), you can set an image blob of any size to an
+infer request using resizable input. Resize is executed during inference using the configured resize algorithm.
 
-But currently resize algorithms are not completely optimized. So expect performance degradation if resizable input is
-specified and an input blob (to be resized) is set (`SetBlob()` is used). Required performance is met for
+But currently, resize algorithms are not completely optimized. So expect performance degradation if resizable input is
+specified and an input blob (to be resized) is set using `SetBlob()`. The best performance is for the 
 [CPU](supported_plugins/CPU.md) plugin only (because enabled openMP* provides parallelism).
 
 Another limitation is that currently, resize algorithms support NCHW layout only. So if you set NHWC layout for an input
diff --git a/docs/IE_DG/Memory_primitives.md b/docs/IE_DG/Memory_primitives.md
index 507757ee650..fd67d0149b1 100644
--- a/docs/IE_DG/Memory_primitives.md
+++ b/docs/IE_DG/Memory_primitives.md
@@ -1,5 +1,12 @@
-Inference Engine Memory primitives {#openvino_docs_IE_DG_Memory_primitives}
-=====================================================================
+# Inference Engine Memory Primitives {#openvino_docs_IE_DG_Memory_primitives}
+
+## Inference Memory Primitives (C++)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
 
 ## Blobs
 
@@ -18,7 +25,7 @@ InferenceEngine::Blob::Ptr blob = InferenceEngine::make_shared_blob<float>(tdesc
 
 This class allows to create planar layouts using the standard formats (like <code>InferenceEngine::Layout::NCDHW</code>, <code>InferenceEngine::Layout::NCHW</code>, <code>InferenceEngine::Layout::NC</code>, <code>InferenceEngine::Layout::C</code> and etc) and also non-planar layouts using <code>InferenceEngine::BlockingDesc</code>.
 
-In order to create a complex layout you should use <code>InferenceEngine::BlockingDesc</code> which allows to define the blocked memory with offsets and strides.
+In order to create a complex layout you should use <code>InferenceEngine::BlockingDesc</code>, which allows you to define the blocked memory with offsets and strides.
 
 ## Examples
 
@@ -27,13 +34,12 @@ In order to create a complex layout you should use <code>InferenceEngine::Blocki
 InferenceEngine::BlockingDesc({1, 20, 20, 25}, {0, 2, 3, 1}); // or
 InferenceEngine::BlockingDesc({1, 20, 20, 25}, InferenceEngine::Layout::NHWC);
 </pre>
-2. If you have a memory with real dimensions {N: 1, C: 25, H: 20, W: 20} but with channels which are blocked by 8, you can define it using next parameters:<br/>
+2. If you have a memory with real dimensions {N: 1, C: 25, H: 20, W: 20} but with channels that are blocked by 8, you can define it using next parameters:<br/>
 <pre class="brush:cpp">
 InferenceEngine::BlockingDesc({1, 4, 20, 20, 8}, {0, 1, 2, 3, 1})
 </pre>
 3. Also you can set strides and offsets if layout contains it.
-4. If you have a complex blob layout and you don't want to calculate the real offset to data you can use methods
-<code>InferenceEngine::TensorDesc::offset(size_t l)</code> or <code>InferenceEngine::TensorDesc::offset(SizeVector v)</code>.<br/>
+4. If you have a complex blob layout and you don't want to calculate the real offset to data you can use the <code>InferenceEngine::TensorDesc::offset(size_t l)</code> or <code>InferenceEngine::TensorDesc::offset(SizeVector v)</code> methods.<br/>
 For example:
 <pre class="brush:cpp">
 InferenceEngine::BlockingDesc blk({1, 4, 20, 20, 8}, {0, 1, 2, 3, 1});
@@ -43,8 +49,7 @@ tdesc.offset(1); // = 8
 tdesc.offset({0, 0, 0, 2}); // = 16
 tdesc.offset({0, 1, 0, 2}); // = 17
 </pre>
-5. If you would like to create a TensorDesc with a planar format and for N dimensions (N can be different 1, 2, 4 and etc), you can use the method
-<code>InferenceEngine::TensorDesc::getLayoutByDims</code>.
+5. If you would like to create a TensorDesc with a planar format and for N dimensions (N can be different 1, 2, 4 and etc), you can use the <code>InferenceEngine::TensorDesc::getLayoutByDims</code> method.
 <pre class="brush:cpp">
 InferenceEngine::TensorDesc::getLayoutByDims({1}); // InferenceEngine::Layout::C
 InferenceEngine::TensorDesc::getLayoutByDims({1, 2}); // InferenceEngine::Layout::NC
@@ -52,4 +57,4 @@ InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3, 4}); // InferenceEngine::
 InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3}); // InferenceEngine::Layout::BLOCKED
 InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3, 4, 5}); // InferenceEngine::Layout::NCDHW
 InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3, 4, 5, ...}); // InferenceEngine::Layout::BLOCKED
-</pre>
\ No newline at end of file
+</pre>
diff --git a/docs/IE_DG/Model_Downloader.md b/docs/IE_DG/Model_Downloader.md
new file mode 100644
index 00000000000..9656e5b2a9b
--- /dev/null
+++ b/docs/IE_DG/Model_Downloader.md
@@ -0,0 +1,446 @@
+# Model Downloader {#openvino_docs_IE_DG_Tools_Model_Downloader}
+
+
+This directory contains scripts that automate certain model-related tasks
+based on configuration files in the models' directories.
+
+* Model Downloader: `downloader.py`  downloads model files from online sources
+  and, if necessary, patches them to make them more usable with Model
+  Optimizer;
+
+* Model Converter: `converter.py` converts the models that are not in the
+  Inference Engine IR format into that format using Model Optimizer.
+
+* Model Quantizer: `quantizer.py` quantizes full-precision models in the IR
+  format into low-precision versions using Post-Training Optimization Toolkit.
+
+*  Model Information Dumper: `info_dumper.py` prints information about the models
+  in a stable machine-readable format.
+
+
+> **TIP**: You can quick start with the Model Downloader inside the OpenVINO™ Deep Learning Workbench (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI that enables you to import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment on various Intel® platforms.
+
+## Prerequisites
+
+1. Install Python (version 3.6 or higher)
+2. Install the tools' dependencies with the following command:
+
+```sh
+python3 -mpip install --user -r ./requirements.in
+```
+
+For the model converter, you will also need to install the OpenVINO&trade;
+toolkit and the prerequisite libraries for Model Optimizer. See the
+[OpenVINO toolkit documentation](https://docs.openvinotoolkit.org/) for details.
+
+To convert models from certain frameworks, you will also need to install
+additional dependencies.
+
+@sphinxdirective
+   
+.. tab:: Caffe2
+   
+  .. code-block:: python
+   
+      python3 -mpip install --user -r ./requirements-caffe2.in
+
+.. tab:: Pytorch
+      
+  .. code-block:: python
+
+      python3 -mpip install --user -r ./requirements-pytorch.in  
+   
+.. tab:: TensorFlow
+      
+  .. code-block:: python
+         
+     python3 -mpip install --user -r ./requirements-tensorflow.in
+
+@endsphinxdirective
+
+
+## Model Downloader 
+
+The basic usage is to run the script like this:
+
+```sh
+./downloader.py --all
+
+```
+This will download all models. The `--all` option can be replaced with
+other filter options to download only a subset of models. See the "Shared options"
+section.
+
+### Model Downloader Starting Parameters
+
+@sphinxdirective
+
++---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------+
+| Parameter                 | Explanation                                                                                                                                                                                                                                                                                                                                                                                      | Example                                                                             |
++===========================+==================================================================================================================================================================================================================================================================================================================================================================================================+=====================================================================================+
+| ``-o``/``--output_dir``   | By default, the script will download models into a directory tree rooted in the current directory. Use this parameter to download into a different directory.                                                                                                                                                                                                                                    | ``./downloader.py --all --output_dir my/download/directory``                        |
++---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------+
+| ``--precisions``          | Specify comma separated precisions of weights to be downloaded                                                                                                                                                                                                                                                                                                                                   | ``./downloader.py --name face-detection-retail-0004 --precisions FP16,FP16-INT8``   |
++---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------+
+| ``--num_attempts``        | By default, the script will attempt to download each file only once. Use this parameter to change that and increase the robustness of the download process                                                                                                                                                                                                                                       | ``./downloader.py --all --num_attempts 5 # attempt each download five times``       |
++---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------+
+| ``--cache_dir``           | Make the script use the specified directory as a cache. The script will place a copy of each downloaded file in the cache, or, if it is already there, retrieve it from the cache instead of downloading it again. The cache format is intended to remain compatible in future Open Model Zoo versions, so you can use a cache to avoid redownloading most files when updating Open Model Zoo.   | ``./downloader.py --all --cache_dir my/cache/directory``                            |
++---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------+
+| ``-j``/``--jobs``         | The script downloads files for multiple models concurrently.                                                                                                                                                                                                                                                                                                                                     | ``./downloader.py --all -j8 # download up to 8 models at a time``                   |
++---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------+
+| ``--progress_format``     | By default, the script outputs progress information as unstructured, human-readable text. Use this option, if you want to consume progress information programmatically.                                                                                                                                                                                                                         | ``./downloader.py --all --progress_format=json``                                    |
++---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------+
+@endsphinxdirective
+
+When this option is set to `json`, the script's standard output is replaced by
+a machine-readable progress report, whose format is documented in the
+"JSON progress report format" section. This option does not affect errors and
+warnings, which will still be printed to the standard error stream in a
+human-readable format.
+
+You can also set this option to `text` to explicitly request the default text
+format.
+
+See the "Shared options" section for information on other options accepted by
+the script.
+
+### JSON progress report format
+
+This section documents the format of the progress report produced by the script
+when the `--progress_format=json` option is specified.
+
+The report consists of a sequence of events, where each event is represented
+by a line containing a JSON-encoded object. Each event has a member with the
+name `$type` whose value determines the type of the event, as well as which
+additional members it contains.
+
+@sphinxdirective
+
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Event type                         | Additional members                                                      | Explanation                                                                                                                                                                                                                                                                                                                                    |
++====================================+=========================================================================+================================================================================================================================================================================================================================================================================================================================================+
+| ``model_download_begin``           | ``model`` (string), ``num_files`` (integer)                             | The script started downloading the model named by ``model``. ``num_files`` is the number of files that will be downloaded for this model. This event will always be followed by a corresponding ``model_download_end`` event.                                                                                                                  |
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``model_download_end``             | ``model`` (string), ``successful`` (boolean)                            | The script stopped downloading the model named by ``model``. ``successful`` is true if every file was downloaded successfully.                                                                                                                                                                                                                 |
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``model_file_download_begin``      | ``model`` (string), ``model_file`` (string), ``size`` (integer)         | The script started downloading the file named by ``model_file`` of the model named by ``model``. ``size`` is the size of the file in bytes. This event will always occur between ``model_download_begin`` and ``model_download_end`` events for the model, and will always be followed by a corresponding ``model_file_download_end`` event.   |
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``model_file_download_end``        | ``model`` (string), ``model_file`` (string), ``successful`` (boolean)   | The script stopped downloading the file named by ``model_file`` of the model named by ``model``. ``successful`` is true if the file was downloaded successfully.                                                                                                                                                                               |
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``model_file_download_progress``   | ``model`` (string), ``model_file`` (string), ``size`` (integer)         | The script downloaded ``size`` bytes of the file named by ``model_file`` of the model named by ``model`` so far. Note that ``size`` can decrease in a subsequent event if the download is interrupted and retried. This event will always occur between ``model_file_download_begin`` and ``model_file_download_end`` events for the file.     |
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``model_postprocessing_begin``     | ``model``                                                               | The script started post-download processing on the model named by ``model``. This event will always be followed by a corresponding ``model_postprocessing_end`` event.                                                                                                                                                                         |
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``model_postprocessing_end``       | ``model``                                                               | The script stopped post-download processing on the model named by ``model``.                                                                                                                                                                                                                                                                   |
++------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+@endsphinxdirective
+
+
+Additional event types and members may be added in the future.
+
+Tools parsing the machine-readable format should avoid relying on undocumented details.
+In particular:
+
+* Tools should not assume that any given event will occur for a given model/file
+  (unless specified otherwise above) or will only occur once.
+
+* Tools should not assume that events will occur in a certain order beyond
+  the ordering constraints specified above. In particular, when the `--jobs` option
+  is set to a value greater than 1, event sequences for different files or models
+  may get interleaved.
+
+## Model Converter 
+
+The basic usage is to run the script like this:
+
+```sh
+./converter.py --all
+```
+
+This will convert all models into the Inference Engine IR format. Models that
+were originally in that format are ignored. Models in PyTorch and Caffe2 formats will be
+converted in ONNX format first.
+
+The `--all` option can be replaced with other filter options to convert only
+a subset of models. See the "Shared options" section.
+
+### Model Converter Starting Parameters
+
+@sphinxdirective
+
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+| Parameter                   | Explanation                                                                                                                                                                                                                                                      | Example                                                                                          |
++=============================+==================================================================================================================================================================================================================================================================+==================================================================================================+
+| ``-d``/``--download_dir``   | The current directory must be the root of a download tree created by the model downloader. Use this parameter to specify a different download tree path.                                                                                                         | ``./converter.py --all --download_dir my/download/directory``                                    |
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+| ``-o``/``--output_dir``     | By default, the script will download models into a directory tree rooted in the current directory. Use this parameter to download into a different directory. Note: models in intermediate format are placed to this directory too.                              | ``./converter.py --all --output_dir my/output/directory``                                        |
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+| ``--precisions``            | By default, the script will produce models in every precision that is supported for conversion. Use this parameter to only produce models in a specific precision. If the specified precision is not supported for a model, that model will be skipped.          | ``./converter.py --all --precisions=FP16``                                                       |
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+| ``--add_mo_arg``            | Add extra Model Optimizer arguments to the ones specified in the model configuration. The option can be repeated to add multiple arguments                                                                                                                       | ``./converter.py --name=caffenet --add_mo_arg=--reverse_input_channels --add_mo_arg=--silent``   |
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+| ``-j``/``--jobs``           | Run multiple conversion commands concurrently. The argument to the option must be either a maximum number of concurrently executed commands, or "auto", in which case the number of CPUs in the system is used. By default, all commands are run sequentially.   | ``./converter.py --all -j8 # run up to 8 commands at a time``                                    |
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+| ``--dry_run``               | Print the conversion commands without actually running them..                                                                                                                                                                                                    | ``./converter.py --all --dry_run``                                                               |
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+| ``-p``/``--python``         | By default, the script will run Model Optimizer using the same Python executable that was used to run the script itself. Apply this parameter to use a different Python executable.                                                                              | ``./converter.py --all --python my/python``                                                      |
++-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+@endsphinxdirective
+
+
+The Python script will attempt to locate Model Optimizer using several methods:
+
+1. If the `--mo` option was specified, then its value will be used as the path
+   to the script to run:
+
+   ```sh
+   ./converter.py --all --mo my/openvino/path/model_optimizer/mo.py
+   ```
+
+2. Otherwise, if the selected Python executable can import the `mo` package,
+   then that package will be used.
+
+3. Otherwise, if the OpenVINO&trade; toolkit's `setupvars.sh`/`setupvars.bat`
+   script has been executed, the environment variables set by that script will
+   be used to locate Model Optimizer within the toolkit.
+
+4. Otherwise, the script will fail.
+
+
+See the "Shared options" section for information on other options accepted by
+the script.
+
+## Model Quantizer 
+
+Before you run the model quantizer, you must prepare a directory with
+the datasets required for the quantization process. This directory will be
+referred to as `<DATASET_DIR>` below. You can find more detailed information
+about dataset preparation in the [Dataset Preparation Guide](../../data/datasets.md).
+
+The basic usage is to run the script like this:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR>
+```
+
+This will quantize all models for which quantization is supported. Other models
+are ignored.
+
+The `--all` option can be replaced with other filter options to quantize only
+a subset of models. See the "Shared options" section.
+
+### Model Quantizer Starting Parameters
+
+@sphinxdirective
+
++---------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------+
+| Parameter                 | Explanation                                                                                                                                                                                                                                                                                                         | Example                                                                                 |
++===========================+=====================================================================================================================================================================================================================================================================================================================+=========================================================================================+
+| ``--model_dir``           | The current directory must be the root of a tree of model files create by the model converter. Use this parameter to specify a different model tree path                                                                                                                                                            | ``./quantizer.py --all --dataset_dir <DATASET_DIR> --model_dir my/model/directory``     |
++---------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------+
+| ``-o``/``--output_dir``   | By default, the script will download models into a directory tree rooted in the current directory. Use this parameter to download into a different directory. Note: models in intermediate format are placed to this directory too.                                                                                 | ``./quantizer.py --all --dataset_dir <DATASET_DIR> --output_dir my/output/directory``   |
++---------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------+
+| ``--precisions``          | By default, the script will produce models in every precision that is supported as a quantization output. Use this parameter to only produce models in a specific precision.                                                                                                                                        | ``./quantizer.py --all --dataset_dir <DATASET_DIR> --precisions=FP16-INT8``             |
++---------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------+
+| ``--target_device``       | It's possible to specify a target device for Post-Training Optimization Toolkitto optimize for. The supported values are those accepted by the "target\_device" option in Post-Training Optimization Toolkit's config files. If this option is unspecified, Post-Training Optimization Toolkit's default is used.   | ``../quantizer.py --all --dataset_dir <DATASET_DIR> --target_device VPU``               |
++---------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------+
+| ``--dry_run``             | The script can print the quantization commands without actually running them. With this option specified, the configuration file for Post-Training Optimization Toolkit will still be created, so that you can inspect it.                                                                                          | ``./quantizer.py --all --dataset_dir <DATASET_DIR> --dry_run``                          |
++---------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------+
+| ``-p``/``--python``       | By default, the script will run Model Optimizer using the same Python executable that was used to run the script itself. Apply this parameter to use a different Python executable.                                                                                                                                 | ``./quantizer.py --all --dataset_dir <DATASET_DIR> --python my/python``                 |
++---------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------+
+@endsphinxdirective
+
+
+The script will attempt to locate Post-Training Optimization Toolkit using several methods:
+
+1. If the `--pot` option was specified, then its value will be used as the path
+   to the script to run:
+
+   ```sh
+   ./quantizer.py --all --dataset_dir <DATASET_DIR> --pot my/openvino/path/post_training_optimization_toolkit/main.py
+   ```
+
+2. Otherwise, if the selected Python executable can import the `pot` package,
+   then that package will be used.
+
+3. Otherwise, if the OpenVINO&trade; toolkit's `setupvars.sh`/`setupvars.bat`
+   script has been executed, the environment variables set by that script will
+   be used to locate Post-Training Optimization Toolkit within the OpenVINO toolkit.
+
+4. Otherwise, the script will fail.
+
+
+See the "Shared options" section for information on other options accepted by
+the script.
+
+## Model Information Dumper 
+
+The basic usage is to run the script like this:
+
+```sh
+./info_dumper.py --all
+```
+
+This will print to standard output information about all models.
+
+The only options accepted by the script are those described in the "Shared options"
+section.
+
+The script's output is a JSON array, each element of which is a JSON object
+describing a single model. Each such object has the following keys:
+
+@sphinxdirective
+
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Parameter                            | Explanation                                                                                                                                                                                                                                                                         |
++======================================+=====================================================================================================================================================================================================================================================================================+
+| ``name``                             | the identifier of the model, as accepted by the ``--name`` option.                                                                                                                                                                                                                  |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``composite_model_name``             | the identifier of the composite model name, if the model is a part of composition of several models (e.g. encoder-decoder), otherwise - ``null``                                                                                                                                    |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``description``                      | text describing the model. Paragraphs are separated by line feed characters.                                                                                                                                                                                                        |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``framework``                        | a string identifying the framework whose format the model is downloaded in. Current possible values are ``dldt`` (Inference Engine IR), ``caffe``, ``caffe2``, ``mxnet``, ``onnx``, ``pytorch`` and ``tf`` (TensorFlow). Additional possible values might be added in the future.   |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``license_url``                      | a URL for the license that the model is distributed under.                                                                                                                                                                                                                          |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``quantization_output_precisions``   | the list of precisions that the model can be quantized to by the model quantizer. Current possible values are ``FP16-INT8`` and ``FP32-INT8``; additional possible values might be added in the future.                                                                             |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``quantization_output_precisions``   | the list of precisions that the model can be quantized to by the model quantizer. Current possible values are ``FP16-INT8`` and ``FP32-INT8``; additional possible values might be added in the future.                                                                             |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| ``subdirectory``                     | the subdirectory of the output tree into which the downloaded or converted files will be placed by the downloader or the converter, respectively.                                                                                                                                   |
++--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+@endsphinxdirective
+
+
+* `precisions`: the list of precisions that the model has IR files for. For models downloaded
+  in a format other than the Inference Engine IR format, these are the precisions that the model
+  converter can produce IR files in. Current possible values are:
+
+  * `FP16`
+  * `FP16-INT1`
+  * `FP16-INT8`
+  * `FP32`
+  * `FP32-INT1`
+  * `FP32-INT8`
+
+  Additional possible values might be added in the future.
+
+
+* `task_type`: a string identifying the type of task that the model performs. 
+  are:
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="Current possible values">
+
+@endsphinxdirective
+
+
+
+  * `action_recognition`
+  * `classification`
+  * `colorization`
+  * `detection`
+  * `face_recognition`
+  * `feature_extraction`
+  * `head_pose_estimation`
+  * `human_pose_estimation`
+  * `image_inpainting`
+  * `image_processing`
+  * `image_translation`
+  * `instance_segmentation`
+  * `machine_translation`
+  * `monocular_depth_estimation`
+  * `named_entity_recognition`
+  * `noise_suppression`
+  * `object_attributes`
+  * `optical_character_recognition`
+  * `place_recognition`
+  * `question_answering`
+  * `salient_object_detection`
+  * `semantic_segmentation`
+  * `sound_classification`
+  * `speech_recognition`
+  * `style_transfer`
+  * `text_to_speech`
+  * `time_series`
+  * `token_recognition`
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+  Additional possible values might be added in the future.
+
+## Shared options
+
+The are certain options that all tools accept.
+
+`-h`/`--help` can be used to print a help message:
+
+```sh
+./TOOL.py --help
+```
+There are several mutually exclusive filter options that select the models the
+tool will process:
+
+@sphinxdirective
+
++--------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
+| Parameter    | Explanation                                                                                                                                                                                                                                                                       | Example                                   |
++==============+===================================================================================================================================================================================================================================================================================+===========================================+
+| ``--all``    | Selects all models                                                                                                                                                                                                                                                                | ``./TOOL.py --all``                       |
++--------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
+| ``--name``   | takes a comma-separated list of patterns and selects models that match at least one of these patterns. The patterns may contain shell-style wildcards. For composite models, the name of composite model is accepted, as well as the names of individual models it consists of.   | ``./TOOL.py --name 'mtcnn,densenet-*'``   |
++--------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
+@endsphinxdirective
+
+
+ See https://docs.python.org/3/library/fnmatch.html for a full description of
+the pattern syntax.
+
+ `--list` takes a path to a file that must contain a list of patterns and
+  selects models that match at least one of those patterns.
+  For composite models, the name of composite model is accepted, as well as the names
+  of individual models it consists of.
+
+  ```sh
+  ./TOOL.py --list my.lst
+  ```
+
+  The file must contain one pattern per line. The pattern syntax is the same
+  as for the `--name` option. Blank lines and comments starting with `#` are
+  ignored. For example:
+
+  ```
+  mtcnn # get all three models: mtcnn-o, mtcnn-p, mtcnn-r
+  densenet-* # get all DenseNet variants
+  ```
+
+To see the available models, you can use the `--print_all` option. When this
+option is specified, the tool will print all model names defined in the
+configuration file and exit:
+
+```
+$ ./TOOL.py --print_all
+action-recognition-0001-decoder
+action-recognition-0001-encoder
+age-gender-recognition-retail-0013
+driver-action-recognition-adas-0002-decoder
+driver-action-recognition-adas-0002-encoder
+emotions-recognition-retail-0003
+face-detection-adas-0001
+face-detection-retail-0004
+face-detection-retail-0005
+[...]
+```
+
+Either `--print_all` or one of the filter options must be specified.
\ No newline at end of file
diff --git a/docs/IE_DG/Model_caching_overview.md b/docs/IE_DG/Model_caching_overview.md
index d480d7626d9..c387611ec5a 100644
--- a/docs/IE_DG/Model_caching_overview.md
+++ b/docs/IE_DG/Model_caching_overview.md
@@ -1,65 +1,136 @@
 # Model Caching Overview {#openvino_docs_IE_DG_Model_caching_overview}
 
-## Introduction
+## Introduction (C++)
 
-As described in [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md), common application flow consists of the following steps:
+@sphinxdirective
+.. raw:: html
 
-1. **Create Inference Engine Core object**
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
 
-2. **Read the Intermediate Representation** - Read an Intermediate Representation file into an object of the `InferenceEngine::CNNNetwork`
+As described in the [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md), a common application flow consists of the following steps:
 
-3. **Prepare inputs and outputs**
+1. **Create an Inference Engine Core object**: First step to manage available devices and read network objects
 
-4. **Set configuration** Pass device-specific loading configurations to the device
+2. **Read the Intermediate Representation**: Read an Intermediate Representation file into an object of the `InferenceEngine::CNNNetwork`
 
-5. **Compile and Load Network to device** - Use the `InferenceEngine::Core::LoadNetwork()` method with specific device
+3. **Prepare inputs and outputs**: If needed, manipulate precision, memory layout, size or color format
 
-6. **Set input data**
+4. **Set configuration**: Pass device-specific loading configurations to the device
 
-7. **Execute**
+5. **Compile and Load Network to device**: Use the `InferenceEngine::Core::LoadNetwork()` method with a specific device
 
-Step #5 can potentially perform several time-consuming device-specific optimizations and network compilations,
-and such delays can lead to bad user experience on application startup. To avoid this, some devices offer
-Import/Export network capability, and it is possible to either use [Compile tool](../../tools/compile_tool/README.md)
+6. **Set input data**: Specify input blob
+
+7. **Execute**: Carry out inference and process results
+
+Step 5 can potentially perform several time-consuming device-specific optimizations and network compilations,
+and such delays can lead to a bad user experience on application startup. To avoid this, some devices offer
+import/export network capability, and it is possible to either use the [Compile tool](../../tools/compile_tool/README.md)
 or enable model caching to export compiled network automatically. Reusing cached networks can significantly reduce load network time.
 
+### Set "CACHE_DIR" config option to enable model caching
 
-## Set "CACHE_DIR" config option to enable model caching
-
-To enable model caching, the application must specify the folder where to store cached blobs. It can be done like this
-
+To enable model caching, the application must specify a folder to store cached blobs, which is done like this:
 
 @snippet snippets/InferenceEngine_Caching0.cpp part0
 
-With this code, if device supports Import/Export network capability, cached blob is automatically created inside the `myCacheFolder` folder
-CACHE_DIR config is set to the Core object. If device does not support Import/Export capability, cache is just not created and no error is thrown
+With this code, if the device specified by `LoadNetwork` supports import/export network capability, a cached blob is automatically created inside the `myCacheFolder` folder.
+CACHE_DIR config is set to the Core object. If the device does not support import/export capability, cache is not created and no error is thrown.
 
 Depending on your device, total time for loading network on application startup can be significantly reduced.
-Please also note that very first LoadNetwork (when cache is not yet created) takes slightly longer time to 'export' compiled blob into a cache file
+Also note that the very first LoadNetwork (when cache is not yet created) takes slightly longer time to "export" the compiled blob into a cache file:
+
 ![caching_enabled]
 
-## Even faster: use LoadNetwork(modelPath)
+### Even faster: use LoadNetwork(modelPath)
 
-In some cases, applications do not need to customize inputs and outputs every time. Such applications always
+In some cases, applications do not need to customize inputs and outputs every time. Such an application always
 call `cnnNet = ie.ReadNetwork(...)`, then `ie.LoadNetwork(cnnNet, ..)` and it can be further optimized.
-For such cases, more convenient API to load network in one call is introduced in the 2021.4 release.
+For these cases, the 2021.4 release introduces a more convenient API to load the network in a single call, skipping the export step:
 
 @snippet snippets/InferenceEngine_Caching1.cpp part1
 
-With enabled model caching, total load time is even smaller - in case that ReadNetwork is optimized as well
+With model caching enabled, total load time is even smaller, if ReadNetwork is optimized as well.
 
 @snippet snippets/InferenceEngine_Caching2.cpp part2
 
 ![caching_times]
 
+### Advanced Examples
 
-## Advanced examples
-
-Not every device supports network import/export capability, enabling of caching for such devices do not have any effect.
+Not every device supports network import/export capability. For those that don't, enabling caching has no effect.
 To check in advance if a particular device supports model caching, your application can use the following code:
 
 @snippet snippets/InferenceEngine_Caching3.cpp part3
 
+## Introduction (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+As described in Inference Engine Developer Guide, a common application flow consists of the following steps:
+
+1. **Create an Inference Engine Core Object**
+2. **Read the Intermediate Representation** - Read an Intermediate Representation file into an object of the [ie_api.IENetwork](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html)
+3. **Prepare inputs and outputs**
+4. **Set configuration** - Pass device-specific loading configurations to the device
+5. **Compile and Load Network to device** - Use the `IECore.load_network()` method and specify the target device
+6. **Set input data**
+7. **Execute the model** - Run inference
+
+Step #5 can potentially perform several time-consuming device-specific optimizations and network compilations, and such delays can lead to bad user experience on application startup. To avoid this, some devices offer Import/Export network capability, and it is possible to either use the [Compile tool](../../inference-engine/tools/compile_tool/README.md) or enable model caching to export the compiled network automatically. Reusing cached networks can significantly reduce load network time.
+
+### Set the “CACHE_DIR” config option to enable model caching
+
+To enable model caching, the application must specify the folder where to store cached blobs. It can be done using [IECore.set_config](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.set_config).
+
+``` python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+ie.set_config(config={"CACHE_DIR": path_to_cache}, device_name=device)
+net = ie.read_network(model=path_to_xml_file)
+exec_net = ie.load_network(network=net, device_name=device)
+```
+
+With this code, if a device supports the Import/Export network capability, a cached blob is automatically created inside the path_to_cache directory `CACHE_DIR` config is set to the Core object. If device does not support Import/Export capability, cache is just not created and no error is thrown
+
+Depending on your device, total time for loading network on application startup can be significantly reduced. Please also note that very first [IECore.load_network](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.load_network) (when the cache is not yet created) takes slightly longer time to ‘export’ the compiled blob into a cache file.
+
+![caching_enabled]
+
+
+### Even Faster: Use IECore.load_network(path_to_xml_file)
+
+In some cases, applications do not need to customize inputs and outputs every time. These applications always call [IECore.read_network](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.read_network), then `IECore.load_network(model=path_to_xml_file)` and may be further optimized. For such cases, it's more convenient to load the network in a single call to `ie.load_network()`
+A model can be loaded directly to the device, with model caching enabled:
+
+``` python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+ie.set_config(config={"CACHE_DIR" : path_to_cache}, device_name=device)
+ie.load_network(network=path_to_xml_file, device_name=device)
+```
+
+![caching_times]
+
+### Advanced Examples
+
+Not every device supports network import/export capability, enabling of caching for such devices does not have any effect. To check in advance if a particular device supports model caching, your application can use the following code:
+
+```python
+all_metrics = ie.get_metric(device_name=device, metric_name="SUPPORTED_METRICS")
+# Find the 'IMPORT_EXPORT_SUPPORT' metric in supported metrics
+allows_caching = "IMPORT_EXPORT_SUPPORT" in all_metrics
+```
+
+> **NOTE**: The GPU plugin does not have the IMPORT_EXPORT_SUPPORT capability, and does not support model caching yet. However, the GPU plugin supports caching kernels (see the [GPU plugin documentation](supported_plugins/GPU.md)). Kernel caching for the GPU plugin can be accessed the same way as model caching: by setting the `CACHE_DIR` configuration key to a folder where the cache should be stored.
+
 
 [caching_enabled]: ../img/caching_enabled.png
 [caching_times]: ../img/caching_times.png
diff --git a/docs/IE_DG/ONNX_Support.md b/docs/IE_DG/ONNX_Support.md
index 5b85b9185f0..4a8010ba73d 100644
--- a/docs/IE_DG/ONNX_Support.md
+++ b/docs/IE_DG/ONNX_Support.md
@@ -1,41 +1,37 @@
-# ONNX format support in the OpenVINO™ {#openvino_docs_IE_DG_ONNX_Support}
+# ONNX Format Support {#openvino_docs_IE_DG_ONNX_Support}
 
-Starting from the 2020.4 release, OpenVINO™ supports reading native ONNX models.
-`Core::ReadNetwork()` method provides a uniform way to read models from IR or ONNX format, it is a recommended approach to reading models.
-Example:
+## Introduction (C++)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
+
+Starting with the 2020.4 release, OpenVINO™ supports reading native ONNX models. The `Core::ReadNetwork()` method provides a uniform way to read models from IR or ONNX format, it is a recommended approach to reading models. Example:
 
 ```cpp
 InferenceEngine::Core core;
 auto network = core.ReadNetwork("model.onnx");
 ```
 
-**Reshape feature:**
+### Reshape Feature
+OpenVINO™ does not provide a mechanism to specify pre-processing (like mean values subtraction, reverse input channels) for the ONNX format. If an ONNX model contains dynamic shapes for input, please use the `CNNNetwork::reshape` method to reshape the model.
 
-OpenVINO™ doesn't provide a mechanism to specify pre-processing (like mean values subtraction, reverse input channels) for the ONNX format.
-If an ONNX model contains dynamic shapes for input, please use the `CNNNetwork::reshape` method for shape specialization.
+### Weights Saved in External Files
 
-**Weights saved in external files:**
-
-OpenVINO™ supports ONNX models that store weights in external files. It is especially useful for models larger than 2GB because of protobuf limitations.
-To read such models, use the `ReadNetwork` overload which takes `modelPath` as input parameter (both `std::string` and `std::wstring`).
-Note that the `binPath` argument of `ReadNetwork` should be empty in this case, because paths to external weights are saved directly in an ONNX model.
-Otherwise, a runtime exception is thrown.
-Reading models with external weights is not supported by the `ReadNetwork(const std::string& model, const Blob::CPtr& weights)` overload.
+OpenVINO™ supports ONNX models that store weights in external files. It is especially useful for models larger than 2GB because of protobuf limitations. To read such models, use the `ReadNetwork` overload which takes `modelPath` as input parameter (both `std::string` and `std::wstring`). Note that the `binPath` argument of `ReadNetwork` should be empty in this case, because paths to external weights are saved directly in an ONNX model.
+Otherwise, a runtime exception is thrown. Reading models with external weights is not supported by the `ReadNetwork(const std::string& model, const Blob::CPtr& weights)` overload.
 
 Paths to external weight files are saved in an ONNX model; these paths are relative to the model's directory path.
-It means that if a model is located at:
-`home/user/workspace/models/model.onnx`
-and a file that contains external weights:
-`home/user/workspace/models/data/weights.bin`
-the path saved in model should be:
-`data/weights.bin`.
+It means that if a model is located at `home/user/workspace/models/model.onnx` and a file that contains external weights is in `home/user/workspace/models/data/weights.bin`, then the path saved in the model should be:
+  `data/weights.bin`
 
-**NOTE**
+> **NOTE**: A single model can use many external weights files.
 
-* A single model can use many external weights files.
-* Data of many tensors can be stored in a single external weights file (it is processed using offset and length values, which can be also saved in a model).
+> **NOTE**: Data of many tensors can be stored in a single external weights file (it is processed using offset and length values, which can be also saved in a model).
 
-The described mechanism is the only possibility to read weights from external files. The following input parameters of the `ReadNetwork` function overloads are NOT supported for ONNX models and should be passed as empty:
+The described mechanism is the only way to read weights from external files. The following input parameters of the `ReadNetwork` function overloads are NOT supported for ONNX models and should be passed as empty:
 * `const std::wstring& binPath`
 * `const std::string& binPath`
 * `const Blob::CPtr& weights`
@@ -43,8 +39,53 @@ The described mechanism is the only possibility to read weights from external fi
 You can find more details about the external data mechanism in [ONNX documentation](https://github.com/onnx/onnx/blob/master/docs/ExternalData.md).
 To convert a model to use the external data feature, you can use [ONNX helper functions](https://github.com/onnx/onnx/blob/master/onnx/external_data_helper.py).
 
-**Unsupported types of tensors:**
+Unsupported types of tensors:
+* string
+* complex64
+* complex128
 
-* `string`,
-* `complex64`,
-* `complex128`.
+## Introduction (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+Starting with the 2020.4 release, OpenVINO™ supports reading native ONNX models. The `IECore.read_network()` method provides a uniform way to read models from IR or ONNX format, it is a recommended approach to reading models. Example:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_onnx_file)
+```
+
+### Reshape Feature
+OpenVINO™ does not provide a mechanism to specify pre-processing (like mean values subtraction, reverse input channels) for the ONNX format. If an ONNX model contains dynamic shapes for input, please use the [IENetwork.reshape](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.reshape) method to reshape the model.
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_onnx_file)
+input_layer = next(iter(net.input_info))
+net.reshape({input_layer: new_shape})
+```
+
+### Weights Saved in External Files
+
+OpenVINO™ supports ONNX models that store weights in external files. It is especially useful for models larger than 2GB because of protobuf limitations. To read such models, use the `model` parameter in the `IECore.read_network(model=path_to_onnx_file)` method. Note that the parameter for the path to the binary weight file, `weights=` should be empty in this case, because paths to external weights are saved directly in an ONNX model. Otherwise, a runtime exception is thrown. Reading models with external weights is **NOT** supported by the `read_network(weights=path_to_bin_file)` parameter.
+
+Paths to external weight files are saved in an ONNX model; these paths are relative to the model’s directory path. It means that if a model is located at: `$HOME/workspace/models/model.onnx` and a file that contains external weights: `$HOME/workspace/models/data/weights.bin`, the path saved in model should be: data/weights.bin.
+
+**NOTE**: 
+* A single model can use many external weights files.
+* Data of many tensors can be stored in a single external weights file (it is processed using offset and length values, which can be also saved in a model).
+
+The described mechanism is the only possibility to read weights from external files. The `weights` input parameter of the [IECore.read_network](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.read_network) function is NOT supported for ONNX models and should not be passed, or set as None.
+
+Unsupported types of tensors:
+* string
+* complex64
+* complex128
diff --git a/docs/IE_DG/Operations_specifications.md b/docs/IE_DG/Operations_specifications.md
new file mode 100644
index 00000000000..1cc4f07cfcb
--- /dev/null
+++ b/docs/IE_DG/Operations_specifications.md
@@ -0,0 +1,194 @@
+# Operations Specifications {#openvino_docs_operations_specifications}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   
+   openvino_docs_ops_arithmetic_Abs_1
+   openvino_docs_ops_arithmetic_Acos_1
+   openvino_docs_ops_arithmetic_Acosh_3
+   openvino_docs_ops_pooling_AdaptiveAvgPool_8
+   openvino_docs_ops_pooling_AdaptiveMaxPool_8
+   openvino_docs_ops_arithmetic_Add_1
+   openvino_docs_ops_arithmetic_Asin_1
+   openvino_docs_ops_arithmetic_Asinh_3
+   openvino_docs_ops_infrastructure_Assign_3
+   openvino_docs_ops_arithmetic_Atan_1
+   openvino_docs_ops_arithmetic_Atanh_3
+   openvino_docs_ops_pooling_AvgPool_1
+   openvino_docs_ops_normalization_BatchNormInference_1
+   openvino_docs_ops_normalization_BatchNormInference_5
+   openvino_docs_ops_movement_BatchToSpace_2
+   openvino_docs_ops_convolution_BinaryConvolution_1
+   openvino_docs_ops_movement_Broadcast_1
+   openvino_docs_ops_movement_Broadcast_3
+   openvino_docs_ops_condition_Bucketize_3
+   openvino_docs_ops_sequence_CTCGreedyDecoder_1
+   openvino_docs_ops_sequence_CTCGreedyDecoderSeqLen_6
+   openvino_docs_ops_arithmetic_Ceiling_1
+   openvino_docs_ops_activation_Clamp_1
+   openvino_docs_ops_movement_Concat_1
+   openvino_docs_ops_infrastructure_Constant_1
+   openvino_docs_ops_type_ConvertLike_1
+   openvino_docs_ops_type_Convert_1
+   openvino_docs_ops_convolution_ConvolutionBackpropData_1
+   openvino_docs_ops_convolution_Convolution_1
+   openvino_docs_ops_arithmetic_Cos_1
+   openvino_docs_ops_arithmetic_Cosh_1
+   openvino_docs_ops_sequence_CTCLoss_4
+   openvino_docs_ops_arithmetic_CumSum_3
+   openvino_docs_ops_convolution_DeformableConvolution_8
+   openvino_docs_ops_detection_DeformablePSROIPooling_1
+   openvino_docs_ops_movement_DepthToSpace_1
+   openvino_docs_ops_detection_DetectionOutput_1
+   openvino_docs_ops_detection_DetectionOutput_8
+   openvino_docs_ops_signals_DFT_7
+   openvino_docs_ops_arithmetic_Divide_1
+   openvino_docs_ops_matrix_Einsum_7
+   openvino_docs_ops_activation_Elu_1
+   openvino_docs_ops_sparse_EmbeddingBagOffsetsSum_3
+   openvino_docs_ops_sparse_EmbeddingBagPackedSum_3
+   openvino_docs_ops_sparse_EmbeddingSegmentsSum_3
+   openvino_docs_ops_comparison_Equal_1
+   openvino_docs_ops_arithmetic_Erf_1
+   openvino_docs_ops_activation_Exp_1
+   openvino_docs_ops_detection_ExperimentalDetectronDetectionOutput_6
+   openvino_docs_ops_detection_ExperimentalDetectronGenerateProposalsSingleImage_6
+   openvino_docs_ops_detection_ExperimentalDetectronPriorGridGenerator_6
+   openvino_docs_ops_detection_ExperimentalDetectronROIFeatureExtractor_6
+   openvino_docs_ops_sort_ExperimentalDetectronTopKROIs_6
+   openvino_docs_ops_movement_ExtractImagePatches_3
+   openvino_docs_ops_quantization_FakeQuantize_1
+   openvino_docs_ops_arithmetic_FloorMod_1
+   openvino_docs_ops_arithmetic_Floor_1
+   openvino_docs_ops_normalization_GRN_1
+   openvino_docs_ops_sequence_GRUCell_3
+   openvino_docs_ops_sequence_GRUSequence_5
+   openvino_docs_ops_movement_GatherTree_1
+   openvino_docs_ops_movement_Gather_1
+   openvino_docs_ops_movement_Gather_7
+   openvino_docs_ops_movement_Gather_8
+   openvino_docs_ops_movement_GatherElements_6
+   openvino_docs_ops_movement_GatherND_5
+   openvino_docs_ops_movement_GatherND_8
+   openvino_docs_ops_activation_GELU_2
+   openvino_docs_ops_activation_GELU_7
+   openvino_docs_ops_comparison_GreaterEqual_1
+   openvino_docs_ops_comparison_Greater_1
+   openvino_docs_ops_convolution_GroupConvolutionBackpropData_1
+   openvino_docs_ops_convolution_GroupConvolution_1
+   openvino_docs_ops_activation_HardSigmoid_1
+   openvino_docs_ops_activation_HSigmoid_5
+   openvino_docs_ops_activation_HSwish_4
+   openvino_docs_ops_image_I420toBGR_8
+   openvino_docs_ops_image_I420toRGB_8
+   openvino_docs_ops_signals_IDFT_7
+   openvino_docs_ops_condition_If_8
+   openvino_docs_ops_image_Interpolate_1
+   openvino_docs_ops_image_Interpolate_4
+   openvino_docs_ops_normalization_LRN_1
+   openvino_docs_ops_sequence_LSTMCell_1
+   openvino_docs_ops_sequence_LSTMSequence_1
+   openvino_docs_ops_comparison_LessEqual_1
+   openvino_docs_ops_comparison_Less_1
+   openvino_docs_ops_arithmetic_Log_1
+   openvino_docs_ops_logical_LogicalAnd_1
+   openvino_docs_ops_logical_LogicalNot_1
+   openvino_docs_ops_logical_LogicalOr_1
+   openvino_docs_ops_logical_LogicalXor_1
+   openvino_docs_ops_activation_LogSoftmax_5
+   openvino_docs_ops_infrastructure_Loop_5
+   openvino_docs_ops_normalization_MVN_1
+   openvino_docs_ops_normalization_MVN_6
+   openvino_docs_ops_matrix_MatMul_1
+   openvino_docs_ops_sort_MatrixNonMaxSuppression_8
+   openvino_docs_ops_pooling_MaxPool_1
+   openvino_docs_ops_arithmetic_Maximum_1
+   openvino_docs_ops_arithmetic_Minimum_1
+   openvino_docs_ops_activation_Mish_4
+   openvino_docs_ops_arithmetic_Mod_1
+   openvino_docs_ops_sort_MulticlassNonMaxSuppression_8
+   openvino_docs_ops_arithmetic_Multiply_1
+   openvino_docs_ops_arithmetic_Negative_1
+   openvino_docs_ops_sort_NonMaxSuppression_1
+   openvino_docs_ops_sort_NonMaxSuppression_3
+   openvino_docs_ops_sort_NonMaxSuppression_4
+   openvino_docs_ops_sort_NonMaxSuppression_5
+   openvino_docs_ops_condition_NonZero_3
+   openvino_docs_ops_normalization_NormalizeL2_1
+   openvino_docs_ops_comparison_NotEqual_1
+   openvino_docs_ops_image_NV12toBGR_8
+   openvino_docs_ops_image_NV12toRGB_8
+   openvino_docs_ops_sequence_OneHot_1
+   openvino_docs_ops_activation_PReLU_1
+   openvino_docs_ops_detection_PSROIPooling_1
+   openvino_docs_ops_movement_Pad_1
+   openvino_docs_ops_infrastructure_Parameter_1
+   openvino_docs_ops_arithmetic_Power_1
+   openvino_docs_ops_detection_PriorBoxClustered_1
+   openvino_docs_ops_detection_PriorBox_1
+   openvino_docs_ops_detection_PriorBox_8
+   openvino_docs_ops_detection_Proposal_1
+   openvino_docs_ops_detection_Proposal_4
+   openvino_docs_ops_generation_RandomUniform_8
+   openvino_docs_ops_generation_Range_1
+   openvino_docs_ops_generation_Range_4
+   openvino_docs_ops_infrastructure_ReadValue_3
+   openvino_docs_ops_activation_ReLU_1
+   openvino_docs_ops_reduction_ReduceL1_4
+   openvino_docs_ops_reduction_ReduceL2_4
+   openvino_docs_ops_reduction_ReduceLogicalAnd_1
+   openvino_docs_ops_reduction_ReduceLogicalOr_1
+   openvino_docs_ops_reduction_ReduceMax_1
+   openvino_docs_ops_reduction_ReduceMean_1
+   openvino_docs_ops_reduction_ReduceMin_1
+   openvino_docs_ops_reduction_ReduceProd_1
+   openvino_docs_ops_reduction_ReduceSum_1
+   openvino_docs_ops_detection_RegionYolo_1
+   openvino_docs_ops_detection_ReorgYolo_1
+   openvino_docs_ops_shape_Reshape_1
+   openvino_docs_ops_infrastructure_Result_1
+   openvino_docs_ops_movement_Reverse_1
+   openvino_docs_ops_movement_ReverseSequence_1
+   openvino_docs_ops_sequence_RNNCell_3
+   openvino_docs_ops_sequence_RNNSequence_5
+   openvino_docs_ops_detection_ROIAlign_3
+   openvino_docs_ops_detection_ROIPooling_1
+   openvino_docs_ops_movement_Roll_7
+   openvino_docs_ops_arithmetic_Round_5
+   openvino_docs_ops_movement_ScatterElementsUpdate_3
+   openvino_docs_ops_movement_ScatterNDUpdate_3
+   openvino_docs_ops_movement_ScatterUpdate_3
+   openvino_docs_ops_condition_Select_1
+   openvino_docs_ops_activation_Selu_1
+   openvino_docs_ops_shape_ShapeOf_1
+   openvino_docs_ops_shape_ShapeOf_3
+   openvino_docs_ops_movement_ShuffleChannels_1
+   openvino_docs_ops_activation_Sigmoid_1
+   openvino_docs_ops_arithmetic_Sign_1
+   openvino_docs_ops_arithmetic_Sin_1
+   openvino_docs_ops_arithmetic_Sinh_1
+   openvino_docs_ops_activation_SoftMax_1
+   openvino_docs_ops_activation_SoftMax_8
+   openvino_docs_ops_activation_SoftPlus_4
+   openvino_docs_ops_movement_SpaceToBatch_2
+   openvino_docs_ops_movement_SpaceToDepth_1
+   openvino_docs_ops_movement_Split_1
+   openvino_docs_ops_arithmetic_Sqrt_1
+   openvino_docs_ops_arithmetic_SquaredDifference_1
+   openvino_docs_ops_shape_Squeeze_1
+   openvino_docs_ops_movement_StridedSlice_1
+   openvino_docs_ops_arithmetic_Subtract_1
+   openvino_docs_ops_activation_Swish_4
+   openvino_docs_ops_arithmetic_Tan_1
+   openvino_docs_ops_arithmetic_Tanh_1
+   openvino_docs_ops_infrastructure_TensorIterator_1
+   openvino_docs_ops_movement_Tile_1
+   openvino_docs_ops_sort_TopK_1
+   openvino_docs_ops_sort_TopK_3
+   openvino_docs_ops_movement_Transpose_1
+   openvino_docs_ops_shape_Unsqueeze_1
+   openvino_docs_ops_movement_VariadicSplit_1
+
+@endsphinxdirective
diff --git a/docs/IE_DG/Samples_Overview.md b/docs/IE_DG/Samples_Overview.md
index c96cdd9d5f6..b0724fb5094 100644
--- a/docs/IE_DG/Samples_Overview.md
+++ b/docs/IE_DG/Samples_Overview.md
@@ -1,5 +1,36 @@
 # Inference Engine Samples {#openvino_docs_IE_DG_Samples_Overview}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_inference_engine_samples_classification_sample_async_README
+   openvino_inference_engine_ie_bridges_python_sample_classification_sample_async_README
+   openvino_inference_engine_samples_hello_classification_README
+   openvino_inference_engine_ie_bridges_c_samples_hello_classification_README
+   openvino_inference_engine_ie_bridges_python_sample_hello_classification_README
+   openvino_inference_engine_samples_hello_reshape_ssd_README
+   openvino_inference_engine_ie_bridges_python_sample_hello_reshape_ssd_README
+   openvino_inference_engine_samples_hello_nv12_input_classification_README
+   openvino_inference_engine_ie_bridges_c_samples_hello_nv12_input_classification_README
+   openvino_inference_engine_samples_hello_query_device_README
+   openvino_inference_engine_ie_bridges_python_sample_hello_query_device_README
+   openvino_inference_engine_samples_ngraph_function_creation_sample_README
+   openvino_inference_engine_ie_bridges_python_sample_ngraph_function_creation_sample_README
+   openvino_inference_engine_samples_object_detection_sample_ssd_README
+   openvino_inference_engine_ie_bridges_python_sample_object_detection_sample_ssd_README
+   openvino_inference_engine_ie_bridges_c_samples_object_detection_sample_ssd_README
+   openvino_inference_engine_samples_speech_sample_README
+   openvino_inference_engine_ie_bridges_python_sample_speech_sample_README
+   openvino_inference_engine_samples_style_transfer_sample_README
+   openvino_inference_engine_ie_bridges_python_sample_style_transfer_sample_README
+   openvino_inference_engine_samples_benchmark_app_README
+   openvino_inference_engine_tools_benchmark_tool_README
+
+@endsphinxdirective
+
 The Inference Engine sample applications are simple console applications that show how to utilize specific Inference Engine capabilities within an application, assist developers in executing specific tasks such as loading a model, running inference, querying specific device capabilities and etc.
 
 After installation of Intel® Distribution of OpenVINO™ toolkit, С, C++ and Python* sample applications are available in the following directories, respectively:
@@ -102,7 +133,7 @@ for the debug configuration — in `<path_to_build_directory>/intel64/Debug/`.
 
 The recommended Windows* build environment is the following:
 * Microsoft Windows* 10
-* Microsoft Visual Studio* 2017, or 2019. Make sure that C++ CMake tools for Windows is [enabled](https://docs.microsoft.com/en-us/cpp/build/cmake-projects-in-visual-studio?view=msvc-160#:~:text=The%20Visual%20C%2B%2B%20Tools%20for,Visual%20Studio%20generators%20are%20supported).
+* Microsoft Visual Studio* 2017, or 2019
 * CMake* version 3.10 or higher
 
 > **NOTE**: If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14.
diff --git a/docs/IE_DG/ShapeInference.md b/docs/IE_DG/ShapeInference.md
index a265f2e9703..be7480d493b 100644
--- a/docs/IE_DG/ShapeInference.md
+++ b/docs/IE_DG/ShapeInference.md
@@ -1,27 +1,37 @@
-Using Shape Inference {#openvino_docs_IE_DG_ShapeInference}
-==========================================
+# Using the Reshape Inference Feature {#openvino_docs_IE_DG_ShapeInference}
 
-OpenVINO™ provides the following methods for runtime model reshaping:
+## Introduction (C++)
 
-* **Set a new input shape** with the `InferenceEngine::CNNNetwork::reshape` method.<br>
-   The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
+
+OpenVINO™ provides two methods for runtime model reshaping: setting a new input shape and setting a new batch dimension value.
+
+### Set a new input shape with the reshape() method
+
+The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
    
 > **NOTES**:
 > - Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping in most cases.
 > - Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.<br>
 > - If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
 
-* **Set a new batch dimension value** with the `InferenceEngine::CNNNetwork::setBatchSize` method.<br>     
-   The meaning of a model batch may vary depending on the model design.
-   This method does not deduce batch placement for inputs from the model architecture.
-   It assumes that the batch is placed at the zero index in the shape for all inputs and uses the `InferenceEngine::CNNNetwork::reshape` method to propagate updated shapes through the model.
+### Set a new batch dimension value with the setBatchSize() method
 
-   The method transforms the model before a new shape propagation to relax a hard-coded batch dimension in the model, if any.
+The meaning of a model batch may vary depending on the model design.
+This method does not deduce batch placement for inputs from the model architecture.
+It assumes that the batch is placed at the zero index in the shape for all inputs and uses the `InferenceEngine::CNNNetwork::reshape` method to propagate updated shapes through the model.
 
-   Use `InferenceEngine::CNNNetwork::reshape` instead of `InferenceEngine::CNNNetwork::setBatchSize` to set new input shapes for the model in case the model has:
-   * Multiple inputs with different zero-index dimension meanings
-   * Input without a batch dimension
-   * 0D, 1D, or 3D shape
+The method transforms the model before a new shape propagation to relax a hard-coded batch dimension in the model, if any.
+
+Use `InferenceEngine::CNNNetwork::reshape` instead of `InferenceEngine::CNNNetwork::setBatchSize` to set new input shapes for the model if the model has one of the following:
+
+* Multiple inputs with different zero-index dimension meanings
+* Input without a batch dimension
+* 0D, 1D, or 3D shape
 
    The `InferenceEngine::CNNNetwork::setBatchSize` method is a high-level API method that wraps the `InferenceEngine::CNNNetwork::reshape` method call and works for trivial models from the batch placement standpoint.
    Use `InferenceEngine::CNNNetwork::reshape` for other models.
@@ -37,10 +47,11 @@ Inference Engine takes three kinds of a model description as an input, which are
 3. [nGraph function](../nGraph_DG/nGraph_dg.md) through the constructor of `InferenceEngine::CNNNetwork`
 
 `InferenceEngine::CNNNetwork` keeps an `ngraph::Function` object with the model description internally.
-The object should have fully defined input shapes to be successfully loaded to the Inference Engine plugins.
-To resolve undefined input dimensions of a model, call the `CNNNetwork::reshape` method providing new input shapes before loading to the Inference Engine plugin.
+The object should have fully-defined input shapes to be successfully loaded to Inference Engine plugins.
+To resolve undefined input dimensions of a model, call the `CNNNetwork::reshape` method to provide new input shapes before loading to the Inference Engine plugin.
 
 Run the following code right after `InferenceEngine::CNNNetwork` creation to explicitly check for model input names and shapes:
+
 ```cpp
 CNNNetwork network = ... // read IR / ONNX model or create from nGraph::Function explicitly
 const auto parameters = network.getFunction()->get_parameters();
@@ -55,37 +66,13 @@ To feed input data of a shape that is different from the model input shape, resh
 
 Once the input shape of `InferenceEngine::CNNNetwork` is set, call the `InferenceEngine::Core::LoadNetwork` method to get an `InferenceEngine::ExecutableNetwork` object for inference with updated shapes.
 
-There are other approaches to reshape the model during the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](../nGraph_DG/build_function.md).
+There are other approaches to reshape the model during the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](../nGraph_DG/build_function.md).
 
 Practically, some models are not ready to be reshaped. In this case, a new input shape cannot be set with the Model Optimizer or the `InferenceEngine::CNNNetwork::reshape` method.
 
-## Troubleshooting Reshape Errors
+### Usage of Reshape Method <a name="usage_of_reshape_method"></a>
 
-Operation semantics may impose restrictions on input shapes of the operation. 
-Shape collision during shape propagation may be a sign that a new shape does not satisfy the restrictions. 
-Changing the model input shape may result in intermediate operations shape collision.
-
-Examples of such operations:
-- [Reshape](../ops/shape/Reshape_1.md) operation with a hard-coded output shape value
-- [MatMul](../ops/matrix/MatMul_1.md) operation with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
-
-Model structure and logic should not change significantly after model reshaping.
-- The Global Pooling operation is commonly used to reduce output feature map of classification models output.
-Having the input of the shape [N, C, H, W], Global Pooling returns the output of the shape [N, C, 1, 1].
-Model architects usually express Global Pooling with the help of the `Pooling` operation with the fixed kernel size [H, W].
-During spatial reshape, having the input of the shape [N, C, H1, W1], Pooling with the fixed kernel size [H, W] returns the output of the shape [N, C, H2, W2], where H2 and W2 are commonly not equal to `1`.
-It breaks the classification model structure.
-For example, [publicly available Inception family models from TensorFlow*](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models) have this issue.
-
-- Changing the model input shape may significantly affect its accuracy.
-For example, Object Detection models from TensorFlow have resizing restrictions by design. 
-To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the `pipeline.config` file. 
-For details, refer to the <a href="_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html#tf_od_custom_input_shape">Tensorflow Object Detection API models resizing techniques</a>.
-
-## Usage of Reshape Method <a name="usage_of_reshape_method"></a>
-
-The primary method of the feature is `InferenceEngine::CNNNetwork::reshape`.
-It gets new input shapes and propagates it from input to output for all intermediates layers of the given network.
+The primary method of the feature is `InferenceEngine::CNNNetwork::reshape`. It gets new input shapes and propagates it from input to output for all intermediates layers of the given network.
 The method takes `InferenceEngine::ICNNNetwork::InputShapes` - a map of pairs: name of input data and its dimension.
 
 The algorithm for resizing network is the following:
@@ -100,9 +87,140 @@ Here is a code example:
 
 @snippet snippets/ShapeInference.cpp part0
 
-Shape Inference feature is used in [Smart Classroom Demo](@ref omz_demos_smart_classroom_demo_cpp).
+The Shape Inference feature is used in [Smart Classroom Demo](@ref omz_demos_smart_classroom_demo_cpp).
 
-## Extensibility
+### Troubleshooting Reshape Errors
 
-Inference Engine provides a special mechanism that allows to add the support of shape inference for custom operations. 
-This mechanism is described in the [Extensibility documentation](Extensibility_DG/Intro.md)
+Operation semantics may impose restrictions on input shapes of the operation. 
+Shape collision during shape propagation may be a sign that a new shape does not satisfy the restrictions. 
+Changing the model input shape may result in intermediate operations shape collision.
+
+Examples of such operations:
+* [Reshape](../ops/shape/Reshape_1.md) operation with a hard-coded output shape value
+* [MatMul](../ops/matrix/MatMul_1.md) operation with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
+
+Model structure and logic should not change significantly after model reshaping.
+- The Global Pooling operation is commonly used to reduce output feature map of classification models output.
+Having the input of the shape [N, C, H, W], Global Pooling returns the output of the shape [N, C, 1, 1].
+Model architects usually express Global Pooling with the help of the `Pooling` operation with the fixed kernel size [H, W].
+During spatial reshape, having the input of the shape [N, C, H1, W1], Pooling with the fixed kernel size [H, W] returns the output of the shape [N, C, H2, W2], where H2 and W2 are commonly not equal to `1`.
+It breaks the classification model structure.
+For example, [publicly available Inception family models from TensorFlow*](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models) have this issue.
+
+- Changing the model input shape may significantly affect its accuracy.
+For example, Object Detection models from TensorFlow have resizing restrictions by design. 
+To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the `pipeline.config` file. 
+For details, refer to the <a href="_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html#tf_od_custom_input_shape">Tensorflow Object Detection API models resizing techniques</a>.
+
+### Extensibility
+The Inference Engine provides a special mechanism that allows adding support of shape inference for custom operations. This mechanism is described in the [Extensibility documentation](Extensibility_DG/Intro.md)
+
+## Introduction (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+OpenVINO™ provides the following methods for runtime model reshaping:
+
+* Set a new input shape with the [IENetwork.reshape](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.reshape) method.
+
+  The [IENetwork.reshape](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.reshape) method updates input shapes and propagates them down to the outputs of the model through all intermediate layers.
+
+  **NOTES**:
+  * Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping in most cases.
+  * Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.
+  * If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
+
+
+* Set a new batch dimension value with the [IENetwork.batch_size](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.batch_size) method.
+
+   The meaning of a model batch may vary depending on the model design. This method does not deduce batch placement for inputs from the model architecture. It assumes that the batch is placed at the zero index in the shape for all inputs and uses the [IENetwork.reshape](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.reshape) method to propagate updated shapes through the model.
+
+The method transforms the model before a new shape propagation to relax a hard-coded batch dimension in the model, if any.
+
+Use [IENetwork.reshape](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.reshape) rather than [IENetwork.batch_size](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.batch_size) to set new input shapes for the model if the model has:
+
+  * Multiple inputs with different zero-index dimension meanings
+  * Input without a batch dimension
+  * 0D, 1D, or 3D shape
+
+The [IENetwork.batch_size](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.batch_size) method is a high-level API method that wraps the [IENetwork.reshape](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.reshape)  method call and works for trivial models from the batch placement standpoint. Use [IENetwork.reshape](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.reshape) for other models.
+
+Using the [IENetwork.batch_size](api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.batch_size) method for models with a non-zero index batch placement or for models with inputs that do not have a batch dimension may lead to undefined behaviour.
+
+You can change input shapes multiple times using the `IENetwork.reshape` and `IENetwork.batch_size` methods in any order. If a model has a hard-coded batch dimension, use `IENetwork.batch_size` first to change the batch, then call `IENetwork.reshape` to update other dimensions, if needed.
+
+Inference Engine takes three kinds of a model description as an input, which are converted into an IENetwork object:
+
+1. Intermediate Representation (IR) through `IECore.read_network`
+2. ONNX model through `IECore.read_network`
+3. nGraph function through the constructor of IENetwork
+
+IENetwork keeps an `ngraph::Function` object with the model description internally. The object should have fully defined input shapes to be successfully loaded to the Inference Engine plugins. To resolve undefined input dimensions of a model, call the `IENetwork.reshape` method providing new input shapes before loading to the Inference Engine plugin.
+
+Run the following code right after IENetwork creation to explicitly check for model input names and shapes:
+
+To feed input data of a shape that is different from the model input shape, reshape the model first.
+
+Once the input shape of IENetwork is set, call the `IECore.load_network` method to get an ExecutableNetwork object for inference with updated shapes.
+
+There are other approaches to reshape the model during the stage of IR generation or [nGraph function](https://docs.openvinotoolkit.org/latest/openvino_docs_nGraph_DG_PythonAPI.html#create_an_ngraph_function_from_a_graph) creation.
+
+Practically, some models are not ready to be reshaped. In this case, a new input shape cannot be set with the Model Optimizer or the `IENetwork.reshape` method.
+
+### Troubleshooting Reshape Errors
+Operation semantics may impose restrictions on input shapes of the operation. Shape collision during shape propagation may be a sign that a new shape does not satisfy the restrictions. Changing the model input shape may result in intermediate operations shape collision.
+
+Examples of such operations:
+
+* Reshape operation with a hard-coded output shape value
+* MatMul operation with the Const second input cannot be resized by spatial dimensions due to operation semantics
+
+A model's structure and logic should not significantly change after model reshaping.
+
+* The Global Pooling operation is commonly used to reduce output feature map of classification models output. Having the input of the shape [N, C, H, W], Global Pooling returns the output of the shape [N, C, 1, 1]. Model architects usually express Global Pooling with the help of the Pooling operation with the fixed kernel size [H, W]. During spatial reshape, having the input of the shape [N, C, H1, W1], Pooling with the fixed kernel size [H, W] returns the output of the shape [N, C, H2, W2], where H2 and W2 are commonly not equal to 1. It breaks the classification model structure. For example, publicly available Inception family models from TensorFlow* have this issue.
+
+* Changing the model input shape may significantly affect its accuracy. For example, Object Detection models from TensorFlow have resizing restrictions by design. To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the pipeline.config file. For details, refer to the Tensorflow Object Detection API models resizing techniques.
+
+
+### Usage of the Reshape Method
+
+The primary method of the feature is `IENetwork.reshape`. It gets new input shapes and propagates it from input to output for all intermediates layers of the given network. Use `IENetwork.input_info` to get names of input_layers and `.tensor_desc.dims` to get the current network input shape.
+
+The following code example shows how to reshape a model to the size of an input image.
+
+```python
+import cv2
+import numpy as np
+from openvino.inference_engine import IECore
+
+ie = IECore()
+
+# Read an input image and transpose input to NCWH
+image = cv2.imread(path_to_image_file)
+input_image = image.transpose((2, 0, 1))
+input_image = np.expand_dims(input_image, axis=0)
+
+# Load the model and get input info
+# Note that this model must support arbitrary input shapes
+net = ie.read_network(model=path_to_xml_file)
+input_layer = next(iter(net.input_info))
+print(f"Input shape: {net.input_info[input_blob].tensor_desc.dims}")
+
+# Call reshape
+net.reshape({input_layer: input_image.shape})
+print(f"New input shape: {net.input_info[input_blob].tensor_desc.dims}")
+
+# Load the model to the device and proceed with inference
+exec_net = ie.load_network(network=net, device_name="CPU")
+```
+
+### Extensibility
+The Inference Engine provides a special mechanism that allows adding support of shape inference for custom operations. This mechanism is described in the [Extensibility documentation](Extensibility_DG/Intro.md)
+
+### See Also:
+
+[Hello Reshape Python Sample](../../inference_engine/ie_bridges/python/sample/hello_reshape_ssd/README.html)
\ No newline at end of file
diff --git a/docs/IE_DG/img/BASIC_FLOW_IE_C.svg b/docs/IE_DG/img/BASIC_FLOW_IE_C.svg
new file mode 100644
index 00000000000..577dea97620
--- /dev/null
+++ b/docs/IE_DG/img/BASIC_FLOW_IE_C.svg
@@ -0,0 +1,32 @@
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"/>
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deleted file mode 100644
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diff --git a/docs/IE_DG/inference_engine_intro.md b/docs/IE_DG/inference_engine_intro.md
index cc6ae5254c0..e48b7578211 100644
--- a/docs/IE_DG/inference_engine_intro.md
+++ b/docs/IE_DG/inference_engine_intro.md
@@ -1,11 +1,11 @@
 # Introduction to Inference Engine {#openvino_docs_IE_DG_inference_engine_intro}
 
-> **NOTE:** [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019).
+> **NOTE**: [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019).
 
 This Guide provides an overview of the Inference Engine describing the typical workflow for performing
 inference of a pre-trained and optimized deep learning model and a set of sample applications.
 
-> **NOTE:** Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer or built directly in run-time using nGraph API. To learn about how to use Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to [Pre-Trained Models](@ref omz_models_group_intel).
+> **NOTE**: Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer or built directly in run-time using nGraph API. To learn about how to use Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to [Pre-Trained Models](@ref omz_models_group_intel).
 
 After you have used the Model Optimizer to create an Intermediate Representation (IR), use the Inference Engine to infer the result for a given input data.
 
diff --git a/docs/IE_DG/integrate_with_customer_application_python.md b/docs/IE_DG/integrate_with_customer_application_python.md
new file mode 100644
index 00000000000..89e5a4712e0
--- /dev/null
+++ b/docs/IE_DG/integrate_with_customer_application_python.md
@@ -0,0 +1,152 @@
+# Integrate Inference Engine with Your Python Application {#openvino_docs_IE_DG_integrate_with_customer_application_python}
+
+This document explains how to integrate and use the Inference Engine API with your Python application.   
+
+The following diagram illustrates the typical Inference Engine Python API workflow:
+![ie_api_flow_python] 
+
+Read the sections below to learn about each item.
+
+## Link with Inference Engine Library
+
+To make use of the Inference Engine functionality, import IECore to your application: 
+
+```py
+from openvino.inference_engine import IECore
+``` 
+ 
+## Use Inference Engine API Implement Inference Pipeline
+
+This section provides step-by-step instructions to implement a typical inference pipeline with the Inference Engine Python API:   
+
+![ie_api_use_python]
+
+### Step 1. Create Inference Engine Core
+
+Use the following code to create Inference Engine Core to manage available devices and read network objects: 
+```py
+ie = IECore()
+``` 
+### Step 2 (Optional). Read model. Configure Input and Output of the Model
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="collapsible-section">
+@endsphinxdirective
+
+Optionally, configure input and output of the model using the steps below: 
+
+1. Read model 
+   @sphinxdirective
+      
+   .. tab:: IR
+   
+      .. code-block:: python
+   
+         net = ie.read_network(model="model.xml")
+   
+   .. tab:: ONNX
+      
+      .. code-block:: python
+         
+         net = ie.read_network(model="model.onnx")
+   
+   .. tab:: nGraph
+      
+      .. code-block:: python
+         
+         // TBD
+   
+   @endsphinxdirective
+
+2. Request input and output information using input_info, outputs 
+   ```py
+   input_name = next(iter(net.input_info))  
+
+   output_name = next(iter(net.outputs)) 
+   ``` 
+   Information for this input layer is stored in input_info. The next cell prints the input layout, precision and shape. 
+   ```py
+   print(f"input layout: {net.input_info[input_layer].layout}") 
+   print(f"input precision: {net.input_info[input_layer].precision}") 
+   print(f"input shape: {net.input_info[input_layer].tensor_desc.dims}") 
+   ```
+   This cell output tells us that the model expects inputs with a shape of [1,3,224,224], and that this is in NCHW layout. This means that the model expects input data with a batch size (N) of 1, 3 channels (C), and images of a height (H) and width (W) of 224. The input data is expected to be of FP32 (floating point) precision. 
+    
+   Getting the output layout, precision and shape is similar to getting the input layout, precision and shape. 
+   ```py
+   print(f"output layout: {net.outputs[output_layer].layout}") 
+   print(f"output precision: {net.outputs[output_layer].precision}") 
+   print(f"output shape: {net.outputs[output_layer].shape}") 
+   ```
+   This cell output shows that the model returns outputs with a shape of [1, 1001], where 1 is the batch size (N) and 1001 the number of classes (C). The output is returned as 32-bit floating point. 
+
+@sphinxdirective
+.. raw:: html
+
+    </div>
+@endsphinxdirective 
+
+### Step 3. Load model to the Device 
+
+Load the model to the device using `load_network()`:
+
+@sphinxdirective
+   
+.. tab:: IR
+
+   .. code-block:: python
+
+      exec_net = ie.load_network(network= "model.xml", device_name="CPU") 
+.. tab:: ONNX
+   
+   .. code-block:: python
+      
+      exec_net = ie.load_network(network= "model.onnx", device_name="CPU") 
+
+.. tab:: nGraph
+   
+   .. code-block:: python
+      
+      // TBD
+
+.. tab:: Model from Step 2
+   
+   .. code-block:: python
+   
+      exec_net = ie.load_network(network=net, device_name="CPU")
+
+@endsphinxdirective
+
+### Step 4. Prepare input 
+```py
+import cv2 
+import numpy as np 
+
+image = cv2.imread("image.png") 
+
+# Resize with OpenCV your image if needed to match with net input shape 
+# res_image = cv2.resize(src=image, dsize=(W, H)) 
+
+# Converting image to NCHW format with FP32 type 
+input_data = np.expand_dims(np.transpose(image, (2, 0, 1)), 0).astype(np.float32) 
+```
+
+### Step 5. Start Inference
+```py
+input_name = next(iter(net.input_info))
+result = exec_net.infer({input_name: input_data}) 
+``` 
+
+### Step 6. Process the Inference Results 
+```py
+output_name = next(iter(net.outputs))
+output = result[output_name] 
+```
+
+## Run Application
+
+[ie_api_flow_python]: img/ie_api_python.png
+[ie_api_use_python]: img/ie_api_integration_python.png
+ 
\ No newline at end of file
diff --git a/docs/IE_DG/network_state_intro.md b/docs/IE_DG/network_state_intro.md
index 9b07ce81b3e..908dc58dcf6 100644
--- a/docs/IE_DG/network_state_intro.md
+++ b/docs/IE_DG/network_state_intro.md
@@ -7,7 +7,7 @@ This section describes how to work with stateful networks in OpenVINO toolkit, s
 
 The section additionally provides small examples of stateful network and code to infer it.
 
-## What is a Stateful Network
+## What is a Stateful Network?
 
  Several use cases require processing of data sequences. When length of a sequence is known and small enough, 
  we can process it with RNN like networks that contain a cycle inside. But in some cases, like online speech recognition of time series 
@@ -316,7 +316,7 @@ InferenceEngine::lowLatency2(cnnNetwork, false);
 
 	![low_latency_limitation_2](./img/low_latency_limitation_2.png)
 
-	**Current solution:** Trim non-reshapable layers via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model_General.md) `--input`, `--output`. For example, the parameter and the problematic constant in the picture above can be trimmed using the following command line option: 
+	**Current solution:** Trim non-reshapable layers via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model.md) `--input`, `--output`. For example, the parameter and the problematic constant in the picture above can be trimmed using the following command line option: 
 	`--input Reshape_layer_name`. The problematic constant can be also replaced using ngraph, as shown in the example below.
 
 ```cpp
@@ -403,7 +403,7 @@ InferenceEngine::LowLatency(cnnNetwork);
 4. Use state API. See sections [OpenVINO state API](#openvino-state-api), [Example of stateful network inference](#example-of-stateful-network-inference).
 
  
-### Known Limitations
+### Known Limitations for LowLatency [DEPRECATED]
 1. Parameters connected directly to ReadValues (States) after the transformation is applied are not allowed.
 
 	Unnecessary parameters may remain on the graph after applying the transformation. The automatic handling of this case inside the transformation is not possible now. Such Parameters should be removed manually from `ngraph::Function` or replaced with a Constant.
@@ -411,7 +411,7 @@ InferenceEngine::LowLatency(cnnNetwork);
 	![low_latency_limitation_1](./img/low_latency_limitation_1.png)
 
 	**Current solutions:** 
-	* Replace Parameter with Constant (freeze) with the value [0, 0, 0 … 0] via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model_General.md) `--input` or `--freeze_placeholder_with_value`.
+	* Replace Parameter with Constant (freeze) with the value [0, 0, 0 … 0] via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model.md) `--input` or `--freeze_placeholder_with_value`.
 	* Use ngraph API to replace Parameter with Constant.
 
 		```cpp
@@ -436,7 +436,7 @@ InferenceEngine::LowLatency(cnnNetwork);
 
 	![low_latency_limitation_2](./img/low_latency_limitation_2.png)
 
-	**Current solution:** trim non-reshapable layers via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model_General.md) `--input`, `--output`. For example, we can trim the Parameter and the problematic Constant in the picture above, using the following command line option: 
+	**Current solution:** trim non-reshapable layers via [ModelOptimizer CLI](../MO_DG/prepare_model/convert_model/Converting_Model.md) `--input`, `--output`. For example, we can trim the Parameter and the problematic Constant in the picture above, using the following command line option: 
 	`--input Reshape_layer_name`. We can also replace the problematic Constant using ngraph, as shown in the example below.
 
 ```cpp
diff --git a/docs/IE_DG/protecting_model_guide.md b/docs/IE_DG/protecting_model_guide.md
index 78fe08b3318..207fdc23280 100644
--- a/docs/IE_DG/protecting_model_guide.md
+++ b/docs/IE_DG/protecting_model_guide.md
@@ -1,7 +1,7 @@
 # Using Encrypted Models with OpenVINO&trade;  {#openvino_docs_IE_DG_protecting_model_guide}
 
 Deploying deep-learning capabilities to edge devices can present security
-challenges. For example, ensuring inference integrity or providing copyright
+challenges, for example, ensuring inference integrity or providing copyright
 protection of your deep-learning models.
 
 One possible solution is to use cryptography to protect models as they are
@@ -14,43 +14,41 @@ This guide demonstrates how to use OpenVINO securely with protected models.
 
 ## Secure Model Deployment
 
-After a model is optimized by the OpenVINO Model Optimizer, it's then deployed
+After a model is optimized by the OpenVINO Model Optimizer, it's deployed
 to target devices in the Intermediate Representation (IR) format. An optimized
-model is stored on an edge device and executed by the Inference Engine.
+model is stored on an edge device and executed by the Inference Engine. 
+(ONNX and nGraph models can also be read natively by the Inference Engine.)
 
 To protect deep-learning models, you can encrypt an optimized model before
 deploying it to the edge device. The edge device should keep the stored model
 protected at all times and have the model decrypted **in runtime only** for use
 by the Inference Engine.
 
-![deploy_encrypted_model]
+![deploy_encrypted_model](img/deploy_encrypted_model.png)
 
 ## Loading Encrypted Models
 
 The OpenVINO Inference Engine requires model decryption before loading. Allocate
-a temporary memory block for model decryption, and use
-`InferenceEngine::Core::ReadNetwork` method to load the model from memory buffer.
-For more information, see the `InferenceEngine::Core` Class
-Reference Documentation.
+a temporary memory block for model decryption and use the 
+`InferenceEngine::Core::ReadNetwork` method to load the model from a memory buffer.
+For more information, see the `InferenceEngine::Core` Class Reference Documentation.
 
 @snippet snippets/protecting_model_guide.cpp part0
 
-Hardware-based protection, such as Intel&reg; Software Guard Extensions
-(Intel&reg; SGX), can be utilized to protect decryption operation secrets and
+Hardware-based protection such as Intel&reg; Software Guard Extensions
+(Intel&reg; SGX) can be utilized to protect decryption operation secrets and
 bind them to a device. For more information, go to [Intel&reg; Software Guard
 Extensions](https://software.intel.com/en-us/sgx).
 
 Use `InferenceEngine::Core::ReadNetwork()` to set model representations and
 weights respectively.
 
-Currently there are no possibility to read external weights from memory for ONNX models.
+Currently there is no way to read external weights from memory for ONNX models.
 The `ReadNetwork(const std::string& model, const Blob::CPtr& weights)` function
 should be called with `weights` passed as an empty `Blob`.
 
 @snippet snippets/protecting_model_guide.cpp part1
 
-[deploy_encrypted_model]: img/deploy_encrypted_model.png
-
 ## Additional Resources
 
 - Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
diff --git a/docs/IE_DG/supported_plugins/AUTO.md b/docs/IE_DG/supported_plugins/AUTO.md
index 99b5388094f..5374e9283df 100644
--- a/docs/IE_DG/supported_plugins/AUTO.md
+++ b/docs/IE_DG/supported_plugins/AUTO.md
@@ -1,55 +1,57 @@
 # Auto-Device Plugin {#openvino_docs_IE_DG_supported_plugins_AUTO}
 
-## Auto-Device Plugin Execution
+## Auto-Device Plugin Execution (C++)
 
-Auto-device is a new special "virtual" or "proxy" device in the OpenVINO™ toolkit. 
+@sphinxdirective
+.. raw:: html
 
-Use "AUTO" as the device name to delegate selection of an actual accelerator to OpenVINO. 
-With the 2021.4 release, Auto-device internally recognizes and selects devices from CPU, 
-integrated GPU and discrete Intel GPUs (when available) depending on the device capabilities and the characteristic of CNN models, 
-for example, precisions. Then Auto-device assigns inference requests to the selected device.
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
 
-From the application point of view, this is just another device that handles all accelerators in full system. 
+The AUTO device is a new, special "virtual" or "proxy" device in the OpenVINO™ toolkit.
+
+Use "AUTO" as the device name to delegate selection of an actual accelerator to OpenVINO. The Auto-device plugin internally recognizes and selects devices from among CPU, integrated GPU and discrete Intel GPUs (when available) depending on the device capabilities and the characteristics of CNN models (for example, precision). Then the Auto-device assigns inference requests to the selected device.
+
+From the application's point of view, this is just another device that handles all accelerators in the full system.
 
 With the 2021.4 release, Auto-device setup is done in three major steps:
-* Step 1: Configure each device as usual (for example, via the conventional <code>SetConfig</code> method)
-* Step 2: Load a network to the Auto-device plugin. This is the only change needed in your application
-* Step 3: Just like with any other executable network (resulted from <code>LoadNetwork</code>), create as many requests as needed to saturate the devices. 
-These steps are covered below in details.
+1. Configure each device as usual (for example, via the conventional `SetConfig()` method)
+2. Load a network to the Auto-device plugin. This is the only change needed in your application.
+3. As with any other executable network resulting from `LoadNetwork()`, create as many requests as needed to saturate the devices. 
 
+These steps are covered below in detail.
 
-## Defining and Configuring the Auto-Device Plugin
-Following the OpenVINO notions of “devices”, the Auto-device has “AUTO” name. The only configuration option for Auto-device is a limited device list:
+### Defining and Configuring the Auto-Device Plugin
+Following the OpenVINO convention for devices names, the Auto-device uses the label "AUTO". The only configuration option for Auto-device is a limited device list:
 
 | Parameter name     | Parameter values      | Default            |             Description                                                      |
 | :---               | :---                  | :---               |:-----------------------------------------------------------------------------|
 | "MULTI_DEVICE_PRIORITIES" | comma-separated device names <span style="color:red">with no spaces</span>| N/A | Device candidate list to be selected    |
 
-You can use the configuration name directly as a string or use <code>InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES</code> from <code>multi-device/multi_device_config.hpp</code>,
-which defines the same string.
+You can use the configuration name directly as a string or use `InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES` from `multi-device/multi_device_config.hpp`, which defines the same string.
 
 There are two ways to use Auto-device:
-1. Directly indicate device by “AUTO” or empty string:
-
+1. Directly indicate device by "AUTO" or an empty string:
 @snippet snippets/AUTO0.cpp part0
 
-2. Use Auto-device configuration to limit the device candidates list to be selected:
-
+2. Use the Auto-device configuration:
 @snippet snippets/AUTO1.cpp part1
 
-Auto-device supports query device optimization capabilities in metric;
+Both methods allow limiting the list of device candidates for the AUTO plugin.
+
+> **NOTE**: The Inference Engine lets you use "GPU" as an alias for "GPU.0" in function calls. 
+
+The Auto-device plugin supports query device optimization capabilities in metric.
 
 | Parameter name                 | Parameter values         |
 | :---                           | :---                     |
 | "OPTIMIZATION_CAPABILITIES"    | Auto-Device capabilities |
 
-## Enumerating Available Devices and Auto-Device Selecting Logic
+### Enumerating Devices and Selection Logic
 
-### Enumerating Available Devices
-
-Inference Engine now features a dedicated API to enumerate devices and their capabilities. 
-See [Hello Query Device C++ Sample](../../../samples/cpp/hello_query_device/README.md).
-This is the example output from the sample (truncated to the devices' names only):
+The Inference Engine now features a dedicated API to enumerate devices and their capabilities. 
+See [Hello Query Device C++ Sample](../../../inference-engine/samples/hello_query_device/README.md).
+This is the example output from the sample (truncated to device names only):
 
 ```sh
 ./hello_query_device
@@ -61,14 +63,15 @@ Available devices:
     Device: GPU.1
 ```
 
-###	Default Auto-Device selecting logic
+### Default Auto-Device Selection Logic
 
-With the 2021.4 release, Auto-Device selects the most suitable device with following default logic:
-1.	Check if dGPU, iGPU and CPU device are available
-2.	Get the precision of the input model, such as FP32
-3.	According to the priority of dGPU, iGPU and CPU (in this order), if the device supports the precision of input network, select it as the most suitable device
+With the 2021.4 release, the Auto-Device selects the most suitable device using the following default logic:
 
-For example, CPU, dGPU and iGPU can support below precision and optimization capabilities:
+1. Check if dGPU (discrete), iGPU (integrated) and CPU devices are available
+2. Get the precision of the input model, such as FP32
+3. According to the priority of dGPU, iGPU, and CPU (in this order), if the device supports the precision of the input network, select it as the most suitable device
+
+For example, CPU, dGPU and iGPU can support the following precision and optimization capabilities:
 
 | Device   | OPTIMIZATION_CAPABILITIES       |
 | :---     | :---                            |
@@ -76,53 +79,254 @@ For example, CPU, dGPU and iGPU can support below precision and optimization cap
 | dGPU     | FP32 BIN BATCHED_BLOB FP16 INT8 |
 | iGPU     | FP32 BIN BATCHED_BLOB FP16 INT8 |
 
-When application use Auto-device to run FP16 IR on system with CPU, dGPU and iGPU, Auto-device will offload this workload to dGPU.
+* When the application uses the Auto-device to run FP16 IR on a system with CPU, dGPU and iGPU, Auto-device will offload this workload to dGPU.
+* When the application uses the Auto-device to run FP16 IR on a system with CPU and iGPU, Auto-device will offload this workload to iGPU.
+* When the application uses the Auto-device to run WINOGRAD-enabled IR on a system with CPU, dGPU and iGPU, Auto-device will offload this workload to CPU.
 
-When application use Auto-device to run FP16 IR on system with CPU and iGPU, Auto-device will offload this workload to iGPU.
+In cases when loading the network to dGPU or iGPU fails, CPU is the fall-back choice.
 
-When application use Auto-device to run WINOGRAD-enabled IR on system with CPU, dGPU and iGPU, Auto-device will offload this workload to CPU.
-
-In any case, when loading the network to dGPU or iGPU fails, the networks falls back to CPU as the last choice.
-
-### Limit Auto Target Devices Logic
-
-According to the Auto-device selection logic from the previous section, 
-the most suitable device from available devices to load mode as follows:
+According to the Auto-device selection logic from the previous section, tell the Inference Engine 
+to use the most suitable device from available devices as follows:
 
 @snippet snippets/AUTO2.cpp part2
 
-Another way to load mode to device from limited choice of devices is with Auto-device:
+You can also use the Auto-device plugin to choose a device from a limited choice of devices, in this example CPU and GPU:
 
 @snippet snippets/AUTO3.cpp part3
 
-## Configuring the Individual Devices and Creating the Auto-Device on Top
+### Configuring the Individual Devices and Creating the Auto-Device on Top
 
-As described in the first section, configure each individual device as usual and then just create the "AUTO" device on top:
+It is possible to configure each individual device as usual and create the "AUTO" device on top:
 
 @snippet snippets/AUTO4.cpp part4
 
-Alternatively, you can combine all the individual device settings into single config and load it, 
-allowing the Auto-device plugin to parse and apply it to the right devices. See the code example here:
+Alternatively, you can combine all the individual device settings into single config file and load it, allowing the Auto-device plugin to parse and apply it to the right devices. See the code example here:
 
 @snippet snippets/AUTO5.cpp part5
 
-## Using the Auto-Device with OpenVINO Samples and Benchmark App
+### Using the Auto-Device with OpenVINO Samples and Benchmark App
+
+Note that every OpenVINO sample or application that supports the "-d" (which stands for "device") command-line option transparently accepts the Auto-device. The Benchmark Application is the best example of the optimal usage of the Auto-device. You do not need to set the number of requests and CPU threads, as the application provides optimal out-of-the-box performance. Below is the example command-line to evaluate AUTO performance with that:
+
+@sphinxdirective
+.. tab:: Package, Docker, open-source installation
+
+   .. code-block:: sh
+
+      ./benchmark_app.py –d AUTO –m <model>
+
+.. tab:: pip installation
+
+    .. code-block:: sh
+
+      benchmark_app –d AUTO –m <model>
+
+@endsphinxdirective
 
-Note that every OpenVINO sample that supports "-d" (which stands for "device") command-line option transparently accepts the Auto-device. 
-The Benchmark Application is the best example of the optimal usage of the Auto-device. 
-You do not need to set the number of requests and CPU threads, as the application provides optimal out-of-the-box performance. 
-Below is the example command-line to evaluate AUTO performance with that:
 
-```sh
-./benchmark_app –d AUTO –m <model> -i <input> -niter 1000
-```
 You can also use the auto-device with limit device choice:
 
-```sh
-./benchmark_app –d AUTO:CPU,GPU –m <model> -i <input> -niter 1000
-```
-Note that the default CPU stream is 1 if using “-d AUTO”.
+@sphinxdirective
+.. tab:: Package, Docker, open-source installation
 
-Note that you can use the FP16 IR to work with auto-device.
-Also note that no demos are (yet) fully optimized for the auto-device, by means of selecting the most suitable device, 
-using the GPU streams/throttling, and so on.
+   .. code-block:: sh
+
+      ./benchmark_app.py –d AUTO:CPU,GPU –m <model>
+
+.. tab:: pip installation
+
+    .. code-block:: sh
+
+      benchmark_app –d AUTO:CPU,GPU –m <model>
+
+@endsphinxdirective
+
+**NOTES:**
+* The default CPU stream is 1 if using `-d AUTO`. 
+* You can use the FP16 IR to work with Auto-device.
+* No demos are fully optimized for Auto-device yet to select the most suitable device, 
+use GPU streams/throttling, and so on.
+
+## Auto-Device Plugin Execution (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+The AUTO device is a new, special "virtual" or "proxy" device in the OpenVINO™ toolkit.
+
+Use "AUTO" as the device name to delegate selection of an actual accelerator to OpenVINO. The Auto-device plugin internally recognizes and selects devices from among CPU, integrated GPU and discrete Intel GPUs (when available) depending on the device capabilities and the characteristics of CNN models (for example, precision). Then the Auto-device assigns inference requests to the selected device.
+
+From the application's point of view, this is just another device that handles all accelerators in the full system.
+
+With the 2021.4 release, Auto-device setup is done in three major steps:
+
+1. Configure each device as usual (for example, via the conventional [IECore.set_config](https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html#a2c738cee90fca27146e629825c039a05) method).
+2. Load a network to the Auto-device plugin. This is the only change needed in your application.
+3. As with any other executable network resulting from [IECore.load_network](https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html#ac9a2e043d14ccfa9c6bbf626cfd69fcc), create as many requests as needed to saturate the devices. 
+
+These steps are covered below in detail.
+
+### Defining and Configuring the Auto-Device Plugin
+Following the OpenVINO convention for devices names, the Auto-device uses the label "AUTO". The only configuration option for Auto-device is a limited device list:
+
+| Parameter name | Parameter values | Default | Description |
+| -------------- | ---------------- | ------- | ----------- |
+| "AUTO_DEVICE_LIST" | comma-separated device names with no spaces | N/A | Device candidate list to be selected
+
+There are two ways to use the Auto-device plugin:
+
+1. Directly indicate device by "AUTO" or an empty string.
+2. Use the Auto-device configuration
+
+Both methods allow limiting the list of device candidates for the AUTO plugin.
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+# Read a network in IR or ONNX format
+net = ie.read_network(model=path_to_model)
+
+# Load a network on the "AUTO" device
+exec_net = ie.load_network(network=net, device_name="AUTO")
+
+# Optionally specify the list of device candidates for the AUTO plugin
+# The following two lines are equivalent
+exec_net = ie.load_network(network=net, device_name="AUTO:CPU,GPU")
+exec_net = ie.load_network(network=net, device_name="AUTO",
+                           config={"AUTO_DEVICE_LIST": "CPU,GPU"})
+```
+
+The Auto-device plugin supports query device optimization capabilities in metric.
+
+| Parameter name | Parameter values |
+| --- | --- |
+| "OPTIMIZATION_CAPABILITIES" | Auto-Device capabilities |
+
+### Enumerating Devices and Selection Logic
+
+The Inference Engine now features a dedicated API to enumerate devices and their capabilities. See the [Hello Query Device Python Sample](../../../inference_engine/ie_bridges/python/sample_hello_query_device_README.html) for code.
+
+This is the example output from the sample (truncated to device names only):
+
+```python
+./hello_query_device
+
+Available devices:
+    Device: CPU
+...
+    Device: GPU.0
+...
+    Device: GPU.1
+```
+
+### Default Auto-Device Selection Logic
+
+With the 2021.4 release, the Auto-Device selects the most suitable device using the following default logic:
+
+1. Check if dGPU (discrete), iGPU (integrated) and CPU devices are available
+2. Get the precision of the input model, such as FP32
+3. According to the priority of dGPU, iGPU, and CPU (in this order), if the device supports the precision of the input network, select it as the most suitable device
+
+For example, CPU, dGPU and iGPU can support the following precision and optimization capabilities:
+
+| Device | OPTIMIZATION_CAPABILITIES |
+| --- | --- |
+| CPU | WINOGRAD FP32 FP16 INT8 BIN |
+| dGPU | FP32 BIN BATCHED_BLOB FP16 INT8 |
+| iGPU | FP32 BIN BATCHED_BLOB FP16 INT8 |
+
+* When the application uses the Auto-device to run FP16 IR on a system with CPU, dGPU and iGPU, Auto-device will offload this workload to dGPU.
+* When the application uses the Auto-device to run FP16 IR on a system with CPU and iGPU, Auto-device will offload this workload to iGPU.
+* When the application uses the Auto-device to run WINOGRAD-enabled IR on a system with CPU, dGPU and iGPU, Auto-device will offload this workload to CPU.
+
+In cases when loading the network to dGPU or iGPU fails, CPU is the fall-back choice.
+
+To show the capabilities for a specific device, query the OPTIMIZATION_CAPABILITIES metric:
+
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+ie.get_metric(device_name=device,
+              metric_name="OPTIMIZATION_CAPABILITIES")
+```
+
+### Configuring the Individual Devices and Creating the Auto-Device on Top
+
+It is possible to configure each individual device as usual and create the "AUTO" device on top:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_model)
+
+cpu_config = {}
+gpu_config = {}
+
+ie.set_config(config=cpu_config, device_name="CPU")
+ie.set_config(config=gpu_config, device_name="GPU")
+
+# Load the network to the AUTO device
+exec_net = ie.load_network(network=net, device_name="AUTO")
+```
+
+Alternatively, you can combine all the individual device settings into single config file and load it, allowing the Auto-device plugin to parse and apply it to the right devices. See the code example here:
+
+```python
+from openvino.inference_engine import IECore
+
+# Init the Inference Engine Core
+ie = IECore()
+
+# Read a network in IR or ONNX format
+net = ie.read_network(model=path_to_model)
+
+full_config = {}
+
+# Load the network to the AUTO device
+exec_net = ie.load_network(network=net, device_name="AUTO", config=full_config)
+```
+
+### Using the Auto-Device with OpenVINO Samples and Benchmark App
+
+Note that every OpenVINO sample or application that supports the "-d" (which stands for "device") command-line option transparently accepts the Auto-device. The Benchmark Application is the best example of the optimal usage of the Auto-device. You do not need to set the number of requests and CPU threads, as the application provides optimal out-of-the-box performance. Below is the example command-line to evaluate AUTO performance with that:
+
+@sphinxdirective
+.. tab:: Package, Docker, open-source installation
+
+   .. code-block:: sh
+
+      ./benchmark_app.py –d AUTO –m <model>
+
+.. tab:: pip installation
+
+    .. code-block:: sh
+
+      benchmark_app –d AUTO –m <model>
+
+@endsphinxdirective
+
+You can also use the auto-device with limit device choice:
+
+@sphinxdirective
+.. tab:: Package, Docker, open-source installation
+
+   .. code-block:: sh
+
+      ./benchmark_app.py –d AUTO:CPU,GPU –m <model>
+
+.. tab:: pip installation
+
+    .. code-block:: sh
+
+      benchmark_app –d AUTO:CPU,GPU –m <model>
+
+@endsphinxdirective
+
+> **NOTE**: If you installed OpenVINO with pip, use `benchmark_app -d AUTO:CPU,GPU -m <model>`
diff --git a/docs/IE_DG/supported_plugins/CPU.md b/docs/IE_DG/supported_plugins/CPU.md
index 3ee93613cc9..2ccef00cb25 100644
--- a/docs/IE_DG/supported_plugins/CPU.md
+++ b/docs/IE_DG/supported_plugins/CPU.md
@@ -1,8 +1,8 @@
 CPU Plugin {#openvino_docs_IE_DG_supported_plugins_CPU}
 =======
 
-## Introducing CPU Plugin
-The CPU plugin was developed in order to provide opportunity for high performance scoring of neural networks on CPU, using the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN).
+## Introducing the CPU Plugin
+The CPU plugin was developed to achieve high performance of neural networks on CPU, using the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN).
 
 Currently, the CPU plugin uses Intel® Threading Building Blocks (Intel® TBB) in order to parallelize calculations. Please refer to the [Optimization Guide](../../optimization_guide/dldt_optimization_guide.md) for associated performance considerations.
 
@@ -10,56 +10,59 @@ The set of supported layers can be expanded with [the Extensibility mechanism](.
 
 ## Supported Platforms
 
-OpenVINO™ toolkit is officially supported and validated on the following platforms:
+OpenVINO™ toolkit, including the CPU plugin, is officially supported and validated on the following platforms:
 
 | Host              | OS (64-bit)                              |
 | :---              | :---                                     |
-| Development       | Ubuntu* 18.04, CentOS* 7.5, MS Windows* 10 |
-| Target            | Ubuntu* 18.04, CentOS* 7.5, MS Windows* 10 |
+| Development       | Ubuntu* 18.04 or 20.04, CentOS* 7.6, MS Windows* 10, macOS* 10.15 |
+| Target            | Ubuntu* 18.04 or 20.04, CentOS* 7.6, MS Windows* 10, macOS* 10.15 |
 
-The CPU Plugin supports inference on Intel® Xeon® with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel® Core™
+The CPU plugin supports inference on Intel® Xeon® with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel® Core™
 Processors with Intel® AVX2, Intel Atom® Processors with Intel® Streaming SIMD Extensions (Intel® SSE).
 
-You can use `-pc` the flag for samples to know which configuration is used by some layer.
-This flag shows execution statistics that you can use to get information about layer name,
-execution status, layer type, execution time, and the type of the execution primitive.
+You can use the `-pc` flag for samples to know which configuration is used by a layer.
+This flag shows execution statistics that you can use to get information about layer name, layer type, 
+execution status, execution time, and the type of the execution primitive.
 
 ## Internal CPU Plugin Optimizations
 
-CPU plugin supports several graph optimization algorithms, such as fusing or removing layers.
+The CPU plugin supports several graph optimization algorithms, such as fusing or removing layers.
 Refer to the sections below for details.
 
 > **NOTE**: For layer descriptions, see the [IR Notation Reference](../../ops/opset.md).
 
 ### Lowering Inference Precision
 
-CPU plugin follows default optimization approach. This approach means that inference is made with lower precision if it is possible on a given platform to reach better performance with acceptable range of accuracy.
+The CPU plugin follows a default optimization approach. This approach means that inference is made with lower precision if it is possible on a given platform to reach better performance with an acceptable range of accuracy.
 
 > **NOTE**: For details, see the [Using Bfloat16 Inference](../Bfloat16Inference.md).
 
 ### Fusing Convolution and Simple Layers
 
-Merge of a Convolution layer and any of the simple layers listed below:
+Merge of a convolution layer and any of the simple layers listed below:
 - Activation: ReLU, ELU, Sigmoid, Clamp
 - Depthwise: ScaleShift, PReLU
 - FakeQuantize
 
 > **NOTE**: You can have any number and order of simple layers.
 
-A combination of a Convolution layer and simple layers results in a single fused layer called 
-*Convolution*:    
+A combination of a convolution layer and simple layers results in a single fused layer called 
+*Convolution*:
+
 ![conv_simple_01]
 
 
 ### Fusing Pooling and FakeQuantize Layers
 
 A combination of Pooling and FakeQuantize layers results in a single fused layer called *Pooling*:  
+
 ![pooling_fakequant_01]
 
 ### Fusing FullyConnected and Activation Layers
 
 A combination of FullyConnected and Activation layers results in a single fused layer called 
-*FullyConnected*:    
+*FullyConnected*:
+
 ![fullyconnected_activation_01]
 
 
@@ -76,16 +79,19 @@ layer and simple layers results in a single layer called *Convolution* (or *Bina
 
 ### Fusing Convolution and Sum Layers
 
-A combination of Convolution, Simple, and Eltwise layers with the sum operation results in a single layer called  *Convolution*:  
+A combination of convolution, simple, and Eltwise layers with the sum operation results in a single layer called *Convolution*:  
+
 ![conv_sum_relu_01]
 
 ### Fusing a Group of Convolutions
 
-If a topology contains the following pipeline, a CPU plugin merges Split, Convolution, and Concatenation layers  into a single Convolution layer with the group parameter:   
-> **NOTE**: Parameters of the Convolution layers must coincide.
+If a topology contains the following pipeline, a CPU plugin merges split, convolution, and concatenation layers into a single convolution layer with the group parameter:   
 
 ![group_convolutions_01]
 
+> **NOTE**: Parameters of the convolution layers must coincide.
+
+
 ### Removing a Power Layer
 
 CPU plugin removes a Power layer from a topology if it has the following parameters:
@@ -97,7 +103,7 @@ CPU plugin removes a Power layer from a topology if it has the following paramet
 ## Supported Configuration Parameters
 
 The plugin supports the configuration parameters listed below.
-All parameters must be set with the <code>InferenceEngine::Core::LoadNetwork()</code> method.
+All parameters must be set with the `InferenceEngine::Core::LoadNetwork()` method.
 When specifying key values as raw strings (that is, when using Python API), omit the `KEY_` prefix.
 Refer to the OpenVINO samples for usage examples: [Benchmark App](../../../samples/cpp/benchmark_app/README.md).
 
@@ -105,18 +111,17 @@ These are general options, also supported by other plugins:
 
 | Parameter name                  | Parameter values      | Default            | Description                                                                                                                  |
 | :---                            | :---                  | :---               | :----------------------------------------------------------------------------------------------------------------------------|
-| `KEY_EXCLUSIVE_ASYNC_REQUESTS`    | `YES`/`NO`                | `NO`                 | Forces async requests (also from different executable networks) to execute serially. This prevents potential oversubscription|
-| `KEY_PERF_COUNT`                 | `YES`/`NO`                | `NO`                 | Enables gathering performance counters                                                                                       |
+| KEY_EXCLUSIVE_ASYNC_REQUESTS    | YES/NO                | NO                 | Forces async requests (also from different executable networks) to execute serially. This prevents potential oversubscription|
+| KEY_PERF_COUNT                  | YES/NO                | NO                 | Enables gathering performance counters                                                                                       |
 
 CPU-specific settings:
 
-
-| Parameter name     | Parameter values      | Default            |             Description                                                      |
-| :---               | :---                  | :---               |:-----------------------------------------------------------------------------|
-| `KEY_CPU_THREADS_NUM`         | `positive integer values`| `0`                 | Specifies the number of threads that CPU plugin should use for inference. Zero (default) means using all (logical) cores|
-| `KEY_CPU_BIND_THREAD`         | `YES`/`NUMA`/`NO`           | `YES`                | Binds inference threads to CPU cores. 'YES' (default) binding option maps threads to cores - this works best for static/synthetic scenarios like benchmarks. The 'NUMA' binding is more relaxed, binding inference threads only to NUMA nodes, leaving further scheduling to specific cores to the OS. This option might perform better in the real-life/contended scenarios. Note that for the latency-oriented cases (number of the streams is less or equal to the number of NUMA nodes, see below) both YES and NUMA options limit number of inference threads to the number of hardware cores (ignoring hyper-threading) on the multi-socket machines. |
-| `KEY_CPU_THROUGHPUT_STREAMS`  | `KEY_CPU_THROUGHPUT_NUMA`, `KEY_CPU_THROUGHPUT_AUTO`, or `positive integer values`| `1` | Specifies number of CPU "execution" streams for the throughput mode. Upper bound for the number of inference requests that can be executed simultaneously. All available CPU cores are evenly distributed between the streams. The default value is 1, which implies latency-oriented behavior for single NUMA-node machine, with all available cores processing requests one by one. On the multi-socket (multiple NUMA nodes) machine, the best latency numbers usually achieved with a number of streams matching the number of NUMA-nodes. <br>`KEY_CPU_THROUGHPUT_NUMA` creates as many streams as needed to accommodate NUMA and avoid associated penalties.<br>`KEY_CPU_THROUGHPUT_AUTO` creates bare minimum of streams to improve the performance; this is the most portable option if you don't know how many cores your target machine has (and what would be the optimal number of streams). Note that your application should provide enough parallel slack (for example, run many inference requests) to leverage the throughput mode. <br> Non-negative integer value creates the requested number of streams. If a number of streams is 0, no internal streams are created and user threads are interpreted as stream master threads.|
-| `KEY_ENFORCE_BF16`            | `YES`/`NO`| `YES` | The name for setting to execute in bfloat16 precision whenever it is possible. This option lets plugin know to downscale the precision where it sees performance benefits from bfloat16 execution. Such option does not guarantee accuracy of the network, you need to verify the accuracy in this mode separately, based on performance and accuracy results. It should be your decision whether to use this option or not. |
+| Parameter name              | Parameter values      | Default            | Description                                               |
+| :---                        | :---                  | :---               | :--- |
+| KEY_CPU_THREADS_NUM         | positive integer values| 0                 | Specifies the number of threads that CPU plugin should use for inference. Zero (default) means using all (logical) cores|
+| KEY_CPU_BIND_THREAD         | YES/NUMA/NO           | YES                | Binds inference threads to CPU cores. 'YES' (default) binding option maps threads to cores - this works best for static/synthetic scenarios like benchmarks. The 'NUMA' binding is more relaxed, binding inference threads only to NUMA nodes, leaving further scheduling to specific cores to the OS. This option might perform better in the real-life/contended scenarios. Note that for the latency-oriented cases (number of the streams is less or equal to the number of NUMA nodes, see below) both YES and NUMA options limit number of inference threads to the number of hardware cores (ignoring hyper-threading) on the multi-socket machines. |
+| KEY_CPU_THROUGHPUT_STREAMS  | KEY_CPU_THROUGHPUT_NUMA, KEY_CPU_THROUGHPUT_AUTO, or positive integer values| 1 | Specifies number of CPU "execution" streams for the throughput mode. Upper bound for the number of inference requests that can be executed simultaneously. All available CPU cores are evenly distributed between the streams. The default value is 1, which implies latency-oriented behavior for single NUMA-node machine, with all available cores processing requests one by one. On the multi-socket (multiple NUMA nodes) machine, the best latency numbers usually achieved with a number of streams matching the number of NUMA-nodes. <br>KEY_CPU_THROUGHPUT_NUMA creates as many streams as needed to accommodate NUMA and avoid associated penalties.<br>KEY_CPU_THROUGHPUT_AUTO creates bare minimum of streams to improve the performance; this is the most portable option if you don't know how many cores your target machine has (and what would be the optimal number of streams). Note that your application should provide enough parallel slack (for example, run many inference requests) to leverage the throughput mode. <br> Non-negative integer value creates the requested number of streams. If a number of streams is 0, no internal streams are created and user threads are interpreted as stream master threads.|
+| KEY_ENFORCE_BF16            | YES/NO| YES | The name for setting to execute in bfloat16 precision whenever it is possible. This option lets plugin know to downscale the precision where it sees performance benefits from bfloat16 execution. Such option does not guarantee accuracy of the network, you need to verify the accuracy in this mode separately, based on performance and accuracy results. It should be your decision whether to use this option or not. |
 
 > **NOTE**: To disable all internal threading, use the following set of configuration parameters: `KEY_CPU_THROUGHPUT_STREAMS=0`, `KEY_CPU_THREADS_NUM=1`, `KEY_CPU_BIND_THREAD=NO`.
 
diff --git a/docs/IE_DG/supported_plugins/Device_Plugins.md b/docs/IE_DG/supported_plugins/Device_Plugins.md
new file mode 100644
index 00000000000..250b69e6371
--- /dev/null
+++ b/docs/IE_DG/supported_plugins/Device_Plugins.md
@@ -0,0 +1,35 @@
+# Device Plugin Support {#openvino_docs_IE_DG_Device_Plugins}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_IE_DG_InferenceEngine_QueryAPI
+   openvino_docs_IE_DG_supported_plugins_CPU
+   openvino_docs_IE_DG_supported_plugins_GPU
+   openvino_docs_IE_DG_supported_plugins_VPU
+   openvino_docs_IE_DG_supported_plugins_GNA
+   openvino_docs_IE_DG_supported_plugins_AUTO
+   openvino_docs_IE_DG_supported_plugins_HETERO
+   openvino_docs_IE_DG_supported_plugins_MULTI
+         
+@endsphinxdirective
+
+Inference Engine uses a plugin architecture. Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel® hardware device: CPU, GPU, VPU, GNA, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.
+
+The Inference Engine provides capabilities to infer deep learning models on the following device types with corresponding plugins:
+
+| Plugin                                   | Device types                                                                                                                                                |
+|------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
+|[GPU plugin](GPU.md)            |Intel&reg; Processor Graphics, including Intel&reg; HD Graphics and Intel&reg; Iris&reg; Graphics                                                            |
+|[CPU plugin](CPU.md)              |Intel&reg; Xeon&reg; with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel&reg; Core&trade; Processors with Intel&reg; AVX2, Intel&reg; Atom&reg; Processors with Intel® Streaming SIMD Extensions (Intel® SSE) |
+|[VPU plugins](VPU.md) (available in the Intel® Distribution of OpenVINO™ toolkit)            |Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X, Intel® Vision Accelerator Design with Intel® Movidius™ VPUs                                                                                           |
+|[GNA plugin](GNA.md) (available in the Intel® Distribution of OpenVINO™ toolkit)              |Intel&reg; Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel&reg; Pentium&reg; Silver J5005 Processor, Intel&reg; Pentium&reg; Silver N5000 Processor, Intel&reg; Celeron&reg; J4005 Processor, Intel&reg; Celeron&reg; J4105 Processor, Intel&reg; Celeron&reg; Processor N4100, Intel&reg; Celeron&reg; Processor N4000, Intel&reg; Core&trade; i3-8121U Processor, Intel&reg; Core&trade; i7-1065G7 Processor, Intel&reg; Core&trade; i7-1060G7 Processor, Intel&reg; Core&trade; i5-1035G4 Processor, Intel&reg; Core&trade; i5-1035G7 Processor, Intel&reg; Core&trade; i5-1035G1 Processor, Intel&reg; Core&trade; i5-1030G7 Processor, Intel&reg; Core&trade; i5-1030G4 Processor, Intel&reg; Core&trade; i3-1005G1 Processor, Intel&reg; Core&trade; i3-1000G1 Processor, Intel&reg; Core&trade; i3-1000G4 Processor|
+|[Multi-Device plugin](MULTI.md) |Multi-Device plugin enables simultaneous inference of the same network on several Intel&reg; devices in parallel    |   
+|[Auto-Device plugin](AUTO.md) |Auto-Device plugin enables selecting Intel&reg; device for inference automatically |   
+|[Heterogeneous plugin](HETERO.md) |Heterogeneous plugin enables automatic inference splitting between several Intel&reg; devices (for example if a device doesn't [support certain layers](#supported-layers)).                                                           |
+
+Devices similar to the ones we have used for benchmarking can be accessed using [Intel® DevCloud for the Edge](https://devcloud.intel.com/edge/), a remote development environment with access to Intel® hardware and the latest versions of the Intel® Distribution of the OpenVINO™ Toolkit. [Learn more](https://devcloud.intel.com/edge/get_started/devcloud/) or [Register here](https://inteliot.force.com/DevcloudForEdge/s/).
+
diff --git a/docs/IE_DG/supported_plugins/GNA.md b/docs/IE_DG/supported_plugins/GNA.md
index ce124072b06..5e3af7dd46b 100644
--- a/docs/IE_DG/supported_plugins/GNA.md
+++ b/docs/IE_DG/supported_plugins/GNA.md
@@ -1,12 +1,11 @@
 # GNA Plugin {#openvino_docs_IE_DG_supported_plugins_GNA}
-
 ## Introducing the GNA Plugin
 
-Intel® Gaussian & Neural Accelerator is a low-power neural coprocessor for continuous inference at the edge.
+The Intel® Gaussian & Neural Accelerator is a low-power neural coprocessor for continuous inference at the edge.
 
-Intel® GNA is not intended to replace classic inference devices such as
-CPU, graphics processing unit (GPU), or vision processing unit (VPU). It is designed for offloading 
-continuous inference workloads including but not limited to noise reduction or speech recognition 
+Intel® GNA is not intended to replace typical inference devices such as the
+CPU, graphics processing unit (GPU), or vision processing unit (VPU). It is designed for offloading
+continuous inference workloads including but not limited to noise reduction or speech recognition
 to save power and free CPU resources.
 
 The GNA plugin provides a way to run inference on Intel® GNA, as well as in the software execution mode on CPU.
@@ -19,33 +18,194 @@ Devices with Intel® GNA support:
 
 * [Amazon Alexa\* Premium Far-Field Developer Kit](https://developer.amazon.com/en-US/alexa/alexa-voice-service/dev-kits/amazon-premium-voice)
 
-* [Intel® Pentium® Silver Processors N5xxx, J5xxx and Intel® Celeron® Processors N4xxx, J4xxx](https://ark.intel.com/content/www/us/en/ark/products/codename/83915/gemini-lake.html):
-	- Intel® Pentium® Silver J5005 Processor
-	- Intel® Pentium® Silver N5000 Processor
-	- Intel® Celeron® J4005 Processor
-	- Intel® Celeron® J4105 Processor
-	- Intel® Celeron® Processor N4100
-	- Intel® Celeron® Processor N4000
+* [Intel® Pentium® Silver Processors N5xxx, J5xxx and Intel® Celeron® Processors N4xxx, J4xxx (formerly codenamed Gemini Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/83915/gemini-lake.html):
+   - Intel® Pentium® Silver J5005 Processor
+   - Intel® Pentium® Silver N5000 Processor
+   - Intel® Celeron® J4005 Processor
+   - Intel® Celeron® J4105 Processor
+   - Intel® Celeron® J4125 Processor
+   - Intel® Celeron® Processor N4100
+   - Intel® Celeron® Processor N4000
 
-* [Intel® Core™ Processors (formerly codenamed Cannon Lake)](https://ark.intel.com/content/www/us/en/ark/products/136863/intel-core-i3-8121u-processor-4m-cache-up-to-3-20-ghz.html):
-Intel® Core™ i3-8121U Processor
+* [Intel® Pentium® Processors N6xxx, J6xxx, Intel® Celeron® Processors N6xxx, J6xxx and Intel Atom® x6xxxxx (formerly codenamed Elkhart Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/128825/products-formerly-elkhart-lake.html)
+
+* [Intel® Core™ Processors (formerly codenamed Cannon Lake)](https://ark.intel.com/content/www/us/en/ark/products/136863/intel-core-i3-8121u-processor-4m-cache-up-to-3-20-ghz.html)
 
 * [10th Generation Intel® Core™ Processors (formerly codenamed Ice Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/74979/ice-lake.html):
-	- Intel® Core™ i7-1065G7 Processor
-	- Intel® Core™ i7-1060G7 Processor
-	- Intel® Core™ i5-1035G4 Processor
-	- Intel® Core™ i5-1035G7 Processor
-	- Intel® Core™ i5-1035G1 Processor
-	- Intel® Core™ i5-1030G7 Processor
-	- Intel® Core™ i5-1030G4 Processor
-	- Intel® Core™ i3-1005G1 Processor
-	- Intel® Core™ i3-1000G1 Processor
-	- Intel® Core™ i3-1000G4 Processor
+	
+* [11th Generation Intel® Core™ Processors (formerly codenamed Tiger Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/88759/tiger-lake.html).
 
-* All [11th Generation Intel® Core™ Processors (formerly codenamed Tiger Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/88759/tiger-lake.html).
+* [12th Generation Intel® Core™ Processors (formerly codenamed Alder Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/147470/products-formerly-alder-lake.html).
 
 > **NOTE**: On platforms where Intel® GNA is not enabled in the BIOS, the driver cannot be installed, so the GNA plugin uses the software emulation mode only.
 
+## Intel® GNA Generational Differences
+
+The first and second versions of Intel® GNA found in 10th and 11th generation Intel® Core™ Processors may be considered to be functionally equivalent.  Intel® GNA 2.0 provided performance improvement with respect to Intel® GNA 1.0.  Starting with 12th Generation Intel® Core™ Processors (formerly codenamed Alder Lake), support for Intel® GNA 3.0 features is being added.
+
+In the rest of this documentation, "GNA 2.0" refers to Intel® GNA hardware delivered on 10th and 11th generation Intel® Core™ processors, and the term "GNA 3.0" will be used to refer to GNA hardware delivered on 12th generation Intel® Core™ processors.
+
+Initially, a limited subset of Intel® GNA 3.0 features are added to the previous feature set including the following:
+
+* **2D VALID Convolution With Small 2D Kernels:**  Two-dimensional convolutions with the following kernel dimensions [H,W] are supported:  [1,1], [2,2], [3,3], [2,1], [3,1], [4,1], [5,1], [6,1], [7,1], [1,2], or [1,3].  Input tensor dimensions are limited to [1,8,16,16] <= [N,C,H,W] <= [1,120,384,240].  Up to 384 channels C may be used with a subset of kernel sizes (see table below).  Up to 256 kernels (output channels) are supported.  Pooling is limited to pool shapes of [1,1], [2,2], or [3,3].  Not all combinations of kernel shape and input tensor shape are supported (see the tables below for exact limitations).
+
+The tables below show that the exact limitation on the input tensor width W depends on the number of input channels C (indicated as Ci below) and the kernel shape.  There is much more freedom to choose the input tensor height and number of output channels.
+
+## Initially Supported Subset of Intel® GNA 2D Convolutions
+
+The following tables provide a more explicit representation of the Intel(R) GNA 3.0 2D convolution operations initially supported.  The limits depend strongly on number of input tensor channels (Ci) and the input tensor width (W).  Other factors are kernel height (KH), kernel width (KW), pool height (PH), pool width (PW), horizontal pool step (SH), and vertical pool step (PW).  For example, the first table shows that for a 3x3 kernel with max pooling, only square pools are supported, and W is limited to 87 when there are 64 input channels.
+
+**Table of Maximum Input Tensor Widths (W) vs. Rest of Parameters** (Input and Kernel Precision: 2 bytes)
+
+|KH|KW|PH|PW|SH|SW|H|W<br>Ci=8<br>Co=256|W<br>Ci=16<br>Co=256|W<br>Ci=32<br>Co=256|W<br>Ci=64<br>Co=256|W<br>Ci=128<br>Co=256|W<br>Ci=256<br>Co=256|W<br>Ci=384<br>Co=256|
+|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|
+|1|1|1|1|1|1|128|240|240|240|240|240|240|170|
+|1|1|1|1|1|1|256|240|240|240|240|240|128|85|
+|1|1|1|1|1|1|384|240|240|240|240|170|85|56|
+|1|2|1|1|1|1|128|240|240|240|240|   |  |  |
+|1|2|1|1|1|1|256|240|240|240|240|   |  |  |
+|1|2|1|1|1|1|384|240|240|240|240|   |  |  |
+|1|3|1|1|1|1|128|240|240|240|240|   |  |  |
+|1|3|1|1|1|1|256|240|240|240|240|   |  |  |
+|1|3|1|1|1|1|384|240|240|240|240|   |  |  |
+|2|1|1|1|1|1|128|192|192|192|192|192|192|128|
+|2|1|1|1|1|1|256|192|192|192|192|192|128|85|
+|2|1|1|1|1|1|384|192|192|192|192|170|85|56|
+|2|2|1|1|1|1|128|193|193|193|193|   |  |  |
+|2|2|1|1|1|1|256|193|193|193|193|   |  |  |
+|2|2|1|1|1|1|384|193|193|193|193|   |  |  |
+|2|2|2|2|1|1|128|193|193|192|179|   |  |  |
+|2|2|2|2|1|1|256|193|193|192|179|   |  |  |
+|2|2|2|2|1|1|384|193|193|192|179|   |  |  |
+|2|2|2|2|1|2|128|193|193|192|179|   |  |  |
+|2|2|2|2|1|2|256|193|193|192|179|   |  |  |
+|2|2|2|2|1|2|384|193|193|192|179|   |  |  |
+|2|2|2|2|2|1|128|193|193|192|179|   |  |  |
+|2|2|2|2|2|1|256|193|193|192|179|   |  |  |
+|2|2|2|2|2|1|384|193|193|192|179|   |  |  |
+|2|2|2|2|2|2|128|193|193|192|179|   |  |  |
+|2|2|2|2|2|2|256|193|193|192|179|   |  |  |
+|2|2|2|2|2|2|384|193|193|192|179|   |  |  |
+|3|1|1|1|1|1|128|128|128|128|128|128|85|42|
+|3|1|1|1|1|1|256|128|128|128|128|128|85|42|
+|3|1|1|1|1|1|384|128|128|128|128|128|85|42|
+|3|3|1|1|1|1|128|130|130|130|87|   |  |  |
+|3|3|1|1|1|1|256|130|130|130|87|   |  |  |
+|3|3|1|1|1|1|384|130|130|130|87|   |  |  |
+|3|3|2|2|1|1|128|130|130|126|87|   |  |  |
+|3|3|2|2|1|1|256|130|130|126|87|   |  |  |
+|3|3|2|2|1|1|384|130|130|126|87|   |  |  |
+|3|3|2|2|1|2|128|130|130|126|87|   |  |  |
+|3|3|2|2|1|2|256|130|130|126|87|   |  |  |
+|3|3|2|2|1|2|384|130|130|126|87|   |  |  |
+|3|3|2|2|2|1|128|130|130|126|87|   |  |  |
+|3|3|2|2|2|1|256|130|130|126|87|   |  |  |
+|3|3|2|2|2|1|384|130|130|126|87|   |  |  |
+|3|3|2|2|2|2|128|130|130|126|87|   |  |  |
+|3|3|2|2|2|2|256|130|130|126|87|   |  |  |
+|3|3|2|2|2|2|384|130|130|126|87|   |  |  |
+|3|3|3|3|1|1|128|130|128|118|87|   |  |  |
+|3|3|3|3|1|1|256|130|128|118|87|   |  |  |
+|3|3|3|3|1|1|384|130|128|118|87|   |  |  |
+|3|3|3|3|1|2|128|130|128|118|87|   |  |  |
+|3|3|3|3|1|2|256|130|128|118|87|   |  |  |
+|3|3|3|3|1|2|384|130|128|118|87|   |  |  |
+|3|3|3|3|1|3|128|130|128|118|87|   |  |  |
+|3|3|3|3|1|3|256|130|128|118|87|   |  |  |
+|3|3|3|3|1|3|384|130|128|118|87|   |  |  |
+|3|3|3|3|2|1|128|130|128|118|87|   |  |  |
+|3|3|3|3|2|1|256|130|128|118|87|   |  |  |
+|3|3|3|3|2|1|384|130|128|118|87|   |  |  |
+|3|3|3|3|2|2|128|130|128|118|87|   |  |  |
+|3|3|3|3|2|2|256|130|128|118|87|   |  |  |
+|3|3|3|3|2|2|384|130|128|118|87|   |  |  |
+|3|3|3|3|2|3|128|130|128|118|87|   |  |  |
+|3|3|3|3|2|3|256|130|128|118|87|   |  |  |
+|3|3|3|3|2|3|384|130|128|118|87|   |  |  |
+|3|3|3|3|3|1|128|130|128|118|87|   |  |  |
+|3|3|3|3|3|1|256|130|128|118|87|   |  |  |
+|3|3|3|3|3|1|384|130|128|118|87|   |  |  |
+|3|3|3|3|3|2|128|130|128|118|87|   |  |  |
+|3|3|3|3|3|2|256|130|128|118|87|   |  |  |
+|3|3|3|3|3|2|384|130|128|118|87|   |  |  |
+|3|3|3|3|3|3|128|130|128|118|87|   |  |  |
+|3|3|3|3|3|3|256|130|128|118|87|   |  |  |
+|3|3|3|3|3|3|384|130|128|118|87|   |  |  |
+|4|1|1|1|1|1|128|96|96|96|96|96|64|32|
+|4|1|1|1|1|1|256|96|96|96|96|96|64|32|
+|4|1|1|1|1|1|384|96|96|96|96|96|64|32|
+|5|1|1|1|1|1|128|76|76|76|76|51|25|  |
+|5|1|1|1|1|1|256|76|76|76|76|51|25|  |
+|5|1|1|1|1|1|384|76|76|76|76|51|25|  |
+|6|1|1|1|1|1|128|64|64|64|64|42|21|  |
+|6|1|1|1|1|1|256|64|64|64|64|42|21|  |
+|6|1|1|1|1|1|384|64|64|64|64|42|21|  |
+|7|1|1|1|1|1|128|54|54|54|54|36|  |  |
+|7|1|1|1|1|1|256|54|54|54|54|36|  |  |
+|7|1|1|1|1|1|384|54|54|54|54|36|  |  |
+
+**Table of Maximum Input Tensor Widths (W) vs. Rest of Parameters** (Input and Kernel Precision: 1 bytes)
+
+|KH|KW|PH|PW|SH|SW|H|W<br>Ci=8<br>Co=256|W<br>Ci=16<br>Co=256|W<br>Ci=32<br>Co=256|W<br>Ci=64<br>Co=256|W<br>Ci=128<br>Co=256|W<br>Ci=256<br>Co=256|W<br>Ci=384<br>Co=256|
+|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|
+|1|1|1|1|1|1|128|240|240|240|240|240|240|240|
+|1|1|1|1|1|1|256|240|240|240|240|240|240|170|
+|1|1|1|1|1|1|384|240|240|240|240|240|170|113|
+|1|2|1|1|1|1|128|240|240|240|240|240|240|240|
+|1|2|1|1|1|1|256|240|240|240|240|240|240|170|
+|1|2|1|1|1|1|384|240|240|240|240|240|170|113|
+|1|3|1|1|1|1|128|240|240|240|240|240|   |   |   
+|1|3|1|1|1|1|256|240|240|240|240|240|   |   |
+|1|3|1|1|1|1|384|240|240|240|240|240|   |   |
+|2|1|1|1|1|1|128|192|192|192|192|192|192|192|
+|2|1|1|1|1|1|256|192|192|192|192|192|192|170|
+|2|1|1|1|1|1|384|192|192|192|192|192|170|113|
+|2|2|1|1|1|1|128|193|193|193|193|193|193|129|
+|2|2|1|1|1|1|256|193|193|193|193|193|193|129|
+|2|2|1|1|1|1|384|193|193|193|193|193|170|113|
+|3|1|1|1|1|1|128|128|128|128|128|128|128|85|
+|3|1|1|1|1|1|256|128|128|128|128|128|128|85|
+|3|1|1|1|1|1|384|128|128|128|128|128|128|85|
+|3|3|1|1|1|1|128|130|130|130|130|87 |   |  |   
+|3|3|1|1|1|1|256|130|130|130|130|87 |   |  |
+|3|3|1|1|1|1|384|130|130|130|130|87 |   |  |
+|4|1|1|1|1|1|128|96|96|96|96|96|96|64|
+|4|1|1|1|1|1|256|96|96|96|96|96|96|64|
+|4|1|1|1|1|1|384|96|96|96|96|96|96|64|
+|5|1|1|1|1|1|128|76|76|76|76|76|51|51|
+|5|1|1|1|1|1|256|76|76|76|76|76|51|51|
+|5|1|1|1|1|1|384|76|76|76|76|76|51|51|
+|6|1|1|1|1|1|128|64|64|64|64|64|42|21|
+|6|1|1|1|1|1|256|64|64|64|64|64|42|21|
+|6|1|1|1|1|1|384|64|64|64|64|64|42|21|
+|7|1|1|1|1|1|128|54|54|54|54|54|36|18|
+|7|1|1|1|1|1|256|54|54|54|54|54|36|18|
+|7|1|1|1|1|1|384|54|54|54|54|54|36|18|
+
+
+> **NOTE**:  The above limitations only apply to the new hardware 2D convolution operation.  When possible, the Intel® GNA plugin graph compiler flattens 2D convolutions so that the second generation Intel® GNA 1D convolution operations (without these limitations) may be used.  The plugin will also flatten 2D convolutions regardless of the sizes  if GNA 2.0 compilation target is selected (see below).
+
+## Intel® GNA Forward and Backward Compatibility
+
+In the general case, there is no guarantee that a model compiled for GNA 2.0 will run on GNA 3.0, or vice versa.
+
+However, in most cases, networks compiled for GNA 2.0 will run as expected on GNA 3.0, although the performance may be worse compared to the case when a network is compiled specifically for the latter.  The exception is networks with convolutions with the number of filters greater than 8192 (see the <a href="#models-and-layers-limitations">Models and Layers Limitations</a> section).
+
+Networks compiled for GNA 3.0 should run on GNA 2.0 with incompatible layers emulated on CPU.
+
+You can use the following options `KEY_GNA_EXEC_TARGET` and `KEY_GNA_COMPILE_TARGET` options  to check interoperability (see the <a href="#supported-configuration-parameters">Supported Configuration Parameters</a> section below):
+
+@sphinxdirective
+.. tab:: C++
+
+   ``KEY_GNA_EXEC_TARGET``,  ``KEY_GNA_COMPILE_TARGET``
+
+.. tab:: Python
+
+   ``GNA_EXEC_TARGET``,  ``GNA_COMPILE_TARGET`` 
+
+@endsphinxdirective
+
 ## Drivers and Dependencies
 
 Intel® GNA hardware requires a driver to be installed on the system.
@@ -56,11 +216,10 @@ Intel® GNA hardware requires a driver to be installed on the system.
 * Windows\* OS:
 Intel® GNA driver for Windows is available through Windows Update\*
 
-## Models and Layers Limitations
+## <a name="models-and-layers-limitations">Models and Layers Limitations</a>
 
 Because of specifics of hardware architecture, Intel® GNA supports a limited set of layers, their kinds and combinations.
-For example, you should not expect the GNA Plugin to be able to run computer vision models, except those specifically adapted 
-for the GNA Plugin, because the plugin does not fully support 2D convolutions.
+For example, you should not expect the GNA Plugin to be able to run computer vision models, except those specifically adapted for the GNA Plugin, because the plugin does not fully support 2D convolutions.
 
 For the list of supported layers, see the **GNA** column of the **Supported Layers** section in [Supported Devices](Supported_Devices.md).
 
@@ -68,12 +227,13 @@ Limitations include:
 
 - Only 1D convolutions are natively supported.
 - The number of output channels for convolutions must be a multiple of 4.
+- The maximum number of filters is 65532 for GNA 2.0 and 8192 for GNA 3.0.
 - Permute layer support is limited to the cases where no data reordering is needed or when reordering is happening for two dimensions, at least one of which is not greater than 8.
 - Splits and concatenations are supported for continuous portions of memory (e.g., split of 1,2,3,4 to 1,1,3,4 and 1,1,3,4 or concats of 1,2,3,4 and 1,2,3,5 to 2,2,3,4).
 
-#### Experimental Support for 2D Convolutions
+### Support for 2D Convolutions in Previous Generations of GNA Hardware
 
-The Intel® GNA hardware natively supports only 1D convolutions.
+The Intel® GNA 1.0 and 2.0 hardware natively supports only 1D convolutions.
 
 However, 2D convolutions can be mapped to 1D when a convolution kernel moves in a single direction. GNA Plugin performs such a transformation for Kaldi `nnet1` convolution. From this perspective, the Intel® GNA hardware convolution operation accepts an `NHWC` input and produces an `NHWC` output. Because OpenVINO™ only supports the `NCHW` layout, you may need to insert `Permute` layers before or after convolutions.
 
@@ -83,59 +243,158 @@ For example, the Kaldi model optimizer inserts such a permute after convolution
 
 Intel® GNA essentially operates in the low-precision mode, which represents a mix of 8-bit (`I8`), 16-bit (`I16`), and 32-bit (`I32`) integer computations. Outputs calculated using a reduced integer precision are different from the scores calculated using the floating point format, for example, `FP32` outputs calculated on CPU using the Inference Engine [CPU Plugin](CPU.md).
 
-Unlike other plugins supporting low-precision execution, the GNA plugin can calculate quantization factors at the model loading time, so you can run a model without calibration using the [Post-Training Optimizaton Tool](@ref pot_README).
+Unlike other plugins supporting low-precision execution, the GNA plugin can calculate quantization factors at the model loading time, so you can run a model without calibration using the [Post-Training Optimization Tool](@ref pot_README).
 However, this mode may not provide satisfactory accuracy because the internal quantization algorithm is based on heuristics which may or may not be efficient, depending on the model and dynamic range of input data.
 
-Starting with 2021.4 release of OpenVINO™, GNA plugin users are encouraged to use the [POT API Usage sample for GNA](@ref pot_sample_speech_README) to get a model with quantization hints based on statistics for the provided dataset.
-
+Starting with 2021.4 release of OpenVINO, GNA plugin users are encouraged to use the [POT API Usage sample for GNA](@ref pot_sample_speech_README) to get a model with quantization hints based on statistics for the provided dataset.
 
 ## <a name="execution-modes">Execution Modes</a>
 
-| Mode | Description |
-| :---------------------------------| :---------------------------------------------------------|
-| `GNA_AUTO` | Uses Intel® GNA if available, otherwise uses software execution mode on CPU. |
-| `GNA_HW` | Uses Intel® GNA if available, otherwise raises an error. |
-| `GNA_SW` | *Deprecated*. Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA, but not in the bit-exact mode. |
-| `GNA_SW_EXACT` | Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA in the bit-exact mode. |
-| `GNA_HW_WITH_SW_FBACK` | Uses Intel® GNA if available, otherwise raises an error. If the HW queue is not empty, automatically falls back to CPU in the bit-exact mode. |
-| `GNA_SW_FP32` | Executes the GNA-compiled graph on CPU but substitutes parameters and calculations from low precision to floating point (`FP32`). |
+@sphinxdirective
+.. tab:: C++
 
-## Supported Configuration Parameters
+   ============================  ==============================================================================================================================================
+   Mode                          Description
+   ============================  ==============================================================================================================================================
+   ``KEY_GNA_AUTO``              Uses Intel® GNA if available, otherwise uses software execution mode on CPU. 
+   ``KEY_GNA_HW``                Uses Intel® GNA if available, otherwise raises an error. 
+   ``KEY_GNA_SW``                *Deprecated*. Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA, but not in the bit-exact mode. 
+   ``KEY_GNA_SW_EXACT``          Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA in the bit-exact mode. 
+   ``KEY_GNA_HW_WITH_SW_FBACK``  Uses Intel® GNA if available, otherwise raises an error. If the hardware queue is not empty, automatically falls back to CPU in the bit-exact mode. 
+   ``KEY_GNA_SW_FP32``           Executes the GNA-compiled graph on CPU but substitutes parameters and calculations from low precision to floating point (``FP32``). 
+   ============================  ==============================================================================================================================================
 
-The plugin supports the configuration parameters listed below.
-The parameters are passed as `std::map<std::string, std::string>` on `InferenceEngine::Core::LoadNetwork` or `InferenceEngine::SetConfig`.
+.. tab:: Python
 
-You can change the `KEY_GNA_DEVICE_MODE` parameter at run time using `InferenceEngine::ExecutableNetwork::SetConfig`, which works for any value excluding `GNA_SW_FP32`. This enables you to switch the
-execution between software emulation mode and hardware emulation mode after the model is loaded.
+   ========================  ==============================================================================================================================================
+   Mode                      Description
+   ========================  ==============================================================================================================================================
+   ``GNA_AUTO``              Uses Intel® GNA if available, otherwise uses software execution mode on CPU. 
+   ``GNA_HW``                Uses Intel® GNA if available, otherwise raises an error. 
+   ``GNA_SW``                *Deprecated*. Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA, but not in the bit-exact mode. 
+   ``GNA_SW_EXACT``          Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA in the bit-exact mode. 
+   ``GNA_HW_WITH_SW_FBACK``  Uses Intel® GNA if available, otherwise raises an error. If the hardware queue is not empty, automatically falls back to CPU in the bit-exact mode. 
+   ``GNA_SW_FP32``           Executes the GNA-compiled graph on CPU but substitutes parameters and calculations from low precision to floating point (``FP32``). 
+   ========================  ==============================================================================================================================================
 
-The parameter names below correspond to their usage through API keys, such as `GNAConfigParams::KEY_GNA_DEVICE_MODE` or `PluginConfigParams::KEY_PERF_COUNT`.
-When specifying key values as raw strings, that is, when using Python API, omit the `KEY_` prefix.
+@endsphinxdirective  
 
-| Parameter Name                    | Parameter Values                                          | Default Value     | Description                                                              |
-| :---------------------------------| :---------------------------------------------------------| :-----------| :------------------------------------------------------------------------|
-| `KEY_GNA_COMPACT_MODE`            | `YES`/`NO`                                                | `NO`       | Enables I/O buffers reuse to save space. Makes debugging harder.              |
-| `KEY_GNA_SCALE_FACTOR`            | `FP32` number                                             | 1.0         | Sets the scale factor to use for input quantization.                               |
-| `KEY_GNA_DEVICE_MODE`             | `GNA_AUTO`/`GNA_HW`/`GNA_SW_EXACT`/`GNA_SW_FP32` | `GNA_AUTO`  |  One of the modes described in <a href="#execution-modes">Execution Modes</a> |
-| `KEY_GNA_FIRMWARE_MODEL_IMAGE`    | `std::string`                                             | `""`        | Sets the name for the embedded model binary dump file.                                 |
-| `KEY_GNA_PRECISION`               | `I16`/`I8`                                                | `I16`       | Sets the preferred integer weight resolution for quantization (ignored for models produced using POT). |
-| `KEY_PERF_COUNT`                  | `YES`/`NO`                                                | `NO`        | Turns on performance counters reporting.                                   |
-| `KEY_GNA_LIB_N_THREADS`           | 1-127 integer number                                      | 1           | Sets the number of GNA accelerator library worker threads used for inference computation in software modes.
+## <a name="supported-configuration-parameters">Supported Configuration Parameters</a>
 
+The plugin supports the configuration parameters listed below. The parameter names correspond to their usage through API keys, such as ``GNAConfigParams::KEY_GNA_DEVICE_MODE`` or ``PluginConfigParams::KEY_PERF_COUNT`` in C++ and ``GNA_DEVICE_MODE`` or ``PERF_COUNT`` in Python.
+
+@sphinxdirective
+.. tab:: C++
+
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | Parameter Name                   | Values                  | Default Value | Description                                                     |
+   +==================================+=========================+===============+=================================================================+
+   | ``KEY_GNA_EXEC_TARGET``          | ``TARGET_2_0``,         | *see below*   | Defines the execution target.                                   |
+   |                                  | ``TARGET_3_0``          |               |                                                                 |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_COMPILE_TARGET``       | ``TARGET_2_0``,         | *see below*   | Defines the compilation target.                                 |
+   |                                  | ``TARGET_3_0``          |               |                                                                 |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_COMPACT_MODE``         | ``YES``, ``NO``         | ``NO``        | Enables I/O buffers reuse to save space.                        |
+   |                                  |                         |               | Makes debugging harder.                                         |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_SCALE_FACTOR``         | FP32 number             | 1.0           | Sets the scale factor to use for input quantization.            |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_DEVICE_MODE``          | ``GNA_AUTO``,           | ``GNA_AUTO``  | One of the modes described                                      |
+   |                                  | ``GNA_HW``,             |               | in `Execution Modes <#execution-modes>`_.                       |
+   |                                  | ``GNA_HW_WITH_SW_FBACK``|               |                                                                 |
+   |                                  | ``GNA_SW_EXACT``,       |               |                                                                 |
+   |                                  | ``GNA_SW_FP32``         |               |                                                                 |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_FIRMWARE_MODEL_IMAGE`` | ``std::string``         | ``""``        | Sets the name for the embedded model binary dump file.          |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_PRECISION``            | ``I16``, ``I8``         | ``I16``       | Sets the preferred integer weight resolution for quantization   |
+   |                                  |                         |               | (ignored for models produced using POT).                        |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_PERF_COUNT``               | ``YES``, ``NO``         | ``NO``        | Turns on performance counters reporting.                        |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_LIB_N_THREADS``        | 1-127 integer number    | 1             | Sets the number of GNA accelerator library worker threads used  |
+   |                                  |                         |               | for inference computation in software modes.                    |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   
+   The parameters are passed as ``std::map<std::string, std::string>`` on ``InferenceEngine::Core::LoadNetwork`` or ``InferenceEngine::SetConfig``.
+
+   Normally, you do not need to select the execution target (``KEY_GNA_EXEC_TARGET``) and compilation target (``KEY_GNA_COMPILE_TARGET``). The default value for the execution target corresponds to available hardware, or latest hardware version supported by the plugin (i.e., GNA 3.0) if there is no GNA HW in the system. The compilation target is the same as the execution target by default. However, you may want to change the targets, for example, if you want to check how a model compiled for one generation would behave on the other generation (using the software emulation mode), or if you are willing to export a model for a specific version of GNA HW.
+   
+   You can change the ``KEY_GNA_DEVICE_MODE`` parameter at run time using ``InferenceEngine::ExecutableNetwork::SetConfig``, which works for any value excluding ``GNA_SW_FP32``. This enables you to switch the execution between software emulation mode and hardware execution mode after the model is loaded.
+
+.. tab:: Python
+
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | Parameter Name                   | Values                  | Default Value | Description                                                     |
+   +==================================+=========================+===============+=================================================================+
+   | ``GNA_EXEC_TARGET``              | ``TARGET_2_0``,         | _see below_   | Defines the execution target.                                   |
+   |                                  | ``TARGET_3_0``          |               |                                                                 |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``GNA_COMPILE_TARGET``           | ``TARGET_2_0``,         | _see below_   | Defines the compilation target.                                 |
+   |                                  | ``TARGET_3_0``          |               |                                                                 |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``GNA_COMPACT_MODE``             | ``YES``, ``NO``         | ``NO``        | Enables I/O buffers reuse to save space.                        |
+   |                                  |                         |               | Makes debugging harder.                                         |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``GNA_SCALE_FACTOR``             | FP32 number             | 1.0           | Sets the scale factor to use for input quantization.            |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``KEY_GNA_DEVICE_MODE``          | ``GNA_AUTO``,           | ``GNA_AUTO``  | One of the modes described                                      |
+   |                                  | ``GNA_HW``,             |               | in `Execution Modes <#execution-modes>`_.                       |
+   |                                  | ``GNA_HW_WITH_SW_FBACK``|               |                                                                 |
+   |                                  | ``GNA_SW_EXACT``,       |               |                                                                 |
+   |                                  | ``GNA_SW_FP32``         |               |                                                                 |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``GNA_FIRMWARE_MODEL_IMAGE``     | ``string``              | ``""``        | Sets the name for the embedded model binary dump file.          |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``GNA_PRECISION``                | ``I16``, ``I8``         | ``I16``       | Sets the preferred integer weight resolution for quantization   |
+   |                                  |                         |               | (ignored for models produced using POT).                        |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``PERF_COUNT``                   | ``YES``, ``NO``         | ``NO``        | Turns on performance counters reporting.                        |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+   | ``GNA_LIB_N_THREADS``            | 1-127 integer number    | 1             | Sets the number of GNA accelerator library worker threads used  |
+   |                                  |                         |               | for inference computation in software modes.                    |
+   +----------------------------------+-------------------------+---------------+-----------------------------------------------------------------+
+
+   The parameters are passed as strings to `IECore.load_network <api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.load_network>`_.
+
+   Normally, you do not need to select the execution target (``GNA_EXEC_TARGET``) and compilation target (``GNA_COMPILE_TARGET``). The default value for the execution target corresponds to available hardware, or latest hardware version supported by the plugin (i.e., GNA 3.0) if there is no GNA HW in the system. The compilation target is the same as the execution target by default. However, you may want to change the targets, for example, if you want to check how a model compiled for one generation would behave on the other generation (using the SW emulation mode), or if you are willing to export a model for a specific version of GNA HW.
+
+   You can change the ``GNA_DEVICE_MODE`` parameter at run time by sending a configuration dict to the `IECore.load_network <api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.load_network>`_ call, which works for any value excluding ``GNA_SW_FP32``. This enables you to switch the execution between software emulation mode and hardware execution mode after the model is loaded.
+
+@endsphinxdirective   
 ## How to Interpret Performance Counters
 
-As a result of collecting performance counters using `InferenceEngine::InferRequest::GetPerformanceCounts`, you can find various performance data about execution on GNA.
-Returned map stores a counter description as a key, and a counter value in the `realTime_uSec` field of the `InferenceEngineProfileInfo` structure. Current GNA implementation calculates counters for the whole utterance scoring and does not provide per-layer information. The API enables you to retrieve counter units in cycles, you can convert cycles to seconds as follows:
+With the following methods, you can collect performance counters that provides various performance data about execution on GNA:
+
+@sphinxdirective
+.. tab:: C++
+
+   ``InferenceEngine::InferRequest::GetPerformanceCounts`` 
+   
+   The returned map stores a counter description as a key, and a counter value in the ``realTime_uSec`` field of the ``InferenceEngineProfileInfo`` structure.
+
+
+.. tab:: Python
+
+   ``openvino.inference_engine.InferRequest.get_perf_counts`` 
+   
+   The returned map stores a counter description as a key, and a counter value in the ``real_time`` field. 
+
+@endsphinxdirective
+
+The current GNA implementation calculates counters for the whole utterance scoring and does not provide per-layer information. The API enables you to retrieve counter units in cycles, you can convert cycles to seconds as follows:
 
 ```
 seconds = cycles / frequency
 ```
 
-Refer to the table below to learn about the frequency of Intel® GNA inside a particular processor.
+Refer to the table below to learn about the frequency of Intel® GNA inside a particular processor:
 Processor | Frequency of Intel® GNA
 ---|---
-Intel® Ice Lake processors| 400MHz
-Intel® Core™ i3-8121U processor| 400MHz
-Intel® Gemini Lake  processors | 200MHz
+Intel® Core™ processors| 400MHz
+Intel® processors formerly codenamed Elkhart Lake | 200MHz
+Intel® processors formerly codenamed Gemini Lake | 200MHz
 
 Performance counters provided for the time being:
 
@@ -147,16 +406,38 @@ Performance counters provided for the time being:
 
 The GNA plugin supports the following configuration parameters for multithreading management:
 
-* `KEY_GNA_LIB_N_THREADS`
+@sphinxdirective
+.. tab:: C++
 
-	By default, the GNA plugin uses one worker thread for inference computations. This parameter allows you to create up to 127 threads for software modes.
+   ``KEY_GNA_LIB_N_THREADS``
 
-> **NOTE:** Multithreading mode does not guarantee the same computation order as the order of issuing. Additionally, in this case, software modes do not implement any serializations.
+.. tab:: Python
+
+   ``GNA_LIB_N_THREADS``
+
+@endsphinxdirective
+
+By default, the GNA plugin uses one worker thread for inference computations. This parameter allows you to create up to 127 threads for software modes.
+
+> **NOTE**: Multithreading mode does not guarantee the same computation order as the order of issuing. Additionally, in this case, software modes do not implement any serializations.
 
 ## Network Batch Size
 
 Intel® GNA plugin supports the processing of context-windowed speech frames in batches of 1-8 frames in one
-input blob using `InferenceEngine::ICNNNetwork::setBatchSize`. Increasing batch size only improves efficiency of `Fully Connected` layers.
+input blob using the following methods:
+
+@sphinxdirective
+.. tab:: C++
+
+   ``InferenceEngine::ICNNNetwork::setBatchSize``
+
+.. tab:: Python
+
+   `IENetwork.batch_size <api/ie_python_api/_autosummary/openvino.inference_engine.IENetwork.html#openvino.inference_engine.IENetwork.batch_size>`_
+
+@endsphinxdirective
+
+Increasing batch size only improves efficiency of `Fully Connected` layers.
 
 > **NOTE**: For networks with `Convolutional`, `LSTM`, or `Memory` layers, the only supported batch size is 1.
 
@@ -164,7 +445,7 @@ input blob using `InferenceEngine::ICNNNetwork::setBatchSize`. Increasing batch
 
 Heterogeneous plugin was tested with the Intel® GNA as a primary device and CPU as a secondary device. To run inference of networks with layers unsupported by the GNA plugin, such as Softmax, use the Heterogeneous plugin with the `HETERO:GNA,CPU` configuration.
 
-> **NOTE:** Due to limitation of the Intel® GNA backend library, heterogenous support is limited to cases where in the resulted sliced graph, only one subgraph is scheduled to run on GNA\_HW or GNA\_SW devices.
+> **NOTE**: Due to limitation of the Intel® GNA backend library, heterogenous support is limited to cases where in the resulted sliced graph, only one subgraph is scheduled to run on GNA\_HW or GNA\_SW devices.
 
 ## Recovery from Interruption by High-Priority Windows Audio Processes\*
 
@@ -175,30 +456,59 @@ For such workloads, processing should be time constrained, otherwise extra delay
 the schedule, thereby causing long running GNA tasks to terminate early.
 
 Applications should be prepared for this situation.
-If an inference in the `GNA_HW` mode cannot be executed because of such an interruption, then `InferRequest::Wait()` returns status code
-`StatusCode::INFER_NOT_STARTED`. In future releases, it will be changed to a more meaningful status code.
+
+If an inference in the `GNA_HW` mode cannot be executed because of such an interruption, then the `wait` method returns the following status code:
+
+@sphinxdirective
+.. tab:: C++
+
+   ``InferRequest::Wait()`` returns status code ``StatusCode::INFER_NOT_STARTED``.
+
+.. tab:: Python
+
+   `InferRequest.wait <api/ie_python_api/_autosummary/openvino.inference_engine.InferRequest.html#openvino.inference_engine.InferRequest.wait>`_ returns status code `INFER_NOT_STARTED`.
+
+@endsphinxdirective
+
+In future releases, it will be changed to a more meaningful status code.
 
 Any application working with GNA must properly react to this code.
 One of the strategies to adapt an application:
 
 1. Immediately switch to the GNA_SW emulation mode:
-```cpp
-std::map<std::string, Parameter> newConfig;
-newConfig[GNAConfigParams::KEY_GNA_DEVICE_MODE] = Parameter("GNA_SW_EXACT");
-executableNet.SetConfig(newConfig);
+@sphinxdirective
+.. tab:: C++
+
+   .. code-block:: cpp
+
+      std::map<std::string, Parameter> newConfig;
+      newConfig[GNAConfigParams::KEY_GNA_DEVICE_MODE] = Parameter("GNA_SW_EXACT");
+      executableNet.SetConfig(newConfig);
+
+.. tab:: Python
+
+   .. code-block:: python
+
+      from openvino.inference_engine import IECore
+
+      ie = IECore()
+      new_cfg = {'GNA_DEVICE_MODE' : 'GNA_SW_EXACT'}
+      net = ie.read_network(model=path_to_model)
+      exec_net = ie.load_network(network=net, device_name="GNA", config=new_cfg)
+
+@endsphinxdirective
 
-```
 2. Resubmit and switch back to GNA_HW expecting that the competing application has finished.
 
-> **NOTE:** This method is deprecated since a new automatic QoS mode has been introduced in 2021.4.1 release of OpenVINO™ (see below).
+   > **NOTE**: This method is deprecated since a new automatic QoS mode has been introduced in 2021.4.1 release of OpenVINO™ (see below).
 
 ## GNA3 Automatic QoS Feature on Windows*
 
-Starting with 2021.4.1 release of OpenVINO and 03.00.00.1363 version of Windows* GNA driver, a new execution mode (GNA_HW_WITH_SW_FBACK) is introduced
+Starting with 2021.4.1 release of OpenVINO and 03.00.00.1363 version of Windows* GNA driver, a new execution mode `GNA_HW_WITH_SW_FBACK` is introduced
 to assure that workloads satisfy real-time execution. In this mode, the GNA driver automatically falls back on CPU for a particular infer request
 if the HW queue is not empty, so there is no need for explicitly switching between GNA and CPU.
 
-**NOTE:** Due to the "first come - first served" nature of GNA driver and the QoS feature, this mode may lead to increased CPU consumption
+> **NOTE**: Due to the "first come - first served" nature of GNA driver and the QoS feature, this mode may lead to increased CPU consumption
 if there are several clients using GNA simultaneously.
 Even a lightweight competing infer request which has not been cleared at the time when the user's GNA client process makes its request,
 can cause the user's request to be executed on CPU, thereby unnecessarily increasing CPU utilization and power.
@@ -207,4 +517,4 @@ can cause the user's request to be executed on CPU, thereby unnecessarily increa
 
 * [Supported Devices](Supported_Devices.md)
 * [Converting Model](../../MO_DG/prepare_model/convert_model/Converting_Model.md)
-* [Convert model from Kaldi](../../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
+* [Convert model from Kaldi](../../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
\ No newline at end of file
diff --git a/docs/IE_DG/supported_plugins/GPU.md b/docs/IE_DG/supported_plugins/GPU.md
index 42657d19acd..ae294da6770 100644
--- a/docs/IE_DG/supported_plugins/GPU.md
+++ b/docs/IE_DG/supported_plugins/GPU.md
@@ -1,6 +1,17 @@
 GPU Plugin {#openvino_docs_IE_DG_supported_plugins_GPU}
 =======
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_IE_DG_supported_plugins_GPU_RemoteBlob_API
+
+      
+@endsphinxdirective
+
 The GPU plugin uses the Intel® Compute Library for Deep Neural Networks (clDNN) to infer deep neural networks.
 clDNN is an open source performance library for Deep Learning (DL) applications intended for acceleration of Deep Learning Inference on Intel® Processor Graphics including Intel® HD Graphics, Intel® Iris® Graphics, Intel® Iris® Xe Graphics, and Intel® Iris® Xe MAX graphics.
 For an in-depth description of clDNN, see [Inference Engine source files](https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_gpu/) and [Accelerate Deep Learning Inference with Intel® Processor Graphics](https://software.intel.com/en-us/articles/accelerating-deep-learning-inference-with-intel-processor-graphics).
@@ -99,7 +110,6 @@ The plugin supports the configuration parameters listed below.
 All parameters must be set before calling <code>InferenceEngine::Core::LoadNetwork()</code> in order to take effect.
 When specifying key values as raw strings (that is, when using Python API), omit the `KEY_` prefix.
 
-
 | Parameter Name          | Parameter Values                | Default         | Description                                               |
 |---------------------|-----------------------------|-----------------|-----------------------------------------------------------|
 | `KEY_CACHE_DIR`      | `"<cache_dir>"`                    | `""`              | Specifies a directory where compiled OCL binaries can be cached. First model loading generates the cache, and all subsequent LoadNetwork calls use precompiled kernels which significantly improves load time. If empty - caching is disabled             |
diff --git a/docs/IE_DG/supported_plugins/GPU_RemoteBlob_API.md b/docs/IE_DG/supported_plugins/GPU_RemoteBlob_API.md
index 9431ae7fd59..a5232b580d9 100644
--- a/docs/IE_DG/supported_plugins/GPU_RemoteBlob_API.md
+++ b/docs/IE_DG/supported_plugins/GPU_RemoteBlob_API.md
@@ -4,13 +4,13 @@ Remote Blob API of GPU Plugin {#openvino_docs_IE_DG_supported_plugins_GPU_Remote
 The GPU plugin implementation of the `RemoteContext` and `RemoteBlob` interfaces supports GPU
 pipeline developers who need video memory sharing and interoperability with existing native APIs
 such as OpenCL\*, Microsoft DirectX\*, or VAAPI\*.
-Using these interfaces allows to avoid any memory copy overhead when plugging the OpenVINO™ inference
-into an existing GPU pipeline. It also enables OpenCL kernels participating in the pipeline to become
+Using these interfaces allows you to avoid any memory copy overhead when plugging the OpenVINO™ inference 
+into an existing GPU pipeline. It also enables OpenCL kernels participating in the pipeline to become 
 native buffer consumers or producers of the OpenVINO™ inference.
 Since the GPU plugin works on top of the clDNN library, the functionality above is also implemented
 using OpenCL and its sharing extensions provided by Intel®.
 
-There are two interoperability scenarios that are supported for the Remote Blob API:
+There are two interoperability scenarios supported by the Remote Blob API:
 
 * GPU plugin context and memory objects can be constructed from low-level device, display, or memory
 handles and used to create the OpenVINO™ `ExecutableNetwork` or `Blob` class.
@@ -76,7 +76,7 @@ the plugin for further execution of inference primitives. Sharing of the queue c
 method to guarantee that submission of inference primitives into given queue is finished before
 returning of control back to calling thread.
 
-This sharing mechanism allows to do pipeline synchonization on app side and avoid blocking of host thread
+This sharing mechanism allows to do pipeline synchronization on app side and avoid blocking of host thread
 on waiting for completion of inference. Pseudocode may look as follows:
 
 @snippet snippets/GPU_RemoteBlob_API3.cpp part0
@@ -87,7 +87,7 @@ on waiting for completion of inference. Pseudocode may look as follows:
    to the command queue. In such cases `StartAsync()` call takes much more time to return control to the calling thread
    as internally it waits for partial or full network completion.
    Examples of operations: Loop, TensorIterator, DetectionOutput, NonMaxSuppression
- - Synchonization of pre/post processing jobs and inference pipleine inside shared queue is the user responsibility
+ - Synchronization of pre/post processing jobs and inference pipeline inside shared queue is the user responsibility
  - Throughput mode is not available when queue sharing is used, i.e. only single stream can be used for each executable network.
 
 ## Low-Level Methods and Their Parameter Description
diff --git a/docs/IE_DG/supported_plugins/HDDL.md b/docs/IE_DG/supported_plugins/HDDL.md
index 5108e303594..6d7784aa9ad 100644
--- a/docs/IE_DG/supported_plugins/HDDL.md
+++ b/docs/IE_DG/supported_plugins/HDDL.md
@@ -6,27 +6,27 @@ The Inference Engine HDDL plugin was developed for inference with neural network
 
 ## Configuring the HDDL Plugin
 
-To configure your Intel® Vision Accelerator Design With Intel® Movidius™ on supported OSs, refer to the Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs section in the installation guides for [Linux](../../install_guides/installing-openvino-linux.md) or [Windows](../../install_guides/installing-openvino-windows.md).
+To configure your Intel® Vision Accelerator Design With Intel® Movidius™ on supported operating systems, refer to the Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs section in the installation guides for [Linux](../../install_guides/installing-openvino-linux.md) or [Windows](../../install_guides/installing-openvino-windows.md).
 
 ## Supported networks
 
-For the "Supported Networks", please reference to [MYRIAD Plugin](MYRIAD.md)
+To see the list of supported networks for the HDDL plugin, refer to the list on the [MYRIAD Plugin page](MYRIAD.md).
 
 ## Supported Configuration Parameters
 
-See VPU common configuration parameters for the [VPU Plugins](VPU.md).
-When specifying key values as raw strings (that is, when using Python API), omit the `KEY_` prefix.
+See VPU common configuration parameters for [VPU Plugins](VPU.md).
+When specifying key values as raw strings (that is, when using the Python API), omit the `KEY_` prefix.
 
-In addition to common parameters for MYRIAD plugin and HDDL plugin, HDDL plugin accepts the following options:
+In addition to common parameters for both VPU plugins, the HDDL plugin accepts the following options:
 
 | Parameter Name                        | Parameter Values | Default      | Description                                                                     |
 | :---                                  | :---             | :---         | :---                                                                            |
-| KEY_PERF_COUNT                        | YES/NO           | NO           | Enable performance counter option.                                               |
+| KEY_PERF_COUNT                        | YES/NO           | NO           | Enable performance counter option.                                              |
 | KEY_VPU_HDDL_GRAPH_TAG                | string           | empty string | Allows to execute network on specified count of devices.                        |
 | KEY_VPU_HDDL_STREAM_ID                | string           | empty string | Allows to execute inference on a specified device.                              |
 | KEY_VPU_HDDL_DEVICE_TAG               | string           | empty string | Allows to allocate/deallocate networks on specified devices.                    |
 | KEY_VPU_HDDL_BIND_DEVICE              | YES/NO           | NO           | Whether the network should bind to a device. Refer to vpu_plugin_config.hpp.    |
-| KEY_VPU_HDDL_RUNTIME_PRIORITY         | signed int       | 0            | Specify the runtime priority of a device among all devices that running a same network Refer to vpu_plugin_config.hpp. |
+| KEY_VPU_HDDL_RUNTIME_PRIORITY         | signed int       | 0            | Specify the runtime priority of a device among all devices running the same network. Refer to vpu_plugin_config.hpp. |
 
 ## See Also
 
diff --git a/docs/IE_DG/supported_plugins/HETERO.md b/docs/IE_DG/supported_plugins/HETERO.md
index 202c04647a6..85cf286df95 100644
--- a/docs/IE_DG/supported_plugins/HETERO.md
+++ b/docs/IE_DG/supported_plugins/HETERO.md
@@ -1,37 +1,57 @@
-Heterogeneous Plugin {#openvino_docs_IE_DG_supported_plugins_HETERO}
-=======
+# Heterogeneous Plugin {#openvino_docs_IE_DG_supported_plugins_HETERO}
 
-## Introducing the Heterogeneous Plugin
+## Introducing the Heterogeneous Plugin (C++)
 
-The heterogeneous plugin enables computing for inference on one network on several devices.
-The purposes of executing networks in heterogeneous mode:
-* Utilize the power of accelerators to calculate heaviest parts of the network and execute unsupported layers on fallback devices like the CPU 
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
+
+The heterogeneous plugin enables computing the inference of one network on several devices. The purposes of executing networks in heterogeneous mode are to:
+
+* Utilize the power of accelerators to process the heaviest parts of the network and to execute unsupported layers on fallback devices like the CPU
 * Utilize all available hardware more efficiently during one inference
 
-The execution through heterogeneous plugin can be divided to two independent steps:
-* Setting of affinity to layers
-* Loading a network to the Heterogeneous plugin, splitting the network to parts, and executing them through the plugin
+The execution through heterogeneous plugin can be divided into two independent steps:
 
-These steps are decoupled. The setting of affinity can be done automatically using fallback policy or in manual mode.
+1. Setting of hardware affinity to layers
+2. Loading a network to the Heterogeneous plugin, splitting the network to parts, and executing them through the plugin
 
-The fallback automatic policy causes "greedy" behavior and assigns all layers that can be executed on certain device according to the priorities you specify (for example, `HETERO:GPU,CPU`).
-Automatic policy does not take into account plugin peculiarities such as the inability to infer some layers without other special layers placed before or after that layer. The plugin is responsible for solving such cases. If the device plugin does not support the subgraph topology constructed by the Hetero plugin, then you should set affinity manually.
+These steps are decoupled. The setting of affinity can be done automatically using the fallback policy or in manual mode.
 
-Some of the topologies are not friendly to heterogeneous execution on some devices or cannot be executed in such mode at all.
-Examples of such networks are networks having activation layers which are not supported on primary device.
-If transmitting data from one part of a network to another part in heterogeneous mode takes more time than in normal mode, it may not make sense to execute them in heterogeneous mode.
-In this case, you can define heaviest part manually and set the affinity to avoid sending data back and forth many times during one inference.
+The fallback automatic policy causes "greedy" behavior and assigns all layers that can be executed on certain device according to the priorities you specify (for example, HETERO:GPU,CPU).
+Automatic policy does not take into account plugin peculiarities such as the inability to infer some layers without other special layers placed before or after that layer. The plugin is responsible for solving such cases. If the device plugin does not support the subgraph topology constructed by the HETERO plugin, then you should set affinity manually.
+
+### Details of Splitting Network and Execution
+During loading of the network to the Heterogeneous plugin, the network is divided into separate parts and loaded to dedicated plugins.
+Intermediate blobs between these subgraphs are allocated automatically in the most efficient way.
+
+### Sample Usage
+
+Inference Engine sample programs can use the Heterogeneous plugin used with the `-d` option:
+
+```sh
+./hello_classification <path_to_model>/squeezenet1.1.xml <path_to_pictures>/picture.jpg HETERO:GPU,CPU
+```
+where:
+- `HETERO` stands for the Heterogeneous plugin
+- `GPU,CPU` points to fallback policy with priority on GPU and fallback to CPU
+
+You can point more than two devices: `-d HETERO:MYRIAD,GPU,CPU`
+
+
+### Annotation of Layers per Device and Default Fallback Policy
 
-## Annotation of Layers per Device and Default Fallback Policy
 Default fallback policy decides which layer goes to which device automatically according to the support in dedicated plugins (GPU, CPU, MYRIAD).
 
-Another way to annotate a network is to set affinity manually using <code>ngraph::Node::get_rt_info</code> with key `"affinity"`:
+Another way to annotate a network is to set affinity manually using `ngraph::Node::get_rt_info` with key `affinity`:
 
 @snippet snippets/HETERO0.cpp part0
 
-The fallback policy does not work if even one layer has an initialized affinity. The sequence should be calling of automating affinity settings and then fix manually.
+The fallback policy does not work if even one layer has an initialized affinity. The sequence should be to call automating affinity settings and then fix manually.
 
-> **NOTE**: If you set affinity manually, be careful at the current moment Inference Engine plugins don't support constant (`Constant`->`Result`) and empty (`Parameter`->`Result`) networks. Please avoid such subgraphs when you set affinity manually.
+> **NOTE**: If you set affinity manually, be careful because currently Inference Engine plugins don't support constant (`Constant`->`Result`) and empty (`Parameter`->`Result`) networks. Please avoid such subgraphs when you set affinity manually.
 
 @snippet snippets/HETERO1.cpp part1
 
@@ -39,51 +59,198 @@ If you rely on the default affinity distribution, you can avoid calling <code>In
 
 @snippet snippets/HETERO2.cpp part2
 
-> **NOTE**: `InferenceEngine::Core::QueryNetwork` does not depend on affinities set by a user, but queries for layer support based on device capabilities.
+> **NOTE**: `InferenceEngine::Core::QueryNetwork` does not depend on affinities set by a user. Instead, it queries for layer support based on device capabilities.
 
+### Handling Difficult Topologies
 
-## Details of Splitting Network and Execution
-During loading of the network to heterogeneous plugin, network is divided to separate parts and loaded to dedicated plugins.
-Intermediate blobs between these sub graphs are allocated automatically in the most efficient way.
+Some topologies are not friendly to heterogeneous execution on some devices or cannot be executed at all with this plugin
+Examples are networks having activation layers that are not supported on the primary device.
+If transmitting data from one part of a network to another part in heterogeneous mode takes more time than in normal mode, it may not make sense to execute them in heterogeneous mode.
+In this case, you can define the heaviest part manually and set the affinity to avoid sending data back and forth many times during one inference.
 
-Samples can be used with the following command:
+### Execution Precision
+Precision for inference in the heterogeneous plugin is defined by:
+* Precision of IR
+* Ability of final plugins to execute in precision defined in IR
 
-```sh
-./hello_classification <path_to_model>/squeezenet1.1.xml <path_to_pictures>/picture.jpg HETERO:GPU,CPU
-```
-where:
-- `HETERO` stands for heterogeneous plugin
-- `GPU,CPU` points to fallback policy with priority on GPU and fallback to CPU
+For example, if you want to execute GPU with CPU fallback with FP16 on GPU, you need to use only FP16 IR.
 
-You can point more than two devices: `-d HETERO:GPU,GPU,CPU`
+### Analyzing Performance Heterogeneous Execution
+After enabling the <code>KEY_HETERO_DUMP_GRAPH_DOT</code> config key (shown in code snippet below), you can dump GraphViz* `.dot` files with annotations of devices per layer.
 
-## Analyzing Heterogeneous Execution
-After enabling of <code>KEY_HETERO_DUMP_GRAPH_DOT</code> config key, you can dump GraphViz* `.dot` files with annotations of devices per layer.
+The Heterogeneous plugin can generate two files:
 
-Heterogeneous plugin can generate two files:
 * `hetero_affinity_<network name>.dot` - annotation of affinities per layer. This file is written to the disk only if default fallback policy was executed
-* `hetero_subgraphs_<network name>.dot` - annotation of affinities per graph. This file is written to the disk during execution of <code>ICNNNetwork::LoadNetwork()</code> for heterogeneous plugin
+* `hetero_subgraphs_<network name>.dot` - annotation of affinities per graph. This file is written to the disk during execution of `ICNNNetwork::LoadNetwork()` for the Heterogeneous plugin
 
 @snippet snippets/HETERO3.cpp part3
 
-You can use GraphViz* utility or converters to `.png` formats. On Ubuntu* operating system, you can use the following utilities:
+You can use the GraphViz* utility or a file converter to view the images. On the Ubuntu* operating system, you can use xdot:
+
 * `sudo apt-get install xdot`
 * `xdot hetero_subgraphs.dot`
 
+You can use performance data (in sample applications, it is the option `-pc`) to get the performance data on each subgraph.
 
-You can use performance data (in samples, it is an option `-pc`) to get performance data on each subgraph.
+Here is an example of the output for Googlenet v1 running on HDDL with fallback to CPU:
 
-Here is an example of the output: for Googlenet v1 running on FPGA with fallback to CPU:
-```cpp
-subgraph1: 1. input preprocessing (mean data/FPGA):EXECUTED       layerType:                    realTime: 129        cpu: 129            execType:
-subgraph1: 2. input transfer to DDR:EXECUTED       layerType:                    realTime: 201        cpu: 0              execType:
-subgraph1: 3. FPGA execute time:EXECUTED       layerType:                    realTime: 3808       cpu: 0              execType:
-subgraph1: 4. output transfer from DDR:EXECUTED       layerType:                    realTime: 55         cpu: 0              execType:
-subgraph1: 5. FPGA output postprocessing:EXECUTED       layerType:                    realTime: 7          cpu: 7              execType:
-subgraph1: 6. copy to IE blob:EXECUTED       layerType:                    realTime: 2          cpu: 2              execType:
-subgraph2: out_prob:          NOT_RUN        layerType: Output             realTime: 0          cpu: 0              execType: unknown
-subgraph2: prob:              EXECUTED       layerType: SoftMax            realTime: 10         cpu: 10             execType: ref
-Total time: 4212     microseconds
 ```
-## See Also
-* [Supported Devices](Supported_Devices.md)
+subgraph1: 1. input preprocessing (mean data/HDDL):EXECUTED layerType:          realTime: 129   cpu: 129  execType:
+subgraph1: 2. input transfer to DDR:EXECUTED                layerType:          realTime: 201   cpu: 0    execType:
+subgraph1: 3. HDDL execute time:EXECUTED                    layerType:          realTime: 3808  cpu: 0    execType:
+subgraph1: 4. output transfer from DDR:EXECUTED             layerType:          realTime: 55    cpu: 0    execType:
+subgraph1: 5. HDDL output postprocessing:EXECUTED           layerType:          realTime: 7     cpu: 7    execType:
+subgraph1: 6. copy to IE blob:EXECUTED                      layerType:          realTime: 2     cpu: 2    execType:
+subgraph2: out_prob:          NOT_RUN                       layerType: Output   realTime: 0     cpu: 0    execType: unknown
+subgraph2: prob:              EXECUTED                      layerType: SoftMax  realTime: 10    cpu: 10   execType: ref
+Total time: 4212 microseconds
+```
+### See Also
+[Supported Devices](Supported_Devices.md)
+
+## Introducing the Heterogeneous Plugin (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+The heterogeneous plugin enables computing the inference of one network on several devices. The purposes of executing networks in heterogeneous mode are to:
+
+* Utilize the power of accelerators to process the heaviest parts of the network and to execute unsupported layers on fallback devices like the CPU
+* Utilize all available hardware more efficiently during one inference
+
+The execution through heterogeneous plugin can be divided into two independent steps:
+
+1. Setting of hardware affinity to layers
+2. Loading a network to the Heterogeneous plugin, splitting the network to parts, and executing them through the plugin
+
+These steps are decoupled. The setting of affinity can be done automatically using the fallback policy or in manual mode.
+
+The fallback automatic policy causes "greedy" behavior and assigns all layers that can be executed on certain device according to the priorities you specify (for example, HETERO:GPU,CPU).
+Automatic policy does not take into account plugin peculiarities such as the inability to infer some layers without other special layers placed before or after that layer. The plugin is responsible for solving such cases. If the device plugin does not support the subgraph topology constructed by the HETERO plugin, then you should set affinity manually.
+
+Some of the topologies are not well-supported for heterogeneous execution on some devices or cannot be executed in this mode at all. Examples of such networks are those having activation layers which are not supported on the primary device. If transmitting data from one part of a network to another part in heterogeneous mode takes more time than in normal mode, it may not make sense to execute them in heterogeneous mode. In this case, you can define the most compute intense part manually and set the affinity to avoid sending data back and forth many times during one inference.
+
+### Use Default Layer Affinities
+
+To use the default affinities, call `load_network` with the "HETERO" device, with an optional list of devices to consider.
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_model)
+exec_net = ie.load_network(network=net, device_name='HETERO:GPU,CPU')
+```
+
+
+### Annotation of Layers per Device and Default Fallback Policy
+
+Default fallback policy decides which layer goes to which device automatically according to the support in dedicated plugins (GPU, CPU, MYRIAD).
+
+Another way to annotate a network is to set affinity manually using code.
+
+### Set Affinity of All Layers to CPU
+```python
+import ngraph as ng
+from openvino.inference_engine import IECore
+
+ie = IECore()
+# Read a network in IR or ONNX format
+net = ie.read_network(path_to_model)
+# Create an Ngraph (graph) function from the network
+ng_func = ng.function_from_cnn(net)
+for node in ng_func.get_ordered_ops():
+    rt_info = node.get_rt_info()
+    rt_info["affinity"] = "CPU"
+```
+
+
+The fallback policy does not work if even one layer has an initialized affinity. The sequence should be calling the default affinity settings and then setting the layers manually.
+
+> **NOTE**: If you set affinity manually, be aware that currently Inference Engine plugins do not support constant (*Constant -> Result*) and empty (*Parameter -> Result*) networks. Please avoid these subgraphs when you set affinity manually.
+
+### Example - Manually Setting Layer Affinities
+
+```python
+import ngraph as ng
+from openvino.inference_engine import IECore
+
+ie = IECore()
+# Read a network in IR or ONNX format
+net = ie.read_network(path_to_model)
+ng_func = ng.function_from_cnn(net)
+
+for node in ng_func.get_ordered_ops():
+    rt_info = node.get_rt_info()
+    rt_info["affinity"] = "CPU"
+
+# Load the network on the target device
+exec_net = ie.load_network(network=net, device_name='HETERO:FPGA,CPU')
+```
+
+> **NOTE**: `ie.query_network` does not depend on affinities set by a user, but queries for layer support based on device capabilities.
+
+### Details of Splitting Network and Execution
+
+During the loading of the network to the heterogeneous plugin, the network is divided into separate parts and loaded to dedicated plugins. Intermediate blobs between these sub graphs are allocated automatically in the most efficient way.
+
+### Execution Precision
+
+The precision for inference in the heterogeneous plugin is defined by:
+* Precision of IR
+* Ability of final plugins to execute in precision defined in IR
+
+For example, if you want to execute GPU with CPU fallback with FP16 on GPU, you need to use only FP16 IR.
+
+OpenVINO samples can be used with the following command:
+```sh
+./hello_classification <path_to_model>/squeezenet1.1.xml <path_to_pictures>/picture.jpg HETERO:GPU,CPU
+```
+
+where `HETERO` stands for the heterogeneous plugin.
+
+You can point to more than two devices, for example: `-d HETERO:MYRIAD,GPU,CPU`
+
+### Analyzing Heterogeneous Execution
+
+After enabling the KEY_HETERO_DUMP_GRAPH_DOT config key, you can dump GraphViz* .dot files with annotations of devices per layer.
+
+The heterogeneous plugin can generate two files:
+
+* `hetero_affinity_<network name>.dot` - annotation of affinities per layer. This file is written to the disk only if the default fallback policy was executed
+* `hetero_subgraphs_<network name>.dot` - annotation of affinities per graph. This file is written to the disk during execution of `ICNNNetwork::LoadNetwork()` for the heterogeneous plugin
+
+#### To Generate the .dot Files
+
+```python
+ie = IECore()
+ie.set_config( config={'HETERO_DUMP_GRAPH_DOT' : 'YES'}, device_name='HETERO')
+```
+
+You can use the GraphViz* utility or a file converter to view the images. On the Ubuntu* operating system, you can use xdot:
+
+* `sudo apt-get install xdot`
+* `xdot hetero_subgraphs.dot`
+
+You can use performance data (in sample applications, it is the option `-pc`) to get the performance data on each subgraph.
+
+Here is an example of the output for Googlenet v1 running on HDDL with fallback to CPU:
+
+```
+subgraph1: 1. input preprocessing (mean data/HDDL):EXECUTED layerType:          realTime: 129   cpu: 129  execType:
+subgraph1: 2. input transfer to DDR:EXECUTED                layerType:          realTime: 201   cpu: 0    execType:
+subgraph1: 3. HDDL execute time:EXECUTED                    layerType:          realTime: 3808  cpu: 0    execType:
+subgraph1: 4. output transfer from DDR:EXECUTED             layerType:          realTime: 55    cpu: 0    execType:
+subgraph1: 5. HDDL output postprocessing:EXECUTED           layerType:          realTime: 7     cpu: 7    execType:
+subgraph1: 6. copy to IE blob:EXECUTED                      layerType:          realTime: 2     cpu: 2    execType:
+subgraph2: out_prob:          NOT_RUN                       layerType: Output   realTime: 0     cpu: 0    execType: unknown
+subgraph2: prob:              EXECUTED                      layerType: SoftMax  realTime: 10    cpu: 10   execType: ref
+Total time: 4212 microseconds
+```
+
+
+### See Also
+[Supported Devices](Supported_Devices.md)
diff --git a/docs/IE_DG/supported_plugins/MULTI.md b/docs/IE_DG/supported_plugins/MULTI.md
index eabc36e4d0a..9adc02c0aea 100644
--- a/docs/IE_DG/supported_plugins/MULTI.md
+++ b/docs/IE_DG/supported_plugins/MULTI.md
@@ -1,31 +1,37 @@
 # Multi-Device Plugin {#openvino_docs_IE_DG_supported_plugins_MULTI}
 
-## Introducing the Multi-Device Plugin
+## Introducing the Multi-Device Plugin (C++)
 
-The Multi-Device plugin automatically assigns inference requests to available computational devices to execute the requests in parallel. Potential gains are as follows:
-* Improved throughput that multiple devices can deliver (compared to single-device execution)
-* More consistent performance, since the devices can now share the inference burden
-(so that if one device is becoming too busy, another device can take more of the load)
+@sphinxdirective
+.. raw:: html
 
-Notice that with multi-device the application logic is left unchanged, so you don't need to explicitly load the network to every device, create and balance the inference requests and so on. From the application point of view, this is just another device that handles the actual machinery. 
-The only thing that is required to leverage performance is to provide the multi-device (and hence the underlying devices) with enough inference requests to crunch.
-For example, if you were processing 4 cameras on the CPU (with 4 inference requests), you may now want to process more cameras (with more requests in flight) to keep CPU+GPU busy via multi-device.
+    <div id="switcher-cpp" class="switcher-anchor">C++</div>
+@endsphinxdirective
 
-The "setup" of Multi-Device can be described in three major steps:
-* First is configuration of each device as usual (e.g. via conventional SetConfig method)
-* Second is loading of a network to the Multi-Device plugin created on top of (prioritized) list of the  configured devices. This is the only change that you need in your application.
-* Finally, just like with any other ExecutableNetwork (resulted from LoadNetwork) you just create as many requests as needed to saturate the devices.
-These steps are covered below in details.
+The Multi-Device plugin automatically assigns inference requests to available computational devices to execute the requests in parallel. By contrast, the Heterogeneous plugin can run different layers on different devices but not in parallel. The potential gains with the Multi-Device plugin are:
 
-## Defining and Configuring the Multi-Device plugin
-Following the OpenVINO notions of "devices", the Multi-Device has a "MULTI" name.
-The only configuration option for the Multi-Device plugin is a prioritized list of devices to use:
+* Improved throughput from using multiple devices (compared to single-device execution)
+* More consistent performance, since the devices share the inference burden (if one device is too busy, another can take more of the load)
 
-| Parameter name                 | Parameter values      | Default            | Description                                                                                                                  |
-| :---                      | :---                  | :---               | :----------------------------------------------------------------------------------------------------------------------------|
-| "MULTI_DEVICE_PRIORITIES"  | comma-separated device names <span style="color:red">with no spaces</span>| N/A              | Prioritized list of devices                 |
+Note that with Multi-Device the application logic is left unchanged, so you don't need to explicitly load the network to every device, create and balance the inference requests and so on. From the application point of view, this is just another device that handles the actual machinery. The only thing that is required to leverage performance is to provide the multi-device (and hence the underlying devices) with enough inference requests to process. For example, if you were processing 4 cameras on the CPU (with 4 inference requests), it might be desirable to process more cameras (with more requests in flight) to keep CPU and GPU busy via Multi-Device.
 
-You can use name of the configuration directly as a string, or use `MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES from the multi/multi_device_config.hpp`, which defines the same string.
+The setup of Multi-Device can be described in three major steps:
+
+1. Configure each device as usual.
+2. Load the network to the Multi-Device plugin created on top of a (prioritized) list of the configured devices. This is the only change needed in the application.
+3. As with any other ExecutableNetwork call (resulting from `InferenceEngine::Core::LoadNetwork`), you create as many requests as needed to saturate the devices.
+
+These steps are covered below in detail.
+
+### Defining and Configuring the Multi-Device Plugin
+
+Following the OpenVINO™ convention of labeling devices, the Multi-Device plugin uses the name "MULTI". The only configuration option for the Multi-Device plugin is a prioritized list of devices to use:
+
+| Parameter name | Parameter values | Default | Description |
+| -------------- | ---------------- | --- | --- |
+| "MULTI_DEVICE_PRIORITIES" | comma-separated device names with no spaces | N/A | Prioritized list of devices |
+
+You can set the configuration directly as a string, or use the metric key `MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES from the `multi/multi_device_config.hpp` file, which defines the same string.
  
 Basically, there are three ways to specify the devices to be use by the "MULTI":
 
@@ -35,71 +41,252 @@ Notice that the priorities of the devices can be changed in real time for the ex
 
 @snippet snippets/MULTI1.cpp part1
 
-Finally, there is a way to specify number of requests that the multi-device will internally keep for each device. Suppose your original app was running 4 cameras with 4 inference requests. You would probably want to share these 4 requests between 2 devices used in the MULTI. The easiest way is to specify a number of requests for each device using parentheses: "MULTI:CPU(2),GPU(2)" and use the same 4 requests in your app. However, such an explicit configuration is not performance-portable and hence not recommended. Instead, the better way is to configure the individual devices and query the resulting number of requests to be used at the application level (see [Configuring the Individual Devices and Creating the Multi-Device On Top](#configuring-the-individual-devices-and-creating-the-multi-device-on-top)).
+Finally, there is a way to specify number of requests that the Multi-Device will internally keep for each device. Suppose your original app was running 4 cameras with 4 inference requests. You would probably want to share these 4 requests between 2 devices used in MULTI. The easiest way is to specify a number of requests for each device using parentheses: "MULTI:CPU(2),GPU(2)" and use the same 4 requests in your app. However, such an explicit configuration is not performance-portable and hence not recommended. Instead, the better way is to configure the individual devices and query the resulting number of requests to be used at the application level (see [Configuring the Individual Devices and Creating the Multi-Device On Top](#configuring-the-individual-devices-and-creating-the-multi-device-on-top)).
 
-## Enumerating Available Devices
-Inference Engine now features a dedicated API to enumerate devices and their capabilities. See [Hello Query Device C++ Sample](../../../samples/cpp/hello_query_device/README.md).  This is example output from the sample (truncated to the devices' names only):
+### Enumerating Available Devices
+The Inference Engine features a dedicated API to enumerate devices and their capabilities. See the [Hello Query Device C++ Sample](../../../samples/cpp/hello_query_device/README.md). This is example output from the sample (truncated to device names only):
 
 ```sh
-./hello_query_device
-Available devices: 
-    Device: CPU
-...
-    Device: GPU.0
-...
-    Device: GPU.1
-...
-    Device: HDDL
+  ./hello_query_device
+  Available devices:
+      Device: CPU
+  ...
+      Device: GPU.0
+  ...
+      Device: GPU.1
+  ...
+      Device: HDDL
 ```
+
 A simple programmatic way to enumerate the devices and use with the multi-device is as follows:
 
 @snippet snippets/MULTI2.cpp part2
 
-Beyond the trivial "CPU", "GPU", "HDDL" and so on, when multiple instances of a device are available the names are more qualified.
-For example, this is how two Intel® Movidius™ Myriad™ X sticks are listed with the hello_query_sample:
+Beyond the trivial "CPU", "GPU", "HDDL" and so on, when multiple instances of a device are available the names are more qualified. For example, this is how two Intel® Movidius™ Myriad™ X sticks are listed with the hello_query_sample:
 ```
 ...
     Device: MYRIAD.1.2-ma2480
 ...
     Device: MYRIAD.1.4-ma2480
 ```
-So the explicit configuration to use both would be "MULTI:MYRIAD.1.2-ma2480,MYRIAD.1.4-ma2480".
-Accordingly, the code that loops over all available devices of "MYRIAD" type only is below:
+
+So the explicit configuration to use both would be "MULTI:MYRIAD.1.2-ma2480,MYRIAD.1.4-ma2480". Accordingly, the code that loops over all available devices of "MYRIAD" type only is below:
 
 @snippet snippets/MULTI3.cpp part3
 
 
-## Configuring the Individual Devices and Creating the Multi-Device On Top
+### Configuring the Individual Devices and Creating the Multi-Device On Top
 As discussed in the first section, you shall configure each individual device as usual and then just create the "MULTI" device on top:
 
 @snippet snippets/MULTI4.cpp part4
 
-Alternatively, you can combine all the individual device settings into single config and load that, allowing the Multi-Device plugin to parse and apply that to the right devices. See code example in the next section.
+An alternative is to combine all the individual device settings into a single config file and load that, allowing the Multi-Device plugin to parse and apply settings to the right devices. See the code example in the next section.
 
-Notice that while the performance of accelerators combines really well with multi-device, the CPU+GPU execution poses some performance caveats, as these devices share the power, bandwidth and other resources. For example it is recommended to enable the GPU throttling hint (which save another CPU thread for the CPU inference).
-See section of the [Using the multi-device with OpenVINO samples and benchmarking the performance](#using-the-multi-device-with-openvino-samples-and-benchmarking-the-performance) below.
+Note that while the performance of accelerators combines really well with Multi-Device, the CPU+GPU execution poses some performance caveats, as these devices share the power, bandwidth and other resources. For example it is recommended to enable the GPU throttling hint (which save another CPU thread for the CPU inference).
+See the [Using the Multi-Device with OpenVINO samples and benchmarking the performance](#using-the-multi-device-with-openvino-samples-and-benchmarking-the-performance) section below.
 
-## Querying the Optimal Number of Inference Requests
-Notice that until R2 you had to calculate number of requests in your application for any device, e.g. you had to know that Intel® Vision Accelerator Design with Intel® Movidius™ VPUs required at least 32 inference requests to perform well. Now you can use the new GetMetric API to query the optimal number of requests. Similarly, when using the multi-device you don't need to sum over included devices yourself, you can query metric directly:
+### Querying the Optimal Number of Inference Requests
+You can use the new GetMetric API to query the optimal number of requests. Similarly, when using the Multi-Device you don't need to sum over included devices yourself, you can query metric directly:
 
 @snippet snippets/MULTI5.cpp part5
 
-## Using the Multi-Device with OpenVINO Samples and Benchmarking the Performance
-Notice that every OpenVINO sample that supports "-d" (which stands for "device") command-line option transparently accepts the multi-device.
-The [Benchmark Application](../../../samples/cpp/benchmark_app/README.md) is the best reference to the optimal usage of the multi-device. As discussed multiple times earlier, you don't need to setup number of requests, CPU streams or threads as the application provides optimal out of the box performance.
-Below is example command-line to evaluate HDDL+GPU performance with that:
+### Using the Multi-Device with OpenVINO Samples and Benchmarking the Performance
+
+Every OpenVINO sample that supports the `-d` (which stands for "device") command-line option transparently accepts Multi-Device. The [Benchmark Application](../../../samples/cpp/benchmark_app/README.md) is the best reference for the optimal usage of Multi-Device. As discussed earlier, you do not need to set up the number of requests, CPU streams or threads because the application provides optimal performance out of the box. Below is an example command to evaluate HDDL+GPU performance with that:
 
 ```sh
 ./benchmark_app –d MULTI:HDDL,GPU –m <model> -i <input> -niter 1000
 ```
-Notice that you can use the FP16 IR to work with multi-device (as CPU automatically upconverts it to the fp32) and rest of devices support it naturally. 
-Also notice that no demos are (yet) fully optimized for the multi-device, by means of supporting the OPTIMAL_NUMBER_OF_INFER_REQUESTS metric, using the GPU streams/throttling, and so on.
 
-## Video: MULTI Plugin
+The Multi-Device plugin supports FP16 IR files. The CPU plugin automatically upconverts it to FP32 and the other devices support it natively. Note that no demos are (yet) fully optimized for Multi-Device, by means of supporting the OPTIMAL_NUMBER_OF_INFER_REQUESTS metric, using the GPU streams/throttling, and so on.
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/xbORYFEmrqU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+### Video: MULTI Plugin
 
-## See Also
-* [Supported Devices](Supported_Devices.md)
+@sphinxdirective
+.. raw:: html
+
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/xbORYFEmrqU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
+
+### See Also
+[Supported Devices](Supported_Devices.md)
+
+## Introducing the Multi-Device Plugin (Python)
+
+@sphinxdirective
+.. raw:: html
+
+    <div id="switcher-python" class="switcher-anchor">Python</div>
+@endsphinxdirective
+
+The Multi-Device plugin automatically assigns inference requests to available computational devices to execute the requests in parallel. By contrast, the Heterogeneous plugin can run different layers on different devices but not in parallel. The potential gains with the Multi-Device plugin are:
+
+* Improved throughput from using multiple devices (compared to single-device execution)
+* More consistent performance, since the devices share the inference burden (if one device is too busy, another can take more of the load)
+
+Note that with Multi-Device the application logic is left unchanged, so you don't need to explicitly load the network to every device, create and balance the inference requests and so on. From the application point of view, this is just another device that handles the actual machinery. The only thing that is required to leverage performance is to provide the multi-device (and hence the underlying devices) with enough inference requests to process. For example, if you were processing 4 cameras on the CPU (with 4 inference requests), it might be desirable to process more cameras (with more requests in flight) to keep CPU and GPU busy via Multi-Device.
+
+The setup of Multi-Device can be described in three major steps:
+
+1. Configure each device as usual (using the conventional [ie_api.IECore.set_config](api/ie_python_api/_autosummary/openvino.inference_engine.IECore.html#openvino.inference_engine.IECore.set_config) method
+2. Load the network to the Multi-Device plugin created on top of a (prioritized) list of the configured devices. This is the only change needed in the application.
+3. As with any other ExecutableNetwork call (resulting from `load_network`), you create as many requests as needed to saturate the devices.
+
+These steps are covered below in detail.
+
+### Defining and Configuring the Multi-Device Plugin
+
+Following the OpenVINO™ convention of labeling devices, the Multi-Device plugin uses the name "MULTI". The only configuration option for the Multi-Device plugin is a prioritized list of devices to use:
+
+| Parameter name | Parameter values | Default | Description |
+| -------------- | ---------------- | --- | --- |
+| "MULTI_DEVICE_PRIORITIES" | comma-separated device names with no spaces | N/A | Prioritized list of devices |
+
+You can set the configuration directly as a string, or use the metric key `MULTI_DEVICE_PRIORITIES` from the `multi/multi_device_config.hpp` file, which defines the same string.
+
+#### The Three Ways to Specify Devices Targets for the MULTI plugin
+
+* Option 1 - Pass a Prioritized List as a Parameter in ie.load_network()
+   ```python
+   from openvino.inference_engine import IECore
+
+   ie = IECore()
+   # Read a network in IR or ONNX format
+   net = ie.read_network(model=path_to_model)
+   exec_net = ie.load_network(network=net, device_name="MULTI:CPU,GPU")
+   ```
+
+* Option 2 - Pass a List as a Parameter, and Dynamically Change Priorities during Execution
+   Notice that the priorities of the devices can be changed in real time for the executable network:
+   ```python
+   from openvino.inference_engine import IECore
+
+   # Init the Inference Engine Core
+   ie = IECore()
+
+   # Read a network in IR or ONNX format
+   net = ie.read_network(model=path_to_model)
+
+   ie.set_config( config={"MULTI_DEVICE_PRIORITIES":"HDDL,GPU"}, device_name="MULTI")
+
+   # Change priorities
+   ie.set_config( config={"MULTI_DEVICE_PRIORITIES":"GPU,HDDL"}, device_name="MULTI")
+   ie.set_config( config={"MULTI_DEVICE_PRIORITIES":"GPU"}, device_name="MULTI")
+   ie.set_config( config={"MULTI_DEVICE_PRIORITIES":"HDDL,GPU"}, device_name="MULTI")
+   ie.set_config( config={"MULTI_DEVICE_PRIORITIES":"CPU,HDDL,GPU"}, device_name="MULTI")
+   ```
+
+* Option 3 - Use Explicit Hints for Controlling Request Numbers Executed by Devices
+   There is a way to specify the number of requests that Multi-Device will internally keep for each device. If the original app was running 4 cameras with 4 inference requests, it might be best to share these 4 requests between 2 devices used in the MULTI. The easiest way is to specify a number of requests for each device using parentheses: “MULTI:CPU(2),GPU(2)” and use the same 4 requests in the app. However, such an explicit configuration is not performance-portable and not recommended. The better way is to configure the individual devices and query the resulting number of requests to be used at the application level. See [Configuring the Individual Devices and Creating the Multi-Device On Top](#configuring-the-individual-devices-and-creating-the-multi-device-on-top).
 
 
+### Enumerating Available Devices
+The Inference Engine features a dedicated API to enumerate devices and their capabilities. See the [Hello Query Device Python Sample](../../../inference-engine/ie_bridges/python/sample/hello_query_device/README.md). This is example output from the sample (truncated to device names only):
+
+```sh
+  ./hello_query_device
+  Available devices:
+      Device: CPU
+  ...
+      Device: GPU.0
+  ...
+      Device: GPU.1
+  ...
+      Device: HDDL
+```
+
+A simple programmatic way to enumerate the devices and use with the multi-device is as follows:
+
+```python
+
+from openvino.inference_engine import IECore
+
+all_devices = "MULTI:"
+ie = IECore()
+net = ie.read_network(model=path_to_model)
+all_devices += ",".join(ie.available_devices)
+exec_net = ie.load_network(network=net, device_name=all_devices)
+```
+
+Beyond the trivial "CPU", "GPU", "HDDL" and so on, when multiple instances of a device are available the names are more qualified. For example, this is how two Intel® Movidius™ Myriad™ X sticks are listed with the hello_query_sample:
+
+```bash
+  ...
+      Device: MYRIAD.1.2-ma2480
+  ...
+      Device: MYRIAD.1.4-ma2480
+```
+
+So the explicit configuration to use both would be "MULTI:MYRIAD.1.2-ma2480,MYRIAD.1.4-ma2480". Accordingly, the code that loops over all available devices of "MYRIAD" type only is below:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+match_list = []
+all_devices = "MULTI:"
+dev_match_str = "MYRIAD"
+net = ie.read_network(model=path_to_model)
+
+for d in ie.available_devices:
+    if dev_match_str in d:
+        match_list.append(d)
+
+all_devices += ",".join(match_list)
+exec_net = ie.load_network(network=net, device_name=all_devices)
+```
+
+### Configuring the Individual Devices and Creating the Multi-Device On Top
+
+It is possible to configure each individual device as usual and then create the "MULTI" device on top:
+
+```python
+from openvino.inference_engine import IECore
+
+ie = IECore()
+net = ie.read_network(model=path_to_model)
+
+cpu_config = {}
+gpu_config = {}
+
+ie.set_config(config=cpu_config, device_name="CPU")
+ie.set_config(config=gpu_config, device_name="GPU")
+
+# Load the network to the multi-device, specifying the priorities
+exec_net = ie.load_network(
+    network=net, device_name="MULTI", config={"MULTI_DEVICE_PRIORITIES": "CPU,GPU"}
+)
+# Query the optimal number of requests
+nireq = exec_net.get_metric("OPTIMAL_NUMBER_OF_INFER_REQUESTS")
+```
+
+An alternative is to combine all the individual device settings into a single config file and load that, allowing the Multi-Device plugin to parse and apply settings to the right devices. See the code example in the next section.
+
+Note that while the performance of accelerators works well with Multi-Device, the CPU+GPU execution poses some performance caveats, as these devices share power, bandwidth and other resources. For example it is recommended to enable the GPU throttling hint (which saves another CPU thread for CPU inferencing). See the section below titled Using the Multi-Device with OpenVINO Samples and Benchmarking the Performance.
+
+
+### Using the Multi-Device with OpenVINO Samples and Benchmarking the Performance
+
+Every OpenVINO sample that supports the `-d` (which stands for "device") command-line option transparently accepts Multi-Device. The [Benchmark application](../../../inference-engine/tools/benchmark_tool/README.md) is the best reference for the optimal usage of Multi-Device. As discussed earlier, you do not need to set up the number of requests, CPU streams or threads because the application provides optimal performance out of the box. Below is an example command to evaluate CPU+GPU performance with the Benchmark application:
+
+```sh
+./benchmark_app.py –d MULTI:CPU,GPU –m <model>
+```
+
+> **NOTE**: If you installed OpenVINO with pip, use `benchmark_app -d MULTI:CPU,GPU -m <model>`
+
+The Multi-Device plugin supports FP16 IR files. The CPU plugin automatically upconverts it to FP32 and the other devices support it natively. Note that no demos are (yet) fully optimized for Multi-Device, by means of supporting the OPTIMAL_NUMBER_OF_INFER_REQUESTS metric, using the GPU streams/throttling, and so on.
+
+### Video: MULTI Plugin
+> **NOTE**: This video is currently available only for C++, but many of the same concepts apply to Python.
+
+@sphinxdirective
+.. raw:: html
+
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/xbORYFEmrqU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
+
+### See Also
+[Supported Devices](Supported_Devices.md)
\ No newline at end of file
diff --git a/docs/IE_DG/supported_plugins/MYRIAD.md b/docs/IE_DG/supported_plugins/MYRIAD.md
index 6425cc5ed4b..53addae9c51 100644
--- a/docs/IE_DG/supported_plugins/MYRIAD.md
+++ b/docs/IE_DG/supported_plugins/MYRIAD.md
@@ -4,63 +4,14 @@
 
 The Inference Engine MYRIAD plugin has been developed for inference of neural networks on Intel&reg; Neural Compute Stick 2.
 
-## Installation on Linux* OS
+## Configuring the MYRIAD Plugin
 
-For installation instructions, refer to the [Installation Guide for Linux*](../../install_guides/installing-openvino-linux.md).
-
-
-## Installation on Windows* OS
-
-For installation instructions, refer to the [Installation Guide for Windows*](../../install_guides/installing-openvino-windows.md).
-
-## Supported networks
-
-The Inference Engine MYRIAD plugin supports the following networks:
-
-**Caffe\***:
-* AlexNet
-* CaffeNet
-* GoogleNet (Inception) v1, v2, v4
-* VGG family (VGG16, VGG19)
-* SqueezeNet v1.0, v1.1
-* ResNet v1 family (18\*\*\*, 50, 101, 152)
-* MobileNet (mobilenet-v1-1.0-224, mobilenet-v2)
-* Inception ResNet v2
-* DenseNet family (121,161,169,201)
-* SSD-300, SSD-512, SSD-MobileNet, SSD-GoogleNet, SSD-SqueezeNet
-
-**TensorFlow\***:
-* AlexNet
-* Inception v1, v2, v3, v4
-* Inception ResNet v2
-* MobileNet v1, v2
-* ResNet v1 family (50, 101, 152)
-* ResNet v2 family (50, 101, 152)
-* SqueezeNet v1.0, v1.1
-* VGG family (VGG16, VGG19)
-* Yolo family (yolo-v2, yolo-v3, tiny-yolo-v1, tiny-yolo-v2, tiny-yolo-v3)
-* faster_rcnn_inception_v2, faster_rcnn_resnet101
-* ssd_mobilenet_v1
-* DeepLab-v3+
-
-**MXNet\***:
-* AlexNet and CaffeNet
-* DenseNet family (121,161,169,201)
-* SqueezeNet v1.1
-* MobileNet v1, v2
-* NiN
-* ResNet v1 (101, 152)
-* ResNet v2 (101)
-* SqueezeNet v1.1
-* VGG family (VGG16, VGG19)
-* SSD-Inception-v3, SSD-MobileNet, SSD-ResNet-50, SSD-300
-
-\*\*\* Network is tested on Intel&reg; Neural Compute Stick 2 with BatchNormalization fusion optimization disabled during Model Optimizer import
+To configure your Intel® Vision Accelerator Design With Intel® Movidius™ on supported operating systemss, refer to the Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs section in the installation guides for [Linux](../../install_guides/installing-openvino-linux.md) or [Windows](../../install_guides/installing-openvino-windows.md).
 
 ## Supported Configuration Parameters
 
 See VPU common configuration parameters for the [VPU Plugins](VPU.md).
-When specifying key values as raw strings (that is, when using Python API), omit the `KEY_` prefix.
+When specifying key values as raw strings (that is, when using the Python API), omit the `KEY_` prefix.
 
 In addition to common parameters, the MYRIAD plugin accepts the following options:
 
@@ -73,9 +24,9 @@ In addition to common parameters, the MYRIAD plugin accepts the following option
 ## Device allocation <a name="MYRIAD_DEVICE_ALLOC">&nbsp;</a>
 
 Each `IExecutableNetwork` instance tries to allocate new device on `InferenceEngine::Core::LoadNetwork`, but if all available devices are already allocated it will use the one with the minimal number of uploaded networks.
-The maximum number of networks single device can handle depends on device memory capacity and the size of the networks.
+The maximum number of networks a single device can handle depends on device memory capacity and the size of the networks.
 
-If `KEY_VPU_MYRIAD_FORCE_RESET` option is set to `YES` the plugin will reset all VPU devices in the system.
+If the `KEY_VPU_MYRIAD_FORCE_RESET` option is set to `YES`, the plugin will reset all VPU devices in the system.
 
 Single device cannot be shared across multiple processes.
 
diff --git a/docs/IE_DG/supported_plugins/Supported_Devices.md b/docs/IE_DG/supported_plugins/Supported_Devices.md
index 5c003fc86bb..cda46c029a6 100644
--- a/docs/IE_DG/supported_plugins/Supported_Devices.md
+++ b/docs/IE_DG/supported_plugins/Supported_Devices.md
@@ -1,7 +1,7 @@
 Supported Devices {#openvino_docs_IE_DG_supported_plugins_Supported_Devices}
 ==================
 
-The Inference Engine can infer models in different formats with various input and output formats. This section provides supported and optimal configurations per device.
+The Inference Engine can infer models in different formats with various input and output formats. This section provides supported and optimal configurations per device. In OpenVINO™ documentation, "device" refers to an Intel® processors used for inference, which can be a supported CPU, GPU, VPU (vision processing unit), or GNA (Gaussian neural accelerator coprocessor), or a combination of those devices.
 
 > **NOTE**: With OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick is no longer supported.
 
@@ -28,7 +28,6 @@ This page shows supported and optimal configurations for each plugin.
 
 | Acronym/Term      | Description                                   |
 | :-----------------| :---------------------------------------------|
-|   DL              | Deep Learning                                 |
 |   FP32 format     | Single-precision floating-point format        |
 |   BF16 format     | Brain floating-point format                   |
 |   FP16 format     | Half-precision floating-point format          |
@@ -37,7 +36,7 @@ This page shows supported and optimal configurations for each plugin.
 |   U16 format      | 2-byte unsigned integer format                |
 |   U8 format       | 1-byte unsigned integer format                |
 
-NHWC, NCHW, and NCDHW refer to the representation of batches of images.
+NHWC, NCHW, and NCDHW refer to the data ordering in batches of images:
 * NHWC and NCHW refer to image data layout.
 * NCDHW refers to image sequence data layout.
 
diff --git a/docs/IE_DG/supported_plugins/VPU.md b/docs/IE_DG/supported_plugins/VPU.md
index 40cf1ad543b..62a390fdbb5 100644
--- a/docs/IE_DG/supported_plugins/VPU.md
+++ b/docs/IE_DG/supported_plugins/VPU.md
@@ -1,20 +1,64 @@
 # VPU Plugins {#openvino_docs_IE_DG_supported_plugins_VPU}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_IE_DG_supported_plugins_MYRIAD
+   openvino_docs_IE_DG_supported_plugins_HDDL
+    
+@endsphinxdirective
+
 This chapter provides information on the Inference Engine plugins that enable inference of deep learning models on the supported VPU devices:
 
 * Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X — Supported by the [MYRIAD Plugin](MYRIAD.md)
 * Intel® Vision Accelerator Design with Intel® Movidius™ VPUs — Supported by the [HDDL Plugin](HDDL.md)
 
-> **NOTE**: With OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick powered by the Intel® Movidius™ Myriad™ 2 is no longer supported.
+> **NOTE**: With the OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick powered by the Intel® Movidius™ Myriad™ 2 is no longer supported.
 
-## Known Layers Limitations
+## Supported Networks
 
-* `ScaleShift` layer is supported for zero value of `broadcast` attribute only.
-* `CTCGreedyDecoder` layer works with `ctc_merge_repeated` attribute equal 1.
-* `DetectionOutput` layer works with zero values of `interpolate_orientation` and `num_orient_classes` parameters only.
-* `MVN` layer uses fixed value for `eps` parameters (1e-9).
-* `Normalize` layer uses fixed value for `eps` parameters (1e-9) and is supported for zero value of `across_spatial` only.
-* `Pad` layer works only with 4D tensors.
+**Caffe\***:
+* AlexNet
+* CaffeNet
+* GoogleNet (Inception) v1, v2, v4
+* VGG family (VGG16, VGG19)
+* SqueezeNet v1.0, v1.1
+* ResNet v1 family (18\*\*\*, 50, 101, 152)
+* MobileNet (mobilenet-v1-1.0-224, mobilenet-v2)
+* Inception ResNet v2
+* DenseNet family (121,161,169,201)
+* SSD-300, SSD-512, SSD-MobileNet, SSD-GoogleNet, SSD-SqueezeNet
+
+**TensorFlow\***:
+* AlexNet
+* Inception v1, v2, v3, v4
+* Inception ResNet v2
+* MobileNet v1, v2
+* ResNet v1 family (50, 101, 152)
+* ResNet v2 family (50, 101, 152)
+* SqueezeNet v1.0, v1.1
+* VGG family (VGG16, VGG19)
+* Yolo family (yolo-v2, yolo-v3, tiny-yolo-v1, tiny-yolo-v2, tiny-yolo-v3)
+* faster_rcnn_inception_v2, faster_rcnn_resnet101
+* ssd_mobilenet_v1
+* DeepLab-v3+
+
+**MXNet\***:
+* AlexNet and CaffeNet
+* DenseNet family (121,161,169,201)
+* SqueezeNet v1.1
+* MobileNet v1, v2
+* NiN
+* ResNet v1 (101, 152)
+* ResNet v2 (101)
+* SqueezeNet v1.1
+* VGG family (VGG16, VGG19)
+* SSD-Inception-v3, SSD-MobileNet, SSD-ResNet-50, SSD-300
+
+\*\*\* Network is tested on Intel&reg; Neural Compute Stick 2 with BatchNormalization fusion optimization disabled during Model Optimizer import
 
 ## Optimizations
 
@@ -24,7 +68,7 @@ VPU plugins support layer fusion and decomposition.
 
 #### Fusing Rules
 
-Certain layers can be merged into Convolution, ReLU, and Eltwise layers according to the patterns below:
+Certain layers can be merged into convolution, ReLU, and Eltwise layers according to the patterns below:
 
 - Convolution
     - Convolution + ReLU → Convolution
@@ -46,6 +90,7 @@ Certain layers can be merged into Convolution, ReLU, and Eltwise layers accordin
 > **NOTE**: Application of these rules depends on tensor sizes and resources available.
 
 Layers can be joined only when the two conditions below are met:
+
 - Layers are located on topologically independent branches. 
 - Layers can be executed simultaneously on the same hardware units.
 
@@ -63,10 +108,9 @@ Layers can be joined only when the two conditions below are met:
 
 > **NOTE**: VPU plugins can add extra layers like Copy.
 
-
 ## VPU Common Configuration Parameters
 
-The VPU plugins supports the configuration parameters listed below.
+VPU plugins support the configuration parameters listed below.
 The parameters are passed as `std::map<std::string, std::string>` on `InferenceEngine::Core::LoadNetwork`
 or `InferenceEngine::Core::SetConfig`.
 When specifying key values as raw strings (that is, when using Python API), omit the `KEY_` prefix.
@@ -83,20 +127,28 @@ When specifying key values as raw strings (that is, when using Python API), omit
 ## Data Transfer Pipelining <a name="VPU_DATA_TRANSFER_PIPELINING">&nbsp;</a>
 
 MYRIAD plugin tries to pipeline data transfer to/from device with computations.
-While one infer request is executed the data for next infer request can be uploaded to device in parallel.
-Same applicable for result downloading.
+While one infer request is executed, the data for next infer request can be uploaded to device in parallel.
+The same applies to result downloading.
 
 `KEY_VPU_PRINT_RECEIVE_TENSOR_TIME` configuration parameter can be used to check the efficiency of current pipelining.
 The new record in performance counters will show the time that device spent waiting for input before starting the inference.
-In perfect pipeline this time should be near to zero, which means that the data was already transferred when new inference started.
+In a perfect pipeline this time should be near zero, which means that the data was already transferred when new inference started.
 
 ## Troubleshooting
 
 **Get the following message when running inference with the VPU plugin: "[VPU] Cannot convert layer <layer_name> due to unsupported layer type <layer_type>"**
 
 This means that your topology has a layer that is unsupported by your target VPU plugin. To resolve this issue, you can implement the custom layer for the target device using the [Inference Engine Extensibility mechanism](../Extensibility_DG/Intro.md). Or, to quickly get a working prototype, you can use the heterogeneous scenario with the default fallback policy (see the [HETERO Plugin](HETERO.md) section). Use the HETERO plugin with a fallback device that supports this layer, for example, CPU: `HETERO:MYRIAD,CPU`.
-For a list of VPU supported layers, see the Supported Layers section of the [Supported Devices](Supported_Devices.md) topic.
+For a list of VPU-supported layers, see the Supported Layers section of the [Supported Devices](Supported_Devices.md) page.
 
+## Known Layers Limitations
+
+* `ScaleShift` layer is supported for zero value of `broadcast` attribute only.
+* `CTCGreedyDecoder` layer works with the `ctc_merge_repeated` attribute equal to 1.
+* `DetectionOutput` layer works with zero values of `interpolate_orientation` and `num_orient_classes` parameters only.
+* `MVN` layer uses fixed value for `eps` parameters (1e-9).
+* `Normalize` layer uses fixed value for `eps` parameters (1e-9) and is supported for zero value of `across_spatial` only.
+* `Pad` layer works only with 4D tensors.
 
 ## See Also
 
diff --git a/docs/IE_PLUGIN_DG/AsyncInferRequest.md b/docs/IE_PLUGIN_DG/AsyncInferRequest.md
index 8250c10b7dd..d9aa2a5d3c0 100644
--- a/docs/IE_PLUGIN_DG/AsyncInferRequest.md
+++ b/docs/IE_PLUGIN_DG/AsyncInferRequest.md
@@ -1,4 +1,4 @@
-# Asynchronous Inference Request {#async_infer_request}
+# Asynchronous Inference Request {#openvino_docs_ie_plugin_dg_async_infer_request}
 
 Asynchronous Inference Request runs an inference pipeline asynchronously in one or several task executors depending on a device pipeline structure.
 Inference Engine Plugin API provides the base InferenceEngine::AsyncInferRequestThreadSafeDefault class:
@@ -16,7 +16,7 @@ Inference Engine Plugin API provides the base InferenceEngine::AsyncInferRequest
 
 #### Class Fields
 
-- `_inferRequest` - a reference to the [synchronous inference request](@ref infer_request) implementation. Its methods are reused in the `AsyncInferRequest` constructor to define a device pipeline.
+- `_inferRequest` - a reference to the [synchronous inference request](@ref openvino_docs_ie_plugin_dg_infer_request) implementation. Its methods are reused in the `AsyncInferRequest` constructor to define a device pipeline.
 - `_waitExecutor` - a task executor that waits for a response from a device about device tasks completion
 
 > **NOTE**: If a plugin can work with several instances of a device, `_waitExecutor` must be device-specific. Otherwise, having a single task executor for several devices does not allow them to work in parallel.
diff --git a/docs/IE_PLUGIN_DG/Building.md b/docs/IE_PLUGIN_DG/Building.md
index 38cb4546c89..35df6b5b165 100644
--- a/docs/IE_PLUGIN_DG/Building.md
+++ b/docs/IE_PLUGIN_DG/Building.md
@@ -1,4 +1,4 @@
-# Build Plugin Using CMake* {#plugin_build}
+# Build Plugin Using CMake* {#openvino_docs_ie_plugin_dg_plugin_build}
 
 Inference Engine build infrastructure provides the Inference Engine Developer Package for plugin development.
 
@@ -35,7 +35,7 @@ Once the commands above are executed, the Inference Engine Developer Package is
        * `IE::ngraphFunctions` - static library with the set of `ngraph::Function` builders
        * `IE::funcSharedTests` - static library with common functional tests
 
-> **Note:** it's enough just to run `cmake --build . --target ie_dev_targets` command to build only targets from the
+> **NOTE**: it's enough just to run `cmake --build . --target ie_dev_targets` command to build only targets from the
 > Inference Engine Developer package.
 
 Build Plugin using Inference Engine Developer Package
diff --git a/docs/IE_PLUGIN_DG/ExecutableNetwork.md b/docs/IE_PLUGIN_DG/ExecutableNetwork.md
index ae82b05e4ed..d9fc8af11ab 100644
--- a/docs/IE_PLUGIN_DG/ExecutableNetwork.md
+++ b/docs/IE_PLUGIN_DG/ExecutableNetwork.md
@@ -1,4 +1,4 @@
-# Executable Network {#executable_network}
+# Executable Network {#openvino_docs_ie_plugin_dg_executable_network}
 
 `ExecutableNetwork` class functionality:
 - Compile an InferenceEngine::ICNNNetwork instance to a backend specific graph representation
@@ -63,8 +63,8 @@ The implementation of the method should write all data to the `model` stream, wh
 
 The method creates an asynchronous inference request and returns it. While the public Inference Engine API has a single interface for inference request, which can be executed in synchronous and asynchronous modes, a plugin library implementation has two separate classes:
 
-- [Synchronous inference request](@ref infer_request), which defines pipeline stages and runs them synchronously in the `Infer` method.
-- [Asynchronous inference request](@ref async_infer_request), which is a wrapper for a synchronous inference request and can run a pipeline asynchronously. Depending on a device pipeline structure, it can has one or several stages:
+- [Synchronous inference request](@ref openvino_docs_ie_plugin_dg_infer_request), which defines pipeline stages and runs them synchronously in the `Infer` method.
+- [Asynchronous inference request](@ref openvino_docs_ie_plugin_dg_async_infer_request), which is a wrapper for a synchronous inference request and can run a pipeline asynchronously. Depending on a device pipeline structure, it can has one or several stages:
    - For single-stage pipelines, there is no need to define this method and create a class derived from InferenceEngine::AsyncInferRequestThreadSafeDefault. For single stage pipelines, a default implementation of this method creates InferenceEngine::AsyncInferRequestThreadSafeDefault wrapping a synchronous inference request and runs it asynchronously in the `_taskExecutor` executor.
    - For pipelines with multiple stages, such as performing some preprocessing on host, uploading input data to a device, running inference on a device, or downloading and postprocessing output data, schedule stages on several task executors to achieve better device use and performance. You can do it by creating a sufficient number of inference requests running in parallel. In this case, device stages of different inference requests are overlapped with preprocessing and postprocessing stage giving better performance.
    > **IMPORTANT**: It is up to you to decide how many task executors you need to optimally execute a device pipeline.
@@ -73,7 +73,7 @@ The method creates an asynchronous inference request and returns it. While the p
 
 ### `CreateInferRequestImpl()`
 
-This is a helper method used by `CreateInferRequest` to create a [synchronous inference request](@ref infer_request), which is later wrapped with the asynchronous inference request class:
+This is a helper method used by `CreateInferRequest` to create a [synchronous inference request](@ref openvino_docs_ie_plugin_dg_infer_request), which is later wrapped with the asynchronous inference request class:
 
 @snippet src/template_executable_network.cpp executable_network:create_infer_request_impl
 
@@ -97,4 +97,4 @@ Returns a current value for a configuration key with the name `name`. The method
 
 This function is the only way to get configuration values when a network is imported and compiled by other developers and tools (for example, the [Compile tool](../_inference_engine_tools_compile_tool_README.html)).
 
-The next step in plugin library implementation is the [Synchronous Inference Request](@ref infer_request) class.
+The next step in plugin library implementation is the [Synchronous Inference Request](@ref openvino_docs_ie_plugin_dg_infer_request) class.
diff --git a/docs/IE_PLUGIN_DG/InferRequest.md b/docs/IE_PLUGIN_DG/InferRequest.md
index c512f42c551..5850c3fc625 100644
--- a/docs/IE_PLUGIN_DG/InferRequest.md
+++ b/docs/IE_PLUGIN_DG/InferRequest.md
@@ -1,8 +1,8 @@
-# Synchronous Inference Request {#infer_request}
+# Synchronous Inference Request {#openvino_docs_ie_plugin_dg_infer_request}
 
 `InferRequest` class functionality:
 - Allocate input and output blobs needed for a backend-dependent network inference.
-- Define functions for inference process stages (for example, `preprocess`, `upload`, `infer`, `download`, `postprocess`). These functions can later be used to define an execution pipeline during [Asynchronous Inference Request](@ref async_infer_request) implementation.
+- Define functions for inference process stages (for example, `preprocess`, `upload`, `infer`, `download`, `postprocess`). These functions can later be used to define an execution pipeline during [Asynchronous Inference Request](@ref openvino_docs_ie_plugin_dg_async_infer_request) implementation.
 - Call inference stages one by one synchronously.
 
 `InferRequest` Class
@@ -80,4 +80,4 @@ The method sets performance counters which were measured during pipeline stages
 
 @snippet src/template_infer_request.cpp infer_request:get_performance_counts
 
-The next step in the plugin library implementation is the [Asynchronous Inference Request](@ref async_infer_request) class.
+The next step in the plugin library implementation is the [Asynchronous Inference Request](@ref openvino_docs_ie_plugin_dg_async_infer_request) class.
diff --git a/docs/IE_PLUGIN_DG/Intro.md b/docs/IE_PLUGIN_DG/Intro.md
index 7437eaa3140..8979d4c74a9 100644
--- a/docs/IE_PLUGIN_DG/Intro.md
+++ b/docs/IE_PLUGIN_DG/Intro.md
@@ -1,4 +1,18 @@
-@mainpage Overview of Inference Engine Plugin Library
+# Overview of Inference Engine Plugin Library {#openvino_docs_ie_plugin_dg_overview}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Converting and Preparing Models
+   :hidden:
+
+   Implement Plugin Functionality <openvino_docs_ie_plugin_dg_plugin>
+   Implement Executable Network Functionality <openvino_docs_ie_plugin_dg_executable_network>
+   Implement Synchronous Inference Request <openvino_docs_ie_plugin_dg_infer_request>
+   Implement Asynchronous Inference Request <openvino_docs_ie_plugin_dg_async_infer_request>
+
+@endsphinxdirective
 
 The plugin architecture of the Inference Engine allows to develop and plug independent inference 
 solutions dedicated to different devices. Physically, a plugin is represented as a dynamic library 
@@ -9,23 +23,23 @@ Inference Engine Plugin Library
 
 Inference Engine plugin dynamic library consists of several main components:
 
-1. [Plugin class](@ref plugin):
+1. [Plugin class](@ref openvino_docs_ie_plugin_dg_plugin):
 	- Provides information about devices of a specific type.
-	- Can create an [executable network](@ref executable_network) instance which represents a Neural 
+	- Can create an [executable network](@ref openvino_docs_ie_plugin_dg_executable_network) instance which represents a Neural 
 	Network backend specific graph structure for a particular device in opposite to the InferenceEngine::ICNNNetwork 
 	interface which is backend-independent.
 	- Can import an already compiled graph structure from an input stream to an 
-	[executable network](@ref executable_network) object.
-2. [Executable Network class](@ref executable_network):
+	[executable network](@ref openvino_docs_ie_plugin_dg_executable_network) object.
+2. [Executable Network class](@ref openvino_docs_ie_plugin_dg_executable_network):
 	- Is an execution configuration compiled for a particular device and takes into account its capabilities.
 	- Holds a reference to a particular device and a task executor for this device.
-	- Can create several instances of [Inference Request](@ref infer_request).
+	- Can create several instances of [Inference Request](@ref openvino_docs_ie_plugin_dg_infer_request).
 	- Can export an internal backend specific graph structure to an output stream.
-3. [Inference Request class](@ref infer_request):
+3. [Inference Request class](@ref openvino_docs_ie_plugin_dg_infer_request):
     - Runs an inference pipeline serially.
     - Can extract performance counters for an inference pipeline execution profiling.
-4. [Asynchronous Inference Request class](@ref async_infer_request):
-    - Wraps the [Inference Request](@ref infer_request) class and runs pipeline stages in parallel 
+4. [Asynchronous Inference Request class](@ref openvino_docs_ie_plugin_dg_async_infer_request):
+    - Wraps the [Inference Request](@ref openvino_docs_ie_plugin_dg_infer_request) class and runs pipeline stages in parallel 
 	on several task executors based on a device-specific pipeline structure.
 
 > **NOTE**: This documentation is written based on the `Template` plugin, which demonstrates plugin 
@@ -35,13 +49,13 @@ at `<dldt source dir>/docs/template_plugin`.
 Detailed guides
 -----------------------
 
-* [Build](@ref plugin_build) a plugin library using CMake\*
-* Plugin and its components [testing](@ref plugin_testing)
-* [Quantized networks](@ref quantized_networks)
-* [Writing ngraph transformations](@ref ngraph_transformation) guide
+* [Build](@ref openvino_docs_ie_plugin_dg_plugin_build) a plugin library using CMake\*
+* Plugin and its components [testing](@ref openvino_docs_ie_plugin_dg_plugin_testing)
+* [Quantized networks](@ref openvino_docs_ie_plugin_dg_quantized_networks)
+* [Writing nGraph transformations](@ref ngraph_transformation) guide
 
 API References
 -----------------------
 
-* [Inference Engine Plugin API](group__ie__dev__api.html)
-* [Inference Engine Transformation API](group__ie__transformation__api.html)
+* [Inference Engine Plugin API](groupie_dev_api.html)
+* [Inference Engine Transformation API](groupie_transformation_api.html)
diff --git a/docs/IE_PLUGIN_DG/LowPrecisionModelRepresentation.md b/docs/IE_PLUGIN_DG/LowPrecisionModelRepresentation.md
index c00507d6c37..0a6366bfb47 100644
--- a/docs/IE_PLUGIN_DG/LowPrecisionModelRepresentation.md
+++ b/docs/IE_PLUGIN_DG/LowPrecisionModelRepresentation.md
@@ -1,4 +1,4 @@
-# Representation of low-precision models {#lp_representation}
+# Representation of low-precision models {#openvino_docs_ie_plugin_dg_lp_representation}
 The goal of this document is to describe how optimized models are represented in OpenVINO Intermediate Representation (IR) and provide guidance on interpretation rules for such models at runtime. 
 Currently, there are two groups of optimization methods that can influence on the IR after applying them to the full-precision model:
 - **Sparsity**. It is represented by zeros inside the weights and this is up to the hardware plugin how to interpret these zeros (use weights as is or apply special compression algorithms and sparse arithmetic). No additional mask is provided with the model.
diff --git a/docs/IE_PLUGIN_DG/Plugin.md b/docs/IE_PLUGIN_DG/Plugin.md
index 6003eb691fc..6cc7571ce6f 100644
--- a/docs/IE_PLUGIN_DG/Plugin.md
+++ b/docs/IE_PLUGIN_DG/Plugin.md
@@ -1,4 +1,4 @@
-# Plugin {#plugin}
+# Plugin {#openvino_docs_ie_plugin_dg_plugin}
 
 Inference Engine Plugin usually represents a wrapper around a backend. Backends can be:
 - OpenCL-like backend (e.g. clDNN library) for GPU devices.
@@ -8,7 +8,7 @@ Inference Engine Plugin usually represents a wrapper around a backend. Backends
 The responsibility of Inference Engine Plugin:
 - Initializes a backend and throw exception in `Engine` constructor if backend cannot be initialized.
 - Provides information about devices enabled by a particular backend, e.g. how many devices, their properties and so on.
-- Loads or imports [executable network](@ref executable_network) objects.
+- Loads or imports [executable network](@ref openvino_docs_ie_plugin_dg_executable_network) objects.
 
 In addition to the Inference Engine Public API, the Inference Engine provides the Plugin API, which is a set of functions and helper classes that simplify new plugin development:
 
@@ -16,7 +16,7 @@ In addition to the Inference Engine Public API, the Inference Engine provides th
 - implementations in the `inference_engine/src/inference_engine` directory
 - symbols in the Inference Engine Core shared library
 
-To build an Inference Engine plugin with the Plugin API, see the [Inference Engine Plugin Building](@ref plugin_build) guide.  
+To build an Inference Engine plugin with the Plugin API, see the [Inference Engine Plugin Building](@ref openvino_docs_ie_plugin_dg_plugin_build) guide.  
 
 Plugin Class
 ------------------------
@@ -39,7 +39,7 @@ The provided plugin class also has several fields:
 As an example, a plugin configuration has three value parameters:
 
 - `deviceId` - particular device ID to work with. Applicable if a plugin supports more than one `Template` device. In this case, some plugin methods, like `SetConfig`, `QueryNetwork`, and `LoadNetwork`, must support the CONFIG_KEY(KEY_DEVICE_ID) parameter. 
-- `perfCounts` - boolean value to identify whether to collect performance counters during [Inference Request](@ref infer_request) execution.
+- `perfCounts` - boolean value to identify whether to collect performance counters during [Inference Request](@ref openvino_docs_ie_plugin_dg_infer_request) execution.
 - `_streamsExecutorConfig` - configuration of `InferenceEngine::IStreamsExecutor` to handle settings of multi-threaded context.
 
 ### Engine Constructor
@@ -69,7 +69,7 @@ InferenceEngine::ICNNNetwork object is supported by a device. In the example abo
 
 The very important part before creation of `ExecutableNetwork` instance is to call `TransformNetwork` method which applies ngraph transformation passes.
 
-Actual graph compilation is done in the `ExecutableNetwork` constructor. Refer to the [ExecutableNetwork Implementation Guide](@ref executable_network) for details.
+Actual graph compilation is done in the `ExecutableNetwork` constructor. Refer to the [ExecutableNetwork Implementation Guide](@ref openvino_docs_ie_plugin_dg_executable_network) for details.
 
 > **NOTE**: Actual configuration map used in `ExecutableNetwork` is constructed as a base plugin 
 > configuration set via `Plugin::SetConfig`, where some values are overwritten with `config` passed to `Plugin::LoadExeNetworkImpl`. 
@@ -82,7 +82,7 @@ The function accepts a const shared pointer to `ngraph::Function` object and per
 1. Deep copies a const object to a local object, which can later be modified.
 2. Applies common and plugin-specific transformations on a copied graph to make the graph more friendly to hardware operations. For details how to write custom plugin-specific transformation, please, refer to [Writing ngraph transformations](@ref ngraph_transformation) guide. See detailed topics about network representation:
     * [Intermediate Representation and Operation Sets](../_docs_MO_DG_IR_and_opsets.html)
-    * [Quantized networks](@ref quantized_networks).
+    * [Quantized networks](@ref openvino_docs_ie_plugin_dg_quantized_networks).
 
 @snippet src/template_plugin.cpp plugin:transform_network
 
@@ -162,7 +162,7 @@ The snippet below provides an example of the implementation for `GetMetric`:
 ### `ImportNetwork()`
 
 The importing network mechanism allows to import a previously exported backend specific graph and wrap it 
-using an [ExecutableNetwork](@ref executable_network) object. This functionality is useful if 
+using an [ExecutableNetwork](@ref openvino_docs_ie_plugin_dg_executable_network) object. This functionality is useful if 
 backend specific graph compilation takes significant time and/or cannot be done on a target host 
 device due to other reasons.
 
@@ -187,4 +187,4 @@ Inference Engine plugin library must export only one function creating a plugin
 
 @snippet src/template_plugin.cpp plugin:create_plugin_engine
 
-Next step in a plugin library implementation is the [ExecutableNetwork](@ref executable_network) class.
+Next step in a plugin library implementation is the [ExecutableNetwork](@ref openvino_docs_ie_plugin_dg_executable_network) class.
diff --git a/docs/IE_PLUGIN_DG/PluginTesting.md b/docs/IE_PLUGIN_DG/PluginTesting.md
index e2aabeff32e..5723893c763 100644
--- a/docs/IE_PLUGIN_DG/PluginTesting.md
+++ b/docs/IE_PLUGIN_DG/PluginTesting.md
@@ -1,10 +1,10 @@
-# Plugin Testing {#plugin_testing}
+# Plugin Testing {#openvino_docs_ie_plugin_dg_plugin_testing}
 
 Inference Engine (IE) tests infrastructure provides a predefined set of functional tests and utilities. They are used to verify a plugin using the Inference Engine public API.
 All the tests are written in the [Google Test C++ framework](https://github.com/google/googletest).
 
 Inference Engine Plugin tests are included in the `IE::funcSharedTests` CMake target which is built within the OpenVINO repository
-(see [Build Plugin Using CMake](@ref plugin_build) guide). This library contains tests definitions (the tests bodies) which can be parametrized and instantiated in plugins depending on whether a plugin supports a particular feature, specific sets of parameters for test on supported operation set and so on.
+(see [Build Plugin Using CMake](@ref openvino_docs_ie_plugin_dg_plugin_build) guide). This library contains tests definitions (the tests bodies) which can be parametrized and instantiated in plugins depending on whether a plugin supports a particular feature, specific sets of parameters for test on supported operation set and so on.
 
 Test definitions are split into tests class declaration (see `inference_engine/tests/functional/plugin/shared/include`) and tests class implementation (see `inference_engine/tests/functional/plugin/shared/src`) and include the following scopes of plugin conformance tests:
 
@@ -39,7 +39,7 @@ To use these tests for your own plugin development, link the `IE::funcSharedTest
 > **NOTE**: A plugin may contain its own tests for use cases that are specific to hardware or need to be extensively tested.
 
 To build test binaries together with other build artifacts, use the `make all` command. For details, see
-[Build Plugin Using CMake*](@ref plugin_build).
+[Build Plugin Using CMake*](@ref openvino_docs_ie_plugin_dg_plugin_build).
 
 ### Tests for plugin-specific ngraph transformations
 
diff --git a/docs/IE_PLUGIN_DG/QuantizedNetworks.md b/docs/IE_PLUGIN_DG/QuantizedNetworks.md
index bbb5a508550..404895c6555 100644
--- a/docs/IE_PLUGIN_DG/QuantizedNetworks.md
+++ b/docs/IE_PLUGIN_DG/QuantizedNetworks.md
@@ -1,9 +1,9 @@
-# Quantized networks compute and restrictions {#quantized_networks}
+# Quantized networks compute and restrictions {#openvino_docs_ie_plugin_dg_quantized_networks}
 
 One of the feature of Inference Engine is the support of quantized networks with different precisions: INT8, INT4, etc.
 However, it is up to the plugin to define what exact precisions are supported by the particular HW.
 All quantized networks which can be expressed in IR have a unified representation by means of *FakeQuantize* operation. 
-For more details about low-precision model representation please refer to this [document](@ref lp_representation).
+For more details about low-precision model representation please refer to this [document](@ref openvino_docs_ie_plugin_dg_lp_representation).
 
 ### Interpreting FakeQuantize at runtime
 During the model load each plugin can interpret quantization rules expressed in *FakeQuantize* operations:
diff --git a/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md b/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
index d865434a578..6e2da56ad3a 100644
--- a/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
+++ b/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
@@ -1,5 +1,20 @@
 # Model Optimizer Developer Guide {#openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model
+   openvino_docs_MO_DG_Additional_Optimization_Use_Cases
+   openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer
+   openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ
+   openvino_docs_MO_DG_Known_Issues_Limitations
+   openvino_docs_MO_DG_Default_Model_Optimizer_Optimizations
+
+@endsphinxdirective
+
 ## Introduction 
 
 Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
@@ -10,7 +25,7 @@ Model Optimizer process assumes you have a network model trained using supported
 
 The scheme below illustrates the typical workflow for deploying a trained deep learning model: 
 
-![](img/workflow_steps.png)
+![](img/BASIC_FLOW_MO_simplified.svg)
 
 The IR is a pair of files describing the model: 
 
@@ -18,37 +33,649 @@ The IR is a pair of files describing the model:
 
 *  <code>.bin</code> - Contains the weights and biases binary data.
 
-Below is a simple command running Model Optimizer to generate an IR for the input model:
+> **TIP**: You also can work with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](https://docs.openvinotoolkit.org/latest/workbench_docs_Workbench_DG_Introduction.html) (DL Workbench).
+> [DL Workbench](https://docs.openvinotoolkit.org/latest/workbench_docs_Workbench_DG_Introduction.html) is a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models.
+
+## Install Model Optimizer Pre-Requisites
+
+Before running the Model Optimizer, you must install the Model Optimizer pre-requisites for the framework that was used to train the model.
+
+@sphinxdirective
+.. tab:: Using configuration scripts
+
+   .. tab:: Linux
+
+      .. tab:: All frameworks
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               ./install_prerequisites.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               ./install_prerequisites.shs
+
+      .. tab:: Caffe
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisitess
+               install_prerequisites_caffe.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_caffe.shs
+
+      .. tab:: Tensorflow 1.x
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_tf.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_tf.sh
+
+      .. tab:: Tensorflow 2.x
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_tf2.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_tf2.sh
+
+      .. tab:: MXNet
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_mxnet.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_mxnet.sh
+
+      .. tab:: ONNX
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_onnx.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_onnx.sh
+
+      .. tab:: Kaldi
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_kaldi.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_kaldi.sh
+
+   .. tab:: Windows
+
+      .. tab:: All frameworks
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites\
+               install_prerequisites.bat
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
+               virtualenv --system-site-packages -p python .\env
+               env\Scripts\activate.bat
+               install_prerequisites.bat
+
+      .. tab:: Caffe
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites\
+               install_prerequisites_caffe.bat
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
+               virtualenv --system-site-packages -p python .\env
+               env\Scripts\activate.bat
+               install_prerequisites_caffe.bat
+
+      .. tab:: Tensorflow 1.x
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites\
+               install_prerequisites_tf.bat
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
+               virtualenv --system-site-packages -p python .\env
+               env\Scripts\activate.bat
+               install_prerequisites_tf.bat
+
+      .. tab:: Tensorflow 2.x
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites\
+               install_prerequisites_tf2.bat
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
+               virtualenv --system-site-packages -p python .\env
+               env\Scripts\activate.bat
+               install_prerequisites_tf2.bat
+
+      .. tab:: MXNet
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites\
+               install_prerequisites_mxnet.bat
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
+               virtualenv --system-site-packages -p python .\env
+               env\Scripts\activate.bat
+               install_prerequisites_mxnet.bat
+
+      .. tab:: ONNX
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites\
+               install_prerequisites_onnx.bat
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
+               virtualenv --system-site-packages -p python .\env
+               env\Scripts\activate.bat
+               install_prerequisites_onnx.bat
+
+      .. tab:: Kaldi
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites\
+               install_prerequisites_kaldi.bat
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
+               virtualenv --system-site-packages -p python .\env
+               env\Scripts\activate.bat
+               install_prerequisites_kaldi.bat
+
+   .. tab:: macOS
+
+      .. tab:: All frameworks
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               ./install_prerequisites.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               ./install_prerequisites.shs
+
+      .. tab:: Caffe
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisitess
+               install_prerequisites_caffe.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_caffe.shs
+
+      .. tab:: Tensorflow 1.x
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_tf.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_tf.sh
+
+      .. tab:: Tensorflow 2.x
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_tf2.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_tf2.sh
+
+      .. tab:: MXNet
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_mxnet.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_mxnet.sh
+
+      .. tab:: ONNX
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_onnx.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_onnx.sh
+
+      .. tab:: Kaldi
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_kaldi.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+               
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               install_prerequisites_kaldi.sh
+
+.. tab:: Using manual configuration process
+
+   .. tab:: Linux
+
+      .. tab:: All frameworks
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements.txt
+
+      .. tab:: Caffe
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_caffe.txt
+
+      .. tab:: Tensorflow 1.x
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_tf.txt
+
+      .. tab:: Tensorflow 2.x
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_tf2.txt
+
+      .. tab:: MXNet
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_mxnet.txt
+
+      .. tab:: ONNX
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_onnx.txt
+
+      .. tab:: Kaldi
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_kaldi.txt
+
+   .. tab:: Windows
+
+      .. tab:: All frameworks
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>\deployment_tools\model_optimizer
+            virtualenv --system-site-packages -p python .\env
+            env\Scripts\activate.bat
+            pip install -r requirements.txt
+
+      .. tab:: Caffe
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>\deployment_tools\model_optimizer
+            virtualenv --system-site-packages -p python .\env
+            env\Scripts\activate.bat
+            pip install -r requirements_caffe.txt
+
+      .. tab:: Tensorflow 1.x
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>\deployment_tools\model_optimizer
+            virtualenv --system-site-packages -p python .\env
+            env\Scripts\activate.bat
+            pip install -r requirements_tf.txt
+
+      .. tab:: Tensorflow 2.x
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>\deployment_tools\model_optimizer
+            virtualenv --system-site-packages -p python .\env
+            env\Scripts\activate.bat
+            pip install -r requirements_tf2.txt
+
+      .. tab:: MXNet
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>\deployment_tools\model_optimizer
+            virtualenv --system-site-packages -p python .\env
+            env\Scripts\activate.bat
+            pip install -r requirements_mxnet.txt
+
+      .. tab:: ONNX
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>\deployment_tools\model_optimizer
+            virtualenv --system-site-packages -p python .\env
+            env\Scripts\activate.bat
+            pip install -r requirements_onnx.txt
+
+      .. tab:: Kaldi
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>\deployment_tools\model_optimizer
+            virtualenv --system-site-packages -p python .\env
+            env\Scripts\activate.bat
+            pip install -r requirements_kaldi.txt
+
+   .. tab:: macOS
+
+      .. tab:: All frameworks
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements.txt
+
+      .. tab:: Caffe
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_caffe.txt
+
+      .. tab:: Tensorflow 1.x
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_tf.txt
+
+      .. tab:: Tensorflow 2.x
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_tf2.txt
+
+      .. tab:: MXNet
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_mxnet.txt
+
+      .. tab:: ONNX
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_onnx.txt
+
+      .. tab:: Kaldi
+      
+         .. code-block:: sh
+               
+            cd <INSTALL_DIR>/deployment_tools/model_optimizer/
+            virtualenv --system-site-packages -p python3 ./venv
+            source ./venv/bin/activate
+            pip3 install -r requirements_kaldi.txt
+
+@endsphinxdirective
+
+## Run Model Optimizer
+
+To convert the model to the Intermediate Representation (IR), run Model Optimizer:
 
 ```sh
- mo --input_model INPUT_MODEL
+mo --input_model INPUT_MODEL --output_dir <OUTPUT_MODEL_DIR>
 ```
-To learn about all Model Optimizer parameters and conversion technics, see the [Converting a Model to IR](prepare_model/convert_model/Converting_Model.md) page.
 
-> **TIP**: You can quick start with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref 
-> openvino_docs_get_started_get_started_dl_workbench) (DL Workbench).
-> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI that enables you to
-> import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for 
-> deployment on various Intel® platforms.
+You need to have have write permissions for an output directory.
+
+> **NOTE**: Some models require using additional arguments to specify conversion parameters, such as `--input_shape`, `--scale`, `--scale_values`, `--mean_values`, `--mean_file`. To learn about when you need to use these parameters, refer to [Converting a Model to Intermediate Representation (IR)](Converting_Model.md).
+
+To adjust the conversion process, you may use general parameters defined in the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) and 
+framework-specific parameters for:
+* [Caffe](Convert_Model_From_Caffe.md)
+* [TensorFlow](Convert_Model_From_TensorFlow.md)
+* [MXNet](Convert_Model_From_MxNet.md)
+* [ONNX](Convert_Model_From_ONNX.md)
+* [Kaldi](Convert_Model_From_Kaldi.md)
 
 ## Videos
 
-<table>
-  <tr>
-    <td>
-<iframe width="220" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-</td>
-<td>
-    <iframe width="220" src="https://www.youtube.com/embed/BBt1rseDcy0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-</td>
-<td>
-<iframe width="220" src="https://www.youtube.com/embed/RF8ypHyiKrY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-</td>
-  </tr>
-  <tr>
-    <td><strong>Model Optimizer Concept</strong>. <br>Duration: 3:56</td>
-    <td><strong>Model Optimizer Basic<br> Operation</strong>. <br>Duration: 2:57.</td>
-    <td><strong>Choosing the Right Precision</strong>. <br>Duration: 4:18.</td>
-  </tr>
-</table>
+@sphinxdirective
 
+.. list-table::
+
+   * - .. raw:: html
+
+           <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="220"
+           src="https://www.youtube.com/embed/Kl1ptVb7aI8">
+           </iframe>
+    
+     - .. raw:: html
+
+           <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="220"
+           src="https://www.youtube.com/embed/BBt1rseDcy0">
+           </iframe>
+
+     - .. raw:: html
+
+           <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="220"
+           src="https://www.youtube.com/embed/RF8ypHyiKrY">
+           </iframe>
+
+   * - **Model Optimizer Concept.**
+     - **Model Optimizer Basic Operation.**
+     - **Choosing the Right Precision.**
+
+   * - Duration: 3:56
+     - Duration: 2:57
+     - Duration: 4:18
+
+@endsphinxdirective
diff --git a/docs/MO_DG/img/BASIC_FLOW_MO_simplified.svg b/docs/MO_DG/img/BASIC_FLOW_MO_simplified.svg
new file mode 100644
index 00000000000..49ccbae9100
--- /dev/null
+++ b/docs/MO_DG/img/BASIC_FLOW_MO_simplified.svg
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diff --git a/docs/MO_DG/img/workflow_steps.png b/docs/MO_DG/img/workflow_steps.png
index fee04b7cb33..0fd0dfcee99 100644
--- a/docs/MO_DG/img/workflow_steps.png
+++ b/docs/MO_DG/img/workflow_steps.png
@@ -1,3 +1,3 @@
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-oid sha256:5281f26cbaa468dc4cafa4ce2fde35d338fe0f658bbb796abaaf793e951939f6
-size 13943
+oid sha256:b89ec8a8231a4fa2c43ce8642d7a22d8f183497f5c99f084c4d1a92edb81df37
+size 14332
diff --git a/docs/MO_DG/prepare_model/Additional_Optimizations.md b/docs/MO_DG/prepare_model/Additional_Optimizations.md
new file mode 100644
index 00000000000..d9b02270203
--- /dev/null
+++ b/docs/MO_DG/prepare_model/Additional_Optimizations.md
@@ -0,0 +1,15 @@
+# Optimize Preprocessing Computation{#openvino_docs_MO_DG_Additional_Optimization_Use_Cases}
+
+Model Optimizer performs preprocessing to a model. It is possible to optimize this step and improve first inference time, to do that, follow the tips bellow:
+
+-	**Image mean/scale parameters**<br>
+	Make sure to use the input image mean/scale parameters (`--scale` and `–mean_values`) with the Model Optimizer when you need pre-processing. It allows the tool to bake the pre-processing into the IR to get accelerated by the Inference Engine.
+
+-	**RGB vs. BGR inputs**<br>
+	If, for example, your network assumes the RGB inputs, the Model Optimizer can swap the channels in the first convolution using the `--reverse_input_channels` command line option, so you do not need to convert your inputs to RGB every time you get the BGR image, for example, from OpenCV*.
+
+-	**Larger batch size**<br>
+	Notice that the devices like GPU are doing better with larger batch size. While it is possible to set the batch size in the runtime using the Inference Engine [ShapeInference feature](../IE_DG/ShapeInference.md).
+
+-	**Resulting IR precision**<br>
+The resulting IR precision, for instance, `FP16` or `FP32`, directly affects performance. As CPU now supports `FP16` (while internally upscaling to `FP32` anyway) and because this is the best precision for a GPU target, you may want to always convert models to `FP16`. Notice that this is the only precision that Intel&reg; Movidius&trade; Myriad&trade; 2 and Intel&reg; Myriad&trade; X VPUs support.
diff --git a/docs/MO_DG/prepare_model/Config_Model_Optimizer.md b/docs/MO_DG/prepare_model/Config_Model_Optimizer.md
index 186b8ddabd5..f2be1fee3cc 100644
--- a/docs/MO_DG/prepare_model/Config_Model_Optimizer.md
+++ b/docs/MO_DG/prepare_model/Config_Model_Optimizer.md
@@ -1,5 +1,334 @@
 # Installing Model Optimizer Pre-Requisites {#openvino_docs_MO_DG_prepare_model_Config_Model_Optimizer}
 
+@sphinxdirective
+
+.. tab:: Using configuration scripts
+
+   .. tab:: Linux
+
+      .. tab:: All frameworks
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               ./install_prerequisites.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               virtualenv --system-site-packages -p python3 ./venv
+               source ./venv/bin/activate  # sh, bash, ksh, or zsh
+               ./install_prerequisites.shs
+
+      .. tab:: Caffe
+      
+         .. tab:: Install globally
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisitess
+               install_prerequisites_caffe.sh
+         
+         .. tab:: Install to virtualenv
+
+            .. code-block:: sh
+
+               cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
+               install_prerequisites_caffe.shs
+
+      .. tab:: Tensorflow 1.x
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Tensorflow 2.x
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: MXNet
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: ONNX
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Kaldi
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+   .. tab:: Windows
+
+      .. tab:: All frameworks
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Caffe
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Tensorflow 1.x
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Tensorflow 2.x
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            tests
+
+      .. tab:: MXNet
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: ONNX
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Kaldi
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+   .. tab:: macOS
+
+      .. tab:: All frameworks
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Caffe
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Tensorflow 1.x
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Tensorflow 2.x
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            tests
+
+      .. tab:: MXNet
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: ONNX
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+      .. tab:: Kaldi
+      
+         .. tab:: Install globally
+
+            test
+         
+         .. tab:: Install to virtualenv
+
+            test
+
+.. tab:: Using manual configuration process
+
+   .. tab:: Linux
+
+      .. tab:: All frameworks
+      
+         test
+
+      .. tab:: Caffe
+      
+         test
+
+      .. tab:: Tensorflow 1.x
+      
+         test
+
+      .. tab:: Tensorflow 2.x
+      
+         test
+
+      .. tab:: MXNet
+      
+         test
+
+      .. tab:: ONNX
+      
+         test
+
+      .. tab:: Kaldi
+      
+         test
+
+
+   .. tab:: Windows
+
+      .. tab:: All frameworks
+      
+         test
+
+      .. tab:: Caffe
+      
+         test
+
+      .. tab:: Tensorflow 1.x
+      
+         test
+
+      .. tab:: Tensorflow 2.x
+      
+         test
+
+      .. tab:: MXNet
+      
+         test
+
+      .. tab:: ONNX
+      
+         test
+
+      .. tab:: Kaldi
+      
+         test
+
+   .. tab:: macOS
+
+      .. tab:: All frameworks
+      
+         test
+
+      .. tab:: Caffe
+      
+         test
+
+      .. tab:: Tensorflow 1.x
+      
+         test
+
+      .. tab:: Tensorflow 2.x
+      
+         test
+
+      .. tab:: MXNet
+      
+         test
+
+      .. tab:: ONNX
+      
+         test
+
+      .. tab:: Kaldi
+      
+         test
+
+@endsphinxdirective
+
 Before running the Model Optimizer, you must install the Model Optimizer pre-requisites for the framework that was used to train the model. This section tells you how to install the pre-requisites either through scripts or by using a manual process.
 
 ## Using Configuration Scripts
@@ -152,8 +481,18 @@ pip3 install -r requirements_onnx.txt
 ```
 
 ## Using the protobuf Library in the Model Optimizer for Caffe\*
-<details>
-    <summary>Click to expand</summary>
+
+
+
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="Click to expand">
+
+@endsphinxdirective
 
 
 
@@ -264,7 +603,15 @@ python3 -m easy_install dist/protobuf-3.6.1-py3.6-win-amd64.egg
 set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
 ```
 
-</details>
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## See Also
 
diff --git a/docs/MO_DG/prepare_model/Default_Model_Optimizer_Optimizations.md b/docs/MO_DG/prepare_model/Default_Model_Optimizer_Optimizations.md
new file mode 100644
index 00000000000..04156027a1e
--- /dev/null
+++ b/docs/MO_DG/prepare_model/Default_Model_Optimizer_Optimizations.md
@@ -0,0 +1,11 @@
+# Default Model Optimizer Optimizations {#openvino_docs_MO_DG_Default_Model_Optimizer_Optimizations}
+
+Model Optimizer not only converts a model to IR format, but also performs a number of optimizations. For example, certain primitives like linear operations (BatchNorm and ScaleShift), are automatically fused into convolutions. Generally, these layers should not be manifested in the resulting IR:
+
+![](../img/optimizations/resnet_269.png)
+
+The picture above shows Caffe\* Resnet269\* topology. The left model is the original model, and the one on the right (after conversion) is the resulting model that the Model Optimizer produces, with BatchNorm and ScaleShift layers  fused into the convolution weights rather than constituting separate layers.
+
+If you still see these operations, inspect the Model Optimizer output carefully while searching for warnings, such as on the tool being unable to fuse. For example, non-linear operations (like activations) in between convolutions and linear operations might prevent the fusing. If performance is of concern, try to change (and potentially re-train) the topology. Refer to the [Model Optimizer Guide](../MO_DG/prepare_model/Model_Optimization_Techniques.md) for more optimizations.
+
+Notice that the activation (`_relu`) is not touched by the Model Optimizer, and while it can be merged into convolution as well, this is rather a device-specific optimization, covered by Inference Engine during the model loading time. You are encouraged to inspect performance counters from plugins that should indicate that these particular layers are not executed (“Optimized out”). For more information, refer to <a href="#performance-counters">Internal Inference Performance Counters</a>.
diff --git a/docs/MO_DG/prepare_model/Getting_performance_numbers.md b/docs/MO_DG/prepare_model/Getting_performance_numbers.md
new file mode 100644
index 00000000000..570715b4fe6
--- /dev/null
+++ b/docs/MO_DG/prepare_model/Getting_performance_numbers.md
@@ -0,0 +1,133 @@
+# Getting Performance Numbers {#openvino_docs_MO_DG_Getting_Performance_Numbers}
+
+
+## Tip 1. Measure the Proper Set of Operations 
+
+When evaluating performance of your model with the Inference Engine, you must measure the proper set of operations. To do so, consider the following tips: 
+
+- Avoid including one-time costs like model loading.
+
+- Track separately the operations that happen outside the Inference Engine, like video decoding. 
+
+> **NOTE**: Some image pre-processing can be baked into the IR and accelerated. For more information, refer to [Model Optimizer Knobs Related to Performance](Additional_Optimizations.md)
+
+## Tip 2. Getting Credible Performance Numbers 
+
+You need to build your performance conclusions on reproducible data. Do the performance measurements with a large number of invocations of the same routine. Since the first iteration is almost always significantly slower than the subsequent ones, you can use an aggregated value for the execution time for final projections:
+
+-	If the warm-up run does not help or execution time still varies, you can try running a large number of iterations and then average or find a mean of the results.
+-	 For time values that range too much, use geomean.
+
+Refer to the [Inference Engine Samples](../IE_DG/Samples_Overview.md) for code examples for the performance measurements. Almost every sample, except interactive demos, has a `-ni` option to specify the number of iterations.
+
+## Getting performance numbers using OpenVINO tool 
+
+To get performance numbers use our Benchmark app.  
+
+[Benchmark App](../../inference-engine/samples/benchmark_app/README.md) sample is the best performance reference.
+It has a lot of device-specific knobs, but the primary usage is as simple as: 
+```bash
+$ ./benchmark_app –d GPU –m <model> -i <input>
+```
+to measure the performance of the model on the GPU. 
+Or
+```bash
+$ ./benchmark_app –d CPU –m <model> -i <input>
+```
+to execute on the CPU instead.
+
+For example, for the CPU throughput mode from the previous section, you can play with number of streams (`-nstreams` command-line param). 
+Try different values of the `-nstreams` argument from `1` to a number of CPU cores and find one that provides the best performance. For example, on a 8-core CPU, compare the `-nstreams 1` (which is a latency-oriented scenario) to the `2`, `4` and `8` streams. Notice that `benchmark_app` automatically queries/creates/runs number of requests required to saturate the given number of streams. 
+
+Finally, notice that when you don't specify number of streams with `-nstreams`, "AUTO" value for the streams is used, e.g. for the CPU this is [CPU_THROUGHPUT_AUTO](supported_plugins/CPU.md). You can spot the actual value behind "AUTO" for your machine in the application output.
+Notice that the "AUTO" number is not necessarily most optimal, so it is generally recommended to play either with the benchmark_app's "-nstreams" as described above, or via  [new Workbench tool](@ref workbench_docs_Workbench_DG_Introduction).This allows you to simplify the app-logic, as you don't need to combine multiple inputs into a batch to achieve good CPU performance.
+Instead, it is possible to keep a separate infer request per camera or another source of input and process the requests in parallel using Async API.
+
+## Comparing Performance with Native/Framework Code 
+
+When comparing the Inference Engine performance with the framework or another reference code, make sure that both versions are as similar as possible:
+
+-	Wrap exactly the inference execution (refer to the [Inference Engine Samples](../IE_DG/Samples_Overview.md) for examples).
+-	Do not include model loading time.
+-	Ensure the inputs are identical for the Inference Engine and the framework. For example, Caffe\* allows to auto-populate the input with random values. Notice that it might give different performance than on real images.
+-	Similarly, for correct performance comparison, make sure the access pattern, for example, input layouts, is optimal for Inference Engine (currently, it is NCHW).
+-	Any user-side pre-processing should be tracked separately.
+-	Make sure to try the same environment settings that the framework developers recommend, for example, for TensorFlow*. In many cases, things that are more machine friendly, like respecting NUMA (see <a href="#cpu-checklist">CPU Checklist</a>), might work well for the Inference Engine as well.
+-	If applicable, use batching with the Inference Engine.
+-	If possible, demand the same accuracy. For example, TensorFlow allows `FP16` support, so when comparing to that, make sure to test the Inference Engine with the `FP16` as well.
+
+## Using Tools <a name="using-tools"></a>
+
+Whether you are tuning for the first time or doing advanced performance optimization, you need a a tool that provides accurate insights. Intel&reg; VTune&trade; Amplifier gives you the tool to mine it and interpret the profiling data.
+
+Alternatively, you can gather the raw profiling data that samples report, the second chapter provides example of how to interpret these.
+
+### Internal Inference Performance Counters <a name="performance-counters"></a>
+
+Almost every sample (inspect command-line options for a specific sample with `-h`) supports a `-pc` command that outputs internal execution breakdown. Refer to the [samples code](../IE_DG/Samples_Overview.md) for the actual Inference Engine API behind that.
+
+Below is example of CPU plugin output for a network (since the device is CPU, the layers wall clock `realTime` and the `cpu` time are the same):
+
+```
+conv1      EXECUTED       layerType: Convolution        realTime: 706        cpu: 706            execType: jit_avx2
+conv2_1_x1  EXECUTED       layerType: Convolution        realTime: 137        cpu: 137            execType: jit_avx2_1x1
+fc6        EXECUTED       layerType: Convolution        realTime: 233        cpu: 233            execType: jit_avx2_1x1
+fc6_nChw8c_nchw      EXECUTED  layerType: Reorder           realTime: 20         cpu: 20             execType: reorder
+out_fc6         EXECUTED       layerType: Output            realTime: 3          cpu: 3              execType: unknown
+relu5_9_x2    OPTIMIZED_OUT     layerType: ReLU             realTime: 0          cpu: 0              execType: undef
+```
+
+This contains layers name (as seen in IR), layers type and execution statistics. Notice the `OPTIMIZED_OUT`, which indicates that the particular activation was fused into adjacent convolution. Also, the `unknown` stays for the Inference Engine specific CPU (helper) primitives that are not part of the Intel MKL-DNN.
+
+Notice that there are some helper layers in the CPU execution breakdown, which were not presented in the original topology. These are automatically added by the plugin. For example, the `Reorder` re-packs the Intel MKL-DNN internal (blocked) layout to the regular plain NCHW (that the user expects as the output). As explained in the <a href="#device-specific-tips">Few Device-Specific Tips</a>, if your custom kernels introduces a lot of outstanding/expensive Reorders, consider blocked implementation for the kernels.
+
+Notice that in the heterogeneous cases, there will be additional information on which subgraph the statistics is about (the first subgraph is GPU, so its `cpu`/host time is really small compared to the actual `realTime`):
+
+```
+subgraph1: squeeze1x1   	  EXECUTED       layerType: Convolution        realTime: 227    cpu:3    execType: GPU
+…
+subgraph2: detection_out      EXECUTED       layerType: DetectionOutput    realTime: 121 cpu:121  execType: unknown
+…
+```
+
+As mentioned earlier, `unknown` here means CPU kernel with unknown (for example, not AVX2 or AVX512) acceleration path.
+Since FPGA execution does not separate individual kernels, only bulk execution/data transfer statistics is available:
+
+```
+subgraph1: 1. input preprocessing (mean data/FPGA):EXECUTED   layerType: preprocessing   realTime: 129     cpu: 129
+subgraph1: 2. input transfer to DDR:EXECUTED       layerType:                    realTime: 201        cpu: 0              
+subgraph1: 3. FPGA execute time:EXECUTED           layerType:                    realTime: 3808       cpu: 0              subgraph1: 4. output transfer from DDR:EXECUTED    layerType:                    realTime: 55         cpu: 0              
+subgraph1: 5. FPGA output postprocessing:EXECUTED  layerType:                    realTime: 7          cpu: 7              
+subgraph1: 6. softmax/copy:   EXECUTED       layerType:                    realTime: 2          cpu: 2              
+subgraph2: out_prob:          NOT_RUN        layerType: Output             realTime: 0          cpu: 0              
+subgraph2: prob:              EXECUTED       layerType: SoftMax            realTime: 10         cpu: 10             
+Total time: 4212     microseconds
+```
+
+The `softmax/copy` is a glue layer that connects the FPGA subgraph to the CPU subgraph (and copies the data).
+
+### Intel&reg; VTune&trade; Examples <a name="vtune-examples"></a>
+
+All major performance calls of the Inference Engine are instrumented with Instrumentation and Tracing Technology APIs. This allows viewing the Inference Engine calls on the Intel&reg; VTune&trade; timelines and aggregations plus correlating them to the underlying APIs, like OpenCL.  In turn, this enables careful per-layer execution breakdown.
+
+When choosing the Analysis type in Intel&reg; VTune&trade; Amplifier, make sure to select the **Analyze user tasks, events, and counters** option:
+
+![](vtune_option.png)
+
+See the [corresponding section in the Intel® VTune™ Amplifier User's Guide](https://software.intel.com/en-us/vtune-amplifier-help-task-analysis) for details.
+
+Example of Inference Engine calls:
+
+-	On the Intel VTune Amplifier timeline.
+	Notice that `Task_runNOThrow` is an Async API wrapper and it is executed in a different thread and triggers the Intel MKL-DNN execution:
+
+	![](vtune_timeline.png)
+	
+-	In the Intel VTune Amplifier **Top-down view**, grouped by the **Task Domain**.
+	Notice the `Task_runNoThrow` and `MKLDNN _INFER` that are bracketing the actual Intel MKL-DNN kernels execution:
+	
+	![](vtune_topdown_view.jpg)
+	
+Similarly, you can use any GPU analysis in the Intel VTune Amplifier and get general correlation with Inference Engine API as well as the execution breakdown for OpenCL kernels.
+
+Just like with regular native application, further drill down in the counters is possible, however, this is mostly useful for <a href="#optimizing-custom-kernels">optimizing custom kernels</a>. Finally, with the Intel VTune Amplifier, the profiling is not limited to your user-level code (see the [corresponding section in the Intel&reg; VTune&trade; Amplifier User's Guide](https://software.intel.com/en-us/vtune-amplifier-help-analyze-performance)).
diff --git a/docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md b/docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md
index 277b612256e..4f577817cdb 100644
--- a/docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md
+++ b/docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md
@@ -616,19 +616,6 @@ You need to specify values for each input of the model. For more information, re
 
 It means that you trying to convert the topology which contains '_contrib_box_nms' operation which is not supported directly. However the sub-graph of operations including the '_contrib_box_nms' could be replaced with DetectionOutput layer if your topology is one of the gluoncv topologies. Specify '--enable_ssd_gluoncv' command line parameter for the Model Optimizer to enable this transformation.
 
-\htmlonly
-
-<script>
-  window.addEventListener('load', function(){
-    var questionID = getURLParameter('question'); /* this function is defined in openvino-layout.js */
-    if (questionID) {
-      window.location = window.location.pathname + '#' + encodeURI(questionID);
-    }
-  });
-</script>
-
-\endhtmlonly
-
 #### 103. What does the message "ModelOptimizer is not able to parse *.caffemodel" mean? <a name="question-103"></a>
 
 If a '*.caffemodel' file exists and it is correct, the error possibly occured due to the use of Python protobuf implementation. In some cases, it shows error message during model parsing, for example: "'utf-8' codec can't decode byte 0xe0 in position 4: invalid continuation byte in field: mo_caffe.SpatialTransformerParameter.transform_type". You can either use Python 3.6/3.7 or build 'cpp' implementation of protobuf yourself for your version of Python. For the complete instructions about building `protobuf` from sources, see the appropriate section in [Converting a Model to Intermediate Representation](Config_Model_Optimizer.md).
diff --git a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md
index 7e7cbb38305..bca2ca4ac42 100644
--- a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md
+++ b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md
@@ -32,22 +32,20 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 * **Semantic segmentation models:**
 	* FCN8
 
-> **NOTE:** It is necessary to specify mean and scale values for most of the Caffe\* models to convert them with the Model Optimizer. The exact values should be determined separately for each model. For example, for Caffe\* models trained on ImageNet, the mean values usually are `123.68`, `116.779`, `103.939` for blue, green and red channels respectively. The scale value is usually `127.5`. Refer to [Framework-agnostic parameters](Converting_Model_General.md) for the information on how to specify mean and scale values.
+> **NOTE**: It is necessary to specify mean and scale values for most of the Caffe\* models to convert them with the Model Optimizer. The exact values should be determined separately for each model. For example, for Caffe\* models trained on ImageNet, the mean values usually are `123.68`, `116.779`, `103.939` for blue, green and red channels respectively. The scale value is usually `127.5`. Refer to the General Conversion Parameters section in [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) for the information on how to specify mean and scale values.
 
 ## Convert a Caffe* Model <a name="Convert_From_Caffe"></a>
 
-To convert a Caffe\* model:
+To convert a Caffe\* model, run Model Optimizer with the path to the input model `.caffemodel` file and the path to an output directory with write permissions:
 
-1. Go to the `$INTEL_OPENVINO_DIR/tools/model_optimizer` directory.
-2. Use the `mo` script to simply convert a model, specifying the path to the input model `.caffemodel` file and the path to an output directory with write permissions:
 ```sh
  mo --input_model <INPUT_MODEL>.caffemodel --output_dir <OUTPUT_MODEL_DIR>
 ```
 
 Two groups of parameters are available to convert your model:
 
-* [Framework-agnostic parameters](Converting_Model_General.md): These parameters are used to convert a model trained with any supported framework.
-* [Caffe-specific parameters](#caffe_specific_conversion_params): Parameters used to convert only Caffe\* models
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+* [Caffe-specific parameters](#caffe_specific_conversion_params) are used to convert only Caffe\* models.
 
 ### Using Caffe\*-Specific Conversion Parameters <a name="caffe_specific_conversion_params"></a>
 
@@ -92,41 +90,38 @@ Caffe*-specific parameters:
 #### Command-Line Interface (CLI) Examples Using Caffe\*-Specific Parameters
 
 * Launching the Model Optimizer for the [bvlc_alexnet.caffemodel](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) with a specified `prototxt` file. This is needed when the name of the Caffe\* model and the `.prototxt` file are different or are placed in different directories. Otherwise, it is enough to provide only the path to the input `model.caffemodel` file. You must have write permissions for the output directory.
-
-```sh
- mo --input_model bvlc_alexnet.caffemodel --input_proto bvlc_alexnet.prototxt --output_dir <OUTPUT_MODEL_DIR>
-```
-
+   ```sh    
+   mo --input_model bvlc_alexnet.caffemodel --input_proto bvlc_alexnet.prototxt --output_dir <OUTPUT_MODEL_DIR>
+   ```
 * Launching the Model Optimizer for the [bvlc_alexnet.caffemodel](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) with a specified `CustomLayersMapping` file. This is the legacy method of quickly enabling model conversion if your model has custom layers. This requires the Caffe\* system on the computer. To read more about this, see [Legacy Mode for Caffe* Custom Layers](../customize_model_optimizer/Legacy_Mode_for_Caffe_Custom_Layers.md).
 Optional parameters without default values and not specified by the user in the `.prototxt` file are removed from the Intermediate Representation, and nested parameters are flattened:
-```sh
- mo --input_model bvlc_alexnet.caffemodel -k CustomLayersMapping.xml --disable_omitting_optional --enable_flattening_nested_params --output_dir <OUTPUT_MODEL_DIR>
-```
-		This example shows a multi-input model with input layers: `data`, `rois`
-```
-layer {
-  name: "data"
-  type: "Input"
-  top: "data"
-  input_param {
-    shape { dim: 1 dim: 3 dim: 224 dim: 224 }
-  }
-}
-layer {
-  name: "rois"
-  type: "Input"
-  top: "rois"
-  input_param {
-    shape { dim: 1 dim: 5 dim: 1 dim: 1 }
-  }
-}
-```
+   ```sh
+   mo --input_model bvlc_alexnet.caffemodel -k CustomLayersMapping.xml --disable_omitting_optional --enable_flattening_nested_params --output_dir <OUTPUT_MODEL_DIR>
+   ```
+   This example shows a multi-input model with input layers: `data`, `rois`
+   ```
+   layer {
+     name: "data"
+     type: "Input"
+     top: "data"
+     input_param {
+       shape { dim: 1 dim: 3 dim: 224 dim: 224 }
+     }
+   }
+   layer {
+     name: "rois"
+     type: "Input"
+     top: "rois"
+     input_param {
+       shape { dim: 1 dim: 5 dim: 1 dim: 1 }
+     }
+   }
+   ```
 
 * Launching the Model Optimizer for a multi-input model with two inputs and providing a new shape for each input in the order they are passed to the Model Optimizer along with a writable output directory. In particular, for data, set the shape to `1,3,227,227`. For rois, set the shape to `1,6,1,1`:
-```sh
- mo --input_model /path-to/your-model.caffemodel --input data,rois --input_shape (1,3,227,227),[1,6,1,1] --output_dir <OUTPUT_MODEL_DIR>
-```
-
+   ```sh
+   mo --input_model /path-to/your-model.caffemodel --input data,rois --input_shape (1,3,227,227),[1,6,1,1] --output_dir <OUTPUT_MODEL_DIR>
+   ```
 ## Custom Layer Definition
 
 Internally, when you run the Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list of known layers. If your topology contains any layers that are not in this list of known layers, the Model Optimizer classifies them as custom.
diff --git a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md
index fe20450f12a..02ea0d232b5 100644
--- a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md
+++ b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md
@@ -1,5 +1,15 @@
 # Converting a Kaldi* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model
+
+@endsphinxdirective
+
 A summary of the steps for optimizing and deploying a model that was trained with Kaldi\*:
 
 1. [Configure the Model Optimizer](../Config_Model_Optimizer.md) for Kaldi\*.
@@ -7,7 +17,7 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 3. Test the model in the Intermediate Representation format using the [Inference Engine](../../../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided Inference Engine [sample applications](../../../IE_DG/Samples_Overview.md).
 4. [Integrate](../../../IE_DG/Samples_Overview.md) the [Inference Engine](../../../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) in your application to deploy the model in the target environment.
 
-> **NOTE:** The Model Optimizer supports the [nnet1](http://kaldi-asr.org/doc/dnn1.html) and [nnet2](http://kaldi-asr.org/doc/dnn2.html) formats of Kaldi models. Support of the [nnet3](http://kaldi-asr.org/doc/dnn3.html) format is limited.
+> **NOTE**: The Model Optimizer supports the [nnet1](http://kaldi-asr.org/doc/dnn1.html) and [nnet2](http://kaldi-asr.org/doc/dnn2.html) formats of Kaldi models. Support of the [nnet3](http://kaldi-asr.org/doc/dnn3.html) format is limited.
 
 ## Supported Topologies
 * Convolutional Neural Networks (CNN):
@@ -31,18 +41,15 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 ## Convert a Kaldi* Model <a name="Convert_From_Kaldi"></a>
 
-To convert a Kaldi\* model:
-
-1. Go to the `<INSTALL_DIR>/tools/model_optimizer` directory.
-2. Use the `mo` script to simply convert a model with the path to the input model `.nnet` or `.mdl` file and to an output directory where you have write permissions:
+To convert a Kaldi\* model, run Model Optimizer with the path to the input model `.nnet` or `.mdl` file and to an output directory where you have write permissions:
 ```sh
- mo --input_model <INPUT_MODEL>.nnet --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model <INPUT_MODEL>.nnet --output_dir <OUTPUT_MODEL_DIR>
 ```
 
 Two groups of parameters are available to convert your model:
 
-* [Framework-agnostic parameters](Converting_Model_General.md): These parameters are used to convert any model trained in any supported framework.
-* [Kaldi-specific parameters](#kaldi_specific_conversion_params): Parameters used to convert only Kaldi\* models.
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+* [Kaldi-specific parameters](#kaldi_specific_conversion_params) are used to convert only Kaldi\* models.
 
 ### Using Kaldi\*-Specific Conversion Parameters <a name="kaldi_specific_conversion_params"></a>
 
@@ -59,14 +66,15 @@ Kaldi-specific parameters:
 ### Examples of CLI Commands
 
 * To launch the Model Optimizer for the wsj_dnn5b_smbr model with the specified `.nnet` file and an output directory where you have write permissions:
-```sh
- mo --input_model wsj_dnn5b_smbr.nnet --output_dir <OUTPUT_MODEL_DIR>
-```
+   ```sh
+   mo --input_model wsj_dnn5b_smbr.nnet --output_dir <OUTPUT_MODEL_DIR>
+   ```
 
 * To launch the Model Optimizer for the wsj_dnn5b_smbr model with existing file that contains counts for the last layer with biases and a writable output directory:
-```sh
- mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --output_dir <OUTPUT_MODEL_DIR>_
-```
+   ```sh
+   mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --output_dir <OUTPUT_MODEL_DIR>
+   ```
+
   * The Model Optimizer normalizes сounts in the following way:
 	\f[
 	S = \frac{1}{\sum_{j = 0}^{|C|}C_{j}}
@@ -81,15 +89,16 @@ Kaldi-specific parameters:
   > **NOTE:** Model Optimizer will show warning if model contains counts values inside model and `--counts` option is not used.
 
 * If you want to remove the last SoftMax layer in the topology, launch the Model Optimizer with the
-`--remove_output_softmax` flag.
-```sh
- mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --remove_output_softmax --output_dir <OUTPUT_MODEL_DIR>_
-```
+`--remove_output_softmax` flag:
+   ```sh
+   mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --remove_output_softmax --output_dir <OUTPUT_MODEL_DIR>_
+   ```
+
 The Model Optimizer finds the last layer of the topology and removes this layer only if it is a SoftMax layer.
 
-  > **NOTE:** Model Optimizer can remove SoftMax layer only if the topology has one output.
+  > **NOTE**: Model Optimizer can remove SoftMax layer only if the topology has one output.
  
-  > **NOTE:** For sample inference of Kaldi models, you can use the Inference Engine Speech Recognition sample application. The sample supports models with one output. If your model has several outputs, specify the desired one with the `--output` option.    
+  > **NOTE**: For sample inference of Kaldi models, you can use the Inference Engine Speech Recognition sample application. The sample supports models with one output. If your model has several outputs, specify the desired one with the `--output` option.    
   
  If you want to convert a model for inference on Intel® Movidius™ Myriad™, use the `--remove_memory` option. 
 It removes Memory layers from the IR. Instead of it, additional inputs and outputs appear in the IR. 
diff --git a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md
index 763861c17d8..a399bd93008 100644
--- a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md
+++ b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md
@@ -1,5 +1,16 @@
 # Converting an MXNet* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet
+   openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models
+
+@endsphinxdirective
+
 A summary of the steps for optimizing and deploying a model that was trained with the MXNet\* framework:
 
 1. [Configure the Model Optimizer](../Config_Model_Optimizer.md) for MXNet* (MXNet was used to train your model)
@@ -9,7 +20,7 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 ## Supported Topologies
 
-> **NOTE:** SSD models from the table require converting to the deploy mode. For details, see the [Conversion Instructions](https://github.com/zhreshold/mxnet-ssd/#convert-model-to-deploy-mode) in the GitHub MXNet-SSD repository.
+> **NOTE**: SSD models from the table require converting to the deploy mode. For details, see the [Conversion Instructions](https://github.com/zhreshold/mxnet-ssd/#convert-model-to-deploy-mode) in the GitHub MXNet-SSD repository.
 
 | Model Name| Model File |
 | ------------- |:-------------:|
@@ -27,12 +38,14 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 |SSD-ResNet-50|	[Repo](https://github.com/zhreshold/mxnet-ssd), [Symbol + Params](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.6/resnet50_ssd_512_voc0712_trainval.zip)|
 |SSD-VGG-16-300|	[Repo](https://github.com/zhreshold/mxnet-ssd), [Symbol + Params](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.5-beta/vgg16_ssd_300_voc0712_trainval.zip)|
 |SSD-Inception v3|	[Repo](https://github.com/zhreshold/mxnet-ssd), [Symbol + Params](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.7-alpha/ssd_inceptionv3_512_voc0712trainval.zip)|
+|FCN8 (Semantic Segmentation)|	[Repo](https://github.com/apache/incubator-mxnet/tree/master/example/fcn-xs), [Symbol](https://www.dropbox.com/sh/578n5cxej7ofd6m/AAA9SFCBN8R_uL2CnAd3WQ5ia/FCN8s_VGG16-symbol.json?dl=0), [Params](https://www.dropbox.com/sh/578n5cxej7ofd6m/AABHWZHCtA2P6iR6LUflkxb_a/FCN8s_VGG16-0019-cpu.params?dl=0)|
 |MTCNN part 1 (Face Detection)| [Repo](https://github.com/pangyupo/mxnet_mtcnn_face_detection), [Symbol](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det1-symbol.json), [Params](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det1-0001.params)|
 |MTCNN part 2 (Face Detection)| [Repo](https://github.com/pangyupo/mxnet_mtcnn_face_detection), [Symbol](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det2-symbol.json), [Params](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det2-0001.params)|
 |MTCNN part 3 (Face Detection)| [Repo](https://github.com/pangyupo/mxnet_mtcnn_face_detection), [Symbol](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det3-symbol.json), [Params](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det3-0001.params)|
 |MTCNN part 4 (Face Detection)| [Repo](https://github.com/pangyupo/mxnet_mtcnn_face_detection), [Symbol](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det4-symbol.json), [Params](https://github.com/pangyupo/mxnet_mtcnn_face_detection/blob/master/model/det4-0001.params)|
 |Lightened_moon| [Repo](https://github.com/tornadomeet/mxnet-face/tree/master/model/lightened_moon), [Symbol](https://github.com/tornadomeet/mxnet-face/blob/master/model/lightened_moon/lightened_moon_fuse-symbol.json), [Params](https://github.com/tornadomeet/mxnet-face/blob/master/model/lightened_moon/lightened_moon_fuse-0082.params)|
 |RNN-Transducer| [Repo](https://github.com/HawkAaron/mxnet-transducer) |
+|word_lm| [Repo](https://github.com/apache/incubator-mxnet/tree/master/example/rnn/word_lm) |
 
 **Other supported topologies**
 
@@ -41,18 +54,16 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 ## Convert an MXNet* Model <a name="ConvertMxNet"></a>
 
-To convert an MXNet\* model:
+To convert an MXNet\* model, run Model Optimizer with a path to the input model `.params` file and to an output directory where you have write permissions:
 
-1. Go to the `<INSTALL_DIR>/tools/model_optimizer` directory.
-2. To convert an MXNet\* model contained in a `model-file-symbol.json` and `model-file-0000.params`, run the Model Optimizer launch script `mo`, specifying a path to the input model file and a path to an output directory with write permissions:
 ```sh
  mo --input_model model-file-0000.params --output_dir <OUTPUT_MODEL_DIR>
 ```
 
 Two groups of parameters are available to convert your model:
 
-* [Framework-agnostic parameters](Converting_Model_General.md): These parameters are used to convert any model trained in any supported framework.
-* [MXNet-specific parameters](#mxnet_specific_conversion_params): Parameters used to convert only MXNet models
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+* [MXNet-specific parameters](#mxnet_specific_conversion_params) are used to convert only MXNet models.
 
 
 ### Using MXNet\*-Specific Conversion Parameters <a name="mxnet_specific_conversion_params"></a>
@@ -78,7 +89,7 @@ MXNet-specific parameters:
             Use only if your topology is one of ssd gluoncv topologies
 ```
 
-> **NOTE:** By default, the Model Optimizer does not use the MXNet loader, as it transforms the topology to another format, which is compatible with the latest
+> **NOTE**: By default, the Model Optimizer does not use the MXNet loader, as it transforms the topology to another format, which is compatible with the latest
 > version of MXNet, but it is required for models trained with lower version of MXNet. If your model was trained with MXNet version lower than 1.0.0, specify the
 > `--legacy_mxnet_model` key to enable the MXNet loader. However, the loader does not support models with custom layers. In this case, you must manually
 > recompile MXNet with custom layers and install it to your environment.
diff --git a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md
index 36052e70cfb..d308a6ea4b5 100644
--- a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md
+++ b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md
@@ -1,5 +1,19 @@
 # Converting a ONNX* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Faster_RCNN
+   openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Mask_RCNN
+   openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_GPT2
+   openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM
+   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch
+
+@endsphinxdirective
+
 ## Introduction to ONNX
 
 [ONNX*](https://github.com/onnx/onnx) is a representation format for deep learning models. ONNX allows AI developers easily transfer models between different frameworks that helps to choose the best combination for them. Today, PyTorch\*, Caffe2\*, Apache MXNet\*, Microsoft Cognitive Toolkit\* and other tools are developing ONNX support.
@@ -57,15 +71,11 @@ The list of supported topologies from the [models v1.5](https://github.com/Paddl
 ## Convert an ONNX* Model <a name="Convert_From_ONNX"></a>
 The Model Optimizer process assumes you have an ONNX model that was directly downloaded from a public repository or converted from any framework that supports exporting to the ONNX format.
 
-To convert an ONNX\* model:
-
-1. Go to the `<INSTALL_DIR>/tools/model_optimizer` directory.
-2. Use the `mo` script to simply convert a model with the path to the input model `.nnet` file and an output directory where you have write permissions:
+To convert an ONNX\* model, run Model Optimizer with the path to the input model `.nnet` file and an output directory where you have write permissions:
 ```sh
  mo --input_model <INPUT_MODEL>.onnx --output_dir <OUTPUT_MODEL_DIR>
 ```
-
-There are no ONNX\* specific parameters, so only [framework-agnostic parameters](Converting_Model_General.md) are available to convert your model.
+There are no ONNX\* specific parameters, so only framework-agnostic parameters are available to convert your model. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
 
 ## Supported ONNX\* Layers
 Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
diff --git a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
index fdf5dcbb983..63149f4a9e0 100644
--- a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
+++ b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
@@ -1,5 +1,18 @@
 # Converting a PyTorch* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net
+   openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet
+   openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RNNT
+   openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT
+
+@endsphinxdirective
+
 ## Supported Topologies
 
 Here is the list of models that are tested and guaranteed to be supported. However, you can also use these instructions to convert PyTorch\* models that are not presented in the list.
diff --git a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md
index eb5565e4a5f..703a106768a 100644
--- a/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md
+++ b/docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md
@@ -1,5 +1,29 @@
 # Converting a TensorFlow* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_RetinaNet_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_AttentionOCR_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_FaceNet_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_NCF_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_DeepSpeech_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_lm_1b_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Slim_Library_Models
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_CRNN_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_GNMT_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_BERT_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_XLNet_From_Tensorflow
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_WideAndDeep_Family_Models
+   openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_EfficientDet_Models
+
+@endsphinxdirective
+
 A summary of the steps for optimizing and deploying a model that was trained with the TensorFlow\* framework:
 
 1. [Configure the Model Optimizer](../Config_Model_Optimizer.md) for TensorFlow\* (TensorFlow was used to train your model).
@@ -37,7 +61,7 @@ Detailed information on how to convert models from the <a href="https://github.c
 
 **Supported Pre-Trained Topologies from TensorFlow 1 Detection Model Zoo**
 
-Detailed information on how to convert models from the <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md">TensorFlow 1 Object Detection Models Zoo</a> and <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md">TensorFlow 2 Object Detection Models Zoo</a> is available in the [Converting TensorFlow Object Detection API Models](tf_specific/Convert_Object_Detection_API_Models.md) chapter. The table below contains models from the Object Detection Models Zoo that are supported.
+Detailed information on how to convert models from the <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md">TensorFlow 1 Detection Model Zoo</a> is available in the [Converting TensorFlow Object Detection API Models](tf_specific/Convert_Object_Detection_API_Models.md) chapter. The table below contains models from the Object Detection Models zoo that are supported.
 
 | Model Name| TensorFlow 1 Object Detection API Models|
 | :------------- | -----:|
@@ -259,10 +283,10 @@ To convert a TensorFlow model:
 
 Two groups of parameters are available to convert your model:
 
-* [Framework-agnostic parameters](Converting_Model_General.md): These parameters are used to convert any model trained in any supported framework.
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
 * [TensorFlow-specific parameters](#tensorflow_specific_conversion_params): Parameters used to convert only TensorFlow models.
 
-> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](Converting_Model_General.md).
+> **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model to Intermediate Representation (IR)](Converting_Model.md).
 
 ### Using TensorFlow\*-Specific Conversion Parameters  <a name="tensorflow_specific_conversion_params"></a>
 The following list provides the TensorFlow\*-specific parameters.
@@ -304,7 +328,7 @@ TensorFlow*-specific parameters:
                         with previous releases.
 ```
 
-> **NOTE:** Models produces with TensorFlow\* usually have not fully defined shapes (contain `-1` in some dimensions). It is necessary to pass explicit shape for the input using command line parameter `--input_shape` or `-b` to override just batch dimension. If the shape is fully defined, then there is no need to specify either `-b` or `--input_shape` options.
+> **NOTE**: Models produces with TensorFlow\* usually have not fully defined shapes (contain `-1` in some dimensions). It is necessary to pass explicit shape for the input using command line parameter `--input_shape` or `-b` to override just batch dimension. If the shape is fully defined, then there is no need to specify either `-b` or `--input_shape` options.
 
 #### Command-Line Interface (CLI) Examples Using TensorFlow\*-Specific Parameters
 
@@ -384,7 +408,7 @@ tf.saved_model.save(model,'model')
 
 Then follow the above instructions for the SavedModel format.
 
-> **NOTE:** Do not use other hacks to resave TensorFlow* 2 models into TensorFlow* 1 formats.
+> **NOTE**: Do not use other hacks to resave TensorFlow* 2 models into TensorFlow* 1 formats.
 
 
 ## Custom Layer Definition
@@ -408,7 +432,15 @@ The Model Optimizer provides explanatory messages if it is unable to run to comp
 
 ## Video: Converting a TensorFlow Model
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/QW6532LtiTc" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+@sphinxdirective
+
+.. raw:: html
+
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315"
+    src="https://www.youtube.com/embed/QW6532LtiTc">
+    </iframe>
+
+@endsphinxdirective
 
 ## Summary
 In this document, you learned:
diff --git a/docs/MO_DG/prepare_model/convert_model/Converting_Model.md b/docs/MO_DG/prepare_model/convert_model/Converting_Model.md
index a66cba7a8fd..6b384ea81a4 100644
--- a/docs/MO_DG/prepare_model/convert_model/Converting_Model.md
+++ b/docs/MO_DG/prepare_model/convert_model/Converting_Model.md
@@ -1,14 +1,36 @@
 # Converting a Model to Intermediate Representation (IR)  {#openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model}
 
-Use the <code>mo</code> script to run the Model Optimizer and convert the model to the Intermediate Representation (IR): 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow
+   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe
+   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet
+   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi
+   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX
+   openvino_docs_MO_DG_prepare_model_Model_Optimization_Techniques
+   openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model
+   openvino_docs_MO_DG_prepare_model_Supported_Frameworks_Layers
+   openvino_docs_MO_DG_prepare_model_convert_model_IR_suitable_for_INT8_inference
+   openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Subgraph_Replacement_Model_Optimizer
+   openvino_docs_MO_DG_prepare_model_convert_model_Legacy_IR_Layers_Catalog_Spec
+
+@endsphinxdirective
+
+To convert the model to the Intermediate Representation (IR), run Model Optimizer using the following command:
+
 ```sh
 mo --input_model INPUT_MODEL --output_dir <OUTPUT_MODEL_DIR>
 ```
-You need to have have write permissions for an output directory.
 
-> **NOTE**: Some models require using additional arguments to specify conversion parameters, such as `--input_shape`, `--scale`, `--scale_values`, `--mean_values`. To learn about when you need to use these parameters, refer to [Converting a Model Using General Conversion Parameters](Converting_Model_General.md).
+The output directory must have write permissions, so you can run Model Optimizer from the output directory or specify an output path with the `--output_dir` option.
 
-To adjust the conversion process, you may use general parameters defined in the [Converting a Model Using General Conversion Parameters](Converting_Model_General.md) and 
+> **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For details, refer to [When to Reverse Input Channels](#when_to_reverse_input_channels).
+
+To adjust the conversion process, you may use general parameters defined in the [General Conversion Parameters](#general_conversion_parameters) and 
 Framework-specific parameters for:
 * [Caffe](Convert_Model_From_Caffe.md)
 * [TensorFlow](Convert_Model_From_TensorFlow.md)
@@ -17,6 +39,236 @@ Framework-specific parameters for:
 * [Kaldi](Convert_Model_From_Kaldi.md)
 
 
+## General Conversion Parameters
+
+To adjust the conversion process, you can also use the general (framework-agnostic) parameters:
+
+```sh
+optional arguments:
+  -h, --help            show this help message and exit
+  --framework {tf,caffe,mxnet,kaldi,onnx}
+                        Name of the framework used to train the input model.
+
+Framework-agnostic parameters:
+  --input_model INPUT_MODEL, -w INPUT_MODEL, -m INPUT_MODEL
+                        Tensorflow*: a file with a pre-trained model (binary
+                        or text .pb file after freezing). Caffe*: a model
+                        proto file with model weights
+  --model_name MODEL_NAME, -n MODEL_NAME
+                        Model_name parameter passed to the final create_ir
+                        transform. This parameter is used to name a network in
+                        a generated IR and output .xml/.bin files.
+  --output_dir OUTPUT_DIR, -o OUTPUT_DIR
+                        Directory that stores the generated IR. By default, it
+                        is the directory from where the Model Optimizer is
+                        launched.
+  --input_shape INPUT_SHAPE
+                        Input shape(s) that should be fed to an input node(s)
+                        of the model. Shape is defined as a comma-separated
+                        list of integer numbers enclosed in parentheses or
+                        square brackets, for example [1,3,227,227] or
+                        (1,227,227,3), where the order of dimensions depends
+                        on the framework input layout of the model. For
+                        example, [N,C,H,W] is used for Caffe* models and
+                        [N,H,W,C] for TensorFlow* models. Model Optimizer
+                        performs necessary transformations to convert the
+                        shape to the layout required by Inference Engine
+                        (N,C,H,W). The shape should not contain undefined
+                        dimensions (? or -1) and should fit the dimensions
+                        defined in the input operation of the graph. If there
+                        are multiple inputs in the model, --input_shape should
+                        contain definition of shape for each input separated
+                        by a comma, for example: [1,3,227,227],[2,4] for a
+                        model with two inputs with 4D and 2D shapes.
+                        Alternatively, specify shapes with the --input
+                        option.
+  --scale SCALE, -s SCALE
+                        All input values coming from original network inputs
+                        will be divided by this value. When a list of inputs
+                        is overridden by the --input parameter, this scale is
+                        not applied for any input that does not match with the
+                        original input of the model.
+  --reverse_input_channels
+                        Switch the input channels order from RGB to BGR (or
+                        vice versa). Applied to original inputs of the model
+                        if and only if a number of channels equals 3.
+                        When --mean_values/--scale_values are also specified,
+                        reversing of channels will be applied to user's input
+                        data first, so that numbers in --mean_values and
+                        --scale_values go in the order of channels used in
+                        the original model. In other words, if both options are
+                        specified then the data flow in the model looks as following:
+                        Parameter -> ReverseInputChannels -> Mean/Scale apply -> the original body of the model.                        
+  --log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
+                        Logger level
+  --input INPUT         Quoted list of comma-separated input nodes names with
+                        shapes, data types, and values for freezing. The shape
+                        and value are specified as space-separated lists. The
+                        data type of input node is specified in braces and can
+                        have one of the values: f64 (float64), f32 (float32),
+                        f16 (float16), i64 (int64), i32 (int32), u8 (uint8),
+                        boolean. For example, use the following format to set
+                        input port 0 of the node `node_name1` with the shape
+                        [3 4] as an input node and freeze output port 1 of the
+                        node `node_name2` with the value [20 15] of the int32
+                        type and shape [2]: "0:node_name1[3
+                        4],node_name2:1[2]{i32}->[20 15]".
+  --output OUTPUT       The name of the output operation of the model. For
+                        TensorFlow*, do not add :0 to this name.
+  --mean_values MEAN_VALUES, -ms MEAN_VALUES
+                        Mean values to be used for the input image per
+                        channel. Values to be provided in the (R,G,B) or
+                        [R,G,B] format. Can be defined for desired input of
+                        the model, for example: "--mean_values
+                        data[255,255,255],info[255,255,255]". The exact
+                        meaning and order of channels depend on how the
+                        original model was trained.
+  --scale_values SCALE_VALUES
+                        Scale values to be used for the input image per
+                        channel. Values are provided in the (R,G,B) or [R,G,B]
+                        format. Can be defined for desired input of the model,
+                        for example: "--scale_values
+                        data[255,255,255],info[255,255,255]". The exact
+                        meaning and order of channels depend on how the
+                        original model was trained.
+  --data_type {FP16,FP32,half,float}
+                        Data type for all intermediate tensors and weights. If
+                        original model is in FP32 and --data_type=FP16 is
+                        specified, all model weights and biases are compressed
+                        to FP16.
+  --disable_fusing      Turn off fusing of linear operations to Convolution
+  --disable_resnet_optimization
+                        Turn off resnet optimization
+  --finegrain_fusing FINEGRAIN_FUSING
+                        Regex for layers/operations that won't be fused.
+                        Example: --finegrain_fusing Convolution1,.*Scale.*
+  --disable_gfusing     Turn off fusing of grouped convolutions
+  --enable_concat_optimization
+                        Turn on Concat optimization.
+  --extensions EXTENSIONS
+                        Directory or a comma separated list of directories
+                        with extensions. To disable all extensions including
+                        those that are placed at the default location, pass an
+                        empty string.
+  --batch BATCH, -b BATCH
+                        Input batch size
+  --version             Version of Model Optimizer
+  --silent              Prevent any output messages except those that
+                        correspond to log level equals ERROR, that can be set
+                        with the following option: --log_level. By default,
+                        log level is already ERROR.
+  --freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE
+                        Replaces input layer with constant node with provided
+                        value, for example: "node_name->True". It will be
+                        DEPRECATED in future releases. Use --input option to
+                        specify a value for freezing.
+  --static_shape        Enables IR generation for fixed input shape (folding
+                        `ShapeOf` operations and shape-calculating sub-graphs
+                        to `Constant`). Changing model input shape using
+                        the Inference Engine API in runtime may fail for such an IR.
+  --disable_weights_compression
+                        Disable compression and store weights with original
+                        precision.
+  --progress            Enable model conversion progress display.
+  --stream_output       Switch model conversion progress display to a
+                        multiline mode.
+  --transformations_config TRANSFORMATIONS_CONFIG
+                        Use the configuration file with transformations
+                        description.
+```
+
+The sections below provide details on using particular parameters and examples of CLI commands.
+
+## When to Specify Mean and Scale Values
+Usually neural network models are trained with the normalized input data. This means that the input data values are converted to be in a specific range, for example, `[0, 1]` or `[-1, 1]`. Sometimes the mean values (mean images) are subtracted from the input data values as part of the pre-processing. There are two cases how the input data pre-processing is implemented.
+ * The input pre-processing operations are a part of a topology. In this case, the application that uses the framework to infer the topology does not pre-process the input.
+ * The input pre-processing operations are not a part of a topology and the pre-processing is performed within the application which feeds the model with an input data.
+ 
+In the first case, the Model Optimizer generates the IR with required pre-processing layers and Inference Engine samples may be used to infer the model. 
+ 
+In the second case, information about mean/scale values should be provided to the Model Optimizer to embed it to the generated IR. Model Optimizer provides a number of command line parameters to specify them: `--scale`, `--scale_values`, `--mean_values`. 
+
+If both mean and scale values are specified, the mean is subtracted first and then scale is applied. Input values are *divided* by the scale value(s). 
+
+There is no a universal recipe for determining the mean/scale values for a particular model. The steps below could help to determine them:
+* Read the model documentation. Usually the documentation describes mean/scale value if the pre-processing is required.
+* Open the example script/application executing the model and track how the input data is read and passed to the framework.
+* Open the model in a visualization tool and check for layers performing subtraction or multiplication (like `Sub`, `Mul`, `ScaleShift`, `Eltwise` etc) of the input data. If such layers exist, pre-processing is probably part of the model.
+
+## When to Specify Input Shapes <a name="when_to_specify_input_shapes"></a>
+There are situations when the input data shape for the model is not fixed, like for the fully-convolutional neural networks. In this case, for example, TensorFlow\* models contain `-1` values in the `shape` attribute of the `Placeholder` operation. Inference Engine does not support input layers with undefined size, so if the input shapes are not defined in the model, the Model Optimizer fails to convert the model. The solution is to provide the input shape(s) using the `--input` or `--input_shape` command line parameter for all input(s) of the model or provide the batch size using the `-b` command line parameter if the model contains just one input with undefined batch size only. In the latter case, the `Placeholder` shape for the TensorFlow\* model looks like this `[-1, 224, 224, 3]`. 
+
+## When to Reverse Input Channels <a name="when_to_reverse_input_channels"></a>
+Input data for your application can be of RGB or BRG color input order. For example, Inference Engine samples load input images in the BGR channels order. However, the model may be trained on images loaded with the opposite order (for example, most TensorFlow\* models are trained with images in RGB order). In this case, inference results using the Inference Engine samples may be incorrect. The solution is to provide `--reverse_input_channels` command line parameter. Taking this parameter, the Model Optimizer performs first convolution or other channel dependent operation weights modification so these operations output will be like the image is passed with RGB channels order.
+
+## When to Specify `--static_shape` Command Line Parameter
+If the `--static_shape` command line parameter is specified the Model Optimizer evaluates shapes of all operations in the model (shape propagation) for a fixed input(s) shape(s). During the shape propagation the Model Optimizer evaluates operations *Shape* and removes them from the computation graph. With that approach, the initial model which can consume inputs of different shapes may be converted to IR working with the input of one fixed shape only. For example, consider the case when some blob is reshaped from 4D of a shape *[N, C, H, W]* to a shape *[N, C, H \* W]*. During the model conversion the Model Optimize calculates output shape as a constant 1D blob with values *[N, C, H \* W]*. So if the input shape changes to some other value *[N,C,H1,W1]* (it is possible scenario for a fully convolutional model) then the reshape layer becomes invalid.
+Resulting Intermediate Representation will not be resizable with the help of Inference Engine.
+
+## Examples of CLI Commands
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with debug log level:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --log_level DEBUG --output_dir <OUTPUT_MODEL_DIR>
+```
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with the output IR called `result.*` in the specified `output_dir`:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --model_name result --output_dir /../../models/
+```
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with one input with scale values:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --scale_values [59,59,59] --output_dir <OUTPUT_MODEL_DIR>
+```
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with multiple inputs with scale values:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --input data,rois --scale_values [59,59,59],[5,5,5] --output_dir <OUTPUT_MODEL_DIR>
+```
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with multiple inputs with scale and mean values specified for the particular nodes:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --input data,rois --mean_values data[59,59,59] --scale_values rois[5,5,5] --output_dir <OUTPUT_MODEL_DIR>
+```
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with specified input layer, overridden input shape, scale 5, batch 8 and specified name of an output operation:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --input "data[1 3 224 224]" --output pool5 -s 5 -b 8 --output_dir <OUTPUT_MODEL_DIR>
+```
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with disabled fusing for linear operations to Convolution and grouped convolutions:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --disable_fusing --disable_gfusing --output_dir <OUTPUT_MODEL_DIR>
+```
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with reversed input channels order between RGB and BGR, specified mean values to be used for the input image per channel and specified data type for input tensor values:
+```sh
+mo --input_model bvlc_alexnet.caffemodel --reverse_input_channels --mean_values [255,255,255] --data_type FP16 --output_dir <OUTPUT_MODEL_DIR>
+```
+
+Launch the Model Optimizer for the Caffe bvlc_alexnet model with extensions listed in specified directories, specified mean_images binaryproto 
+ file. For more information about extensions, please refer to [this](../customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md) page.
+```sh
+mo --input_model bvlc_alexnet.caffemodel --extensions /home/,/some/other/path/ --mean_file /path/to/binaryproto --output_dir <OUTPUT_MODEL_DIR>
+```
+
+Launch the Model Optimizer for TensorFlow* FaceNet* model with a placeholder freezing value. 
+It replaces the placeholder with a constant layer that contains the passed value.
+For more information about FaceNet conversion, please refer to [this](tf_specific/Convert_FaceNet_From_Tensorflow.md) page.
+```sh
+mo --input_model FaceNet.pb --input "phase_train->False" --output_dir <OUTPUT_MODEL_DIR>
+```
+Launch the Model Optimizer for any model with a placeholder freezing tensor of values. 
+It replaces the placeholder with a constant layer that contains the passed values.
+
+Tensor here is represented in square brackets with each value separated from another by a whitespace. 
+If data type is set in the model, this tensor will be reshaped to a placeholder shape and casted to placeholder data type.
+Otherwise, it will be casted to data type passed to `--data_type` parameter (by default, it is FP32).
+```sh
+mo --input_model FaceNet.pb --input "placeholder_layer_name->[0.1 1.2 2.3]" --output_dir <OUTPUT_MODEL_DIR>
+```
+
+
 ## See Also
 * [Configuring the Model Optimizer](../Config_Model_Optimizer.md)
 * [IR Notation Reference](../../IR_and_opsets.md)
diff --git a/docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md b/docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md
deleted file mode 100644
index 07f8886a0e9..00000000000
--- a/docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md
+++ /dev/null
@@ -1,238 +0,0 @@
-# Converting a Model Using General Conversion Parameters {#openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model_General}
-
-To simply convert a model trained by any supported framework, run the Model Optimizer launch script ``mo`` specifying a path to the input model file and an output directory where you have write permissions:
-```sh
- mo --input_model INPUT_MODEL --output_dir <OUTPUT_MODEL_DIR>
-```
-
-The script is in `$INTEL_OPENVINO_DIR/tools/model_optimizer/`. The output directory must have write permissions, so you can run mo from the output directory or specify an output path with the `--output_dir` option.
-
-> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For details, refer to [When to Reverse Input Channels](#when_to_reverse_input_channels).
-
-To adjust the conversion process, you can also use the general (framework-agnostic) parameters:
-
-```sh
-optional arguments:
-  -h, --help            show this help message and exit
-  --framework {tf,caffe,mxnet,kaldi,onnx}
-                        Name of the framework used to train the input model.
-
-Framework-agnostic parameters:
-  --input_model INPUT_MODEL, -w INPUT_MODEL, -m INPUT_MODEL
-                        Tensorflow*: a file with a pre-trained model (binary
-                        or text .pb file after freezing). Caffe*: a model
-                        proto file with model weights
-  --model_name MODEL_NAME, -n MODEL_NAME
-                        Model_name parameter passed to the final create_ir
-                        transform. This parameter is used to name a network in
-                        a generated IR and output .xml/.bin files.
-  --output_dir OUTPUT_DIR, -o OUTPUT_DIR
-                        Directory that stores the generated IR. By default, it
-                        is the directory from where the Model Optimizer is
-                        launched.
-  --input_shape INPUT_SHAPE
-                        Input shape(s) that should be fed to an input node(s)
-                        of the model. Shape is defined as a comma-separated
-                        list of integer numbers enclosed in parentheses or
-                        square brackets, for example [1,3,227,227] or
-                        (1,227,227,3), where the order of dimensions depends
-                        on the framework input layout of the model. For
-                        example, [N,C,H,W] is used for Caffe* models and
-                        [N,H,W,C] for TensorFlow* models. Model Optimizer
-                        performs necessary transformations to convert the
-                        shape to the layout required by Inference Engine
-                        (N,C,H,W). The shape should not contain undefined
-                        dimensions (? or -1) and should fit the dimensions
-                        defined in the input operation of the graph. If there
-                        are multiple inputs in the model, --input_shape should
-                        contain definition of shape for each input separated
-                        by a comma, for example: [1,3,227,227],[2,4] for a
-                        model with two inputs with 4D and 2D shapes.
-                        Alternatively, specify shapes with the --input
-                        option.
-  --scale SCALE, -s SCALE
-                        All input values coming from original network inputs
-                        will be divided by this value. When a list of inputs
-                        is overridden by the --input parameter, this scale is
-                        not applied for any input that does not match with the
-                        original input of the model.
-  --reverse_input_channels
-                        Switch the input channels order from RGB to BGR (or
-                        vice versa). Applied to original inputs of the model
-                        if and only if a number of channels equals 3.
-                        When --mean_values/--scale_values are also specified,
-                        reversing of channels will be applied to user's input
-                        data first, so that numbers in --mean_values and
-                        --scale_values go in the order of channels used in
-                        the original model. In other words, if both options are
-                        specified then the data flow in the model looks as following:
-                        Parameter -> ReverseInputChannels -> Mean/Scale apply -> the original body of the model.
-  --log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
-                        Logger level
-  --input INPUT         Quoted list of comma-separated input nodes names with
-                        shapes, data types, and values for freezing. The shape
-                        and value are specified as space-separated lists. The
-                        data type of input node is specified in braces and can
-                        have one of the values: f64 (float64), f32 (float32),
-                        f16 (float16), i64 (int64), i32 (int32), u8 (uint8),
-                        boolean. For example, use the following format to set
-                        input port 0 of the node `node_name1` with the shape
-                        [3 4] as an input node and freeze output port 1 of the
-                        node `node_name2` with the value [20 15] of the int32
-                        type and shape [2]: "0:node_name1[3
-                        4],node_name2:1[2]{i32}->[20 15]".
-  --output OUTPUT       The name of the output operation of the model. For
-                        TensorFlow*, do not add :0 to this name.
-  --mean_values MEAN_VALUES, -ms MEAN_VALUES
-                        Mean values to be used for the input image per
-                        channel. Values to be provided in the (R,G,B) or
-                        [R,G,B] format. Can be defined for desired input of
-                        the model, for example: "--mean_values
-                        data[255,255,255],info[255,255,255]". The exact
-                        meaning and order of channels depend on how the
-                        original model was trained.
-  --scale_values SCALE_VALUES
-                        Scale values to be used for the input image per
-                        channel. Values are provided in the (R,G,B) or [R,G,B]
-                        format. Can be defined for desired input of the model,
-                        for example: "--scale_values
-                        data[255,255,255],info[255,255,255]". The exact
-                        meaning and order of channels depend on how the
-                        original model was trained.
-  --data_type {FP16,FP32,half,float}
-                        Data type for all intermediate tensors and weights. If
-                        original model is in FP32 and --data_type=FP16 is
-                        specified, all model weights and biases are compressed
-                        to FP16.
-  --disable_fusing      Turn off fusing of linear operations to Convolution
-  --disable_resnet_optimization
-                        Turn off resnet optimization
-  --finegrain_fusing FINEGRAIN_FUSING
-                        Regex for layers/operations that won't be fused.
-                        Example: --finegrain_fusing Convolution1,.*Scale.*
-  --disable_gfusing     Turn off fusing of grouped convolutions
-  --enable_concat_optimization
-                        Turn on Concat optimization.
-  --extensions EXTENSIONS
-                        Directory or a comma separated list of directories
-                        with extensions. To disable all extensions including
-                        those that are placed at the default location, pass an
-                        empty string.
-  --batch BATCH, -b BATCH
-                        Input batch size
-  --version             Version of Model Optimizer
-  --silent              Prevent any output messages except those that
-                        correspond to log level equals ERROR, that can be set
-                        with the following option: --log_level. By default,
-                        log level is already ERROR.
-  --freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE
-                        Replaces input layer with constant node with provided
-                        value, for example: "node_name->True". It will be
-                        DEPRECATED in future releases. Use --input option to
-                        specify a value for freezing.
-  --static_shape        Enables IR generation for fixed input shape (folding
-                        `ShapeOf` operations and shape-calculating sub-graphs
-                        to `Constant`). Changing model input shape using
-                        the Inference Engine API in runtime may fail for such an IR.
-  --disable_weights_compression
-                        Disable compression and store weights with original
-                        precision.
-  --progress            Enable model conversion progress display.
-  --stream_output       Switch model conversion progress display to a
-                        multiline mode.
-  --transformations_config TRANSFORMATIONS_CONFIG
-                        Use the configuration file with transformations
-                        description.
-```
-
-The sections below provide details on using particular parameters and examples of CLI commands.
-
-## When to Specify Mean and Scale Values
-Usually neural network models are trained with the normalized input data. This means that the input data values are converted to be in a specific range, for example, `[0, 1]` or `[-1, 1]`. Sometimes the mean values (mean images) are subtracted from the input data values as part of the pre-processing. There are two cases how the input data pre-processing is implemented.
- * The input pre-processing operations are a part of a topology. In this case, the application that uses the framework to infer the topology does not pre-process the input.
- * The input pre-processing operations are not a part of a topology and the pre-processing is performed within the application which feeds the model with an input data.
- 
-In the first case, the Model Optimizer generates the IR with required pre-processing layers and Inference Engine samples may be used to infer the model. 
- 
-In the second case, information about mean/scale values should be provided to the Model Optimizer to embed it to the generated IR. Model Optimizer provides a number of command line parameters to specify them: `--scale`, `--scale_values`, `--mean_values`. 
-
-If both mean and scale values are specified, the mean is subtracted first and then scale is applied. Input values are *divided* by the scale value(s). 
-
-There is no a universal recipe for determining the mean/scale values for a particular model. The steps below could help to determine them:
-* Read the model documentation. Usually the documentation describes mean/scale value if the pre-processing is required.
-* Open the example script/application executing the model and track how the input data is read and passed to the framework.
-* Open the model in a visualization tool and check for layers performing subtraction or multiplication (like `Sub`, `Mul`, `ScaleShift`, `Eltwise` etc) of the input data. If such layers exist, pre-processing is probably part of the model.
-
-## When to Specify Input Shapes <a name="when_to_specify_input_shapes"></a>
-There are situations when the input data shape for the model is not fixed, like for the fully-convolutional neural networks. In this case, for example, TensorFlow\* models contain `-1` values in the `shape` attribute of the `Placeholder` operation. Inference Engine does not support input layers with undefined size, so if the input shapes are not defined in the model, the Model Optimizer fails to convert the model. The solution is to provide the input shape(s) using the `--input` or `--input_shape` command line parameter for all input(s) of the model or provide the batch size using the `-b` command line parameter if the model contains just one input with undefined batch size only. In the latter case, the `Placeholder` shape for the TensorFlow\* model looks like this `[-1, 224, 224, 3]`. 
-
-## When to Reverse Input Channels <a name="when_to_reverse_input_channels"></a>
-Input data for your application can be of RGB or BRG color input order. For example, Inference Engine samples load input images in the BGR channels order. However, the model may be trained on images loaded with the opposite order (for example, most TensorFlow\* models are trained with images in RGB order). In this case, inference results using the Inference Engine samples may be incorrect. The solution is to provide `--reverse_input_channels` command line parameter. Taking this parameter, the Model Optimizer performs first convolution or other channel dependent operation weights modification so these operations output will be like the image is passed with RGB channels order.
-
-## When to Specify `--static_shape` Command Line Parameter
-If the `--static_shape` command line parameter is specified the Model Optimizer evaluates shapes of all operations in the model (shape propagation) for a fixed input(s) shape(s). During the shape propagation the Model Optimizer evaluates operations *Shape* and removes them from the computation graph. With that approach, the initial model which can consume inputs of different shapes may be converted to IR working with the input of one fixed shape only. For example, consider the case when some blob is reshaped from 4D of a shape *[N, C, H, W]* to a shape *[N, C, H \* W]*. During the model conversion the Model Optimize calculates output shape as a constant 1D blob with values *[N, C, H \* W]*. So if the input shape changes to some other value *[N,C,H1,W1]* (it is possible scenario for a fully convolutional model) then the reshape layer becomes invalid.
-Resulting Intermediate Representation will not be resizable with the help of Inference Engine.
-
-## Examples of CLI Commands
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with debug log level:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --log_level DEBUG --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with the output IR called `result.*` in the specified `output_dir`:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --model_name result --output_dir /../../models/ 
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with one input with scale values:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --scale_values [59,59,59] --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with multiple inputs with scale values:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --input data,rois --scale_values [59,59,59],[5,5,5] --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with multiple inputs with scale and mean values specified for the particular nodes:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --input data,rois --mean_values data[59,59,59] --scale_values rois[5,5,5] --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with specified input layer, overridden input shape, scale 5, batch 8 and specified name of an output operation:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --input "data[1 3 224 224]" --output pool5 -s 5 -b 8 --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with disabled fusing for linear operations to Convolution and grouped convolutions:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --disable_fusing --disable_gfusing --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with reversed input channels order between RGB and BGR, specified mean values to be used for the input image per channel and specified data type for input tensor values:
-```sh
- mo --input_model bvlc_alexnet.caffemodel --reverse_input_channels --mean_values [255,255,255] --data_type FP16 --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for the Caffe bvlc_alexnet model with extensions listed in specified directories, specified mean_images binaryproto file. For more information about extensions, please refer to [this](../customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md) page.
-```sh
- mo --input_model bvlc_alexnet.caffemodel --extensions /home/,/some/other/path/ --mean_file /path/to/binaryproto --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for TensorFlow* FaceNet* model with a placeholder freezing value. 
-It replaces the placeholder with a constant layer that contains the passed value.
-For more information about FaceNet conversion, please refer to [this](tf_specific/Convert_FaceNet_From_Tensorflow.md) page
-```sh
- mo --input_model FaceNet.pb --input "phase_train->False" --output_dir <OUTPUT_MODEL_DIR>
-```
-
-Launch the Model Optimizer for any model with a placeholder freezing tensor of values. 
-It replaces the placeholder with a constant layer that contains the passed values.
-
-Tensor here is represented in square brackets with each value separated from another by a whitespace. 
-If data type is set in the model, this tensor will be reshaped to a placeholder shape and casted to placeholder data type.
-Otherwise, it will be casted to data type passed to `--data_type` parameter (by default, it is FP32).
-```sh
- mo --input_model FaceNet.pb --input "placeholder_layer_name->[0.1 1.2 2.3]" --output_dir <OUTPUT_MODEL_DIR>
-```
diff --git a/docs/MO_DG/prepare_model/convert_model/Cutting_Model.md b/docs/MO_DG/prepare_model/convert_model/Cutting_Model.md
index 8d5c76677d2..aac7d73f006 100644
--- a/docs/MO_DG/prepare_model/convert_model/Cutting_Model.md
+++ b/docs/MO_DG/prepare_model/convert_model/Cutting_Model.md
@@ -39,8 +39,7 @@ In the TensorBoard, it looks the following way together with some predecessors:
 
 Convert this model and put the results in a writable output directory:
 ```sh
-${INTEL_OPENVINO_DIR}/tools/model_optimizer
- mo --input_model inception_v1.pb -b 1 --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model inception_v1.pb -b 1 --output_dir <OUTPUT_MODEL_DIR>
 ```
 (The other examples on this page assume that you first cd to the `model_optimizer` directory and add the `--output_dir` argument with a directory where you have write permissions.)
 
@@ -81,9 +80,9 @@ The last layer in the model is `InceptionV1/Logits/Predictions/Reshape_1`, which
 ```
 Due to automatic identification of inputs and outputs, you do not need to provide the `--input` and `--output` options to convert the whole model. The following commands are equivalent for the Inception V1 model:
 ```sh
- mo --input_model inception_v1.pb -b 1 --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model inception_v1.pb -b 1 --output_dir <OUTPUT_MODEL_DIR>
 
- mo --input_model inception_v1.pb -b 1 --input input --output InceptionV1/Logits/Predictions/Reshape_1 --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model inception_v1.pb -b 1 --input input --output InceptionV1/Logits/Predictions/Reshape_1 --output_dir <OUTPUT_MODEL_DIR>
 ```
 The Intermediate Representations are identical for both conversions. The same is true if the model has multiple inputs and/or outputs.
 
@@ -98,9 +97,9 @@ Now consider how to cut some parts of the model off. This chapter uses the first
 If you want to cut your model at the end, you have the following options:
 
 1. The following command cuts off the rest of the model after the `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu`, making this node the last in the model:
-```sh
- mo --input_model inception_v1.pb -b 1 --output=InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
-```
+   ```sh
+   mo --input_model inception_v1.pb -b 1 --output=InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
+   ```
    The resulting Intermediate Representation has three layers:
 ```xml
 <?xml version="1.0" ?>
@@ -142,9 +141,9 @@ If you want to cut your model at the end, you have the following options:
    As you can see in the TensorBoard picture, the original model has more nodes than Intermediate Representation. Model Optimizer has fused batch normalization `InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm` to the convolution `InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution`, and it is not present in the final Intermediate Representation. This is not an effect of the `--output` option, it is usual behavior of the Model Optimizer for batch normalizations and convolutions. The effect of the `--output` is that the `ReLU` layer becomes the last one in the converted model.
 
 2. The following command cuts the edge that comes from 0 output port of the `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` and the rest of the model, making this node the last one in the model:
-```sh
- mo --input_model inception_v1.pb -b 1 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu:0 --output_dir <OUTPUT_MODEL_DIR>
-```
+   ```sh
+   mo --input_model inception_v1.pb -b 1 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu:0 --output_dir <OUTPUT_MODEL_DIR>
+   ```
    The resulting Intermediate Representation has three layers, which are the same as in the previous case:
 ```xml
 <?xml version="1.0" ?>
@@ -186,9 +185,9 @@ If you want to cut your model at the end, you have the following options:
    This type of cutting is useful to cut edges in case of multiple output edges.
 
 3. The following command cuts the edge that comes to 0 input port of the `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` and the rest of the model including `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu`, deleting this node and making the previous node `InceptionV1/InceptionV1/Conv2d_1a_7x7/Conv2D` the last in the model:
-```sh
- mo --input_model inception_v1.pb -b 1 --output=0:InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
-```
+   ```sh
+   mo --input_model inception_v1.pb -b 1 --output=0:InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
+   ```
    The resulting Intermediate Representation has two layers, which are the same as the first two layers in the previous case:
 ```xml
 <?xml version="1.0" ?>
@@ -224,9 +223,9 @@ If you want to cut your model at the end, you have the following options:
 If you want to go further and cut the beginning of the model, leaving only the `ReLU` layer, you have the following options:
 
 1.  You can use the following command line, where `--input` and `--output` specify the same node in the graph:
-```sh
- mo --input_model=inception_v1.pb -b 1 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --input InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
-```
+   ```sh
+   mo --input_model=inception_v1.pb -b 1 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --input InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
+   ```
    The resulting Intermediate Representation looks as follows:
 ```xml
 <xml version="1.0">
@@ -256,9 +255,9 @@ Even though `--input_shape` is not specified in the command line, the shapes for
 
 2. You can cut edge incoming to layer by port number. To specify incoming port use notation `--input=port:input_node`. 
 So, to cut everything before `ReLU` layer, cut edge incoming in port 0 of `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` node:
-```sh
- mo --input_model inception_v1.pb -b 1 --input 0:InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
-```
+   ```sh
+   mo --input_model inception_v1.pb -b 1 --input 0:InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
+   ```
    The resulting Intermediate Representation looks as follows:
 ```xml
 <xml version="1.0">
@@ -288,9 +287,9 @@ Even though `--input_shape` is not specified in the command line, the shapes for
 
 3. You can cut edge outcoming from layer by port number. To specify outcoming port use notation `--input=input_node:port`.
 So, to cut everything before `ReLU` layer, cut edge from `InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm/batchnorm/add_1` node to `ReLU`:
-```sh
- mo --input_model inception_v1.pb -b 1 --input InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm/batchnorm/add_1:0 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
-```
+   ```sh
+   mo --input_model inception_v1.pb -b 1 --input InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm/batchnorm/add_1:0 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
+   ```
    The resulting Intermediate Representation looks as follows:
 ```xml
 <xml version="1.0">
@@ -318,7 +317,7 @@ So, to cut everything before `ReLU` layer, cut edge from `InceptionV1/InceptionV
 
 ## Shape Override for New Inputs
 
-The input shape can be overridden with `--input_shape`. In this case, the shape is applied to the node referenced in `--input`, not to the original `Placeholder` in the model. For example, this command line
+The input shape can be overridden with `--input_shape`. In this case, the shape is applied to the node referenced in `--input`, not to the original `Placeholder` in the model. For example, the command below
 ```sh
  mo --input_model inception_v1.pb --input_shape=[1,5,10,20] --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --input InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
 ```
@@ -369,14 +368,14 @@ There are operations that contain more than one input ports. In the example cons
 Following this behavior, the Model Optimizer creates an `Input` layer for port 0 only, leaving port 1 as a constant. So the result of:
 
 ```sh
- mo --input_model inception_v1.pb -b 1 --input InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model inception_v1.pb -b 1 --input InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --output_dir <OUTPUT_MODEL_DIR>
 ```
 
 is identical to the result of conversion of the model as a whole, because this convolution is the first executable operation in Inception V1.
 
 Different behavior occurs when `--input_shape` is also used as an attempt to override the input shape:
 ```sh
- mo --input_model inception_v1.pb--input=InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --input_shape [1,224,224,3]  --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model inception_v1.pb--input=InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --input_shape [1,224,224,3]  --output_dir <OUTPUT_MODEL_DIR>
 ```
 An error occurs (for more information, see <a href="MO_FAQ.html#FAQ30">FAQ #30</a>):
 ```sh
@@ -388,5 +387,5 @@ In this case, when `--input_shape` is specified and the node contains multiple i
 
 The correct command line is:
 ```sh
- mo --input_model inception_v1.pb --input 0:InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --input_shape=[1,224,224,3] --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model inception_v1.pb --input 0:InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --input_shape=[1,224,224,3] --output_dir <OUTPUT_MODEL_DIR>
 ```
diff --git a/docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md b/docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md
index 4f9baa1386c..bf117d90d69 100644
--- a/docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md
+++ b/docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md
@@ -9,7 +9,7 @@ Intermediate Representation (IR) should be specifically formed to be suitable fo
 Such an IR is called a Low Precision IR and you can generate it in two ways:
 - [Quantize regular IR with the Post-Training Optimization tool](@ref pot_README)
 - Use the Model Optimizer for a model pretrained for Low Precision inference: TensorFlow\* pre-TFLite models (`.pb` model file with `FakeQuantize*` operations) and ONNX\* quantized models.
-Both TensorFlow and ONNX quantized models could be prepared by [Neural Network Compression Framework](https://github.com/openvinotoolkit/nncf/blob/develop/README.md). 
+Both TensorFlow and ONNX quantized models could be prepared by [Neural Network Compression Framework](https://github.com/openvinotoolkit/nncf/blob/develop/README.md).
 
 For an operation to be executed in INT8, it must have `FakeQuantize` operations as inputs.
 See the [specification of `FakeQuantize` operation](../../../ops/quantization/FakeQuantize_1.md) for details. 
diff --git a/docs/MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md b/docs/MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md
index abd997f36c3..b94e2b5d331 100644
--- a/docs/MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md
+++ b/docs/MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md
@@ -1,4 +1,4 @@
-# Intermediate Representation Notation Reference Catalog {#openvino_docs_MO_DG_prepare_model_convert_model_Legacy_IR_Layers_Catalog_Spec}
+# [DEPRECATED] Intermediate Representation Notation Reference Catalog {#openvino_docs_MO_DG_prepare_model_convert_model_Legacy_IR_Layers_Catalog_Spec}
 
 > **NOTE**: This IR Notation Reference is no longer supported since the new concept of operation sets is introduced in OpenVINO 2020.1 version. For a complete list of supported operations, see the [Intermediate Representation and Operation Sets](../../IR_and_opsets.md) topic.
 
diff --git a/docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md b/docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md
index 2606987bf47..86a4990ee58 100644
--- a/docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md
+++ b/docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md
@@ -88,9 +88,9 @@ maker.generate('output.jpg', '../images/tubingen.jpg')
 ```
 Where the `models/13` string is composed of the following substrings: 
 * `models/`: path to the folder that contains .nd files with pre-trained styles weights 
-* `13`: prefix pointing to 13_decoder, which is the default decoder for the repository
+* `13`: prefix pointing to 13_decoder, which is the default decoder for the repository.
 
->**NOTE**: If you get an error saying "No module named 'cPickle'", try running the script from this step in Python 2. Then return to Python 3 for the remaining steps.
+> **NOTE**: If you get an error saying "No module named 'cPickle'", try running the script from this step in Python 2. Then return to Python 3 for the remaining steps.
 
 You can choose any style from [collection of pre-trained weights](https://pan.baidu.com/s/1skMHqYp). (On the Chinese-language page, click the down arrow next to a size in megabytes. Then wait for an overlay box to appear, and click the blue button in it to download.) The `generate()` function generates `nst_vgg19-symbol.json` and `vgg19-symbol.json` files for the specified shape. In the code, it is [1024 x 768] for a 4:3 ratio, and you can specify another, for example, [224,224] for a square ratio.
 
@@ -112,6 +112,6 @@ cp models/13_decoder_auxs.nd nst_model
 
 3. Run the Model Optimizer for MXNet. Use the `--nd_prefix_name` option to specify the decoder prefix and `--input_shape` to specify input shapes in [N,C,W,H] order. For example:<br>
 ```sh
- mo --input_symbol <path/to/nst_model>/nst_vgg19-symbol.json --framework mxnet --output_dir <path/to/output_dir> --input_shape [1,3,224,224] --nd_prefix_name 13_decoder --pretrained_model <path/to/nst_model>/vgg19-0000.params
+mo --input_symbol <path/to/nst_model>/nst_vgg19-symbol.json --framework mxnet --output_dir <path/to/output_dir> --input_shape [1,3,224,224] --nd_prefix_name 13_decoder --pretrained_model <path/to/nst_model>/vgg19-0000.params
 ```
 4. The IR is generated (`.bin`, `.xml` and `.mapping` files) in the specified output directory and ready to be consumed by the Inference Engine. 
diff --git a/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_DLRM.md b/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_DLRM.md
index 6816481a009..8e87467a528 100644
--- a/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_DLRM.md
+++ b/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_DLRM.md
@@ -11,7 +11,7 @@ If you train the model using the [script provided in model repository](https://g
 
 **Step 2**. To generate the Intermediate Representation (IR) of the model, change your current working directory to the Model Optimizer installation directory and run the Model Optimizer with the following parameters:
 ```sh
- mo --input_model dlrm_s_pytorch.onnx
+mo --input_model dlrm_s_pytorch.onnx
 ```
 
 Note that Pytorch model uses operation `torch.nn.EmbeddingBag`. This operation converts to onnx as custom `ATen` layer and not directly supported by OpenVINO*, but it is possible to convert this operation to:
diff --git a/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_GPT2.md b/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_GPT2.md
index 1d8eb7ac9c5..2e7af9bf042 100644
--- a/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_GPT2.md
+++ b/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_GPT2.md
@@ -13,5 +13,5 @@ To download the model and sample test data, click **Download** on [https://githu
 
 To generate the Intermediate Representation (IR) of the model GPT-2, run the Model Optimizer with the following parameters:
 ```sh
- mo --input_model gpt2-10.onnx --input_shape [X,Y,Z] --output_dir <OUTPUT_MODEL_DIR>
+mo --input_model gpt2-10.onnx --input_shape [X,Y,Z] --output_dir <OUTPUT_MODEL_DIR>
 ```
diff --git a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_Bert_ner.md b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_Bert_ner.md
index 2af175f0a77..2bd66bd1228 100644
--- a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_Bert_ner.md
+++ b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_Bert_ner.md
@@ -49,7 +49,7 @@ The script generates ONNX* model file `bert-ner.onnx`.
 ## Convert ONNX* BERT-NER model to IR
 
 ```bash
- mo --input_model bert-ner.onnx --input "input_mask[1 128],segment_ids[1 128],input_ids[1 128]"
+mo --input_model bert-ner.onnx --input "input_mask[1 128],segment_ids[1 128],input_ids[1 128]"
 ```
 
 where `1` is `batch_size` and `128` is `sequence_length`.
\ No newline at end of file
diff --git a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_F3Net.md b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_F3Net.md
index bf34e38e27c..d26b712d7fa 100644
--- a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_F3Net.md
+++ b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_F3Net.md
@@ -2,17 +2,18 @@
 
 [F3Net](https://github.com/weijun88/F3Net): Fusion, Feedback and Focus for Salient Object Detection
 
-## Clone the F3Net Model Repository
+## Clone the F3Net Repository
 
 To clone the repository, run the following command:
-```bash
-git clone http://github.com/weijun88/F3Net.git
+
+```sh
+git clone http://github.com/weijun88/F3Net.git 
 ```
 
 ## Download and Convert the Model to ONNX*
 
 To download the pre-trained model or train the model yourself, refer to the 
-[instruction](https://github.com/weijun88/F3Net/blob/master/README.md) in the F3Net model repository. First, convert the model to ONNX\* format. Create and run the script with the following content in the `src` directory of the model repository:
+[instruction](https://github.com/weijun88/F3Net/blob/master/README.md) in the F3Net model repository. First, convert the model to ONNX\* format. Create and run the following Python script in the `src` directory of the model repository:
 ```python
 import torch
 from dataset import Config
@@ -23,7 +24,7 @@ net = F3Net(cfg)
 image = torch.zeros([1, 3, 352, 352])
 torch.onnx.export(net, image, 'f3net.onnx', export_params=True, do_constant_folding=True, opset_version=11)
 ```
-The script generates the ONNX\* model file `f3net.onnx`. This model conversion was tested with the repository hash commit `eecace3adf1e8946b571a4f4397681252f9dc1b8`.
+The script generates the ONNX\* model file f3net.onnx. This model conversion was tested with the repository hash commit `eecace3adf1e8946b571a4f4397681252f9dc1b8`.
 
 ## Convert ONNX* F3Net Model to IR
 
diff --git a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md
index cc73d5f285f..b3eafbaea77 100644
--- a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md
+++ b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md
@@ -96,9 +96,9 @@ python3 export_rnnt_to_onnx.py
 
 After completing this step, the files `rnnt_encoder.onnx`, `rnnt_prediction.onnx`, and `rnnt_joint.onnx` will be saved in the current directory. 
 
-**Step 6**. Run the conversion command:
+**Step 6**. Run the conversion commands:
 
-```bash
+```sh
 mo --input_model rnnt_encoder.onnx --input "input[157 1 240],feature_length->157"
 mo --input_model rnnt_prediction.onnx --input "symbol[1 1],hidden_in_1[2 1 320],hidden_in_2[2 1 320]"
 mo --input_model rnnt_joint.onnx --input "0[1 1 1024],1[1 1 320]"
diff --git a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md
index 483bda226bb..aa68f9d6185 100644
--- a/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md
+++ b/docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md
@@ -138,7 +138,7 @@ git checkout 57b8f2d95e62e2e649b382f516ab41f949b57239
 
 3. Set up the environment as described in `README.md`.
 
-**Step 2**. Download a pre-trained model from the list attached in the `Evaluation` section of the [README.md](https://github.com/dbolya/yolact/blob/master/README.md) document, for example `yolact_base_54_800000.pth`.
+**Step 2**. Download a pre-trained model from the list attached in the `Evaluation` section of `README.md` document, for example `yolact_base_54_800000.pth`.
 
 **Step 3**. Export the model to ONNX* format.
 
@@ -161,7 +161,6 @@ python3 eval.py \
 3. The script may fail, but you should get `yolact.onnx` file.
 
 **Step 4**. Convert the model to the IR:
-
 ```sh
 mo --input_model /path/to/yolact.onnx
 ```
@@ -171,7 +170,6 @@ mo --input_model /path/to/yolact.onnx
 To get performance gain by offloading to the OpenVINO application of mean/scale values and RGB->BGR conversion, use the following options of the Model Optimizer (MO):
 
 * If the backbone of the model is Resnet50-FPN or Resnet101-FPN, use the following MO command line:
-
 ```sh
 mo \
     --input_model /path/to/yolact.onnx \
@@ -180,11 +178,10 @@ mo \
     --scale_values "[58.40, 57.12, 57.38]"
 ```
 
-* If the backbone of the model is Darknet53-FPN, use the following MO command line:
-
+* If the backbone of the model is Darknet53-FPN, use the following command line:
 ```sh
 mo \
     --input_model /path/to/yolact.onnx \
     --reverse_input_channels \
     --scale 255
-```
\ No newline at end of file
+```
diff --git a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md
index 727bb5e343e..4ec00886f59 100644
--- a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md
+++ b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md
@@ -41,7 +41,7 @@ As a result the frozen model file `savedmodeldir/efficientdet-d4_frozen.pb` will
 
 > **NOTE**: For custom trained models, specify `--hparams` flag to `config.yaml` which was used during training.
 
-> **NOTE:** If you see an error `AttributeError: module 'tensorflow_core.python.keras.api._v2.keras.initializers' has no attribute 'variance_scaling'` apply the fix from the [patch](https://github.com/google/automl/pull/846).
+> **NOTE**: If you see an error `AttributeError: module 'tensorflow_core.python.keras.api._v2.keras.initializers' has no attribute 'variance_scaling'` apply the fix from the [patch](https://github.com/google/automl/pull/846).
 
 ### Convert EfficientDet TensorFlow Model to the IR
 
@@ -65,7 +65,7 @@ to the Model Optimizer on how to convert the model and trigger transformations i
 train the model yourself and modified the `hparams_config` file or the parameters are different from the ones used for EfficientDet-D4.
 The attribute names are self-explanatory or match the name in the `hparams_config` file.
 
-> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).
+> **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model to Intermediate Representation (IR)](../Converting_Model.md).
 
 OpenVINO&trade; toolkit provides samples that can be used to infer EfficientDet model. For more information, refer to 
 [Open Model Zoo Demos](@ref omz_demos) and 
diff --git a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md
index 1ee1ee511b1..2de32733bb6 100644
--- a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md
+++ b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md
@@ -45,7 +45,7 @@ To convert a TensorFlow\* Object Detection API model, go to the `<INSTALL_DIR>/t
 * `--tensorflow_object_detection_api_pipeline_config <path_to_pipeline.config>` --- A special configuration file that describes the topology hyper-parameters and structure of the TensorFlow Object Detection API model. For the models downloaded from the TensorFlow\* Object Detection API zoo, the configuration file is named `pipeline.config`. If you plan to train a model yourself, you can find templates for these files in the [models repository](https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs).
 * `--input_shape` (optional) --- A custom input image shape. Refer to [Custom Input Shape](#tf_od_custom_input_shape) for more information how the `--input_shape` parameter is handled for the TensorFlow* Object Detection API models.
 
-> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. If you convert a TensorFlow\* Object Detection API model to use with the Inference Engine sample applications, you must specify the `--reverse_input_channels` parameter. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).
+> **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. If you convert a TensorFlow\* Object Detection API model to use with the Inference Engine sample applications, you must specify the `--reverse_input_channels` parameter. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model to Intermediate Representation (IR)](../Converting_Model.md).
 
 Additionally to the mandatory parameters listed above you can use optional conversion parameters if needed. A full list of parameters is available in the [Converting a TensorFlow* Model](../Convert_Model_From_TensorFlow.md) topic.
 
diff --git a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_RetinaNet_From_Tensorflow.md b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_RetinaNet_From_Tensorflow.md
index 99511612509..f18a97301bf 100644
--- a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_RetinaNet_From_Tensorflow.md
+++ b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_RetinaNet_From_Tensorflow.md
@@ -7,7 +7,7 @@ To convert this model to the TensorFlow\* format, you can use [Reproduce Keras*
 
 After you convert the model to TensorFlow* format, run the Model Optimizer command below:
 ```sh
- mo --input "input_1[1 1333 1333 3]" --input_model retinanet_resnet50_coco_best_v2.1.0.pb --data_type FP32 --transformations_config front/tf/retinanet.json
+mo --input "input_1[1 1333 1333 3]" --input_model retinanet_resnet50_coco_best_v2.1.0.pb --data_type FP32 --transformations_config front/tf/retinanet.json
 ```
 
 Where `transformations_config` command-line parameter specifies the configuration json file containing model conversion hints for the Model Optimizer.
diff --git a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_XLNet_From_Tensorflow.md b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_XLNet_From_Tensorflow.md
index 547b48fc54a..cb05866d6a6 100644
--- a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_XLNet_From_Tensorflow.md
+++ b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_XLNet_From_Tensorflow.md
@@ -24,15 +24,13 @@ To get pb-file from the archive contents, you need to do the following.
 1. Run commands
 
 ```sh
-cd ~
-mkdir XLNet-Base
-cd XLNet-Base
-git clone https://github.com/zihangdai/xlnet
-wget https://storage.googleapis.com/xlnet/released_models/cased_L-12_H-768_A-12.zip
-unzip cased_L-12_H-768_A-12.zip
-mkdir try_save
-cd xlnet
-sed -i "s/tf\.train\.Optimizer/tf\.train.Optimizer if tf.version < '1.15' else tf.compat.v1.train.Optimizer/g" model_utils.py
+   cd ~
+   mkdir XLNet-Base
+   cd XLNet-Base
+   git clone https://github.com/zihangdai/xlnet
+   wget https://storage.googleapis.com/xlnet/released_models/cased_L-12_H-768_A-12.zip
+   unzip cased_L-12_H-768_A-12.zip
+   mkdir try_save
 ```
 
    
diff --git a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md
index 9eee61b7eae..cac5a80a4d7 100644
--- a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md
+++ b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md
@@ -35,9 +35,9 @@ python converter.py
 
 4. Run Model Optimizer to converter the model from the TensorFlow 2 format to an IR:
 
-> **NOTE:** Before you run the convertion, make sure you have installed all the Model Optimizer dependencies for TensorFlow 2.
+> **NOTE:** Before you run the conversion, make sure you have installed all the Model Optimizer dependencies for TensorFlow 2.
 ```sh
- mo --saved_model_dir yolov4 --output_dir models/IRs --input_shape [1,608,608,3] --model_name yolov4 
+mo --saved_model_dir yolov4 --output_dir models/IRs --input_shape [1,608,608,3] --model_name yolov4 
 ```
 
 ## <a name="yolov3-to-ir"></a>Convert YOLOv3 Model to IR
@@ -66,17 +66,17 @@ cd tensorflow-yolo-v3
 git checkout ed60b90
 ```
 3. Download [coco.names](https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names) file from the DarkNet website **OR** use labels that fit your task.
-4. Download the [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) (for the YOLOv3 model) or [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) (for the YOLOv3-tiny model) file **OR** use your pre-trained weights with the same structure
+4. Download the [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) (for the YOLOv3 model) or [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) (for the YOLOv3-tiny model) file **OR** use your pre-trained weights with the same structure.
 5. Install PIL, which is used by the conversion script in the repo:
 ```sh
 pip install PIL
 ```
 6. Run a converter:
-- for YOLO-v3:
+- For YOLO-v3:
 ```sh
 python3 convert_weights_pb.py --class_names coco.names --data_format NHWC --weights_file yolov3.weights
 ```
-- for YOLOv3-tiny:
+- For YOLOv3-tiny:
 ```sh
 python3 convert_weights_pb.py --class_names coco.names --data_format NHWC --weights_file yolov3-tiny.weights --tiny
 ```
@@ -145,7 +145,7 @@ where:
 * `--batch` defines shape of model input. In the example, `--batch` is equal to 1, but you can also specify other integers larger than 1.
 * `--transformations_config` adds missing `Region` layers to the model. In the IR, the `Region` layer has name `RegionYolo`.
 
-> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).
+> **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model to Intermediate Representation (IR)](../Converting_Model.md).
 
 OpenVINO&trade; toolkit provides a demo that uses YOLOv3 model. For more information, refer to [Object Detection C++ Demo](@ref omz_demos_object_detection_demo_cpp).
 
@@ -177,25 +177,25 @@ cd darkflow
 
 #### <a name="yolov1-v2-to-tf"></a>Convert DarkNet\* YOLOv1 or YOLOv2 Model to TensorFlow\*
 
-To convert YOLOv1 or YOLOv2 model to TensorFlow, go to the root directory of the cloned DarkFlow repository and run the following command:<br>
-For Yolo V1:
+To convert YOLOv1 or YOLOv2 model to TensorFlow, go to the root directory of the cloned DarkFlow repository, place downloaded above \*.cfg and \*.weights files in the current directory and run the following command:<br>
+- For YOLOv1:
 ```sh
 python3 flow --model yolov1.cfg --load yolov1.weights --savepb
 ```
 
-For Yolo V2 with VOC dataset `--labels` argument should be specified and additional changes in the original exporting script are required. 
+- For YOLOv2 with VOC dataset `--labels` argument should be specified and additional changes in the original exporting script are required. 
 In the file [https://github.com/thtrieu/darkflow/blob/b187c65/darkflow/utils/loader.py#L121](https://github.com/thtrieu/darkflow/blob/b187c65630f9aa1bb8b809c33ec67c8cc5d60124/darkflow/utils/loader.py#L121) 
 change line 121 from `self.offset = 16` to `self.offset = 20`. Then run:
 ```sh
 python3 flow --model yolov2-voc.cfg --load yolov2-voc.weights --labels voc-labels.txt --savepb
 ```
-voc-labels can be found by the following link https://raw.githubusercontent.com/szaza/android-yolo-v2/master/assets/tiny-yolo-voc-labels.txt
+VOC labels can be found on the following link https://raw.githubusercontent.com/szaza/android-yolo-v2/master/assets/tiny-yolo-voc-labels.txt
 
 General conversion command is:
 ```sh
 python3 flow --model <path_to_model>/<model_name>.cfg --load <path_to_model>/<model_name>.weights --labels <path_to_dataset_labels_file> --savepb
 ```
-Where argument `--labels` for Yolo V1 can be skipped. If the model was successfully converted, you can find the `<model_name>.meta` and `<model_name>.pb` files
+For YOLOv1  the argument `--labels` can be skipped. If the model was successfully converted, you can find the `<model_name>.meta` and `<model_name>.pb` files
 in `built_graph`  subdirectory of the cloned DarkFlow repository.
 
 File `<model_name>.pb` is a TensorFlow representation of the YOLO model.
@@ -227,4 +227,4 @@ The model was trained with input values in the range `[0,1]`. OpenVINO&trade; to
 * `--transformations_config` adds missing `Region` layers to the model. In the IR, the `Region` layer has name `RegionYolo`.
 For other applicable parameters, refer to [Convert Model from TensorFlow](../Convert_Model_From_TensorFlow.md).
 
-> **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).
\ No newline at end of file
+> **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model to Intermediate Representation (IR)](../Converting_Model.md).
diff --git a/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md b/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
index d29b2edeb6c..ffbf1eeb645 100644
--- a/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
+++ b/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
@@ -1,5 +1,18 @@
 # Model Optimizer Extensibility {#openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Extending_Model_Optimizer_with_New_Primitives
+   openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Extending_Model_Optimizer_With_Caffe_Python_Layers
+   openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Extending_Model_Optimizer_with_New_Primitives
+   openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Legacy_Mode_for_Caffe_Custom_Layers
+
+@endsphinxdirective
+
 - <a href="#model-representation-in-memory">Model Representation in Memory</a>
 - <a href="#model-conversion-pipeline">Model Conversion Pipeline</a>
   - <a href="#model-loading">Model Loading</a>
@@ -34,7 +47,7 @@
 
 <a name="model-optimizer-extensibility"></a>Model Optimizer extensibility mechanism enables support of new operations and custom transformations to generate the optimized intermediate representation (IR) as described in the 
 [Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™](../../IR_and_opsets.md). This
-mechanism is a core part of the Model Optimizer, which uses it under the hood, so the Model Optimizer itself is a huge set of examples for adding custom logic to support your model.
+mechanism is a core part of the Model Optimizer. The Model Optimizer itself uses it under the hood, being a huge set of examples on how to add custom logic to support your model.
 
 There are several cases when the customization is needed:
 
diff --git a/docs/MO_DG/prepare_model/vtune_option.png b/docs/MO_DG/prepare_model/vtune_option.png
new file mode 100644
index 00000000000..f284465314b
--- /dev/null
+++ b/docs/MO_DG/prepare_model/vtune_option.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a82b414dbc4f7ce2eae625bb7c9c7b88c154a7c476374683dd9886564560f67
+size 7951
diff --git a/docs/MO_DG/prepare_model/vtune_timeline.png b/docs/MO_DG/prepare_model/vtune_timeline.png
new file mode 100644
index 00000000000..f29cc161cda
--- /dev/null
+++ b/docs/MO_DG/prepare_model/vtune_timeline.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c514316f78f04e8c000f6b95dc579d8c63c57f00c4c980ea4d358a6a4f1b9d7e
+size 8744
diff --git a/docs/MO_DG/prepare_model/vtune_topdown_view.jpg b/docs/MO_DG/prepare_model/vtune_topdown_view.jpg
new file mode 100644
index 00000000000..fd3431c97ec
--- /dev/null
+++ b/docs/MO_DG/prepare_model/vtune_topdown_view.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40c4b9096ef264807d930fe64d427f53a69ce2247c836415e64c5aa72d9f245e
+size 36468
diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css
new file mode 100644
index 00000000000..07533f91be3
--- /dev/null
+++ b/docs/_static/css/custom.css
@@ -0,0 +1,55 @@
+.switcher-set {
+    margin-bottom:1rem;
+}
+
+main img {
+    cursor: pointer;
+}
+
+.doxyrest-title-code-block {
+    margin-bottom: 0;
+}
+
+/* Performance Benchmark Graphs */
+
+.chart {
+    height: 500px;
+    padding:0;
+}
+
+/* Table Sort Button */
+
+.sort-header {
+    cursor: pointer;
+}
+
+.sort-btn {
+    content: "";
+    background-image:url('media/arrow-small-opposite-v.svg');
+    background-size: 100% 100%;
+    background-position: center;
+    background-repeat: no-repeat;
+    display: inline-block;
+    height: 1.5rem;
+    width:1.5rem;
+    position:relative;
+    top:0.5rem;
+}
+
+.sort-btn.sort-active.ascending,
+.sort-btn.sort-active {
+    background-size: 100% 70%;
+}
+
+.sort-btn.sort-active.ascending {
+    background-image: url('media/union-down.svg');
+}
+
+.sort-btn.sort-active {
+    background-image: url('media/union-up.svg');
+}
+
+.highlight .err {
+    border:none;
+    color:inherit;
+}
diff --git a/docs/_static/css/getstarted_style.css b/docs/_static/css/getstarted_style.css
new file mode 100644
index 00000000000..1d6b35a1a5f
--- /dev/null
+++ b/docs/_static/css/getstarted_style.css
@@ -0,0 +1,101 @@
+/* overrides */
+.switcher-set {display: none;}
+
+
+
+
+/* === INTRO TEXT ======================================================================== */
+
+#GSG_introtext a {
+	background-color: #0068B5;
+	height: 100px;
+	padding: 9px;
+	float: left;
+	margin: 20px 1% 35px 1%;
+	width: 32%;
+	color: #fff;
+	text-align: center;
+}
+#GSG_introtext a::after {
+	display: block;
+	font-size: 0.8em;
+	margin-top: 6px;
+	padding-top:3px;
+	border-top: solid 1px #fff;
+}
+
+#GSG_introtext a:nth-of-type(1)::after {content: "Intel Distribution";}
+#GSG_introtext a:nth-of-type(2)::after {content: "PIP, Docker, Conda, Apt, Yum";}
+#GSG_introtext a:nth-of-type(3)::after {content: "GitHub";}
+
+#GSG_introtext a:nth-of-type(1) {margin-left: 0;}
+#GSG_introtext a:nth-of-type(3) {margin-right: 0;}
+
+
+
+
+/* === SECOND STEP CHOICE ================================================================ */
+#GSG_nextstepchoice {
+	margin: 0 auto;
+	width: 100%;
+}
+#GSG_nextstepchoice a {
+	display: block;
+	width: 100%;
+	padding: 10px 15px;
+}
+#GSG_nextstepchoice a h4 {
+	display: inline-block;
+    height: 30px;	
+    color: #0068B5;
+    font-size: 1.1em;
+    line-height: 2em;
+    font-weight: bold;
+    margin: 0;
+
+    border-bottom: 1px solid #0068B5;
+	
+	transition: all 0.5s;
+	-webkit-transition: all 0.5s;
+	-moz-transition: all 0.5s;
+	-o-transition: all 0.5s;
+	-ms-transition: all 0.5s;
+	
+}
+#GSG_nextstepchoice a p {
+	color: #0068B5;
+	text-align: left;
+	line-height:1.3em;
+	margin: 0;
+}
+
+#GSG_nextstepchoice a:hover h4 {padding-left: 15px;}
+
+#GSG_nextstepchoice a:hover,
+#GSG_nextstepchoice a:hover h4,
+#GSG_nextstepchoice a:hover p {
+	text-decoration:none;	
+}
+
+
+
+
+
+
+
+
+/* === screen narrow =========================================================================== */
+@media only screen and (max-width: 800px) {
+	#GSG_introtext a {							/* === intro tiles float === */
+		width: 80%;
+		clear: both;
+		margin-left: auto;
+		margin-right: auto;
+	}
+	#GSG_nextstepchoice a {						/* === further steps height === */
+		height: 140px;
+	}
+	
+}
+
+
diff --git a/docs/_static/css/homepage_style.css b/docs/_static/css/homepage_style.css
new file mode 100644
index 00000000000..b1b02dd0424
--- /dev/null
+++ b/docs/_static/css/homepage_style.css
@@ -0,0 +1,206 @@
+/* overrides */
+.switcher-set {display: none;}
+
+#openvino-documentation > h1 {
+	display: none;
+}
+
+img {
+	cursor: default;
+}
+
+/* === SEPARATOR HEADERS ================================================= */
+.HP_separator-header { 
+	font-weight: bold;
+	text-align: center;
+	width: 100%;
+	height: 2em;
+	line-height: 2em;
+	margin: 40px auto;
+}
+
+.HP_separator-header p { 
+	display: inline-block;
+	line-height: 1em;
+	margin: 0 auto;
+	color: #0068B5;
+	background-color: #fff;
+	padding: 0 15px;
+}
+
+.HP_separator-header::before { 
+    display: block;
+	content: "";
+	position: relative;
+	top: 1em;
+	margin: 0;
+    border-top: 1px solid #0068B5;
+    width: 100%;
+	z-index: -1;
+}
+
+/* === HEADER BANNER ===================================================== */
+#HP_head_banner {
+	width: 100%;
+	height: auto;
+	background: left / cover no-repeat url("../images/head_banner.jpg");
+	overflow: hidden;
+	padding: 3% 5% 5% 5%;
+	margin-bottom: 20px;
+	margin-top: -15px;
+}
+#HP_head_banner > p {
+float: right;
+    font-size: 1.5em;
+    font-weight: bold;
+    color: #fff;
+    width: 100%;
+    text-align: right;
+    background-color: #0008;
+    margin-bottom: 40px;
+    margin-top: 10px;
+}
+#HP_head_banner > div {
+	float: left;
+	width: 350px;
+}
+#HP_head_banner > div > a {
+	height: 2em;
+	width: 2em;
+	float: left;
+	margin-right: 5px;
+	text-align: center;
+	background-color: #fff;
+	padding: 3px;
+	
+	transition: all 0.5s;
+	-webkit-transition: all 0.5s;
+	-moz-transition: all 0.5s;
+	-o-transition: all 0.5s;
+	-ms-transition: all 0.5s;
+}
+#HP_head_banner > div > a img{
+	vertical-align: inherit;
+	transition: all 0.5s;
+	-webkit-transition: all 0.5s;
+	-moz-transition: all 0.5s;
+	-o-transition: all 0.5s;
+	-ms-transition: all 0.5s;
+}
+
+#HP_head_banner > div > a:nth-of-type(1) {
+	width: 9em;
+	padding: 4px;
+	font-weight: bold;
+	color: #0068B5;
+	text-decoration: none;
+}
+
+#HP_head_banner > div > a:hover {
+	color: #fff;
+	background-color: #0068B5;
+}
+#HP_head_banner > div > a:hover img{
+	filter: brightness(0%) invert(100%);
+}
+
+#HP_head_banner ~ div ~ p {
+	text-align: left;
+}
+
+.HP_img_chart {
+	display: block;
+	margin: 40px auto 60px auto;
+	width:80%;
+}
+img.HP_img_chart ~ p {
+	margin-top: -40px;
+	font-size: 0.7em;
+}
+
+
+/* === INFORMATION BOXES ================================================= */
+.HP_infoboxes {
+	width: 100%;
+	height: auto;
+	margin: 0; 
+	margin-bottom: 10px;
+}
+.HP_infoboxes a {
+	background-color: #0068B5;
+	height: 150px;
+	padding: 5px 2%;
+	float: left;
+	margin: 0 1% 10px 1%;
+	width: 32%;
+}
+
+.HP_infoboxes a p,
+.HP_infoboxes a h3 {
+	color: #fff;
+	margin: 0;
+	text-align: left;
+	
+	transition: all 0.5s;
+	-webkit-transition: all 0.5s;
+	-moz-transition: all 0.5s;
+	-o-transition: all 0.5s;
+	-ms-transition: all 0.5s;
+}
+.HP_infoboxes a h3 {
+	display: table-cell;
+	width: 300px;
+	vertical-align: middle;
+	height: 35px;
+	border-bottom: solid 1px #fff;
+	font-weight: bold;
+	font-size: 1em;
+	margin-bottom: 5px;
+}
+.HP_infoboxes a p {
+	font-size: 0.9em;
+	line-height: 1.5em;
+	    margin-top: 5px;
+}
+
+.HP_infoboxes a:hover h3 {
+	font-size: 1.1em;
+	text-decoration:none !important;
+}
+/*.HP_infoboxes a:hover p {
+	margin-top: 10px;
+	text-decoration:none !important;
+}*/
+
+/* === screen wide =================================================== */
+/* =================================================================== */
+@media only screen and (min-width: 901px) {
+	.HP_infoboxes a {
+		width: 32%;
+	}	
+	.HP_infoboxes a:nth-of-type(1), 
+	.HP_infoboxes a:nth-of-type(4), 
+	.HP_infoboxes a:nth-of-type(7) {
+		margin-left: 0;
+	}
+	.HP_infoboxes a:nth-of-type(3), 
+	.HP_infoboxes a:nth-of-type(6), 
+	.HP_infoboxes a:nth-of-type(9) {
+		margin-right: 0;
+	}	
+}
+/* === screen narrow =================================================== */
+/* =================================================================== */
+@media only screen and (max-width: 900px) {
+	.HP_infoboxes a {
+		width: 48%;
+		margin-left: 0;
+		margin-right: 2%;
+	}	
+	.HP_infoboxes a:nth-of-type(2n) {
+		margin-left: 2%;
+		margin-right: 0;
+	}	
+}
+
+
diff --git a/docs/_static/css/media/arrow-small-opposite-v.svg b/docs/_static/css/media/arrow-small-opposite-v.svg
new file mode 100644
index 00000000000..6d16d93ed0e
--- /dev/null
+++ b/docs/_static/css/media/arrow-small-opposite-v.svg
@@ -0,0 +1,4 @@
+<svg width="64" height="64" viewBox="0 0 64 64" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M20.0001 40L13.0393 40C12.1484 40 11.7022 41.0771 12.3322 41.7071L23.293 51.6679C23.4882 51.8631 23.7442 52 24.0001 52C24.256 52 24.5119 51.8631 24.7072 51.6679L35.668 41.7071C36.298 41.0771 35.8518 40 34.9609 40L28.0001 40L28.0001 33C28.0001 32.4477 27.5524 32 27.0001 32L21.0001 32C20.4479 32 20.0001 32.4477 20.0001 33L20.0001 40Z" fill="#2D3741"/>
+<path d="M36.0002 24L29.0393 24C28.1484 24 27.7022 22.9229 28.3322 22.2929L39.293 12.3321C39.4883 12.1369 39.7442 12 40.0001 12C40.256 12 40.5119 12.1369 40.7072 12.3321L51.668 22.2929C52.298 22.9229 51.8518 24 50.9609 24L44.0002 24L44.0002 31C44.0002 31.5523 43.5524 32 43.0002 32L37.0002 32C36.4479 32 36.0002 31.5523 36.0002 31L36.0002 24Z" fill="#2D3741"/>
+</svg>
diff --git a/docs/_static/css/media/union-down.svg b/docs/_static/css/media/union-down.svg
new file mode 100644
index 00000000000..747a6a55ad1
--- /dev/null
+++ b/docs/_static/css/media/union-down.svg
@@ -0,0 +1,3 @@
+<svg width="24" height="49" viewBox="0 0 24 49" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M7.99547 36.0049L0.995481 36.0049C-0.0045201 36.0049 -0.254518 37.2549 0.302333 37.7028L11.2844 47.712C11.4819 47.9095 11.7414 48.0071 12.0003 48.0048C12.2591 48.0071 12.5186 47.9095 12.7161 47.712L23.6205 37.6939C24.308 37.0674 23.9955 35.9964 22.9955 35.9964L15.9951 35.9964L15.9951 1.00491C15.9951 0.452621 15.5473 0.00490502 14.9951 0.00490505L8.99547 0.00490531C8.44319 0.00490533 7.99547 0.452621 7.99547 1.00491L7.99547 36.0049Z" fill="#2D3741"/>
+</svg>
diff --git a/docs/_static/css/media/union-up.svg b/docs/_static/css/media/union-up.svg
new file mode 100644
index 00000000000..a518b92e08c
--- /dev/null
+++ b/docs/_static/css/media/union-up.svg
@@ -0,0 +1,3 @@
+<svg width="24" height="49" viewBox="0 0 24 49" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M7.99547 12.0049L0.995481 12.0049C-0.0045201 12.0049 -0.254518 10.7549 0.302333 10.307L11.2796 0.297775C11.4771 0.100294 11.7367 0.00267807 11.9955 0.00492112C12.2543 0.00267809 12.5138 0.100294 12.7113 0.297775L23.6205 10.3159C24.308 10.9424 23.9955 12.0134 22.9955 12.0134L15.9951 12.0134L15.9951 47.0049C15.9951 47.5572 15.5473 48.0049 14.9951 48.0049L8.99547 48.0049C8.44319 48.0049 7.99547 47.5572 7.99547 47.0049L7.99547 12.0049Z" fill="#2D3741"/>
+</svg>
diff --git a/docs/_static/css/viewer.min.css b/docs/_static/css/viewer.min.css
new file mode 100644
index 00000000000..5b32e904ae5
--- /dev/null
+++ b/docs/_static/css/viewer.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Viewer.js v1.10.1
+ * https://fengyuanchen.github.io/viewerjs
+ *
+ * Copyright 2015-present Chen Fengyuan
+ * Released under the MIT license
+ *
+ * Date: 2021-08-01T13:35:44.576Z
+ */.viewer-close:before,.viewer-flip-horizontal:before,.viewer-flip-vertical:before,.viewer-fullscreen-exit:before,.viewer-fullscreen:before,.viewer-next:before,.viewer-one-to-one:before,.viewer-play:before,.viewer-prev:before,.viewer-reset:before,.viewer-rotate-left:before,.viewer-rotate-right:before,.viewer-zoom-in:before,.viewer-zoom-out:before{background-image:url("data:image/png;base64,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");background-repeat:no-repeat;background-size:280px;color:transparent;display:block;font-size:0;height:20px;line-height:0;width:20px}.viewer-zoom-in:before{background-position:0 0;content:"Zoom In"}.viewer-zoom-out:before{background-position:-20px 0;content:"Zoom Out"}.viewer-one-to-one:before{background-position:-40px 0;content:"One to One"}.viewer-reset:before{background-position:-60px 0;content:"Reset"}.viewer-prev:before{background-position:-80px 0;content:"Previous"}.viewer-play:before{background-position:-100px 0;content:"Play"}.viewer-next:before{background-position:-120px 0;content:"Next"}.viewer-rotate-left:before{background-position:-140px 0;content:"Rotate Left"}.viewer-rotate-right:before{background-position:-160px 0;content:"Rotate Right"}.viewer-flip-horizontal:before{background-position:-180px 0;content:"Flip Horizontal"}.viewer-flip-vertical:before{background-position:-200px 0;content:"Flip Vertical"}.viewer-fullscreen:before{background-position:-220px 0;content:"Enter Full Screen"}.viewer-fullscreen-exit:before{background-position:-240px 0;content:"Exit Full Screen"}.viewer-close:before{background-position:-260px 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#fff;box-shadow:0 0 3px #fff;outline:0;position:relative;z-index:1}.viewer-toolbar>ul>li:before{margin:2px}.viewer-toolbar>ul>li+li{margin-left:1px}.viewer-toolbar>ul>.viewer-small{height:18px;margin-bottom:3px;margin-top:3px;width:18px}.viewer-toolbar>ul>.viewer-small:before{margin:-1px}.viewer-toolbar>ul>.viewer-large{height:30px;margin-bottom:-3px;margin-top:-3px;width:30px}.viewer-toolbar>ul>.viewer-large:before{margin:5px}.viewer-tooltip{background-color:rgba(0,0,0,.8);border-radius:10px;color:#fff;display:none;font-size:12px;height:20px;left:50%;line-height:20px;margin-left:-25px;margin-top:-10px;position:absolute;text-align:center;top:50%;width:50px}.viewer-title{color:#ccc;display:inline-block;font-size:12px;line-height:1;margin:0 5% 5px;max-width:90%;opacity:.8;overflow:hidden;text-overflow:ellipsis;-webkit-transition:opacity .15s;transition:opacity 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viewer-spinner{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes viewer-spinner{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}.viewer-loading:after{-webkit-animation:viewer-spinner 1s linear infinite;animation:viewer-spinner 1s linear infinite;border:4px solid hsla(0,0%,100%,.1);border-left-color:hsla(0,0%,100%,.5);border-radius:50%;content:"";display:inline-block;height:40px;left:50%;margin-left:-20px;margin-top:-20px;position:absolute;top:50%;width:40px;z-index:1}@media (max-width:767px){.viewer-hide-xs-down{display:none}}@media (max-width:991px){.viewer-hide-sm-down{display:none}}@media (max-width:1199px){.viewer-hide-md-down{display:none}}
\ No newline at end of file
diff --git a/docs/doxygen/assets/images/favicon.ico b/docs/_static/favicon.ico
similarity index 100%
rename from docs/doxygen/assets/images/favicon.ico
rename to docs/_static/favicon.ico
diff --git a/docs/_static/images/HP_ov_flow.svg b/docs/_static/images/HP_ov_flow.svg
new file mode 100644
index 00000000000..902a0de840d
--- /dev/null
+++ b/docs/_static/images/HP_ov_flow.svg
@@ -0,0 +1,62 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg width="100%" height="100%" viewBox="0 0 1300 421" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:1.5;">
+    <g transform="matrix(1,0,0,1,-3564.04,-690.017)">
+        <g id="Artboard3" transform="matrix(1,0,0,1,-7.53286e-05,0)">
+            <rect x="3400" y="0" width="1650" height="1199.97" style="fill:none;"/>
+            <g transform="matrix(1.42952,0,0,0.459279,-1469.37,725.179)">
+                <rect x="3521.05" y="45.956" width="255.921" height="255.921" style="fill:rgb(0,104,181);"/>
+            </g>
+            <g transform="matrix(2.19776,0,0,13.773,3285.16,-3926.44)">
+                <g transform="matrix(0.455009,-0,-0,0.0726059,126.894,335.181)">
+                    <path d="M429.331,200.779L456.331,209.779L429.331,218.779L429.331,200.779Z" style="fill:rgb(0,104,181);"/>
+                    <path d="M376.331,209.779L434.731,209.779" style="fill:none;stroke:rgb(0,104,181);stroke-width:5px;"/>
+                </g>
+            </g>
+            <g transform="matrix(2.19776,0,0,13.773,3751.98,-3926.44)">
+                <g transform="matrix(0.455009,-0,-0,0.0726059,-85.5147,335.181)">
+                    <path d="M896.153,200.779L923.153,209.779L896.153,218.779L896.153,200.779Z" style="fill:rgb(0,104,181);"/>
+                    <path d="M843.153,209.779L901.553,209.779" style="fill:none;stroke:rgb(0,104,181);stroke-width:5px;"/>
+                </g>
+            </g>
+            <g transform="matrix(1.84815,0,0,1.84815,2999.08,208.138)">
+                <text x="341.467px" y="329.069px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">T<tspan x="351.221px 356.882px " y="329.069px 329.069px ">ra</tspan>in your model</text>
+            </g>
+            <g transform="matrix(1.42952,0,0,0.459279,-1002.55,668.91)">
+                <rect x="3521.05" y="45.956" width="255.921" height="255.921" style="fill:rgb(0,104,181);"/>
+            </g>
+            <g transform="matrix(1.84815,0,0,1.84815,3466.11,151.868)">
+                <text x="352.102px" y="320.291px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">Convert to the</text>
+                <text x="334.16px" y="337.847px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">OpenVINO format*</text>
+            </g>
+            <g transform="matrix(1.42952,0,0,0.459279,-1002.55,819.919)">
+                <rect x="3521.05" y="45.956" width="255.921" height="255.921" style="fill:rgb(0,104,181);"/>
+            </g>
+            <g transform="matrix(1.84815,0,0,1.84815,3466.11,302.878)">
+                <text x="352.098px" y="329.069px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">Run Inference</text>
+            </g>
+            <g transform="matrix(1.42952,0,0,0.459279,-535.727,819.919)">
+                <rect x="3521.05" y="45.956" width="255.921" height="255.921" style="fill:rgb(0,104,181);"/>
+            </g>
+            <g transform="matrix(1.84815,0,0,1.84815,3932.93,302.878)">
+                <text x="317.135px" y="329.069px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">Deploy your application</text>
+            </g>
+            <g transform="matrix(1.42952,0,0,0.459279,-1002.55,971.372)">
+                <rect x="3521.05" y="45.956" width="255.921" height="255.921" style="fill:rgb(0,104,181);"/>
+            </g>
+            <g transform="matrix(1.84815,0,0,1.84815,3466.11,454.331)">
+                <text x="333.994px" y="329.069px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">T<tspan x="343.747px 353.202px " y="329.069px 329.069px ">un</tspan>e and Optimize</text>
+            </g>
+            <g transform="matrix(1.84815,0,0,1.84815,2999.29,319.809)">
+                <text x="392.743px" y="319.908px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:rgb(0,104,181);">OR</text>
+            </g>
+            <g transform="matrix(1.42952,0,0,0.459279,-1469.37,914.659)">
+                <rect x="3521.05" y="45.956" width="255.921" height="255.921" style="fill:rgb(0,104,181);"/>
+            </g>
+            <g transform="matrix(1.84815,0,0,1.84815,2999.08,397.618)">
+                <text x="381.78px" y="320.291px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">Pick a</text>
+                <text x="338.309px" y="337.847px" style="font-family:'ArialMT', 'Arial', sans-serif;font-size:17px;fill:white;">pre-trained model</text>
+            </g>
+        </g>
+    </g>
+</svg>
diff --git a/docs/_static/images/download_btn_github.svg b/docs/_static/images/download_btn_github.svg
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--- /dev/null
+++ b/docs/_static/images/download_btn_github.svg
@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg width="100%" height="100%" viewBox="0 0 24 24" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;">
+    <path d="M12,0C5.374,0 0,5.373 0,12C0,17.302 3.438,21.8 8.207,23.387C8.806,23.498 9,23.126 9,22.81L9,20.576C5.662,21.302 4.967,19.16 4.967,19.16C4.421,17.773 3.634,17.404 3.634,17.404C2.545,16.659 3.717,16.675 3.717,16.675C4.922,16.759 5.556,17.912 5.556,17.912C6.626,19.746 8.363,19.216 9.048,18.909C9.155,18.134 9.466,17.604 9.81,17.305C7.145,17 4.343,15.971 4.343,11.374C4.343,10.063 4.812,8.993 5.579,8.153C5.455,7.85 5.044,6.629 5.696,4.977C5.696,4.977 6.704,4.655 8.997,6.207C9.954,5.941 10.98,5.808 12,5.803C13.02,5.808 14.047,5.941 15.006,6.207C17.297,4.655 18.303,4.977 18.303,4.977C18.956,6.63 18.545,7.851 18.421,8.153C19.191,8.993 19.656,10.064 19.656,11.374C19.656,15.983 16.849,16.998 14.177,17.295C14.607,17.667 15,18.397 15,19.517L15,22.81C15,23.129 15.192,23.504 15.801,23.386C20.566,21.797 24,17.3 24,12C24,5.373 18.627,0 12,0Z" style="fill:rgb(0,104,181);fill-rule:nonzero;"/>
+</svg>
diff --git a/docs/_static/images/download_btn_installer.svg b/docs/_static/images/download_btn_installer.svg
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index 00000000000..c9a23670f77
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+++ b/docs/_static/images/download_btn_installer.svg
@@ -0,0 +1,4 @@
+<svg width="64" height="64" viewBox="0 0 64 64" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M19.039 28H27.9998V5C27.9998 4.44772 28.4475 4 28.9998 4H30.9998C31.5521 4 31.9998 4.44772 31.9998 5V28H40.9606C41.8515 28 42.2977 29.0771 41.6677 29.7071L30.7069 39.707C30.3164 40.0976 29.6832 40.0976 29.2927 39.707L18.3319 29.7071C17.7019 29.0771 18.1481 28 19.039 28Z" fill="#0068B5"/>
+<path d="M15.5858 36C15.851 36 16.1054 36.1054 16.2929 36.2929L29.4179 47.7929C29.8084 48.1834 30.4416 48.1834 30.8321 47.7929L43.7071 36.2929C43.8946 36.1054 44.149 36 44.4142 36H52C54.2091 36 56 37.7909 56 40V56C56 58.2091 54.2091 60 52 60H8C5.79086 60 4 58.2091 4 56V40C4 37.7909 5.79086 36 8 36H15.5858Z" fill="#0068B5"/>
+</svg>
diff --git a/docs/_static/images/head_banner.jpg b/docs/_static/images/head_banner.jpg
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+++ b/docs/_static/images/head_banner.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07de2ee0d18f1d40afe1f2bb5420c7060eff09026a9138399d21b49b35cc0b8e
+size 184552
diff --git a/docs/_static/images/ov_chart.png b/docs/_static/images/ov_chart.png
new file mode 100644
index 00000000000..f91923443c3
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+++ b/docs/_static/images/ov_chart.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:f9a0d138f7f6d2546f0e48d9240d6a90aec18dc6d8092e1082b2fc3125f1ce3d
+size 108434
diff --git a/docs/_static/js/custom.js b/docs/_static/js/custom.js
new file mode 100644
index 00000000000..bf2c5f9e2bb
--- /dev/null
+++ b/docs/_static/js/custom.js
@@ -0,0 +1,179 @@
+var sw_anchors = {};
+var urlSearchParams = new URLSearchParams(window.location.search);
+var sw_timer;
+
+/* Adobe Analytics */
+var wapLocalCode = 'us-en';
+var wapSection = 'openvinotoolkit';
+
+(function () {
+    var host = (window.document.location.protocol == 'http:') ? "http://www.intel.com" : "https://www.intel.com";
+    var url = host + "/content/dam/www/global/wap/tms-loader.js"; //wap file url
+    var po = document.createElement('script');
+    po.type = 'text/javascript'; po.async = true; po.src = url;
+    var s = document.getElementsByTagName('head')[0];
+    s.appendChild(po);
+})();
+
+$(document).ready(function() {
+    init_col_sections();
+    init_switchers();
+    handleSwitcherParam();
+    initViewerJS();
+    var TABLE_SORT = window.TABLE_SORT;
+    if (TABLE_SORT) {
+        addTableSort();
+    }
+});
+
+
+function addTableSort() {
+    var tables = $('table.table');
+    tables.each(function() {
+        var table = $(this);
+        var headings = table.find('th');
+        headings.each(function() {
+            var th = $(this);
+            var index = th.index();
+            var sortBtn = $('<span class="sort-btn"></span>');
+            th.addClass('sort-header');
+            th.click(function(){
+                var counter = 0;
+                sortBtn.addClass('sort-active');
+                sortBy = sortBtn.data('sortby');
+                var trs = table.find('tbody tr');
+                sortBtn.toggleClass('ascending');
+                trs.sort(function(item1, item2) {
+                    
+                    if (sortBtn.hasClass('ascending')) {
+                        var text1 = $(item1).find('td').eq(index).text();
+                        var text2 = $(item2).find('td').eq(index).text();
+                    }
+                    else {
+                        var text1 = $(item2).find('td').eq(index).text();
+                        var text2 = $(item1).find('td').eq(index).text();
+                    }
+                    // try converting to num
+                    var _text1 = parseFloat(text1);
+                    var _text2 = parseFloat(text2);
+
+                    if (!isNaN(_text1) && !isNaN(_text2)) {
+                        text1 = _text1;
+                        text2 = _text2;
+                    }
+                    if (text1 > text2) {
+                        return 1;
+                    }
+                    else if (text1 < text2) {
+                        return -1;
+                    }
+                    else {
+                        return 0;
+                    }
+                }).map(function() {
+                    var row = $(this);
+                    if (counter % 2 === 0) {
+                        row.removeClass('row-odd');
+                        row.addClass('row-even');
+                    }
+                    else {
+                        row.removeClass('row-even');
+                        row.addClass('row-odd');
+                    }
+                    counter++;
+                    table.find('tbody').append(row);
+                });
+
+                headings.each(function() {
+                    if ($(this).index() !== index) {
+                        $(this).find('.sort-btn').removeClass('ascending');
+                        $(this).find('.sort-btn').removeClass('sort-active');
+                    }
+                });
+            });
+            th.find('p').append(sortBtn);
+        });
+    });
+}
+
+function initViewerJS() {
+    try {
+        var images =$('main img[src*="_images"]');
+        images.each(function() {
+            try{
+                new Viewer($(this).get(0));
+            }
+            catch (err) {
+                console.log(err);
+            }
+        });
+    }
+    catch(err) {
+        console.log(err);
+    }
+}
+
+function init_col_sections() {
+    var collapsible_sections = $('div.collapsible-section');
+    collapsible_sections.each(function() {
+        try {
+            var title = $(this).data('title') || 'Click to expand';
+            var summary = $('<summary>' + title + '</summary>');
+            // summary.html(title);
+            var details = $('<details class="col-sect-details"></details>');
+            $(this).wrap(details);
+            summary.insertBefore($(this));
+        }
+        catch(err) {
+            console.log(err);
+        }
+    });
+}
+
+function handleSwitcherParam() {
+    var sw_type = urlSearchParams.get('sw_type');
+    var section_id;
+    if (sw_type && sw_type in sw_anchors) {
+        section_id = sw_anchors[sw_type];
+    }
+    else {
+        section_id = sw_anchors['default'];
+    }
+    $('.reference.internal.nav-link[href="#' + section_id + '"]').parent('li').css('display', 'block');
+    $('#' + section_id).css('display', 'block');
+    $('#button-' + section_id).removeClass('bttn-prm')
+    $('#button-' + section_id).addClass('bttn-act');
+    $('#button-' + section_id).attr('style', 'color: #fff !important');
+}
+
+function init_switchers() {
+    var switcherAnchors = $('.switcher-anchor');
+    if (switcherAnchors.length === 0) {
+        return
+    }
+    var switcherPanel = $('<div></div>');
+    switcherPanel.addClass('switcher-set');
+    for (var i = 0; i < switcherAnchors.length; i++) {
+        var anchor = $(switcherAnchors[i]);
+        var option = $(anchor).text();
+        var id = $(anchor).attr('id');
+        var link = $('<a></a>');
+        link.text(option);
+        link.attr('href', '?sw_type=' + id);
+        link.addClass('button bttn-prm button-size-m');
+        switcherPanel.append(link);
+        var section = $(anchor).parent('div.section');
+        section.css('display', 'none');
+        var section_id = section.attr('id');
+        link.attr('id', 'button-' + section_id);
+        $('.reference.internal.nav-link[href="#' + section_id + '"]').parent('li').css('display', 'none');
+        section.addClass('switcher-content');
+        sw_anchors[id] = section_id;
+        if (i === 0) {
+            sw_anchors['default'] = section_id;
+        }
+    }
+
+    $('main').prepend(switcherPanel);
+    switcherAnchors.remove();
+}
diff --git a/docs/_static/js/graphs.js b/docs/_static/js/graphs.js
new file mode 100644
index 00000000000..a6e375dd13c
--- /dev/null
+++ b/docs/_static/js/graphs.js
@@ -0,0 +1,289 @@
+$(document).ready(function () {
+    var chartBlock = $('.chart-block');
+    chartBlock.each(function () {
+        var url = $(this).data('loadcsv');
+        Papa.parse(url, {
+            download: true,
+            complete: renderData($(this))
+        })
+    });
+
+    function getLabels(data, ieType) {
+        return data
+            .filter((item) => item[1] === ieType)
+            .map((item) => item[2]);
+    }
+
+    var CONFIG = {
+        core: {
+            throughput: {
+                chartTitle: 'Throughput (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS (INT8)' }, { data: null, color: '#0068B5', label: 'FPS (FP32)' }],
+            },
+            latency: {
+                chartTitle: 'Latency (lower is better)',
+                datasets: [{ data: null, color: '#8F5DA2', label: 'Milliseconds' }],
+            },
+            value: {
+                chartTitle: 'Value (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS/$ (INT8)' }],
+            },
+            efficiency: {
+                chartTitle: 'Efficiency (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS/TDP (INT8)' }],
+            }
+        },
+        atom: {
+            throughput: {
+                chartTitle: 'Throughput (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS (INT8)' }, { data: null, color: '#0068B5', label: 'FPS (FP32)' }],
+            },
+            latency: {
+                chartTitle: 'Latency (lower is better)',
+                datasets: [{ data: null, color: '#8F5DA2', label: 'Milliseconds' }],
+            },
+            value: {
+                chartTitle: 'Value (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS/$ (INT8)' }],
+            },
+            efficiency: {
+                chartTitle: 'Efficiency (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS/TDP (INT8)' }],
+            }
+        },
+        xeon: {
+            throughput: {
+                chartTitle: 'Throughput (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS (INT8)' }, { data: null, color: '#0068B5', label: 'FPS (FP32)' }],
+            },
+            latency: {
+                chartTitle: 'Latency (lower is better)',
+                datasets: [{ data: null, color: '#8F5DA2', label: 'Milliseconds' }],
+            },
+            value: {
+                chartTitle: 'Value (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS/$ (INT8)' }],
+            },
+            efficiency: {
+                chartTitle: 'Efficiency (higher is better)',
+                datasets: [{ data: null, color: '#00C7FD', label: 'FPS/TDP (INT8)' }],
+            }
+        },
+        accel: {
+            throughput: {
+                chartTitle: 'Throughput (higher is better)',
+                datasets: [{ data: null, color: '#8BAE46', label: 'FPS (FP16)' }],
+            },
+            latency: {
+                chartTitle: 'Latency (lower is better)',
+                datasets: [{ data: null, color: '#8F5DA2', label: 'Milliseconds' }],
+            },
+            value: {
+                chartTitle: 'Value (higher is better)',
+                datasets: [{ data: null, color: '#8BAE46', label: 'FPS (FP16)' }]
+            },
+            efficiency: {
+                chartTitle: 'Efficiency (higher is better)',
+                datasets: [{ data: null, color: '#8BAE46', label: 'FPS (FP16)' }]
+            }
+        }
+    }
+
+    var titleMapping = {
+        core: '<h3>Intel® Core™</h3>',
+        atom: '<h3>Intel® Atom®</h3>',
+        xeon: '<h3>Intel® Xeon®</h3>',
+        accel: '<h3>Intel® Movidius™ Vision Processing Units</h3>'
+    }
+
+    var labelsMapping = {
+        core: null,
+        atom: null,
+        xeon: null,
+        accel: null
+    }
+
+    function getDataByLabelsAndIndex(data, labels, pos) {
+        return data.filter(item => labels.indexOf(item[2]) !== -1).map(item => parseFloat(item[pos]));
+    }
+
+    function getChartOptions(title, displayLabels) {
+        return {
+            responsive: false,
+            maintainAspectRatio:false,
+            legend: { display: true, position: 'bottom' },
+            title: {
+                display: true,
+                text: title
+            },
+            scales: {
+                xAxes: [{
+                    ticks: {
+                        beginAtZero: true
+                    }
+                }],
+                yAxes: [{
+                    ticks: {
+                        display: displayLabels, //this will remove only the label
+                        beginAtZero: true
+                    }
+                }]
+            },
+            plugins: {
+                datalabels: {
+                    color: "#4A4A4A",
+                    anchor: "end",
+                    align: "end",
+                    clamp: false,
+                    offset: 0,
+                    display: true,
+                    font: {
+                        size: 8,
+                        family: 'Roboto'
+                    }
+                }
+            }
+        }
+    }
+
+    function getChartData(hwType, metric) {
+        return {
+            labels: labelsMapping[hwType],
+            datasets: CONFIG[hwType][metric]['datasets'].map(function (item) {
+                return {
+                    label: item.label,
+                    data: item.data,
+                    backgroundColor: item.color,
+                    borderColor: 'rgba(170,170,170,0)',
+                    barThickness: 12
+                }
+            })
+        };
+    }
+
+    function renderData(currentChart) {
+        return function (result) {
+            var data = result.data;
+            // remove col names
+            data.shift(0);
+
+            var hwTypes = Object.keys(CONFIG);
+            var chartName = data[1][0];
+            var chartSlug = chartName.replace(')', '').replace(' (', '-');
+            var graphContainer = $('<div>');
+            var chartContainer = $('<div>');
+            graphContainer.attr('id', 'ov-graph-container-' + chartSlug);
+            chartContainer.addClass('chart-container');
+            chartContainer.addClass('container');
+
+            hwTypes.forEach(function (hwType) {
+                // add title
+                var chartWrap = $('<div>');
+                chartWrap.addClass('chart-wrap');
+                chartWrap.addClass('container');
+                chartContainer.append(chartWrap);
+                var labels = getLabels(data, hwType);
+                var int8Data = getDataByLabelsAndIndex(data, labels, 3);
+                var fp32Data = getDataByLabelsAndIndex(data, labels, 4);
+                var fp16Data = getDataByLabelsAndIndex(data, labels, 5);
+                var valueData = getDataByLabelsAndIndex(data, labels, 6);
+                var efficiencyData = getDataByLabelsAndIndex(data, labels, 7);
+                var latencyData = getDataByLabelsAndIndex(data, labels, 8);
+
+                labelsMapping[hwType] = labels
+                if (hwType === 'accel') {
+                    CONFIG[hwType].throughput.datasets[0].data = fp16Data;
+                }
+                else {
+                    CONFIG[hwType].throughput.datasets[0].data = int8Data;
+                    CONFIG[hwType].throughput.datasets[1].data = fp32Data;
+                }
+                CONFIG[hwType].latency.datasets[0].data = latencyData;
+                CONFIG[hwType].value.datasets[0].data = valueData;
+                CONFIG[hwType].efficiency.datasets[0].data = efficiencyData;
+
+                metrics = Object.keys(CONFIG[hwType]).filter((metric) => hasData(hwType, metric));
+
+                var througputLatency = $('<div>');
+                througputLatency.addClass('row');
+                var efficiencyValue = $('<div>');
+                efficiencyValue.addClass('row');
+
+                chartWrap.append(througputLatency);
+                chartWrap.append(efficiencyValue);
+
+                var displayWidth = $(window).width();
+
+                if (metrics.includes('throughput') && metrics.includes('latency')) {
+                    processMetric(hwType, 'throughput', througputLatency, 'col-md-8', true);
+                    if (displayWidth < 450) {
+                        processMetric(hwType, 'latency', througputLatency, 'col-md-4', true);
+                    }
+                    else {
+                        processMetric(hwType, 'latency', througputLatency, 'col-md-4', false);
+                    }
+                }
+                else if (metrics.includes('throughput')) {
+                    processMetric(hwType, 'throughput', througputLatency, 'col-md-12', true);
+                }
+                else if (metrics.includes('latency')) {
+                    processMetric(hwType, 'latency', througputLatency, 'col-md-12', true);
+                }
+
+                if (metrics.includes('efficiency') && metrics.includes('value')) {
+                    processMetric(hwType, 'efficiency', througputLatency, 'col-md-8', true);
+                    if (displayWidth < 450) {
+                        processMetric(hwType, 'value', througputLatency, 'col-md-4', true);
+                    }
+                    else {
+                        processMetric(hwType, 'value', througputLatency, 'col-md-4', false);
+                    }
+                }
+                else if (metrics.includes('efficiency')) {
+                    processMetric(hwType, 'efficiency', througputLatency, 'col-md-6', true);
+                }
+                else if (metrics.includes('value')) {
+                    processMetric(hwType, 'value', througputLatency, 'col-md-6', true);
+                }
+
+            })
+            currentChart.append(chartContainer);
+        }
+
+        function processMetric(hwType, metric, container, widthClass, displayLabels) {
+            var chart = $('<div>');
+            chart.addClass('chart');
+            chart.addClass(widthClass);
+            chart.height(labelsMapping[hwType].length * 55 + 30);
+            var canvas = $('<canvas>');
+            chart.append(canvas);
+            container.append(chart);
+            var context = canvas.get(0).getContext('2d');
+            context.canvas.height = labelsMapping[hwType].length * 55 + 30;
+            if (widthClass === 'col-md-8') {
+                context.canvas.width = context.canvas.width * 1.5;
+            }
+            else if(widthClass === 'col-md-12') {
+                context.canvas.width = context.canvas.width * 2.5;
+            }
+            new Chart(context, {
+                type: 'horizontalBar',
+                data: getChartData(hwType, metric),
+                options: getChartOptions(CONFIG[hwType][metric].chartTitle, displayLabels)
+            });
+        }
+
+        function hasData(hwType, metric) {
+            var has = false;
+            CONFIG[hwType][metric]['datasets'].forEach(function (dataset) {
+                for (var i = 0; i < dataset.data.length; i++) {
+                    if (dataset.data[i] > 0) {
+                        has = true;
+                        break;
+                    }
+                }
+            })
+            return has;
+        }
+    }
+});
diff --git a/docs/_static/js/papaparse.min.js b/docs/_static/js/papaparse.min.js
new file mode 100644
index 00000000000..14c98ff82b7
--- /dev/null
+++ b/docs/_static/js/papaparse.min.js
@@ -0,0 +1,7 @@
+/* @license
+Papa Parse
+v5.0.2
+https://github.com/mholt/PapaParse
+License: MIT
+*/
+!function(e,t){"function"==typeof define&&define.amd?define([],t):"object"==typeof module&&"undefined"!=typeof exports?module.exports=t():e.Papa=t()}(this,function s(){"use strict";var f="undefined"!=typeof self?self:"undefined"!=typeof window?window:void 0!==f?f:{};var n=!f.document&&!!f.postMessage,o=n&&/blob:/i.test((f.location||{}).protocol),a={},h=0,b={parse:function(e,t){var r=(t=t||{}).dynamicTyping||!1;q(r)&&(t.dynamicTypingFunction=r,r={});if(t.dynamicTyping=r,t.transform=!!q(t.transform)&&t.transform,t.worker&&b.WORKERS_SUPPORTED){var i=function(){if(!b.WORKERS_SUPPORTED)return!1;var e=(r=f.URL||f.webkitURL||null,i=s.toString(),b.BLOB_URL||(b.BLOB_URL=r.createObjectURL(new Blob(["(",i,")();"],{type:"text/javascript"})))),t=new f.Worker(e);var r,i;return t.onmessage=_,t.id=h++,a[t.id]=t}();return i.userStep=t.step,i.userChunk=t.chunk,i.userComplete=t.complete,i.userError=t.error,t.step=q(t.step),t.chunk=q(t.chunk),t.complete=q(t.complete),t.error=q(t.error),delete 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diff --git a/docs/_static/js/viewer.min.js b/docs/_static/js/viewer.min.js
new file mode 100644
index 00000000000..f8c75a11a54
--- /dev/null
+++ b/docs/_static/js/viewer.min.js
@@ -0,0 +1,10 @@
+/*!
+ * Viewer.js v1.10.1
+ * https://fengyuanchen.github.io/viewerjs
+ *
+ * Copyright 2015-present Chen Fengyuan
+ * Released under the MIT license
+ *
+ * Date: 2021-08-01T13:35:49.731Z
+ */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).Viewer=e()}(this,function(){"use strict";function e(e,t){var i,n=Object.keys(e);return Object.getOwnPropertySymbols&&(i=Object.getOwnPropertySymbols(e),t&&(i=i.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,i)),n}function l(n){for(var t=1;t<arguments.length;t++){var o=null!=arguments[t]?arguments[t]:{};t%2?e(Object(o),!0).forEach(function(t){var e,i;e=n,t=o[i=t],i in e?Object.defineProperty(e,i,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[i]=t}):Object.getOwnPropertyDescriptors?Object.defineProperties(n,Object.getOwnPropertyDescriptors(o)):e(Object(o)).forEach(function(t){Object.defineProperty(n,t,Object.getOwnPropertyDescriptor(o,t))})}return n}function i(t){return(i="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return 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e=this.options,i=this.pointers,n=t.buttons,o=t.button;!this.viewed||this.showing||this.viewing||this.hiding||("mousedown"===t.type||"pointerdown"===t.type&&"mouse"===t.pointerType)&&(J(n)&&1!==n||J(o)&&0!==o||t.ctrlKey)||(t.preventDefault(),t.changedTouches?ot(t.changedTouches,function(t){i[t.identifier]=Et(t)}):i[t.pointerId||0]=Et(t),o=!!e.movable&&c,e.zoomOnTouch&&e.zoomable&&1<Object.keys(i).length?o=d:e.slideOnTouch&&("touch"===t.pointerType||"touchstart"===t.type)&&this.isSwitchable()&&(o=u),!e.transition||o!==c&&o!==d||ct(this.image,S),this.action=o)},pointermove:function(t){var e=this.pointers,i=this.action;this.viewed&&i&&(t.preventDefault(),t.changedTouches?ot(t.changedTouches,function(t){st(e[t.identifier]||{},Et(t,!0))}):st(e[t.pointerId||0]||{},Et(t,!0)),this.change(t))},pointerup:function(t){var e,i=this,n=this.options,o=this.action,s=this.pointers;t.changedTouches?ot(t.changedTouches,function(t){e=s[t.identifier],delete s[t.identifier]}):(e=s[t.pointerId||0],delete 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e=this;this.isShown&&!this.hiding&&(this.fulled&&(this.close(),this.initBody(),this.open()),this.initContainer(),this.initViewer(),this.renderViewer(),this.renderList(),this.viewed&&this.initImage(function(){e.renderImage()}),this.played&&(!this.options.fullscreen||!this.fulled||document.fullscreenElement||document.webkitFullscreenElement||document.mozFullScreenElement||document.msFullscreenElement?ot(this.player.getElementsByTagName("img"),function(t){yt(t,N,e.loadImage.bind(e),{once:!0}),xt(t,N)}):this.stop()))},wheel:function(t){var e,i,n=this;this.viewed&&(t.preventDefault(),this.wheeling||(this.wheeling=!0,setTimeout(function(){n.wheeling=!1},50),e=Number(this.options.zoomRatio)||.1,i=1,t.deltaY?i=0<t.deltaY?1:-1:t.wheelDelta?i=-t.wheelDelta/120:t.detail&&(i=0<t.detail?1:-1),this.zoom(-i*e,!0,t)))}},St={show:function(){var t=0<arguments.length&&void 0!==arguments[0]&&arguments[0],e=this.element,i=this.options;if(i.inline||this.showing||this.isShown||this.showing)return this;if(!this.ready)return this.build(),this.ready&&this.show(t),this;if(nt(i.show)&&yt(e,H,i.show,{once:!0}),!1===xt(e,H)||!this.ready)return this;this.hiding&&this.transitioning.abort(),this.showing=!0,this.open();var n,o=this.viewer;return ct(o,w),o.setAttribute("role","dialog"),o.setAttribute("aria-labelledby",this.title.id),o.setAttribute("aria-modal",!0),o.removeAttribute("aria-hidden"),i.transition&&!t?(n=this.shown.bind(this),this.transitioning={abort:function(){wt(o,P,n),ct(o,z)}},lt(o,S),o.initialOffsetWidth=o.offsetWidth,yt(o,P,n,{once:!0}),lt(o,z)):(lt(o,z),this.shown()),this},hide:function(){var i=this,t=0<arguments.length&&void 0!==arguments[0]&&arguments[0],e=this.element,n=this.options;if(n.inline||this.hiding||!this.isShown&&!this.showing)return this;if(nt(n.hide)&&yt(e,"hide",n.hide,{once:!0}),!1===xt(e,"hide"))return this;this.showing&&this.transitioning.abort(),this.hiding=!0,this.played?this.stop():this.viewing&&this.viewing.abort();var o,s,a=this.viewer,r=this.image,h=function(){ct(a,z),i.hidden()};return n.transition&&!t?(o=function t(e){e&&e.target===a&&(wt(a,P,t),i.hidden())},s=function(){ht(a,S)?(yt(a,P,o),ct(a,z)):h()},this.transitioning={abort:function(){i.viewed&&ht(r,S)?wt(r,P,s):ht(a,S)&&wt(a,P,o)}},this.viewed&&ht(r,S)?(yt(r,P,s,{once:!0}),this.zoomTo(0,!1,null,!0)):s()):h(),this},view:function(){var i=this,t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:this.options.initialViewIndex,t=Number(t)||0;if(this.hiding||this.played||t<0||t>=this.length||this.viewed&&t===this.index)return this;if(!this.isShown)return this.index=t,this.show();this.viewing&&this.viewing.abort();var e=this.element,n=this.options,o=this.title,s=this.canvas,a=this.items[t],r=a.querySelector("img"),h=gt(r,"originalUrl"),l=r.getAttribute("alt"),c=document.createElement("img");if(ot(n.inheritedAttributes,function(t){var e=r.getAttribute(t);null!==e&&c.setAttribute(t,e)}),c.src=h,c.alt=l,nt(n.view)&&yt(e,"view",n.view,{once:!0}),!1===xt(e,"view",{originalImage:this.images[t],index:t,image:c})||!this.isShown||this.hiding||this.played)return this;h=this.items[this.index];h&&(ct(h,m),h.removeAttribute("aria-selected")),lt(a,m),a.setAttribute("aria-selected",!0),n.focus&&a.focus(),this.image=c,this.viewed=!1,this.index=t,this.imageData={},lt(c,D),n.loading&&lt(s,T),s.innerHTML="",s.appendChild(c),this.renderList(),o.innerHTML="";function u(){var t=i.imageData,e=Array.isArray(n.title)?n.title[1]:n.title;o.innerHTML=_(t=nt(e)?e.call(i,c,t):"".concat(l," (").concat(t.naturalWidth," × ").concat(t.naturalHeight,")"))?t.replace(/&(?!amp;|quot;|#39;|lt;|gt;)/g,"&amp;").replace(/"/g,"&quot;").replace(/'/g,"&#39;").replace(/</g,"&lt;").replace(/>/g,"&gt;"):t}var d;return yt(e,V,u,{once:!0}),this.viewing={abort:function(){wt(e,V,u),c.complete?i.imageRendering?i.imageRendering.abort():i.imageInitializing&&i.imageInitializing.abort():(c.src="",wt(c,N,d),i.timeout&&clearTimeout(i.timeout))}},c.complete?this.load():(yt(c,N,d=this.load.bind(this),{once:!0}),this.timeout&&clearTimeout(this.timeout),this.timeout=setTimeout(function(){ct(c,D),i.timeout=!1},1e3)),this},prev:function(){var t=this.index-1;return t<0&&(t=0<arguments.length&&void 0!==arguments[0]&&arguments[0]?this.length-1:0),this.view(t),this},next:function(){var t=this.length-1,e=this.index+1;return this.view(e=t<e?0<arguments.length&&void 0!==arguments[0]&&arguments[0]?0:t:e),this},move:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:t,i=this.imageData;return this.moveTo(Q(t)?t:i.x+Number(t),Q(e)?e:i.y+Number(e)),this},moveTo:function(t){var e=this,i=1<arguments.length&&void 0!==arguments[1]?arguments[1]:t,n=2<arguments.length&&void 0!==arguments[2]?arguments[2]:null,o=this.element,s=this.options,a=this.imageData;if(t=Number(t),i=Number(i),this.viewed&&!this.played&&s.movable){var r=a.x,h=a.y,l=!1;if(J(t)?l=!0:t=r,J(i)?l=!0:i=h,l){if(nt(s.move)&&yt(o,"move",s.move,{once:!0}),!1===xt(o,"move",{x:t,y:i,oldX:r,oldY:h,originalEvent:n}))return this;a.x=t,a.y=i,a.left=t,a.top=i,this.moving=!0,this.renderImage(function(){e.moving=!1,nt(s.moved)&&yt(o,"moved",s.moved,{once:!0}),xt(o,"moved",{x:t,y:i,oldX:r,oldY:h,originalEvent:n},{cancelable:!1})})}}return this},rotate:function(t){return this.rotateTo((this.imageData.rotate||0)+Number(t)),this},rotateTo:function(t){var e=this,i=this.element,n=this.options,o=this.imageData;if(J(t=Number(t))&&this.viewed&&!this.played&&n.rotatable){var s=o.rotate;if(nt(n.rotate)&&yt(i,"rotate",n.rotate,{once:!0}),!1===xt(i,"rotate",{degree:t,oldDegree:s}))return this;o.rotate=t,this.rotating=!0,this.renderImage(function(){e.rotating=!1,nt(n.rotated)&&yt(i,U,n.rotated,{once:!0}),xt(i,U,{degree:t,oldDegree:s},{cancelable:!1})})}return this},scaleX:function(t){return this.scale(t,this.imageData.scaleY),this},scaleY:function(t){return this.scale(this.imageData.scaleX,t),this},scale:function(t){var e=this,i=1<arguments.length&&void 0!==arguments[1]?arguments[1]:t,n=this.element,o=this.options,s=this.imageData;if(t=Number(t),i=Number(i),this.viewed&&!this.played&&o.scalable){var a=s.scaleX,r=s.scaleY,h=!1;if(J(t)?h=!0:t=a,J(i)?h=!0:i=r,h){if(nt(o.scale)&&yt(n,"scale",o.scale,{once:!0}),!1===xt(n,"scale",{scaleX:t,scaleY:i,oldScaleX:a,oldScaleY:r}))return this;s.scaleX=t,s.scaleY=i,this.scaling=!0,this.renderImage(function(){e.scaling=!1,nt(o.scaled)&&yt(n,"scaled",o.scaled,{once:!0}),xt(n,"scaled",{scaleX:t,scaleY:i,oldScaleX:a,oldScaleY:r},{cancelable:!1})})}}return this},zoom:function(t){var e=1<arguments.length&&void 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this;this.zooming=!0,a?(p={left:(p=(p=this.viewer).getBoundingClientRect()).left+(window.pageXOffset-document.documentElement.clientLeft),top:p.top+(window.pageYOffset-document.documentElement.clientTop)},c=c&&Object.keys(c).length?(o=n=i=0,ot(c,function(t){var e=t.startX,t=t.startY;i+=e,n+=t,o+=1}),{pageX:i/=o,pageY:n/=o}):{pageX:a.pageX,pageY:a.pageY},u.x-=(c.pageX-p.left-d)/g*w,u.y-=(c.pageY-p.top-m)/f*v):(u.x-=w/2,u.y-=v/2),u.left=u.x,u.top=u.y,u.width=r,u.height=b,u.oldRatio=y,u.ratio=t,this.renderImage(function(){e.zooming=!1,nt(l.zoomed)&&yt(h,"zoomed",l.zoomed,{once:!0}),xt(h,"zoomed",{ratio:t,oldRatio:y,originalEvent:a},{cancelable:!1})}),s&&this.tooltip()}return this},play:function(){var e=this,t=0<arguments.length&&void 0!==arguments[0]&&arguments[0];if(!this.isShown||this.played)return this;var i=this.element,o=this.options;if(nt(o.play)&&yt(i,"play",o.play,{once:!0}),!1===xt(i,"play"))return this;var s=this.player,a=this.loadImage.bind(this),r=[],h=0,l=0;return this.played=!0,this.onLoadWhenPlay=a,t&&this.requestFullscreen(t),lt(s,A),ot(this.items,function(t,e){var i=t.querySelector("img"),n=document.createElement("img");n.src=gt(i,"originalUrl"),n.alt=i.getAttribute("alt"),n.referrerPolicy=i.referrerPolicy,h+=1,lt(n,v),ut(n,S,o.transition),ht(t,m)&&(lt(n,z),l=e),r.push(n),yt(n,N,a,{once:!0}),s.appendChild(n)}),J(o.interval)&&0<o.interval&&1<h&&function t(){e.playing=setTimeout(function(){ct(r[l],z),lt(r[l=(l+=1)<h?l:0],z),t()},o.interval)}(),this},stop:function(){var e=this;if(!this.played)return this;var t=this.element,i=this.options;if(nt(i.stop)&&yt(t,"stop",i.stop,{once:!0}),!1===xt(t,"stop"))return this;t=this.player;return this.played=!1,clearTimeout(this.playing),ot(t.getElementsByTagName("img"),function(t){wt(t,N,e.onLoadWhenPlay)}),ct(t,A),t.innerHTML="",this.exitFullscreen(),this},full:function(){var t=this,e=this.options,i=this.viewer,n=this.image,o=this.list;return!this.isShown||this.played||this.fulled||!e.inline||(this.fulled=!0,this.open(),lt(this.button,h),e.transition&&(ct(o,S),this.viewed&&ct(n,S)),lt(i,p),i.setAttribute("role","dialog"),i.setAttribute("aria-labelledby",this.title.id),i.setAttribute("aria-modal",!0),i.removeAttribute("style"),rt(i,{zIndex:e.zIndex}),e.focus&&this.enforceFocus(),this.initContainer(),this.viewerData=st({},this.containerData),this.renderList(),this.viewed&&this.initImage(function(){t.renderImage(function(){e.transition&&setTimeout(function(){lt(n,S),lt(o,S)},0)})})),this},exit:function(){var t=this,e=this.options,i=this.viewer,n=this.image,o=this.list;return this.isShown&&!this.played&&this.fulled&&e.inline&&(this.fulled=!1,this.close(),ct(this.button,h),e.transition&&(ct(o,S),this.viewed&&ct(n,S)),e.focus&&this.clearEnforceFocus(),i.removeAttribute("role"),i.removeAttribute("aria-labelledby"),i.removeAttribute("aria-modal"),ct(i,p),rt(i,{zIndex:e.zIndexInline}),this.viewerData=st({},this.parentData),this.renderViewer(),this.renderList(),this.viewed&&this.initImage(function(){t.renderImage(function(){e.transition&&setTimeout(function(){lt(n,S),lt(o,S)},0)})})),this},tooltip:function(){var t=this,e=this.options,i=this.tooltipBox,n=this.imageData;return this.viewed&&!this.played&&e.tooltip&&(i.textContent="".concat(Math.round(100*n.ratio),"%"),this.tooltipping?clearTimeout(this.tooltipping):e.transition?(this.fading&&xt(i,P),lt(i,A),lt(i,v),lt(i,S),i.removeAttribute("aria-hidden"),i.initialOffsetWidth=i.offsetWidth,lt(i,z)):(lt(i,A),i.removeAttribute("aria-hidden")),this.tooltipping=setTimeout(function(){e.transition?(yt(i,P,function(){ct(i,A),ct(i,v),ct(i,S),i.setAttribute("aria-hidden",!0),t.fading=!1},{once:!0}),ct(i,z),t.fading=!0):(ct(i,A),i.setAttribute("aria-hidden",!0)),t.tooltipping=!1},1e3)),this},toggle:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:null;return 1===this.imageData.ratio?this.zoomTo(this.imageData.oldRatio,!0,t):this.zoomTo(1,!0,t),this},reset:function(){return this.viewed&&!this.played&&(this.imageData=st({},this.initialImageData),this.renderImage()),this},update:function(){var e=this,t=this.element,i=this.options,n=this.isImg;if(n&&!t.parentNode)return this.destroy();var o,s=[];return ot(n?[t]:t.querySelectorAll("img"),function(t){nt(i.filter)?i.filter.call(e,t)&&s.push(t):e.getImageURL(t)&&s.push(t)}),s.length&&(this.images=s,this.length=s.length,this.ready?(o=[],ot(this.items,function(t,e){var i=t.querySelector("img"),t=s[e];t&&i&&t.src===i.src&&t.alt===i.alt||o.push(e)}),rt(this.list,{width:"auto"}),this.initList(),this.isShown&&(this.length?this.viewed&&(0<=(t=o.indexOf(this.index))?(this.viewed=!1,this.view(Math.max(Math.min(this.index-t,this.length-1),0))):(lt(t=this.items[this.index],m),t.setAttribute("aria-selected",!0))):(this.image=null,this.viewed=!1,this.index=0,this.imageData={},this.canvas.innerHTML="",this.title.innerHTML=""))):this.build()),this},destroy:function(){var t=this.element,e=this.options;return t[g]&&(this.destroyed=!0,this.ready?(this.played&&this.stop(),e.inline?(this.fulled&&this.exit(),this.unbind()):this.isShown?(this.viewing&&(this.imageRendering?this.imageRendering.abort():this.imageInitializing&&this.imageInitializing.abort()),this.hiding&&this.transitioning.abort(),this.hidden()):this.showing&&(this.transitioning.abort(),this.hidden()),this.ready=!1,this.viewer.parentNode.removeChild(this.viewer)):e.inline&&(this.delaying?this.delaying.abort():this.initializing&&this.initializing.abort()),e.inline||wt(t,O,this.onStart),t[g]=void 0),this}},Ot={getImageURL:function(t){var e=this.options.url;return e=_(e)?t.getAttribute(e):nt(e)?e.call(this,t):""},enforceFocus:function(){var i=this;this.clearEnforceFocus(),yt(document,R,this.onFocusin=function(t){var e=i.viewer,t=t.target;t===document||t===e||e.contains(t)||null!==t.getAttribute("tabindex")&&"true"===t.getAttribute("aria-modal")||e.focus()})},clearEnforceFocus:function(){this.onFocusin&&(wt(document,R,this.onFocusin),this.onFocusin=null)},open:function(){var t=this.body;lt(t,I),t.style.paddingRight="".concat(this.scrollbarWidth+(parseFloat(this.initialBodyComputedPaddingRight)||0),"px")},close:function(){var t=this.body;ct(t,I),t.style.paddingRight=this.initialBodyPaddingRight},shown:function(){var t=this.element,e=this.options,i=this.viewer;this.fulled=!0,this.isShown=!0,this.render(),this.bind(),this.showing=!1,e.focus&&(i.focus(),this.enforceFocus()),nt(e.shown)&&yt(t,B,e.shown,{once:!0}),!1!==xt(t,B)&&this.ready&&this.isShown&&!this.hiding&&this.view(this.index)},hidden:function(){var t=this.element,e=this.options,i=this.viewer;e.fucus&&this.clearEnforceFocus(),this.fulled=!1,this.viewed=!1,this.isShown=!1,this.close(),this.unbind(),lt(i,w),i.removeAttribute("role"),i.removeAttribute("aria-labelledby"),i.removeAttribute("aria-modal"),i.setAttribute("aria-hidden",!0),this.resetList(),this.resetImage(),this.hiding=!1,this.destroyed||(nt(e.hidden)&&yt(t,"hidden",e.hidden,{once:!0}),xt(t,"hidden",null,{cancelable:!1}))},requestFullscreen:function(t){var e=this.element.ownerDocument;this.fulled&&!(e.fullscreenElement||e.webkitFullscreenElement||e.mozFullScreenElement||e.msFullscreenElement)&&((e=e.documentElement).requestFullscreen?it(t)?e.requestFullscreen(t):e.requestFullscreen():e.webkitRequestFullscreen?e.webkitRequestFullscreen(Element.ALLOW_KEYBOARD_INPUT):e.mozRequestFullScreen?e.mozRequestFullScreen():e.msRequestFullscreen&&e.msRequestFullscreen())},exitFullscreen:function(){var 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diff --git a/docs/_static/logo.svg b/docs/_static/logo.svg
new file mode 100644
index 00000000000..7df60e35f9e
--- /dev/null
+++ b/docs/_static/logo.svg
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diff --git a/docs/api/api_reference.rst b/docs/api/api_reference.rst
new file mode 100644
index 00000000000..c526811b8f3
--- /dev/null
+++ b/docs/api/api_reference.rst
@@ -0,0 +1,18 @@
+.. _api_reference
+
+API Reference
+-------------
+
+The OpenVINO toolkit has APIs available for Python, C, and C++. Most features exist for all APIs, but there are some differences. The C++ API is the most comprehensive.
+
+There is also an nGraph API available for Python and C++, with enhanced operation sets and other features. Read `nGraph <https://www.intel.com/content/www/us/en/artificial-intelligence/ngraph.html>`_ and `Introduction to the nGraph <../openvino_docs_nGraph_DG_Introduction.html>`_ for more information. 
+
+API references available:
+
+
+.. toctree::
+   :maxdepth: 2
+   
+   ../groupie_cpp_api
+   ../groupie_c_api
+   ie_python_api/api
diff --git a/docs/api/ie_python_api/api.rst b/docs/api/ie_python_api/api.rst
new file mode 100644
index 00000000000..9a9ebc39d10
--- /dev/null
+++ b/docs/api/ie_python_api/api.rst
@@ -0,0 +1,8 @@
+Inference Engine Python API
+===========================
+
+.. autosummary::
+   :toctree: _autosummary
+   :template: custom-module-template.rst
+
+   openvino.inference_engine
diff --git a/docs/benchmarks/performance_benchmarks.md b/docs/benchmarks/performance_benchmarks.md
index 7969b2929ff..2e11602b110 100644
--- a/docs/benchmarks/performance_benchmarks.md
+++ b/docs/benchmarks/performance_benchmarks.md
@@ -1,5 +1,17 @@
 # Performance Benchmarks {#openvino_docs_performance_benchmarks}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_performance_benchmarks_openvino
+   openvino_docs_performance_benchmarks_ovms
+
+      
+@endsphinxdirective
+
 The [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html) helps accelerate deep learning inference across a variety of Intel® processors and accelerators.  
 
 The benchmarks below demonstrate high performance gains on several public neural networks on multiple Intel® CPUs, GPUs and VPUs covering a broad performance range. Use this data to help you decide which hardware is best for your applications and solutions, or to plan your AI workload on the Intel computing already included in your solutions. 
diff --git a/docs/benchmarks/performance_benchmarks_faq.md b/docs/benchmarks/performance_benchmarks_faq.md
index e7fd00866b1..c4cf081fe4d 100644
--- a/docs/benchmarks/performance_benchmarks_faq.md
+++ b/docs/benchmarks/performance_benchmarks_faq.md
@@ -68,17 +68,4 @@ The web site format has changed in order to support the more common search appro
 #### 13. How is Latency measured?
 Latency is measured by running the OpenVINO™ inference engine in synchronous mode. In synchronous mode each frame or image is processed through the entire set of stages (pre-processing, inference, post-processing) before the next frame or image is processed. This KPI is relevant for applications where the inference on a single image is required, for example the analysis of an ultra sound image in a medical application or the analysis of a seismic image in the oil & gas industry. Other use cases include real-time or near real-time applications like an industrial robot's response to changes in its environment and obstacle avoidance for autonomous vehicles where a quick response to the result of the inference is required.
 
-\htmlonly
-<style>
-    .footer {
-        display: none;
-    }
-</style>
-<div class="opt-notice-wrapper">
-<p class="opt-notice">
-\endhtmlonly
 For more complete information about performance and benchmark results, visit: [www.intel.com/benchmarks](https://www.intel.com/benchmarks) and [Optimization Notice](https://software.intel.com/articles/optimization-notice). [Legal Information](../Legal_Information.md).
-\htmlonly
-</p>
-</div>
-\endhtmlonly
diff --git a/docs/benchmarks/performance_benchmarks_openvino.md b/docs/benchmarks/performance_benchmarks_openvino.md
index be7c46410d7..bb475992a49 100644
--- a/docs/benchmarks/performance_benchmarks_openvino.md
+++ b/docs/benchmarks/performance_benchmarks_openvino.md
@@ -1,5 +1,17 @@
 # Intel® Distribution of OpenVINO™ toolkit Benchmark Results {#openvino_docs_performance_benchmarks_openvino}
 
+@sphinxdirective
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_performance_benchmarks_faq
+   Download Performance Data Spreadsheet in MS Excel* Format <https://docs.openvinotoolkit.org/downloads/benchmark_files/OV-2021.4-Download-Excel.xlsx>
+   openvino_docs_performance_int8_vs_fp32
+
+      
+@endsphinxdirective
+
 This benchmark setup includes a single machine on which both the benchmark application and the OpenVINO™ installation reside.  
 
 The benchmark application loads the Inference Engine (SW) at run time and executes inferences on the specified hardware inference engine, (CPU, GPU or VPU). The benchmark application measures the time spent on actual inferencing (excluding any pre or post processing) and then reports on the inferences per second (or Frames Per Second). For more information on the benchmark application, please also refer to the entry 5 of the [FAQ section](performance_benchmarks_faq.md). 
@@ -14,95 +26,169 @@ Measuring inference performance involves many variables and is extremely use-cas
 - **Latency** - This measures the synchronous execution of inference requests and is reported in milliseconds. Each inference request (for example: preprocess, infer, postprocess) is allowed to complete before the next is started. This performance metric is relevant in usage scenarios where a single image input needs to be acted upon as soon as possible. An example would be the healthcare sector where medical personnel only request analysis of a single ultra sound scanning image or in real-time or near real-time applications for example an industrial robot's response to actions in its environment or obstacle avoidance for autonomous vehicles. 
 
 
-\htmlonly
-<!-- these CDN links and scripts are required.  Add them to the <head> of your website -->
-<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@100;300;400;500;600;700;900&display=swap" rel="stylesheet" type="text/css">
-<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" type="text/css">
-<script src="https://cdn.jsdelivr.net/npm/chart.js@2.9.3/dist/Chart.min.js"></script>
-<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-datalabels"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/chartjs-plugin-annotation/0.5.7/chartjs-plugin-annotation.min.js"></script> 
-<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-barchart-background@1.3.0/build/Plugin.Barchart.Background.min.js"></script>
-<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-deferred@1"></script>
-<!-- download this file and place on your server (or include the styles inline) -->
-<link rel="stylesheet" href="ovgraphs.css" type="text/css">
-\endhtmlonly
+## bert-large-uncased-whole-word-masking-squad-int8-0001 [384]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/bert-large-uncased-whole-word-masking-squad-int8-0001-384.csv"></div>
+    
+@endsphinxdirective
+
+## deeplabv3-TF [513x513]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/deeplabv3-TF-513x513.csv"></div>
+    
+@endsphinxdirective
+
+## densenet-121-TF [224x224]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/densenet-121-TF-224x224.csv"></div>
+    
+@endsphinxdirective
+
+## faster-rcnn-resnet50-coco-TF [600x1024]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/faster_rcnn_resnet50_coco-TF-600x1024.csv"></div>
+    
+@endsphinxdirective
 
 
-\htmlonly
-<script src="bert-large-uncased-whole-word-masking-squad-int8-0001-384-ov-2021-4-569.js" id="bert-large-uncased-whole-word-masking-squad-int8-0001-384-ov-2021-4-569"></script>
-\endhtmlonly
+## inception-v3-TF [299x299]
 
-\htmlonly
-<script src="deeplabv3-tf-513x513-ov-2021-4-569.js" id="deeplabv3-tf-513x513-ov-2021-4-569"></script>
-\endhtmlonly
+@sphinxdirective
+.. raw:: html
 
-\htmlonly
-<script src="densenet-121-tf-224x224-ov-2021-4-569.js" id="densenet-121-tf-224x224-ov-2021-4-569"></script>
-\endhtmlonly
+    <div class="chart-block" data-loadcsv="csv/inception-v3-TF-299x299.csv"></div>
+    
+@endsphinxdirective
 
-\htmlonly
-<script src="faster-rcnn-resnet50-coco-tf-600x1024-ov-2021-4-569.js" id="faster-rcnn-resnet50-coco-tf-600x1024-ov-2021-4-569"></script>
-\endhtmlonly
+## inception-v4-TF [299x299]
 
-\htmlonly
-<script src="inception-v3-tf-299x299-ov-2021-4-569.js" id="inception-v3-tf-299x299-ov-2021-4-569"></script>
-\endhtmlonly
+@sphinxdirective
+.. raw:: html
 
-\htmlonly
-<script src="inception-v4-tf-299x299-ov-2021-4-569.js" id="inception-v4-tf-299x299-ov-2021-4-569"></script>
-\endhtmlonly
+    <div class="chart-block" data-loadcsv="csv/inception-v4-TF-299x299.csv"></div>
+    
+@endsphinxdirective
 
-\htmlonly
-<script src="mobilenet-ssd-cf-300x300-ov-2021-4-569.js" id="mobilenet-ssd-cf-300x300-ov-2021-4-569"></script>
-\endhtmlonly
+## mobilenet-ssd-CF [300x300]
 
-\htmlonly
-<script src="mobilenet-v2-pytorch-224x224-ov-2021-4-569.js" id="mobilenet-v2-pytorch-224x224-ov-2021-4-569"></script>
-\endhtmlonly
+@sphinxdirective
+.. raw:: html
 
-\htmlonly
-<script src="resnet-18-pytorch-224x224-ov-2021-4-569.js" id="resnet-18-pytorch-224x224-ov-2021-4-569"></script>
-\endhtmlonly
+    <div class="chart-block" data-loadcsv="csv/mobilenet-ssd-CF-300x300.csv"></div>
+    
+@endsphinxdirective
 
-\htmlonly
-<script src="resnet-50-tf-224x224-ov-2021-4-569.js" id="resnet-50-tf-224x224-ov-2021-4-569"></script>
-\endhtmlonly
+## mobilenet-v2-pytorch [224x224]
 
-\htmlonly
-<script src="se-resnext-50-cf-224x224-ov-2021-4-569.js" id="se-resnext-50-cf-224x224-ov-2021-4-569"></script>
-\endhtmlonly
+@sphinxdirective
+.. raw:: html
 
-\htmlonly
-<script src="squeezenet1-1-cf-227x227-ov-2021-4-569.js" id="squeezenet1-1-cf-227x227-ov-2021-4-569"></script>
-\endhtmlonly
+    <div class="chart-block" data-loadcsv="csv/mobilenet-v2-pytorch-224x224.csv"></div>
+    
+@endsphinxdirective
+
+## resnet-18-pytorch [224x224]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/resnet-18-pytorch-224x224.csv"></div>
+    
+@endsphinxdirective
+
+## resnet_50_TF [224x224]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/resnet-50-TF-224x224.csv"></div>
+    
+@endsphinxdirective
+
+## se-resnext-50-CF [224x224]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/se-resnext-50-CF-224x224.csv"></div>
+    
+@endsphinxdirective
+
+## squeezenet1.1-CF [227x227]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/squeezenet1.1-CF-227x227.csv"></div>
+    
+@endsphinxdirective
+
+## ssd300-CF [300x300]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/ssd300-CF-300x300.csv"></div>
+    
+@endsphinxdirective
+
+## yolo-v3-tiny-tf [416x416]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/yolo-v3-tiny-tf-416x416.csv"></div>
+    
+@endsphinxdirective
+
+## yolo_v4-tf [608x608]
+
+@sphinxdirective
+.. raw:: html
+
+    <div class="chart-block" data-loadcsv="csv/yolo_v4-tf-608x608.csv"></div>
+    
+@endsphinxdirective
 
 
-\htmlonly
-<script src="ssd300-cf-300x300-ov-2021-4-569.js" id="ssd300-cf-300x300-ov-2021-4-569"></script>
-\endhtmlonly
+## unet-camvid-onnx-0001 [368x480]
 
-\htmlonly
-<script src="yolo-v3-tiny-tf-416x416-ov-2021-4-569.js" id="yolo-v3-tiny-tf-416x416-ov-2021-4-569"></script>
-\endhtmlonly
+@sphinxdirective
+.. raw:: html
 
-\htmlonly
-<script src="yolo-v4-tf-608x608-ov-2021-4-569.js" id="yolo-v4-tf-608x608-ov-2021-4-569"></script>
-\endhtmlonly
+    <div class="chart-block" data-loadcsv="csv/unet-camvid-onnx-0001-368x480.csv"></div>
+    
+@endsphinxdirective
 
-\htmlonly
-<script src="unet-camvid-onnx-0001-368x480-ov-2021-4-569.js" id="unet-camvid-onnx-0001-368x480-ov-2021-4-569"></script>
-\endhtmlonly
+## ssd-resnet34-1200-onnx [1200x1200]
 
-\htmlonly
-<script src="ssd-resnet34-1200-onnx-1200x1200-ov-2021-4-569.js" id="ssd-resnet34-1200-onnx-1200x1200-ov-2021-4-569"></script>
-\endhtmlonly
+@sphinxdirective
+.. raw:: html
 
-\htmlonly
-<script src="vgg19-caffe-224x224-ov-2021-4-569.js" id="vgg19-caffe-224x224-ov-2021-4-569"></script>
-\endhtmlonly
+    <div class="chart-block" data-loadcsv="csv/ssd-resnet34-1200-onnx-1200x1200.csv"></div>
+    
+@endsphinxdirective
 
+## vgg19-caffe [224x224]
 
+@sphinxdirective
+.. raw:: html
 
+    <div class="chart-block" data-loadcsv="csv/vgg19-caffe-224x224.csv"></div>
+    
+@endsphinxdirective
 
 ## Platform Configurations
 
@@ -122,7 +208,7 @@ Testing by Intel done on: see test date for each HW platform below.
 
 **CPU Inference Engines**
 
-|                                 | Intel® Xeon® E-2124G               | Intel® Xeon® W1290P                | Intel® Xeon® Silver 4216R               | 
+| Configuration                   | Intel® Xeon® E-2124G               | Intel® Xeon® W1290P                | Intel® Xeon® Silver 4216R               | 
 | ------------------------------- | ----------------------             | ---------------------------        | ----------------------------            |
 | Motherboard                     | ASUS* WS C246 PRO                  | ASUS* WS W480-ACE                  | Intel® Server Board S2600STB            |
 | CPU                             | Intel® Xeon® E-2124G CPU @ 3.40GHz | Intel® Xeon® W-1290P CPU @ 3.70GHz | Intel® Xeon® Silver 4216R CPU @ 2.20GHz |
@@ -144,7 +230,7 @@ Testing by Intel done on: see test date for each HW platform below.
 
 **CPU Inference Engines (continue)**
 
-|                                 | Intel® Xeon® Gold 5218T                 | Intel® Xeon® Platinum 8270               | Intel® Xeon® Platinum 8380               |
+| Configuration                   | Intel® Xeon® Gold 5218T                 | Intel® Xeon® Platinum 8270               | Intel® Xeon® Platinum 8380               |
 | ------------------------------- | ----------------------------            | ----------------------------             | -----------------------------------------|
 | Motherboard                     | Intel® Server Board S2600STB            | Intel® Server Board S2600STB             | Intel Corporation / WilsonCity           |
 | CPU                             | Intel® Xeon® Gold 5218T CPU @ 2.10GHz   | Intel® Xeon® Platinum 8270 CPU @ 2.70GHz | Intel® Xeon® Platinum 8380 CPU @ 2.30GHz |
@@ -167,7 +253,7 @@ Testing by Intel done on: see test date for each HW platform below.
 
 **CPU Inference Engines (continue)**
 
-|                      | Intel® Core™ i7-8700T               | Intel® Core™ i9-10920X               |
+| Configuration        | Intel® Core™ i7-8700T               | Intel® Core™ i9-10920X               |
 | -------------------- | ----------------------------------- |--------------------------------------|
 | Motherboard          | GIGABYTE* Z370M DS3H-CF             | ASUS* PRIME X299-A II                |
 | CPU                  | Intel® Core™ i7-8700T CPU @ 2.40GHz | Intel® Core™ i9-10920X CPU @ 3.50GHz |
@@ -188,7 +274,7 @@ Testing by Intel done on: see test date for each HW platform below.
 | CPU Price/socket on June 21, 2021, USD<br>Prices may vary    | [303](https://ark.intel.com/content/www/us/en/ark/products/129948/intel-core-i7-8700t-processor-12m-cache-up-to-4-00-ghz.html)                | [700](https://ark.intel.com/content/www/us/en/ark/products/198012/intel-core-i9-10920x-x-series-processor-19-25m-cache-3-50-ghz.html) |
 
 **CPU Inference Engines (continue)**
-|                      | 11th Gen Intel® Core™ i7-1185G7 | 11th Gen Intel® Core™ i7-11850HE |
+| Configuration        | 11th Gen Intel® Core™ i7-1185G7 | 11th Gen Intel® Core™ i7-11850HE |
 | -------------------- | --------------------------------|----------------------------------|
 | Motherboard          | Intel Corporation<br>internal/Reference<br>Validation Platform | Intel Corporation<br>internal/Reference<br>Validation Platform |
 | CPU                  | 11th Gen Intel® Core™ i7-1185G7 @ 3.00GHz | 11th Gen Intel® Core™ i7-11850HE @ 2.60GHz |
@@ -210,7 +296,7 @@ Testing by Intel done on: see test date for each HW platform below.
 
 **CPU Inference Engines (continue)**
 
-|                      | Intel® Core™ i3-8100               | Intel® Core™ i5-8500               | Intel® Core™ i5-10500TE               |
+| Configuration        | Intel® Core™ i3-8100               | Intel® Core™ i5-8500               | Intel® Core™ i5-10500TE               |
 | -------------------- |----------------------------------- | ---------------------------------- | -----------------------------------   |
 | Motherboard          | GIGABYTE* Z390 UD                  | ASUS* PRIME Z370-A                 | GIGABYTE* Z490 AORUS PRO AX           |
 | CPU                  | Intel® Core™ i3-8100 CPU @ 3.60GHz | Intel® Core™ i5-8500 CPU @ 3.00GHz | Intel® Core™ i5-10500TE CPU @ 2.30GHz |
@@ -233,7 +319,7 @@ Testing by Intel done on: see test date for each HW platform below.
 
 **CPU Inference Engines (continue)**
 
-|                      | Intel Atom® x5-E3940                  | Intel Atom® x6425RE                               | Intel® Celeron® 6305E            |
+| Configuration        | Intel Atom® x5-E3940                  | Intel Atom® x6425RE                               | Intel® Celeron® 6305E            |
 | -------------------- | --------------------------------------|-------------------------------                    |----------------------------------|
 | Motherboard          | Intel Corporation<br>internal/Reference<br>Validation Platform | Intel Corporation<br>internal/Reference<br>Validation Platform | Intel Corporation<br>internal/Reference<br>Validation Platform      |
 | CPU                  | Intel Atom® Processor E3940 @ 1.60GHz | Intel Atom® x6425RE<br>Processor @ 1.90GHz        | Intel® Celeron®<br>6305E @ 1.80GHz  |
@@ -257,7 +343,7 @@ Testing by Intel done on: see test date for each HW platform below.
 
 **Accelerator Inference Engines**
 
-|                                         | Intel® Neural Compute Stick 2         | Intel® Vision Accelerator Design<br>with Intel® Movidius™ VPUs (Mustang-V100-MX8) | 
+| Configuration                           | Intel® Neural Compute Stick 2         | Intel® Vision Accelerator Design<br>with Intel® Movidius™ VPUs (Mustang-V100-MX8) | 
 | --------------------------------------- | ------------------------------------- | ------------------------------------- |
 | VPU                                     | 1 X Intel® Movidius™ Myriad™ X MA2485 | 8 X Intel® Movidius™ Myriad™ X MA2485 |
 | Connection                              | USB 2.0/3.0                           | PCIe X4                               |
@@ -281,17 +367,4 @@ Testing by Intel done on: see test date for each HW platform below.
 
 Please follow this link for more detailed configuration descriptions: [Configuration Details](https://docs.openvinotoolkit.org/resources/benchmark_files/system_configurations_2021.4.html)
 
-\htmlonly
-<style>
-    .footer {
-        display: none;
-    }
-</style>
-<div class="opt-notice-wrapper">
-<p class="opt-notice">
-\endhtmlonly
 Results may vary. For workloads and configurations visit: [www.intel.com/PerformanceIndex](https://www.intel.com/PerformanceIndex) and [Legal Information](../Legal_Information.md).
-\htmlonly
-</p>
-</div>
-\endhtmlonly
diff --git a/docs/benchmarks/performance_benchmarks_ovms.md b/docs/benchmarks/performance_benchmarks_ovms.md
index 5e897955baa..57495835d86 100644
--- a/docs/benchmarks/performance_benchmarks_ovms.md
+++ b/docs/benchmarks/performance_benchmarks_ovms.md
@@ -47,408 +47,422 @@ OpenVINO™ Model Server is measured in multiple-client-single-server configurat
 
 OpenVINO™ Model Server performance benchmark numbers are based on release 2021.4. Performance results are based on testing as of June 17, 2021 and may not reflect all publicly available updates. 
 
-**Platform with Intel® Xeon® Platinum 8260M**
+### Platform with Intel® Xeon® Platinum 8260M
 
-<table>
-  <tr>
-    <th></th>
-    <th><strong>Server Platform</strong></th>
-    <th><strong>Client Platform</strong></th>
-  </tr>
-  <tr>
-    <td><strong>Motherboard</strong></td>
-    <td>Inspur YZMB-00882-104 NF5280M5</td>
-    <td>Intel® Server Board S2600WF H48104-872</td>
-  </tr>
-  <tr>
-    <td><strong>Memory</strong></td>
-    <td>Samsung 16 x 16GB @ 2666 MT/s DDR4</td>
-    <td>Hynix 16 x 16GB @ 2666 MT/s DDR4</td>
-  </tr>
-  <tr>
-    <td><strong>CPU</strong></td>
-    <td>Intel® Xeon® Platinum 8260M CPU @ 2.40GHz</td>
-    <td>Intel® Xeon® Gold 6252 CPU @ 2.10GHz</td>
-  </tr>
-  <tr>
-    <td><strong>Selected CPU Flags</strong></td>
-    <td>Hyper Threading, Turbo Boost, DL Boost</td>
-    <td>Hyper Threading, Turbo Boost, DL Boost</td>
-  </tr>
-  <tr>
-    <td><strong>CPU Thermal Design Power</strong></td>
-    <td>162 W</td>
-    <td>150 W</td>
-  </tr>
-  <tr>
-    <td><strong>Operating System</strong></td>
-    <td>Ubuntu 20.04.2 LTS</td>
-    <td>Ubuntu 20.04.2 LTS</td>
-  </tr>
-  <tr>
-    <td><strong>Kernel Version</strong></td>
-    <td>5.4.0-54-generic</td>
-    <td>5.4.0-65-generic</td>
-  </tr>
-  <tr>
-    <td><strong>BIOS Vendor</strong></td>
-    <td>American Megatrends Inc.</td>
-    <td>Intel® Corporation</td>
-  </tr>
-  <tr>
-    <td><strong>BIOS Version & Release</strong></td>
-    <td>4.1.16, date: 06/23/2020</td>
-    <td>SE5C620.86B.02.01, date: 03/26/2020</td>
-  </tr>
-  <tr>
-    <td><strong>Docker Version</strong></td>
-    <td>20.10.3</td>
-    <td>20.10.3</td>
-  </tr>
-  <tr>
-    <td><strong>Network Speed</strong></td>
-    <td colspan="2">40 Gb/s</td>
-  </tr>
-</table>
+@sphinxdirective
+.. raw:: html
 
-**Platform with Intel® Xeon® Gold 6252**
+    <table class="table">
+      <tr>
+        <th></th>
+        <th><strong>Server Platform</strong></th>
+        <th><strong>Client Platform</strong></th>
+      </tr>
+      <tr>
+        <td><strong>Motherboard</strong></td>
+        <td>Inspur YZMB-00882-104 NF5280M5</td>
+        <td>Intel® Server Board S2600WF H48104-872</td>
+      </tr>
+      <tr>
+        <td><strong>Memory</strong></td>
+        <td>Samsung 16 x 16GB @ 2666 MT/s DDR4</td>
+        <td>Hynix 16 x 16GB @ 2666 MT/s DDR4</td>
+      </tr>
+      <tr>
+        <td><strong>CPU</strong></td>
+        <td>Intel® Xeon® Platinum 8260M CPU @ 2.40GHz</td>
+        <td>Intel® Xeon® Gold 6252 CPU @ 2.10GHz</td>
+      </tr>
+      <tr>
+        <td><strong>Selected CPU Flags</strong></td>
+        <td>Hyper Threading, Turbo Boost, DL Boost</td>
+        <td>Hyper Threading, Turbo Boost, DL Boost</td>
+      </tr>
+      <tr>
+        <td><strong>CPU Thermal Design Power</strong></td>
+        <td>162 W</td>
+        <td>150 W</td>
+      </tr>
+      <tr>
+        <td><strong>Operating System</strong></td>
+        <td>Ubuntu 20.04.2 LTS</td>
+        <td>Ubuntu 20.04.2 LTS</td>
+      </tr>
+      <tr>
+        <td><strong>Kernel Version</strong></td>
+        <td>5.4.0-54-generic</td>
+        <td>5.4.0-65-generic</td>
+      </tr>
+      <tr>
+        <td><strong>BIOS Vendor</strong></td>
+        <td>American Megatrends Inc.</td>
+        <td>Intel® Corporation</td>
+      </tr>
+      <tr>
+        <td><strong>BIOS Version & Release</strong></td>
+        <td>4.1.16, date: 06/23/2020</td>
+        <td>SE5C620.86B.02.01, date: 03/26/2020</td>
+      </tr>
+      <tr>
+        <td><strong>Docker Version</strong></td>
+        <td>20.10.3</td>
+        <td>20.10.3</td>
+      </tr>
+      <tr>
+        <td><strong>Network Speed</strong></td>
+        <td colspan="2">40 Gb/s</td>
+      </tr>
+    </table>
 
-<table>
-  <tr>
-    <th></th>
-    <th><strong>Server Platform</strong></th>
-    <th><strong>Client Platform</strong></th>
-  </tr>
-  <tr>
-    <td><strong>Motherboard</strong></td>
-    <td>Intel® Server Board S2600WF H48104-872</td>
-    <td>Inspur YZMB-00882-104 NF5280M5</td>
-  </tr>
-  <tr>
-    <td><strong>Memory</strong></td>
-    <td>Hynix 16 x 16GB @ 2666 MT/s DDR4</td>
-    <td>Samsung 16 x 16GB @ 2666 MT/s DDR4</td>
-  </tr>
-  <tr>
-    <td><strong>CPU</strong></td>
-    <td>Intel® Xeon® Gold 6252 CPU @ 2.10GHz</td>
-    <td>Intel® Xeon® Platinum 8260M CPU @ 2.40GHz</td>
-  </tr>
-  <tr>
-    <td><strong>Selected CPU Flags</strong></td>
-    <td>Hyper Threading, Turbo Boost, DL Boost</td>
-    <td>Hyper Threading, Turbo Boost, DL Boost</td>
-  </tr>
-  <tr>
-    <td><strong>CPU Thermal Design Power</strong></td>
-    <td>150 W</td>
-    <td>162 W</td>
-</tr>
-  <tr>
-    <td><strong>Operating System</strong></td>
-    <td>Ubuntu 20.04.2 LTS</td>
-    <td>Ubuntu 20.04.2 LTS</td>
-  </tr>
-  <tr>
-    <td><strong>Kernel Version</strong></td>
-    <td>5.4.0-65-generic</td>
-    <td>5.4.0-54-generic</td>
-  </tr>
-  <tr>
-    <td><strong>BIOS Vendor</strong></td>
-    <td>Intel® Corporation</td>
-    <td>American Megatrends Inc.</td>
-  </tr>
-  <tr>
-    <td><strong>BIOS Version and Release Date</strong></td>
-    <td>SE5C620.86B.02.01, date: 03/26/2020</td>
-    <td>4.1.16, date: 06/23/2020</td>
-  </tr>
-  <tr>
-    <td><strong>Docker Version</strong></td>
-    <td>20.10.3</td>
-    <td>20.10.3</td>
-  </tr>
-  <tr>
-    <td><strong>Network Speed</strong></td>
-    <td colspan="2" align="center">40 Gb/s</td>
-  </tr>
-</table>
+@endsphinxdirective
 
-**Platform with Intel® Core™ i9-10920X**
+### Platform with Intel® Xeon® Gold 6252
 
-<table>
-<tr>
-  <th></th>
-  <th><strong>Server Platform</strong></th>
-  <th><strong>Client Platform</strong></th>
-</tr>
-<tr>
-  <td><strong>Motherboard</strong></td>
-  <td>ASUSTeK COMPUTER INC. PRIME X299-A II</td>
-  <td>ASUSTeK COMPUTER INC. PRIME Z370-P</td>
-</tr>
-<tr>
-  <td><strong>Memory</strong></td>
-  <td>Corsair 4 x 16GB @ 2666 MT/s DDR4</td>
-  <td>Corsair 4 x 16GB @ 2133 MT/s DDR4</td>
-</tr>
-<tr>
-  <td><strong>CPU</strong></td>
-  <td>Intel® Core™ i9-10920X CPU @ 3.50GHz</td>
-  <td>Intel® Core™ i7-8700T CPU @ 2.40GHz</td>
-</tr>
-<tr>
-  <td><strong>Selected CPU Flags</strong></td>
-  <td>Hyper Threading, Turbo Boost, DL Boost</td>
-  <td>Hyper Threading, Turbo Boost</td>
-</tr>
-<tr>
-  <td><strong>CPU Thermal Design Power</strong></td>
-  <td>165 W</td>
-  <td>35 W</td>
-</tr>
-<tr>
-  <td><strong>Operating System</strong></td>
-  <td>Ubuntu 20.04.1 LTS</td>
-  <td>Ubuntu 20.04.1 LTS</td>
-</tr>
+@sphinxdirective
+.. raw:: html
 
-<tr>
-  <td><strong>Kernel Version</strong></td>
-  <td>5.4.0-52-generic</td>
-  <td>5.4.0-56-generic</td>
-</tr>
-<tr>
-  <td><strong>BIOS Vendor</strong></td>
-  <td>American Megatrends Inc.</td>
-  <td>American Megatrends Inc.</td>
-</tr>
-<tr>
-  <td><strong>BIOS Version and Release Date</strong></td>
-  <td>0603, date: 03/05/2020</td>
-  <td>2401, date: 07/15/2019</td>
-</tr>
-<tr>
-  <td><strong>Docker Version</strong></td>
-  <td>19.03.13</td>
-  <td>19.03.14</td>
-</tr>
-</tr>
-<tr>
-  <td><strong>Network Speed</strong></td>
-  <td colspan="2" align="center">10 Gb/s</td>
-</tr>
-</table>
+    <table class="table">
+      <tr>
+        <th></th>
+        <th><strong>Server Platform</strong></th>
+        <th><strong>Client Platform</strong></th>
+      </tr>
+      <tr>
+        <td><strong>Motherboard</strong></td>
+        <td>Intel® Server Board S2600WF H48104-872</td>
+        <td>Inspur YZMB-00882-104 NF5280M5</td>
+      </tr>
+      <tr>
+        <td><strong>Memory</strong></td>
+        <td>Hynix 16 x 16GB @ 2666 MT/s DDR4</td>
+        <td>Samsung 16 x 16GB @ 2666 MT/s DDR4</td>
+      </tr>
+      <tr>
+        <td><strong>CPU</strong></td>
+        <td>Intel® Xeon® Gold 6252 CPU @ 2.10GHz</td>
+        <td>Intel® Xeon® Platinum 8260M CPU @ 2.40GHz</td>
+      </tr>
+      <tr>
+        <td><strong>Selected CPU Flags</strong></td>
+        <td>Hyper Threading, Turbo Boost, DL Boost</td>
+        <td>Hyper Threading, Turbo Boost, DL Boost</td>
+      </tr>
+      <tr>
+        <td><strong>CPU Thermal Design Power</strong></td>
+        <td>150 W</td>
+        <td>162 W</td>
+    </tr>
+      <tr>
+        <td><strong>Operating System</strong></td>
+        <td>Ubuntu 20.04.2 LTS</td>
+        <td>Ubuntu 20.04.2 LTS</td>
+      </tr>
+      <tr>
+        <td><strong>Kernel Version</strong></td>
+        <td>5.4.0-65-generic</td>
+        <td>5.4.0-54-generic</td>
+      </tr>
+      <tr>
+        <td><strong>BIOS Vendor</strong></td>
+        <td>Intel® Corporation</td>
+        <td>American Megatrends Inc.</td>
+      </tr>
+      <tr>
+        <td><strong>BIOS Version and Release Date</strong></td>
+        <td>SE5C620.86B.02.01, date: 03/26/2020</td>
+        <td>4.1.16, date: 06/23/2020</td>
+      </tr>
+      <tr>
+        <td><strong>Docker Version</strong></td>
+        <td>20.10.3</td>
+        <td>20.10.3</td>
+      </tr>
+      <tr>
+        <td><strong>Network Speed</strong></td>
+        <td colspan="2" align="center">40 Gb/s</td>
+      </tr>
+    </table>
 
-**Platform with Intel® Core™ i7-8700T**
+@endsphinxdirective
 
-<table>
-<tr>
-  <th></th>
-  <th><strong>Server Platform</strong></th>
-  <th><strong>Client Platform</strong></th>
-</tr>
-<tr>
-  <td><strong>Motherboard</strong></td>
-  <td>ASUSTeK COMPUTER INC. PRIME Z370-P</td>
-  <td>ASUSTeK COMPUTER INC. PRIME X299-A II</td>
-</tr>
-<tr>
-  <td><strong>Memory</strong></td>
-  <td>Corsair 4 x 16GB @ 2133 MT/s DDR4</td>
-  <td>Corsair 4 x 16GB @ 2666 MT/s DDR4</td>
-</tr>
-<tr>
-  <td><strong>CPU</strong></td>
-  <td>Intel® Core™ i7-8700T CPU @ 2.40GHz</td>
-  <td>Intel® Core™ i9-10920X CPU @ 3.50GHz</td>
-</tr>
-<tr>
-  <td><strong>Selected CPU Flags</strong></td>
-  <td>Hyper Threading, Turbo Boost</td>
-  <td>Hyper Threading, Turbo Boost, DL Boost</td>
-</tr>
-<tr>
-  <td><strong>CPU Thermal Design Power</strong></td>
-  <td>35 W</td>
-  <td>165 W</td>
-</tr>
-<tr>
-  <td><strong>Operating System</strong></td>
-  <td>Ubuntu 20.04.1 LTS</td>
-  <td>Ubuntu 20.04.1 LTS</td>
-</tr>
+### Platform with Intel® Core™ i9-10920X
 
-<tr>
-  <td><strong>Kernel Version</strong></td>
-  <td>5.4.0-56-generic</td>
-  <td>5.4.0-52-generic</td>
-</tr>
-<tr>
-  <td><strong>BIOS Vendor</strong></td>
-  <td>American Megatrends Inc.</td>
-  <td>American Megatrends Inc.</td>
-</tr>
-<tr>
-  <td><strong>BIOS Version and Release Date</strong></td>
-  <td>2401, date: 07/15/2019</td>
-  <td>0603, date: 03/05/2020</td>
-</tr>
-<tr>
-  <td><strong>Docker Version</strong></td>
-  <td>19.03.14</td>
-  <td>19.03.13</td>
-</tr>
-</tr>
-<tr>
-  <td><strong>Network Speed</strong></td>
-  <td colspan="2" align="center">10 Gb/s</td>
-</tr>
-</table>
+@sphinxdirective
+.. raw:: html
 
-**Platform with Intel® Core™ i5-8500**
+    <table class="table">
+    <tr>
+      <th></th>
+      <th><strong>Server Platform</strong></th>
+      <th><strong>Client Platform</strong></th>
+    </tr>
+    <tr>
+      <td><strong>Motherboard</strong></td>
+      <td>ASUSTeK COMPUTER INC. PRIME X299-A II</td>
+      <td>ASUSTeK COMPUTER INC. PRIME Z370-P</td>
+    </tr>
+    <tr>
+      <td><strong>Memory</strong></td>
+      <td>Corsair 4 x 16GB @ 2666 MT/s DDR4</td>
+      <td>Corsair 4 x 16GB @ 2133 MT/s DDR4</td>
+    </tr>
+    <tr>
+      <td><strong>CPU</strong></td>
+      <td>Intel® Core™ i9-10920X CPU @ 3.50GHz</td>
+      <td>Intel® Core™ i7-8700T CPU @ 2.40GHz</td>
+    </tr>
+    <tr>
+      <td><strong>Selected CPU Flags</strong></td>
+      <td>Hyper Threading, Turbo Boost, DL Boost</td>
+      <td>Hyper Threading, Turbo Boost</td>
+    </tr>
+    <tr>
+      <td><strong>CPU Thermal Design Power</strong></td>
+      <td>165 W</td>
+      <td>35 W</td>
+    </tr>
+    <tr>
+      <td><strong>Operating System</strong></td>
+      <td>Ubuntu 20.04.1 LTS</td>
+      <td>Ubuntu 20.04.1 LTS</td>
+    </tr>
+    <tr>
+      <td><strong>Kernel Version</strong></td>
+      <td>5.4.0-52-generic</td>
+      <td>5.4.0-56-generic</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Vendor</strong></td>
+      <td>American Megatrends Inc.</td>
+      <td>American Megatrends Inc.</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Version and Release Date</strong></td>
+      <td>0603, date: 03/05/2020</td>
+      <td>2401, date: 07/15/2019</td>
+    </tr>
+    <tr>
+      <td><strong>Docker Version</strong></td>
+      <td>19.03.13</td>
+      <td>19.03.14</td>
+    </tr>
+    </tr>
+    <tr>
+      <td><strong>Network Speed</strong></td>
+      <td colspan="2" align="center">10 Gb/s</td>
+    </tr>
+    </table>
 
-<table>
-<tr>
-  <th></th>
-  <th><strong>Server Platform</strong></th>
-  <th><strong>Client Platform</strong></th>
-</tr>
-<tr>
-  <td><strong>Motherboard</strong></td>
-  <td>ASUSTeK COMPUTER INC. PRIME Z370-A</td>
-  <td>Gigabyte Technology Co., Ltd. Z390 UD</td>
-</tr>
-<tr>
-  <td><strong>Memory</strong></td>
-  <td>Corsair 2 x 16GB @ 2133 MT/s DDR4</td>
-  <td>029E 4 x 8GB @ 2400 MT/s DDR4</td>
-</tr>
-<tr>
-  <td><strong>CPU</strong></td>
-  <td>Intel® Core™ i5-8500 CPU @ 3.00GHz</td>
-  <td>Intel® Core™ i3-8100 CPU @ 3.60GHz</td>
-</tr>
-<tr>
-  <td><strong>Selected CPU Flags</strong></td>
-  <td>Turbo Boost</td>
-  <td>-</td>
-</tr>
-<tr>
-  <td><strong>CPU Thermal Design Power</strong></td>
-  <td>65 W</td>
-  <td>65 W</td>
-</tr>
-<tr>
-  <td><strong>Operating System</strong></td>
-  <td>Ubuntu 20.04.1 LTS</td>
-  <td>Ubuntu 20.04.1 LTS</td>
-</tr>
-<tr>
-  <td><strong>Kernel Version</strong></td>
-  <td>5.4.0-52-generic</td>
-  <td>5.4.0-52-generic</td>
-</tr>
-<tr>
-  <td><strong>BIOS Vendor</strong></td>
-  <td>American Megatrends Inc.</td>
-  <td>American Megatrends Inc.</td>
-</tr>
-<tr>
-  <td><strong>BIOS Version and Release Date</strong></td>
-  <td>2401, date: 07/12/2019</td>
-  <td>F10j, date: 09/16/2020</td>
-</tr>
-<tr>
-  <td><strong>Docker Version</strong></td>
-  <td>19.03.13</td>
-  <td>20.10.0</td>
-</tr>
-</tr>
-<tr>
-  <td><strong>Network Speed</strong></td>
-  <td colspan="2" align="center">40 Gb/s</td>
-</tr>
-</table>
+@endsphinxdirective
 
-**Platform with Intel® Core™ i3-8100**
-<table>
-<tr>
-  <th></th>
-  <th><strong>Server Platform</strong></th>
-  <th><strong>Client Platform</strong></th>
-</tr>
-<tr>
-  <td><strong>Motherboard</strong></td>
-  <td>Gigabyte Technology Co., Ltd. Z390 UD</td>
-  <td>ASUSTeK COMPUTER INC. PRIME Z370-A</td>
-</tr>
-<tr>
-  <td><strong>Memory</strong></td>
-  <td>029E 4 x 8GB @ 2400 MT/s DDR4</td>
-  <td>Corsair 2 x 16GB @ 2133 MT/s DDR4</td>
-</tr>
-<tr>
-  <td><strong>CPU</strong></td>
-  <td>Intel® Core™ i3-8100 CPU @ 3.60GHz</td>
-  <td>Intel® Core™ i5-8500 CPU @ 3.00GHz</td>
-</tr>
-<tr>
-  <td><strong>Selected CPU Flags</strong></td>
-  <td>-</td>
-  <td>Turbo Boost</td>
-</tr>
-<tr>
-  <td><strong>CPU Thermal Design Power</strong></td>
-  <td>65 W</td>
-  <td>65 W</td>
-</tr>
-<tr>
-  <td><strong>Operating System</strong></td>
-  <td>Ubuntu 20.04.1 LTS</td>
-  <td>Ubuntu 20.04.1 LTS</td>
-</tr>
-<tr>
-  <td><strong>Kernel Version</strong></td>
-  <td>5.4.0-52-generic</td>
-  <td>5.4.0-52-generic</td>
-</tr>
-<tr>
-  <td><strong>BIOS Vendor</strong></td>
-  <td>American Megatrends Inc.</td>
-  <td>American Megatrends Inc.</td>
-</tr>
-<tr>
-  <td><strong>BIOS Version and Release Date</strong></td>
-  <td>F10j, date: 09/16/2020</td>
-  <td>2401, date: 07/12/2019</td>
-</tr>
-<tr>
-  <td><strong>Docker Version</strong></td>
-  <td>20.10.0</td>
-  <td>19.03.13</td>
-</tr>
-</tr>
-<tr>
-  <td><strong>Network Speed</strong></td>
-  <td colspan="2" align="center">40 Gb/s</td>
-</tr>
-</table>
+### Platform with Intel® Core™ i7-8700T
 
+@sphinxdirective
+.. raw:: html
+
+    <table class="table">
+    <tr>
+      <th></th>
+      <th><strong>Server Platform</strong></th>
+      <th><strong>Client Platform</strong></th>
+    </tr>
+    <tr>
+      <td><strong>Motherboard</strong></td>
+      <td>ASUSTeK COMPUTER INC. PRIME Z370-P</td>
+      <td>ASUSTeK COMPUTER INC. PRIME X299-A II</td>
+    </tr>
+    <tr>
+      <td><strong>Memory</strong></td>
+      <td>Corsair 4 x 16GB @ 2133 MT/s DDR4</td>
+      <td>Corsair 4 x 16GB @ 2666 MT/s DDR4</td>
+    </tr>
+    <tr>
+      <td><strong>CPU</strong></td>
+      <td>Intel® Core™ i7-8700T CPU @ 2.40GHz</td>
+      <td>Intel® Core™ i9-10920X CPU @ 3.50GHz</td>
+    </tr>
+    <tr>
+      <td><strong>Selected CPU Flags</strong></td>
+      <td>Hyper Threading, Turbo Boost</td>
+      <td>Hyper Threading, Turbo Boost, DL Boost</td>
+    </tr>
+    <tr>
+      <td><strong>CPU Thermal Design Power</strong></td>
+      <td>35 W</td>
+      <td>165 W</td>
+    </tr>
+    <tr>
+      <td><strong>Operating System</strong></td>
+      <td>Ubuntu 20.04.1 LTS</td>
+      <td>Ubuntu 20.04.1 LTS</td>
+    </tr>
+    <tr>
+      <td><strong>Kernel Version</strong></td>
+      <td>5.4.0-56-generic</td>
+      <td>5.4.0-52-generic</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Vendor</strong></td>
+      <td>American Megatrends Inc.</td>
+      <td>American Megatrends Inc.</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Version and Release Date</strong></td>
+      <td>2401, date: 07/15/2019</td>
+      <td>0603, date: 03/05/2020</td>
+    </tr>
+    <tr>
+      <td><strong>Docker Version</strong></td>
+      <td>19.03.14</td>
+      <td>19.03.13</td>
+    </tr>
+    </tr>
+    <tr>
+      <td><strong>Network Speed</strong></td>
+      <td colspan="2" align="center">10 Gb/s</td>
+    </tr>
+    </table>
+
+@endsphinxdirective
+
+### Platform with Intel® Core™ i5-8500
+
+@sphinxdirective
+.. raw:: html
+
+    <table class="table">
+    <tr>
+      <th></th>
+      <th><strong>Server Platform</strong></th>
+      <th><strong>Client Platform</strong></th>
+    </tr>
+    <tr>
+      <td><strong>Motherboard</strong></td>
+      <td>ASUSTeK COMPUTER INC. PRIME Z370-A</td>
+      <td>Gigabyte Technology Co., Ltd. Z390 UD</td>
+    </tr>
+    <tr>
+      <td><strong>Memory</strong></td>
+      <td>Corsair 2 x 16GB @ 2133 MT/s DDR4</td>
+      <td>029E 4 x 8GB @ 2400 MT/s DDR4</td>
+    </tr>
+    <tr>
+      <td><strong>CPU</strong></td>
+      <td>Intel® Core™ i5-8500 CPU @ 3.00GHz</td>
+      <td>Intel® Core™ i3-8100 CPU @ 3.60GHz</td>
+    </tr>
+    <tr>
+      <td><strong>Selected CPU Flags</strong></td>
+      <td>Turbo Boost</td>
+      <td>-</td>
+    </tr>
+    <tr>
+      <td><strong>CPU Thermal Design Power</strong></td>
+      <td>65 W</td>
+      <td>65 W</td>
+    </tr>
+    <tr>
+      <td><strong>Operating System</strong></td>
+      <td>Ubuntu 20.04.1 LTS</td>
+      <td>Ubuntu 20.04.1 LTS</td>
+    </tr>
+    <tr>
+      <td><strong>Kernel Version</strong></td>
+      <td>5.4.0-52-generic</td>
+      <td>5.4.0-52-generic</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Vendor</strong></td>
+      <td>American Megatrends Inc.</td>
+      <td>American Megatrends Inc.</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Version and Release Date</strong></td>
+      <td>2401, date: 07/12/2019</td>
+      <td>F10j, date: 09/16/2020</td>
+    </tr>
+    <tr>
+      <td><strong>Docker Version</strong></td>
+      <td>19.03.13</td>
+      <td>20.10.0</td>
+    </tr>
+    </tr>
+    <tr>
+      <td><strong>Network Speed</strong></td>
+      <td colspan="2" align="center">40 Gb/s</td>
+    </tr>
+    </table>
+
+@endsphinxdirective
+
+### Platform with Intel® Core™ i3-8100
+
+@sphinxdirective
+.. raw:: html
+
+    <table class="table">
+    <tr>
+      <th></th>
+      <th><strong>Server Platform</strong></th>
+      <th><strong>Client Platform</strong></th>
+    </tr>
+    <tr>
+      <td><strong>Motherboard</strong></td>
+      <td>Gigabyte Technology Co., Ltd. Z390 UD</td>
+      <td>ASUSTeK COMPUTER INC. PRIME Z370-A</td>
+    </tr>
+    <tr>
+      <td><strong>Memory</strong></td>
+      <td>029E 4 x 8GB @ 2400 MT/s DDR4</td>
+      <td>Corsair 2 x 16GB @ 2133 MT/s DDR4</td>
+    </tr>
+    <tr>
+      <td><strong>CPU</strong></td>
+      <td>Intel® Core™ i3-8100 CPU @ 3.60GHz</td>
+      <td>Intel® Core™ i5-8500 CPU @ 3.00GHz</td>
+    </tr>
+    <tr>
+      <td><strong>Selected CPU Flags</strong></td>
+      <td>-</td>
+      <td>Turbo Boost</td>
+    </tr>
+    <tr>
+      <td><strong>CPU Thermal Design Power</strong></td>
+      <td>65 W</td>
+      <td>65 W</td>
+    </tr>
+    <tr>
+      <td><strong>Operating System</strong></td>
+      <td>Ubuntu 20.04.1 LTS</td>
+      <td>Ubuntu 20.04.1 LTS</td>
+    </tr>
+    <tr>
+      <td><strong>Kernel Version</strong></td>
+      <td>5.4.0-52-generic</td>
+      <td>5.4.0-52-generic</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Vendor</strong></td>
+      <td>American Megatrends Inc.</td>
+      <td>American Megatrends Inc.</td>
+    </tr>
+    <tr>
+      <td><strong>BIOS Version and Release Date</strong></td>
+      <td>F10j, date: 09/16/2020</td>
+      <td>2401, date: 07/12/2019</td>
+    </tr>
+    <tr>
+      <td><strong>Docker Version</strong></td>
+      <td>20.10.0</td>
+      <td>19.03.13</td>
+    </tr>
+    </tr>
+    <tr>
+      <td><strong>Network Speed</strong></td>
+      <td colspan="2" align="center">40 Gb/s</td>
+    </tr>
+    </table>
+
+@endsphinxdirective    
 
-\htmlonly
-<style>
-    .footer {
-        display: none;
-    }
-</style>
-<div class="opt-notice-wrapper">
-<p class="opt-notice">
-\endhtmlonly
 Results may vary. For workloads and configurations visit: [www.intel.com/PerformanceIndex](https://www.intel.com/PerformanceIndex) and [Legal Information](../Legal_Information.md).
-\htmlonly
-</p>
-</div>
-\endhtmlonly
-
diff --git a/docs/benchmarks/performance_int8_vs_fp32.md b/docs/benchmarks/performance_int8_vs_fp32.md
index e6e85d02df5..e060d3bbe73 100644
--- a/docs/benchmarks/performance_int8_vs_fp32.md
+++ b/docs/benchmarks/performance_int8_vs_fp32.md
@@ -2,446 +2,443 @@
 
 The table below illustrates the speed-up factor for the performance gain by switching from an FP32 representation of an OpenVINO™ supported model to its INT8 representation. 
 
-<table>
-  <tr align="left">
-    <th></th>
-    <th></th>
-    <th>Intel® Core™ <br>i7-8700T</th>
-    <th>Intel® Core™ <br>i7-1185G7</th>
-    <th>Intel® Xeon® <br>W-1290P</th>
-    <th>Intel® Xeon® <br>Platinum <br>8270</th>
-  </tr>
-  <tr align="left">
-    <th>OpenVINO <br>benchmark <br>model name</th>
-    <th>Dataset</th>
-    <th colspan="3" align="center">Throughput speed-up FP16-INT8 vs FP32</th>
-  </tr>
-  <tr>
-    <td>bert-large-<br>uncased-whole-word-<br>masking-squad-0001</td>
-    <td>SQuAD</td>
-    <td>1.6</td>
-    <td>3.1</td>
-    <td>1.5</td>
-    <td>2.5</td>
-  </tr>
-  <tr>
-    <td>brain-tumor-<br>segmentation-<br>0001-MXNET</td>
-    <td>BraTS</td>
-    <td>1.6</td>
-    <td>2.0</td>
-    <td>1.8</td>
-    <td>1.8</td>
-  </tr>
-  <tr>
-    <td>deeplabv3-TF</td>
-    <td>VOC 2012<br>Segmentation</td>
-    <td>1.9</td>
-    <td>3.0</td>
-    <td>2.8</td>
-    <td>3.1</td>
-  </tr>
-  <tr>
-    <td>densenet-121-TF</td>
-    <td>ImageNet</td>
-    <td>1.8</td>
-    <td>3.5</td>
-    <td>1.9</td>
-    <td>3.8</td>
-  </tr>
-  <tr>
-    <td>facenet-<br>20180408-<br>102900-TF</td>
-    <td>LFW</td>
-    <td>2.1</td>
-    <td>3.6</td>
-    <td>2.2</td>
-    <td>3.7</td>
-  </tr>
-  <tr>
-    <td>faster_rcnn_<br>resnet50_coco-TF</td>
-    <td>MS COCO</td>
-    <td>1.9</td>
-    <td>3.7</td>
-    <td>2.0</td>
-    <td>3.4</td>
-  </tr>
-  <tr>
-    <td>inception-v3-TF</td>
-    <td>ImageNet</td>
-    <td>1.9</td>
-    <td>3.8</td>
-    <td>2.0</td>
-    <td>4.1</td>
-  </tr>
-  <tr>
-    <td>mobilenet-<br>ssd-CF</td>
-    <td>VOC2012</td>
-    <td>1.6</td>
-    <td>3.1</td>
-    <td>1.9</td>
-    <td>3.6</td>
-  </tr>
-  <tr>
-    <td>mobilenet-v2-1.0-<br>224-TF</td>
-    <td>ImageNet</td>
-    <td>1.5</td>
-    <td>2.4</td>
-    <td>1.8</td>
-    <td>3.9</td>
-  </tr>
-  <tr>
-    <td>mobilenet-v2-<br>pytorch</td>
-    <td>ImageNet</td>
-    <td>1.7</td>
-    <td>2.4</td>
-    <td>1.9</td>
-    <td>4.0</td>
-  </tr>
-  <tr>
-    <td>resnet-18-<br>pytorch</td>
-    <td>ImageNet</td>
-    <td>1.9</td>
-    <td>3.7</td>
-    <td>2.1</td>
-    <td>4.2</td>
-  </tr>
-  <tr>
-    <td>resnet-50-<br>pytorch</td>
-    <td>ImageNet</td>
-    <td>1.9</td>
-    <td>3.6</td>
-    <td>2.0</td>
-    <td>3.9</td>
-  </tr>
-  <tr>
-    <td>resnet-50-<br>TF</td>
-    <td>ImageNet</td>
-    <td>1.9</td>
-    <td>3.6</td>
-    <td>2.0</td>
-    <td>3.9</td>
-  </tr>
-  <tr>
-    <td>squeezenet1.1-<br>CF</td>
-    <td>ImageNet</td>
-    <td>1.7</td>
-    <td>3.2</td>
-    <td>1.8</td>
-    <td>3.4</td>
-  </tr>
-  <tr>
-    <td>ssd_mobilenet_<br>v1_coco-tf</td>
-    <td>VOC2012</td>
-    <td>1.8</td>
-    <td>3.1</td>
-    <td>2.0</td>
-    <td>3.6</td>
-  </tr>
-  <tr>
-    <td>ssd300-CF</td>
-    <td>MS COCO</td>
-    <td>1.8</td>
-    <td>4.2</td>
-    <td>1.9</td>
-    <td>3.9</td>
-  </tr>
-  <tr>
-    <td>ssdlite_<br>mobilenet_<br>v2-TF</td>
-    <td>MS COCO</td>
-    <td>1.7</td>
-    <td>2.5</td>
-    <td>2.4</td>
-    <td>3.5</td>
-  </tr>
-  <tr>
-    <td>yolo_v4-TF</td>
-    <td>MS COCO</td>
-    <td>1.9</td>
-    <td>3.6</td>
-    <td>2.0</td>
-    <td>3.4</td>
-  </tr>
-  <tr>
-    <td>unet-camvid-onnx-0001</td>
-    <td>MS COCO</td>
-    <td>1.7</td>
-    <td>3.9</td>
-    <td>1.7</td>
-    <td>3.7</td>
-  </tr>
-  <tr>
-    <td>ssd-resnet34-<br>1200-onnx</td>
-    <td>MS COCO</td>
-    <td>1.7</td>
-    <td>4.0</td>
-    <td>1.7</td>
-    <td>3.4</td>
-  </tr>
-  <tr>
-    <td>googlenet-v4-tf</td>
-    <td>ImageNet</td>
-    <td>1.9</td>
-    <td>3.9</td>
-    <td>2.0</td>
-    <td>4.1</td>
-  </tr>
-  <tr>
-    <td>vgg19-caffe</td>
-    <td>ImageNet</td>
-    <td>1.9</td>
-    <td>4.7</td>
-    <td>2.0</td>
-    <td>4.5</td>
-  </tr>
-  <tr>
-    <td>yolo-v3-tiny-tf</td>
-    <td>MS COCO</td>
-    <td>1.7</td>
-    <td>3.4</td>
-    <td>1.9</td>
-    <td>3.5</td>
-  </tr>
-</table>
+@sphinxdirective
+.. raw:: html
+
+    <table class="table">
+      <tr align="left">
+        <th></th>
+        <th></th>
+        <th>Intel® Core™ <br>i7-8700T</th>
+        <th>Intel® Core™ <br>i7-1185G7</th>
+        <th>Intel® Xeon® <br>W-1290P</th>
+        <th>Intel® Xeon® <br>Platinum <br>8270</th>
+      </tr>
+      <tr align="left">
+        <th>OpenVINO <br>benchmark <br>model name</th>
+        <th>Dataset</th>
+        <th colspan="4" align="center">Throughput speed-up FP16-INT8 vs FP32</th>
+      </tr>
+      <tr>
+        <td>bert-large-<br>uncased-whole-word-<br>masking-squad-0001</td>
+        <td>SQuAD</td>
+        <td>1.6</td>
+        <td>3.1</td>
+        <td>1.5</td>
+        <td>2.5</td>
+      </tr>
+      <tr>
+        <td>brain-tumor-<br>segmentation-<br>0001-MXNET</td>
+        <td>BraTS</td>
+        <td>1.6</td>
+        <td>2.0</td>
+        <td>1.8</td>
+        <td>1.8</td>
+      </tr>
+      <tr>
+        <td>deeplabv3-TF</td>
+        <td>VOC 2012<br>Segmentation</td>
+        <td>1.9</td>
+        <td>3.0</td>
+        <td>2.8</td>
+        <td>3.1</td>
+      </tr>
+      <tr>
+        <td>densenet-121-TF</td>
+        <td>ImageNet</td>
+        <td>1.8</td>
+        <td>3.5</td>
+        <td>1.9</td>
+        <td>3.8</td>
+      </tr>
+      <tr>
+        <td>facenet-<br>20180408-<br>102900-TF</td>
+        <td>LFW</td>
+        <td>2.1</td>
+        <td>3.6</td>
+        <td>2.2</td>
+        <td>3.7</td>
+      </tr>
+      <tr>
+        <td>faster_rcnn_<br>resnet50_coco-TF</td>
+        <td>MS COCO</td>
+        <td>1.9</td>
+        <td>3.7</td>
+        <td>2.0</td>
+        <td>3.4</td>
+      </tr>
+      <tr>
+        <td>inception-v3-TF</td>
+        <td>ImageNet</td>
+        <td>1.9</td>
+        <td>3.8</td>
+        <td>2.0</td>
+        <td>4.1</td>
+      </tr>
+      <tr>
+        <td>mobilenet-<br>ssd-CF</td>
+        <td>VOC2012</td>
+        <td>1.6</td>
+        <td>3.1</td>
+        <td>1.9</td>
+        <td>3.6</td>
+      </tr>
+      <tr>
+        <td>mobilenet-v2-1.0-<br>224-TF</td>
+        <td>ImageNet</td>
+        <td>1.5</td>
+        <td>2.4</td>
+        <td>1.8</td>
+        <td>3.9</td>
+      </tr>
+      <tr>
+        <td>mobilenet-v2-<br>pytorch</td>
+        <td>ImageNet</td>
+        <td>1.7</td>
+        <td>2.4</td>
+        <td>1.9</td>
+        <td>4.0</td>
+      </tr>
+      <tr>
+        <td>resnet-18-<br>pytorch</td>
+        <td>ImageNet</td>
+        <td>1.9</td>
+        <td>3.7</td>
+        <td>2.1</td>
+        <td>4.2</td>
+      </tr>
+      <tr>
+        <td>resnet-50-<br>pytorch</td>
+        <td>ImageNet</td>
+        <td>1.9</td>
+        <td>3.6</td>
+        <td>2.0</td>
+        <td>3.9</td>
+      </tr>
+      <tr>
+        <td>resnet-50-<br>TF</td>
+        <td>ImageNet</td>
+        <td>1.9</td>
+        <td>3.6</td>
+        <td>2.0</td>
+        <td>3.9</td>
+      </tr>
+      <tr>
+        <td>squeezenet1.1-<br>CF</td>
+        <td>ImageNet</td>
+        <td>1.7</td>
+        <td>3.2</td>
+        <td>1.8</td>
+        <td>3.4</td>
+      </tr>
+      <tr>
+        <td>ssd_mobilenet_<br>v1_coco-tf</td>
+        <td>VOC2012</td>
+        <td>1.8</td>
+        <td>3.1</td>
+        <td>2.0</td>
+        <td>3.6</td>
+      </tr>
+      <tr>
+        <td>ssd300-CF</td>
+        <td>MS COCO</td>
+        <td>1.8</td>
+        <td>4.2</td>
+        <td>1.9</td>
+        <td>3.9</td>
+      </tr>
+      <tr>
+        <td>ssdlite_<br>mobilenet_<br>v2-TF</td>
+        <td>MS COCO</td>
+        <td>1.7</td>
+        <td>2.5</td>
+        <td>2.4</td>
+        <td>3.5</td>
+      </tr>
+      <tr>
+        <td>yolo_v4-TF</td>
+        <td>MS COCO</td>
+        <td>1.9</td>
+        <td>3.6</td>
+        <td>2.0</td>
+        <td>3.4</td>
+      </tr>
+      <tr>
+        <td>unet-camvid-onnx-0001</td>
+        <td>MS COCO</td>
+        <td>1.7</td>
+        <td>3.9</td>
+        <td>1.7</td>
+        <td>3.7</td>
+      </tr>
+      <tr>
+        <td>ssd-resnet34-<br>1200-onnx</td>
+        <td>MS COCO</td>
+        <td>1.7</td>
+        <td>4.0</td>
+        <td>1.7</td>
+        <td>3.4</td>
+      </tr>
+      <tr>
+        <td>googlenet-v4-tf</td>
+        <td>ImageNet</td>
+        <td>1.9</td>
+        <td>3.9</td>
+        <td>2.0</td>
+        <td>4.1</td>
+      </tr>
+      <tr>
+        <td>vgg19-caffe</td>
+        <td>ImageNet</td>
+        <td>1.9</td>
+        <td>4.7</td>
+        <td>2.0</td>
+        <td>4.5</td>
+      </tr>
+      <tr>
+        <td>yolo-v3-tiny-tf</td>
+        <td>MS COCO</td>
+        <td>1.7</td>
+        <td>3.4</td>
+        <td>1.9</td>
+        <td>3.5</td>
+      </tr>
+    </table>
+
+@endsphinxdirective
 
 The following table shows the absolute accuracy drop that is calculated as the difference in accuracy between the FP32 representation of a model and its INT8 representation.
 
-<table>
-  <tr align="left">
-    <th></th>
-    <th></th>
-    <th></th>
-    <th>Intel® Core™ <br>i9-10920X CPU<br>@ 3.50GHZ (VNNI)</th>
-    <th>Intel® Core™ <br>i9-9820X CPU<br>@ 3.30GHz (AVX512)</th>
-    <th>Intel® Core™ <br>i7-6700K CPU<br>@ 4.0GHz (AVX2)</th>
-    <th>Intel® Core™ <br>i7-1185G7 CPU<br>@ 4.0GHz (TGL VNNI)</th>
-  </tr>
-  <tr align="left">
-    <th>OpenVINO Benchmark <br>Model Name</th>
-    <th>Dataset</th>
-    <th>Metric Name</th>
-    <th colspan="4" align="center">Absolute Accuracy Drop, %</th>
-  </tr>
-  <tr>
-    <td>bert-large-uncased-whole-word-masking-squad-0001</td>
-    <td>SQuAD</td>
-    <td>F1</td>
-    <td>0.62</td>
-    <td>0.71</td>
-    <td>0.62</td>
-    <td>0.62</td>
-  </tr>
-  <tr>
-    <td>brain-tumor-<br>segmentation-<br>0001-MXNET</td>
-    <td>BraTS</td>
-    <td>Dice-index@ <br>Mean@ <br>Overall Tumor</td>
-    <td>0.08</td>
-    <td>0.10</td>
-    <td>0.10</td>
-    <td>0.08</td>
-  </tr>
-  <tr>
-    <td>deeplabv3-TF</td>
-    <td>VOC 2012<br>Segmentation</td>
-    <td>mean_iou</td>
-    <td>0.09</td>
-    <td>0.41</td>
-    <td>0.41</td>
-    <td>0.09</td>
-  </tr>
-  <tr>
-    <td>densenet-121-TF</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.49</td>
-    <td>0.56</td>
-    <td>0.56</td>
-    <td>0.49</td>
-  </tr>
-  <tr>
-    <td>facenet-<br>20180408-<br>102900-TF</td>
-    <td>LFW</td>
-    <td>pairwise_<br>accuracy<br>_subsets</td>
-    <td>0.05</td>
-    <td>0.12</td>
-    <td>0.12</td>
-    <td>0.05</td>
-  </tr>
-  <tr>
-    <td>faster_rcnn_<br>resnet50_coco-TF</td>
-    <td>MS COCO</td>
-    <td>coco_<br>precision</td>
-    <td>0.09</td>
-    <td>0.09</td>
-    <td>0.09</td>
-    <td>0.09</td>
-  </tr>
-  <tr>
-    <td>inception-v3-TF</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.02</td>
-    <td>0.01</td>
-    <td>0.01</td>
-    <td>0.02</td>
-  </tr>
-  <tr>
-    <td>mobilenet-<br>ssd-CF</td>
-    <td>VOC2012</td>
-    <td>mAP</td>
-    <td>0.06</td>
-    <td>0.04</td>
-    <td>0.04</td>
-    <td>0.06</td>
-  </tr>
-  <tr>
-    <td>mobilenet-v2-1.0-<br>224-TF</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.40</td>
-    <td>0.76</td>
-    <td>0.76</td>
-    <td>0.40</td>
-  </tr>
-  <tr>
-    <td>mobilenet-v2-<br>PYTORCH</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.36</td>
-    <td>0.52</td>
-    <td>0.52</td>
-    <td>0.36</td>
-  </tr>
-  <tr>
-    <td>resnet-18-<br>pytorch</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.25</td>
-    <td>0.25</td>
-    <td>0.25</td>
-    <td>0.25</td>
-  </tr>
-  <tr>
-    <td>resnet-50-<br>PYTORCH</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.19</td>
-    <td>0.21</td>
-    <td>0.21</td>
-    <td>0.19</td>
-  </tr>
-  <tr>
-    <td>resnet-50-<br>TF</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.11</td>
-    <td>0.11</td>
-    <td>0.11</td>
-    <td>0.11</td>
-  </tr>
-  <tr>
-    <td>squeezenet1.1-<br>CF</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.64</td>
-    <td>0.66</td>
-    <td>0.66</td>
-    <td>0.64</td>
-  </tr>
-  <tr>
-    <td>ssd_mobilenet_<br>v1_coco-tf</td>
-    <td>VOC2012</td>
-    <td>COCO mAp</td>
-    <td>0.17</td>
-    <td>2.96</td>
-    <td>2.96</td>
-    <td>0.17</td>
-  </tr>
-  <tr>
-    <td>ssd300-CF</td>
-    <td>MS COCO</td>
-    <td>COCO mAp</td>
-    <td>0.18</td>
-    <td>3.06</td>
-    <td>3.06</td>
-    <td>0.18</td>
-  </tr>
-  <tr>
-    <td>ssdlite_<br>mobilenet_<br>v2-TF</td>
-    <td>MS COCO</td>
-    <td>COCO mAp</td>
-    <td>0.11</td>
-    <td>0.43</td>
-    <td>0.43</td>
-    <td>0.11</td>
-  </tr>
-  <tr>
-    <td>yolo_v4-TF</td>
-    <td>MS COCO</td>
-    <td>COCO mAp</td>
-    <td>0.06</td>
-    <td>0.03</td>
-    <td>0.03</td>
-    <td>0.06</td>
-  </tr>
-  <tr>
-    <td>unet-camvid-<br>onnx-0001</td>
-    <td>MS COCO</td>
-    <td>COCO mAp</td>
-    <td>0.29</td>
-    <td>0.29</td>
-    <td>0.31</td>
-    <td>0.29</td>
-  </tr>
-  <tr>
-    <td>ssd-resnet34-<br>1200-onnx</td>
-    <td>MS COCO</td>
-    <td>COCO mAp</td>
-    <td>0.02</td>
-    <td>0.03</td>
-    <td>0.03</td>
-    <td>0.02</td>
-  </tr>
-  <tr>
-    <td>googlenet-v4-tf</td>
-    <td>ImageNet</td>
-    <td>COCO mAp</td>
-    <td>0.08</td>
-    <td>0.06</td>
-    <td>0.06</td>
-    <td>0.06</td>
-  </tr>
-  <tr>
-    <td>vgg19-caffe</td>
-    <td>ImageNet</td>
-    <td>COCO mAp</td>
-    <td>0.02</td>
-    <td>0.04</td>
-    <td>0.04</td>
-    <td>0.02</td>
-  </tr>
-  <tr>
-    <td>yolo-v3-tiny-tf</td>
-    <td>MS COCO</td>
-    <td>COCO mAp</td>
-    <td>0.02</td>
-    <td>0.6</td>
-    <td>0.6</td>
-    <td>0.02</td>
-  </tr>
-</table>
+@sphinxdirective
+.. raw:: html
 
-![INT8 vs FP32 Comparison](img/int8vsfp32.png)
+    <table class="table">
+      <tr align="left">
+        <th></th>
+        <th></th>
+        <th></th>
+        <th>Intel® Core™ <br>i9-10920X CPU<br>@ 3.50GHZ (VNNI)</th>
+        <th>Intel® Core™ <br>i9-9820X CPU<br>@ 3.30GHz (AVX512)</th>
+        <th>Intel® Core™ <br>i7-6700K CPU<br>@ 4.0GHz (AVX2)</th>
+        <th>Intel® Core™ <br>i7-1185G7 CPU<br>@ 4.0GHz (TGL VNNI)</th>
+      </tr>
+      <tr align="left">
+        <th>OpenVINO Benchmark <br>Model Name</th>
+        <th>Dataset</th>
+        <th>Metric Name</th>
+        <th colspan="4" align="center">Absolute Accuracy Drop, %</th>
+      </tr>
+      <tr>
+        <td>bert-large-uncased-whole-word-masking-squad-0001</td>
+        <td>SQuAD</td>
+        <td>F1</td>
+        <td>0.62</td>
+        <td>0.71</td>
+        <td>0.62</td>
+        <td>0.62</td>
+      </tr>
+      <tr>
+        <td>brain-tumor-<br>segmentation-<br>0001-MXNET</td>
+        <td>BraTS</td>
+        <td>Dice-index@ <br>Mean@ <br>Overall Tumor</td>
+        <td>0.08</td>
+        <td>0.10</td>
+        <td>0.10</td>
+        <td>0.08</td>
+      </tr>
+      <tr>
+        <td>deeplabv3-TF</td>
+        <td>VOC 2012<br>Segmentation</td>
+        <td>mean_iou</td>
+        <td>0.09</td>
+        <td>0.41</td>
+        <td>0.41</td>
+        <td>0.09</td>
+      </tr>
+      <tr>
+        <td>densenet-121-TF</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.49</td>
+        <td>0.56</td>
+        <td>0.56</td>
+        <td>0.49</td>
+      </tr>
+      <tr>
+        <td>facenet-<br>20180408-<br>102900-TF</td>
+        <td>LFW</td>
+        <td>pairwise_<br>accuracy<br>_subsets</td>
+        <td>0.05</td>
+        <td>0.12</td>
+        <td>0.12</td>
+        <td>0.05</td>
+      </tr>
+      <tr>
+        <td>faster_rcnn_<br>resnet50_coco-TF</td>
+        <td>MS COCO</td>
+        <td>coco_<br>precision</td>
+        <td>0.09</td>
+        <td>0.09</td>
+        <td>0.09</td>
+        <td>0.09</td>
+      </tr>
+      <tr>
+        <td>inception-v3-TF</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.02</td>
+        <td>0.01</td>
+        <td>0.01</td>
+        <td>0.02</td>
+      </tr>
+      <tr>
+        <td>mobilenet-<br>ssd-CF</td>
+        <td>VOC2012</td>
+        <td>mAP</td>
+        <td>0.06</td>
+        <td>0.04</td>
+        <td>0.04</td>
+        <td>0.06</td>
+      </tr>
+      <tr>
+        <td>mobilenet-v2-1.0-<br>224-TF</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.40</td>
+        <td>0.76</td>
+        <td>0.76</td>
+        <td>0.40</td>
+      </tr>
+      <tr>
+        <td>mobilenet-v2-<br>PYTORCH</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.36</td>
+        <td>0.52</td>
+        <td>0.52</td>
+        <td>0.36</td>
+      </tr>
+      <tr>
+        <td>resnet-18-<br>pytorch</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.25</td>
+        <td>0.25</td>
+        <td>0.25</td>
+        <td>0.25</td>
+      </tr>
+      <tr>
+        <td>resnet-50-<br>PYTORCH</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.19</td>
+        <td>0.21</td>
+        <td>0.21</td>
+        <td>0.19</td>
+      </tr>
+      <tr>
+        <td>resnet-50-<br>TF</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.11</td>
+        <td>0.11</td>
+        <td>0.11</td>
+        <td>0.11</td>
+      </tr>
+      <tr>
+        <td>squeezenet1.1-<br>CF</td>
+        <td>ImageNet</td>
+        <td>acc@top-1</td>
+        <td>0.64</td>
+        <td>0.66</td>
+        <td>0.66</td>
+        <td>0.64</td>
+      </tr>
+      <tr>
+        <td>ssd_mobilenet_<br>v1_coco-tf</td>
+        <td>VOC2012</td>
+        <td>COCO mAp</td>
+        <td>0.17</td>
+        <td>2.96</td>
+        <td>2.96</td>
+        <td>0.17</td>
+      </tr>
+      <tr>
+        <td>ssd300-CF</td>
+        <td>MS COCO</td>
+        <td>COCO mAp</td>
+        <td>0.18</td>
+        <td>3.06</td>
+        <td>3.06</td>
+        <td>0.18</td>
+      </tr>
+      <tr>
+        <td>ssdlite_<br>mobilenet_<br>v2-TF</td>
+        <td>MS COCO</td>
+        <td>COCO mAp</td>
+        <td>0.11</td>
+        <td>0.43</td>
+        <td>0.43</td>
+        <td>0.11</td>
+      </tr>
+      <tr>
+        <td>yolo_v4-TF</td>
+        <td>MS COCO</td>
+        <td>COCO mAp</td>
+        <td>0.06</td>
+        <td>0.03</td>
+        <td>0.03</td>
+        <td>0.06</td>
+      </tr>
+      <tr>
+        <td>unet-camvid-<br>onnx-0001</td>
+        <td>MS COCO</td>
+        <td>COCO mAp</td>
+        <td>0.29</td>
+        <td>0.29</td>
+        <td>0.31</td>
+        <td>0.29</td>
+      </tr>
+      <tr>
+        <td>ssd-resnet34-<br>1200-onnx</td>
+        <td>MS COCO</td>
+        <td>COCO mAp</td>
+        <td>0.02</td>
+        <td>0.03</td>
+        <td>0.03</td>
+        <td>0.02</td>
+      </tr>
+      <tr>
+        <td>googlenet-v4-tf</td>
+        <td>ImageNet</td>
+        <td>COCO mAp</td>
+        <td>0.08</td>
+        <td>0.06</td>
+        <td>0.06</td>
+        <td>0.06</td>
+      </tr>
+      <tr>
+        <td>vgg19-caffe</td>
+        <td>ImageNet</td>
+        <td>COCO mAp</td>
+        <td>0.02</td>
+        <td>0.04</td>
+        <td>0.04</td>
+        <td>0.02</td>
+      </tr>
+      <tr>
+        <td>yolo-v3-tiny-tf</td>
+        <td>MS COCO</td>
+        <td>COCO mAp</td>
+        <td>0.02</td>
+        <td>0.6</td>
+        <td>0.6</td>
+        <td>0.02</td>
+      </tr>
+    </table>
+
+@endsphinxdirective
+
+![INT8 vs FP32 Comparison](../img/int8vsfp32.png)
 
-\htmlonly
-<style>
-    .footer {
-        display: none;
-    }
-</style>
-<div class="opt-notice-wrapper">
-<p class="opt-notice">
-\endhtmlonly
 For more complete information about performance and benchmark results, visit: [www.intel.com/benchmarks](https://www.intel.com/benchmarks) and [Optimization Notice](https://software.intel.com/articles/optimization-notice). [Legal Information](../Legal_Information.md).
-\htmlonly
-</p>
-</div>
-\endhtmlonly
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 00000000000..2f364b00aa8
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,150 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+import json
+import shutil
+from sphinx.util import logging
+from json import JSONDecodeError
+from sphinx.ext.autodoc import ClassDocumenter
+
+
+sys.path.insert(0, os.path.abspath('doxyrest-sphinx'))
+
+# -- Project information -----------------------------------------------------
+
+project = 'OpenVINO™'
+copyright = '2021, Intel®'
+author = 'Intel®'
+
+language = 'en'
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx_inline_tabs',
+    'sphinx_copybutton',
+    'doxyrest',
+    'cpplexer',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx_sitemap'
+]
+
+html_baseurl = 'https://docs.openvino.ai/latest/'
+
+sitemap_url_scheme = "{link}"
+
+html_favicon = '_static/favicon.ico'
+autodoc_default_flags = ['members']
+autosummary_generate = True
+autosummary_imported_members = True
+
+html_logo = '_static/logo.svg'
+html_copy_source = False
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'openvino/inference-engine']
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "openvino_sphinx_theme"
+
+html_theme_path = ['_themes']
+
+html_theme_options = {
+    "navigation_depth": 5,
+    "use_edit_page_button": True,
+    "github_url": "https://github.com/openvinotoolkit/openvino",
+}
+
+html_context = {
+    'current_version': '2021.4',
+    'current_language': 'English',
+    'languages': (('English', '/latest'), ('Chinese', '/cn/latest')),
+    'versions': (('Latest', '/latest'), ('2021.4', '/2021.4'),
+                ('2021.3', '/2021.3'), ('2021.2', '/2021.2'), ('2021.1', '/2021.1'),
+                 ('2020.4', '/2020.4'), ('2020.3', '/2020.3'), ('2020.2', '/2020.1'),
+                  ('2020.1', '/2020.1'), ('2019_R3.1', '/2019_R3.1'), ('2019_R3', '/2019_R3'),
+                   ('2019_R2', '/2019_R2'), ('2019_R1.1', '/2019_R1.1'),  ('2019_R1.01', '/2019_R1.01'),
+                    ('2019_R1', '/2019_R1'), ('2018_R5', '/2018_R5'),),
+    'download_docs_url': '/archives/2021.4.zip'
+}
+
+repositories = {
+    'openvino': {
+        'github_user': 'openvinotoolkit',
+        'github_repo': 'openvino',
+        'github_version': 'releases/2021/4',
+        'host_url': 'https://github.com'
+    },
+    'open_model_zoo': {
+        'github_user': 'openvinotoolkit',
+        'github_repo': 'open_model_zoo',
+        'github_version': 'master',
+        'host_url': 'https://github.com'
+    }
+}
+
+try:
+    doxygen_mapping_file = '@DOXYGEN_MAPPING_FILE@'
+    with open(doxygen_mapping_file, 'r', encoding='utf-8') as f:
+        doxygen_mapping_file = json.load(f)
+except JSONDecodeError:
+    doxygen_mapping_file = dict()
+except FileNotFoundError:
+    doxygen_mapping_file = dict()
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# monkeypatch sphinx api doc to prevent showing inheritance from object and enum.Enum
+add_line = ClassDocumenter.add_line
+
+def add_line_no_base_object(self, line, *args, **kwargs):
+    if line.strip() in ['Bases: :class:`object`', 'Bases: :class:`enum.Enum`']:
+        return
+    else:
+        add_line(self, line, *args, **kwargs)
+
+
+ClassDocumenter.add_line = add_line_no_base_object
+
+
+def setup(app):
+    logger = logging.getLogger(__name__)
+    app.add_config_value('doxygen_mapping_file', doxygen_mapping_file, rebuild=True)
+    app.add_config_value('repositories', repositories, rebuild=True)
+    app.add_css_file('css/viewer.min.css')
+    app.add_css_file('css/custom.css')
+    app.add_js_file('js/viewer.min.js')
+    app.add_js_file('js/custom.js')
+    app.add_js_file('js/graphs.js')
+    try:
+        shutil.copytree(os.path.join(app.srcdir, 'csv'), os.path.join(app.outdir, 'csv'), dirs_exist_ok=True)
+    except FileNotFoundError:
+        logger.warning('csv directory not found.')
diff --git a/docs/documentation.md b/docs/documentation.md
new file mode 100644
index 00000000000..bd4444f1469
--- /dev/null
+++ b/docs/documentation.md
@@ -0,0 +1,106 @@
+# Documentation {#documentation}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Converting and Preparing Models
+   :hidden:
+
+   openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide
+   openvino_docs_HOWTO_Custom_Layers_Guide
+   omz_tools_downloader
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Deploying Inference
+   :hidden:
+
+   openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide
+   openvino_docs_nGraph_DG_DevGuide
+   openvino_docs_install_guides_deployment_manager_tool
+   openvino_inference_engine_tools_compile_tool_README
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Tuning for Performance
+   :hidden:
+
+   openvino_docs_performance_benchmarks
+   openvino_docs_optimization_guide_dldt_optimization_guide
+   openvino_docs_MO_DG_Getting_Performance_Numbers
+   pot_README
+   openvino_docs_tuning_utilities
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Graphical Web Interface for OpenVINO™ toolkit  
+   :hidden:
+
+   workbench_docs_Workbench_DG_Introduction
+   workbench_docs_Workbench_DG_Install
+   workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets
+   workbench_docs_Workbench_DG_User_Guide
+   workbench_docs_security_Workbench
+   workbench_docs_Workbench_DG_Troubleshooting
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Media Processing
+
+   DL Streamer API Reference <https://openvinotoolkit.github.io/dlstreamer_gst/>
+   gst_samples_README
+   openvino_docs_gapi_gapi_intro
+   OpenVX Developer Guide <https://software.intel.com/en-us/openvino-ovx-guide>
+   OpenVX API Reference <https://khronos.org/openvx>
+   OpenCV* Developer Guide <https://docs.opencv.org/master/>
+   OpenCL™ Developer Guide <https://software.intel.com/en-us/openclsdk-devguide>   
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Add-Ons
+   :hidden:
+
+   openvino_docs_ovms
+   ovsa_get_started
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Developing Inference Engine Plugins 
+   :hidden:
+
+   Inference Engine Plugin Developer Guide <openvino_docs_ie_plugin_dg_overview>
+   groupie_dev_api
+   
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Use OpenVINO™ Toolkit Securely
+   
+   openvino_docs_security_guide_introduction
+   openvino_docs_security_guide_workbench
+   openvino_docs_IE_DG_protecting_model_guide
+   ovsa_get_started
+
+@endsphinxdirective
+
+This section provides reference documents that guide you through developing your own deep learning applications with the OpenVINO™ toolkit. These documents will most helpful if you have first gone through the [Get Started](get_started.md) guide.
+
+## Converting and Preparing Models
+With the [Model Downloader](@ref omz_tools_downloader) and [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) guides, you will learn to download pre-trained models and convert them for use with the OpenVINO™ toolkit. You can provide your own model or choose a public or Intel model from a broad selection provided in the [Open Model Zoo](model_zoo.md).
+
+## Deploying Inference
+The [Inference Engine Developer Guide](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) explains the process of creating your own application that runs inference with the OpenVINO™ toolkit. The [API Reference](./api_references.html) defines the Inference Engine API for Python, C++, and C and the nGraph API for Python and C++. The Inference Engine API is what you'll use to create an OpenVINO™ application, while the nGraph API is available for using enhanced operations sets and other features. After writing your application, you can use the [Deployment Manager](install_guides/deployment-manager-tool.md) for deploying to target devices.
+
+## Tuning for Performance
+The toolkit provides a [Performance Optimization Guide](optimization_guide/dldt_optimization_guide.md) and utilities for squeezing the best performance out of your application, including [Accuracy Checker](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_README), and other tools for measuring accuracy, benchmarking performance, and tuning your application.
+
+## Graphical Web Interface for OpenVINO™ Toolkit
+You can choose to use the [OpenVINO™ Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), a web-based tool that guides you through the process of converting, measuring, optimizing, and deploying models. This tool also serves as a low-effort introduction to the toolkit and provides a variety of useful interactive charts for understanding performance.
+
+## Media Processing
+The OpenVINO™ toolkit comes with several sets of libraries and tools that add capability and flexibility to the toolkit. These include [DL Streamer](@ref gst_samples_README), a utility that eases creation of pipelines via command line or API, and optimized versions of OpenCV and OpenCL.
\ No newline at end of file
diff --git a/docs/documentation_build_instructions.md b/docs/documentation_build_instructions.md
new file mode 100644
index 00000000000..dc0c33a388d
--- /dev/null
+++ b/docs/documentation_build_instructions.md
@@ -0,0 +1,37 @@
+# Build Documentation Using CMake
+
+1. Clone submodules:
+```
+cd openvino
+git submodule update --init --recursive
+```
+2. Install build dependencies using the `install_build_dependencies.sh` script in the project root folder.
+```
+chmod +x install_build_dependencies.sh
+./install_build_dependencies.sh
+```
+
+3. Install [doxyrest](https://github.com/vovkos/doxyrest/releases/tag/doxyrest-2.1.2) and put the `bin` folder in your path
+
+4. Install python dependencies:
+
+```
+python -m pip install -r docs/requirements.txt
+```
+
+5. Install the sphinx theme
+
+6. Create a build folder:
+
+```
+mkdir build && cd build
+```
+
+7. Build documentation using these commands:
+```
+cmake .. -DENABLE_DOCS=ON -DENABLE_DOCKER=ON
+```
+
+```
+cmake --build . --target sphinx_docs
+```
diff --git a/docs/doxygen-xfail.txt b/docs/doxygen-xfail.txt
new file mode 100644
index 00000000000..18ba88378a5
--- /dev/null
+++ b/docs/doxygen-xfail.txt
@@ -0,0 +1,245 @@
+inference-engine/include/ie_api.h
+inference-engine/ie_bridges/c/include/c_api/ie_c_api.h
+doxygen_errors
+inference-engine/include/ie_parallel.hpp
+openvino/itt/include/openvino/itt.hpp
+inference-engine/src/transformations/include/ngraph_ops/type_relaxed.hpp
+openvino/docs/IE_DG/supported_plugins/GNA.md
+inference-engine/src/plugin_api/ie_system_conf.h
+openvino/docs/documentation.md
+openvino/docs/gapi/face_beautification.md
+openvino/docs/gapi/gapi_face_analytics_pipeline.md
+openvino/docs/get_started/get_started_demos.md
+openvino/docs/get_started/get_started_dl_workbench.md
+openvino/docs/get_started/get_started_linux.md
+openvino/docs/get_started/get_started_macos.md
+openvino/docs/get_started/get_started_raspbian.md
+openvino/docs/get_started/get_started_windows.md
+openvino/docs/HOWTO/Custom_Layers_Guide.md
+openvino/docs/IE_DG/inference_engine_intro.md
+openvino/docs/IE_DG/Int8Inference.md
+openvino/docs/IE_DG/Intro_to_Performance.md
+openvino/docs/IE_DG/Model_Downloader.md
+openvino/docs/IE_DG/protecting_model_guide.md
+openvino/docs/IE_DG/Samples_Overview.md
+openvino/docs/IE_DG/ShapeInference.md
+openvino/docs/IE_DG/Tools_Overview.md
+openvino/docs/IE_PLUGIN_DG/PluginTesting.md
+openvino/docs/index.md
+openvino/docs/install_guides/deployment-manager-tool.md
+openvino/docs/install_guides/installing-openvino-apt.md
+openvino/docs/install_guides/installing-openvino-docker-linux.md
+openvino/docs/install_guides/installing-openvino-docker-windows.md
+openvino/docs/install_guides/installing-openvino-images.md
+openvino/docs/install_guides/installing-openvino-linux-ivad-vpu.md
+openvino/docs/install_guides/installing-openvino-linux.md
+openvino/docs/install_guides/installing-openvino-macos.md
+openvino/docs/install_guides/installing-openvino-raspbian.md
+openvino/docs/install_guides/installing-openvino-windows.md
+openvino/docs/install_guides/installing-openvino-yum.md
+openvino/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
+openvino/docs/MO_DG/prepare_model/Additional_Optimizations.md
+openvino/docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md
+openvino/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md
+openvino/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md
+openvino/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
+openvino/docs/MO_DG/prepare_model/Default_Model_Optimizer_Optimizations.md
+openvino/docs/MO_DG/prepare_model/Getting_performance_numbers.md
+openvino/docs/model_zoo.md
+openvino/docs/optimization_guide/dldt_deployment_optimization_guide.md
+openvino/docs/optimization_guide/dldt_deployment_optimization_guide_additional.md
+openvino/docs/ovsa/ovsa_get_started.md
+openvino/docs/resources/introduction.md
+openvino/docs/security_guide/workbench.md
+openvino/inference-engine/ie_bridges/c/docs/api_overview.md
+openvino/inference-engine/ie_bridges/c/samples/hello_classification/README.md
+openvino/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/README.md
+openvino/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/README.md
+openvino/inference-engine/ie_bridges/python/sample/classification_sample_async/README.md
+openvino/inference-engine/ie_bridges/python/sample/hello_classification/README.md
+openvino/inference-engine/ie_bridges/python/sample/hello_reshape_ssd/README.md
+openvino/inference-engine/ie_bridges/python/sample/ngraph_function_creation_sample/README.md
+openvino/inference-engine/ie_bridges/python/sample/object_detection_sample_ssd/README.md
+openvino/inference-engine/ie_bridges/python/sample/speech_sample/README.md
+openvino/inference-engine/ie_bridges/python/sample/style_transfer_sample/README.md
+openvino/inference-engine/samples/benchmark_app/README.md
+openvino/inference-engine/samples/classification_sample_async/README.md
+openvino/inference-engine/samples/hello_classification/README.md
+openvino/inference-engine/samples/hello_nv12_input_classification/README.md
+openvino/inference-engine/samples/hello_reshape_ssd/README.md
+openvino/inference-engine/samples/object_detection_sample_ssd/README.md
+openvino/inference-engine/samples/speech_sample/README.md
+openvino/inference-engine/samples/style_transfer_sample/README.md
+openvino/inference-engine/tools/benchmark_tool/README.md
+inference-engine/src/plugin_api/exec_graph_info.hpp
+inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
+inference-engine/src/plugin_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp
+inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp
+inference-engine/src/plugin_api/threading/ie_istreams_executor.hpp
+inference-engine/include/gpu/gpu_context_api_ocl.hpp
+inference-engine/src/transformations/include/transformations/rt_info/primitives_priority_attribute.hpp
+inference-engine/src/transformations/include/ngraph_ops/convolution_ie.hpp
+gst/docs/index.md
+open_model_zoo/models/public/i3d-rgb-tf/README.md
+open_model_zoo/models/public/index.md
+open_model_zoo/tools/accuracy_checker/accuracy_checker/annotation_converters/README.md
+open_model_zoo/tools/accuracy_checker/README.md
+workbench/configs/README.md
+workbench/docs/CLI.md
+workbench/docs/FrequentlyAskedQuestions.md
+workbench/docs/InstallationGuide.md
+workbench/README.md
+workbench/README_dev.md
+pot/configs/README.md
+pot/docs/CLI.md
+pot/docs/FrequentlyAskedQuestions.md
+pot/docs/InstallationGuide.md
+pot/README.md
+pot/README_dev.md
+workbench/docs/Workbench_DG/Set_Accuracy_Config.md
+workbench/docs/Workbench_DG/Int-8_Quantization.md
+workbench/docs/Workbench_DG/Remote_Profiling.md
+workbench/docs/Workbench_DG/Run_Baseline_Inference.md
+workbench/docs/Workbench_DG/Run_Range_of_Inferences.md
+workbench/docs/Workbench_DG/Run_Single_Inference.md
+workbench/docs/Workbench_DG/Select_Models.md
+workbench/docs/Workbench_DG/Visualize_Accuracy.md
+openvino/docs/IE_DG/network_state_intro.md
+workbench/docs/Workbench_DG/Compare_Performance_between_Two_Versions_of_Models.md
+openvino/docs/benchmarks/performance_benchmarks_faq.md
+openvino/docs/IE_DG/Bfloat16Inference.md
+openvino/docs/IE_DG/InferenceEngine_QueryAPI.md
+openvino/docs/IE_DG/Integrate_with_customer_application_new_API.md
+openvino/docs/IE_DG/Model_caching_overview.md
+openvino/docs/IE_DG/PythonPackage_Overview.md
+src/common/legacy/include/legacy/ie_reader.hpp
+src/core/builder/include/ngraph/builder/make_constant.hpp
+src/core/include/ngraph/except.hpp
+src/core/include/ngraph/factory_adapter.hpp
+src/core/include/ngraph/opsets/opset1.hpp
+src/core/include/ngraph/opsets/opset2.hpp
+src/core/include/ngraph/opsets/opset3.hpp
+src/core/include/ngraph/opsets/opset4.hpp
+src/core/include/ngraph/opsets/opset5.hpp
+src/core/include/ngraph/opsets/opset6.hpp
+src/core/include/ngraph/opsets/opset7.hpp
+src/core/include/ngraph/opsets/opset8.hpp
+src/core/include/ngraph/pass/low_latency.hpp
+src/core/include/ngraph/pass/pass.hpp
+src/core/include/openvino/op/constant.hpp
+src/core/include/openvino/opsets/opset1.hpp
+src/core/include/openvino/opsets/opset2.hpp
+src/core/include/openvino/opsets/opset3.hpp
+src/core/include/openvino/opsets/opset4.hpp
+src/core/include/openvino/opsets/opset5.hpp
+src/core/include/openvino/opsets/opset6.hpp
+src/core/include/openvino/opsets/opset7.hpp
+src/core/include/openvino/opsets/opset8.hpp
+src/core/reference/include/ngraph/coordinate_transform.hpp
+src/inference/include/ie/ie_api.h
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l=ft(+n)?[+n,0]:Tt(n,r,s,a),"left"===a?(r.top+=l[0],r.left-=l[1]):"right"===a?(r.top+=l[0],r.left+=l[1]):"top"===a?(r.left+=l[0],r.top-=l[1]):"bottom"===a&&(r.left+=l[0],r.top+=l[1]),e.popper=r,e},offset:0},preventOverflow:{order:300,enabled:!0,fn:function(e,i){var t=i.boundariesElement||Fe(e.instance.popper);e.instance.reference===t&&(t=Fe(t));var n=lt("transform"),o=e.instance.popper.style,r=o.top,s=o.left,a=o[n];o.top="",o.left="",o[n]="";var l=Ze(e.instance.popper,e.instance.reference,i.padding,t,e.positionFixed);o.top=r,o.left=s,o[n]=a,i.boundaries=l;var c=i.priority,h=e.offsets.popper,u={primary:function(e){var t=h[e];return h[e]<l[e]&&!i.escapeWithReference&&(t=Math.max(h[e],l[e])),Ye({},e,t)},secondary:function(e){var t="right"===e?"left":"top",n=h[t];return h[e]>l[e]&&!i.escapeWithReference&&(n=Math.min(h[t],l[e]-("right"===e?h.width:h.height))),Ye({},t,n)}};return c.forEach(function(e){var 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o=e.placement.split("-")[0],r=e.offsets,s=r.popper,a=r.reference,l=-1!==["left","right"].indexOf(o),c=l?"height":"width",h=l?"Top":"Left",u=h.toLowerCase(),f=l?"left":"top",d=l?"bottom":"right",p=nt(i)[c];a[d]-p<s[u]&&(e.offsets.popper[u]-=s[u]-(a[d]-p)),a[u]+p>s[d]&&(e.offsets.popper[u]+=a[u]+p-s[d]),e.offsets.popper=Xe(e.offsets.popper);var m=a[u]+a[c]/2-p/2,g=ke(e.instance.popper),_=parseFloat(g["margin"+h],10),v=parseFloat(g["border"+h+"Width"],10),y=m-e.offsets.popper[u]-_-v;return y=Math.max(Math.min(s[c]-p,y),0),e.arrowElement=i,e.offsets.arrow=(Ye(n={},u,Math.round(y)),Ye(n,f,""),n),e},element:"[x-arrow]"},flip:{order:600,enabled:!0,fn:function(m,g){if(at(m.instance.modifiers,"inner"))return m;if(m.flipped&&m.placement===m.originalPlacement)return m;var _=Ze(m.instance.popper,m.instance.reference,g.padding,g.boundariesElement,m.positionFixed),v=m.placement.split("-")[0],y=it(v),E=m.placement.split("-")[1]||"",b=[];switch(g.behavior){case Et:b=[v,y];break;case 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n=m.offsets.popper,i=m.offsets.reference,o=Math.floor,r="left"===v&&o(n.right)>o(i.left)||"right"===v&&o(n.left)<o(i.right)||"top"===v&&o(n.bottom)>o(i.top)||"bottom"===v&&o(n.top)<o(i.bottom),s=o(n.left)<o(_.left),a=o(n.right)>o(_.right),l=o(n.top)<o(_.top),c=o(n.bottom)>o(_.bottom),h="left"===v&&s||"right"===v&&a||"top"===v&&l||"bottom"===v&&c,u=-1!==["top","bottom"].indexOf(v),f=!!g.flipVariations&&(u&&"start"===E&&s||u&&"end"===E&&a||!u&&"start"===E&&l||!u&&"end"===E&&c),d=!!g.flipVariationsByContent&&(u&&"start"===E&&a||u&&"end"===E&&s||!u&&"start"===E&&c||!u&&"end"===E&&l),p=f||d;(r||h||p)&&(m.flipped=!0,(r||h)&&(v=b[t+1]),p&&(E=function(e){return"end"===e?"start":"start"===e?"end":e}(E)),m.placement=v+(E?"-"+E:""),m.offsets.popper=ze({},m.offsets.popper,ot(m.instance.popper,m.offsets.reference,m.placement)),m=st(m.instance.modifiers,m,"flip"))}),m},behavior:"flip",padding:5,boundariesElement:"viewport",flipVariations:!1,flipVariationsByContent:!1},inner:{order:700,enabled:!1,fn:function(e){var t=e.placement,n=t.split("-")[0],i=e.offsets,o=i.popper,r=i.reference,s=-1!==["left","right"].indexOf(n),a=-1===["top","left"].indexOf(n);return o[s?"left":"top"]=r[n]-(a?o[s?"width":"height"]:0),e.placement=it(t),e.offsets.popper=Xe(o),e}},hide:{order:800,enabled:!0,fn:function(e){if(!gt(e.instance.modifiers,"hide","preventOverflow"))return e;var t=e.offsets.reference,n=rt(e.instance.modifiers,function(e){return"preventOverflow"===e.name}).boundaries;if(t.bottom<n.top||t.left>n.right||t.top>n.bottom||t.right<n.left){if(!0===e.hide)return e;e.hide=!0,e.attributes["x-out-of-boundaries"]=""}else{if(!1===e.hide)return e;e.hide=!1,e.attributes["x-out-of-boundaries"]=!1}return e}},computeStyle:{order:850,enabled:!0,fn:function(e,t){var n=t.x,i=t.y,o=e.offsets.popper,r=rt(e.instance.modifiers,function(e){return"applyStyle"===e.name}).gpuAcceleration;void 0!==r&&console.warn("WARNING: `gpuAcceleration` option moved to `computeStyle` modifier and will not be supported in future versions of 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e={instance:this,styles:{},arrowStyles:{},attributes:{},flipped:!1,offsets:{}};e.offsets.reference=tt(this.state,this.popper,this.reference,this.options.positionFixed),e.placement=et(this.options.placement,e.offsets.reference,this.popper,this.reference,this.options.modifiers.flip.boundariesElement,this.options.modifiers.flip.padding),e.originalPlacement=e.placement,e.positionFixed=this.options.positionFixed,e.offsets.popper=ot(this.popper,e.offsets.reference,e.placement),e.offsets.popper.position=this.options.positionFixed?"fixed":"absolute",e=st(this.modifiers,e),this.state.isCreated?this.options.onUpdate(e):(this.state.isCreated=!0,this.options.onCreate(e))}}.call(this)}},{key:"destroy",value:function(){return function(){return this.state.isDestroyed=!0,at(this.modifiers,"applyStyle")&&(this.popper.removeAttribute("x-placement"),this.popper.style.position="",this.popper.style.top="",this.popper.style.left="",this.popper.style.right="",this.popper.style.bottom="",this.popper.style.willChange="",this.popper.style[lt("transform")]=""),this.disableEventListeners(),this.options.removeOnDestroy&&this.popper.parentNode.removeChild(this.popper),this}.call(this)}},{key:"enableEventListeners",value:function(){return function(){this.state.eventsEnabled||(this.state=ht(this.reference,this.options,this.state,this.scheduleUpdate))}.call(this)}},{key:"disableEventListeners",value:function(){return ut.call(this)}}]),Dt);function Dt(e,t){var n=this,i=2<arguments.length&&void 0!==arguments[2]?arguments[2]:{};!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,Dt),this.scheduleUpdate=function(){return 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this._config.reference.jquery&&(o=this._config.reference[0])),"scrollParent"!==this._config.boundary&&p(i).addClass(Wt),this._popper=new St(o,this._menu,this._getPopperConfig())}"ontouchstart"in document.documentElement&&0===p(i).closest(Kt).length&&p(document.body).children().on("mouseover",null,p.noop),this._element.focus(),this._element.setAttribute("aria-expanded",!0),p(this._menu).toggleClass(jt),p(i).toggleClass(jt).trigger(p.Event(Pt.SHOWN,t))}}},e.hide=function(){if(!this._element.disabled&&!p(this._element).hasClass(xt)&&p(this._menu).hasClass(jt)){var e={relatedTarget:this._element},t=p.Event(Pt.HIDE,e),n=c._getParentFromElement(this._element);p(n).trigger(t),t.isDefaultPrevented()||(this._popper&&this._popper.destroy(),p(this._menu).toggleClass(jt),p(n).toggleClass(jt).trigger(p.Event(Pt.HIDDEN,e)))}},e.dispose=function(){p.removeData(this._element,At),p(this._element).off(Ot),this._element=null,(this._menu=null)!==this._popper&&(this._popper.destroy(),this._popper=null)},e.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},e._addEventListeners=function(){var t=this;p(this._element).on(Pt.CLICK,function(e){e.preventDefault(),e.stopPropagation(),t.toggle()})},e._getConfig=function(e){return e=l({},this.constructor.Default,{},p(this._element).data(),{},e),m.typeCheckConfig(It,e,this.constructor.DefaultType),e},e._getMenuElement=function(){if(!this._menu){var e=c._getParentFromElement(this._element);e&&(this._menu=e.querySelector(qt))}return this._menu},e._getPlacement=function(){var e=p(this._element.parentNode),t=zt;return e.hasClass(Ht)?(t=Vt,p(this._menu).hasClass(Mt)&&(t=Yt)):e.hasClass(Rt)?t=Gt:e.hasClass(Ft)?t=$t:p(this._menu).hasClass(Mt)&&(t=Xt),t},e._detectNavbar=function(){return 0<p(this._element).closest(".navbar").length},e._getOffset=function(){var t=this,e={};return"function"==typeof this._config.offset?e.fn=function(e){return e.offsets=l({},e.offsets,{},t._config.offset(e.offsets,t._element)||{}),e}:e.offset=this._config.offset,e},e._getPopperConfig=function(){var e={placement:this._getPlacement(),modifiers:{offset:this._getOffset(),flip:{enabled:this._config.flip},preventOverflow:{boundariesElement:this._config.boundary}}};return"static"===this._config.display&&(e.modifiers.applyStyle={enabled:!1}),l({},e,{},this._config.popperConfig)},c._jQueryInterface=function(t){return this.each(function(){var e=p(this).data(At);if(e||(e=new c(this,"object"==typeof t?t:null),p(this).data(At,e)),"string"==typeof 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n&&(t=document.querySelector(n)),t||e.parentNode},c._dataApiKeydownHandler=function(e){if((/input|textarea/i.test(e.target.tagName)?!(32===e.which||27!==e.which&&(40!==e.which&&38!==e.which||p(e.target).closest(qt).length)):Lt.test(e.which))&&(e.preventDefault(),e.stopPropagation(),!this.disabled&&!p(this).hasClass(xt))){var t=c._getParentFromElement(this),n=p(t).hasClass(jt);if(n||27!==e.which)if(n&&(!n||27!==e.which&&32!==e.which)){var i=[].slice.call(t.querySelectorAll(Qt)).filter(function(e){return p(e).is(":visible")});if(0!==i.length){var o=i.indexOf(e.target);38===e.which&&0<o&&o--,40===e.which&&o<i.length-1&&o++,o<0&&(o=0),i[o].focus()}}else{if(27===e.which){var r=t.querySelector(Ut);p(r).trigger("focus")}p(this).trigger("click")}}},s(c,null,[{key:"VERSION",get:function(){return"4.4.1"}},{key:"Default",get:function(){return Jt}},{key:"DefaultType",get:function(){return 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tn="modal",nn="bs.modal",on="."+nn,rn=p.fn[tn],sn={backdrop:!0,keyboard:!0,focus:!0,show:!0},an={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean",show:"boolean"},ln={HIDE:"hide"+on,HIDE_PREVENTED:"hidePrevented"+on,HIDDEN:"hidden"+on,SHOW:"show"+on,SHOWN:"shown"+on,FOCUSIN:"focusin"+on,RESIZE:"resize"+on,CLICK_DISMISS:"click.dismiss"+on,KEYDOWN_DISMISS:"keydown.dismiss"+on,MOUSEUP_DISMISS:"mouseup.dismiss"+on,MOUSEDOWN_DISMISS:"mousedown.dismiss"+on,CLICK_DATA_API:"click"+on+".data-api"},cn="modal-dialog-scrollable",hn="modal-scrollbar-measure",un="modal-backdrop",fn="modal-open",dn="fade",pn="show",mn="modal-static",gn=".modal-dialog",_n=".modal-body",vn='[data-toggle="modal"]',yn='[data-dismiss="modal"]',En=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",bn=".sticky-top",wn=function(){function o(e,t){this._config=this._getConfig(t),this._element=e,this._dialog=e.querySelector(gn),this._backdrop=null,this._isShown=!1,this._isBodyOverflowing=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollbarWidth=0}var e=o.prototype;return e.toggle=function(e){return this._isShown?this.hide():this.show(e)},e.show=function(e){var t=this;if(!this._isShown&&!this._isTransitioning){p(this._element).hasClass(dn)&&(this._isTransitioning=!0);var n=p.Event(ln.SHOW,{relatedTarget:e});p(this._element).trigger(n),this._isShown||n.isDefaultPrevented()||(this._isShown=!0,this._checkScrollbar(),this._setScrollbar(),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),p(this._element).on(ln.CLICK_DISMISS,yn,function(e){return t.hide(e)}),p(this._dialog).on(ln.MOUSEDOWN_DISMISS,function(){p(t._element).one(ln.MOUSEUP_DISMISS,function(e){p(e.target).is(t._element)&&(t._ignoreBackdropClick=!0)})}),this._showBackdrop(function(){return t._showElement(e)}))}},e.hide=function(e){var t=this;if(e&&e.preventDefault(),this._isShown&&!this._isTransitioning){var n=p.Event(ln.HIDE);if(p(this._element).trigger(n),this._isShown&&!n.isDefaultPrevented()){this._isShown=!1;var i=p(this._element).hasClass(dn);if(i&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),p(document).off(ln.FOCUSIN),p(this._element).removeClass(pn),p(this._element).off(ln.CLICK_DISMISS),p(this._dialog).off(ln.MOUSEDOWN_DISMISS),i){var o=m.getTransitionDurationFromElement(this._element);p(this._element).one(m.TRANSITION_END,function(e){return t._hideModal(e)}).emulateTransitionEnd(o)}else this._hideModal()}}},e.dispose=function(){[window,this._element,this._dialog].forEach(function(e){return p(e).off(on)}),p(document).off(ln.FOCUSIN),p.removeData(this._element,nn),this._config=null,this._element=null,this._dialog=null,this._backdrop=null,this._isShown=null,this._isBodyOverflowing=null,this._ignoreBackdropClick=null,this._isTransitioning=null,this._scrollbarWidth=null},e.handleUpdate=function(){this._adjustDialog()},e._getConfig=function(e){return e=l({},sn,{},e),m.typeCheckConfig(tn,e,an),e},e._triggerBackdropTransition=function(){var e=this;if("static"===this._config.backdrop){var t=p.Event(ln.HIDE_PREVENTED);if(p(this._element).trigger(t),t.defaultPrevented)return;this._element.classList.add(mn);var n=m.getTransitionDurationFromElement(this._element);p(this._element).one(m.TRANSITION_END,function(){e._element.classList.remove(mn)}).emulateTransitionEnd(n),this._element.focus()}else this.hide()},e._showElement=function(e){var t=this,n=p(this._element).hasClass(dn),i=this._dialog?this._dialog.querySelector(_n):null;this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.appendChild(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),p(this._dialog).hasClass(cn)&&i?i.scrollTop=0:this._element.scrollTop=0,n&&m.reflow(this._element),p(this._element).addClass(pn),this._config.focus&&this._enforceFocus();function o(){t._config.focus&&t._element.focus(),t._isTransitioning=!1,p(t._element).trigger(r)}var r=p.Event(ln.SHOWN,{relatedTarget:e});if(n){var s=m.getTransitionDurationFromElement(this._dialog);p(this._dialog).one(m.TRANSITION_END,o).emulateTransitionEnd(s)}else o()},e._enforceFocus=function(){var t=this;p(document).off(ln.FOCUSIN).on(ln.FOCUSIN,function(e){document!==e.target&&t._element!==e.target&&0===p(t._element).has(e.target).length&&t._element.focus()})},e._setEscapeEvent=function(){var t=this;this._isShown&&this._config.keyboard?p(this._element).on(ln.KEYDOWN_DISMISS,function(e){27===e.which&&t._triggerBackdropTransition()}):this._isShown||p(this._element).off(ln.KEYDOWN_DISMISS)},e._setResizeEvent=function(){var t=this;this._isShown?p(window).on(ln.RESIZE,function(e){return t.handleUpdate(e)}):p(window).off(ln.RESIZE)},e._hideModal=function(){var e=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._isTransitioning=!1,this._showBackdrop(function(){p(document.body).removeClass(fn),e._resetAdjustments(),e._resetScrollbar(),p(e._element).trigger(ln.HIDDEN)})},e._removeBackdrop=function(){this._backdrop&&(p(this._backdrop).remove(),this._backdrop=null)},e._showBackdrop=function(e){var 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n=t.style.marginRight,i=p(t).css("margin-right");p(t).data("margin-right",n).css("margin-right",parseFloat(i)-o._scrollbarWidth+"px")});var n=document.body.style.paddingRight,i=p(document.body).css("padding-right");p(document.body).data("padding-right",n).css("padding-right",parseFloat(i)+this._scrollbarWidth+"px")}p(document.body).addClass(fn)},e._resetScrollbar=function(){var e=[].slice.call(document.querySelectorAll(En));p(e).each(function(e,t){var n=p(t).data("padding-right");p(t).removeData("padding-right"),t.style.paddingRight=n||""});var t=[].slice.call(document.querySelectorAll(""+bn));p(t).each(function(e,t){var n=p(t).data("margin-right");"undefined"!=typeof n&&p(t).css("margin-right",n).removeData("margin-right")});var n=p(document.body).data("padding-right");p(document.body).removeData("padding-right"),document.body.style.paddingRight=n||""},e._getScrollbarWidth=function(){var e=document.createElement("div");e.className=hn,document.body.appendChild(e);var 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this._leave(null,this);this._enter(null,this)}},e.dispose=function(){clearTimeout(this._timeout),p.removeData(this.element,this.constructor.DATA_KEY),p(this.element).off(this.constructor.EVENT_KEY),p(this.element).closest(".modal").off("hide.bs.modal",this._hideModalHandler),this.tip&&p(this.tip).remove(),this._isEnabled=null,this._timeout=null,this._hoverState=null,this._activeTrigger=null,this._popper&&this._popper.destroy(),this._popper=null,this.element=null,this.config=null,this.tip=null},e.show=function(){var t=this;if("none"===p(this.element).css("display"))throw new Error("Please use show on visible elements");var e=p.Event(this.constructor.Event.SHOW);if(this.isWithContent()&&this._isEnabled){p(this.element).trigger(e);var n=m.findShadowRoot(this.element),i=p.contains(null!==n?n:this.element.ownerDocument.documentElement,this.element);if(e.isDefaultPrevented()||!i)return;var o=this.getTipElement(),r=m.getUID(this.constructor.NAME);o.setAttribute("id",r),this.element.setAttribute("aria-describedby",r),this.setContent(),this.config.animation&&p(o).addClass(Un);var s="function"==typeof this.config.placement?this.config.placement.call(this,o,this.element):this.config.placement,a=this._getAttachment(s);this.addAttachmentClass(a);var l=this._getContainer();p(o).data(this.constructor.DATA_KEY,this),p.contains(this.element.ownerDocument.documentElement,this.tip)||p(o).appendTo(l),p(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new St(this.element,o,this._getPopperConfig(a)),p(o).addClass(Bn),"ontouchstart"in document.documentElement&&p(document.body).children().on("mouseover",null,p.noop);var c=function(){t.config.animation&&t._fixTransition();var e=t._hoverState;t._hoverState=null,p(t.element).trigger(t.constructor.Event.SHOWN),e===Mn&&t._leave(null,t)};if(p(this.tip).hasClass(Un)){var 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Boolean(this.getTitle())},e.addAttachmentClass=function(e){p(this.getTipElement()).addClass(Ln+"-"+e)},e.getTipElement=function(){return this.tip=this.tip||p(this.config.template)[0],this.tip},e.setContent=function(){var e=this.getTipElement();this.setElementContent(p(e.querySelectorAll(qn)),this.getTitle()),p(e).removeClass(Un+" "+Bn)},e.setElementContent=function(e,t){"object"!=typeof t||!t.nodeType&&!t.jquery?this.config.html?(this.config.sanitize&&(t=In(t,this.config.whiteList,this.config.sanitizeFn)),e.html(t)):e.text(t):this.config.html?p(t).parent().is(e)||e.empty().append(t):e.text(p(t).text())},e.getTitle=function(){var e=this.element.getAttribute("data-original-title");return e=e||("function"==typeof this.config.title?this.config.title.call(this.element):this.config.title)},e._getPopperConfig=function(e){var t=this;return l({},{placement:e,modifiers:{offset:this._getOffset(),flip:{behavior:this.config.fallbackPlacement},arrow:{element:Kn},preventOverflow:{boundariesElement:this.config.boundary}},onCreate:function(e){e.originalPlacement!==e.placement&&t._handlePopperPlacementChange(e)},onUpdate:function(e){return t._handlePopperPlacementChange(e)}},{},this.config.popperConfig)},e._getOffset=function(){var t=this,e={};return"function"==typeof this.config.offset?e.fn=function(e){return e.offsets=l({},e.offsets,{},t.config.offset(e.offsets,t.element)||{}),e}:e.offset=this.config.offset,e},e._getContainer=function(){return!1===this.config.container?document.body:m.isElement(this.config.container)?p(this.config.container):p(document).find(this.config.container)},e._getAttachment=function(e){return Hn[e.toUpperCase()]},e._setListeners=function(){var i=this;this.config.trigger.split(" ").forEach(function(e){if("click"===e)p(i.element).on(i.constructor.Event.CLICK,i.config.selector,function(e){return 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-        url('fonts/Lato/lato-v16-latin-300italic.woff') format('woff'), /* Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-300italic.ttf') format('truetype'), /* Safari, Android, iOS */
-        url('fonts/Lato/lato-v16-latin-300italic.svg#Lato') format('svg'); /* Legacy iOS */
-}
-/* lato-regular - latin */
-@font-face {
-  font-family: 'Lato';
-  font-style: normal;
-  font-weight: 400;
-  src: url('fonts/Lato/lato-v16-latin-regular.eot'); /* IE9 Compat Modes */
-  src: local('Lato Regular'), local('Lato-Regular'),
-        url('fonts/Lato/lato-v16-latin-regular.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */
-        url('fonts/Lato/lato-v16-latin-regular.woff2') format('woff2'), /* Super Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-regular.woff') format('woff'), /* Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-regular.ttf') format('truetype'), /* Safari, Android, iOS */
-        url('fonts/Lato/lato-v16-latin-regular.svg#Lato') format('svg'); /* Legacy iOS */
-}
-/* lato-italic - latin */
-@font-face {
-  font-family: 'Lato';
-  font-style: italic;
-  font-weight: 400;
-  src: url('fonts/Lato/lato-v16-latin-italic.eot'); /* IE9 Compat Modes */
-  src: local('Lato Italic'), local('Lato-Italic'),
-        url('fonts/Lato/lato-v16-latin-italic.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */
-        url('fonts/Lato/lato-v16-latin-italic.woff2') format('woff2'), /* Super Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-italic.woff') format('woff'), /* Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-italic.ttf') format('truetype'), /* Safari, Android, iOS */
-        url('fonts/Lato/lato-v16-latin-italic.svg#Lato') format('svg'); /* Legacy iOS */
-}
-/* lato-700 - latin */
-@font-face {
-  font-family: 'Lato';
-  font-style: normal;
-  font-weight: 700;
-  src: url('fonts/Lato/lato-v16-latin-700.eot'); /* IE9 Compat Modes */
-  src: local('Lato Bold'), local('Lato-Bold'),
-        url('fonts/Lato/lato-v16-latin-700.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */
-        url('fonts/Lato/lato-v16-latin-700.woff2') format('woff2'), /* Super Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-700.woff') format('woff'), /* Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-700.ttf') format('truetype'), /* Safari, Android, iOS */
-        url('fonts/Lato/lato-v16-latin-700.svg#Lato') format('svg'); /* Legacy iOS */
-}
-/* lato-700italic - latin */
-@font-face {
-  font-family: 'Lato';
-  font-style: italic;
-  font-weight: 700;
-  src: url('fonts/Lato/lato-v16-latin-700italic.eot'); /* IE9 Compat Modes */
-  src: local('Lato Bold Italic'), local('Lato-BoldItalic'),
-        url('fonts/Lato/lato-v16-latin-700italic.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */
-        url('fonts/Lato/lato-v16-latin-700italic.woff2') format('woff2'), /* Super Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-700italic.woff') format('woff'), /* Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-700italic.ttf') format('truetype'), /* Safari, Android, iOS */
-        url('fonts/Lato/lato-v16-latin-700italic.svg#Lato') format('svg'); /* Legacy iOS */
-}
-/* lato-900 - latin */
-@font-face {
-  font-family: 'Lato';
-  font-style: normal;
-  font-weight: 900;
-  src: url('fonts/Lato/lato-v16-latin-900.eot'); /* IE9 Compat Modes */
-  src: local('Lato Black'), local('Lato-Black'),
-        url('fonts/Lato/lato-v16-latin-900.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */
-        url('fonts/Lato/lato-v16-latin-900.woff2') format('woff2'), /* Super Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-900.woff') format('woff'), /* Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-900.ttf') format('truetype'), /* Safari, Android, iOS */
-        url('fonts/Lato/lato-v16-latin-900.svg#Lato') format('svg'); /* Legacy iOS */
-}
-/* lato-900italic - latin */
-@font-face {
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-  font-weight: 900;
-  src: url('fonts/Lato/lato-v16-latin-900italic.eot'); /* IE9 Compat Modes */
-  src: local('Lato Black Italic'), local('Lato-BlackItalic'),
-        url('fonts/Lato/lato-v16-latin-900italic.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */
-        url('fonts/Lato/lato-v16-latin-900italic.woff2') format('woff2'), /* Super Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-900italic.woff') format('woff'), /* Modern Browsers */
-        url('fonts/Lato/lato-v16-latin-900italic.ttf') format('truetype'), /* Safari, Android, iOS */
-        url('fonts/Lato/lato-v16-latin-900italic.svg#Lato') format('svg'); /* Legacy iOS */
-}
-
-* {
-  box-sizing: border-box;
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-div.opt-notice a{
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-/* Failsafe in case JS is turned off */
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-.image {
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-hr {
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-.blockquote_caution {
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-.blockquote_tip {
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-/*
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-#container {
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-.textblock {
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-div.summary {
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-/* make sure that the width of the contents section is everything but left and right columns */
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-  }
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-
-/* at smaller breakpoints, only account for left nav column & gutter */
-/*
-@media screen and (max-width: 1200px) {
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-h2,
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-h3 {
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-h4 {
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-}
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-/* "H1" headings */
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-div.header {
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-div.headertitle {
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-
-.title {
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-}
-
-/* END "H1" headings */
-
-/* Tables */
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-}
-
-table,
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-table tr.heading td {
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-
-table.doxtable td,
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-table.markdownTable th {
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-}
-
-table.doxtable td:first-child,
-table.doxtable th:first-child,
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-table.markdownTable th:first-child {
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-}
-
-table.doxtable td:last-child,
-table.doxtable th:last-child,
-table.markdownTable td:last-child,
-table.markdownTable th:last-child {
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-
-table.doxtable th,
-table.markdownTable th {
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-
-table.doxtable th.markdownTableHeadCenter,
-table.markdownTable th.markdownTableHeadCenter {
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-
-table.doxtable th.markdownTableHeadRight,
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-
-table .image {
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-
-table .markdownTableBodyNone .image,
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-table h2.groupheader {
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-table.memberdecls td.memSeparator {
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-
-.mdescLeft,
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-.memTemplParams {
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-}
-/* END tables */
-
-/* =========================================================== */
-/* H E A D E R */
-
-#top {
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-#titlearea {
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-#projectalign {
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-#projectalign,
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-#projectname {
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-a.homelink-id {
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-a.homelink-id > img {
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-a.homelink-id > p {
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-#projectnumber {
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-}
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-#versionsSelector {
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-div.ovino-btn {
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-div.ovino-btn:hover {
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-
-div.ovino-btn > a {
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-
-#versionsSelector button.version-toggle {
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-}
-
-#versionsList {
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-#versionsList.opened {
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-#versionsList li {
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-#versionsList li a {
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-#versionsList li.active a,
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-#versionsList li.active a:visited {
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-
-#nav-path {
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-#main-nav {
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-#main-nav ul#main-menu {
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-
-#main-nav ul#main-menu > li {
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-#main-nav ul#main-menu > li:last-child {
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-}
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-ul.dropdown-menu {
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-
-#main-nav ul.dropdown-menu a {
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-#main-nav ul#main-menu > li:hover>ul.dropdown-menu {
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-#main-nav ul.dropdown-menu > li {
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-#main-nav ul.dropdown-menu > li > a {
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-#main-nav ul.dropdown-menu > li > a {
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-#main-nav ul.dropdown-menu ul {
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-
-#main-nav ul#main-menu > li > a {
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-#main-nav ul#main-menu > li > a:hover {
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-a.see-all {
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-@media screen and (max-width: 1300px) {
-  #main-nav ul#main-menu > li > a {
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-  #main-nav ul#main-menu > li {
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-  #main-nav ul#main-menu {
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-  #projectalign, #MSearchBox, #MSearchBox .left {
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-  #install-link {
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-
-#main-nav ul#main-menu > li > a.active {
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-}
-
-div.old-version > p {
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-  background: #ffb133;
-  padding: 5px;
-  color: white;
-}
-
-#secondnav {
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-  padding:8px;
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-
-#download-link {
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-}
-
-#install-link {
-    margin-right: 3.75rem;
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-
-.nav-placeholder {
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-  height:30px;
-}
-
-/* =========================================================== */
-/* L E F T - N A V */
-
-#left-nav {
-  left: 0;
-  overflow-x: hidden;
-  overflow-y: auto;
-  position: fixed;
-  width: 17.3125rem;
-  margin-top: 20px;
-  max-height: 690px;
-}
-
-#left-nav a {
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-}
-
-#left-nav a:hover {
-  color: #368dcc;
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-div.accordion-heading {
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-
-#left-nav ul,
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-}
-
-#left-nav ul {
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-  padding: 0;
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diff --git a/docs/doxygen/assets/openvino-layout.js b/docs/doxygen/assets/openvino-layout.js
deleted file mode 100644
index db73c971843..00000000000
--- a/docs/doxygen/assets/openvino-layout.js
+++ /dev/null
@@ -1,864 +0,0 @@
-/*
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
-*/
-
-"use strict";
-
-/**
- * Builds menus dynamically and displays proper side navigation based on current page URL.
- * Document overview is on the left of the page, in-page navigation is on the right
- * (Same nav shows up embedded at top of page on smaller breakpoints)
- * @author Amplified - http://amplifiedbydesign.com/
- * @returns {function}
- */
-function openVinoMenu() {
-    // Get active page path as "currentPath"
-    var currentPath = getCurrentPath();
-    var activeMenu = "";
-
-    /**
-     * Initialize/do this on page load
-     * @public
-     */
-    function init() {
-        var mainNav = document.getElementById("main-nav");
-
-        if (mainNav) {
-            // Remove first (unused "home" link) and last (search box) list items that are added
-            // automatically by Doxygen script
-            $("ul#main-menu li").first().remove();
-            $("ul#main-menu li").last().remove();
-
-            var $initialMenuContent = $("#main-nav #main-menu > li");
-            // Have to make a copy of the menu because the first one is being so heavily manipulated
-            var $secondaryMenuContent = $("#main-nav #main-menu").clone();
-
-            // Remove .sub-arrow span from all menu content
-            $initialMenuContent.find("span.sub-arrow").remove();
-            $secondaryMenuContent.find("span.sub-arrow").remove();
-            var $mainMenuItems = $(
-                'a[href ="' + currentPath + '"]',
-                $initialMenuContent
-            )
-                .parents("ul > li")
-                .last();
-            
-            var $leftMenuContent = $($mainMenuItems).clone();
-
-            var $activeMenuItem = $(
-                'a[href ="' + currentPath + '"]',
-                $secondaryMenuContent
-            )
-                .parents("ul > li")
-                .last();
-            var $activeMenu = $activeMenuItem.find("ul").first() || null;
-
-            activeMenu = $mainMenuItems
-                .find("a")
-                .first()
-                .text();
-
-            // if current path is search.html no menu is renderedered
-            // Else if "nav-path" exists OR activeMenu returns an empty string,
-            //  then assume we're dealing with a generated API page and manually
-            //  set the activeMenu/$activeMenu variables.
-            // If the API REFERENCES category name changes, update this with correct name!
-            if (currentPath.indexOf('g-search.html') != -1) {
-                activeMenu = '';
-                $activeMenu = null;
-            }
-            else if ($("#top #nav-path").length > 0 || activeMenu === "") {
-                activeMenu = "API REFERENCES";
-                $activeMenu = $($secondaryMenuContent)
-                                .find('a[href^="' + activeMenu + '"]')
-                                .last()
-                                .addClass("active")
-                                .parent("li")
-                                .addClass("active");
-            }
-            
-            renderMainMenu($initialMenuContent);
-            renderLeftMenu($leftMenuContent);
-            renderContentsMenus();
-
-            $("#container").css({
-                marginTop: document.getElementById("top").offsetHeight + 40
-            });
-        }
-    }
-
-    /**
-     * Moves window scroll position to element, needed to handle clicks on both
-     * in-page links and if you copy/paste a link from elsewhere
-     * @public
-     * @param {DOM Element} el
-     */
-    function scrollToElement(el) {
-        var headerHeight = document.getElementById("top").offsetHeight;
-        // Handle the in-page links we created elsewhere
-        if (["H2", "H3", "H4"].includes(el.parentElement.nodeName)) {
-            el = el.parentElement;
-        } else {
-            el = el.nextElementSibling;
-        }
-
-        // uses jQuery's offset method in order to get around issue with headings found in tables
-        var offset = $(el).offset();
-        var scrollValue = offset.top - headerHeight - 30;
-
-        window.scrollTo(0, scrollValue);
-    }
-
-    /**
-     * Makes click function accessible from outside openVinoMenu function
-     * @public
-     * @param {String} href
-     * @returns function
-     */
-    function handleScrollToClick(href) {
-        return function (event) {
-            event.preventDefault();
-            event.stopPropagation();
-            event.stopImmediatePropagation();
-
-            var anchor = document.getElementById(href);
-
-            if (window.location.hash) {
-                window.location.href = window.location.href.replace(
-                    window.location.hash,
-                    "#" + href
-                );
-            } else {
-                window.location.href = window.location.href + "#" + href;
-            }
-
-            scrollToElement(anchor);
-        };
-    }
-
-    /**
-     * Builds out the "contents" menu, based on H2 and H3 DOM Elements found
-     * within the current document. Returns markup.
-     * @private
-     */
-    function makeContentsMenu() {
-        function makeTree(data, className) {
-            var result = "";
-            if (data.length > 0) {
-                if (className) {
-                    result += '<ul class="' + className + '">';
-                } else {
-                    result += "<ul>";
-                }
-                // Create starting list item as either list-with-children or a straight list item
-                for (var i = 0, dataLength = data.length; i < dataLength; i++) {
-                    if (data[i].nodeName === "H2" && data[i].children.length > 0) {
-                        result += '<li class="has-children">';
-                    } else {
-                        result += "<li>";
-                    }
-                    result +=
-                        '<a href="' +
-                        data[i].url +
-                        '">' +
-                        data[i].text +
-                        "</a>" +
-                        makeTree(data[i].children) +
-                        "</li>";
-                }
-                result += "</ul>";
-            }
-            return result;
-        }
-
-        /**
-         Create the list of links
-         **/
-        function generateList(arr) {
-            if (arr && arr.length > 0) {
-                var contentsArr = [];
-                for (var i = 0, arrLength = arr.length; i < arrLength; i++) {
-                    var obj = {
-                        nodeName: arr[i].nodeName,
-                        text: arr[i].innerText.replace(/[◆§]/gi, ""),
-                        children: []
-                    };
-                    if (arr[i].querySelector("a.anchor")) {
-                        obj.url = "#" + arr[i].querySelector("a.anchor").id;
-                    } else if (arr[i].querySelector("span.permalink a")) {
-                        obj.url = arr[i]
-                            .querySelector("span.permalink a")
-                            .getAttribute("href");
-                    }
-                    // handle in-page h2 and h3 links
-                    if (
-                        contentsArr.length > 0 &&
-                        (arr[i].nodeName === "H3" ||
-                            (arr[i].nodeName === "H2" &&
-                                arr[i].classList.contains("memtitle")))
-                    ) {
-                        contentsArr[contentsArr.length - 1].children.push(obj);
-                    }
-                    if (
-                        arr[i].classList &&
-                        arr[i].nodeName === "H2" &&
-                        !arr[i].classList.contains("memtitle")
-                    ) {
-                        contentsArr.push(obj);
-                    }
-                }
-
-                return makeTree(contentsArr, "contents-list");
-            }
-        }
-
-        // Create "anchors" on each heading we are navigating to (if they do not exist)
-        $("h2, h3", ".contents").each(function () {
-            if ($("a.anchor, span.permalink a", this).length > 0) return;
-            var urlHash = this.innerText
-                .replace(/[`~!@#$%^&*()|+=?;:'",.<>®™◆•§\{\}\[\]\\\/]/gi, "")
-                .replace(/[\s/-]/g, "_")
-                .toLowerCase();
-            var anchor = $('<a class="anchor"></a>').attr("id", urlHash);
-            $(this).prepend(anchor);
-        });
-
-        // Create/Format list of heading anchors
-        return generateList($("h2, h3", ".contents"));
-    }
-
-    /**
-     * Handle all scrolling effects relating to LEFT NAV
-     * @param {Object} menu
-     */
-    function handleScrolling(menu) {
-        var headerHeight = document.getElementById("top").offsetHeight;
-        var footerOffset = document.querySelector(".footer").getBoundingClientRect()
-            .top;
-        var windowHeight = window.innerHeight;
-        var maxHeight =
-            windowHeight < footerOffset
-                ? windowHeight - headerHeight - 60
-                : footerOffset - headerHeight - 60;
-
-        menu.css({
-            top: headerHeight + 30,
-            "max-height": maxHeight
-        });
-
-        if (menu[0].id === "contents-nav") {
-            $("ul a", menu).each(function () {
-                var $this = $(this);
-                var href = $this.attr("href");
-                if (href[0] !== "#") {
-                    var regEx = new RegExp(currentPath + "\\#\\w+", "ig");
-                    if (href.match(regEx)) {
-                        href = href.replace(href, href.match(/\#\w+/gi)[0]).substring(1);
-                    }
-                } else {
-                    href = href.substring(1);
-                }
-                var anchor = document.getElementById(href);
-
-                // Handle scrolling for in-page links
-                if (anchor) {
-                    if (["H2", "H3"].includes(anchor.parentElement.nodeName)) {
-                        anchor = anchor.parentElement;
-                    } else {
-                        anchor = anchor.nextElementSibling;
-                    }
-
-                    if (
-                        anchor.getBoundingClientRect().top < headerHeight + 40 &&
-                        $("ul a.active", menu) !== $this
-                    ) {
-                        $("ul a", menu).removeClass("active");
-                        $this.addClass("active");
-                        $this.parent("ul.contents-menu > li > ul").show();
-                    }
-
-                    $this.on("click", handleScrollToClick(href));
-                }
-            });
-        }
-    }
-
-    /**
-     ** Builds main menu based on the array provided to it when called
-     * @private
-     * @param {Object} menu
-     */
-    function renderMainMenu(menu) {
-        $.each(menu, function () {
-            var $link = $(this).children('a').first();
-            var $submenu = $(this).children('ul').first();
-            if ($submenu) {
-                $submenu.addClass('dropdown-menu');
-            }
-            $('ul ul', $submenu).remove();
-
-            $('> li', $submenu).addClass('col-lg-2 col-md-auto');
-
-            $('ul', $submenu).each(function() {
-                var sectionItems = $('> li', this);
-                if (sectionItems.length > 5) {
-                    sectionItems.slice(6).hide();
-                    var sectionUrl = $('> a', $(this).parent('li')).last().attr('href');
-                    var $seeAll = $('<li><a class="see-all" href="' + sectionUrl + '">See all ></a></li>');
-                    $(this).append($seeAll);
-                }
-            });
-            if ($link.text() === activeMenu) {
-                $link.addClass("active");
-            }
-            return this;
-        });
-
-        $("#main-nav").insertAfter($('#projectalign'));
-    }
-
-    /**
-     * Builds entire left menu
-     * @private
-     * @param {Object} leftMenuContent
-     */
-    function renderLeftMenu(leftMenuContent) {
-        // Handle all accordion logic and functionality
-        function handleAccordion() {
-            var $target = $(this);
-            var $parent = $target.parent();
-            if ($parent.hasClass('accordion-heading')) {
-                $target = $parent;
-            }
-            var $list = $target.siblings("ul");
-
-            $target.parent("li").toggleClass("accordion-opened");
-            $list.toggle();
-        }
-
-        // Set up LEFT NAV
-        if (leftMenuContent) {
-            var $leftNav = $('<nav id="left-nav"></nav>');
-            var $menu = $('<ul class="main-menu"></ul>');
-            $menu.append(leftMenuContent);
-            var $listItems = $("li", $menu);
-            var $listItemsWithChildren = $listItems.filter(function () {
-                return $("ul", this).length > 0;
-            });
-
-            $listItemsWithChildren.each(function() {
-                $(this).addClass("has-children accordion");
-                var $heading = $('<div class="accordion-heading"></div>');
-                $('> a', this).wrap($heading);
-                $('> div.accordion-heading', this).prepend('<span class="accordion-trigger"></span>')
-            });
-
-            $menu
-                .addClass("main-menu")
-                .removeAttr(
-                    "aria-hidden aria-labelledby aria-expanded aria-haspopup aria-controls"
-                );
-
-            $leftNav.append($menu);
-        }
-
-        // Set 'active' class on currently selected nav link
-        $leftNav
-            .find('a[href^="' + currentPath + '"]')
-            .last()
-            .closest("li")
-            .addClass("active");
-
-        // Add LEFT NAV to page
-        if ($("a", $leftNav).length > 0) {
-            $leftNav.insertBefore($(".contents"));
-            var activeListItem = $("li.active", $leftNav)[0];
-            // Show/Expand active list accordion
-            var activeListItemStack = $(activeListItem).parents(
-                "ul.main-menu li"
-            );
-            activeListItemStack.push(activeListItem);
-            
-            // get 2nd lvl node
-            var secondLvlItems = $('> ul > li > ul > li.has-children.accordion', $leftNav)
-            
-            if (secondLvlItems) {
-                activeListItemStack.push.apply(activeListItemStack, secondLvlItems);
-            }
-
-            if (activeListItemStack.length > 0 && activeListItem) {
-                activeListItemStack.each(function () {
-                    if ($(this).hasClass("accordion")) {
-                        $(this).addClass("accordion-opened");
-                        $("> ul", this).show();
-                    }
-                });
-
-                // Scroll active list item to top of menu on page load
-                $leftNav[0].scrollTop = $(activeListItem).offset().top - $($leftNav).offset().top - 60;
-            }
-
-            $("ul.main-menu li.has-children span.accordion-trigger", $leftNav).on(
-                "click",
-                handleAccordion
-            );
-
-            // Add event listeners to this navigation so we can detect scroll and resize when necessary
-            window.addEventListener(
-                "scroll",
-                function () {
-                    handleScrolling($leftNav);
-                },
-                false
-            );
-            window.addEventListener(
-                "resize",
-                function () {
-                    handleScrolling($leftNav);
-                },
-                false
-            );
-            handleScrolling($leftNav);
-        }
-    }
-
-    /**
-     * Builds entire right/document in-page contents menu
-     */
-    function renderContentsMenus() {
-        var $contentsNav = $('<nav id="contents-nav" class="contents-nav"></nav>');
-
-        $contentsNav.append(
-            $('<h2 class="contents-nav-title">In This Document</h2>')
-        );
-        $contentsNav.append(makeContentsMenu());
-
-        var $innerContentsNav = $contentsNav.clone();
-        $innerContentsNav[0].id = "inner-contents-nav";
-
-        // add RIGHT NAV to page twice, once for the right column and a second time
-        // to the top of the page so we can show it inline on smaller breakpoints
-        if ($("a", $contentsNav).length > 0) {
-            $contentsNav.insertAfter("#left-nav");
-            $innerContentsNav.insertAfter($(".contents .headertitle"));
-
-            // Add event handlers to in-page navigation to we can detect scroll and resize when necessary
-            window.addEventListener(
-                "scroll",
-                function () {
-                    handleScrolling($contentsNav);
-                },
-                false
-            );
-            window.addEventListener(
-                "resize",
-                function () {
-                    handleScrolling($contentsNav);
-                },
-                false
-            );
-            handleScrolling($contentsNav);
-        }
-    }
-
-    return {
-        init: init,
-        scrollToElement: scrollToElement,
-        handleScrollToClick: handleScrollToClick
-    };
-}
-
-/**
- * Restructures DOM content & layout.
- * @author Amplified - http://amplifiedbydesign.com/
- * @returns {object}
- */
-function openVinoContent() {
-    /**
-     * Initialize/do this on page load
-     * @public
-     */
-    function init() {
-        // Init viewer.js instance
-        new Viewer(document.querySelector('.contents'));
-
-        var $container = $('<div id="container"></div>');
-        // Move Search Box to titlearea
-        $("#titlearea").append($("#MSearchBox"));
-        // Add search slider
-        var searchSlider = $('<div class="closed" id="search-slider"></div>');
-        $('#MSearchBox').prepend(searchSlider);
-        searchSlider.on('click', function() {
-            $(this).toggleClass('closed open');
-            $("#MSearchField").animate({width:'toggle'},200);
-            $('#MSearchField').focus();
-        });
-       if (['http:', 'https:'].indexOf(window.location.protocol) !== -1) {
-           $('#MSearchField').replaceWith('<input type="text" name="query" id="MSearchField" value="Search" accesskey="S" onfocus="searchBox.OnSearchFieldFocus(true)">');
-           if (!$('#FSearchBox').length) {
-                $('#MSearchField').wrap('<form id="FSearchBox" action="g-search.html" method="get"></form>');
-           }
-           var currentVersion = getCurrentVersion();
-           if (currentVersion === 'cn') {
-               currentVersion = 'latest';
-           }
-           if (currentVersion === 'latest') {
-               var latestVersion = '';
-               try {
-                   latestVersion = JSON.parse(data)[1].version;
-               }
-               catch(e) {};
-               currentVersion = latestVersion;
-           }
-           if(currentVersion) {
-                $('#FSearchBox').append('<input type="hidden" name="version" value="' + currentVersion  + '">');
-           }
-       }
-        
-        // Add favicon.ico
-        $('<link/>', {
-            'href': '/assets/images/favicon.ico',
-            'rel': 'shortcut icon'
-        }).appendTo('head');
-
-        // Restructure DOM contents
-        if (!$("body").hasClass("homepage")) {
-            $container.append($(".header, .contents")).insertAfter($("#top"));
-            $(".contents").prepend($(".header"));
-        }
-
-        // assign clipboard button for each .fragment element
-        $('.fragment').wrap('<div class="code-container"></div>');
-        $('.code-container').prepend($('<div class="code-header"></div>'));
-        var $copyButton = $('<span class="material-icons copy-button">content_copy</span>');
-        $copyButton.click(function() {
-            var self = this;
-            $(self).text('check_circle_outline')
-                   .css('color', '#003C71')
-                   .css("pointer-events", 'none');;
-            $(self).next('.copy-tooltip')
-                   .attr('data-original-title', 'Copied!')
-                   .tooltip('show')
-                   .addClass('active');
-            var fragment = $(self.parentElement.parentElement).children('div.fragment')[0];
-            var text = [];
-            $(fragment).children('div.line').each(function(key, val) {
-                text.push(val.innerText);
-            });
-            text = text.join('\n');
-            var $placeholder = $('<textarea>');
-            $placeholder.val(text);
-            $(document.body).append($placeholder);
-            $placeholder.select();
-            document.execCommand('copy');
-            $placeholder.remove();
-            setTimeout(function() {
-                $(self).text('content_copy')
-                       .css({'color':'inherit', "pointer-events": 'all'});
-                $(self).next('.copy-tooltip')
-                       .attr('data-original-title', 'Copy')
-                       .tooltip('hide')
-                       .removeClass('active');
-            }, 2000);
-        }).mouseenter(function() {
-            $(this).next('.copy-tooltip').tooltip('show');
-        }).mouseleave(function() {
-            if (!$(this).next('.copy-tooltip').hasClass('active')) {
-                $(this).next('.copy-tooltip').tooltip('hide'); 
-            }
-        });
-        $('.code-header').append($copyButton);
-        $('.code-header').append($('<span class="copy-tooltip" data-toggle="tooltip" trigger="manual" data-placement="right" title="Copy"></span>'));
-        // activate tooltips
-        $('[data-toggle="tooltip"]').tooltip();
-
-        // Detect any anchor in the content that links to an image and add target="_blank" to it
-        // This lets a user see very large images if they appear too narrow on page
-        // Less processor-intensive than interpreting every single image dimension at pageload
-        $(".textblock a").each(function () {
-            if (this.href.indexOf('.png' || '.jpg' || 'jpeg' || 'gif') != -1) {
-                $(this).attr('target', '_blank')
-            }
-        });
-
-        // Look for any anchor that has an href of [none] and remove that href, add class so
-        // it doesn't have a hover state
-        $("a").each(function () {
-            if (this.href.indexOf('[none]') != -1) {
-                $(this).removeAttr("href").addClass("removehover");
-            }
-        });
-
-        // Wrap tables in a class so we can style them
-        $(".contents > table, .textblock > table").wrap(
-            '<div class="table-wrapper"></div>'
-        );
-
-        // Handle any blockquotes and added the classes needed to style them
-        // Doxygen doesn't handle hand-inserted HTML very well so we are left with blockquotes
-        // Get the first bold item in a blockquote, apply class based on content
-        $("blockquote p b").each(function () {
-            // Set up variable to call as object literal
-            var blockquoteDeclarations = {
-                "NOTE": "blockquote_note",
-                "NOTES": "blockquote_note",
-                "TIP": "blockquote_tip",
-                "TIPS": "blockquote_tip",
-                "IMPORTANT": "blockquote_caution",
-                "CAUTION": "blockquote_caution",
-                "WARNING": "blockquote_warning"
-            };
-            // Strip punctuation from label so we do not force everybody to add every keyword and all variants
-            var blockquoteLabel = $(this).html().replace(/[.,\/#!$%\^&\*;:{}=\-_`~()]/g, "");
-            $(this).parent().parent().addClass(blockquoteDeclarations[blockquoteLabel] || '');
-        });
-    }
-
-    return {
-        init: init
-    };
-}
-
-function getURLParameter(name) {
-    return decodeURIComponent((new RegExp('[?|&]'+name+
-           '='+'([^&;]+?)(&|#|;|$)').exec(location.search)
-           ||[,""])[1].replace(/\+/g, '%20'))||null;
-}
-
-/**
- * Handle building the versions links, updating URLs on "homepage", and managing
- * the versions "select".
- * @author Amplified - http://amplifiedbydesign.com/
- * @returns {object}
- */
-function openVinoVersions() {
-    /**
-     * Initialize/do this on page load
-     * @public
-     */
-    function init() {
-        buildVersionsContent();
-
-        if ($("body").hasClass("homepage")) {
-            updateAllLinks();
-        }
-    }
-
-    function getPageUrlWithVersion(url, version) {
-        var fullURL = window.location.pathname.split('/');
-        var lastElement = fullURL.slice(-1)[0];
-        var newURL = url.replace(getCurrentVersion(), version);
-
-        if ($("body").hasClass("homepage")) {
-            if (window.location.protocol == "file:") {
-                newURL = url.replace("index.html", version + "/" + landingPage);
-            }
-            if (lastElement == "404.html") {
-                newURL = url.replace("404.html", version + "/" + landingPage);
-            } else {
-                newURL = documentationHost + "/" + version + "/" + landingPage;
-            }
-        }
-
-        return newURL;
-    }
-
-    function buildVersionsContent() {
-        if (versions && versions.length > 0) {
-            var $versionsContent = $('<div id="versionsSelector"></div>');
-            var $selectorButton = $(
-                '<button type="button" class="current-version version-toggle"></button>'
-            );
-            var $versionsList = $('<ul id="versionsList"></ul>');
-            var currentVersion = getCurrentVersion();
-            var listItems = "";
-            var url = window.location.href;
-            var max_length = 0;
-
-            for (var i = 0, verLength = versions.length; i < verLength; i++) {
-                var ver = versions[i];
-                if (ver.version === currentVersion) {
-                    listItems += '<li class="active">';
-                } else {
-                    listItems += "<li>";
-                }
-                listItems +=
-                    '<a href="' +
-                    getPageUrlWithVersion(url, ver.version) +
-                    '">Version ' +
-                    ver.version +
-                    "</a>";
-                listItems += "</li>";
-                if (ver.version.length > max_length){
-                    max_length = ver.version.length;
-                }
-            }
-
-            var scale = 7;
-            var versionLength = 'version '.length;
-            var align = 5;
-            $versionsList.css("width", ((max_length + versionLength + align) * scale).toString() + "px");
-
-            var handleVersionBlur = function () {
-                if ($versionsList.hasClass("opened")) {
-                    window.removeEventListener("click", handleVersionBlur, false);
-                    $versionsList.removeClass("opened");
-                } else {
-                    $versionsList.toggleClass("opened");
-                }
-            };
-
-            $selectorButton.on("click", function () {
-                window.addEventListener("click", handleVersionBlur, false);
-            });
-
-            $versionsList.append(listItems);
-            $versionsContent.append(
-                $selectorButton.html("Version <strong>" + currentVersion + "</strong>")
-            );
-            $versionsContent.append($versionsList);
-
-            if ($("#MSearchBox").length > 0) {
-                $versionsContent.insertBefore($("#top #MSearchBox"));
-            } else {
-                $("#titlearea").append($versionsContent);
-            }
-        }
-    }
-
-    return {
-        init: init
-    };
-}
-
-function getDomainName() {
-    var protocol = window.location.protocol + "//";
-    var index = window.location.href.indexOf(protocol);
-    var link = window.location.href.slice(index + protocol.length);
-    return window.location.protocol + "//" + link.split('/')[0];
-}
-
-function getCurrentPath() {
-    var windowLocationArr = window.location.pathname.split("/");
-    var currentPath = windowLocationArr[windowLocationArr.length - 1];
-    return currentPath;
-}
-
-// Get current version from URL
-function getCurrentVersion() {
-    var protocol = window.location.protocol + "//";
-    var index = window.location.href.indexOf(protocol);
-    var link = window.location.href.slice(index + protocol.length).split('/');
-    var wordAfterDomain = link[1];
-    if (wordAfterDomain === 'cn') {
-        wordAfterDomain = link[2];
-    }
-    if (["index.html", "404.html", "", "latest"].indexOf(wordAfterDomain) >= 0) {
-        /*
-        * If this landing page, 404 or domain.com we should get first version
-        * */
-        return versions[0].version;
-    }
-    return encodeURI(wordAfterDomain);
-}
-
-function updateAllLinks() {
-    var currentVersion = getCurrentVersion();
-    var domainName = getDomainName();
-    $(".openvino-content a, .api-content a, .footer a").each(function () {
-        $(this).attr("href", function (index, old) {
-            return old.replace('<version_placeholder>', currentVersion);
-        });
-    });
-    $(".homelink-id, a.download").each(function () {
-        $(this).attr("href", function (index, old) {
-            return old.replace('<domain_placeholder>', domainName);
-        });
-    });
-    $('[property="og:url"]').each(function () {
-        $(this).attr("content", function (index, old) {
-            return old.replace('<domain_placeholder>', domainName);
-        });
-    });
-}
-
-/**
- * Takes content generated by Doxygen and manipulates the DOM in order to "theme"
- * the output.
- * @author Amplified - http://amplifiedbydesign.com/
- */
-(function openVinoLayout() {
-    var LayoutBuilder = openVinoContent();
-    var MenuBuilder = openVinoMenu();
-
-    // Fire off init functions once the DOM content is loaded
-    window.addEventListener(
-        "DOMContentLoaded",
-        function load() {
-            window.removeEventListener("DOMContentLoaded", load, false);
-            LayoutBuilder.init();
-            MenuBuilder.init();
-        },
-        false
-    );
-
-    // Have to wait for FULL page load before firing the following functions...
-    window.addEventListener(
-        "load",
-        function fullLoad() {
-            window.removeEventListener("loaded", fullLoad, false);
-            
-            function setScrolling() {
-                if (window.location.hash) {
-                    var hash = window.location.hash;
-                    var $selectedEl = $(hash)[0];
-    
-                    if ($selectedEl) {
-                        MenuBuilder.scrollToElement($selectedEl);
-                    }
-                }
-            }
-
-            // check if "onhashchange" event is supported by Browser
-            if ("onhashchange" in window) {
-                $(window).on('hashchange', function() {
-                    setScrolling();
-                });
-            }
-
-            var contentsAnchors = document.querySelectorAll('.contents a[href^="#"]');
-            if (contentsAnchors.length > 0) {
-                for (
-                    var i = 0, anchorsLength = contentsAnchors.length;
-                    i < anchorsLength;
-                    i++
-                ) {
-                    contentsAnchors[i].addEventListener(
-                        "click",
-                        MenuBuilder.handleScrollToClick(
-                            contentsAnchors[i].hash.substring(1)
-                        )
-                    );
-                }
-            }
-
-            setScrolling();
-            updateAllLinks();
-        },
-        false
-    );
-})();
diff --git a/docs/doxygen/build_main_layout.py b/docs/doxygen/build_main_layout.py
deleted file mode 100644
index e8a23d729b5..00000000000
--- a/docs/doxygen/build_main_layout.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import argparse
-import os
-from lxml import etree
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--openvino', type=str, required=True, default=None, help='openvino_docs.xml')
-    args = parser.parse_args()
-    result = build_layout(args.openvino)
-    with open(args.openvino, 'wb') as f:
-        f.write(result)
-
-
-def build_layout(openvino):
-    ns = {"xi": "http://www.w3.org/2001/XInclude"}
-    root = etree.parse(openvino)
-    root.xinclude()
-    return etree.tostring(root, pretty_print=True, encoding='utf-8')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/docs/doxygen/doxy_md_filter.py b/docs/doxygen/doxy_md_filter.py
deleted file mode 100644
index 8d2b9fa1813..00000000000
--- a/docs/doxygen/doxy_md_filter.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import os
-import re
-import glob
-import argparse
-
-
-def get_label(file):
-    """
-    Read lines of a file and try to find a doxygen label.
-    If the label is not found return None.
-    """
-    for line in file:
-        label = re.search(r'\{\#(.+)\}', line)
-        if label:
-            return label.group(1)
-    return
-
-
-def replace_links(content, items, folder, labels, docs_folder):
-    """
-    Replace markdown links with doxygen labels.
-    """
-    for item in items:
-        link = item[0]
-        ext = item[1]
-        link_path = os.path.abspath(os.path.join(folder, link))
-        if os.path.exists(link_path):
-            if ext == 'md':
-                if link_path in labels:
-                    label = labels.get(link_path)
-                else:
-                    with open(link_path, 'r', encoding='utf-8') as file:
-                        lines = []
-                        i = 0
-                        while i < 5:
-                            try:
-                                lines.append(next(file))
-                            except StopIteration:
-                                break
-                            i += 1
-                        label = get_label(lines)
-                        labels[link_path] = label
-                if label:
-                    content = content.replace(link, '@ref ' + label)
-            else:
-                rel_path = os.path.relpath(link_path, docs_folder).replace('\\', '/')
-                content = content.replace(link, rel_path)
-    return content
-
-
-def add_htmlonly(content):
-    content = content.replace('<details>', '\n\\htmlonly\n<details>')
-    content = content.replace('</summary>', '</summary>\n\\endhtmlonly')
-    content = content.replace('</details>', '\n\\htmlonly\n</details>\n\\endhtmlonly')
-    content = content.replace('<iframe', '\n\\htmlonly\n<iframe')
-    content = content.replace('</iframe>', '</iframe>\n\\endhtmlonly')
-    return content
-
-
-def process_github_md_links(content, items):
-    """
-    This is a workaround to support github markdown links in doxygen 1.8.12.
-    """
-    for item in items:
-        orig = item[0]
-        link_name = item[1]
-        link_url = item[2]
-        html_link = '<a href="{}">{}</a>'.format(link_url, link_name)
-        content = content.replace(orig, html_link)
-    return content
-
-
-def process(docs_folder):
-    """
-    Recursively find markdown files in docs_folder and
-    replace links to markdown files with doxygen labels (ex. @ref label_name).
-    """
-    labels = dict()  # store labels in dictionary
-    md_files = glob.glob(os.path.join(docs_folder, '**/*.md'), recursive=True)
-    for md_file in md_files:
-        md_folder = os.path.dirname(md_file)
-        with open(md_file, 'r', encoding='utf-8') as f:
-            content = f.read()
-        inline_links = set(re.findall(r'!?\[.*?\]\(([\w\/\-\.]+\.(md|png|jpg|gif))\)', content, flags=re.IGNORECASE))
-        github_md_links = set(re.findall(r'(\[(.+?)\]\((https:[\w\.\/-]+?\.md)\))', content, flags=re.IGNORECASE))
-        reference_links = set(re.findall(r'\[.+\]\:\s*?([\w\/\-\.]+\.(md|png|jpg|gif))', content, flags=re.IGNORECASE))
-        content = replace_links(content, inline_links, md_folder, labels, docs_folder)
-        content = replace_links(content, reference_links, md_folder, labels, docs_folder)
-        content = process_github_md_links(content, github_md_links)
-        content = add_htmlonly(content)
-        if inline_links or reference_links or github_md_links:
-            with open(md_file, 'w', encoding='utf-8') as f:
-                f.write(content)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('docs', type=str, help='Path to a folder containing .md files.')
-    args = parser.parse_args()
-    process(args.docs)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/docs/doxygen/doxygen-ignore.txt b/docs/doxygen/doxygen-ignore.txt
deleted file mode 100644
index 77e59419d8f..00000000000
--- a/docs/doxygen/doxygen-ignore.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-openvino/samples/cpp/hello_reshape_ssd/README.md
-openvino/docs/index.md
-openvino/docs/get_started/get_started_dl_workbench.md
-openvino/docs/get_started/get_started_linux.md
-openvino/docs/get_started/get_started_raspbian.md
-openvino/docs/get_started/get_started_windows.md
-openvino/docs/how_tos/how-to-links.md
-openvino/docs/HOWTO/Custom_Layers_Guide.md
-openvino/docs/install_guides/deployment-manager-tool.md
-openvino/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
-openvino/docs/ovsa/ovsa_get_started.md
-openvino/src/bindings/c/docs/api_overview.md
-openvino/docs/benchmarks/performance_int8_vs_fp32.md
-openvino/docs/get_started/get_started_macos.md
-build/docs/openvino_docs.xml
-openvino/docs/install_guides/installing-openvino-linux-ivad-vpu.md
-src/inference/include/ie/ie_parallel.hpp
-src/inference/include/ie/ie_plugin_config.hpp
-src/inference/include/ie/vpu/myriad_config.hpp
-src/inference/include/ie/vpu/vpu_config.hpp
-src/inference/include/ie/vpu/vpu_plugin_config.hpp
diff --git a/docs/doxygen/footer.html.in b/docs/doxygen/footer.html.in
deleted file mode 100644
index 0cea34b411f..00000000000
--- a/docs/doxygen/footer.html.in
+++ /dev/null
@@ -1,35 +0,0 @@
-<!--
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
--->
-
-<div class="footer">
-    <div id="nav-path" class="navpath"></div>
-    <div class="footer-content">
-      <div class="opt-notice">For more complete information about compiler optimizations, see our 
-      <a class="el" href="https://software.intel.com/articles/optimization-notice">Optimization Notice</a>
-      </div>
-      <div class="footer-row">
-        <ul>
-          <li><a href="https://software.intel.com/en-us/forums/computer-vision">Support</a></li>
-          <li><a href="/<version_placeholder>/openvino_docs_Legal_Information.html">Legal Information</a></li>
-          <li><a href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">Cookies and Similar Technologies Notice</a></li>
-        </ul>
-      </div>
-    </div>
-  </div>
-</body>
-</html>
diff --git a/docs/doxygen/header.html.in b/docs/doxygen/header.html.in
deleted file mode 100644
index 60bb64916ad..00000000000
--- a/docs/doxygen/header.html.in
+++ /dev/null
@@ -1,76 +0,0 @@
-<!--
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
--->
-
-<!DOCTYPE html>
-<html lang="en">
-<head>
-<!-- HTML header for doxygen $doxygenversion -->
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen $doxygenversion"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
-<!--BEGIN PROJECT_NAME--><title>$title - $projectname</title><!--END PROJECT_NAME-->
-<!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME-->
-<!-- <script type="text/javascript" src="$relpath^jquery.js"></script> -->
-<!-- JQuery 2.2.4 -->
-<script src="assets/jquery-2.2.4.min.js"></script>
-<script type="text/javascript" src="$relpath^dynsections.js"></script>
-<!-- Bootstrap 4.4.1 -->
-<link href="assets/bootstrap.min.css" rel="stylesheet"
-    type="text/css" />
-<script src="assets/bootstrap.bundle.min.js"
-    type="text/javascript"></script>
-<!-- $treeview -->
-$search
-$mathjax
-<link href="$relpath^$stylesheet" rel="stylesheet" type="text/css" />
-<link href="assets/customdoxygen.css" rel="stylesheet" type="text/css" />
-$extrastylesheet
-<script type="text/javascript" src="assets/openvino-layout.js"></script>
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/viewerjs/1.6.2/viewer.css" integrity="sha512-Fd7AWkSYX3RBadbJlBrETnOyX5tkcQu2oN+nO11c+kewdhPlNWQ1Hj83W3hN1odgyh+k7rpvvv7IxZq9bRomCg==" crossorigin="anonymous" />
-<script src="https://cdnjs.cloudflare.com/ajax/libs/viewerjs/1.6.2/viewer.min.js" integrity="sha512-VzJLwaOOYyQemqxRypvwosaCDSQzOGqmBFRrKuoOv7rF2DZPlTaamK1zadh7i2FRmmpdUPAE/VBkCwq2HKPSEQ==" crossorigin="anonymous"></script>
-<link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
-</head>
-<body>
-<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
-<!--BEGIN TITLEAREA-->
-<div id="titlearea">
-  <!--BEGIN PROJECT_NAME-->
-  <div id="projectalign">
-   <div id="projectname">
-     <a href="<domain_placeholder>" class="homelink-id">
-       <img src="assets/images/int-openvino-wht.svg" alt="OpenVINO™ Toolkit">
-       <p>Documentation</p>
-      </a>
-    </div>
-  </div>
-  <!--END PROJECT_NAME-->
-  <!--BEGIN DISABLE_INDEX-->
-   <!--BEGIN SEARCHENGINE-->
-   <div>$searchbox</div>
-   <!--END SEARCHENGINE-->
-  <!--END DISABLE_INDEX-->
-</div>
-<!--END TITLEAREA-->
-<div id="secondnav">
-    <div id="versionsSelector" class="nav-placeholder"></div>
-    <div id="download-link" class="nav-placeholder"></div>
-    <div id="install-link" class="ovino-btn"><a href="https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html" target="_blank">Install OpenVINO™</a></div>
-</div>
-<!-- end header part -->
diff --git a/docs/doxygen/ie_c_api.config b/docs/doxygen/ie_c_api.config
deleted file mode 100644
index a7050a57d0f..00000000000
--- a/docs/doxygen/ie_c_api.config
+++ /dev/null
@@ -1,43 +0,0 @@
-# Doxyfile 1.8.18
-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
-#
-# All text after a double hash (##) is considered a comment and is placed in
-# front of the TAG it is preceding.
-#
-# All text after a single hash (#) is considered a comment and will be ignored.
-# The format is:
-# TAG = value [value, ...]
-# For lists, items can also be appended using:
-# TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (\" \").
-
-@INCLUDE = "@IE_CONFIG_BUILD@"
-
-EXCLUDE_SYMBOLS        = INFERENCE_ENGINE_C_API_EXTERN \
-                         INFERENCE_ENGINE_C_API \
-                         INFERENCE_ENGINE_C_API_CALLBACK \
-                         IE_NODISCARD
-
-PREDEFINED             = "__attribute__(x)=" \
-                         "__VA_ARGS__=" \
-                         "INFERENCE_ENGINE_C_API_EXTERN=" \
-                         "INFERENCE_ENGINE_C_API_CALLBACK=" \
-                         "INFERENCE_ENGINE_C_API=" \
-                         "IE_NODISCARD=" \
-                         "__cdecl=" \
-                         "__declspec(x)=" \
-                         "_WIN32"
-
-FILE_PATTERNS          = *.h
-
-LAYOUT_FILE            = "@C_LAYOUT_BUILD@"
-
-INPUT                  = "@C_API@"
-
-HTML_OUTPUT            = "@C_OUTPUT@""
-
-GENERATE_TAGFILE       = "@DOCS_BUILD_DIR@/ie_c_api.tag"
-
-WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ie_c_api.log"
diff --git a/docs/doxygen/ie_c_api.xml b/docs/doxygen/ie_c_api.xml
deleted file mode 100644
index 8045df95de6..00000000000
--- a/docs/doxygen/ie_c_api.xml
+++ /dev/null
@@ -1,228 +0,0 @@
-<!--
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
--->
-
-<doxygenlayout version="1.0">
-  <!-- Generated by doxygen 1.8.12 -->
-  <!-- Navigation index tabs for HTML output -->
-  <navindex>
-    <tab type="mainpage" title="OpenVINO Home" url="../index.html"/>
-    <tab type="user" title="Get Started" url="../index.html"/>
-    <tab type="user" title="Documentation" url="../documentation.html"/>
-    <tab type="user" title="Examples" url="../examples.html"/>
-    <tab type="usergroup" title="API REFERENCES" url="../api_references.html">
-      <!-- OpenVX -->
-      <tab type="user" title="OpenVX Developer Guide" url="https://khronos.org/openvx"/>
-      <!-- OpenCV -->
-      <tab type="user" title="OpenCV Developer Guide" url="https://docs.opencv.org/master/"/>
-      <!-- IE C -->
-      <tab type="usergroup" title="Inference Engine C API Reference" url="modules.html">
-        <tab type="modules" visible="yes" title="Groups">
-          <tab type="user" title="Core" url="group__Core.html"/>
-          <tab type="user" title="ExecutableNetwork" url="group__ExecutableNetwork.html"/>
-          <tab type="user" title="InferRequest" url="group__InferRequest.html"/>
-          <tab type="user" title="Network" url="group__Network.html"/>
-          <tab type="user" title="Blob" url="group__Blob.html"/>
-        </tab>
-        <tab type="classlist" visible="yes" title=""/>
-        <tab type="pages" visible="yes"/>
-        <tab type="filelist" visible="yes"/>
-        <tab type="globals" visible="yes"/>
-      </tab>
-      <tab type="user" title="Inference Engine С++ API Reference" url="../annotated.html"/>
-      <tab type="user" title="Inference Engine Python API Reference" url="../ie_python_api/annotated.html"/>
-      <!-- DL Streamer -->
-      <tab type="user" title="DL Streamer API Reference" url="https://openvinotoolkit.github.io/dlstreamer_gst/"/>
-      <!-- nGraph C++ API -->
-      <tab type="user" title="nGraph C++ API Reference" url="../ngraph_cpp_api/annotated.html"/>
-      <!-- nGraph Python API -->
-      <tab type="user" title="nGraph Python API Reference" url="../ngraph_python_api/files.html"/>
-    </tab>
-    <!-- Chinese docs -->
-    <tab type="user" title="中文文件" url="https://docs.openvinotoolkit.org/cn/index.html"/>
-  </navindex>
-
-  <!-- Layout definition for a class page -->
-  <class>
-    <briefdescription visible="yes"/>
-    <includes visible="$SHOW_INCLUDE_FILES"/>
-    <inheritancegraph visible="$CLASS_GRAPH"/>
-    <collaborationgraph visible="$COLLABORATION_GRAPH"/>
-    <memberdecl>
-      <nestedclasses visible="yes" title=""/>
-      <publictypes title=""/>
-      <services title=""/>
-      <interfaces title=""/>
-      <publicslots title=""/>
-      <signals title=""/>
-      <publicmethods title=""/>
-      <publicstaticmethods title=""/>
-      <publicattributes title=""/>
-      <publicstaticattributes title=""/>
-      <protectedtypes title=""/>
-      <protectedslots title=""/>
-      <protectedmethods title=""/>
-      <protectedstaticmethods title=""/>
-      <protectedattributes title=""/>
-      <protectedstaticattributes title=""/>
-      <packagetypes title=""/>
-      <packagemethods title=""/>
-      <packagestaticmethods title=""/>
-      <packageattributes title=""/>
-      <packagestaticattributes title=""/>
-      <properties title=""/>
-      <events title=""/>
-      <privatetypes title=""/>
-      <privateslots title=""/>
-      <privatemethods title=""/>
-      <privatestaticmethods title=""/>
-      <privateattributes title=""/>
-      <privatestaticattributes title=""/>
-      <friends title=""/>
-      <related title="" subtitle=""/>
-      <membergroups visible="yes"/>
-    </memberdecl>
-    <detaileddescription title=""/>
-    <memberdef>
-      <inlineclasses title=""/>
-      <typedefs title=""/>
-      <enums title=""/>
-      <services title=""/>
-      <interfaces title=""/>
-      <constructors title=""/>
-      <functions title=""/>
-      <related title=""/>
-      <variables title=""/>
-      <properties title=""/>
-      <events title=""/>
-    </memberdef>
-    <allmemberslink visible="yes"/>
-    <usedfiles visible="$SHOW_USED_FILES"/>
-    <authorsection visible="yes"/>
-  </class>
-
-  <!-- Layout definition for a namespace page -->
-  <namespace>
-    <briefdescription visible="yes"/>
-    <memberdecl>
-      <nestednamespaces visible="yes" title=""/>
-      <constantgroups visible="yes" title=""/>
-      <classes visible="yes" title=""/>
-      <typedefs title=""/>
-      <enums title=""/>
-      <functions title=""/>
-      <variables title=""/>
-      <membergroups visible="yes"/>
-    </memberdecl>
-    <detaileddescription title=""/>
-    <memberdef>
-      <inlineclasses title=""/>
-      <typedefs title=""/>
-      <enums title=""/>
-      <functions title=""/>
-      <variables title=""/>
-    </memberdef>
-    <authorsection visible="yes"/>
-  </namespace>
-
-  <!-- Layout definition for a file page -->
-  <file>
-    <briefdescription visible="yes"/>
-    <includes visible="$SHOW_INCLUDE_FILES"/>
-    <includegraph visible="$INCLUDE_GRAPH"/>
-    <includedbygraph visible="$INCLUDED_BY_GRAPH"/>
-    <sourcelink visible="yes"/>
-    <memberdecl>
-      <classes visible="yes" title=""/>
-      <namespaces visible="yes" title=""/>
-      <constantgroups visible="yes" title=""/>
-      <defines title=""/>
-      <typedefs title=""/>
-      <enums title=""/>
-      <functions title=""/>
-      <variables title=""/>
-      <membergroups visible="yes"/>
-    </memberdecl>
-    <detaileddescription title=""/>
-    <memberdef>
-      <inlineclasses title=""/>
-      <defines title=""/>
-      <typedefs title=""/>
-      <enums title=""/>
-      <functions title=""/>
-      <variables title=""/>
-    </memberdef>
-    <authorsection/>
-  </file>
-
-  <!-- Layout definition for a group page -->
-  <group>
-    <briefdescription visible="yes"/>
-    <groupgraph visible="$GROUP_GRAPHS"/>
-    <memberdecl>
-      <nestedgroups visible="yes" title=""/>
-      <dirs visible="yes" title=""/>
-      <files visible="yes" title=""/>
-      <namespaces visible="yes" title=""/>
-      <classes visible="yes" title=""/>
-      <defines title=""/>
-      <typedefs title=""/>
-      <enums title=""/>
-      <enumvalues title=""/>
-      <functions title=""/>
-      <variables title=""/>
-      <signals title=""/>
-      <publicslots title=""/>
-      <protectedslots title=""/>
-      <privateslots title=""/>
-      <events title=""/>
-      <properties title=""/>
-      <friends title=""/>
-      <membergroups visible="yes"/>
-    </memberdecl>
-    <detaileddescription title=""/>
-    <memberdef>
-      <pagedocs/>
-      <inlineclasses title=""/>
-      <defines title=""/>
-      <typedefs title=""/>
-      <enums title=""/>
-      <enumvalues title=""/>
-      <functions title=""/>
-      <variables title=""/>
-      <signals title=""/>
-      <publicslots title=""/>
-      <protectedslots title=""/>
-      <privateslots title=""/>
-      <events title=""/>
-      <properties title=""/>
-      <friends title=""/>
-    </memberdef>
-    <authorsection visible="yes"/>
-  </group>
-
-  <!-- Layout definition for a directory page -->
-  <directory>
-    <briefdescription visible="yes"/>
-    <directorygraph visible="yes"/>
-    <memberdecl>
-      <dirs visible="yes"/>
-      <files visible="yes"/>
-    </memberdecl>
-    <detaileddescription title=""/>
-  </directory>
-</doxygenlayout>
diff --git a/docs/doxygen/ie_docs.xml b/docs/doxygen/ie_docs.xml
deleted file mode 100644
index 3a0f6d854eb..00000000000
--- a/docs/doxygen/ie_docs.xml
+++ /dev/null
@@ -1,383 +0,0 @@
-<!--
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
--->
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-                <tab type="user" title="ReduceL1-4" url="@ref openvino_docs_ops_reduction_ReduceL1_4"/>
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-                <tab type="user" title="ReduceLogicalOr-1" url="@ref openvino_docs_ops_reduction_ReduceLogicalOr_1"/>
-                <tab type="user" title="ReduceMax-1" url="@ref openvino_docs_ops_reduction_ReduceMax_1"/>
-                <tab type="user" title="ReduceMean-1" url="@ref openvino_docs_ops_reduction_ReduceMean_1"/>
-                <tab type="user" title="ReduceMin-1" url="@ref openvino_docs_ops_reduction_ReduceMin_1"/>
-                <tab type="user" title="ReduceProd-1" url="@ref openvino_docs_ops_reduction_ReduceProd_1"/>
-                <tab type="user" title="ReduceSum-1" url="@ref openvino_docs_ops_reduction_ReduceSum_1"/>
-                <tab type="user" title="RegionYolo-1" url="@ref openvino_docs_ops_detection_RegionYolo_1"/>
-                <tab type="user" title="ReorgYolo-1" url="@ref openvino_docs_ops_detection_ReorgYolo_1"/>
-                <tab type="user" title="Reshape-1" url="@ref openvino_docs_ops_shape_Reshape_1"/>
-                <tab type="user" title="Result-1" url="@ref openvino_docs_ops_infrastructure_Result_1"/>
-                <tab type="user" title="Reverse" url="@ref openvino_docs_ops_movement_Reverse_1"/>
-                <tab type="user" title="ReverseSequence-1" url="@ref openvino_docs_ops_movement_ReverseSequence_1"/>
-                <tab type="user" title="RNNCell-3" url="@ref openvino_docs_ops_sequence_RNNCell_3"/>
-                <tab type="user" title="RNNSequence-5" url="@ref openvino_docs_ops_sequence_RNNSequence_5"/>
-                <tab type="user" title="ROIAlign-3" url="@ref openvino_docs_ops_detection_ROIAlign_3"/>
-                <tab type="user" title="ROIPooling-1" url="@ref openvino_docs_ops_detection_ROIPooling_1"/>
-                <tab type="user" title="Roll-7" url="@ref openvino_docs_ops_movement_Roll_7"/>
-                <tab type="user" title="Round-5" url="@ref openvino_docs_ops_arithmetic_Round_5"/>
-                <tab type="user" title="ScatterElementsUpdate-3" url="@ref openvino_docs_ops_movement_ScatterElementsUpdate_3"/>
-                <tab type="user" title="ScatterNDUpdate" url="@ref openvino_docs_ops_movement_ScatterNDUpdate_3"/>
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-                <tab type="user" title="Selu-1" url="@ref openvino_docs_ops_activation_Selu_1"/>
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-                <tab type="user" title="ShapeOf-3" url="@ref openvino_docs_ops_shape_ShapeOf_3"/>
-                <tab type="user" title="ShuffleChannels-1" url="@ref openvino_docs_ops_movement_ShuffleChannels_1"/>
-                <tab type="user" title="Sigmoid-1" url="@ref openvino_docs_ops_activation_Sigmoid_1"/>
-                <tab type="user" title="Sign-1" url="@ref openvino_docs_ops_arithmetic_Sign_1"/>
-                <tab type="user" title="Sin-1" url="@ref openvino_docs_ops_arithmetic_Sin_1"/>
-                <tab type="user" title="Sinh-1" url="@ref openvino_docs_ops_arithmetic_Sinh_1"/>
-                <tab type="user" title="SoftMax-1" url="@ref openvino_docs_ops_activation_SoftMax_1"/>
-                <tab type="user" title="SoftMax-8" url="@ref openvino_docs_ops_activation_SoftMax_8"/>
-                <tab type="user" title="SoftPlus-4" url="@ref openvino_docs_ops_activation_SoftPlus_4"/>
-                <tab type="user" title="SpaceToBatch-2" url="@ref openvino_docs_ops_movement_SpaceToBatch_2"/>
-                <tab type="user" title="SpaceToDepth-1" url="@ref openvino_docs_ops_movement_SpaceToDepth_1"/>
-                <tab type="user" title="Split-1" url="@ref openvino_docs_ops_movement_Split_1"/>
-                <tab type="user" title="Sqrt-1" url="@ref openvino_docs_ops_arithmetic_Sqrt_1"/>
-                <tab type="user" title="SquaredDifference-1" url="@ref openvino_docs_ops_arithmetic_SquaredDifference_1"/>
-                <tab type="user" title="Squeeze-1" url="@ref openvino_docs_ops_shape_Squeeze_1"/>
-                <tab type="user" title="StridedSlice-1" url="@ref openvino_docs_ops_movement_StridedSlice_1"/>
-                <tab type="user" title="Subtract-1" url="@ref openvino_docs_ops_arithmetic_Subtract_1"/>
-                <tab type="user" title="Swish-4" url="@ref openvino_docs_ops_activation_Swish_4"/>
-                <tab type="user" title="Tan-1" url="@ref openvino_docs_ops_arithmetic_Tan_1"/>
-                <tab type="user" title="Tanh-1" url="@ref openvino_docs_ops_arithmetic_Tanh_1"/>
-                <tab type="user" title="TensorIterator-1" url="@ref openvino_docs_ops_infrastructure_TensorIterator_1"/>
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-                <tab type="user" title="TopK-1" url="@ref openvino_docs_ops_sort_TopK_1"/>
-                <tab type="user" title="TopK-3" url="@ref openvino_docs_ops_sort_TopK_3"/>
-                <tab type="user" title="Transpose-1" url="@ref openvino_docs_ops_movement_Transpose_1"/>
-                <tab type="user" title="Unsqueeze-1" url="@ref openvino_docs_ops_shape_Unsqueeze_1"/>
-                <tab type="user" title="VariadicSplit-1" url="@ref openvino_docs_ops_movement_VariadicSplit_1"/>
-            </tab>
-        </tab>
-
-        <tab id="deploying_inference" type="usergroup" title="Deploying Inference" url="@ref openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide">
-            <!-- Inference Engine Developer Guide -->
-            <tab type="usergroup" title="Inference Engine Developer Guide" url="@ref openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide">
-                <tab type="user" title="Inference Engine API Changes History" url="@ref openvino_docs_IE_DG_API_Changes"/>
-                <tab type="user" title="Inference Engine Memory primitives" url="@ref openvino_docs_IE_DG_Memory_primitives"/>
-                <tab type="user" title="Inference Engine Device Query API" url="@ref openvino_docs_IE_DG_InferenceEngine_QueryAPI"/>
-                <tab type="user" title="Inference Engine Model Caching" url="@ref openvino_docs_IE_DG_Model_caching_overview"/>
-                <tab type="usergroup" title="Inference Engine Extensibility Mechanism" url="@ref openvino_docs_IE_DG_Extensibility_DG_Intro">
-                    <tab type="user" title="Extension Library" url="@ref openvino_docs_IE_DG_Extensibility_DG_Extension"/>
-                    <tab type="user" title="Custom Operations" url="@ref openvino_docs_IE_DG_Extensibility_DG_AddingNGraphOps"/>
-                    <tab type="user" title="CPU Kernels Extensibility" url="@ref openvino_docs_IE_DG_Extensibility_DG_CPU_Kernel"/>
-                    <tab type="user" title="GPU Kernels Extensibility" url="@ref openvino_docs_IE_DG_Extensibility_DG_GPU_Kernel"/>
-                    <tab type="user" title="VPU Kernels Extensibility" url="@ref openvino_docs_IE_DG_Extensibility_DG_VPU_Kernel"/>
-                    <tab type="user" title="Build Extension Library Using CMake" url="@ref openvino_docs_IE_DG_Extensibility_DG_Building"/>
-                    <tab type="user" title="Custom ONNX operators" url="@ref openvino_docs_IE_DG_Extensibility_DG_Custom_ONNX_Ops"/>
-                </tab>
-                <tab type="user" title="Integrate the Inference Engine with Your Application" url="@ref openvino_docs_IE_DG_Integrate_with_customer_application_new_API"/>
-                <tab type="user" title="Introduction to Performance Topics" url="@ref openvino_docs_IE_DG_Intro_to_Performance"/>
-                <tab type="user" title="Inference Engine Python* API Overview" url="@ref openvino_inference_engine_ie_bridges_python_docs_api_overview"/>
-                <tab type="user" title="Read an ONNX model" url="@ref openvino_docs_IE_DG_ONNX_Support"/>
-                <tab type="user" title="Using Dynamic Batching Feature" url="@ref openvino_docs_IE_DG_DynamicBatching"/>
-                <tab type="user" title="Using Static Shape Infer Feature" url="@ref openvino_docs_IE_DG_ShapeInference"/>
-                <tab type="usergroup" title="Using Bfloat16 Inference" url="@ref openvino_docs_IE_DG_Bfloat16Inference">
-                </tab>
-                <tab type="usergroup" title="Using Low-Precision 8-bit Integer Inference" url="@ref openvino_docs_IE_DG_Int8Inference">
-                </tab>
-                <tab type="usergroup" title="Utilities to Validate Your Converted Model" url="@ref openvino_inference_engine_tools_cross_check_tool_README">
-                    <tab type="user" title="Using Cross Check Tool for Per-Layer Comparison Between Plugins" url="@ref openvino_inference_engine_tools_cross_check_tool_README"/>
-                </tab>
-                <tab type="user" title="Introduction to OpenVINO state API" url="@ref openvino_docs_IE_DG_network_state_intro"/>
-                <tab type="usergroup" title="Supported Devices" url="@ref openvino_docs_IE_DG_supported_plugins_Supported_Devices">
-                    <tab type="usergroup" title="GPU Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_GPU">
-                        <tab type="user" title="RemoteBlob API of GPU Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_GPU_RemoteBlob_API"/>
-                    </tab>
-                    <tab type="user" title="CPU Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_CPU"/>
-                    <tab type="usergroup" title="VPU Plugins" url="@ref openvino_docs_IE_DG_supported_plugins_VPU">
-                        <tab type="user" title="MYRIAD Plugin " url="@ref openvino_docs_IE_DG_supported_plugins_MYRIAD"/>
-                        <tab type="user" title="HDDL Plugin " url="@ref openvino_docs_IE_DG_supported_plugins_HDDL"/>
-                    </tab>
-                    <tab type="user" title="Heterogeneous Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_HETERO"/>
-                    <tab type="user" title="Multi-Device Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_MULTI"/>
-                    <tab type="user" title="Auto-Device Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_AUTO"/>
-                    <tab type="user" title="GNA Plugin" url="@ref openvino_docs_IE_DG_supported_plugins_GNA"/>
-                </tab>
-                <tab type="user" title="Known Issues" url="@ref openvino_docs_IE_DG_Known_Issues_Limitations"/>
-                <tab type="user" title="Glossary" url="@ref openvino_docs_IE_DG_Glossary"/>
-            </tab>
-
-            <!-- nGraph -->
-            <tab type="usergroup" title="nGraph Developer Guide" url="@ref openvino_docs_nGraph_DG_DevGuide">
-                <tab type="user" title="Introduction" url="@ref openvino_docs_nGraph_DG_Introduction"/>
-                <tab type="user" title="Basic Concepts" url="@ref openvino_docs_nGraph_DG_basic_concepts"/>
-                <tab type="user" title="Operation sets" url="@ref openvino_docs_MO_DG_IR_and_opsets"/>
-                <tab type="user" title="Graph building" url="@ref openvino_docs_nGraph_DG_build_function"/>
-                <tab type="user" title="Transfomations" url="@ref ngraph_transformation"/>
-                <tab type="user" title="Debug capabilities" url="@ref openvino_docs_nGraph_DG_Debug_capabilities"/>
-                <tab type="user" title="Python API" url="@ref openvino_docs_nGraph_DG_PythonAPI"/>
-            </tab>
-
-            <!-- Deployment Manager Guide -->
-            <tab type="user" title="Deployment Manager Guide" url="@ref openvino_docs_install_guides_deployment_manager_tool"/>
-
-            <!-- Compile Tool -->
-            <tab type="user" title="Compile Tool" url="@ref openvino_inference_engine_tools_compile_tool_README"/>
-
-            <!-- API References -->
-            <tab id="api_references" type="usergroup" title="API References">
-                <!-- IE C -->
-                <tab type="user" title="Inference Engine C API Reference" url="ie_c_api/modules.html"/>
-                <!-- IE C++-->
-                <tab type="classes" visible="yes" title="Inference Engine C++ API Reference" url="annotated.html">
-                    <tab type="classlist" visible="yes" title=""/>
-                    <tab type="hierarchy" visible="yes" title=""/>
-                    <tab type="namespacemembers" visible="yes" title="" intro=""/>
-                    <tab type="pages" visible="no"/>
-                    <tab type="files" visible="no"/>
-                    <tab type="filelist" visible="no"/>
-                    <tab type="globals" visible="no"/>
-                </tab>
-                <!-- IE Python -->
-                <tab type="user" title="Inference Engine Python API Reference" url="ie_python_api/annotated.html"/>
-                <!-- nGraph C++ -->
-                <tab type="user" title="nGraph C++ API Reference" url="ngraph_cpp_api/annotated.html"/>
-                <!-- nGraph Python -->
-                <tab type="user" title="nGraph Python API Reference" url="ngraph_python_api/files.html"/>
-            </tab>
-            <!-- Inference Engine Plugin Development Guide-->
-            <tab type="user" title="Inference Engine Plugin Development Guide" url="ie_plugin_api/index.html"/>
-        </tab>
-    </navindex>
-</doxygenlayout>
\ No newline at end of file
diff --git a/docs/doxygen/ie_plugin_api.config b/docs/doxygen/ie_plugin_api.config
deleted file mode 100644
index 4304ffaa9a4..00000000000
--- a/docs/doxygen/ie_plugin_api.config
+++ /dev/null
@@ -1,85 +0,0 @@
-# Doxyfile 1.8.18
-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
-#
-# All text after a double hash (##) is considered a comment and is placed in
-# front of the TAG it is preceding.
-#
-# All text after a single hash (#) is considered a comment and will be ignored.
-# The format is:
-# TAG = value [value, ...]
-# For lists, items can also be appended using:
-# TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (\" \").
-
-@INCLUDE = "@IE_CONFIG_BUILD@"
-
-LAYOUT_FILE            = "@PLUGIN_LAYOUT_BUILD@"
-
-HTML_OUTPUT            = "@PLUGIN_OUTPUT@""
-
-GENERATE_TAGFILE       = "@DOCS_BUILD_DIR@/ie_plugin_api.tag"
-
-EXTRACT_LOCAL_CLASSES  = NO
-
-INPUT                  = "@DOCS_BUILD_DIR@/docs/IE_PLUGIN_DG" \
-                         "@OV_INFERENCE_DIR@/dev_api" \
-                         "@OpenVINO_SOURCE_DIR@/src/common/transformations/include" \
-                         "@OpenVINO_SOURCE_DIR@/src/common/itt/include/openvino"
-
-
-RECURSIVE              = YES
-
-FILE_PATTERNS          = *.c \
-                         *.cpp \
-                         *.c++ \
-                         *.h \
-                         *.hpp \
-                         *.md
-
-EXCLUDE_SYMBOLS        = InferenceEngine::details
-
-TAGFILES               = "@DOCS_BUILD_DIR@/ie_api.tag=.." \
-                         "@DOCS_BUILD_DIR@/ngraph_cpp_api.tag=../ngraph_cpp_api"
-
-EXAMPLE_PATH           = "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/src" \
-                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/include" \
-                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/src/CMakeLists.txt" \
-                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/tests/functional/CMakeLists.txt" \
-                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/tests/functional/transformations" \
-                         "@CMAKE_CURRENT_SOURCE_DIR@/template_plugin/tests/functional/shared_tests_instances/" \
-                         "@CMAKE_CURRENT_SOURCE_DIR@/snippets"
-                         "@IE_SOURCE_DIR@/tests/functional/plugin/shared/include" \
-
-EXAMPLE_PATTERNS       = *.cpp \
-                         *.hpp
-
-ENUM_VALUES_PER_LINE   = 1
-
-EXPAND_ONLY_PREDEF     = YES
-
-PREDEFINED             = "INFERENCE_ENGINE_API=" \
-                         "INFERENCE_ENGINE_API_CPP=" \
-                         "INFERENCE_ENGINE_API_CLASS=" \
-                         "OPENVINO_RUNTIME_API_C=" \
-                         "OPENVINO_RUNTIME_API=" \
-                         "OPENVINO_EXTERN_C=" \
-                         "OPENVINO_CORE_IMPORTS=" \
-                         "OPENVINO_CORE_EXPORTS=" \
-                         "INFERENCE_ENGINE_DEPRECATED=" \
-                         "OPENVINO_DEPRECATED=" \
-                         "IMPLEMENT_OPENVINO_API" \
-                         "TRANSFORMATIONS_API=" \
-                         "NGRAPH_HELPER_DLL_EXPORT=" \
-                         "NGRAPH_HELPER_DLL_IMPORT=" \
-                         "OPENVINO_CDECL=" \
-                         "OPENVINO_STDCALL=" \
-                         "IE_SUPPRESS_DEPRECATED_START=" \
-                         "IE_SUPPRESS_DEPRECATED_END=" \
-                         "_IE_SUPPRESS_DEPRECATED_START_GCC=" \
-                         "_IE_SUPPRESS_DEPRECATED_END_GCC=" \
-                         "IE_THREAD=IE_THREAD_TBB" \
-                         "NGRAPH_RTTI_DECLARATION="
-
-WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ie_plugin_api.log"
diff --git a/docs/doxygen/ie_plugin_api.xml b/docs/doxygen/ie_plugin_api.xml
deleted file mode 100644
index 3bd60bb49c9..00000000000
--- a/docs/doxygen/ie_plugin_api.xml
+++ /dev/null
@@ -1,44 +0,0 @@
-<!--
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
--->
-
-<doxygenlayout version="1.0">
-    <navindex>
-        <!-- Steps -->
-        <tab type="usergroup" url="index.html" visibile="yes" title="GUIDE">
-            <tab type="usergroup" url="index.html" title="Developer Guide for Inference Engine Plugin Library">
-                <tab type="user" url="@ref plugin" visibile="yes" title="Implement Plugin Functionality"/>
-                <tab type="user" url="@ref executable_network" visibile="yes" title="Implement Executable Network Functionality"/>
-                <tab type="user" url="@ref infer_request" visibile="yes" title="Implement Synchronous Inference Request"/>
-                <tab type="user" url="@ref async_infer_request" visibile="yes" title="Implement Asynchronous Inference Request"/>
-            </tab>
-        </tab>
-        <!-- Additional resources -->
-         <tab type="usergroup" visibile="no" title="ADDITIONAL RESOURCES">
-            <tab type="user" url="@ref plugin_build" visibile="yes" title="Build Your Plugin with CMake*"/>
-            <tab type="user" url="@ref plugin_testing" visibile="yes" title="Test Your Plugin"/>
-        </tab>
-        <!-- API References -->
-        <tab type="usergroup" title="API REFERENCE">
-            <!-- IE Plugin API Reference -->
-            <tab type="user" url="group__ie__dev__api.html" visible="yes" title="Inference Engine Plugin API Reference"/>
-            <!-- IE Transformations API Reference -->
-            <tab type="user" url="group__ie__transformation__api.html" visible="yes" title="Inference Engine Transformations API Reference"/>
-        </tab>
-        <tab type="usergroup" title="MAIN OPENVINO™ DOCS" url="../index.html"/>
-    </navindex>
-</doxygenlayout>
diff --git a/docs/doxygen/ie_py_api.config b/docs/doxygen/ie_py_api.config
deleted file mode 100644
index cfae70036f5..00000000000
--- a/docs/doxygen/ie_py_api.config
+++ /dev/null
@@ -1,52 +0,0 @@
-# Doxyfile 1.8.18
-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
-#
-# All text after a double hash (##) is considered a comment and is placed in
-# front of the TAG it is preceding.
-#
-# All text after a single hash (#) is considered a comment and will be ignored.
-# The format is:
-# TAG = value [value, ...]
-# For lists, items can also be appended using:
-# TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (\" \").
-
-@INCLUDE = "@IE_CONFIG_BUILD@"
-
-EXCLUDE_SYMBOLS        = ie_api::BlobBuffer \
-                         *impl* \
-                         *device_name* \
-                         *num_requests* \
-                         *exec_net* \
-                         *c_config* \
-                         *ie_core_impl* \
-                         *plugin_impl* \
-                         *extension_str* \
-                         *buffer* \
-                         *__cinit__*
-
-PREDEFINED             = "__attribute__(x)=" \
-                         "__VA_ARGS__=" \
-                         "INFERENCE_ENGINE_C_API_EXTERN=" \
-                         "INFERENCE_ENGINE_C_API=" \
-                         "IE_NODISCARD=" \
-                         "__cdecl=" \
-                         "__declspec(x)=" \
-                         "__GNUC__=" \
-                         "_WIN32"
-
-EXTENSION_MAPPING      = pyx=Python
-
-FILE_PATTERNS          = *.pyx
-
-LAYOUT_FILE            = "@PY_LAYOUT_BUILD@"
-
-INPUT                  = "@PYTHON_API_OUT@"
-
-HTML_OUTPUT            = "@PY_OUTPUT@""
-
-GENERATE_TAGFILE       = "@DOCS_BUILD_DIR@/ie_python_api.tag"
-
-WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ie_py_api.log"
diff --git a/docs/doxygen/ie_py_api.xml b/docs/doxygen/ie_py_api.xml
deleted file mode 100644
index 8c9fa0fa9c4..00000000000
--- a/docs/doxygen/ie_py_api.xml
+++ /dev/null
@@ -1,226 +0,0 @@
-<!--
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
--->
-
-<doxygenlayout version="1.0">
-  <!-- Generated by doxygen 1.8.12 -->
-  <!-- Navigation index tabs for HTML output -->
-  <navindex>
-    <tab type="mainpage" title="OpenVINO Home" url="../index.html"/>
-    <tab type="user" title="Get Started" url="../index.html"/>
-    <tab type="user" title="Documentation" url="../documentation.html"/>
-    <tab type="user" title="Examples" url="../examples.html"/>
-    <tab type="usergroup" title="API REFERENCES" url="../api_references.html">
-      <!-- OpenVX -->
-      <tab type="user" title="OpenVX Developer Guide" url="https://khronos.org/openvx"/>
-      <!-- OpenCV -->
-      <tab type="user" title="OpenCV Developer Guide" url="https://docs.opencv.org/master/"/>
-      <!-- IE C -->
-      <tab type="usergroup" title="Inference Engine C API Reference" url="../ie_c_api/modules.html"/>
-      <!-- IE C++-->
-      <tab type="user" title="Inference Engine С++ API Reference" url="../annotated.html"/>
-      <!-- IE Python -->
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diff --git a/docs/doxygen/ngraph_cpp_api.config b/docs/doxygen/ngraph_cpp_api.config
deleted file mode 100644
index 5ee7a2d6f24..00000000000
--- a/docs/doxygen/ngraph_cpp_api.config
+++ /dev/null
@@ -1,39 +0,0 @@
-# Doxyfile 1.8.18
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diff --git a/docs/doxygen/ngraph_cpp_api.xml b/docs/doxygen/ngraph_cpp_api.xml
deleted file mode 100644
index 10c16fefeb7..00000000000
--- a/docs/doxygen/ngraph_cpp_api.xml
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@@ -1,224 +0,0 @@
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diff --git a/docs/doxygen/ngraph_py_api.config b/docs/doxygen/ngraph_py_api.config
deleted file mode 100644
index 321b514d5c6..00000000000
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diff --git a/docs/doxygen/ngraph_py_api.xml b/docs/doxygen/ngraph_py_api.xml
deleted file mode 100644
index f2669bc7cd9..00000000000
--- a/docs/doxygen/ngraph_py_api.xml
+++ /dev/null
@@ -1,215 +0,0 @@
-<!--
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\ No newline at end of file
diff --git a/docs/doxygen/openvino_docs.xml b/docs/doxygen/openvino_docs.xml
deleted file mode 100644
index ea7c05aad76..00000000000
--- a/docs/doxygen/openvino_docs.xml
+++ /dev/null
@@ -1,373 +0,0 @@
-<!--
-******************************************************************************
-Copyright 2017-2021 Intel Corporation
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-******************************************************************************
--->
-
-<doxygenlayout version="1.0" xmlns:xi="http://www.w3.org/2001/XInclude">
-    <!-- Navigation index tabs for HTML output -->
-    <navindex>
-        <tab type="mainpage" title="OpenVINO Home" url="@ref index"/>
-        
-        <!-- GET STARTED category -->
-        <tab type="usergroup" title="GET STARTED" url="index.html">
-            <!-- Install Directly -->
-            <tab type="usergroup" title="Installation Guides" url=""><!--automatically generated-->
-                <tab type="usergroup" title="Linux" url="@ref openvino_docs_install_guides_installing_openvino_linux">
-                    <tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Linux* OS" url="@ref openvino_docs_install_guides_installing_openvino_linux"/>
-                </tab>     
-                <tab type="usergroup" title="Windows" url="@ref openvino_docs_install_guides_installing_openvino_windows">
-                    <tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Windows* 10" url="@ref openvino_docs_install_guides_installing_openvino_windows"/>
-                </tab>
-                <tab type="user" title="macOS" url="@ref openvino_docs_install_guides_installing_openvino_macos"/>
-                <tab type="user" title="Raspbian OS" url="@ref openvino_docs_install_guides_installing_openvino_raspbian"/>
-            </tab> 
-            <!-- Install From Images and Repositories -->  
-            <tab type="usergroup" title="Install From Images and Repositories" url="@ref openvino_docs_install_guides_installing_openvino_images">
-                <tab type="usergroup" title="Docker" url="@ref openvino_docs_install_guides_installing_openvino_docker_linux">
-                    <tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Linux* from a Docker* Image" url="@ref openvino_docs_install_guides_installing_openvino_docker_linux"/>
-                    <tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Windows* from a Docker* Image" url="@ref openvino_docs_install_guides_installing_openvino_docker_windows"/>
-                </tab>
-                <tab type="user" title="Docker with DL Workbench" url="./workbench_docs_Workbench_DG_Run_Locally.html"/><!-- Link to the original Workbench topic -->
-                <tab type="user" title="APT" url="@ref openvino_docs_install_guides_installing_openvino_apt"/>
-                <tab type="user" title="YUM" url="@ref openvino_docs_install_guides_installing_openvino_yum"/>
-                <tab type="user" title="Anaconda Cloud" url="@ref openvino_docs_install_guides_installing_openvino_conda"/>
-                <tab type="user" title="Yocto" url="@ref openvino_docs_install_guides_installing_openvino_yocto"/>
-                <tab type="user" title="PyPI" url="@ref openvino_docs_install_guides_installing_openvino_pip"/>
-                <tab type="user" title="Build from Source" url="https://github.com/openvinotoolkit/openvino/wiki/BuildingCode"/>
-            </tab>
-            <!-- Get Started Guides-->
-            <tab type="usergroup" title="Get Started Guides" url=""><!--automatically generated-->
-                <tab type="user" title="OpenVINO™ Toolkit Overview" url="@ref index"/>
-                <tab type="user" title="Linux" url="@ref openvino_docs_get_started_get_started_linux"/>
-                <tab type="user" title="Windows" url="@ref openvino_docs_get_started_get_started_windows"/>
-                <tab type="user" title="macOS" url="@ref openvino_docs_get_started_get_started_macos"/>
-                <tab type="user" title="Raspbian" url="@ref openvino_docs_get_started_get_started_raspbian"/>
-                <tab type="user" title="DL Workbench: Quick Start with OpenVINO™ Toolkit" url="@ref openvino_docs_get_started_get_started_dl_workbench"/>
-                <tab type="user" title="Legal Information" url="@ref openvino_docs_Legal_Information"/>
-            </tab>
-            <!-- Configuration for Hardware -->
-            <tab type="usergroup" title="Configuration for Hardware" url=""><!--automatically generated-->
-                <tab type="usergroup" title="VPUs" url="@ref openvino_docs_install_guides_movidius_setup_guide">
-                    <tab type="user" title="Configuration Guide for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs on Linux" url="@ref openvino_docs_install_guides_installing_openvino_linux_ivad_vpu"/>
-                    <tab type="user" title="Intel® Movidius™ VPUs Setup Guide" url="@ref openvino_docs_install_guides_movidius_setup_guide"/>
-                    <tab type="user" title="Intel® Movidius™ VPUs Programming Guide" url="@ref openvino_docs_install_guides_movidius_programming_guide"/>
-                </tab>
-            </tab>
-            <!-- Security -->
-            <tab type="usergroup" title="Security" url="@ref openvino_docs_security_guide_introduction"><!--automatically generated-->
-                <tab type="user" title="Introduction" url="@ref openvino_docs_security_guide_introduction"/>
-                <tab type="user" title="Using DL Workbench Securely" url="@ref openvino_docs_security_guide_workbench"/>
-                <tab type="user" title="Using Encrypted Models" url="@ref openvino_docs_IE_DG_protecting_model_guide"/>
-                <tab type="user" title="Security Add-on" url="@ref ovsa_get_started"/>
-            </tab>
-        </tab>
-
-        <!-- DOCUMENTATION category -->
-        <tab type="usergroup" title="DOCUMENTATION"><!--automatically generated-->
-            <!-- DLDT Documentation-->
-            <xi:include href="ie_docs.xml" xpointer="xpointer(//tab[@id='converting_and_preparing_models'])">
-                <xi:fallback/>
-            </xi:include>
-            <xi:include href="ie_docs.xml" xpointer="xpointer(//tab[@id='intermediate_representaton_and_operations_sets'])">
-                <xi:fallback/>
-            </xi:include>
-            <xi:include href="ie_docs.xml" xpointer="xpointer(//tab[@id='deploying_inference'])">
-                <xi:fallback/>
-            </xi:include>
-            <!-- Workbench -->
-            <xi:include href="workbench_docs.xml" xpointer="xpointer(//tab[@id='deep_learning_workbench'])">
-                <xi:fallback/>
-            </xi:include>
-            <!-- Tuning for Performance -->
-            <tab type="usergroup" title="Tuning for Performance">
-                <!-- Performance Benchmarks -->
-                <tab type="usergroup" title="Performance Benchmark Results" url="@ref openvino_docs_performance_benchmarks">
-                    <tab type="usergroup" title="Intel® Distribution of OpenVINO™ toolkit Benchmark Results" url="@ref openvino_docs_performance_benchmarks_openvino">
-                        <tab type="user" title="Performance Information Frequently Asked Questions" url="@ref openvino_docs_performance_benchmarks_faq"/>
-                        <tab type="user" title="Download Performance Data Spreadsheet in MS Excel* Format" url="https://docs.openvinotoolkit.org/downloads/benchmark_files/OV-2021.4-Download-Excel.xlsx"/>
-                        <tab type="user" title="INT8 vs. FP32 Comparison on Select Networks and Platforms" url="@ref openvino_docs_performance_int8_vs_fp32"/>
-                    </tab>
-                    <tab type="user" title="OpenVINO™ Model Server Benchmark Results" url="@ref openvino_docs_performance_benchmarks_ovms"/>
-                </tab>    
-                <tab type="user" title="Performance Optimization Guide" url="@ref openvino_docs_optimization_guide_dldt_optimization_guide"/>
-                <!-- POT DevGuide -->
-                <xi:include href="pot_docs.xml" xpointer="xpointer(//tab[@id='pot'])">
-                    <xi:fallback/>
-                </xi:include>
-                <!-- Tuning Utilities -->
-                <tab type="usergroup" title="Tuning Utilities" url="">
-                    <xi:include href="omz_docs.xml" xpointer="omz_tools_accuracy_checker">
-                        <xi:fallback/>
-                    </xi:include>
-                    <xi:include href="omz_docs.xml" xpointer="omz_data">
-                        <xi:fallback/>
-                    </xi:include>
-                    <tab type="user" title="Using Cross Check Tool for Per-Layer Comparison Between Plugins" url="@ref openvino_inference_engine_tools_cross_check_tool_README"/>
-                </tab>
-                <tab type="user" title="Case Studies" url="https://www.intel.com/openvino-success-stories"/>
-            </tab>
-                        
-            <tab type="usergroup" title="Media Processing">
-                <!-- DL Streamer -->
-                <tab type="user" title="DL Streamer API Reference" url="https://openvinotoolkit.github.io/dlstreamer_gst/"/>
-                <!-- DL Streamer Examples -->
-                <tab type="usergroup" title="DL Streamer Examples" url="@ref gst_samples_README">
-                </tab>
-                <!-- G-API -->
-                <tab type="usergroup" title="Graph API (G-API) Developer Guide" url="@ref openvino_docs_gapi_gapi_intro">
-                    <tab type="user" title="Introduction to G-API" url="@ref openvino_docs_gapi_gapi_intro"/>
-                    <tab type="user" title="G-API Kernel API" url="@ref openvino_docs_gapi_kernel_api"/>
-                    <tab type="user" title="Use Cases: Implementing a Face Beautification Algorithm" url="@ref openvino_docs_gapi_face_beautification"/>
-                    <tab type="user" title="Use Cases: Building a Face Analytics Pipeline" url="@ref openvino_docs_gapi_gapi_face_analytics_pipeline"/>
-                    <tab type="usergroup" title="API Reference" url="">
-                        <tab type="user" title="G-API Core functionality" url="https://docs.opencv.org/4.2.0/df/d1f/group__gapi__core.html"/>
-                        <tab type="user" title="G-API Image processing functionality" url="https://docs.opencv.org/4.2.0/d2/d00/group__gapi__imgproc.html"/>
-                        <tab type="user" title="G-API Drawing and composition functionality" url="https://docs.opencv.org/4.2.0/df/de4/group__gapi__draw.html"/>
-                        <tab type="user" title="G-API Framework" url="https://docs.opencv.org/4.2.0/d7/d0d/group__gapi.html"/>
-                    </tab>
-                </tab>    
-                <!-- OpenVX -->
-                <tab type="user" title="OpenVX Developer Guide" url="https://software.intel.com/en-us/openvino-ovx-guide"/>
-                <tab type="user" title="OpenVX API Reference" url="https://khronos.org/openvx"/>
-                <!-- OpenCV -->
-                <tab type="user" title="OpenCV* Developer Guide" url="https://docs.opencv.org/master/"/>
-                <!-- OpenCL -->
-                <tab type="user" title="OpenCL™ Developer Guide" url="https://software.intel.com/en-us/openclsdk-devguide"/>
-            </tab>    
-        </tab>
-
-        <!-- RESOURCES category -->
-        <tab type="usergroup" title="RESOURCES">
-            <!-- Models and Demos Documentation-->
-            <xi:include href="omz_docs.xml" xpointer="omz_models">
-                <xi:fallback/>
-            </xi:include>
-            <xi:include href="omz_docs.xml" xpointer="omz_demos">
-                <xi:fallback/>
-            </xi:include>
-            <!-- IE Code Samples -->
-            <tab type="usergroup" title="Inference Engine Code Samples" url="@ref openvino_docs_IE_DG_Samples_Overview">
-                <tab type="user" title="Image Classification Async C++ Sample" url="@ref openvino_inference_engine_samples_classification_sample_async_README"/>
-                <tab type="user" title="Image Classification Async Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_classification_sample_async_README"/>
-                <tab type="user" title="Hello Classification C++ Sample" url="@ref openvino_inference_engine_samples_hello_classification_README"/>
-                <tab type="user" title="Hello Classification C Sample" url="@ref openvino_inference_engine_ie_bridges_c_samples_hello_classification_README"/>
-                <tab type="user" title="Hello Classification Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_hello_classification_README"/>
-                <tab type="user" title="Hello Reshape SSD C++ Sample" url="@ref openvino_inference_engine_samples_hello_reshape_ssd_README"/>
-                <tab type="user" title="Hello Reshape SSD Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_hello_reshape_ssd_README"/>
-                <tab type="user" title="Hello NV12 Input Classification C++ Sample" url="@ref openvino_inference_engine_samples_hello_nv12_input_classification_README"/>
-                <tab type="user" title="Hello NV12 Input Classification C Sample" url="@ref openvino_inference_engine_ie_bridges_c_samples_hello_nv12_input_classification_README"/>
-                <tab type="user" title="Hello Query Device C++ Sample" url="@ref openvino_inference_engine_samples_hello_query_device_README"/>
-                <tab type="user" title="Hello Query Device Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_hello_query_device_README"/>
-                <tab type="user" title="nGraph Function Creation C++ Sample" url="@ref openvino_inference_engine_samples_ngraph_function_creation_sample_README"/>
-                <tab type="user" title="nGraph Function Creation Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_ngraph_function_creation_sample_README"/>
-                <tab type="user" title="Automatic Speech Recognition C++ Sample" url="@ref openvino_inference_engine_samples_speech_sample_README"/>
-                <tab type="user" title="Automatic Speech Recognition Python Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_speech_sample_README"/>
-                <tab type="user" title="Benchmark C++ Tool" url="@ref openvino_inference_engine_samples_benchmark_app_README"/>
-                <tab type="user" title="Benchmark Python* Tool" url="@ref openvino_inference_engine_tools_benchmark_tool_README"/>
-            </tab>
-            <!-- DL Streamer Examples -->
-            <tab type="usergroup" title="DL Streamer Examples" url="@ref gst_samples_README">
-                <tab type="usergroup" title="Command Line Samples" url="">
-                    <tab type="user" title="Audio Detection Sample" url="@ref gst_samples_gst_launch_audio_detect_README"/>
-                    <tab type="user" title="Face Detection And Classification Sample" url="@ref gst_samples_gst_launch_face_detection_and_classification_README"/>
-                    <tab type="user" title="Vehicle and Pedestrian Tracking Sample" url="@ref gst_samples_gst_launch_vehicle_pedestrian_tracking_README"/>
-                    <tab type="usergroup" title="Metadata Publishing Sample" url="@ref gst_samples_gst_launch_metapublish_README">
-                        <tab type="user" title="MetaPublish Listeners" url="@ref gst_samples_gst_launch_metapublish_listener"/>
-                    </tab>
-                    <tab type="user" title="gvapython Sample" url="@ref gst_samples_gst_launch_gvapython_face_detection_and_classification_README"/>
-                    <tab type="user" title="Action Recognition Sample" url="@ref gst_samples_gst_launch_action_recognition_README"/>
-                    <tab type="user" title="Human Pose Estimation Sample" url="@ref gst_samples_gst_launch_human_pose_estimation_README"/>
-                </tab>
-                <tab type="user" title="Draw Face Attributes C++ Sample" url="@ref gst_samples_cpp_draw_face_attributes_README"/>
-                <tab type="user" title="Draw Face Attributes Python Sample" url="@ref gst_samples_python_draw_face_attributes_README"/>
-                <tab type="user" title="Benchmark Sample" url="@ref gst_samples_benchmark_README"/>
-            </tab>
-            <tab type="usergroup" title="Add-Ons" url="">
-              <tab type="user" title="Model Server" url="@ref openvino_docs_ovms"/>
-              <tab type="user" title="Security Add-on" url="./ovsa_get_started.html"/>  
-            </tab>
-        </tab>
-
-        <!-- Chinese docs -->
-        <tab type="user" title="中文文件" url="https://docs.openvinotoolkit.org/cn/index.html"/>
-    </navindex>
-
-    <!-- Layout definition for a class page -->
-    <class>
-        <briefdescription visible="yes"/>
-        <includes visible="$SHOW_INCLUDE_FILES"/>
-        <inheritancegraph visible="$CLASS_GRAPH"/>
-        <collaborationgraph visible="$COLLABORATION_GRAPH"/>
-        <memberdecl>
-            <nestedclasses visible="yes" title=""/>
-            <publictypes title=""/>
-            <services title=""/>
-            <interfaces title=""/>
-            <publicslots title=""/>
-            <signals title=""/>
-            <publicmethods title=""/>
-            <publicstaticmethods title=""/>
-            <publicattributes title=""/>
-            <publicstaticattributes title=""/>
-            <protectedtypes title=""/>
-            <protectedslots title=""/>
-            <protectedmethods title=""/>
-            <protectedstaticmethods title=""/>
-            <protectedattributes title=""/>
-            <protectedstaticattributes title=""/>
-            <packagetypes title=""/>
-            <packagemethods title=""/>
-            <packagestaticmethods title=""/>
-            <packageattributes title=""/>
-            <packagestaticattributes title=""/>
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-            <events title=""/>
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-            <privateattributes title=""/>
-            <privatestaticattributes title=""/>
-            <friends title=""/>
-            <related title="" subtitle=""/>
-            <membergroups visible="yes"/>
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-        <detaileddescription title=""/>
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-            <related title=""/>
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-            <events title=""/>
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-        <allmemberslink visible="yes"/>
-        <usedfiles visible="$SHOW_USED_FILES"/>
-        <authorsection visible="yes"/>
-    </class>
-    <!-- Layout definition for a namespace page -->
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-    </namespace>
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-        <sourcelink visible="yes"/>
-        <memberdecl>
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diff --git a/docs/doxygen/pyx_filter.py b/docs/doxygen/pyx_filter.py
deleted file mode 100644
index 44ae37aedd2..00000000000
--- a/docs/doxygen/pyx_filter.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import re
-import argparse
-
-
-def process_pyx(pyx_file):
-    """
-    Convert .pyx file to a more readable format for doxygen.
-    """
-    with open(pyx_file, 'r') as f:
-        source = f.readlines()
-    idx = 0
-    while idx < len(source):
-        line = source[idx]
-        striped_line = line.lstrip()
-        tabs = ' ' * (len(line) - len(striped_line))  # Keep indentation
-        striped_line = striped_line.rstrip()
-        if striped_line == '@property':  # Python functions wrapped with @property decorator
-            new_getter = convert_getter(source, idx)
-            if new_getter:
-                indent = tabs + ' ' * 4
-                new_func, comments, shift = new_getter
-                func_name = re.search(r'def\s+?([A-Za-z0-9_]+)\s*?\(', new_func).group(1)
-                source[idx + 1] = tabs + new_func + '\n'
-                for i in range(shift):
-                    source.pop(idx + 2)
-                # This is a workaround to help Doxygen understand "@property" functions as class properties.
-                for comm in comments:
-                    source.insert(idx + 2, '{indent}{comment}\n'.format(indent=indent, comment=comm))
-                    idx += 1
-                source.insert(idx + 2, '{indent}self.{func_name} = {func_name}\n'.format(
-                    indent=indent,
-                    func_name=func_name
-                ))
-                idx += 1
-        if re.search(r'c?p?def.+\(', striped_line):  # Convert cython functions to python format
-            new_sign = get_signature(source, idx)
-            if new_sign:
-                new_func, shift = new_sign
-                args = re.search(r'\((.+)\)', new_func)
-                if args:
-                    new_func = new_func.replace(args.group(1), process_args(args.group(1))).replace('cpdef', 'def')
-                source[idx] = tabs + new_func + '\n'
-                for i in range(shift):
-                    source.pop(idx + 1)
-        if '__cinit__' in striped_line:  # Doxygen only interprets "__init__" constructors
-            source[idx] = source[idx].replace('__cinit__', '__init__')
-        idx += 1
-
-    with open(pyx_file, 'w') as f:
-        f.writelines(source)
-
-
-def process_args(str_args):
-    """
-    Convert function arguments to the doxygen readable format.
-    """
-    args = re.sub(r'\[.*?\]', r'', str_args)
-    args = re.sub(r'\(.*?\)', r'', args)
-    args = args.split(',')
-    for idx, arg in enumerate(args):
-        arg = arg.replace('&', '').strip()
-        if arg.startswith('const'):
-            arg = arg.replace('const', '').strip()
-        if ':' in arg:
-            arg = arg.split(':')[0]
-        match = re.match(r'^[\w\.]+\s+(\w.+)', arg)
-        if match:
-            arg = match.group(1)
-        args[idx] = arg.strip()
-    return ', '.join(args)
-
-
-def convert_getter(source, start):
-    """
-    Process a function that is wrapped with @property decorator
-    """
-    current = source[start + 1].strip()
-    if not current.startswith('def'):  # Base Case
-        return
-    new_sign = get_signature(source, start + 1)
-    if new_sign:
-        new_func, shift = new_sign
-        new_func += ':'
-        # get comments
-        comments = []
-        if start > 1:
-            idx = start - 1
-            while source[idx].lstrip().startswith('#') and idx >= 0:
-                comments.append(source[idx].strip())
-                idx -= 1
-            comments.reverse()
-        return new_func, comments, shift
-
-
-def get_signature(source, start):
-    """
-    Get function signature and process it
-    """
-    match = re.search(r'c?p?def.+\(', source[start].strip())
-    if not match:
-        return
-    start_j = match.span()[1]
-    open_brackets = 1
-    new_sign = match.group()
-
-    for i in range(start, len(source)):
-        line = source[i].strip()
-        for j in range(start_j, len(line)):
-            char = line[j]
-            if char == ')':
-                open_brackets -= 1
-            if char == '(':
-                open_brackets += 1
-            new_sign += char
-            if not open_brackets:
-                return new_sign + ':\n', i - start
-        start_j = 0
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('pyx_file', type=str, nargs='+', help='Path to a .pyx file.')
-    args = parser.parse_args()
-    for pyx in args.pyx_file:
-        process_pyx(pyx)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/docs/doxyrest-config.lua b/docs/doxyrest-config.lua
new file mode 100644
index 00000000000..afc534d77c7
--- /dev/null
+++ b/docs/doxyrest-config.lua
@@ -0,0 +1,464 @@
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+--!
+--! \defgroup frame-config
+--! \grouporder 2
+--! \title Frame Settings
+--!
+--! This section describes frame settings controlling input and output paths,
+--! titles, force-includes, declaration coding style, etc.
+--!
+--! A default ``doxyrest-config.lua`` file for standard frames can be found at
+--! ``$DOXYREST_FRAME_DIR/doxyrest-config.lua``. Copy it to your project
+--! directory and then adjust all the necessary parameters.
+--!
+--! @{
+--!
+
+--!
+--! Table containing a list of frame directories. All frame files will be
+--! searched in directories -- and in the sequence -- specified here.
+--!
+--
+FRAME_DIR_LIST = { "@DOXYREST_OUT@/frame/cfamily", "@DOXYREST_OUT@/frame/common"}
+
+--!
+--! The output master (index) reStructuredText file. Usually, the index frame
+--! also generates auxillary files -- they will be placed next to the master
+--! file. The command line option ``-f`` *overrides* this value.
+--! If neither ``FRAME_FILE`` nor ``-f`` is specified, ``index.rst.in`` will be
+--! used as the default frame file.
+--!
+
+FRAME_FILE = '@DOXYREST_OUT@/frame/cfamily/index.rst.in'
+
+--!
+--! The input master (index) XML file. Specifying it here allows calling
+--! ``doxyrest`` without parameters; otherwise, the master XML *must* be passed
+--! via the command line. If both ``INPUT_FILE`` and command line parameter are
+--! specified, the command line takes precedence.
+--!
+
+INPUT_FILE = '@XML_OUTPUT@/index.xml'
+
+--!
+--! The output master (index) reStructuredText file. Usually, the index frame
+--! also generates auxillary files -- they will be placed next to the master
+--! file. The command line option ``-o`` *overrides* this value. If neither
+--! ``OUTPUT_FILE`` nor ``-o`` is specified, ``index.rst`` will be used as
+--! the default output master file.
+--!
+
+OUTPUT_FILE = "@RST_OUTPUT@/index.rst"
+
+--!
+--! File with project-specific reStructuredText definitions. When non``nil``,
+--! this file will be included at the top of every generated ``.rst`` file.
+--!
+
+FORCE_INCLUDE_FILE = nil
+
+--!
+--! If you want to add extra reStructuredText documentation pages, do so
+--! by adding them to this list.
+--!
+
+EXTRA_PAGE_LIST = {}
+
+--!
+--! The title of the main (index) page. This only is used when ``INTRO_FILE``
+--! is not set (otherwise, the title of intro file will be used).
+--!
+
+INDEX_TITLE = "Inference Engine C++ API Reference"
+
+--!
+--! File with project introduction (reStructuredText). When non-nil, this file
+--! will be included into ``index.rst`` file and NOT added to the list of other
+--! documentation pages.
+--!
+
+INTRO_FILE = nil
+
+--!
+--! Specify whether to sort groups lexicographically (by ``title``) or
+--! logically (by ``id``). To maintain the original order (in which a group has
+--! been seen in the XML database), use ``originalIdx``.
+--!
+--! Omitting ``SORT_GROUPS_BY`` (or setting it to ``nil``) results in groups
+--! being sorted by ``title``.
+--!
+
+SORT_GROUPS_BY = nil
+
+
+--[[!
+	By default, the page for the global namespace page will be called
+	"Global Namespace" and will contain no description except that for the
+	global compounds and members.
+
+	It's possible to override this behaviour by defining an auxillary compound
+	(page or group) with a special ``id``; this page/group may contain a
+	user-defined title,  a brief description and a detailed description. Use
+	``GLOBAL_AUX_COMPOUND_ID`` to define this special id.
+
+	.. note::
+
+		To make sure you use the correct **Doxygen** XML ID of the group/page,
+		find the definition of the group in one of ``.xml`` files and copy
+		the value of ``id`` attribute.
+
+		For example, if the group was declared as ``\defgroup global`` then
+		the its ``id`` will probably be either ``group_<your-group-name>`` or
+		``group__<your-group-name>``.
+]]
+
+GLOBAL_AUX_COMPOUND_ID = "group_global"
+
+--[[!
+	Doxyrest offers a workaround for the lack of footnotes in Doxygen by
+	getting documentation blocks for specially named pseudo-members and
+	converting those into footnotes.
+
+	``FOOTNOTE_MEMBER_PREFIX`` specifies the name prefix for such
+	pseudo-members. If it is set to ``nil`` or an empty string, Doxyrest
+	will not attempt to convert any members to footnotes.
+
+	\sa :ref:`footnotes`
+]]
+
+FOOTNOTE_MEMBER_PREFIX = nil
+
+--!
+--! Specify the main language of your project; this string will be used for
+--! the reStructuredText ``.. code-block::`` sections and for conditional
+--! formatting of module item declarations.
+--!
+
+LANGUAGE = cpp
+
+--!
+--! Convert ``\verbatim`` sections in doxy-comments to ``.. code-block::``
+--! sections in the output reStructuredText. The string value of
+--! ``VERBATIM_TO_CODE_BLOCK`` will be used as the language of
+--! ``.. code-block::`` section. By default, it's ``"none"`` which results in
+--! no syntax highlighting. To disable conversion at all, use ``nil``.
+--!
+
+VERBATIM_TO_CODE_BLOCK = "cpp"
+
+--!
+--! If the original doxy comments contain asterisks, they have to be escaped in
+--! reStructuredText (asterisks are used to mark **bold** or *italic* blocks).
+--!
+
+ESCAPE_ASTERISKS = true
+
+--!
+--! If the original doxy comments contain pipe characters ``|``, they have to be
+--! escaped in reStructuredText (pipes are used for substitution references).
+--!
+
+ESCAPE_PIPES = false
+
+--!
+--! If the original doxy comments contain trailingasterisks, they have to be
+--! escaped in reStructuredText (trailing underscores are used for internal
+--! links).
+--!
+
+ESCAPE_TRAILING_UNDERSCORES = false
+
+--!
+--! Exclude items declared in specific locations. Use a regular expression to
+--! define a mask of directories/source files to completely exclude from the
+--! final documentation. For example, ``.*/impl/.*lua$`` will exclude all
+--! ``.lua`` files located in ``impl/`` directory.
+--!
+
+EXCLUDE_LOCATION_PATTERN = nil
+
+--!
+--! Exclude variables and functions without any documentation (no doxy-comments).
+--!
+
+EXCLUDE_UNDOCUMENTED_ITEMS = true
+
+--!
+--! \subgroup
+--!
+--! By default, Doxyrest tries to translate Doxygen ``\section``,
+--! ``\subsection``, and ``\subsubsection`` commands (as well as the
+--! ``<heading level="n">`` blocks generated by Doxygen from Markdown titles
+--! inside comments) into reStructuredText titles.
+--!
+--! This sometimes leads to problems because Doxygen headings may appear outside
+--! of the global scope (e.g. inside lists) while reStructuredText titles are
+--! only allowed at the global scope. Another issue is Doxygen headings may
+--! yield inconsistent title structure (e.g. a title level 1 followed by level
+--! 3).
+--!
+--! If you run into these issues, use ``SECTION_TO_RUBRIC`` or
+--! ``HEADING_TO_RUBRIC``  to always convert Doxygen sections or ``<heading>``
+--! blocks or into reStructuredText ``.. rubric::`` directives. This yields
+--! uni-level headings, but solves both aforementioned problems.
+--!
+
+SECTION_TO_RUBRIC = false
+HEADING_TO_RUBRIC = false
+
+--[[!
+	By default, Doxyrest frames build a Python dictionary to be used later on by
+	the ``:cref:`` (code-reference) role. This database maps language-specific
+	qualified names of items to their IDs.
+
+	This, together with setting the Sphinx ``default_role`` to ``cref``, allows
+	to conveniently reference items from Doxy-comments or regular ``.rst`` files
+	as such:
+
+	.. code-block:: none
+
+		The `ui::Dialog` class is a base to the `ui::FileDialog` class.
+
+	However, if this facility is not used, building (and loading) of the
+	cref-database can be omitted to save both space and time.
+]]
+
+CREF_DB = false
+
+--[[!
+	Exclude items with higher protection level than ``PROTECTION_FILTER``:
+
+		1. ``public``
+		2. ``protected``
+		3. ``private``
+		4. ``package``
+
+	By default, only public items are included into documentation.
+]]
+
+PROTECTION_FILTER = "public"
+
+--!
+--! In many projects empty defines are *only* used as include-guards (and as
+--! such, should be excluded from the documentation). If this is not the case
+--! and empty defines should be kept in the final documentation, change this
+--! setting to ``false``.
+--!
+
+EXCLUDE_EMPTY_DEFINES = true
+
+--!
+--! If non-``nil``, each define will be checked using this regular expression
+--! and if its name matches, this define will be excluded from the documentation.
+--!
+
+EXCLUDE_DEFINE_PATTERN = nil
+
+--!
+--! Usually providing documentation blocks for default constructors is
+--! not necessary (as to avoid redundant meaningless "Constructs a new object"
+--! paragraphs). Change this to ``false`` if default constructors *should* be
+--! included.
+--!
+
+EXCLUDE_DEFAULT_CONSTRUCTORS = true
+
+--!
+--! Usually providing documentation blocks for a destructors is
+--! not necessary (as to avoid redundant meaningless "Destructs an object"
+--! paragraphs). Change this to ``false`` if destructors *should* be
+--! included.
+--!
+
+EXCLUDE_DESTRUCTORS = true
+
+--[[!
+	Usually providing documentation blocks for primitive C typedefs such as:
+
+	.. code-block:: C
+
+		typedef struct S S;
+
+	is not necessary. Change this to ``false`` if such typedefs *should* be
+	included.
+]]
+
+EXCLUDE_PRIMITIVE_TYPEDEFS = false
+
+--!
+--! For a base class/struct, show all the types directly derived from it.
+--!
+
+SHOW_DIRECT_DESCENDANTS = true
+
+--[[!
+	\subgroup
+
+	Insert space between function name and parameter list like this:
+
+	.. code-block:: C
+
+		void foo ();
+
+	By default, ``PRE_PARAM_LIST_SPACE`` is ``false`` which yields:
+
+	.. code-block:: C
+
+		void foo();
+]]
+
+PRE_PARAM_LIST_SPACE = false
+PRE_OPERATOR_NAME_SPACE = true
+PRE_OPERATOR_PARAM_LIST_SPACE = true
+
+--[[!
+	Insert a new line before the body of a enum, struct, class, etc.
+
+	When ``PRE_BODY_NL`` is ``false``:
+
+	.. code-block:: cpp
+
+		class MyClass {
+			...
+		}
+
+	When ``PRE_BODY_NL`` is ``true``:
+
+	.. code-block:: cpp
+
+		class MyClass
+		{
+			...
+		}
+]]
+
+PRE_BODY_NL = true
+
+--[[!
+	Use multi-line parameter lists in function declarations if parameter count is
+	greater than this threshold. ``nil`` means don't use parameter count
+	threshold.
+
+	For example, when ``ML_PARAM_LIST_COUNT_THRESHOLD`` is ``2``, the function
+	declarations will look as such:
+
+	.. code-block:: C
+
+		void fn0();
+		void fn1(int a);
+		void fn2(int a, int b);
+
+		void fn3(
+			int a,
+			int b
+			int c
+			);
+]]
+
+ML_PARAM_LIST_COUNT_THRESHOLD = nil
+
+--[[!
+	Use multi-line parameter lists in function declarations if single-line
+	declaration length parameter count is greater than this threshold.
+	``nil`` means don't use length threshold.
+
+	Similar to ``ML_PARAM_LIST_COUNT_THRESHOLD``, but the threshold parameter
+	here is *declaration length* and not *declaration parameter count*.
+
+	Example:
+
+	.. code-block:: C
+
+		void foo(int a, int b, int c);
+
+		void bar(
+			int veryLongParameterName,
+			int anotherVeryLongParameterName
+			);
+
+]]
+
+ML_PARAM_LIST_LENGTH_THRESHOLD = 80
+
+--[[!
+	Use multi-line specifier-modifier lists in function declarations, i.e
+	allocate a dedicated line for each type specifier/morifier.
+
+	For example, when ``ML_SPECIFIER_MODIFIER_LIST`` is ``true``, the function
+	declarations will look as such:
+
+	.. code-block:: C
+
+		void
+		foo();
+
+		static
+		bool
+		__cdecl
+		bar(int a);
+]]
+
+ML_SPECIFIER_MODIFIER_LIST = false
+
+--[[!
+	Use the new C++11 syntax for ``typedef``-s:
+
+	When ``TYPEDEF_TO_USING`` is ``false``:
+
+	.. code-block:: cpp
+
+		typedef unsigned int uint_t;
+		typedef void Foo(int);
+		typedef void (*FooPtr)(int);
+
+	When ``TYPEDEF_TO_USING`` is ``true``:
+
+	.. code-block:: cpp
+
+		using uint_t = unsigned int;
+		using Foo typedef void (int);
+		using FooPtr typedef void (*)(int);
+]]
+
+TYPEDEF_TO_USING = false
+
+--[[!
+	Sometimes, it's required to redirect a Doxygen link to some external location.
+	In this case, add an entry to ``EXTERNAL_CREF_DB`` with the target URL, e.g.:
+
+	.. code-block:: lua
+
+		EXTERNAL_CREF_DB =
+		{
+			[ "jnc.Scheduler" ] = "https://vovkos.github.io/jancy/stdlib/class_jnc_Scheduler.html",
+			[ "strlen"] = "https://vovkos.github.io/jancy/stdlib/group_crt.html#doxid-function-strlen"
+
+			-- ...
+		}
+
+	The key of the map is an ``importid`` attribute. This is a non-standard Doxygen
+	attribute; Jancy compiler generates is when a referenced item is contained in an
+	imported extensions library (``.jncx``)
+]]
+
+EXTERNAL_CREF_DB = {}
+
+--[[!
+	Usually providing documentation blocks for Lua ``local`` variables or
+	functions is not desired. Change this to ``false`` if local items
+	*should* be included in the final documentation.
+]]
+
+EXCLUDE_LUA_LOCALS = true
+
+--! @}
diff --git a/docs/doxyrest-sphinx/conf/doxyrest-docutils.conf.in b/docs/doxyrest-sphinx/conf/doxyrest-docutils.conf.in
new file mode 100644
index 00000000000..54d8314e40f
--- /dev/null
+++ b/docs/doxyrest-sphinx/conf/doxyrest-docutils.conf.in
@@ -0,0 +1,9 @@
+[general]
+footnote_backlinks = false
+
+[restructuredtext parser]
+trim_footnote_reference_space = false
+tab_width = %tab_width%
+
+[html4css1 writer]
+footnote_references = superscript
diff --git a/docs/doxyrest-sphinx/cpplexer.py b/docs/doxyrest-sphinx/cpplexer.py
new file mode 100644
index 00000000000..4d3a8f85f0b
--- /dev/null
+++ b/docs/doxyrest-sphinx/cpplexer.py
@@ -0,0 +1,315 @@
+#...............................................................................
+#
+#  This file is part of the Doxyrest toolkit.
+#
+#  Doxyrest is distributed under the MIT license.
+#  For details see accompanying license.txt file,
+#  the public copy of which is also available at:
+#  http://tibbo.com/downloads/archive/doxyrest/license.txt
+#
+#...............................................................................
+
+# -*- coding: utf-8 -*-
+"""
+    pygments.lexers.c_cpp
+    ~~~~~~~~~~~~~~~~~~~~~
+
+    Lexers for C/C++ languages.
+
+    :copyright: Copyright 2006-2015 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import re
+
+from pygments.lexer import RegexLexer, include, bygroups, using, \
+    this, inherit, default, words
+from pygments.util import get_bool_opt
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Punctuation, Error
+from sphinx.highlighting import lexers
+
+
+class CFamilyLexer(RegexLexer):
+    """
+    For C family source code.  This is used as a base class to avoid repetitious
+    definitions.
+    """
+
+    #: optional Comment or Whitespace
+    _ws = r'(?:\s|//.*?\n|/[*].*?[*]/)+'
+
+    # The trailing ?, rather than *, avoids a geometric performance drop here.
+    #: only one /* */ style comment
+    _ws1 = r'\s*(?:/[*].*?[*]/\s*)?'
+
+    tokens = {
+        'whitespace': [
+            # preprocessor directives: without whitespace
+            ('^#if\s+0', Comment.Preproc, 'if0'),
+            ('^#', Comment.Preproc, 'macro'),
+            # or with whitespace
+            ('^(' + _ws1 + r')(#if\s+0)',
+             bygroups(using(this), Comment.Preproc), 'if0'),
+            ('^(' + _ws1 + ')(#)',
+             bygroups(using(this), Comment.Preproc), 'macro'),
+            (r'\n', Text),
+            (r'\s+', Text),
+            (r'\\\n', Text),  # line continuation
+            (r'//(\n|(.|\n)*?[^\\](\n|$))', Comment.Single),
+            (r'/(\\\n)?[*](.|\n)*?[*](\\\n)?/', Comment.Multiline),
+        ],
+        'statements': [
+            (r'L?"', String, 'string'),
+            (r"L?'", String.Char, 'char'),
+            (r'(\d+\.\d*|\.\d+|\d+)[eE][+-]?\d+[LlUu]*', Number.Float),
+            (r'(\d+\.\d*|\.\d+|\d+[fF])[fF]?', Number.Float),
+            (r'0x[0-9a-fA-F]+[LlUu]*', Number.Hex),
+            (r'0[0-7]+[LlUu]*', Number.Oct),
+            (r'\d+[LlUu]*', Number.Integer),
+            (r'\*/', Error),
+            (r'[~!%^&*+=|?:<>/-]', Operator),
+            (r'[()\[\],.]', Punctuation),
+            (words(('auto', 'break', 'case', 'const', 'continue', 'default', 'do',
+                    'else', 'enum', 'extern', 'for', 'goto', 'if', 'register',
+                    'restricted', 'return', 'sizeof', 'static', 'struct',
+                    'switch', 'typedef', 'union', 'volatile', 'while'),
+                   suffix=r'\b'), Keyword),
+            (r'(bool|int|long|float|short|double|char|unsigned|signed|void)\b',
+             Keyword.Type),
+            (words(('inline', '_inline', '__inline', 'naked', 'restrict',
+                    'thread', 'typename'), suffix=r'\b'), Keyword.Reserved),
+            # Vector intrinsics
+            (r'(__m(128i|128d|128|64))\b', Keyword.Reserved),
+            # Microsoft-isms
+            (words((
+                'asm', 'int8', 'based', 'except', 'int16', 'stdcall', 'cdecl',
+                'fastcall', 'int32', 'declspec', 'finally', 'int64', 'try',
+                'leave', 'wchar_t', 'w64', 'unaligned', 'raise', 'noop',
+                'identifier', 'forceinline', 'assume'),
+                prefix=r'__', suffix=r'\b'), Keyword.Reserved),
+            (r'(true|false|NULL)\b', Name.Builtin),
+            (r'([a-zA-Z_]\w*)(\s*)(:)(?!:)', bygroups(Name.Label, Text, Punctuation)),
+            ('[a-zA-Z_]\w*', Name),
+        ],
+        'root': [
+            include('whitespace'),
+            # functions
+            (r'((?:[\w*\s])+?(?:\s|[*]))'  # return arguments
+             r'([a-zA-Z_]\w*)'             # method name
+             r'(\s*\([^;]*?\))'            # signature
+             r'([^;{]*)(\{)',
+             bygroups(using(this), Name.Function, using(this), using(this),
+                      Punctuation),
+             'function'),
+            # function declarations
+            (r'((?:[\w*\s])+?(?:\s|[*]))'  # return arguments
+             r'([a-zA-Z_]\w*)'             # method name
+             r'(\s*\([^;]*?\))'            # signature
+             r'([^;]*)(;)',
+             bygroups(using(this), Name.Function, using(this), using(this),
+                      Punctuation)),
+            default('statement'),
+        ],
+        'statement': [
+            include('whitespace'),
+            include('statements'),
+            ('[{}]', Punctuation),
+            (';', Punctuation, '#pop'),
+        ],
+        'function': [
+            include('whitespace'),
+            include('statements'),
+            (';', Punctuation),
+            (r'\{', Punctuation, '#push'),
+            (r'\}', Punctuation, '#pop'),
+        ],
+        'string': [
+            (r'"', String, '#pop'),
+            (r'\\([\\abefnrtv"\']|x[a-fA-F0-9]{2,4}|'
+             r'u[a-fA-F0-9]{4}|U[a-fA-F0-9]{8}|[0-7]{1,3})', String.Escape),
+            (r'[^\\"\n]+', String),  # all other characters
+            (r'\\\n', String),  # line continuation
+            (r'\\', String),  # stray backslash
+        ],
+        'char': [
+            (r"'", String.Char, '#pop'),
+            (r'\\([\\abefnrtv"\']|x[a-fA-F0-9]{2,4}|'
+             r'u[a-fA-F0-9]{4}|U[a-fA-F0-9]{8}|[0-7]{1,3})', String.Escape),
+            (r"[^\\'\n]+", String.Char),  # all other characters
+            (r'\\\n', String.Char),  # line continuation
+            (r'\\', String.Char),  # stray backslash
+        ],
+        'macro': [
+            (r'(include)(' + _ws1 + ')([^\n]+)', bygroups(Comment.Preproc, Text, Comment.PreprocFile)),
+            (r'[^/\n]+', Comment.Preproc),
+            (r'/[*](.|\n)*?[*]/', Comment.Multiline),
+            (r'//.*?\n', Comment.Single, '#pop'),
+            (r'/', Comment.Preproc),
+            (r'(?<=\\)\n', Comment.Preproc),
+            (r'\n', Comment.Preproc, '#pop'),
+        ],
+        'if0': [
+            (r'^\s*#if.*?(?<!\\)\n', Comment.Preproc, '#push'),
+            (r'^\s*#el(?:se|if).*\n', Comment.Preproc, '#pop'),
+            (r'^\s*#endif.*?(?<!\\)\n', Comment.Preproc, '#pop'),
+            (r'.*?\n', Comment),
+        ]
+    }
+
+    stdlib_types = set((
+        'size_t', 'ssize_t', 'off_t', 'wchar_t', 'ptrdiff_t', 'sig_atomic_t', 'fpos_t',
+        'clock_t', 'time_t', 'va_list', 'jmp_buf', 'FILE', 'DIR', 'div_t', 'ldiv_t',
+        'mbstate_t', 'wctrans_t', 'wint_t', 'wctype_t'))
+    c99_types = set((
+        '_Bool', '_Complex', 'int8_t', 'int16_t', 'int32_t', 'int64_t', 'uint8_t',
+        'uint16_t', 'uint32_t', 'uint64_t', 'int_least8_t', 'int_least16_t',
+        'int_least32_t', 'int_least64_t', 'uint_least8_t', 'uint_least16_t',
+        'uint_least32_t', 'uint_least64_t', 'int_fast8_t', 'int_fast16_t', 'int_fast32_t',
+        'int_fast64_t', 'uint_fast8_t', 'uint_fast16_t', 'uint_fast32_t', 'uint_fast64_t',
+        'intptr_t', 'uintptr_t', 'intmax_t', 'uintmax_t'))
+    linux_types = set((
+        'clockid_t', 'cpu_set_t', 'cpumask_t', 'dev_t', 'gid_t', 'id_t', 'ino_t', 'key_t',
+        'mode_t', 'nfds_t', 'pid_t', 'rlim_t', 'sig_t', 'sighandler_t', 'siginfo_t',
+        'sigset_t', 'sigval_t', 'socklen_t', 'timer_t', 'uid_t'))
+
+    def __init__(self, **options):
+        self.stdlibhighlighting = get_bool_opt(options, 'stdlibhighlighting', True)
+        self.c99highlighting = get_bool_opt(options, 'c99highlighting', True)
+        self.platformhighlighting = get_bool_opt(options, 'platformhighlighting', True)
+        RegexLexer.__init__(self, **options)
+
+    def get_tokens_unprocessed(self, text):
+        for index, token, value in \
+                RegexLexer.get_tokens_unprocessed(self, text):
+            if token is Name:
+                if self.stdlibhighlighting and value in self.stdlib_types:
+                    token = Keyword.Type
+                elif self.c99highlighting and value in self.c99_types:
+                    token = Keyword.Type
+                elif self.platformhighlighting and value in self.linux_types:
+                    token = Keyword.Type
+            yield index, token, value
+
+
+class CLexer(CFamilyLexer):
+    """
+    For C source code with preprocessor directives.
+    """
+    name = 'C'
+    aliases = ['c']
+    filenames = ['*.c', '*.h', '*.idc']
+    mimetypes = ['text/x-chdr', 'text/x-csrc']
+    priority = 0.1
+
+    def __init__(self, **options):
+        CFamilyLexer.__init__(self, **options)
+
+    def analyse_text(text):
+        if re.search('^\s*#include [<"]', text, re.MULTILINE):
+            return 0.1
+        if re.search('^\s*#ifn?def ', text, re.MULTILINE):
+            return 0.1
+
+
+class CppLexer(CFamilyLexer):
+    """
+    For C++ source code with preprocessor directives.
+    """
+    name = 'C++'
+    aliases = ['cpp', 'c++']
+    filenames = ['*.cpp', '*.hpp', '*.c++', '*.h++',
+                 '*.cc', '*.hh', '*.cxx', '*.hxx',
+                 '*.C', '*.H', '*.cp', '*.CPP']
+    mimetypes = ['text/x-c++hdr', 'text/x-c++src']
+    priority = 0.1
+
+    tokens = {
+        'statements': [
+            (words((
+                'asm', 'catch', 'class', 'const_cast', 'delete', 'dynamic_cast', 'explicit',
+                'export', 'friend', 'mutable', 'namespace', 'new', 'operator',
+                'private', 'protected', 'public', 'reinterpret_cast',
+                'restrict', 'static_cast', 'template', 'this', 'throw', 'throws',
+                'try', 'typeid', 'typename', 'using', 'virtual',
+                'constexpr', 'nullptr', 'decltype', 'thread_local',
+                'alignas', 'alignof', 'static_assert', 'noexcept', 'override',
+                'final'), suffix=r'\b'), Keyword),
+            (r'char(16_t|32_t)\b', Keyword.Type),
+            # C++11 raw strings
+            (r'R"\(', String, 'rawstring'),
+            inherit,
+        ],
+        'root': [
+            inherit,
+            # C++ Microsoft-isms
+            (words(('virtual_inheritance', 'uuidof', 'super', 'single_inheritance',
+                    'multiple_inheritance', 'interface', 'event'),
+                   prefix=r'__', suffix=r'\b'), Keyword.Reserved),
+            # Offload C++ extensions, http://offload.codeplay.com/
+            (r'__(offload|blockingoffload|outer)\b', Keyword.Pseudo),
+        ],
+        'rawstring': [
+            (r'\)"', String, '#pop'),
+            (r'[^)]+', String),
+            (r'\)', String),
+        ],
+    }
+
+    def __init__(self, **options):
+        CFamilyLexer.__init__(self, **options)
+
+    def analyse_text(text):
+        if re.search('#include <[a-z_]+>', text):
+            return 0.2
+        if re.search('using namespace ', text):
+            return 0.4
+
+
+class IdlLexer(CppLexer):
+    """
+    For Intefce Definition Language source code.
+    """
+    name = 'IDL'
+    aliases = ['idl']
+    filenames = ['*.idl']
+    mimetypes = ['text/x-idl']
+    priority = 0.1
+
+    tokens = {
+        'statements': [
+            (words((
+                'interface', 'protocol', 'exception', 'service', 'singleton', 'module',
+                'any', 'boolean', 'string', 'sequence'), suffix=r'\b'), Keyword),
+            inherit,
+        ],
+        'root': [
+            inherit,
+        ],
+    }
+
+    def __init__(self, **options):
+        CppLexer.__init__(self, **options)
+
+
+def setup(app):
+    options = {}
+    options['stripnl'] = False
+    options['ensurenl'] = False
+
+    c_lexer = CLexer(**options)
+    cpp_lexer = CppLexer(**options)
+    idl_lexer = IdlLexer(**options)
+
+    lexers['c'] = c_lexer
+    lexers['cpp'] = cpp_lexer
+    lexers['cxx'] = cpp_lexer
+    lexers['c++'] = cpp_lexer
+    lexers['c++'] = cpp_lexer
+    lexers['idl'] = idl_lexer
+
+    return {
+        'version': 'builtin',
+        'parallel_read_safe': True,
+        'parallel_write_safe': True,
+    }
diff --git a/docs/doxyrest-sphinx/css/doxyrest-pygments.css b/docs/doxyrest-sphinx/css/doxyrest-pygments.css
new file mode 100644
index 00000000000..e06f2f28b86
--- /dev/null
+++ b/docs/doxyrest-sphinx/css/doxyrest-pygments.css
@@ -0,0 +1,21 @@
+.highlight .k  { color: #902000 !important; font-weight: bold !important } /* Keyword */
+.highlight .kc { color: #902000 !important; font-weight: bold !important } /* Keyword.Constant */
+.highlight .kd { color: #902000 !important; font-weight: bold !important } /* Keyword.Declaration */
+.highlight .kn { color: #902000 !important; font-weight: bold !important } /* Keyword.Namespace */
+.highlight .kp { color: #902000 !important; font-weight: bold !important } /* Keyword.Pseudo */
+.highlight .kr { color: #902000 !important; font-weight: bold !important } /* Keyword.Reserved */
+.highlight .kt { color: #902000 !important; font-weight: bold !important } /* Keyword.Type */
+
+.highlight .nb { color: #902000 !important; font-weight: bold !important } /* Name.Builtin */
+
+.highlight .na { color: #000000 !important; font-weight: normal !important } /* Name.Attribute */
+.highlight .nc { color: #000000 !important; font-weight: normal !important } /* Name.Class */
+.highlight .no { color: #000000 !important; font-weight: normal !important } /* Name.Constant */
+.highlight .nd { color: #000000 !important; font-weight: normal !important } /* Name.Decorator */
+.highlight .ni { color: #000000 !important; font-weight: normal !important } /* Name.Entity */
+.highlight .ne { color: #000000 !important; font-weight: normal !important } /* Name.Exception */
+.highlight .nf { color: #000000 !important; font-weight: normal !important } /* Name.Function */
+.highlight .nl { color: #000000 !important; font-weight: normal !important } /* Name.Label */
+.highlight .nn { color: #000000 !important; font-weight: normal !important } /* Name.Namespace */
+.highlight .nt { color: #000000 !important; font-weight: normal !important } /* Name.Tag */
+.highlight .nv { color: #000000 !important; font-weight: normal !important } /* Name.Variable */
diff --git a/docs/doxyrest-sphinx/css/doxyrest-sphinx_rtd_theme.css b/docs/doxyrest-sphinx/css/doxyrest-sphinx_rtd_theme.css
new file mode 100644
index 00000000000..aee0b912d82
--- /dev/null
+++ b/docs/doxyrest-sphinx/css/doxyrest-sphinx_rtd_theme.css
@@ -0,0 +1,51 @@
+code { font-family: 'Consolas', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace !important; }
+
+pre {
+	font-family: 'Consolas', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace !important;
+	border: 1px dashed #ccc !important;
+	background-color: #ffffe0 !important;
+}
+
+div[class^="highlight"] {
+	border: none !important;
+}
+
+pre.doxyrest-overview-code-block {
+	border: 1px solid #ccc !important;
+	background-color: #fff !important;
+}
+
+pre.doxyrest-overview-inherited-code-block {
+	border: 1px solid #ccc !important;
+	background-color: #eee !important;
+}
+
+pre.doxyrest-title-code-block {
+	font-size: larger !important;
+	border: none !important;
+	border-top: solid #ccc !important;
+	background-color: #f0f0f0 !important;
+}
+
+code a { color:#000000 !important; text-decoration: underline !important }
+code a:visited:hover { color: #3091d1 !important }
+code a:visited { color: #000000 !important }
+
+pre a { color: #000000 !important; text-decoration: underline !important }
+pre a:visited:hover { color: #3091d1 !important }
+pre a:visited { color: #000000 !important }
+
+td {
+	white-space: normal !important;
+}
+
+.doxyrest-target-highlight {
+    animation-name: doxyrest-target-highlight-flash;
+    animation-duration: 2s;
+}
+
+@keyframes doxyrest-target-highlight-flash {
+    0%   { box-shadow: none; }
+    25%  { box-shadow: 0 0 8px 8px #8cf, 0 0 8px 8px #8cf inset; }
+    100% { box-shadow: none; }
+}
diff --git a/docs/doxyrest-sphinx/css/doxyrest-sphinxdoc.css b/docs/doxyrest-sphinx/css/doxyrest-sphinxdoc.css
new file mode 100644
index 00000000000..e74669c93a6
--- /dev/null
+++ b/docs/doxyrest-sphinx/css/doxyrest-sphinxdoc.css
@@ -0,0 +1,36 @@
+code { font-family: 'Consolas', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace !important }
+
+pre {
+    font-family: 'Consolas', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace !important;
+    border: 1px dashed #ccc !important;
+    background-color: #ffffe0 !important;
+}
+
+pre.doxyrest-overview-code-block {
+    border: 1px solid #ccc !important;
+    background-color: #f8f8f8 !important;
+}
+
+pre.doxyrest-overview-inherited-code-block {
+    border: 1px solid #ccc !important;
+    background-color: #eee !important;
+}
+
+pre.doxyrest-title-code-block {
+	font-size: larger !important;
+    border: 1px solid #ccc !important;
+    background-color: #f8f8f8 !important;
+}
+
+code a { color: #333 !important }
+
+.doxyrest-target-highlight {
+    animation-name: doxyrest-target-highlight-flash;
+    animation-duration: 2s;
+}
+
+@keyframes doxyrest-target-highlight-flash {
+    0%   { box-shadow: none; }
+    25%  { box-shadow: 0 0 8px 8px #fda, 0 0 8px 8px #fda inset; }
+    100% { box-shadow: none; }
+}
diff --git a/docs/doxyrest-sphinx/doxyrest.py b/docs/doxyrest-sphinx/doxyrest.py
new file mode 100644
index 00000000000..06da3dae585
--- /dev/null
+++ b/docs/doxyrest-sphinx/doxyrest.py
@@ -0,0 +1,494 @@
+#...............................................................................
+#
+#  This file is part of the Doxyrest toolkit.
+#
+#  Doxyrest is distributed under the MIT license.
+#  For details see accompanying license.txt file,
+#  the public copy of which is also available at:
+#  http://tibbo.com/downloads/archive/doxyrest/license.txt
+#
+#...............................................................................
+
+import os
+import re
+import warnings
+from packaging import version
+from docutils import nodes
+from docutils.parsers.rst import Directive, directives
+from docutils.transforms import Transform
+from sphinx import __version__ as sphinx_version_string, roles, addnodes, config
+from sphinx.directives.other import Include
+from sphinx.domains import Domain
+
+#...............................................................................
+#
+#  utils
+#
+
+sphinx_version = version.parse(sphinx_version_string)
+this_dir = os.path.dirname(os.path.realpath(__file__))
+url_re_prog = re.compile('(ftp|https?)://')
+crefdb = {}
+
+def get_cref_target(text):
+    if text in crefdb:
+        return crefdb[text]
+
+    warnings.warn('target not found for cref: ' + text, Warning, 2)
+    return None
+
+# Sphinx.add_javascript was renamed to add_js_file in 1.8.0
+# Sphinx.add_stylesheet was renamed to add_css_file in 1.8.0
+
+if sphinx_version >= version.parse('1.8.0'):
+    def add_js_file(app, filename):
+        app.add_js_file(filename)
+
+    def add_css_file(app, filename):
+        app.add_css_file(filename)
+else:
+    def add_js_file(app, filename):
+        app.add_javascript(filename)
+
+    def add_css_file(app, filename):
+        app.add_stylesheet(filename)
+
+#...............................................................................
+#
+#  Sphinx nodes
+#
+
+class HighlightedText(nodes.General, nodes.TextElement):
+    def __init__(self, *args, **kwargs):
+        nodes.TextElement.__init__(self, *args, **kwargs)
+
+
+def visit_highlighted_text_node(self, node):
+    text_node = node.children[0]
+    language = node['language']
+
+    if language == 'none':
+        self.body.append(text_node)
+    else:
+        options = {}
+        options['stripnl'] = False
+        options['ensurenl'] = False
+
+        text = text_node
+
+        # pygments always ensures a newline at the end and there is no way to tell
+        # if the newline is originating from source or was it inserted by pygments
+        # as a workaround, duplicate trailing newline and then strip it
+
+        if text.endswith('\n'):
+            text = text + '\n'
+
+        highlighted = self.highlighter.highlight_block(text, language, options)
+
+        match = re.search('<pre>(.*)\n</pre>', highlighted, re.DOTALL)
+        if not match:
+            self.body.append(text_node)
+        else:
+            self.body.append(match.group(1))
+    raise nodes.SkipNode
+
+
+
+def visit_doxyrest_literalblock_node(self, node):
+    classes = ' '.join(node.attributes['classes'])
+    self.body.append('<div class="{}"><div class="highlight"><pre>'.format(classes))
+
+
+def depart_doxyrest_literalblock_node(self, node):
+    self.body.append('</pre></div></div>')
+
+
+def create_ref_node(raw_text, text, target):
+    if not target:
+        return nodes.Text(text, text)
+
+    if url_re_prog.match(target):
+        return nodes.reference(raw_text, text, refuri=target)
+
+    node = addnodes.pending_xref(raw_text)
+    node['reftype'] = 'ref'
+    node['refdomain'] = 'std'
+    node['reftarget'] = target
+    node['refwarn'] = True
+    node['refexplicit'] = True
+    node += nodes.Text(text, text)
+    return node
+
+
+def create_target_node(raw_text, text, target, highlight_language, lineno, document, extra_classes=[]):
+    node = nodes.target(raw_text, '')
+    node['names'] += [target]
+    node['classes'] += extra_classes
+    node.line = lineno
+
+    if text:
+        if highlight_language:
+            node += HighlightedText(text, text, language=highlight_language)
+        else:
+            node += nodes.Text(text, text)
+
+    document.note_explicit_target(node)
+    return node
+
+
+#...............................................................................
+#
+#  Sphinx directives
+#
+
+
+class DoxyrestLiteralBlock(nodes.literal_block):
+    pass
+
+
+class RefCodeBlock(Directive):
+    has_content = True
+    required_arguments = 0
+    optional_arguments = 1
+    final_argument_whitespace = False
+
+    option_spec = {
+        'linenos': directives.flag,
+        'dedent': int,
+        'lineno-start': int,
+        'emphasize-lines': directives.unchanged_required,
+        'caption': directives.unchanged_required,
+        'class': directives.class_option,
+        'name': directives.unchanged,
+    }
+
+    def __init__(self, *args, **kwargs):
+        Directive.__init__(self, *args, **kwargs)
+
+        role_re_src = '(:ref:|:cref:|:target:)'
+        if self.state.document.settings.env.config.default_role == 'cref':
+            role_re_src += '?' # explicit role is optional
+
+        role_re_src += '`(.+?)(\s*<([^<>]*)>)?`'
+        self.role_re_prog = re.compile(role_re_src)
+        self.ws_re_prog = re.compile('\s+')
+
+    def run(self):
+        config = self.state.document.settings.env.config
+        code = u'\n'.join(self.content)
+        pos = 0
+        node = DoxyrestLiteralBlock('.', '') # single char to prevent sphinx from trying to highlight it
+        node['classes'] += ['highlight']    # we are stripping pyments-generated <div>
+        node['classes'] += self.options.get('class', [])
+
+        if len(self.arguments) >= 1:
+            language = self.arguments[0]
+        else:
+            language = config.highlight_language
+
+        while True:
+            match = self.role_re_prog.search(code, pos)
+            if match is None:
+                plain_text = code[pos:]
+                if plain_text != "":
+                    node += HighlightedText(plain_text, plain_text, language=language)
+                break
+
+            plain_text = code[pos:match.start()]
+            if plain_text != "":
+                node += HighlightedText(plain_text, plain_text, language=language)
+
+            raw_text = match.group(0)
+            role = match.group(1)
+            text = match.group(2)
+            target = match.group(4)
+
+            pos = match.end()
+
+            if text:
+                text = text.replace('\\<', '<') # restore escaped left-chevron
+
+            if role == ':target:':
+                if not target:
+                    target = text
+                    text = None
+                    ws_match = self.ws_re_prog.match(code, pos)
+                    if ws_match: # drop whitespace right after empty target
+                        pos = ws_match.end()
+
+                new_node = create_target_node(
+                    raw_text,
+                    text,
+                    target,
+                    language,
+                    None,
+                    self.state.document,
+                    ['doxyrest-code-target']
+                    )
+
+            else:
+                if not role or role == ':cref:':
+                    target = get_cref_target(target if target else text)
+                elif not target:
+                    target = text
+
+                new_node = create_ref_node(raw_text, text, target)
+
+            node += new_node
+
+        self.add_name(node)
+        return [node]
+
+#...............................................................................
+#
+#  Sphinx transforms
+#
+
+class RefTransform(Transform):
+    default_priority = 100
+
+    node_classes = {
+        nodes.literal,
+        nodes.strong,
+        nodes.emphasis
+    }
+
+    def __init__(self, document, startnode=None):
+        Transform.__init__(self, document, startnode)
+
+        re_src = '(:c?ref:)'
+        if document.settings.env.config.default_role == 'cref':
+            re_src += '?' # explicit role is optional
+
+        re_src += '`(.+?)(\s*<([^<>]*)>)?`'
+        self.re_prog = re.compile(re_src)
+
+    @staticmethod
+    def node_filter(node):
+        for node_class in RefTransform.node_classes:
+            if isinstance (node, node_class):
+                return node['classes'] == []
+
+        return False
+
+    def apply(self):
+        for node in self.document.traverse(RefTransform.node_filter):
+            code = node.astext()
+            node.children = []
+            pos = 0
+
+            while True:
+                match = self.re_prog.search(code, pos)
+                if match is None:
+                    plain_text = code[pos:]
+                    if plain_text != "":
+                        node += nodes.Text(plain_text, plain_text)
+                    break
+
+                plain_text = code[pos:match.start()]
+                if plain_text != "":
+                    node += nodes.Text(plain_text, plain_text)
+
+                raw_text = match.group(0)
+                role = match.group(1)
+                text = match.group(2)
+                target = match.group(4)
+
+                if not role or role == ':cref:':
+                    target = get_cref_target(text)
+
+                node += create_ref_node(raw_text, text, target)
+                pos = match.end()
+
+
+#...............................................................................
+#
+#  Sphinx roles
+#
+
+def cref_role(typ, raw_text, text, lineno, inliner, options={}, content=[]):
+    target = get_cref_target(text)
+
+    if text.find(' ') == -1:
+        node = nodes.literal(raw_text, '')
+    else:
+        node = nodes.inline(raw_text, '')
+
+    node['classes'] += ['doxyrest-cref']
+    node += create_ref_node(raw_text, text, target)
+    return [node], []
+
+def target_role(typ, raw_text, text, lineno, inliner, options={}, content=[]):
+    node = create_target_node(raw_text, None, text, None, lineno, inliner.document)
+    return [node], []
+
+
+#...............................................................................
+#
+#  Doxyrest domain -- so that the cref-database is checked for :any: refs
+#
+
+class DoxyrestDomain(Domain):
+    name = 'doxyrest'
+    label = 'Doxyrest'
+
+    def merge_domaindata(self, docnames, otherdata):
+        pass # as to avoid errors on parallel builds
+
+    def resolve_any_xref(self, env, fromdocname, builder, target, node, contnode):
+        cref_target = get_cref_target(target)
+        if not cref_target:
+            return []
+
+        std = env.get_domain('std')
+        node['refexplicit'] = True
+        resolved_node = std.resolve_xref(env, fromdocname, builder, 'ref', cref_target, node, contnode)
+        if not resolved_node: # shouldn't really happen
+            return []
+
+        result_node = nodes.literal(target, '')
+        result_node += resolved_node
+        return [('std:ref', result_node)]
+
+
+#...............................................................................
+#
+#  Sphinx source inputs --  a workaround for sphinx-2.0.1 (and below)
+#  completely ignoring docutils.conf:tab_width
+#
+
+is_sphinx_tab_aware = sphinx_version >= version.parse('2.1.0')
+
+if not is_sphinx_tab_aware:
+    from sphinx.io import SphinxBaseFileInput, SphinxRSTFileInput
+    from docutils.statemachine import StringList, string2lines
+
+
+    class TabAwareSphinxRSTFileInput(SphinxRSTFileInput):
+        def read(self):
+            # type: () -> StringList
+            inputstring = SphinxBaseFileInput.read(self)
+            tab_width = self.env.config.doxyrest_tab_width
+            lines = string2lines(inputstring, convert_whitespace=True, tab_width=tab_width)
+
+            content = StringList()
+            for lineno, line in enumerate(lines):
+                content.append(line, self.source_path, lineno)
+
+            if self.env.config.rst_prolog:
+                self.prepend_prolog(content, self.env.config.rst_prolog)
+            if self.env.config.rst_epilog:
+                self.append_epilog(content, self.env.config.rst_epilog)
+
+            return content
+
+
+    class TabAwareInclude(Include):
+        def run(self):
+            # update tab_width setting
+            self.state.document.settings.tab_width = \
+            self.state.document.settings.env.config.doxyrest_tab_width
+            return Include.run(self)
+
+#...............................................................................
+#
+#  Sphinx events
+#
+
+def on_builder_inited(app):
+    app.config.html_static_path += [
+        this_dir + '/css/doxyrest-pygments.css',
+        this_dir + '/js/target-highlight.js'
+    ]
+
+    add_css_file(app, 'doxyrest-pygments.css')
+    add_js_file(app, 'target-highlight.js')
+
+    supported_themes = {
+        'sphinx_rtd_theme',
+        'sphinxdoc'
+    }
+
+    if app.config.html_theme in supported_themes:
+        css_file = 'doxyrest-' + app.config.html_theme + '.css'
+        app.config.html_static_path += [this_dir + '/css/' + css_file];
+        add_css_file(app, css_file);
+
+    for basedir, dirnames, filenames in os.walk(app.srcdir):
+        if 'crefdb.py' in filenames:
+            crefdb_path = os.path.join(basedir, 'crefdb.py')
+            src = open(crefdb_path).read()
+            ns = {}
+            exec(src, ns)
+            new_crefdb = ns['crefdb']
+            if isinstance(new_crefdb, dict):
+                global crefdb
+                crefdb.update(new_crefdb)
+
+def on_config_inited(app, config):
+
+    # prepare and register doxyrest-specific docutils.conf
+
+    # it's OK to put yet another docutils.conf into your config dir to
+    # override settings if necessary
+
+    docutils_conf_in_path = this_dir + '/conf/doxyrest-docutils.conf.in'
+    docutils_conf_path = app.doctreedir + '/doxyrest-docutils.conf'
+
+    src_file = open(docutils_conf_in_path, 'r')
+    contents = src_file.read()
+    contents = contents.replace('%tab_width%', str(config.doxyrest_tab_width))
+    src_file.close()
+
+    if not os.path.exists(app.doctreedir):
+        os.makedirs(app.doctreedir)
+
+    dst_file = open(docutils_conf_path, 'w')
+    dst_file.write(contents)
+    dst_file.close()
+
+    if 'DOCUTILSCONFIG' in os.environ:
+        prev_docutils_conf = os.environ['DOCUTILSCONFIG']
+        os.environ['DOCUTILSCONFIG'] = docutils_conf_path + os.pathsep + prev_docutils_conf
+    else:
+        os.environ['DOCUTILSCONFIG'] = docutils_conf_path
+
+#...............................................................................
+#
+#  Doxyrest extenstion setup
+#
+
+
+def setup(app):
+    app.add_domain(DoxyrestDomain)
+
+    app.add_node(
+        HighlightedText,
+        html=(visit_highlighted_text_node, None),
+        latex=(visit_highlighted_text_node, None)
+        )
+
+    app.add_node(
+        DoxyrestLiteralBlock,
+        html=(visit_doxyrest_literalblock_node, depart_doxyrest_literalblock_node),
+        latex=(visit_doxyrest_literalblock_node, depart_doxyrest_literalblock_node)
+    )
+
+    app.add_role('cref', cref_role)
+    app.add_role('target', target_role)
+    app.add_config_value('doxyrest_cref_file', default=None, rebuild=True)
+    app.add_config_value('doxyrest_tab_width', default=4, rebuild=True)
+    directives.register_directive('ref-code-block', RefCodeBlock)
+    app.add_transform(RefTransform)
+    app.connect('builder-inited', on_builder_inited)
+    app.connect('config-inited', on_config_inited)
+
+    if not is_sphinx_tab_aware:
+        app.registry.source_inputs['restructuredtext'] = TabAwareSphinxRSTFileInput
+        directives.register_directive('include', TabAwareInclude)
+
+    return {
+        'version': 'builtin',
+        'parallel_read_safe': True,
+        'parallel_write_safe': True,
+    }
diff --git a/docs/doxyrest-sphinx/js/target-highlight.js b/docs/doxyrest-sphinx/js/target-highlight.js
new file mode 100644
index 00000000000..76725eb320a
--- /dev/null
+++ b/docs/doxyrest-sphinx/js/target-highlight.js
@@ -0,0 +1,68 @@
+//..............................................................................
+//
+//  This file is part of the Doxyrest toolkit.
+//
+//  Doxyrest is distributed under the MIT license.
+//  For details see accompanying license.txt file,
+//  the public copy of which is also available at:
+//  http://tibbo.com/downloads/archive/doxyrest/license.txt
+//
+//..............................................................................
+
+(function () {
+
+var g_oldTarget = $([]);
+var g_suitableTargetClass = "pre, table, a, h2, h3, h4, h5, h6"; // don't highlight <h1>
+
+function adjustTarget(target) {
+	if (target.is("div"))
+		target = target.children(":first");
+
+	if (target.is(g_suitableTargetClass))
+		return target;
+
+	if (!target.is("span"))
+		return $([]);
+
+	if (target.hasClass("doxyrest-code-target")) {
+		return target;
+	}
+
+	// ascend to the top-level <span>
+
+	var parent = target.parent();
+	while (parent.is("span")) {
+		target = parent;
+		parent = parent.parent();
+	}
+
+	// check the direct parent of the top-level <span>
+
+	if (parent.is("pre"))
+		return parent;
+
+	// nope, skip all sibling spans
+
+	target = target.next();
+	while (target.is("span"))
+		target = target.next();
+
+	if (target.is(g_suitableTargetClass))
+		return target;
+
+	return $([]); // no target to highlight
+}
+
+function updateTarget() {
+	var target = adjustTarget($(":target"));
+	g_oldTarget.removeClass("doxyrest-target-highlight");
+	target.addClass("doxyrest-target-highlight");
+	g_oldTarget = target;
+}
+
+$(document).ready(updateTarget);
+$(window).on('hashchange', updateTarget);
+
+} ());
+
+//..............................................................................
diff --git a/docs/doxyrest-sphinx/sty/doxyrest.sty b/docs/doxyrest-sphinx/sty/doxyrest.sty
new file mode 100644
index 00000000000..9a96aeae16e
--- /dev/null
+++ b/docs/doxyrest-sphinx/sty/doxyrest.sty
@@ -0,0 +1,26 @@
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]
+\ProvidesPackage{doxyrest}[2016/09/05 Doxyrest package]
+
+% override \DURole
+% don't add \csname inside verbatim blocks (alltt environment)
+
+% \providecommand + \renewcommand pair works no matter \DUrole is defined or not
+
+\providecommand*{\DUrole}[2]{}
+
+\renewcommand*{\DUrole}[2]{%
+  \def\@tempa{alltt}%
+  \ifx\@tempa\@currenvir%
+    #2%
+  \else% default DURole implementation
+    \ifcsname DUrole#1\endcsname%
+      \csname DUrole#1\endcsname{#2}%
+    \else% backwards compatibility: try \docutilsrole#1{#2}
+      \ifcsname docutilsrole#1\endcsname%
+        \csname docutilsrole#1\endcsname{#2}%
+      \else%
+        #2%
+      \fi%
+    \fi%
+  \fi%
+}
diff --git a/docs/doxyrest/frame/cfamily/class.rst.in b/docs/doxyrest/frame/cfamily/class.rst.in
new file mode 100644
index 00000000000..cb905adb3d1
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/class.rst.in
@@ -0,0 +1,48 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local classes = argArray[1]
+
+local templatePrefix = ""
+
+local level = 1
+for i = 1, #classes do
+
+class = classes[i]
+
+if i == 1 then
+	level = 1
+else
+	level = 6
+end
+
+local titleName = getItemQualifiedName(class)
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+}
+.. index:: pair: $(class.compoundKind); $titleName
+.. _doxid-$(class.id):
+
+$(getTitle(templatePrefix .. class.compoundKind .. " " .. titleName, level))
+
+%{
+includeFile("compound.rst.in", class)
+}
+
+%{
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/compound.rst.in b/docs/doxyrest/frame/cfamily/compound.rst.in
new file mode 100644
index 00000000000..7c9589de14d
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/compound.rst.in
@@ -0,0 +1,144 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+local variableSectionName
+local functionSectionName
+local prevGetItemName = getItemName
+local prevCompoundId = g_currentCompoundId
+local indent
+
+if compound.compoundKind == "namespace" or compound.compoundKind == "group" then
+	variableSectionName = "Global Variables"
+	functionSectionName = "Global Functions"
+	indent = ""
+else
+	variableSectionName = "Fields"
+	functionSectionName = "Methods"
+	indent = "\t"
+end
+
+if compound.compoundKind == "group" then
+	getItemName = getItemQualifiedName
+else
+	getItemName = getItemSimpleName
+	g_currentCompoundId = compound.id
+end
+
+local compoundStats = prepareCompound(compound)
+}
+$(getCompoundTocTree(compound))
+
+%{
+local hasDetails =
+	compoundStats.hasDocumentedItems or
+	compoundStats.hasBriefDocumentation and compoundStats.hasDetailedDocumentation
+
+if hasDetails then
+}
+Overview
+~~~~~~~~
+
+$(getItemBriefDocumentation(compound))
+
+%{
+else
+}
+$(getItemDetailedDocumentation(compound))
+
+%{
+end -- if
+
+if #compound.groupArray > 0 or compound.hasGlobalNamespace then
+	local groupTree = ""
+
+	for i = 1, #compound.groupArray do
+		local item = compound.groupArray[i]
+		local targetFileName = getItemFileName(item)
+
+		generateFile(targetFileName, "group.rst.in", item)
+		groupTree = groupTree .. getGroupTree(item)
+	end -- for
+
+	if compound.hasGlobalNamespace then
+		groupTree = groupTree .. getGroupTree(g_globalNamespace)
+	end
+}
+$groupTree
+
+%{
+end -- if
+
+if compoundStats.hasItems or
+	compound.baseCompound or
+	compound.derivedTypeArray and #compound.derivedTypeArray > 0 then
+
+	includeFile("overview_compound.rst.in", compound, variableSectionName, functionSectionName)
+end
+
+if hasDetails then
+}
+.. _details-$(compound.id):
+
+Detailed Documentation
+~~~~~~~~~~~~~~~~~~~~~~
+
+$(getItemDetailedDocumentation(compound))
+
+%{
+end -- if
+
+if compoundStats.hasDocumentedUnnamedEnumValues then
+	includeFile("details_unnamed_enum_values.rst.in", compound)
+end
+
+if compoundStats.hasDocumentedTypedefs then
+	includeFile("details_typedefs.rst.in", compound)
+end
+
+if compoundStats.hasDocumentedVariables then
+	includeFile("details_variables.rst.in", compound, variableSectionName)
+end
+
+if compoundStats.hasDocumentedProperties then
+	includeFile("details_properties.rst.in", compound)
+end
+
+if compoundStats.hasDocumentedEvents then
+	includeFile("details_events.rst.in", compound)
+end
+
+if compoundStats.hasDocumentedConstruction then
+	includeFile("details_construction.rst.in", compound)
+end
+
+if compoundStats.hasDocumentedFunctions then
+	includeFile("details_functions.rst.in", compound, functionSectionName)
+end
+
+if compoundStats.hasDocumentedAliases then
+	includeFile("details_aliases.rst.in", compound)
+end
+
+if compoundStats.hasDocumentedDefines then
+	includeFile("details_defines.rst.in", compound)
+end
+
+if #compound.footnoteArray > 0 then
+	includeFile("footnotes.rst.in", compound)
+end
+
+getItemName = prevGetItemName
+g_currentCompoundId = prevCompoundId
+}
diff --git a/docs/doxyrest/frame/cfamily/crefdb.py.in b/docs/doxyrest/frame/cfamily/crefdb.py.in
new file mode 100644
index 00000000000..79597255bce
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/crefdb.py.in
@@ -0,0 +1,18 @@
+#...............................................................................
+#
+#  This file is part of the Doxyrest toolkit.
+#
+#  Doxyrest is distributed under the MIT license.
+#  For details see accompanying license.txt file,
+#  the public copy of which is also available at:
+#  http://tibbo.com/downloads/archive/doxyrest/license.txt
+#
+#...............................................................................
+
+crefdb = {
+%{
+	includeFile("crefdb_groups.py.in", g_groupArray)
+	includeFile("crefdb_members.py.in", g_globalNamespace)
+	includeFile("crefdb_external.py.in")
+}
+}
diff --git a/docs/doxyrest/frame/cfamily/crefdb_enums.py.in b/docs/doxyrest/frame/cfamily/crefdb_enums.py.in
new file mode 100644
index 00000000000..1afcc54060b
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/crefdb_enums.py.in
@@ -0,0 +1,35 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local itemArray = argArray[1]
+
+for i = 1, #itemArray do
+	local item = itemArray[i]
+	if not isUnnamedItem(item) then
+}
+	'$(getItemQualifiedName(item))' : 'doxid-$(item.id)',
+%{
+	end
+
+	for j = 1, #item.enumValueArray do
+		local enumValue = item.enumValueArray[j]
+		local enumValueName = getItemQualifiedName(enumValue)
+
+		-- remove parent enum name (C++ only)
+		enumValueName = string.gsub(enumValueName, "([%w_]+)::([%w_]+)$", "%2")
+}
+	'$enumValueName' : 'doxid-$(enumValue.id)',
+%{
+	end -- for
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/crefdb_items.py.in b/docs/doxyrest/frame/cfamily/crefdb_items.py.in
new file mode 100644
index 00000000000..e5dd0abb5b0
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/crefdb_items.py.in
@@ -0,0 +1,28 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local itemArray = argArray[1]
+
+for i = 1, #itemArray do
+	local item = itemArray[i]
+	if not isUnnamedItem(item) then
+}
+	'$(getItemQualifiedName(item))' : 'doxid-$(item.id)',
+%{
+	end
+
+	if item.compoundKind then
+		includeFile("crefdb_members.py.in", item)
+	end
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/crefdb_members.py.in b/docs/doxyrest/frame/cfamily/crefdb_members.py.in
new file mode 100644
index 00000000000..d76e79fdd33
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/crefdb_members.py.in
@@ -0,0 +1,33 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile("crefdb_enums.py.in", compound.enumArray)
+includeFile("crefdb_items.py.in", compound.namespaceArray)
+includeFile("crefdb_items.py.in", compound.structArray)
+includeFile("crefdb_items.py.in", compound.unionArray)
+includeFile("crefdb_items.py.in", compound.classArray)
+includeFile("crefdb_items.py.in", compound.interfaceArray)
+includeFile("crefdb_items.py.in", compound.protocolArray)
+includeFile("crefdb_items.py.in", compound.exceptionArray)
+includeFile("crefdb_items.py.in", compound.singletonArray)
+includeFile("crefdb_items.py.in", compound.serviceArray)
+includeFile("crefdb_items.py.in", compound.typedefArray)
+includeFile("crefdb_items.py.in", compound.variableArray)
+includeFile("crefdb_items.py.in", compound.propertyArray)
+includeFile("crefdb_items.py.in", compound.eventArray)
+includeFile("crefdb_items.py.in", compound.functionArray)
+includeFile("crefdb_items.py.in", compound.aliasArray)
+includeFile("crefdb_items.py.in", compound.defineArray)
+}
diff --git a/docs/doxyrest/frame/cfamily/details_aliases.rst.in b/docs/doxyrest/frame/cfamily/details_aliases.rst.in
new file mode 100644
index 00000000000..59183fb5436
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_aliases.rst.in
@@ -0,0 +1,45 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+}
+Aliases
+-------
+
+%{
+for i = 1, #compound.aliasArray do
+	local item = compound.aliasArray[i]
+	if isItemInCompoundDetails(item, compound) then
+}
+$(getItemRefTargetString(item))
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-title-code-block
+
+	alias $(getItemName(item)) $(getLinkedTextString(item.initializer, true))
+%{
+		if item.isSubGroupHead then
+			for j = 1, #item.subGroupSlaveArray do
+				slaveItem = item.subGroupSlaveArray[j]
+}
+	alias $(getItemName(slaveItem)) $(getLinkedTextString(slaveItem.initializer, true))
+%{
+			end -- for
+		end -- if
+}
+
+$(getItemDetailedDocumentation(item))
+
+%{
+	end -- if
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/details_construction.rst.in b/docs/doxyrest/frame/cfamily/details_construction.rst.in
new file mode 100644
index 00000000000..f1b25b4d590
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_construction.rst.in
@@ -0,0 +1,40 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+}
+Construction
+------------
+
+%{
+includeFile(
+	"details.rst.in",
+	compound,
+	compound.constructorArray,
+	getVoidFunctionDeclString
+	)
+
+if not EXCLUDE_DESTRUCTORS and compound.destructor and compound.destructor.hasDocumentation then
+	item = compound.destructor
+}
+.. _doxid-$(item.id):
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-title-code-block
+
+	$(getVoidFunctionDeclString(item, g_simpleItemNameTemplate, "\t"))
+
+$(getItemDetailedDocumentation(item))
+
+%{
+end -- if
+}
diff --git a/docs/doxyrest/frame/cfamily/details_defines.rst.in b/docs/doxyrest/frame/cfamily/details_defines.rst.in
new file mode 100644
index 00000000000..076b1a2929e
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_defines.rst.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"details.rst.in",
+	compound,
+	compound.defineArray,
+	getDefineDeclString,
+	"Macros"
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/details_events.rst.in b/docs/doxyrest/frame/cfamily/details_events.rst.in
new file mode 100644
index 00000000000..5ac3365dae5
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_events.rst.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"details.rst.in",
+	compound,
+	compound.eventArray,
+	getEventDeclString,
+	"Events"
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/details_functions.rst.in b/docs/doxyrest/frame/cfamily/details_functions.rst.in
new file mode 100644
index 00000000000..1a92a4e05e0
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_functions.rst.in
@@ -0,0 +1,24 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local sectionName = argArray[2]
+
+includeFile(
+	"details.rst.in",
+	compound,
+	compound.functionArray,
+	getFunctionDeclString,
+	sectionName
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/details_properties.rst.in b/docs/doxyrest/frame/cfamily/details_properties.rst.in
new file mode 100644
index 00000000000..82af1f9b30d
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_properties.rst.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"details.rst.in",
+	compound,
+	compound.propertyArray,
+	getPropertyDeclString,
+	"Properties"
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/details_typedefs.rst.in b/docs/doxyrest/frame/cfamily/details_typedefs.rst.in
new file mode 100644
index 00000000000..0d6a53f4af1
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_typedefs.rst.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"details.rst.in",
+	compound,
+	compound.typedefArray,
+	getTypedefDeclString,
+	"Typedefs"
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/details_unnamed_enum_values.rst.in b/docs/doxyrest/frame/cfamily/details_unnamed_enum_values.rst.in
new file mode 100644
index 00000000000..f66ce07ca48
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_unnamed_enum_values.rst.in
@@ -0,0 +1,51 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+}
+Enum Values
+-----------
+
+%{
+for i = 1, #compound.enumArray do
+	local item = compound.enumArray[i]
+
+	if isUnnamedItem(item) then
+		for j = 1, #item.enumValueArray do
+			local subItem = item.enumValueArray[j]
+			if subItem.hasDocumentation then
+}
+$(getItemRefTargetString(subItem))
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-title-code-block
+
+	$(subItem.name)
+%{
+				if subItem.isSubGroupHead then
+					for j = 1, #subItem.subGroupSlaveArray do
+						slaveItem = subItem.subGroupSlaveArray[j]
+	}
+	$(slaveItem.name)
+%{
+					end -- for
+				end -- if
+}
+
+$(getItemDetailedDocumentation(subItem))
+
+%{
+			end -- if
+		end -- for
+	end -- if
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/details_variables.rst.in b/docs/doxyrest/frame/cfamily/details_variables.rst.in
new file mode 100644
index 00000000000..4da50ab5609
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/details_variables.rst.in
@@ -0,0 +1,98 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local sectionName = argArray[2]
+}
+$(getTitle(sectionName, 3))
+
+%{
+for i = 1, #compound.variableArray do
+	local item = compound.variableArray[i]
+	if not isUnnamedItem(item) and isItemInCompoundDetails(item, compound) then
+		local modifiers = ""
+		local suffix = ""
+
+		if item.modifiers ~= "" then
+			modifiers = " " .. item.modifiers
+		end
+
+		if item.argString ~= "" then
+			suffix = g_preParamSpace .. item.argString
+		elseif item.bitField  ~= "" then
+			suffix = g_preParamSpace .. ":" .. item.bitField
+		end
+
+		local type = getLinkedTextString(item.type, true)
+		if string.find(type, "@") then
+			type = "unnamed-" .. string.match(type, "[a-z]+")
+			-- e.g.: unnamed-struct
+		end
+
+		local static = ""
+		if string.find(item.flags, "static") then
+			static = "static "
+		end
+
+		local initializer = getLinkedTextString(item.initializer, true)
+		if string.len(initializer) > 0 then
+			initializer = " " .. initializer
+		end
+}
+$(getItemRefTargetString(item))
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-title-code-block
+
+	$static$type$modifiers $(getItemName(item))$suffix$initializer
+%{
+		if item.isSubGroupHead then
+			for j = 1, #item.subGroupSlaveArray do
+				slaveItem = item.subGroupSlaveArray[j]
+				if not isUnnamedItem(slaveItem) then
+					local modifiers = ""
+					local suffix = ""
+
+					if slaveItem.modifiers ~= "" then
+						modifiers = " " .. slaveItem.modifiers
+					end
+
+					if slaveItem.argString ~= "" then
+						suffix = " " .. slaveItem.argString
+					elseif slaveItem.bitField  ~= "" then
+						suffix = " :" .. slaveItem.bitField
+					end
+
+					local type = getLinkedTextString(slaveItem.type, true)
+					if string.find(type, "@") then
+						type = "unnamed-" .. string.match(type, "[a-z]+")
+						-- e.g.: unnamed-struct
+					end
+
+					local static = ""
+					if string.find(item.flags, "static") then
+						static = "static "
+					end
+}
+	$static$type$modifiers $(getItemName(slaveItem))$suffix
+%{
+				end -- if
+			end -- for
+		end -- if
+}
+
+$(getItemDetailedDocumentation(item))
+
+%{
+	end -- if
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/enum.rst.in b/docs/doxyrest/frame/cfamily/enum.rst.in
new file mode 100644
index 00000000000..3bcfc40c83c
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/enum.rst.in
@@ -0,0 +1,116 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local enum = argArray[1]
+
+local enumKind = getItemKindString(enum, "enum")
+local enumStats = prepareEnum(enum)
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+}
+.. index:: pair: enum; $(enum.name)
+.. _doxid-$(enum.id):
+
+$(getTitle(enumKind .. " " .. getItemQualifiedName(enum), 1))
+
+%{
+local hasDetails =
+	enumStats.hasDocumentedEnumValues or
+	enumStats.hasBriefDocumentation and enumStats.hasDetailedDocumentation
+
+if hasDetails then
+}
+Overview
+~~~~~~~~
+
+$(getItemBriefDocumentation(enum))
+
+%{
+else
+}
+$(getItemDetailedDocumentation(enum))
+
+%{
+end -- if
+}
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-overview-code-block
+
+	$(getEnumImportString(enum))
+	$enumKind $(enum.name)$g_preBodySpace{
+%{
+for i = 1, #enum.enumValueArray do
+	local item = enum.enumValueArray[i]
+	local initializer = getLinkedTextString(item.initializer, true)
+
+	if string.len(initializer) > 0 then
+		local spaceCount = enumStats.maxEnumValueNameLength - string.len(item.name) + 1
+		initializer = string.rep(" ", spaceCount) .. initializer
+	end
+}
+	    $(getItemNameForOverview(item))$initializer,
+%{
+end -- for
+}
+	};
+
+%{
+if hasDetails then
+}
+.. _details-$(enum.id):
+
+Detailed Documentation
+~~~~~~~~~~~~~~~~~~~~~~
+
+$(getItemDetailedDocumentation(enum))
+
+%{
+end -- if
+
+if enumStats.hasDocumentedEnumValues then
+}
+Enum Values
+-----------
+
+%{
+	for i = 1, #enum.enumValueArray do
+		local item = enum.enumValueArray[i]
+		if item.hasDocumentation then
+}
+$(getItemRefTargetString(item))
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-title-code-block
+
+	$(item.name)
+%{
+		if item.isSubGroupHead then
+			for j = 1, #item.subGroupSlaveArray do
+				slaveItem = item.subGroupSlaveArray[j]
+}
+	$(slaveItem.name)
+%{
+			end -- for
+		end -- if
+}
+
+$(getItemDetailedDocumentation(item))
+
+%{
+		end -- if
+	end -- for
+end -- if
+}
diff --git a/docs/doxyrest/frame/cfamily/example.rst.in b/docs/doxyrest/frame/cfamily/example.rst.in
new file mode 100644
index 00000000000..188849f739f
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/example.rst.in
@@ -0,0 +1,27 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local example = argArray[1]
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+}
+.. index:: pair: example; $(example.name)
+.. _doxid-$(example.id):
+
+$(getTitle(example.name, 1))
+
+$(getItemDetailedDocumentation(example))
diff --git a/docs/doxyrest/frame/cfamily/global.rst.in b/docs/doxyrest/frame/cfamily/global.rst.in
new file mode 100644
index 00000000000..a25312c69d5
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/global.rst.in
@@ -0,0 +1,30 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+if not g_cfamilyUtilsIncluded then
+	dofile(g_frameDir .. "/utils.lua")
+end
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+}
+.. _global:
+.. index:: pair: namespace; global
+
+$(getTitle(g_globalNamespace.title, 1))
+
+%{
+includeFile("compound.rst.in", g_globalNamespace)
+}
diff --git a/docs/doxyrest/frame/cfamily/index.rst.in b/docs/doxyrest/frame/cfamily/index.rst.in
new file mode 100644
index 00000000000..7a17fa074e5
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/index.rst.in
@@ -0,0 +1,19 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+dofile(g_frameDir .. "/utils.lua")
+includeFile("index_main.rst.in")
+
+if CREF_DB then
+	generateFile("crefdb.py", "crefdb.py.in")
+end
+}
diff --git a/docs/doxyrest/frame/cfamily/namespace.rst.in b/docs/doxyrest/frame/cfamily/namespace.rst.in
new file mode 100644
index 00000000000..e720da614f2
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/namespace.rst.in
@@ -0,0 +1,31 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local namespace = argArray[1]
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+
+local name = getItemQualifiedName(namespace)
+}
+.. index:: pair: namespace; $name
+.. _doxid-$(namespace.id):
+
+$(getTitle("namespace " .. name, 1))
+
+%{
+includeFile("compound.rst.in", namespace)
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_aliases.rst.in b/docs/doxyrest/frame/cfamily/overview_aliases.rst.in
new file mode 100644
index 00000000000..83966fa481b
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_aliases.rst.in
@@ -0,0 +1,25 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+}
+	// aliases
+
+%{
+for i = 1, #compound.aliasArray do
+	local item = compound.aliasArray[i]
+}
+	alias $(getItemNameForOverview(item)) $(getLinkedTextString(item.initializer, true));
+%{
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_classes.rst.in b/docs/doxyrest/frame/cfamily/overview_classes.rst.in
new file mode 100644
index 00000000000..20bfd1798ae
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_classes.rst.in
@@ -0,0 +1,44 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local itemArray = argArray[2]
+local sectionName = argArray[3]
+
+if sectionName == 'interfaces' then
+	sectionName = 'templates'
+end
+
+}
+	// $sectionName
+
+%{
+local isPrevMl = false
+
+local mapping = {}
+
+for i = 1, #itemArray do
+	local item = itemArray[i]
+	local targetFileName = getItemFileName(item)
+	local decl = getClassDeclString(item, g_refItemNameTemplate, '\t')
+
+	if mapping[getItemFileNameNoSuffix(item)] == nil then
+		mapping[getItemFileNameNoSuffix(item)] = true
+		generateFile(targetFileName, "class.rst.in", g_itemFileNameMap[getItemFileNameNoSuffix(item)].items)
+	end
+
+}
+$extraSep	$decl;
+%{
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_common.rst.in b/docs/doxyrest/frame/cfamily/overview_common.rst.in
new file mode 100644
index 00000000000..05e2808d5d3
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_common.rst.in
@@ -0,0 +1,47 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local itemArray = argArray[2]
+local getDeclString = argArray[3]
+local sectionName = argArray[4]
+local omitSemicolon = argArray[5]
+
+local semicolon = ";"
+
+if omitSemicolon then
+	semicolon = ""
+end
+}
+	// $sectionName
+
+%{
+local isPrevMl = false
+
+for i = 1, #itemArray do
+	local item = itemArray[i]
+	local linkage
+	local decl = getDeclString(item, getItemNameTemplate(item), "\t")
+	local isMl = string.find(decl, "\n")
+	local extraSep = ""
+
+	if i > 1 and isMl or isPrevMl then
+		extraSep = "\n"
+	end
+
+	isPrevMl = isMl
+}
+$extraSep	$decl$semicolon
+%{
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_compound.rst.in b/docs/doxyrest/frame/cfamily/overview_compound.rst.in
new file mode 100644
index 00000000000..203c50726fb
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_compound.rst.in
@@ -0,0 +1,104 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local variableSectionName = string.lower(argArray[2])
+local functionSectionName = string.lower(argArray[3])
+local destructorRefTargetString = ""
+}
+
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-overview-code-block
+
+	$(getItemImportString(compound))
+%{
+if compound.compoundKind == "group" then
+	-- no open scope snippet
+	includeFile("overview_members.rst.in", compound, variableSectionName, functionSectionName, "")
+elseif compound.compoundKind == "namespace" then
+	if not compound.name then
+		-- no open scope snippet
+		includeFile("overview_members.rst.in", compound, variableSectionName, functionSectionName, "")
+	else
+		includeFile("scope_namespace.rst.in", compound, true)
+		includeFile("overview_members.rst.in", compound, variableSectionName, functionSectionName, "")
+		trimOutput()
+		emit("\n")
+		includeFile("scope_namespace.rst.in", compound, false)
+	end
+else
+	includeFile("scope_class.rst.in", compound, true)
+
+	local hasProtectionLabel = false
+	local defaultProtectionKind = getDefaultProtectionKind(compound)
+
+	for i = g_minProtectionValue, g_maxProtectionValue do
+		if compound.protectionCompoundArray[i] then
+			if hasProtectionLabel or i ~= defaultProtectionKind then
+}
+	$(g_protectionNameMap[i]):
+%{
+				hasProtectionLabel = true
+			end
+
+			includeFile(
+				"overview_members.rst.in",
+				compound.protectionCompoundArray[i],
+				variableSectionName,
+				functionSectionName,
+				"\t"
+				)
+		end -- if
+	end -- for
+
+	trimOutput()
+	emit("\n")
+	includeFile("scope_class.rst.in", compound, false)
+
+	if compound.baseCompound and compound.baseCompound.hasItems then
+		compound = compound.baseCompound
+}
+
+Inherited Members
+-----------------
+
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-overview-inherited-code-block
+
+%{
+		-- always link inherited members (even when no documentation)
+
+		local prevHasItemDocumentation = hasItemDocumentation
+		hasItemDocumentation = function(item)
+			return true
+		end
+
+		for i = g_minProtectionValue, g_maxProtectionValue do
+			if compound.protectionCompoundArray[i] then
+}
+	$(g_protectionNameMap[i]):
+%{
+				includeFile(
+					"overview_members.rst.in",
+					compound.protectionCompoundArray[i],
+					variableSectionName,
+					functionSectionName,
+					"\t"
+					)
+			end -- if
+		end -- for
+
+		hasItemDocumentation = prevHasItemDocumentation
+	end -- if
+end -- if
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_construction.rst.in b/docs/doxyrest/frame/cfamily/overview_construction.rst.in
new file mode 100644
index 00000000000..3a3feefcec1
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_construction.rst.in
@@ -0,0 +1,34 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"overview_common.rst.in",
+	compound,
+	compound.constructorArray,
+	getVoidFunctionDeclString,
+	"construction"
+	)
+
+if not EXCLUDE_DESTRUCTORS and compound.destructor then
+	local item = compound.destructor
+	local extraSep = ""
+	if isPrevMl then
+		extraSep = "\n"
+	end
+}
+$extraSep	$(getVoidFunctionDeclString(item, getItemNameTemplate(item), "\t"));
+%{
+end -- if
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_defines.rst.in b/docs/doxyrest/frame/cfamily/overview_defines.rst.in
new file mode 100644
index 00000000000..f56a0ec9156
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_defines.rst.in
@@ -0,0 +1,24 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"overview_common.rst.in",
+	compound,
+	compound.defineArray,
+	getDefineDeclString,
+	"macros",
+	true
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_enums.rst.in b/docs/doxyrest/frame/cfamily/overview_enums.rst.in
new file mode 100644
index 00000000000..ff47024fb1a
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_enums.rst.in
@@ -0,0 +1,67 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+}
+	// enums
+
+%{
+local isLastUnnamed = false
+
+for i = 1, #compound.enumArray do
+	local item = compound.enumArray[i]
+	local enumKind = getItemKindString(item, "enum")
+
+	if isUnnamedItem(item) then
+		if i > 1 then
+			emit("\n")
+		end
+}
+	$enumKind$g_preBodySpace{
+%{
+		local enumStats = prepareEnum(item)
+
+		for j = 1, #item.enumValueArray do
+			local subItem = item.enumValueArray[j]
+			local initializer = getLinkedTextString(subItem.initializer, true)
+
+			if string.len(initializer) > 0 then
+				local spaceCount = enumStats.maxEnumValueNameLength - string.len(subItem.name) + 1
+				initializer = string.rep(" ", spaceCount) .. initializer
+			end
+}
+	    $(getItemNameForOverview(subItem))$initializer,
+%{
+		end -- for
+
+		isLastUnnamed = true
+}
+	};
+%{
+	else
+		local targetFileName = getItemFileName(item)
+
+		if compound.compoundKind ~= "group" and not compound.isBaseCompound then
+			generateFile(targetFileName, "enum.rst.in", item)
+		end
+
+		if isLastUnnamed then
+			emit("\n")
+			isLastUnnamed = false
+		end
+}
+	$enumKind :ref:`$(getItemName(item))<doxid-$(item.id)>`;
+%{
+	end -- if
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_events.rst.in b/docs/doxyrest/frame/cfamily/overview_events.rst.in
new file mode 100644
index 00000000000..2b1faf3d38c
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_events.rst.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"overview_common.rst.in",
+	compound,
+	compound.eventArray,
+	getEventDeclString,
+	"events"
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_functions.rst.in b/docs/doxyrest/frame/cfamily/overview_functions.rst.in
new file mode 100644
index 00000000000..acf1976f3a2
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_functions.rst.in
@@ -0,0 +1,24 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local sectionName = argArray[2]
+
+includeFile(
+	"overview_common.rst.in",
+	compound,
+	compound.functionArray,
+	getFunctionDeclString,
+	sectionName
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_members.rst.in b/docs/doxyrest/frame/cfamily/overview_members.rst.in
new file mode 100644
index 00000000000..b982df3b731
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_members.rst.in
@@ -0,0 +1,108 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local variableSectionName = string.lower(argArray[2])
+local functionSectionName = string.lower(argArray[3])
+local indent = argArray[4]
+
+if #compound.namespaceArray > 0 then
+	includeFileWithIndent(indent, "overview_namespaces.rst.in", compound)
+	emit("\n")
+end
+
+if #compound.typedefArray > 0 then
+	includeFileWithIndent(indent, "overview_typedefs.rst.in", compound)
+	emit("\n")
+end
+
+if #compound.enumArray > 0 then
+	includeFileWithIndent(indent, "overview_enums.rst.in", compound)
+	emit("\n")
+end
+
+if #compound.structArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.structArray, "structs")
+	emit("\n")
+end
+
+if #compound.unionArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.unionArray, "unions")
+	emit("\n")
+end
+
+if #compound.interfaceArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.interfaceArray, "interfaces")
+	emit("\n")
+end
+
+if #compound.protocolArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.protocolArray, "protocols")
+	emit("\n")
+end
+
+if #compound.exceptionArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.exceptionArray, "exceptions")
+	emit("\n")
+end
+
+if #compound.classArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.classArray, "classes")
+	emit("\n")
+end
+
+if #compound.singletonArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.singletonArray, "singletons")
+	emit("\n")
+end
+
+if #compound.serviceArray > 0 then
+	includeFileWithIndent(indent, "overview_classes.rst.in", compound, compound.serviceArray, "services")
+	emit("\n")
+end
+
+if #compound.variableArray > 0 then
+	includeFileWithIndent(indent, "overview_variables.rst.in", compound, variableSectionName)
+	emit("\n")
+end
+
+if #compound.propertyArray > 0 then
+	includeFileWithIndent(indent, "overview_properties.rst.in", compound)
+	emit("\n")
+end
+
+if #compound.eventArray > 0 then
+	includeFileWithIndent(indent, "overview_events.rst.in", compound)
+	emit("\n")
+end
+
+if #compound.constructorArray > 0 or not EXCLUDE_DESTRUCTORS and compound.destructor then
+	includeFileWithIndent(indent, "overview_construction.rst.in", compound)
+	emit("\n")
+end
+
+if #compound.functionArray > 0 then
+	includeFileWithIndent(indent, "overview_functions.rst.in", compound, functionSectionName)
+	emit("\n")
+end
+
+if #compound.aliasArray > 0 then
+	includeFileWithIndent(indent, "overview_aliases.rst.in", compound)
+	emit("\n")
+end
+
+if #compound.defineArray > 0 then
+	includeFileWithIndent(indent, "overview_defines.rst.in", compound)
+	emit("\n")
+end
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_namespaces.rst.in b/docs/doxyrest/frame/cfamily/overview_namespaces.rst.in
new file mode 100644
index 00000000000..471cf68fd52
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_namespaces.rst.in
@@ -0,0 +1,29 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+}
+	// namespaces
+
+%{
+for i = 1, #compound.namespaceArray do
+	local item = compound.namespaceArray[i]
+	local targetFileName = getItemFileName(item)
+
+	if compound.compoundKind ~= "group" then
+		generateFile(targetFileName, "namespace.rst.in", item)
+	end
+
+	emit(getNamespaceTree(item))
+end -- for
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_properties.rst.in b/docs/doxyrest/frame/cfamily/overview_properties.rst.in
new file mode 100644
index 00000000000..95e4ea5d98d
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_properties.rst.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"overview_common.rst.in",
+	compound,
+	compound.propertyArray,
+	getPropertyDeclString,
+	"properties"
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_typedefs.rst.in b/docs/doxyrest/frame/cfamily/overview_typedefs.rst.in
new file mode 100644
index 00000000000..f21ee02c78a
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_typedefs.rst.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+
+includeFile(
+	"overview_common.rst.in",
+	compound,
+	compound.typedefArray,
+	getTypedefDeclString,
+	"typedefs"
+	)
+}
diff --git a/docs/doxyrest/frame/cfamily/overview_variables.rst.in b/docs/doxyrest/frame/cfamily/overview_variables.rst.in
new file mode 100644
index 00000000000..6e9dc1a9435
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/overview_variables.rst.in
@@ -0,0 +1,139 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local sectionName = argArray[2]
+local indent = ""
+local unnamedStack = {}
+local unnamedStackSize = 0
+local isLastUnnamed = false
+}
+	// $sectionName
+
+%{
+local i = 1
+while i <= #compound.variableArray do
+	local item = compound.variableArray[i]
+
+	if not isUnnamedItem(item) then
+		local modifiers = ""
+		local suffix = ""
+
+		if item.modifiers ~= "" then
+			modifiers = " " .. item.modifiers
+		end
+
+		if item.argString ~= "" then
+			suffix = g_preParamSpace .. item.argString
+		elseif item.bitField  ~= "" then
+			suffix = g_preParamSpace .. ":" .. item.bitField
+		end
+
+		local unnamed = isMemberOfUnnamedType(item)
+		if unnamed then
+			if i > 1 then
+				emit("\n")
+			end
+
+			while unnamed ~= "" do
+				local m, t, n = string.match(unnamed, "^((%a+)[/:](%d+)/*)")
+				if not m then
+					m, t = string.match(unnamed, "^((%a+)/*)")
+					if not m then
+						break
+					end
+				end
+}
+	$indent$t$g_preBodySpace$indent{
+%{
+				unnamed = string.sub(unnamed, string.len(m) + 1)
+				unnamedStackSize = unnamedStackSize + 1
+				unnamedStack[unnamedStackSize] = n
+				indent = indent .. "\t"
+			end -- while
+
+			isLastUnnamed = false
+		end
+
+		if isLastUnnamed then
+			emit("\n")
+			isLastUnnamed = false
+		end
+
+		local static = ""
+		if string.find(item.flags, "static") then
+			static = "static "
+		end
+
+		local initializer = getLinkedTextString(item.initializer, true)
+		if string.len(initializer) > 0 then
+			initializer = " " .. initializer
+		end
+}
+	$indent$static$(getLinkedTextString(item.type, true))$modifiers $(getItemNameForOverview(item))$suffix$initializer;
+%{
+	else -- unnamed item
+		local j
+		for j = 1, unnamedStackSize do
+			if not unnamedStack[j] then -- terminate this stack entry and everything above
+				local k
+				for k = j, unnamedStackSize do
+					unnamedStack[k] = 0
+				end -- for
+
+				break
+			end -- if
+		end -- for
+	end -- if
+
+	while unnamedStackSize > 0 do
+		if not unnamedStack[unnamedStackSize] then
+			break -- wait for unnamed variable @n to terminate this stack entry
+		end
+
+		unnamedStack[unnamedStackSize] = unnamedStack[unnamedStackSize] - 1
+		if unnamedStack[unnamedStackSize] > 0 then
+			break
+		end
+
+		indent = string.sub(indent, 2) -- remove 1 tab
+		unnamedStackSize = unnamedStackSize - 1
+		isLastUnnamed = true
+
+		local useNextItem = false
+		local nextItem = compound.variableArray[i + 1]
+		if nextItem and not isUnnamedItem(nextItem) then
+			local typeString = getLinkedTextString(nextItem.type, false)
+			useNextItem = string.find(typeString, "@") -- next item is a field of THIS unnamed type
+		end
+
+		if useNextItem then
+			local modifiers = ""
+			if nextItem.modifiers ~= "" then
+				modifiers = " " .. nextItem.modifiers
+			end
+
+			i = i + 1 -- skip next
+}
+	$indent}$modifiers $(getItemNameForOverview(nextItem));
+%{
+		else
+}
+	$indent};
+%{
+		end -- if
+	end -- while
+
+	i = i + 1
+end -- while
+}
diff --git a/docs/doxyrest/frame/cfamily/scope_class.rst.in b/docs/doxyrest/frame/cfamily/scope_class.rst.in
new file mode 100644
index 00000000000..4df111cdf93
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/scope_class.rst.in
@@ -0,0 +1,74 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local isOpen = argArray[2]
+
+if isOpen then
+	local decl = getClassDeclString(compound, g_simpleItemNameTemplate, "\t")
+
+	if #compound.baseTypeArray == 0 then
+}
+	$decl$g_preBodySpace{
+%{
+	elseif #compound.baseTypeArray == 1 then
+		local item = compound.baseTypeArray[1]
+		local protection = compound.baseTypeProtectionArray[1]
+}
+	$decl: $(getBaseClassString(item, protection))$g_preBodySpace{
+%{
+	else
+}
+	$decl:
+%{
+		for i = 1, #compound.baseTypeArray do
+			local item = compound.baseTypeArray[i]
+			local protection = compound.baseTypeProtectionArray[i]
+			local suffix = ""
+
+			if i < #compound.baseTypeArray then
+				suffix = ","
+			else
+				suffix = g_preBodySpace .. "{"
+			end
+}
+	    $(getBaseClassString(item, protection))$suffix
+%{
+		end -- for
+	end -- if
+else
+}
+	};
+%{
+	if #compound.derivedTypeArray > 0 and SHOW_DIRECT_DESCENDANTS then
+		-- always link derived types (even when no documentation)
+
+		local prevHasItemDocumentation = hasItemDocumentation
+		hasItemDocumentation = function(item)
+			return true
+		end
+
+		emit("\n")
+
+		includeFile(
+			"overview_common.rst.in",
+			compound,
+			compound.derivedTypeArray,
+			getClassDeclString,
+			"direct descendants"
+			)
+
+		hasItemDocumentation = prevHasItemDocumentation
+	end
+end -- if
+}
diff --git a/docs/doxyrest/frame/cfamily/scope_namespace.rst.in b/docs/doxyrest/frame/cfamily/scope_namespace.rst.in
new file mode 100644
index 00000000000..d109211663a
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/scope_namespace.rst.in
@@ -0,0 +1,28 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local namespace = argArray[1]
+local isOpen = argArray[2]
+
+if isOpen then
+}
+	namespace $(namespace.name) {
+
+%{
+else
+}
+
+	} // namespace $(namespace.name)
+%{
+end -- if
+}
diff --git a/docs/doxyrest/frame/cfamily/utils.lua b/docs/doxyrest/frame/cfamily/utils.lua
new file mode 100644
index 00000000000..d5fb55cb18d
--- /dev/null
+++ b/docs/doxyrest/frame/cfamily/utils.lua
@@ -0,0 +1,1155 @@
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+g_cfamilyUtilsIncluded = true
+
+dofile(g_frameDir .. "/../common/string.lua")
+dofile(g_frameDir .. "/../common/table.lua")
+dofile(g_frameDir .. "/../common/item.lua")
+dofile(g_frameDir .. "/../common/doc.lua")
+dofile(g_frameDir .. "/../common/toc.lua")
+
+if not LANGUAGE then
+	LANGUAGE = "cpp"
+end
+
+if not INDEX_TITLE then
+	INDEX_TITLE = "My Project Documentation"
+end
+
+if not EXTRA_PAGE_LIST then
+	EXTRA_PAGE_LIST = {}
+end
+
+if not EXTERNAL_CREF_DB then
+	EXTERNAL_CREF_DB = {}
+end
+
+if ML_PARAM_LIST_COUNT_THRESHOLD then
+	ML_PARAM_LIST_COUNT_THRESHOLD = tonumber(ML_PARAM_LIST_COUNT_THRESHOLD)
+end
+
+if ML_PARAM_LIST_LENGTH_THRESHOLD then
+	ML_PARAM_LIST_LENGTH_THRESHOLD = tonumber(ML_PARAM_LIST_LENGTH_THRESHOLD)
+end
+
+if string.match(LANGUAGE, "^c[px+]*$") then
+	g_nameDelimiter = "::"
+else
+	g_nameDelimiter = "."
+end
+
+g_protectionKindMap = {
+	public    = 0,
+	protected = 1,
+	private   = 2,
+	package   = 3,
+}
+
+g_protectionNameMap = {
+	[0] = "public",
+	[1] = "protected",
+	[2] = "private",
+	[3] = "package",
+}
+
+g_minProtectionValue = 0
+g_maxProtectionValue = 3
+
+if PROTECTION_FILTER then
+	g_protectionFilterValue = g_protectionKindMap[PROTECTION_FILTER]
+else
+	g_protectionFilterValue = 0
+end
+
+if PRE_PARAM_LIST_SPACE then
+	g_preParamSpace = " "
+	g_preOperatorParamSpace = ""
+elseif PRE_OPERATOR_PARAM_LIST_SPACE then
+	g_preParamSpace = ""
+	g_preOperatorParamSpace = " "
+else
+	g_preParamSpace = ""
+	g_preOperatorParamSpace = ""
+end
+
+if PRE_OPERATOR_NAME_SPACE then
+	g_preOperatorNameSpace = " "
+else
+	g_preOperatorNameSpace = ""
+end
+
+if PRE_BODY_NL then
+	g_preBodySpace = "\n\t"
+else
+	g_preBodySpace = " "
+end
+
+if not g_globalNamespace.title then
+	g_globalNamespace.title = "Global Namespace"
+end
+
+g_currentCompoundId = nil
+
+-------------------------------------------------------------------------------
+
+function getNormalizedCppString(string)
+	local s = string
+
+	s = string.gsub(s, "%s*%*", "*")
+	s = string.gsub(s, "%s*&", "&")
+	s = string.gsub(s, "%s*<%s*", g_preParamSpace .. "<")
+	s = string.gsub(s, "%s+>", ">")
+	s = string.gsub(s, "%s*%(%s*", g_preParamSpace .. "(")
+	s = string.gsub(s, "%s+%)", ")")
+
+	return s
+end
+
+function getLinkedTextString(text, isRef)
+	if not text then
+		return ""
+	end
+
+	if not isRef then
+		return getNormalizedCppString(text.plainText)
+	end
+
+	local s = ""
+
+	for i = 1, #text.refTextArray do
+		local refText = text.refTextArray[i]
+		local text = getNormalizedCppString(refText.text)
+
+		if refText.id ~= "" and refText.id ~= g_currentCompoundId then
+			text = string.gsub(text, "<", "\\<") -- escape left chevron
+			s = s .. ":ref:`" .. text .. "<doxid-" .. refText.id .. ">`"
+		else
+			s = s .. text
+		end
+	end
+
+	s = string.gsub(s, "\n", " ") -- callsites of getLinkedTextString don't expect newline chars
+
+	return s
+end
+
+-------------------------------------------------------------------------------
+
+-- param array formatting
+
+function getParamString(param, isRef)
+	local s = ""
+	local name
+
+	if not param.type.isEmpty then
+		s = s .. getLinkedTextString(param.type, isRef)
+	end
+
+	if param.declarationName ~= "" then
+		name = param.declarationName
+	else
+		name = param.definitionName
+	end
+
+	if name ~= "" then
+		if s ~= "" then
+			s = s .. " "
+		end
+
+		s = s .. getNormalizedCppString(name)
+	end
+
+	if param.array ~= "" then
+		s = s .. g_preParamSpace .. param.array
+	end
+
+	if not param.defaultValue.isEmpty then
+		s = s .. " = " .. getLinkedTextString(param.defaultValue, isRef)
+	end
+
+	return s
+end
+
+function getParamArrayString_sl(paramArray, isRef, lbrace, rbrace)
+	local s
+	local count = #paramArray
+
+	if count == 0 then
+		s = lbrace .. rbrace
+	else
+		s = lbrace .. getParamString(paramArray[1], isRef)
+
+		for i = 2, count do
+			s = s .. ", " .. getParamString(paramArray[i], isRef)
+		end
+
+		s = s .. rbrace
+	end
+
+	return s
+end
+
+function getParamArrayString_ml(paramArray, isRef, lbrace, rbrace, indent, nl)
+	local s
+	local count = #paramArray
+
+	if count == 0 then
+		s = lbrace .. rbrace
+	elseif count == 1  then
+		s = lbrace .. getParamString(paramArray[1], isRef) .. rbrace
+	else
+		s = lbrace .. nl .. indent .. "\t"
+
+		for i = 1, count do
+			s = s .. getParamString(paramArray[i], isRef)
+
+			if i ~= count then
+				s = s .. ","
+			end
+
+			s = s .. nl .. indent .. "\t"
+		end
+		s = s .. rbrace
+	end
+
+	return s
+end
+
+function getParamArrayString(prefix, paramArray, isRef, lbrace, rbrace, indent, nl)
+	if not lbrace then
+		lbrace = "("
+	end
+
+	if not rbrace then
+		rbrace = ")"
+	end
+
+	if not indent then
+		indent = "\t"
+	end
+
+	if not nl then
+		nl = "\n"
+	end
+
+	local s = ""
+	local space = ""
+
+	if PRE_PARAM_LIST_SPACE then
+		space = " "
+	end
+
+	if ML_PARAM_LIST_COUNT_THRESHOLD and
+		#paramArray > ML_PARAM_LIST_COUNT_THRESHOLD then
+		s = getParamArrayString_ml(paramArray, isRef, lbrace, rbrace, indent, nl)
+	else
+		s = getParamArrayString_sl(paramArray, isRef, lbrace, rbrace)
+
+		if ML_PARAM_LIST_LENGTH_THRESHOLD then
+			local decl = prefix .. space .. s
+			decl = replaceRolesWithPlainText(decl)
+
+			if string.len(decl) > ML_PARAM_LIST_LENGTH_THRESHOLD then
+				s = getParamArrayString_ml(paramArray, isRef, lbrace, rbrace, indent, nl)
+			end
+		end
+	end
+
+	s = space .. s
+	return s
+end
+
+-------------------------------------------------------------------------------
+
+function getItemKindString(item, itemKindString)
+	local s = ""
+
+	if item.modifiers ~= "" then
+		s = item.modifiers .. " "
+	end
+
+	s = s .. itemKindString
+	return s
+end
+
+function getItemImportArray(item)
+	if item.importArray and next(item.importArray) ~= nil then
+		return item.importArray
+	end
+
+	local text = getItemInternalDocumentation(item)
+	local importArray = {}
+	local i = 1
+	for import in string.gmatch(text, ":import%(([^:]+)%)") do
+		importArray[i] = import
+		i = i + 1
+	end
+
+	return importArray
+end
+
+function getItemImportString(item)
+	local importArray = getItemImportArray(item)
+	if next(importArray) == nil then
+		return ""
+	end
+
+	local indent = getLineText()
+	local importPrefix
+	local importSuffix
+
+	if string.match(LANGUAGE, "^c[px+]*$") or LANGUAGE == "idl" then
+		importPrefix = "#include <"
+		importSuffix = ">\n" .. indent
+	elseif string.match(LANGUAGE, "^ja?ncy?$") then
+		importPrefix = "import \""
+		importSuffix = "\"\n" .. indent
+	else
+		importPrefix = "import "
+		importSuffix = "\n" .. indent
+	end
+
+	local s = ""
+
+	for i = 1, #importArray do
+		local import = importArray[i]
+		s = s .. importPrefix .. import .. importSuffix
+	end
+
+	return s
+end
+
+function getEnumImportString(item)
+	local importArray = getItemImportArray(item)
+	if next(importArray) ~= nil then
+		return getItemImportArray(item)
+	end
+
+	local file = string.gsub(item.location.file, ".*[\\/]", "")
+
+	if string.match(LANGUAGE, "^c[px+]*$") and string.match(file, "%.h[px+]*$") then
+		return "#include <" .. file .. ">\n"
+	else
+		return ""
+	end
+end
+
+function getTemplateSpecSuffix(item)
+	if not item.templateSpecParamArray or #item.templateSpecParamArray == 0 then
+		return ""
+	end
+
+	return getParamArrayString_sl(item.templateSpecParamArray, false, "<", ">")
+end
+
+function getItemSimpleName(item)
+	return item.name .. getTemplateSpecSuffix(item)
+end
+
+function getItemQualifiedName(item)
+	local name = string.gsub(item.path, "/", g_nameDelimiter)
+	return name .. getTemplateSpecSuffix(item)
+end
+
+getItemName = getItemQualifiedName
+
+function getItemNameForOverview(item)
+	local template = getItemNameTemplate(item)
+	local name = getItemName(item)
+	return fillItemNameTemplate(template, name, item.id)
+end
+
+function getFunctionModifierDelimiter(indent)
+	if ML_SPECIFIER_MODIFIER_LIST then
+		return "\n" .. indent
+	else
+		return " "
+	end
+end
+
+function getPropertyDeclString(item, nameTemplate, indent)
+	local s = getLinkedTextString(item.returnType, true)
+
+	if item.modifiers ~= "" then
+		s = string.gsub(s, "property$", item.modifiers .. " property")
+	end
+
+	s = s .. getFunctionModifierDelimiter(indent)
+	s = s .. fillItemNameTemplate(nameTemplate, getItemName(item), item.id)
+
+	if #item.paramArray > 0 then
+		s = s .. getParamArrayString(s, item.paramArray, true, "(", ")", indent)
+	end
+
+	return s
+end
+
+function getFunctionDeclStringImpl(item, returnType, nameTemplate, indent)
+	local s = ""
+
+	if item.templateParamArray and #item.templateParamArray > 0 or
+		item.templateSpecParamArray and #item.templateSpecParamArray > 0 then
+		s = "template " .. getParamArrayString(s, item.templateParamArray, false, "<", ">") .. "\n" .. indent
+	end
+
+	if string.find(item.flags, "static") then
+		s = s .. "static" .. getFunctionModifierDelimiter(indent)
+	elseif item.virtualKind == "pure-virtual" then
+		s = s .. "virtual" .. getFunctionModifierDelimiter(indent)
+	elseif item.virtualKind ~= "non-virtual" then
+		s = s .. item.virtualKind .. getFunctionModifierDelimiter(indent)
+	end
+
+	if returnType and returnType ~= "" then
+		s = s .. returnType .. getFunctionModifierDelimiter(indent)
+	end
+
+	if item.modifiers ~= "" then
+		s = s .. item.modifiers .. getFunctionModifierDelimiter(indent)
+	end
+
+	local name = getItemName(item)
+	local isOperator = string.find(name, "operator")
+
+	if isOperator then
+		name = string.gsub(
+			name,
+			"operator%s*([()[%]+%-*&=!<>]+)%s*",
+			"operator" .. g_preOperatorNameSpace .. "%1"
+			)
+
+		name = string.gsub(name, "%%", "%%%%")
+	end
+
+	s = s .. fillItemNameTemplate(nameTemplate, name, item.id)
+
+	if isOperator then -- ensure spacce after operator
+		s = s .. g_preOperatorParamSpace
+	end
+
+	s = s .. getParamArrayString(s, item.paramArray, true, "(", ")", indent)
+
+	if string.find(item.flags, "const") then
+		s = s .. " const"
+	end
+
+	if item.virtualKind == "pure-virtual" then
+		s = s .. " = 0"
+	end
+
+	return s
+end
+
+function getFunctionDeclString(func, nameTemplate, indent)
+	return getFunctionDeclStringImpl(
+		func,
+		getLinkedTextString(func.returnType, true),
+		nameTemplate,
+		indent
+		)
+end
+
+function getVoidFunctionDeclString(func, nameTemplate, indent)
+	return getFunctionDeclStringImpl(
+		func,
+		nil,
+		nameTemplate,
+		indent
+		)
+end
+
+function getEventDeclString(event, nameTemplate, indent)
+	return getFunctionDeclStringImpl(
+		event,
+		"event",
+		nameTemplate,
+		indent
+		)
+end
+
+function getDefineDeclString(define, nameTemplate, indent)
+	local s = "#define "
+
+	s = s .. fillItemNameTemplate(nameTemplate, define.name, define.id)
+
+	if #define.paramArray > 0 then
+		-- no space between name and params!
+
+		s = s .. getParamArrayString(s, define.paramArray, false, "(", ")", indent, " \\\n")
+	end
+
+	return s
+end
+
+function getTypedefDeclString(typedef, nameTemplate, indent)
+	local s
+	local name = fillItemNameTemplate(nameTemplate, getItemName(typedef), typedef.id)
+	local declName
+
+	if TYPEDEF_TO_USING then
+		s = "using " .. name .. " ="
+		declName = ""
+	else
+		s = "typedef"
+		declName = name
+	end
+
+	if next(typedef.paramArray) == nil then
+		local type = getLinkedTextString(typedef.type, true)
+		if string.match(type, "%(%*$") then -- quickfix for function pointers
+			s = s .. " " .. string.sub(type, 1, -3) ..  " (*"
+		else
+			s = s .. " " .. type .. " "
+		end
+
+		s = s .. declName
+
+		if typedef.argString ~= "" then
+			s = s .. formatArgDeclString(typedef.argString, indent)
+		end
+
+		return s
+	end
+
+ 	s = s .. getFunctionModifierDelimiter(indent) .. getLinkedTextString(typedef.type, true) .. getFunctionModifierDelimiter(indent)
+	s = s .. declName
+	s = s .. getParamArrayString(s, typedef.paramArray, true, "(", ")", indent)
+
+	return s
+end
+
+function getClassDeclString(class, nameTemplate, indent)
+	local s = ""
+
+	if #class.templateParamArray > 0 or #class.templateSpecParamArray > 0 then
+		s = "template"
+		if not PRE_PARAM_LIST_SPACE then
+			s = s .. " "
+		end
+		s = s .. getParamArrayString(s, class.templateParamArray, false, "<", ">", indent) .. "\n" .. indent
+	end
+
+	local compoundKind = ''
+	if class.compoundKind == 'interface' then
+		compoundKind = 'template'
+	else
+		compoundKind = class.compoundKind
+	end
+	s = s .. compoundKind .. " "
+	s = s .. fillItemNameTemplate(nameTemplate, getItemName(class), class.id)
+
+	return s
+end
+
+function getBaseClassString(class, protection)
+	local s = ""
+
+	if string.match(LANGUAGE, "^c[px+]*$") then
+		s = protection .. " "
+	end
+
+	if class.id ~= "" then
+		s = s .. ":ref:`" .. getItemQualifiedName(class) .. "<doxid-" .. class.id .. ">`"
+	else
+		-- class without id (imported)
+
+		local url = EXTERNAL_CREF_DB[class.name]
+		if url then
+			s = s .. ":ref:`" .. class.name .. "<" .. url .. ">`"
+		else
+			s = s .. class.name
+		end
+	end
+
+	return s
+end
+
+function isMemberOfUnnamedType(item)
+	local text = getItemInternalDocumentation(item)
+	return string.match(text, ":unnamed%(([%w/:]+)%)")
+end
+
+g_closingBracketChar =
+{
+	["("] = ")",
+	["["] = "]",
+	["<"] = ">",
+	["{"] = "}",
+}
+
+function formatArgDeclString(decl, indent)
+	local bracket = {}
+	bracket[0] = {}
+	bracket[0].result = ""
+
+	local level = 0
+	local pos = 1
+	local len = string.len(decl)
+
+	for i = 1, len do
+		local c = string.sub(decl, i, i)
+
+		if c == "(" or c == "[" or c == "<" or c == "{" then
+			local chunk = trimLeadingWhitespace(string.sub(decl, pos, i))
+			pos = i + 1
+
+			bracket[level].result = bracket[level].result .. chunk
+
+			level = level + 1
+			bracket[level] = {}
+			bracket[level].closingChar = g_closingBracketChar[c]
+			bracket[level].result = ""
+		elseif level > 0 then
+			if c == ',' then
+				if not bracket[level].delimiter then
+					bracket[level].delimiter = "\n" .. indent .. string.rep("\t", level)
+					bracket[level].result = bracket[level].delimiter .. bracket[level].result
+				end
+
+				local chunk = trimLeadingWhitespace(string.sub(decl, pos, i))
+				pos = i + 1
+
+				bracket[level].result = bracket[level].result .. chunk .. bracket[level].delimiter
+				pos = i + 1
+			elseif c == bracket[level].closingChar then
+				local chunk = trimLeadingWhitespace(string.sub(decl, pos, i - 1))
+				pos = i
+
+				bracket[level].result = bracket[level].result .. chunk
+
+				if bracket[level].delimiter then
+					bracket[level].result = bracket[level].result .. bracket[level].delimiter
+				end
+
+				level = level - 1
+				bracket[level].result = bracket[level].result .. bracket[level + 1].result
+			end
+		end
+	end
+
+	if pos <= len then
+		bracket[0].result = bracket[0].result .. string.sub(decl, pos)
+	end
+
+	return bracket[0].result
+end
+
+function getNamespaceTree(nspace, indent)
+	local s = ""
+
+	if not indent then
+		indent = ""
+	end
+
+	s = "\t" .. indent .. "namespace :ref:`" .. getItemQualifiedName(nspace) .. "<doxid-" .. nspace.id ..">`;\n"
+
+	for i = 1, #nspace.namespaceArray do
+		s = s .. getNamespaceTree(nspace.namespaceArray[i], indent .. "\t")
+	end
+
+	return s
+end
+
+-------------------------------------------------------------------------------
+
+-- item filtering utils
+
+function getDefaultProtectionKind(compound)
+	if compound.compoundKind ~= "class" or string.match(LANGUAGE, "^ja?ncy?$") then
+		return 0
+	else
+		return 2
+	end
+end
+
+function getProtectionValue(protectionKind)
+	local protectionValue = g_protectionKindMap[protectionKind]
+
+	if not protectionValue then
+		protectionValue = 0 -- assume public
+	end
+
+	return protectionValue
+end
+
+function isItemExcludedByProtectionFilter(item)
+
+	local protectionValue = g_protectionKindMap[item.protectionKind]
+	if protectionValue and protectionValue > g_protectionFilterValue then
+		return true
+	end
+
+	return false
+end
+
+function hasNonPublicItems(array)
+	if next(array) == nil then
+		return false
+	end
+
+	local lastItem = array[#array]
+	local protectionValue = g_protectionKindMap[lastItem.protectionKind]
+	return protectionValue and protectionValue > 0
+end
+
+function isItemExcludedByLocationFilter(item)
+	if not item.location then
+		return false
+	end
+
+	return
+		-- exclude explicitly specified locations
+		EXCLUDE_LOCATION_PATTERN and
+		string.match(item.location.file, EXCLUDE_LOCATION_PATTERN)
+end
+
+function filterItemArray(itemArray)
+	if next(itemArray) == nil then
+		return
+	end
+
+	for i = #itemArray, 1, -1 do
+		local item = itemArray[i]
+		local isExcluded =
+			isItemExcludedByProtectionFilter(item) or
+			isItemExcludedByLocationFilter(item)
+
+		if isExcluded then
+			table.remove(itemArray, i)
+		end
+	end
+end
+
+function filterEnumArray(enumArray)
+	if next(enumArray) == nil then
+		return
+	end
+
+	for i = #enumArray, 1, -1 do
+		local enum = enumArray[i]
+		local isExcluded =
+			isItemExcludedByProtectionFilter(enum) or
+			isItemExcludedByLocationFilter(enum) or
+			isUnnamedItem(enum) and #enum.enumValueArray == 0
+
+		if isExcluded then
+			table.remove(enumArray, i)
+		end
+	end
+end
+
+function filterNamespaceArray(namespaceArray)
+	if next(namespaceArray) == nil then
+		return
+	end
+
+	for i = #namespaceArray, 1, -1 do
+		local item = namespaceArray[i]
+		local isExcluded =
+			isUnnamedItem(item) or
+			isItemExcludedByLocationFilter(item)
+
+		if isExcluded then
+			table.remove(namespaceArray, i)
+		end
+	end
+end
+
+function filterConstructorArray(constructorArray)
+	filterItemArray(constructorArray)
+
+	if #constructorArray == 1 then
+		local item = constructorArray[1]
+		local isExcluded =
+			isItemExcludedByProtectionFilter(item) or
+			EXCLUDE_DEFAULT_CONSTRUCTORS and #item.paramArray == 0
+
+		if isExcluded then
+			table.remove(constructorArray, 1)
+		end
+	end
+end
+
+function filterDefineArray(defineArray)
+	if next(defineArray) == nil then
+		return
+	end
+
+	for i = #defineArray, 1, -1 do
+		local item = defineArray[i]
+
+		local isExcluded =
+			isItemExcludedByLocationFilter(item) or
+			EXCLUDE_EMPTY_DEFINES and item.initializer.isEmpty or
+			EXCLUDE_DEFINE_PATTERN and string.match(item.name, EXCLUDE_DEFINE_PATTERN)
+
+		if isExcluded then
+			table.remove(defineArray, i)
+		end
+	end
+end
+
+function filterTypedefArray(typedefArray)
+	if next(typedefArray) == nil then
+		return
+	end
+
+	for i = #typedefArray, 1, -1 do
+		local item = typedefArray[i]
+		local isExcluded =
+			isItemExcludedByProtectionFilter(item) or
+			isItemExcludedByLocationFilter(item)
+
+		if not isExcluded and EXCLUDE_PRIMITIVE_TYPEDEFS then
+			local typeKind, name = string.match(
+				item.type.plainText,
+				"(%a+)%s+(%w[%w_]*)"
+				)
+
+			if name == item.name then
+				isExcluded =
+					item.briefDescription.isEmpty and
+					item.detailedDescription.isEmpty
+			end
+		end
+
+		if isExcluded then
+			table.remove(typedefArray, i)
+		end
+	end
+end
+
+-------------------------------------------------------------------------------
+
+-- base compound is an artificial compound holding all inherited members
+
+function addToBaseCompound(baseCompound, baseTypeArray)
+	for i = 1, #baseTypeArray do
+		local baseType = baseTypeArray[i]
+
+		-- prevent adding the same base type multiple times
+
+		if not baseCompound.baseTypeMap[baseType] and
+			baseType.compoundKind ~= "<undefined>" then
+
+			baseCompound.baseTypeMap[baseType] = true
+			prepareCompound(baseType)
+
+			if next(baseType.baseTypeArray) ~= nil then
+				addToBaseCompound(baseCompound, baseType.baseTypeArray)
+			end
+
+			concatenateTables(baseCompound.typedefArray, baseType.typedefArray)
+			concatenateTables(baseCompound.enumArray, baseType.enumArray)
+			concatenateTables(baseCompound.structArray, baseType.structArray)
+			concatenateTables(baseCompound.unionArray, baseType.unionArray)
+			concatenateTables(baseCompound.interfaceArray, baseType.interfaceArray)
+			concatenateTables(baseCompound.protocolArray, baseType.protocolArray)
+			concatenateTables(baseCompound.exceptionArray, baseType.exceptionArray)
+			concatenateTables(baseCompound.classArray, baseType.classArray)
+			concatenateTables(baseCompound.singletonArray, baseType.singletonArray)
+			concatenateTables(baseCompound.serviceArray, baseType.serviceArray)
+			concatenateTables(baseCompound.variableArray, baseType.variableArray)
+			concatenateTables(baseCompound.propertyArray, baseType.propertyArray)
+			concatenateTables(baseCompound.eventArray, baseType.eventArray)
+			concatenateTables(baseCompound.functionArray, baseType.functionArray)
+			concatenateTables(baseCompound.aliasArray, baseType.aliasArray)
+		end
+	end
+end
+
+function createPseudoCompound(compoundKind)
+	local compound = {}
+
+	compound.compoundKind = compoundKind
+	compound.baseTypeMap = {}
+	compound.namespaceArray = {}
+	compound.typedefArray = {}
+	compound.enumArray = {}
+	compound.structArray = {}
+	compound.unionArray = {}
+	compound.interfaceArray = {}
+	compound.protocolArray = {}
+	compound.exceptionArray = {}
+	compound.classArray = {}
+	compound.singletonArray = {}
+	compound.serviceArray = {}
+	compound.variableArray = {}
+	compound.propertyArray = {}
+	compound.eventArray = {}
+	compound.constructorArray = {}
+	compound.functionArray = {}
+	compound.aliasArray = {}
+	compound.defineArray = {}
+
+	return compound
+end
+
+function createBaseCompound(compound)
+	local baseCompound = createPseudoCompound("base-compound")
+	baseCompound.isBaseCompound = true
+	addToBaseCompound(baseCompound, compound.baseTypeArray)
+	baseCompound.hasItems = hasCompoundItems(baseCompound)
+	handleCompoundProtection(baseCompound)
+
+	compound.baseCompound = baseCompound
+end
+
+function sortByProtection(array)
+	if next(array) == nil then
+		return
+	end
+
+	local bucketArray  = {}
+	for i = g_minProtectionValue, g_maxProtectionValue do
+		bucketArray[i] = {}
+	end
+
+	for i = 1, #array do
+		local item = array[i]
+		local protectionValue = getProtectionValue(item.protectionKind)
+		table.insert(bucketArray[protectionValue], item)
+	end
+
+	local result = {}
+	local k = 1
+
+	for i = g_minProtectionValue, g_maxProtectionValue do
+		local bucket = bucketArray[i]
+
+		for j = 1, #bucket do
+			array[k] = bucket[j]
+			k = k + 1
+		end
+	end
+
+	assert(k == #array + 1)
+
+	return #array - #bucketArray[0]
+end
+
+function addToProtectionCompounds(compound, arrayName)
+	local array = compound[arrayName]
+
+	if next(array) == nil then
+		return
+	end
+
+	for i = 1, #array do
+		local item = array[i]
+		local protectionValue = getProtectionValue(item.protectionKind)
+		local protectionCompound = compound.protectionCompoundArray[protectionValue]
+		local targetArray = protectionCompound[arrayName]
+
+		table.insert(targetArray, item)
+		protectionCompound.isEmpty = false
+	end
+end
+
+function handleCompoundProtection(compound)
+
+	compound.protectionCompoundArray = {}
+
+	for i = g_minProtectionValue, g_maxProtectionValue do
+		local protectionCompound = createPseudoCompound("protection-compound")
+		protectionCompound.isEmpty = true
+		protectionCompound.isBaseCompound = compound.isBaseCompound
+		compound.protectionCompoundArray[i] = protectionCompound
+	end
+
+	addToProtectionCompounds(compound, "namespaceArray")
+	addToProtectionCompounds(compound, "typedefArray")
+	addToProtectionCompounds(compound, "enumArray")
+	addToProtectionCompounds(compound, "structArray")
+	addToProtectionCompounds(compound, "unionArray")
+	addToProtectionCompounds(compound, "interfaceArray")
+	addToProtectionCompounds(compound, "protocolArray")
+	addToProtectionCompounds(compound, "exceptionArray")
+	addToProtectionCompounds(compound, "classArray")
+	addToProtectionCompounds(compound, "singletonArray")
+	addToProtectionCompounds(compound, "serviceArray")
+	addToProtectionCompounds(compound, "variableArray")
+	addToProtectionCompounds(compound, "propertyArray")
+	addToProtectionCompounds(compound, "eventArray")
+	addToProtectionCompounds(compound, "constructorArray")
+	addToProtectionCompounds(compound, "functionArray")
+	addToProtectionCompounds(compound, "aliasArray")
+	addToProtectionCompounds(compound, "defineArray")
+
+	if compound.destructor then
+		local protectionValue = getProtectionValue(compound.destructor.protectionKind)
+		local protectionCompound = compound.protectionCompoundArray[protectionValue]
+		protectionCompound.destructor = compound.destructor
+		protectionCompound.isEmpty = false
+	end
+
+	-- eliminate empty protection compounds
+
+	for i = g_minProtectionValue, g_maxProtectionValue do
+		if compound.protectionCompoundArray[i].isEmpty then
+			compound.protectionCompoundArray[i] = nil
+		end
+	end
+end
+
+-------------------------------------------------------------------------------
+
+-- compound & enum prep
+
+function hasCompoundItems(compound)
+	return
+		#compound.namespaceArray ~= 0 or
+		#compound.typedefArray ~= 0 or
+		#compound.enumArray ~= 0 or
+		#compound.structArray ~= 0 or
+		#compound.unionArray ~= 0 or
+		#compound.interfaceArray ~= 0 or
+		#compound.protocolArray ~= 0 or
+		#compound.exceptionArray ~= 0 or
+		#compound.classArray ~= 0 or
+		#compound.singletonArray ~= 0 or
+		#compound.serviceArray ~= 0 or
+		#compound.variableArray ~= 0 or
+		#compound.propertyArray ~= 0 or
+		#compound.eventArray ~= 0 or
+		#compound.constructorArray ~= 0 or
+		#compound.functionArray ~= 0 or
+		#compound.aliasArray ~= 0 or
+		#compound.defineArray ~= 0
+end
+
+function prepareCompound(compound)
+	if compound.stats then
+		return compound.stats
+	end
+
+	local stats = {}
+
+	-- filter invisible items out
+
+	if compound.derivedTypeArray then
+		filterItemArray(compound.derivedTypeArray)
+	end
+
+	filterNamespaceArray(compound.namespaceArray)
+	filterTypedefArray(compound.typedefArray)
+	filterEnumArray(compound.enumArray)
+	filterItemArray(compound.structArray)
+	filterItemArray(compound.unionArray)
+	filterItemArray(compound.interfaceArray)
+	filterItemArray(compound.protocolArray)
+	filterItemArray(compound.exceptionArray)
+	filterItemArray(compound.classArray)
+	filterItemArray(compound.singletonArray)
+	filterItemArray(compound.serviceArray)
+	filterItemArray(compound.variableArray)
+	filterItemArray(compound.propertyArray)
+	filterItemArray(compound.eventArray)
+	filterConstructorArray(compound.constructorArray)
+	filterItemArray(compound.functionArray)
+	filterItemArray(compound.aliasArray)
+	filterDefineArray(compound.defineArray)
+
+	stats.hasItems = hasCompoundItems(compound)
+
+	-- scan for documentation and create subgroups
+
+	stats.hasUnnamedEnums = false
+	stats.hasDocumentedUnnamedEnumValues = false
+
+	for i = 1, #compound.enumArray do
+		local item = compound.enumArray[i]
+
+		if isUnnamedItem(item) then
+			stats.hasUnnamedEnums = true
+
+			if prepareItemArrayDocumentation(item.enumValueArray, compound) then
+				stats.hasDocumentedUnnamedEnumValues = true
+			end
+		end
+	end
+
+	stats.hasDocumentedTypedefs = prepareItemArrayDocumentation(compound.typedefArray, compound)
+	stats.hasDocumentedVariables = prepareItemArrayDocumentation(compound.variableArray, compound)
+	stats.hasDocumentedProperties = prepareItemArrayDocumentation(compound.propertyArray, compound)
+	stats.hasDocumentedEvents = prepareItemArrayDocumentation(compound.eventArray, compound)
+	stats.hasDocumentedFunctions = prepareItemArrayDocumentation(compound.functionArray, compound)
+	stats.hasDocumentedAliases = prepareItemArrayDocumentation(compound.aliasArray, compound)
+	stats.hasDocumentedDefines = prepareItemArrayDocumentation(compound.defineArray, compound)
+	stats.hasDocumentedConstruction = prepareItemArrayDocumentation(compound.constructorArray, compound)
+
+	if not EXCLUDE_DESTRUCTORS and compound.destructor then
+		prepareItemDocumentation(compound.destructor, compound)
+		stats.hasDocumentedConstruction = stats.hasDocumentedConstruction or compound.destructor.hasDocumentation
+	end
+
+	stats.hasDocumentedItems =
+		stats.hasDocumentedUnnamedEnumValues or
+		stats.hasDocumentedTypedefs or
+		stats.hasDocumentedVariables or
+		stats.hasDocumentedProperties or
+		stats.hasDocumentedEvents or
+		stats.hasDocumentedConstruction or
+		stats.hasDocumentedFunctions or
+		stats.hasDocumentedAliases or
+		stats.hasDocumentedDefines
+
+	stats.hasBriefDocumentation = not isDocumentationEmpty(compound.briefDescription)
+	stats.hasDetailedDocumentation = not isDocumentationEmpty(compound.detailedDescription)
+
+	-- sort items -- only the ones producing separate pages;
+	-- also, defines, which always go to the global namespace
+
+	table.sort(compound.groupArray, cmpGroups)
+	table.sort(compound.namespaceArray, cmpNames)
+	table.sort(compound.enumArray, cmpNames)
+	table.sort(compound.structArray, cmpNames)
+	table.sort(compound.unionArray, cmpNames)
+	table.sort(compound.interfaceArray, cmpNames)
+	table.sort(compound.protocolArray, cmpNames)
+	table.sort(compound.exceptionArray, cmpNames)
+	table.sort(compound.classArray, cmpNames)
+	table.sort(compound.serviceArray, cmpNames)
+	table.sort(compound.defineArray, cmpNames)
+
+	compound.stats = stats
+
+	if compound.baseTypeArray and next(compound.baseTypeArray) ~= nil then
+		createBaseCompound(compound)
+	end
+
+	handleCompoundProtection(compound)
+
+	return stats
+end
+
+function prepareEnum(enum)
+	local stats = {}
+
+	local maxLength = 0
+	for i = 1, #enum.enumValueArray do
+		local item = enum.enumValueArray[i]
+		local length = string.len(item.name)
+		if length > maxLength then
+			maxLength = length
+		end
+	end
+
+	stats.hasDocumentedEnumValues = prepareItemArrayDocumentation(enum.enumValueArray)
+	stats.hasBriefDocumentation = not isDocumentationEmpty(enum.briefDescription)
+	stats.hasDetailedDocumentation = not isDocumentationEmpty(enum.detailedDescription)
+	stats.maxEnumValueNameLength = maxLength
+
+	return stats
+end
+
+-------------------------------------------------------------------------------
diff --git a/docs/doxyrest/frame/common/crefdb_external.py.in b/docs/doxyrest/frame/common/crefdb_external.py.in
new file mode 100644
index 00000000000..68cbd9dfff0
--- /dev/null
+++ b/docs/doxyrest/frame/common/crefdb_external.py.in
@@ -0,0 +1,20 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+
+for id, url in pairs(EXTERNAL_CREF_DB) do
+}
+	'$id' : '$url',
+%{
+end -- for
+}
diff --git a/docs/doxyrest/frame/common/crefdb_groups.py.in b/docs/doxyrest/frame/common/crefdb_groups.py.in
new file mode 100644
index 00000000000..ac646cdbea4
--- /dev/null
+++ b/docs/doxyrest/frame/common/crefdb_groups.py.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local groupArray = argArray[1]
+
+for i = 1, #groupArray do
+	local group = groupArray[i]
+}
+	'$(group.title)' : 'doxid-$(group.id)',
+%{
+	includeFile("crefdb_groups.py.in", group.groupArray)
+end -- for
+}
diff --git a/docs/doxyrest/frame/common/crefdb_items.py.in b/docs/doxyrest/frame/common/crefdb_items.py.in
new file mode 100644
index 00000000000..93ce25a6cd6
--- /dev/null
+++ b/docs/doxyrest/frame/common/crefdb_items.py.in
@@ -0,0 +1,23 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local itemArray = argArray[1]
+local namePrefix = argArray[2]
+
+for i = 1, #itemArray do
+	local item = itemArray[i]
+}
+	'$namePrefix$(item.name)' : 'doxid-$(item.id)',
+%{
+end -- for
+}
diff --git a/docs/doxyrest/frame/common/details.rst.in b/docs/doxyrest/frame/common/details.rst.in
new file mode 100644
index 00000000000..744262cd2e3
--- /dev/null
+++ b/docs/doxyrest/frame/common/details.rst.in
@@ -0,0 +1,63 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+local itemArray = argArray[2]
+local getDeclString = argArray[3]
+local sectionName = argArray[4]
+
+if sectionName then
+}
+$(getTitle(sectionName, 3))
+
+%{
+end -- if
+
+for i = 1, #itemArray do
+	local item = itemArray[i]
+	if isItemInCompoundDetails(item, compound) then
+		local decl = getDeclString(item, g_simpleItemNameTemplate, "\t");
+}
+$(getItemRefTargetString(item))
+.. ref-code-block:: $LANGUAGE
+	:class: doxyrest-title-code-block
+
+	$decl
+%{
+		if item.isSubGroupHead then
+			local isPrevMl = string.find(decl, "\n")
+
+			for j = 1, #item.subGroupSlaveArray do
+				slaveItem = item.subGroupSlaveArray[j]
+				local decl = getDeclString(slaveItem, g_simpleItemNameTemplate, "\t")
+				local isMl = string.find(decl, "\n")
+				local extraSep = ""
+
+				if isMl or isPrevMl then
+					extraSep = "\n"
+				end
+
+				isPrevMl = isMl
+}
+$extraSep	$decl
+%{
+			end -- for
+		end -- if
+}
+
+$(getItemDetailedDocumentation(item))
+
+%{
+	end -- if
+end -- for
+}
diff --git a/docs/doxyrest/frame/common/doc.lua b/docs/doxyrest/frame/common/doc.lua
new file mode 100644
index 00000000000..780cb1ac74a
--- /dev/null
+++ b/docs/doxyrest/frame/common/doc.lua
@@ -0,0 +1,592 @@
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+function concatDocBlockContents(s1, s2)
+	local length1 = string.len(s1)
+	local length2 = string.len(s2)
+
+	if length1 == 0 then
+		return s2
+	elseif length2 == 0 then
+		return s1
+	end
+
+	if string.match(s1, "[%s[{(<]$") or string.match(s2, "^[%s%]})>%.,;!?]") then
+		return s1 .. s2
+	else
+		return s1 .. " " .. s2
+	end
+end
+
+function getCodeDocBlockContents(block, context)
+	context.codeBlockKind = block.blockKind
+	local s = getDocBlockListContentsImpl(block.childBlockList, context)
+	context.codeBlockKind = nil
+
+	return s
+end
+
+function getDocBlockText(block, context)
+	local text = block.text
+	local childContents = getDocBlockListContentsImpl(block.childBlockList, context)
+
+	if not context.codeBlockKind then
+		if ESCAPE_ASTERISKS then
+			text = string.gsub(text, "%*", "\\*")
+			-- workaround for doxygen
+			text = string.gsub(text, "%\\%\\%*", "\\*")
+		end
+
+		if ESCAPE_PIPES then
+			text = string.gsub(text, "|", "\\|")
+		end
+
+		if ESCAPE_TRAILING_UNDERSCORES then
+			text = string.gsub(text .. " ", "_([%s%p%c])", "\\_%1")
+		end
+
+		text = trimWhitespace(text)
+		text = concatDocBlockContents(text, childContents)
+	else
+		text = text .. childContents
+	end
+
+	return text
+end
+
+-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+
+-- handling of different block kinds
+
+function formatDocBlock_computeroutput(block, context)
+	if context.codeBlockKind then
+		return block.text .. getDocBlockListContentsImpl(block.childBlockList, context)
+	end
+
+	local code = block.text .. getCodeDocBlockContents(block, context)
+	if not string.find(code, "\n") then
+		return "``" .. trimWhitespace(code) .. "``"
+	end
+
+	code = replaceCommonSpacePrefix(code, "\t")
+	code = trimTrailingWhitespace(code)
+	return "\n\n.. code-block:: none\n\n" .. code
+end
+
+function formatDocBlock_programlisting(block, context)
+	local code = getCodeDocBlockContents(block, context)
+	code = replaceCommonSpacePrefix(code, "\t")
+	code = trimTrailingWhitespace(code)
+
+	return "\n\n.. ref-code-block:: " .. LANGUAGE .. "\n\n" .. code .. "\n\n"
+end
+
+function formatDocBlock_preformatted(block, context)
+	local code = getCodeDocBlockContents(block, context)
+	code = replaceCommonSpacePrefix(code, "\t")
+	code = trimTrailingWhitespace(code)
+
+	-- raw seems like a better approach, but need to figure something out with indents
+	return "\n\n.. code-block:: none\n\n" .. code .. "\n\n"
+end
+
+function formatDocBlock_formula(block, context)
+	local code = getCodeDocBlockContents(block, context)
+	local isInline = string.sub(code, 1, 1) == "$"
+
+	-- take away framing tokens
+	code = string.gsub(code, "^\\?[$%[]", "")
+	code = string.gsub(code, "\\?[$%]]$", "")
+
+	if isInline then
+		code = trimWhitespace(code)
+		return ":math:`" .. code .. "`"
+	end
+
+	code = replaceCommonSpacePrefix(code, "\t")
+	code = trimTrailingWhitespace(code)
+
+	-- raw seems like a better approach, but need to figure something out with indents
+	return "\n\n.. math::\n\n" .. code .. "\n\n"
+end
+
+function formatDocBlock_verbatim(block, context)
+	if not VERBATIM_TO_CODE_BLOCK then
+		return getDocBlockText(block, context)
+	end
+
+	local code = getCodeDocBlockContents(block, context)
+	code = replaceCommonSpacePrefix(code, "\t")
+	code = trimTrailingWhitespace(code)
+
+	-- probably also need to assign some div class
+	return "\n\n.. code-block:: " .. VERBATIM_TO_CODE_BLOCK .. "\n\n" .. code .. "\n\n"
+end
+
+function formatDocBlock_list(block, context, bullet)
+	local prevBullet = context.listItemBullet
+	context.listItemBullet = bullet
+
+	local s = getDocBlockListContentsImpl(block.childBlockList, context)
+	s = "\n\n" .. trimTrailingWhitespace(s) .. "\n\n"
+
+	context.listItemBullet = prevBullet
+
+	return s
+end
+
+function formatDocBlock_variablelist(block, context)
+	local prevList = context.dlList
+	context.dlList = {}
+
+	getDocBlockListContentsImpl(block.childBlockList, context)
+
+	local s =
+		"\n\n.. list-table::\n" ..
+		"\t:widths: 20 80\n\n"
+
+	for i = 1, #context.dlList do
+		s = s .. "\t*\n"
+
+		local entry = context.dlList[i]
+		local title = replaceCommonSpacePrefix(entry.title, "\t\t  ")
+		local description = replaceCommonSpacePrefix(entry.description, "\t\t  ")
+
+		s = s .. "\t\t- " .. trimLeadingWhitespace(title) .. "\n\n"
+		s = s .. "\t\t- " .. trimLeadingWhitespace(description) .. "\n\n"
+	end
+
+	context.dlList = prevList
+
+	return s
+end
+
+function formatDocBlock_ref(block, context)
+	local text = getDocBlockText(block, context)
+	text = string.gsub(text, "<", "\\<") -- escape left chevron
+	return ":ref:`" .. text .. " <doxid-" .. block.id .. ">`"
+end
+
+function formatDocBlock_anchor(block, context)
+	local text = getDocBlockText(block, context)
+	return ":target:`doxid-" .. block.id .. "`" .. text
+end
+
+function formatDocBlock_image(block, context)
+	if block.imageKind == 'html' then
+		local s = "\n\n.. image:: " .. block.name .. "\n"
+		if block.width ~= 0 then
+			s = s .. "\t:width: " .. block.width .. "\n"
+		end
+		if block.height ~= 0 then
+			s = s .. "\t:height: " .. block.height .. "\n"
+		end
+		if block.text ~= "" then
+			s = s .. "\t:alt: " .. block.text .. "\n"
+		end
+		return s .. "\n"
+	end
+	return ''
+end
+
+function formatDocBlock_font(block, context, token)
+	local text = getDocBlockText(block, context)
+
+	if not string.match(text, "[\n`*]") then -- single line, no inline markup
+		return token .. text .. token
+	end
+
+	return text
+end
+
+function formatDocBlock_heading(block, context)
+	local text = getDocBlockText(block, context)
+
+	if HEADING_TO_RUBRIC or not text or text == "" then
+		return "\n\n.. rubric:: " .. text .. "\n\n"
+	else
+		return "\n\n" .. getTitle(text, block.level + 1) .. "\n\n"
+	end
+end
+
+function formatDocBlock_varlistentry(block, context)
+	if not context.dlList then
+		error("unexpected <varlistentry>")
+	end
+
+	local text = getDocBlockText(block, context)
+	local count = #context.dlList
+	local entry = {}
+	entry.title = trimWhitespace(text)
+	entry.description = ""
+	context.dlList[count + 1] = entry
+
+	return ""
+end
+
+function formatDocBlock_listitem(block, context)
+	local s = ""
+	local text = getDocBlockText(block, context)
+
+	if context.dlList then
+		local count = #context.dlList
+		local entry = context.dlList[count]
+		if entry then
+			entry.description = trimWhitespace(text)
+		end
+	else
+		if not context.listItemBullet then
+			error("unexpected <listitem>")
+		end
+
+		s = context.listItemBullet .. " "
+		local indent = string.rep(' ', string.len(s))
+
+		text = replaceCommonSpacePrefix(text, indent)
+		text = trimWhitespace(text)
+
+		s = s .. text .. "\n\n"
+	end
+
+	return s
+end
+
+function formatDocBlock_para(block, context)
+	local s = getDocBlockText(block, context)
+	s = trimWhitespace(s)
+
+	if s ~= "" then
+		s = s .. "\n\n"
+	end
+
+	return s
+end
+
+function formatDocBlock_parametername(block, context)
+	local text = getDocBlockText(block, context)
+	text = trimWhitespace(text)
+
+	if not context.paramSection then
+		context.paramSection = {}
+	end
+
+	local i = #context.paramSection + 1
+
+	context.paramSection[i] = {}
+	context.paramSection[i].name = text
+	context.paramSection[i].description = ""
+
+	return ""
+end
+
+function formatDocBlock_parameterdescription(block, context)
+	local text = getDocBlockText(block, context)
+	text = trimWhitespace(text)
+
+	if string.find(text, "\n") then
+		text = "\n" .. replaceCommonSpacePrefix(text, "\t\t  ") -- add paramter table offset "- "
+	end
+
+	if context.paramSection then
+		local count = #context.paramSection
+		context.paramSection[count].description = text
+	end
+
+	return ""
+end
+
+function formatDocBlock_sect(block, context, level)
+	local s = "\n\n"
+	local text = getDocBlockText(block, context)
+
+	if block.id and block.id ~= "" then
+		s = s .. ".. _doxid-" .. block.id .. ":\n\n"
+	end
+
+	if block.title and block.title ~= "" then
+		if SECTION_TO_RUBRIC then
+			s = s .. ".. rubric:: " .. block.title .. ":"
+		else
+			s = s .. getTitle(block.title, level + 1)
+		end
+	end
+
+	return s .. "\n\n" .. text .. "\n\n"
+end
+
+function formatDocBlock_simplesect(block, context)
+	local s = ""
+	local text = getDocBlockText(block, context)
+
+	if block.simpleSectionKind == "return" then
+		if not context.returnSection then
+			context.returnSection = {}
+		end
+
+		local count = #context.returnSection
+		context.returnSection[count + 1] = text
+	elseif block.simpleSectionKind == "see" then
+		if not context.seeSection then
+			context.seeSection = {}
+		end
+
+		local count = #context.seeSection
+		context.seeSection[count + 1] = text
+	else
+		s = text
+	end
+
+	return s
+end
+
+function formatDocBlock_ulink(block, context)
+	return "`" .. block.text .. " <" .. block.url .. ">`__"
+end
+
+function formatDocBlock_table(b, context)
+	local blockList = b.childBlockList
+	local tbl = {}
+	local maxwidths = {}
+
+	for i = 1, #blockList do
+		local block = blockList [i]
+		if block.blockKind == 'row' then
+			local rowblock = block.childBlockList
+			local row = {}
+			for rownum = 1, #rowblock do
+				if rowblock[rownum].blockKind == 'entry' then
+					local entryblock = rowblock[rownum].childBlockList
+					for entry = 1, #entryblock do
+						if entryblock[entry].blockKind == 'para' then
+							local t = ''
+							local parablock = entryblock[entry].childBlockList
+							for para = 1, #parablock do
+								if parablock[para].blockKind == 'ref' then
+									local blockText = getDocBlockText(parablock[para], context)
+									blockText = string.gsub(blockText, "<", "\\<") -- escape left chevron
+									t = t .. ":ref:`" .. blockText .. " <doxid-" .. parablock[para].id .. ">` "
+								elseif parablock[para].blockKind == 'bold' then
+									local blockText = getDocBlockText(parablock[para], context)
+									t = t .. '**' .. blockText .. '** '
+								elseif parablock[para].blockKind == 'emphasis' then
+									local blockText = getDocBlockText(parablock[para], context)
+									t = t .. '*' .. blockText .. '* '
+								elseif parablock[para].blockKind == 'ulink' then
+									t = t .. "`" .. parablock[para].text .. " <" .. parablock[para].url .. ">`__ "
+								elseif parablock[para].blockKind == 'formula' then
+										local code = getCodeDocBlockContents(parablock[para], context)
+										-- take away framing tokens
+										code = string.gsub(code, "^\\?[$%[]", "")
+										code = string.gsub(code, "\\?[$%]]$", "")
+										code = trimWhitespace(code)
+										t = t .. ":math:`" .. code .. "` "
+								elseif parablock[para].blockKind == 'computeroutput' then
+									local code = getCodeDocBlockContents(parablock[para], context)
+									t = t .. "``" .. code .. "`` "
+								else
+									moreT = trimWhitespace(parablock[para].text)
+									if string.len(moreT) > 0 then
+										moreT = moreT .. ' '
+									end
+									t = t .. moreT
+								end
+							end
+							local tlen = string.len(t)
+							table.insert(row, t)
+							if maxwidths[#row] == nil or maxwidths[#row] < tlen then
+								maxwidths[#row] = tlen
+							end
+						end
+					end
+				end
+			end
+			table.insert(tbl, row)
+		end
+	end
+
+	if #tbl == 0 then
+		return ''
+	end
+
+	local s = '.. list-table::\n'
+	local s = s .. '    :header-rows: 1\n\n'
+	for r = 1, #tbl do
+		for c = 1, #tbl[r] do
+			if c == 1 then
+				s = s .. '    * - '
+			else
+				s = s .. '      - '
+			end
+			tblrc = trimWhitespace(tbl[r][c])
+			s = s .. tblrc .. '\n'
+			-- s = s .. tblrc .. string.rep(' ', 2 + maxwidths[c] - string.len(tblrc))
+		end
+	end
+	return '\n\n' .. s .. '\n'
+end
+
+
+function formatDocBlock_graph(block, context, graphtype)
+	local code = getCodeDocBlockContents(block, context)
+	code = replaceCommonSpacePrefix(code, "\t")
+	code = trimTrailingWhitespace(code)
+
+	return "\n\n.. " .. graphtype .. "::\n\n" .. code .. "\n\n"
+end
+
+function formatDocBlock_blockquote(block, context)
+	local text = getDocBlockText(block, context)
+	if text.find(text,'**NOTE**') then
+		text = string.gsub(text, '^%s*%*%*NOTE%*%*%s*:%s*', '')
+		return "\n\n.. note:: " .. text .. "\n\n"
+	end
+	if text.find(text,'**IMPORTANT**') then
+		text = string.gsub(text, '^%s*%*%*IMPORTANT%*%*%s*:%s*', '')
+		return "\n\n.. warning:: " .. text .. "\n\n"
+	end
+	if text.find(text,'**TIP**') then
+		text = string.gsub(text, '^%s*%*%*TIP%*%*%s*:%s*', '')
+		return "\n\n.. tip:: " .. text .. "\n\n"
+	end
+	return "\t" .. text
+end
+
+function formatDocBlock_sphinxdirective(block, context)
+	local code = getCodeDocBlockContents(block, context)
+	return "\n\n" .. code .. "\n\n"
+end
+
+-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+
+g_blockKindFormatMap =
+{
+	["computeroutput"]       = formatDocBlock_computeroutput,
+	["programlisting"]       = formatDocBlock_programlisting,
+	["preformatted"]         = formatDocBlock_preformatted,
+	["formula"]              = formatDocBlock_formula,
+	["verbatim"]             = formatDocBlock_verbatim,
+	["itemizedlist"]         = function(b, c) return formatDocBlock_list(b, c, "*") end,
+	["orderedlist"]          = function(b, c) return formatDocBlock_list(b, c, "#.") end,
+	["variablelist"]         = formatDocBlock_variablelist,
+	["linebreak"]            = function(b, c) return "\n\n" end,
+	["ref"]                  = formatDocBlock_ref,
+	["anchor"]               = formatDocBlock_anchor,
+	["image"]                = formatDocBlock_image,
+	["bold"]                 = function(b, c) return formatDocBlock_font(b, c, "**") end,
+	["emphasis"]             = function(b, c) return formatDocBlock_font(b, c, "*") end,
+	["sp"]                   = function(b, c) return " " end,
+	["varlistentry"]         = formatDocBlock_varlistentry,
+	["listitem"]             = formatDocBlock_listitem,
+	["heading"]              = formatDocBlock_heading,
+	["para"]                 = formatDocBlock_para,
+	["parametername"]        = formatDocBlock_parametername,
+	["parameterdescription"] = formatDocBlock_parameterdescription,
+	["sect1"]                = function(b, c) return formatDocBlock_sect(b, c, 1) end,
+	["sect2"]                = function(b, c) return formatDocBlock_sect(b, c, 2) end,
+	["sect3"]                = function(b, c) return formatDocBlock_sect(b, c, 3) end,
+	["sect4"]                = function(b, c) return formatDocBlock_sect(b, c, 4) end,
+	["simplesect"]           = formatDocBlock_simplesect,
+	["ulink"]                = formatDocBlock_ulink,
+	["table"]                = formatDocBlock_table,
+	["dot"]                  = function(b, c) return formatDocBlock_graph(b, c, "graphviz") end,
+	["plantuml"]             = function(b, c) return formatDocBlock_graph(b, c, "uml") end,
+	["msc"]                  = function(b, c) return formatDocBlock_graph(b, c, "msc") end,
+	["blockquote"]           = formatDocBlock_blockquote,
+	["sphinxdirective"]      = formatDocBlock_sphinxdirective
+}
+
+function getDocBlockContents(block, context)
+	format = g_blockKindFormatMap[block.blockKind]
+	if format then
+		return format(block, context)
+	else
+		return getDocBlockText(block, context)
+	end
+end
+
+-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+
+function getDocBlockListContentsImpl(blockList, context)
+	local s = ""
+
+	for i = 1, #blockList do
+		local block = blockList[i]
+		if block.blockKind ~= "internal" then
+			local blockContents = getDocBlockContents(block, context)
+			if not context.codeBlockKind then
+				s = concatDocBlockContents(s, blockContents)
+			else
+				s = s .. blockContents
+			end
+		end
+	end
+
+	return s
+end
+
+function getDocBlockListContents(blockList)
+	local context = {}
+	local s = getDocBlockListContentsImpl(blockList, context)
+
+	if context.paramSection then
+		s = s .. "\n\n.. rubric:: Parameters:\n\n"
+		s = s .. ".. list-table::\n"
+		s = s .. "\t:widths: 20 80\n\n"
+
+		for i = 1, #context.paramSection do
+			s = s .. "\t*\n"
+			s = s .. "\t\t- " .. context.paramSection[i].name .. "\n\n"
+			s = s .. "\t\t- " .. context.paramSection[i].description .. "\n\n"
+		end
+	end
+
+	if context.returnSection then
+		s = s .. "\n\n.. rubric:: Returns:\n\n"
+
+		for i = 1, #context.returnSection do
+			s = s .. context.returnSection[i]
+		end
+	end
+
+	if context.seeSection then
+		s = s .. "\n\n.. rubric:: See also:\n\n"
+
+		for i = 1, #context.seeSection do
+			s = s .. context.seeSection[i]
+		end
+	end
+
+	s = trimTrailingWhitespace(s)
+
+	return replaceCommonSpacePrefix(s, "")
+end
+
+function getSimpleDocBlockListContents(blockList)
+	local s = ""
+
+	for i = 1, #blockList do
+		local block = blockList[i]
+		s = s .. block.text .. getSimpleDocBlockListContents(block.childBlockList)
+	end
+
+	return s
+end
+
+function isDocumentationEmpty(description)
+	if description.isEmpty then
+		return true
+	end
+
+	local text = getDocBlockListContents(description.docBlockList)
+	return string.len(text) == 0
+end
+
+-------------------------------------------------------------------------------
diff --git a/docs/doxyrest/frame/common/footnotes.rst.in b/docs/doxyrest/frame/common/footnotes.rst.in
new file mode 100644
index 00000000000..3109ab34b56
--- /dev/null
+++ b/docs/doxyrest/frame/common/footnotes.rst.in
@@ -0,0 +1,33 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local compound = argArray[1]
+}
+
+---------------------
+
+.. rubric:: Footnotes
+
+%{
+	for i = 1, #compound.footnoteArray do
+		local item = compound.footnoteArray[i]
+		local text = getItemDetailedDocumentation(item)
+		text = replaceCommonSpacePrefix(text, "\t")
+}
+.. [#$(item.name)]
+
+$text
+
+%{
+	end -- for
+}
diff --git a/docs/doxyrest/frame/common/group.rst.in b/docs/doxyrest/frame/common/group.rst.in
new file mode 100644
index 00000000000..d6d14c702a5
--- /dev/null
+++ b/docs/doxyrest/frame/common/group.rst.in
@@ -0,0 +1,31 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local group = argArray[1]
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+
+local name = getGroupName(group)
+}
+.. index:: pair: group; $(name)
+.. _doxid-$(group.id):
+
+$(getTitle(name, 1))
+
+%{
+includeFile("compound.rst.in", group)
+}
diff --git a/docs/doxyrest/frame/common/index_main.rst.in b/docs/doxyrest/frame/common/index_main.rst.in
new file mode 100644
index 00000000000..45f4349dac7
--- /dev/null
+++ b/docs/doxyrest/frame/common/index_main.rst.in
@@ -0,0 +1,139 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+
+if INTRO_FILE then
+}
+.. include:: $INTRO_FILE
+
+%{
+else
+}
+$(getTitle(INDEX_TITLE, 1))
+
+%{
+end --if
+
+if #g_groupArray > 0 then
+}
+.. toctree::
+	:hidden:
+
+%{
+	table.sort(g_groupArray, cmpGroups)
+	local groupTree = ""
+
+	for i = 1, #g_groupArray do
+		local group = g_groupArray[i]
+		local targetFileName = getItemFileName(group)
+		prepareCompound(group)
+		generateFile(targetFileName, "group.rst.in", group)
+		groupTree = groupTree .. getGroupTree(group)
+}
+	$(targetFileName)
+%{
+	end -- for
+}
+
+.. rubric:: Further Reading:
+
+$groupTree
+
+%{
+end -- if
+
+if #g_pageArray > 0 or #EXTRA_PAGE_LIST > 0 then
+}
+.. toctree::
+	:hidden:
+
+%{
+	table.sort(g_pageArray, cmpTitles)
+	local pageTree = ""
+
+	for i = 1, #g_pageArray do
+		local page = g_pageArray[i]
+		local targetFileName = getItemFileName(page)
+
+		generateFile(targetFileName, "page.rst.in", page)
+
+		if targetFileName ~= INTRO_FILE then
+			pageTree = pageTree .. getPageTree(page, targetFileName)
+}
+	$(targetFileName)
+%{
+		end -- if
+	end -- for
+
+	for i = 1, #EXTRA_PAGE_LIST do
+		local fileName = EXTRA_PAGE_LIST[i]
+		local docName = string.gsub(fileName, "%.rst$", "")
+		pageTree = pageTree .. "|\t:doc:`" .. docName .."`\n"
+}
+	$(fileName)
+%{
+	end -- for
+}
+
+.. rubric:: Related Pages:
+
+$pageTree
+
+%{
+end -- if
+
+if #g_exampleArray > 0 then
+}
+.. toctree::
+	:hidden:
+
+%{
+	table.sort(g_exampleArray, cmpNames)
+	local exampleList = ""
+
+	for i = 1, #g_exampleArray do
+		local example = g_exampleArray[i]
+		local targetFileName = getItemFileName(example)
+		local docName = string.gsub(targetFileName, "%.rst$", "")
+
+		generateFile(targetFileName, "example.rst.in", example)
+		exampleList = exampleList .. "|\t:doc:`" .. docName .."`\n"
+}
+	$(targetFileName)
+%{
+	end -- for
+}
+
+.. rubric:: Examples:
+
+$exampleList
+
+%{
+end -- if
+
+generateFile("global.rst", "global.rst.in")
+}
+.. rubric:: Reference and Index:
+
+.. toctree::
+	:hidden:
+
+	global.rst
+
+|	:doc:`global`
+|	:ref:`genindex`
+
diff --git a/docs/doxyrest/frame/common/item.lua b/docs/doxyrest/frame/common/item.lua
new file mode 100644
index 00000000000..18595ff9d78
--- /dev/null
+++ b/docs/doxyrest/frame/common/item.lua
@@ -0,0 +1,280 @@
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+g_itemFileNameMap = {}
+
+function ensureUniqueItemName(item, name, map, sep)
+	local mapValue = map[name]
+
+	if mapValue == nil then
+		mapValue = {}
+		mapValue.itemMap = {}
+		mapValue.itemMap[item.id] = 1
+		mapValue.items = {}
+		mapValue.count = 1
+		mapValue.items[mapValue.count] = item
+		map[name] = mapValue
+	else
+		local index = mapValue.itemMap[item.id]
+
+		if index == nil then
+			index = mapValue.count + 1
+			mapValue.itemMap[item.id] = index
+			mapValue.items[index] = item
+			mapValue.count = mapValue.count + 1
+		end
+	end
+
+	return name
+end
+
+function getItemFileName(item, suffix)
+	local s
+	
+	if item.compoundKind == 'page' then
+		s = ''
+	elseif item.compoundKind == "interface" then
+		s = 'class'
+	elseif item.compoundKind then
+		s = item.compoundKind
+	elseif item.memberKind then
+		s = item.memberKind
+	else
+		s = "undef_"
+	end
+
+	if item.compoundKind == "group" then
+		s = s .. item.name
+	else
+		local path = string.gsub(item.path, "/operator[%s%p]+$", "/operator")
+		s = s .. string.gsub(path, "/", "_1_1")
+	end
+
+	s = ensureUniqueItemName(item, s, g_itemFileNameMap, "-")
+
+	if not suffix then
+		suffix = ".rst"
+	end
+
+	return s .. suffix
+end
+
+function getItemFileNameNoSuffix(item)
+	local s
+	
+	if item.compoundKind == 'page' then
+		s = ''
+	elseif item.compoundKind == "interface" then
+		s = 'class'
+	elseif item.compoundKind then
+		s = item.compoundKind
+	elseif item.memberKind then
+		s = item.memberKind
+	else
+		s = "undef_"
+	end
+
+	if item.compoundKind == "group" then
+		s = s .. item.name
+	else
+		local path = string.gsub(item.path, "/operator[%s%p]+$", "/operator")
+		s = s .. string.gsub(path, "/", "_1_1")
+	end
+
+	return s
+end
+
+function getItemRefTargetString(item)
+	if string.match(item.name, ":$") then -- Objective C methods (simple handling for now)
+		return ".. _doxid-" .. item.id .. ":\n"
+	end
+
+	local s =
+		".. index:: pair: " .. item.memberKind .. "; " .. item.name .. "\n" ..
+		".. _doxid-" .. item.id .. ":\n"
+
+	if item.isSubGroupHead then
+		for j = 1, #item.subGroupSlaveArray do
+			slaveItem = item.subGroupSlaveArray[j]
+
+			s = s ..
+				".. index:: pair: " .. slaveItem.memberKind .. "; " .. slaveItem.name .. "\n" ..
+				".. _doxid-" .. slaveItem.id .. ":\n"
+		end
+	end
+
+	return s
+end
+
+function hasItemDocumentation(item)
+	return item.hasDocumentation or item.subGroupHead -- in which case subgroup head has doc
+end
+
+g_simpleItemNameTemplate = "$n"
+g_refItemNameTemplate = ":ref:`$n<doxid-$i>`"
+g_targetItemNameTemplate = ":target:`$n<doxid-$i>`"
+
+function getItemNameTemplate(item)
+	if hasItemDocumentation(item) then
+		return g_refItemNameTemplate
+	else
+		return g_targetItemNameTemplate
+	end
+end
+
+function fillItemNameTemplate(template, name, id)
+	local s = string.gsub(template, "$n", name)
+	s = string.gsub(s, "$i", id)
+	return s
+end
+
+function isTocTreeItem(compound, item)
+	return not item.group or item.group == compound
+end
+
+function isUnnamedItem(item)
+	return item.name == "" or string.sub(item.name, 1, 1) == "@"
+end
+
+function getItemInternalDocumentation(item)
+	local s = ""
+
+	for i = 1, #item.detailedDescription.docBlockList do
+		local block = item.detailedDescription.docBlockList[i]
+		if block.blockKind == "internal" then
+			s = s .. block.text .. getSimpleDocBlockListContents(block.childBlockList)
+		end
+	end
+
+	return s
+end
+
+function getItemBriefDocumentation(item)
+	local s = getDocBlockListContents(item.briefDescription.docBlockList)
+
+	if string.len(s) == 0 then
+		s = getDocBlockListContents(item.detailedDescription.docBlockList)
+		if string.len(s) == 0 then
+			return ""
+		end
+
+		-- generate brief description from first sentence only
+		-- matching space is to handle qualified identifiers(e.g. io.File.open)
+
+		local i = string.find(s, "%.%s", 1)
+		if i then
+			s = string.sub(s, 1, i)
+		end
+
+		s = trimTrailingWhitespace(s)
+	end
+
+	return s .. " :ref:`More...<details-" .. item.id .. ">`"
+end
+
+function getItemDetailedDocumentation(item)
+	local brief = getDocBlockListContents(item.briefDescription.docBlockList)
+	local detailed = getDocBlockListContents(item.detailedDescription.docBlockList)
+
+	if string.len(detailed) == 0 then
+		return brief
+	elseif string.len(brief) == 0 then
+		return detailed
+	else
+		return brief .. "\n\n" .. detailed
+	end
+end
+
+function prepareItemDocumentation(item, compound)
+	local hasBriefDocuemtnation = not isDocumentationEmpty(item.briefDescription)
+	local hasDetailedDocuemtnation = not isDocumentationEmpty(item.detailedDescription)
+
+	item.hasDocumentation = hasBriefDocuemtnation or hasDetailedDocuemtnation
+	if not item.hasDocumentation then
+		return false
+	end
+
+	if hasDetailedDocuemtnation then
+		local text = getItemInternalDocumentation(item)
+
+		item.isSubGroupHead = string.match(text, ":subgroup:") ~= nil
+		if item.isSubGroupHead then
+			item.subGroupSlaveArray = {}
+		end
+	end
+
+	return not compound or not item.group or item.group == compound
+end
+
+function prepareItemArrayDocumentation(itemArray, compound)
+
+	local hasDocumentation = false
+	local subGroupHead = nil
+
+	for i = 1, #itemArray do
+		local item = itemArray[i]
+
+		local result = prepareItemDocumentation(item, compound)
+		if result then
+			hasDocumentation = true
+			if item.isSubGroupHead then
+				subGroupHead = item
+			else
+				subGroupHead = nil
+			end
+		elseif subGroupHead then
+			table.insert(subGroupHead.subGroupSlaveArray, item)
+			item.subGroupHead = subGroupHead
+		end
+	end
+
+	return hasDocumentation
+end
+
+function isItemInCompoundDetails(item, compound)
+	if not item.hasDocumentation then
+		return false
+	end
+
+	return not item.group or item.group == compound
+end
+
+function cmpIds(i1, i2)
+	return i1.id < i2.id
+end
+
+function cmpNames(i1, i2)
+	return i1.name < i2.name
+end
+
+function cmpTitles(i1, i2)
+	return i1.title < i2.title
+end
+
+if not SORT_GROUPS_BY then
+	SORT_GROUPS_BY = "title"
+end
+
+function cmpGroups(g1, g2)
+	s1 = getItemInternalDocumentation(g1)
+	s2 = getItemInternalDocumentation(g2)
+
+	o1 = string.match(s1, ":grouporder%(([^)]*)%)")
+	o2 = string.match(s2, ":grouporder%(([^)]*)%)")
+
+	if o1 and o2 then
+		return tonumber(o1) < tonumber(o2)
+	else
+		return g1[SORT_GROUPS_BY] < g2[SORT_GROUPS_BY]
+	end
+end
+
+-------------------------------------------------------------------------------
diff --git a/docs/doxyrest/frame/common/page.rst.in b/docs/doxyrest/frame/common/page.rst.in
new file mode 100644
index 00000000000..56d913bbf0e
--- /dev/null
+++ b/docs/doxyrest/frame/common/page.rst.in
@@ -0,0 +1,61 @@
+%{
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+local argArray = table.pack(...)
+local page = argArray[1]
+
+if FORCE_INCLUDE_FILE then
+}
+.. include:: $FORCE_INCLUDE_FILE
+%{
+end -- if
+}
+.. index:: pair: page; $(page.title)
+.. _doxid-$(page.id):
+
+
+$(getTitle(page.title, 1))
+
+$(getItemDetailedDocumentation(page))
+
+%{
+if #page.subPageArray > 0 then
+}
+.. toctree::
+	:hidden:
+
+%{
+	table.sort(page.subPageArray, cmpTitles)
+	local pageTree = ""
+
+	for i = 1, #page.subPageArray do
+		local page = page.subPageArray[i]
+		local targetFileName = getItemFileName(page)
+
+		generateFile(targetFileName, "page.rst.in", page)
+
+		if targetFileName ~= INTRO_FILE then
+			pageTree = pageTree .. getPageTree(page, targetFileName)
+}
+	$(targetFileName)
+%{
+		end -- if
+	end -- for
+}
+
+.. rubric:: Related Pages:
+
+$pageTree
+
+%{
+end -- if
+}
diff --git a/docs/doxyrest/frame/common/string.lua b/docs/doxyrest/frame/common/string.lua
new file mode 100644
index 00000000000..a7dbe6d7412
--- /dev/null
+++ b/docs/doxyrest/frame/common/string.lua
@@ -0,0 +1,106 @@
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+function captialize(string)
+	local s = string.gsub(string, "^%l", string.upper)
+	return s
+end
+
+function trimLeadingWhitespace(string)
+	local s = string.gsub(string, "^%s+", "")
+	return s
+end
+
+function trimTrailingWhitespace(string)
+	local s = string.gsub(string, "%s+$", "")
+	return s
+end
+
+function trimWhitespace(string)
+	local s = trimLeadingWhitespace(string)
+	return trimTrailingWhitespace(s)
+end
+
+g_titleUnderlineCharMap =
+{
+	"=", "~", "-", "+", "*", "^" -- 1 to 6
+}
+
+function getTitle(title, level)
+	if not title or title == "" then
+		title = "<Untitled>"
+	end
+
+	-- escape trailing underscores
+	title = string.gsub(title, "(_+)(%s+)", "\\%1%2")
+	title = string.gsub(title, "(_+)$", "\\%1")
+
+	local underline = g_titleUnderlineCharMap[level]
+	if not underline then
+		underline = "^"
+	end
+
+	return title .. "\n" .. string.rep(underline, #title)
+end
+
+function replaceAnySpacePrefix(source, replacement)
+	local s = "\n" .. source -- add leading '\n'
+	s = string.gsub(s, "\n[ \t]*", "\n" .. replacement)
+	s = string.sub(s, 2) -- remove leading '\n'
+	return s
+end
+
+function replaceCommonSpacePrefix(source, replacement)
+	local s = "\n" .. source -- add leading '\n'
+	local prefix = nil
+	local len = 0
+
+	for newPrefix in string.gmatch(s, "(\n[ \t]*)[^%s]") do
+		if not prefix then
+			prefix = newPrefix
+			len = string.len(prefix)
+		else
+			local newLen = string.len(newPrefix)
+			if newLen < len then
+				len = newLen
+			end
+
+			for i = 2, len do
+				if string.byte(prefix, i) ~= string.byte(newPrefix, i) then
+					len = i - 1
+					break
+				end
+			end
+
+			prefix = string.sub(prefix, 1, len)
+		end
+
+		if len < 2 then
+			break
+		end
+	end
+
+	if not prefix or len < 2 and replacement == "" then
+		return source
+	end
+
+	s = string.gsub(s, prefix, "\n" .. replacement) -- replace common prefix
+	s = string.sub(s, 2) -- remove leading '\n'
+
+	return s
+end
+
+function replaceRolesWithPlainText(source)
+	local s = string.gsub(source, ":[^:]*:`([^<`]*)[^`]*`", "%1")
+	return s
+end
+
+-------------------------------------------------------------------------------
diff --git a/docs/doxyrest/frame/common/table.lua b/docs/doxyrest/frame/common/table.lua
new file mode 100644
index 00000000000..9207f62b775
--- /dev/null
+++ b/docs/doxyrest/frame/common/table.lua
@@ -0,0 +1,38 @@
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+function printTable(t)
+	for key, value in pairs(t) do
+		print(key, " -> ", value)
+	end
+end
+
+function concatenateTables(t1, t2)
+	local j = #t1 + 1
+	for i = 1, #t2 do
+		t1 [j] = t2 [i]
+		j = j + 1
+	end
+end
+
+function filterArray(a, filter)
+	if next(a) == nil then
+		return
+	end
+
+	for i = #a, 1, -1 do
+		if not filter(a[i]) then
+			table.remove(a, i)
+		end
+	end
+end
+
+--------------------------------------------------------------------------------
diff --git a/docs/doxyrest/frame/common/toc.lua b/docs/doxyrest/frame/common/toc.lua
new file mode 100644
index 00000000000..d368f2dd207
--- /dev/null
+++ b/docs/doxyrest/frame/common/toc.lua
@@ -0,0 +1,219 @@
+--------------------------------------------------------------------------------
+--
+--  This file is part of the Doxyrest toolkit.
+--
+--  Doxyrest is distributed under the MIT license.
+--  For details see accompanying license.txt file,
+--  the public copy of which is also available at:
+--  http://tibbo.com/downloads/archive/doxyrest/license.txt
+--
+--------------------------------------------------------------------------------
+
+function getCompoundTocTree(compound)
+
+	local s = ""
+	if #compound.groupArray > 0 or #compound.namespaceArray > 0 or #compound.enumArray > 0 or
+	#compound.structArray > 0 or #compound.unionArray > 0 or #compound.interfaceArray > 0 or
+	#compound.protocolArray > 0 or #compound.exceptionArray > 0 or #compound.classArray > 0 or
+	#compound.singletonArray > 0 or #compound.serviceArray > 0 or compound.hasGlobalNamespace then
+		s = s .. ".. toctree::\n\t:hidden:\n\n"
+	end
+
+	local hash = {}
+	for i = 1, #compound.groupArray do
+		local item = compound.groupArray[i]
+		local fileName = getItemFileName(item)
+		local isDuplicate = hash[fileName]
+		if isDuplicate == nil then
+			s = s .. "\t" .. fileName .. "\n"
+			hash[fileName] = true
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.namespaceArray do
+		local item = compound.namespaceArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.enumArray do
+		local item = compound.enumArray[i]
+		if isTocTreeItem(compound, item) and not isUnnamedItem(item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.structArray do
+		local item = compound.structArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.unionArray do
+		local item = compound.unionArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.interfaceArray do
+		local item = compound.interfaceArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.protocolArray do
+		local item = compound.protocolArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.exceptionArray do
+		local item = compound.exceptionArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.classArray do
+		local item = compound.classArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.singletonArray do
+		local item = compound.singletonArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. fileName .. "\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	local hash = {}
+	for i = 1, #compound.serviceArray do
+		local item = compound.serviceArray[i]
+		if isTocTreeItem(compound, item) then
+			local fileName = getItemFileName(item)
+			local isDuplicate = hash[fileName]
+			if isDuplicate == nil then
+				s = s .. "\t" .. item.name .. " <" .. fileName .. ">\n"
+				hash[fileName] = true
+			end
+		end
+	end
+
+	if compound.hasGlobalNamespace then
+		s = s .. "\t" .. "global.rst" .. "\n"
+	end
+
+	return trimTrailingWhitespace(s)
+end
+
+function getGroupName(group)
+	local s
+	if string.len(group.title) ~= 0 then
+		s = group.title
+	else
+		s = group.name
+	end
+
+	return s
+end
+
+function getGroupTree(group, indent)
+	local s = ""
+
+	if not indent then
+		indent = ""
+	end
+
+	local name = getGroupName(group)
+	name = string.gsub(name, "<", "\\<") -- escape left chevron
+
+	s = "|\t" .. indent .. ":ref:`" .. name .. "<doxid-" ..group.id .. ">`\n"
+
+	for i = 1, #group.groupArray do
+		s = s .. getGroupTree(group.groupArray[i], indent .. "\t")
+	end
+
+	return s
+end
+
+function getPageTree(page, fileName, indent)
+	local s = ""
+
+	if not indent then
+		indent = ""
+	end
+
+	local docName = string.gsub(fileName, "%.rst$", "")
+
+	s = "|\t" .. indent .. ":doc:`" .. docName .. "`\n"
+
+	for i = 1, #page.subPageArray do
+		local subPage = page.subPageArray[i]
+		s = s .. getPageTree(subPage, getItemFileName(subPage), indent .. "\t")
+	end
+
+	return s
+end
+
+--------------------------------------------------------------------------------
diff --git a/docs/get_started.md b/docs/get_started.md
new file mode 100644
index 00000000000..d34b46c2f72
--- /dev/null
+++ b/docs/get_started.md
@@ -0,0 +1,120 @@
+# Get Started {#get_started}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Install From Release Packages
+   
+   Linux <openvino_docs_install_guides_installing_openvino_linux>
+   Windows <openvino_docs_install_guides_installing_openvino_windows>
+   macOS <openvino_docs_install_guides_installing_openvino_macos>
+   Raspbian OS <openvino_docs_install_guides_installing_openvino_raspbian>
+   Uninstalling <openvino_docs_install_guides_uninstalling_openvino>
+
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Install From Images and Repositories
+   
+   Overview <openvino_docs_install_guides_installing_openvino_images>
+   PIP<openvino_docs_install_guides_installing_openvino_pip>
+   Docker for Linux <openvino_docs_install_guides_installing_openvino_docker_linux>
+   Docker for Windows <openvino_docs_install_guides_installing_openvino_docker_windows>
+   Docker with DL Workbench <workbench_docs_Workbench_DG_Run_Locally>
+   APT <openvino_docs_install_guides_installing_openvino_apt>
+   YUM <openvino_docs_install_guides_installing_openvino_yum>
+   Conda <openvino_docs_install_guides_installing_openvino_conda>
+   Yocto <openvino_docs_install_guides_installing_openvino_yocto>
+   Build from Source <https://github.com/openvinotoolkit/openvino/wiki/BuildingCode>
+
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Configuration for Hardware
+   
+   Configure Intel® Vision Accelerator Design with Intel® Movidius™ VPUs on Linux*<openvino_docs_install_guides_installing_openvino_linux_ivad_vpu>
+   Intel® Movidius™ VPUs Setup Guide <openvino_docs_install_guides_movidius_setup_guide>
+   Intel® Movidius™ VPUs Programming Guide <openvino_docs_install_guides_movidius_programming_guide>
+   
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Get Started Guides
+   
+   Get Started with One-Command Demo <openvino_docs_get_started_get_started_scripts>
+   Get Started with Step-by-step Demo <openvino_docs_get_started_get_started_demos>
+   Get Started with Tutorials <tutorials>
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Inference Engine Code Samples
+
+   openvino_docs_IE_DG_Samples_Overview
+
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Reference Implementations For Speech Recognition Apps
+
+   openvino_inference_engine_samples_speech_libs_and_demos_Speech_libs_and_demos
+   openvino_inference_engine_samples_speech_libs_and_demos_Speech_library
+   openvino_inference_engine_samples_speech_libs_and_demos_Offline_speech_recognition_demo
+   openvino_inference_engine_samples_speech_libs_and_demos_Live_speech_recognition_demo
+   openvino_inference_engine_samples_speech_libs_and_demos_Kaldi_SLM_conversion_tool
+
+@endsphinxdirective
+   
+   
+@sphinxdirective
+.. raw:: html
+    
+   <link rel="stylesheet" type="text/css" href="_static/css/getstarted_style.css">
+   <p id="GSG_introtext">To get started with OpenVINO, the first thing to do is to actually install it. If you haven't done it yet, choose the installation type that best suits   your needs and follow the instructions:<br />
+     <a href="openvino_docs_install_guides_installing_openvino_linux.html" >Install<br /> Package </a>
+     <a href="openvino_docs_install_guides_installing_openvino_images.html" >Install from <br /> images or repositories</a>
+     <a href="https://github.com/openvinotoolkit/openvino/wiki/BuildingCode" >Build <br /> from source</a>
+   </p>
+   <div style="clear:both;"> </div>   
+   <p>With OpenVINO installed, you are ready to run your first inference and learn the workflow. <br /> Here is a set of hands-on demonstrations of various complexity levels to    guide you through the process: from performing sample inference with just one command, to running code samples, demo application or Jupyter notebooks. If you prefer working   with  GUI, you can also get started with the DL Workbench application. This way you can choose the right level for you.<br /></p>
+   
+   <h3>Choose how you want to progress:</h3>
+   
+   <div id="GSG_nextstepchoice">
+     <a href="openvino_docs_get_started_get_started_scripts.html" >
+        <h4>One-command demo 		</h4>
+        <p>Execute just one command and watch all the steps happening before your eyes. </p>
+     </a>  		
+     <a href="openvino_docs_get_started_get_started_demos.html" >
+        <h4>Step-by-step demo		</h4>
+        <p>Follow the step-by-step instructions to execute simple tasks with OpenVINO. </p>
+     </a>
+     <a href="tutorials.html" >
+        <h4>Python Tutorials		</h4>
+        <p>Learn from a choice of interactive Python tutorials targeting typical OpenVINO use cases.	</p>
+     </a> 		
+     <a href="workbench_docs_Workbench_DG_Introduction.html" >
+        <h4>DL Workbench		</h4>
+        <p>Use a web-based version of OpenVINO with a Graphical User Interface. Installing a DL Workbench container is required. </p>
+     </a> 
+     <a href="openvino_docs_IE_DG_Samples_Overview.html" >
+        <h4>Inference Engine samples	</h4>
+        <p>See ready-made applications explaining OpenVINO features and various use-cases.		</p>
+     </a> 
+     <a href="openvino_docs_IE_DG_Samples_Overview.html" >
+        <h4>Reference Implementation For Speech Recognition Apps</h4>
+        <p>Use a speech recognition demo and Kaldi* model conversion tool as reference. </p>
+     </a>
+     <a href="http://devcloud.intel.com/edge/" >
+        <h4>Intel® DevCloud 	</h4>
+        <p>Develop, test, and run your OpenVINO solution for free on a cluster of the latest Intel® hardware. </p>
+     </a> 
+   </div>
+   <div style="clear:both;"> </div>
+ 
+@endsphinxdirective
\ No newline at end of file
diff --git a/docs/get_started/get_started_demos.md b/docs/get_started/get_started_demos.md
new file mode 100644
index 00000000000..856ffe40a51
--- /dev/null
+++ b/docs/get_started/get_started_demos.md
@@ -0,0 +1,874 @@
+# Get Started with Sample and Demo Applications {#openvino_docs_get_started_get_started_demos}
+
+## Introduction
+
+This section guides you through a simplified workflow for the Intel® Distribution of OpenVINO™ toolkit using code samples and demo applications.
+You will perform the following steps:
+
+1. <a href="#download-models">Use the Model Downloader to download suitable models.</a>
+2. <a href="#convert-models-to-intermediate-representation">Convert the models with the Model Optimizer.</a> 
+3. <a href="download-media">Download media files to run inference on.</a>
+4. <a href="run-image-classification">Run inference on the sample and see the results:</a>
+    - <a href="run-image-classification">Image Classification Code Sample</a>
+    - <a href="run-security-barrier">Security Barrier Camera Demo application</a>
+
+If you installed OpenVINO™ via `pip` you need to change commands listed below. Details are listed in one of [tutorials](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/002-openvino-api/002-openvino-api.ipynb).
+
+This guide assumes you completed all installation and configuration steps. If you have not yet installed and configured the toolkit:
+
+@sphinxdirective
+.. tab:: Linux
+
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for Linux* <openvino_docs_install_guides_installing_openvino_linux>`
+
+.. tab:: Windows
+
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for Windows* <openvino_docs_install_guides_installing_openvino_windows>`
+
+.. tab:: macOS
+
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for macOS* <openvino_docs_install_guides_installing_openvino_macos>`
+  
+@endsphinxdirective
+
+## Build Samples and Demos
+
+If you have already built the demos and samples, you can skip this section. The build will take about 5-10 minutes, depending on your system.
+
+To build OpenVINO samples:
+@sphinxdirective
+.. tab:: Linux
+
+   Go to the :doc:`Inference Engine Samples page <openvino_docs_IE_DG_Samples_Overview>` and see the "Build the Sample Applications on Linux*" section.
+
+.. tab:: Windows
+
+   Go to the :doc:`Inference Engine Samples page <openvino_docs_IE_DG_Samples_Overview>` and see the "Build the Sample Applications on Microsoft Windows* OS" section.
+
+.. tab:: macOS
+
+   Go to the :doc:`Inference Engine Samples page <openvino_docs_IE_DG_Samples_Overview>` and see the "Build the Sample Applications on macOS*" section. 
+
+@endsphinxdirective
+
+To build OpenVINO demos:
+@sphinxdirective
+.. tab:: Linux
+
+   Go to the :doc:`Open Model Zoo Demos page <omz_demos>` and see the "Build the Demo Applications on Linux*" section.
+
+.. tab:: Windows
+
+   Go to the :doc:`Open Model Zoo Demos page <omz_demos>` and see the "Build the Demo Applications on Microsoft Windows* OS" section.
+
+.. tab:: macOS
+
+   Go to the :doc:`Open Model Zoo Demos page <omz_demos>` and see the "Build the Demo Applications on Linux*" section. You can use the requirements from "To build OpenVINO samples" above and adapt the Linux build steps for macOS*.
+
+@endsphinxdirective
+
+## <a name="download-models"></a> Step 1: Download the Models
+
+You must have a model that is specific for your inference task. Example model types are:
+
+- Classification (AlexNet, GoogleNet, SqueezeNet, others): Detects one type of element in an image
+- Object Detection (SSD, YOLO): Draws bounding boxes around multiple types of objects in an image
+- Custom: Often based on SSD
+
+Options to find a model suitable for the OpenVINO™ toolkit:
+
+- Download public or Intel pre-trained models from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader)
+- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, etc.
+- Train your own model with machine learning tools
+  
+This guide uses the OpenVINO™ Model Downloader to get pre-trained models. You can use one of the following commands to find a model:
+
+### List the models available in the downloader
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
+      python3 info_dumper.py --print_all
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      cd <INSTALL_DIR>\deployment_tools\tools\model_downloader\
+      python info_dumper.py --print_all
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
+      python3 info_dumper.py --print_all
+
+@endsphinxdirective
+
+### Use `grep` to list models that have a specific name pattern
+
+``` sh
+   python3 info_dumper.py --print_all | grep <model_name>
+```
+
+Use the Model Downloader to download the models to a models directory. This guide uses `<models_dir>` and `<models_name>` as placeholders for the models directory and model name:
+@sphinxdirective
+.. tab:: Linux
+
+   Don't run downloader with `sudo`. It will further lead to complications
+   .. code-block:: sh
+
+      python3 downloader.py --name <model_name> --output_dir <models_dir>
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      python downloader.py --name <model_name> --output_dir <models_dir>
+
+.. tab:: macOS
+
+   Don't run downloader with `sudo`. It will further lead to complications
+   .. code-block:: sh
+
+      python3 downloader.py --name <model_name> --output_dir <models_dir>
+
+@endsphinxdirective
+
+Download the following models to run the Image Classification Sample and Security Barrier Camera Demo applications:
+
+|Model Name                                     | Code Sample or Demo App                  |
+|-----------------------------------------------|------------------------------------------|
+|`squeezenet1.1`                                | Image Classification Sample              |
+|`vehicle-license-plate-detection-barrier-0106` | Security Barrier Camera Demo             |
+|`vehicle-attributes-recognition-barrier-0039`  | Security Barrier Camera Demo             |
+|`license-plate-recognition-barrier-0001`       | Security Barrier Camera Demo             |
+
+@sphinxdirective
+.. raw:: html
+
+   <div class="collapsible-section" data-title="Click for an example of downloading the SqueezeNet Caffe* model">
+
+@endsphinxdirective
+
+To download the SqueezeNet 1.1 Caffe* model to the `models` folder:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      python3 downloader.py --name squeezenet1.1 --output_dir ~/models
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      python downloader.py --name squeezenet1.1 --output_dir C:\Users\<USER_ID>\Documents\models
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      python3 downloader.py --name squeezenet1.1 --output_dir ~/models
+
+@endsphinxdirective
+
+Your screen looks similar to this after the download and shows the paths of downloaded files:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ###############|| Downloading models ||###############
+
+      ========= Downloading /home/username/models/public/squeezenet1.1/squeezenet1.1.prototxt
+
+      ========= Downloading /home/username/models/public/squeezenet1.1/squeezenet1.1.caffemodel
+      ... 100%, 4834 KB, 3157 KB/s, 1 seconds passed
+
+      ###############|| Post processing ||###############
+
+      ========= Replacing text in /home/username/models/public/squeezenet1.1/squeezenet1.1.prototxt =========
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      ################|| Downloading models ||################
+
+      ========== Downloading C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.prototxt
+      ... 100%, 9 KB, ? KB/s, 0 seconds passed
+
+      ========== Downloading C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.caffemodel
+      ... 100%, 4834 KB, 571 KB/s, 8 seconds passed
+
+      ################|| Post-processing ||################
+
+      ========== Replacing text in C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.prototxt
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ###############|| Downloading models ||###############
+
+      ========= Downloading /Users/username/models/public/squeezenet1.1/squeezenet1.1.prototxt
+      ... 100%, 9 KB, 44058 KB/s, 0 seconds passed
+
+      ========= Downloading /Users/username/models/public/squeezenet1.1/squeezenet1.1.caffemodel
+      ... 100%, 4834 KB, 4877 KB/s, 0 seconds passed
+
+      ###############|| Post processing ||###############
+
+      ========= Replacing text in /Users/username/models/public/squeezenet1.1/squeezenet1.1.prototxt =========
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   <div class="collapsible-section" data-title="Click for an example of downloading models for the Security Barrier Camera Demo application">
+
+@endsphinxdirective
+
+To download all three pre-trained models in FP16 precision to the `models` folder in your home folder:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      python3 downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir ~/models --precisions FP16
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      python downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir C:\Users\<USER_ID>\Documents\models --precisions FP16
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      python3 downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir ~/models --precisions FP16
+
+@endsphinxdirective
+
+Your screen looks similar to this after the download:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ################|| Downloading models ||################
+
+      ========== Downloading /home/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml
+      ... 100%, 204 KB, 183949 KB/s, 0 seconds passed
+
+      ========== Downloading /home/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.bin
+      ... 100%, 1256 KB, 3948 KB/s, 0 seconds passed
+
+      ========== Downloading /home/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
+      ... 100%, 32 KB, 133398 KB/s, 0 seconds passed
+
+      ========== Downloading /home/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.bin
+      ... 100%, 1222 KB, 3167 KB/s, 0 seconds passed
+
+      ========== Downloading /home/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml
+      ... 100%, 47 KB, 85357 KB/s, 0 seconds passed
+
+      ========== Downloading /home/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.bin
+      ... 100%, 2378 KB, 5333 KB/s, 0 seconds passed
+
+      ################|| Post-processing ||################
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      ################|| Downloading models ||################
+
+      ========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml
+      ... 100%, 207 KB, 13810 KB/s, 0 seconds passed
+
+      ========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.bin
+      ... 100%, 1256 KB, 70 KB/s, 17 seconds passed
+
+      ========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml
+      ... 100%, 32 KB, ? KB/s, 0 seconds passed
+
+      ========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.bin
+      ... 100%, 1222 KB, 277 KB/s, 4 seconds passed
+
+      ========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml
+      ... 100%, 47 KB, ? KB/s, 0 seconds passed
+
+      ========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.bin
+      ... 100%, 2378 KB, 120 KB/s, 19 seconds passed
+
+      ################|| Post-processing ||################
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ################|| Downloading models ||################
+
+      ========== Downloading /Users/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml
+      ... 100%, 207 KB, 313926 KB/s, 0 seconds passed
+
+      ========== Downloading /Users/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.bin
+      ... 100%, 1256 KB, 2552 KB/s, 0 seconds passed
+
+      ========== Downloading /Users/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
+      ... 100%, 32 KB, 172042 KB/s, 0 seconds passed
+
+      ========== Downloading /Users/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.bin
+      ... 100%, 1222 KB, 2712 KB/s, 0 seconds passed
+
+      ========== Downloading /Users/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml
+      ... 100%, 47 KB, 217130 KB/s, 0 seconds passed
+
+      ========== Downloading /Users/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.bin
+      ... 100%, 2378 KB, 4222 KB/s, 0 seconds passed
+
+      ################|| Post-processing ||################
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+## <a name="convert-models-to-intermediate-representation"></a>Step 2: Convert the Model with Model Optimizer
+
+In this step, your trained models are ready to run through the Model Optimizer to convert them to the IR (Intermediate Representation) format. For most model types, this is required before using the Inference Engine with the model.
+
+Models in the IR format always include an `.xml` and `.bin` file and may also include other files such as `.json` or `.mapping`. Make sure you have these files together in a single directory so the Inference Engine can find them.
+
+REQUIRED: `model_name.xml`
+REQUIRED: `model_name.bin`
+OPTIONAL: `model_name.json`, `model_name.mapping`, etc.
+
+This tutorial uses the public SqueezeNet 1.1 Caffe* model to run the Image Classification Sample. See the example in the Download Models section of this page to learn how to download this model.
+
+The SqueezeNet1.1 model is downloaded in the Caffe* format. You must use the Model Optimizer to convert the model to IR. The `vehicle-license-plate-detection-barrier-0106`, `vehicle-attributes-recognition-barrier-0039`, and `license-plate-recognition-barrier-0001` models are downloaded in IR format. You don't need to use the Model Optimizer on them because they are Intel models that have previously been converted. Public models will need converting with Model Optimizer.
+
+Create an `<ir_dir>` directory to contain the model's Intermediate Representation (IR).
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      mkdir ~/ir
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      mkdir C:\Users\<USER_ID>\Documents\ir
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      mkdir ~/ir
+
+@endsphinxdirective
+
+The Inference Engine can perform inference on different precision formats, such as FP32, FP16, or INT8. To generate an IR with a specific precision, run the Model Optimizer with the appropriate `--data_type` option.
+
+Generic Model Optimizer script:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino/deployment_tools/model_optimizer
+      python3 mo.py --input_model <model_dir>/<model_file> --data_type <model_precision> --output_dir <ir_dir>
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      cd <INSTALL_DIR>\deployment_tools\model_optimizer
+      python mo.py --input_model <model_dir>\<model_file> --data_type <model_precision> --output_dir <ir_dir>
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino/deployment_tools/model_optimizer
+      python3 ./mo.py --input_model <model_dir>/<model_file> --data_type <model_precision> --output_dir <ir_dir>
+
+@endsphinxdirective
+
+IR files produced by the script are written to the <ir_dir> directory.
+
+The command with most placeholders filled in and FP16 precision:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino/deployment_tools/model_optimizer
+      python3 mo.py --input_model ~/models/public/squeezenet1.1/squeezenet1.1.caffemodel --data_type FP16 --output_dir ~/ir
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      cd <INSTALL_DIR>\deployment_tools\model_optimizer
+      python mo.py --input_model C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\squeezenet1.1.caffemodel --data_type FP16 --output_dir C:\Users\<USER_ID>\Documents\ir
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino/deployment_tools/model_optimizer
+      python3 mo.py --input_model ~/models/public/squeezenet1.1/squeezenet1.1.caffemodel --data_type FP16 --output_dir ~/ir
+
+@endsphinxdirective
+
+## <a name="download-media"></a> Step 3: Download a Video or Still Photo as Media
+
+Many sources are available from which you can download video media to use the code samples and demo applications. Possibilities include:
+
+- [Pexels](https://pexels.com)
+- [Google Images](https://images.google.com)
+
+As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes several sample images and videos that you can use for running code samples and demo applications:
+@sphinxdirective
+.. tab:: Linux
+
+- ``/opt/intel/openvino_2021/deployment_tools/demo/car.png``
+- ``/opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp``
+- `Sample images and video <https://storage.openvinotoolkit.org/data/test_data/>`_
+- `Sample videos <https://github.com/intel-iot-devkit/sample-videos>`_
+
+.. tab:: Windows
+
+- ``<INSTALL_DIR>\deployment_tools\demo\car.png``
+- ``<INSTALL_DIR>\deployment_tools\demo\car_1.bmp``
+- `Sample images and video <https://storage.openvinotoolkit.org/data/test_data/>`_
+- `Sample videos <https://github.com/intel-iot-devkit/sample-videos>`_
+
+.. tab:: macOS
+
+- ``/opt/intel/openvino_2021/deployment_tools/demo/car.png``
+- ``/opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp``
+- `Sample images and video <https://storage.openvinotoolkit.org/data/test_data/>`_
+- `Sample videos <https://github.com/intel-iot-devkit/sample-videos>`_
+
+@endsphinxdirective
+
+## <a name="run-image-classification"></a>Step 4: Run Inference on the Sample
+
+### Run the Image Classification Code Sample
+
+To run the **Image Classification** code sample with an input image using the IR model:
+
+1. Set up the OpenVINO environment variables:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      source /opt/intel/openvino/bin/setupvars.sh
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      <INSTALL_DIR>\openvino\bin\setupvars.bat
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      source /opt/intel/openvino/bin/setupvars.sh
+
+@endsphinxdirective
+
+2. Go to the code samples release directory created when you built the samples earlier:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      cd ~/inference_engine_cpp_samples_build/intel64/Release
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      cd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_samples_build\intel64\Release
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      cd ~/inference_engine_cpp_samples_build/intel64/Release
+
+@endsphinxdirective
+
+3. Run the code sample executable, specifying the input media file, the IR for your model, and a target device for performing inference:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      classification_sample_async -i <path_to_media> -m <path_to_model> -d <target_device>
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      classification_sample_async.exe -i <path_to_media> -m <path_to_model> -d <target_device>
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      classification_sample_async -i <path_to_media> -m <path_to_model> -d <target_device>
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   <div class="collapsible-section" data-title="Click for examples of running the Image Classification code sample on different devices">
+
+@endsphinxdirective
+
+The following commands run the Image Classification Code Sample using the `car.png` file from the `demo` directory as an input image, the model in IR format from the `ir` directory, and on different hardware devices:
+
+   **CPU:**  
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\classification_sample_async.exe -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d CPU
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
+
+@endsphinxdirective
+
+   **GPU:**
+   > **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires 
+    [additional hardware configuration steps](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps), as described earlier on this page. Running on GPU is not compatible with macOS*.
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d GPU
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\classification_sample_async.exe -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d GPU
+
+@endsphinxdirective
+
+   **MYRIAD:**
+   > **NOTE**: Running inference on VPU devices (Intel® Movidius™ Neural Compute
+   Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires 
+    [additional hardware configuration steps](inference-engine/README.md#optional-additional-installation-steps-for-the-intel-movidius-neural-compute-stick-and-neural-compute-stick-2), as described earlier on this page.
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\classification_sample_async.exe -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d MYRIAD
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ./classification_sample -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
+
+@endsphinxdirective
+
+When the sample application is complete, you see the label and confidence for the top 10 categories on the display. Below is a sample output with inference results on CPU:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      Top 10 results:
+
+      Image /opt/intel/deployment-tools/demo/car.png
+
+      classid probability label
+      ------- ----------- -----
+      817     0.8363345   sports car, sport car
+      511     0.0946488   convertible
+      479     0.0419131   car wheel
+      751     0.0091071   racer, race car, racing car
+      436     0.0068161   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+      656     0.0037564   minivan
+      586     0.0025741   half track
+      717     0.0016069   pickup, pickup truck
+      864     0.0012027   tow truck, tow car, wrecker
+      581     0.0005882   grille, radiator grille
+
+      [ INFO ] Execution successful
+      
+      [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+   Top 10 results:
+
+   Image C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\demo\car.png
+
+      classid probability label
+      ------- ----------- -----
+      817     0.8363345   sports car, sport car
+      511     0.0946488   convertible
+      479     0.0419131   car wheel
+      751     0.0091071   racer, race car, racing car
+      436     0.0068161   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+      656     0.0037564   minivan
+      586     0.0025741   half track
+      717     0.0016069   pickup, pickup truck
+      864     0.0012027   tow truck, tow car, wrecker
+      581     0.0005882   grille, radiator grille
+
+      [ INFO ] Execution successful
+      
+      [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      Top 10 results:
+
+      Image /opt/intel/deployment-tools/demo/car.png
+
+      classid probability label
+      ------- ----------- -----
+      817     0.8363345   sports car, sport car
+      511     0.0946488   convertible
+      479     0.0419131   car wheel
+      751     0.0091071   racer, race car, racing car
+      436     0.0068161   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+      656     0.0037564   minivan
+      586     0.0025741   half track
+      717     0.0016069   pickup, pickup truck
+      864     0.0012027   tow truck, tow car, wrecker
+      581     0.0005882   grille, radiator grille
+
+      [ INFO ] Execution successful
+      
+      [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+### <a name="run-security-barrier"></a>Run the Security Barrier Camera Demo Application
+
+To run the **Security Barrier Camera Demo Application** using an input image on the prepared IR models:
+
+1. Set up the OpenVINO environment variables:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      source /opt/intel/openvino/bin/setupvars.sh
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      <INSTALL_DIR>\bin\setupvars.bat
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      source /opt/intel/openvino/bin/setupvars.sh
+
+@endsphinxdirective
+
+2. Go to the demo application build directory:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      cd ~/inference_engine_demos_build/intel64/Release
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      cd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_demos_build\intel64\Release
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      cd ~/inference_engine_demos_build/intel64/Release
+
+@endsphinxdirective
+
+3. Run the demo executable, specifying the input media file, list of model IRs, and a target device for performing inference:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./security_barrier_camera_demo -i <path_to_media> -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_vehicle_attributes model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_license_plate_recognition_model>/license-plate-recognition-barrier-0001.xml -d <target_device>
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\security_barrier_camera_demo.exe -i <path_to_media> -m <path_to_vehicle-license-plate-detection_model_xml> -m_va <path_to_vehicle_attributes_model_xml> -m_lpr <path_to_license_plate_recognition_model_xml> -d <target_device>
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ./security_barrier_camera_demo -i <path_to_media> -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_vehicle_attributes model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_license_plate_recognition_model>/license-plate-recognition-barrier-0001.xml -d <target_device>
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   <div class="collapsible-section" data-title="Click for examples of running the Security Barrier Camera demo application on different devices">
+
+@endsphinxdirective
+
+**CPU:**
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d CPU
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\security_barrier_camera_demo.exe -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml -m_va C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml -m_lpr C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml -d CPU
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d CPU
+
+@endsphinxdirective
+
+**GPU:**
+> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires [additional hardware configuration steps](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps), as described earlier on this page. Running on GPU is not compatible with macOS*.
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\security_barrier_camera_demo.exe -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
+
+@endsphinxdirective
+
+**MYRIAD:**
+> **NOTE**: Running inference on VPU devices (Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires     [additional hardware configuration steps](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps), as described earlier on this page.
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d MYRIAD
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\security_barrier_camera_demo.exe -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d MYRIAD
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+## Other Demos/Samples
+
+For more samples and demos, you can visit the samples and demos pages below. You can review samples and demos by complexity or by usage, run the relevant application, and adapt the code for your use.
+
+[Samples](../IE_DG/Samples_Overview.md)
+
+[Demos](@ref omz_demos)
\ No newline at end of file
diff --git a/docs/get_started/get_started_dl_workbench.md b/docs/get_started/get_started_dl_workbench.md
index 0812f543495..4403f78d588 100644
--- a/docs/get_started/get_started_dl_workbench.md
+++ b/docs/get_started/get_started_dl_workbench.md
@@ -14,38 +14,51 @@ Start working with the OpenVINO™ toolkit right from your browser: import a mod
 * Analyze the quality of your model and visualize output.
 * Use preconfigured JupyterLab\* environment to learn OpenVINO™ workflow. 
 
+
 ## Run DL Workbench 
 
 You can [run DL Workbench](@ref workbench_docs_Workbench_DG_Install) on your local system or in the Intel® DevCloud for the Edge. Ensure that you have met the [prerequisites](@ref workbench_docs_Workbench_DG_Prerequisites).
 
 Run DL Workbench on your local system by using the installation form. Select your options and run the commands on the local machine:
 
-<iframe style="width: 100%; height: 620px;" src="https://openvinotoolkit.github.io/workbench_aux/"  frameborder="0" allow="clipboard-write;"></iframe>
+@sphinxdirective
+.. raw:: html
+
+   <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen style="width: 100%; height: 620px;" src="https://openvinotoolkit.github.io/workbench_aux/"  frameborder="0" allow="clipboard-write;"></iframe>
+
+@endsphinxdirective
 
 Once DL Workbench is set up, open the http://127.0.0.1:5665 link.
 
 ![](./dl_workbench_img/active_projects_page.png)
 
+Watch the video to learn more detailed information on how to run DL Workbench:
+
+@sphinxdirective
+.. list-table::
+
+   * - .. raw:: html
+          
+          <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="100%" src="https://www.youtube.com/embed/JBDG2g5hsoM" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+   * - **Inference Engine Concept**. Duration: 3:43
+     
+@endsphinxdirective
+
+@sphinxdirective
+.. list-table::
+
+   * - .. raw:: html
+          
+          <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="100%" src="https://www.youtube.com/embed/on8xSSTKCt8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+   * - **Inference Engine Concept**. Duration: 3:43
+     
+@endsphinxdirective
+
 
 Congratulations, you have installed DL Workbench. Your next step is to [Get Started with DL Workbench](@ref workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets) and create your first project. 
 
-## Videos
-
-<table>
-  <tr>
-    <td>
-<iframe width="320" src="https://www.youtube.com/embed/on8xSSTKCt8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-</td>
-<td>
-    <iframe width="320" src="https://www.youtube.com/embed/JBDG2g5hsoM" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-</td>
-  </tr>
-  <tr>
-    <td><strong>What is the OpenVINO™ toolkit DL Workbench</strong>. <br>Duration: 1:31</td>
-    <td><strong>How to Install the OpenVINO™ toolkit DL Workbench</strong>. <br>Duration: 8:20</td>
-  </tr>
-</table>
-
 ## See Also
 * [Get Started with DL Workbench](@ref workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets)
 * [DL Workbench Overview](@ref workbench_docs_Workbench_DG_Introduction)
diff --git a/docs/get_started/get_started_instructions.md b/docs/get_started/get_started_instructions.md
new file mode 100644
index 00000000000..65609f50801
--- /dev/null
+++ b/docs/get_started/get_started_instructions.md
@@ -0,0 +1,20 @@
+# Get Started with Instructions {#openvino_docs_get_started_get_started_instructions}
+
+# Introduction 
+
+This guide assumes you completed all installation and configuration steps. If you have not yet installed and configured the toolkit:
+
+@sphinxdirective
+.. tab:: Linux
+ 
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for Linux* <openvino_docs_install_guides_installing_openvino_linux>`
+ 
+.. tab:: Windows
+ 
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for Windows* <openvino_docs_install_guides_installing_openvino_windows>`
+ 
+.. tab:: macOS
+ 
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for macOS* <openvino_docs_install_guides_installing_openvino_macos>`
+  
+@endsphinxdirective
diff --git a/docs/get_started/get_started_linux.md b/docs/get_started/get_started_linux.md
index daf7325ed11..b9ace433ec1 100644
--- a/docs/get_started/get_started_linux.md
+++ b/docs/get_started/get_started_linux.md
@@ -221,7 +221,7 @@ Use the Model Downloader to download the models to a models directory. This guid
 ```sh
 sudo python3 ./downloader.py --name <model_name> --output_dir <models_dir>
 ```
-> **NOTE:** Always run the downloader with `sudo`.
+> **NOTE**: Always run the downloader with `sudo`.
 
 Download the following models if you want to run the Image Classification Sample:
 
diff --git a/docs/get_started/get_started_macos.md b/docs/get_started/get_started_macos.md
index aaedca23cd1..ffbf3a926c5 100644
--- a/docs/get_started/get_started_macos.md
+++ b/docs/get_started/get_started_macos.md
@@ -31,10 +31,18 @@ For simplicity, a symbolic link to the latest installation is also created: `/ho
 
 If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `/opt/intel` or `/home/<USER>/` with the directory in which you installed the software.
 
-The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>/tools` directory.
-<details>
-    <summary><strong>Click for the Intel® Distribution of OpenVINO™ toolkit directory structure</strong></summary>
+The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>/deployment_tools` directory.
 
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for the Intel® Distribution of OpenVINO™ toolkit directory structure</strong>">
+
+@endsphinxdirective
+
+
+   
 
 | Directory&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Description                                                                           |  
 |:----------------------------------------|:--------------------------------------------------------------------------------------|
@@ -55,7 +63,16 @@ The primary tools for deploying your models and applications are installed to th
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`tools/`        | Model Downloader and Accuracy Checker tools. |
 | `tools/`                                | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
 
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## <a name="workflow-overview"></a>OpenVINO™ Workflow Overview
 
@@ -92,8 +109,16 @@ The script:
 3. Builds the Image Classification Sample Async application.
 4. Runs the compiled sample with the `car.png` image located in the `demo` directory.
 
-<details>
-    <summary><strong>Click for an example of running the Image Classification sample script</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of running the Image Classification demo script</strong>">
+
+@endsphinxdirective
+
+
 
 To run the script to view the sample image and perform inference on the CPU:
 
@@ -126,19 +151,82 @@ classid probability label
 [ INFO ] Classification sample execution successful
 ```
 
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ### Benchmark Sample Script
 The `run_sample_benchmark_app` script illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices.
 
 The script:
+1. Downloads three pre-trained model IRs.
+2. Builds the Security Barrier Camera Demo application.
+3. Runs the application with the downloaded models and the `car_1.bmp` image from the `demo` directory to show an inference pipeline. 
+
+This application:
+
+1. Identifies an object identified as a vehicle. 
+2. Uses the vehicle identification as input to the second model, which identifies specific vehicle attributes, including the license plate.
+3. Uses the the license plate as input to the third model, which recognizes specific characters in the license plate.
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of Running the Pipeline demo script</strong>">
+
+@endsphinxdirective
+
+
+    
+To run the script performing inference on a CPU:
+
+```sh
+./demo_security_barrier_camera.sh
+```
+
+When the verification script completes, you see an image that displays the resulting frame with detections rendered as bounding boxes, and text:
+
+![](../img/inference_pipeline_script_mac.png) 
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+### Benchmark Demo Script
+The `demo_benchmark_app` script illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices.
+
+The script: 
 1. Downloads a SqueezeNet model.
 2. Runs the Model Optimizer to convert the model to the IR.
 3. Builds the Inference Engine Benchmark tool.
 4. Runs the tool with the `car.png` image located in the `demo` directory.
 
-<details>
-    <summary><strong>Click for an example of running the Benchmark sample script</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of running the Benchmark demo script</strong>">
+
+@endsphinxdirective
+
+
 
 To run the script that performs inference on a CPU:
 
@@ -146,7 +234,16 @@ To run the script that performs inference on a CPU:
 ./run_sample_benchmark_app.sh
 ```
 When the verification script completes, you see the performance counters, resulting latency, and throughput values displayed on the screen.
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## <a name="using-sample-application"></a>Use Code Samples and Demo Applications to Learn the Workflow
 
@@ -205,7 +302,7 @@ Use the Model Downloader to download the models to a models directory. This guid
 ```sh
 sudo python3 ./downloader.py --name <model_name> --output_dir <models_dir>
 ```
-> **NOTE:** Always run the downloader with `sudo`.
+> **NOTE**: Always run the downloader with `sudo`.
 
 Download the following models if you want to run the Image Classification Sample:
 
@@ -213,8 +310,16 @@ Download the following models if you want to run the Image Classification Sample
 |-----------------------------------------------|-----------------------------------------------------|
 |`squeezenet1.1`                                | Image Classification Sample                         |
 
-<details>
-    <summary><strong>Click for an example of downloading the SqueezeNet Caffe* model</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of downloading the SqueezeNet Caffe* model</strong>">
+
+@endsphinxdirective
+
+
 
 To download the SqueezeNet 1.1 Caffe* model to the `~/models` folder:
 
@@ -236,7 +341,68 @@ Your screen looks similar to this after the download:
 
 ========= Replacing text in /Users/username/models/public/squeezenet1.1/squeezenet1.1.prototxt =========
 ```
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of downloading models for the Security Barrier Camera Demo application</strong>">
+
+@endsphinxdirective
+
+
+
+To download all three pre-trained models in FP16 precision to the `~/models` folder:   
+
+```sh
+./downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir ~/models --precisions FP16
+```   
+Your screen looks similar to this after the download:
+```
+################|| Downloading models ||################
+
+========== Downloading /Users/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml
+... 100%, 207 KB, 313926 KB/s, 0 seconds passed
+
+========== Downloading /Users/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.bin
+... 100%, 1256 KB, 2552 KB/s, 0 seconds passed
+
+========== Downloading /Users/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
+... 100%, 32 KB, 172042 KB/s, 0 seconds passed
+
+========== Downloading /Users/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.bin
+... 100%, 1222 KB, 2712 KB/s, 0 seconds passed
+
+========== Downloading /Users/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml
+... 100%, 47 KB, 217130 KB/s, 0 seconds passed
+
+========== Downloading /Users/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.bin
+... 100%, 2378 KB, 4222 KB/s, 0 seconds passed
+
+################|| Post-processing ||################
+```
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+   
 
 ### <a name="convert-models-to-intermediate-representation"></a> Step 2: Convert the Models to the Intermediate Representation
 
@@ -265,8 +431,16 @@ The `squeezenet1.1` model is downloaded in the Caffe* format. You must use the M
    ```
    The produced IR files are in the `<ir_dir>` directory.
 
-<details>
-    <summary><strong>Click for an example of converting the SqueezeNet Caffe* model</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of converting the SqueezeNet Caffe* model</strong>">
+
+@endsphinxdirective
+
+
 
 The following command converts the public SqueezeNet 1.1 Caffe\* model to the FP16 IR and saves to the `~/models/public/squeezenet1.1/ir` output directory:
 
@@ -283,7 +457,16 @@ Copy the `squeezenet1.1.labels` file from the `/opt/intel/openvino_2022/samples/
    ```sh
    cp /opt/intel/openvino_2022/samples/scripts/squeezenet1.1.labels <ir_dir>
    ```
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ### <a name="download-media"></a> Step 3: Download a Video or a Still Photo as Media
 
@@ -313,8 +496,16 @@ To run the **Image Classification** code sample with an input image on the IR:
    ```sh
    ./classification_sample_async -i <path_to_media> -m <path_to_model> -d <target_device>
    ```
-<details>
-    <summary><strong>Click for examples of running the Image Classification code sample on different devices</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for examples of running the Image Classification code sample on different devices</strong>">
+
+@endsphinxdirective
+
+
 
 The following commands run the Image Classification Code Sample using the `car.png` file from the `/opt/intel/openvino_2022/samples/scripts/` directory as an input image, the IR of your model from `~/models/public/squeezenet1.1/ir` and on different hardware devices:
 
@@ -350,10 +541,73 @@ classid probability label
 864     0.0012045   tow truck, tow car, wrecker
 581     0.0005833   grille, radiator grille
 
-[ INFO ] Classification sample execution successful
+[ INFO ] Execution successful
 ```
 
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
+
+> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos) section.
+
+To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
+
+1. Set up the OpenVINO environment variables:
+   ```sh
+   source /opt/intel/openvino_2021/bin/setupvars.sh
+   ``` 
+2. Go to the demo application build directory:
+   ```sh
+   cd ~/inference_engine_demos_build/intel64/Release
+   ```
+3. Run the demo executable, specifying the input media file, list of model IRs, and a target device on which to perform inference:
+   ```sh
+   ./security_barrier_camera_demo -i <path_to_media> -m <path_to_vehicle-license-plate-detection_model_xml> -m_va <path_to_vehicle_attributes_model_xml> -m_lpr <path_to_license_plate_recognition_model_xml> -d <target_device>
+   ```
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for examples of running the Security Barrier Camera demo application on different devices</strong>">
+
+@endsphinxdirective
+
+
+
+**CPU:**
+
+```sh
+./security_barrier_camera_demo -i /opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp -m ~/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_va ~/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml -m_lpr ~/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -d CPU
+```
+
+**MYRIAD:** 
+   
+> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-macos.md).
+```sh   
+./classification_sample_async -i <INSTALL_DIR>/inference-engine/samples/sample_data/car.png -m <ir_dir>/squeezenet1.1.xml -d MYRIAD
+```
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## <a name="basic-guidelines-sample-application"></a>Basic Guidelines for Using Code Samples and Demo Applications
 
diff --git a/docs/get_started/get_started_raspbian.md b/docs/get_started/get_started_raspbian.md
index e53beffaedd..6a1f49db701 100644
--- a/docs/get_started/get_started_raspbian.md
+++ b/docs/get_started/get_started_raspbian.md
@@ -22,10 +22,18 @@ This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit
 
 The OpenVINO toolkit for Raspbian* OS is distributed without installer. This document refers to the directory to which you unpacked the toolkit package as `<INSTALL_DIR>`.
 
-The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>/tools` directory.
-<details>
-    <summary><strong>Click for the <code>tools</code> directory structure</strong></summary>
+The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>/deployment_tools` directory.
 
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for the <code>deployment_tools</code> directory structure</strong>">
+
+@endsphinxdirective
+
+
+   
 
 | Directory&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Description                                                                           |
 |:----------------------------------------|:--------------------------------------------------------------------------------------|
@@ -36,7 +44,16 @@ The primary tools for deploying your models and applications are installed to th
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`samples/`      | Inference Engine samples. Contains source code for C++ and Python* samples and build scripts. See the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md). |
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`share/`        | CMake configuration files for linking with Inference Engine.|
 
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## <a name="workflow-overview"></a>OpenVINO™ Workflow Overview
 
diff --git a/docs/get_started/get_started_scripts.md b/docs/get_started/get_started_scripts.md
new file mode 100644
index 00000000000..4cb55aa4338
--- /dev/null
+++ b/docs/get_started/get_started_scripts.md
@@ -0,0 +1,357 @@
+# Getting Started with Demo Scripts {#openvino_docs_get_started_get_started_scripts}
+
+## Introduction
+
+A set of demo scripts in the `openvino_2021/deployment_tools/demo` directory give you a starting point for learning the OpenVINO™ workflow. These scripts automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios. The demo steps let you see how to: 
+* Compile several samples from the source files delivered as part of the OpenVINO™ toolkit.
+* Download trained models.
+* Convert the models to IR (Intermediate Representation format used by OpenVINO™) with Model Optimizer.
+* Perform pipeline steps and see the output on the console.
+
+This guide assumes you completed all installation and configuration steps. If you have not yet installed and configured the toolkit:
+
+@sphinxdirective
+.. tab:: Linux
+ 
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for Linux* <openvino_docs_install_guides_installing_openvino_linux>`
+ 
+.. tab:: Windows
+ 
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for Windows* <openvino_docs_install_guides_installing_openvino_windows>`
+ 
+.. tab:: macOS
+ 
+   See :doc:`Install Intel® Distribution of OpenVINO™ toolkit for macOS* <openvino_docs_install_guides_installing_openvino_macos>`
+  
+@endsphinxdirective
+
+The demo scripts can run inference on any [supported target device](https://software.intel.com/en-us/openvino-toolkit/hardware). Although the default inference device (i.e., processor) is the CPU, you can add the `-d` parameter to specify a different inference device. The general command to run a demo script is as follows:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      #If you installed in a location other than /opt/intel, substitute that path.
+      cd /opt/intel/openvino_2021/deployment_tools/demo/
+      ./<script_name> -d [CPU, GPU, MYRIAD, HDDL]
+
+.. tab:: Windows
+
+   .. code-block:: sh
+
+      rem If you installed in a location other than the default, substitute that path.
+      cd "C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\demo"
+      .\<script_name> -d [CPU, GPU, MYRIAD, HDDL]
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      #If you installed in a location other than /opt/intel, substitute that path.
+      cd /opt/intel/openvino_2021/deployment_tools/demo/
+      ./<script_name> -d [CPU, MYRIAD]
+
+@endsphinxdirective
+
+Before running the demo applications on Intel® Processor Graphics or on an Intel® Neural Compute Stick 2 device, you must complete additional configuration steps. 
+
+@sphinxdirective
+.. tab:: Linux
+
+   For details, see the following sections in the :doc:`installation instructions <openvino_docs_install_guides_installing_openvino_linux>`:
+   
+   * Steps for Intel® Processor Graphics (GPU) 
+   * Steps for Intel® Neural Compute Stick 2
+
+.. tab:: Windows
+
+   For details, see the following sections in the :doc:`installation instructions <openvino_docs_install_guides_installing_openvino_windows>`:
+   
+   * Additional Installation Steps for Intel® Processor Graphics (GPU)
+   * Additional Installation Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
+
+.. tab:: macOS
+
+   For details, see the following sections in the :doc:`installation instructions <openvino_docs_install_guides_installing_openvino_macos>`:
+   
+   * Steps for Intel® Neural Compute Stick 2
+
+@endsphinxdirective
+
+The following sections describe each demo script.
+
+## Image Classification Demo Script
+The `demo_squeezenet_download_convert_run` script illustrates the image classification pipeline.
+
+The script: 
+1. Downloads a SqueezeNet model. 
+2. Runs the Model Optimizer to convert the model to the IR format used by OpenVINO™.
+3. Builds the Image Classification Sample Async application.
+4. Runs the compiled sample with the `car.png` image located in the `demo` directory.
+
+### Example of Running the Image Classification Demo Script
+
+@sphinxdirective
+.. raw:: html
+    <div class="collapsible-section">
+
+@endsphinxdirective
+**Click for an example of running the Image Classification demo script**
+
+To preview the image that the script will classify:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino_2021/deployment_tools/demo
+      eog car.png
+
+.. tab:: Windows
+
+   .. code-block:: sh
+
+      car.png
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      cd /opt/intel/openvino_2021/deployment_tools/demo
+      open car.png
+
+@endsphinxdirective
+
+To run the script and perform inference on the CPU:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./demo_squeezenet_download_convert_run.sh
+
+.. tab:: Windows
+
+      .. code-block:: bat
+
+         .\demo_squeezenet_download_convert_run.bat
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ./demo_squeezenet_download_convert_run.sh
+
+@endsphinxdirective
+
+When the script completes, you see the label and confidence for the top 10 categories:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      Top 10 results:
+
+      Image /opt/intel/openvino_2021/deployment_tools/demo/car.png
+
+      classid probability label
+      ------- ----------- -----
+      817     0.8363345   sports car, sport car
+      511     0.0946488   convertible
+      479     0.0419131   car wheel
+      751     0.0091071   racer, race car, racing car
+      436     0.0068161   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+      656     0.0037564   minivan
+      586     0.0025741   half track
+      717     0.0016069   pickup, pickup truck
+      864     0.0012027   tow truck, tow car, wrecker
+      581     0.0005882   grille, radiator grille
+
+      [ INFO ] Execution successful
+
+      [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
+
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      Top 10 results:
+
+      Image C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\demo\car.png
+
+      classid probability label
+      ------- ----------- -----
+      817     0.8363345   sports car, sport car
+      511     0.0946488   convertible
+      479     0.0419131   car wheel
+      751     0.0091071   racer, race car, racing car
+      436     0.0068161   beach wagon, station wagon, wagon, estate car, beach wagon, station wagon, wagon
+      656     0.0037564   minivan
+      586     0.0025741   half track
+      717     0.0016069   pickup, pickup truck
+      864     0.0012027   tow truck, tow car, wrecker
+      581     0.0005882   grille, radiator grille
+
+      [ INFO ] Execution successful
+
+      [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
+
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      Top 10 results:
+
+      Image /Users/colin/intel/openvino_2021/deployment_tools/demo/car.png
+
+      classid probability label
+      ------- ----------- -----
+      817     0.8363345   sports car, sport car
+      511     0.0946488   convertible
+      479     0.0419131   car wheel
+      751     0.0091071   racer, race car, racing car
+      436     0.0068161   beach wagon, station wagon, wagon, estate car, beach wagon, station wagon, wagon
+      656     0.0037564   minivan
+      586     0.0025741   half track
+      717     0.0016069   pickup, pickup truck
+      864     0.0012027   tow truck, tow car, wrecker
+      581     0.0005882   grille, radiator grille
+
+      [ INFO ] Execution successful
+
+      [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+    </div>
+
+@endsphinxdirective
+
+## Inference Pipeline Demo Script
+The `demo_security_barrier_camera` application uses vehicle recognition in which vehicle attributes build on each other to narrow in on a specific attribute.
+
+The script:
+1. Downloads three pre-trained models, already converted to IR format.
+2. Builds the Security Barrier Camera Demo application.
+3. Runs the application with the three models and the `car_1.bmp` image from the `demo` directory to show an inference pipeline.
+
+This application:
+
+1. Gets the boundaries an object identified as a vehicle with the first model.
+2. Uses the vehicle identification as input to the second model, which identifies specific vehicle attributes, including the license plate.
+3. Uses the license plate as input to the third model, which recognizes specific characters in the license plate.
+
+### Example of Running the Pipeline Demo Script
+@sphinxdirective
+.. raw:: html
+
+   <div class="collapsible-section">
+
+@endsphinxdirective
+**Click for an example of Running the Pipeline demo script**
+    
+To run the script performing inference on Intel® Processor Graphics:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./demo_security_barrier_camera.sh -d GPU
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\demo_security_barrier_camera.bat -d GPU
+
+@endsphinxdirective
+
+When the verification script is complete, you see an image that displays the resulting frame with detections rendered as bounding boxes and overlaid text:
+
+@sphinxdirective
+.. tab:: Linux
+
+   .. image:: ../img/inference_pipeline_script_lnx.png
+
+.. tab:: Windows
+
+   .. image:: ../img/inference_pipeline_script_win.png
+
+.. tab:: macOS
+
+   .. image:: ../img/inference_pipeline_script_mac.png
+
+@endsphinxdirective
+
+@sphinxdirective
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+## Benchmark Demo Script
+The `demo_benchmark_app` script illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices. 
+
+The script:
+1. Downloads a SqueezeNet model.
+2. Runs the Model Optimizer to convert the model to IR format.
+3. Builds the Inference Engine Benchmark tool.
+4. Runs the tool with the `car.png` image located in the `demo` directory.
+
+### Example of Running the Benchmark Demo Script
+
+@sphinxdirective
+.. raw:: html
+
+   <div class="collapsible-section">
+
+@endsphinxdirective
+**Click for an example of running the Benchmark demo script**
+
+To run the script that performs measures inference performance:
+@sphinxdirective
+.. tab:: Linux
+
+   .. code-block:: sh
+
+      ./demo_benchmark_app.sh
+
+.. tab:: Windows
+
+   .. code-block:: bat
+
+      .\demo_benchmark_app.bat
+
+.. tab:: macOS
+
+   .. code-block:: sh
+
+      ./demo_benchmark_app.sh
+
+@endsphinxdirective
+
+When the verification script is complete, you see the performance counters, resulting latency, and throughput values displayed on the screen.
+@sphinxdirective
+.. raw:: html
+	
+   </div>
+
+@endsphinxdirective
+
+## Other Get Started Documents
+
+For more get started documents, visit the pages below:
+
+[Get Started with Sample and Demo Applications](get_started_demos.md)
+
+[Get Started with Instructions](get_started_instructions.md)
diff --git a/docs/get_started/get_started_windows.md b/docs/get_started/get_started_windows.md
index 00e5bf4cf1e..fdd1047d88c 100644
--- a/docs/get_started/get_started_windows.md
+++ b/docs/get_started/get_started_windows.md
@@ -4,7 +4,7 @@ The OpenVINO™ toolkit optimizes and runs Deep Learning Neural Network models o
 
 In this guide, you will:
 * Learn the OpenVINO™ inference workflow
-* Run ыфьзду scripts that illustrate the workflow and perform the steps for you
+* Run sample scripts that illustrate the workflow and perform the steps for you
 * Run the workflow steps yourself, using detailed instructions with a code sample and demo application
 
 ## <a name="openvino-components"></a>OpenVINO™ toolkit Components
@@ -26,10 +26,18 @@ This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit
 
 By default, the installation directory is `C:\Program Files (x86)\Intel\openvino_<version>`, referred to as `<INSTALL_DIR>`. If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `C:\Program Files (x86)\Intel` with the directory in which you installed the software. For simplicity, a shortcut to the latest installation is also created: `C:\Program Files (x86)\Intel\openvino_2022`.
 
-The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>\tools` directory.
-<details>
-    <summary><strong>Click for the <code>tools</code> directory structure</strong></summary>
+The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>\deployment_tools` directory.
 
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for the <code>deployment_tools</code> directory structure</strong>">
+
+@endsphinxdirective
+
+
+   
 
 | Directory&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Description                                                                           |
 |:----------------------------------------|:--------------------------------------------------------------------------------------|
@@ -52,7 +60,16 @@ The primary tools for deploying your models and applications are installed to th
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`tools\`        | Model Downloader and Accuracy Checker tools. |
 | `tools\`                                | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
 
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## <a name="workflow-overview"></a>OpenVINO™ Workflow Overview
 
@@ -91,8 +108,16 @@ The script:
 3. Builds the Image Classification Sample Async application.
 4. Runs the compiled sample with the `car.png` image located in the `demo` directory.
 
-<details>
-    <summary><strong>Click for an example of running the Image Classification sample script</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of running the Image Classification demo script</strong>">
+
+@endsphinxdirective
+
+
 
 To preview the image that the script will classify open the `car.png` file in any image viewer.
 
@@ -125,19 +150,82 @@ classid probability label
 [ INFO ] Classification sample execution successful
 ```
 
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ### Benchmark Sample Script
 The `run_sample_benchmark_app` script illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices.
 
 The script:
+1. Downloads three pre-trained model IRs.
+2. Builds the Security Barrier Camera Demo application.
+3. Runs the application with the downloaded models and the `car_1.bmp` image from the `demo` directory to show an inference pipeline.
+
+This application:
+
+1. Identifies an object identified as a vehicle.
+2. Uses the vehicle identification as input to the second model, which identifies specific vehicle attributes, including the license plate.
+3. Uses the the license plate as input to the third model, which recognizes specific characters in the license plate.
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of Running the Pipeline demo script</strong>">
+
+@endsphinxdirective
+
+
+    
+To run the script performing inference on Intel® Processor Graphics:
+
+```bat
+.\demo_security_barrier_camera.bat -d GPU
+```
+
+When the verification script completes, you see an image that displays the resulting frame with detections rendered as bounding boxes, and text:
+
+![](../img/inference_pipeline_script_win.png) 
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+### Benchmark Demo Script
+The `demo_benchmark_app` script illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices. 
+
+The script: 
 1. Downloads a SqueezeNet model.
 2. Runs the Model Optimizer to convert the model to the IR.
 3. Builds the Inference Engine Benchmark tool.
 4. Runs the tool with the `car.png` image located in the `demo` directory.
 
-<details>
-    <summary><strong>Click for an example of running the Benchmark sample script</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of running the Benchmark demo script</strong>">
+
+@endsphinxdirective
+
+
 
 To run the script that performs inference (runs on CPU by default):
 
@@ -145,7 +233,16 @@ To run the script that performs inference (runs on CPU by default):
 .\run_sample_benchmark_app.bat
 ```
 When the verification script completes, you see the performance counters, resulting latency, and throughput values displayed on the screen.
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## <a name="using-sample-application"></a>Use Code Samples and Demo Applications to Learn the Workflow
 
@@ -211,8 +308,16 @@ Download the following models if you want to run the Image Classification Sample
 |-----------------------------------------------|-----------------------------------------------------|
 |`squeezenet1.1`                                | Image Classification Sample                         |
 
-<details>
-    <summary><strong>Click for an example of downloading the SqueezeNet Caffe* model</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of downloading the SqueezeNet Caffe* model</strong>">
+
+@endsphinxdirective
+
+
 
 To download the SqueezeNet 1.1 Caffe* model to the `C:\Users\<USER_ID>\Documents\models` folder:
 
@@ -234,7 +339,68 @@ Your screen looks similar to this after the download:
 
 ========== Replacing text in C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.prototxt
 ```
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of downloading models for the Security Barrier Camera Demo application</strong>">
+
+@endsphinxdirective
+
+
+
+To download all three pre-trained models in FP16 precision to the `C:\Users\<USER_ID>\Documents\models` folder:   
+
+```bat
+python .\downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir C:\Users\username\Documents\models --precisions FP16
+```   
+Your screen looks similar to this after the download:
+```
+################|| Downloading models ||################
+
+========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml
+... 100%, 207 KB, 13810 KB/s, 0 seconds passed
+
+========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.bin
+... 100%, 1256 KB, 70 KB/s, 17 seconds passed
+
+========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml
+... 100%, 32 KB, ? KB/s, 0 seconds passed
+
+========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.bin
+... 100%, 1222 KB, 277 KB/s, 4 seconds passed
+
+========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml
+... 100%, 47 KB, ? KB/s, 0 seconds passed
+
+========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.bin
+... 100%, 2378 KB, 120 KB/s, 19 seconds passed
+
+################|| Post-processing ||################
+```
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+   
 
 ### <a name="convert-models-to-intermediate-representation"></a> Step 2: Convert the Models to the Intermediate Representation
 
@@ -261,8 +427,16 @@ The `squeezenet1.1` model is downloaded in the Caffe* format. You must use the M
    ```
    The produced IR files are in the `<ir_dir>` directory.
 
-<details>
-    <summary><strong>Click for an example of converting the SqueezeNet Caffe* model</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for an example of converting the SqueezeNet Caffe* model</strong>">
+
+@endsphinxdirective
+
+
 
 The following command converts the public SqueezeNet 1.1 Caffe\* model to the FP16 IR and saves to the `C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir` output directory:
 
@@ -276,7 +450,16 @@ Copy the `squeezenet1.1.labels` file from the `<INSTALL_DIR>\samples\scripts\` t
    ```batch
    cp <INSTALL_DIR>\samples\scripts\squeezenet1.1.labels <ir_dir>
    ```
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ### <a name="download-media"></a> Step 3: Download a Video or a Still Photo as Media
 
@@ -306,8 +489,16 @@ To run the **Image Classification** code sample with an input image on the IR:
    ```bat
    classification_sample_async.exe -i <path_to_media> -m <path_to_model> -d <target_device>
    ```
-<details>
-    <summary><strong>Click for examples of running the Image Classification code sample on different devices</strong></summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for examples of running the Image Classification code sample on different devices</strong>">
+
+@endsphinxdirective
+
+
 
 The following commands run the Image Classification Code Sample using the `car.png` file from the `<INSTALL_DIR>\samples\scripts` directory as an input image, the IR of your model from `C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir` and on different hardware devices:
 
@@ -348,10 +539,79 @@ classid probability label
 864     0.0012045   tow truck, tow car, wrecker
 581     0.0005833   grille, radiator grille
 
-[ INFO ] Classification sample execution successful
+[ INFO ] Execution successful
 ```
 
-</details>
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
+
+> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos) section.
+
+To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
+
+1. Set up the OpenVINO environment variables:
+   ```bat
+   <INSTALL_DIR>\bin\setupvars.bat
+   ``` 
+2. Go to the demo application build directory:
+   ```bat
+   cd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_demos_build\intel64\Release
+   ```
+3. Run the demo executable, specifying the input media file, list of model IRs, and a target device on which to perform inference:
+   ```bat
+   .\security_barrier_camera_demo -i <path_to_media> -m <path_to_vehicle-license-plate-detection_model_xml> -m_va <path_to_vehicle_attributes_model_xml> -m_lpr <path_to_license_plate_recognition_model_xml> -d <target_device>
+   ```
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>Click for examples of running the Security Barrier Camera demo application on different devices</strong>">
+
+@endsphinxdirective
+
+
+
+**CPU:**
+
+```bat
+.\security_barrier_camera_demo -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml -m_va C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml -m_lpr C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml -d CPU
+```
+
+**GPU:**
+   
+> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps. For details, see the Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-windows.md).
+```bat
+.\security_barrier_camera_demo -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
+```
+
+**MYRIAD:** 
+   
+```bat   
+.\classification_sample_async -i <INSTALL_DIR>\inference-engine\samples\sample_data\car.png -m <ir_dir>\squeezenet1.1.xml -d MYRIAD
+```
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
 
 ## <a name="basic-guidelines-sample-application"></a>Basic Guidelines for Using Code Samples and Demo Applications
 
@@ -374,10 +634,10 @@ This section explains how to build and use the sample and demo applications prov
 To build all the demos and samples:
 
 ```sh
-cd $INTEL_OPENVINO_DIR\inference_engine_samples\cpp
-# to compile C samples, go here also: cd <INSTALL_DIR>\samples\c
+cd %INTEL_OPENVINO_DIR%\inference_engine_samples\cpp
+# to compile C samples, go here also: cd <INSTALL_DIR>\inference_engine\samples\c
 build_samples_msvc.bat
-cd $INTEL_OPENVINO_DIR\extras\open_model_zoo\demos
+cd %INTEL_OPENVINO_DIR%\deployment_tools\open_model_zoo\demos
 build_demos_msvc.bat
 ```
 
diff --git a/docs/how_tos/how-to-links.md b/docs/how_tos/how-to-links.md
index 869c6f7736c..5511f5803b5 100644
--- a/docs/how_tos/how-to-links.md
+++ b/docs/how_tos/how-to-links.md
@@ -17,32 +17,57 @@ To learn about what is *custom operation* and how to work with them in the Deep
  
 ## Introducing OpenVINO™ and Computer Vision | IoT Developer Show Season 2 | Intel Software
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/M6Nyh2JDLQs" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+@sphinxdirective
+.. raw:: html
 
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/M6Nyh2JDLQs" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
 
 ## OpenVINO™ Toolkit and Two Hardware Development Kits | IoT Developer Show Season 2 | Intel Software
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/GtJPBYjuyVU" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+@sphinxdirective
+.. raw:: html
 
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/GtJPBYjuyVU" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
 
 ## Intel Demonstration of High Performance Vision Deployment - The OpenVINO Toolkit in Action
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/1_iI_4Zgufw" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+@sphinxdirective
+.. raw:: html
 
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/1_iI_4Zgufw" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
 
 ## Computer Vision at the Edge with OpenVINO by Krishnakumar Shetti at ODSC_India
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/RfRCrq35LXg" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+@sphinxdirective
+..raw:: html
 
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/RfRCrq35LXg" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
 
 ## Model optimizer concept
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+@sphinxdirective
+.. raw:: html
+
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
 
 ## Computer Vision with Intel
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/FZZD4FCvO9c" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+@sphinxdirective
+.. raw:: html
 
+    <iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen width="560" height="315" src="https://www.youtube.com/embed/FZZD4FCvO9c" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+@endsphinxdirective
 
 ## Case Studies
 
diff --git a/docs/img/BASIC_FLOW_IE_ C.svg b/docs/img/BASIC_FLOW_IE_ C.svg
new file mode 100644
index 00000000000..577dea97620
--- /dev/null
+++ b/docs/img/BASIC_FLOW_IE_ C.svg	
@@ -0,0 +1,32 @@
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"/>
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--- a/docs/index.md
+++ b/docs/index.md
@@ -1,4 +1,4 @@
-# OpenVINO™ Toolkit Overview {#index}
+# OpenVINO™ Toolkit Overview {#openvino_overview}
 
 ## Introduction
 
@@ -94,7 +94,7 @@ Intel® Distribution of OpenVINO™ toolkit includes the following components:
 - [Open Model Zoo](@ref omz_models_group_intel)
     - [Demos](@ref omz_demos): Console applications that provide robust application templates to help you implement specific deep learning scenarios.
     - Additional Tools: A set of tools to work with your models including [Accuracy Checker Utility](@ref omz_tools_accuracy_checker) and [Model Downloader](@ref omz_tools_downloader).
-    - [Documentation for Pre-trained Models](@ref omz_models_group_intel): Documentation for pre-trained models that are available in the [Open Model Zoo repository](https://github.com/openvinotoolkit/open_model_zoo).
+    - [Documentation for Pretrained Models](@ref omz_models_group_intel): Documentation for pre-trained models that are available in the [Open Model Zoo repository](https://github.com/openvinotoolkit/open_model_zoo).
 - Deep Learning Streamer (DL Streamer): Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. DL Streamer can be installed by the Intel® Distribution of OpenVINO™ toolkit installer. Its open-source version is available on [GitHub](https://github.com/openvinotoolkit/dlstreamer_gst). For the DL Streamer documentation, see:
     - [DL Streamer Samples](@ref gst_samples_README)
     - [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/)
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 00000000000..43e956118eb
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,103 @@
+.. OpenVINO Toolkit documentation master file, created by
+   sphinx-quickstart on Wed Jul  7 10:46:56 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+.. meta::
+   :google-site-verification: _YqumYQ98cmXUTwtzM_0WIIadtDc6r_TMYGbmGgNvrk
+
+OpenVINO™ Documentation
+=======================
+
+.. raw:: html
+
+   <div class="section" id="welcome-to-openvino-toolkit-s-documentation">
+
+   <link rel="stylesheet" type="text/css" href="_static/css/homepage_style.css">
+      <div style="clear:both;"> </div>
+
+      <p>
+         OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
+         <ul>
+            <li>Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks </li>
+            <li>Use models trained with popular frameworks like TensorFlow, PyTorch and more </li>
+            <li>Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud </li>
+         </ul>
+     </p>
+      
+      <img class="HP_img_chart" src="_static/images/ov_chart.png"
+         alt="OpenVINO allows to process models built with Caffe, Keras, mxnet, TensorFlow, ONNX, and PyTorch. They can be easily optimized and deployed on devices running Windows, Linux, or MacOS." />
+      <div style="clear:both;"> </div>
+      <p>Check the full range of supported hardware in the 
+         <a href="openvino_docs_IE_DG_supported_plugins_Supported_Devices.html"> Supported Devices page</a> and see how it stacks up in our
+         <a href="openvino_docs_performance_benchmarks.html"> Performance Benchmarks page.</a> <br />
+	 Supports deployment on Windows, Linux, and macOS.
+      </p>      
+      <div class="HP_separator-header">
+         <p> Train, Optimize, Deploy </p>
+      </div>
+      <div style="clear:both;"> </div>
+      <img class="HP_img_chart" src="_static/images/HP_ov_flow.svg" alt="" />
+      <p>* The ONNX format is also supported, but conversion to OpenVINO is recommended for better performance.</p>
+      <div style="clear:both;"> </div>
+      
+      <div style="clear:both;"> </div>
+      <div class="HP_separator-header">
+         <p> Want to know more? </p>
+      </div>
+      <div style="clear:both;"> </div>
+      
+      <div class="HP_infoboxes">
+         <a href="get_started.html">
+            <h3>Get Started </H3>
+            <p> Learn how to download, install, and configure OpenVINO. </p>
+         </a>
+	 <a href="model_zoo.html" >
+	    <h3>Open Model Zoo </h3>
+	    <p> Browse through over 200 publicly available neural networks and pick the right one for your solution. </p>
+	 </a>
+         <a href="openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html" >
+	    <h3>Model Optimizer </h3>
+	    <p> Learn how to convert your model and optimize it for use with OpenVINO. </p> 
+	 </a>
+	 <a href="tutorials.html" >
+	    <h3>Tutorials </h3>
+	    <p> Learn how to use OpenVINO based on our training material. </p>  
+	 </a>
+	 <a href="openvino_docs_IE_DG_Samples_Overview.html" >
+	    <h3>Samples </h3>
+	    <p> Try OpenVINO using ready-made applications explaining various use cases. </p>  
+	 </a>
+	 <a href="workbench_docs_Workbench_DG_Introduction.html" >
+	    <h3>DL Workbench </h3>
+	    <p> Learn about the alternative, web-based version of OpenVINO. DL Workbench container installation Required. </p>
+	 </a>
+	 <a href="openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html" >
+	    <h3>Inference Engine </h3>
+	    <p> Learn about OpenVINO's inference mechanism which executes the IR and ONNX models on target devices. </p>  
+	 </a>
+	 <a href="openvino_docs_optimization_guide_dldt_optimization_guide.html" >
+	    <h3>Tune & Optimize </h3>
+	    <p> Use quantization, pruning, and sparsity algorithms to make your application as efficient as possible. </p> 
+	 </a>
+	 <a href="openvino_docs_performance_benchmarks.html" >
+	    <h3>Performance<br /> Benchmarks </h3>
+	    <p> View performance benchmark results for various models on Intel platforms. </p>  
+	 </a>
+      </div>
+      <div style="clear:both;"> </div>
+   </div>
+
+
+
+
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+   
+   get_started
+   documentation
+   tutorials
+   api/api_reference
+   model_zoo
+   resources
diff --git a/docs/install_guides/deployment-manager-tool.md b/docs/install_guides/deployment-manager-tool.md
index a7a7783767b..6963b7f35c6 100644
--- a/docs/install_guides/deployment-manager-tool.md
+++ b/docs/install_guides/deployment-manager-tool.md
@@ -1,123 +1,224 @@
 # OpenVINO™ Deployment Manager Guide {#openvino_docs_install_guides_deployment_manager_tool}
 
-The Deployment Manager of Intel® Distribution of OpenVINO™ creates a deployment package by assembling the model, IR files, your application, and associated dependencies into a runtime package for your target device.
+The Deployment Manager is a Python* command-line tool that creates a deployment package by assembling the model, IR files, your application, and associated dependencies into a runtime package for your target device. This tool is delivered within the Intel® Distribution of OpenVINO™ toolkit for Linux*, Windows* and macOS* release packages and is available after installation in the `<INSTALL_DIR>/tools/deployment_manager` directory.
 
-The Deployment Manager is a Python\* command-line tool that is delivered within the Intel® Distribution of OpenVINO™ toolkit for Linux\* and Windows\* release packages and available after installation in the `<INSTALL_DIR>/tools/deployment_manager` directory.
+## Prerequisites
 
-## Pre-Requisites
-
-* Intel® Distribution of OpenVINO™ toolkit for Linux\* (version 2019 R3 or higher) or Intel® Distribution of OpenVINO™ toolkit for Windows\* (version 2019 R4 or higher) installed on your development machine.
-* Python\* 3.6 or higher is required to run the Deployment Manager.
+* Intel® Distribution of OpenVINO™ toolkit
 * To run inference on a target device other than CPU, device drivers must be pre-installed:
-   * For **Linux**, see the following sections in the [installation instructions for Linux](../install_guides/installing-openvino-linux.md): 
+   * **For Linux**, see the following sections in the [installation instructions for Linux](../install_guides/installing-openvino-linux.md): 
      * Steps for Intel® Processor Graphics (GPU) section 
      * Steps for Intel® Neural Compute Stick 2 section
      * Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
    * **For Windows**, see the following sections in the [installation instructions for Windows](../install_guides/installing-openvino-windows.md):
      * Steps for Intel® Processor Graphics (GPU)
      * Steps for the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
+   * **For macOS**, see the following section in the [installation instructions for macOS](../install_guides/installing-openvino-macos.md): 
+     * Steps for Intel® Neural Compute Stick 2 section
      
-
-> **IMPORTANT**: The operating system on the target host must be the same as the development system on which you are creating the package. For example, if the target system is Ubuntu 18.04, the deployment package must be created from the OpenVINO™ toolkit installed on Ubuntu 18.04.     
+> **IMPORTANT**: The operating system on the target system must be the same as the development system on which you are creating the package. For example, if the target system is Ubuntu 18.04, the deployment package must be created from the OpenVINO™ toolkit installed on Ubuntu 18.04.     
 
 ## Create Deployment Package Using Deployment Manager
 
 There are two ways to create a deployment package that includes inference-related components of the OpenVINO™ toolkit: you can run the Deployment Manager tool in either interactive or standard CLI mode.
 
 ### Run Interactive Mode
-<details>
-  <summary>Click to expand/collapse</summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+    <div class="collapsible-section" data-title="Click to expand/collapse">
+    
+@endsphinxdirective
   
 Interactive mode provides a user-friendly command-line interface that will guide you through the process with text prompts.
 
-1. To launch the Deployment Manager in the interactive mode, open a new terminal window, go to the Deployment Manager tool directory and run the tool script without parameters:
-   ```sh
-   <INSTALL_DIR>/tools/deployment_manager
-   ```
-   ```sh
-   ./deployment_manager.py
-   ``` 
-2. The target device selection dialog is displayed:
-![Deployment Manager selection dialog](../img/selection_dialog.png)
-Use the options provided on the screen to complete selection of the target devices and press **Enter** to proceed to the package generation dialog. if you want to interrupt the generation process and exit the program, type **q** and press **Enter**.
-3. Once you accept the selection, the package generation dialog is displayed:
-![Deployment Manager configuration dialog](../img/configuration_dialog.png)
-   1. The target devices you have selected at the previous step appear on the screen. If you want to change the selection, type **b** and press **Enter** to go back to the previous screen. 
+1. To launch the Deployment Manager in interactive mode, open a new terminal window, go to the Deployment Manager tool directory and run the tool script without parameters:
+  
+   @sphinxdirective
    
-   2. Use the options provided to configure the generation process, or use the default settings.
+   .. tab:: Linux  
    
-   3. Once all the parameters are set, type **g** and press **Enter** to generate the package for the selected target devices. If you want to interrupt the generation process and exit the program, type **q** and press **Enter**.
+      .. code-block:: sh
+   
+         cd <INSTALL_DIR>/tools/deployment_manager
+      
+         ./deployment_manager.py  
+      
+   .. tab:: Windows  
+   
+      .. code-block:: bat  
+      
+        cd <INSTALL_DIR>\deployment_tools\tools\deployment_manager
+        .\deployment_manager.py  
+        
+   .. tab:: macOS  
+   
+      .. code-block:: sh
+      
+         cd <INSTALL_DIR>/tools/deployment_manager
+         ./deployment_manager.py  
+      
+@endsphinxdirective
 
-The script successfully completes and the deployment package is generated in the output directory specified. 
-</details>
+2. The target device selection dialog is displayed:
+  
+   ![Deployment Manager selection dialog](../img/selection_dialog.png)
+
+   Use the options provided on the screen to complete selection of the target devices and press **Enter** to proceed to the package generation dialog. if you want to interrupt the generation process and exit the program, type **q** and press **Enter**.
+
+3. Once you accept the selection, the package generation dialog is displayed:
+  
+   ![Deployment Manager configuration dialog](../img/configuration_dialog.png)
+
+   The target devices you have selected at the previous step appear on the screen. To go back and change the selection, type **b** and press **Enter**. Use the options provided to configure the generation process, or use the default settings.
+   
+   * `o. Change output directory` (optional): Path to the output directory. By default, it's set to your home directory.
+
+   * `u. Provide (or change) path to folder with user data` (optional): Path to a directory with user data (IRs, models, datasets, etc.) files and subdirectories required for inference, which will be added to the deployment archive. By default, it's set to `None`, which means you will separately copy the user data to the target system.
+
+   * `t. Change archive name` (optional): Deployment archive name without extension. By default, it is set to `openvino_deployment_package`.
+ 
+4. Once all the parameters are set, type **g** and press **Enter** to generate the package for the selected target devices. To interrupt the generation process and exit the program, type **q** and press **Enter**.
+
+   The script successfully completes and the deployment package is generated in the specified output directory. 
+
+@sphinxdirective
+
+.. raw:: html  
+
+    </div>  
+    
+@endsphinxdirective
 
 ### Run Standard CLI Mode
-<details>
-  <summary>Click to expand/collapse</summary>
+  
+@sphinxdirective
+
+.. raw:: html
+
+    <div class="collapsible-section" data-title="Click to expand/collapse">
+    
+@endsphinxdirective
 
 Alternatively, you can run the Deployment Manager tool in the standard CLI mode. In this mode, you specify the target devices and other parameters as command-line arguments of the Deployment Manager Python script. This mode facilitates integrating the tool in an automation pipeline.
 
 To launch the Deployment Manager tool in the standard mode, open a new terminal window, go to the Deployment Manager tool directory and run the tool command with the following syntax:
-```sh
-./deployment_manager.py <--targets> [--output_dir] [--archive_name] [--user_data]
-```
+
+@sphinxdirective
+
+.. tab:: Linux
+
+   .. code-block:: sh
+   
+      cd <INSTALL_DIR>/tools/deployment_manager
+      ./deployment_manager.py <--targets> [--output_dir] [--archive_name] [--user_data]  
+  
+.. tab:: Windows  
+
+   .. code-block:: bat  
+
+      cd <INSTALL_DIR>\deployment_tools\tools\deployment_manager
+      .\deployment_manager.py <--targets> [--output_dir] [--archive_name] [--user_data]
+  
+.. tab:: macOS  
+
+   .. code-block:: sh
+
+      cd <INSTALL_DIR>/tools/deployment_manager
+      ./deployment_manager.py <--targets> [--output_dir] [--archive_name] [--user_data]
+  
+@endsphinxdirective
 
 The following options are available:
 
-* `<--targets>` — (Mandatory) List of target devices to run inference. To specify more than one target, separate them with spaces. For example: `--targets cpu gpu vpu`. You can get a list of currently available targets running the tool's help: 
-   ```sh
-   ./deployment_manager.py -h
-   ```
-*	`[--output_dir]` — (Optional) Path to the output directory. By default, it set to your home directory.
+* `<--targets>` (required): List of target devices to run inference. To specify more than one target, separate them with spaces. For example: `--targets cpu gpu vpu`. You can get a list of currently available targets by running the program with the `-h` option. 
 
-*	`[--archive_name]` — (Optional) Deployment archive name without extension. By default, it is set to `openvino_deployment_package`.
+* `[--output_dir]` (optional): Path to the output directory. By default, it is set to your home directory.
 
-*	`[--user_data]` — (Optional) Path to a directory with user data (IRs, models, datasets, etc.) required for inference. By default, it's set to `None`, which means that the user data are already present on the target host machine.
+* `[--archive_name]` (optional): Deployment archive name without extension. By default, it is set to `openvino_deployment_package`.
 
-The script successfully completes and the deployment package is generated in the output directory specified.
-</details>
+* `[--user_data]` (optional): Path to a directory with user data (IRs, models, datasets, etc.) files and subdirectories required for inference, which will be added to the deployment archive. By default, it's set to `None`, which means you will separately copy the user data to the target system.
 
-## Deploy Package on Target Hosts
+The script successfully completes, and the deployment package is generated in the output directory specified.
 
-After the Deployment Manager has successfully completed, you can find the generated `.tar.gz` (for Linux) or `.zip` (for Windows) package in the output directory you specified. 
+@sphinxdirective
 
-To deploy the Inference Engine components from the development machine to the target host, perform the following steps:
+.. raw:: html  
 
-1. Transfer the generated archive to the target host using your preferred method.
+    </div>  
+    
+@endsphinxdirective
 
-2. Unpack the archive into the destination directory on the target host (if your archive name is different from the default shown below, replace the `openvino_deployment_package` with the name you use).
-   * For Linux:
-   ```sh
-   tar xf openvino_deployment_package.tar.gz -C <destination_dir>
-   ```
-   * For Windows, use an archiver your prefer.  
+## Deploy Package on Target Systems
+
+After the Deployment Manager has successfully completed, you can find the generated `.tar.gz` (for Linux or macOS) or `.zip` (for Windows) package in the output directory you specified. 
+
+To deploy the Inference Engine components from the development machine to the target system, perform the following steps:
+
+1. Copy the generated archive to the target system using your preferred method.
+
+2. Unpack the archive into the destination directory on the target system (if your archive name is different from the default shown below, replace the `openvino_deployment_package` with the name you use).
+
+   @sphinxdirective
    
-   The package is unpacked to the destination directory and the following files and subdirectories are created:
-      * `setupvars.sh` — copy of `setupvars.sh`
-      * `runtime` — Contains the OpenVINO runtime binary files.
-      * `install_dependencies` — Snapshot of the `install_dependencies` directory from the OpenVINO installation directory.
-      * `<user_data>` — The directory with the user data (IRs, datasets, etc.) you specified while configuring the package.
-3. For Linux, to run inference on a target Intel® GPU, Intel® Movidius™ VPU, or Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, you need to install additional dependencies by running the `install_openvino_dependencies.sh` script:
+   .. tab:: Linux  
+   
+      .. code-block:: sh 
+   
+         tar xf openvino_deployment_package.tar.gz -C <destination_dir>
+     
+   .. tab:: Windows  
+   
+      Use the archiver of your choice to unzip the file.  
+      
+   .. tab:: macOS  
+   
+      .. code-block:: sh
+         
+         tar xf openvino_deployment_package.tar.gz -C <destination_dir>
+     
+   @endsphinxdirective
+
+The package is unpacked to the destination directory and the following files and subdirectories are created:
+   
+   * `setupvars.sh` — Copy of `setupvars.sh`
+   * `runtime` — Contains the OpenVINO runtime binary files.
+   * `install_dependencies` — Snapshot of the `install_dependencies` directory from the OpenVINO installation directory.
+   * `<user_data>` — The directory with the user data (IRs, datasets, etc.) you specified while configuring the package.
+
+3. For Linux, to run inference on a target Intel® GPU, Intel® Movidius™ VPU, or Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, you need to install additional dependencies by running the `install_openvino_dependencies.sh` script on the target machine:
    ```sh
    cd <destination_dir>/openvino/install_dependencies
-   ```
-   ```sh
    sudo -E ./install_openvino_dependencies.sh
    ```
+  
 4. Set up the environment variables:
-   * For Linux:
-   ```sh
-   cd <destination_dir>/openvino/
-   ```
-   ```sh
-   source ./setupvars.sh
-   ```
-   * For Windows:
-   ```
-   cd <destination_dir>\openvino\
-   ```
-   ```
-   .\setupvars.bat
-   ```
+  
+   @sphinxdirective  
+   
+   .. tab:: Linux  
+   
+      .. code-block:: sh
+      
+         cd <destination_dir>/openvino/
+         source ./setupvars.sh
+     
+   .. tab:: Windows  
+   
+      .. code-block:: bat  
+      
+         cd <destination_dir>\openvino\
+         .\setupvars.bat
+     
+   .. tab:: macOS  
+   
+      .. code-block:: sh
+      
+         cd <destination_dir>/openvino/
+         source ./setupvars.sh
+     
+   @endsphinxdirective
 
-Congratulations, you have finished the deployment of the Inference Engine components to the target host. 
\ No newline at end of file
+You have now finished the deployment of the Inference Engine components to the target system.
diff --git a/docs/install_guides/installing-openvino-linux.md b/docs/install_guides/installing-openvino-linux.md
index 8cf4f4dc213..f7541989984 100644
--- a/docs/install_guides/installing-openvino-linux.md
+++ b/docs/install_guides/installing-openvino-linux.md
@@ -1,48 +1,29 @@
-# Install Intel® Distribution of OpenVINO™ toolkit for Linux* {#openvino_docs_install_guides_installing_openvino_linux}
+# Install and Configure Intel® Distribution of OpenVINO™ toolkit for Linux* {#openvino_docs_install_guides_installing_openvino_linux}
 
-> **NOTES**:
-> - These steps apply to Ubuntu\*, CentOS\*, and Yocto\*.
-> - If you are using Intel® Distribution of OpenVINO™ toolkit on Windows\* OS, see the [Installation Guide for Windows*](installing-openvino-windows.md).
-> - CentOS and Yocto installations will require some modifications that are not covered in this guide.
-> - An internet connection is required to follow the steps in this guide.
-
-
-> **TIP**: If you want to [quick start with OpenVINO™ toolkit](@ref 
-> openvino_docs_get_started_get_started_dl_workbench), you can use 
-> the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI 
-> that enables you to import a
-> model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment
-> on various Intel® platforms. 
+> **NOTE**: These steps apply to Ubuntu\* and with some modifications shown below, also Red Hat\* Enterprise Linux\*.
 
 ## Introduction
 
-OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
-
-The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
-- Enables CNN-based deep learning inference on the edge
-- Supports heterogeneous execution across Intel® CPU, Intel® GPU, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
-- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
-- Includes optimized calls for computer vision standards including OpenCV\* and OpenCL™
-
-**Included with the Installation and installed by default:**
+By default, the [OpenVINO™ Toolkit](https://docs.openvinotoolkit.org/latest/index.html) installation on this page installs the following components:
 
 | Component                                                                                           | Description                                                                                                                                                                                                                                                                                                   |  
 |-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br>Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*.                                                                              |
-| [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)               | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.                                                                                                                                                                |
-| Intel® Media SDK                                                                                    | Offers access to hardware accelerated video codecs and frame processing                                                                                                                                                                                                                                       |
-| [OpenCV](https://docs.opencv.org/master/)                                                           | OpenCV\* community version compiled for Intel® hardware                                                                                                                                                                                                                                                       |
-| [Inference Engine Code Samples](../IE_DG/Samples_Overview.md)           | A set of simple console applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more. |
-| [Demo Applications](@ref omz_demos)           | A set of simple console applications that provide robust application templates to help you implement specific deep learning scenarios. |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo).  |
-| Deep Learning Streamer (DL Streamer)   | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
+| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br> Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*. |
+| [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
+| Intel® Media SDK | Offers access to hardware accelerated video codecs and frame processing |
+| [OpenCV\*](https://docs.opencv.org/master/) | OpenCV\* community version compiled for Intel® hardware |
+| [Inference Engine Code Samples](../IE_DG/Samples_Overview.md) | A set of simple command-line applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more. |
+| [Demo Applications](@ref omz_demos) | A set of command-line applications that serve as robust templates to help you implement multi-stage pipelines and specific deep learning scenarios. |
+| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and others |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo). |
+| Deep Learning Streamer (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
 
 ## System Requirements
 
 **Hardware**
 
-* 6th to 11th generation Intel® Core™ processors and Intel® Xeon® processors 
+Optimized for these processors:
+* 6th to 12th generation Intel® Core™ processors and Intel® Xeon® processors 
 * 3rd generation Intel® Xeon® Scalable processor (formerly code named Cooper Lake)
 * Intel® Xeon® Scalable processor (formerly Skylake and Cascade Lake)
 * Intel Atom® processor with support for Intel® Streaming SIMD Extensions 4.1 (Intel® SSE4.1)
@@ -51,21 +32,19 @@ The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
 * Intel® Neural Compute Stick 2
 * Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
 
-> **NOTE**: With OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick is no longer supported.
+> **NOTE**: Since the OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick is not supported.
 
-**Processor Notes:**
+**Processor Notes**
 
-- Processor graphics are not included in all processors. See [Product Specifications](https://ark.intel.com/) for information about your processor.
-- A chipset that supports processor graphics is required for Intel® Xeon® processors.
+- Processor graphics are not included in all processors. See [Product Specifications](https://ark.intel.com/) for information about your processor.
 
 **Operating Systems**
 
 - Ubuntu 18.04.x long-term support (LTS), 64-bit
 - Ubuntu 20.04.0 long-term support (LTS), 64-bit
-- CentOS 7.6, 64-bit (for target only)
-- Yocto Project v3.0, 64-bit (for target only and requires modifications)
-- For deployment scenarios on Red Hat* Enterprise Linux* 8.2 (64 bit), you can use the of Intel® Distribution of OpenVINO™ toolkit run-time package that includes the Inference Engine core libraries, nGraph, OpenCV, Python bindings, CPU and GPU plugins. The package is available as: 
-   - [Downloadable archive](https://storage.openvinotoolkit.org/repositories/openvino/packages/2021.3/l_openvino_toolkit_runtime_rhel8_p_2021.3.394.tgz)
+- CentOS 7.6, 64-bit (for deployment only, not development)
+- For deployment on Red Hat* Enterprise Linux* 8.2, 64-bit, you can use the Intel® Distribution of OpenVINO™ toolkit runtime package that includes the Inference Engine core libraries, nGraph, OpenCV, Python bindings, and CPU and GPU plugins. The package is available as: 
+   - [Downloadable archive](https://storage.openvinotoolkit.org/repositories/openvino/packages/2021.4.1/l_openvino_toolkit_runtime_rhel8_p_2021.4.689.tgz)
    - [PyPi package](https://pypi.org/project/openvino/)
    - [Docker image](https://catalog.redhat.com/software/containers/intel/openvino-runtime/606ff4d7ecb5241699188fb3)
 
@@ -73,80 +52,87 @@ The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
 
 This guide provides step-by-step instructions on how to install the Intel® Distribution of OpenVINO™ toolkit. Links are provided for each type of compatible hardware including downloads, initialization and configuration steps. The following steps will be covered:
 
-1. <a href="#install-openvino">Install the Intel® Distribution of OpenVINO™ Toolkit </a>
-2. <a href="#install-external-dependencies">Install External software dependencies</a>
-3. <a href="#set-the-environment-variables">Set the OpenVINO™ Environment Variables: Optional Update to .bashrc</a>.
-4. <a href="#configure-model-optimizer">Configure the Model Optimizer </a>
-5. <a href="#additional-GPU-steps">Steps for Intel® Processor Graphics (GPU)</a>
-6. <a href="#additional-NCS-steps">Steps for Intel® Neural Compute Stick 2</a>
-7. <a href="#install-VPU">Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPU</a><br>
-After installing your Intel® Movidius™ VPU, you will return to this guide to complete OpenVINO™ installation.
-8. <a href="#get-started">Get Started with Code Samples and Demo Applications</a>
-9. <a href="#uninstall">Steps to uninstall the Intel® Distribution of OpenVINO™ Toolkit.</a>
+1. <a href="#install-openvino">Install the Intel® Distribution of OpenVINO™ Toolkit</a>
+2. <a href="#install-external-dependencies">Install External Software Dependencies</a>
+3. <a href="#set-the-environment-variables">Configure the Environment</a>
+4. <a href="#model-optimizer">Configure the Model Optimizer</a>
+5. <a href="#optional-steps">Configure Inference on non-CPU Devices (Optional):</a>
+   - <a href="#install-gpu">Steps for Intel® Processor Graphics (GPU)</a>
+   - <a href="#install-ncs2">Steps for Intel® Neural Compute Stick 2</a>
+   - <a href="#install-vpu">Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPU</a><br>
+   After installing your Intel® Movidius™ VPU, you will return to this guide to complete OpenVINO™ installation.  
+6. <a href="#get-started">Start Using the Toolkit</a>
 
-## <a name="install-openvino"></a>Install the Intel® Distribution of OpenVINO™ Toolkit Core Components
+- [Steps to uninstall the Intel® Distribution of OpenVINO™ Toolkit](uninstalling-openvino.md)
 
-Download the Intel® Distribution of OpenVINO™ toolkit package file from [Intel® Distribution of OpenVINO™ toolkit for Linux*](https://software.intel.com/en-us/openvino-toolkit/choose-download).
-Select the Intel® Distribution of OpenVINO™ toolkit for Linux package from the dropdown menu.
+## <a name="install-openvino"></a>Step 1: Install the Intel® Distribution of OpenVINO™ Toolkit Core Components
 
-1. Open a command prompt terminal window.
-2. Change directories to where you downloaded the Intel Distribution of
+1. Download the Intel® Distribution of OpenVINO™ toolkit package file from [Intel® Distribution of OpenVINO™ toolkit for Linux*](https://software.intel.com/en-us/openvino-toolkit/choose-download).
+   Select the Intel® Distribution of OpenVINO™ toolkit for Linux package from the dropdown menu.
+
+2. Open a command prompt terminal window. You can use the keyboard shortcut: Ctrl+Alt+T
+3. Change directories to where you downloaded the Intel Distribution of
 OpenVINO toolkit for Linux\* package file.<br>
-If you downloaded the package file to the current user's `Downloads` directory:
-```sh
-cd ~/Downloads/
-```
-   By default, the file is saved as `l_openvino_toolkit_p_<version>.tgz`.
-3. Unpack the .tgz file:
-```sh
-tar -xvzf l_openvino_toolkit_p_<version>.tgz
-```
-   The files are unpacked to the `l_openvino_toolkit_p_<version>` directory.
-4. Go to the `l_openvino_toolkit_p_<version>` directory:
-```sh
-cd l_openvino_toolkit_p_<version>
-```
+   If you downloaded the package file to the current user's `Downloads` directory:
+   ```sh
+   cd ~/Downloads/
+   ```
+   By default, the file is saved as `l_openvino_toolkit_p_<version>.tgz`, e.g., `l_openvino_toolkit_p_2021.4.689.tgz`.
+4. Unpack the .tgz file:
+   ```sh
+   tar -xvzf l_openvino_toolkit_p_<version>.tgz
+   ```
+   The files are unpacked to the `l_openvino_toolkit_p_<version>` directory.
+5. Go to the `l_openvino_toolkit_p_<version>` directory:
+   ```sh
+   cd l_openvino_toolkit_p_<version>
+   ```
    If you have a previous version of the Intel Distribution of OpenVINO
 toolkit installed, rename or delete these two directories:
    - `~/inference_engine_samples_build`
    - `~/openvino_models`
-5. Choose your installation option and run the related script as root to use either a GUI installation wizard or command line instructions (CLI).<br>    
-   Screenshots are provided for the GUI, but not for CLI. The following information also applies to CLI and will be helpful to your installation where you will be presented with the same choices and tasks.
-   - **Option 1:** GUI Installation Wizard:
-```sh
-sudo ./install_GUI.sh
-```
-   - **Option 2:** Command Line Instructions:
-```sh
-sudo ./install.sh
-```
-   - **Option 3:** Command Line Silent Instructions:
-```sh
-sudo sed -i 's/decline/accept/g' silent.cfg
-sudo ./install.sh -s silent.cfg
-```   
-   You can select which OpenVINO components will be installed by modifying the `COMPONENTS` parameter in the `silent.cfg` file. For example, to install only CPU runtime for the Inference Engine, set `COMPONENTS=intel-openvino-ie-rt-cpu__x86_64` in `silent.cfg`. To get a full list of available components for installation, run the `./install.sh --list_components` command from the unpacked OpenVINO™ toolkit package.
-6. Follow the instructions on your screen. Watch for informational messages such as the following in case you must complete additional steps:
-   ![](../img/openvino-install-linux-01.png)
-7. If you select the default options, the **Installation summary** GUI screen looks like this:
-   ![](../img/openvino-install-linux-02.png)
-   By default, the Intel® Distribution of OpenVINO™ is installed to the following directory, referred to as `<INSTALL_DIR>`:
-      * For root or administrator: `/opt/intel/openvino_<version>/`
-      * For regular users: `/home/<USER>/intel/openvino_<version>/`
-   For simplicity, a symbolic link to the latest installation is also created: `/opt/intel/openvino_2022/`.
 
-8. **Optional**: You can choose **Customize** to change the installation directory or the components you want to install:
-> **NOTE**: If there is an OpenVINO™ toolkit version previously installed on your system, the installer will use the same destination directory for next installations. If you want to install a newer version to a different directory, you need to uninstall the previously installed versions.
-   ![](../img/openvino-install-linux-03.png)
+6. Choose your installation option and run the related script as root to use either a graphical user interface (GUI) installation wizard or command line instructions (CLI).<br>    
+   Screenshots are provided for the GUI, but not for CLI. The following information also applies to CLI and will be helpful to your installation, where you will be presented with the same choices and tasks.
+   - **Option 1:** GUI Installation Wizard:
+   ```sh
+   sudo ./install_GUI.sh
+   ```
+   - **Option 2:** Command Line Instructions:
+   ```sh
+   sudo ./install.sh
+   ```
+   - **Option 3:** Command Line Silent Instructions:
+   ```sh
+   sudo sed -i 's/decline/accept/g' silent.cfg
+   sudo ./install.sh -s silent.cfg
+   ```   
+   In Option 3 you can select which OpenVINO components will be installed by modifying the `COMPONENTS` parameter in the `silent.cfg` file. For example, to install only CPU runtime for the Inference Engine, set `COMPONENTS=intel-openvino-ie-rt-cpu__x86_64` in `silent.cfg`. To get a full list of available components for installation, run the `./install.sh --list_components` command from the unpacked OpenVINO™ toolkit package.
+
+7. Follow the instructions on your screen. Watch for informational messages such as the following in case you must complete additional steps:
+   ![](../img/openvino-install-linux-01.png)
+
+   By default, the Intel® Distribution of OpenVINO™ is installed to the following directory:
+   * For root or administrator: `/opt/intel/openvino_<version>/`
+   * For regular users: `/home/<USER>/intel/openvino_<version>/`
+
+   For simplicity, a symbolic link to the latest installation is also created: `/opt/intel/openvino_2022/` or `/home/<USER>/intel/openvino_2022/`
+
+8. **Optional**: You can choose **Customize** to change the installation directory or the components you want to install.
+   > **NOTE**: If there is an OpenVINO™ toolkit version previously installed on your system, the installer will use the same destination directory for the next installation. If you want to install a newer version to a different directory, you need to uninstall the previously installed versions.
+   
    > **NOTE**: The Intel® Media SDK component is always installed in the `/opt/intel/mediasdk` directory regardless of the OpenVINO installation path chosen.
-9. A Complete screen indicates that the core components have been installed:
+
+9. The **Finish** screen indicates that the core components have been installed:
    ![](../img/openvino-install-linux-04.png)
 
-The first core components are installed. Continue to the next section to install additional dependencies.
+   Once you click **Finish** to close the installation wizard, a new browser window will open with this documentation. It jumps to the section with your next installation steps.
 
-## <a name="install-external-dependencies"></a>Install External Software Dependencies
+The core components are now installed. Continue to the next section to install additional dependencies.
 
-> **NOTE**: If you installed the Intel® Distribution of OpenVINO™ to the non-default install directory, replace `/opt/intel` with the directory in which you installed the software.
+## <a name="install-external-dependencies"></a>Step 2: Install External Software Dependencies
+
+> **NOTE**: If you installed the Intel® Distribution of OpenVINO™ to a non-default directory, replace `/opt/intel` with the directory in which you installed the software.
 
 These dependencies are required for:
 
@@ -154,184 +140,137 @@ These dependencies are required for:
 - Deep Learning Inference Engine
 - Deep Learning Model Optimizer tools
 
-1. Change to the `install_dependencies` directory:
-```sh
-cd /opt/intel/openvino_2022/install_dependencies
-```
+1. Go to the `install_dependencies` directory:
+   ```sh
+   cd /opt/intel/openvino_2022/install_dependencies
+   ```
 2. Run a script to download and install the external software dependencies:
-```sh
-sudo -E ./install_openvino_dependencies.sh
-```
-   The dependencies are installed. Continue to the next section to set your environment variables.
+   ```sh
+   sudo -E ./install_openvino_dependencies.sh
+   ```
+   
+   Once the dependencies are installed, continue to the next section to set your environment variables.
 
-## <a name="set-the-environment-variables"></a>Set the Environment Variables
+## <a name="set-the-environment-variables"></a>Step 3: Configure the Environment
 
-You must update several environment variables before you can compile and run OpenVINO™ applications. Run the following script to temporarily set your environment variables:
+You must update several environment variables before you can compile and run OpenVINO™ applications. Set persistent environment variables as follows, using vi (as below) or your preferred editor:
 
-```sh
-source /opt/intel/openvino_2022/setupvars.sh
-```  
+1. Open the `.bashrc` file in `/home/<USER>`:
+   ```sh
+   vi ~/.bashrc
+   ```
 
-**Optional:** The OpenVINO environment variables are removed when you close the shell. As an option, you can permanently set the environment variables as follows:
+2. Press the **i** key to switch to insert mode.
 
-1. Open the `.bashrc` file in `<user_directory>`:
-```sh
-vi <user_directory>/.bashrc
-```
+3. Add this line to the end of the file:
+   ```sh
+   source /opt/intel/openvino_2022/bin/setupvars.sh
+   ```
 
-2. Add this line to the end of the file:
-```sh
-source /opt/intel/openvino_2022/setupvars.sh
-```
+4. Save and close the file: press the **Esc** key and type `:wq`.
 
-3. Save and close the file: press the **Esc** key and type `:wq`.
+5. To verify the change, open a new terminal. You will see `[setupvars.sh] OpenVINO environment initialized`.
 
-4. To test your change, open a new terminal. You will see `[setupvars.sh] OpenVINO environment initialized`.
+   **Optional:** If you don't want to change your shell profile, you can run the following script to temporarily set your environment variables for each terminal instance when working with OpenVINO™:
 
-The environment variables are set. Continue to the next section to configure the Model Optimizer.
+   ```sh
+   source /opt/intel/openvino_2022/bin/setupvars.sh
+   ```  
 
-## <a name="configure-model-optimizer"></a>Configure the Model Optimizer
+The environment variables are set. Next, you will configure the Model Optimizer.
+
+## <a name="model-optimizer">Step 4: Configure the Model Optimizer
+
+> **NOTE**: Since the TensorFlow framework is not officially supported on CentOS*, the Model Optimizer for TensorFlow can't be configured and run on that operating system.  
 
 The Model Optimizer is a Python\*-based command line tool for importing
 trained models from popular deep learning frameworks such as Caffe\*,
 TensorFlow\*, Apache MXNet\*, ONNX\* and Kaldi\*.
 
-The Model Optimizer is a key component of the Intel Distribution of OpenVINO toolkit. You cannot perform inference on your trained model without
-running the model through the Model Optimizer. When you run a pre-trained model through the Model Optimizer, your output is an
-Intermediate Representation (IR) of the network. The Intermediate Representation is a pair of files that describe the whole model:
+The Model Optimizer is a key component of the Intel Distribution of OpenVINO toolkit. Performing inference on a model 
+(with the exception of ONNX and nGraph models) requires running the model through the Model Optimizer. When you run a pre-trained 
+model through the Model Optimizer, your output is an Intermediate Representation (IR) of the network. The Intermediate 
+Representation is a pair of files that describe the whole model:
 
 - `.xml`: Describes the network topology
 - `.bin`: Contains the weights and biases binary data
 
-For more information about the Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). 
+For more information about the Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). 
 
-### Model Optimizer Configuration Steps
-
-You can choose to either configure all supported frameworks at once **OR** configure one framework at a time. Choose the option that best suits your needs. If you see error messages, make sure you installed all dependencies.
-
-> **NOTE**: Since the TensorFlow framework is not officially supported on CentOS*, the Model Optimizer for TensorFlow can't be configured and ran on those systems.  
-
-> **IMPORTANT**: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
-
-**Option 1: Configure all supported frameworks at the same time**
-
-1.  Go to the Model Optimizer prerequisites directory:
-```sh
-cd /opt/intel/openvino_2022/tools/model_optimizer/install_prerequisites
-```
-2.  Run the script to configure the Model Optimizer for Caffe,
-    TensorFlow 1.x, MXNet, Kaldi\*, and ONNX:
-```sh
-sudo ./install_prerequisites.sh
-```
-
-**Option 2: Configure each framework separately**
-
-Configure individual frameworks separately **ONLY** if you did not select **Option 1** above.
-
-1.  Go to the Model Optimizer prerequisites directory:
-```sh
-cd /opt/intel/openvino_2022/tools/model_optimizer/install_prerequisites
-```
-2.  Run the script for your model framework. You can run more than one script:
-
-   - For **Caffe**:
+1. Go to the Model Optimizer prerequisites directory:
    ```sh
-   sudo ./install_prerequisites_caffe.sh
+   cd /opt/intel/openvino_2022/tools/model_optimizer/install_prerequisites
    ```
 
-   - For **TensorFlow 1.x**:
+2. Run the script to configure the Model Optimizer for Caffe, TensorFlow 2.x, MXNet, Kaldi, and ONNX:
    ```sh
-   sudo ./install_prerequisites_tf.sh
+   sudo ./install_prerequisites.sh
    ```
 
-   - For **TensorFlow 2.x**:
-   ```sh
-   sudo ./install_prerequisites_tf2.sh
-   ```
-
-   - For **MXNet**:
-   ```sh
-   sudo ./install_prerequisites_mxnet.sh
-   ```
-
-   - For **ONNX**:
-   ```sh
-   sudo ./install_prerequisites_onnx.sh
-   ```
-
-   - For **Kaldi**:
-   ```sh
-   sudo ./install_prerequisites_kaldi.sh
-   ```
+3. **Optional:** You can choose to install Model Optimizer support for only certain frameworks. In the same directory are individual scripts for Caffe, TensorFlow 1.x, TensorFlow 2.x, MXNet, Kaldi, and ONNX (`install_prerequisites_caffe.sh`, etc.). If you see error messages, make sure you installed all dependencies. 
+   
 The Model Optimizer is configured for one or more frameworks.
 
-You have completed all required installation, configuration and build steps in this guide to use your CPU to work with your trained models. 
+You have now completed all required installation, configuration, and build steps in this guide to use your CPU to work with your trained models. 
 
-To enable inference on other hardware, see:
-- <a href="#additional-GPU-steps">Steps for Intel® Processor Graphics (GPU)</a>
-- <a href="#additional-NCS-steps">Steps for Intel® Neural Compute Stick 2</a>
-- <a href="#install-VPU">Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs</a><br>
+To enable inference on other hardware, see below:
+- <a href="#install-gpu">Steps for Intel® Processor Graphics (GPU)</a>
+- <a href="#install-ncs2">Steps for Intel® Neural Compute Stick 2</a>
+- <a href="#install-vpu">Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs</a><br>
 
-Or proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
+Or proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-## <a name="additional-GPU-steps"></a>Steps for Intel® Processor Graphics (GPU)
+## <a name="optional-steps"></a>Step 5 (Optional): Configure Inference on non-CPU Devices:
+
+### <a name="install-gpu"></a>Optional: Steps for Intel® Processor Graphics (GPU)
 
 The steps in this section are required only if you want to enable the toolkit components to use processor graphics (GPU) on your system.
 
 1. Go to the install_dependencies directory:
-```sh
-cd /opt/intel/openvino_2022/install_dependencies/
-```
+   ```sh
+   cd /opt/intel/openvino_2022/install_dependencies/
+   ```
 
-2. Install the **Intel® Graphics Compute Runtime for OpenCL™** driver components required to use the GPU plugin and write custom layers for Intel® Integrated Graphics. The drivers are not included in the package and must be installed separately.
-   > **NOTE**: To use the **Intel® Iris® Xe MAX Graphics**, see the [Intel® Iris® Xe MAX Graphics with Linux*](https://dgpu-docs.intel.com/devices/iris-xe-max-graphics/index.html) page for driver installation instructions.
-
-   To install the drivers, make sure you have the internet connection and run the installation script:
+2. Install the **Intel® Graphics Compute Runtime for OpenCL™** driver components required to use the GPU plugin and write custom layers for Intel® Integrated Graphics. The drivers are not included in the package. To install, run this script:
    ```sh
    sudo -E ./install_NEO_OCL_driver.sh
    ```
-   The script compares the driver version on the system to the current version. If the driver version on the system is higher or equal to the current version, the script does not install a new driver. If the version of the driver is lower than the current version, the script uninstalls the lower and installs the current version with your permission:
+   > **NOTE**: To use the **Intel® Iris® Xe MAX Graphics**, see the [Intel® Iris® Xe MAX Graphics with Linux*](https://dgpu-docs.intel.com/devices/iris-xe-max-graphics/index.html) page for driver installation instructions.
+   
+   The script compares the driver version on the system to the current version. If the driver version on the system is higher or equal to the current version, the script does 
+   not install a new driver. If the version of the driver is lower than the current version, the script uninstalls the lower version and installs the current version with your permission:
    ![](../img/NEO_check_agreement.png) 
-   Higher hardware versions require a higher driver version, namely 20.35 instead of 19.41. If the script fails to uninstall the driver, uninstall it manually. During the script execution, you may see the following command line output:  
-```sh
-Add OpenCL user to video group    
-```
-   Ignore this suggestion and continue.<br>You can also find the most recent version of the driver, installation procedure and other information on the [Intel® software for general purpose GPU capabilities](https://dgpu-docs.intel.com/index.html) site.
 
-3. **Optional** Install header files to allow compiling a new code. You can find the header files at [Khronos OpenCL™ API Headers](https://github.com/KhronosGroup/OpenCL-Headers.git).
+   Higher hardware versions require a higher driver version, namely 20.35 instead of 19.41. If the script fails to uninstall the driver, uninstall it manually. During the script execution, you may see the following command line output:  
+   ```sh
+   Add OpenCL user to video group    
+   ```
+   Ignore this suggestion and continue.<br>
+   You can also find the most recent version of the driver, installation procedure and other information on the [Intel® software for general purpose GPU capabilities](https://dgpu-docs.intel.com/index.html) site.
+
+3. **Optional:** Install header files to allow compilation of new code. You can find the header files at [Khronos OpenCL™ API Headers](https://github.com/KhronosGroup/OpenCL-Headers.git).
 
 You've completed all required configuration steps to perform inference on processor graphics. 
-Proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
+Proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-## <a name="additional-NCS-steps"></a>Steps for Intel® Neural Compute Stick 2
+### <a name="install-ncs2"></a>Optional: Steps for Intel® Neural Compute Stick 2
 
-These steps are only required if you want to perform inference on Intel® Movidius™ NCS powered by the Intel® Movidius™ Myriad™ 2 VPU or Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X VPU. See also the [Get Started page for Intel® Neural Compute Stick 2:](https://software.intel.com/en-us/neural-compute-stick/get-started)
+These steps are only required if you want to perform inference on Intel® Movidius™ NCS powered by the Intel® Movidius™ Myriad™ 2 VPU or Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X VPU. For more details, see the [Get Started page for Intel® Neural Compute Stick 2:](https://software.intel.com/en-us/neural-compute-stick/get-started).
 
-1. Add the current Linux user to the `users` group:
-```sh
-sudo usermod -a -G users "$(whoami)"
-```
-   Log out and log in for it to take effect.
-2. To perform inference on Intel® Neural Compute Stick 2, install the USB rules as follows:
+1. Go to the install_dependencies directory:
    ```sh
-   sudo cp /opt/intel/openvino_2022/runtime/3rdparty/97-myriad-usbboot.rules /etc/udev/rules.d/
+   cd /opt/intel/openvino_2022/install_dependencies/
    ```
-   ```sh
-   sudo udevadm control --reload-rules
+2. Run the `install_NCS_udev_rules.sh` script:
    ```
-   ```sh
-   sudo udevadm trigger
+   ./install_NCS_udev_rules.sh
    ```
-   ```sh
-   sudo ldconfig
-   ```
-> **NOTE**: You may need to reboot your machine for this to take effect.
+3. You may need to reboot your machine for this to take effect.
 
 You've completed all required configuration steps to perform inference on Intel® Neural Compute Stick 2. 
-Proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
+Proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-## <a name="install-VPU"></a>Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
+### <a name="install-vpu"></a>Optional: Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
 
 To install and configure your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, see the [Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Configuration Guide](installing-openvino-linux-ivad-vpu.md).
 
@@ -340,80 +279,58 @@ To install and configure your Intel® Vision Accelerator Design with Intel® Mov
 After configuration is done, you are ready to run the verification scripts with the HDDL Plugin for your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs:
 
 1. Go to the **Inference Engine demo** directory:
-```sh
-cd /opt/intel/openvino_2022/samples/scripts
-```
+   ```sh
+   cd /opt/intel/openvino_2022/samples/scripts
+   ```
 
 2. Run the **Image Classification verification script**. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
-```sh
-./run_sample_squeezenet.sh -d HDDL
-```
+   ```sh
+   ./run_sample_squeezenet.sh -d HDDL
+   ```
 
 You've completed all required configuration steps to perform inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs. 
-Proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
+Proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-## <a name="get-started"></a>Get Started
+## <a name="get-started"></a>Step 6: Start Using the Toolkit
 
-Now you are ready to get started. To continue, see the following pages:
-* [OpenVINO™ Toolkit Overview](../index.md)
-* [Get Started Guide for Linux](../get_started/get_started_linux.md) to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications with pre-trained models on different inference devices.
-
-## <a name="uninstall"></a>Uninstall the Intel® Distribution of OpenVINO™ Toolkit
-Choose one of the options provided below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system.
-
-### Uninstall with GUI
-1. Run the uninstallation script from `<INSTALL_DIR>/openvino_toolkit_uninstaller`:
-   ```sh
-   sudo ./uninstall_GUI.sh
-   ```
-2. Follow the uninstallation wizard instructions.
-
-
-### Uninstall with Command Line (Interactive Mode)
-1. Run the uninstallation script from `<INSTALL_DIR>/openvino_toolkit_uninstaller`:
-   ```sh
-   sudo ./uninstall.sh
-   ```
-2. Follow the instructions on your screen.
-4. When uninstallation is complete, press **Enter**.
-
-### Uninstall with Command Line (Silent Mode)
-1. Run the following command from `<INSTALL_DIR>/openvino_toolkit_uninstaller`:
-   ```sh
-   sudo ./uninstall.sh -s
-   ```
-2. Intel® Distribution of OpenVINO™ Toolkit is now uninstalled from your system.
+Now you are ready to try out the toolkit. To continue, see the [Get Started Guide](../get_started/get_started.md) section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications with pre-trained models on different inference devices.
 
 ## Troubleshooting
 
-PRC developers might encounter pip installation related issues during OpenVINO™ installation. To resolve the issues, you may use one of the following options at your discretion:
-* Add the download source with `-i` parameter in the `pip` command. For example: 
-```
-pip install numpy.py -i https://mirrors.aliyun.com/pypi/simple/
-```
+PRC developers might encounter pip errors during OpenVINO™ installation. To resolve the issues, try one of the following options:
+* Add the download source using the `-i` parameter with the Python `pip` command. For example: 
+
+   ```
+   pip install numpy.py -i https://mirrors.aliyun.com/pypi/simple/
+   ```
 Use the `--trusted-host` parameter if the URL above is `http` instead of `https`.
 
-* Modify or create `~/.pip/pip.conf` file to change the default download source with the content below:
-```
-[global]
-index-url = http://mirrors.aliyun.com/pypi/simple/
-[install]
-trusted-host = mirrors.aliyun.com
-```
+* Modify or create the `~/.pip/pip.conf` file to change the default download source with the content below:
+
+   ```
+   [global]
+   index-url = http://mirrors.aliyun.com/pypi/simple/
+   [install]
+   trusted-host = mirrors.aliyun.com
+   ```
+## <a name="uninstall"></a>Uninstall the Intel® Distribution of OpenVINO™ Toolkit
+
+To uninstall the toolkit, follow the steps on the [Uninstalling page](uninstalling-openvino.md).
 
 ## Additional Resources
 
+- Get started with samples and demos: [Get Started Guide](../get_started.md)
 - Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
-- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
-- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
-- [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
-- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
-- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
-- For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
+- Convert models for use with OpenVINO™: [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
+- Write your own OpenVINO™ applications: [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
+- Information on sample applications: [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
+- Information on a supplied set of models: [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
+- IoT libraries and code samples: [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit)
 
-To learn more about converting models, go to:
+To learn more about converting models from specific frameworks, go to:
 
 - [Convert Your Caffe* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md)
 - [Convert Your TensorFlow* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
 - [Convert Your MXNet* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md)
+- [Convert Your Kaldi* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
 - [Convert Your ONNX* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
diff --git a/docs/install_guides/installing-openvino-macos.md b/docs/install_guides/installing-openvino-macos.md
index 36249196da0..269f2307ba9 100644
--- a/docs/install_guides/installing-openvino-macos.md
+++ b/docs/install_guides/installing-openvino-macos.md
@@ -1,51 +1,28 @@
-# Install Intel® Distribution of OpenVINO™ toolkit for macOS* {#openvino_docs_install_guides_installing_openvino_macos}
+# Install and Configure Intel® Distribution of OpenVINO™ toolkit for macOS* {#openvino_docs_install_guides_installing_openvino_macos}
 
-> **NOTES**:
-> - The Intel® Distribution of OpenVINO™ is supported on macOS\* 10.15.x versions.
-> - An internet connection is required to follow the steps in this guide. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
-
-> **TIP**: If you want to [quick start with OpenVINO™ toolkit](@ref 
-> openvino_docs_get_started_get_started_dl_workbench), you can use 
-> the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI 
-> that enables you to import a
-> model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment
-> on various Intel® platforms. 
+> **NOTE**: The Intel® Distribution of OpenVINO™ is supported on macOS\* version 10.15.x with Intel® processor-based machines.
 
 ## Introduction
 
-The Intel® Distribution of OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance.
+By default, the [OpenVINO™ Toolkit](https://docs.openvinotoolkit.org/latest/index.html) installation on this page installs the following components:
 
-The Intel® Distribution of OpenVINO™ toolkit for macOS*:
+| Component                                                                                           | Description                                                                                                                                                                                                                                                                                                   |
+|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br> Popular frameworks include Caffe*, TensorFlow*, MXNet\*, Kaldi\* and ONNX\*. |
+| [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications |
+| [OpenCV\*](https://docs.opencv.org/master/) | OpenCV\* community version compiled for Intel® hardware |
+| [Inference Engine Code Samples](../IE_DG/Samples_Overview.md) | A set of simple command-line applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more. |
+| [Demo Applications](@ref omz_demos) | A set of command-line applications that serve as robust templates to help you implement multi-stage pipelines and specific deep learning scenarios. |
+| Additional Tools | A set of tools to work with your models, including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_README), [Model Downloader](@ref omz_tools_downloader), and others |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo). |
 
-- Enables CNN-based deep learning inference on the edge  
-- Supports heterogeneous execution across Intel® CPU and Intel® Neural Compute Stick 2 with Intel® Movidius™ VPUs
-- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
-- Includes optimized calls for computer vision standards including OpenCV\*
-
-**Included with the Installation**
-
-The following components are installed by default:
-
-| Component                                                                                           | Description                                                                                                                                                                                                                                                  |
-| :-------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | This tool imports, converts, and optimizes models, which were trained in popular frameworks, to a format usable by Intel tools, especially the Inference Engine. <br> Popular frameworks include Caffe*, TensorFlow*, MXNet\*, and ONNX\*. |
-| [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)               | This is the engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications                                                                                                               |
-| [OpenCV\*](https://docs.opencv.org/master/)                                                         | OpenCV\* community version compiled for Intel® hardware                                                                                                                                                                                                      |
-| [Sample Applications](../IE_DG/Samples_Overview.md)                                                                                | A set of simple console applications demonstrating how to use the Inference Engine in your applications. |
-| [Demos](@ref omz_demos)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo)  |
-
-
-## Development and Target Platform
-
-The development and target platforms have the same requirements, but you can select different components during the installation, based on your intended use.
+## System Requirements
 
 **Hardware**
 
-> **NOTE**: The current version of the Intel® Distribution of OpenVINO™ toolkit for macOS* supports inference on Intel CPUs and Intel® Neural Compute Sticks 2 only.
+> **NOTE**: The current version of the Intel® Distribution of OpenVINO™ toolkit for macOS* supports inference on Intel CPUs and Intel® Neural Compute Stick 2 devices only.
 
-* 6th to 11th generation Intel® Core™ processors and Intel® Xeon® processors 
+* 6th to 12th generation Intel® Core™ processors and Intel® Xeon® processors 
 * 3rd generation Intel® Xeon® Scalable processor (formerly code named Cooper Lake)
 * Intel® Xeon® Scalable processor (formerly Skylake and Cascade Lake)
 * Intel® Neural Compute Stick 2
@@ -55,8 +32,8 @@ The development and target platforms have the same requirements, but you can sel
 * CMake 3.13 or higher
 	+ [Install](https://cmake.org/download/) (choose "macOS 10.13 or later")
 	+ Add `/Applications/CMake.app/Contents/bin` to path (for default install) 
-* Python 3.6 - 3.7
-	+ [Install](https://www.python.org/downloads/mac-osx/) (choose 3.6.x or 3.7.x, not latest)
+* Python 3.6 - 3.8
+	+ [Install](https://www.python.org/downloads/mac-osx/) (choose 3.6.x - 3.8.x, not latest)
 	+ Add to path
 * Apple Xcode\* Command Line Tools
 	+ In the terminal, run `xcode-select --install` from any directory
@@ -68,87 +45,72 @@ The development and target platforms have the same requirements, but you can sel
 
 ## Overview
 
-This guide provides step-by-step instructions on how to install the Intel® Distribution of OpenVINO™ 2020.1 toolkit for macOS*.
+This guide provides step-by-step instructions on how to install the Intel® Distribution of OpenVINO™ toolkit for macOS*. The following steps will be covered:
 
-The following steps will be covered:
+1. <a href="#install-core">Install the Intel® Distribution of OpenVINO™ Toolkit</a>
+2. <a href="#set-the-environment-variables">Configure the Environment</a>
+3. <a href="#model-optimizer">Configure the Model Optimizer</a>
+4. <a href="#configure-ncs2">Configure the Intel® Neural Compute Stick 2 (Optional)</a>
+5. <a href="#get-started">Start Using the Toolkit</a>
 
-1. <a href="#Install-Core">Install the Intel® Distribution of OpenVINO™ Toolkit</a>.
-2. <a href="#set-the-environment-variables">Set the OpenVINO environment variables and (optional) Update to <code>.bash_profile</code></a>.
-3. <a href="#configure-the-model-optimizer">Configure the Model Optimizer</a>.
-4. <a href="#get-started">Get Started with Code Samples and Demo Applications</a>.
-5. <a href="#uninstall">Uninstall the Intel® Distribution of OpenVINO™ Toolkit</a>.
+- [Steps to uninstall the Intel® Distribution of OpenVINO™ Toolkit](../install_guides/uninstalling-openvino.md)
 
-## <a name="Install-Core"></a>Install the Intel® Distribution of OpenVINO™ Toolkit Core Components
+## <a name="install-core"></a>Step 1: Install the Intel® Distribution of OpenVINO™ Toolkit Core Components
 
 If you have a previous version of the Intel® Distribution of OpenVINO™ toolkit installed, rename or delete these two directories:
 
-- `/home/<user>/inference_engine_samples`
-- `/home/<user>/openvino_models`
+- `~/inference_engine_samples`
+- `~/openvino_models`
 
-[Download the latest version of OpenVINO toolkit for macOS*](https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-macos) then return to this guide to proceed with the installation.
+[Download the latest version of OpenVINO toolkit for macOS*](https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-macos), then return to this guide to proceed with the installation.
 
 Install the OpenVINO toolkit core components:
 
-1. Go to the directory in which you downloaded the Intel® Distribution of OpenVINO™ toolkit. This document assumes this is your `Downloads` directory. By default, the disk image file is saved as `m_openvino_toolkit_p_<version>.dmg`.
+1. Go to the directory where you downloaded the Intel® Distribution of OpenVINO™ toolkit. This document assumes this is your `Downloads` directory. By default, the disk image file is saved as `m_openvino_toolkit_p_<version>.dmg`.
 
 2. Double-click the `m_openvino_toolkit_p_<version>.dmg` file to mount.
-The disk image is mounted to `/Volumes/m_openvino_toolkit_p_<version>` and automatically opened in a separate window.
+The disk image is mounted to `/Volumes/m_openvino_toolkit_p_<version>` and automatically opens in a separate window.
 
-3. Run the installation wizard application `m_openvino_toolkit_p_<version>.app`
+3. Run the installation wizard application `m_openvino_toolkit_p_<version>.app`.
 
 4. On the **User Selection** screen, choose a user account for the installation:
     - Root
     - Administrator
     - Current user
 
-    ![](../img/openvino-install-macos-01.png)
-
    The default installation directory path depends on the privileges you choose for the installation.
 
-5. Click **Next** and follow the instructions on your screen.
-
-6. If you are missing external dependencies, you will see a warning screen. Take note of any dependencies you are missing. After installing the Intel® Distribution of OpenVINO™ toolkit core components, you will need to install the missing dependencies. For example, the screen example below indicates you are missing a dependency:
+5. Follow the instructions on your screen. Watch for informational messages such as the following in case you must complete additional steps:
    ![](../img/openvino-install-macos-02.png)
 
-7. Click **Next**.
-
-8. The **Installation summary** screen shows you the default component set to install:
-   ![](../img/openvino-install-macos-03.png)
-   By default, the Intel® Distribution of OpenVINO™ is installed to the following directory, referred to as `<INSTALL_DIR>`:
+6. The **Installation summary** screen shows you the default component set to install. By default, the Intel® Distribution of OpenVINO™ is installed in the following directory, referred to as `<INSTALL_DIR>` elsewhere in the documentation:
 
    * For root or administrator: `/opt/intel/openvino_<version>/`
-   * For regular users: `/home/<USER>/intel/openvino_<version>/` 
+   * For regular users: `~/intel/openvino_<version>/`
 
-   For simplicity, a symbolic link to the latest installation is also created: `/home/<user>/intel/openvino_2022/`.
-9. If needed, click **Customize** to change the installation directory or the components you want to install:
-   ![](../img/openvino-install-macos-04.png)
-   > **NOTE**: If there is an OpenVINO™ toolkit version previously installed on your system, the installer will use the same destination directory for next installations. If you want to install a newer version to a different directory, you need to uninstall the previously installed versions.
-10. Click **Next** to save the installation options and show the Installation summary screen.
+   For simplicity, a symbolic link to the latest installation is also created: `<INSTALL_DIR>/intel/openvino_2021/`.
 
-11. On the **Installation summary** screen, click **Install** to begin the installation.
+7. **Optional**: You can choose **Customize** to change the installation directory or the components you want to install.
+> **NOTE**: If there is an OpenVINO™ toolkit version previously installed on your system, the installer will use the same destination directory for next installations. If you want to install a newer version to a different directory, you need to uninstall the previously installed versions.
 
-12. When the first part of installation is complete, the final screen informs you that the core components have been installed
-   and additional steps still required:
+8. The **Finish** screen indicates that the core components have been installed:
    ![](../img/openvino-install-macos-05.png)
 
-13. Click **Finish** to close the installation wizard. A new browser window opens to the next section of the Installation Guide to set the environment variables. If the installation did not indicate you must install dependencies, you can move ahead to [Set the Environment Variables](#set-the-environment-variables).  If you received a message that you were missing external software dependencies, listed under **Software Requirements** at the top of this guide, you need to install them now before continuing on to the next section.
+   Once you click **Finish** to close the installation wizard, a new browser window opens with the document you’re reading now (in case you installed without it) and jumps to the section with the next installation steps.
 
-## <a name="set-the-environment-variables"></a>Set the Environment Variables
+The core components are now installed. If you received a message that you were missing external software dependencies (list available under Software Requirements at the top of this guide), you need to install them now before continuing to the next section.
 
-You need to update several environment variables before you can compile and run OpenVINO™ applications. Open the macOS Terminal\* or a command-line interface shell you prefer and run the following script to temporarily set your environment variables:
+## <a name="set-the-environment-variables"></a>Step 2: Configure the Environment
 
-   ```sh
-   source /opt/intel/openvino_2022/setupvars.sh
-   ```  
+> **NOTE**: If you installed OpenVINO to a non-default installation directory, replace `/opt/intel/` with the directory where you installed the software.
 
-If you didn't choose the default installation option, replace `/opt/intel/openvino_2022` with your directory.
-
-<strong>Optional</strong>: The OpenVINO environment variables are removed when you close the shell. You can permanently set the environment variables as follows:
+You must update several environment variables before you can compile and run OpenVINO™ applications. Set persistent environment variables as follows, using vi (as below) or your preferred editor in the terminal:
 
 1. Open the `.bash_profile` file in the current user home directory:
    ```sh
    vi ~/.bash_profile
    ```
+
 2. Press the **i** key to switch to insert mode.
 
 3. Add this line to the end of the file:
@@ -156,15 +118,21 @@ If you didn't choose the default installation option, replace `/opt/intel/openvi
    source /opt/intel/openvino_2022/setupvars.sh
    ```
 
-If you didn't choose the default installation option, replace `/opt/intel/openvino_2022` with your directory.
+   If you didn't choose the default installation option, replace `/opt/intel/openvino_2022` with your directory.
 
 4. Save and close the file: press the **Esc** key, type `:wq` and press the **Enter** key.
 
-5. To verify your change, open a new terminal. You will see `[setupvars.sh] OpenVINO environment initialized`.
+5. To verify the change, open a new terminal. You will see `[setupvars.sh] OpenVINO environment initialized`.
+
+   **Optional:** If you don't want to change your shell profile, you can run the following script to temporarily set your environment variables when working with the OpenVINO* toolkit:
+
+   ```sh
+   source /opt/intel/openvino_2021/bin/setupvars.sh
+   ```  
 
 The environment variables are set. Continue to the next section to configure the Model Optimizer.
 
-## <a name="configure-the-model-optimizer"></a>Configure the Model Optimizer
+## <a name="model-optimizer"></a>Step 3: Configure the Model Optimizer
 
 The Model Optimizer is a Python\*-based command line tool for importing
 trained models from popular deep learning frameworks such as Caffe\*,
@@ -179,77 +147,32 @@ The Inference Engine reads, loads, and infers the IR files, using a common API o
 
 For more information about the Model Optimizer, see the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 
-### Model Optimizer Configuration Steps
-
-You can choose to either configure the Model Optimizer for all supported frameworks at once, **OR** for one framework at a time. Choose the option that best suits your needs. If you see error messages, verify that you installed all dependencies listed under **Software Requirements** at the top of this guide.
-
-> **NOTE**: If you installed OpenVINO to a non-default installation directory, replace `/opt/intel/` with the directory where you installed the software.
-
-**Option 1: Configure the Model Optimizer for all supported frameworks at the same time:**
+If you see error messages, verify that you installed all dependencies listed under **Software Requirements** at the top of this guide.
 
 1. Go to the Model Optimizer prerequisites directory:
    ```sh
    cd /opt/intel/openvino_2022/tools/model_optimizer/install_prerequisites
    ```
 
-2. Run the script to configure the Model Optimizer for Caffe, TensorFlow 1.x, MXNet, Kaldi\*, and ONNX:
+2. Run the script to configure the Model Optimizer for Caffe, TensorFlow 2.x, MXNet, Kaldi\*, and ONNX:
    ```sh
    sudo ./install_prerequisites.sh
    ```
 
-**Option 2: Configure the Model Optimizer for each framework separately:**
-
-Configure individual frameworks separately **ONLY** if you did not select **Option 1** above.
-
-1. Go to the Model Optimizer prerequisites directory:
-   ```sh
-   cd /opt/intel/openvino_2022/tools/model_optimizer/install_prerequisites
-   ```
-
-2. Run the script for your model framework. You can run more than one script:
-
-   - For **Caffe**:
-   ```sh
-   sudo ./install_prerequisites_caffe.sh
-   ```
-
-   - For **TensorFlow 1.x**:
-   ```sh
-   sudo ./install_prerequisites_tf.sh
-   ```
-
-   - For **TensorFlow 2.x**:
-   ```sh
-   sudo ./install_prerequisites_tf2.sh
-   ```
-
-   - For **MXNet**:
-   ```sh
-   sudo ./install_prerequisites_mxnet.sh
-   ```
-
-   - For **ONNX**:
-   ```sh
-   sudo ./install_prerequisites_onnx.sh
-   ```
-
-   - For **Kaldi**:
-   ```sh
-   sudo ./install_prerequisites_kaldi.sh
-   ```
+3. **Optional:** You can choose to install Model Optimizer support for only certain frameworks. In the same directory are individual scripts for Caffe, TensorFlow 1.x, TensorFlow 2.x, MXNet, Kaldi, and ONNX (`install_prerequisites_caffe.sh`, etc.). If you see error messages, make sure you installed all dependencies.
 
 The Model Optimizer is configured for one or more frameworks.
 
 You have completed all required installation, configuration and build steps in this guide to use your CPU to work with your trained models. 
 
-To enable inference on Intel® Neural Compute Stick 2, see the <a href="#additional-NCS2-steps">Steps for Intel® Neural Compute Stick 2</a>. 
+To enable inference on Intel® Neural Compute Stick 2, see the <a href="#configure-ncs2">Configure the Intel® Neural Compute Stick 2 </a>. 
 
-Or proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
+Or proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-## <a name="additional-NCS2-steps"></a>Steps for Intel® Neural Compute Stick 2
+## <a name="configure-ncs2"></a>Step 4 (Optional): Configure the Intel® Neural Compute Stick 2 
 
-These steps are only required if you want to perform inference on Intel® Neural Compute Stick 2
-powered by the Intel® Movidius™ Myriad™ X VPU. See also the
+These steps are required only if you want to perform inference on Intel® Neural Compute Stick 2
+powered by the Intel® Movidius™ Myriad™ X VPU. For more details, see also the
 [Get Started page for Intel® Neural Compute Stick 2](https://software.intel.com/en-us/neural-compute-stick/get-started).
 
 To perform inference on Intel® Neural Compute Stick 2, the `libusb` library is required. You can build it from the [source code](https://github.com/libusb/libusb) or install using the macOS package manager you prefer: [Homebrew*](https://brew.sh/), [MacPorts*](https://www.macports.org/) or other.
@@ -259,40 +182,31 @@ For example, to install the `libusb` library using Homebrew\*, use the following
 brew install libusb
 ```
 
-You've completed all required configuration steps to perform inference on your Intel® Neural Compute Stick 2. 
-Proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
+You've completed all required configuration steps to perform inference on your Intel® Neural Compute Stick 2.
+Proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-## <a name="get-started"></a>Get Started
+## <a name="get-started"></a>Step 5: Start Using the Toolkit
 
-Now you are ready to get started. To continue, see the following pages:
-* [OpenVINO™ Toolkit Overview](../index.md)
-* [Get Started Guide for macOS](../get_started/get_started_macos.md) to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications with pre-trained models on different inference devices.
+Now you are ready to try out the toolkit. To continue, see the [Get Started Guide](../get_started/get_started.md) section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications with pre-trained models on different inference devices.
 
 ## <a name="uninstall"></a>Uninstall the Intel® Distribution of OpenVINO™ Toolkit
 
-Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system:
-
-1. From the installation directory (by default, `/opt/intel/openvino_2022`), locate and open `openvino_toolkit_uninstaller.app`.
-2. Follow the uninstallation wizard instructions.
-3. When uninstallation is complete, click **Finish**. 
-
+To uninstall, follow the steps on the [Uninstalling page](uninstalling_openvino.md).
 
 ## Additional Resources
 
-- To learn more about the verification applications, see `README.txt` in `/opt/intel/openvino_2022/samples/scripts/`.
+- Get started with samples and demos: [Get Started Guide](../get_started.md)
+- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
+- Convert models for use with OpenVINO™: [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
+- Write your own OpenVINO™ applications: [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
+- Information on sample applications: [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
+- Information on a supplied set of models: [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
+- IoT libraries and code samples: [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit)
 
-- For detailed description of the pre-trained models, go to the [Overview of OpenVINO toolkit Pre-Trained Models](@ref omz_models_group_intel) page.
-
-- More information on [sample applications](../IE_DG/Samples_Overview.md).
+To learn more about converting models from specific frameworks, go to:
 
 - [Convert Your Caffe* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md)
-
 - [Convert Your TensorFlow* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
-
 - [Convert Your MXNet* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md)
-
+- [Convert Your Kaldi* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
 - [Convert Your ONNX* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
-
-- [Intel Distribution of OpenVINO Toolkit home page](https://software.intel.com/en-us/openvino-toolkit)
-
-- [Intel Distribution of OpenVINO Toolkit documentation](https://docs.openvinotoolkit.org)
diff --git a/docs/install_guides/installing-openvino-windows.md b/docs/install_guides/installing-openvino-windows.md
index 3a1da1a24d7..6e1d69c7e1f 100644
--- a/docs/install_guides/installing-openvino-windows.md
+++ b/docs/install_guides/installing-openvino-windows.md
@@ -1,81 +1,25 @@
-# Install Intel® Distribution of OpenVINO™ toolkit for Windows* 10 {#openvino_docs_install_guides_installing_openvino_windows}
-
-> **NOTE**:
-> - This guide applies to Microsoft Windows\* 10 64-bit. For Linux* OS information and instructions, see the [Installation Guide for Linux](installing-openvino-linux.md).
-
-> **TIP**: If you want to [quick start with OpenVINO™ toolkit](@ref 
-> openvino_docs_get_started_get_started_dl_workbench), you can use 
-> the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI 
-> that enables you to import a
-> model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment
-> on various Intel® platforms. 
+# Install and Configure Intel® Distribution of OpenVINO™ toolkit for Windows* 10 {#openvino_docs_install_guides_installing_openvino_windows}
 
 ## Introduction
 
-> **IMPORTANT**:
-> - All steps in this guide are required, unless otherwise stated.<br>
-> - In addition to the download package, you must install dependencies and complete configuration steps.
+By default, the [OpenVINO™ Toolkit](https://docs.openvinotoolkit.org/latest/index.html) installation on this page installs the following components:
 
-Your installation is complete when these are all completed:
+| Component                                                                                           | Description                                                                                                                                                                                                                                                                                                   |  
+|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br> Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, ONNX\* and Kaldi\*. |
+| [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
+| [OpenCV\*](https://docs.opencv.org/master/) | OpenCV\* community version compiled for Intel® hardware |
+| [Inference Engine Code Samples](../IE_DG/Samples_Overview.md) | A set of simple command-line applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more. |
+| [Demo Applications](@ref omz_demos) | A set of command-line applications that serve as robust templates to help you implement multi-stage pipelines and specific deep learning scenarios. |
+| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and others |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo). |
 
-1. Install the <a href="#Install-Core-Components">Intel® Distribution of OpenVINO™ toolkit core components</a>
-
-2. Install the dependencies:
-
-   - [Microsoft Visual Studio* 2019 with MSBuild](http://visualstudio.microsoft.com/downloads/)
-   - [CMake 3.14 or higher 64-bit](https://cmake.org/download/)
-   - [Python **3.6** - **3.8** 64-bit](https://www.python.org/downloads/windows/)
-   > **IMPORTANT**: As part of this installation, make sure you click the option **[Add Python 3.x to PATH](https://docs.python.org/3/using/windows.html#installation-steps)** to add Python to your `PATH` environment variable.
-
-3. <a href="#set-the-environment-variables">Set Environment Variables</a>         
-
-4. <a href="#Configure_MO">Configure the Model Optimizer</a>
-
-5. Optional: 
-
-    - <a href="#Install-GPU">Install the Intel® Graphics Driver for Windows*</a>
-
-    - <a href="#hddl-myriad">Install the drivers and software for the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs</a>
-
-    - <a href="#Update-Path">Update Windows* environment variables</a> (necessary if you didn't choose the option to add Python to the path when you installed Python)
-
-Also, the following steps will be covered in the guide:
-- <a href="#get-started">Get Started with Code Samples and Demo Applications</a>
-- <a href="#uninstall">Uninstall the Intel® Distribution of OpenVINO™ Toolkit</a>
-
-### About the Intel® Distribution of OpenVINO™ toolkit
-
-OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
-
-For more information, see the online [Intel® Distribution of OpenVINO™ toolkit Overview](https://software.intel.com/en-us/OpenVINO-toolkit) page.
-
-The Intel® Distribution of OpenVINO™ toolkit for Windows\* 10 OS:
-
-- Enables CNN-based deep learning inference on the edge
-- Supports heterogeneous execution across Intel® CPU, Intel® GPU, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
-- Speeds time-to-market through an easy-to-use library of computer vision functions and pre-optimized kernels
-- Includes optimized calls for computer vision standards including OpenCV\* and OpenCL™
-
-#### <a name="InstallPackageContents"></a>Included in the Installation Package
-
-The following components are installed by default:  
-
-| Component                                                                                          | Description                                                                                                                                                                                                                                   |
-|:---------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-|[Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) |This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine.<br><strong>NOTE</strong>: Popular frameworks include such frameworks as Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*.         |
-|[Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)               |This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.                                                                                                 |
-|[OpenCV\*](https://docs.opencv.org/master/)                                                         |OpenCV* community version compiled for Intel® hardware                                                                                                                                                                                         |
-|[Inference Engine Samples](../IE_DG/Samples_Overview.md)                             |A set of simple console applications demonstrating how to use Intel's Deep Learning Inference Engine in your applications.  |
-| [Demos](@ref omz_demos)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo)  |
-
-
-### System Requirements
+## System Requirements
 
 **Hardware**
 
-* 6th to 11th generation Intel® Core™ processors and Intel® Xeon® processors 
+Optimized for these processors:
+* 6th to 12th generation Intel® Core™ processors and Intel® Xeon® processors 
 * 3rd generation Intel® Xeon® Scalable processor (formerly code named Cooper Lake)
 * Intel® Xeon® Scalable processor (formerly Skylake and Cascade Lake)
 * Intel Atom® processor with support for Intel® Streaming SIMD Extensions 4.1 (Intel® SSE4.1)
@@ -84,193 +28,171 @@ The following components are installed by default:
 * Intel® Neural Compute Stick 2
 * Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
 
-> **NOTE**: With OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick is no longer supported.
-
-**Processor Notes:**
-
-- Processor graphics are not included in all processors. See [Processors specifications](https://ark.intel.com/#@Processors) for information about your processor.
-- A chipset that supports processor graphics is required if you're using an Intel Xeon processor. See [Chipset specifications](https://ark.intel.com/#@Chipsets) for information about your chipset.
+> **NOTE**: Processor graphics are not included in all processors. See [Product Specifications](https://ark.intel.com/) for information about your processor.
 
 **Operating System**
 
-- Microsoft Windows\* 10 64-bit
+- Microsoft Windows* 10, 64-bit
 
 **Software**
-- [Microsoft Visual Studio* with C++ **2019 or 2017** with MSBuild](http://visualstudio.microsoft.com/downloads/). Make sure that C++ CMake tools for Windows is [enabled](https://docs.microsoft.com/en-us/cpp/build/cmake-projects-in-visual-studio?view=msvc-160#:~:text=The%20Visual%20C%2B%2B%20Tools%20for,Visual%20Studio%20generators%20are%20supported).
-- [CMake **3.10 or higher** 64-bit](https://cmake.org/download/). If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14.
-- [Python **3.6** - **3.8** 64-bit](https://www.python.org/downloads/windows/)
+- [Microsoft Visual Studio 2019 with MSBuild](http://visualstudio.microsoft.com/downloads/)
+- [CMake 3.14 or higher, 64-bit](https://cmake.org/download/)
+- [Python 3.6 - 3.8, 64-bit](https://www.python.org/downloads/windows/)
+    > **IMPORTANT**: As part of this installation, make sure you click the option **Add Python 3.x to PATH** to [add Python](https://docs.python.org/3/using/windows.html#installation-steps) to your `PATH` environment variable.
 
-## Installation Steps
+## Overview
 
-### <a name="Install-Core-Components"></a>Install the Intel® Distribution of OpenVINO™ toolkit Core Components
+This guide provides step-by-step instructions on how to install the Intel® Distribution of OpenVINO™ toolkit. Links are provided for each type of compatible hardware including downloads, initialization and configuration steps. The following steps will be covered:
 
-1. If you have not downloaded the Intel® Distribution of OpenVINO™ toolkit, [download the latest version](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html). By default, the file is saved to the `Downloads` directory as `w_openvino_toolkit_p_<version>.exe`.
+1. <a href="#install-external-dependencies">Install External Software Dependencies</a>
+2. <a href="#install-openvino">Install the Intel® Distribution of OpenVINO™ Toolkit</a>
+3. <a href="#set-the-environment-variables">Configure the Environment</a>
+4. <a href="#model-optimizer">Configure the Model Optimizer</a>
+5. <a href="#optional-steps">Configure Inference on non-CPU Devices (Optional):</a>
+   - <a href="#install-gpu">Steps for Intel® Processor Graphics (GPU)</a>
+   - <a href="#hddl-myriad">Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPU</a><br>
+   After installing your Intel® Movidius™ VPU, you will return to this guide to complete OpenVINO™ installation.<br>   
+5. <a href="#get-started">Start Using the Toolkit</a>
+
+- [Steps to uninstall the Intel® Distribution of OpenVINO™ Toolkit](../install_guides/uninstalling-openvino.md)
+
+## <a name="install-external-dependencies"></a>Step 1: Install External Software Dependencies
+    
+Install these dependencies:
+
+1. [Microsoft Visual Studio* 2019 with MSBuild](http://visualstudio.microsoft.com/downloads/)
+   > **NOTE**: You can choose to download Community version. Use [Microsoft Visual Studio installation guide](https://docs.microsoft.com/en-us/visualstudio/install/install-visual-studio?view=vs-2019) to walk you through the installation. During installation in the **Workloads** tab, choose **Desktop development with C++**.
+   
+2. [CMake 3.14 or higher 64-bit](https://cmake.org/download/)
+   > **NOTE**: You can either use `cmake<version>.msi` which is the installation wizard or `cmake<version>.zip` where you have to go into the `bin` folder and then manually add the path to environmental variables.
+   
+3. [Python **3.6** - **3.8** 64-bit](https://www.python.org/downloads/windows/)
+   
+   > **IMPORTANT**: As part of this installation, make sure you click the option **Add Python 3.x to PATH** to [add Python](https://docs.python.org/3/using/windows.html#installation-steps) to your `PATH` environment variable.
+
+## <a name="install-openvino"></a>Step 2: Install the Intel® Distribution of OpenVINO™ toolkit Core Components
+
+1. Download the Intel® Distribution of OpenVINO™ toolkit package file from [Intel® Distribution of OpenVINO™ toolkit for Windows*](https://software.intel.com/en-us/openvino-toolkit/choose-download).
+   Select the Intel® Distribution of OpenVINO™ toolkit for Windows* package from the dropdown menu.
+   
 2. Go to the `Downloads` folder and double-click `w_openvino_toolkit_p_<version>.exe`. A window opens to let you choose your installation directory and components. 
    ![](../img/openvino-install-windows-01.png)
-   The default installation directory is `C:\Program Files (x86)\Intel\openvino_<version>`, for simplicity, a shortcut to the latest installation is also created: `C:\Program Files (x86)\Intel\openvino_2022`. If you choose a different installation directory, the installer will create the directory for you.
-   > **NOTE**: If there is an OpenVINO™ toolkit version previously installed on your system, the installer will use the same destination directory for next installations. If you want to install a newer version to a different directory, you need to uninstall the previously installed versions.    
-3. Click **Next**.
-4. You are asked if you want to provide consent to gather information. Choose the option of your choice. Click **Next**.
-5. If you are missing external dependencies, you will see a warning screen. Write down the dependencies you are missing. **You need to take no other action at this time**. After installing the Intel® Distribution of OpenVINO™ toolkit core components, install the missing dependencies.
-The screen example below indicates you are missing two dependencies:
+   
+3. Follow the instructions on your screen. Watch for informational messages such as the following in case you must complete additional steps:
    ![](../img/openvino-install-windows-02.png)
-6. Click **Next**.
-7. When the first part of installation is complete, the final screen informs you that the core components have been installed and additional steps still required:
+
+4. By default, the Intel® Distribution of OpenVINO™ is installed to the following directory, referred to as `<INSTALL_DIR>` elsewhere in the documentation: `C:\Program Files (x86)\Intel\openvino_<version>`. For simplicity, a shortcut to the latest installation is also created: `C:\Program Files (x86)\Intel\openvino_2022`.
+
+5. **Optional**: You can choose **Customize** to change the installation directory or the components you want to install.
+   > **NOTE**: If there is an OpenVINO™ toolkit version previously installed on your system, the installer will use the same destination directory for next installations. If you want to install a newer version to a different directory, you need to uninstall the previously installed versions.
+
+6. The **Finish** screen indicates that the core components have been installed: 
    ![](../img/openvino-install-windows-03.png)
-8. Click **Finish** to close the installation wizard. A new browser window opens to the next section of the installation guide to set the environment variables. You are in the same document. The new window opens in case you ran the installation without first opening this installation guide. 
-9. If the installation indicated you must install dependencies, install them first. If there are no missing dependencies, you can go ahead and <a href="#set-the-environment-variables">set the environment variables</a>.  
+ 
+7. Click **Finish** to close the installation wizard.
 
-### Set the Environment Variables <a name="set-the-environment-variables"></a>
+   Once you click **Finish** to close the installation wizard, a new browser window opens with the document you’re reading now (in case you installed without it) and jumps to the section with the next installation steps.
 
-> **NOTE**: If you installed the Intel® Distribution of OpenVINO™ to the non-default install directory, replace `C:\Program Files (x86)\Intel` with the directory in which you installed the software.
+The core components are now installed. Continue to the next section to install additional dependencies.
+
+## <a name="set-the-environment-variables">Step 3: Configure the Environment
+
+> **NOTE**: If you installed the Intel® Distribution of OpenVINO™ to a non-default install directory, replace `C:\Program Files (x86)\Intel` with that directory in this guide's instructions.
 
 You must update several environment variables before you can compile and run OpenVINO™ applications. Open the Command Prompt, and run the `setupvars.bat` batch file to temporarily set your environment variables:
-```sh
-"C:\Program Files (x86)\Intel\openvino_2022\setupvars.bat"
-```
-> **IMPORTANT**: Windows PowerShell* is not recommended to run the configuration commands, please use the Command Prompt instead.
 
-<strong>(Optional)</strong>: OpenVINO toolkit environment variables are removed when you close the Command Prompt window. As an option, you can permanently set the environment variables manually.
+```sh
+"C:\Program Files (x86)\Intel\openvino_2022\bin\setupvars.bat"
+```
+
+> **IMPORTANT**: Windows PowerShell* is not recommended to run the configuration commands. Please use the command prompt (cmd) instead.
+
+**Optional**: OpenVINO toolkit environment variables are removed when you close the command prompt window. As an option, you can permanently set the environment variables manually.
 
 > **NOTE**: If you see an error indicating Python is not installed when you know you installed it, your computer might not be able to find the program. For the instructions to add Python to your system environment variables, see <a href="#Update-Path">Update Your Windows Environment Variables</a>.
 
-The environment variables are set. Continue to the next section to configure the Model Optimizer.
+The environment variables are set. Next, you will configure the Model Optimizer.
 
-## Configure the Model Optimizer <a name="Configure_MO"></a>
+## <a name="model-optimizer">Step 4: Configure the Model Optimizer
 
-> **IMPORTANT**: These steps are required. You must configure the Model Optimizer for at least one framework. The Model Optimizer will fail if you do not complete the steps in this section.
+The Model Optimizer is a Python\*-based command line tool for importing trained models from popular deep learning frameworks such as Caffe\*,
+TensorFlow\*, Apache MXNet\*, ONNX\* and Kaldi\*.
 
-The Model Optimizer is a key component of the Intel® Distribution of OpenVINO™ toolkit. You cannot do inference on your trained model without running the model through the Model Optimizer. When you run a pre-trained model through the Model Optimizer, your output is an Intermediate Representation (IR) of the network. The IR is a pair of files that describe the whole model:
+The Model Optimizer is a key component of the Intel Distribution of OpenVINO toolkit. Performing inference on a model 
+(with the exception of ONNX and nGraph models) requires running the model through the Model Optimizer. When you convert a pre-trained 
+model through the Model Optimizer, your output is an Intermediate Representation (IR) of the network. The Intermediate 
+Representation is a pair of files that describe the whole model:
 
 - `.xml`: Describes the network topology
 - `.bin`: Contains the weights and biases binary data
 
-The Inference Engine reads, loads, and infers the IR files, using a common API across the CPU, GPU, or VPU hardware.  
+For more information about the Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). 
 
-The Model Optimizer is a Python*-based command line tool (`mo`), which is located in `C:\Program Files (x86)\Intel\openvino_2022\tools\model_optimizer`. Use this tool on models trained with popular deep learning frameworks such as Caffe\*, TensorFlow\*, MXNet\*, and ONNX\* to convert them to an optimized IR format that the Inference Engine can use.
+If you see error messages, make sure you installed all dependencies. These steps use a command prompt to make sure you see error messages.
 
-This section explains how to use scripts to configure the Model Optimizer either for all of the supported frameworks at the same time or for individual frameworks. If you want to manually configure the Model Optimizer instead of using scripts, see the **Using Manual Configuration Process** section on the [Configuring the Model Optimizer](../MO_DG/prepare_model/Config_Model_Optimizer.md) page.
-
-For more information about the Model Optimizer, see the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
-
-
-### Model Optimizer Configuration Steps
-
-You can configure the Model Optimizer either for all supported frameworks at once or for one framework at a time. Choose the option that best suits your needs. If you see error messages, make sure you installed all dependencies.
-
-> **IMPORTANT**: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your environment.
-
-> **NOTE**:
-> In the steps below:
-> - If you you want to use the Model Optimizer from another installed versions of Intel® Distribution of OpenVINO™ toolkit installed, replace `openvino_2022` with `openvino_<version>`, where `<version>` is the required version.
-> - If you installed the Intel® Distribution of OpenVINO™ toolkit to the non-default installation directory, replace `C:\Program Files (x86)\Intel` with the directory where you installed the software.
-
-These steps use a command prompt to make sure you see error messages.
-
-#### Option 1: Configure the Model Optimizer for all supported frameworks at the same time:
-
-1. Open a command prompt. To do so, type `cmd` in your **Search Windows** box and then press **Enter**.
+1. Open a command prompt by typing `cmd` in your **Search Windows** box and then pressing **Enter**.
 Type commands in the opened window:
-
    ![](../img/command_prompt.PNG)
 
 2. Go to the Model Optimizer prerequisites directory.<br>
-```sh
-cd C:\Program Files (x86)\Intel\openvino_2022\tools\model_optimizer\install_prerequisites
-```
-
-3. Run the following batch file to configure the Model Optimizer for Caffe\*, TensorFlow\* 1.x, MXNet\*, Kaldi\*, and ONNX\*:<br>
-```sh
-install_prerequisites.bat
-```
-
-#### Option 2: Configure the Model Optimizer for each framework separately:
-
-1. Go to the Model Optimizer prerequisites directory:<br>
-```sh
-cd C:\Program Files (x86)\Intel\openvino_2022\tools\model_optimizer\install_prerequisites
-```
-
-2. Run the batch file for the framework you will use with the Model Optimizer. You can use more than one:
-
-   * For **Caffe**:<br>
    ```sh
-   install_prerequisites_caffe.bat
+   cd C:\Program Files (x86)\Intel\openvino_2022\tools\model_optimizer\install_prerequisites
    ```
 
-   * For **TensorFlow 1.x**:<br>
+3.  Run this batch file to configure the Model Optimizer for Caffe, TensorFlow 2.x, MXNet, Kaldi\*, and ONNX:<br>
    ```sh
-   install_prerequisites_tf.bat
+   install_prerequisites.bat
    ```
 
-   * For **TensorFlow 2.x**:<br>
-   ```sh
-   install_prerequisites_tf2.bat
-   ```
+3. **Optional:** You can choose to install Model Optimizer support for only certain frameworks. In the same directory are individual batch files for Caffe, TensorFlow 1.x, TensorFlow 2.x, MXNet, Kaldi, and ONNX (`install_prerequisites_caffe.bat`, etc.). If you see error messages, make sure you installed all dependencies. 
+   
+The Model Optimizer is configured for one or more frameworks.
 
-   * For **MXNet**:<br>
-   ```sh
-   install_prerequisites_mxnet.bat
-   ```
-
-   * For **ONNX**:
-   ```sh
-   install_prerequisites_onnx.bat
-   ```
-
-   * For **Kaldi**:
-   ```sh
-   install_prerequisites_kaldi.bat
-   ```
-
-The Model Optimizer is configured for one or more frameworks. Success is indicated by a screen similar to this:
-
-![](../img/Configure-MO.PNG)
-
-You have completed all required installation, configuration and build steps in this guide to use your CPU to work with your trained models. 
+You have now completed all required installation, configuration and build steps in this guide to use your CPU to work with your trained models. 
 
 If you want to use a GPU or VPU, or update your Windows* environment variables, read through the <a href="#optional-steps">Optional Steps</a> section:
 
-- <a href="#Install-GPU">Steps for Intel® Processor Graphics (GPU)</a>
+- <a href="#install-gpu">Steps for Intel® Processor Graphics (GPU)</a>
 - <a href="#hddl-myriad">Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs</a>
 - <a href="#Update-Path">Add CMake* or Python* to your Windows* environment variables</a><br>
 
-Or proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
-## <a name="optional-steps"></a>Optional Steps
+Or proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-###  <a name="Install-GPU"></a>Optional: Additional Installation Steps for Intel® Processor Graphics (GPU)
+## <a name="optional-steps"></a>Step 5 (Optional): Configure Inference on non-CPU Devices:
+
+### <a name="install-gpu"></a>Optional: Steps for Intel® Processor Graphics (GPU)
 
 > **NOTE**: These steps are required only if you want to use an Intel® integrated GPU.
 
-If your applications offload computation to **Intel® Integrated Graphics**, you must have the Intel Graphics Driver for Windows installed for your hardware. 
+This section will help you check if you require driver installation. Install indicated version or higher.
+
+If your applications offload computation to **Intel® Integrated Graphics**, you must have the Intel Graphics Driver for Windows installed on your hardware.
 [Download and install the recommended version](https://downloadcenter.intel.com/download/30079/Intel-Graphics-Windows-10-DCH-Drivers). 
 
 To check if you have this driver installed:
 
-1. Type **device manager** in your **Search Windows** box. The **Device Manager** opens.
-
-2. Click the drop-down arrow to view the **Display adapters**. You see the adapter that is installed in your computer:
+1. Type **device manager** in your **Search Windows** box and press Enter. The **Device Manager** opens.
 
+2. Click the drop-down arrow to view the **Display adapters**. You can see the adapter that is installed in your computer:
    ![](../img/DeviceManager.PNG)
 
 3. Right-click the adapter name and select **Properties**.
 
 4. Click the **Driver** tab to see the driver version. 
-
    ![](../img/DeviceDriverVersion.PNG)
 
-You are done updating your device driver and are ready to use your GPU. Proceed to the <a href="#get-started">Get Started</a> to get started with running code samples and demo applications.
+You are done updating your device driver and are ready to use your GPU. Proceed to the <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-### <a name="hddl-myriad"></a> Optional: Additional Installation Steps for the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
+### <a name="hddl-myriad"></a> Optional: Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
 
 > **NOTE**: These steps are required only if you want to use Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
 
-To perform inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, the following additional installation steps are required:
+To enable inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, the following additional installation steps are required:
 
   1. Download and install <a href="https://www.microsoft.com/en-us/download/details.aspx?id=48145">Visual C++ Redistributable for Visual Studio 2017</a>
   2. Check with a support engineer if your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs card requires SMBUS connection to PCIe slot (most unlikely). Install the SMBUS driver only if confirmed (by default, it's not required):
       1. Go to the `<INSTALL_DIR>\runtime\3rdparty\hddl\drivers\SMBusDriver` directory, where `<INSTALL_DIR>` is the directory in which the Intel Distribution of OpenVINO toolkit is installed.
-      2. Right click on the `hddlsmbus.inf` file and choose **Install** from the pop-up menu.
+      2. Right click on the `hddlsmbus.inf` file and choose **Install** from the pop up menu.
 
 You are done installing your device driver and are ready to use your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
 
@@ -280,15 +202,15 @@ See also:
 
 * After you've configurated your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, see [Intel® Movidius™ VPUs Programming Guide for Use with Intel® Distribution of OpenVINO™ toolkit](movidius-programming-guide.md) to learn how to distribute a model across all 8 VPUs to maximize performance.
 
-After configuration is done, you are ready to <a href="#get-started">Get Started</a> with running code samples and demo applications.
+After configuration is done, you are ready to go to <a href="#get-started">Start Using the Toolkit</a> section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.
 
-### <a name="Update-Path"></a>Optional: Update Your Windows Environment Variables
+## <a name="Update-Path"></a>Optional: Update Your Windows Environment Variables
 
 > **NOTE**: These steps are only required under special circumstances, such as if you forgot to check the box during the CMake\* or Python\* installation to add the application to your Windows `PATH` environment variable.
 
-Use these steps to update your Windows `PATH` if a command you execute returns an error message stating that an application cannot be found. This might happen if you do not add CMake or Python to your `PATH` environment variable during the installation.
+Use these steps to update your Windows `PATH` if a command you execute returns an error message stating that an application cannot be found.
 
-1. In your **Search Windows** box, type **Edit the system environment variables** and press **Enter**. A window similar to the following displays:
+1. In your **Search Windows** box, type **Edit the system environment variables** and press **Enter**. A window like the following appears:
    ![](../img/System_Properties.PNG)
 
 2. At the bottom of the screen, click **Environment Variables**.
@@ -301,49 +223,34 @@ Use these steps to update your Windows `PATH` if a command you execute returns a
 
 5. If you need to add CMake to the `PATH`, browse to the directory in which you installed CMake. The default directory is `C:\Program Files\CMake`.
 
-6. If you need to add Python to the `PATH`, browse to the directory in which you installed Python. The default directory is `C:\Users\<USER_ID>\AppData\Local\Programs\Python\Python36\Python`. Note that the `AppData` folder is hidden by default. To view hidden files and folders, see the [Windows 10 instructions](https://support.microsoft.com/en-us/windows/view-hidden-files-and-folders-in-windows-10-97fbc472-c603-9d90-91d0-1166d1d9f4b5). 
+6. If you need to add Python to the `PATH`, browse to the directory in which you installed Python. The default directory is `C:\Users\<USER_ID>\AppData\Local\Programs\Python\Python36\Python`. Note that the `AppData` folder is hidden by default. To view hidden files and folders, see these [Windows 10 instructions](https://support.microsoft.com/en-us/windows/view-hidden-files-and-folders-in-windows-10-97fbc472-c603-9d90-91d0-1166d1d9f4b5). 
 
 7. Click **OK** repeatedly to close each screen.
 
 Your `PATH` environment variable is updated. If the changes don't take effect immediately, you may need to reboot.
 
-## <a name="get-started"></a>Get Started
+## <a name="get-started"></a>Step 6: Start Using the Toolkit
 
-Now you are ready to get started. To continue, see the following pages:
-* [OpenVINO™ Toolkit Overview](../index.md)
-* [Get Started Guide for Windows](../get_started/get_started_windows.md) to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications with pre-trained models on different inference devices.
+Now you are ready to try out the toolkit. To continue, see the [Get Started Guide](../get_started/get_started.md) section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications with pre-trained models on different inference devices.
 
 ## <a name="uninstall"></a>Uninstall the Intel® Distribution of OpenVINO™ Toolkit
-Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system:
-1. Choose the **Apps & Features** option from the Windows* Settings app.
-2. From the list of installed applications, select the Intel® Distribution of OpenVINO™ Toolkit and click **Uninstall**.
-3. Follow the uninstallation wizard instructions.
-4. When uninstallation is complete, click **Finish**. 
 
-## <a name="Summary"></a>Summary
+To uninstall the toolkit, follow the steps on the [Uninstalling](uninstalling_openvino.md) page.
 
-In this document, you installed the Intel® Distribution of OpenVINO™ toolkit and its dependencies. You also configured the Model Optimizer for one or more frameworks. After the software was installed and configured, you ran two verification scripts. You might have also installed drivers that will let you use a GPU or VPU to infer your models and run the Image Classification Sample application.
+## Additional Resources
 
-You are now ready to learn more about converting models trained with popular deep learning frameworks to the Inference Engine format, following the links below, or you can move on to running the [sample applications](../IE_DG/Samples_Overview.md).
+- Get started with samples and demos: [Get Started Guide](../get_started.md)
+- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
+- Convert models for use with OpenVINO™: [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
+- Write your own OpenVINO™ applications: [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
+- Information on sample applications: [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
+- Information on a supplied set of models: [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
+- IoT libraries and code samples: [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit)
 
-To learn more about converting deep learning models, go to:
+To learn more about converting models from specific frameworks, go to:
 
 - [Convert Your Caffe* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md)
 - [Convert Your TensorFlow* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
 - [Convert Your MXNet* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md)
-- [Convert Your ONNX* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
 - [Convert Your Kaldi* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
-
-## Additional Resources
-
-- [Intel Distribution of OpenVINO Toolkit home page](https://software.intel.com/en-us/openvino-toolkit)
-- [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
-- [Introduction to Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
-- [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
-- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-- [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
-- [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
-- [Intel® Neural Compute Stick 2 Get Started](https://software.intel.com/en-us/neural-compute-stick/get-started)
-
-
-[myriad_driver]: ../img/myriad_driver.png
+- [Convert Your ONNX* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
\ No newline at end of file
diff --git a/docs/install_guides/installing-openvino-yum.md b/docs/install_guides/installing-openvino-yum.md
index 3c3ad19ef7b..8ab04742ddc 100644
--- a/docs/install_guides/installing-openvino-yum.md
+++ b/docs/install_guides/installing-openvino-yum.md
@@ -30,7 +30,7 @@ The following components are installed with the OpenVINO runtime package:
 
 ## Set up the Repository
 
-> **NOTE:** You must be logged in as root to set up and install the repository.
+> **NOTE**: You must be logged in as root to set up and install the repository.
 <br>
 Configure YUM with the OpenVINO repository to install OpenVINO. You have two options for this, using the `yum-config-manager` or manually by creating a text file and pointing YUM to the file. 
 
diff --git a/docs/install_guides/movidius-programming-guide.md b/docs/install_guides/movidius-programming-guide.md
index 184910a1471..031e4c270c4 100644
--- a/docs/install_guides/movidius-programming-guide.md
+++ b/docs/install_guides/movidius-programming-guide.md
@@ -3,10 +3,10 @@
 ## See Also
 
 - [Intel® Movidius™ VPUs Setup Guide for use with the Intel® Distribution of OpenVINO™](movidius-setup-guide.md)
-- <a class="download" href="<domain_placeholder>/downloads/595850_Intel_Vision_Accelerator_Design_with_Intel_Movidius™_VPUs-HAL Configuration Guide_rev1.3.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs HAL Configuration Guide</a>
-- <a class="download" href="<domain_placeholder>/downloads/613514_Intel Vision Accelerator Design with Intel Movidius™ VPUs Workload Distribution_UG_r0.9.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Workload Distribution User Guide</a>
-- <a class="download" href="<domain_placeholder>/downloads/613759_Intel Vision Accelerator Design with Intel Movidius™ VPUs Scheduler_UG_r0.9.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Scheduler User Guide</a>
-- <a class="download" href="<domain_placeholder>/downloads/Intel Vision Accelerator Design with Intel Movidius™ VPUs Errata.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Errata</a>
+- <a class="download" href="<domain_placeholder>/downloads/595850_Intel_Vision_Accelerator_Design_with_Intel_Movidius_VPUs-HAL Configuration Guide_rev1.3.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs HAL Configuration Guide</a>
+- <a class="download" href="<domain_placeholder>/downloads/613514_Intel Vision Accelerator Design with Intel Movidius VPUs Workload Distribution_UG_r0.9.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Workload Distribution User Guide</a>
+- <a class="download" href="<domain_placeholder>/downloads/613759_Intel Vision Accelerator Design with Intel Movidius VPUs Scheduler_UG_r0.9.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Scheduler User Guide</a>
+- <a class="download" href="<domain_placeholder>/downloads/Intel Vision Accelerator Design with Intel Movidius VPUs Errata.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Errata</a>
 
 The following section provides information on how to distribute a model across all 8 VPUs to maximize performance.
 
diff --git a/docs/install_guides/movidius-setup-guide.md b/docs/install_guides/movidius-setup-guide.md
index 0bb5de0fe69..236f8a4b0a6 100644
--- a/docs/install_guides/movidius-setup-guide.md
+++ b/docs/install_guides/movidius-setup-guide.md
@@ -5,10 +5,10 @@
 ## See Also
 
 - [Intel® Movidius™ VPUs Programming Guide for use with the Intel® Distribution of OpenVINO™](movidius-programming-guide.md)
-- <a class="download" href="<domain_placeholder>/downloads/595850_Intel_Vision_Accelerator_Design_with_Intel_Movidius™_VPUs-HAL Configuration Guide_rev1.3.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs HAL Configuration Guide</a>
-- <a class="download" href="<domain_placeholder>/downloads/613514_Intel Vision Accelerator Design with Intel Movidius™ VPUs Workload Distribution_UG_r0.9.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Workload Distribution User Guide</a>
-- <a class="download" href="<domain_placeholder>/downloads/613759_Intel Vision Accelerator Design with Intel Movidius™ VPUs Scheduler_UG_r0.9.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Scheduler User Guide</a>
-- <a class="download" href="<domain_placeholder>/downloads/Intel Vision Accelerator Design with Intel Movidius™ VPUs Errata.pdf">Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Errata</a>
+- [Intel® Vision Accelerator Design with Intel® Movidius™ VPUs HAL Configuration Guide](/downloads/595850_Intel_Vision_Accelerator_Design_with_Intel_Movidius_VPUs-HAL Configuration Guide_rev1.3.pdf)
+- [Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Workload Distribution User Guide](/downloads/613514_Intel Vision Accelerator Design with Intel Movidius VPUs Workload Distribution_UG_r0.9.pdf)
+- [Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Scheduler User Guide](/downloads/613759_Intel Vision Accelerator Design with Intel Movidius VPUs Scheduler_UG_r0.9.pdf)
+- [Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Errata](/downloads/Intel Vision Accelerator Design with Intel Movidius VPUs Errata.pdf)
 
 The IEI Mustang-V100-MX8 is an OEM version of the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
 This guide assumes you have installed the [Mustang-V100-MX8](https://download.ieiworld.com/) and the [Intel® Distribution of OpenVINO™ Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html).
@@ -63,7 +63,7 @@ If you have installed OpenVINO&trade; in a different directory on your system, y
 
 Below are some possible configuration options. 
 
-> **NOTE:**  After changing a configuration file, the `hddldaemon` must be restarted. 
+> **NOTE**:  After changing a configuration file, the `hddldaemon` must be restarted. 
 
 ### Recommended Settings
 
@@ -121,7 +121,7 @@ This setting reports the amount of memory being used by each VPU.
 `"max_cycle_switchout": 3`                   
 Requires the squeeze scheduler.  This setting might speed up performance significantly, depending on the app.  
 
-> **NOTE:** This setting works in conjunction with: `max_task_number_switch_out`.
+> **NOTE**: This setting works in conjunction with: `max_task_number_switch_out`.
 
 `"client_fps" : "off"`                          
 This setting reports the total FPS for the dispatching hddl_service (which will have one or more clients per app).
diff --git a/docs/install_guides/uninstalling-openvino.md b/docs/install_guides/uninstalling-openvino.md
new file mode 100644
index 00000000000..25e075f8dff
--- /dev/null
+++ b/docs/install_guides/uninstalling-openvino.md
@@ -0,0 +1,48 @@
+# Uninstalling the Intel® Distribution of OpenVINO™ Toolkit {#openvino_docs_install_guides_uninstalling_openvino}
+
+> **NOTE**: Uninstallation procedures remove all Intel® Distribution of OpenVINO™ Toolkit component files but don't affect user files in the installation directory.
+
+## <a name="uninstall"></a>Uninstall the Toolkit in Linux
+Choose one of the options provided below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system.
+
+### Uninstall with GUI
+1. Run the uninstallation script from `<INSTALL_DIR>/openvino_toolkit_uninstaller`:
+   ```sh
+   sudo ./uninstall_GUI.sh
+   ```
+2. Follow the uninstallation wizard instructions.
+
+
+### Uninstall with Command Line (Interactive Mode)
+1. Run the uninstallation script from `<INSTALL_DIR>/openvino_toolkit_uninstaller`:
+   ```sh
+   sudo ./uninstall.sh
+   ```
+2. Follow the instructions on your screen.
+4. When uninstallation is complete, press **Enter**.
+
+### Uninstall with Command Line (Silent Mode)
+1. Run the following command from `<INSTALL_DIR>/openvino_toolkit_uninstaller`:
+   ```sh
+   sudo ./uninstall.sh -s
+   ```
+2. Intel® Distribution of OpenVINO™ Toolkit is now uninstalled from your system.
+
+## <a name="uninstall"></a>Uninstall the Toolkit in Windows
+
+Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system:
+1. Choose the **Apps & Features** option from the Windows* Settings app.
+2. From the list of installed applications, select the Intel® Distribution of OpenVINO™ Toolkit and click **Uninstall**.
+3. Follow the uninstallation wizard instructions.
+4. When uninstallation is complete, click **Finish**. 
+
+## <a name="uninstall"></a>Uninstall the Toolkit in macOS
+
+Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system:
+
+1. Run the uninstall application from the installation directory (by default, `/opt/intel/openvino_2021`):
+   ```sh
+   open openvino_toolkit_uninstaller.app
+   ```
+2. Follow the uninstallation wizard instructions.
+3. When uninstallation is complete, click **Finish**. 
diff --git a/docs/model_server/README.md b/docs/model_server/README.md
index e6c7144f3cb..2fe6c550b0c 100644
--- a/docs/model_server/README.md
+++ b/docs/model_server/README.md
@@ -72,7 +72,7 @@ For more detailed guides on using the Model Server in various scenarios, visit t
 
 OpenVINO&trade; Model Server gRPC API is documented in the proto buffer files in [tensorflow_serving_api](https://github.com/tensorflow/serving/tree/r2.2/tensorflow_serving/apis).
 
-> **NOTE:** The implementations for `Predict`, `GetModelMetadata`, and `GetModelStatus` function calls are currently available.
+> **NOTE**: The implementations for `Predict`, `GetModelMetadata`, and `GetModelStatus` function calls are currently available.
 > These are the most generic function calls and should address most of the usage scenarios.
 
 [Predict proto](https://github.com/tensorflow/serving/blob/r2.2/tensorflow_serving/apis/predict.proto) defines two message specifications: `PredictRequest` and `PredictResponse` used while calling Prediction endpoint.
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
new file mode 100644
index 00000000000..52f34826f67
--- /dev/null
+++ b/docs/model_zoo.md
@@ -0,0 +1,28 @@
+# Model Zoo {#model_zoo}
+
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Pre-Trained Models
+   
+   omz_models_group_intel
+   omz_models_group_public
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Demo Applications
+
+   omz_demos
+
+   
+@endsphinxdirective
+
+Open Model Zoo for OpenVINO™ toolkit provides a wide variety of free, pre-trained deep learning models and demo applications that provide full application templates to help you implement deep learning in Python, C++, or OpenCV Graph API (G-API).
+
+Browse through over 200 neural network models, both [public](@ref omz_models_group_public) and from [Intel](@ref omz_models_group_intel), and pick the right one for your solution. Types include object detection, classification, image segmentation, handwriting recognition, text to speech, pose estimation, and others. The Intel models have already been converted to work with OpenVINO™ software, while public models can easily be converted using the [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide) utility.
+
+Get started with simple [step-by-step procedures](../get_started/get_started_demos.md) to learn how to build and run demo applications or discover the [full set of demos](@ref omz_demos) and adapt them for implementing specific deep learning scenarios in your applications.
\ No newline at end of file
diff --git a/docs/nGraph_DG/nGraphTransformation.md b/docs/nGraph_DG/nGraphTransformation.md
index 2dd3e0d73ad..a0907b0521b 100644
--- a/docs/nGraph_DG/nGraphTransformation.md
+++ b/docs/nGraph_DG/nGraphTransformation.md
@@ -378,7 +378,7 @@ Another example shows how multiple matcher passes can be united into single Grap
 
 @snippet src/transformations/template_pattern_transformation.cpp matcher_pass:manager2
 
-> **Note:** nGraph used to have the `pass::PassConfig` class for transformation pipeline manipulation.
+> **NOTE**: nGraph used to have the `pass::PassConfig` class for transformation pipeline manipulation.
 This mechanism is now obsolete and the `pass::PassConfig` class will be removed in future release.
 
 ## How to debug transformations <a name="how_to_debug_transformations"></a>
diff --git a/docs/nGraph_DG/nGraph_dg.md b/docs/nGraph_DG/nGraph_dg.md
index a59d501a103..c4aa4a9db5b 100644
--- a/docs/nGraph_DG/nGraph_dg.md
+++ b/docs/nGraph_DG/nGraph_dg.md
@@ -1,11 +1,16 @@
 # nGraph Developer Guide {#openvino_docs_nGraph_DG_DevGuide}
 
-## Table of Contents
+@sphinxdirective
 
-* [Introduction to the nGraph](intro.md)
-* [Basic Concepts](./nGraph_basic_concepts.md)
-* [Operation Sets](../ops/opset.md)
-* [Graph Constructing](./build_function.md)
-* [Transformations](./nGraphTransformation.md)
-* [Debug Capabilities](./nGraph_debug_capabilities.md)
-* [Python API](./nGraph_Python_API.md)
+.. toctree::
+   :maxdepth: 1
+      
+   openvino_docs_nGraph_DG_Introduction
+   openvino_docs_nGraph_DG_basic_concepts
+   openvino_docs_MO_DG_IR_and_opsets
+   openvino_docs_nGraph_DG_build_function
+   openvino_docs_nGraph_DG_Debug_capabilities
+   ngraph_transformation
+   openvino_docs_nGraph_DG_PythonAPI
+      
+@endsphinxdirective
\ No newline at end of file
diff --git a/docs/nbdoc/README.md b/docs/nbdoc/README.md
new file mode 100644
index 00000000000..ea655fa32b5
--- /dev/null
+++ b/docs/nbdoc/README.md
@@ -0,0 +1,52 @@
+# Jupyter notebooks autodoc
+
+Auto fetching documentations designed for openvino notebooks tutorials.
+This module is responsible for fetching artifats, in this particular example jupyter tutorial notebooks and converting them to notebook documentation.
+
+## Step 0. Prepare venv
+
+To start new venv based on system interpreter
+
+``` bash
+python -m venv ./venv
+```
+
+Linux:
+
+``` bash
+source ./venv/bin/activate
+```
+
+Windows (cmd):
+
+``` bash
+venv/Scripts/activate
+```
+
+## Step 1. Download requirements
+
+``` bash
+python -m pip install -r requirements.txt
+```
+
+## Step 2. Download personal token from github account
+
+[Tutorial how to do it.](https://docs.github.com/en/github/authenticating-to-github/keeping-your-account-and-data-secure/creating-a-personal-access-token). By default configuration you should place it on path ../.secret.
+
+## Step 3. Configure consts to meet project directions
+
+[Consts file](consts.py) contains multiple variables that might differ for different environments.
+
+## Step 4. Add classes with methods to makefile or other executed file
+
+[Main file](main.py) contains example usecases of auto generator for notebooks. Informations placed in [main](main.py) should be further used to add it to makefile and possibly fully automate notebook documentation process.
+
+## Step 5. Run python file (optional)
+
+If step 4 was skipped use command
+
+``` bash
+python main.py
+```
+
+or use any other command that is responsible for generating documentation
diff --git a/docs/nbdoc/__init__.py b/docs/nbdoc/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/docs/nbdoc/consts.py b/docs/nbdoc/consts.py
new file mode 100644
index 00000000000..0fd5053cb6b
--- /dev/null
+++ b/docs/nbdoc/consts.py
@@ -0,0 +1,67 @@
+notebooks_docs = "notebooks.rst"
+
+notebooks_path = "notebooks"
+
+repo_directory = "notebooks"
+
+repo_owner = "openvinotoolkit"
+
+repo_name = "openvino_notebooks"
+
+section_names = ["Getting Started", "Convert & Optimize", "Model Demos", "Model Training", "Live Demos"]
+
+# Templates
+
+binder_template = """
+This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. 
+See the |installation_link| for instructions to run this tutorial locally on Windows, Linux or macOS. 
+To run without installing anything, click the launch binder button.
+
+|binder_link| |github_link|
+
+.. |installation_link| raw:: html
+
+   <a href="https://github.com/{{ owner }}/{{ repo }}#-installation-guide" target="_blank">installation guide</a>
+
+.. |binder_link| raw:: html 
+
+   <a href="https://mybinder.org/v2/gh/{{ owner }}/{{ repo }}/HEAD?filepath={{ folder }}%2F{{ notebook }}%2F{{ notebook }}.ipynb" target="_blank"><img src="https://mybinder.org/badge_logo.svg" alt="Binder"></a>
+
+.. |github_link| raw:: html
+
+   <a href="https://github.com/{{ owner }}/{{ repo }}" target="_blank"><img src="https://badgen.net/badge/icon/github?icon=github&label" alt="Github"></a>
+
+\n
+"""
+no_binder_template = """
+This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. 
+See the |installation_link| for instructions to run this tutorial locally on Windows, Linux or macOS.
+
+|github_link|
+
+.. |installation_link| raw:: html
+
+   <a href="https://github.com/{{ owner }}/{{ repo }}#-installation-guide" target="_blank">installation guide</a>
+
+.. |github_link| raw:: html
+
+   <a href="https://github.com/{{ owner }}/{{ repo }}" target="_blank"><img src="https://badgen.net/badge/icon/github?icon=github&label" alt="Github"></a>
+
+\n
+"""
+
+rst_template = """
+OpenVINO notebooks documentation
+================================
+
+{% for section in sections %}
+{{section.name}}
+--------------------------------
+
+.. toctree::
+   :maxdepth: 1
+
+{% for notebook in section.notebooks %}   {{notebook.path}}\n{% endfor %}
+{% endfor %}
+
+"""
diff --git a/docs/nbdoc/nbdoc.py b/docs/nbdoc/nbdoc.py
new file mode 100644
index 00000000000..e936093e229
--- /dev/null
+++ b/docs/nbdoc/nbdoc.py
@@ -0,0 +1,214 @@
+import argparse
+from pathlib import Path
+from utils import (
+    create_content,
+    add_content_below,
+    load_secret,
+    process_notebook_name,
+    find_latest_artifact,
+    verify_notebook_name,
+    generate_artifact_link,
+    remove_existing,
+    split_notebooks_into_sections,
+)
+from consts import (
+    binder_template,
+    no_binder_template,
+    rst_template,
+    notebooks_path,
+    repo_owner,
+    repo_name,
+    repo_directory,
+    notebooks_docs,
+    section_names
+)
+from notebook import Notebook
+from section import Section
+from io import BytesIO
+from glob import glob
+from jinja2 import Template
+from requests import get
+from zipfile import ZipFile
+import os
+
+
+class NbDownloader:
+    """Class responsible for downloading and extracting notebooks"""
+
+    def __init__(self, secret_path: str) -> None:
+        self.secret = load_secret(secret_path)
+        self.headers = {
+            "Accept": "application/vnd.github.v3+json",
+            "Authorization": f"token {self.secret}",
+        }
+        self.artifact_link = generate_artifact_link(repo_owner, repo_name)
+
+    def default_pipeline(self, path: str = notebooks_path) -> bool:
+        """Default pipeline for fetching, downloading and extracting rst files
+
+        :param path: Path to folder that will contain notebooks. Defaults to notebooks_path.
+        :type path: str
+        :returns: Returns if status is sucessful
+        :rtype: bool
+
+        """
+        artifacts = self.fetch_artifacts()
+        latest_artifact = find_latest_artifact(artifacts)
+        download_link = self.generate_artifact_download_link(latest_artifact)
+        zipfile = self.download_rst_files(download_link)
+        if zipfile.testzip() is None:
+            remove_existing(path)
+        return self.extract_artifacts(zipfile, path=path)
+
+    def fetch_artifacts(self) -> dict:
+        """Fetching artifcats from github actions
+
+        :returns: Artifacts in repo
+        :rtype: dict
+
+        """
+        return get(self.artifact_link, headers=self.headers).json()
+
+    def generate_artifact_download_link(self, artifact_id: int) -> str:
+        """Generate link based on link and latest artifact id containing rst files
+
+        :param artifact_id: Latest artifact id containing rst files
+        :type artifact_id: int
+        :returns: Link to download rst files
+        :rtype: str
+
+        """
+        return f"{self.artifact_link}/{artifact_id}/zip"
+
+    def download_rst_files(self, artifact_download_link: str) -> ZipFile:
+        """Downloading rst files
+
+        :param artifact_download_link: Generated link for downloading rst
+        :type artifact_download_link: str
+        :returns: Zipped archive of rst files
+        :rtype: ZipFile
+
+        """
+        artifact = get(artifact_download_link, headers=self.headers)
+        return ZipFile(BytesIO(artifact.content))
+
+    def extract_artifacts(self, zipfile: ZipFile, path: str) -> bool:
+        """Extracting all artifacts from zipped archive
+
+        :param zipfile: zipped rst files
+        :type zipfile: ZipFile
+        :param path: path to extract files to
+        :type path: str
+        :returns: Returns if status is sucessful
+        :rtype: bool
+
+        """
+        try:
+            zipfile.extractall(path=path)
+            return True
+        except ValueError:
+            return False
+
+
+class NbProcessor:
+    def __init__(self, nb_path: str = notebooks_path):
+        self.nb_path = nb_path
+        notebooks = [
+                Notebook(
+                    name=process_notebook_name(notebook),
+                    path=notebook,
+                )
+                for notebook in os.listdir(self.nb_path)
+                if verify_notebook_name(notebook)
+        ]
+        notebooks = split_notebooks_into_sections(notebooks)
+        self.rst_data = {
+            "sections": [
+                Section(name=section_name, notebooks=section_notebooks)
+                for section_name, section_notebooks in zip(section_names, notebooks)
+            ]
+            
+        }
+        self.binder_data = {
+            "owner": repo_owner,
+            "repo": repo_name,
+            "folder": repo_directory,
+        }
+
+    def fetch_binder_list(self, file_format: str = 'txt') -> list:
+        """Funtion that fetches list of notebooks with binder buttons
+
+        :param file_format: Format of file containing list of notebooks with button. Defaults to 'txt'
+        :type file_format: str
+        :return: List of notebooks conaining binder buttons
+        :rtype: list
+        """
+        list_of_buttons = glob(f"{self.nb_path}/*.{file_format}")
+        if list_of_buttons:
+            with open(list_of_buttons[0]) as file:
+                list_of_buttons = file.read().splitlines()
+            return list_of_buttons
+        else:
+            return []
+
+    def add_binder(self, buttons_list: list,  template_with_binder: str = binder_template, template_without_binder: str = no_binder_template):
+        """Function working as an example how to add binder button to existing rst files
+
+        :param buttons_list: List of notebooks that work on Binder.
+        :type buttons_list: list
+        :param template_with_binder: Template of button added to rst file if Binder is available. Defaults to binder_template.
+        :type template_with_binder: str
+        :param template_without_binder: Template of button added to rst file if Binder isn't available. Defaults to no_binder_template.
+        :type template_without_binder: str
+        :raises FileNotFoundError: In case of failure of adding content, error will appear
+
+        """
+        for notebook in [
+            nb for nb in os.listdir(self.nb_path) if verify_notebook_name(nb)
+        ]:
+            if '-'.join(notebook.split('-')[:-2]) in buttons_list:
+                button_text = create_content(template_with_binder, self.binder_data, notebook)
+                if not add_content_below(button_text, f"{self.nb_path}/{notebook}"):
+                    raise FileNotFoundError("Unable to modify file")
+            else:
+                button_text = create_content(template_without_binder, self.binder_data, notebook)
+                if not add_content_below(button_text, f"{self.nb_path}/{notebook}"):
+                    raise FileNotFoundError("Unable to modify file")
+
+    def render_rst(self, path: str = notebooks_docs, template: str = rst_template):
+        """Rendering rst file for all notebooks
+
+        :param path: Path to notebook main rst file. Defaults to notebooks_docs.
+        :type path: str
+        :param template: Template for default rst page. Defaults to rst_template.
+        :type template: str
+
+        """
+        with open(path, "w+") as nb_file:
+            nb_file.writelines(Template(template).render(self.rst_data))
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('secret', type=Path)
+    parser.add_argument('outdir', type=Path)
+    args = parser.parse_args()
+    secret = args.secret
+    outdir = args.outdir
+    outdir.mkdir(parents=True, exist_ok=True)
+    # Step 1. Create secret file
+    # link: https://docs.github.com/en/github/authenticating-to-github/keeping-your-account-and-data-secure/creating-a-personal-access-token
+    # For this notebooks purpose only repo -> public_repo box is required
+    nbd = NbDownloader(secret)
+    # Step 2. Run default pipeline for downloading
+    if not nbd.default_pipeline(outdir):
+        raise FileExistsError("Files not downloaded")
+    # Step 3. Run processing on downloaded file
+    nbp = NbProcessor(outdir)
+    buttons_list = nbp.fetch_binder_list('txt')
+    nbp.add_binder(buttons_list)
+    nbp.render_rst(outdir.joinpath(notebooks_docs))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/nbdoc/notebook.py b/docs/nbdoc/notebook.py
new file mode 100644
index 00000000000..4dfb3449bb1
--- /dev/null
+++ b/docs/nbdoc/notebook.py
@@ -0,0 +1,4 @@
+class Notebook:
+    def __init__(self, name, path):
+        self.name = name
+        self.path = path
diff --git a/docs/nbdoc/requirements.txt b/docs/nbdoc/requirements.txt
new file mode 100644
index 00000000000..20f9b475d25
--- /dev/null
+++ b/docs/nbdoc/requirements.txt
@@ -0,0 +1,2 @@
+jinja2
+requests
\ No newline at end of file
diff --git a/docs/nbdoc/section.py b/docs/nbdoc/section.py
new file mode 100644
index 00000000000..c71f0d75681
--- /dev/null
+++ b/docs/nbdoc/section.py
@@ -0,0 +1,4 @@
+class Section:
+    def __init__(self, name: str, notebooks: list):
+        self.name = name
+        self.notebooks = notebooks
diff --git a/docs/nbdoc/utils.py b/docs/nbdoc/utils.py
new file mode 100644
index 00000000000..c00524b63f4
--- /dev/null
+++ b/docs/nbdoc/utils.py
@@ -0,0 +1,142 @@
+from jinja2 import Template
+from os import path, remove
+from shutil import rmtree
+
+
+def create_content(template: str, notebooks_data: dict, file_name: str):
+    """Filling rst template with data
+
+    :param template: jinja template that will be filled with notebook data
+    :type template: str
+    :param notebooks_data: data structure containing information required to fill template
+    :type notebooks_data: dict
+    :param file_name: file name
+    :type file_name: str
+    :returns: Filled template
+    :rtype: str
+
+    """
+    template = Template(template)
+    notebooks_data["notebook"] = "-".join(file_name.split("-")[:-2])
+    return template.render(notebooks_data)
+
+
+def add_content_below(text: str, path: str, line=3) -> bool:
+    """Add additional content (like binder button) to existing rst file
+
+    :param text: Text that will be added inside rst file
+    :type text: str
+    :param path: Path to modified file
+    :type path: str
+    :param line: Line number that content will be added. Defaults to 3.
+    :type line: int
+    :returns: Informs about success or failure in modifying file
+    :rtype: bool
+
+    """
+    try:
+        with open(path, "r+", encoding="utf-8") as file:
+            current_file = file.readlines()
+            current_file[line:line] = text
+            file.seek(0)
+            file.writelines(current_file)
+            return True
+    except FileNotFoundError:
+        return False
+
+
+def load_secret(path: str = "../.secret") -> str:
+    """Loading secret file
+
+    :param path: Path to secret file. Defaults to "../.secret".
+    :type path: str
+    :returns: Secret key
+    :rtype: str
+
+    """
+    with open(path, "r+") as file:
+        return file.readline().strip()
+
+
+def process_notebook_name(notebook_name: str) -> str:
+    """Processes notebook name
+
+    :param notebook_name: Notebook name by default keeps convention:
+        [3 digit]-name-with-dashes-with-output.rst,
+        example: 001-hello-world-with-output.rst
+    :type notebook_name: str
+    :returns: Processed notebook name,
+        001-hello-world-with-output.rst -> 001. hello world
+    :rtype: str
+
+    """
+    return (
+        notebook_name[:3]
+        + "."
+        + " ".join(notebook_name[4:].split(".")[0].split("-")[:-2])
+    )
+
+
+def find_latest_artifact(artifacts_dict: dict, name: str = "rst_files") -> int:
+    """Finds id of latest artifact that can be downloaded
+
+    :param artifacts_dict: Fetched github actions
+    :type artifacts_dict: dict
+    :param name: Name of searched artifact. Defaults to "rst_files".
+    :type name: str
+    :returns: Id of latest artifact containing rst files
+    :rtype: int
+
+    """
+    return max([r["id"] for r in artifacts_dict["artifacts"] if r["name"] == name])
+
+
+def verify_notebook_name(notebook_name: str) -> bool:
+    """Verification based on notebook name
+
+    :param notebook_name: Notebook name by default keeps convention:
+        [3 digit]-name-with-dashes-with-output.rst,
+        example: 001-hello-world-with-output.rst
+    :type notebook_name: str
+    :returns: Return if notebook meets requirements
+    :rtype: bool
+
+    """
+    return notebook_name[:3].isdigit() and notebook_name[-4:] == ".rst"
+
+
+def generate_artifact_link(owner: str, name: str) -> str:
+    """Generate link for downloading artifacts
+
+    :param owner: Github repo owner name
+    :type owner: str
+    :param name: Github repo name
+    :type name: str
+    :returns: Link to api to download artifacts
+    :rtype: str
+
+    """
+    return f"https://api.github.com/repos/{owner}/{name}/actions/artifacts"
+
+
+def remove_existing(notebooks_path: str) -> None:
+    """Removes file if already existed
+
+    :param notebooks_path: path to file to be removed
+    :type notebooks_path: str
+
+    """
+    if path.exists(notebooks_path):
+        if path.isdir(notebooks_path):
+            rmtree(notebooks_path)
+        else:
+            remove(notebooks_path)
+
+def split_notebooks_into_sections(notebooks: list) -> list:
+    series = [list() for _ in range(5)]
+    for notebook in notebooks:
+        try:
+            series[int(notebook.name[0])].append(notebook)
+        except IndexError:
+            pass
+    return series
\ No newline at end of file
diff --git a/docs/openvino_sphinx_theme/.gitignore b/docs/openvino_sphinx_theme/.gitignore
new file mode 100644
index 00000000000..3d5242e6138
--- /dev/null
+++ b/docs/openvino_sphinx_theme/.gitignore
@@ -0,0 +1,7 @@
+.idea
+.vscode
+__pycache__
+build
+dist
+*.egg-info
+example/_build
diff --git a/docs/openvino_sphinx_theme/MANIFEST.in b/docs/openvino_sphinx_theme/MANIFEST.in
new file mode 100644
index 00000000000..bfbf945b216
--- /dev/null
+++ b/docs/openvino_sphinx_theme/MANIFEST.in
@@ -0,0 +1,3 @@
+include openvino_sphinx_theme/theme.conf
+recursive-include openvino_sphinx_theme *.html *.rst
+recursive-include openvino_sphinx_theme/static *.js *.png *.css *.svg
diff --git a/docs/openvino_sphinx_theme/README.md b/docs/openvino_sphinx_theme/README.md
new file mode 100644
index 00000000000..2ccbc160ace
--- /dev/null
+++ b/docs/openvino_sphinx_theme/README.md
@@ -0,0 +1,47 @@
+# openvino_sphinx_theme
+
+## Installation and usage
+
+1. Install the `openvino_sphinx_theme` using `python`:
+```
+python setup.py install --user
+```
+
+2. Update the `html_theme` variable in your `conf.py`:
+
+```
+html_theme = 'openvino_sphinx_theme'
+```
+
+## Configuration
+
+### Theme Logo
+
+To add a logo at the left of your navigation bar, use `html_logo` variable to set the path to the logo file.
+
+```
+html_logo = <path to the logo file>
+```
+
+### Version and language selectors
+
+To enable a version and language selectors, add the following configuration to your `conf.py` in `html_context`:
+
+```
+html_context = {
+    'current_version': 'latest',
+    'current_language': 'en',
+    'languages': (('English', '/en/latest/'), ('Chinese', '/cn/latest/')),
+    'versions': (('latest', '/en/latest/'), ('2022.1', '/en/2022.1'))
+}
+```
+
+You can add selectors only for versions or languages.
+If you want to add version selector you must define both `current_version` and `versions` properties.
+If you want to add version selector you must define both `current_language` and `languages` properties.
+
+
+### Maintainers
+
+* Nikolay Tyukaev <nikolay.tyukaev@intel.com>
+* Andrey Zaytsev <andrey.zaytsev@intel.com>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/__init__.py b/docs/openvino_sphinx_theme/openvino_sphinx_theme/__init__.py
new file mode 100644
index 00000000000..ddc231ddf49
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/__init__.py
@@ -0,0 +1,224 @@
+import os
+import sys
+from sphinx.errors import ExtensionError
+import jinja2
+from pathlib import Path
+from bs4 import BeautifulSoup as bs
+from sphinx.util import logging
+from pydata_sphinx_theme import index_toctree
+
+
+SPHINX_LOGGER = logging.getLogger(__name__)
+
+
+def setup_edit_url(app, pagename, templatename, context, doctree):
+    """Add a function that jinja can access for returning the edit URL of a page."""
+
+    def has_github_page():
+        doxygen_mapping_file = app.config['doxygen_mapping_file']
+        name = pagename.rsplit('-')[0]
+        if name in doxygen_mapping_file:
+            return True
+        return False
+
+    def get_edit_url():
+        """Return a URL for an "edit this page" link."""
+        doc_context = dict()
+        doc_context.update(**context)
+
+        # Make sure that doc_path has a path separator only if it exists (to avoid //)
+        doc_path = doc_context.get("doc_path", "")
+        if doc_path and not doc_path.endswith("/"):
+            doc_path = f"{doc_path}/"
+
+        # ensure custom URL is checked first, if given
+        url_template = doc_context.get("edit_page_url_template")
+
+        if url_template is not None:
+            if "file_name" not in url_template:
+                raise ExtensionError(
+                    "Missing required value for `use_edit_page_button`. "
+                    "Ensure `file_name` appears in `edit_page_url_template`: "
+                    f"{url_template}"
+                )
+            return jinja2.Template(url_template).render(**doc_context)
+
+        url_template = '{{ github_url }}/{{ github_user }}/{{ github_repo }}' \
+                       '/edit/{{ github_version }}/{{ doc_path }}{{ file_name }}'
+
+        doxygen_mapping_file = app.config['doxygen_mapping_file']
+        rst_name = pagename.rsplit('-')[0]
+        file_name = doxygen_mapping_file[rst_name]
+        parent_folder = Path(os.path.dirname(file_name)).parts[0]
+        file_name = Path(*Path(file_name).parts[1:]).as_posix()
+
+        doc_context.update(doc_path=doc_path, file_name=file_name)
+        try:
+            repositories = app.config.repositories
+        except AttributeError:
+            raise ExtensionError("Missing required value for `use_edit_page_button`. "
+                                 "Ensure `repositories` is set in conf.py.")
+
+        required = ['github_user', 'github_repo', 'github_version', 'host_url']
+        for repo, config in repositories.items():
+            for key, val in config.items():
+                if key not in required or not val:
+                    raise ExtensionError(f'Missing required value for `{repo}` entry in `repositories`'
+                                         f'Ensure {required} all set.')
+            if parent_folder == repo:
+                doc_context.update(github_user=config['github_user'])
+                doc_context.update(github_repo=config['github_repo'])
+                doc_context.update(github_version=config['github_version'])
+                doc_context.update(github_url=config['host_url'])
+                return jinja2.Template(url_template).render(**doc_context)
+
+    context["get_edit_url"] = get_edit_url
+    context['has_github_page'] = has_github_page()
+
+    # Ensure that the max TOC level is an integer
+    context["theme_show_toc_level"] = int(context.get("theme_show_toc_level", 1))
+
+
+def get_theme_path():
+    theme_path = os.path.abspath(os.path.dirname(__file__))
+    return theme_path
+
+
+# override pydata_sphinx_theme
+def _add_collapse_checkboxes(soup, open_first=False):
+    # based on https://github.com/pradyunsg/furo
+
+    toctree_checkbox_count = 0
+
+    for element in soup.find_all("li", recursive=True):
+        # We check all "li" elements, to add a "current-page" to the correct li.
+        classes = element.get("class", [])
+
+        # Nothing more to do, unless this has "children"
+        if not element.find("ul"):
+            continue
+
+        # Add a class to indicate that this has children.
+        element["class"] = classes + ["has-children"]
+
+        # We're gonna add a checkbox.
+        toctree_checkbox_count += 1
+        checkbox_name = f"toctree-checkbox-{toctree_checkbox_count}"
+
+        # Add the "label" for the checkbox which will get filled.
+        if soup.new_tag is None:
+            continue
+        label = soup.new_tag("label", attrs={"for": checkbox_name})
+        label.append(soup.new_tag("i", attrs={"class": "fas fa-chevron-down"}))
+        element.insert(1, label)
+
+        # Add the checkbox that's used to store expanded/collapsed state.
+        checkbox = soup.new_tag(
+            "input",
+            attrs={
+                "type": "checkbox",
+                "class": ["toctree-checkbox"],
+                "id": checkbox_name,
+                "name": checkbox_name,
+            },
+        )
+        # if this has a "current" class, be expanded by default
+        # (by checking the checkbox)
+        if "current" in classes or (open_first and toctree_checkbox_count == 1):
+            checkbox.attrs["checked"] = ""
+
+
+        element.insert(1, checkbox)
+
+def add_toctree_functions(app, pagename, templatename, context, doctree):
+
+    # override pydata_sphinx_theme
+    def generate_sidebar_nav(kind, startdepth=None, **kwargs):
+        """
+        Return the navigation link structure in HTML. Arguments are passed
+        to Sphinx "toctree" function (context["toctree"] below).
+
+        We use beautifulsoup to add the right CSS classes / structure for bootstrap.
+
+        See https://www.sphinx-doc.org/en/master/templating.html#toctree.
+
+        Parameters
+        ----------
+        kind : ["navbar", "sidebar", "raw"]
+            The kind of UI element this toctree is generated for.
+        startdepth : int
+            The level of the toctree at which to start. By default, for
+            the navbar uses the normal toctree (`startdepth=0`), and for
+            the sidebar starts from the second level (`startdepth=1`).
+        kwargs: passed to the Sphinx `toctree` template function.
+
+        Returns
+        -------
+        HTML string (if kind in ["navbar", "sidebar"])
+        or BeautifulSoup object (if kind == "raw")
+        """
+
+        open_first = False
+        if 'open_first' in kwargs:
+            open_first = kwargs.pop('open_first')
+
+        if startdepth is None:
+            startdepth = 1 if kind == "sidebar" else 0
+
+        if startdepth == 0:
+            toc_sphinx = context["toctree"](**kwargs)
+        else:
+            # select the "active" subset of the navigation tree for the sidebar
+            toc_sphinx = index_toctree(app, pagename, startdepth, **kwargs)
+
+        soup = bs(toc_sphinx, "html.parser")
+
+        # pair "current" with "active" since that's what we use w/ bootstrap
+        for li in soup("li", {"class": "current"}):
+            li["class"].append("active")
+
+        # Remove navbar/sidebar links to sub-headers on the page
+        for li in soup.select("li"):
+            # Remove
+            if li.find("a"):
+                href = li.find("a")["href"]
+                if "#" in href and href != "#":
+                    li.decompose()
+
+        if kind == "navbar":
+            # Add CSS for bootstrap
+            for li in soup("li"):
+                li["class"].append("nav-item")
+                li.find("a")["class"].append("nav-link")
+            # only select li items (not eg captions)
+            out = "\n".join([ii.prettify() for ii in soup.find_all("li")])
+
+        elif kind == "sidebar":
+            # Add bootstrap classes for first `ul` items
+            for ul in soup("ul", recursive=False):
+                ul.attrs["class"] = ul.attrs.get("class", []) + ["nav", "bd-sidenav"]
+
+            # Add icons and labels for collapsible nested sections
+            _add_collapse_checkboxes(soup, open_first=open_first)
+
+            out = soup.prettify()
+
+        elif kind == "raw":
+            out = soup
+
+        return out
+
+
+    context["generate_sidebar_nav"] = generate_sidebar_nav
+
+
+def setup(app):
+    theme_path = get_theme_path()
+    templates_path = os.path.join(theme_path, 'templates')
+    static_path = os.path.join(theme_path, 'static')
+    app.config.templates_path.append(templates_path)
+    app.config.html_static_path.append(static_path)
+    app.connect("html-page-context", setup_edit_url, priority=sys.maxsize)
+    app.connect("html-page-context", add_toctree_functions)
+    app.add_html_theme('openvino_sphinx_theme', theme_path)
+    return {'parallel_read_safe': True, 'parallel_write_safe': True}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/__init__.py b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/button.css b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/button.css
new file mode 100644
index 00000000000..84ecc7ea568
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/button.css
@@ -0,0 +1,160 @@
+:root {
+    --bttn-act-color: #ffffff;
+    --bttn-act-bg: #0068b5;
+    --bttn-act-bg-hover: #004a86;
+    --bttn-act-bg-active: #00285a;
+    --bttn-act-bg-focus: #0068b5;
+    --bttn-act-inset-border-focus: #ffffff;
+    --bttn-prm-color: #2b2c30;
+    --bttn-prm-bg-color: #ffffff;
+    --bttn-prm-bg-color-hover: #e9eaeb;
+    --bttn-prm-bg-color-active: #c9cace;
+    --bttn-prm-bg-color-focus: #e9eaeb;
+    --bttn-prm-border-color-focus: #0068b5;
+    --bttn-sec-border-color: #c9cace;
+}
+
+.bttn-act {
+    color: var(--bttn-act-color)!important;
+    border-color: transparent;
+    background-color: var(--bttn-act-bg);
+}
+.bttn-act:hover {
+    background-color: var(--bttn-act-bg-hover);
+}
+.bttn-act:focus {
+    background-color: var(--bttn-act-bg-focus);
+}
+.bttn-act:focus-visible {
+    box-shadow: inset 0px 0px 0px 1px var(--bttn-act-inset-border-focus);
+}
+.bttn-act:active {
+    background-color: var(--bttn-act-bg-active);
+}
+.bttn-act:disabled {
+    color: var(--button-color-disabled);
+    cursor: default;
+    background-color: var(--button-bg-disabled);
+}
+.bttn-prm {
+    color: var(--bttn-prm-color)!important;
+    border-color: var(--bttn-prm-color);
+    background-color: var(--bttn-prm-bg-color);
+}
+.bttn-prm:hover {
+    background-color: var(--bttn-prm-bg-color-hover);
+}
+.bttn-prm:focus {
+    background-color: var(--bttn-prm-bg-color-focus);
+}
+.bttn-prm:focus-visible {
+    box-shadow: inset 0px 0px 0px 1px var(--bttn-prm-border-color-focus);
+    border-color: var(--bttn-prm-border-color-focus);
+}
+.bttn-prm:active {
+    background-color: var(--bttn-prm-bg-color-active);
+}
+.bttn-prm:disabled {
+    color: var(--button-color-disabled);
+    cursor: default;
+    background-color: var(--button-bg-disabled);
+}
+.bttn-sec {
+    color: var(--bttn-prm-color)!important;
+    border-color: var(--bttn-sec-border-color);
+    background-color: var(--bttn-prm-bg-color);
+}
+.bttn-sec:hover {
+    background-color: var(--bttn-prm-bg-color-hover);
+}
+.bttn-sec:focus {
+    background-color: var(--bttn-prm-bg-color-focus);
+}
+.bttn-sec:focus-visible {
+    box-shadow: inset 0px 0px 0px 1px var(--bttn-prm-border-color-focus);
+    border-color: var(--bttn-prm-border-color-focus);
+}
+.bttn-sec:active {
+    background-color: var(--bttn-prm-bg-color-active);
+}
+.bttn-sec:disabled {
+    color: var(--button-color-disabled);
+    cursor: default;
+    background-color: var(--button-bg-disabled);
+}
+.button-ghost {
+    color: var(--bttn-prm-color)!important;
+    border-color: transparent;
+    background-color: var(--bttn-prm-bg-color);
+}
+.button-ghost:hover {
+    background-color: var(--bttn-prm-bg-color-hover);
+}
+.button-ghost:focus {
+    background-color: var(--bttn-prm-bg-color-focus);
+}
+.button-ghost:focus-visible {
+    box-shadow: inset 0px 0px 0px 1px var(--bttn-prm-border-color-focus);
+    border-color: var(--bttn-prm-border-color-focus);
+}
+.button-ghost:active {
+    background-color: var(--bttn-prm-bg-color-active);
+}
+.button-ghost:disabled {
+    color: var(--button-color-disabled);
+    cursor: default;
+    background-color: transparent;
+}
+
+.button-size-l {
+    height: 40px;
+    font-size: 16px;
+    line-height: 18px;
+    padding-top: 8px;
+    padding-bottom: 8px;
+    padding-inline-end: 20px;
+    padding-inline-start: 20px;
+}
+.button-size-l.squared {
+    padding-inline-end: 14px;
+    padding-inline-start: 14px;
+}
+.button-size-m {
+    height: 32px;
+    font-size: 14px;
+    line-height: 16px;
+    padding-top: 6px;
+    padding-bottom: 6px;
+    padding-inline-end: 16px;
+    padding-inline-start: 16px;
+}
+.button-size-m.squared {
+    padding-inline-end: 10px;
+    padding-inline-start: 10px;
+}
+.button-size-s {
+    height: 24px;
+    font-size: 12px;
+    line-height: 14px;
+    padding-top: 4px;
+    padding-bottom: 4px;
+    padding-inline-end: 12px;
+    padding-inline-start: 12px;
+}
+.button-size-s.squared {
+    padding-inline-end: 6px;
+    padding-inline-start: 6px;
+}
+.button {
+    cursor: pointer;
+    box-sizing: border-box;
+    border-style: solid;
+    border-width: 1px;
+    text-decoration: none!important;
+    color: inherit!important;
+    white-space: nowrap!important;
+    outline: none!important;
+}
+.button:focus-visible {
+    outline: none;
+}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/input.css b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/input.css
new file mode 100644
index 00000000000..03e1bee16b4
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/input.css
@@ -0,0 +1,66 @@
+:root {
+    --input-color: #2b2c30;
+    --input-color-disabled: #b2b3b9;
+    --input-color-placeholder: #6a6d75;
+    --input-color-placeholder-hover: #494b51;
+    --input-border-color: #c9cace;
+    --input-border-color-hover: #8b8e97;
+    --input-border-color-focus: #0068b5;
+    --input-border-color-invalid: #c81326;
+    --input-border-color-disabled: #e2e2e4;
+}
+
+.input-quiet {
+    border-width: 0;
+    background-color: transparent;
+    border-bottom-width: 1px;
+}
+.input-quiet:focus {
+    border-bottom-width: 2px;
+}
+.input-size-l {
+    height: 40px;
+}
+.input-size-m {
+    height: 32px;
+}
+.input-size-s {
+    height: 24px;
+}
+.input {
+    color: var(--input-color);
+    cursor: pointer;
+    box-sizing: border-box;
+    border-color: var(--input-border-color);
+    border-style: solid;
+}
+.input:hover {
+    border-color: var(--input-border-color-hover);
+}
+.input:focus {
+    border-color: var(--input-border-color-focus);
+}
+.input:disabled {
+    color: var(--input-color-disabled);
+    cursor: default;
+    border-color: var(--input-border-color-disabled);
+}
+.input:invalid,
+.input.is-invalid {
+    border-color: var(--input-border-color-invalid);
+}
+.input:focus-visible {
+    outline: none;
+}
+.input::placeholder {
+    color: var(--input-color-placeholder);
+}
+.input:hover:not(:disabled)::placeholder {
+    color: var(--input-color-placeholder-hover);
+}
+.input::-webkit-search-cancel-button{
+    display: none;
+    right:20px;  
+
+    -webkit-appearance: none;
+}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/burger.svg b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/burger.svg
new file mode 100644
index 00000000000..61dea2793de
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/burger.svg
@@ -0,0 +1 @@
+<svg xmlns='http://www.w3.org/2000/svg' width='30' height='30'><path stroke='rgba(255,255,255,0.5)' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/chevron-active.svg b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/chevron-active.svg
new file mode 100644
index 00000000000..718c66d9ad6
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/chevron-active.svg
@@ -0,0 +1,3 @@
+<svg width="40" height="20" viewBox="0 0 40 20" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M38.9219 0H1.07813C0.119543 0 -0.360524 1.09992 0.317302 1.74321L19.2392 19.7009C19.6594 20.0997 20.3406 20.0997 20.7608 19.7009L39.6827 1.74321C40.3605 1.09992 39.8805 0 38.9219 0Z" fill="white"/>
+</svg>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/chevron.svg b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/chevron.svg
new file mode 100644
index 00000000000..b45e831043a
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/chevron.svg
@@ -0,0 +1,3 @@
+<svg width="20" height="40" viewBox="0 0 20 40" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M0 1.07813V38.9219C0 39.8805 1.09992 40.3605 1.74321 39.6827L19.7009 20.7608C20.0997 20.3406 20.0997 19.6594 19.7009 19.2392L1.74321 0.317303C1.09992 -0.360525 0 0.119543 0 1.07813Z" fill="white"/>
+</svg>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/favicon.ico b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/favicon.ico
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diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/github-mark-light.svg b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/github-mark-light.svg
new file mode 100644
index 00000000000..f40c50275aa
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/media/github-mark-light.svg
@@ -0,0 +1,9 @@
+<svg width="28" height="28" viewBox="0 0 28 28" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<rect width="28" height="28" fill="url(#pattern0)"/>
+<defs>
+<pattern id="pattern0" patternContentUnits="objectBoundingBox" width="1" height="1">
+<use xlink:href="#image0" transform="scale(0.03125)"/>
+</pattern>
+<image id="image0" width="32" height="32" xlink:href="data:image/png;base64,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"/>
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+</svg>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/openvino_sphinx_theme.css b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/openvino_sphinx_theme.css
new file mode 100644
index 00000000000..20209035500
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/openvino_sphinx_theme.css
@@ -0,0 +1,475 @@
+:root {
+    /* OpenVino Sphinx Theme Variables */
+    --ost-font-size:16px;
+    --ost-color-primary:0, 104, 181;
+    --ost-color-primary-variant:0, 74, 134;
+    --ost-color-secondary: 0, 163, 246;
+    --ost-color-text-primary:38, 38, 38;
+    --ost-color-text-primary-variant:43, 44, 48;
+    --ost-color-sidebar-link-active: var(--ost-color-text-primary);
+    --ost-color-sidebar-link-hover: var(--ost-color-sidebar-link-active);
+    --ost-color-sidebar-link: var(--ost-color-text-primary-variant);
+    --ost-color-link: var(--ost-color-primary);
+    --ost-color-link-hover:var(--ost-color-primary);
+    --ost-tabs--label-text--active: var(--white);
+    --ost-tabs--border:var(--ost-tabs--label-text--active);
+    --ost-tabs--label-background: 247, 247, 247;
+    --ost-tabs--label-background-active: var(--ost-tabs--label-background);
+    --ost-tabs--label-background-hover: rgb(var(--ost-tabs--label-background));
+    --ost-color-h1: var(--ost-color-text-primary);
+    --ost-color-h2: var(--pst-color-h1);
+    --ost-color-active-navigation:var(--ost-color-text-primary);
+    --ost-color-navbar-background: var(--ost-color-primary);
+    --ost-color-sst-dropdown-text:255, 255, 255;
+    --ost-color-sst-dropdown-background:var(--ost-color-navbar-background);
+    --ost-color-sst-dropdown-background-active:var(--ost-color-primary-variant);
+    /* Override Pydata Sphinx Theme Variables */
+    --pst-font-family-base: var(--ost-font-family), var(--pst-font-family-base-system);
+    --pst-font-family-heading: var(--ost-font-family), var(--pst-font-family-base-system);
+    --pst-font-size-base: var(--ost-font-size);
+    --pst-color-sidebar-link-active: var(--ost-color-sidebar-link-active);
+    --pst-color-sidebar-link-hover: var(--ost-color-sidebar-link-hover);
+    --pst-color-sidebar-link: var(--ost-color-sidebar-link);
+    --pst-color-primary: var(--ost-color-primary);
+    --pst-color-link: var(--ost-color-link);
+    --pst-color-link-hover: var(--ost-color-link-hover);
+    --tabs--label-text--active: var(--ost-tabs--label-text--active);
+    --tabs--label-background--active: rgb(var(--ost-color-primary));
+    --tabs--border: var(--ost-tabs--border);
+    --tabs--label-background: var(--ost-tabs--label-background);
+    --pst-color-h1: var(--ost-color-h1);
+    --pst-color-h2: var(--ost-color-h2);
+    --pst-color-active-navigation: var(--ost-color-active-navigation);
+}
+
+body {
+    -webkit-font-smoothing: antialiased;
+}
+
+.bd-sidebar .nav .active > a {
+    font-weight: 600!important;
+}
+
+.toc-entry >.nav-link.active {
+    font-weight: 500!important;
+    border-color: rgb(var(--ost-color-primary));
+}
+
+/* Syntax Highlighting */
+
+code {
+    color: rgb(var(--ost-color-primary));
+}
+
+code.descname, code.descclassname {
+    font-size: 1rem;
+}
+
+.highlight-default,
+.highlight-default .highlight,
+.highlight-default pre,
+.highlight-default .highlight .k,
+.highlight-default .highlight .kc,
+.highlight-default .highlight .kd,
+.highlight-default .highlight .nb,
+.highlight-default .highlight .kn,
+.highlight-default .highlight .kp,
+.highlight-default .highlight .kr,
+.highlight-default .highlight .kt,
+.highlight-default span {
+    background-color:transparent;
+    font-weight: normal !important;
+    color: #000000 !important;
+}
+
+.highlight-default pre {
+    border: none;
+    box-shadow: none;
+}
+
+em.sig-param,
+.sig-paren {
+    font-weight: normal;
+    font-style: italic;
+    font-size: inherit;
+}
+
+.highlight .p {
+    font-weight: normal;
+}
+
+.sst-github {
+    vertical-align: middle;
+    content: url('media/github-mark-light.svg');
+}
+
+#navbar-icon-links {
+    transform: translateY(-50%);
+    top:50%;
+    position: relative;
+}
+
+@media print {
+    .bd-sidebar, .prev-next-bottom, .editthispage {
+        display: none;
+    }
+}
+
+blockquote {
+    background: #F9F9F9;
+    padding:1rem;
+}
+
+blockquote p {
+    margin-bottom:0;
+}
+
+@media (min-width: 720px) {
+    .pl-md-5, .px-md-5 {
+        padding-left: 3.2rem!important;
+    }
+}
+
+@media (min-width: 720px) {
+    .pr-md-4, .px-md-4 {
+        padding-right: 3.2rem!important;
+    }
+}
+
+/* Edit this page */
+
+.editthispage {
+    text-align: end;
+    padding-top:0;
+}
+
+.editthispage a {
+    color: rgba(var(--ost-color-link), 1);
+}
+
+.editthispage a:hover {
+    color: rgba(var(--ost-color-link-hover), 1);
+}
+
+/* Toctree */
+
+.toctree-wrapper.compound p, .bd-toc-item.active p {
+    margin-bottom: 0;
+}
+
+/* Toc */
+
+.bd-toc-item.active p {
+    color: rgba(var(--ost-color-primary),1);
+}
+
+.bd-toc .onthispage {
+    color: rgba(var(--ost-color-h1),1);
+    font-weight: 500;
+}
+
+/* Navbar */
+
+.navbar-brand {
+    padding-left:0.5rem;
+}
+
+@media (max-width:426px) {
+    .navbar-toggler {
+        padding-right:1.5rem;
+    }
+}
+
+.navbar-light .navbar-toggler-icon {
+    background-image: url('media/burger.svg');
+}
+
+.navbar-brand > .title {
+    position: absolute;
+    transform: translateY(-50%);
+    top: 50%;
+}
+
+.navbar {
+    background:rgba(var(--ost-color-navbar-background), 1) !important;
+}
+
+.navbar-light .navbar-nav li a.nav-link {
+    color: #fff !important;
+    font-size: 1rem;
+    white-space: nowrap!important;
+    padding-right:0;
+    padding: 0 .8rem;
+}
+
+@media (max-width: 1300px) {
+    #navbar-collapsible {
+        max-width: 100%;
+        display: none !important;
+    }
+
+    .navbar-brand {
+        padding-left:1rem;
+    }
+
+    #navbar-collapsible.show {
+        display: block !important;
+    }
+
+    .navbar-expand-lg>.container,
+    .navbar-expand-lg>.container-fluid,
+    .navbar-expand-lg>.container-lg,
+    .navbar-expand-lg>.container-md,
+    .navbar-expand-lg>.container-sm,
+    .navbar-expand-lg>.container-xl {
+        flex-wrap: wrap;
+    }
+
+    .navbar-nav {
+        flex-direction: column !important;
+    }
+
+    .container-xl {
+        display: block;
+    }
+
+    .navbar-toggler {
+        display: block !important;
+    }
+}
+
+@media (max-width:426px) {
+    .bd-content {
+        padding-left:1.5rem;
+        padding-right:1.5rem;
+    }
+}
+
+#navbar-collapsible {
+    padding-left:0;
+}
+
+#navbar-end {
+    display: flex;
+    flex-direction: row;
+    padding-left:0.1rem;
+}
+
+.navbar-end-item {
+    display:block;
+}
+
+.navbar-brand img {
+    width:120px;
+    height: auto;
+    margin-top: 8px;
+}
+
+/* Tabs */
+
+html .tab-set > label {
+    background: rgba(var(--tabs--label-background),1);
+    margin-right:10px;
+    border:none;
+}
+
+html .tab-set > label:hover {
+    color: var(--tabs--label-text--active);
+    background: var(--tabs--label-background--active);
+}
+
+.tab-set, .tab-content {
+    padding-left:0;
+    padding-right:0;
+}
+
+.tab-content {
+    box-shadow:none;
+    padding-top: 5px;
+    border-top: 1px solid rgba(0,0,0,.1);
+}
+
+/* Prev Next Buttons */
+.prev-next-bottom {
+    float: right;
+}
+
+.prev-next-bottom a.left-prev:before {
+    content: '';
+}
+
+.prev-next-bottom a.right-next:after {
+    content: '';
+}
+
+.prev-next-bottom a.left-prev {
+    float: none;
+}
+
+.prev-next-bottom a.right-next {
+    float: none;
+}
+
+.footer {
+    font-size: 14px;
+}
+
+.prev-next-bottom a.left-prev, .prev-next-bottom a.right-next {
+    font-weight:bold;
+    padding:7px 20px 7px 20px;
+    font-size:14px;
+    border: 1px solid rgba(0,0,0,.2);
+    max-width: 45%;
+    overflow-x: hidden;
+    color: rgba(0,0,0,.65);
+}
+
+/* NAVBAR SELECTORS */
+
+.sst-btn,
+.sst-btn:hover,
+.sst-btn:active,
+.sst-btn:focus,
+.sst-dropdown .dropdown-menu,
+.sst-dropdown .dropdown-item {
+    color: rgba(var(--ost-color-sst-dropdown-text),1) !important;
+}
+
+.sst-dropown {
+    position:relative;
+}
+
+.sst-dropdown .dropdown-menu {
+    min-width:0px;
+    width:100%;
+    border:0;
+    max-height:200px;
+    overflow-y: auto;
+}
+
+.sst-btn,
+.sst-dropdown .dropdown-menu {
+    background-color: rgba(var(--ost-color-sst-dropdown-background),1);
+    margin:0;
+    border-radius:0;
+}
+
+.sst-btn.focus,
+.sst-btn:active,
+.sst-btn:focus {
+    box-shadow: none;
+}
+
+.sst-btn:hover,
+.sst-dropdown:hover .sst-btn,
+.sst-dropdown.show .sst-btn,
+.sst-dropdown .dropdown-menu.show,
+.sst-dropdown.show .dropdown-item,
+.sst-dropdown .dropdown-item.active,
+.sst-dropdown .dropdown-item:active,
+.sst-dropdown .dropdown-item:hover {
+    background-color: rgba(var(--ost-color-sst-dropdown-background-active), 1);
+}
+
+.sst-dropdown .sst-btn {
+    text-align: left;
+    padding: 0.5rem 0.65625rem 0.5rem 0.96875rem;
+}
+
+.sst-dropdown .dropdown-toggle:before {
+    display: inline-block;
+    content: '';
+    background-image:url('media/chevron.svg');
+    background-repeat: no-repeat;
+    background-position: center center;
+    height: 9px;
+    width: 9px;
+    background-size: contain;
+    margin-right: 0.625rem;
+    vertical-align: middle;
+}
+
+.sst-dropdown.show .dropdown-toggle:before {
+    background-image:url('media/chevron-active.svg');
+}
+
+.sst-dropdown .dropdown-toggle::after {
+    content:none;
+}
+
+/* Download button */
+
+.download-docs {
+    padding-top:1rem;
+}
+
+.tocsection .sst-btn,
+.download-docs .sst-btn,
+.download-docs .dropdown-menu {
+    font-size: 0.875rem;
+    min-width:8.125rem;
+}
+
+.download-docs .sst-dropdown {
+    display:inline-block;
+}
+
+.download-docs .sst-btn::before {
+    content: none;
+}
+
+/* Doc navigation mobile devices */
+
+#collapse-nav {
+    width: 100%;
+}
+
+#collapse-nav .fa-chevron-down {
+    position: absolute;
+    right: 15%;
+}
+
+#collapse-nav[aria-expanded="true"] .fa-chevron-down {
+    transform: rotate( 180deg);
+}
+
+@media (min-width: 720px) {
+    #collapse-nav {
+        display: none;
+    }
+    #collapse-nav-wrapper {
+        display: none;
+    }
+    #nav-tree:not(.show) {
+        display: block;
+    }
+}
+
+@media (max-width: 720px) {
+    #nav-tree {
+        position: sticky;
+        top:92px;
+        z-index: 1000;
+        background: #fff;
+        max-height: 300px;
+        overflow-y: auto;
+        border-bottom:1px solid var(--bttn-prm-color);
+    }
+
+    #nav-tree {
+        display: none;
+    }
+
+    #nav-tree.show {
+        display: block;
+    }
+
+    .bd-sidebar .nav .active > a,
+    .bd-sidebar .nav .active > a:hover {
+        background: #76c3ff;
+    }
+
+    #collapse-nav {
+        display: block;
+    }
+}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/tabs.css b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/tabs.css
new file mode 100644
index 00000000000..24624a88bc1
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/tabs.css
@@ -0,0 +1,104 @@
+:root {
+  --tabs--label-text: #8c8c8c;
+  --tabs--label-text--active: #368ce2;
+  --tabs--label-text--hover: #1d73c9;
+  --tabs--label-background--active: transparent;
+  --tabs--label-background--hover: #f7f7f7;
+  --tabs--border: #e6e6e6;
+  --tabs--border--active: var(--tabs--label-text--active);
+}
+/* Hide radio buttons */
+.tab-set > input {
+  position: absolute;
+  opacity: 0;
+}
+
+/* Tab set container */
+.tab-set {
+  border-radius: 2px;
+  display: flex;
+  flex-wrap: wrap;
+  margin: 0.75em 0;
+  position: relative;
+}
+
+/* Tab label */
+.tab-set > label {
+  z-index: 1;
+  cursor: pointer;
+  box-sizing: border-box;
+  border-style: solid;
+  border-width: 1px;
+  color: inherit;
+  width: auto;
+  height: 32px;
+  font-size: 14px;
+  padding-top: 6px;
+  padding-bottom: 6px;
+  padding-inline-end: 16px;
+  padding-inline-start: 16px;
+  font-weight: 500;
+  line-height: 1.5;
+
+  color: var(--bttn-prm-color)!important;
+  border-color: var(--bttn-sec-border-color)!important;
+  background-color: var(--bttn-prm-bg-color)!important;
+}
+
+.tab-set > label:hover {
+  background-color: var(--bttn-prm-bg-color-hover)!important;
+}
+
+/* Active tab label */
+.tab-set > input:checked + label {
+  color: var(--bttn-act-color)!important;
+  border-color: transparent!important;
+  background-color: var(--bttn-act-bg)!important;
+}
+.tab-set > input:checked + label:hover {
+  background-color: var(--bttn-act-bg-hover)!important;
+}
+
+/* Focused tab label */
+.tab-set > input:focus + label {
+  outline-style: auto;
+}
+
+.tab-set > input:not(.focus-visible) + label {
+  outline: none;
+  -webkit-tap-highlight-color: transparent;
+}
+
+/* Tab content */
+.tab-content {
+  order: 99;
+  display: none;
+  width: 100%;
+  box-shadow: 0 -0.0625rem var(--tabs--border);
+}
+/* Show content, when input is checked. */
+.tab-set > input:checked + label + .tab-content {
+  display: block;
+}
+.tab-content > p:first-child {
+  margin-top: 0.75rem;
+}
+/* Remove the top border on first code block */
+.tab-content > [class^="highlight-"]:first-child .highlight {
+  border-top: none;
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+
+/* Remove margins on children */
+.tab-content > *:first-child {
+  margin-top: 0;
+}
+.tab-content > *:last-child {
+  margin-bottom: 0;
+}
+
+/* Remove margins on nested tabs */
+.tab-content > .tab-set {
+  margin: 0;
+}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/textfield.css b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/textfield.css
new file mode 100644
index 00000000000..f1f6b4a8f09
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/css/textfield.css
@@ -0,0 +1,43 @@
+:root {
+    --textfield-color: #2b2c30;
+    --textfield-color-valid: #708541;
+    --textfield-color-invalid: #c81326;
+    --textfield-color-disabled: #b2b3b9;
+}
+
+.textfield-q .left-slot-container {
+    left: 0;
+}
+.textfield-q.textfield-size-m.left-slot .input,
+.textfield-q.textfield-size-s.left-slot .input {
+    padding-left: 20px;
+}
+.textfield-q.textfield-size-l.left-slot .input {
+    padding-left: 24px;
+}
+.textfield {
+    position: relative;
+    box-sizing: border-box;
+}
+.textfield .input {
+    width: 100%;
+}
+.textfield .is-valid {
+    color: var(--textfield-color-valid);
+}
+.textfield.is-disabled {
+    color: var(--textfield-color-disabled);
+}
+.textfield :not(.input).is-invalid {
+    color: var(--textfield-color-invalid);
+}
+.textfield .slot {
+    top: 0;
+    bottom: 0;
+    display: flex;
+    position: absolute;
+    align-items: center;
+}
+.textfield.is-invalid + .message {
+    color: var(--textfield-color-invalid);
+}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/js/openvino_sphinx_theme.js b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/js/openvino_sphinx_theme.js
new file mode 100644
index 00000000000..8830ed6a0d1
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/static/js/openvino_sphinx_theme.js
@@ -0,0 +1,21 @@
+document.addEventListener('DOMContentLoaded', function () {
+    adjustWidthforNavbarSelectors();
+});
+
+function adjustWidthforNavbarSelectors() {
+    // adjust button size for navbar selectors
+    var navbarSelectors = $('.sst-dropdown-navbar');
+    navbarSelectors.each(function () {
+        var dropdownButton = $(this).find('.btn');
+        var maxWidth = dropdownButton.width();
+        var dropdownItems = $(this).find('.dropdown-item');
+        var itemWidth;
+        dropdownItems.each(function () {
+            itemWidth = $(this).text().length * 10;
+            if (itemWidth > maxWidth) {
+                maxWidth = itemWidth;
+            }
+        });
+        dropdownButton.width(maxWidth);
+    });
+}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/custom-class-template.rst b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/custom-class-template.rst
new file mode 100644
index 00000000000..710766c8aae
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/custom-class-template.rst
@@ -0,0 +1,32 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+   :show-inheritance:
+   :inherited-members:
+
+   {% block methods %}
+   .. automethod:: __init__
+
+   {% if methods %}
+   .. rubric:: Methods
+
+   .. autosummary::
+   {% for met in methods %}
+      ~{{ name }}.{{ met }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: Attributes
+
+   .. autosummary::
+   {% for att in attributes %}
+      ~{{ name }}.{{ att }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/custom-module-template.rst b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/custom-module-template.rst
new file mode 100644
index 00000000000..80cac03cf9e
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/custom-module-template.rst
@@ -0,0 +1,56 @@
+{{ fullname | escape | underline}}
+
+.. automodule:: {{ fullname }}
+  
+{% block attributes %}
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:
+   {% for attr in attributes %}
+      {{ attr }}
+   {% endfor %}
+{% endblock %}
+
+{% block functions %}
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   {% for func in functions %}
+      {{ func }}
+   {%- endfor %}
+{% endblock %}
+
+{% block classes %}
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   {% for cl in classes %}
+      {{ cl }}
+   {%- endfor %}
+{% endblock %}
+
+{% block exceptions %}
+   .. rubric:: Exceptions
+
+   .. autosummary::
+      :toctree:
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+{% endblock %}
+
+{% block modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom-module-template.rst
+   :recursive:
+{% for mod in modules %}
+   {{ mod }}
+{%- endfor %}
+{% endblock %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/download-docs.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/download-docs.html
new file mode 100644
index 00000000000..f9039642688
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/download-docs.html
@@ -0,0 +1,14 @@
+<div class="tocsection download-docs">
+  <div class="dropdown sst-dropdown">
+    <button class="button bttn-prm button-size-m" data-display="static" type="button" id="download-options"
+      data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
+      Download Docs
+    </button>
+    <div class="dropdown-menu" aria-labelledby="download-options">
+      <a class="dropdown-item" href="#" onclick="window.print()">.pdf</a>
+      {% if download_docs_url %}
+      <a class="dropdown-item" href="{{download_docs_url}}">.zip</a>
+      {% endif %}
+    </div>
+  </div>
+</div>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/edit-this-page.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/edit-this-page.html
new file mode 100644
index 00000000000..1b494d5b96f
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/edit-this-page.html
@@ -0,0 +1,8 @@
+{% if sourcename is defined and theme_use_edit_page_button==true and page_source_suffix and has_github_page %}
+{% set src = sourcename.split('.') %}
+<div class="editthispage">
+    <a class='button button-ghost button-size-m' href="{{ get_edit_url() }}">
+        <i class="fas fa-pencil-alt"></i> {{ _("Edit this page") }}
+    </a>
+</div>
+{% endif %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/icon-links.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/icon-links.html
new file mode 100644
index 00000000000..97f075202a9
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/icon-links.html
@@ -0,0 +1,19 @@
+{%- macro icon_link_nav_item(url, icon, name) -%}
+  {%- if url | length > 2 %}
+        <li class="nav-item">
+          <a class="nav-link" href="{{ url }}" rel="noopener" target="_blank" title="{{ _(name) }}">
+            <span><i class="{{ icon }}"></i></span>
+            <label class="sr-only">{{ _(name) }}</label>
+          </a>
+        </li>
+  {%- endif -%}
+{%- endmacro -%}
+
+<ul id="navbar-icon-links" class="navbar-nav" aria-label="{{ _(theme_icon_links_label) }}">
+{%- block icon_link_shortcuts -%}
+{{ icon_link_nav_item(theme_github_url, "sst-github", "GitHub") -}}
+{% endblock -%}
+{%- for icon_link in theme_icon_links -%}
+{{ icon_link_nav_item(icon_link["url"], icon_link["icon"], icon_link["name"]) -}}
+{%- endfor %}
+</ul>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/language-selector.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/language-selector.html
new file mode 100644
index 00000000000..2cc47053ed7
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/language-selector.html
@@ -0,0 +1,15 @@
+{# Add language selector #}
+{% if languages and current_language %}
+<div class="dropdown sst-dropdown sst-dropdown-navbar">
+  <button class="btn sst-btn dropdown-toggle" type="button" id="language-selector" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">{{current_language}}</button>
+  <div class="dropdown-menu" aria-labelledby="language-selector">
+    {% for slug, url in languages %}
+      {% if slug == current_language %}
+        <a class="dropdown-item font-weight-bold" href="{{ url }}/{{ pagename }}.html">{{ slug }}</a>
+      {% else %}
+        <a  class="dropdown-item" href="{{ url }}/{{ pagename }}.html">{{ slug }}</a>
+      {% endif %}
+    {% endfor %}
+  </div>
+</div>
+{% endif %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/layout.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/layout.html
new file mode 100644
index 00000000000..a7abff646e1
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/layout.html
@@ -0,0 +1,102 @@
+{%- extends "pydata_sphinx_theme/layout.html" %}
+{# prev/next buttons #}
+{% macro ovino_prev_next(prev, next, prev_title='', next_title='') %}
+ {%- if prev %}
+    <a class='button bttn-sec button-size-l' id="prev-link" href="{{ prev.link|e }}" title="{{ _('previous page')}}">Prev</a>
+ {%- endif %}
+ {%- if next %}
+    <a class='button bttn-sec button-size-l' id="next-link" href="{{ next.link|e }}" title="{{ _('next page')}}">Next</a>
+ {%- endif %}
+{% endmacro %}
+
+{% block css %}
+    {{ super() }}
+    <link href="{{ pathto('_static/css/media/favicon.ico', 1) }}" rel="shortcut icon">
+    <link rel="stylesheet" href="{{ pathto('_static/css/openvino_sphinx_theme.css', 1) }}" type="text/css" />
+    <link rel="stylesheet" href="{{ pathto('_static/css/button.css', 1) }}" type="text/css" />
+    <link rel="stylesheet" href="{{ pathto('_static/css/input.css', 1) }}" type="text/css" />
+    <link rel="stylesheet" href="{{ pathto('_static/css/textfield.css', 1) }}" type="text/css" />
+    <link rel="stylesheet" href="{{ pathto('_static/css/tabs.css', 1) }}" type="text/css" />
+    <script src="{{ pathto('_static/js/openvino_sphinx_theme.js', 1) }}"></script>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@2.9.3/dist/Chart.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-datalabels"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/chartjs-plugin-annotation/0.5.7/chartjs-plugin-annotation.min.js"></script> 
+    <script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-barchart-background@1.3.0/build/Plugin.Barchart.Background.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-deferred@1"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/PapaParse/5.3.1/papaparse.min.js"></script>
+{% endblock %}
+
+{%- block content %}
+    {# Added to support a banner with an alert #}
+    <div class="container-fluid" id="banner"></div>
+
+    {% block docs_navbar %}
+      <nav class="navbar navbar-light navbar-expand-lg bg-light fixed-top bd-navbar" id="navbar-main">
+        {%- include "docs-navbar.html" %}
+        <div id="collapse-nav-wrapper" class="container-xl">
+          <button id="collapse-nav" class="button bttn-prm button-size-m" type="button" data-toggle="collapse" data-target="#nav-tree" aria-expanded="false" aria-controls="nav-tree">
+            Documentation navigation <i class="fas fa-chevron-down"></i>
+          </button>
+        </div>
+      </nav>
+    {% endblock %}
+
+    <div class="container-xl">
+      <div class="row">
+          {% block docs_sidebar %}
+            {% if sidebars %}
+            <!-- Only show if we have sidebars configured, else just a small margin  -->
+            <div class="col-12 col-md-3 bd-sidebar" id="nav-tree">
+                {%- for sidebartemplate in sidebars %}
+                {%- include sidebartemplate %}
+                {%- endfor %}
+            </div>
+            {% else %}
+            <div class="col-12 col-md-1 col-xl-2 bd-sidebar no-sidebar"></div>
+            {% endif %}
+          {% endblock %}
+
+          {% block docs_toc %}
+          <div class="d-none d-xl-block col-xl-2 bd-toc">
+            {% if meta is defined and not (meta is not none and 'notoc' in meta) %}
+              {% for toc_item in theme_page_sidebar_items %}
+              <div class="toc-item">
+                {% include toc_item %}
+              </div>
+              {% endfor %}
+            {% endif %}
+          </div>
+          {% endblock %}
+
+          {% block docs_main %}
+          {% if sidebars %}
+              {% set content_col_class = "col-md-9 col-xl-7" %}
+          {% else %}
+              {% set content_col_class = "col-md-11 col-xl-8" %}
+          {% endif %}
+          <main class="col-12 {{ content_col_class }} py-md-5 pl-md-5 pr-md-4 bd-content" role="main">
+            {%- block edit_page -%}
+              {%- include "edit-this-page.html" with context -%}
+            {%- endblock %}
+            {% block docs_body %}
+                <div>
+                  {% block body %} {% endblock %}
+                </div>
+            {% endblock %}
+            {% if theme_show_prev_next %}
+                <div class='prev-next-bottom'>
+                  {{ ovino_prev_next(prev, next) }}
+                </div>
+            {% endif %}
+          </main>
+          {% endblock %}
+
+      </div>
+    </div>
+
+
+  {%- block scripts_end %}
+  {{ _webpack.body_post() }}
+  {%- endblock %}
+
+{%- endblock %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-language-selector.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-language-selector.html
new file mode 100644
index 00000000000..1a7da433d32
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-language-selector.html
@@ -0,0 +1,3 @@
+{%- block icon_links -%}
+{%- include "language-selector.html" with context -%}
+{%- endblock %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-logo.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-logo.html
new file mode 100644
index 00000000000..b561735fca9
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-logo.html
@@ -0,0 +1,19 @@
+{% if logo %}
+{% if not theme_logo_link %}
+<a class="navbar-brand" href="{{ pathto(master_doc) }}">
+  <img src="{{ pathto('_static/' + logo, 1) }}" class="logo" alt="logo">
+</a>
+{% elif theme_logo_link[:4] == 'http' %}
+<a class="navbar-brand" href="{{ theme_logo_link }}">
+  <img src="{{ pathto('_static/' + logo, 1) }}" class="logo" alt="logo">
+</a>
+{% else %}
+<a class="navbar-brand" href="{{ pathto(theme_logo_link) }}">
+  <img src="{{ pathto('_static/' + logo, 1) }}" class="logo" alt="logo">
+</a>
+{% endif %}
+{% else %}
+<a class="navbar-brand" href="{{ pathto(master_doc) }}">
+<p class="title">{{ project }}</p>
+</a>
+{% endif %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-version-selector.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-version-selector.html
new file mode 100644
index 00000000000..2dda5c0b668
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-version-selector.html
@@ -0,0 +1,3 @@
+{%- block icon_links -%}
+{%- include "version-selector.html" with context -%}
+{%- endblock %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/page-toc.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/page-toc.html
new file mode 100644
index 00000000000..42c0a46eff3
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/page-toc.html
@@ -0,0 +1,9 @@
+{% set page_toc = generate_toc_html() %}
+
+<div class="tocsection onthispage pt-5 pb-3">
+    {{ _("On this page") }}
+</div>
+
+<nav id="bd-toc-nav">
+    {{ page_toc }}
+</nav>
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/search-field.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/search-field.html
new file mode 100644
index 00000000000..6d69674e970
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/search-field.html
@@ -0,0 +1,16 @@
+<form style="padding: 0 0.5rem;" class="d-flex align-items-center" action="{{ pathto('search') }}" method="get">
+  <div style="width:100%;" class="textfield textfield-q textfield-size-m left-slot">
+    <div class="slot left-slot-container">
+      <span class="icon fas fa-search"></span>
+    </div>
+    <input 
+      class="input input-quiet input-size-m" 
+      type="search" 
+      name="q" 
+      id="search-input" 
+      placeholder="{{ _(theme_search_bar_text) }}" 
+      aria-label="{{ theme_search_bar_text }}" 
+      autocomplete="off"
+    >
+  </div>
+</form>
\ No newline at end of file
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/sidebar-nav-bs.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/sidebar-nav-bs.html
new file mode 100644
index 00000000000..57b5c55ce06
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/sidebar-nav-bs.html
@@ -0,0 +1,15 @@
+<nav class="bd-links" id="bd-docs-nav" aria-label="{{ _('Main navigation') }}">
+    <div class="bd-toc-item active">
+      {% set open_first = false %}
+      {% if pagename == 'tutorials' %}
+      {% set open_first = true %}
+      {% endif %}
+      {{ generate_sidebar_nav("sidebar",
+                           maxdepth=theme_navigation_depth|int,
+                           collapse=theme_collapse_navigation|tobool,
+                           includehidden=True,
+                           titles_only=True,
+                           open_first=open_first) }}
+    </div>
+  </nav>
+  
\ No newline at end of file
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/version-selector.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/version-selector.html
new file mode 100644
index 00000000000..dfc32de42a8
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/version-selector.html
@@ -0,0 +1,15 @@
+{# Add version selector #}
+{% if versions and current_version %}
+<div class="dropdown sst-dropdown sst-dropdown-navbar">
+  <button class="btn sst-btn dropdown-toggle" type="button" id="version-selector" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">{{current_version}}</button>
+  <div class="dropdown-menu" aria-labelledby="version-selector">
+    {% for slug, url in versions%}
+      {% if slug == current_version %}
+        <a class="dropdown-item font-weight-bold" href="{{ url }}/{{ pagename }}.html">{{ slug }}</a>
+      {% else %}
+        <a  class="dropdown-item" href="{{ url }}/{{ pagename }}.html">{{ slug }}</a>
+      {% endif %}
+    {% endfor %}
+  </div>
+</div>
+{% endif %}
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/theme.conf b/docs/openvino_sphinx_theme/openvino_sphinx_theme/theme.conf
new file mode 100644
index 00000000000..cdd43785897
--- /dev/null
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/theme.conf
@@ -0,0 +1,6 @@
+[theme]
+inherit = pydata_sphinx_theme
+
+[options]
+navbar_end = navbar-icon-links.html, navbar-version-selector.html, navbar-language-selector.html
+page_sidebar_items = page-toc.html, download-docs.html
diff --git a/docs/openvino_sphinx_theme/setup.py b/docs/openvino_sphinx_theme/setup.py
new file mode 100644
index 00000000000..99e90f57124
--- /dev/null
+++ b/docs/openvino_sphinx_theme/setup.py
@@ -0,0 +1,19 @@
+from setuptools import setup
+
+setup(
+    name='openvino-sphinx-theme',
+    version='0.0.1',
+    packages=['openvino_sphinx_theme'],
+    maintainer='Nikolay Tyukaev',
+    maintainer_email='nikolay.tyukaev@intel.com',
+    include_package_data=True,
+    entry_points={"sphinx.html_themes": ["openvino_sphinx_theme = openvino_sphinx_theme"]},
+    install_requires=['pydata_sphinx_theme', 'sphinx_inline_tabs'],
+    python_requires='>=3.5',
+    url='https://github.com/openvinotoolkit/openvino',
+    license='',
+    author='Intel Corporation',
+    author_email='',
+    description='A Sphinx theme',
+    long_description=''
+)
diff --git a/docs/ops/activation/Clamp_1.md b/docs/ops/activation/Clamp_1.md
index bc6b7edd3c9..a0ff821c75c 100644
--- a/docs/ops/activation/Clamp_1.md
+++ b/docs/ops/activation/Clamp_1.md
@@ -1,4 +1,4 @@
-## Clamp<a name="Clamp"></a> {#openvino_docs_ops_activation_Clamp_1}
+# Clamp {#openvino_docs_ops_activation_Clamp_1}
 
 **Versioned name**: *Clamp-1*
 
diff --git a/docs/ops/activation/Elu_1.md b/docs/ops/activation/Elu_1.md
index 50537dc6ea6..cffa21150ac 100644
--- a/docs/ops/activation/Elu_1.md
+++ b/docs/ops/activation/Elu_1.md
@@ -1,4 +1,4 @@
-## Elu<a name="Elu"></a> {#openvino_docs_ops_activation_Elu_1}
+# Elu {#openvino_docs_ops_activation_Elu_1}
 
 **Versioned name**: *Elu-1*
 
diff --git a/docs/ops/activation/Exp_1.md b/docs/ops/activation/Exp_1.md
index e31e425e7e2..6b91ec371d4 100644
--- a/docs/ops/activation/Exp_1.md
+++ b/docs/ops/activation/Exp_1.md
@@ -1,4 +1,4 @@
-## Exp<a name="Exp"></a> {#openvino_docs_ops_activation_Exp_1}
+# Exp {#openvino_docs_ops_activation_Exp_1}
 
 **Versioned name**: *Exp-1*
 
diff --git a/docs/ops/activation/GELU_2.md b/docs/ops/activation/GELU_2.md
index 3d2adaa14de..e93791bc856 100644
--- a/docs/ops/activation/GELU_2.md
+++ b/docs/ops/activation/GELU_2.md
@@ -1,4 +1,4 @@
-## GELU- Gaussian Error Linear Unit <a name="Gelu"></a> {#openvino_docs_ops_activation_GELU_2}
+# GELU- Gaussian Error Linear Unit {#openvino_docs_ops_activation_GELU_2}
 
 **Versioned name**: *Gelu-2*
 
diff --git a/docs/ops/activation/GELU_7.md b/docs/ops/activation/GELU_7.md
index f11a4813a07..216ada0ac10 100644
--- a/docs/ops/activation/GELU_7.md
+++ b/docs/ops/activation/GELU_7.md
@@ -1,4 +1,4 @@
-## GELU- Gaussian Error Linear Unit <a name="Gelu"></a> {#openvino_docs_ops_activation_GELU_7}
+# GELU- Gaussian Error Linear Unit {#openvino_docs_ops_activation_GELU_7}
 
 **Versioned name**: *Gelu-7*
 
diff --git a/docs/ops/activation/HSigmoid_5.md b/docs/ops/activation/HSigmoid_5.md
index 367327a4f85..adc83867085 100644
--- a/docs/ops/activation/HSigmoid_5.md
+++ b/docs/ops/activation/HSigmoid_5.md
@@ -1,4 +1,4 @@
-## HSigmoid<a name="HSigmoid"></a> {#openvino_docs_ops_activation_HSigmoid_5}
+# HSigmoid {#openvino_docs_ops_activation_HSigmoid_5}
 
 **Versioned name**: *HSigmoid-5*
 
diff --git a/docs/ops/activation/HSwish_4.md b/docs/ops/activation/HSwish_4.md
index b5870983af3..71a9aa18899 100644
--- a/docs/ops/activation/HSwish_4.md
+++ b/docs/ops/activation/HSwish_4.md
@@ -1,4 +1,4 @@
-## HSwish <a name="HSwish"></a> {#openvino_docs_ops_activation_HSwish_4}
+# HSwish {#openvino_docs_ops_activation_HSwish_4}
 
 **Versioned name**: *HSwish-4*
 
diff --git a/docs/ops/activation/HardSigmoid_1.md b/docs/ops/activation/HardSigmoid_1.md
index 8403ca8a1ec..b3ca7a6a0df 100644
--- a/docs/ops/activation/HardSigmoid_1.md
+++ b/docs/ops/activation/HardSigmoid_1.md
@@ -1,4 +1,4 @@
-## HardSigmoid <a name="HardSigmoid"></a> {#openvino_docs_ops_activation_HardSigmoid_1}
+# HardSigmoid {#openvino_docs_ops_activation_HardSigmoid_1}
 
 **Versioned name**: *HardSigmoid-1*
 
diff --git a/docs/ops/activation/LogSoftmax_5.md b/docs/ops/activation/LogSoftmax_5.md
index 25296571319..e8537e968bb 100644
--- a/docs/ops/activation/LogSoftmax_5.md
+++ b/docs/ops/activation/LogSoftmax_5.md
@@ -1,4 +1,4 @@
-## LogSoftMax <a name="LogSoftmax"></a> {#openvino_docs_ops_activation_LogSoftmax_5}
+# LogSoftMax {#openvino_docs_ops_activation_LogSoftmax_5}
 
 **Versioned name**: *LogSoftmax-5*
 
diff --git a/docs/ops/activation/Mish_4.md b/docs/ops/activation/Mish_4.md
index 30b98590fd2..7c33184801f 100644
--- a/docs/ops/activation/Mish_4.md
+++ b/docs/ops/activation/Mish_4.md
@@ -1,4 +1,4 @@
-## Mish <a name="Mish"></a> {#openvino_docs_ops_activation_Mish_4}
+# Mish {#openvino_docs_ops_activation_Mish_4}
 
 **Versioned name**: *Mish-4*
 
diff --git a/docs/ops/activation/PReLU_1.md b/docs/ops/activation/PReLU_1.md
index a8d4b7ec499..f41748c4e4f 100644
--- a/docs/ops/activation/PReLU_1.md
+++ b/docs/ops/activation/PReLU_1.md
@@ -1,4 +1,4 @@
-## PReLU <a name="PReLU"></a> {#openvino_docs_ops_activation_PReLU_1}
+# PReLU {#openvino_docs_ops_activation_PReLU_1}
 
 **Versioned name**: *PReLU-1*
 
diff --git a/docs/ops/activation/ReLU_1.md b/docs/ops/activation/ReLU_1.md
index 5b401dbc908..626f3dce22c 100644
--- a/docs/ops/activation/ReLU_1.md
+++ b/docs/ops/activation/ReLU_1.md
@@ -1,4 +1,4 @@
-## ReLU <a name="ReLU"></a> {#openvino_docs_ops_activation_ReLU_1}
+# ReLU {#openvino_docs_ops_activation_ReLU_1}
 
 **Versioned name**: *ReLU-1*
 
diff --git a/docs/ops/activation/Selu_1.md b/docs/ops/activation/Selu_1.md
index 4be64167157..3807513b87e 100644
--- a/docs/ops/activation/Selu_1.md
+++ b/docs/ops/activation/Selu_1.md
@@ -1,4 +1,4 @@
-## Selu <a name="Selu"></a> {#openvino_docs_ops_activation_Selu_1}
+# Selu {#openvino_docs_ops_activation_Selu_1}
 
 **Versioned name**: *Selu-1*
 
diff --git a/docs/ops/activation/Sigmoid_1.md b/docs/ops/activation/Sigmoid_1.md
index 3206d3953f8..b9cdf70b4a5 100644
--- a/docs/ops/activation/Sigmoid_1.md
+++ b/docs/ops/activation/Sigmoid_1.md
@@ -1,4 +1,4 @@
-## Sigmoid<a name="Sigmoid"></a> {#openvino_docs_ops_activation_Sigmoid_1}
+# Sigmoid {#openvino_docs_ops_activation_Sigmoid_1}
 
 **Versioned name**: *Sigmoid-1*
 
diff --git a/docs/ops/activation/SoftMax_1.md b/docs/ops/activation/SoftMax_1.md
index ace4aefa332..d24ac81e54b 100644
--- a/docs/ops/activation/SoftMax_1.md
+++ b/docs/ops/activation/SoftMax_1.md
@@ -1,4 +1,4 @@
-## SoftMax <a name="SoftMax"></a> {#openvino_docs_ops_activation_SoftMax_1}
+# SoftMax {#openvino_docs_ops_activation_SoftMax_1}
 
 **Versioned name**: *SoftMax-1*
 
diff --git a/docs/ops/activation/SoftMax_8.md b/docs/ops/activation/SoftMax_8.md
index 170d5802d60..9659ad24f55 100644
--- a/docs/ops/activation/SoftMax_8.md
+++ b/docs/ops/activation/SoftMax_8.md
@@ -1,4 +1,4 @@
-## SoftMax <a name="SoftMax"></a> {#openvino_docs_ops_activation_SoftMax_8}
+# SoftMax {#openvino_docs_ops_activation_SoftMax_8}
 
 **Versioned name**: *SoftMax-8*
 
diff --git a/docs/ops/activation/SoftPlus_4.md b/docs/ops/activation/SoftPlus_4.md
index e6307f56a4f..b4c98cd82cf 100644
--- a/docs/ops/activation/SoftPlus_4.md
+++ b/docs/ops/activation/SoftPlus_4.md
@@ -1,4 +1,4 @@
-## SoftPlus <a name="SoftPlus"></a> {#openvino_docs_ops_activation_SoftPlus_4}
+# SoftPlus {#openvino_docs_ops_activation_SoftPlus_4}
 
 **Versioned name**: *SoftPlus-4*
 
diff --git a/docs/ops/activation/Swish_4.md b/docs/ops/activation/Swish_4.md
index ac81c794fcd..f6237f51010 100644
--- a/docs/ops/activation/Swish_4.md
+++ b/docs/ops/activation/Swish_4.md
@@ -1,4 +1,4 @@
-## Swish <a name="Swish"></a> {#openvino_docs_ops_activation_Swish_4}
+# Swish {#openvino_docs_ops_activation_Swish_4}
 
 **Versioned name**: *Swish-4*
 
diff --git a/docs/ops/arithmetic/Abs_1.md b/docs/ops/arithmetic/Abs_1.md
index 457085a36f9..30f44ed856e 100644
--- a/docs/ops/arithmetic/Abs_1.md
+++ b/docs/ops/arithmetic/Abs_1.md
@@ -1,4 +1,4 @@
-## Abs <a name="Abs"></a> {#openvino_docs_ops_arithmetic_Abs_1}
+# Abs {#openvino_docs_ops_arithmetic_Abs_1}
 
 **Versioned name**: *Abs-1*
 
diff --git a/docs/ops/arithmetic/Acos_1.md b/docs/ops/arithmetic/Acos_1.md
index 5b866047551..e8d7ccf2e5d 100644
--- a/docs/ops/arithmetic/Acos_1.md
+++ b/docs/ops/arithmetic/Acos_1.md
@@ -1,4 +1,4 @@
-## Acos <a name="Acos"></a> {#openvino_docs_ops_arithmetic_Acos_1}
+# Acos {#openvino_docs_ops_arithmetic_Acos_1}
 
 **Versioned name**: *Acos-1*
 
diff --git a/docs/ops/arithmetic/Acosh_3.md b/docs/ops/arithmetic/Acosh_3.md
index 20f8dee4969..75344c50f93 100644
--- a/docs/ops/arithmetic/Acosh_3.md
+++ b/docs/ops/arithmetic/Acosh_3.md
@@ -1,4 +1,4 @@
-## Acosh <a name="Acosh"></a> {#openvino_docs_ops_arithmetic_Acosh_3}
+# Acosh {#openvino_docs_ops_arithmetic_Acosh_3}
 
 **Versioned name**: *Acosh-3*
 
diff --git a/docs/ops/arithmetic/Add_1.md b/docs/ops/arithmetic/Add_1.md
index c71d182cc9f..c6470b9c24c 100644
--- a/docs/ops/arithmetic/Add_1.md
+++ b/docs/ops/arithmetic/Add_1.md
@@ -1,4 +1,4 @@
-## Add <a name="Add"></a> {#openvino_docs_ops_arithmetic_Add_1}
+# Add {#openvino_docs_ops_arithmetic_Add_1}
 
 **Versioned name**: *Add-1*
 
diff --git a/docs/ops/arithmetic/Asin_1.md b/docs/ops/arithmetic/Asin_1.md
index 69be38d84ab..819c6e3ccf6 100644
--- a/docs/ops/arithmetic/Asin_1.md
+++ b/docs/ops/arithmetic/Asin_1.md
@@ -1,4 +1,4 @@
-## Asin <a name="Asin"></a> {#openvino_docs_ops_arithmetic_Asin_1}
+# Asin {#openvino_docs_ops_arithmetic_Asin_1}
 
 **Versioned name**: *Asin-1*
 
diff --git a/docs/ops/arithmetic/Asinh_3.md b/docs/ops/arithmetic/Asinh_3.md
index 093161399f0..9f58e9372ba 100644
--- a/docs/ops/arithmetic/Asinh_3.md
+++ b/docs/ops/arithmetic/Asinh_3.md
@@ -1,4 +1,4 @@
-## Asinh <a name="Asinh"></a> {#openvino_docs_ops_arithmetic_Asinh_3}
+# Asinh {#openvino_docs_ops_arithmetic_Asinh_3}
 
 **Versioned name**: *Asinh-3*
 
diff --git a/docs/ops/arithmetic/Atan_1.md b/docs/ops/arithmetic/Atan_1.md
index 4439477f2a9..9685c355180 100644
--- a/docs/ops/arithmetic/Atan_1.md
+++ b/docs/ops/arithmetic/Atan_1.md
@@ -1,4 +1,4 @@
-## Atan <a name="Atan"></a> {#openvino_docs_ops_arithmetic_Atan_1}
+# Atan  {#openvino_docs_ops_arithmetic_Atan_1}
 
 **Versioned name**: *Atan-1*
 
diff --git a/docs/ops/arithmetic/Atanh_3.md b/docs/ops/arithmetic/Atanh_3.md
index 0f01caaade2..a71b7ffbdc9 100644
--- a/docs/ops/arithmetic/Atanh_3.md
+++ b/docs/ops/arithmetic/Atanh_3.md
@@ -1,4 +1,4 @@
-## Atanh <a name="Atanh"></a> {#openvino_docs_ops_arithmetic_Atanh_3}
+# Atanh {#openvino_docs_ops_arithmetic_Atanh_3}
 
 **Versioned name**: *Atanh-3*
 
diff --git a/docs/ops/arithmetic/Ceiling_1.md b/docs/ops/arithmetic/Ceiling_1.md
index 4f8a5e983d4..fb0d387db7b 100644
--- a/docs/ops/arithmetic/Ceiling_1.md
+++ b/docs/ops/arithmetic/Ceiling_1.md
@@ -1,4 +1,4 @@
-## Ceiling <a name="Ceiling"></a> {#openvino_docs_ops_arithmetic_Ceiling_1}
+# Ceiling  {#openvino_docs_ops_arithmetic_Ceiling_1}
 
 **Versioned name**: *Ceiling-1*
 
diff --git a/docs/ops/arithmetic/Cos_1.md b/docs/ops/arithmetic/Cos_1.md
index 6b134d17f9b..8eeb2a1f789 100644
--- a/docs/ops/arithmetic/Cos_1.md
+++ b/docs/ops/arithmetic/Cos_1.md
@@ -1,4 +1,4 @@
-## Cos <a name="Cos"></a> {#openvino_docs_ops_arithmetic_Cos_1}
+# Cos {#openvino_docs_ops_arithmetic_Cos_1}
 
 **Versioned name**: *Cos-1*
 
diff --git a/docs/ops/arithmetic/Cosh_1.md b/docs/ops/arithmetic/Cosh_1.md
index e736b2fb11f..48a2a34ce07 100644
--- a/docs/ops/arithmetic/Cosh_1.md
+++ b/docs/ops/arithmetic/Cosh_1.md
@@ -1,4 +1,4 @@
-## Cosh <a name="Cosh"></a> {#openvino_docs_ops_arithmetic_Cosh_1}
+# Cosh  {#openvino_docs_ops_arithmetic_Cosh_1}
 
 **Versioned name**: *Cosh-1*
 
diff --git a/docs/ops/arithmetic/CumSum_3.md b/docs/ops/arithmetic/CumSum_3.md
index c33524a39f2..3e2c766aca9 100644
--- a/docs/ops/arithmetic/CumSum_3.md
+++ b/docs/ops/arithmetic/CumSum_3.md
@@ -1,4 +1,4 @@
-## CumSum <a name="CumSum"></a> {#openvino_docs_ops_arithmetic_CumSum_3}
+# CumSum {#openvino_docs_ops_arithmetic_CumSum_3}
 
 **Versioned name**: *CumSum-3*
 
diff --git a/docs/ops/arithmetic/Divide_1.md b/docs/ops/arithmetic/Divide_1.md
index ef07ea3e1fa..081a799ea35 100644
--- a/docs/ops/arithmetic/Divide_1.md
+++ b/docs/ops/arithmetic/Divide_1.md
@@ -1,4 +1,4 @@
-## Divide <a name="Divide"></a> {#openvino_docs_ops_arithmetic_Divide_1}
+# Divide {#openvino_docs_ops_arithmetic_Divide_1}
 
 **Versioned name**: *Divide-1*
 
diff --git a/docs/ops/arithmetic/Erf_1.md b/docs/ops/arithmetic/Erf_1.md
index ff137416254..7b13f4e9eed 100644
--- a/docs/ops/arithmetic/Erf_1.md
+++ b/docs/ops/arithmetic/Erf_1.md
@@ -1,4 +1,4 @@
-## Erf <a name="Erf"></a> {#openvino_docs_ops_arithmetic_Erf_1}
+# Erf {#openvino_docs_ops_arithmetic_Erf_1}
 
 **Versioned name**: *Erf-1*
 
diff --git a/docs/ops/arithmetic/FloorMod_1.md b/docs/ops/arithmetic/FloorMod_1.md
index eccad4177ef..31180e07ef0 100644
--- a/docs/ops/arithmetic/FloorMod_1.md
+++ b/docs/ops/arithmetic/FloorMod_1.md
@@ -1,4 +1,4 @@
-## FloorMod <a name="FloorMod"></a> {#openvino_docs_ops_arithmetic_FloorMod_1}
+# FloorMod  {#openvino_docs_ops_arithmetic_FloorMod_1}
 
 **Versioned name**: *FloorMod-1*
 
diff --git a/docs/ops/arithmetic/Floor_1.md b/docs/ops/arithmetic/Floor_1.md
index d62c604e32b..23fde8a3a33 100644
--- a/docs/ops/arithmetic/Floor_1.md
+++ b/docs/ops/arithmetic/Floor_1.md
@@ -1,4 +1,4 @@
-## Floor <a name="Floor"></a> {#openvino_docs_ops_arithmetic_Floor_1}
+# Floor  {#openvino_docs_ops_arithmetic_Floor_1}
 
 **Versioned name**: *Floor-1*
 
diff --git a/docs/ops/arithmetic/Log_1.md b/docs/ops/arithmetic/Log_1.md
index f24e3d21542..c928132fb23 100644
--- a/docs/ops/arithmetic/Log_1.md
+++ b/docs/ops/arithmetic/Log_1.md
@@ -1,4 +1,4 @@
-## Log <a name="Log"></a> {#openvino_docs_ops_arithmetic_Log_1}
+# Log  {#openvino_docs_ops_arithmetic_Log_1}
 
 **Versioned name**: *Log-1*
 
diff --git a/docs/ops/arithmetic/Maximum_1.md b/docs/ops/arithmetic/Maximum_1.md
index 8ef87a94552..594eb644cea 100644
--- a/docs/ops/arithmetic/Maximum_1.md
+++ b/docs/ops/arithmetic/Maximum_1.md
@@ -1,4 +1,4 @@
-## Maximum <a name="Maximum"></a> {#openvino_docs_ops_arithmetic_Maximum_1}
+# Maximum  {#openvino_docs_ops_arithmetic_Maximum_1}
 
 **Versioned name**: *Maximum-1*
 
diff --git a/docs/ops/arithmetic/Minimum_1.md b/docs/ops/arithmetic/Minimum_1.md
index 8131ffa6bc9..2138ec4039d 100644
--- a/docs/ops/arithmetic/Minimum_1.md
+++ b/docs/ops/arithmetic/Minimum_1.md
@@ -1,4 +1,4 @@
-## Minimum <a name="Minimum"></a> {#openvino_docs_ops_arithmetic_Minimum_1}
+# Minimum  {#openvino_docs_ops_arithmetic_Minimum_1}
 
 **Versioned name**: *Minimum-1*
 
diff --git a/docs/ops/arithmetic/Mod_1.md b/docs/ops/arithmetic/Mod_1.md
index febac678ed6..818c3405381 100644
--- a/docs/ops/arithmetic/Mod_1.md
+++ b/docs/ops/arithmetic/Mod_1.md
@@ -1,4 +1,4 @@
-## Mod <a name="Mod"></a> {#openvino_docs_ops_arithmetic_Mod_1}
+# Mod  {#openvino_docs_ops_arithmetic_Mod_1}
 
 **Versioned name**: *Mod-1*
 
diff --git a/docs/ops/arithmetic/Multiply_1.md b/docs/ops/arithmetic/Multiply_1.md
index c6fd0b11ea2..c882c812c98 100644
--- a/docs/ops/arithmetic/Multiply_1.md
+++ b/docs/ops/arithmetic/Multiply_1.md
@@ -1,4 +1,4 @@
-## Multiply <a name="Multiply"></a> {#openvino_docs_ops_arithmetic_Multiply_1}
+# Multiply {#openvino_docs_ops_arithmetic_Multiply_1}
 
 **Versioned name**: *Multiply-1*
 
diff --git a/docs/ops/arithmetic/Negative_1.md b/docs/ops/arithmetic/Negative_1.md
index 5fa0e630dda..8036eaac21c 100644
--- a/docs/ops/arithmetic/Negative_1.md
+++ b/docs/ops/arithmetic/Negative_1.md
@@ -1,4 +1,4 @@
-## Negative <a name="Negative"></a> {#openvino_docs_ops_arithmetic_Negative_1}
+# Negative  {#openvino_docs_ops_arithmetic_Negative_1}
 
 **Versioned name**: *Negative-1*
 
diff --git a/docs/ops/arithmetic/Power_1.md b/docs/ops/arithmetic/Power_1.md
index f4de24bf038..17880d8ab4b 100644
--- a/docs/ops/arithmetic/Power_1.md
+++ b/docs/ops/arithmetic/Power_1.md
@@ -1,4 +1,4 @@
-## Power <a name="Power"></a> {#openvino_docs_ops_arithmetic_Power_1}
+# Power  {#openvino_docs_ops_arithmetic_Power_1}
 
 **Versioned name**: *Power-1*
 
diff --git a/docs/ops/arithmetic/Round_5.md b/docs/ops/arithmetic/Round_5.md
index ae423dcd091..9e08eb5e6fd 100644
--- a/docs/ops/arithmetic/Round_5.md
+++ b/docs/ops/arithmetic/Round_5.md
@@ -1,4 +1,4 @@
-## Round <a name="Round"></a> {#openvino_docs_ops_arithmetic_Round_5}
+# Round {#openvino_docs_ops_arithmetic_Round_5}
 
 **Versioned name**: *Round-5*
 
diff --git a/docs/ops/arithmetic/Sign_1.md b/docs/ops/arithmetic/Sign_1.md
index f9691bf5488..698c4845980 100644
--- a/docs/ops/arithmetic/Sign_1.md
+++ b/docs/ops/arithmetic/Sign_1.md
@@ -1,4 +1,4 @@
-## Sign <a name="Sign"></a> {#openvino_docs_ops_arithmetic_Sign_1}
+# Sign  {#openvino_docs_ops_arithmetic_Sign_1}
 
 **Versioned name**: *Sign-1*
 
diff --git a/docs/ops/arithmetic/Sin_1.md b/docs/ops/arithmetic/Sin_1.md
index 36bef086315..416b6290b78 100644
--- a/docs/ops/arithmetic/Sin_1.md
+++ b/docs/ops/arithmetic/Sin_1.md
@@ -1,4 +1,4 @@
-## Sin <a name="Sin"></a> {#openvino_docs_ops_arithmetic_Sin_1}
+# Sin {#openvino_docs_ops_arithmetic_Sin_1}
 
 **Versioned name**: *Sin-1*
 
diff --git a/docs/ops/arithmetic/Sinh_1.md b/docs/ops/arithmetic/Sinh_1.md
index aeb99c7918d..4641e3cf488 100644
--- a/docs/ops/arithmetic/Sinh_1.md
+++ b/docs/ops/arithmetic/Sinh_1.md
@@ -1,4 +1,4 @@
-## Sinh <a name="Sinh"></a> {#openvino_docs_ops_arithmetic_Sinh_1}
+# Sinh {#openvino_docs_ops_arithmetic_Sinh_1}
 
 **Versioned name**: *Sinh-1*
 
diff --git a/docs/ops/arithmetic/Sqrt_1.md b/docs/ops/arithmetic/Sqrt_1.md
index a69dfd6458a..71063c42a89 100644
--- a/docs/ops/arithmetic/Sqrt_1.md
+++ b/docs/ops/arithmetic/Sqrt_1.md
@@ -1,4 +1,4 @@
-## Sqrt <a name="Sqrt"></a> {#openvino_docs_ops_arithmetic_Sqrt_1}
+# Sqrt {#openvino_docs_ops_arithmetic_Sqrt_1}
 
 **Versioned name**: *Sqrt-1*
 
diff --git a/docs/ops/arithmetic/SquaredDifference_1.md b/docs/ops/arithmetic/SquaredDifference_1.md
index b20ec7aa1ce..a8eb3c5366b 100644
--- a/docs/ops/arithmetic/SquaredDifference_1.md
+++ b/docs/ops/arithmetic/SquaredDifference_1.md
@@ -1,4 +1,4 @@
-## SquaredDifference <a name="SquaredDifference"></a> {#openvino_docs_ops_arithmetic_SquaredDifference_1}
+# SquaredDifference {#openvino_docs_ops_arithmetic_SquaredDifference_1}
 
 **Versioned name**: *SquaredDifference-1*
 
diff --git a/docs/ops/arithmetic/Subtract_1.md b/docs/ops/arithmetic/Subtract_1.md
index 6a35d32ac35..596cd207b69 100644
--- a/docs/ops/arithmetic/Subtract_1.md
+++ b/docs/ops/arithmetic/Subtract_1.md
@@ -1,4 +1,4 @@
-## Subtract <a name="Subtract"></a> {#openvino_docs_ops_arithmetic_Subtract_1}
+# Subtract {#openvino_docs_ops_arithmetic_Subtract_1}
 
 **Versioned name**: *Subtract-1*
 
diff --git a/docs/ops/arithmetic/Tan_1.md b/docs/ops/arithmetic/Tan_1.md
index 07491c3c9d1..f03e0f0179e 100644
--- a/docs/ops/arithmetic/Tan_1.md
+++ b/docs/ops/arithmetic/Tan_1.md
@@ -1,4 +1,4 @@
-## Tan <a name="Tan"></a> {#openvino_docs_ops_arithmetic_Tan_1}
+# Tan  {#openvino_docs_ops_arithmetic_Tan_1}
 
 **Versioned name**: *Tan-1*
 
diff --git a/docs/ops/arithmetic/Tanh_1.md b/docs/ops/arithmetic/Tanh_1.md
index 0c90d4b496d..3bd45016f49 100644
--- a/docs/ops/arithmetic/Tanh_1.md
+++ b/docs/ops/arithmetic/Tanh_1.md
@@ -1,4 +1,4 @@
-## Tanh<a name="Tanh"></a> {#openvino_docs_ops_arithmetic_Tanh_1}
+# Tanh {#openvino_docs_ops_arithmetic_Tanh_1}
 
 **Versioned name**: *Tanh-1*
 
diff --git a/docs/ops/comparison/Equal_1.md b/docs/ops/comparison/Equal_1.md
index bf813ca6c8c..980ed5d533d 100644
--- a/docs/ops/comparison/Equal_1.md
+++ b/docs/ops/comparison/Equal_1.md
@@ -1,4 +1,4 @@
-## Equal <a name="Equal"></a> {#openvino_docs_ops_comparison_Equal_1}
+# Equal  {#openvino_docs_ops_comparison_Equal_1}
 
 **Versioned name**: *Equal-1*
 
diff --git a/docs/ops/comparison/GreaterEqual_1.md b/docs/ops/comparison/GreaterEqual_1.md
index 5cc5f2d8b6a..bdad9606033 100644
--- a/docs/ops/comparison/GreaterEqual_1.md
+++ b/docs/ops/comparison/GreaterEqual_1.md
@@ -1,4 +1,4 @@
-## GreaterEqual <a name="GreaterEqual"></a> {#openvino_docs_ops_comparison_GreaterEqual_1}
+# GreaterEqual {#openvino_docs_ops_comparison_GreaterEqual_1}
 
 **Versioned name**: *GreaterEqual-1*
 
diff --git a/docs/ops/comparison/Greater_1.md b/docs/ops/comparison/Greater_1.md
index 4076251fd1a..b786b0e4a47 100644
--- a/docs/ops/comparison/Greater_1.md
+++ b/docs/ops/comparison/Greater_1.md
@@ -1,4 +1,4 @@
-## Greater <a name="Greater"></a> {#openvino_docs_ops_comparison_Greater_1}
+# Greater {#openvino_docs_ops_comparison_Greater_1}
 
 **Versioned name**: *Greater-1*
 
diff --git a/docs/ops/comparison/LessEqual_1.md b/docs/ops/comparison/LessEqual_1.md
index dbdf80c3716..f714e83a8ca 100644
--- a/docs/ops/comparison/LessEqual_1.md
+++ b/docs/ops/comparison/LessEqual_1.md
@@ -1,4 +1,4 @@
-## LessEqual <a name="LessEqual"></a> {#openvino_docs_ops_comparison_LessEqual_1}
+# LessEqual {#openvino_docs_ops_comparison_LessEqual_1}
 
 **Versioned name**: *LessEqual-1*
 
diff --git a/docs/ops/comparison/Less_1.md b/docs/ops/comparison/Less_1.md
index ca24f17cd16..5d0209ed750 100644
--- a/docs/ops/comparison/Less_1.md
+++ b/docs/ops/comparison/Less_1.md
@@ -1,4 +1,4 @@
-## Less <a name="Less"></a> {#openvino_docs_ops_comparison_Less_1}
+# Less {#openvino_docs_ops_comparison_Less_1}
 
 **Versioned name**: *Less-1*
 
diff --git a/docs/ops/comparison/NotEqual_1.md b/docs/ops/comparison/NotEqual_1.md
index dd077b9d2fc..0b6f7b77e17 100644
--- a/docs/ops/comparison/NotEqual_1.md
+++ b/docs/ops/comparison/NotEqual_1.md
@@ -1,4 +1,4 @@
-## NotEqual <a name="NotEqual"></a> {#openvino_docs_ops_comparison_NotEqual_1}
+# NotEqual {#openvino_docs_ops_comparison_NotEqual_1}
 
 **Versioned name**: *NotEqual-1*
 
diff --git a/docs/ops/condition/Bucketize_3.md b/docs/ops/condition/Bucketize_3.md
index 2de9a166aea..5f77963ced7 100644
--- a/docs/ops/condition/Bucketize_3.md
+++ b/docs/ops/condition/Bucketize_3.md
@@ -1,4 +1,4 @@
-## Bucketize <a name="Bucketize"></a> {#openvino_docs_ops_condition_Bucketize_3}
+# Bucketize {#openvino_docs_ops_condition_Bucketize_3}
 
 **Versioned name**: *Bucketize-3*
 
diff --git a/docs/ops/condition/NonZero_3.md b/docs/ops/condition/NonZero_3.md
index 6d71ea808f3..f76585f0e1b 100644
--- a/docs/ops/condition/NonZero_3.md
+++ b/docs/ops/condition/NonZero_3.md
@@ -1,4 +1,4 @@
-## NonZero <a name="NonZero"></a> {#openvino_docs_ops_condition_NonZero_3}
+# NonZero {#openvino_docs_ops_condition_NonZero_3}
 
 **Versioned name**: *NonZero-3*
 
diff --git a/docs/ops/condition/Select_1.md b/docs/ops/condition/Select_1.md
index 78ec5d5a5d2..2321fc03221 100644
--- a/docs/ops/condition/Select_1.md
+++ b/docs/ops/condition/Select_1.md
@@ -1,4 +1,4 @@
-## Select <a name="Select"></a> {#openvino_docs_ops_condition_Select_1}
+# Select {#openvino_docs_ops_condition_Select_1}
 
 **Versioned name**: *Select-1*
 
diff --git a/docs/ops/convolution/BinaryConvolution_1.md b/docs/ops/convolution/BinaryConvolution_1.md
index 9176e3621cd..975baa7e401 100644
--- a/docs/ops/convolution/BinaryConvolution_1.md
+++ b/docs/ops/convolution/BinaryConvolution_1.md
@@ -1,4 +1,4 @@
-## BinaryConvolution<a name="BinaryConvolution"></a> {#openvino_docs_ops_convolution_BinaryConvolution_1}
+# BinaryConvolution {#openvino_docs_ops_convolution_BinaryConvolution_1}
 
 **Versioned name**: *BinaryConvolution-1*
 
diff --git a/docs/ops/convolution/ConvolutionBackpropData_1.md b/docs/ops/convolution/ConvolutionBackpropData_1.md
index a630a113cf7..fadcc01ed08 100644
--- a/docs/ops/convolution/ConvolutionBackpropData_1.md
+++ b/docs/ops/convolution/ConvolutionBackpropData_1.md
@@ -1,4 +1,4 @@
-## ConvolutionBackpropData<a name="ConvolutionBackpropData"></a> {#openvino_docs_ops_convolution_ConvolutionBackpropData_1}
+# ConvolutionBackpropData {#openvino_docs_ops_convolution_ConvolutionBackpropData_1}
 
 **Versioned name**: *ConvolutionBackpropData-1*
 
diff --git a/docs/ops/convolution/Convolution_1.md b/docs/ops/convolution/Convolution_1.md
index 068bad8b210..43cb3161edd 100644
--- a/docs/ops/convolution/Convolution_1.md
+++ b/docs/ops/convolution/Convolution_1.md
@@ -1,4 +1,4 @@
-## Convolution <a name="Convolution"></a> {#openvino_docs_ops_convolution_Convolution_1}
+# Convolution {#openvino_docs_ops_convolution_Convolution_1}
 
 **Versioned name**: *Convolution-1*
 
diff --git a/docs/ops/convolution/DeformableConvolution_1.md b/docs/ops/convolution/DeformableConvolution_1.md
index ed64bb0e71c..80938a0a97d 100644
--- a/docs/ops/convolution/DeformableConvolution_1.md
+++ b/docs/ops/convolution/DeformableConvolution_1.md
@@ -1,4 +1,4 @@
-## DeformableConvolution<a name="DeformableConvolution"></a> {#openvino_docs_ops_convolution_DeformableConvolution_1}
+# DeformableConvolution {#openvino_docs_ops_convolution_DeformableConvolution_1}
 
 **Versioned name**: *DeformableConvolution-1*
 
diff --git a/docs/ops/convolution/GroupConvolutionBackpropData_1.md b/docs/ops/convolution/GroupConvolutionBackpropData_1.md
index 94f7bdf267c..f98865fb47b 100644
--- a/docs/ops/convolution/GroupConvolutionBackpropData_1.md
+++ b/docs/ops/convolution/GroupConvolutionBackpropData_1.md
@@ -1,4 +1,4 @@
-## GroupConvolutionBackpropData<a name="GroupConvolutionBackpropData"></a> {#openvino_docs_ops_convolution_GroupConvolutionBackpropData_1}
+# GroupConvolutionBackpropData {#openvino_docs_ops_convolution_GroupConvolutionBackpropData_1}
 
 **Versioned name**: *GroupConvolutionBackpropData-1*
 
diff --git a/docs/ops/convolution/GroupConvolution_1.md b/docs/ops/convolution/GroupConvolution_1.md
index 9739274147c..9a5df5df547 100644
--- a/docs/ops/convolution/GroupConvolution_1.md
+++ b/docs/ops/convolution/GroupConvolution_1.md
@@ -1,4 +1,4 @@
-## GroupConvolution <a name="GroupConvolution"></a> {#openvino_docs_ops_convolution_GroupConvolution_1}
+# GroupConvolution  {#openvino_docs_ops_convolution_GroupConvolution_1}
 
 **Versioned name**: *GroupConvolution-1*
 
diff --git a/docs/ops/detection/DeformablePSROIPooling_1.md b/docs/ops/detection/DeformablePSROIPooling_1.md
index 1d4fbfb8abe..d81290fd050 100644
--- a/docs/ops/detection/DeformablePSROIPooling_1.md
+++ b/docs/ops/detection/DeformablePSROIPooling_1.md
@@ -1,4 +1,4 @@
-## DeformablePSROIPooling <a name="DeformablePSROIPooling"></a> {#openvino_docs_ops_detection_DeformablePSROIPooling_1}
+# DeformablePSROIPooling {#openvino_docs_ops_detection_DeformablePSROIPooling_1}
 
 **Versioned name**: *DeformablePSROIPooling-1*
 
diff --git a/docs/ops/detection/DetectionOutput_1.md b/docs/ops/detection/DetectionOutput_1.md
index 1a7eb285157..2278619ec15 100644
--- a/docs/ops/detection/DetectionOutput_1.md
+++ b/docs/ops/detection/DetectionOutput_1.md
@@ -1,4 +1,4 @@
-## DetectionOutput <a name="DetectionOutput"></a> {#openvino_docs_ops_detection_DetectionOutput_1}
+# DetectionOutput {#openvino_docs_ops_detection_DetectionOutput_1}
 
 **Versioned name**: *DetectionOutput-1*
 
diff --git a/docs/ops/detection/DetectionOutput_8.md b/docs/ops/detection/DetectionOutput_8.md
index c4138f2ec8f..87c6dbba724 100644
--- a/docs/ops/detection/DetectionOutput_8.md
+++ b/docs/ops/detection/DetectionOutput_8.md
@@ -1,4 +1,4 @@
-## DetectionOutput <a name="DetectionOutput"></a> {#openvino_docs_ops_detection_DetectionOutput_8}
+# DetectionOutput {#openvino_docs_ops_detection_DetectionOutput_8}
 
 **Versioned name**: *DetectionOutput-8*
 
@@ -21,7 +21,7 @@ yielding \f$(c + 4)kmn\f$ outputs for a *m \* n* feature map.
 
 **Attributes**:
 
-**NOTE:** *num_classes*, a number of classes attribute, presents in [DetectionOutput_1](DetectionOutput_1.md) has been removed.
+**NOTE**: *num_classes*, a number of classes attribute, presents in [DetectionOutput_1](DetectionOutput_1.md) has been removed.
 It can be computed as `cls_pred_shape[-1] // num_prior_boxes` where `cls_pred_shape` and `num_prior_boxes` are class predictions tensor shape and
 a number of prior boxes.
 
diff --git a/docs/ops/detection/ExperimentalDetectronDetectionOutput_6.md b/docs/ops/detection/ExperimentalDetectronDetectionOutput_6.md
index a1008ec14be..7bbe7bf7ab6 100644
--- a/docs/ops/detection/ExperimentalDetectronDetectionOutput_6.md
+++ b/docs/ops/detection/ExperimentalDetectronDetectionOutput_6.md
@@ -1,4 +1,4 @@
-## ExperimentalDetectronDetectionOutput <a name="ExperimentalDetectronDetectionOutput"></a> {#openvino_docs_ops_detection_ExperimentalDetectronDetectionOutput_6}
+# ExperimentalDetectronDetectionOutput {#openvino_docs_ops_detection_ExperimentalDetectronDetectionOutput_6}
 
 **Versioned name**: *ExperimentalDetectronDetectionOutput-6*
 
diff --git a/docs/ops/detection/ExperimentalDetectronGenerateProposalsSingleImage_6.md b/docs/ops/detection/ExperimentalDetectronGenerateProposalsSingleImage_6.md
index 915a269d10f..5a9ec0b5b96 100644
--- a/docs/ops/detection/ExperimentalDetectronGenerateProposalsSingleImage_6.md
+++ b/docs/ops/detection/ExperimentalDetectronGenerateProposalsSingleImage_6.md
@@ -1,4 +1,4 @@
-## ExperimentalDetectronGenerateProposalsSingleImage <a name="ExperimentalDetectronGenerateProposalsSingleImage"></a> {#openvino_docs_ops_detection_ExperimentalDetectronGenerateProposalsSingleImage_6}
+# ExperimentalDetectronGenerateProposalsSingleImage {#openvino_docs_ops_detection_ExperimentalDetectronGenerateProposalsSingleImage_6}
 
 **Versioned name**: *ExperimentalDetectronGenerateProposalsSingleImage-6*
 
diff --git a/docs/ops/detection/ExperimentalDetectronPriorGridGenerator_6.md b/docs/ops/detection/ExperimentalDetectronPriorGridGenerator_6.md
index 31078c09516..336d6b5d52f 100644
--- a/docs/ops/detection/ExperimentalDetectronPriorGridGenerator_6.md
+++ b/docs/ops/detection/ExperimentalDetectronPriorGridGenerator_6.md
@@ -1,4 +1,4 @@
-## ExperimentalDetectronPriorGridGenerator <a name="ExperimentalDetectronPriorGridGenerator"></a> {#openvino_docs_ops_detection_ExperimentalDetectronPriorGridGenerator_6}
+# ExperimentalDetectronPriorGridGenerator {#openvino_docs_ops_detection_ExperimentalDetectronPriorGridGenerator_6}
 
 **Versioned name**: *ExperimentalDetectronPriorGridGenerator-6*
 
diff --git a/docs/ops/detection/ExperimentalDetectronROIFeatureExtractor_6.md b/docs/ops/detection/ExperimentalDetectronROIFeatureExtractor_6.md
index b6536e71a12..fb67a6fafe9 100644
--- a/docs/ops/detection/ExperimentalDetectronROIFeatureExtractor_6.md
+++ b/docs/ops/detection/ExperimentalDetectronROIFeatureExtractor_6.md
@@ -1,4 +1,4 @@
-## ExperimentalDetectronROIFeatureExtractor <a name="ExperimentalDetectronROIFeatureExtractor"></a> {#openvino_docs_ops_detection_ExperimentalDetectronROIFeatureExtractor_6}
+# ExperimentalDetectronROIFeatureExtractor {#openvino_docs_ops_detection_ExperimentalDetectronROIFeatureExtractor_6}
 
 **Versioned name**: *ExperimentalDetectronROIFeatureExtractor-6*
 
diff --git a/docs/ops/detection/PSROIPooling_1.md b/docs/ops/detection/PSROIPooling_1.md
index 4b4546f7efb..cf4fe8ef735 100644
--- a/docs/ops/detection/PSROIPooling_1.md
+++ b/docs/ops/detection/PSROIPooling_1.md
@@ -1,4 +1,4 @@
-## PSROIPooling <a name="PSROIPooling"></a> {#openvino_docs_ops_detection_PSROIPooling_1}
+# PSROIPooling {#openvino_docs_ops_detection_PSROIPooling_1}
 
 **Versioned name**: *PSROIPooling-1*
 
diff --git a/docs/ops/detection/PriorBoxClustered_1.md b/docs/ops/detection/PriorBoxClustered_1.md
index 1815e5a72b3..5e8783e3fed 100644
--- a/docs/ops/detection/PriorBoxClustered_1.md
+++ b/docs/ops/detection/PriorBoxClustered_1.md
@@ -1,4 +1,4 @@
-## PriorBoxClustered <a name="PriorBoxClustered"></a> {#openvino_docs_ops_detection_PriorBoxClustered_1}
+# PriorBoxClustered {#openvino_docs_ops_detection_PriorBoxClustered_1}
 
 **Versioned name**: *PriorBoxClustered-1*
 
diff --git a/docs/ops/detection/PriorBox_1.md b/docs/ops/detection/PriorBox_1.md
index 39ea4afecb2..8230352baf3 100644
--- a/docs/ops/detection/PriorBox_1.md
+++ b/docs/ops/detection/PriorBox_1.md
@@ -1,4 +1,4 @@
-## PriorBox<a name="PriorBox"></a> {#openvino_docs_ops_detection_PriorBox_1}
+# PriorBox {#openvino_docs_ops_detection_PriorBox_1}
 
 **Versioned name**: *PriorBox-1*
 
diff --git a/docs/ops/detection/PriorBox_8.md b/docs/ops/detection/PriorBox_8.md
index ebefd133232..5880a5b19a7 100644
--- a/docs/ops/detection/PriorBox_8.md
+++ b/docs/ops/detection/PriorBox_8.md
@@ -1,4 +1,4 @@
-## PriorBox<a name="PriorBox"></a> {#openvino_docs_ops_detection_PriorBox_8}
+# PriorBox {#openvino_docs_ops_detection_PriorBox_8}
 
 **Versioned name**: *PriorBox-8*
 
diff --git a/docs/ops/detection/Proposal_1.md b/docs/ops/detection/Proposal_1.md
index ce8cb3ad82a..70d25aff9b3 100644
--- a/docs/ops/detection/Proposal_1.md
+++ b/docs/ops/detection/Proposal_1.md
@@ -1,4 +1,4 @@
-## Proposal <a name="Proposal"></a> {#openvino_docs_ops_detection_Proposal_1}
+# Proposal {#openvino_docs_ops_detection_Proposal_1}
 
 **Versioned name**: *Proposal-1*
 
diff --git a/docs/ops/detection/Proposal_4.md b/docs/ops/detection/Proposal_4.md
index 630c16f79f3..31f94644443 100644
--- a/docs/ops/detection/Proposal_4.md
+++ b/docs/ops/detection/Proposal_4.md
@@ -1,4 +1,4 @@
-## Proposal <a name="Proposal"></a> {#openvino_docs_ops_detection_Proposal_4}
+# Proposal {#openvino_docs_ops_detection_Proposal_4}
 
 **Versioned name**: *Proposal-4*
 
diff --git a/docs/ops/detection/ROIAlign_3.md b/docs/ops/detection/ROIAlign_3.md
index 23ad2911039..22de43ebbb5 100644
--- a/docs/ops/detection/ROIAlign_3.md
+++ b/docs/ops/detection/ROIAlign_3.md
@@ -1,4 +1,4 @@
-## ROIAlign <a name="ROIAlign"></a> {#openvino_docs_ops_detection_ROIAlign_3}
+# ROIAlign {#openvino_docs_ops_detection_ROIAlign_3}
 
 **Versioned name**: *ROIAlign-3*
 
diff --git a/docs/ops/detection/ROIPooling_1.md b/docs/ops/detection/ROIPooling_1.md
index 2597fa3d20b..7cd1be0fcb9 100644
--- a/docs/ops/detection/ROIPooling_1.md
+++ b/docs/ops/detection/ROIPooling_1.md
@@ -1,4 +1,4 @@
-## ROIPooling <a name="ROIPooling"></a> {#openvino_docs_ops_detection_ROIPooling_1}
+# ROIPooling {#openvino_docs_ops_detection_ROIPooling_1}
 
 **Versioned name**: *ROIPooling-1*
 
diff --git a/docs/ops/detection/RegionYolo_1.md b/docs/ops/detection/RegionYolo_1.md
index f669593151d..a90c6905c8c 100644
--- a/docs/ops/detection/RegionYolo_1.md
+++ b/docs/ops/detection/RegionYolo_1.md
@@ -1,4 +1,4 @@
-## RegionYolo <a name="RegionYolo"></a> {#openvino_docs_ops_detection_RegionYolo_1}
+# RegionYolo {#openvino_docs_ops_detection_RegionYolo_1}
 
 **Versioned name**: *RegionYolo-1*
 
diff --git a/docs/ops/detection/ReorgYolo_1.md b/docs/ops/detection/ReorgYolo_1.md
index 0c20ed9ee3b..b9637dd1cc7 100644
--- a/docs/ops/detection/ReorgYolo_1.md
+++ b/docs/ops/detection/ReorgYolo_1.md
@@ -1,4 +1,4 @@
-## ReorgYolo Layer <a name="ReorgYolo"></a> {#openvino_docs_ops_detection_ReorgYolo_1}
+# ReorgYolo Layer {#openvino_docs_ops_detection_ReorgYolo_1}
 
 **Versioned name**: *ReorgYolo-1*
 
diff --git a/docs/ops/generation/Range_1.md b/docs/ops/generation/Range_1.md
index 3852f021594..26813c56999 100644
--- a/docs/ops/generation/Range_1.md
+++ b/docs/ops/generation/Range_1.md
@@ -1,4 +1,4 @@
-## Range<a name="Range"></a> {#openvino_docs_ops_generation_Range_1}
+# Range {#openvino_docs_ops_generation_Range_1}
 
 **Versioned name**: *Range-1*
 
diff --git a/docs/ops/generation/Range_4.md b/docs/ops/generation/Range_4.md
index bf57e13412f..def3f5f7ac7 100644
--- a/docs/ops/generation/Range_4.md
+++ b/docs/ops/generation/Range_4.md
@@ -1,4 +1,4 @@
-## Range<a name="Range"></a> {#openvino_docs_ops_generation_Range_4}
+# Range {#openvino_docs_ops_generation_Range_4}
 
 **Versioned name**: *Range-4*
 
diff --git a/docs/ops/image/I420toBGR_8.md b/docs/ops/image/I420toBGR_8.md
index aa7fad206ce..c3faf0af388 100644
--- a/docs/ops/image/I420toBGR_8.md
+++ b/docs/ops/image/I420toBGR_8.md
@@ -1,4 +1,4 @@
-## I420toBGR <a name="I420toBGR"></a> {#openvino_docs_ops_image_I420toBGR_8}
+# I420toBGR {#openvino_docs_ops_image_I420toBGR_8}
 
 **Versioned name**: *I420toBGR-8*
 
diff --git a/docs/ops/image/I420toRGB_8.md b/docs/ops/image/I420toRGB_8.md
index 6b5aba144e2..5bf71c937e5 100644
--- a/docs/ops/image/I420toRGB_8.md
+++ b/docs/ops/image/I420toRGB_8.md
@@ -1,4 +1,4 @@
-## I420toRGB <a name="I420toRGB"></a> {#openvino_docs_ops_image_I420toRGB_8}
+# I420toRGB {#openvino_docs_ops_image_I420toRGB_8}
 
 **Versioned name**: *I420toRGB-8*
 
diff --git a/docs/ops/image/Interpolate_1.md b/docs/ops/image/Interpolate_1.md
index 4cb3ad21030..dec59de3cb4 100644
--- a/docs/ops/image/Interpolate_1.md
+++ b/docs/ops/image/Interpolate_1.md
@@ -1,4 +1,4 @@
-## Interpolate <a name="Interpolate"></a> {#openvino_docs_ops_image_Interpolate_1}
+# Interpolate {#openvino_docs_ops_image_Interpolate_1}
 
 **Versioned name**: *Interpolate-1*
 
diff --git a/docs/ops/image/Interpolate_4.md b/docs/ops/image/Interpolate_4.md
index ef159ab2b82..920ba422b2f 100644
--- a/docs/ops/image/Interpolate_4.md
+++ b/docs/ops/image/Interpolate_4.md
@@ -1,4 +1,4 @@
-## Interpolate <a name="Interpolate"></a> {#openvino_docs_ops_image_Interpolate_4}
+# Interpolate {#openvino_docs_ops_image_Interpolate_4}
 
 **Versioned name**: *Interpolate-4*
 
diff --git a/docs/ops/image/NV12toBGR_8.md b/docs/ops/image/NV12toBGR_8.md
index f4009a1aa28..3e9dbf65bad 100644
--- a/docs/ops/image/NV12toBGR_8.md
+++ b/docs/ops/image/NV12toBGR_8.md
@@ -1,4 +1,4 @@
-## NV12toBGR <a name="NV12toBGR"></a> {#openvino_docs_ops_image_NV12toBGR_8}
+# NV12toBGR {#openvino_docs_ops_image_NV12toBGR_8}
 
 **Versioned name**: *NV12toBGR-8*
 
diff --git a/docs/ops/image/NV12toRGB_8.md b/docs/ops/image/NV12toRGB_8.md
index 0d5b3480443..2d8911138f7 100644
--- a/docs/ops/image/NV12toRGB_8.md
+++ b/docs/ops/image/NV12toRGB_8.md
@@ -1,4 +1,4 @@
-## NV12toRGB <a name="NV12toRGB"></a> {#openvino_docs_ops_image_NV12toRGB_8}
+# NV12toRGB {#openvino_docs_ops_image_NV12toRGB_8}
 
 **Versioned name**: *NV12toRGB-8*
 
diff --git a/docs/ops/infrastructure/Assign_3.md b/docs/ops/infrastructure/Assign_3.md
index c7256e09024..e8e264075ba 100644
--- a/docs/ops/infrastructure/Assign_3.md
+++ b/docs/ops/infrastructure/Assign_3.md
@@ -1,4 +1,4 @@
-## Assign <a name="Assign"></a> {#openvino_docs_ops_infrastructure_Assign_3}
+# Assign {#openvino_docs_ops_infrastructure_Assign_3}
 
 **Versioned name**: *Assign-3*
 
diff --git a/docs/ops/infrastructure/Constant_1.md b/docs/ops/infrastructure/Constant_1.md
index b9211590e72..3018057638f 100644
--- a/docs/ops/infrastructure/Constant_1.md
+++ b/docs/ops/infrastructure/Constant_1.md
@@ -1,4 +1,4 @@
-## Constant <a name="Constant"></a> {#openvino_docs_ops_infrastructure_Constant_1}
+# Constant {#openvino_docs_ops_infrastructure_Constant_1}
 
 **Versioned name**: *Constant-1*
 
diff --git a/docs/ops/infrastructure/Loop_5.md b/docs/ops/infrastructure/Loop_5.md
index 3774c1dc68d..d945211fea4 100644
--- a/docs/ops/infrastructure/Loop_5.md
+++ b/docs/ops/infrastructure/Loop_5.md
@@ -1,4 +1,4 @@
-## Loop <a name="Loop"></a> {#openvino_docs_ops_infrastructure_Loop_5}
+# Loop  {#openvino_docs_ops_infrastructure_Loop_5}
 
 **Versioned name**: *Loop-5*
 
diff --git a/docs/ops/infrastructure/Parameter_1.md b/docs/ops/infrastructure/Parameter_1.md
index 373e8ed2b15..326e679750d 100644
--- a/docs/ops/infrastructure/Parameter_1.md
+++ b/docs/ops/infrastructure/Parameter_1.md
@@ -1,4 +1,4 @@
-## Parameter <a name="Parameter"></a> {#openvino_docs_ops_infrastructure_Parameter_1}
+# Parameter  {#openvino_docs_ops_infrastructure_Parameter_1}
 
 **Versioned name**: *Parameter-1*
 
diff --git a/docs/ops/infrastructure/ReadValue_3.md b/docs/ops/infrastructure/ReadValue_3.md
index 604f577344b..6504816c2c0 100644
--- a/docs/ops/infrastructure/ReadValue_3.md
+++ b/docs/ops/infrastructure/ReadValue_3.md
@@ -1,4 +1,4 @@
-## ReadValue <a name="ReadValue"></a> {#openvino_docs_ops_infrastructure_ReadValue_3}
+# ReadValue {#openvino_docs_ops_infrastructure_ReadValue_3}
 
 **Versioned name**: *ReadValue-3*
 
diff --git a/docs/ops/infrastructure/Result_1.md b/docs/ops/infrastructure/Result_1.md
index b34d4a4436c..f6d48748aa8 100644
--- a/docs/ops/infrastructure/Result_1.md
+++ b/docs/ops/infrastructure/Result_1.md
@@ -1,4 +1,4 @@
-## Result <a name="Result"></a> {#openvino_docs_ops_infrastructure_Result_1}
+# Result {#openvino_docs_ops_infrastructure_Result_1}
 
 **Versioned name**: *Result-1*
 
diff --git a/docs/ops/infrastructure/TensorIterator_1.md b/docs/ops/infrastructure/TensorIterator_1.md
index 996b0b1d321..60310734d04 100644
--- a/docs/ops/infrastructure/TensorIterator_1.md
+++ b/docs/ops/infrastructure/TensorIterator_1.md
@@ -1,4 +1,4 @@
-## TensorIterator <a name="TensorIterator"></a> {#openvino_docs_ops_infrastructure_TensorIterator_1}
+# TensorIterator {#openvino_docs_ops_infrastructure_TensorIterator_1}
 
 **Versioned name**: *TensorIterator-1*
 
diff --git a/docs/ops/logical/LogicalAnd_1.md b/docs/ops/logical/LogicalAnd_1.md
index b8c935a7769..d77877bb80b 100644
--- a/docs/ops/logical/LogicalAnd_1.md
+++ b/docs/ops/logical/LogicalAnd_1.md
@@ -1,4 +1,4 @@
-## LogicalAnd <a name="LogicalAnd"></a> {#openvino_docs_ops_logical_LogicalAnd_1}
+# LogicalAnd {#openvino_docs_ops_logical_LogicalAnd_1}
 
 **Versioned name**: *LogicalAnd-1*
 
diff --git a/docs/ops/logical/LogicalNot_1.md b/docs/ops/logical/LogicalNot_1.md
index 9c5f35081cc..82a485b7860 100644
--- a/docs/ops/logical/LogicalNot_1.md
+++ b/docs/ops/logical/LogicalNot_1.md
@@ -1,4 +1,4 @@
-## LogicalNot <a name="LogicalNot"></a> {#openvino_docs_ops_logical_LogicalNot_1}
+# LogicalNot {#openvino_docs_ops_logical_LogicalNot_1}
 
 **Versioned name**: *LogicalNot-1*
 
diff --git a/docs/ops/logical/LogicalOr_1.md b/docs/ops/logical/LogicalOr_1.md
index 54c6344b0c5..bed4e71c6b5 100644
--- a/docs/ops/logical/LogicalOr_1.md
+++ b/docs/ops/logical/LogicalOr_1.md
@@ -1,4 +1,4 @@
-## LogicalOr <a name="LogicalOr"></a> {#openvino_docs_ops_logical_LogicalOr_1}
+# LogicalOr {#openvino_docs_ops_logical_LogicalOr_1}
 
 **Versioned name**: *LogicalOr-1*
 
diff --git a/docs/ops/logical/LogicalXor_1.md b/docs/ops/logical/LogicalXor_1.md
index 52f25caae67..5eba95c887e 100644
--- a/docs/ops/logical/LogicalXor_1.md
+++ b/docs/ops/logical/LogicalXor_1.md
@@ -1,4 +1,4 @@
-## LogicalXor <a name="LogicalXor"></a> {#openvino_docs_ops_logical_LogicalXor_1}
+# LogicalXor {#openvino_docs_ops_logical_LogicalXor_1}
 
 **Versioned name**: *LogicalXor-1*
 
diff --git a/docs/ops/matrix/Einsum_7.md b/docs/ops/matrix/Einsum_7.md
index 94cb1387282..2e4325dc685 100644
--- a/docs/ops/matrix/Einsum_7.md
+++ b/docs/ops/matrix/Einsum_7.md
@@ -1,4 +1,4 @@
-## Einsum <a name="Einsum"></a> {#openvino_docs_ops_matrix_Einsum_7}
+# Einsum {#openvino_docs_ops_matrix_Einsum_7}
 
 **Versioned name**: *Einsum-7*
 
diff --git a/docs/ops/matrix/MatMul_1.md b/docs/ops/matrix/MatMul_1.md
index 33ed6695324..4c5b555df91 100644
--- a/docs/ops/matrix/MatMul_1.md
+++ b/docs/ops/matrix/MatMul_1.md
@@ -1,4 +1,4 @@
-## MatMul <a name="MatMul"></a> {#openvino_docs_ops_matrix_MatMul_1}
+# MatMul {#openvino_docs_ops_matrix_MatMul_1}
 
 **Versioned name**: *MatMul-1*
 
diff --git a/docs/ops/movement/BatchToSpace_2.md b/docs/ops/movement/BatchToSpace_2.md
index 8ebe11861d1..7649897a9f9 100644
--- a/docs/ops/movement/BatchToSpace_2.md
+++ b/docs/ops/movement/BatchToSpace_2.md
@@ -1,4 +1,4 @@
-## BatchToSpace <a name="BatchToSpace"></a> {#openvino_docs_ops_movement_BatchToSpace_2}
+# BatchToSpace {#openvino_docs_ops_movement_BatchToSpace_2}
 
 **Versioned name**: *BatchToSpace-2*
 
diff --git a/docs/ops/movement/Broadcast_1.md b/docs/ops/movement/Broadcast_1.md
index be1db9cc658..7d187d8b251 100644
--- a/docs/ops/movement/Broadcast_1.md
+++ b/docs/ops/movement/Broadcast_1.md
@@ -1,4 +1,4 @@
-## Broadcast <a name="Broadcast"></a> {#openvino_docs_ops_movement_Broadcast_1}
+# Broadcast {#openvino_docs_ops_movement_Broadcast_1}
 
 **Versioned name**: *Broadcast-1*
 
diff --git a/docs/ops/movement/Broadcast_3.md b/docs/ops/movement/Broadcast_3.md
index cd24431324a..9b389293f5e 100644
--- a/docs/ops/movement/Broadcast_3.md
+++ b/docs/ops/movement/Broadcast_3.md
@@ -1,4 +1,4 @@
-## Broadcast <a name="Broadcast"></a> {#openvino_docs_ops_movement_Broadcast_3}
+# Broadcast {#openvino_docs_ops_movement_Broadcast_3}
 
 **Versioned name**: *Broadcast-3*
 
diff --git a/docs/ops/movement/Concat_1.md b/docs/ops/movement/Concat_1.md
index 371ae8bd9a8..a6f71c2e9c6 100644
--- a/docs/ops/movement/Concat_1.md
+++ b/docs/ops/movement/Concat_1.md
@@ -1,4 +1,4 @@
-## Concat <a name="Concat"></a> {#openvino_docs_ops_movement_Concat_1}
+# Concat {#openvino_docs_ops_movement_Concat_1}
 
 **Versioned name**: *Concat-1*
 
diff --git a/docs/ops/movement/DepthToSpace_1.md b/docs/ops/movement/DepthToSpace_1.md
index 0e9252288ed..cbca9e70f13 100644
--- a/docs/ops/movement/DepthToSpace_1.md
+++ b/docs/ops/movement/DepthToSpace_1.md
@@ -1,4 +1,4 @@
-## DepthToSpace <a name="DepthToSpace"></a> {#openvino_docs_ops_movement_DepthToSpace_1}
+# DepthToSpace {#openvino_docs_ops_movement_DepthToSpace_1}
 
 **Versioned name**: *DepthToSpace-1*
 
diff --git a/docs/ops/movement/ExtractImagePatches_3.md b/docs/ops/movement/ExtractImagePatches_3.md
index fc1d724f701..c312caac007 100644
--- a/docs/ops/movement/ExtractImagePatches_3.md
+++ b/docs/ops/movement/ExtractImagePatches_3.md
@@ -1,4 +1,4 @@
-## ExtractImagePatches <a name="ExtractImagePatches"></a> {#openvino_docs_ops_movement_ExtractImagePatches_3}
+# ExtractImagePatches {#openvino_docs_ops_movement_ExtractImagePatches_3}
 
 **Versioned name**: *ExtractImagePatches-3*
 
diff --git a/docs/ops/movement/GatherElements_6.md b/docs/ops/movement/GatherElements_6.md
index 153b52e89c1..3a88cec16a3 100644
--- a/docs/ops/movement/GatherElements_6.md
+++ b/docs/ops/movement/GatherElements_6.md
@@ -1,4 +1,4 @@
-## GatherElements <a name="GatherElements"></a> {#openvino_docs_ops_movement_GatherElements_6}
+# GatherElements {#openvino_docs_ops_movement_GatherElements_6}
 
 **Versioned name**: *GatherElements-6*
 
diff --git a/docs/ops/movement/GatherND_5.md b/docs/ops/movement/GatherND_5.md
index cf8dc097021..2f546b94e46 100644
--- a/docs/ops/movement/GatherND_5.md
+++ b/docs/ops/movement/GatherND_5.md
@@ -1,4 +1,4 @@
-## GatherND <a name="GatherND"></a> {#openvino_docs_ops_movement_GatherND_5}
+# GatherND {#openvino_docs_ops_movement_GatherND_5}
 
 **Versioned name**: *GatherND-5*
 
diff --git a/docs/ops/movement/GatherND_8.md b/docs/ops/movement/GatherND_8.md
index 1b3944ef4d8..0fde20c5c75 100644
--- a/docs/ops/movement/GatherND_8.md
+++ b/docs/ops/movement/GatherND_8.md
@@ -1,4 +1,4 @@
-## GatherND <a name="GatherND"></a> {#openvino_docs_ops_movement_GatherND_8}
+# GatherND {#openvino_docs_ops_movement_GatherND_8}
 
 **Versioned name**: *GatherND-8*
 
diff --git a/docs/ops/movement/GatherTree_1.md b/docs/ops/movement/GatherTree_1.md
index 5c4441b37b3..d8ddff60af7 100644
--- a/docs/ops/movement/GatherTree_1.md
+++ b/docs/ops/movement/GatherTree_1.md
@@ -1,4 +1,4 @@
-## GatherTree <a name="GatherTree"></a> {#openvino_docs_ops_movement_GatherTree_1}
+# GatherTree {#openvino_docs_ops_movement_GatherTree_1}
 
 **Versioned name**: *GatherTree-1*
 
diff --git a/docs/ops/movement/Gather_1.md b/docs/ops/movement/Gather_1.md
index d105a1dfd9b..d0b48f30666 100644
--- a/docs/ops/movement/Gather_1.md
+++ b/docs/ops/movement/Gather_1.md
@@ -1,4 +1,4 @@
-## Gather <a name="Gather"></a> {#openvino_docs_ops_movement_Gather_1}
+# Gather {#openvino_docs_ops_movement_Gather_1}
 
 **Versioned name**: *Gather-1*
 
diff --git a/docs/ops/movement/Gather_7.md b/docs/ops/movement/Gather_7.md
index 19e5854f0ca..2d6e67dda59 100644
--- a/docs/ops/movement/Gather_7.md
+++ b/docs/ops/movement/Gather_7.md
@@ -1,4 +1,4 @@
-## Gather <a name="Gather"></a> {#openvino_docs_ops_movement_Gather_7}
+# Gather {#openvino_docs_ops_movement_Gather_7}
 
 **Versioned name**: *Gather-7*
 
diff --git a/docs/ops/movement/Pad_1.md b/docs/ops/movement/Pad_1.md
index 5aa1fc6fdb2..e91f2bbd3ac 100644
--- a/docs/ops/movement/Pad_1.md
+++ b/docs/ops/movement/Pad_1.md
@@ -1,4 +1,4 @@
-## Pad <a name="Pad"></a> {#openvino_docs_ops_movement_Pad_1}
+# Pad {#openvino_docs_ops_movement_Pad_1}
 
 **Versioned name**: *Pad-1*
 
diff --git a/docs/ops/movement/ReverseSequence_1.md b/docs/ops/movement/ReverseSequence_1.md
index 63c4fa10e5c..0f7a67775b6 100644
--- a/docs/ops/movement/ReverseSequence_1.md
+++ b/docs/ops/movement/ReverseSequence_1.md
@@ -1,4 +1,4 @@
-## ReverseSequence <a name="ReverseSequence"></a> {#openvino_docs_ops_movement_ReverseSequence_1}
+# ReverseSequence {#openvino_docs_ops_movement_ReverseSequence_1}
 
 **Versioned name**: *ReverseSequence-1*
 
diff --git a/docs/ops/movement/Reverse_1.md b/docs/ops/movement/Reverse_1.md
index 31f04969953..8c292d9cf2c 100644
--- a/docs/ops/movement/Reverse_1.md
+++ b/docs/ops/movement/Reverse_1.md
@@ -1,4 +1,4 @@
-## Reverse <a name="Reverse"></a> {#openvino_docs_ops_movement_Reverse_1}
+# Reverse {#openvino_docs_ops_movement_Reverse_1}
 
 **Versioned name**: *Reverse-1*
 
diff --git a/docs/ops/movement/Roll_7.md b/docs/ops/movement/Roll_7.md
index fc58a033926..345dec77391 100644
--- a/docs/ops/movement/Roll_7.md
+++ b/docs/ops/movement/Roll_7.md
@@ -1,4 +1,4 @@
-## Roll <a name="Roll"></a> {#openvino_docs_ops_movement_Roll_7}
+# Roll {#openvino_docs_ops_movement_Roll_7}
 
 **Versioned name**: *Roll-7*
 
diff --git a/docs/ops/movement/ScatterElementsUpdate_3.md b/docs/ops/movement/ScatterElementsUpdate_3.md
index 271e98a0b08..acf7512c377 100644
--- a/docs/ops/movement/ScatterElementsUpdate_3.md
+++ b/docs/ops/movement/ScatterElementsUpdate_3.md
@@ -1,4 +1,4 @@
-## ScatterElementsUpdate <a name="ScatterElementsUpdate"></a> {#openvino_docs_ops_movement_ScatterElementsUpdate_3}
+# ScatterElementsUpdate {#openvino_docs_ops_movement_ScatterElementsUpdate_3}
 
 **Versioned name**: *ScatterElementsUpdate-3*
 
diff --git a/docs/ops/movement/ScatterNDUpdate_3.md b/docs/ops/movement/ScatterNDUpdate_3.md
index 242d4d9b37d..692694a12d8 100644
--- a/docs/ops/movement/ScatterNDUpdate_3.md
+++ b/docs/ops/movement/ScatterNDUpdate_3.md
@@ -1,4 +1,4 @@
-## ScatterNDUpdate <a name="ScatterNDUpdate"></a> {#openvino_docs_ops_movement_ScatterNDUpdate_3}
+# ScatterNDUpdate {#openvino_docs_ops_movement_ScatterNDUpdate_3}
 
 **Versioned name**: *ScatterNDUpdate-3*
 
diff --git a/docs/ops/movement/ScatterUpdate_3.md b/docs/ops/movement/ScatterUpdate_3.md
index 1ba52d09e3e..c27309a8cf1 100644
--- a/docs/ops/movement/ScatterUpdate_3.md
+++ b/docs/ops/movement/ScatterUpdate_3.md
@@ -1,4 +1,4 @@
-## ScatterUpdate <a name="ScatterUpdate"></a> {#openvino_docs_ops_movement_ScatterUpdate_3}
+# ScatterUpdate {#openvino_docs_ops_movement_ScatterUpdate_3}
 
 **Versioned name**: *ScatterUpdate-3*
 
diff --git a/docs/ops/movement/ShuffleChannels_1.md b/docs/ops/movement/ShuffleChannels_1.md
index 47f3518746c..e5fd0b1f594 100644
--- a/docs/ops/movement/ShuffleChannels_1.md
+++ b/docs/ops/movement/ShuffleChannels_1.md
@@ -1,4 +1,4 @@
-## ShuffleChannels <a name="ShuffleChannels"></a> {#openvino_docs_ops_movement_ShuffleChannels_1}
+# ShuffleChannels {#openvino_docs_ops_movement_ShuffleChannels_1}
 
 **Versioned name**: *ShuffleChannels-1*
 
diff --git a/docs/ops/movement/SpaceToBatch_2.md b/docs/ops/movement/SpaceToBatch_2.md
index 0820999ba2f..0b824534b79 100644
--- a/docs/ops/movement/SpaceToBatch_2.md
+++ b/docs/ops/movement/SpaceToBatch_2.md
@@ -1,4 +1,4 @@
-## SpaceToBatch <a name="SpaceToBatch"></a> {#openvino_docs_ops_movement_SpaceToBatch_2}
+# SpaceToBatch {#openvino_docs_ops_movement_SpaceToBatch_2}
 
 **Versioned name**: *SpaceToBatch-2*
 
diff --git a/docs/ops/movement/SpaceToDepth_1.md b/docs/ops/movement/SpaceToDepth_1.md
index 6733faefbd8..ae44ab0d7f5 100644
--- a/docs/ops/movement/SpaceToDepth_1.md
+++ b/docs/ops/movement/SpaceToDepth_1.md
@@ -1,4 +1,4 @@
-## SpaceToDepth <a name="SpaceToDepth"></a> {#openvino_docs_ops_movement_SpaceToDepth_1}
+# SpaceToDepth {#openvino_docs_ops_movement_SpaceToDepth_1}
 
 **Versioned name**: *SpaceToDepth-1*
 
diff --git a/docs/ops/movement/Split_1.md b/docs/ops/movement/Split_1.md
index 0f78b20e0e0..dbdbfe653a8 100644
--- a/docs/ops/movement/Split_1.md
+++ b/docs/ops/movement/Split_1.md
@@ -1,4 +1,4 @@
-## Split <a name="Split"></a> {#openvino_docs_ops_movement_Split_1}
+# Split {#openvino_docs_ops_movement_Split_1}
 
 **Versioned name**: *Split-1*
 
diff --git a/docs/ops/movement/StridedSlice_1.md b/docs/ops/movement/StridedSlice_1.md
index 238bd4c0c36..e39d83459f9 100644
--- a/docs/ops/movement/StridedSlice_1.md
+++ b/docs/ops/movement/StridedSlice_1.md
@@ -1,4 +1,4 @@
-## StridedSlice <a name="StridedSlice"></a> {#openvino_docs_ops_movement_StridedSlice_1}
+# StridedSlice {#openvino_docs_ops_movement_StridedSlice_1}
 
 **Versioned name**: *StridedSlice-1*
 
diff --git a/docs/ops/movement/Tile_1.md b/docs/ops/movement/Tile_1.md
index 76ba4f58490..4c7f35cc30a 100644
--- a/docs/ops/movement/Tile_1.md
+++ b/docs/ops/movement/Tile_1.md
@@ -1,4 +1,4 @@
-## Tile<a name="Tile"></a> {#openvino_docs_ops_movement_Tile_1}
+# Tile {#openvino_docs_ops_movement_Tile_1}
 
 **Versioned name**: *Tile-1*
 
diff --git a/docs/ops/movement/Transpose_1.md b/docs/ops/movement/Transpose_1.md
index d60387dd8b3..6f6799e7610 100644
--- a/docs/ops/movement/Transpose_1.md
+++ b/docs/ops/movement/Transpose_1.md
@@ -1,4 +1,4 @@
-## Transpose<a name="Transpose"></a> {#openvino_docs_ops_movement_Transpose_1}
+# Transpose {#openvino_docs_ops_movement_Transpose_1}
 
 **Versioned name**: *Transpose-1*
 
diff --git a/docs/ops/movement/VariadicSplit_1.md b/docs/ops/movement/VariadicSplit_1.md
index da15f9fc809..6455ea199f9 100644
--- a/docs/ops/movement/VariadicSplit_1.md
+++ b/docs/ops/movement/VariadicSplit_1.md
@@ -1,4 +1,4 @@
-## VariadicSplit <a name="VariadicSplit"></a> {#openvino_docs_ops_movement_VariadicSplit_1}
+# VariadicSplit {#openvino_docs_ops_movement_VariadicSplit_1}
 
 **Versioned name**: *VariadicSplit-1*
 
diff --git a/docs/ops/normalization/BatchNormInference_1.md b/docs/ops/normalization/BatchNormInference_1.md
index a0a9cac3fa1..b9aee39e7d1 100644
--- a/docs/ops/normalization/BatchNormInference_1.md
+++ b/docs/ops/normalization/BatchNormInference_1.md
@@ -1,4 +1,4 @@
-## BatchNormInference <a name="BatchNormInference"></a> {#openvino_docs_ops_normalization_BatchNormInference_1}
+# BatchNormInference {#openvino_docs_ops_normalization_BatchNormInference_1}
 
 **Versioned name**: *BatchNormInference-1*
 
diff --git a/docs/ops/normalization/BatchNormInference_5.md b/docs/ops/normalization/BatchNormInference_5.md
index 61a9c252603..c6bf50f92b8 100644
--- a/docs/ops/normalization/BatchNormInference_5.md
+++ b/docs/ops/normalization/BatchNormInference_5.md
@@ -1,4 +1,4 @@
-## BatchNormInference <a name="BatchNormInference"></a> {#openvino_docs_ops_normalization_BatchNormInference_5}
+# BatchNormInference {#openvino_docs_ops_normalization_BatchNormInference_5}
 
 **Versioned name**: *BatchNormInference-5*
 
diff --git a/docs/ops/normalization/GRN_1.md b/docs/ops/normalization/GRN_1.md
index de796e681a7..2c7c7c2ba2a 100644
--- a/docs/ops/normalization/GRN_1.md
+++ b/docs/ops/normalization/GRN_1.md
@@ -1,4 +1,4 @@
-## GRN <a name="GRN"></a> {#openvino_docs_ops_normalization_GRN_1}
+# GRN {#openvino_docs_ops_normalization_GRN_1}
 
 **Versioned name**: *GRN-1*
 
diff --git a/docs/ops/normalization/LRN_1.md b/docs/ops/normalization/LRN_1.md
index d5a92f8e64a..47ba7d2295a 100644
--- a/docs/ops/normalization/LRN_1.md
+++ b/docs/ops/normalization/LRN_1.md
@@ -1,4 +1,4 @@
-## LRN <a name="LRN"></a> {#openvino_docs_ops_normalization_LRN_1}
+# LRN {#openvino_docs_ops_normalization_LRN_1}
 
 **Versioned name**: *LRN-1*
 
diff --git a/docs/ops/normalization/MVN_1.md b/docs/ops/normalization/MVN_1.md
index a82c9a9ca40..07b93713b34 100644
--- a/docs/ops/normalization/MVN_1.md
+++ b/docs/ops/normalization/MVN_1.md
@@ -1,4 +1,4 @@
-## MVN <a name="MVN"></a> {#openvino_docs_ops_normalization_MVN_1}
+# MVN {#openvino_docs_ops_normalization_MVN_1}
 
 **Versioned name**: *MVN-1*
 
diff --git a/docs/ops/normalization/MVN_6.md b/docs/ops/normalization/MVN_6.md
index f89cf60e92d..313a4fa1e9c 100644
--- a/docs/ops/normalization/MVN_6.md
+++ b/docs/ops/normalization/MVN_6.md
@@ -1,4 +1,4 @@
-## MVN <a name="MVN"></a> {#openvino_docs_ops_normalization_MVN_6}
+# MVN {#openvino_docs_ops_normalization_MVN_6}
 
 **Versioned name**: *MVN-6*
 
diff --git a/docs/ops/normalization/NormalizeL2_1.md b/docs/ops/normalization/NormalizeL2_1.md
index 4668519030f..ed8b2909055 100644
--- a/docs/ops/normalization/NormalizeL2_1.md
+++ b/docs/ops/normalization/NormalizeL2_1.md
@@ -1,4 +1,4 @@
-## NormalizeL2 <a name="NormalizeL2"></a> {#openvino_docs_ops_normalization_NormalizeL2_1}
+# NormalizeL2 {#openvino_docs_ops_normalization_NormalizeL2_1}
 
 **Versioned name**: *NormalizeL2-1*
 
diff --git a/docs/ops/opset.md b/docs/ops/opset.md
index e114f5b2267..af166242766 100644
--- a/docs/ops/opset.md
+++ b/docs/ops/opset.md
@@ -1,11 +1,29 @@
 # Available Operations Sets {#openvino_docs_ops_opset}
 
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_ops_opset8
+   openvino_docs_ops_opset7
+   openvino_docs_ops_opset6
+   openvino_docs_ops_opset5
+   openvino_docs_ops_opset4
+   openvino_docs_ops_opset3
+   openvino_docs_ops_opset2
+   openvino_docs_ops_opset1
+      
+@endsphinxdirective
+
 According to capabilities of supported deep learning frameworks and hardware capabilities of a target inference device, all operations are combined into operations sets each fully supported in a specific version of OpenVINO™ toolkit. 
 
 This topic provides a complete list of available sets of operations supported in different versions of OpenVINO™ toolkit. It's highly recommended to use the actual version of the operations set for a particular release. For a list of operations included into an operations set, click a link in the table.  
 
 | OpenVINO™ Version | Actual Operations Set            | 
 | :---------------- | :------------------------------- | 
+| 2022.1            | [opset8](opset8.md)   |
 | 2021.4            | [opset7](opset7.md)   | 
 | 2021.3            | [opset6](opset6.md)   | 
 | 2021.2            | [opset5](opset5.md)   | 
diff --git a/docs/ops/opset1.md b/docs/ops/opset1.md
index eec109ad9c3..3eee791065d 100644
--- a/docs/ops/opset1.md
+++ b/docs/ops/opset1.md
@@ -1,4 +1,4 @@
-# Operation Set `opset1` Specification {#openvino_docs_ops_opset1}
+# opset1 {#openvino_docs_ops_opset1}
 
 This specification document describes `opset1` operation set supported in OpenVINO.
 Support for each particular operation from the list below depends on the capabilities available in a inference plugin
diff --git a/docs/ops/opset2.md b/docs/ops/opset2.md
index 67c51385a78..04f9dfe048f 100644
--- a/docs/ops/opset2.md
+++ b/docs/ops/opset2.md
@@ -1,4 +1,4 @@
-# Operation Set `opset2` Specification {#openvino_docs_ops_opset2}
+# opset2 {#openvino_docs_ops_opset2}
 
 This specification document describes `opset2` operation set supported in OpenVINO.
 Support for each particular operation from the list below depends on the capabilities available in a inference plugin
diff --git a/docs/ops/opset3.md b/docs/ops/opset3.md
index 52af67efc0f..525e25d3449 100644
--- a/docs/ops/opset3.md
+++ b/docs/ops/opset3.md
@@ -1,4 +1,4 @@
-# Operation Set `opset3` Specification {#openvino_docs_ops_opset3}
+# opset3 {#openvino_docs_ops_opset3}
 
 This specification document describes `opset3` operation set supported in OpenVINO.
 Support for each particular operation from the list below depends on the capabilities available in a inference plugin
diff --git a/docs/ops/opset4.md b/docs/ops/opset4.md
index 71607453ee9..b20fe4ac619 100644
--- a/docs/ops/opset4.md
+++ b/docs/ops/opset4.md
@@ -1,4 +1,4 @@
-# Operation Set `opset4` Specification {#openvino_docs_ops_opset4}
+# opset4 {#openvino_docs_ops_opset4}
 
 This specification document describes `opset4` operation set supported in OpenVINO.
 Support for each particular operation from the list below depends on the capabilities available in a inference plugin
diff --git a/docs/ops/opset5.md b/docs/ops/opset5.md
index 6c79caca19b..f980a96a043 100644
--- a/docs/ops/opset5.md
+++ b/docs/ops/opset5.md
@@ -1,4 +1,4 @@
-# Operation Set `opset5` Specification {#openvino_docs_ops_opset5}
+# opset5 {#openvino_docs_ops_opset5}
 
 This specification document describes `opset5` operation set supported in OpenVINO.
 Support for each particular operation from the list below depends on the capabilities available in a inference plugin
diff --git a/docs/ops/opset6.md b/docs/ops/opset6.md
index 92deb4a6fbe..3154484d56e 100644
--- a/docs/ops/opset6.md
+++ b/docs/ops/opset6.md
@@ -1,4 +1,4 @@
-# Operation Set `opset6` Specification {#openvino_docs_ops_opset6}
+# opset6 {#openvino_docs_ops_opset6}
 
 This specification document describes `opset6` operation set supported in OpenVINO.
 Support for each particular operation from the list below depends on the capabilities available in a inference plugin
diff --git a/docs/ops/opset7.md b/docs/ops/opset7.md
index c00935b9451..8a786fc2b39 100644
--- a/docs/ops/opset7.md
+++ b/docs/ops/opset7.md
@@ -1,4 +1,4 @@
-# Operation Set `opset7` Specification {#openvino_docs_ops_opset7}
+# opset7 {#openvino_docs_ops_opset7}
 
 This specification document describes the `opset7` operation set supported in OpenVINO™.
 Support for each particular operation from the list below depends on the capabilities available in an inference plugin
diff --git a/docs/ops/pooling/AvgPool_1.md b/docs/ops/pooling/AvgPool_1.md
index 705c86d5b2d..09c3ee1a513 100644
--- a/docs/ops/pooling/AvgPool_1.md
+++ b/docs/ops/pooling/AvgPool_1.md
@@ -1,4 +1,4 @@
-## AvgPool<a name="AvgPool"></a> {#openvino_docs_ops_pooling_AvgPool_1}
+# AvgPool {#openvino_docs_ops_pooling_AvgPool_1}
 
 **Versioned name**: *AvgPool-1*
 
diff --git a/docs/ops/pooling/MaxPool_1.md b/docs/ops/pooling/MaxPool_1.md
index 9ea1be7380f..734d4cc0ba7 100644
--- a/docs/ops/pooling/MaxPool_1.md
+++ b/docs/ops/pooling/MaxPool_1.md
@@ -1,4 +1,4 @@
-## MaxPool <a name="MaxPool"></a> {#openvino_docs_ops_pooling_MaxPool_1}
+# MaxPool {#openvino_docs_ops_pooling_MaxPool_1}
 
 **Versioned name**: *MaxPool-1*
 
diff --git a/docs/ops/quantization/FakeQuantize_1.md b/docs/ops/quantization/FakeQuantize_1.md
index 6598de09d48..9d24d27317b 100644
--- a/docs/ops/quantization/FakeQuantize_1.md
+++ b/docs/ops/quantization/FakeQuantize_1.md
@@ -1,4 +1,4 @@
-## FakeQuantize <a name="FakeQuantize"></a> {#openvino_docs_ops_quantization_FakeQuantize_1}
+# FakeQuantize {#openvino_docs_ops_quantization_FakeQuantize_1}
 
 **Versioned name**: *FakeQuantize-1*
 
diff --git a/docs/ops/reduction/ReduceL1_4.md b/docs/ops/reduction/ReduceL1_4.md
index bb8305f8319..b00f1b27aa7 100644
--- a/docs/ops/reduction/ReduceL1_4.md
+++ b/docs/ops/reduction/ReduceL1_4.md
@@ -1,4 +1,4 @@
-## ReduceLp <a name="ReduceL1"></a> {#openvino_docs_ops_reduction_ReduceL1_4}
+# ReduceLp {#openvino_docs_ops_reduction_ReduceL1_4}
 
 **Versioned name**: *ReduceL1-4*
 
diff --git a/docs/ops/reduction/ReduceL2_4.md b/docs/ops/reduction/ReduceL2_4.md
index ccf7fb5858d..36f764bc0d8 100644
--- a/docs/ops/reduction/ReduceL2_4.md
+++ b/docs/ops/reduction/ReduceL2_4.md
@@ -1,4 +1,4 @@
-## ReduceLp <a name="ReduceL2"></a> {#openvino_docs_ops_reduction_ReduceL2_4}
+# ReduceLp {#openvino_docs_ops_reduction_ReduceL2_4}
 
 **Versioned name**: *ReduceL2-4*
 
diff --git a/docs/ops/reduction/ReduceLogicalAnd_1.md b/docs/ops/reduction/ReduceLogicalAnd_1.md
index c8718a1a4d3..072f7b55128 100644
--- a/docs/ops/reduction/ReduceLogicalAnd_1.md
+++ b/docs/ops/reduction/ReduceLogicalAnd_1.md
@@ -1,4 +1,4 @@
-## ReduceLogicalAnd <a name="ReduceLogicalAnd"></a> {#openvino_docs_ops_reduction_ReduceLogicalAnd_1}
+# ReduceLogicalAnd {#openvino_docs_ops_reduction_ReduceLogicalAnd_1}
 
 **Versioned name**: *ReduceLogicalAnd-1*
 
diff --git a/docs/ops/reduction/ReduceLogicalOr_1.md b/docs/ops/reduction/ReduceLogicalOr_1.md
index 8506b786613..0fd4c680bcb 100644
--- a/docs/ops/reduction/ReduceLogicalOr_1.md
+++ b/docs/ops/reduction/ReduceLogicalOr_1.md
@@ -1,4 +1,4 @@
-## ReduceLogicalOr <a name="ReduceLogicalOr"></a> {#openvino_docs_ops_reduction_ReduceLogicalOr_1}
+# ReduceLogicalOr {#openvino_docs_ops_reduction_ReduceLogicalOr_1}
 
 **Versioned name**: *ReduceLogicalOr-1*
 
diff --git a/docs/ops/reduction/ReduceMax_1.md b/docs/ops/reduction/ReduceMax_1.md
index 5f0bd684310..dea2977a318 100644
--- a/docs/ops/reduction/ReduceMax_1.md
+++ b/docs/ops/reduction/ReduceMax_1.md
@@ -1,4 +1,4 @@
-## ReduceMax <a name="ReduceMax"></a> {#openvino_docs_ops_reduction_ReduceMax_1}
+# ReduceMax {#openvino_docs_ops_reduction_ReduceMax_1}
 
 **Versioned name**: *ReduceMax-1*
 
diff --git a/docs/ops/reduction/ReduceMean_1.md b/docs/ops/reduction/ReduceMean_1.md
index 3e66f20fb36..be7bb436061 100644
--- a/docs/ops/reduction/ReduceMean_1.md
+++ b/docs/ops/reduction/ReduceMean_1.md
@@ -1,4 +1,4 @@
-## ReduceMean <a name="ReduceMean"></a> {#openvino_docs_ops_reduction_ReduceMean_1}
+# ReduceMean {#openvino_docs_ops_reduction_ReduceMean_1}
 
 **Versioned name**: *ReduceMean-1*
 
diff --git a/docs/ops/reduction/ReduceMin_1.md b/docs/ops/reduction/ReduceMin_1.md
index 3d2a37d7ae2..2fc4ff449f8 100644
--- a/docs/ops/reduction/ReduceMin_1.md
+++ b/docs/ops/reduction/ReduceMin_1.md
@@ -1,4 +1,4 @@
-## ReduceMin <a name="ReduceMin"></a> {#openvino_docs_ops_reduction_ReduceMin_1}
+# ReduceMin {#openvino_docs_ops_reduction_ReduceMin_1}
 
 **Versioned name**: *ReduceMin-1*
 
diff --git a/docs/ops/reduction/ReduceProd_1.md b/docs/ops/reduction/ReduceProd_1.md
index 4571b4ac194..69c3a41701d 100644
--- a/docs/ops/reduction/ReduceProd_1.md
+++ b/docs/ops/reduction/ReduceProd_1.md
@@ -1,4 +1,4 @@
-## ReduceProd <a name="ReduceProd"></a> {#openvino_docs_ops_reduction_ReduceProd_1}
+# ReduceProd {#openvino_docs_ops_reduction_ReduceProd_1}
 
 **Versioned name**: *ReduceProd-1*
 
diff --git a/docs/ops/reduction/ReduceSum_1.md b/docs/ops/reduction/ReduceSum_1.md
index b4cfa0dd16e..1ca610db5b4 100644
--- a/docs/ops/reduction/ReduceSum_1.md
+++ b/docs/ops/reduction/ReduceSum_1.md
@@ -1,4 +1,4 @@
-## ReduceSum <a name="ReduceSum"></a> {#openvino_docs_ops_reduction_ReduceSum_1}
+# ReduceSum {#openvino_docs_ops_reduction_ReduceSum_1}
 
 **Versioned name**: *ReduceSum-1*
 
diff --git a/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md b/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md
index 956ca032179..4bcbf6d312e 100644
--- a/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md
+++ b/docs/ops/sequence/CTCGreedyDecoderSeqLen_6.md
@@ -1,4 +1,4 @@
-## CTCGreedyDecoderSeqLen <a name="CTCGreedyDecoderSeqLen"></a> {#openvino_docs_ops_sequence_CTCGreedyDecoderSeqLen_6}
+# CTCGreedyDecoderSeqLen  {#openvino_docs_ops_sequence_CTCGreedyDecoderSeqLen_6}
 
 **Versioned name**: *CTCGreedyDecoderSeqLen-6*
 
diff --git a/docs/ops/sequence/CTCGreedyDecoder_1.md b/docs/ops/sequence/CTCGreedyDecoder_1.md
index cfaced502a4..5fd281fd84e 100644
--- a/docs/ops/sequence/CTCGreedyDecoder_1.md
+++ b/docs/ops/sequence/CTCGreedyDecoder_1.md
@@ -1,4 +1,4 @@
-## CTCGreedyDecoder <a name="CTCGreedyDecoder"></a> {#openvino_docs_ops_sequence_CTCGreedyDecoder_1}
+# CTCGreedyDecoder {#openvino_docs_ops_sequence_CTCGreedyDecoder_1}
 
 **Versioned name**: *CTCGreedyDecoder-1*
 
diff --git a/docs/ops/sequence/CTCLoss_4.md b/docs/ops/sequence/CTCLoss_4.md
index cb092511077..4f6ad270023 100644
--- a/docs/ops/sequence/CTCLoss_4.md
+++ b/docs/ops/sequence/CTCLoss_4.md
@@ -1,4 +1,4 @@
-## CTCLoss <a name="CTCLoss"></a> {#openvino_docs_ops_sequence_CTCLoss_4}
+# CTCLoss  {#openvino_docs_ops_sequence_CTCLoss_4}
 
 **Versioned name**: *CTCLoss-4*
 
diff --git a/docs/ops/sequence/GRUCell_3.md b/docs/ops/sequence/GRUCell_3.md
index 4453c6c1028..88a072357aa 100644
--- a/docs/ops/sequence/GRUCell_3.md
+++ b/docs/ops/sequence/GRUCell_3.md
@@ -1,4 +1,4 @@
-## GRUCell <a name="GRUCell"></a> {#openvino_docs_ops_sequence_GRUCell_3}
+# GRUCell  {#openvino_docs_ops_sequence_GRUCell_3}
 
 **Versioned name**: *GRUCell-3*
 
diff --git a/docs/ops/sequence/GRUSequence_5.md b/docs/ops/sequence/GRUSequence_5.md
index c090933cb44..bf0b48d5c62 100644
--- a/docs/ops/sequence/GRUSequence_5.md
+++ b/docs/ops/sequence/GRUSequence_5.md
@@ -1,4 +1,4 @@
-## GRUSequence <a name="GRUSequence"></a> {#openvino_docs_ops_sequence_GRUSequence_5}
+# GRUSequence  {#openvino_docs_ops_sequence_GRUSequence_5}
 
 **Versioned name**: *GRUSequence-5*
 
diff --git a/docs/ops/sequence/LSTMCell_1.md b/docs/ops/sequence/LSTMCell_1.md
index dd73b1b7dd9..c6a6c6c10c5 100644
--- a/docs/ops/sequence/LSTMCell_1.md
+++ b/docs/ops/sequence/LSTMCell_1.md
@@ -1,4 +1,4 @@
-## LSTMCell <a name="LSTMCell"></a> {#openvino_docs_ops_sequence_LSTMCell_1}
+# LSTMCell  {#openvino_docs_ops_sequence_LSTMCell_1}
 
 **Versioned name**: *LSTMCell-1*
 
diff --git a/docs/ops/sequence/LSTMSequence_1.md b/docs/ops/sequence/LSTMSequence_1.md
index a759197548a..72c7f589314 100644
--- a/docs/ops/sequence/LSTMSequence_1.md
+++ b/docs/ops/sequence/LSTMSequence_1.md
@@ -1,4 +1,4 @@
-## LSTMSequence <a name="LSTMSequence"></a> {#openvino_docs_ops_sequence_LSTMSequence_1}
+# LSTMSequence {#openvino_docs_ops_sequence_LSTMSequence_1}
 
 **Versioned name**: *LSTMSequence-1*
 
diff --git a/docs/ops/sequence/OneHot_1.md b/docs/ops/sequence/OneHot_1.md
index bc1e37a446f..7462e4985e0 100644
--- a/docs/ops/sequence/OneHot_1.md
+++ b/docs/ops/sequence/OneHot_1.md
@@ -1,4 +1,4 @@
-## OneHot <a name="OneHot"></a> {#openvino_docs_ops_sequence_OneHot_1}
+# OneHot {#openvino_docs_ops_sequence_OneHot_1}
 
 **Versioned name**: *OneHot-1*
 
diff --git a/docs/ops/sequence/RNNCell_3.md b/docs/ops/sequence/RNNCell_3.md
index 874143a097d..58f1b9ddd87 100644
--- a/docs/ops/sequence/RNNCell_3.md
+++ b/docs/ops/sequence/RNNCell_3.md
@@ -1,4 +1,4 @@
-## RNNCell <a name="RNNCell"></a> {#openvino_docs_ops_sequence_RNNCell_3}
+# RNNCell  {#openvino_docs_ops_sequence_RNNCell_3}
 
 **Versioned name**: *RNNCell-3*
 
diff --git a/docs/ops/sequence/RNNSequence_5.md b/docs/ops/sequence/RNNSequence_5.md
index 9bd3ce3add8..e9537b3db28 100644
--- a/docs/ops/sequence/RNNSequence_5.md
+++ b/docs/ops/sequence/RNNSequence_5.md
@@ -1,4 +1,4 @@
-## RNNSequence <a name="RNNSequence"></a> {#openvino_docs_ops_sequence_RNNSequence_5}
+# RNNSequence {#openvino_docs_ops_sequence_RNNSequence_5}
 
 **Versioned name**: *RNNSequence-5*
 
diff --git a/docs/ops/shape/Reshape_1.md b/docs/ops/shape/Reshape_1.md
index 324a2dab9ec..62a451f83eb 100644
--- a/docs/ops/shape/Reshape_1.md
+++ b/docs/ops/shape/Reshape_1.md
@@ -1,4 +1,4 @@
-## Reshape <a name="Reshape"></a> {#openvino_docs_ops_shape_Reshape_1}
+# Reshape {#openvino_docs_ops_shape_Reshape_1}
 
 **Versioned name**: *Reshape-1*
 
diff --git a/docs/ops/shape/ShapeOf_1.md b/docs/ops/shape/ShapeOf_1.md
index cda4bd74c61..be8ddb54b45 100644
--- a/docs/ops/shape/ShapeOf_1.md
+++ b/docs/ops/shape/ShapeOf_1.md
@@ -1,4 +1,4 @@
-## ShapeOf <a name="ShapeOf"></a> {#openvino_docs_ops_shape_ShapeOf_1}
+# ShapeOf {#openvino_docs_ops_shape_ShapeOf_1}
 
 **Versioned name**: *ShapeOf-1*
 
diff --git a/docs/ops/shape/ShapeOf_3.md b/docs/ops/shape/ShapeOf_3.md
index 3ca8013d627..eea83589614 100644
--- a/docs/ops/shape/ShapeOf_3.md
+++ b/docs/ops/shape/ShapeOf_3.md
@@ -1,4 +1,4 @@
-## ShapeOf <a name="ShapeOf"></a> {#openvino_docs_ops_shape_ShapeOf_3}
+# ShapeOf {#openvino_docs_ops_shape_ShapeOf_3}
 
 **Versioned name**: *ShapeOf-3*
 
diff --git a/docs/ops/shape/Squeeze_1.md b/docs/ops/shape/Squeeze_1.md
index 535e60c4ac9..af9f0a5f6f0 100644
--- a/docs/ops/shape/Squeeze_1.md
+++ b/docs/ops/shape/Squeeze_1.md
@@ -1,4 +1,4 @@
-## Squeeze<a name="Squeeze"></a> {#openvino_docs_ops_shape_Squeeze_1}
+# Squeeze {#openvino_docs_ops_shape_Squeeze_1}
 
 **Versioned name**: *Squeeze-1*
 
diff --git a/docs/ops/shape/Unsqueeze_1.md b/docs/ops/shape/Unsqueeze_1.md
index f98616c0ba6..00383a9ae24 100644
--- a/docs/ops/shape/Unsqueeze_1.md
+++ b/docs/ops/shape/Unsqueeze_1.md
@@ -1,4 +1,4 @@
-## Unsqueeze <a name="Unsqueeze"></a> {#openvino_docs_ops_shape_Unsqueeze_1}
+# Unsqueeze {#openvino_docs_ops_shape_Unsqueeze_1}
 
 **Versioned name**: *Unsqueeze-1*
 
diff --git a/docs/ops/signals/DFT_7.md b/docs/ops/signals/DFT_7.md
index aea3f68337e..8bda2ab794c 100644
--- a/docs/ops/signals/DFT_7.md
+++ b/docs/ops/signals/DFT_7.md
@@ -1,4 +1,4 @@
-## Discrete Fourier Transformation (DFT) <a name="DFT"></a> {#openvino_docs_ops_signals_DFT_7}
+# Discrete Fourier Transformation (DFT) {#openvino_docs_ops_signals_DFT_7}
 
 **Versioned name**: *DFT-7*
 
diff --git a/docs/ops/signals/IDFT_7.md b/docs/ops/signals/IDFT_7.md
index 272480d751d..625db9cd78c 100644
--- a/docs/ops/signals/IDFT_7.md
+++ b/docs/ops/signals/IDFT_7.md
@@ -1,4 +1,4 @@
-## Inverse Discrete Fourier Transformation (IDFT) <a name="IDFT"></a> {#openvino_docs_ops_signals_IDFT_7}
+# Inverse Discrete Fourier Transformation (IDFT) {#openvino_docs_ops_signals_IDFT_7}
 
 **Versioned name**: *IDFT-7*
 
diff --git a/docs/ops/sort/ExperimentalDetectronTopKROIs_6.md b/docs/ops/sort/ExperimentalDetectronTopKROIs_6.md
index 389974558c7..82e503735ea 100644
--- a/docs/ops/sort/ExperimentalDetectronTopKROIs_6.md
+++ b/docs/ops/sort/ExperimentalDetectronTopKROIs_6.md
@@ -1,4 +1,4 @@
-## ExperimentalDetectronTopKROIs <a name="ExperimentalDetectronTopKROIs"></a> {#openvino_docs_ops_sort_ExperimentalDetectronTopKROIs_6}
+# ExperimentalDetectronTopKROIs  {#openvino_docs_ops_sort_ExperimentalDetectronTopKROIs_6}
 
 **Versioned name**: *ExperimentalDetectronTopKROIs-6*
 
diff --git a/docs/ops/sort/NonMaxSuppression_1.md b/docs/ops/sort/NonMaxSuppression_1.md
index 6b5b78c261c..68d1ea1271c 100644
--- a/docs/ops/sort/NonMaxSuppression_1.md
+++ b/docs/ops/sort/NonMaxSuppression_1.md
@@ -1,4 +1,4 @@
-## NonMaxSuppression<a name="NonMaxSuppression"></a> {#openvino_docs_ops_sort_NonMaxSuppression_1}
+# NonMaxSuppression {#openvino_docs_ops_sort_NonMaxSuppression_1}
 
 **Versioned name**: *NonMaxSuppression-1*
 
diff --git a/docs/ops/sort/NonMaxSuppression_3.md b/docs/ops/sort/NonMaxSuppression_3.md
index 2beb902d5f7..78ac95d61fe 100644
--- a/docs/ops/sort/NonMaxSuppression_3.md
+++ b/docs/ops/sort/NonMaxSuppression_3.md
@@ -1,4 +1,4 @@
-## NonMaxSuppression<a name="NonMaxSuppression"></a> {#openvino_docs_ops_sort_NonMaxSuppression_3}
+# NonMaxSuppression {#openvino_docs_ops_sort_NonMaxSuppression_3}
 
 **Versioned name**: *NonMaxSuppression-3*
 
diff --git a/docs/ops/sort/NonMaxSuppression_4.md b/docs/ops/sort/NonMaxSuppression_4.md
index 2024e49f010..241cab5ef7e 100644
--- a/docs/ops/sort/NonMaxSuppression_4.md
+++ b/docs/ops/sort/NonMaxSuppression_4.md
@@ -1,4 +1,4 @@
-## NonMaxSuppression<a name="NonMaxSuppression"></a> {#openvino_docs_ops_sort_NonMaxSuppression_4}
+# NonMaxSuppression {#openvino_docs_ops_sort_NonMaxSuppression_4}
 
 **Versioned name**: *NonMaxSuppression-4*
 
diff --git a/docs/ops/sort/NonMaxSuppression_5.md b/docs/ops/sort/NonMaxSuppression_5.md
index 1339b7d3adf..d9c9ababd7a 100644
--- a/docs/ops/sort/NonMaxSuppression_5.md
+++ b/docs/ops/sort/NonMaxSuppression_5.md
@@ -1,4 +1,4 @@
-## NonMaxSuppression<a name="NonMaxSuppression"></a> {#openvino_docs_ops_sort_NonMaxSuppression_5}
+# NonMaxSuppression {#openvino_docs_ops_sort_NonMaxSuppression_5}
 
 **Versioned name**: *NonMaxSuppression-5*
 
diff --git a/docs/ops/sort/TopK_1.md b/docs/ops/sort/TopK_1.md
index 1b2da3f69e5..824ae65fa28 100644
--- a/docs/ops/sort/TopK_1.md
+++ b/docs/ops/sort/TopK_1.md
@@ -1,4 +1,4 @@
-## TopK <a name="TopK"></a> {#openvino_docs_ops_sort_TopK_1}
+# TopK {#openvino_docs_ops_sort_TopK_1}
 
 **Versioned name**: *TopK-1*
 
diff --git a/docs/ops/sort/TopK_3.md b/docs/ops/sort/TopK_3.md
index 5986b9996be..d5d4d3a4085 100644
--- a/docs/ops/sort/TopK_3.md
+++ b/docs/ops/sort/TopK_3.md
@@ -1,4 +1,4 @@
-## TopK <a name="TopK"></a> {#openvino_docs_ops_sort_TopK_3}
+# TopK  {#openvino_docs_ops_sort_TopK_3}
 
 **Versioned name**: *TopK-3*
 
diff --git a/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md b/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md
index e74b9f78e4d..08dabfe9530 100644
--- a/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md
+++ b/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md
@@ -1,4 +1,4 @@
-## EmbeddingBagOffsetsSum <a name="EmbeddingBagOffsetsSum"></a> {#openvino_docs_ops_sparse_EmbeddingBagOffsetsSum_3}
+# EmbeddingBagOffsetsSum {#openvino_docs_ops_sparse_EmbeddingBagOffsetsSum_3}
 
 **Versioned name**: *EmbeddingBagOffsetsSum-3*
 
diff --git a/docs/ops/sparse/EmbeddingBagPackedSum_3.md b/docs/ops/sparse/EmbeddingBagPackedSum_3.md
index 41b66668440..745b217e900 100644
--- a/docs/ops/sparse/EmbeddingBagPackedSum_3.md
+++ b/docs/ops/sparse/EmbeddingBagPackedSum_3.md
@@ -1,4 +1,4 @@
-## EmbeddingBagPackedSum <a name="EmbeddingBagPackedSum"></a> {#openvino_docs_ops_sparse_EmbeddingBagPackedSum_3}
+# EmbeddingBagPackedSum {#openvino_docs_ops_sparse_EmbeddingBagPackedSum_3}
 
 **Versioned name**: *EmbeddingBagPackedSum-3*
 
diff --git a/docs/ops/sparse/EmbeddingSegmentsSum_3.md b/docs/ops/sparse/EmbeddingSegmentsSum_3.md
index b1c8404aaca..828c268a59b 100644
--- a/docs/ops/sparse/EmbeddingSegmentsSum_3.md
+++ b/docs/ops/sparse/EmbeddingSegmentsSum_3.md
@@ -1,4 +1,4 @@
-## EmbeddingSegmentsSum <a name="EmbeddingSegmentsSum"></a> {#openvino_docs_ops_sparse_EmbeddingSegmentsSum_3}
+# EmbeddingSegmentsSum {#openvino_docs_ops_sparse_EmbeddingSegmentsSum_3}
 
 **Versioned name**: *EmbeddingSegmentsSum-3*
 
diff --git a/docs/ops/type/ConvertLike_1.md b/docs/ops/type/ConvertLike_1.md
index a70e3dd5bff..e08f9341cc4 100644
--- a/docs/ops/type/ConvertLike_1.md
+++ b/docs/ops/type/ConvertLike_1.md
@@ -1,4 +1,4 @@
-## ConvertLike <a name="ConvertLike"></a> {#openvino_docs_ops_type_ConvertLike_1}
+# ConvertLike {#openvino_docs_ops_type_ConvertLike_1}
 
 **Versioned name**: *ConvertLike-1*
 
diff --git a/docs/ops/type/Convert_1.md b/docs/ops/type/Convert_1.md
index 4ee9dfdda43..36ceb522b97 100644
--- a/docs/ops/type/Convert_1.md
+++ b/docs/ops/type/Convert_1.md
@@ -1,4 +1,4 @@
-## Convert <a name="Convert"></a> {#openvino_docs_ops_type_Convert_1}
+# Convert {#openvino_docs_ops_type_Convert_1}
 
 **Versioned name**: *Convert-1*
 
diff --git a/docs/optimization_guide/dldt_deployment_optimization_guide.md b/docs/optimization_guide/dldt_deployment_optimization_guide.md
new file mode 100644
index 00000000000..f3990a17b7e
--- /dev/null
+++ b/docs/optimization_guide/dldt_deployment_optimization_guide.md
@@ -0,0 +1,303 @@
+# Deployment Optimization Guide {#openvino_docs_deployment_optimization_guide_dldt_optimization_guide}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   
+   openvino_docs_deployment_optimization_guide_dldt_optimization_guide_additional
+
+@endsphinxdirective
+
+To optimize your performance results during runtime step it is possible to experiment with: 
+
+* Preprocess 
+
+* Throughput mode  
+
+* Async API 
+
+* Lowering inference precision 
+
+* Device optimization 
+
+* Combination of devices 
+
+## Preprocess
+
+### Letting the Inference Engine Accelerate Image Pre-processing/Conversion <a name="image-preprocessing"></a>
+
+In many cases, a network expects a pre-processed image, so make sure you do not perform unnecessary steps in your code:
+- Model Optimizer can efficiently bake the mean and normalization (scale) values into the model (for example, weights of the first convolution). See <a href="#mo-knobs-related-to-performance">Model Optimizer Knobs Related to Performance</a>.
+- If regular 8-bit per channel images are your native media (for instance, decoded frames), do not convert to the `FP32` on your side, as this is something that plugins can accelerate. Use the `InferenceEngine::Precision::U8` as your input format:<br>
+
+@snippet snippets/dldt_optimization_guide1.cpp part1
+
+Note that in many cases, you can directly share the (input) data with the Inference Engine.
+
+## Throughput Mode
+
+One way to increase computational efficiency is batching, which combines many (potentially tens) of input images to achieve optimal throughput. Internally, the execution resources are split/pinned into execution *streams*. Using this feature gains much better performance for the networks that originally are not scaled well with a number of threads (for example, lightweight topologies). This is especially pronounced for the many-core server machines. 
+
+![](../img/THROUGHPUT.svg)
+
+Run the Benchmark App and play with number of infer requests running in parallel, next section. Try different values of the -nstreams argument from 1 to a number of CPU cores and find one that provides the best performance. 
+
+The throughput mode relaxes the requirement to saturate the CPU by using a large batch: running multiple independent inference requests in parallel often gives much better performance, than using a batch only. This allows you to simplify the app-logic, as you don't need to combine multiple inputs into a batch to achieve good CPU performance. Instead, it is possible to keep a separate infer request per camera or another source of input and process the requests in parallel using Async API. 
+
+## Inference Engine Async API 
+
+Inference Engine Async API can improve overall frame rate of the application. While accelerator is busy with the inference, the application can continue doing things on the host rather than wait for the inference to complete.
+
+In the example below, inference is applied to the results of the video decoding. So it is possible to keep two parallel infer requests, and while the current is processed, the input frame for the next is being captured. This essentially hides the latency of capturing, so that the overall frame rate is rather determined only by the slowest part of the pipeline (decoding IR inference) and not by the sum of the stages.
+
+You can compare the pseudo-codes for the regular and async-based approaches:
+
+-	In the regular way, the frame is captured with OpenCV and then immediately processed:<br>
+
+@snippet snippets/dldt_optimization_guide8.cpp part8
+
+![Intel&reg; VTune&trade; screenshot](../img/vtune_regular.png)
+
+-	In the "true" async mode, the `NEXT` request is populated in the main (application) thread, while the `CURRENT` request is processed:<br>
+
+@snippet snippets/dldt_optimization_guide9.cpp part9
+
+![Intel&reg; VTune&trade; screenshot](../img/vtune_async.png)
+
+The technique can be generalized to any available parallel slack. For example, you can do inference and simultaneously encode the resulting or previous frames or run further inference, like emotion detection on top of the face detection results.
+
+There are important performance caveats though: for example, the tasks that run in parallel should try to avoid oversubscribing the shared compute resources. If the inference is performed on the HDDL and the CPU is essentially idle, it makes sense to do things on the CPU in parallel. However, multiple infer requests can oversubscribe that. Notice that heterogeneous execution can implicitly use the CPU, refer to <a href="#heterogeneity">Heterogeneity</a>.
+
+Also, if the inference is performed on the graphics processing unit (GPU), it can take little gain to do the encoding, for instance, of the resulting video, on the same GPU in parallel, because the device is already busy.
+
+Refer to the [Object Detection SSD Demo](@ref omz_demos_object_detection_demo_ssd_async_README) (latency-oriented Async API showcase) and [Benchmark App Sample](../../inference-engine/samples/benchmark_app/README.md) (which has both latency and throughput-oriented modes) for complete examples of the Async API in action.
+
+### Request-Based API and “GetBlob” Idiom <a name="new-request-based-api"></a>
+
+Infer Request based API offers two types of request: Sync and Async. The Sync is considered below. The Async splits (synchronous) `Infer` into `StartAsync` and `Wait` (see <a href="#ie-async-api">Inference Engine Async API</a>).
+
+More importantly, an infer request encapsulates the reference to the “executable” network and actual inputs/outputs. Now, when you load the network to the plugin, you get a reference to the executable network (you may consider that as a queue). Actual infer requests are created by the executable network:
+
+```sh
+
+@snippet snippets/dldt_optimization_guide6.cpp part6
+```
+
+`GetBlob` is a recommend way to communicate with the network, as it internally allocates the data with right padding/alignment for the device. For example, the GPU inputs/outputs blobs are mapped to the host (which is fast) if the `GetBlob` is used. But if you called the `SetBlob`, the copy (from/to the blob you have set) into the internal GPU plugin structures will happen.
+
+### Performance Aspects of Running Multiple Requests Simultaneously <a name="running-multiple-requests-simultaneously"></a>
+
+If your application simultaneously executes multiple infer requests:
+
+- 	For the CPU, the best solution, you can use the <a href="#cpu-streams">CPU "throughput" mode</a>.
+	-	If latency is of more concern, you can try the `EXCLUSIVE_ASYNC_REQUESTS` [configuration option](../IE_DG/supported_plugins/CPU.md) that limits the number of the simultaneously executed requests for all (executable) networks that share the specific device to just one:<br>
+
+@snippet snippets/dldt_optimization_guide7.cpp part7
+
+		<br>For more information on the executable networks notation, see <a href="#new-request-based-api">Request-Based API and “GetBlob” Idiom</a>.
+
+	-	The heterogeneous device uses the `EXCLUSIVE_ASYNC_REQUESTS` by default.
+
+	-	`KEY_EXCLUSIVE_ASYNC_REQUESTS` option affects only device queues of the individual application.
+
+-	For GPU, the actual work is serialized by a plugin and/or a driver anyway.
+
+- 	Finally, for <a href="#myriad">any VPU flavor</a>, using multiple requests is a must for achieving good throughput. 
+
+In the Inference Engine, there is no notion of requests priorities. It is left to the user side (for example, not queuing the low priority infer request, until another higher priority is waiting). Notice that it would require additional logic to synchronize between executable networks (queues) in your application code.
+
+## Automatic Lowering of the Inference Precision 
+
+Inference precision directly affects the performance. 
+
+Model Optimizer can produce an IR with different precision. For example, an FP16 IR initially targets VPU and GPU devices, while, for example, for the CPU, an FP16 IR is    typically up-scaled to the regular FP32 automatically upon loading. But notice that further device-specific inference precision settings are available, 
+for example, [8-bit integer](Int8Inference.md) or [bfloat16](Bfloat16Inference.md), which is specific to the CPU inference, below.
+Note that for the [MULTI device](supported_plugins/MULTI.md) plugin that supports automatic inference on multiple devices in parallel, you can use an FP16 IR (no need for FP32).
+You can find more information, including preferred data types for specific devices, in the
+[Supported Devices](supported_plugins/Supported_Devices.md) document.
+
+
+By default, plugins enable the optimizations that allow lower precision if the acceptable range of accuracy is preserved.
+For example, for the CPU that supports the AVX512_BF16 instructions, an FP16/FP32 model is converted to a [bfloat16](Bfloat16Inference.md) IR to accelerate inference.
+
+To compare the associated speedup, run the example command below to disable this feature on the CPU device with the AVX512_BF16 support and get regular FP32 execution:
+
+```sh
+$ benchmark_app -m <model.xml> -enforcebf16=false
+ ```
+
+Notice that for quantized (e.g. INT8) models the bfloat16 calculations (of the layers that remain in FP32) is disabled by default.
+Refer to the [CPU Plugin documentation](supported_plugins/CPU.md) for more details.
+
+Similarly, the GPU device automatically executes FP16 for the layers that remain in FP16 in the quantized models (assuming that the FP16 model was quantized).
+Refer to the ENABLE_FP16_FOR_QUANTIZED_MODELS key in the [GPU Plugin documentation](supported_plugins/GPU.md).
+
+## Device Optimizations
+
+The Inference Engine supports several target devices (CPU, GPU, Intel&reg; Movidius&trade; Myriad&trade; 2 VPU, Intel&reg; Movidius&trade; Myriad&trade; X VPU, Intel® Vision Accelerator Design with Intel® Movidius™ Vision Processing Units (VPU)), and each of them has a corresponding plugin. If you want to optimize a specific device, you must keep in mind the following tips to increase the performance.
+
+### CPU Checklist <a name="cpu-checklist"></a>
+
+CPU plugin completely relies on the Intel&reg; Math Kernel Library for Deep Neural Networks (Intel&reg; MKL-DNN) for major primitives acceleration, for example, Convolutions or FullyConnected.
+
+The only hint you can get from that is how the major primitives are accelerated (and you cannot change this). For example, on the Core machines, you should see variations of the `jit_avx2` when inspecting the <a href="#performance-counters">internal inference performance counters</a> (and additional '_int8' postfix for [int8 inference](../IE_DG/Int8Inference.md)). If you are an advanced user, you can further trace the CPU execution with (see <a href="#vtune-examples">Intel&reg; VTune&trade;</a>).
+
+Internally, the Inference Engine has a threading abstraction level, which allows for compiling the [open source version](https://github.com/opencv/dldt) with either Intel&reg; Threading Building Blocks (Intel&reg; TBB) which is now default, or OpenMP* as an alternative parallelism solution. When using inference on the CPU, this is particularly important to align threading model with the rest of your application (and any third-party libraries that you use) to avoid oversubscription. For more information, see <a href="#note-on-app-level-threading">Note on the App-Level Threading</a> section.
+
+ Since R1 2019, the OpenVINO&trade; toolkit comes pre-compiled with Intel TBB,
+ so any  OpenMP* API or environment settings (like `OMP_NUM_THREADS`) has no effect.
+ Certain tweaks (like number of threads used for inference on the CPU) are still possible via  [CPU configuration options](../IE_DG/supported_plugins/CPU.md).
+ Finally, the OpenVINO CPU inference is NUMA-aware, please refer to the <a href="#note-on-numa">Tips for inference on NUMA systems</a> section.
+
+Other general recommendations:
+-	Usually, batching improves CPU performance. However, the need to gather frames in the batch might complicate the application logic. Instead, you can keep a separate infer request per camera or other source of input and process the requests in parallel. For more information, see the next section.
+-	If your application simultaneously performs inference of multiple models on the same CPU, make sure you do not oversubscribe the machine. See <a href="#running-multiple-requests-simultaneously">Performance Aspects of Running Multiple Requests Simultaneously</a> for more information.
+-	Notice that the heterogeneous execution might implicitly load the CPU. For details, refer to the <a href="#heterogeneity">Heterogeneity</a> section.
+- 	Consider [8-bit integer inference on the CPU](../IE_DG/Int8Inference.md).
+
+#### Throughput Mode for CPU <a name="cpu-streams"></a>
+Unlike most accelerators, CPU is perceived as an inherently latency-oriented device.
+In fact, the OpenVINO does support the "throughput" mode for the CPU, which allows the Inference Engine to efficiently run multiple inference requests on the CPU simultaneously, greatly improving the overall throughput.
+
+Internally, the execution resources are split/pinned into execution "streams".
+This feature usually provides much better performance for the networks than batching. This is especially true for the many-core server machines:
+![](../img/cpu_streams_explained_1.png)
+
+Compared with the batching, the parallelism is somewhat transposed (i.e. performed over inputs, and much less within CNN ops):
+![](../img/cpu_streams_explained.png)
+
+Try the [Benchmark App](../../inference-engine/samples/benchmark_app/README.md) sample and play with number of streams running in parallel. The rule of thumb is tying up to a number of CPU cores on your machine.
+For example, on an 8-core CPU, compare the `-nstreams 1` (which is a legacy, latency-oriented scenario) to the 2, 4, and 8 streams.
+
+In addition, you can play with the batch size to find the throughput sweet spot.
+
+If your application is hard or impossible to change in accordance with the multiple-requests logic, consider the "multiple-instance" trick to improve the throughput:  
+-   For multi-socket execution, it is recommended to set   [`KEY_CPU_THREADS_NUM`](../IE_DG/supported_plugins/CPU.md) to the number of cores per socket, and run as many instances of the application as you have sockets.
+-   Similarly, for extremely lightweight networks (running faster than 1ms) and/or many-core machines (16+ cores), try limiting the number of CPU inference  threads to just `#&zwj;phys` cores and further, while trying to saturate the machine with running multiple instances of the application.
+
+### GPU Checklist <a name="gpu-checklist"></a>
+
+Inference Engine relies on the [Compute Library for Deep Neural Networks (clDNN)](https://01.org/cldnn) for Convolutional Neural Networks acceleration on Intel&reg; GPUs. Internally, clDNN uses OpenCL&trade; to implement the kernels. Thus, many general tips apply:
+
+-	Prefer `FP16` over `FP32`, as the Model Optimizer can generate both variants and the `FP32` is default.
+- 	Try to group individual infer jobs by using batches.
+-	Notice that using the GPU introduces one-time overhead (order of few seconds) of compiling the OpenCL kernels. The compilation happens upon loading the network to the GPU plugin and does not affect the inference time.
+-	If your application is simultaneously using the inference on the CPU or otherwise loads the host heavily, make sure that the OpenCL driver threads do not starve. You can use [CPU configuration options](../IE_DG/supported_plugins/CPU.md) to limit number of inference threads for the CPU plugin.
+-	In the GPU-only scenario, a GPU driver might occupy a CPU core with spin-looped polling for completion. If the _CPU_ utilization is a concern, consider the `KEY_CLDND_PLUGIN_THROTTLE` configuration option.
+
+> **NOTE**: See the [Benchmark App Sample](../../inference-engine/samples/benchmark_app/README.md) code for a usage example. 
+Notice that while disabling the polling, this option might reduce the GPU performance, so usually this option is used with multiple [GPU streams](../IE_DG/supported_plugins/CL_DNN.md). 
+
+
+### Intel&reg; Movidius&trade; Myriad&trade; X Visual Processing Unit and Intel&reg; Vision Accelerator Design with Intel&reg; Movidius&trade; VPUs  <a name="myriad"></a>
+
+Since Intel&reg; Movidius&trade; Myriad&trade; X Visual Processing Unit (Intel&reg; Movidius&trade; Myriad&trade; 2 VPU) communicates with the host over USB, minimum four infer requests in flight are recommended to hide the data transfer costs. See <a href="#new-request-based-api">Request-Based API and “GetBlob” Idiom</a> and [Benchmark App Sample](../../inference-engine/samples/benchmark_app/README.md) for more information.
+
+Intel&reg; Vision Accelerator Design with Intel&reg; Movidius&trade; VPUs requires to keep at least 32 inference requests in flight to fully saturate the device.  
+
+## Heterogeneity <a name="heterogeneity"></a>
+
+Heterogeneous execution (constituted by the dedicated Inference Engine [“Hetero” plugin](../IE_DG/supported_plugins/HETERO.md)) enables to schedule a network inference to the multiple devices.
+
+### Typical Heterogeneous Scenarios of Concern <a name="heterogeneous-scenarios-of-concern"></a>
+
+The primary points for executing a network in heterogeneous mode are as follows:
+
+-	Calculate the heaviest pieces of the network with an accelerator while falling back to the CPU for the layers that are not supported by the accelerator.<br>
+	This is particularly useful when certain custom (user) kernels are implemented only for the CPU (and much harder or even impossible to implement for the accelerator).
+
+-	Use all available compute devices more efficiently, for example, by running branches of the network on the different devices.
+
+### Heterogeneous Flow <a name="heterogeneous-flow"></a>
+
+The execution through heterogeneous plugin has three distinct steps:
+
+1.	**Applying affinity setting for the layers**, that is, binding them to the devices.
+
+	-	This can be done automatically using *fallback priorities*, or on the *per-layer* basis.
+
+	-	The affinity setting is made before loading the network to the (heterogeneous) plugin, so this is always a **static** setup with respect to execution.
+
+2.	**Loading a network to the heterogeneous plugin**, which internally splits the network into subgraphs.<br>
+	You can check the decisions the plugin makes, see <a href="#analyzing-heterogeneous-execution">Analysing the Heterogeneous Execution</a>.
+
+3.	**Executing the infer requests**. From user’s side, this looks identical to a single-device case, while internally, the subgraphs are executed by actual plugins/devices.
+
+Performance benefits of the heterogeneous execution depend heavily on the communications granularity between devices. If transmitting/converting data from one part device to another takes more time than the execution, the heterogeneous approach makes little or no sense. Using Intel&reg; VTune&trade; helps to visualize the execution flow on a timeline (see <a href="#vtune-examples">Intel&reg; VTune&trade; Examples</a>).
+
+Similarly, if there are too much subgraphs, the synchronization and data transfers might eat the entire performance. In some cases, you can define the (coarser) affinity manually to avoid sending data back and forth many times during one inference.
+
+The general affinity “rule of thumb” is to keep computationally-intensive kernels on the accelerator, and "glue" or helper  kernels on the CPU. Notice that this includes the granularity considerations. For example, running some custom activation (that comes after every accelerator-equipped convolution) on the CPU might result in performance degradation due to too much data type and/or layout conversions, even though the activation itself can be extremely fast. In this case, it might make sense to consider implementing the kernel for the accelerator (see <a href="#optimizing-custom-kernels">Optimizing Custom Kernels</a>). The conversions typically manifest themselves as outstanding (comparing to CPU-only execution) 'Reorder' entries (see <a href="#performance-counters">Internal Inference Performance Counters</a>).
+
+For general details on the heterogeneous plugin, refer to the [corresponding section in the Inference Engine Developer Guide](../IE_DG/supported_plugins/HETERO.md).
+
+### Trying the Heterogeneous Plugin with Inference Engine Samples <a name="heterogeneous-plugin-with-samples"></a>
+
+Every Inference Engine sample supports the `-d` (device) option.
+
+For example, here is a command to run an [Object Detection Sample SSD Sample](../../inference-engine/samples/object_detection_sample_ssd/README.md):
+
+```sh
+./object_detection_sample_ssd -m  <path_to_model>/ModelSSD.xml -i <path_to_pictures>/picture.jpg -d HETERO:GPU,CPU
+```
+
+where:
+
+-	`HETERO` stands for Heterogeneous plugin.
+-	`GPU,CPU` points to fallback policy with first priority on GPU and further fallback to CPU.
+
+You can point more than two devices: `-d HETERO:HDDL,GPU,CPU`.
+
+### General Tips on GPU/CPU Execution <a name="tips-on-gpu-cpu-execution"></a>
+
+The following tips are provided to give general guidance on optimizing execution on GPU/CPU devices.
+
+-	Generally, GPU performance is better on heavy kernels (like Convolutions) and large inputs. So if the network inference time is already too small (~1ms of execution time), using the GPU would unlikely give a boost.
+
+-	A typical strategy to start with is to test the CPU-only and GPU-only scenarios first (with samples this is plain `-d CPU` or `-d GPU`). If there are specific kernels that are not supported by the GPU, the best option to try is the `HETERO:GPU,CPU` that automatically applies default splitting (based on the plugins layers support). Then, you can play with the manual affinity settings (for example, to further minimize the number of subgraphs).  
+
+-	The general affinity “rule of thumb” is to keep computationally-intensive kernels on the accelerator, and "glue" (or helper) kernels on the CPU. Notice that this includes the granularity considerations. For example, running some (custom) activation on the CPU would result in too many conversions.
+
+-	It is advised to do <a href="#analyzing-hetero-execution">performance analysis</a> to determine “hotspot” kernels, which should be the first candidates for offloading. At the same time, it is often more efficient to offload some reasonably sized sequence of kernels, rather than individual kernels, to minimize scheduling and other run-time overheads.
+
+-	Notice that GPU can be busy with other tasks (like rendering). Similarly, the CPU can be in charge for the general OS routines and other application threads (see <a href="#note-on-app-level-threading">Note on the App-Level Threading</a>). Also, a high interrupt rate due to many subgraphs can raise the frequency of the one device and drag the frequency of another down.
+
+-	Device performance can be affected by dynamic frequency scaling. For example, running long kernels on both devices simultaneously might eventually result in one or both devices stopping use of the Intel&reg; Turbo Boost Technology. This might result in overall performance decrease, even comparing to single-device scenario.
+
+-	Mixing the `FP16` (GPU) and `FP32` (CPU) execution results in conversions and, thus, performance issues. If you are seeing a lot of heavy outstanding (compared to the CPU-only execution) Reorders, consider implementing actual GPU kernels. Refer to <a href="#performance-counters">Internal Inference Performance Counters</a> for more information.
+
+### Analyzing Heterogeneous Execution <a name="analyzing-heterogeneous-execution"></a>
+
+There is a dedicated configuration option that enables dumping the visualization of the subgraphs created by the heterogeneous plugin, please see code example in the [HETERO plugin documentation](https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_HETERO.html#analyzing_heterogeneous_execution)
+
+After enabling the configuration key, the heterogeneous plugin generates two files:
+
+-	`hetero_affinity.dot` - per-layer affinities. This file is generated only if default fallback policy was executed (as otherwise you have set the affinities by yourself, so you know them).
+-	`hetero_subgraphs.dot` - affinities per sub-graph. This file is written to the disk during execution of `Core::LoadNetwork` for the heterogeneous flow.
+
+You can use GraphViz\* utility or `.dot` converters (for example, to `.png` or `.pdf`), like xdot\*, available on Linux\* OS with `sudo apt-get install xdot`. 
+
+You can also use performance data (in the [Benchmark App](../../inference-engine/samples/benchmark_app/README.md), it is an option `-pc`) to get performance data on each subgraph. Again, refer to the [HETERO plugin documentation](https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_HETERO.html#analyzing_heterogeneous_execution) and to <a href="#performance-counters">Internal Inference Performance Counters</a> for a general counters information.
+
+## Multi-Device Execution <a name="multi-device-optimizations"></a>
+OpenVINO&trade; toolkit supports automatic multi-device execution, please see [MULTI-Device plugin description](../IE_DG/supported_plugins/MULTI.md).
+In the next chapter you can find the device-specific tips, while this section covers few recommendations 
+for the multi-device execution:
+-	MULTI usually performs best when the fastest device is specified first in the list of the devices. 
+    This is particularly important when the parallelism is not sufficient 
+    (e.g. the number of request in the flight is not enough to saturate all devices).
+- It is highly recommended to query the optimal number of inference requests directly from the instance of the ExecutionNetwork 
+  (resulted from the LoadNetwork call with the specific multi-device configuration as a parameter). 
+Please refer to the code of the [Benchmark App](../../inference-engine/samples/benchmark_app/README.md) sample for details.    
+-   Notice that for example CPU+GPU execution performs better with certain knobs 
+    which you can find in the code of the same [Benchmark App](../../inference-engine/samples/benchmark_app/README.md) sample.
+    One specific example is disabling GPU driver polling, which in turn requires multiple GPU streams (which is already a default for the GPU) to amortize slower 
+    inference completion from the device to the host.
+-	Multi-device logic always attempts to save on the (e.g. inputs) data copies between device-agnostic, user-facing inference requests 
+    and device-specific 'worker' requests that are being actually scheduled behind the scene. 
+    To facilitate the copy savings, it is recommended to start the requests in the order that they were created 
+    (with ExecutableNetwork's CreateInferRequest).
+  
+Refer to [Deployment Optimization Guide Additional Configurations](dldt_deployment_optimization_guide_additional.md) to read more about performance during deployment step and learn about threading, working with multi-socket CPUs and Basic Interoperability with Other APIs.
\ No newline at end of file
diff --git a/docs/optimization_guide/dldt_deployment_optimization_guide_additional.md b/docs/optimization_guide/dldt_deployment_optimization_guide_additional.md
new file mode 100644
index 00000000000..19133dea90f
--- /dev/null
+++ b/docs/optimization_guide/dldt_deployment_optimization_guide_additional.md
@@ -0,0 +1,70 @@
+# Deployment Optimization Guide Additional Configurations {#openvino_docs_deployment_optimization_guide_dldt_optimization_guide_additional}
+
+To optimize your performance results during runtime step, you can experiment with:
+
+* multi socket CPUs
+
+* threading
+
+* Basic Interoperability with Other APIs
+
+
+## Best Latency on the Multi-Socket CPUs
+Note that when latency is of concern, there are additional tips for multi-socket systems.
+When input is limited to the single image, the only way to achieve the best latency is to limit execution to the single socket.
+The reason is that single image is simply not enough
+to saturate more than one socket. Also NUMA overheads might dominate the execution time.
+Below is the example command line that limits the execution to the single socket using numactl for the best *latency* value
+(assuming the machine with 28 phys cores per socket):
+```
+limited to the single socket).
+$ numactl -m 0 --physcpubind 0-27  benchmark_app -m <model.xml> -api sync -nthreads 28
+ ```
+Note that if you have more than one input, running as many inference requests as you have NUMA nodes (or sockets)
+usually gives the same best latency as a single request on the single socket, but much higher throughput. Assuming two NUMA nodes machine:
+```
+$ benchmark_app -m <model.xml> -nstreams 2
+ ```
+Number of NUMA nodes on the machine can be queried via 'lscpu'.
+Please see more on the NUMA support in the [Optimization Guide](supported_plugins/MULTI.md).
+ 
+  
+ ##  Threading 
+
+ - As explained in the <a href="#cpu-checklist">CPU Checklist</a> section, by default the Inference Engine uses Intel TBB as a parallel engine. Thus, any OpenVINO-internal threading (including CPU inference) uses the same threads pool, provided by the TBB. But there are also other threads in your application, so oversubscription is possible at the application level:
+- The rule of thumb is that you should try to have the overall number of active threads in your application equal to the number of cores in your machine. Keep in mind the spare core(s) that the OpenCL driver under the GPU plugin might also need.
+- One specific workaround to limit the number of threads for the Inference Engine is using the [CPU configuration options](../IE_DG/supported_plugins/CPU.md).
+- To avoid further oversubscription, use the same threading model in all modules/libraries that your application uses. Notice that third party components might bring their own threading. For example, using Inference Engine which is now compiled with the TBB by default might lead to [performance troubles](https://www.threadingbuildingblocks.org/docs/help/reference/appendices/known_issues/interoperability.html) when mixed in the same app with another computationally-intensive library, but compiled with OpenMP. You can try to compile the [open source version](https://github.com/opencv/dldt) of the Inference Engine to use the OpenMP as well. But notice that in general, the TBB offers much better composability, than other threading solutions.
+- If your code (or third party libraries) uses GNU OpenMP, the Intel&reg; OpenMP (if you have recompiled Inference Engine with that) must be initialized first. This can be achieved by linking your application with the Intel OpenMP instead of GNU OpenMP, or using `LD_PRELOAD` on Linux* OS.
+
+## Basic Interoperability with Other APIs <a name="basic-interoperability-with-other-apis"></a>
+
+The general approach for sharing data between Inference Engine and media/graphics APIs like Intel&reg; Media Server Studio (Intel&reg; MSS) is based on sharing the *system* memory.  That is, in your code, you should map or copy the data from the API to the CPU address space first.
+
+For Intel MSS, it is recommended to perform a viable pre-processing, for example, crop/resize, and then convert to RGB again with the [Video Processing Procedures (VPP)](https://software.intel.com/en-us/node/696108). Then lock the result and create an Inference Engine blob on top of that. The resulting pointer can be used for the `SetBlob`:
+
+@snippet snippets/dldt_optimization_guide2.cpp part2
+
+**WARNING**: The `InferenceEngine::NHWC` layout is not supported natively by most InferenceEngine plugins so internal conversion might happen.
+
+@snippet snippets/dldt_optimization_guide3.cpp part3
+
+Alternatively, you can use RGBP (planar RGB) output from Intel MSS. This allows to wrap the (locked) result as regular NCHW which is generally friendly for most plugins (unlike NHWC). Then you can use it with `SetBlob` just like in previous example:
+
+@snippet snippets/dldt_optimization_guide4.cpp part4
+
+The only downside of this approach is that VPP conversion to RGBP is not hardware accelerated (and performed on the GPU EUs). Also, it is available only on LInux.
+
+## OpenCV* Interoperability Example <a name="opencv-interoperability"></a>
+
+Unlike APIs that use dedicated address space and/or special data layouts (for instance, compressed OpenGL* textures), regular OpenCV data objects like `cv::Mat` reside in the conventional system memory. That is, the memory can be actually shared with the Inference Engine and only data ownership to be transferred.
+
+Again, if the OpenCV and Inference Engine layouts match, the data can be wrapped as Inference Engine (input/output) blob. Notice that by default, Inference Engine accepts the **planar** and **not interleaved** inputs in NCHW, so the NHWC (which is exactly the interleaved layout) should be specified explicitly:
+
+**WARNING**: The `InferenceEngine::NHWC` layout is not supported natively by most InferenceEngine plugins so internal conversion might happen.
+
+@snippet snippets/dldt_optimization_guide5.cpp part5
+
+Notice that original `cv::Mat`/blobs cannot be used simultaneously by the application and the Inference Engine. Alternatively, the data that the pointer references to can be copied to unlock the original data and return ownership to the original API.
+
+To learn more about optimizations during developing step, visit [Deployment Optimization Guide](dldt_deployment_optimization_guide.md) page.
\ No newline at end of file
diff --git a/docs/optimization_guide/dldt_optimization_guide.md b/docs/optimization_guide/dldt_optimization_guide.md
index efe4d8a7fe6..7381545800e 100644
--- a/docs/optimization_guide/dldt_optimization_guide.md
+++ b/docs/optimization_guide/dldt_optimization_guide.md
@@ -1,514 +1,40 @@
-# Optimization Guide {#openvino_docs_optimization_guide_dldt_optimization_guide}
+# Performance Optimization Guide {#openvino_docs_optimization_guide_dldt_optimization_guide}
 
-## Introduction
 
-The purpose of this document is to give you performance-related insights to every step of the network deployment process.
+Before exploring optimization techniques, let us first define what performance is and how it is measured.
 
-For information on the general workflow, refer to the documentation in <a href="#see-also">See Also</a>. For an example Inference Engine API snippet, see <a href="#new-request-based-api">Request-Based API and “GetBlob” Idiom</a>.
+## What Is Performance 
 
-### Deep Learning Inference Engine Overview <a name="dldt-overview"></a>
+Performance means how fast the model is in deployment. Two key metrics are used to measure performance: latency and throughput. 
 
-Deep Learning Inference Engine is a part of OpenVINO™ toolkit. Inference Engine facilitates deployment of deep learning solutions by delivering a unified, device-agnostic API.
+![](../img/LATENCY_VS_THROUGHPUT.svg)
 
-Below, there are the three main steps of the deployment process:
+Latency measures inference time (ms) required to process a single input. When it comes to batch input need to measure throughput (images per second or frames per second, FPS). To calculate throughput, divide number of frames that were processed by the processing time.   
 
-1.	**Conversion**<br>
-	Trained models are converted from a specific framework, like TensorFlow\*, or format, like ONNX\*, to the framework-agnostic Intermediate Representation (IR) format.
+> **NOTE**: To get performance numbers for OpenVINO, as well as tips how to measure it and compare with native framework, check [Getting performance numbers](../MO_DG/prepare_model/Getting_performance_numbers.md) page.
+ 
+## How to Improve Performance 
 
-	- *Performance flow*: This is an offline step where general topology-level optimizations happen automatically (see <a href="#mo-knobs-related-to-performance">Model Optimizer Knobs Related to Performance</a>).
+> **NOTE**: Make sure that your model can be successfully inferred with OpenVINO Inference Engine. 
 
-	- *Tools*: OpenVINO™ features the Model Optimizer, which enables automatic and seamless transition from a training to deployment environment.
+Inside OpenVINO there are two ways how to get better performance number: during developing and deployment your model. **It is possible to combine both developing and deployment optimizations**. 
 
-2.	**Model Inference/Execution**<br>
-	After conversion, Inference Engine consumes the IR to perform inference. While Inference Engine API itself is target-agnostic, internally, it has a notion of plugins, which are device-specific libraries facilitating the hardware-assisted acceleration.
+- **Developing step** includes model modification. Inside developing optimization there are three ways to optimize your model:  
 
-	-	*Performance flow*: Upon conversion to IR, the execution starts with existing [Inference Engine samples](../IE_DG/Samples_Overview.md) to measure and tweak the performance of the network on different devices.<br>
-		> **NOTE**: While consuming the same IR, each plugin performs additional device-specific optimizations at load time, so the resulting accuracy might differ. Also, enabling and optimizing custom kernels is error-prone (see <a href="#optimizing-custom-kernels">Optimizing Custom Kernels</a>).
+    - **Post-training Optimization tool** (POT) is designed to optimize the inference of deep learning models by applying special methods without model retraining or fine-tuning, like post-training quantization.  
 
-	-	*Tools*: Beyond inference performance that samples report (see <a href="#latency-vs-throughput">Latency vs. Throughput</a>), you can get further device- and kernel-level timing with the <a href="#performance-counters">Inference Engine performance counters</a> and <a href="#vtune-examples">Intel® VTune™</a>.
+    - **Neural Network Compression Framework (NNCF)** provides a suite of advanced algorithms for Neural Networks inference optimization with minimal accuracy drop, available quantization, pruning and sparsity optimization algorithms.
 
-3.	**Integration to the product**<br>
-	After model inference is verified with the [samples](../IE_DG/Samples_Overview.md), the Inference Engine code is typically integrated into a real application or pipeline.
+    - **Model Optimizer** implement some optimization to a model, most of them added by default, but you can configure mean/scale values, batch size RGB vs BGR input channels and other parameters to speed-up preprocess of a model ([Additional Optimization Use Cases](../MO_DG/prepare_model/Additional_Optimizations.md)) 
 
-	- *Performance flow*: The most important point is to preserve the sustained performance achieved with the stand-alone model execution. Take precautions when combining with other APIs and be careful testing the performance of every integration step.
+- **Deployment step**  includes tuning inference parameters and optimizing model execution, to read more visit [Deployment Optimization Guide](../optimization_guide/dldt_deployment_optimization_guide.md).
 
-	- *Tools*: Beyond tracking the actual wall-clock time of your application, see <a href="#vtune-examples">Intel® VTune™ Examples</a> for application-level and system-level information.
+More detailed workflow: 
 
+![](../img/DEVELOPMENT_FLOW_V3_crunch.svg)
 
-## Gathering the Performance Numbers <a name="gathering-performance-numbers"></a>
+To understand when to use each development optimization tool, follow this diagram: 
 
-Performance data comes in a variety of forms. For example, one of the the most common performance metrics is latency, which represents the time required to complete a unit of work (for instance, inference time for a single image). In the following sections, you will see important recommendations for measuring the performance.
+POT is the easiest way to get optimized models and it is also really fast and usually takes several minutes depending on the model size and used HW. NNCF can be considered as an alternative or an addition when the first does not give accurate results. 
 
-### Measure the Proper Set of Operations <a name="measure-proper-set-of-operations"></a>
-
-When evaluating performance of your model with the Inference Engine, you must measure the proper set of operations. To do so, consider the following tips:
-
--	Avoid including one-time costs like model loading. For examples, refer to the [Inference Engine samples](../IE_DG/Samples_Overview.md).
-- 	Track separately the operations that happen outside the Inference Engine, like video decoding.
-
-> **NOTE**: Some image pre-processing can be baked into the IR and accelerated. For more information, refer to <a href="#mo-knobs-related-to-performance">Model Optimizer Knobs Related to Performance</a>.
-
-### Latency vs. Throughput <a name="latency-vs-throughput"></a>
-
-In the asynchronous case (see <a href="#new-request-based-api">Request-Based API and “GetBlob” Idiom</a>), the performance of an individual infer request is usually of less concern. Instead, you typically execute multiple requests asynchronously and measure the throughput in images per second by dividing the number of images that were processed by the processing time. 
-In contrast, for latency-oriented tasks, the time to a single frame is more important.
-
-Refer to the [Benchmark App](../../samples/cpp/benchmark_app/README.md) sample, which allows latency vs. throughput measuring.
-
-> **NOTE**: The [Benchmark App](../../samples/cpp/benchmark_app/README.md) sample also supports batching, that is, automatically packing multiple input images into a single request. However, high batch size results in a latency penalty. So for more real-time oriented usages, batch sizes that are as low as a single input are usually used. Still, devices like CPU, Intel®Movidius™ Myriad™ 2 VPU, Intel® Movidius™ Myriad™ X VPU, or Intel® Vision Accelerator Design with Intel® Movidius™ VPU require a number of parallel requests instead of batching to leverage the performance. Running multiple requests should be coupled with a device configured to the corresponding number of streams. See <a href="#cpu-streams">details on CPU streams</a> for an example.
-
-[OpenVINO™ Deep Learning Workbench tool](https://docs.openvinotoolkit.org/latest/workbench_docs_Workbench_DG_Introduction.html) provides throughput versus latency charts for different numbers of streams, requests, and batch sizes to find the performance sweet spot.
-
-### Comparing Performance with Native/Framework Code <a name="comparing-performance-with-native-framework-code"></a>
-
-When comparing the Inference Engine performance with the framework or another reference code, make sure that both versions are as similar as possible:
-
--	Wrap exactly the inference execution (refer to the [Benchmark App](../../samples/cpp/benchmark_app/README.md) sample for an example).
--	Track model loading time separately.
--	Ensure the inputs are identical for the Inference Engine and the framework. For example, Caffe\* allows you to auto-populate the input with random values. Notice that it might give different performance than on real images.
--	Similarly, for correct performance comparison, make sure the access pattern, for example, input layouts, is optimal for Inference Engine (currently, it is NCHW).
--	Any user-side pre-processing should be tracked separately.
--	Make sure to try the same environment settings that the framework developers recommend, for example, for TensorFlow*. In many cases, things that are more machine friendly, like respecting NUMA (see <a href="#cpu-checklist">CPU Checklist</a>), might work well for the Inference Engine as well.
--	If applicable, use batching with the Inference Engine.
--	If possible, demand the same accuracy. For example, TensorFlow allows `FP16` support, so when comparing to that, make sure to test the Inference Engine with the `FP16` as well.
-
-### Getting Credible Performance Numbers <a name="getting-credible-performance-numbers"></a>
-
-You need to build your performance conclusions on reproducible data. Do the performance measurements with a large number of invocations of the same routine. Since the first iteration is almost always significantly slower than the subsequent ones, you can use an aggregated value for the execution time for final projections:
-
--	If the warm-up run does not help or execution time still varies, you can try running a large number of iterations and then average or find a mean of the results.
--	 For time values that range too much, use geomean.
-
-Refer to the [Benchmark App](../../samples/cpp/benchmark_app/README.md) for code examples of performance measurements. Almost every sample, except interactive demos, has the `-ni` option to specify the number of iterations.
-
-## Model Optimizer Knobs Related to Performance <a name="mo-knobs-related-to-performance"></a>
-
-Network training is typically done on high-end data centers, using popular training frameworks like Caffe\*, TensorFlow\*, and MXNet\*. Model Optimizer converts the trained model in original proprietary formats to IR that describes the topology. IR is accompanied by a binary file with weights. These files in turn are consumed by the Inference Engine and used for scoring.
-
-![](../img/workflow_steps.png)
-
-As described in the [Model Optimizer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md), there are a number of device-agnostic optimizations the tool performs. For example, certain primitives like linear operations (BatchNorm and ScaleShift) are automatically fused into convolutions. Generally, these layers should not be manifested in the resulting IR:
-
-![](../img/resnet_269.png)
-
-The picture above shows Caffe\* Resnet269\* topology. The left model is the original model, and the one on the right (after conversion) is the resulting model that the Model Optimizer produces, with BatchNorm and ScaleShift layers  fused into the convolution weights rather than constituting separate layers.
-
-If you still see these operations, inspect the Model Optimizer output carefully while searching for warnings, such as on the tool being unable to fuse. For example, non-linear operations (like activations) in between convolutions and linear operations might prevent the fusing. If performance is of concern, try to change (and potentially re-train) the topology. Refer to the [Model Optimizer Guide](../MO_DG/prepare_model/Model_Optimization_Techniques.md) for more optimizations.
-
-Notice that the activation (`_relu`) is not touched by the Model Optimizer, and while it can be merged into convolution as well, this is rather a device-specific optimization, covered by Inference Engine during the model loading time. You are encouraged to inspect performance counters from plugins that should indicate that these particular layers are not executed (“Optimized out”). For more information, refer to <a href="#performance-counters">Internal Inference Performance Counters</a>.
-
-Also:
-
--	**Image mean/scale parameters**<br>
-	Make sure to use the input image mean/scale parameters (`--scale` and `–mean_values`) with the Model Optimizer when you need pre-processing. It allows the tool to bake the pre-processing into the IR to get accelerated by the Inference Engine.
-
--	**RGB vs. BGR inputs**<br>
-	If, for example, your network assumes the RGB inputs, the Model Optimizer can swap the channels in the first convolution using the `--reverse_input_channels` command line option, so you do not need to convert your inputs to RGB every time you get the BGR image, for example, from OpenCV*.
-
--	**Larger batch size**<br>
-	Notice that the devices like GPU are doing better with larger batch size. While it is possible to set the batch size in the runtime using the Inference Engine [ShapeInference feature](../IE_DG/ShapeInference.md).
-
--	**Resulting IR precision**<br>
-The resulting IR precision, for instance, `FP16` or `FP32`, directly affects performance. As CPU now supports `FP16` (while internally upscaling to `FP32` anyway) and because this is the best precision for a GPU target, you may want to always convert models to `FP16`. Notice that this is the only precision that Intel® Movidius™ Myriad™ 2 and Intel® Myriad™ X VPUs support.
-
-
-## Multi-Device Execution <a name="multi-device-optimizations"></a>
-OpenVINO™ toolkit supports automatic multi-device execution, please see [MULTI-Device plugin description](../IE_DG/supported_plugins/MULTI.md).
-In the next chapter you can find the device-specific tips, while this section covers few recommendations
-for the multi-device execution:
--	MULTI usually performs best when the fastest device is specified first in the list of the devices. 
-    This is particularly important when the parallelism is not sufficient 
-    (e.g., the number of request in the flight is not enough to saturate all devices).
-- It is highly recommended to query the optimal number of inference requests directly from the instance of the ExecutionNetwork 
-  (resulted from the LoadNetwork call with the specific multi-device configuration as a parameter). 
-Please refer to the code of the [Benchmark App](../../samples/cpp/benchmark_app/README.md) sample for details.    
--   Notice that for example CPU+GPU execution performs better with certain knobs 
-    which you can find in the code of the same [Benchmark App](../../samples/cpp/benchmark_app/README.md) sample.
-    One specific example is disabling GPU driver polling, which in turn requires multiple GPU streams (which is already a default for the GPU) to amortize slower
-    inference completion from the device to the host.
--	Multi-device logic always attempts to save on the (e.g., inputs) data copies between device-agnostic, user-facing inference requests
-    and device-specific 'worker' requests that are being actually scheduled behind the scene.
-    To facilitate the copy savings, it is recommended to start the requests in the order that they were created
-    (with ExecutableNetwork's CreateInferRequest).
-
-## Device-Specific Optimizations <a name="device-specific-optimizations"></a>
-
-The Inference Engine supports several target devices (CPU, GPU, Intel® Neural Compute Stick 2, Intel® Vision Accelerator Design with Intel® Movidius™ Vision Processing Units (VPU)), and each of them has a corresponding plugin. If you want to optimize a specific device, keep in mind the following tips to increase performance.
-
-### CPU Checklist <a name="cpu-checklist"></a>
-
-The CPU plugin completely relies on the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) for major primitives acceleration, for example, Convolutions or FullyConnected.
-
-The only hint you can get from that is how the major primitives are accelerated (and you cannot change this). For example, on machines with Intel® Core™ processors, you should see variations of the `jit_avx2` when inspecting the <a href="#performance-counters">internal inference performance counters</a> (and additional '_int8' postfix for [int8 inference](../IE_DG/Int8Inference.md)). If you are an advanced user, you can further trace the CPU execution with (see <a href="#vtune-examples">Intel&reg; VTune™</a>).
-
-Internally, the Inference Engine has a threading abstraction level, which allows for compiling the [open source version](https://github.com/openvinotoolkit/openvino) with either Intel® Threading Building Blocks (Intel® TBB) which is now default, or OpenMP* as an alternative parallelism solution. When using inference on the CPU, this is particularly important to align threading model with the rest of your application (and any third-party libraries that you use) to avoid oversubscription. For more information, see <a href="#note-on-app-level-threading">Note on the App-Level Threading</a> section.
-
- Since R1 2019, OpenVINO™ toolkit comes pre-compiled with Intel TBB,
- so any OpenMP* API or environment settings (like `OMP_NUM_THREADS`) have no effect.
- Certain tweaks (like number of threads used for inference on the CPU) are still possible via  [CPU configuration options](../IE_DG/supported_plugins/CPU.md).
- Finally, the OpenVINO CPU inference is NUMA-aware, please refer to the <a href="#note-on-numa">Tips for inference on NUMA systems</a> section.
-
-Other general recommendations:
--	Usually, batching improves CPU performance. However, the need to gather frames in the batch might complicate the application logic. Instead, you can keep a separate infer request per camera or other source of input and process the requests in parallel. For more information, see the next section.
--	If your application simultaneously performs inference of multiple models on the same CPU, make sure you do not oversubscribe the machine. See <a href="#running-multiple-requests-simultaneously">Performance Aspects of Running Multiple Requests Simultaneously</a> for more information.
--	Notice that the heterogeneous execution might implicitly load the CPU. For details, refer to the <a href="#heterogeneity">Heterogeneity</a> section.
-- 	Consider [8-bit integer inference on the CPU](../IE_DG/Int8Inference.md).
--   In order to save power, the OS can decrease the CPU frequency and increase a volatility of the latency values.  Similarly the Intel® Turbo Boost Technolog may also affect the stability of results. For best reproducibility, consider locking the frequency to the processor base frequency (refer to the https://ark.intel.com/ for your specific CPU). For example, in Linux setting the releveant values for the /sys/devices/system/cpu/cpu* entries does the trick. High-level commands like cpupower also exists:
-```bash
-$ cpupower frequency-set --min 3.1GHz
-```
-
-#### Throughput Mode for CPU <a name="cpu-streams"></a>
-Unlike most accelerators, CPU is perceived as an inherently latency-oriented device.
-In fact, OpenVINO supports the "throughput" mode for the CPU, which allows the Inference Engine to efficiently run multiple inference requests on the CPU simultaneously, greatly improving the overall throughput.
-
-Internally, the execution resources are split/pinned into execution "streams".
-This feature usually provides much better performance for the networks than batching. This is especially true for the many-core server machines:
-![](../img/cpu_streams_explained_1.png)
-
-Compared with the batching, the parallelism is somewhat transposed (i.e. performed over inputs, and much less within CNN ops):
-![](../img/cpu_streams_explained.png)
-
-Try the [Benchmark App](../../samples/cpp/benchmark_app/README.md) sample and play with the number of streams running in parallel. The rule of thumb is tying up to a number of CPU cores on your machine.
-For example, on an 8-core CPU, compare the `-nstreams 1` (which is a legacy, latency-oriented scenario) to the 2, 4, and 8 streams.
-Notice that on a multi-socket machine, the bare minimum of streams for a latency scenario equals the number of sockets.
-
-In addition, you can play with the batch size to find the throughput sweet spot.
-
-If your application is hard or impossible to change in accordance with the multiple-requests logic, consider the "multiple-instance" trick to improve the throughput:
--   For multi-socket execution, it is recommended to set   [`KEY_CPU_THREADS_NUM`](../IE_DG/supported_plugins/CPU.md) to the number of cores per socket, and run as many instances of the application as you have sockets.
--   Similarly, for extremely lightweight networks (running faster than 1ms) and/or many-core machines (16+ cores), try limiting the number of CPU inference threads to just `#&zwj;phys` cores and further, while trying to saturate the machine with running multiple instances of the application.
-
-
-### GPU Checklist <a name="gpu-checklist"></a>
-
-Inference Engine relies on the [Compute Library for Deep Neural Networks (clDNN)](https://01.org/cldnn) for Convolutional Neural Networks acceleration on Intel® GPUs. Internally, clDNN uses OpenCL™ to implement the kernels. Thus, many general tips apply:
-
--	Prefer `FP16` over `FP32`, as the Model Optimizer can generate both variants and the `FP32` is default.
-- 	Try to group individual infer jobs by using batches.
--	Notice that using the GPU introduces one-time overhead (order of few seconds) of compiling the OpenCL kernels. The compilation happens upon loading the network to the GPU plugin and does not affect the inference time.
--	If your application is simultaneously using the inference on the CPU or otherwise loads the host heavily, make sure that the OpenCL driver threads do not starve. You can use [CPU configuration options](../IE_DG/supported_plugins/CPU.md) to limit number of inference threads for the CPU plugin.
--	In the GPU-only scenario, a GPU driver might occupy a CPU core with spin-looped polling for completion. If the _CPU_ utilization is a concern, consider the `KEY_CLDND_PLUGIN_THROTTLE` configuration option.
-
-> **NOTE**: See the [Benchmark App Sample](../../samples/cpp/benchmark_app/README.md) code for a usage example.
-Notice that while disabling the polling, this option might reduce the GPU performance, so usually this option is used with multiple [GPU streams](../IE_DG/supported_plugins/GPU.md).
-
-
-### Intel® Movidius™ Myriad™ X Visual Processing Unit and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs  <a name="myriad"></a>
-
-Since Intel® Movidius™ Myriad™ X Visual Processing Unit (Intel® Movidius™ Myriad™ 2 VPU) communicates with the host over USB, minimum four infer requests in flight are recommended to hide the data transfer costs. See <a href="#new-request-based-api">Request-Based API and “GetBlob” Idiom</a> and [Benchmark App Sample](../../samples/cpp/benchmark_app/README.md) for more information.
-
-Intel® Vision Accelerator Design with Intel® Movidius™ VPUs requires keeping at least 32 inference requests in flight to fully saturate the device.
-
-## Heterogeneity <a name="heterogeneity"></a>
-
-Heterogeneous execution (constituted by the dedicated Inference Engine [“Hetero” plugin](../IE_DG/supported_plugins/HETERO.md)) enables to schedule a network inference to the multiple devices.
-
-### Typical Heterogeneous Scenarios of Concern <a name="heterogeneous-scenarios-of-concern"></a>
-
-The primary points for executing a network in heterogeneous mode are as follows:
-
--	Calculate the heaviest pieces of the network with an accelerator while falling back to the CPU for the layers that are not supported by the accelerator.<br>
-	This is particularly useful when certain custom (user) kernels are implemented only for the CPU (and much harder or even impossible to implement for the accelerator).
-
--	Use all available compute devices more efficiently, for example, by running branches of the network on the different devices.
-
-### Heterogeneous Flow <a name="heterogeneous-flow"></a>
-
-The execution through heterogeneous plugin has three distinct steps:
-
-1.	**Applying affinity setting for the layers**, that is, binding them to the devices.
-
-	-	This can be done automatically using *fallback priorities*, or on the *per-layer* basis.
-
-	-	The affinity setting is made before loading the network to the (heterogeneous) plugin, so this is always a **static** setup with respect to execution.
-
-2.	**Loading a network to the heterogeneous plugin**, which internally splits the network into subgraphs.<br>
-	You can check the decisions the plugin makes, see <a href="#analyzing-heterogeneous-execution">Analyzing the Heterogeneous Execution</a>.
-
-3.	**Executing the infer requests**. From user’s side, this looks identical to a single-device case, while internally, the subgraphs are executed by actual plugins/devices.
-
-Performance benefits of the heterogeneous execution depend heavily on the communications granularity between devices. If transmitting/converting data from one part device to another takes more time than the execution, the heterogeneous approach makes little or no sense. Using Intel® VTune™ helps to visualize the execution flow on a timeline (see <a href="#vtune-examples">Intel® VTune™ Examples</a>).
-
-Similarly, if there are too many subgraphs, the synchronization and data transfers might eat the entire performance. In some cases, you can define the (coarser) affinity manually to avoid sending data back and forth many times during one inference.
-
-The general affinity rule of thumb is to keep computationally-intensive kernels on the accelerator, and "glue" or helper kernels on the CPU. Notice that this includes the granularity considerations. For example, running some custom activation (that comes after every accelerator-equipped convolution) on the CPU might result in performance degradation due to too much data type and/or layout conversions, even though the activation itself can be extremely fast. In this case, it might make sense to consider implementing the kernel for the accelerator (see <a href="#optimizing-custom-kernels">Optimizing Custom Kernels</a>). The conversions typically manifest themselves as outstanding (comparing to CPU-only execution) 'Reorder' entries (see <a href="#performance-counters">Internal Inference Performance Counters</a>).
-
-For general details on the heterogeneous plugin, refer to the [corresponding section in the Inference Engine Developer Guide](../IE_DG/supported_plugins/HETERO.md).
-
-### Trying the Heterogeneous Plugin with Inference Engine Samples <a name="heterogeneous-plugin-with-samples"></a>
-
-Target device cand be specified from command line for every Inference Engine sample. 
-
-For example, here is a command to run an [Classification Sample Async](../../samples/cpp/classification_sample_async/README.md):
-
-```sh
-./classification_sample_async -m  <path_to_model>/Model.xml -i <path_to_pictures>/picture.jpg -d HETERO:GPU,CPU
-```
-
-where:
-
--	`HETERO` stands for Heterogeneous plugin.
--	`GPU,CPU` points to fallback policy with first priority on GPU and further fallback to CPU.
-
-You can point more than two devices: `-d HETERO:GPU,MYRIAD,CPU`.
-
-### General Tips on GPU/CPU Execution <a name="tips-on-gpu-cpu-execution"></a>
-
-The following tips are provided to give general guidance on optimizing execution on GPU/CPU devices.
-
--	Generally, GPU performance is better on heavy kernels (like Convolutions) and large inputs. So if the network inference time is already too small (~1ms of execution time), using the GPU would unlikely give a boost.
-
--	A typical strategy to start with is to test the CPU-only and GPU-only scenarios first (with samples this is plain `-d CPU` or `-d GPU`). If there are specific kernels that are not supported by the GPU, the best option to try is the `HETERO:GPU,CPU` that automatically applies default splitting (based on the plugins layers support). Then, you can play with the manual affinity settings (for example, to further minimize the number of subgraphs).
-
--	The general affinity “rule of thumb” is to keep computationally-intensive kernels on the accelerator, and "glue" (or helper) kernels on the CPU. Notice that this includes the granularity considerations. For example, running some (custom) activation on the CPU would result in too many conversions.
-
--	It is advised to do <a href="#analyzing-heterogeneous-execution">performance analysis</a> to determine “hotspot” kernels, which should be the first candidates for offloading. At the same time, it is often more efficient to offload some reasonably sized sequence of kernels, rather than individual kernels, to minimize scheduling and other runtime overhead.
-
--	Notice that the GPU can be busy with other tasks (like rendering). Similarly, the CPU can be in charge for the general OS routines and other application threads (see <a href="#note-on-app-level-threading">Note on the App-Level Threading</a>). Also, a high interrupt rate due to many subgraphs can raise the frequency of the device and drag down the frequency of another.
-
--	Device performance can be affected by dynamic frequency scaling. For example, running long kernels on both devices simultaneously might eventually result in one or both devices stopping use of the Intel® Turbo Boost Technology. This might result in overall performance decrease, even comparing to single-device scenario.
-
--	Mixing the `FP16` (GPU) and `FP32` (CPU) execution results in conversions and, thus, performance issues. If you are seeing a lot of heavy outstanding (compared to the CPU-only execution) Reorders, consider implementing actual GPU kernels. Refer to <a href="#performance-counters">Internal Inference Performance Counters</a> for more information.
-
-### Analyzing Heterogeneous Execution <a name="analyzing-heterogeneous-execution"></a>
-
-There is a dedicated configuration option that enables dumping the visualization of the subgraphs created by the heterogeneous plugin, please see code example in the [HETERO plugin documentation](https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_HETERO.html#analyzing_heterogeneous_execution)
-
-After enabling the configuration key, the heterogeneous plugin generates two files:
-
--	`hetero_affinity.dot` - per-layer affinities. This file is generated only if default fallback policy was executed (as otherwise you have set the affinities by yourself, so you know them).
--	`hetero_subgraphs.dot` - affinities per sub-graph. This file is written to the disk during execution of `Core::LoadNetwork` for the heterogeneous flow.
-
-You can use the GraphViz\* utility or `.dot` converters (for example, to `.png` or `.pdf`), like xdot\*, available on Linux\* OS with `sudo apt-get install xdot`. Below is an example of the output trimmed to the two last layers (one executed on the FPGA and another on the CPU):
-
-![](../img/output_trimmed.png)
-
-You can also use performance data (in the [Benchmark App](../../samples/cpp/benchmark_app/README.md), it is an option `-pc`) to get performance data on each subgraph. Again, refer to the [HETERO plugin documentation](https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_HETERO.html#analyzing_heterogeneous_execution) and to <a href="#performance-counters">Internal Inference Performance Counters</a> for information on general counters.
-
-## Optimizing Custom Kernels <a name="optimizing-custom-kernels"></a>
-
-### A Few Initial Performance Considerations <a name="initial-performance-considerations"></a>
-
-The Inference Engine supports CPU, GPU and VPU custom kernels. Typically, custom kernels are used to quickly implement missing layers for new topologies. You should not override standard layers implementation, especially on the critical path, for example, Convolutions.  Also, overriding existing layers can disable some existing performance optimizations, such as fusing.
-
-It is usually easier to start with the CPU extension and switch to the GPU after debugging with the CPU path. Sometimes, when the custom layers are at the very end of your pipeline, it is easier to implement them as regular post-processing in your application without wrapping them as kernels. This is particularly true for the kernels that do not fit the GPU well, for example, output bounding boxes sorting. In many cases, you can do such post-processing on the CPU.
-
-There are many cases when sequence of the custom kernels can be implemented as a "super" kernel, allowing you to save on data accesses.
-
-Finally, with the heterogeneous execution, it is possible to execute the vast majority of intensive computations with the accelerator and keep the custom pieces on the CPU. The tradeoff is granularity/costs of communication between different devices.
-
-For more details on custom layers in Inference Engine, see [Inference Engine Extensibility Mechanism](../IE_DG/Extensibility_DG/Intro.md)
-
-### Understanding Performance Contribution of Your Custom Kernels <a name="performance-contribution-of-custom-kernels"></a>
-
-In most cases, before actually implementing a full-blown code for the kernel, you can estimate the final performance by doing a simple stub kernel that does nothing (and thus is "infinitely" fast) just to let the topology execute end-to-end. Of course, the estimation is valid only if the kernel output does not affect the performance, for instance, if its output is not driving any branches or loops.
-
-Other than that, when implementing the kernels, you can try the methods from the previous chapter to understand actual contribution and, if any custom kernel is in the hotspots, optimize that.
-
-### A Few Device-Specific Tips <a name="device-specific-tips"></a>
-
-- As already outlined in the <a href="#cpu-checklist">CPU Checklist</a>, align the threading model that you use in your CPU kernels with the model that the rest of the Inference Engine compiled with.
-- For CPU extensions, consider kernel flavor that supports blocked layout, if your kernel is in the hotspots (see <a href="#performance-counters">Internal Inference Performance Counters</a>). Since Intel MKL-DNN internally operates on the blocked layouts, this would save you a data packing (Reorder) on tensor inputs/outputs of your kernel.
-
-## Plugging Inference Engine to Applications <a name="plugging-ie-to-applications"></a>
-
-### Tips for inference on NUMA systems <a name="note-on-numa"></a>
-For inference on the CPU there are multiple threads binding options, see
-[CPU configuration options](../IE_DG/supported_plugins/CPU.md).
- - 'YES' (default) binding option maps threads to cores and works best for static/synthetic scenarios like benchmarks. It uses only available cores (determined thru the process mask) and binds the threads in a round-robin fashion.
- - 'NUMA' binding may perform better in the real-life  scenarios, leaving much more room for the OS scheduling. The main anticipated usage is _contended_ scenarios, for example multiple (CPU) inference-heavy apps executed simultaneously on a single machine.
-
-If you are building an app-level pipeline with third-party components like GStreamer*, the general guidance for NUMA machines is as follows:
-- Whenever possible, use at least one instance of the pipeline per NUMA node:
-   - Pin the _entire_ pipeline instance to the specific NUMA node at the outer-most level (for example, use Kubernetes* and/or `numactl` command with proper settings before  actual GStreamer commands).
-   - Disable any individual pinning by the pipeline components (for example, set [CPU_BIND_THREAD to 'NO'](../IE_DG/supported_plugins/CPU.md)).
-   - Limit each instance with respect to number of inference threads. Use  [CPU_THREADS_NUM](../IE_DG/supported_plugins/CPU.md) or  or other means (e.g. virtualization, Kubernetes*, etc), to avoid oversubscription.
-- If pinning instancing/pinning of the entire pipeline is not possible or desirable, relax the inference threads pinning to just 'NUMA'.
-   - This is less restrictive compared to the default pinning of threads to cores, yet avoids NUMA penalties.
-
-### Note on the App-Level Threading <a name="note-on-app-level-threading"></a>
-
-- As explained in the <a href="#cpu-checklist">CPU Checklist</a> section, by default the Inference Engine uses Intel TBB as a parallel engine. Thus, any OpenVINO-internal threading (including CPU inference) uses the same threads pool, provided by the TBB. But there are also other threads in your application, so oversubscription is possible at the application level:
-- The rule of thumb is that you should try to have the overall number of active threads in your application equal to the number of cores in your machine. Keep in mind the spare core(s) that the OpenCL driver under the GPU plugin might also need.
-- One specific workaround to limit the number of threads for the Inference Engine is using the [CPU configuration options](../IE_DG/supported_plugins/CPU.md).
-- To avoid further oversubscription, use the same threading model in all modules/libraries that your application uses. Notice that third party components might bring their own threading. For example, using Inference Engine which is now compiled with the TBB by default might lead to [performance troubles](https://www.threadingbuildingblocks.org/docs/help/reference/appendices/known_issues/interoperability.html) when mixed in the same app with another computationally-intensive library, but compiled with OpenMP. You can try to compile the [open source version](https://github.com/openvinotoolkit/openvino) of the Inference Engine to use the OpenMP as well. But notice that in general, the TBB offers much better composability, than other threading solutions.
-- If your code (or third party libraries) uses GNU OpenMP, the Intel® OpenMP (if you have recompiled Inference Engine with that) must be initialized first. This can be achieved by linking your application with the Intel OpenMP instead of GNU OpenMP, or using `LD_PRELOAD` on Linux* OS.
-
-### Letting the Inference Engine Accelerate Image Pre-processing/Conversion <a name="image-preprocessing"></a>
-
-In many cases, a network expects a pre-processed image, so make sure you do not perform unnecessary steps in your code:
-- Model Optimizer can efficiently bake the mean and normalization (scale) values into the model (for example, weights of the first convolution). See <a href="#mo-knobs-related-to-performance">Model Optimizer Knobs Related to Performance</a>.
-- If regular 8-bit per channel images are your native media (for instance, decoded frames), do not convert to the `FP32` on your side, as this is something that plugins can accelerate. Use the `InferenceEngine::Precision::U8` as your input format:<br>
-
-@snippet snippets/dldt_optimization_guide1.cpp part1
-
-Note that in many cases, you can directly share the (input) data with the Inference Engine.
-
-### Basic Interoperability with Other APIs <a name="basic-interoperability-with-other-apis"></a>
-
-The general approach for sharing data between Inference Engine and media/graphics APIs like Intel® Media Server Studio (Intel® MSS) is based on sharing the *system* memory.  That is, in your code, you should map or copy the data from the API to the CPU address space first.
-
-For Intel® Media SDK, it is recommended to perform a viable pre-processing, for example, crop/resize, and then convert to RGB again with the [Video Processing Procedures (VPP)](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onevpl.htm). Then lock the result and create an Inference Engine blob on top of that. The resulting pointer can be used for `SetBlob`:
-
-@snippet snippets/dldt_optimization_guide2.cpp part2
-
-Using the `InferenceEngine::NHWC` layout:
-
-@snippet snippets/dldt_optimization_guide3.cpp part3
-
-Alternatively, you can use an RGBP (planar RGB) output from Intel® Media SDK. This allows you to wrap the (locked) result as regular NCHW. Then you can use it with `SetBlob` just like in the previous example:
-
-@snippet snippets/dldt_optimization_guide4.cpp part4
-
-### OpenCV* Interoperability Example <a name="opencv-interoperability"></a>
-
-Unlike APIs that use dedicated address space and/or special data layouts (for instance, compressed OpenGL* textures), regular OpenCV data objects like `cv::Mat` reside in the conventional system memory. That is, the memory can be actually shared with the Inference Engine and only data ownership to be transferred.
-
-Again, if the OpenCV and Inference Engine layouts match, the data can be wrapped as Inference Engine (input/output) blob. Notice that by default, Inference Engine accepts the **planar** and **not interleaved** inputs in NCHW, so the NHWC (which is exactly the interleaved layout) should be specified explicitly:
-
-**WARNING**: The `InferenceEngine::NHWC` layout is not supported natively by most InferenceEngine plugins so internal conversion might happen.
-
-@snippet snippets/dldt_optimization_guide5.cpp part5
-
-Notice that original `cv::Mat`/blobs cannot be used simultaneously by the application and the Inference Engine. Alternatively, the data that the pointer references to can be copied to unlock the original data and return ownership to the original API.
-
-### Request-Based API and “GetBlob” Idiom <a name="new-request-based-api"></a>
-
-Infer Request based API offers two types of request: Sync and Async. The Sync is considered below. The Async splits (synchronous) `Infer` into `StartAsync` and `Wait` (see <a href="#ie-async-api">Inference Engine Async API</a>).
-
-More importantly, an infer request encapsulates the reference to the “executable” network and actual inputs/outputs. Now, when you load the network to the plugin, you get a reference to the executable network (you may consider that as a queue). Actual infer requests are created by the executable network:
-
-@snippet snippets/dldt_optimization_guide6.cpp part6
-
-`GetBlob` is a recommend way to communicate with the network, as it internally allocates the data with right padding/alignment for the device. For example, the GPU inputs/outputs blobs are mapped to the host (which is fast) if the `GetBlob` is used. But if you called the `SetBlob`, the copy (from/to the blob you have set) into the internal GPU plugin structures will happen.
-
-### Performance Aspects of Running Multiple Requests Simultaneously <a name="running-multiple-requests-simultaneously"></a>
-
-If your application simultaneously executes multiple inference requests:
-
-- 	For the CPU, the best solution is to use the <a href="#cpu-streams">CPU "throughput" mode</a>.
-	-	If latency is of more concern, you can try the `EXCLUSIVE_ASYNC_REQUESTS` [configuration option](../IE_DG/supported_plugins/CPU.md), which limits the number of the simultaneously executed requests for all (executable) networks that share the specific device to just one:<br>
-
-@snippet snippets/dldt_optimization_guide7.cpp part7
-
-<br>For more information on the executable networks notation, see <a href="#new-request-based-api">Request-Based API and “GetBlob” Idiom</a>.
-
--	The heterogeneous device uses the `EXCLUSIVE_ASYNC_REQUESTS` by default.
-
--	`KEY_EXCLUSIVE_ASYNC_REQUESTS` option affects only device queues of the individual application.
-
--	For GPU, the actual work is serialized by a plugin and/or a driver anyway.
-
-- 	Finally, for <a href="#myriad">any VPU model</a>, using multiple requests is a must for achieving good throughput. 
-
-In the Inference Engine, there is no notion of requests priorities. It is left to the user side (for example, not queuing the low priority infer request, until another higher priority is waiting). Notice that it would require additional logic to synchronize between executable networks (queues) in your application code.
-
-### Inference Engine Async API <a name="ie-async-api"></a>
-
-Inference Engine Async API can improve overall frame rate of the application. While accelerator is busy with the inference, the application can continue doing things on the host rather than wait for the inference to complete.
-
-In the example below, inference is applied to the results of the video decoding. So it is possible to keep two parallel infer requests, and while the current is processed, the input frame for the next is being captured. This essentially hides the latency of capturing, so that the overall frame rate is rather determined only by the slowest part of the pipeline (decoding IR inference) and not by the sum of the stages.
-
-You can compare the pseudo-codes for the regular and async-based approaches:
-
--	In the regular way, the frame is captured with OpenCV and then immediately processed:<br>
-
-@snippet snippets/dldt_optimization_guide8.cpp part8
-
-![Intel® VTune™ screenshot](../img/vtune_regular.png)
-
--	In the "true" async mode, the `NEXT` request is populated in the main (application) thread, while the `CURRENT` request is processed:<br>
-
-@snippet snippets/dldt_optimization_guide9.cpp part9
-
-![Intel® VTune™ screenshot](../img/vtune_async.png)
-
-The technique can be generalized to any available parallel slack. For example, you can do inference and simultaneously encode the resulting or previous frames or run further inference, like emotion detection on top of the face detection results.
-
-There are important performance caveats though: for example, the tasks that run in parallel should try to avoid oversubscribing the shared compute resources. If the inference is performed on the FPGA and the CPU is essentially idle, it makes sense to do things on the CPU in parallel. However, multiple infer requests can oversubscribe that. Notice that heterogeneous execution can implicitly use the CPU, refer to <a href="#heterogeneity">Heterogeneity</a>.
-
-Also, if the inference is performed on the graphics processing unit (GPU), there is little gain in doing the encoding of the resulting video on the same GPU in parallel, for instance, because the device is already busy.
-
-Refer to the [Object Detection SSD Demo](@ref omz_demos_object_detection_demo_cpp) (latency-oriented Async API showcase) and [Benchmark App Sample](../../samples/cpp/benchmark_app/README.md) (which has both latency and throughput-oriented modes) for complete examples of the Async API in action.
-
-## Using Tools <a name="using-tools"></a>
-
-Whether you are tuning for the first time or doing advanced performance optimization, you need a a tool that provides accurate insights. Intel® VTune™ Amplifier gives you the tool to mine it and interpret the profiling data.
-
-Alternatively, you can gather the raw profiling data that samples report, the second chapter provides example of how to interpret these.
-
-### Intel® VTune™ Examples <a name="vtune-examples"></a>
-
-All major performance calls of the Inference Engine are instrumented with Instrumentation and Tracing Technology APIs. This allows viewing the Inference Engine calls on the Intel® VTune™ timelines and aggregations plus correlating them to the underlying APIs, like OpenCL.  In turn, this enables careful per-layer execution breakdown.
-
-When choosing the Analysis type in Intel® VTune™ Amplifier, make sure to select the **Analyze user tasks, events, and counters** option:
-
-![](../img/vtune_option.jpg)
-
-See the [corresponding section in the Intel® VTune™ Amplifier User's Guide](https://software.intel.com/en-us/vtune-amplifier-help-task-analysis) for details.
-
-Example of Inference Engine calls:
-
--	On the Intel VTune Amplifier timeline.
-	Notice that `Task_runNOThrow` is an Async API wrapper and it is executed in a different thread and triggers the Intel MKL-DNN execution:
-
-	![](../img/vtune_timeline.png)
-
--	In the Intel VTune Amplifier **Top-down view**, grouped by the **Task Domain**.
-	Notice the `Task_runNoThrow` and `MKLDNN _INFER` that are bracketing the actual Intel MKL-DNN kernels execution:
-
-	![](../img/vtune_topdown_view.jpg)
-
-Similarly, you can use any GPU analysis in the Intel VTune Amplifier and get general correlation with Inference Engine API as well as the execution breakdown for OpenCL kernels.
-
-Just like with regular native application, further drill down in the counters is possible, however, this is mostly useful for <a href="#optimizing-custom-kernels">optimizing custom kernels</a>. Finally, with the Intel VTune Amplifier, the profiling is not limited to your user-level code (see the [corresponding section in the Intel® VTune™ Amplifier User's Guide](https://software.intel.com/en-us/vtune-amplifier-help-analyze-performance)).
-
-### Internal Inference Performance Counters <a name="performance-counters"></a>
-
-Almost every sample (inspect command-line options for a specific sample with `-h`) supports a `-pc` command that outputs internal execution breakdown. Refer to the [samples code](../IE_DG/Samples_Overview.md) for the actual Inference Engine API behind that.
-
-Below is example of CPU plugin output for a network (since the device is CPU, the layers wall clock `realTime` and the `cpu` time are the same):
-
-```
-conv1      EXECUTED       layerType: Convolution        realTime: 706        cpu: 706            execType: jit_avx2
-conv2_1_x1  EXECUTED       layerType: Convolution        realTime: 137        cpu: 137            execType: jit_avx2_1x1
-fc6        EXECUTED       layerType: Convolution        realTime: 233        cpu: 233            execType: jit_avx2_1x1
-fc6_nChw8c_nchw      EXECUTED  layerType: Reorder           realTime: 20         cpu: 20             execType: reorder
-out_fc6         EXECUTED       layerType: Output            realTime: 3          cpu: 3              execType: unknown
-relu5_9_x2    OPTIMIZED_OUT     layerType: ReLU             realTime: 0          cpu: 0              execType: undef
-```
-
-This contains layers name (as seen in IR), layers type and execution statistics. Notice the `OPTIMIZED_OUT`, which indicates that the particular activation was fused into adjacent convolution. Also, the `unknown` stays for the Inference Engine specific CPU (helper) primitives that are not part of the Intel MKL-DNN.
-
-Notice that there are some helper layers in the CPU execution breakdown, which were not presented in the original topology. These are automatically added by the plugin. For example, the `Reorder` re-packs the Intel MKL-DNN internal (blocked) layout to the regular plain NCHW (that the user expects as the output). As explained in the <a href="#device-specific-tips">Few Device-Specific Tips</a>, if your custom kernels introduces a lot of outstanding/expensive Reorders, consider blocked implementation for the kernels.
-
-Notice that in the heterogeneous cases, there will be additional information on which subgraph the statistics is about (the first subgraph is GPU, so its `cpu`/host time is really small compared to the actual `realTime`):
-
-```
-subgraph1: squeeze1x1   	  EXECUTED       layerType: Convolution        realTime: 227    cpu:3    execType: GPU
-…
-subgraph2: detection_out      EXECUTED       layerType: DetectionOutput    realTime: 121 cpu:121  execType: unknown
-…
-```
-
-As mentioned earlier, `unknown` here means CPU kernel with unknown (for example, not AVX2 or AVX512) acceleration path.
-Since FPGA execution does not separate individual kernels, only bulk execution/data transfer statistics is available:
-
-```
-subgraph1: 1. input preprocessing (mean data/FPGA):EXECUTED   layerType: preprocessing   realTime: 129     cpu: 129
-subgraph1: 2. input transfer to DDR:EXECUTED       layerType:                    realTime: 201        cpu: 0
-subgraph1: 3. FPGA execute time:EXECUTED           layerType:                    realTime: 3808       cpu: 0              subgraph1: 4. output transfer from DDR:EXECUTED    layerType:                    realTime: 55         cpu: 0
-subgraph1: 5. FPGA output postprocessing:EXECUTED  layerType:                    realTime: 7          cpu: 7
-subgraph1: 6. softmax/copy:   EXECUTED       layerType:                    realTime: 2          cpu: 2
-subgraph2: out_prob:          NOT_RUN        layerType: Output             realTime: 0          cpu: 0
-subgraph2: prob:              EXECUTED       layerType: SoftMax            realTime: 10         cpu: 10
-Total time: 4212     microseconds
-```
-
-The `softmax/copy` is a glue layer that connects the FPGA subgraph to the CPU subgraph (and copies the data).
-
-## See Also <a name="see-also"></a>
-
--	[Inference Engine Developer Guide](https://software.intel.com/en-us/articles/OpenVINO-inferengine)
--	[Model Optimizer Developer Guide](https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer)
+![](../img/WHAT_TO_USE.svg)
diff --git a/docs/ovsa/ovsa_get_started.md b/docs/ovsa/ovsa_get_started.md
index 7f541dd956c..405596a3d48 100644
--- a/docs/ovsa/ovsa_get_started.md
+++ b/docs/ovsa/ovsa_get_started.md
@@ -15,8 +15,16 @@ The OpenVINO™ Security Add-on works with the [OpenVINO™ Model Server](@ref o
 
 The OpenVINO™ Security Add-on consists of three components that run in Kernel-based Virtual Machines (KVMs). These components provide a way to run security-sensitive operations in an isolated environment. A brief description of the three components are as follows. Click each triangled line for more information about each. 
 
-<details>
-    <summary><strong>OpenVINO™ Security Add-on Tool</strong>: As a Model Developer or Independent Software Vendor, you use the OpenVINO™ Security Add-on Tool(`ovsatool`) to generate a access controlled model and master license. </summary>
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>OpenVINO™ Security Add-on Tool</strong>: As a Model Developer or Independent Software Vendor, you use the OpenVINO™ Security Add-on Tool(`ovsatool`) to generate a access controlled model and master license.">
+
+@endsphinxdirective
+
+
 
 - The Model Developer generates a access controlled model from the OpenVINO™ toolkit output. The access controlled model uses the model's Intermediate Representation (IR) files to create a access controlled output file archive that are distributed to Model Users. The Developer can also put the archive file in long-term storage or back it up without additional security. 
 
@@ -24,19 +32,53 @@ The OpenVINO™ Security Add-on consists of three components that run in Kernel-
 
 - The Model Developer generates user-specific licenses in a JSON format file for the access controlled model. The Model Developer can define global or user-specific licenses and attach licensing policies to the licenses. For example, the Model Developer can add a time limit for a model or limit the number of times a user can run a model. 
 
-</details>
 
-<details>
-    <summary><strong>OpenVINO™ Security Add-on License Service</strong>: Use the OpenVINO™ Security Add-on License Service to verify user parameters.</summary>
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>OpenVINO™ Security Add-on License Service</strong>: Use the OpenVINO™ Security Add-on License Service to verify user parameters.">
+
+@endsphinxdirective
+
+
 
 - The Independent Software Vendor hosts the OpenVINO™ Security Add-on License Service, which responds to license validation requests when a user attempts to load a access controlled model in a model server. The licenses are registered with the OpenVINO™ Security Add-on License Service.
 
 - When a user loads the model, the OpenVINO™ Security Add-on Runtime contacts the License Service to make sure the license is valid and within the parameters that the Model Developer defined with the OpenVINO™ Security Add-on Tool (<code>ovsatool</code>). The user must be able to reach the Independent Software Vendor's License Service over the Internet. 
 
-</details>
 
-<details>
-    <summary><strong>OpenVINO™ Security Add-on Runtime</strong>: Users install and use the OpenVINO™ Security Add-on Runtime on a virtual machine. </summary>
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+
+
+
+@sphinxdirective
+
+.. raw:: html
+
+   <div class="collapsible-section" data-title="<strong>OpenVINO™ Security Add-on Runtime</strong>: Users install and use the OpenVINO™ Security Add-on Runtime on a virtual machine. ">
+
+@endsphinxdirective
+
+
 
 Users host the OpenVINO™ Security Add-on Runtime component in a virtual machine. 
 
@@ -44,7 +86,16 @@ Externally from the OpenVINO™ Security Add-on, the User adds the access contro
 
 After the license is successfully validated, the OpenVINO™ Model Server loads the model and services the inference requests. 
 
-</details> 
+
+@sphinxdirective
+
+.. raw:: html
+
+   </div>
+
+@endsphinxdirective
+
+ 
 
 <br>
 **Where the OpenVINO™ Security Add-on Fits into Model Development and Deployment**
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 00000000000..75463025ec9
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,34 @@
+alabaster
+Babel
+beautifulsoup4
+certifi
+charset-normalizer
+Cython
+docutils==0.14
+idna
+imagesize
+Jinja2
+lxml
+MarkupSafe
+mistune
+packaging
+pydata-sphinx-theme
+Pygments
+pyparsing
+pytz
+requests
+snowballstemmer
+soupsieve
+Sphinx
+sphinx-copybutton
+sphinx-inline-tabs
+sphinxcontrib-applehelp
+sphinxcontrib-devhelp
+sphinxcontrib-htmlhelp
+sphinxcontrib-jsmath
+sphinxcontrib-qthelp
+sphinxcontrib-serializinghtml
+urllib3
+sphinx_sitemap
+pytest
+pytest-html
diff --git a/docs/resources.md b/docs/resources.md
new file mode 100644
index 00000000000..842d1526a51
--- /dev/null
+++ b/docs/resources.md
@@ -0,0 +1,55 @@
+# Resources {#resources}
+
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Additional Resources
+   
+   Release Notes <https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html>
+   Supported Frameworks
+   openvino_docs_IE_DG_supported_plugins_Supported_Devices
+   glossary
+   openvino_docs_Legal_Information
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Intermediate Representation and Operations Sets
+   :hidden:
+    
+   openvino_docs_ops_opset
+   openvino_docs_ops_broadcast_rules
+   openvino_docs_operations_specifications
+
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+   :caption: Case Studies
+
+   Case Studies <https://www.intel.com/openvino-success-stories>
+
+@endsphinxdirective
+
+
+This section includes a variety of reference information in three broad categories.
+
+### Additional Resources
+[Release Notes](https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html) contains change logs and notes for each OpenVINO release.
+
+[Supported Devices](IE_DG/supported_plugins/Supported_Devices.md) is compatibility information about supported hardware accelerators.
+
+[Legal Information](Legal_Information.md) has trademark information and other legal statements.
+
+### Intermediate Representation and Operations Sets
+[Available Operation Sets](ops/opset.md) is a list of supported operations and explanation of supported capabilities.
+
+[Broadcast Rules for Elementwise Operations](ops/broadcast_rules.md) explains the rules used for to support an arbitrary number of dimensions in neural nets.
+
+[Operation Specifications](IE_DG/Operations_specifications.md) is a detailed reference of supported operations.
+
+### Case Studies
+Links to [articles](https://www.intel.com/openvino-success-stories) about real-world examples of OpenVINO™ usage.
diff --git a/docs/scripts/copy_images.py b/docs/scripts/copy_images.py
new file mode 100644
index 00000000000..403ff3e3683
--- /dev/null
+++ b/docs/scripts/copy_images.py
@@ -0,0 +1,31 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import shutil
+from pathlib import Path
+
+
+def copy_images(input_dir: Path, output_dir: Path):
+    """
+    Copy images from doxygen xml folder to sphinx folder
+    """
+    output_dir.mkdir(parents=True, exist_ok=True)
+    extensions = ('*.png', '*.jpg', '*.svg', '*.gif', '*.PNG', '*.JPG', '*.SVG', '*.GIF')
+    for extension in extensions:
+        for file in input_dir.glob(extension):
+            shutil.copy(file, output_dir)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('input_dir', type=Path, help='Path to the folder containing images.')
+    parser.add_argument('output_dir', type=Path, help='Path to the output folder')
+    args = parser.parse_args()
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    copy_images(input_dir, output_dir)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/scripts/create_mapping.py b/docs/scripts/create_mapping.py
new file mode 100644
index 00000000000..fb32a12cc5f
--- /dev/null
+++ b/docs/scripts/create_mapping.py
@@ -0,0 +1,85 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import json
+import logging
+from lxml import etree
+from pathlib import Path
+
+REPOSITORIES = [
+    'openvino',
+    'omz'
+]
+
+def create_mapping(xml_input: Path, output_dir: Path, strip_path: Path):
+    """
+    Create a mapping between doxygen label and file path for edit on github button.
+    """
+    xml_input = xml_input.resolve()
+    output_dir = output_dir.resolve()
+    strip_path = strip_path.resolve()
+    mapping = {
+        'get_started': 'openvino/docs/get_started.md',
+        'documentation': 'openvino/docs/documentation.md',
+        'index': 'openvino/docs/index.rst',
+        'model_zoo': 'openvino/docs/model_zoo.md',
+        'resources': 'openvino/docs/resources.md',
+        'tutorials': 'openvino/docs/tutorials.md',
+        'tuning_utilities': 'openvino/docs/tuning_utilities.md'
+    }
+    output_dir.mkdir(parents=True, exist_ok=True)
+    xml_files = xml_input.glob('*.xml')
+    for xml_file in xml_files:
+        try:
+            root = etree.parse(xml_file.as_posix()).getroot()
+            compounds = root.xpath('//compounddef')
+            for compound in compounds:
+                kind = compound.attrib['kind']
+                if kind in ['file', 'dir']:
+                    continue
+                name_tag = compound.find('compoundname')
+                name = name_tag.text
+                name = name.replace('::', '_1_1')
+                if kind == 'page':
+                    exclude = True
+                    for rep in REPOSITORIES:
+                        if name.startswith(rep):
+                            exclude = False
+                    if exclude:
+                        continue
+                else:
+                    name = kind + name
+                location_tag = compound.find('location')
+                file = Path(location_tag.attrib['file'])
+                if not file.suffix:
+                    continue
+                try:
+                    file = file.relative_to(strip_path)
+                except ValueError:
+                    logging.warning('{}: {} is not relative to {}.'.format(xml_file, file, strip_path))
+                mapping[name] = file.as_posix()
+        except AttributeError:
+            logging.warning('{}: Cannot find the origin file.'.format(xml_file))
+        except etree.XMLSyntaxError as e:
+            logging.warning('{}: {}.'.format(xml_file, e))
+
+    with open(output_dir.joinpath('mapping.json'), 'w') as f:
+        json.dump(mapping, f)
+
+
+def main():
+    logging.basicConfig()
+    parser = argparse.ArgumentParser()
+    parser.add_argument('xml_input', type=Path, help='Path to the folder containing doxygen xml files')
+    parser.add_argument('output_dir', type=Path, help='Path to the output folder')
+    parser.add_argument('strip_path', type=Path, help='Strip from path')
+    args = parser.parse_args()
+    xml_input = args.xml_input
+    output_dir = args.output_dir
+    strip_path = args.strip_path
+    create_mapping(xml_input, output_dir, strip_path)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/scripts/doxy_md_filter.py b/docs/scripts/doxy_md_filter.py
new file mode 100644
index 00000000000..651b85d095e
--- /dev/null
+++ b/docs/scripts/doxy_md_filter.py
@@ -0,0 +1,135 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import re
+import argparse
+from pathlib import Path
+import shutil
+import logging
+
+
+def get_label(file):
+    """
+    Read lines of a file and try to find a doxygen label.
+    If the label is not found return None.
+    Assume the label is in the first line
+    """
+    with open(file, 'r', encoding='utf-8') as f:
+        line = f.readline()
+        label = re.search(r'\{\#(.+)\}', line)
+        if label:
+            return label.group(1)
+
+
+def replace_links(content, items, md_folder, labels, docs_folder):
+    """
+    Replace markdown links with doxygen labels.
+    """
+    for item in items:
+        link = item
+        link_path = md_folder.joinpath(link).resolve()
+        if os.path.exists(link_path):
+            content = content.replace(link, '@ref ' + labels[link_path])
+        else:
+            rel_path = os.path.relpath(link_path, docs_folder).replace('\\', '/')
+            content = content.replace(link, rel_path)
+    return content
+
+
+def replace_image_links(content, images, input_dir, md_folder, output_dir):
+    for image in images:
+        new_path = md_folder.joinpath(image).resolve().relative_to(input_dir)
+        new_path = output_dir / new_path
+        content = content.replace(image, new_path.as_posix())
+    return content
+
+
+def add_htmlonly(content):
+    content = content.replace('<details>', '\n\\htmlonly\n<details>')
+    content = content.replace('</summary>', '</summary>\n\\endhtmlonly')
+    content = content.replace('</details>', '\n\\htmlonly\n</details>\n\\endhtmlonly')
+    content = content.replace('<iframe', '\n\\htmlonly\n<iframe')
+    content = content.replace('</iframe>', '</iframe>\n\\endhtmlonly')
+    return content
+
+
+def copy_file(file, content, input_dir, output_dir):
+    rel_path = file.relative_to(input_dir)
+    dest = output_dir.joinpath(rel_path)
+    dest.parents[0].mkdir(parents=True, exist_ok=True)
+    with open(dest, 'w', encoding='utf-8') as f:
+        f.write(content)
+
+
+def copy_image(file, input_dir, output_dir):
+    rel_path = file.relative_to(input_dir)
+    dest = output_dir.joinpath(rel_path)
+    dest.parents[0].mkdir(parents=True, exist_ok=True)
+    try:
+        shutil.copy(file, dest)
+    except FileNotFoundError:
+        logging.warning('{}: file not found'.format(file))
+
+
+def get_refs_by_regex(content, regex):
+    def map_func(path):
+        return (Path(path[0]), path[1]) if isinstance(path, tuple) else Path(path)
+
+    refs = set(map(map_func, re.findall(regex, content, flags=re.IGNORECASE)))
+    return refs
+
+
+def process(input_dir, output_dir, exclude_dirs):
+    """
+    Recursively find markdown files in docs_folder and
+    replace links to markdown files with doxygen labels (ex. @ref label_name).
+    """
+    md_files = input_dir.glob('**/*.md')
+    md_files = filter(lambda x: not any(ex_path in x.parents for ex_path in exclude_dirs), md_files)
+    label_to_file_map = dict(filter(lambda x: x[1], map(lambda f: (Path(f), get_label(f)), md_files)))
+    label_to_file_map.pop(None, None)
+    for md_file in label_to_file_map.keys():
+        md_folder = md_file.parents[0]
+        with open(md_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        inline_links = set(re.findall(r'!?\[.*?\]\(([\w\/\-\.]+\.md)\)', content))
+        reference_links = set(re.findall(r'\[.+\]\:\s*?([\w\/\-\.]+\.md)', content))
+        inline_images = set(re.findall(r'!?\[.*?\]\(([\w\/\-\.]+\.(?:png|jpg|gif|svg))\)', content, flags=re.IGNORECASE))
+        reference_images = set(re.findall(r'\[.+\]\:\s*?([\w\/\-\.]+\.(?:png|jpg|gif|svg))', content, flags=re.IGNORECASE))
+
+        images = inline_images
+        images.update(reference_images)
+
+        content = replace_image_links(content, images, input_dir, md_folder, output_dir)
+
+        md_links = inline_links
+        md_links.update(reference_links)
+        md_links = list(filter(lambda x: md_folder.joinpath(x) in label_to_file_map, md_links))
+        content = replace_links(content, md_links, md_folder, label_to_file_map, input_dir)
+        # content = add_htmlonly(content)
+
+        copy_file(md_file, content, input_dir, output_dir)
+
+        for image in images:
+            path = md_file.parents[0].joinpath(image)
+            copy_image(path, input_dir, output_dir)
+
+
+def main():
+    logging.basicConfig()
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_dir', type=Path, help='Path to a folder containing .md files.')
+    parser.add_argument('--output_dir', type=Path, help='Path to the output folder.')
+    parser.add_argument('--exclude_dir', type=Path, action='append', default=[], help='Ignore a folder.')
+    args = parser.parse_args()
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    exclude_dirs = args.exclude_dir
+    output_dir.mkdir(parents=True, exist_ok=True)
+    process(input_dir, output_dir, exclude_dirs)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/doxygen/log.py b/docs/scripts/log.py
similarity index 100%
rename from docs/doxygen/log.py
rename to docs/scripts/log.py
diff --git a/docs/scripts/prepare_xml.py b/docs/scripts/prepare_xml.py
new file mode 100644
index 00000000000..8e697b71e41
--- /dev/null
+++ b/docs/scripts/prepare_xml.py
@@ -0,0 +1,60 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import re
+import logging
+import argparse
+from lxml import etree
+from pathlib import Path
+from xml.sax import saxutils
+
+
+def prepare_xml(xml_dir: Path):
+    """
+    A preprocessing xml function
+    """
+    pattern = r'\<sphinxdirective\>(.+?)\<\/sphinxdirective>'
+    xml_files = xml_dir.glob('*.xml')
+
+    for xml_file in xml_files:
+        try:
+            with open(xml_file, 'r', encoding='utf-8') as f:
+                contents = f.read()
+
+            matches = re.findall(pattern, contents, flags=re.DOTALL)
+            if matches:
+                for match in matches:
+                    contents = contents.replace(match, saxutils.escape(match))
+            contents = str.encode(contents)
+            root = etree.fromstring(contents)
+            anchors = root.xpath('//anchor')
+            localanchors = list(filter(lambda x: x.attrib['id'].startswith('_1'), anchors))
+            for anc in localanchors:
+                text = anc.attrib['id']
+                heading = anc.getparent()
+                para = heading.getparent()
+                dd = para.getparent()
+                index = dd.index(para)
+                new_para = etree.Element('para')
+                sphinxdirective = etree.Element('sphinxdirective')
+                sphinxdirective.text = '\n\n.. _' + text[2:] + ':\n\n'
+                new_para.append(sphinxdirective)
+                dd.insert(index, new_para)
+
+            with open(xml_file, 'wb') as f:
+                f.write(etree.tostring(root))
+        except UnicodeDecodeError as err:
+            logging.warning('{}:{}'.format(xml_file, err))
+
+
+def main():
+    logging.basicConfig()
+    parser = argparse.ArgumentParser()
+    parser.add_argument('xml_dir', type=Path, help='Path to the folder containing xml files.')
+    args = parser.parse_args()
+    xml_dir = args.xml_dir
+    prepare_xml(xml_dir)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/scripts/remove_xml.py b/docs/scripts/remove_xml.py
new file mode 100644
index 00000000000..ed641b5c360
--- /dev/null
+++ b/docs/scripts/remove_xml.py
@@ -0,0 +1,25 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import shutil
+
+
+def remove_xml_dir(path):
+    """
+    Remove doxygen xml folder
+    """
+    if os.path.exists(path):
+        shutil.rmtree(path, ignore_errors=True)
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('xml_dir')
+    args = parser.parse_args()
+    remove_xml_dir(args.xml_dir)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/scripts/tests/conftest.py b/docs/scripts/tests/conftest.py
new file mode 100644
index 00000000000..9d3cb29d229
--- /dev/null
+++ b/docs/scripts/tests/conftest.py
@@ -0,0 +1,125 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+""" Configuration for tests.
+
+Tests for documentation utilize pytest test framework for tests execution
+and reports generation.
+
+Documentation generation tests process Doxygen log to generate test per
+documentation source file (.hpp, .md, etc. files). Source files
+with errors can be skipped (--doxygen-skip) or excluded temporary
+(--doxygen-xfail).
+
+Usage:
+pytest --doxygen doxygen.log --html doc-generation.html test_doc-generation.py
+"""
+from inspect import getsourcefile
+from contextlib import contextmanager
+import os
+import pytest
+from distutils.util import strtobool
+from utils.log import parse
+
+
+def pytest_addoption(parser):
+    """ Define extra options for pytest options
+    """
+    parser.addoption('--doxygen', help='Doxygen log path to run tests for')
+    parser.addoption(
+        '--doxygen-strip',
+        default='tmp_docs/',
+        help='Path to strip from paths found in doxygen log')
+    parser.addoption(
+        '--doxygen-xfail',
+        action='append',
+        default=[],
+        help='A file with relative paths to a files with known failures')
+    parser.addoption(
+        '--doxygen-skip',
+        action='append',
+        default=[],
+        help='A file with relative paths to a files to exclude from validation')
+    parser.addoption(
+        '--include_omz',
+        type=str,
+        required=False,
+        default='',
+        help='Include link check for omz docs')
+    parser.addoption(
+        '--include_wb',
+        type=str,
+        required=False,
+        default='',
+        help='Include link check for workbench docs')
+    parser.addoption(
+        '--include_pot',
+        type=str,
+        required=False,
+        default='',
+        help='Include link check for pot docs')
+    parser.addoption(
+        '--include_gst',
+        type=str,
+        required=False,
+        default='',
+        help='Include link check for gst docs')
+    parser.addoption(
+        '--include_ovms',
+        type=str,
+        required=False,
+        default='',
+        help='Include link check for ovms')
+
+
+def read_lists(configs):
+    """Read lines from files from configs. Return unique items.
+    """
+    files = set()
+    for config_path in configs:
+        try:
+            with open(config_path, 'r') as config:
+                files.update(map(str.strip, config.readlines()))
+        except OSError:
+            pass
+    return list(files)
+
+
+def pytest_generate_tests(metafunc):
+    """ Generate tests depending on command line options
+    """
+    # read log
+    with open(metafunc.config.getoption('doxygen'), 'r') as log:
+        all_files = parse(log.read(), metafunc.config.getoption('doxygen_strip'))
+
+    exclude_links = {'open_model_zoo', 'workbench', 'pot',  'gst', 'omz', 'ovms'}
+    if metafunc.config.getoption('include_omz'):
+        exclude_links.remove('omz')
+    if metafunc.config.getoption('include_wb'):
+        exclude_links.remove('workbench')
+    if metafunc.config.getoption('include_pot'):
+        exclude_links.remove('pot')
+    if metafunc.config.getoption('include_gst'):
+        exclude_links.remove('gst')
+    if metafunc.config.getoption('include_ovms'):
+        exclude_links.remove('ovms')
+
+    filtered_keys = filter(lambda line: not any([line.startswith(repo) for repo in exclude_links]), all_files)
+    files = {key: all_files[key] for key in filtered_keys}
+    # read mute lists
+    marks = dict()
+    marks.update(
+        (name, pytest.mark.xfail)
+        for name in read_lists(metafunc.config.getoption('doxygen_xfail')))
+    marks.update(
+        (name, pytest.mark.skip)
+        for name in read_lists(metafunc.config.getoption('doxygen_skip')))
+
+    # generate tests
+    if 'doxygen_errors' in metafunc.fixturenames:
+        metafunc.parametrize(
+            'doxygen_errors', [
+                pytest.param(errors, marks=marks[file])
+                if file in marks else errors for file, errors in files.items()
+            ],
+            ids=list(files.keys()))
diff --git a/docs/scripts/tests/test_docs.py b/docs/scripts/tests/test_docs.py
new file mode 100644
index 00000000000..9db3f609780
--- /dev/null
+++ b/docs/scripts/tests/test_docs.py
@@ -0,0 +1,14 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+""" Test for Doxygen based documentation generation.
+Refer to conftest.py on the test usage.
+"""
+
+
+def test_documentation_page(doxygen_errors):
+    """ Test documentation page has no errors generating
+    """
+    if doxygen_errors:
+        assert False, '\n'.join(['documentation has issues:'] +
+                                sorted(doxygen_errors))
diff --git a/docs/scripts/tests/utils/__init__.py b/docs/scripts/tests/utils/__init__.py
new file mode 100644
index 00000000000..dc8ba3c4598
--- /dev/null
+++ b/docs/scripts/tests/utils/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
diff --git a/docs/scripts/tests/utils/layout.py b/docs/scripts/tests/utils/layout.py
new file mode 100644
index 00000000000..02e9c0a12be
--- /dev/null
+++ b/docs/scripts/tests/utils/layout.py
@@ -0,0 +1,55 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+DoxygenLayout.xml parsing routines
+"""
+
+from collections import defaultdict
+import argparse
+from lxml import etree
+import re
+
+
+def parse_arguments():
+    """
+    Parse arguments
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--layout', type=str, required=True, default=None, help='Path to DoxygenLayout.xml file')
+    return parser.parse_args()
+
+
+def format_input(root):
+    """
+    Format input
+    """
+    for elem in root.getiterator():
+        if not hasattr(elem.tag, 'find'):
+            continue
+        elem.tag = re.sub(r'{.+}(.+)', r'\1', elem.tag)
+
+
+def parse_layout(content):
+    """
+    Parse DoxygenLayout.xml
+    """
+    parser = etree.XMLParser(encoding='utf-8')
+    root = etree.fromstring(content, parser=parser)
+    format_input(root)
+    files = defaultdict(lambda: set())
+    md_links = filter(
+        lambda x: 'url' in x.attrib and x.attrib['url'].startswith('./') and x.attrib['url'].endswith('.md'),
+        root.xpath('//tab'))
+    for md_link in map(lambda x: x.attrib['url'], md_links):
+        link = md_link[2:] if md_link.startswith('./') else md_link
+        files[link] = set()
+        files[link].update(["The link to this file located in DoxygenLayout.xml is not converted to a doxygen reference ('@ref filename')"])
+    return files
+
+
+if __name__ == '__main__':
+    arguments = parse_arguments()
+    with open(arguments.layout, 'r', encoding="utf-8") as f:
+        content = f.read()
+    parse_layout(content)
diff --git a/docs/scripts/tests/utils/log.py b/docs/scripts/tests/utils/log.py
new file mode 100644
index 00000000000..8acd864a0e5
--- /dev/null
+++ b/docs/scripts/tests/utils/log.py
@@ -0,0 +1,110 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+""" Doxygen log parsing routines
+"""
+from collections import defaultdict
+import argparse
+import re
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--doxygen', type=str, required=True, default=None, help='Path to doxygen.log file')
+    parser.add_argument('--doxygen-strip', type=str, required=False, default='tmp_docs/', help='Path to doxygen.log file')
+    return parser.parse_args()
+
+
+def strip_timestmp(text):
+    """Strip jenkins timestamp
+    """
+    return text.split(']')[-1]
+
+
+def strip_path(path, strip):
+    """Strip `path` components ends on `strip`
+    """
+    strip = strip.replace('\\', '/')
+    if not strip.endswith('/'):
+        strip = strip + '/'
+    new_path = path.split(strip)[-1]
+    if new_path.startswith('build/docs/'):
+        new_path = new_path.split('build/docs/')[-1]
+    return new_path
+
+
+def _get_file_line(text):
+    """Extracts file and line from Doxygen warning line
+    """
+    if text:
+        location = text.split()[-1]
+        file_line = location.rsplit(':', 1)
+        if len(file_line) == 2:
+            return file_line
+    return '', ''
+
+
+def parse(log, strip):
+    """Extracts {file: errors} from doxygen log
+    """
+    log = log.splitlines()
+    files = defaultdict(lambda: set())  # pylint: disable=unnecessary-lambda
+    idx = 0
+    prev_file = ''
+    prev_line = ''
+    while idx < len(log):  # pylint: disable=too-many-nested-blocks
+        try:
+            log_line = strip_timestmp(log[idx]).strip()
+            processing_verb = next(
+                filter(log_line.startswith,
+                       ('Reading /', 'Parsing file /', 'Preprocessing /')),
+                None)
+            if processing_verb:
+                files[strip_path(log_line[len(processing_verb) - 1:-3],
+                                 strip)] = set()
+            elif 'warning:' in log_line:
+                warning = list(map(str.strip, log_line.split(': warning:')))
+                file, line = _get_file_line(warning[0])
+                file = strip_path(file, strip)
+                if len(warning) == 1:
+                    file = prev_file
+                    line = prev_line
+                    error = warning[0]
+                else:
+                    error = warning[1]
+                if error.endswith(':'):
+                    continuation = []
+                    while idx + 1 < len(log):
+                        peek = strip_timestmp(log[idx + 1])
+                        if not peek.startswith('  '):
+                            break
+                        continuation += [peek]
+                        idx += 1
+                    error += ';'.join(continuation)
+                if line:
+                    error = '{error} (line: {line})'.format(
+                        line=line, error=error)
+                if not file or 'deprecated' in file:
+                    files['doxygen_errors'].update([error])
+                else:
+                    prev_file = file
+                    prev_line = line
+                    files[file].update([error])
+            elif log_line.startswith('explicit link request') and 'in layout file' in log_line:
+                match = re.search(r"\'(.+?)\'", log_line)
+                if match:
+                    file = match.group(1)
+                    files[file].update([log_line])
+                else:
+                    files['doxygen_errors'].update([log_line])
+            idx += 1
+        except:
+            print('Parsing error at line {}\n\n{}\n'.format(idx, log[idx]))
+            raise
+    return files
+
+
+if __name__ == '__main__':
+    arguments = parse_arguments()
+    with open(arguments.doxygen, 'r') as log:
+        files = parse(log.read(), arguments.doxygen_strip)
diff --git a/docs/tuning_utilities.md b/docs/tuning_utilities.md
new file mode 100644
index 00000000000..4fbf800d52f
--- /dev/null
+++ b/docs/tuning_utilities.md
@@ -0,0 +1,13 @@
+# Tuning Utilities {#openvino_docs_tuning_utilities}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Tuning Utilities
+   
+   omz_tools_accuracy_checker
+   omz_data_datasets
+   openvino_inference_engine_tools_cross_check_tool_README 
+
+@endsphinxdirective
\ No newline at end of file
diff --git a/docs/tutorials.md b/docs/tutorials.md
new file mode 100644
index 00000000000..52c69cf2332
--- /dev/null
+++ b/docs/tutorials.md
@@ -0,0 +1,18 @@
+# Tutorials {#tutorials}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Notebooks
+   :hidden:
+
+   notebooks/notebooks
+
+@endsphinxdirective
+
+This collection of Python tutorials are written for running on [Jupyter*](https://jupyter.org) notebooks. The tutorials provide an introduction to the OpenVINO™ toolkit and explain how to use the Python API and tools for optimized deep learning inference. You can run the code one section at a time to see how to integrate your application with OpenVINO™ libraries.
+
+![Binder logo](img/badge_logo.svg)
+
+Tutorials showing this logo may be run remotely using Binder with no setup, although running the notebooks on a local system is recommended for best performance. See the [OpenVINO™ Notebooks Installation Guide](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide) to install and run locally.
diff --git a/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx b/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
index b259a57c278..a945f2caaf8 100644
--- a/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
+++ b/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
@@ -67,16 +67,24 @@ def read_network(path_to_xml : str, path_to_bin : str):
     return net
 
 
-## This class manages data for reset operations
 cdef class VariableState:
-    ## Reset internal variable state for relevant infer request
-    # to a value specified as default for according ReadValue node
+    """
+    This class manages data for reset operations
+    """
+
     def reset(self):
+        """
+        Reset internal variable state for relevant infer request
+        to a value specified as default for according ReadValue node
+        """
         self.impl.reset()
 
-    ## Returns the value of the variable state.
+
     @property
     def state(self):
+        """
+        Returns the value of the variable state.
+        """
         blob = Blob()
         blob._ptr = self.impl.getState()
         blob._is_const = True
@@ -86,15 +94,18 @@ cdef class VariableState:
     def state(self, blob : Blob):
         self.impl.setState(blob._ptr)
 
-    ## A string representing a state name
     @property
     def name(self):
+        """
+        A string representing a state name
+        """
         return to_py_string(self.impl.getName())
 
 
-## This class defines Tensor description
 cdef class TensorDesc:
-
+    """
+    This class defines Tensor description
+    """
     def __eq__(self, other : TensorDesc):
         return self.layout == other.layout and self.precision == other.precision and self.dims == other.dims
 
@@ -104,28 +115,37 @@ cdef class TensorDesc:
     def __deepcopy__(self, memodict={}):
         return TensorDesc(deepcopy(self.precision, memodict), deepcopy(self.dims, memodict), deepcopy(self.layout, memodict))
 
-    ## Class constructor
-    # @param precision: target memory precision
-    # @param dims: target memory dimensions
-    # @param layout: target memory layout
-    # @return Instance of defines class
+
     def __cinit__(self, precision : str, dims : [list, tuple], layout : str):
+        """Class constructor
+
+        :param precision: target memory precision
+        :param dims: target memory dimensions
+        :param layout: target memory layout
+        :return: Instance of defines class
+        """
         if precision not in supported_precisions:
             raise ValueError(f"Unsupported precision {precision}! List of supported precisions: {supported_precisions}")
         self.impl = C.CTensorDesc(C.Precision.FromStr(precision.encode()), dims, layout_str_to_enum[layout])
 
-    ## Shape (dimensions) of the TensorDesc object
+
     @property
     def dims(self):
+        """
+        Shape (dimensions) of the :class:`TensorDesc` object
+        """
         return self.impl.getDims()
 
     @dims.setter
     def dims(self, dims_array : [list, tuple]):
         self.impl.setDims(dims_array)
 
-    ## Precision of the TensorDesc object
+
     @property
     def precision(self):
+        """
+        Precision of the :class:`TensorDesc` object
+        """
         return self.impl.getPrecision().name().decode()
 
     @precision.setter
@@ -134,9 +154,12 @@ cdef class TensorDesc:
             raise ValueError(f"Unsupported precision {precision}! List of supported precisions: {supported_precisions}")
         self.impl.setPrecision(C.Precision.FromStr(precision.encode()))
 
-    ## Layout of the TensorDesc object
+
     @property
     def layout(self):
+        """
+        Layout of the :class:`TensorDesc` object
+        """
         return layout_int_to_str_map[self.impl.getLayout()]
 
     @layout.setter
@@ -146,16 +169,22 @@ cdef class TensorDesc:
                              f"List of supported layouts: {list(layout_str_to_enum.keys())}")
         self.impl.setLayout(layout_str_to_enum[layout])
 
-## This class represents Blob
+
 cdef class Blob:
-    ## Class constructor
-    # @param tensor_desc: TensorDesc object describing creating Blob object.
-    # @param array: numpy.ndarray with data to fill blob memory, The array have to have same elements count
-    #               as specified in tensor_desc.dims attribute and same elements precision corresponding to
-    #               tensor_desc.precision. If array isn't provided empty numpy.ndarray will be created accorsing
-    #               to parameters of tensor_desc
-    # @return Instance of Blob class
+    """
+    This class represents Blob
+    """
+
     def __cinit__(self, TensorDesc tensor_desc = None, array : np.ndarray = None):
+        """Class constructor
+
+        :param tensor_desc: :class:`TensorDesc` object describing creating Blob object.
+        :param array: numpy.ndarray with data to fill blob memory, The array have to have same elements count
+        as specified in tensor_desc.dims attribute and same elements precision corresponding to
+        tensor_desc.precision. If array isn't provided empty numpy.ndarray will be created accorsing
+        to parameters of tensor_desc
+        :return: Instance of Blob class
+        """
         cdef CTensorDesc c_tensor_desc
         cdef float[::1] fp32_array_memview
         cdef double[::1] fp64_array_memview
@@ -256,17 +285,22 @@ cdef class Blob:
         res.buffer[:] = deepcopy(self.buffer[:], memodict)
         return res
 
-    ## Blob's memory as numpy.ndarray representation
     @property
     def buffer(self):
+        """
+        Blob's memory as :class:`numpy.ndarray` representation
+        """
         representation_shape = self._initial_shape if self._initial_shape is not None else []
         cdef BlobBuffer buffer = BlobBuffer()
         buffer.reset(self._ptr, representation_shape)
         return buffer.to_numpy(self._is_const)
 
-    ## TensorDesc of created Blob
+
     @property
     def tensor_desc(self):
+        """
+        :class:`TensorDesc` of created Blob
+        """
         cdef CTensorDesc c_tensor_desc = deref(self._ptr).getTensorDesc()
         precision = c_tensor_desc.getPrecision().name().decode()
         layout = c_tensor_desc.getLayout()
@@ -280,23 +314,33 @@ cdef class Blob:
 
 ## This class represents an Inference Engine entity and allows you to manipulate with plugins using unified interfaces.
 cdef class IECore:
-    ## Class constructor
-    # @param xml_config_file:  A full path to `.xml` file containing plugins configuration.
-    #                          If the parameter is not specified, the default configuration is handled automatically.
-    # @return Instance of IECore class
+    """
+    This class represents an Inference Engine entity and allows you to manipulate with plugins using unified interfaces.
+    """
+
     def __cinit__(self, xml_config_file: str = ""):
+        """Class constructor
+
+        :param xml_config_file:  A full path to `.xml` file containing plugins configuration.
+        If the parameter is not specified, the default configuration is handled automatically.
+        :return: Instance of IECore class
+        """
         cdef string c_xml_config_file = xml_config_file.encode()
         with nogil:
             self.impl = C.IECore(c_xml_config_file)
 
-    ## Get a `namedtuple` object with versions of the plugin specified
-    #  @param device_name: Name of the the registered plugin
-    #  @return Dictionary mapping a plugin name and `Versions` `namedtuple` object with the following fields:
-    #            * `major` - major plugin integer version
-    #            * `minor` - minor plugin integer version
-    #            * `build_number` - plugin build number string
-    #            * `description` - plugin description string
+
     def get_versions(self, device_name: str):
+        """Get a :class:`collections.namedtuple` object with versions of the plugin specified
+        
+        :param device_name: Name of the the registered plugin
+        :return: Dictionary mapping a plugin name and `Versions` :class:`collections.namedtuple` object with the following fields:
+        
+        * `major` - major plugin integer version
+        * `minor` - minor plugin integer version
+        * `build_number` - plugin build number string
+        * `description` - plugin description string
+        """
         cdef  map[string, C.Version] versions_
         versions_ = self.impl.getVersions(device_name.encode())
         versions = {}
@@ -325,6 +369,24 @@ cdef class IECore:
     #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
     #  ```
     cpdef IENetwork read_network(self, model: [str, bytes, os.PathLike], weights: [str, bytes, os.PathLike] = "", init_from_buffer: bool = False):
+        """Reads a network from Intermediate Representation (IR) or ONNX formats and creates an :class:`IENetwork`.
+        
+        :param model: A `.xml`, `.onnx`or `.prototxt` model file or string with IR.
+        :param weights: A `.bin` file of the IR. Depending on `init_from_buffer` value, can be a string path or
+                        bytes with file content.
+        :param init_from_buffer: Defines the way of how `model` and `weights` attributes are interpreted.
+        If  `False`, attributes are interpreted as strings with paths to `.xml` and `.bin` files
+        of IR. If `True`, they are  interpreted as Python `bytes` object with `.xml` and `.bin` files content.
+        
+        :return: An :class:`IENetwork` object
+        
+        Usage example:
+        
+        .. code-block:: python
+        
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+        """
         cdef uint8_t*bin_buffer
         cdef string weights_
         cdef string model_
@@ -352,25 +414,28 @@ cdef class IECore:
                 net.impl = self.impl.readNetwork(model_, weights_)
         return net
 
-    ## Loads a network that was read from the Intermediate Representation (IR) to the plugin with specified device name
-    #    and creates an `ExecutableNetwork` object of the `IENetwork` class.
-    #    You can create as many networks as you need and use them simultaneously (up to the limitation of the hardware
-    #    resources).
-    #  @param network: A valid `IENetwork` instance. Model file name .xml, .onnx can also be passed as argument
-    #  @param device_name: A device name of a target plugin
-    #  @param config: A dictionary of plugin configuration keys and their values
-    #  @param num_requests: A positive integer value of infer requests to be created. Number of infer requests is limited
-    #                       by device capabilities.
-    #                       Value `0` indicates that optimal number of infer requests will be created.
-    #  @return An `ExecutableNetwork` object
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  exec_net = ie.load_network(network=net, device_name="CPU", num_requests=2)
-    #  ```
     cpdef ExecutableNetwork load_network(self, network: [IENetwork, str], str device_name, config=None, int num_requests=1):
+        """Loads a network that was read from the Intermediate Representation (IR) to the plugin with specified device name
+        and creates an :class:`ExecutableNetwork` object of the :class:`IENetwork` class.
+        You can create as many networks as you need and use them simultaneously (up to the limitation of the hardware
+        resources).
+        
+        :param network: A valid :class:`IENetwork` instance. Model file name .xml, .onnx can also be passed as argument
+        :param device_name: A device name of a target plugin
+        :param config: A dictionary of plugin configuration keys and their values
+        :param num_requests: A positive integer value of infer requests to be created.
+        Number of infer requests is limited by device capabilities. Value `0` indicates that optimal number of infer requests will be created.
+        
+        :return: An :class:`ExecutableNetwork` object
+        
+        Usage example:
+        
+        .. code-block:: python
+
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            exec_net = ie.load_network(network=net, device_name="CPU", num_requests=2)
+        """
         cdef ExecutableNetwork exec_net = ExecutableNetwork()
         cdef map[string, string] c_config
         cdef string c_device_name
@@ -390,25 +455,30 @@ cdef class IECore:
                 exec_net.impl = move(self.impl.loadNetwork((<IENetwork>network).impl, c_device_name, c_config, num_requests))
         return exec_net
 
-    ## Creates an executable network from a previously exported network
-    #  @param device_name Name of device load executable network on
-    #  @param model_file Full path to the location of the exported file
-    #  @param config: A dictionary of plugin configuration keys and their values
-    #  @param num_requests: A positive integer value of infer requests to be created. Number of infer requests is limited
-    #                       by device capabilities.
-    #                       Value `0` indicates that optimal number of infer requests will be created.
-    #  @return An `ExecutableNetwork` object
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  exec_net = ie.load_network(network=net, device_name="MYRIAD", num_requests=2)
-    #  # export executable network
-    #  exec_net.export(path_to_file_to_save)
-    #  # import previously exported executable network
-    #  exec_net_imported = ie.import_network(model_file=path_to_file_to_save, device_name="MYRIAD")
-    #  ```
     cpdef ExecutableNetwork import_network(self, str model_file, str device_name, config=None, int num_requests=1):
+        """Creates an executable network from a previously exported network
+        
+        :param device_name: Name of device load executable network on
+        :param model_file: Full path to the location of the exported file
+        :param config: A dictionary of plugin configuration keys and their values
+        :param num_requests: A positive integer value of infer requests to be created. Number of infer requests is limited
+                             by device capabilities.
+                             Value `0` indicates that optimal number of infer requests will be created.
+        
+        :return: An :class:`ExecutableNetwork` object
+        
+        Usage example:
+        
+        .. code-block:: python
+        
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            exec_net = ie.load_network(network=net, device_name="MYRIAD", num_requests=2)
+            # export executable network
+            exec_net.export(path_to_file_to_save)
+            # import previously exported executable network
+            exec_net_imported = ie.import_network(model_file=path_to_file_to_save, device_name="MYRIAD")
+        """
         cdef ExecutableNetwork exec_net = ExecutableNetwork()
         cdef map[string, string] c_config
         if num_requests < 0:
@@ -419,136 +489,173 @@ cdef class IECore:
         exec_net.impl = move(self.impl.importNetwork(model_file.encode(), device_name.encode(), c_config, num_requests))
         return exec_net
 
-    ## Queries the plugin with specified device name what network layers are supported in the current configuration.
-    #  Please note that layers support depends on plugin configuration and loaded extensions.
-    #  @param network: A valid `IENetwork` instance
-    #  @param device_name: A device name of a target plugin
-    #  @param config: A dictionary of plugin configuration keys and their values
-    #  @return A dictionary mapping layers and device names on which they are supported
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  layers_map = ie.query_network(network=net, device_name="HETERO:GPU,CPU")
-    #  ```
+
     def query_network(self, IENetwork network, str device_name, config=None):
+        """Queries the plugin with specified device name what network layers are supported in the current configuration.
+        Please note that layers support depends on plugin configuration and loaded extensions.
+        
+        :param network: A valid :class:`IENetwork` instance
+        :param device_name: A device name of a target plugin
+        :param config: A dictionary of plugin configuration keys and their values
+        :return: A dictionary mapping layers and device names on which they are supported
+    
+        Usage example:
+    
+        .. code-block:: python
+        
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            layers_map = ie.query_network(network=net, device_name="HETERO:GPU,CPU")
+        """
         cdef map[string, string] c_config
         if config:
             c_config = dict_to_c_map(config)
         res = self.impl.queryNetwork(network.impl, device_name.encode(), c_config)
         return c_map_to_dict(res)
 
-    ## Sets a configuration for a plugin
-    #
-    #  \note When specifying a key value of a config, the "KEY_" prefix is omitted.
-    #
-    #  @param config: a dictionary of configuration parameters as keys and their values
-    #  @param device_name: a device name of a target plugin
-    #  @return None
-    #
-    #  Usage examples:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  ie.set_config(config={"DYN_BATCH_ENABLED": "YES"}, device_name="CPU")
-    #  ```
+
     def set_config(self, config: dict, device_name: str):
+        """Sets a configuration for a plugin
+        
+        .. note:: When specifying a key value of a config, the "KEY_" prefix is omitted.
+        
+        :param config: a dictionary of configuration parameters as keys and their values
+        :param device_name: a device name of a target plugin
+        :return: None
+        
+        Usage examples:\n
+        
+        .. code-block:: python
+        
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            ie.set_config(config={"DYN_BATCH_ENABLED": "YES"}, device_name="CPU")
+        """
         cdef map[string, string] c_config = dict_to_c_map(config)
         self.impl.setConfig(c_config, device_name.encode())
 
-    ## Registers plugins specified in an `.xml` configuration file
-    #  @param plugin_name: A name of a plugin. Depending on a platform, plugin_name is wrapped with a shared
-    #                      library suffix and a prefix to identify a full name of the library
-    #  @param device_name: A target device name for the plugin. If not specified, the method registers
-    #                      a plugin with the default name.
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  ie.register_plugin(plugin="MKLDNNPlugin", device_name="MY_NEW_PLUGIN")
-    #  ```
+
     def register_plugin(self, plugin_name: str, device_name: str = ""):
+        """Registers plugins specified in an `.xml` configuration file
+        
+        :param plugin_name: A name of a plugin. Depending on a platform, plugin_name is wrapped with a shared
+                            library suffix and a prefix to identify a full name of the library
+        :param device_name: A target device name for the plugin. If not specified, the method registers
+                            a plugin with the default name.
+        
+        :return: None
+    
+        Usage example:
+
+        .. code-block:: python
+
+            ie = IECore()
+            ie.register_plugin(plugin="MKLDNNPlugin", device_name="MY_NEW_PLUGIN")
+        """
         self.impl.registerPlugin(plugin_name.encode(), device_name.encode())
 
-    ## Registers plugins specified in an `.xml` configuration file
-    # @param xml_config_file: A full path to `.xml` file containing plugins configuration
-    # @return None
-    #
-    #  Usage example:
-    #  ```python
-    #  ie = IECore()
-    #  ie.register_plugins("/localdisk/plugins/my_custom_cfg.xml")
-    #  ```
+
     def register_plugins(self, xml_config_file: str):
+        """Registers plugins specified in an `.xml` configuration file
+        
+        :param xml_config_file: A full path to `.xml` file containing plugins configuration
+        :return: None
+
+        Usage example:
+
+        .. code-block:: python
+
+            ie = IECore()
+            ie.register_plugins("/localdisk/plugins/my_custom_cfg.xml")
+        """
         self.impl.registerPlugins(xml_config_file.encode())
 
-    ## Unregisters a plugin with a specified device name
-    #  @param device_name: A device name of the plugin to unregister
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  ie.unregister_plugin(device_name="GPU")
-    #  ```
+   
     def unregister_plugin(self, device_name: str):
+        """Unregisters a plugin with a specified device name
+        
+        :param device_name: A device name of the plugin to unregister
+        :return: None
+        
+        Usage example:
+
+        .. code-block:: python
+
+            ie = IECore()
+            ie.unregister_plugin(device_name="GPU")
+        """
         self.impl.unregisterPlugin(device_name.encode())
 
-    ## Loads extension library to the plugin with a specified device name
-    #  @param extension_path: Path to the extensions library file to load to a plugin
-    #  @param device_name: A device name of a plugin to load the extensions to
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  ie.add_extension(extension_path="/some_dir/libcpu_extension_avx2.so", device_name="CPU")
-    #  ```
+    
     def add_extension(self, extension_path: str, device_name: str):
+        """Loads extension library to the plugin with a specified device name
+        
+        :param extension_path: Path to the extensions library file to load to a plugin
+        :param device_name: A device name of a plugin to load the extensions to
+        :return: None
+
+        Usage example:\n
+        
+        .. code-block:: python
+        
+             ie = IECore()
+             ie.add_extension(extension_path="/some_dir/libcpu_extension_avx2.so", device_name="CPU")
+        """
         self.impl.addExtension(extension_path.encode(), device_name.encode())
 
-    ## Gets a general runtime metric for dedicated hardware. Enables to request common device properties,
-    #  which are `ExecutableNetwork` agnostic, such as device name, temperature, and other devices-specific values.
-    #  @param device_name: A name of a device to get a metric value.
-    #  @param metric_name: A metric name to request.
-    #  @return A metric value corresponding to a metric key.
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  ie.get_metric(metric_name="SUPPORTED_METRICS", device_name="CPU")
-    #  ```
     def get_metric(self, device_name: str, metric_name: str):
+        """
+        Gets a general runtime metric for dedicated hardware. Enables to request common device properties,
+        which are :class:`ExecutableNetwork` agnostic, such as device name, temperature, and other devices-specific values.
+        
+        :param device_name: A name of a device to get a metric value.
+        :param metric_name: A metric name to request.
+        :return: A metric value corresponding to a metric key.
+
+        Usage example:
+
+        .. code-block:: python
+
+            ie = IECore()
+            ie.get_metric(metric_name="SUPPORTED_METRICS", device_name="CPU")
+        """
         return self.impl.getMetric(device_name.encode(), metric_name.encode())
 
-    ## Gets a configuration dedicated to device behavior. The method targets to extract information
-    #  which can be set via set_config method.
-    #
-    #  \note When specifying a key value of a config, the "KEY_" prefix is omitted.
-    #
-    #  @param device_name: A name of a device to get a config value.
-    #  @param config_name: A config name to request.
-    #  @return A config value corresponding to a config key.
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  ie.get_config(device_name="CPU", config_name="CPU_BIND_THREAD")
-    #  ```
+    
     def get_config(self, device_name: str, config_name: str):
+        """Gets a configuration dedicated to device behavior. The method targets to extract information
+        which can be set via set_config method.
+
+        .. note:: When specifying a key value of a config, the "KEY_" prefix is omitted.
+
+        :param device_name: A name of a device to get a config value.
+        :param config_name: A config name to request.
+        :return: A config value corresponding to a config key.
+        
+        Usage example:
+
+        .. code-block:: python
+
+            ie = IECore()
+            ie.get_config(device_name="CPU", config_name="CPU_BIND_THREAD")
+        """
         return self.impl.getConfig(device_name.encode(), config_name.encode())
 
     ## A list of devices. The devices are returned as \[CPU, GPU.0, GPU.1, MYRIAD\].
     # If there are more than one device of a specific type, they all are listed followed by a dot and a number.
     @property
     def available_devices(self):
+        """
+        A list of devices. The devices are returned as \[CPU, FPGA.0, FPGA.1, MYRIAD\].
+        If there are more than one device of a specific type, they all are listed followed by a dot and a number.
+        """
         cdef vector[string] c_devices = self.impl.getAvailableDevices()
         return [d.decode() for d in c_devices]
 
-## This structure stores info about pre-processing of network inputs (scale, mean image, ...)
 cdef class PreProcessChannel:
+    """
+    This structure stores info about pre-processing of network inputs (scale, mean image, ...)
+    """
     property mean_value:
         def __get__(self):
             return deref(self._ptr).meanValue
@@ -570,8 +677,11 @@ cdef class PreProcessChannel:
         def __set__(self, Blob mean_data):
             deref(self._ptr).meanData = mean_data._ptr
 
-## This class stores pre-process information for the input
+
 cdef class PreProcessInfo:
+    """
+    This class stores pre-process information for the input
+    """
     def __cinit__(self):
         self._ptr = new CPreProcessInfo()
         self._cptr = self._ptr
@@ -587,39 +697,53 @@ cdef class PreProcessInfo:
         channel._ptr = c_channel
         return channel
 
-    ## Returns a number of channels to preprocess
+
     def get_number_of_channels(self):
+        """
+        Returns a number of channels to preprocess
+        """
         return deref(self._cptr).getNumberOfChannels()
 
-    ## Initializes with given number of channels
+
     def init(self, const size_t number_of_channels):
+        """
+        Initializes with given number of channels
+        """
         if not self._ptr:
             raise TypeError("Cannot initialized when created from constant")
         deref(self._ptr).init(number_of_channels)
 
-    ## Sets mean image values if operation is applicable.
-    #  Also sets the mean type to MEAN_IMAGE for all channels
+
     def set_mean_image(self, Blob mean_image):
+        """
+        Sets mean image values if operation is applicable.
+        Also sets the mean type to MEAN_IMAGE for all channels
+        """
         if not self._ptr:
             raise TypeError("Cannot set mean image when called from constant")
         deref(self._ptr).setMeanImage(mean_image._ptr)
 
-    ## Sets mean image values if operation is applicable.
-    #  Also sets the mean type to MEAN_IMAGE for a particular channel
+
     def set_mean_image_for_channel(self, Blob mean_image, size_t channel):
+        """
+        Sets mean image values if operation is applicable.
+        Also sets the mean type to MEAN_IMAGE for a particular channel
+        """
         if not self._ptr:
             raise TypeError("Cannot set mean image for channel when called from constant")
         deref(self._ptr).setMeanImageForChannel(mean_image._ptr, channel)
 
-    ## Mean Variant to be applied for input before inference if needed.
-    #
-    #  Usage example:\n
-    #  ```python
-    #  net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  net.input_info['data'].preprocess_info.mean_variant = MeanVariant.MEAN_IMAGE
-    #  ```
     @property
     def mean_variant(self):
+        """Mean Variant to be applied for input before inference if needed.
+        
+        Usage example:
+
+        .. code-block:: python
+        
+            net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            net.input_info['data'].preprocess_info.mean_variant = MeanVariant.MEAN_IMAGE
+        """
         return MeanVariant(deref(self._cptr).getMeanVariant())
 
     @mean_variant.setter
@@ -628,23 +752,28 @@ cdef class PreProcessInfo:
             raise TypeError("Cannot set mean image when called from constant")
         deref(self._ptr).setVariant(variant.value)
 
-    ## Resize Algorithm to be applied for input before inference if needed.
-    #
-    #  \note It's need to set your input via the set_blob method.
-    #
-    #  Usage example:\n
-    #  ```python
-    #  net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  net.input_info['data'].preprocess_info.resize_algorithm = ResizeAlgorithm.RESIZE_BILINEAR
-    #  exec_net = ie_core.load_network(net, 'CPU')
-    #  tensor_desc = ie.TensorDesc("FP32", [1, 3, image.shape[2], image.shape[3]], "NCHW")
-    #  img_blob = ie.Blob(tensor_desc, image)
-    #  request = exec_net.requests[0]
-    #  request.set_blob('data', img_blob)
-    #  request.infer()
-    #  ```
+    
     @property
     def resize_algorithm(self):
+        """
+        Resize Algorithm to be applied for input before inference if needed.
+        .. note::
+        
+            It's need to set your input via the set_blob method.
+        
+        Usage example:
+
+        .. code-block:: python
+
+            net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            net.input_info['data'].preprocess_info.resize_algorithm = ResizeAlgorithm.RESIZE_BILINEAR
+            exec_net = ie_core.load_network(net, 'CPU')
+            tensor_desc = ie.TensorDesc("FP32", [1, 3, image.shape[2], image.shape[3]], "NCHW")
+            img_blob = ie.Blob(tensor_desc, image)
+            request = exec_net.requests[0]
+            request.set_blob('data', img_blob)
+            request.infer()
+        """
         return ResizeAlgorithm(deref(self._cptr).getResizeAlgorithm())
 
     @resize_algorithm.setter
@@ -653,15 +782,19 @@ cdef class PreProcessInfo:
             raise TypeError("Cannot set resize algorithm when called from constant")
         deref(self._ptr).setResizeAlgorithm(alg.value)
 
-    ## Color format to be used in on-demand color conversions applied to input before inference
-    #
-    #  Usage example:\n
-    #  ```python
-    #  net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  net.input_info['data'].preprocess_info.color_format = ColorFormat.BGR
-    #  ```
+
     @property
     def color_format(self):
+        """
+        Color format to be used in on-demand color conversions applied to input before inference
+
+        Usage example:
+
+        .. code-block:: python
+       
+            net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            net.input_info['data'].preprocess_info.color_format = ColorFormat.BGR
+        """
         return ColorFormat(deref(self._cptr).getColorFormat())
 
     @color_format.setter
@@ -671,16 +804,23 @@ cdef class PreProcessInfo:
         deref(self._ptr).setColorFormat(fmt.value)
 
 
-## This class contains information about each input of the network
 cdef class InputInfoPtr:
-    ## Name of this input
+    """
+    This class contains information about each input of the network
+    """
+
     @property
     def name(self):
+        """
+        Name of this input
+        """
         return deref(self._ptr).name().decode()
 
-    ## Precision of this input
     @property
     def precision(self):
+        """
+        Precision of this input
+        """
         return deref(self._ptr).getPrecision().name().decode()
 
     @precision.setter
@@ -689,9 +829,11 @@ cdef class InputInfoPtr:
             raise ValueError(f"Unsupported precision {precision}! List of supported precisions: {supported_precisions}")
         deref(self._ptr).setPrecision(C.Precision.FromStr(precision.encode()))
 
-    ## Layout of this input
     @property
     def layout(self):
+        """
+        Layout of this input
+        """
         return layout_int_to_str_map[deref(self._ptr).getLayout()]
 
     @layout.setter
@@ -701,15 +843,18 @@ cdef class InputInfoPtr:
                              f"List of supported layouts: {list(layout_str_to_enum.keys())}")
         deref(self._ptr).setLayout(layout_str_to_enum[layout])
 
-    ## Gets pre-process info for the input
-    #
-    #  Usage example:\n
-    #  ```python
-    #  net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  net.input_info['data'].preprocess_info.color_format = ColorFormat.BGR
-    #  ```
+   
     @property
     def preprocess_info(self):
+        """Gets pre-process info for the input
+    
+        Usage example:
+        
+        .. code-block:: python
+
+            net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            net.input_info['data'].preprocess_info.color_format = ColorFormat.BGR
+        """
         cdef CPreProcessInfo* c_preprocess_info = &deref(self._ptr).getPreProcess()
         preprocess_info = PreProcessInfo()
         del preprocess_info._ptr
@@ -728,9 +873,11 @@ cdef class InputInfoPtr:
         tensor_desc.impl = c_tensor_desc
         return tensor_desc
 
-    ## Get access to DataPtr object
     @property
     def input_data(self):
+        """
+        Get access to DataPtr object
+        """
         cdef C.DataPtr c_data_ptr = deref(self._ptr).getInputData()
         data_ptr = DataPtr()
         data_ptr._ptr_network = self._ptr_network
@@ -742,31 +889,42 @@ cdef class InputInfoPtr:
         deref(self._ptr).setInputData(input_ptr._ptr)
 
 
-## This class contains const information about each input of the network.
-#  Provides same interface as InputInfoPtr object except properties setters
 cdef class InputInfoCPtr:
-    ## Name of this input
+    """
+    This class contains const information about each input of the network.
+    Provides same interface as InputInfoPtr object except properties setters
+    """
+    
     @property
     def name(self):
+        """
+        Name of this input
+        """
         return deref(self._ptr).name().decode()
 
-    ## Precision of this input
     @property
     def precision(self):
+        """
+        Precision of this input
+        """
         return deref(self._ptr).getPrecision().name().decode()
 
-    ## Get access to DataPtr object
     @property
     def input_data(self):
+        """
+        Get access to DataPtr object
+        """
         cdef C.DataPtr c_data_ptr = deref(self._ptr).getInputData()
         data_ptr = DataPtr()
         data_ptr._ptr = c_data_ptr
         data_ptr._ptr_plugin = self._ptr_plugin
         return data_ptr
 
-    ## tensor_desc of this input
     @property
     def tensor_desc(self):
+        """
+        tensor_desc of this input
+        """
         cdef CTensorDesc c_tensor_desc = deref(self._ptr).getTensorDesc()
         precision = c_tensor_desc.getPrecision().name().decode()
         layout = c_tensor_desc.getLayout()
@@ -776,20 +934,29 @@ cdef class InputInfoCPtr:
         return tensor_desc
 
 
-## This class is the layer data representation.
 cdef class DataPtr:
-    ## Default constructor
+    """
+    This class is the layer data representation.
+    """
+    
     def __init__(self):
+        """
+        Default constructor
+        """
         self._ptr_network = NULL
 
-    ## Name of the data object
     @property
     def name(self):
+        """
+        Name of the data object
+        """
         return deref(self._ptr).getName().decode()
 
-    ## Precision of the data object
     @property
     def precision(self):
+        """
+        Precision of the data object
+        """
         return deref(self._ptr).getPrecision().name().decode()
 
     @precision.setter
@@ -798,14 +965,18 @@ cdef class DataPtr:
             raise ValueError(f"Unsupported precision {precision}! List of supported precisions: {supported_precisions}")
         deref(self._ptr).setPrecision(C.Precision.FromStr(precision.encode()))
 
-    ## Shape (dimensions) of the data object
     @property
     def shape(self):
+        """
+        Shape (dimensions) of the data object
+        """
         return deref(self._ptr).getDims()
 
-    ## Layout of the data object
     @property
     def layout(self):
+        """
+        Layout of the data object
+        """
         return layout_int_to_str_map[deref(self._ptr).getLayout()]
 
     @layout.setter
@@ -815,9 +986,11 @@ cdef class DataPtr:
                              f"List of supported layouts: {list(layout_str_to_enum.keys())}")
         deref(self._ptr).setLayout(layout_str_to_enum[layout])
 
-    ## Checks if the current data object is resolved
     @property
     def initialized(self):
+        """
+        Checks if the current data object is resolved
+        """
         return deref(self._ptr).isInitialized()
 
     @property
@@ -829,31 +1002,44 @@ cdef class DataPtr:
         return C.getPartialShape_capsule(self._ptr)
 
 
-## This class is the layer constant data representation. Provides same interface as DataPtr object except properties setters
 cdef class CDataPtr:
-    ## Name of the data object
+    """
+    This class is the layer constant data representation. Provides same interface as DataPtr object except properties setters
+    """
+
     @property
     def name(self):
+        """
+        Name of the data object
+        """
         return deref(self._ptr).getName().decode()
 
-    ## Precision of the data object
     @property
     def precision(self):
+        """
+        Precision of the data object
+        """
         return deref(self._ptr).getPrecision().name().decode()
 
-    ## Shape (dimensions) of the data object
     @property
     def shape(self):
+        """
+        Shape (dimensions) of the data object
+        """
         return deref(self._ptr).getDims()
 
-    ## Layout of the data object
     @property
     def layout(self):
+        """
+        Layout of the data object
+        """
         return layout_int_to_str_map[deref(self._ptr).getLayout()]
 
-    ## Checks if the current data object is resolved
     @property
     def initialized(self):
+        """
+        Checks if the current data object is resolved
+        """
         return deref(self._ptr).isInitialized()
 
     @property
@@ -865,34 +1051,42 @@ cdef class CDataPtr:
         return C.getPartialShape_capsule(self._ptr)
 
 
-## This class represents a network instance loaded to plugin and ready for inference.
 cdef class ExecutableNetwork:
-    ## There is no explicit class constructor. To make a valid instance of `ExecutableNetwork`,
-    #  use `load_network()` method of the `IECore` class.
+    """
+    This class represents a network instance loaded to plugin and ready for inference.
+    """
+
     def __init__(self):
+        """
+        There is no explicit class constructor. To make a valid instance of :class:`ExecutableNetwork`,
+        use :func:`IECore.load_network` method of the :class:`IECore` class.
+        """
         self._infer_requests = []
 
-    ## Starts synchronous inference for the first infer request of the executable network and returns output data.
-    #  Wraps `infer()` method of the `InferRequest` class
-    #  @param inputs:  A dictionary that maps input layer names to `numpy.ndarray` objects of proper shape with
-    #                  input data for the layer
-    #  @return A dictionary that maps output layer names to `numpy.ndarray` objects with output data of the layer
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie_core = IECore()
-    #  net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
-    #  res = exec_net.infer({'data': img})
-    #  res
-    #  {'prob': array([[[[2.83426580e-08]],
-    #                  [[2.40166020e-08]],
-    #                  [[1.29469613e-09]],
-    #                  [[2.95946148e-08]]
-    #                  ......
-    #                 ]])}
-    #  ```
     def infer(self, inputs=None):
+        """Starts synchronous inference for the first infer request of the executable network and returns output data.
+        Wraps :func:`InferRequest.infer` method of the :class:`InferRequest` class
+        
+        :param inputs:  A dictionary that maps input layer names to :class:`numpy.ndarray` objects of proper shape with
+                        input data for the layer
+        :return: A dictionary that maps output layer names to :class:`numpy.ndarray` objects with output data of the layer
+        
+        Usage example:
+
+        .. code-block:: python
+
+            ie_core = IECore()
+            net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
+            res = exec_net.infer({'data': img})
+            res
+            {'prob': array([[[[2.83426580e-08]],
+                          [[2.40166020e-08]],
+                          [[1.29469613e-09]],
+                          [[2.95946148e-08]]
+                          ......
+                         ]])}
+        """
         current_request = self.requests[0]
         current_request.infer(inputs)
         res = {}
@@ -900,29 +1094,36 @@ cdef class ExecutableNetwork:
             res[name] = deepcopy(value.buffer)
         return res
 
-    ## Starts asynchronous inference for specified infer request.
-    #  Wraps `async_infer()` method of the `InferRequest` class.
-    #  @param request_id: Index of infer request to start inference
-    #  @param inputs: A dictionary that maps input layer names to `numpy.ndarray` objects of proper
-    #                 shape with input data for the layer
-    #  @return A handler of specified infer request, which is an instance of the `InferRequest` class.
-    #
-    #  Usage example:\n
-    #  ```python
-    #  infer_request_handle = exec_net.start_async(request_id=0, inputs={input_blob: image})
-    #  infer_status = infer_request_handle.wait()
-    #  res = infer_request_handle.output_blobs[out_blob_name]
-    #  ```
     def start_async(self, request_id, inputs=None):
+        """
+        Starts asynchronous inference for specified infer request.
+        Wraps :func:`InferRequest.async_infer` method of the :class:`InferRequest` class.
+        
+        :param request_id: Index of infer request to start inference
+        :param inputs: A dictionary that maps input layer names to :class:`numpy.ndarray` objects of proper
+                       shape with input data for the layer
+        :return: A handler of specified infer request, which is an instance of the :class:`InferRequest` class.
+
+        Usage example:
+        
+        .. code-block:: python
+        
+            infer_request_handle = exec_net.start_async(request_id=0, inputs={input_blob: image})
+            infer_status = infer_request_handle.wait()
+            res = infer_request_handle.output_blobs[out_blob_name]
+        """
         if request_id not in list(range(len(self.requests))):
             raise ValueError("Incorrect request_id specified!")
         current_request = self.requests[request_id]
         current_request.async_infer(inputs)
         return current_request
 
-    ## A tuple of `InferRequest` instances
+
     @property
     def requests(self):
+        """
+        A tuple of :class:`InferRequest` instances
+        """
         cdef size_t c_infer_requests_size = deref(self.impl).infer_requests.size()
         if len(self._infer_requests) == 0:
             for i in range(c_infer_requests_size):
@@ -939,9 +1140,11 @@ cdef class ExecutableNetwork:
 
         return self._infer_requests
 
-    ## A dictionary that maps input layer names to InputInfoCPtr objects
     @property
     def input_info(self):
+        """
+        A dictionary that maps input layer names to InputInfoCPtr objects
+        """
         cdef map[string, C.InputInfo.CPtr] c_inputs = deref(self.impl).getInputsInfo()
         inputs = {}
         cdef InputInfoCPtr input_info_ptr
@@ -955,6 +1158,9 @@ cdef class ExecutableNetwork:
     ## A dictionary that maps output layer names to CDataPtr objects
     @property
     def outputs(self):
+        """
+        A dictionary that maps output layer names to CDataPtr objects
+        """
         cdef map[string, C.CDataPtr] c_outputs = deref(self.impl).getOutputs()
         outputs = {}
         cdef CDataPtr data_ptr
@@ -965,50 +1171,62 @@ cdef class ExecutableNetwork:
             outputs[in_.first.decode()] = data_ptr
         return outputs
 
-    ## Gets executable graph information from a device
-    #  @return An instance of `IENetwork`
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie_core = IECore()
-    #  net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  exec_net = ie_core.load_network(net, device, num_requests=2)
-    #  exec_graph = exec_net.get_exec_graph_info()
-    #  ```
+
     def get_exec_graph_info(self):
+        """Gets executable graph information from a device
+        
+        :return: An instance of :class:`IENetwork`
+        
+        Usage example:
+
+        .. code-block:: python
+        
+            ie_core = IECore()
+            net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            exec_net = ie_core.load_network(net, device, num_requests=2)
+            exec_graph = exec_net.get_exec_graph_info()
+        """
         ie_network = IENetwork()
         ie_network.impl = deref(self.impl).GetExecGraphInfo()
         ie_network._ptr_plugin = deref(self.impl).getPluginLink()
         return ie_network
 
-    ## Gets general runtime metric for an executable network. It can be network name, actual device ID on
-    #  which executable network is running or all other properties which cannot be changed dynamically.
-    #  @param metric_name: A metric name to request.
-    #  @return A metric value corresponding to a metric key.
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  exec_net = ie.load_network(net, "CPU")
-    #  exec_net.get_metric("NETWORK_NAME")
-    #  ```
+
     def get_metric(self, metric_name: str):
+        """Gets general runtime metric for an executable network. It can be network name, actual device ID on
+        which executable network is running or all other properties which cannot be changed dynamically.
+        
+        :param metric_name: A metric name to request.
+        :return: A metric value corresponding to a metric key.
+       
+        Usage example:
+       
+        .. code-block:: python
+
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            exec_net = ie.load_network(net, "CPU")
+            exec_net.get_metric("NETWORK_NAME")
+        """
         return deref(self.impl).getMetric(metric_name.encode())
 
-    ## Gets configuration for current executable network. The method is responsible to extract information
-    #  which affects executable network execution
-    #  @param config_name: A configuration parameter name to request.
-    #  @return A configuration value corresponding to a configuration key.
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  exec_net = ie.load_network(net, "CPU")
-    #  config = exec_net.get_config("CPU_BIND_THREAD")
-    #  ```
+   
     def get_config(self, config_name: str):
+        """Gets configuration for current executable network. The method is responsible to extract information
+        which affects executable network execution
+        
+        :param config_name: A configuration parameter name to request.
+        :return: A configuration value corresponding to a configuration key.
+        
+        Usage example:
+        
+        .. code-block:: python
+        
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            exec_net = ie.load_network(net, "CPU")
+            config = exec_net.get_config("CPU_BIND_THREAD")
+        """
         return deref(self.impl).getConfig(config_name.encode())
 
     ## Sets configuration for current executable network.
@@ -1038,15 +1256,29 @@ cdef class ExecutableNetwork:
     #  exec_net.export(path_to_file_to_save)
     #  ```
     def export(self, model_file: str):
+        """Exports the current executable network.
+        
+        :param model_file: Full path to the target exported file location
+        :return: None
+
+        .. code-block:: python
+    
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            exec_net = ie.load_network(network=net, device_name="MYRIAD", num_requests=2)
+            exec_net.export(path_to_file_to_save)
+        """
         deref(self.impl).exportNetwork(model_file.encode())
 
-    ## Waits when the result from any request becomes available. Blocks until specified timeout elapses or the result.
-    #  @param num_requests: Number of idle requests for which wait.
-    #                       If not specified, `num_requests` value is set to number of requests by default.
-    #  @param timeout: Time to wait in milliseconds or special (0, -1) cases described above.
-    #                  If not specified, `timeout` value is set to -1 by default.
-    #  @return Request status code: OK or RESULT_NOT_READY
     cpdef wait(self, num_requests=None, timeout=None):
+        """Waits when the result from any request becomes available. Blocks until specified timeout elapses or the result.
+        
+        :param num_requests: Number of idle requests for which wait.
+                             If not specified, `num_requests` value is set to number of requests by default.
+        :param timeout: Time to wait in milliseconds or special (0, -1) cases described above.
+                        If not specified, `timeout` value is set to -1 by default.
+        :return: Request status code: `OK` or `RESULT_NOT_READY`
+        """
         cdef int status_code
         cdef int64_t c_timeout
         cdef int c_num_requests
@@ -1060,20 +1292,30 @@ cdef class ExecutableNetwork:
             status_code = deref(self.impl).wait(c_num_requests, c_timeout)
         return status_code
 
-    ## Get idle request ID
-    #  @return Request index
+    
     cpdef get_idle_request_id(self):
+        """
+        Get idle request ID
+        
+        :return: Request index
+        """
         return deref(self.impl).getIdleRequestId()
 
 ctypedef extern void (*cb_type)(void*, int) with gil
 
-## This class provides an interface to infer requests of `ExecutableNetwork` and serves to handle infer requests execution
-#  and to set and get output data.
+
 cdef class InferRequest:
-    ## There is no explicit class constructor. To make a valid `InferRequest` instance, use `load_network()`
-    #  method of the `IECore` class with specified number of requests to get `ExecutableNetwork` instance
-    #  which stores infer requests.
+    """
+    This class provides an interface to infer requests of :class:`ExecutableNetwork` and serves
+    to handle infer requests execution and to set and get output data.
+    """
+    
     def __init__(self):
+        """
+        There is no explicit class constructor. To make a valid :class:`InferRequest` instance, use :func:`IECore.load_network`
+        method of the :class:`IECore` class with specified number of requests to get :class:`ExecutableNetwork` instance
+        which stores infer requests.
+        """
         self._user_blobs = {}
         self._inputs_list = []
         self._outputs_list = []
@@ -1085,25 +1327,27 @@ cdef class InferRequest:
         if self._py_callback:
             self._py_callback(status, self._py_data)
 
-    ## Description: Sets a callback function that is called on success or failure of an asynchronous request
-    #
-    #  @param py_callback - Any defined or lambda function
-    #  @param py_data - Data that is passed to the callback function
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  callback = lambda status, py_data: print(f"Request with id {py_data} finished with status {status}")
-    #  ie = IECore()
-    #  net = ie.read_network(model="./model.xml", weights="./model.bin")
-    #  exec_net = ie.load_network(net, "CPU", num_requests=4)
-    #  for id, req in enumerate(exec_net.requests):
-    #      req.set_completion_callback(py_callback=callback, py_data=id)
-    #
-    #  for req in exec_net.requests:
-    #      req.async_infer({"data": img})
-    #  ```
     def set_completion_callback(self, py_callback, py_data = None):
+        """Description: Sets a callback function that is called on success or failure of an asynchronous request
+        
+        :param py_callback: Any defined or lambda function
+        :param py_data: Data that is passed to the callback function
+        :return: None
+        
+        Usage example:
+
+        .. code-block:: python
+        
+            callback = lambda status, py_data: print(f"Request with id {py_data} finished with status {status}")
+            ie = IECore()
+            net = ie.read_network(model="./model.xml", weights="./model.bin")
+            exec_net = ie.load_network(net, "CPU", num_requests=4)
+            for id, req in enumerate(exec_net.requests):
+                req.set_completion_callback(py_callback=callback, py_data=id)
+                
+            for req in exec_net.requests:
+                req.async_infer({"data": img})
+        """
         self._py_callback = py_callback
         self._py_data = py_data
         deref(self.impl).setCyCallback(<cb_type> self.user_callback, <void *> self)
@@ -1115,9 +1359,12 @@ cdef class InferRequest:
         buffer.reset(blob_ptr)
         return buffer
 
-    ## Dictionary that maps input layer names to corresponding Blobs
+
     @property
     def input_blobs(self):
+        """
+        Dictionary that maps input layer names to corresponding Blobs
+        """
         input_blobs = {}
         for input in self._inputs_list:
             # TODO: will not work for setting data via .inputs['data'][:]
@@ -1129,9 +1376,11 @@ cdef class InferRequest:
                 input_blobs[input] = blob
         return input_blobs
 
-    ## Dictionary that maps output layer names to corresponding Blobs
     @property
     def output_blobs(self):
+        """
+        Dictionary that maps output layer names to corresponding Blobs
+        """
         output_blobs = {}
         for output in self._outputs_list:
             blob = Blob()
@@ -1139,9 +1388,11 @@ cdef class InferRequest:
             output_blobs[output] = deepcopy(blob)
         return output_blobs
 
-    ## Dictionary that maps input layer names to corresponding preprocessing information
     @property
     def preprocess_info(self):
+        """
+        Dictionary that maps input layer names to corresponding preprocessing information
+        """
         preprocess_info = {}
         for input_blob in self.input_blobs.keys():
             preprocess = PreProcessInfo()
@@ -1152,10 +1403,11 @@ cdef class InferRequest:
             preprocess_info[input_blob] = preprocess
         return preprocess_info
 
-    ## Gets state control interface for given infer request
-    # State control essential for recurrent networks
-    # @return A vector of Memory State objects
     def query_state(self):
+        """Gets state control interface for given infer request
+        State control essential for recurrent networks
+        :return: A vector of Memory State objects
+        """
         cdef vector[C.CVariableState] c_mem_state_vec = deref(self.impl).queryState()
         mem_state_vec = []
         for ms in c_mem_state_vec:
@@ -1164,80 +1416,94 @@ cdef class InferRequest:
             mem_state_vec.append(state)
         return mem_state_vec
 
-    ## Sets user defined Blob for the infer request
-    #  @param blob_name: A name of input blob
-    #  @param blob: Blob object to set for the infer request
-    #  @param preprocess_info: PreProcessInfo object to set for the infer request.
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = IENetwork("./model.xml", "./model.bin")
-    #  exec_net = ie.load_network(net, "CPU", num_requests=2)
-    #  td = TensorDesc("FP32", (1, 3, 224, 224), "NCHW")
-    #  blob_data = np.ones(shape=(1, 3, 224, 224), dtype=np.float32)
-    #  blob = Blob(td, blob_data)
-    #  exec_net.requests[0].set_blob(blob_name="input_blob_name", blob=blob),
-    #  ```
     def set_blob(self, blob_name : str, blob : Blob, preprocess_info: PreProcessInfo = None):
+        """Sets user defined Blob for the infer request
+        
+        :param blob_name: A name of input blob
+        :param blob: Blob object to set for the infer request
+        :param preprocess_info: PreProcessInfo object to set for the infer request.
+        :return: None
+        
+        Usage example:
+
+        .. code-block:: python
+    
+            ie = IECore()
+            net = IENetwork("./model.xml", "./model.bin")
+            exec_net = ie.load_network(net, "CPU", num_requests=2)
+            td = TensorDesc("FP32", (1, 3, 224, 224), "NCHW")
+            blob_data = np.ones(shape=(1, 3, 224, 224), dtype=np.float32)
+            blob = Blob(td, blob_data)
+            exec_net.requests[0].set_blob(blob_name="input_blob_name", blob=blob),
+        """
         if preprocess_info:
             deref(self.impl).setBlob(blob_name.encode(), blob._ptr, deref(preprocess_info._ptr))
         else:
             deref(self.impl).setBlob(blob_name.encode(), blob._ptr)
         self._user_blobs[blob_name] = blob
-    ## Starts synchronous inference of the infer request and fill outputs array
-    #
-    #  @param inputs: A dictionary that maps input layer names to `numpy.ndarray` objects of proper shape with
-    #                 input data for the layer
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
-    #  exec_net.requests[0].infer({input_blob: image})
-    #  res = exec_net.requests[0].output_blobs['prob']
-    #  np.flip(np.sort(np.squeeze(res)),0)
-    #  array([4.85416055e-01, 1.70385033e-01, 1.21873841e-01, 1.18894853e-01,
-    #         5.45198545e-02, 2.44456064e-02, 5.41366823e-03, 3.42589128e-03,
-    #         2.26027006e-03, 2.12283316e-03 ...])
-    #  ```
+
     cpdef infer(self, inputs=None):
+        """Starts synchronous inference of the infer request and fill outputs array
+        
+        :param inputs: A dictionary that maps input layer names to :class:`numpy.ndarray` objects of proper shape with
+                       input data for the layer
+        :return: None
+
+        Usage example:
+
+        .. code-block:: python
+    
+            exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
+            exec_net.requests[0].infer({input_blob: image})
+            res = exec_net.requests[0].output_blobs['prob']
+            np.flip(np.sort(np.squeeze(res)),0)
+
+            # array([4.85416055e-01, 1.70385033e-01, 1.21873841e-01, 1.18894853e-01,
+            #         5.45198545e-02, 2.44456064e-02, 5.41366823e-03, 3.42589128e-03,
+            #         2.26027006e-03, 2.12283316e-03 ...])
+        """
         if inputs is not None:
             self._fill_inputs(inputs)
         deref(self.impl).infer()
 
-    ## Starts asynchronous inference of the infer request and fill outputs array
-    #
-    #  @param inputs: A dictionary that maps input layer names to `numpy.ndarray` objects of proper shape with input data for the layer
-    #  @return: None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
-    #  exec_net.requests[0].async_infer({input_blob: image})
-    #  request_status = exec_net.requests[0].wait()
-    #  res = exec_net.requests[0].output_blobs['prob']
-    #  ```
     cpdef async_infer(self, inputs=None):
+        """Starts asynchronous inference of the infer request and fill outputs array
+        
+        :param inputs: A dictionary that maps input layer names to :class:`numpy.ndarray` objects
+                       of proper shape with input data for the layer
+        :return: None
+        
+        Usage example:
+    
+        .. code-block:: python
+        
+            exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
+            exec_net.requests[0].async_infer({input_blob: image})
+            request_status = exec_net.requests[0].wait()
+            res = exec_net.requests[0].output_blobs['prob']
+        """
         if inputs is not None:
             self._fill_inputs(inputs)
         deref(self.impl).infer_async()
 
-    ## Waits for the result to become available. Blocks until specified timeout elapses or the result
-    #  becomes available, whichever comes first.
-    #
-    #  \note There are special values of the timeout parameter:
-    #  * 0 - Immediately returns the inference status. It does not block or interrupt execution.
-    #        To find statuses meaning, please refer to InferenceEngine::StatusCode in Inference Engine C++ documentation
-    #  * -1 - Waits until inference result becomes available (default value)
-    #
-    #  @param timeout: Time to wait in milliseconds or special (0, -1) cases described above.
-    #                  If not specified, `timeout` value is set to -1 by default.
-    #  @return Request status code.
-    #
-    #  Usage example: See `async_infer()` method of the the `InferRequest` class.
     cpdef wait(self, timeout=None):
+        """Waits for the result to become available. Blocks until specified timeout elapses or the result
+        becomes available, whichever comes first.
+
+        :param timeout: Time to wait in milliseconds or special (0, -1) cases described above.
+                        If not specified, `timeout` value is set to -1 by default.
+        :return: Request status code.
+
+        .. note::
+        
+            There are special values of the timeout parameter:
+            
+            * 0 - Immediately returns the inference status. It does not block or interrupt execution.
+              To find statuses meaning, please refer to :ref:`enum_InferenceEngine_StatusCode` in Inference Engine C++ documentation
+            * -1 - Waits until inference result becomes available (default value)
+    
+        Usage example: See :func:`InferRequest.async_infer` method of the the :class:`InferRequest` class.
+        """
         cdef int status
         cdef int64_t c_timeout
         if timeout is None:
@@ -1247,31 +1513,34 @@ cdef class InferRequest:
             status = deref(self.impl).wait(c_timeout)
         return status
 
-    ## Queries performance measures per layer to get feedback of what is the most time consuming layer.
-    #
-    #  \note Performance counters data and format depends on the plugin
-    #
-    #  @return Dictionary containing per-layer execution information.
-    #
-    #  Usage example:
-    #  ```python
-    #  exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
-    #  exec_net.requests[0].infer({input_blob: image})
-    #  exec_net.requests[0].get_perf_counts()
-    #  {'Conv2D': {'exec_type': 'jit_avx2_1x1',
-    #              'real_time': 154,
-    #              'cpu_time': 154,
-    #              'status': 'EXECUTED',
-    #              'layer_type': 'Convolution'},
-    #   'Relu6':  {'exec_type': 'undef',
-    #              'real_time': 0,
-    #              'cpu_time': 0,
-    #              'status': 'NOT_RUN',
-    #              'layer_type': 'Clamp'}
-    #   ...
-    #  }
-    #  ```
+
     cpdef get_perf_counts(self):
+        """Queries performance measures per layer to get feedback of what is the most time consuming layer.
+        
+        .. note:: Performance counters data and format depends on the plugin
+
+        :return: Dictionary containing per-layer execution information.
+    
+        Usage example:
+        
+        .. code-block:: python
+
+            exec_net = ie_core.load_network(network=net, device_name="CPU", num_requests=2)
+            exec_net.requests[0].infer({input_blob: image})
+            exec_net.requests[0].get_perf_counts()
+            #  {'Conv2D': {'exec_type': 'jit_avx2_1x1',
+            #              'real_time': 154,
+            #              'cpu_time': 154,
+            #              'status': 'EXECUTED',
+            #              'layer_type': 'Convolution'},
+            #   'Relu6':  {'exec_type': 'undef',
+            #              'real_time': 0,
+            #              'cpu_time': 0,
+            #              'status': 'NOT_RUN',
+            #              'layer_type': 'Clamp'}
+            #   ...
+            #  }
+        """
         cdef map[string, C.ProfileInfo] c_profile = deref(self.impl).getPerformanceCounts()
         profile = {}
         for line in c_profile:
@@ -1285,29 +1554,34 @@ cdef class InferRequest:
     ## Current infer request inference time in milliseconds
     @property
     def latency(self):
+        """
+        Current infer request inference time in milliseconds
+        """
         return self.impl.exec_time
 
-    ## Sets new batch size for certain infer request when dynamic batching is enabled in executable network
-    #  that created this request.
-    #
-    #  \note Support of dynamic batch size depends on the target plugin.
-    #
-    #  @param size: New batch size to be used by all the following inference calls for this request
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  # Set max batch size
-    #  net.batch = 10
-    #  ie.set_config(config={"DYN_BATCH_ENABLED": "YES"}, device_name=device)
-    #  exec_net = ie.load_network(network=net, device_name=device)
-    #  # Set batch size for certain network.
-    #  # NOTE: Input data shape will not be changed, but will be used partially in inference which increases performance
-    #  exec_net.requests[0].set_batch(2)
-    #  ```
     def set_batch(self, size):
+        """Sets new batch size for certain infer request when dynamic batching is enabled in executable network
+        that created this request.
+        
+        .. note:: Support of dynamic batch size depends on the target plugin.
+
+        :param size: New batch size to be used by all the following inference calls for this request
+        :return: None
+
+        Usage example:
+    
+        .. code-block:: python
+
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            # Set max batch size
+            # net.batch = 10
+            ie.set_config(config={"DYN_BATCH_ENABLED": "YES"}, device_name=device)
+            exec_net = ie.load_network(network=net, device_name=device)
+            # Set batch size for certain network.
+            # NOTE: Input data shape will not be changed, but will be used partially in inference which increases performance
+            exec_net.requests[0].set_batch(2)
+        """
         if size <= 0:
             raise ValueError(f"Batch size should be positive integer number but {size} specified")
         deref(self.impl).setBatch(size)
@@ -1324,8 +1598,6 @@ cdef class InferRequest:
                 self.input_blobs[k].buffer[:] = v
 
 
-## This class contains the information about the network model read from IR and allows you to manipulate with
-#  some model parameters such as layers affinity and output layers.
 cdef class IENetwork:
     ## Class constructor
     #
@@ -1348,15 +1620,19 @@ cdef class IENetwork:
             with nogil:
                 self.impl = C.IENetwork()
 
-    ## Name of the loaded network
     @property
     def name(self):
+        """
+        Name of the loaded network
+        """
         name = bytes(self.impl.name)
         return name.decode()
 
-    ## A dictionary that maps input layer names to InputInfoPtr objects.
     @property
     def input_info(self):
+        """
+        A dictionary that maps input layer names to InputInfoPtr objects.
+        """
         cdef map[string, C.InputInfo.Ptr] c_inputs = self.impl.getInputsInfo()
         inputs = {}
         cdef InputInfoPtr input_info_ptr
@@ -1370,6 +1646,9 @@ cdef class IENetwork:
     ## A dictionary that maps output layer names to DataPtr objects
     @property
     def outputs(self):
+        """
+        A dictionary that maps output layer names to DataPtr objects
+        """
         cdef map[string, C.DataPtr] c_outputs = self.impl.getOutputs()
         outputs = {}
         cdef DataPtr data_ptr
@@ -1380,18 +1659,21 @@ cdef class IENetwork:
             outputs[output.first.decode()] = data_ptr
         return outputs
 
-    ## Batch size of the network. Provides getter and setter interfaces to get and modify the
-    #  network batch size. For example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  print(net.batch_size)
-    #  net.batch_size = 4
-    #  print(net.batch_size)
-    #  print(net.input_info['data'].input_data.shape)
-    #  ```
+    
     @property
     def batch_size(self):
+        """Batch size of the network. Provides getter and setter interfaces to get and modify the
+        network batch size. For example:
+       
+        .. code-block:: python
+        
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            print(net.batch_size)
+            net.batch_size = 4
+            print(net.batch_size)
+            print(net.input_info['data'].input_data.shape)
+        """
         return self.impl.getBatch()
 
     @batch_size.setter
@@ -1400,19 +1682,23 @@ cdef class IENetwork:
             raise AttributeError(f"Invalid batch size {batch}! Batch size should be positive integer value")
         self.impl.setBatch(batch)
 
-    ## Marks any intermediate layer as output layer to retrieve the inference results from the specified layers.
-    #  @param outputs: List of layers to be set as model outputs. The list can contain strings with layer names to be set
-    #                  as outputs or tuples with layer name as first element and output port id as second element.
-    #                  In case of setting one layer as output, string or tuple with one layer can be provided.
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  net.add_outputs(["conv5_1', conv2_1', (split_2, 1)])]
-    #  ```
     def add_outputs(self, outputs):
+        """Marks any intermediate layer as output layer to retrieve the inference results from the specified layers.
+        
+        :param outputs: List of layers to be set as model outputs. The list can contain strings with layer names to be set
+                        as outputs or tuples with layer name as first element and output port id as second element.
+                        In case of setting one layer as output, string or tuple with one layer can be provided.
+        
+        :return: None
+
+        Usage example:
+    
+        .. code-block:: python
+    
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+            net.add_outputs(["conv5_1', conv2_1', (split_2, 1)])]
+        """
         if not isinstance(outputs, list):
             outputs = [outputs]
         for i, line in enumerate(outputs):
@@ -1425,38 +1711,44 @@ cdef class IENetwork:
                                 "Expected string with layer name or tuple with two elements: layer name as "
                                 "first element and port id as second")
 
-    ## Serializes the network and stores it in files.
-    #
-    #  @param path_to_xml: Path to a file, where a serialized model will be stored
-    #  @param path_to_bin: Path to a file, where serialized weights will be stored
-    #  @return None
-    #
-    #  Usage example:
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml, weights=path_to_bin)
-    #  net.serialize(path_to_xml, path_to_bin)
-    #  ```
     def serialize(self, path_to_xml, path_to_bin: str = ""):
+        """Serializes the network and stores it in files.
+       
+        :param path_to_xml: Path to a file, where a serialized model will be stored
+        :param path_to_bin: Path to a file, where serialized weights will be stored
+        :return: None
+    
+        Usage example:
+    
+        .. code-block:: python
+
+            ie = IECore()
+            net = ie.read_network(model=path_to_xml, weights=path_to_bin)
+            net.serialize(path_to_xml, path_to_bin)
+        """
         self.impl.serialize(path_to_xml.encode(), path_to_bin.encode())
 
-    ## Reshapes the network to change spatial dimensions, batch size, or any dimension.
-    #
-    #  \note Before using this method, make sure that the target shape is applicable for the network.
-    #        Changing the network shape to an arbitrary value may lead to unpredictable behaviour.
-    #
-    #  @param input_shapes: A dictionary that maps input layer names to tuples with the target shape
-    #  @return None
-    #
-    #  Usage example:\n
-    #  ```python
-    #  ie = IECore()
-    #  net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
-    #  input_layer = next(iter(net.input_info))
-    #  n, c, h, w = net.input_info[input_layer].input_data.shape
-    #  net.reshape({input_layer: (n, c, h*2, w*2)})
-    #  ```
     def reshape(self, input_shapes: dict):
+        """Reshapes the network to change spatial dimensions, batch size, or any dimension.
+        
+        :param input_shapes: A dictionary that maps input layer names to tuples with the target shape
+        :return: None
+
+        .. note::
+        
+           Before using this method, make sure that the target shape is applicable for the network.
+           Changing the network shape to an arbitrary value may lead to unpredictable behaviour.
+
+        Usage example:
+       
+        .. code-block:: python
+    
+           ie = IECore()
+           net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file)
+           input_layer = next(iter(net.input_info))
+           n, c, h, w = net.input_info[input_layer].input_data.shape
+           net.reshape({input_layer: (n, c, h*2, w*2)})
+        """
         cdef map[string, vector[vector[int64_t]]] c_input_shapes
         cdef vector[vector[int64_t]] c_shape
         cdef vector[int64_t] dim
diff --git a/samples/cpp/benchmark_app/README.md b/samples/cpp/benchmark_app/README.md
index 5b67cbf13ac..affacb9adc7 100644
--- a/samples/cpp/benchmark_app/README.md
+++ b/samples/cpp/benchmark_app/README.md
@@ -3,7 +3,7 @@
 This topic demonstrates how to use the Benchmark C++ Tool to estimate deep learning inference performance on supported devices.
 Performance can be measured for two inference modes: latency- and throughput-oriented.
 
-> **NOTE:** This topic describes usage of C++ implementation of the Benchmark Tool. For the Python* implementation, refer to [Benchmark Python* Tool](../../../tools/benchmark_tool/README.md).
+> **NOTE**: This topic describes usage of C++ implementation of the Benchmark Tool. For the Python* implementation, refer to [Benchmark Python* Tool](../../../tools/benchmark_tool/README.md).
 
 > **TIP**: You can quick start with the Benchmark Tool inside the OpenVINO™ [Deep Learning Workbench](@ref openvino_docs_get_started_get_started_dl_workbench) (DL Workbench).
 > [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI you to
@@ -169,7 +169,7 @@ To run the tool, you can use [public](@ref omz_models_group_public) or [Intel's]
 
 This section provides step-by-step instructions on how to run the Benchmark Tool with the `googlenet-v1` public model on CPU or GPU devices. As an input, the `car.png` file from the `<INSTALL_DIR>/samples/scripts/` directory is used.
 
-> **NOTE:** The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
+> **NOTE**: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
 
 1. Download the model. Go to the the Model Downloader directory and run the `downloader.py` script with specifying the model name and directory to download the model to:
    ```sh
@@ -178,12 +178,9 @@ This section provides step-by-step instructions on how to run the Benchmark Tool
    ```sh
    python3 downloader.py --name googlenet-v1 -o <models_dir>
    ```
-2. Convert the model to the Inference Engine IR format. Go to the Model Optimizer directory and run the `mo.py` script with specifying the path to the model, model format (which must be FP32 for CPU and FPG) and output directory to generate the IR files:
+2. Convert the model to the Inference Engine IR format. Run the Model Optimizer using the `mo` command with the path to the model, model format (which must be FP32 for CPU and FPG) and output directory to generate the IR files:
    ```sh
-   cd <INSTALL_DIR>/tools/model_optimizer
-   ```
-   ```sh
-   python3 mo.py --input_model <models_dir>/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP32 --output_dir <ir_dir>
+   mo --input_model <models_dir>/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP32 --output_dir <ir_dir>
    ```
 3. Run the tool with specifying the `<INSTALL_DIR>/samples/scripts/car.png` file as an input image, the IR of the `googlenet-v1` model and a device to perform inference on. The following commands demonstrate running the Benchmark Tool in the asynchronous mode on CPU and GPU devices:
 
diff --git a/samples/cpp/speech_sample/README.md b/samples/cpp/speech_sample/README.md
index d73a5c5ea80..560fe8b1d92 100644
--- a/samples/cpp/speech_sample/README.md
+++ b/samples/cpp/speech_sample/README.md
@@ -27,7 +27,7 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
 
 ## How It Works
 
-Upon the start-up, the application reads command line parameters, loads a specified model and input data to the Inference Engine plugin, performs synchronous inference on all speech utterances stored in the input file. Context-windowed speech frames are processed in batches of 1-8
+On startup, the application reads command line parameters, loads a specified model and input data to the Inference Engine plugin, performs synchronous inference on all speech utterances stored in the input file. Context-windowed speech frames are processed in batches of 1-8
 frames according to the `-bs` parameter.  Batching across utterances is not supported by this sample.  When inference is done, the application creates an output file.  If the `-r` option is given, error
 statistics are provided for each speech utterance as shown above.
 
@@ -46,7 +46,7 @@ Three modes are supported:
 utterance in the input file is scanned for dynamic range.  The scale factor (floating point scalar multiplier) required to scale the maximum input value of the first utterance to 16384 (15 bits) is used
 for all subsequent inputs.  The neural network is quantized to accommodate the scaled input dynamic range.
 - *dynamic* - The scale factor for each input batch is computed
-just before inference on that batch.  The input and network are (re)quantized on-the-fly using an efficient procedure.
+just before inference on that batch.  The input and network are (re)quantized on the fly using an efficient procedure.
 - *user-defined* - The user may specify a scale factor via the `-sf` flag that will be used for static quantization.
 
 The `-qb` flag provides a hint to the GNA plugin regarding the preferred target weight resolution for all layers.  For example, when `-qb 8` is specified, the plugin will use 8-bit weights wherever possible in the
@@ -72,7 +72,7 @@ Several execution modes are supported via the `-d` flag:
 
 The GNA plugin supports loading and saving of the GNA-optimized model (non-IR) via the `-rg` and `-wg` flags.  Thereby, it is possible to avoid the cost of full model quantization at run time. The GNA plugin also supports export of firmware-compatible embedded model images for the Intel® Speech Enabling Developer Kit and Amazon Alexa* Premium Far-Field Voice Development Kit via the `-we` flag (save only).
 
-In addition to performing inference directly from a GNA model file, these options make it possible to:
+In addition to performing inference directly from a GNA model file, these combinations of options make it possible to:
 
 - Convert from IR format to GNA format model file (`-m`, `-wg`)
 - Convert from IR format to embedded format model file (`-m`, `-we`)
@@ -86,7 +86,8 @@ To build the sample, please use instructions available at [Build the Sample Appl
 
 Running the application with the `-h` option yields the following usage message:
 
-```
+```sh
+./speech_sample -h
 [ INFO ] InferenceEngine:
         API version ............ <version>
         Build .................. <number>
@@ -128,14 +129,11 @@ Running the application with the empty list of options yields the usage message
 
 ### Model Preparation
 
-You can use the following model optimizer command to convert a Kaldi nnet1 or nnet2 neural network to Inference Engine Intermediate Representation format:
+Run Model Optimizer to convert a Kaldi nnet1 or nnet2 neural network to Inference Engine Intermediate Representation format:
 
-```
-python <path_to_mo>/mo.py --framework kaldi --input_model wsj_dnn5b.nnet --counts wsj_dnn5b.counts --remove_output_softmax --output_dir <OUTPUT_MODEL_DIR>
-```
+mo --framework kaldi --input_model wsj_dnn5b.nnet --counts wsj_dnn5b.counts --remove_output_softmax --output_dir <OUTPUT_MODEL_DIR>
 
-Assuming that the model optimizer (`mo.py`), Kaldi-trained neural network, `wsj_dnn5b.nnet`, and Kaldi class counts file, `wsj_dnn5b.counts`, are in the working directory this produces
-the IE IR network consisting of `wsj_dnn5b.xml` and `wsj_dnn5b.bin`.
+Assuming that the Kaldi-trained neural network, `wsj_dnn5b.nnet`, and Kaldi class counts file, `wsj_dnn5b.counts`, are in the working directory this produces the IR network consisting of `wsj_dnn5b.xml` and `wsj_dnn5b.bin`.
 
 The following pre-trained models are available:
 
@@ -147,9 +145,9 @@ All of them can be downloaded from [https://storage.openvinotoolkit.org/models_c
 
 ### Speech Inference
 
-Once the IR is created, you can use the following command to do inference on Intel&reg; Processors with the GNA co-processor (or emulation library):
+Once the IR is created, you can use the following command to do inference on Intel® Processors with the GNA co-processor (or emulation library):
 
-```
+```sh
 <path_to_sample>/speech_sample -d GNA_AUTO -bs 2 -i <path_to_ark>/dev93_10.ark -m <path_to_model>/wsj_dnn5b.xml -o scores.ark -r <path_to_ark>/dev93_scores_10.ark
 ```
 
@@ -168,7 +166,7 @@ All of them can be downloaded from [https://storage.openvinotoolkit.org/models_c
 The acoustic log likelihood sequences for all utterances are stored in the file. Example `scores.ark` or `scores.npz`.  If the `-r` option is used, a report on the statistical score error is generated for each utterance such as
 the following:
 
-```
+```sh
 ./speech_sample -d GNA_AUTO -bs 2 -i dev93_10.ark -m wsj_dnn5b.xml -o scores.ark -r dev93_scores_10.ark
 [ INFO ] InferenceEngine:
         API version ............ <version>
@@ -211,8 +209,8 @@ End of Utterance X
 
 ## Use of Sample in Kaldi* Speech Recognition Pipeline
 
-The Wall Street Journal DNN model used in this example was prepared using the Kaldi s5 recipe and the Kaldi Nnet (nnet1) framework.  It is possible to recognize speech by substituting the `speech_sample` for
-Kaldi's nnet-forward command. Since the speech_sample does not yet use pipes, it is necessary to use temporary files for speaker-transformed feature vectors and scores when running the Kaldi speech recognition pipeline. The following operations assume that feature extraction was already performed according to the `s5` recipe and that the working directory within the Kaldi source tree is `egs/wsj/s5`.
+The Wall Street Journal DNN model used in this example was prepared using the Kaldi s5 recipe and the Kaldi Nnet (nnet1) framework. It is possible to recognize speech by substituting the `speech_sample` for
+Kaldi's nnet-forward command. Since the `speech_sample` does not yet use pipes, it is necessary to use temporary files for speaker-transformed feature vectors and scores when running the Kaldi speech recognition pipeline. The following operations assume that feature extraction was already performed according to the `s5` recipe and that the working directory within the Kaldi source tree is `egs/wsj/s5`.
 
 1. Prepare a speaker-transformed feature set given the feature transform specified in `final.feature_transform` and the feature files specified in `feats.scp`:
 
diff --git a/samples/python/ngraph_function_creation_sample/README.md b/samples/python/ngraph_function_creation_sample/README.md
index b38287ed63c..0306c659aa9 100644
--- a/samples/python/ngraph_function_creation_sample/README.md
+++ b/samples/python/ngraph_function_creation_sample/README.md
@@ -32,7 +32,7 @@ each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_custo
 
 Run the application with the `-h` option to see the usage message:
 
-```
+```sh
 python <path_to_sample>/ngraph_function_creation_sample.py -h
 ```
 
@@ -59,9 +59,7 @@ Options:
                         Optional. Number of top results.
 ```
 
-To run the sample, you need specify a model weights and image:
-
-- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
+To run the sample, you need specify a model weights and image. You can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
 
 > **NOTE**:
 >
@@ -73,9 +71,9 @@ To run the sample, you need specify a model weights and image:
 >
 > - The white over black images will be automatically inverted in color for a better predictions.
 
-For example, you can do inference of `3.png` using the pre-trained model on a `GPU`:
+For example, you can do inference of [3.png](https://storage.openvinotoolkit.org/data/test_data/images/3.png) using the pre-trained model on a `GPU`:
 
-```
+```sh
 python <path_to_sample>/ngraph_function_creation_sample.py -m <path_to_sample>/lenet.bin -i <path_to_image>/3.png -d GPU
 ```
 
@@ -109,23 +107,6 @@ The sample application logs each step in a standard output stream and outputs to
 [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
 ```
 
-## Deprecation Notice
-
-<table>
-  <tr>
-    <td><strong>Deprecation Begins</strong></td>
-    <td>June 1, 2020</td>
-  </tr>
-  <tr>
-    <td><strong>Removal Date</strong></td>
-    <td>December 1, 2020</td>
-  </tr>
-</table>
-
-*Starting with the OpenVINO™ toolkit 2020.2 release, all of the features previously available through nGraph have been merged into the OpenVINO™ toolkit. As a result, all the features previously available through ONNX RT Execution Provider for nGraph have been merged with ONNX RT Execution Provider for OpenVINO™ toolkit.*
-
-*Therefore, ONNX RT Execution Provider for nGraph will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.*
-
 ## See Also
 
 - [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
diff --git a/samples/python/speech_sample/README.md b/samples/python/speech_sample/README.md
index 2263ef219a9..82f0109f5c3 100644
--- a/samples/python/speech_sample/README.md
+++ b/samples/python/speech_sample/README.md
@@ -108,8 +108,7 @@ Options:
   -i INPUT, --input INPUT
                         Required. Path to an input file (.ark or .npz).
   -o OUTPUT, --output OUTPUT
-                        Optional. Output file name to save inference results
-                        (.ark or .npz).
+                        Optional. Output file name to save inference results (.ark or .npz).
   -r REFERENCE, --reference REFERENCE
                         Optional. Read reference score file and compare
                         scores.
@@ -176,8 +175,8 @@ Options:
 
 You can use the following model optimizer command to convert a Kaldi nnet1 or nnet2 neural network to Inference Engine Intermediate Representation format:
 
-```
-python <path_to_mo>/mo.py --framework kaldi --input_model wsj_dnn5b.nnet --counts wsj_dnn5b.counts --remove_output_softmax --output_dir <path_to_dir>
+```sh
+mo --framework kaldi --input_model wsj_dnn5b.nnet --counts wsj_dnn5b.counts --remove_output_softmax --output_dir <OUTPUT_MODEL_DIR>
 ```
 
 The following pre-trained models are available:
diff --git a/src/bindings/c/include/c_api/ie_c_api.h b/src/bindings/c/include/c_api/ie_c_api.h
index fdc50ae2efe..a9d415f2d8f 100644
--- a/src/bindings/c/include/c_api/ie_c_api.h
+++ b/src/bindings/c/include/c_api/ie_c_api.h
@@ -14,6 +14,11 @@
  * is allocated and manage inference flow.
 **/
 
+/**
+ *  @defgroup ie_c_api Inference Engine C API
+ *  Inference Engine C API
+ */
+
 #ifndef IE_C_API_H
 #define IE_C_API_H
 
@@ -329,6 +334,7 @@ INFERENCE_ENGINE_C_API(void) ie_param_free(ie_param_t *param);
 
 /**
  * @defgroup Core Core
+ * @ingroup ie_c_api
  * Set of functions dedicated to working with registered plugins and loading
  * network to the registered devices.
  * @{
@@ -558,6 +564,7 @@ INFERENCE_ENGINE_C_API(void) ie_core_available_devices_free(ie_available_devices
 
 /**
  * @defgroup ExecutableNetwork ExecutableNetwork
+ * @ingroup ie_c_api
  * Set of functions representing of neural networks been loaded to device.
  * @{
  */
@@ -619,6 +626,7 @@ INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_exec_network_get_config(con
 
 /**
  * @defgroup InferRequest InferRequest
+ * @ingroup ie_c_api
  * Set of functions responsible for dedicated inference for certain
  * ExecutableNetwork.
  * @{
@@ -703,6 +711,7 @@ INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_infer_request_set_batch(ie_
 
 /**
  * @defgroup Network Network
+ * @ingroup ie_c_api
  * Set of functions managing network been read from the IR before loading
  * of it to the device.
  * @{
@@ -943,6 +952,7 @@ INFERENCE_ENGINE_C_API(void) ie_network_name_free(char **name);
 
 /**
  * @defgroup Blob Blob
+ * @ingroup ie_c_api
  * Set of functions allowing to research memory from infer requests or make new
  * memory objects to be passed to InferRequests.
  * @{
diff --git a/src/bindings/python/src/compatibility/ngraph/opset1/ops.py b/src/bindings/python/src/compatibility/ngraph/opset1/ops.py
index 25a2a05b639..58346cdd6f5 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset1/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset1/ops.py
@@ -49,9 +49,9 @@ _get_node_factory_opset1 = partial(_get_node_factory, "opset1")
 def absolute(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies f(x) = abs(x) to the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with Abs operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with Abs operation applied on it.
     """
     return _get_node_factory_opset1().create("Abs", [node])
 
@@ -60,9 +60,9 @@ def absolute(node: NodeInput, name: Optional[str] = None) -> Node:
 def acos(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply inverse cosine function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with arccos operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with arccos operation applied on it.
     """
     return _get_node_factory_opset1().create("Acos", [node])
 
@@ -84,9 +84,9 @@ def add(
 def asin(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply inverse sine function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with arcsin operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with arcsin operation applied on it.
     """
     return _get_node_factory_opset1().create("Asin", [node])
 
@@ -95,9 +95,9 @@ def asin(node: NodeInput, name: Optional[str] = None) -> Node:
 def atan(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply inverse tangent function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with arctan operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with arctan operation applied on it.
     """
     return _get_node_factory_opset1().create("Atan", [node])
 
@@ -116,19 +116,19 @@ def avg_pool(
 ) -> Node:
     """Return average pooling node.
 
-    @param data_batch:      The input node providing data.
-    @param strides:         The window movement strides.
-    @param pads_begin:      The input data optional padding below filled with zeros.
-    @param pads_end:        The input data optional padding below filled with zeros.
-    @param kernel_shape:    The pooling window shape.
-    @param exclude_pad:     Whether or not to include zero padding in average computations.
-    @param rounding_type:   Determines used rounding schema when computing output shape. Acceptable
+    :param data_batch:      The input node providing data.
+    :param strides:         The window movement strides.
+    :param pads_begin:      The input data optional padding below filled with zeros.
+    :param pads_end:        The input data optional padding below filled with zeros.
+    :param kernel_shape:    The pooling window shape.
+    :param exclude_pad:     Whether or not to include zero padding in average computations.
+    :param rounding_type:   Determines used rounding schema when computing output shape. Acceptable
                             values are: ['floor', 'ceil']
-    @param auto_pad:        Determines how the padding is calculated. Acceptable values:
+    :param auto_pad:        Determines how the padding is calculated. Acceptable values:
                             [None, 'same_upper', 'same_lower', 'valid']
-    @param name:            Optional name for the new output node.
+    :param name:            Optional name for the new output node.
 
-    @return New node with AvgPool operation applied on its data.
+    :return: New node with AvgPool operation applied on its data.
     """
     if auto_pad is None:
         auto_pad = "explicit"
@@ -159,15 +159,15 @@ def batch_norm_inference(
 ) -> Node:
     """Perform layer normalizes a input tensor by mean and variance with appling scale and offset.
 
-    @param data: The input tensor with data for normalization.
-    @param gamma: The scalar scaling for normalized value.
-    @param beta: The bias added to the scaled normalized value.
-    @param mean: The value for mean normalization.
-    @param variance: The value for variance normalization.
-    @param epsilon: The  number to be added to the variance to avoid division
+    :param data: The input tensor with data for normalization.
+    :param gamma: The scalar scaling for normalized value.
+    :param beta: The bias added to the scaled normalized value.
+    :param mean: The value for mean normalization.
+    :param variance: The value for variance normalization.
+    :param epsilon: The  number to be added to the variance to avoid division
                     by zero when normalizing a value.
-    @param name: The optional name of the output node.
-    @return The new node which performs BatchNormInference.
+    :param name: The optional name of the output node.
+    :return: The new node which performs BatchNormInference.
     """
     inputs = as_nodes(gamma, beta, data, mean, variance)
     return _get_node_factory_opset1().create("BatchNormInference", inputs, {"epsilon": epsilon})
@@ -188,17 +188,17 @@ def binary_convolution(
 ) -> Node:
     """Create node performing convolution with binary weights, binary input and integer output.
 
-    @param data: The node providing data batch tensor.
-    @param filter: The node providing filters tensor.
-    @param strides: The kernel window movement strides.
-    @param pads_begin: The number of pixels to add to the beginning along each axis.
-    @param pads_end: The number of pixels to add to the end along each axis.
-    @param dilations: The distance in width and height between elements (weights) in the filter.
-    @param mode: Defines how input tensor 0/1 values and weights 0/1 are interpreted.
-    @param pad_value: Floating-point value used to fill pad area.
-    @param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
-    @param name: The optional new name for output node.
-    @return New node performing binary convolution operation.
+    :param data: The node providing data batch tensor.
+    :param filter: The node providing filters tensor.
+    :param strides: The kernel window movement strides.
+    :param pads_begin: The number of pixels to add to the beginning along each axis.
+    :param pads_end: The number of pixels to add to the end along each axis.
+    :param dilations: The distance in width and height between elements (weights) in the filter.
+    :param mode: Defines how input tensor 0/1 values and weights 0/1 are interpreted.
+    :param pad_value: Floating-point value used to fill pad area.
+    :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
+    :param name: The optional new name for output node.
+    :return: New node performing binary convolution operation.
     """
     return _get_node_factory_opset1().create(
         "BinaryConvolution",
@@ -225,14 +225,14 @@ def broadcast(
 ) -> Node:
     """Create a node which broadcasts the input node's values along specified axes to a desired shape.
 
-    @param data: The node with input tensor data.
-    @param target_shape: The node with a new shape we want to broadcast tensor to.
-    @param axes_mapping: The node with a axis positions (0-based) in the result
+    :param data: The node with input tensor data.
+    :param target_shape: The node with a new shape we want to broadcast tensor to.
+    :param axes_mapping: The node with a axis positions (0-based) in the result
                            that are being broadcast.
-    @param mode: The type of broadcasting that specifies mapping of input tensor axes
+    :param mode: The type of broadcasting that specifies mapping of input tensor axes
                            to output shape axes. Range of values: NUMPY, EXPLICIT.
-    @param name: Optional new name for output node.
-    @return New node with broadcast shape.
+    :param name: Optional new name for output node.
+    :return: New node with broadcast shape.
     """
     inputs = as_nodes(data, target_shape)
     if mode.upper() == "EXPLICIT":
@@ -251,11 +251,11 @@ def ctc_greedy_decoder(
 ) -> Node:
     """Perform greedy decoding on the logits given in input (best path).
 
-    @param data: Logits on which greedy decoding is performed.
-    @param sequence_mask: The tensor with sequence masks for each sequence in the batch.
-    @param merge_repeated: The flag for merging repeated labels during the CTC calculation.
-    @param name: Optional name for output node.
-    @return The new node performing an CTCGreedyDecoder operation on input tensor.
+    :param data: Logits on which greedy decoding is performed.
+    :param sequence_mask: The tensor with sequence masks for each sequence in the batch.
+    :param merge_repeated: The flag for merging repeated labels during the CTC calculation.
+    :param name: Optional name for output node.
+    :return: The new node performing an CTCGreedyDecoder operation on input tensor.
     """
     node_inputs = as_nodes(data, sequence_mask)
     return _get_node_factory_opset1().create(
@@ -267,9 +267,9 @@ def ctc_greedy_decoder(
 def ceiling(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies ceiling to the input node element-wise.
 
-    @param node: The node providing data to ceiling operation.
-    @param name: Optional name for output node.
-    @return The node performing element-wise ceiling.
+    :param node: The node providing data to ceiling operation.
+    :param name: Optional name for output node.
+    :return: The node performing element-wise ceiling.
     """
     return _get_node_factory_opset1().create("Ceiling", [node])
 
@@ -280,11 +280,11 @@ def clamp(
 ) -> Node:
     """Perform clamp element-wise on data from input node.
 
-    @param data: Input tensor. One of: input node, array or scalar.
-    @param min_value: The lower bound of the <min_value;max_value> range. Scalar value.
-    @param max_value: The upper bound of the <min_value;max_value> range. Scalar value.
-    @param name: Optional output node name.
-    @return The new node performing a clamp operation on its input data element-wise.
+    :param data: Input tensor. One of: input node, array or scalar.
+    :param min_value: The lower bound of the <min_value;max_value> range. Scalar value.
+    :param max_value: The upper bound of the <min_value;max_value> range. Scalar value.
+    :param name: Optional output node name.
+    :return: The new node performing a clamp operation on its input data element-wise.
 
     Performs a clipping operation on an input value between a pair of boundary values.
 
@@ -295,12 +295,12 @@ def clamp(
 
     Clamp uses the following logic:
 
-    @code{.py}
+    .. code-block:: python
+
         if data < min_value:
             data=min_value
         elif data > max_value:
             data=max_value
-    @endcode
     """
     return _get_node_factory_opset1().create(
         "Clamp", [as_node(data)], {"min": min_value, "max": max_value}
@@ -311,10 +311,10 @@ def clamp(
 def concat(nodes: List[NodeInput], axis: int, name: Optional[str] = None) -> Node:
     """Concatenate input nodes into single new node along specified axis.
 
-    @param nodes: The nodes we want concatenate into single new node.
-    @param axis: The axis along which we want to concatenate input nodes.
-    @param name: The optional new name for output node.
-    @return Return new node that is a concatenation of input nodes.
+    :param nodes: The nodes we want concatenate into single new node.
+    :param axis: The axis along which we want to concatenate input nodes.
+    :param name: The optional new name for output node.
+    :return: Return new node that is a concatenation of input nodes.
     """
     return _get_node_factory_opset1().create("Concat", as_nodes(*nodes), {"axis": axis})
 
@@ -323,10 +323,10 @@ def concat(nodes: List[NodeInput], axis: int, name: Optional[str] = None) -> Nod
 def constant(value: NumericData, dtype: NumericType = None, name: Optional[str] = None) -> Constant:
     """Create a Constant node from provided value.
 
-    @param value: One of: array of values or scalar to initialize node with.
-    @param dtype: The data type of provided data.
-    @param name: Optional name for output node.
-    @return The Constant node initialized with provided data.
+    :param value: One of: array of values or scalar to initialize node with.
+    :param dtype: The data type of provided data.
+    :param name: Optional name for output node.
+    :return: The Constant node initialized with provided data.
     """
     return make_constant_node(value, dtype)
 
@@ -337,10 +337,10 @@ def convert(
 ) -> Node:
     """Return node which casts input node values to specified type.
 
-    @param data: Node which produces the input tensor.
-    @param destination_type: Provides the target type for the conversion.
-    @param name: Optional name for the output node.
-    @return New node performing the conversion operation.
+    :param data: Node which produces the input tensor.
+    :param destination_type: Provides the target type for the conversion.
+    :param name: Optional name for the output node.
+    :return: New node performing the conversion operation.
     """
     if not isinstance(destination_type, str):
         destination_type = get_element_type_str(destination_type)
@@ -353,10 +353,10 @@ def convert(
 def convert_like(data: NodeInput, like: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which casts data node values to the type of another node.
 
-    @param data: Node which produces the input tensor
-    @param like: Node which provides the target type information for the conversion
-    @param name: Optional name for the output node.
-    @return New node performing the conversion operation.
+    :param data: Node which produces the input tensor
+    :param like: Node which provides the target type information for the conversion
+    :param name: Optional name for the output node.
+    :return: New node performing the conversion operation.
     """
     return _get_node_factory_opset1().create("ConvertLike", [data, like])
 
@@ -374,15 +374,15 @@ def convolution(
 ) -> Node:
     """Return node performing batched convolution operation.
 
-    @param data: The node providing data batch tensor.
-    @param filter: The node providing filters tensor.
-    @param strides: The kernel window movement strides.
-    @param pads_begin: The number of zero padding elements to add on each axis below 0 coordinate.
-    @param pads_end: The number of zero padding elements to add on each axis above max coordinate
-    @param dilations: The data batch dilation strides.
-    @param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
-    @param name: The optional new name for output node.
-    @return New node performing batched convolution operation.
+    :param data: The node providing data batch tensor.
+    :param filter: The node providing filters tensor.
+    :param strides: The kernel window movement strides.
+    :param pads_begin: The number of zero padding elements to add on each axis below 0 coordinate.
+    :param pads_end: The number of zero padding elements to add on each axis above max coordinate
+    :param dilations: The data batch dilation strides.
+    :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
+    :param name: The optional new name for output node.
+    :return: New node performing batched convolution operation.
     """
     return _get_node_factory_opset1().create(
         "Convolution",
@@ -412,18 +412,18 @@ def convolution_backprop_data(
 ) -> Node:
     """Create node performing a batched-convolution backprop data operation.
 
-    @param      data:         The node producing data from forward-prop
-    @param      filters:      The node producing the filters from forward-prop.
-    @param      output_shape: The node producing output delta.
-    @param      strides:      The distance (in pixels) to slide the filter on the feature map
+    :param      data:         The node producing data from forward-prop
+    :param      filters:      The node producing the filters from forward-prop.
+    :param      output_shape: The node producing output delta.
+    :param      strides:      The distance (in pixels) to slide the filter on the feature map
                               over the axes.
-    @param      pads_begin:   The number of pixels to add to the beginning along each axis.
-    @param      pads_end:     The number of pixels to add to the end along each axis.
-    @param      dilations:    The distance in width and height between elements (weights)
+    :param      pads_begin:   The number of pixels to add to the beginning along each axis.
+    :param      pads_end:     The number of pixels to add to the end along each axis.
+    :param      dilations:    The distance in width and height between elements (weights)
                               in the filter.
-    @param      name:         The node name.
+    :param      name:         The node name.
 
-    @return   The node object representing ConvolutionBackpropData  operation.
+    :return:   The node object representing ConvolutionBackpropData  operation.
     """
     spatial_dim_count = len(strides)
     if pads_begin is None:
@@ -458,9 +458,9 @@ def convolution_backprop_data(
 def cos(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply cosine function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with cos operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with cos operation applied on it.
     """
     return _get_node_factory_opset1().create("Cos", [node])
 
@@ -469,9 +469,9 @@ def cos(node: NodeInput, name: Optional[str] = None) -> Node:
 def cosh(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply hyperbolic cosine function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with cosh operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with cosh operation applied on it.
     """
     return _get_node_factory_opset1().create("Cosh", [node])
 
@@ -492,18 +492,18 @@ def deformable_convolution(
 ) -> Node:
     """Create node performing deformable convolution.
 
-    @param data: The node providing data batch tensor.
-    @param filter: The node providing filters tensor.
-    @param strides: The distance (in pixels) to slide the filter on the feature map over the axes.
-    @param pads_begin: The number of pixels to add to the beginning along each axis.
-    @param pads_end: The number of pixels to add to the end along each axis.
-    @param dilations: The distance in width and height between elements (weights) in the filter.
-    @param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
-    @param group: The number of groups which both output and input should be split into.
-    @param deformable_group: The number of groups which deformable values and output should be split
+    :param data: The node providing data batch tensor.
+    :param filter: The node providing filters tensor.
+    :param strides: The distance (in pixels) to slide the filter on the feature map over the axes.
+    :param pads_begin: The number of pixels to add to the beginning along each axis.
+    :param pads_end: The number of pixels to add to the end along each axis.
+    :param dilations: The distance in width and height between elements (weights) in the filter.
+    :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
+    :param group: The number of groups which both output and input should be split into.
+    :param deformable_group: The number of groups which deformable values and output should be split
                              into along the channel axis.
-    @param name: The optional new name for output node.
-    @return New node performing deformable convolution operation.
+    :param name: The optional new name for output node.
+    :return: New node performing deformable convolution operation.
     """
     return _get_node_factory_opset1().create(
         "DeformableConvolution",
@@ -540,19 +540,19 @@ def deformable_psroi_pooling(
     DeformablePSROIPooling computes position-sensitive pooling
     on regions of interest specified by input.
 
-    @param feature_maps: 4D tensor with feature maps.
-    @param coords: 2D tensor describing box consisting of tuples: [batch_id, x_1, y_1, x_2, y_2].
-    @param output_dim: A pooled output channel number.
-    @param spatial_scale: A multiplicative spatial scale factor to translate ROI.
-    @param group_size: The number of groups to encode position-sensitive score.
-    @param mode: Specifies mode for pooling. Range of values: ['bilinear_deformable'].
-    @param spatial_bins_x: Specifies numbers of bins to divide the input feature maps over width.
-    @param spatial_bins_y: Specifies numbers of bins to divide the input feature maps over height.
-    @param trans_std: The value that all transformation (offset) values are multiplied with.
-    @param part_size: The number of parts the output tensor spatial dimensions are divided into.
-    @param offsets: Optional node. 4D input blob with transformation values (offsets).
-    @param name: The optional new name for output node.
-    @return New node performing DeformablePSROIPooling operation.
+    :param feature_maps: 4D tensor with feature maps.
+    :param coords: 2D tensor describing box consisting of tuples: [batch_id, x_1, y_1, x_2, y_2].
+    :param output_dim: A pooled output channel number.
+    :param spatial_scale: A multiplicative spatial scale factor to translate ROI.
+    :param group_size: The number of groups to encode position-sensitive score.
+    :param mode: Specifies mode for pooling. Range of values: ['bilinear_deformable'].
+    :param spatial_bins_x: Specifies numbers of bins to divide the input feature maps over width.
+    :param spatial_bins_y: Specifies numbers of bins to divide the input feature maps over height.
+    :param trans_std: The value that all transformation (offset) values are multiplied with.
+    :param part_size: The number of parts the output tensor spatial dimensions are divided into.
+    :param offsets: Optional node. 4D input blob with transformation values (offsets).
+    :param name: The optional new name for output node.
+    :return: New node performing DeformablePSROIPooling operation.
     """
     node_inputs = as_nodes(feature_maps, coords)
     if offsets is not None:
@@ -587,16 +587,16 @@ def depth_to_space(node: Node, mode: str, block_size: int = 1, name: str = None)
 
     [N, C * `block_size` * `block_size`, H / `block_size`, W / `block_size`]
 
-    @param node: The node with input tensor data.
-    @param mode: Specifies how the input depth dimension is split to block coordinates
+    :param node: The node with input tensor data.
+    :param mode: Specifies how the input depth dimension is split to block coordinates
 
                  blocks_first: The input is divided to [block_size, ..., block_size, new_depth]
                  depth_first: The input is divided to [new_depth, block_size, ..., block_size]
 
-    @param block_size: The size of the spatial block of values describing
+    :param block_size: The size of the spatial block of values describing
                        how the tensor's data is to be rearranged.
-    @param name: Optional output node name.
-    @return The new node performing an DepthToSpace operation on its input tensor.
+    :param name: Optional output node name.
+    :return: The new node performing an DepthToSpace operation on its input tensor.
     """
     return _get_node_factory_opset1().create(
         "DepthToSpace", [node], {"mode": mode, "block_size": block_size},
@@ -615,14 +615,14 @@ def detection_output(
 ) -> Node:
     """Generate the detection output using information on location and confidence predictions.
 
-    @param  box_logits:         The 2D input tensor with box logits.
-    @param  class_preds:        The 2D input tensor with class predictions.
-    @param  proposals:          The 3D input tensor with proposals.
-    @param  attrs:              The dictionary containing key, value pairs for attributes.
-    @param  aux_class_preds:    The 2D input tensor with additional class predictions information.
-    @param  aux_box_preds:      The 2D input tensor with additional box predictions information.
-    @param  name:               Optional name for the output node.
-    @return Node representing DetectionOutput operation.
+    :param  box_logits:         The 2D input tensor with box logits.
+    :param  class_preds:        The 2D input tensor with class predictions.
+    :param  proposals:          The 3D input tensor with proposals.
+    :param  attrs:              The dictionary containing key, value pairs for attributes.
+    :param  aux_class_preds:    The 2D input tensor with additional class predictions information.
+    :param  aux_box_preds:      The 2D input tensor with additional box predictions information.
+    :param  name:               Optional name for the output node.
+    :return: Node representing DetectionOutput operation.
 
      Available attributes are:
 
@@ -714,7 +714,9 @@ def detection_output(
                             Required: no
 
     Example of attribute dictionary:
-    @code{.py}
+
+    .. code-block:: python
+
         # just required ones
         attrs = {
             'num_classes': 85,
@@ -731,7 +733,6 @@ def detection_output(
             'input_height': [32],
             'input_width': [32],
         }
-    @endcode
 
     Optional attributes which are absent from dictionary will be set with corresponding default.
     """
@@ -774,11 +775,11 @@ def divide(
 ) -> Node:
     """Return node which applies f(x) = A/B to the input nodes element-wise.
 
-    @param left_node: The node providing dividend data.
-    @param right_node: The node providing divisor data.
-    @param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors.
-    @param name: Optional name for output node.
-    @return The node performing element-wise division.
+    :param left_node: The node providing dividend data.
+    :param right_node: The node providing divisor data.
+    :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors.
+    :param name: Optional name for output node.
+    :return: The node performing element-wise division.
     """
     return _get_node_factory_opset1().create(
         "Divide", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -794,10 +795,10 @@ def elu(data: NodeInput, alpha: NumericType, name: Optional[str] = None) -> Node
     For more information refer to:
     [Fast and Accurate Deep Network Learning by Exponential Linear Units](http://arxiv.org/abs/1511.07289)
 
-    @param data: Input tensor. One of: input node, array or scalar.
-    @param alpha: Scalar multiplier for negative values.
-    @param name: Optional output node name.
-    @return The new node performing an ELU operation on its input data element-wise.
+    :param data: Input tensor. One of: input node, array or scalar.
+    :param alpha: Scalar multiplier for negative values.
+    :param name: Optional output node name.
+    :return: The new node performing an ELU operation on its input data element-wise.
     """
     return _get_node_factory_opset1().create("Elu", [as_node(data)], {"alpha": alpha})
 
@@ -811,12 +812,12 @@ def equal(
 ) -> Node:
     """Return node which checks if input nodes are equal element-wise.
 
-    @param left_node: The first input node for equal operation.
-    @param right_node: The second input node for equal operation.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param left_node: The first input node for equal operation.
+    :param right_node: The second input node for equal operation.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @param name: The optional name for output new node.
-    @return The node performing element-wise equality check.
+    :param name: The optional name for output new node.
+    :return: The node performing element-wise equality check.
     """
     return _get_node_factory_opset1().create(
         "Equal", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -827,9 +828,9 @@ def equal(
 def erf(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which calculates Gauss error function element-wise with given tensor.
 
-    @param node: The node providing data for operation.
-    @param name: The optional name for new output node.
-    @return The new node performing element-wise Erf operation.
+    :param node: The node providing data for operation.
+    :param name: The optional name for new output node.
+    :return: The new node performing element-wise Erf operation.
     """
     return _get_node_factory_opset1().create("Erf", [node])
 
@@ -838,9 +839,9 @@ def erf(node: NodeInput, name: Optional[str] = None) -> Node:
 def exp(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies exponential function to the input node element-wise.
 
-    @param node: The node providing data for operation.
-    @param name: The optional name for new output node.
-    @return The new node performing natural exponential operation.
+    :param node: The node providing data for operation.
+    :param name: The optional name for new output node.
+    :return: The new node performing natural exponential operation.
     """
     return _get_node_factory_opset1().create("Exp", [node])
 
@@ -858,33 +859,34 @@ def fake_quantize(
 ) -> Node:
     r"""Perform an element-wise linear quantization on input data.
 
-    @param data:           The node with data tensor.
-    @param input_low:      The node with the minimum for input values.
-    @param input_high:     The node with the maximum for input values.
-    @param output_low:     The node with the minimum quantized value.
-    @param output_high:    The node with the maximum quantized value.
-    @param levels:         The number of quantization levels. Integer value.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param data:           The node with data tensor.
+    :param input_low:      The node with the minimum for input values.
+    :param input_high:     The node with the maximum for input values.
+    :param output_low:     The node with the minimum quantized value.
+    :param output_high:    The node with the maximum quantized value.
+    :param levels:         The number of quantization levels. Integer value.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @return New node with quantized value.
+    :return: New node with quantized value.
 
     Input floating point values are quantized into a discrete set of floating point values.
 
-    @code{.py}
+    .. code-block:: python
+
         if x <= input_low:
             output = output_low
         if x > input_high:
             output = output_high
         else:
             output = fake_quantize(output)
-    @endcode
 
     Fake quantize uses the following logic:
 
-    \f[ output =
-            \dfrac{round( \dfrac{data - input\_low}{(input\_high - input\_low)\cdot (levels-1)})}
-            {(levels-1)\cdot (output\_high - output\_low)} + output\_low
-    \f]
+    .. math::
+
+        output =
+        \dfrac{round( \dfrac{data - input\_low}{(input\_high - input\_low)\cdot (levels-1)})}
+        {(levels-1)\cdot (output\_high - output\_low)} + output\_low
     """
     return _get_node_factory_opset1().create(
         "FakeQuantize",
@@ -897,9 +899,9 @@ def fake_quantize(
 def floor(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies floor to the input node element-wise.
 
-    @param node: The input node providing data.
-    @param name: The optional name for new output node.
-    @return The node performing element-wise floor operation.
+    :param node: The input node providing data.
+    :param name: The optional name for new output node.
+    :return: The node performing element-wise floor operation.
     """
     return _get_node_factory_opset1().create("Floor", [node])
 
@@ -913,11 +915,11 @@ def floor_mod(
 ) -> Node:
     """Return node performing element-wise FloorMod (division reminder) with two given tensors.
 
-    @param left_node: The first input node for FloorMod operation.
-    @param right_node: The second input node for FloorMod operation.
-    @param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors.
-    @param name: Optional name for output node.
-    @return The node performing element-wise FloorMod operation.
+    :param left_node: The first input node for FloorMod operation.
+    :param right_node: The second input node for FloorMod operation.
+    :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors.
+    :param name: Optional name for output node.
+    :return: The node performing element-wise FloorMod operation.
     """
     return _get_node_factory_opset1().create(
         "FloorMod", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -930,11 +932,11 @@ def gather(
 ) -> Node:
     """Return Gather node which takes slices from axis of data according to indices.
 
-    @param data: The tensor from which slices are gathered.
-    @param indices: Tensor with indexes to gather.
-    @param axis: The dimension index to gather data from.
-    @param name: Optional name for output node.
-    @return The new node performing a Gather operation on the data input tensor.
+    :param data: The tensor from which slices are gathered.
+    :param indices: Tensor with indexes to gather.
+    :param axis: The dimension index to gather data from.
+    :param name: Optional name for output node.
+    :return: The new node performing a Gather operation on the data input tensor.
     """
     node_inputs = as_nodes(data, indices, axis)
     return _get_node_factory_opset1().create("Gather", node_inputs)
@@ -950,18 +952,19 @@ def gather_tree(
 ) -> Node:
     """Perform GatherTree operation.
 
-    @param step_ids: The tensor with indices from per each step.
-    @param parent_idx: The tensor with with parent beam indices.
-    @param max_seq_len: The tensor with maximum lengths for each sequence in the batch.
-    @param end_token: The scalar tensor with value of the end marker in a sequence.
-    @param name: Optional name for output node.
-    @return The new node performing a GatherTree operation.
+    :param step_ids: The tensor with indices from per each step.
+    :param parent_idx: The tensor with with parent beam indices.
+    :param max_seq_len: The tensor with maximum lengths for each sequence in the batch.
+    :param end_token: The scalar tensor with value of the end marker in a sequence.
+    :param name: Optional name for output node.
+    :return: The new node performing a GatherTree operation.
 
     The GatherTree node generates the complete beams from the indices per each step
     and the parent beam indices.
     GatherTree uses the following logic:
 
-    @code{.py}
+    .. code-block:: python
+
         for batch in range(BATCH_SIZE):
             for beam in range(BEAM_WIDTH):
                 max_sequence_in_beam = min(MAX_TIME, max_seq_len[batch])
@@ -972,7 +975,7 @@ def gather_tree(
                     final_idx[level, batch, beam] = step_idx[level, batch, parent]
 
                     parent = parent_idx[level, batch, parent]
-    @endcode
+
     """
     node_inputs = as_nodes(step_ids, parent_idx, max_seq_len, end_token)
     return _get_node_factory_opset1().create("GatherTree", node_inputs)
@@ -987,12 +990,12 @@ def greater(
 ) -> Node:
     """Return node which checks if left input node is greater than the right node element-wise.
 
-    @param left_node: The first input node providing data.
-    @param right_node: The second input node providing data.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param left_node: The first input node providing data.
+    :param right_node: The second input node providing data.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @param name: The optional new name for output node.
-    @return The node performing element-wise check whether left_node is greater than right_node.
+    :param name: The optional new name for output node.
+    :return: The node performing element-wise check whether left_node is greater than right_node.
     """
     return _get_node_factory_opset1().create(
         "Greater", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1008,12 +1011,12 @@ def greater_equal(
 ) -> Node:
     """Return node which checks if left node is greater or equal to the right node element-wise.
 
-    @param left_node: The first input node providing data.
-    @param right_node: The second input node providing data.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param left_node: The first input node providing data.
+    :param right_node: The second input node providing data.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @param name: The optional new name for output node.
-    @return The node performing element-wise check whether left_node is greater than or equal
+    :param name: The optional new name for output node.
+    :return: The node performing element-wise check whether left_node is greater than or equal
              right_node.
     """
     return _get_node_factory_opset1().create(
@@ -1026,12 +1029,12 @@ def grn(data: Node, bias: float, name: Optional[str] = None) -> Node:
 
     Computes GRN operation on channels for input tensor:
 
-    \f[ output_i = \dfrac{input_i}{\sqrt{\sum_{i}^{C} input_i}} \f]
+    .. math:: output_i = \dfrac{input_i}{\sqrt{\sum_{i}^{C} input_i}}
 
-    @param data: The node with data tensor.
-    @param bias: The bias added to the variance. Scalar value.
-    @param name: Optional output node name.
-    @return The new node performing a GRN operation on tensor's channels.
+    :param data: The node with data tensor.
+    :param bias: The bias added to the variance. Scalar value.
+    :param name: Optional output node name.
+    :return: The new node performing a GRN operation on tensor's channels.
     """
     return _get_node_factory_opset1().create("GRN", [data], {"bias": bias})
 
@@ -1049,24 +1052,24 @@ def group_convolution(
 ) -> Node:
     """Perform Group Convolution operation on data from input node.
 
-    @param data:        The node producing input data.
-    @param filters:     The node producing filters data.
-    @param strides:     The distance (in pixels) to slide the filter on the feature map
+    :param data:        The node producing input data.
+    :param filters:     The node producing filters data.
+    :param strides:     The distance (in pixels) to slide the filter on the feature map
                         over the axes.
-    @param pads_begin:  The number of pixels to add at the beginning along each axis.
-    @param pads_end:    The number of pixels to add at the end along each axis.
-    @param dilations:   The distance in width and height between elements (weights) in the filter.
-    @param auto_pad:    Describes how to perform padding. Possible values:
+    :param pads_begin:  The number of pixels to add at the beginning along each axis.
+    :param pads_end:    The number of pixels to add at the end along each axis.
+    :param dilations:   The distance in width and height between elements (weights) in the filter.
+    :param auto_pad:    Describes how to perform padding. Possible values:
                         EXPLICIT:   Pad dimensions are explicity specified
                         SAME_LOWER: Pad dimensions computed to match input shape
-                                    Ceil(num_dims/2) at the beginning and
-                                    Floor(num_dims/2) at the end
+                        Ceil(num_dims/2) at the beginning and
+                        Floor(num_dims/2) at the end
                         SAME_UPPER: Pad dimensions computed to match input shape
-                                    Floor(num_dims/2) at the beginning and
-                                    Ceil(num_dims/2) at the end
+                        Floor(num_dims/2) at the beginning and
+                        Ceil(num_dims/2) at the end
                         VALID:      No padding
-    @param name: Optional output node name.
-    @return The new node performing a Group Convolution operation on tensor from input node.
+    :param name: Optional output node name.
+    :return: The new node performing a Group Convolution operation on tensor from input node.
     """
     return _get_node_factory_opset1().create(
         "GroupConvolution",
@@ -1096,28 +1099,28 @@ def group_convolution_backprop_data(
 ) -> Node:
     """Perform Group Convolution operation on data from input node.
 
-    @param data:            The node producing input data.
-    @param filters:         The node producing filter data.
-    @param strides:         The distance (in pixels) to slide the filter on the feature map
+    :param data:            The node producing input data.
+    :param filters:         The node producing filter data.
+    :param strides:         The distance (in pixels) to slide the filter on the feature map
                             over the axes.
-    @param output_shape:    The node that specifies spatial shape of the output.
-    @param pads_begin:      The number of pixels to add at the beginning along each axis.
-    @param pads_end:        The number of pixels to add at the end along each axis.
-    @param dilations:       The distance in width and height between elements (weights)
+    :param output_shape:    The node that specifies spatial shape of the output.
+    :param pads_begin:      The number of pixels to add at the beginning along each axis.
+    :param pads_end:        The number of pixels to add at the end along each axis.
+    :param dilations:       The distance in width and height between elements (weights)
                             in the filter.
-    @param auto_pad:        Describes how to perform padding. Possible values:
+    :param auto_pad:        Describes how to perform padding. Possible values:
                             EXPLICIT:   Pad dimensions are explicity specified
                             SAME_LOWER: Pad dimensions computed to match input shape
-                                        Ceil(num_dims/2) at the beginning and
-                                        Floor(num_dims/2) at the end
+                            Ceil(num_dims/2) at the beginning and
+                            Floor(num_dims/2) at the end
                             SAME_UPPER: Pad dimensions computed to match input shape
-                                        Floor(num_dims/2) at the beginning and
-                                        Ceil(num_dims/2) at the end
+                            Floor(num_dims/2) at the beginning and
+                            Ceil(num_dims/2) at the end
                             VALID:      No padding
-    @param output_padding:  The additional amount of paddings added per each spatial axis
+    :param output_padding:  The additional amount of paddings added per each spatial axis
                             in the output tensor.
-    @param name: Optional output node name.
-    @return The new node performing a Group Convolution operation on tensor from input node.
+    :param name: Optional output node name.
+    :return: The new node performing a Group Convolution operation on tensor from input node.
     """
     spatial_dim_count = len(strides)
     if dilations is None:
@@ -1150,17 +1153,18 @@ def group_convolution_backprop_data(
 def hard_sigmoid(data: Node, alpha: NodeInput, beta: NodeInput, name: Optional[str] = None) -> Node:
     """Perform Hard Sigmoid operation element-wise on data from input node.
 
-    @param data: The node with data tensor.
-    @param alpha: A node producing the alpha parameter.
-    @param beta: A node producing the beta parameter
-    @param name: Optional output node name.
-    @return The new node performing a Hard Sigmoid element-wise on input tensor.
+    :param data: The node with data tensor.
+    :param alpha: A node producing the alpha parameter.
+    :param beta: A node producing the beta parameter
+    :param name: Optional output node name.
+    :return: The new node performing a Hard Sigmoid element-wise on input tensor.
 
     Hard Sigmoid uses the following logic:
 
-    @code{.py}
+    .. code-block:: python
+
         y = max(0, min(1, alpha * data + beta))
-    @endcode
+
     """
     return _get_node_factory_opset1().create("HardSigmoid", [data, as_node(alpha), as_node(beta)])
 
@@ -1171,11 +1175,11 @@ def interpolate(
 ) -> Node:
     """Perform interpolation of independent slices in input tensor.
 
-    @param  image:         The node providing input tensor with data for interpolation.
-    @param  output_shape:  1D tensor describing output shape for spatial axes.
-    @param  attrs:         The dictionary containing key, value pairs for attributes.
-    @param  name:          Optional name for the output node.
-    @return Node representing interpolation operation.
+    :param  image:         The node providing input tensor with data for interpolation.
+    :param  output_shape:  1D tensor describing output shape for spatial axes.
+    :param  attrs:         The dictionary containing key, value pairs for attributes.
+    :param  name:          Optional name for the output node.
+    :return: Node representing interpolation operation.
 
     Available attributes are:
 
@@ -1210,7 +1214,9 @@ def interpolate(
                         Required: no
 
     Example of attribute dictionary:
-    @code{.py}
+
+    .. code-block:: python
+
         # just required ones
         attrs = {
             'axes': [2, 3],
@@ -1223,7 +1229,7 @@ def interpolate(
             'antialias': True,
             'pads_begin': [2, 2, 2],
         }
-    @endcode
+
     Optional attributes which are absent from dictionary will be set with corresponding default.
     """
     requirements = [
@@ -1249,12 +1255,12 @@ def less(
 ) -> Node:
     """Return node which checks if left input node is less than the right node element-wise.
 
-    @param left_node: The first input node providing data.
-    @param right_node: The second input node providing data.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param left_node: The first input node providing data.
+    :param right_node: The second input node providing data.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @param name: The optional new name for output node.
-    @return The node performing element-wise check whether left_node is less than the right_node.
+    :param name: The optional new name for output node.
+    :return: The node performing element-wise check whether left_node is less than the right_node.
     """
     return _get_node_factory_opset1().create(
         "Less", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1270,12 +1276,12 @@ def less_equal(
 ) -> Node:
     """Return node which checks if left input node is less or equal the right node element-wise.
 
-    @param left_node: The first input node providing data.
-    @param right_node: The second input node providing data.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param left_node: The first input node providing data.
+    :param right_node: The second input node providing data.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @param name: The optional new name for output node.
-    @return The node performing element-wise check whether left_node is less than or equal the
+    :param name: The optional new name for output node.
+    :return: The node performing element-wise check whether left_node is less than or equal the
              right_node.
     """
     return _get_node_factory_opset1().create(
@@ -1287,9 +1293,9 @@ def less_equal(
 def log(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies natural logarithm to the input node element-wise.
 
-    @param node: The input node providing data for operation.
-    @param name: The optional new name for output node.
-    @return The new node performing log operation element-wise.
+    :param node: The input node providing data for operation.
+    :param name: The optional new name for output node.
+    :return: The new node performing log operation element-wise.
     """
     return _get_node_factory_opset1().create("Log", [node])
 
@@ -1303,12 +1309,12 @@ def logical_and(
 ) -> Node:
     """Return node which perform logical and operation on input nodes element-wise.
 
-    @param left_node: The first input node providing data.
-    @param right_node: The second input node providing data.
-    @param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
+    :param left_node: The first input node providing data.
+    :param right_node: The second input node providing data.
+    :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
                            to output shape axes. Range of values: numpy, explicit.
-    @param name: The optional new name for output node.
-    @return The node performing logical and operation on input nodes corresponding elements.
+    :param name: The optional new name for output node.
+    :return: The node performing logical and operation on input nodes corresponding elements.
     """
     return _get_node_factory_opset1().create(
         "LogicalAnd", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1319,9 +1325,9 @@ def logical_and(
 def logical_not(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies element-wise logical negation to the input node.
 
-    @param node: The input node providing data.
-    @param name: The optional new name for output node.
-    @return The node performing element-wise logical NOT operation with given tensor.
+    :param node: The input node providing data.
+    :param name: The optional new name for output node.
+    :return: The node performing element-wise logical NOT operation with given tensor.
     """
     return _get_node_factory_opset1().create("LogicalNot", [node])
 
@@ -1335,12 +1341,12 @@ def logical_or(
 ) -> Node:
     """Return node which performs logical OR operation on input nodes element-wise.
 
-    @param left_node: The first input node providing data.
-    @param right_node: The second input node providing data.
-    @param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
+    :param left_node: The first input node providing data.
+    :param right_node: The second input node providing data.
+    :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
                            to output shape axes. Range of values: numpy, explicit.
-    @param name: The optional new name for output node.
-    @return The node performing logical or operation on input nodes corresponding elements.
+    :param name: The optional new name for output node.
+    :return: The node performing logical or operation on input nodes corresponding elements.
     """
     return _get_node_factory_opset1().create(
         "LogicalOr", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1356,12 +1362,12 @@ def logical_xor(
 ) -> Node:
     """Return node which performs logical XOR operation on input nodes element-wise.
 
-    @param left_node: The first input node providing data.
-    @param right_node: The second input node providing data.
-    @param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
+    :param left_node: The first input node providing data.
+    :param right_node: The second input node providing data.
+    :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
                            to output shape axes. Range of values: numpy, explicit.
-    @param name: The optional new name for output node.
-    @return The node performing logical or operation on input nodes corresponding elements.
+    :param name: The optional new name for output node.
+    :return: The node performing logical or operation on input nodes corresponding elements.
     """
     return _get_node_factory_opset1().create(
         "LogicalXor", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1380,13 +1386,13 @@ def lrn(
 ) -> Node:
     """Return a node which performs element-wise Local Response Normalization (LRN) operation.
 
-    @param data: Input data.
-    @param alpha: A scale factor (usually positive).
-    @param beta: An exponent.
-    @param bias: An offset (usually positive) to avoid dividing by 0.
-    @param size: Width of the 1-D normalization window.
-    @param name: An optional name of the output node.
-    @return The new node which performs LRN.
+    :param data: Input data.
+    :param alpha: A scale factor (usually positive).
+    :param beta: An exponent.
+    :param bias: An offset (usually positive) to avoid dividing by 0.
+    :param size: Width of the 1-D normalization window.
+    :param name: An optional name of the output node.
+    :return: The new node which performs LRN.
     """
     attributes = {"alpha": alpha, "beta": beta, "bias": bias, "size": size}
     return _get_node_factory_opset1().create("LRN", as_nodes(data, axes), attributes)
@@ -1409,20 +1415,20 @@ def lstm_cell(
 ) -> Node:
     """Return a node which performs LSTMCell operation.
 
-    @param X: The input tensor with shape: [batch_size, input_size].
-    @param initial_hidden_state: The hidden state tensor with shape: [batch_size, hidden_size].
-    @param initial_cell_state: The cell state tensor with shape: [batch_size, hidden_size].
-    @param W: The weight tensor with shape: [4*hidden_size, input_size].
-    @param R: The recurrence weight tensor with shape: [4*hidden_size, hidden_size].
-    @param B: The bias tensor for gates with shape: [4*hidden_size].
-    @param hidden_size: Specifies hidden state size.
-    @param activations: The list of three activation functions for gates.
-    @param activations_alpha: The list of alpha parameters for activation functions.
-    @param activations_beta: The list of beta parameters for activation functions.
-    @param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
-    @param name: An optional name of the output node.
+    :param X: The input tensor with shape: [batch_size, input_size].
+    :param initial_hidden_state: The hidden state tensor with shape: [batch_size, hidden_size].
+    :param initial_cell_state: The cell state tensor with shape: [batch_size, hidden_size].
+    :param W: The weight tensor with shape: [4*hidden_size, input_size].
+    :param R: The recurrence weight tensor with shape: [4*hidden_size, hidden_size].
+    :param B: The bias tensor for gates with shape: [4*hidden_size].
+    :param hidden_size: Specifies hidden state size.
+    :param activations: The list of three activation functions for gates.
+    :param activations_alpha: The list of alpha parameters for activation functions.
+    :param activations_beta: The list of beta parameters for activation functions.
+    :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
+    :param name: An optional name of the output node.
 
-    @return The new node represents LSTMCell. Node outputs count: 2.
+    :return: The new node represents LSTMCell. Node outputs count: 2.
     """
     if activations is None:
         activations = ["sigmoid", "tanh", "tanh"]
@@ -1475,28 +1481,28 @@ def lstm_sequence(
 ) -> Node:
     """Return a node which performs LSTMSequence operation.
 
-    @param X: The input tensor. Shape: [batch_size, seq_length, input_size].
-    @param initial_hidden_state:    The hidden state tensor.
+    :param X: The input tensor. Shape: [batch_size, seq_length, input_size].
+    :param initial_hidden_state:    The hidden state tensor.
                                     Shape: [batch_size, num_directions, hidden_size].
-    @param initial_cell_state:      The cell state tensor.
+    :param initial_cell_state:      The cell state tensor.
                                     Shape: [batch_size, num_directions, hidden_size].
-    @param sequence_lengths:        Specifies real sequence lengths for each batch element.
+    :param sequence_lengths:        Specifies real sequence lengths for each batch element.
                                     Shape: [batch_size]. Integer type.
-    @param W: Tensor with weights for matrix multiplication operation with input portion of data.
+    :param W: Tensor with weights for matrix multiplication operation with input portion of data.
               Shape: [num_directions, 4*hidden_size, input_size].
-    @param R: The tensor with weights for matrix multiplication operation with hidden state.
+    :param R: The tensor with weights for matrix multiplication operation with hidden state.
               Shape: [num_directions, 4*hidden_size, hidden_size].
-    @param B: The tensor with biases.
+    :param B: The tensor with biases.
               Shape: [num_directions, 4*hidden_size].
-    @param hidden_size: Specifies hidden state size.
-    @param direction: Specifies if the RNN is forward, reverse, or bidirectional.
-    @param activations: The list of three activation functions for gates.
-    @param activations_alpha: The list of alpha parameters for activation functions.
-    @param activations_beta: The list of beta parameters for activation functions.
-    @param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
-    @param name: An optional name of the output node.
+    :param hidden_size: Specifies hidden state size.
+    :param direction: Specifies if the RNN is forward, reverse, or bidirectional.
+    :param activations: The list of three activation functions for gates.
+    :param activations_alpha: The list of alpha parameters for activation functions.
+    :param activations_beta: The list of beta parameters for activation functions.
+    :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
+    :param name: An optional name of the output node.
 
-    @return The new node represents LSTMSequence. Node outputs count: 3.
+    :return: The new node represents LSTMSequence. Node outputs count: 3.
     """
     if activations is None:
         activations = ["sigmoid", "tanh", "tanh"]
@@ -1545,11 +1551,11 @@ def matmul(
 ) -> Node:
     """Return the Matrix Multiplication operation.
 
-    @param data_a: left-hand side matrix
-    @param data_b: right-hand side matrix
-    @param transpose_a: should the first matrix be transposed before operation
-    @param transpose_b: should the second matrix be transposed
-    @return MatMul operation node
+    :param data_a: left-hand side matrix
+    :param data_b: right-hand side matrix
+    :param transpose_a: should the first matrix be transposed before operation
+    :param transpose_b: should the second matrix be transposed
+    :return: MatMul operation node
     """
     print("transpose_a", transpose_a, "transpose_b", transpose_b)
     return _get_node_factory_opset1().create(
@@ -1570,19 +1576,19 @@ def max_pool(
 ) -> Node:
     """Perform max pooling operation with given parameters on provided data.
 
-    @param  data:           The node providing input data.
-    @param  strides:        The distance (in pixels) to slide the filter on the feature map
+    :param  data:           The node providing input data.
+    :param  strides:        The distance (in pixels) to slide the filter on the feature map
                             over the axes.
-    @param  pads_begin:     The number of pixels to add at the beginning along each axis.
-    @param  pads_end:       The number of pixels to add at the end along each axis.
-    @param  kernel_shape:   The pooling operation kernel shape.
-    @param  rounding_type:  Determines used rounding schema when computing output shape. Acceptable
+    :param  pads_begin:     The number of pixels to add at the beginning along each axis.
+    :param  pads_end:       The number of pixels to add at the end along each axis.
+    :param  kernel_shape:   The pooling operation kernel shape.
+    :param  rounding_type:  Determines used rounding schema when computing output shape. Acceptable
                             values are: ['floor', 'ceil']
-    @param  auto_pad:       Determines how the padding is calculated. Acceptable values:
+    :param  auto_pad:       Determines how the padding is calculated. Acceptable values:
                             [None, 'same_upper', 'same_lower', 'valid']
-    @param  name:           The optional name for the created output node.
+    :param  name:           The optional name for the created output node.
 
-    @return   The new node performing max pooling operation.
+    :return:   The new node performing max pooling operation.
     """
     if auto_pad is None:
         auto_pad = "explicit"
@@ -1635,11 +1641,11 @@ def mod(
 ) -> Node:
     """Return node performing element-wise division reminder with two given tensors.
 
-    @param left_node: The first input node for mod operation.
-    @param right_node: The second input node for mod operation.
-    @param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors.
-    @param name: Optional name for output node.
-    @return The node performing element-wise Mod operation.
+    :param left_node: The first input node for mod operation.
+    :param right_node: The second input node for mod operation.
+    :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors.
+    :param name: Optional name for output node.
+    :return: The node performing element-wise Mod operation.
     """
     return _get_node_factory_opset1().create(
         "Mod", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1678,16 +1684,16 @@ def non_max_suppression(
 ) -> Node:
     """Return a node which performs NonMaxSuppression.
 
-    @param boxes: Tensor with box coordinates.
-    @param scores: Tensor with box scores.
-    @param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
+    :param boxes: Tensor with box coordinates.
+    :param scores: Tensor with box scores.
+    :param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
                                         to be selected per class.
-    @param iou_threshold: Tensor specifying intersection over union threshold
-    @param score_threshold: Tensor specifying minimum score to consider box for the processing.
-    @param box_encoding: Format of boxes data encoding. Range of values: corner or cente.
-    @param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
+    :param iou_threshold: Tensor specifying intersection over union threshold
+    :param score_threshold: Tensor specifying minimum score to consider box for the processing.
+    :param box_encoding: Format of boxes data encoding. Range of values: corner or cente.
+    :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
                                    boxes across batches or not.
-    @return The new node which performs NonMaxSuppression
+    :return: The new node which performs NonMaxSuppression
     """
     if max_output_boxes_per_class is None:
         max_output_boxes_per_class = make_constant_node(0, np.int64)
@@ -1711,11 +1717,11 @@ def normalize_l2(
 ) -> Node:
     """Construct an NormalizeL2 operation.
 
-    @param data: Node producing the input tensor
-    @param axes: Node indicating axes along which L2 reduction is calculated
-    @param eps: The epsilon added to L2 norm
-    @param eps_mode: how eps is combined with L2 value (`add` or `max`)
-    @return New node which performs the L2 normalization.
+    :param data: Node producing the input tensor
+    :param axes: Node indicating axes along which L2 reduction is calculated
+    :param eps: The epsilon added to L2 norm
+    :param eps_mode: how eps is combined with L2 value (`add` or `max`)
+    :return: New node which performs the L2 normalization.
     """
     return _get_node_factory_opset1().create(
         "NormalizeL2", as_nodes(data, axes), {"eps": eps, "mode": eps_mode}
@@ -1731,12 +1737,12 @@ def not_equal(
 ) -> Node:
     """Return node which checks if input nodes are unequal element-wise.
 
-    @param left_node: The first input node for not-equal operation.
-    @param right_node: The second input node for not-equal operation.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param left_node: The first input node for not-equal operation.
+    :param right_node: The second input node for not-equal operation.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @param name: The optional name for output new node.
-    @return The node performing element-wise inequality check.
+    :param name: The optional name for output new node.
+    :return: The node performing element-wise inequality check.
     """
     return _get_node_factory_opset1().create(
         "NotEqual", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1754,16 +1760,16 @@ def one_hot(
 ) -> Node:
     """Create node performing one-hot encoding on input data.
 
-    @param indices: Input tensor of rank N with indices of any supported integer data type.
-    @param depth: Scalar of any supported integer type that specifies number of classes and
+    :param indices: Input tensor of rank N with indices of any supported integer data type.
+    :param depth: Scalar of any supported integer type that specifies number of classes and
                   the size of one-hot dimension.
-    @param on_value: Scalar of any type that is the value that the locations
+    :param on_value: Scalar of any type that is the value that the locations
                      in output tensor represented by indices in input take.
-    @param off_value: Scalar of any type that is the value that the locations not represented
+    :param off_value: Scalar of any type that is the value that the locations not represented
                       by indices in input take.
 
-    @param name: The optional name for new output node.
-    @return New node performing one-hot operation.
+    :param name: The optional name for new output node.
+    :return: New node performing one-hot operation.
     """
     return _get_node_factory_opset1().create(
         "OneHot", as_nodes(indices, depth, on_value, off_value), {"axis": axis}
@@ -1781,13 +1787,13 @@ def pad(
 ) -> Node:
     """Return a generic padding operation.
 
-    @param arg: The node producing input tensor to be padded.
-    @param pads_begin: number of padding elements to be added before position 0
+    :param arg: The node producing input tensor to be padded.
+    :param pads_begin: number of padding elements to be added before position 0
                        on each axis of arg.
-    @param pads_end: number of padding elements to be added after the last element.
-    @param pad_mode: "constant", "edge", "reflect" or "symmetric"
-    @param arg_pad_value: value used for padding if pad_mode is "constant"
-    @return Pad operation node.
+    :param pads_end: number of padding elements to be added after the last element.
+    :param pad_mode: "constant", "edge", "reflect" or "symmetric"
+    :param arg_pad_value: value used for padding if pad_mode is "constant"
+    :return: Pad operation node.
     """
     input_nodes = as_nodes(arg, pads_begin, pads_end)
     if arg_pad_value:
@@ -1815,12 +1821,12 @@ def power(
 ) -> Node:
     """Return node which perform element-wise exponentiation operation.
 
-    @param left_node: The node providing the base of operation.
-    @param right_node: The node providing the exponent of operation.
-    @param name: The optional name for the new output node.
-    @param auto_broadcast: The type of broadcasting specifies rules used for
+    :param left_node: The node providing the base of operation.
+    :param right_node: The node providing the exponent of operation.
+    :param name: The optional name for the new output node.
+    :param auto_broadcast: The type of broadcasting specifies rules used for
                            auto-broadcasting of input tensors.
-    @return The new node performing element-wise exponentiation operation on input nodes.
+    :return: The new node performing element-wise exponentiation operation on input nodes.
     """
     return _get_node_factory_opset1().create(
         "Power", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -1831,19 +1837,20 @@ def power(
 def prelu(data: NodeInput, slope: NodeInput, name: Optional[str] = None) -> Node:
     """Perform Parametrized Relu operation element-wise on data from input node.
 
-    @param data: The node with data tensor.
-    @param slope: The node with the multipliers for negative values.
-    @param name: Optional output node name.
-    @return The new node performing a PRelu operation on tensor's channels.
+    :param data: The node with data tensor.
+    :param slope: The node with the multipliers for negative values.
+    :param name: Optional output node name.
+    :return: The new node performing a PRelu operation on tensor's channels.
 
     PRelu uses the following logic:
 
-    @code{.py}
+    .. code-block:: python
+
         if data < 0:
             data = data * slope
         elif data >= 0:
             data = data
-    @endcode
+
     """
     return _get_node_factory_opset1().create("PRelu", as_nodes(data, slope))
 
@@ -1854,13 +1861,13 @@ def prior_box_clustered(
 ) -> Node:
     """Generate prior boxes of specified sizes normalized to the input image size.
 
-    @param  output_size:    1D tensor with two integer elements [height, width]. Specifies the
+    :param  output_size:    1D tensor with two integer elements [height, width]. Specifies the
                             spatial size of generated grid with boxes.
-    @param  image_size:     1D tensor with two integer elements [image_height, image_width] that
+    :param  image_size:     1D tensor with two integer elements [image_height, image_width] that
                             specifies shape of the image for which boxes are generated.
-    @param  attrs:          The dictionary containing key, value pairs for attributes.
-    @param  name:           Optional name for the output node.
-    @return Node representing PriorBoxClustered operation.
+    :param  attrs:          The dictionary containing key, value pairs for attributes.
+    :param  name:           Optional name for the output node.
+    :return: Node representing PriorBoxClustered operation.
 
      Available attributes are:
 
@@ -1901,7 +1908,9 @@ def prior_box_clustered(
                     Required: no
 
     Example of attribute dictionary:
-    @code{.py}
+
+    .. code-block:: python
+
         # just required ones
         attrs = {
             'offset': 85,
@@ -1912,7 +1921,6 @@ def prior_box_clustered(
             'clip': False,
             'step_widths': [1.5, 2.0, 2.5]
         }
-    @endcode
 
     Optional attributes which are absent from dictionary will be set with corresponding default.
     """
@@ -1939,11 +1947,11 @@ def prior_box(
 ) -> Node:
     """Generate prior boxes of specified sizes and aspect ratios across all dimensions.
 
-    @param  layer_shape:  Shape of layer for which prior boxes are computed.
-    @param  image_shape:  Shape of image to which prior boxes are scaled.
-    @param  attrs:        The dictionary containing key, value pairs for attributes.
-    @param  name:         Optional name for the output node.
-    @return Node representing prior box operation.
+    :param  layer_shape:  Shape of layer for which prior boxes are computed.
+    :param  image_shape:  Shape of image to which prior boxes are scaled.
+    :param  attrs:        The dictionary containing key, value pairs for attributes.
+    :param  name:         Optional name for the output node.
+    :return: Node representing prior box operation.
 
     Available attributes are:
 
@@ -2011,7 +2019,9 @@ def prior_box(
                         Required: no
 
     Example of attribute dictionary:
-    @code{.py}
+
+    .. code-block:: python
+
         # just required ones
         attrs = {
             'offset': 85,
@@ -2023,7 +2033,6 @@ def prior_box(
             'clip': True,
             'fixed_size': [32, 64, 128]
         }
-    @endcode
 
     Optional attributes which are absent from dictionary will be set with corresponding default.
     """
@@ -2057,12 +2066,12 @@ def proposal(
 ) -> Node:
     """Filter bounding boxes and outputs only those with the highest prediction confidence.
 
-    @param  class_probs:        4D input floating point tensor with class prediction scores.
-    @param  bbox_deltas:         4D input floating point tensor with box logits.
-    @param  image_shape:        The 1D input tensor with 3 or 4 elements describing image shape.
-    @param  attrs:              The dictionary containing key, value pairs for attributes.
-    @param  name:               Optional name for the output node.
-    @return Node representing Proposal operation.
+    :param  class_probs:        4D input floating point tensor with class prediction scores.
+    :param  bbox_deltas:         4D input floating point tensor with box logits.
+    :param  image_shape:        The 1D input tensor with 3 or 4 elements describing image shape.
+    :param  attrs:              The dictionary containing key, value pairs for attributes.
+    :param  name:               Optional name for the output node.
+    :return: Node representing Proposal operation.
 
     * base_size     The size of the anchor to which scale and ratio attributes are applied.
                     Range of values: a positive unsigned integer number
@@ -2142,19 +2151,19 @@ def proposal(
 
     Example of attribute dictionary:
 
-    @code{.py}
-    # just required ones
-    attrs = {
-        'base_size': 85,
-        'pre_nms_topn': 10,
-        'post_nms_topn': 20,
-        'nms_thresh': 0.34,
-        'feat_stride': 16,
-        'min_size': 32,
-        'ratio': [0.1, 1.5, 2.0, 2.5],
-        'scale': [2, 3, 3, 4],
-    }
-    @endcode
+    .. code-block:: python
+
+        # just required ones
+        attrs = {
+            'base_size': 85,
+            'pre_nms_topn': 10,
+            'post_nms_topn': 20,
+            'nms_thresh': 0.34,
+            'feat_stride': 16,
+            'min_size': 32,
+            'ratio': [0.1, 1.5, 2.0, 2.5],
+            'scale': [2, 3, 3, 4],
+        }
 
     Optional attributes which are absent from dictionary will be set with corresponding default.
     """
@@ -2196,15 +2205,15 @@ def psroi_pooling(
 ) -> Node:
     """Return a node which produces a PSROIPooling operation.
 
-    @param input: Input feature map {N, C, ...}
-    @param coords: Coordinates of bounding boxes
-    @param output_dim: Output channel number
-    @param group_size: Number of groups to encode position-sensitive scores
-    @param spatial_scale: Ratio of input feature map over input image size
-    @param spatial_bins_x: Numbers of bins to divide the input feature maps over
-    @param spatial_bins_y: Numbers of bins to divide the input feature maps over
-    @param mode: Mode of pooling - "avg" or "bilinear"
-    @return PSROIPooling node
+    :param input: Input feature map {N, C, ...}
+    :param coords: Coordinates of bounding boxes
+    :param output_dim: Output channel number
+    :param group_size: Number of groups to encode position-sensitive scores
+    :param spatial_scale: Ratio of input feature map over input image size
+    :param spatial_bins_x: Numbers of bins to divide the input feature maps over
+    :param spatial_bins_y: Numbers of bins to divide the input feature maps over
+    :param mode: Mode of pooling - "avg" or "bilinear"
+    :return: PSROIPooling node
     """
     mode = mode.lower()
     return _get_node_factory_opset1().create(
@@ -2225,11 +2234,11 @@ def psroi_pooling(
 def range(start: Node, stop: NodeInput, step: NodeInput, name: Optional[str] = None) -> Node:
     """Return a node which produces the Range operation.
 
-    @param start:  The start value of the generated range
-    @param stop:   The stop value of the generated range
-    @param step:   The step value for the generated range
-    @param name:   Optional name for output node.
-    @return Range node
+    :param start:  The start value of the generated range
+    :param stop:   The stop value of the generated range
+    :param step:   The step value for the generated range
+    :param name:   Optional name for output node.
+    :return: Range node
     """
     return _get_node_factory_opset1().create("Range", as_nodes(start, stop, step))
 
@@ -2238,9 +2247,9 @@ def range(start: Node, stop: NodeInput, step: NodeInput, name: Optional[str] = N
 def relu(node: NodeInput, name: Optional[str] = None) -> Node:
     """Perform rectified linear unit operation on input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: The optional output node name.
-    @return The new node performing relu operation on its input element-wise.
+    :param node: One of: input node, array or scalar.
+    :param name: The optional output node name.
+    :return: The new node performing relu operation on its input element-wise.
     """
     return _get_node_factory_opset1().create("Relu", [node])
 
@@ -2251,11 +2260,11 @@ def reduce_logical_and(
 ) -> Node:
     """Logical AND reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to reduce.
-    @param reduction_axes: The axes to eliminate through AND operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           Optional name for output node.
-    @return The new node performing reduction operation.
+    :param node:           The tensor we want to reduce.
+    :param reduction_axes: The axes to eliminate through AND operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           Optional name for output node.
+    :return: The new node performing reduction operation.
     """
     return _get_node_factory_opset1().create(
         "ReduceLogicalAnd", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -2268,11 +2277,11 @@ def reduce_logical_or(
 ) -> Node:
     """Logical OR reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to reduce.
-    @param reduction_axes: The axes to eliminate through OR operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           Optional name for output node.
-    @return The new node performing reduction operation.
+    :param node:           The tensor we want to reduce.
+    :param reduction_axes: The axes to eliminate through OR operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           Optional name for output node.
+    :return: The new node performing reduction operation.
     """
     return _get_node_factory_opset1().create(
         "ReduceLogicalOr", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -2285,10 +2294,10 @@ def reduce_max(
 ) -> Node:
     """Max-reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to max-reduce.
-    @param reduction_axes: The axes to eliminate through max operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name: Optional name for output node.
+    :param node:           The tensor we want to max-reduce.
+    :param reduction_axes: The axes to eliminate through max operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name: Optional name for output node.
     """
     return _get_node_factory_opset1().create(
         "ReduceMax", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -2301,11 +2310,11 @@ def reduce_mean(
 ) -> Node:
     """Mean-reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to mean-reduce.
-    @param reduction_axes: The axes to eliminate through mean operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           Optional name for output node.
-    @return The new node performing mean-reduction operation.
+    :param node:           The tensor we want to mean-reduce.
+    :param reduction_axes: The axes to eliminate through mean operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           Optional name for output node.
+    :return: The new node performing mean-reduction operation.
     """
     return _get_node_factory_opset1().create(
         "ReduceMean", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -2318,10 +2327,10 @@ def reduce_min(
 ) -> Node:
     """Min-reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to min-reduce.
-    @param reduction_axes: The axes to eliminate through min operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           Optional name for output node.
+    :param node:           The tensor we want to min-reduce.
+    :param reduction_axes: The axes to eliminate through min operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           Optional name for output node.
     """
     return _get_node_factory_opset1().create(
         "ReduceMin", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -2334,11 +2343,11 @@ def reduce_prod(
 ) -> Node:
     """Product-reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to product-reduce.
-    @param reduction_axes: The axes to eliminate through product operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           Optional name for output node.
-    @return The new node performing product-reduction operation.
+    :param node:           The tensor we want to product-reduce.
+    :param reduction_axes: The axes to eliminate through product operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           Optional name for output node.
+    :return: The new node performing product-reduction operation.
     """
     return _get_node_factory_opset1().create(
         "ReduceProd", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -2351,11 +2360,11 @@ def reduce_sum(
 ) -> Node:
     """Perform element-wise sums of the input tensor, eliminating the specified reduction axes.
 
-    @param node:           The node providing data for operation.
-    @param reduction_axes: The axes to eliminate through summation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           The optional new name for output node.
-    @return The new node performing summation along `reduction_axes` element-wise.
+    :param node:           The node providing data for operation.
+    :param reduction_axes: The axes to eliminate through summation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           The optional new name for output node.
+    :return: The new node performing summation along `reduction_axes` element-wise.
     """
     return _get_node_factory_opset1().create(
         "ReduceSum", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -2377,17 +2386,17 @@ def region_yolo(
 ) -> Node:
     """Return a node which produces the RegionYolo operation.
 
-    @param input:       Input data
-    @param coords:      Number of coordinates for each region
-    @param classes:     Number of classes for each region
-    @param num:         Number of regions
-    @param do_softmax:  Compute softmax
-    @param mask:        Mask
-    @param axis:        Axis to begin softmax on
-    @param end_axis:    Axis to end softmax on
-    @param anchors:     A flattened list of pairs `[width, height]` that describes prior box sizes
-    @param name:        Optional name for output node.
-    @return RegionYolo node
+    :param input:       Input data
+    :param coords:      Number of coordinates for each region
+    :param classes:     Number of classes for each region
+    :param num:         Number of regions
+    :param do_softmax:  Compute softmax
+    :param mask:        Mask
+    :param axis:        Axis to begin softmax on
+    :param end_axis:    Axis to end softmax on
+    :param anchors:     A flattened list of pairs `[width, height]` that describes prior box sizes
+    :param name:        Optional name for output node.
+    :return: RegionYolo node
     """
     if anchors is None:
         anchors = []
@@ -2414,9 +2423,9 @@ def reshape(
 ) -> Node:
     """Return reshaped node according to provided parameters.
 
-    @param node: The tensor we want to reshape.
-    @param output_shape: The node with a new shape for input tensor.
-    @param special_zero: The boolean variable that controls how zero values in shape are
+    :param node: The tensor we want to reshape.
+    :param output_shape: The node with a new shape for input tensor.
+    :param special_zero: The boolean variable that controls how zero values in shape are
                          interpreted. If special_zero is false, then 0 is interpreted as-is
                          which means that output shape will contain a zero dimension at the
                          specified location. Input and output tensors are empty in this case.
@@ -2433,8 +2442,8 @@ def reshape(
 def result(data: NodeInput, name: Optional[str] = None) -> Node:
     """Return a node which represents an output of a graph (Function).
 
-    @param data: The tensor containing the input data
-    @return Result node
+    :param data: The tensor containing the input data
+    :return: Result node
     """
     return _get_node_factory_opset1().create("Result", [data])
 
@@ -2449,11 +2458,11 @@ def reverse_sequence(
 ) -> Node:
     """Return a node which produces a ReverseSequence operation.
 
-    @param input: tensor with input data to reverse
-    @param seq_lengths: 1D tensor of integers with sequence lengths in the input tensor.
-    @param batch_axis: index of the batch dimension.
-    @param seq_axis: index of the sequence dimension.
-    @return ReverseSequence node
+    :param input: tensor with input data to reverse
+    :param seq_lengths: 1D tensor of integers with sequence lengths in the input tensor.
+    :param batch_axis: index of the batch dimension.
+    :param seq_axis: index of the sequence dimension.
+    :return: ReverseSequence node
     """
     return _get_node_factory_opset1().create(
         "ReverseSequence",
@@ -2472,14 +2481,14 @@ def select(
 ) -> Node:
     """Perform an element-wise selection operation on input tensors.
 
-    @param cond: Tensor with selection mask of type `boolean`.
-    @param then_node: Tensor providing data to be selected if respective `cond`
+    :param cond: Tensor with selection mask of type `boolean`.
+    :param then_node: Tensor providing data to be selected if respective `cond`
                         item value is `True`.
-    @param else_node: Tensor providing data to be selected if respective `cond`
+    :param else_node: Tensor providing data to be selected if respective `cond`
                         item value is `False`.
-    @param auto_broadcast: Mode specifies rules used for auto-broadcasting of input tensors.
-    @param name: The optional new name for output node.
-    @return The new node with values selected according to provided arguments.
+    :param auto_broadcast: Mode specifies rules used for auto-broadcasting of input tensors.
+    :param name: The optional new name for output node.
+    :return: The new node with values selected according to provided arguments.
     """
     inputs = as_nodes(cond, then_node, else_node)
     return _get_node_factory_opset1().create(
@@ -2495,11 +2504,11 @@ def selu(
 ) -> Node:
     """Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise.
 
-    @param data: input node, array or scalar.
-    @param alpha: Alpha coefficient of SELU operation
-    @param lambda_value: Lambda coefficient of SELU operation
-    @param name: The optional output node name.
-    @return The new node performing relu operation on its input element-wise.
+    :param data: input node, array or scalar.
+    :param alpha: Alpha coefficient of SELU operation
+    :param lambda_value: Lambda coefficient of SELU operation
+    :param name: The optional output node name.
+    :return: The new node performing relu operation on its input element-wise.
     """
     return _get_node_factory_opset1().create("Selu", as_nodes(data, alpha, lambda_value))
 
@@ -2508,8 +2517,8 @@ def selu(
 def shape_of(data: NodeInput, name: Optional[str] = None) -> Node:
     """Return a node which produces a tensor containing the shape of its input data.
 
-    @param data: The tensor containing the input data.
-    @return ShapeOf node
+    :param data: The tensor containing the input data.
+    :return: ShapeOf node
     """
     return _get_node_factory_opset1().create("ShapeOf", [as_node(data)])
 
@@ -2518,8 +2527,8 @@ def shape_of(data: NodeInput, name: Optional[str] = None) -> Node:
 def sigmoid(data: NodeInput, name: Optional[str] = None) -> Node:
     """Return a node which applies the sigmoid function element-wise.
 
-    @param data: The tensor containing the input data
-    @return Sigmoid node
+    :param data: The tensor containing the input data
+    :return: Sigmoid node
     """
     return _get_node_factory_opset1().create("Sigmoid", [data])
 
@@ -2528,9 +2537,9 @@ def sigmoid(data: NodeInput, name: Optional[str] = None) -> Node:
 def sign(node: NodeInput, name: Optional[str] = None) -> Node:
     """Perform element-wise sign operation.
 
-    @param node: One of: input node, array or scalar.
-    @param name: The optional new name for output node.
-    @return The node with mapped elements of the input tensor to -1 (if it is negative),
+    :param node: One of: input node, array or scalar.
+    :param name: The optional new name for output node.
+    :return: The node with mapped elements of the input tensor to -1 (if it is negative),
              0 (if it is zero), or 1 (if it is positive).
     """
     return _get_node_factory_opset1().create("Sign", [node])
@@ -2540,9 +2549,9 @@ def sign(node: NodeInput, name: Optional[str] = None) -> Node:
 def sin(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply sine function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with sin operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with sin operation applied on it.
     """
     return _get_node_factory_opset1().create("Sin", [node])
 
@@ -2551,9 +2560,9 @@ def sin(node: NodeInput, name: Optional[str] = None) -> Node:
 def sinh(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply hyperbolic sine function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with sin operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with sin operation applied on it.
     """
     return _get_node_factory_opset1().create("Sinh", [node])
 
@@ -2562,9 +2571,9 @@ def sinh(node: NodeInput, name: Optional[str] = None) -> Node:
 def softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node:
     """Apply softmax operation on each element of input tensor.
 
-    @param data: The tensor providing input data.
-    @param axis: An axis along which Softmax should be calculated
-    @return The new node with softmax operation applied on each element.
+    :param data: The tensor providing input data.
+    :param axis: An axis along which Softmax should be calculated
+    :return: The new node with softmax operation applied on each element.
     """
     return _get_node_factory_opset1().create("Softmax", [as_node(data)], {"axis": axis})
 
@@ -2577,15 +2586,15 @@ def space_to_depth(data: Node, mode: str, block_size: int = 1, name: str = None)
     The operator returns a copy of the input tensor where values from the height
     and width dimensions are moved to the depth dimension.
 
-    @param data: The node with data tensor.
-    @param mode: Specifies how the output depth dimension is gathered from block coordinates.
+    :param data: The node with data tensor.
+    :param mode: Specifies how the output depth dimension is gathered from block coordinates.
 
                  blocks_first: The output depth is gathered from [block_size, ..., block_size, C]
                  depth_first: The output depth is gathered from [C, block_size, ..., block_size]
 
-    @param block_size: The size of the block of values to be moved. Scalar value.
-    @param name: Optional output node name.
-    @return The new node performing a SpaceToDepth operation on input tensor.
+    :param block_size: The size of the block of values to be moved. Scalar value.
+    :param name: Optional output node name.
+    :return: The new node performing a SpaceToDepth operation on input tensor.
     """
     return _get_node_factory_opset1().create(
         "SpaceToDepth", [data], {"mode": mode, "block_size": block_size},
@@ -2596,10 +2605,10 @@ def space_to_depth(data: Node, mode: str, block_size: int = 1, name: str = None)
 def split(data: NodeInput, axis: NodeInput, num_splits: int, name: Optional[str] = None) -> Node:
     """Return a node which splits the input tensor into same-length slices.
 
-    @param data: The input tensor to be split
-    @param axis: Axis along which the input data will be split
-    @param num_splits: Number of the output tensors that should be produced
-    @return Split node
+    :param data: The input tensor to be split
+    :param axis: Axis along which the input data will be split
+    :param num_splits: Number of the output tensors that should be produced
+    :return: Split node
     """
     return _get_node_factory_opset1().create(
         "Split",
@@ -2612,9 +2621,9 @@ def split(data: NodeInput, axis: NodeInput, num_splits: int, name: Optional[str]
 def sqrt(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies square root to the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return The new node with sqrt operation applied element-wise.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: The new node with sqrt operation applied element-wise.
     """
     return _get_node_factory_opset1().create("Sqrt", [node])
 
@@ -2625,14 +2634,14 @@ def squared_difference(
 ) -> Node:
     r"""Perform an element-wise squared difference between two tensors.
 
-    \f[ y[i] = (x_1[i] - x_2[i])^2 \f]
+    ..math:: y[i] = (x_1[i] - x_2[i])^2
 
-    @param x1: The node with first input tensor.
-    @param x2: The node with second input tensor.
-    @param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
+    :param x1: The node with first input tensor.
+    :param x2: The node with second input tensor.
+    :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
                            to output shape axes. Range of values: numpy, explicit.
-    @param name: Optional new name for output node.
-    @return The new node performing a squared difference between two tensors.
+    :param name: Optional new name for output node.
+    :return: The new node performing a squared difference between two tensors.
     """
     return _get_node_factory_opset1().create(
         "SquaredDifference", [x1, x2], {"auto_broadcast": auto_broadcast.upper()}
@@ -2643,11 +2652,11 @@ def squared_difference(
 def squeeze(data: NodeInput, axes: NodeInput, name: Optional[str] = None) -> Node:
     """Perform squeeze operation on input tensor.
 
-    @param data: The node with data tensor.
-    @param axes: List of non-negative integers, indicate the dimensions to squeeze.
+    :param data: The node with data tensor.
+    :param axes: List of non-negative integers, indicate the dimensions to squeeze.
                   One of: input node or array.
-    @param name: Optional new name for output node.
-    @return The new node performing a squeeze operation on input tensor.
+    :param name: Optional new name for output node.
+    :return: The new node performing a squeeze operation on input tensor.
 
     Remove single-dimensional entries from the shape of a tensor.
     Takes a parameter `axes` with a list of axes to squeeze.
@@ -2679,18 +2688,18 @@ def strided_slice(
 ) -> Node:
     """Return a node which dynamically repeats(replicates) the input data tensor.
 
-    @param      data:              The tensor to be sliced
-    @param      begin:             1D tensor with begin indexes for input blob slicing
-    @param      end:               1D tensor with end indexes for input blob slicing
-    @param      strides:           The slicing strides
-    @param      begin_mask:        A mask applied to the 'begin' input indicating which elements
+    :param      data:              The tensor to be sliced
+    :param      begin:             1D tensor with begin indexes for input blob slicing
+    :param      end:               1D tensor with end indexes for input blob slicing
+    :param      strides:           The slicing strides
+    :param      begin_mask:        A mask applied to the 'begin' input indicating which elements
                                    shoud be ignored
-    @param      end_mask:          A mask applied to the 'end' input indicating which elements
+    :param      end_mask:          A mask applied to the 'end' input indicating which elements
                                    shoud be ignored
-    @param      new_axis_mask:     A mask indicating dimensions where '1' should be inserted
-    @param      shrink_axis_mask:  A mask indicating which dimensions should be deleted
-    @param      ellipsis_mask:     Indicates positions where missing dimensions should be inserted
-    @return   StridedSlice node
+    :param      new_axis_mask:     A mask indicating dimensions where '1' should be inserted
+    :param      shrink_axis_mask:  A mask indicating which dimensions should be deleted
+    :param      ellipsis_mask:     Indicates positions where missing dimensions should be inserted
+    :return:   StridedSlice node
     """
     if new_axis_mask is None:
         new_axis_mask = []
@@ -2720,12 +2729,12 @@ def subtract(
 ) -> Node:
     """Return node which applies f(x) = A-B to the input nodes element-wise.
 
-    @param left_node: The node providing data for left hand side of operator.
-    @param right_node: The node providing data for right hand side of operator.
-    @param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
+    :param left_node: The node providing data for left hand side of operator.
+    :param right_node: The node providing data for right hand side of operator.
+    :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes
                            to output shape axes. Range of values: numpy, explicit.
-    @param name: The optional name for output node.
-    @return The new output node performing subtraction operation on both tensors element-wise.
+    :param name: The optional name for output node.
+    :return: The new output node performing subtraction operation on both tensors element-wise.
     """
     return _get_node_factory_opset1().create(
         "Subtract", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()}
@@ -2736,9 +2745,9 @@ def subtract(
 def tan(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply tangent function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with tan operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with tan operation applied on it.
     """
     return _get_node_factory_opset1().create("Tan", [node])
 
@@ -2747,9 +2756,9 @@ def tan(node: NodeInput, name: Optional[str] = None) -> Node:
 def tanh(node: NodeInput, name: Optional[str] = None) -> Node:
     """Return node which applies hyperbolic tangent to the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with tanh operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with tanh operation applied on it.
     """
     return _get_node_factory_opset1().create("Tanh", [node])
 
@@ -2767,23 +2776,23 @@ def tensor_iterator(
 ) -> Node:
     """Perform recurrent execution of the network described in the body, iterating through the data.
 
-    @param      inputs:                The provided to TensorIterator operator.
-    @param      graph_body:            The graph representing the body we execute.
-    @param      slice_input_desc:      The descriptors describing sliced inputs, that is nodes
+    :param      inputs:                The provided to TensorIterator operator.
+    :param      graph_body:            The graph representing the body we execute.
+    :param      slice_input_desc:      The descriptors describing sliced inputs, that is nodes
                                        representing tensors we iterate through, processing single
                                        data slice in one iteration.
-    @param      merged_input_desc:     The descriptors describing merged inputs, that is nodes
+    :param      merged_input_desc:     The descriptors describing merged inputs, that is nodes
                                        representing variables with initial value at first iteration,
                                        which may be changing through iterations.
-    @param      invariant_input_desc:  The descriptors describing invariant inputs, that is nodes
+    :param      invariant_input_desc:  The descriptors describing invariant inputs, that is nodes
                                        representing variable with persistent value through all
                                        iterations.
-    @param      body_output_desc:      The descriptors describing body outputs from specified
+    :param      body_output_desc:      The descriptors describing body outputs from specified
                                        iteration.
-    @param      concat_output_desc:    The descriptors describing specified output values through
+    :param      concat_output_desc:    The descriptors describing specified output values through
                                        all the iterations concatenated into one node.
-    @param      name:                  The optional name for output node.
-    @return   Node representing TensorIterator operation.
+    :param      name:                  The optional name for output node.
+    :return:   Node representing TensorIterator operation.
     """
     attributes = {
         "body": graph_body.serialize(),
@@ -2801,9 +2810,9 @@ def tensor_iterator(
 def tile(data: NodeInput, repeats: NodeInput, name: Optional[str] = None) -> Node:
     """Return a node which dynamically repeats(replicates) the input data tensor.
 
-    @param data: The input tensor to be tiled
-    @param repeats: Per-dimension replication factors
-    @return Tile node
+    :param data: The input tensor to be tiled
+    :param repeats: Per-dimension replication factors
+    :return: Tile node
     """
     return _get_node_factory_opset1().create("Tile", as_nodes(data, repeats))
 
@@ -2819,12 +2828,12 @@ def topk(
 ) -> Node:
     """Return a node which performs TopK.
 
-    @param data: Input data.
-    @param k: K.
-    @param axis: TopK Axis.
-    @param mode: Compute TopK largest ('max') or smallest ('min')
-    @param sort: Order of output elements (sort by: 'none', 'index' or 'value')
-    @return The new node which performs TopK (both indices and values)
+    :param data: Input data.
+    :param k: K.
+    :param axis: TopK Axis.
+    :param mode: Compute TopK largest ('max') or smallest ('min')
+    :param sort: Order of output elements (sort by: 'none', 'index' or 'value')
+    :return: The new node which performs TopK (both indices and values)
     """
     return _get_node_factory_opset1().create(
         "TopK",
@@ -2837,9 +2846,9 @@ def topk(
 def transpose(data: NodeInput, input_order: NodeInput, name: Optional[str] = None) -> Node:
     """Return a node which transposes the data in the input tensor.
 
-    @param data: The input tensor to be transposed
-    @param input_order: Permutation of axes to be applied to the input tensor
-    @return Transpose node
+    :param data: The input tensor to be transposed
+    :param input_order: Permutation of axes to be applied to the input tensor
+    :return: Transpose node
     """
     return _get_node_factory_opset1().create("Transpose", as_nodes(data, input_order))
 
@@ -2854,10 +2863,10 @@ def unsqueeze(data: NodeInput, axes: NodeInput, name: Optional[str] = None) -> N
     For example: Inputs: tensor with shape [3, 4, 5], axes=[0, 4]
                  Result: tensor with shape [1, 3, 4, 5, 1]
 
-    @param data: The node with data tensor.
-    @param axes: List of non-negative integers, indicate the dimensions to be inserted.
+    :param data: The node with data tensor.
+    :param axes: List of non-negative integers, indicate the dimensions to be inserted.
                   One of: input node or array.
-    @return The new node performing an unsqueeze operation on input tensor.
+    :return: The new node performing an unsqueeze operation on input tensor.
     """
     return _get_node_factory_opset1().create("Unsqueeze", as_nodes(data, axes))
 
@@ -2868,9 +2877,9 @@ def variadic_split(
 ) -> Node:
     """Return a node which splits the input tensor into variadic length slices.
 
-    @param data: The input tensor to be split
-    @param axis: Axis along which the input data will be split
-    @param split_lengths: Sizes of the output tensors along the split axis
-    @return VariadicSplit node
+    :param data: The input tensor to be split
+    :param axis: Axis along which the input data will be split
+    :param split_lengths: Sizes of the output tensors along the split axis
+    :return: VariadicSplit node
     """
     return _get_node_factory_opset1().create("VariadicSplit", as_nodes(data, axis, split_lengths))
diff --git a/src/bindings/python/src/compatibility/ngraph/opset2/ops.py b/src/bindings/python/src/compatibility/ngraph/opset2/ops.py
index a623b8b9780..a55e7887c60 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset2/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset2/ops.py
@@ -57,12 +57,12 @@ def batch_to_space(
 
     BatchToSpace permutes data from the batch dimension of the data tensor into spatial dimensions.
 
-    @param data: Node producing the data tensor.
-    @param block_shape: The sizes of the block of values to be moved.
-    @param crops_begin: Specifies the amount to crop from the beginning along each axis of `data`.
-    @param crops_end: Specifies the amount to crop from the end along each axis of `data`.
-    @param name: Optional output node name.
-    @return The new node performing a BatchToSpace operation.
+    :param data: Node producing the data tensor.
+    :param block_shape: The sizes of the block of values to be moved.
+    :param crops_begin: Specifies the amount to crop from the beginning along each axis of `data`.
+    :param crops_end: Specifies the amount to crop from the end along each axis of `data`.
+    :param name: Optional output node name.
+    :return: The new node performing a BatchToSpace operation.
     """
     return _get_node_factory_opset2().create(
         "BatchToSpace", as_nodes(data, block_shape, crops_begin, crops_end)
@@ -75,13 +75,13 @@ def gelu(node: NodeInput, name: Optional[str] = None) -> Node:
 
     Computes GELU function:
 
-    \f[ f(x) = 0.5\cdot x\cdot(1 + erf( \dfrac{x}{\sqrt{2}}) \f]
+    .. math:: f(x) = 0.5\cdot x\cdot(1 + erf( \dfrac{x}{\sqrt{2}})
 
     For more information refer to [Gaussian Error Linear Unit (GELU)](https://arxiv.org/pdf/1606.08415.pdf>)
 
-    @param node: Input tensor. One of: input node, array or scalar.
-    @param name: Optional output node name.
-    @return The new node performing a GELU operation on its input data element-wise.
+    :param node: Input tensor. One of: input node, array or scalar.
+    :param name: Optional output node name.
+    :return: The new node performing a GELU operation on its input data element-wise.
     """
     return _get_node_factory_opset2().create("Gelu", [node])
 
@@ -98,15 +98,15 @@ def mvn(
 
     Computes MVN on the input tensor `data` (called `X`) using formula:
 
-    \f[ Y = \dfrac{X-EX}{\sqrt{E(X-EX)^2}} \f]
+    ..math:: Y = \dfrac{X-EX}{\sqrt{E(X-EX)^2}}
 
-    @param data: The node with data tensor.
-    @param across_channels: Denotes if mean values are shared across channels.
-    @param normalize_variance: Denotes whether to perform variance normalization.
-    @param eps: The number added to the variance to avoid division by zero
+    :param data: The node with data tensor.
+    :param across_channels: Denotes if mean values are shared across channels.
+    :param normalize_variance: Denotes whether to perform variance normalization.
+    :param eps: The number added to the variance to avoid division by zero
                when normalizing the value. Scalar value.
-    @param name: Optional output node name.
-    @return The new node performing a MVN operation on input tensor.
+    :param name: Optional output node name.
+    :return: The new node performing a MVN operation on input tensor.
     """
     return _get_node_factory_opset2().create(
         "MVN",
@@ -119,10 +119,10 @@ def mvn(
 def reorg_yolo(input: Node, stride: List[int], name: Optional[str] = None) -> Node:
     """Return a node which produces the ReorgYolo operation.
 
-    @param input:   Input data
-    @param stride:  Stride to reorganize input by
-    @param name:    Optional name for output node.
-    @return ReorgYolo node
+    :param input:   Input data
+    :param stride:  Stride to reorganize input by
+    :param name:    Optional name for output node.
+    :return: ReorgYolo node
     """
     return _get_node_factory_opset2().create("ReorgYolo", [input], {"stride": stride})
 
@@ -138,12 +138,12 @@ def roi_pooling(
 ) -> Node:
     """Return a node which produces an ROIPooling operation.
 
-    @param input:          Input feature map {N, C, ...}
-    @param coords:         Coordinates of bounding boxes
-    @param output_size:    Height/Width of ROI output features (shape)
-    @param spatial_scale:  Ratio of input feature map over input image size (float)
-    @param method:         Method of pooling - string: "max" or "bilinear"
-    @return               ROIPooling node
+    :param input:          Input feature map {N, C, ...}
+    :param coords:         Coordinates of bounding boxes
+    :param output_size:    Height/Width of ROI output features (shape)
+    :param spatial_scale:  Ratio of input feature map over input image size (float)
+    :param method:         Method of pooling - string: "max" or "bilinear"
+    :return:               ROIPooling node
     """
     method = method.lower()
     return _get_node_factory_opset2().create(
@@ -167,12 +167,12 @@ def space_to_batch(
     The operator returns a copy of the input tensor where values from spatial blocks dimensions
     are moved in the batch dimension
 
-    @param data: Node producing the data tensor.
-    @param block_shape: The sizes of the block of values to be moved.
-    @param pads_begin: Specifies the padding for the beginning along each axis of `data`.
-    @param pads_end: Specifies the padding for the ending along each axis of `data`.
-    @param name: Optional output node name.
-    @return The new node performing a SpaceToBatch operation.
+    :param data: Node producing the data tensor.
+    :param block_shape: The sizes of the block of values to be moved.
+    :param pads_begin: Specifies the padding for the beginning along each axis of `data`.
+    :param pads_end: Specifies the padding for the ending along each axis of `data`.
+    :param name: Optional output node name.
+    :return: The new node performing a SpaceToBatch operation.
     """
     return _get_node_factory_opset2().create(
         "SpaceToBatch", as_nodes(data, block_shape, pads_begin, pads_end)
diff --git a/src/bindings/python/src/compatibility/ngraph/opset3/ops.py b/src/bindings/python/src/compatibility/ngraph/opset3/ops.py
index 7cb1acc2364..4a718a1d73f 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset3/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset3/ops.py
@@ -49,10 +49,10 @@ _get_node_factory_opset3 = partial(_get_node_factory, "opset3")
 def assign(new_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node:
     """Return a node which produces the Assign operation.
 
-    @param new_value:    Node producing a value to be assigned to a variable.
-    @param variable_id:  Id of a variable to be updated.
-    @param name:         Optional name for output node.
-    @return Assign node
+    :param new_value:    Node producing a value to be assigned to a variable.
+    :param variable_id:  Id of a variable to be updated.
+    :param name:         Optional name for output node.
+    :return: Assign node
     """
     return _get_node_factory_opset3().create(
         "Assign",
@@ -71,14 +71,14 @@ def broadcast(
 ) -> Node:
     """Create a node which broadcasts the input node's values along specified axes to a desired shape.
 
-    @param data: The node with input tensor data.
-    @param target_shape: The node with a new shape we want to broadcast tensor to.
-    @param axes_mapping: The node with a axis positions (0-based) in the result
+    :param data: The node with input tensor data.
+    :param target_shape: The node with a new shape we want to broadcast tensor to.
+    :param axes_mapping: The node with a axis positions (0-based) in the result
                            that are being broadcast.
-    @param broadcast_spec: The type of broadcasting that specifies mapping of input tensor axes
+    :param broadcast_spec: The type of broadcasting that specifies mapping of input tensor axes
                            to output shape axes. Range of values: NUMPY, EXPLICIT, BIDIRECTIONAL.
-    @param name: Optional new name for output node.
-    @return New node with broadcast shape.
+    :param name: Optional new name for output node.
+    :return: New node with broadcast shape.
     """
     inputs = as_nodes(data, target_shape)
     if broadcast_spec.upper() == "EXPLICIT":
@@ -98,13 +98,13 @@ def bucketize(
 ) -> Node:
     """Return a node which produces the Bucketize operation.
 
-    @param data:              Input data to bucketize
-    @param buckets:           1-D of sorted unique boundaries for buckets
-    @param output_type:       Output tensor type, "i64" or "i32", defaults to i64
-    @param with_right_bound:  indicates whether bucket includes the right or left
+    :param data:              Input data to bucketize
+    :param buckets:           1-D of sorted unique boundaries for buckets
+    :param output_type:       Output tensor type, "i64" or "i32", defaults to i64
+    :param with_right_bound:  indicates whether bucket includes the right or left
                               edge of interval. default true = includes right edge
-    @param name:              Optional name for output node.
-    @return Bucketize node
+    :param name:              Optional name for output node.
+    :return: Bucketize node
     """
     return _get_node_factory_opset3().create(
         "Bucketize",
@@ -123,11 +123,11 @@ def cum_sum(
 ) -> Node:
     """Construct a cumulative summation operation.
 
-    @param arg: The tensor to be summed.
-    @param axis: zero dimension tensor specifying axis position along which sum will be performed.
-    @param exclusive: if set to true, the top element is not included
-    @param reverse: if set to true, will perform the sums in reverse direction
-    @return New node performing the operation
+    :param arg: The tensor to be summed.
+    :param axis: zero dimension tensor specifying axis position along which sum will be performed.
+    :param exclusive: if set to true, the top element is not included
+    :param reverse: if set to true, will perform the sums in reverse direction
+    :return: New node performing the operation
     """
     return _get_node_factory_opset3().create(
         "CumSum", as_nodes(arg, axis), {"exclusive": exclusive, "reverse": reverse}
@@ -145,13 +145,13 @@ def embedding_bag_offsets_sum(
 ) -> Node:
     """Return a node which performs sums of bags of embeddings without the intermediate embeddings.
 
-    @param emb_table: Tensor containing the embedding lookup table.
-    @param indices: Tensor with indices.
-    @param offsets: Tensor containing the starting index positions of each bag in indices.
-    @param per_sample_weights: Tensor with weights for each sample.
-    @param default_index: Scalar containing default index in embedding table to fill empty bags.
-    @param name: Optional name for output node.
-    @return The new node which performs EmbeddingBagOffsetsSum
+    :param emb_table: Tensor containing the embedding lookup table.
+    :param indices: Tensor with indices.
+    :param offsets: Tensor containing the starting index positions of each bag in indices.
+    :param per_sample_weights: Tensor with weights for each sample.
+    :param default_index: Scalar containing default index in embedding table to fill empty bags.
+    :param name: Optional name for output node.
+    :return: The new node which performs EmbeddingBagOffsetsSum
     """
     inputs = [emb_table, as_node(indices), as_node(offsets)]
     if per_sample_weights is not None:
@@ -175,11 +175,11 @@ def embedding_bag_packed_sum(
     EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
     input tensor with a row (from the weights matrix) at that index
 
-    @param emb_table: Tensor containing the embedding lookup table.
-    @param indices: Tensor with indices.
-    @param per_sample_weights: Weights to be multiplied with embedding table.
-    @param name: Optional name for output node.
-    @return EmbeddingBagPackedSum node
+    :param emb_table: Tensor containing the embedding lookup table.
+    :param indices: Tensor with indices.
+    :param per_sample_weights: Weights to be multiplied with embedding table.
+    :param name: Optional name for output node.
+    :return: EmbeddingBagPackedSum node
     """
     inputs = [as_node(emb_table), as_node(indices)]
     if per_sample_weights is not None:
@@ -203,14 +203,14 @@ def embedding_segments_sum(
     EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
     input tensor with a row (from the weights matrix) at that index
 
-    @param emb_table: Tensor containing the embedding lookup table.
-    @param indices: Tensor with indices.
-    @param segment_ids: Tensor with indices into the output Tensor
-    @param num_segments: Tensor with number of segments.
-    @param default_index: Scalar containing default index in embedding table to fill empty bags.
-    @param per_sample_weights: Weights to be multiplied with embedding table.
-    @param name: Optional name for output node.
-    @return EmbeddingSegmentsSum node
+    :param emb_table: Tensor containing the embedding lookup table.
+    :param indices: Tensor with indices.
+    :param segment_ids: Tensor with indices into the output Tensor
+    :param num_segments: Tensor with number of segments.
+    :param default_index: Scalar containing default index in embedding table to fill empty bags.
+    :param per_sample_weights: Weights to be multiplied with embedding table.
+    :param name: Optional name for output node.
+    :return: EmbeddingSegmentsSum node
     """
     inputs = [as_node(emb_table), as_node(indices), as_node(segment_ids)]
     if per_sample_weights is not None:
@@ -237,13 +237,13 @@ def extract_image_patches(
 ) -> Node:
     """Return a node which produces the ExtractImagePatches operation.
 
-    @param image:     4-D Input data to extract image patches.
-    @param sizes:     Patch size in the format of [size_rows, size_cols].
-    @param strides:   Patch movement stride in the format of [stride_rows, stride_cols]
-    @param rates:     Element seleciton rate for creating a patch.
-    @param auto_pad:  Padding type.
-    @param name:      Optional name for output node.
-    @return ExtractImagePatches node
+    :param image:     4-D Input data to extract image patches.
+    :param sizes:     Patch size in the format of [size_rows, size_cols].
+    :param strides:   Patch movement stride in the format of [stride_rows, stride_cols]
+    :param rates:     Element seleciton rate for creating a patch.
+    :param auto_pad:  Padding type.
+    :param name:      Optional name for output node.
+    :return: ExtractImagePatches node
     """
     return _get_node_factory_opset3().create(
         "ExtractImagePatches",
@@ -274,29 +274,29 @@ def gru_cell(
 
     Note this class represents only single *cell* and not whole *layer*.
 
-    @param X:                       The input tensor with shape: [batch_size, input_size].
-    @param initial_hidden_state:    The hidden state tensor at current time step with shape:
+    :param X:                       The input tensor with shape: [batch_size, input_size].
+    :param initial_hidden_state:    The hidden state tensor at current time step with shape:
                                     [batch_size, hidden_size].
-    @param W:                       The weights for matrix multiplication, gate order: zrh.
+    :param W:                       The weights for matrix multiplication, gate order: zrh.
                                     Shape: [3*hidden_size, input_size].
-    @param R:                       The recurrence weights for matrix multiplication.
+    :param R:                       The recurrence weights for matrix multiplication.
                                     Shape: [3*hidden_size, hidden_size].
-    @param B:                       The sum of biases (weight and recurrence).
+    :param B:                       The sum of biases (weight and recurrence).
                                     For linear_before_reset set True the shape is [4*hidden_size].
                                     Otherwise the shape is [3*hidden_size].
-    @param hidden_size:             The number of hidden units for recurrent cell.
+    :param hidden_size:             The number of hidden units for recurrent cell.
                                     Specifies hidden state size.
-    @param activations:             The vector of activation functions used inside recurrent cell.
-    @param activation_alpha:        The vector of alpha parameters for activation functions in
+    :param activations:             The vector of activation functions used inside recurrent cell.
+    :param activation_alpha:        The vector of alpha parameters for activation functions in
                                     order respective to activation list.
-    @param activation_beta:         The vector of beta parameters for activation functions in order
+    :param activation_beta:         The vector of beta parameters for activation functions in order
                                     respective to activation list.
-    @param clip:                    The value defining clipping range [-clip, clip] on input of
+    :param clip:                    The value defining clipping range [-clip, clip] on input of
                                     activation functions.
-    @param linear_before_reset:     Flag denotes if the layer behaves according to the modification
+    :param linear_before_reset:     Flag denotes if the layer behaves according to the modification
                                     of GRUCell described in the formula in the ONNX documentation.
-    @param name:                    Optional output node name.
-    @return   The new node performing a GRUCell operation on tensor from input node.
+    :param name:                    Optional output node name.
+    :return:   The new node performing a GRUCell operation on tensor from input node.
     """
     if activations is None:
         activations = ["sigmoid", "tanh"]
@@ -331,17 +331,17 @@ def non_max_suppression(
 ) -> Node:
     """Return a node which performs NonMaxSuppression.
 
-    @param boxes: Tensor with box coordinates.
-    @param scores: Tensor with box scores.
-    @param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
+    :param boxes: Tensor with box coordinates.
+    :param scores: Tensor with box scores.
+    :param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
                                         to be selected per class.
-    @param iou_threshold: Tensor specifying intersection over union threshold
-    @param score_threshold: Tensor specifying minimum score to consider box for the processing.
-    @param box_encoding: Format of boxes data encoding.
-    @param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
+    :param iou_threshold: Tensor specifying intersection over union threshold
+    :param score_threshold: Tensor specifying minimum score to consider box for the processing.
+    :param box_encoding: Format of boxes data encoding.
+    :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
                                    boxes across batches or not.
-    @param output_type: Output element type.
-    @return The new node which performs NonMaxSuppression
+    :param output_type: Output element type.
+    :return: The new node which performs NonMaxSuppression
     """
     if max_output_boxes_per_class is None:
         max_output_boxes_per_class = make_constant_node(0, np.int64)
@@ -364,10 +364,10 @@ def non_max_suppression(
 def non_zero(data: NodeInput, output_type: str = "i64", name: Optional[str] = None,) -> Node:
     """Return the indices of the elements that are non-zero.
 
-    @param data: Input data.
-    @param output_type: Output tensor type.
+    :param data: Input data.
+    :param output_type: Output tensor type.
 
-    @return The new node which performs NonZero
+    :return: The new node which performs NonZero
     """
     return _get_node_factory_opset3().create(
         "NonZero",
@@ -380,10 +380,10 @@ def non_zero(data: NodeInput, output_type: str = "i64", name: Optional[str] = No
 def read_value(init_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node:
     """Return a node which produces the Assign operation.
 
-    @param init_value:   Node producing a value to be returned instead of an unassigned variable.
-    @param variable_id:  Id of a variable to be read.
-    @param name:         Optional name for output node.
-    @return ReadValue node
+    :param init_value:   Node producing a value to be returned instead of an unassigned variable.
+    :param variable_id:  Id of a variable to be read.
+    :param name:         Optional name for output node.
+    :return: ReadValue node
     """
     return _get_node_factory_opset3().create(
         "ReadValue",
@@ -413,24 +413,24 @@ def rnn_cell(
 
     Note this class represents only single *cell* and not whole RNN *layer*.
 
-    @param X:                       The input tensor with shape: [batch_size, input_size].
-    @param initial_hidden_state:    The hidden state tensor at current time step with shape:
+    :param X:                       The input tensor with shape: [batch_size, input_size].
+    :param initial_hidden_state:    The hidden state tensor at current time step with shape:
                                     [batch_size, hidden_size].
-    @param W:                       The weight tensor with shape: [hidden_size, input_size].
-    @param R:                       The recurrence weight tensor with shape: [hidden_size,
+    :param W:                       The weight tensor with shape: [hidden_size, input_size].
+    :param R:                       The recurrence weight tensor with shape: [hidden_size,
                                     hidden_size].
-    @param B:                       The sum of biases (weight and recurrence) with shape: [hidden_size].
-    @param hidden_size:             The number of hidden units for recurrent cell.
+    :param B:                       The sum of biases (weight and recurrence) with shape: [hidden_size].
+    :param hidden_size:             The number of hidden units for recurrent cell.
                                     Specifies hidden state size.
-    @param activations:             The vector of activation functions used inside recurrent cell.
-    @param activation_alpha:        The vector of alpha parameters for activation functions in
+    :param activations:             The vector of activation functions used inside recurrent cell.
+    :param activation_alpha:        The vector of alpha parameters for activation functions in
                                     order respective to activation list.
-    @param activation_beta:         The vector of beta parameters for activation functions in order
+    :param activation_beta:         The vector of beta parameters for activation functions in order
                                     respective to activation list.
-    @param clip:                    The value defining clipping range [-clip, clip] on input of
+    :param clip:                    The value defining clipping range [-clip, clip] on input of
                                     activation functions.
-    @param name:                    Optional output node name.
-    @return   The new node performing a RNNCell operation on tensor from input node.
+    :param name:                    Optional output node name.
+    :return:   The new node performing a RNNCell operation on tensor from input node.
     """
     if activations is None:
         activations = ["tanh"]
@@ -464,18 +464,18 @@ def roi_align(
 ) -> Node:
     """Return a node which performs ROIAlign.
 
-    @param data: Input data.
-    @param rois: RoIs (Regions of Interest) to pool over.
-    @param batch_indices: Tensor with each element denoting the index of
+    :param data: Input data.
+    :param rois: RoIs (Regions of Interest) to pool over.
+    :param batch_indices: Tensor with each element denoting the index of
                           the corresponding image in the batch.
-    @param pooled_h: Height of the ROI output feature map.
-    @param pooled_w: Width of the ROI output feature map.
-    @param sampling_ratio: Number of bins over height and width to use to calculate
+    :param pooled_h: Height of the ROI output feature map.
+    :param pooled_w: Width of the ROI output feature map.
+    :param sampling_ratio: Number of bins over height and width to use to calculate
                            each output feature map element.
-    @param spatial_scale: Multiplicative spatial scale factor to translate ROI coordinates.
-    @param mode: Method to perform pooling to produce output feature map elements.
+    :param spatial_scale: Multiplicative spatial scale factor to translate ROI coordinates.
+    :param mode: Method to perform pooling to produce output feature map elements.
 
-    @return The new node which performs ROIAlign
+    :return: The new node which performs ROIAlign
     """
     inputs = as_nodes(data, rois, batch_indices)
     attributes = {
@@ -498,11 +498,11 @@ def scatter_elements_update(
 ) -> Node:
     """Return a node which produces a ScatterElementsUpdate operation.
 
-    @param data:    The input tensor to be updated.
-    @param indices: The tensor with indexes which will be updated.
-    @param updates: The tensor with update values.
-    @param axis:    The axis for scatter.
-    @return ScatterElementsUpdate node
+    :param data:    The input tensor to be updated.
+    :param indices: The tensor with indexes which will be updated.
+    :param updates: The tensor with update values.
+    :param axis:    The axis for scatter.
+    :return: ScatterElementsUpdate node
 
     ScatterElementsUpdate creates a copy of the first input tensor with updated elements
     specified with second and third input tensors.
@@ -527,11 +527,11 @@ def scatter_update(
 
     ScatterUpdate sets new values to slices from data addressed by indices.
 
-    @param data:    The input tensor to be updated.
-    @param indices: The tensor with indexes which will be updated.
-    @param updates: The tensor with update values.
-    @param axis:    The axis at which elements will be updated.
-    @return ScatterUpdate node
+    :param data:    The input tensor to be updated.
+    :param indices: The tensor with indexes which will be updated.
+    :param updates: The tensor with update values.
+    :param axis:    The axis at which elements will be updated.
+    :return: ScatterUpdate node
     """
     return _get_node_factory_opset3().create(
         "ScatterUpdate",
@@ -543,9 +543,9 @@ def scatter_update(
 def shape_of(data: NodeInput, output_type: str = "i64", name: Optional[str] = None) -> Node:
     """Return a node which produces a tensor containing the shape of its input data.
 
-    @param data: The tensor containing the input data.
-    @param output_type: Output element type.
-    @return ShapeOf node
+    :param data: The tensor containing the input data.
+    :param output_type: Output element type.
+    :return: ShapeOf node
     """
     return _get_node_factory_opset3().create(
         "ShapeOf",
@@ -558,14 +558,14 @@ def shape_of(data: NodeInput, output_type: str = "i64", name: Optional[str] = No
 def shuffle_channels(data: Node, axis: int, group: int, name: Optional[str] = None) -> Node:
     """Perform permutation on data in the channel dimension of the input tensor.
 
-    @param data: The node with input tensor.
-    @param axis: Channel dimension index in the data tensor.
+    :param data: The node with input tensor.
+    :param axis: Channel dimension index in the data tensor.
                  A negative value means that the index should be calculated
                  from the back of the input data shape.
-    @param group: The channel dimension specified by the axis parameter
+    :param group: The channel dimension specified by the axis parameter
                  should be split into this number of groups.
-    @param name: Optional output node name.
-    @return The new node performing a permutation on data in the channel dimension
+    :param name: Optional output node name.
+    :return: The new node performing a permutation on data in the channel dimension
              of the input tensor.
 
     The operation is the equivalent with the following transformation of the input tensor
@@ -579,7 +579,8 @@ def shuffle_channels(data: Node, axis: int, group: int, name: Optional[str] = No
 
     For example:
 
-    @code{.py}
+    .. code-block:: python
+
         Inputs: tensor of shape [1, 6, 2, 2]
 
                 data = [[[[ 0.,  1.], [ 2.,  3.]],
@@ -600,7 +601,6 @@ def shuffle_channels(data: Node, axis: int, group: int, name: Optional[str] = No
                            [[ 4.,  5.], [ 6.,  7.]],
                            [[12., 13.], [14., 15.]],
                            [[20., 21.], [22., 23.]]]]
-    @endcode
     """
     return _get_node_factory_opset3().create(
         "ShuffleChannels", [as_node(data)], {"axis": axis, "group": group}
@@ -619,13 +619,13 @@ def topk(
 ) -> Node:
     """Return a node which performs TopK.
 
-    @param data: Input data.
-    @param k: K.
-    @param axis: TopK Axis.
-    @param mode: Compute TopK largest ('max') or smallest ('min')
-    @param sort: Order of output elements (sort by: 'none', 'index' or 'value')
-    @param index_element_type: Type of output tensor with indices.
-    @return The new node which performs TopK (both indices and values)
+    :param data: Input data.
+    :param k: K.
+    :param axis: TopK Axis.
+    :param mode: Compute TopK largest ('max') or smallest ('min')
+    :param sort: Order of output elements (sort by: 'none', 'index' or 'value')
+    :param index_element_type: Type of output tensor with indices.
+    :return: The new node which performs TopK (both indices and values)
     """
     return _get_node_factory_opset3().create(
         "TopK",
diff --git a/src/bindings/python/src/compatibility/ngraph/opset4/ops.py b/src/bindings/python/src/compatibility/ngraph/opset4/ops.py
index 6526f17cc17..230f32e97ec 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset4/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset4/ops.py
@@ -59,15 +59,15 @@ def ctc_loss(
 ) -> Node:
     """Return a node which performs CTCLoss.
 
-    @param logits:                        3-D tensor of logits.
-    @param logit_length:                  1-D tensor of lengths for each object from a batch.
-    @param labels:                        2-D tensor of labels for which likelihood is estimated using logits.
-    @param label_length:                  1-D tensor of length for each label sequence.
-    @param blank_index:                   Scalar used to mark a blank index.
-    @param preprocess_collapse_repeated:  Flag for preprocessing labels before loss calculation.
-    @param ctc_merge_repeated:            Flag for merging repeated characters in a potential alignment.
-    @param unique:                        Flag to find unique elements in a target.
-    @return The new node which performs CTCLoss
+    :param logits:                        3-D tensor of logits.
+    :param logit_length:                  1-D tensor of lengths for each object from a batch.
+    :param labels:                        2-D tensor of labels for which likelihood is estimated using logits.
+    :param label_length:                  1-D tensor of length for each label sequence.
+    :param blank_index:                   Scalar used to mark a blank index.
+    :param preprocess_collapse_repeated:  Flag for preprocessing labels before loss calculation.
+    :param ctc_merge_repeated:            Flag for merging repeated characters in a potential alignment.
+    :param unique:                        Flag to find unique elements in a target.
+    :return: The new node which performs CTCLoss
     """
     if blank_index is not None:
         inputs = as_nodes(logits, logit_length, labels, label_length, blank_index)
@@ -97,17 +97,17 @@ def non_max_suppression(
 ) -> Node:
     """Return a node which performs NonMaxSuppression.
 
-    @param boxes: Tensor with box coordinates.
-    @param scores: Tensor with box scores.
-    @param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
+    :param boxes: Tensor with box coordinates.
+    :param scores: Tensor with box scores.
+    :param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
                                         to be selected per class.
-    @param iou_threshold: Tensor specifying intersection over union threshold
-    @param score_threshold: Tensor specifying minimum score to consider box for the processing.
-    @param box_encoding: Format of boxes data encoding.
-    @param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
+    :param iou_threshold: Tensor specifying intersection over union threshold
+    :param score_threshold: Tensor specifying minimum score to consider box for the processing.
+    :param box_encoding: Format of boxes data encoding.
+    :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
                                    boxes across batches or not.
-    @param output_type: Output element type.
-    @return The new node which performs NonMaxSuppression
+    :param output_type: Output element type.
+    :return: The new node which performs NonMaxSuppression
     """
     if max_output_boxes_per_class is None:
         max_output_boxes_per_class = make_constant_node(0, np.int64)
@@ -130,8 +130,8 @@ def non_max_suppression(
 def softplus(data: NodeInput, name: Optional[str] = None) -> Node:
     """Apply SoftPlus operation on each element of input tensor.
 
-    @param data: The tensor providing input data.
-    @return The new node with SoftPlus operation applied on each element.
+    :param data: The tensor providing input data.
+    :return: The new node with SoftPlus operation applied on each element.
     """
     return _get_node_factory_opset4().create("SoftPlus", as_nodes(data), {})
 
@@ -140,8 +140,8 @@ def softplus(data: NodeInput, name: Optional[str] = None) -> Node:
 def mish(data: NodeInput, name: Optional[str] = None,) -> Node:
     """Return a node which performs Mish.
 
-    @param data: Tensor with input data floating point type.
-    @return The new node which performs Mish
+    :param data: Tensor with input data floating point type.
+    :return: The new node which performs Mish
     """
     return _get_node_factory_opset4().create("Mish", as_nodes(data), {})
 
@@ -150,8 +150,8 @@ def mish(data: NodeInput, name: Optional[str] = None,) -> Node:
 def hswish(data: NodeInput, name: Optional[str] = None,) -> Node:
     """Return a node which performs HSwish (hard version of Swish).
 
-    @param data: Tensor with input data floating point type.
-    @return The new node which performs HSwish
+    :param data: Tensor with input data floating point type.
+    :return: The new node which performs HSwish
     """
     return _get_node_factory_opset4().create("HSwish", as_nodes(data), {})
 
@@ -164,8 +164,8 @@ def swish(
 ) -> Node:
     """Return a node which performing Swish activation function Swish(x, beta=1.0) = x * sigmoid(x * beta)).
 
-    @param data: Tensor with input data floating point type.
-    @return The new node which performs Swish
+    :param data: Tensor with input data floating point type.
+    :return: The new node which performs Swish
     """
     if beta is None:
         beta = make_constant_node(1.0, np.float32)
@@ -176,9 +176,9 @@ def swish(
 def acosh(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply hyperbolic inverse cosine function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with arccosh operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with arccosh operation applied on it.
     """
     return _get_node_factory_opset4().create("Acosh", [node])
 
@@ -187,9 +187,9 @@ def acosh(node: NodeInput, name: Optional[str] = None) -> Node:
 def asinh(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply hyperbolic inverse sinus function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with arcsinh operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with arcsinh operation applied on it.
     """
     return _get_node_factory_opset4().create("Asinh", [node])
 
@@ -198,9 +198,9 @@ def asinh(node: NodeInput, name: Optional[str] = None) -> Node:
 def atanh(node: NodeInput, name: Optional[str] = None) -> Node:
     """Apply hyperbolic inverse tangent function on the input node element-wise.
 
-    @param node: One of: input node, array or scalar.
-    @param name: Optional new name for output node.
-    @return New node with arctanh operation applied on it.
+    :param node: One of: input node, array or scalar.
+    :param name: Optional new name for output node.
+    :return: New node with arctanh operation applied on it.
     """
     return _get_node_factory_opset4().create("Atanh", [node])
 
@@ -215,11 +215,11 @@ def proposal(
 ) -> Node:
     """Filter bounding boxes and outputs only those with the highest prediction confidence.
 
-    @param  class_probs:        4D input floating point tensor with class prediction scores.
-    @param  bbox_deltas:        4D input floating point tensor with corrected predictions of bounding boxes
-    @param  image_shape:        The 1D input tensor with 3 or 4 elements describing image shape.
-    @param  attrs:              The dictionary containing key, value pairs for attributes.
-    @param  name:               Optional name for the output node.
+    :param  class_probs:        4D input floating point tensor with class prediction scores.
+    :param  bbox_deltas:        4D input floating point tensor with corrected predictions of bounding boxes
+    :param  image_shape:        The 1D input tensor with 3 or 4 elements describing image shape.
+    :param  attrs:              The dictionary containing key, value pairs for attributes.
+    :param  name:               Optional name for the output node.
     * base_size     The size of the anchor to which scale and ratio attributes are applied.
                     Range of values: a positive unsigned integer number
                     Default value: None
@@ -284,7 +284,9 @@ def proposal(
                     Required: no
 
     Example of attribute dictionary:
-    @code{.py}
+
+    .. code-block:: python
+
         # just required ones
         attrs = {
             'base_size': 85,
@@ -296,9 +298,9 @@ def proposal(
             'ratio': [0.1, 1.5, 2.0, 2.5],
             'scale': [2, 3, 3, 4],
         }
-    @endcode
+
     Optional attributes which are absent from dictionary will be set with corresponding default.
-    @return Node representing Proposal operation.
+    :return: Node representing Proposal operation.
     """
     requirements = [
         ("base_size", True, np.unsignedinteger, is_positive_value),
@@ -330,11 +332,11 @@ def reduce_l1(
 ) -> Node:
     """L1-reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to mean-reduce.
-    @param reduction_axes: The axes to eliminate through mean operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           Optional name for output node.
-    @return The new node performing mean-reduction operation.
+    :param node:           The tensor we want to mean-reduce.
+    :param reduction_axes: The axes to eliminate through mean operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           Optional name for output node.
+    :return: The new node performing mean-reduction operation.
     """
     return _get_node_factory_opset4().create(
         "ReduceL1", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -347,11 +349,11 @@ def reduce_l2(
 ) -> Node:
     """L2-reduction operation on input tensor, eliminating the specified reduction axes.
 
-    @param node:           The tensor we want to mean-reduce.
-    @param reduction_axes: The axes to eliminate through mean operation.
-    @param keep_dims:      If set to True it holds axes that are used for reduction
-    @param name:           Optional name for output node.
-    @return The new node performing mean-reduction operation.
+    :param node:           The tensor we want to mean-reduce.
+    :param reduction_axes: The axes to eliminate through mean operation.
+    :param keep_dims:      If set to True it holds axes that are used for reduction
+    :param name:           Optional name for output node.
+    :return: The new node performing mean-reduction operation.
     """
     return _get_node_factory_opset4().create(
         "ReduceL2", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
@@ -375,20 +377,20 @@ def lstm_cell(
 ) -> Node:
     """Return a node which performs LSTMCell operation.
 
-    @param X: The input tensor with shape: [batch_size, input_size].
-    @param initial_hidden_state: The hidden state tensor with shape: [batch_size, hidden_size].
-    @param initial_cell_state: The cell state tensor with shape: [batch_size, hidden_size].
-    @param W: The weight tensor with shape: [4*hidden_size, input_size].
-    @param R: The recurrence weight tensor with shape: [4*hidden_size, hidden_size].
-    @param B: The bias tensor for gates with shape: [4*hidden_size].
-    @param hidden_size: Specifies hidden state size.
-    @param activations: The list of three activation functions for gates.
-    @param activations_alpha: The list of alpha parameters for activation functions.
-    @param activations_beta: The list of beta parameters for activation functions.
-    @param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
-    @param name: An optional name of the output node.
+    :param X: The input tensor with shape: [batch_size, input_size].
+    :param initial_hidden_state: The hidden state tensor with shape: [batch_size, hidden_size].
+    :param initial_cell_state: The cell state tensor with shape: [batch_size, hidden_size].
+    :param W: The weight tensor with shape: [4*hidden_size, input_size].
+    :param R: The recurrence weight tensor with shape: [4*hidden_size, hidden_size].
+    :param B: The bias tensor for gates with shape: [4*hidden_size].
+    :param hidden_size: Specifies hidden state size.
+    :param activations: The list of three activation functions for gates.
+    :param activations_alpha: The list of alpha parameters for activation functions.
+    :param activations_beta: The list of beta parameters for activation functions.
+    :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
+    :param name: An optional name of the output node.
 
-    @return The new node represents LSTMCell. Node outputs count: 2.
+    :return: The new node represents LSTMCell. Node outputs count: 2.
     """
     if activations is None:
         activations = ["sigmoid", "tanh", "tanh"]
diff --git a/src/bindings/python/src/compatibility/ngraph/opset5/ops.py b/src/bindings/python/src/compatibility/ngraph/opset5/ops.py
index 134dd3b8292..ca81ee55e25 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset5/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset5/ops.py
@@ -57,15 +57,15 @@ def batch_norm_inference(
 ) -> Node:
     """Perform layer normalizes a input tensor by mean and variance with appling scale and offset.
 
-    @param data: The input tensor with data for normalization.
-    @param gamma: The scalar scaling for normalized value.
-    @param beta: The bias added to the scaled normalized value.
-    @param mean: The value for mean normalization.
-    @param variance: The value for variance normalization.
-    @param epsilon: The  number to be added to the variance to avoid division
+    :param data: The input tensor with data for normalization.
+    :param gamma: The scalar scaling for normalized value.
+    :param beta: The bias added to the scaled normalized value.
+    :param mean: The value for mean normalization.
+    :param variance: The value for variance normalization.
+    :param epsilon: The  number to be added to the variance to avoid division
                     by zero when normalizing a value.
-    @param name: The optional name of the output node.
-    @return: The new node which performs BatchNormInference.
+    :param name: The optional name of the output node.
+    :return: The new node which performs BatchNormInference.
     """
     inputs = as_nodes(data, gamma, beta, mean, variance)
     return _get_node_factory_opset5().create("BatchNormInference", inputs, {"epsilon": epsilon})
@@ -80,10 +80,10 @@ def gather_nd(
 ) -> Node:
     """Return a node which performs GatherND.
 
-    @param data:       N-D tensor with data for gathering
-    @param indices:    K-D tensor of tuples with indices by which data is gathered
-    @param batch_dims: Scalar value of batch dimensions
-    @return: The new node which performs GatherND
+    :param data:       N-D tensor with data for gathering
+    :param indices:    K-D tensor of tuples with indices by which data is gathered
+    :param batch_dims: Scalar value of batch dimensions
+    :return: The new node which performs GatherND
     """
     inputs = as_nodes(data, indices)
 
@@ -98,9 +98,9 @@ def gather_nd(
 def log_softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node:
     """Apply LogSoftmax operation on each element of input tensor.
 
-    @param data: The tensor providing input data.
-    @param axis: An axis along which LogSoftmax should be calculated
-    @return: The new node with LogSoftmax operation applied on each element.
+    :param data: The tensor providing input data.
+    :param axis: An axis along which LogSoftmax should be calculated
+    :return: The new node with LogSoftmax operation applied on each element.
     """
     return _get_node_factory_opset5().create("LogSoftmax", [as_node(data)], {"axis": axis})
 
@@ -120,18 +120,18 @@ def non_max_suppression(
 ) -> Node:
     """Return a node which performs NonMaxSuppression.
 
-    @param boxes: Tensor with box coordinates.
-    @param scores: Tensor with box scores.
-    @param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
+    :param boxes: Tensor with box coordinates.
+    :param scores: Tensor with box scores.
+    :param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
                                         to be selected per class.
-    @param iou_threshold: Tensor specifying intersection over union threshold
-    @param score_threshold: Tensor specifying minimum score to consider box for the processing.
-    @param soft_nms_sigma: Tensor specifying the sigma parameter for Soft-NMS.
-    @param box_encoding: Format of boxes data encoding.
-    @param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
+    :param iou_threshold: Tensor specifying intersection over union threshold
+    :param score_threshold: Tensor specifying minimum score to consider box for the processing.
+    :param soft_nms_sigma: Tensor specifying the sigma parameter for Soft-NMS.
+    :param box_encoding: Format of boxes data encoding.
+    :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
                                    boxes across batches or not.
-    @param output_type: Output element type.
-    @return: The new node which performs NonMaxSuppression
+    :param output_type: Output element type.
+    :return: The new node which performs NonMaxSuppression
     """
     if max_output_boxes_per_class is None:
         max_output_boxes_per_class = make_constant_node(0, np.int64)
@@ -161,12 +161,12 @@ def non_max_suppression(
 def round(data: NodeInput, mode: str = "half_to_even", name: Optional[str] = None) -> Node:
     """Apply Round operation on each element of input tensor.
 
-    @param data: The tensor providing input data.
-    @param mode: Rule to round halfway cases. If set to 'half_to_even' then halfs round to the nearest even
+    :param data: The tensor providing input data.
+    :param mode: Rule to round halfway cases. If set to 'half_to_even' then halfs round to the nearest even
         integer or rounding in such a way that the result heads away from zero if `mode` attribute is
         'half_away_from_zero`.
-    @param name: An optional name of the output node.
-    @return: The new node with Round operation applied on each element.
+    :param name: An optional name of the output node.
+    :return: The new node with Round operation applied on each element.
     """
     return _get_node_factory_opset5().create("Round", as_nodes(data), {"mode": mode.upper()})
 
@@ -190,30 +190,30 @@ def lstm_sequence(
 ) -> Node:
     """Return a node which performs LSTMSequence operation.
 
-    @param X: The input tensor. Shape: [batch_size, seq_length, input_size].
-    @param initial_hidden_state:    The hidden state tensor.
+    :param X: The input tensor. Shape: [batch_size, seq_length, input_size].
+    :param initial_hidden_state:    The hidden state tensor.
                                     Shape: [batch_size, num_directions, hidden_size].
-    @param initial_cell_state:      The cell state tensor.
+    :param initial_cell_state:      The cell state tensor.
                                     Shape: [batch_size, num_directions, hidden_size].
-    @param sequence_lengths:        Specifies real sequence lengths for each batch element.
+    :param sequence_lengths:        Specifies real sequence lengths for each batch element.
                                     Shape: [batch_size]. Integer type.
-    @param W: Tensor with weights for matrix multiplication operation with input portion of data.
+    :param W: Tensor with weights for matrix multiplication operation with input portion of data.
               Expected format: fico
               Shape: [num_directions, 4*hidden_size, input_size].
-    @param R: The tensor with weights for matrix multiplication operation with hidden state.
+    :param R: The tensor with weights for matrix multiplication operation with hidden state.
               Expected format: fico
               Shape: [num_directions, 4*hidden_size, hidden_size].
-    @param B: The sum of biases (weight and recurrence). Expected format: fico
+    :param B: The sum of biases (weight and recurrence). Expected format: fico
               Shape: [num_directions, 4*hidden_size].
-    @param hidden_size: Specifies hidden state size.
-    @param direction: Specifies if the RNN is forward, reverse, or bidirectional.
-    @param activations: The list of three activation functions for gates.
-    @param activations_alpha: The list of alpha parameters for activation functions.
-    @param activations_beta: The list of beta parameters for activation functions.
-    @param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
-    @param name: An optional name of the output node.
+    :param hidden_size: Specifies hidden state size.
+    :param direction: Specifies if the RNN is forward, reverse, or bidirectional.
+    :param activations: The list of three activation functions for gates.
+    :param activations_alpha: The list of alpha parameters for activation functions.
+    :param activations_beta: The list of beta parameters for activation functions.
+    :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
+    :param name: An optional name of the output node.
 
-    @return: The new node represents LSTMSequence. Node outputs count: 3.
+    :return: The new node represents LSTMSequence. Node outputs count: 3.
     """
     if activations is None:
         activations = ["sigmoid", "tanh", "tanh"]
@@ -238,8 +238,8 @@ def lstm_sequence(
 def hsigmoid(data: NodeInput, name: Optional[str] = None,) -> Node:
     """Return a node which performs HSigmoid.
 
-    @param data: Tensor with input data floating point type.
-    @return: The new node which performs HSigmoid
+    :param data: Tensor with input data floating point type.
+    :return: The new node which performs HSigmoid
     """
     return _get_node_factory_opset5().create("HSigmoid", as_nodes(data), {})
 
@@ -263,29 +263,29 @@ def gru_sequence(
 ) -> Node:
     """Return a node which performs GRUSequence operation.
 
-    @param X: The input tensor. Shape: [batch_size, seq_length, input_size].
-    @param initial_hidden_state:    The hidden state tensor.
+    :param X: The input tensor. Shape: [batch_size, seq_length, input_size].
+    :param initial_hidden_state:    The hidden state tensor.
                                     Shape: [batch_size, num_directions, hidden_size].
-    @param sequence_lengths:        Specifies real sequence lengths for each batch element.
+    :param sequence_lengths:        Specifies real sequence lengths for each batch element.
                                     Shape: [batch_size]. Integer type.
-    @param W: Tensor with weights for matrix multiplication operation with input portion of data.
+    :param W: Tensor with weights for matrix multiplication operation with input portion of data.
               Shape: [num_directions, 3*hidden_size, input_size].
-    @param R: The tensor with weights for matrix multiplication operation with hidden state.
+    :param R: The tensor with weights for matrix multiplication operation with hidden state.
               Shape: [num_directions, 3*hidden_size, hidden_size].
-    @param B: The sum of biases (weight and recurrence).
+    :param B: The sum of biases (weight and recurrence).
               For linear_before_reset set True the shape is [num_directions, 4*hidden_size].
               Otherwise the shape is [num_directions, 3*hidden_size].
-    @param hidden_size: Specifies hidden state size.
-    @param direction: Specifies if the RNN is forward, reverse, or bidirectional.
-    @param activations: The list of three activation functions for gates.
-    @param activations_alpha: The list of alpha parameters for activation functions.
-    @param activations_beta: The list of beta parameters for activation functions.
-    @param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
-    @param linear_before_reset: Flag denotes if the layer behaves according to the modification
+    :param hidden_size: Specifies hidden state size.
+    :param direction: Specifies if the RNN is forward, reverse, or bidirectional.
+    :param activations: The list of three activation functions for gates.
+    :param activations_alpha: The list of alpha parameters for activation functions.
+    :param activations_beta: The list of beta parameters for activation functions.
+    :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
+    :param linear_before_reset: Flag denotes if the layer behaves according to the modification
                                 of GRU described in the formula in the ONNX documentation.
-    @param name: An optional name of the output node.
+    :param name: An optional name of the output node.
 
-    @return: The new node represents GRUSequence. Node outputs count: 2.
+    :return: The new node represents GRUSequence. Node outputs count: 2.
     """
     if activations is None:
         activations = ["sigmoid", "tanh"]
@@ -326,26 +326,26 @@ def rnn_sequence(
 ) -> Node:
     """Return a node which performs RNNSequence operation.
 
-    @param X: The input tensor. Shape: [batch_size, seq_length, input_size].
-    @param initial_hidden_state:    The hidden state tensor.
+    :param X: The input tensor. Shape: [batch_size, seq_length, input_size].
+    :param initial_hidden_state:    The hidden state tensor.
                                     Shape: [batch_size, num_directions, hidden_size].
-    @param sequence_lengths:        Specifies real sequence lengths for each batch element.
+    :param sequence_lengths:        Specifies real sequence lengths for each batch element.
                                     Shape: [batch_size]. Integer type.
-    @param W: Tensor with weights for matrix multiplication operation with input portion of data.
+    :param W: Tensor with weights for matrix multiplication operation with input portion of data.
               Shape: [num_directions, hidden_size, input_size].
-    @param R: The tensor with weights for matrix multiplication operation with hidden state.
+    :param R: The tensor with weights for matrix multiplication operation with hidden state.
               Shape: [num_directions, hidden_size, hidden_size].
-    @param B: The sum of biases (weight and recurrence).
+    :param B: The sum of biases (weight and recurrence).
               Shape: [num_directions, hidden_size].
-    @param hidden_size: Specifies hidden state size.
-    @param direction: Specifies if the RNN is forward, reverse, or bidirectional.
-    @param activations: The list of three activation functions for gates.
-    @param activations_alpha: The list of alpha parameters for activation functions.
-    @param activations_beta: The list of beta parameters for activation functions.
-    @param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
-    @param name: An optional name of the output node.
+    :param hidden_size: Specifies hidden state size.
+    :param direction: Specifies if the RNN is forward, reverse, or bidirectional.
+    :param activations: The list of three activation functions for gates.
+    :param activations_alpha: The list of alpha parameters for activation functions.
+    :param activations_beta: The list of beta parameters for activation functions.
+    :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations.
+    :param name: An optional name of the output node.
 
-    @return: The new node represents RNNSequence. Node outputs count: 2.
+    :return: The new node represents RNNSequence. Node outputs count: 2.
     """
     if activations is None:
         activations = ["tanh"]
@@ -385,33 +385,33 @@ def loop(
 ) -> Node:
     """Perform recurrent execution of the network described in the body, iterating through the data.
 
-    @param trip_count: A scalar or 1D tensor with 1 element specifying
+    :param trip_count: A scalar or 1D tensor with 1 element specifying
         maximum number of iterations.
-    @param execution_condition: A scalar or 1D tensor with 1 element
+    :param execution_condition: A scalar or 1D tensor with 1 element
         specifying whether to execute the first iteration or not.
-    @param      inputs:                The provided to TensorIterator operator.
-    @param      graph_body:            The graph representing the body we execute.
-    @param      slice_input_desc:      The descriptors describing sliced inputs, that is nodes
+    :param      inputs:                The provided to TensorIterator operator.
+    :param      graph_body:            The graph representing the body we execute.
+    :param      slice_input_desc:      The descriptors describing sliced inputs, that is nodes
                                        representing tensors we iterate through, processing single
                                        data slice in one iteration.
-    @param      merged_input_desc:     The descriptors describing merged inputs, that is nodes
+    :param      merged_input_desc:     The descriptors describing merged inputs, that is nodes
                                        representing variables with initial value at first iteration,
                                        which may be changing through iterations.
-    @param      invariant_input_desc:  The descriptors describing invariant inputs, that is nodes
+    :param      invariant_input_desc:  The descriptors describing invariant inputs, that is nodes
                                        representing variable with persistent value through all
                                        iterations.
-    @param      body_output_desc:      The descriptors describing body outputs from specified
+    :param      body_output_desc:      The descriptors describing body outputs from specified
                                        iteration.
-    @param      concat_output_desc:    The descriptors describing specified output values through
+    :param      concat_output_desc:    The descriptors describing specified output values through
                                        all the iterations concatenated into one node.
-    @param      body_condition_output_idx:    Determines the purpose of the corresponding result in
+    :param      body_condition_output_idx:    Determines the purpose of the corresponding result in
                                               the graph_body. This result will determine the dynamic
                                               exit condition. If the value of this result is False,
                                               then iterations stop.
-    @param      current_iteration_input_idx:  Determines the purpose of the corresponding parameter
+    :param      current_iteration_input_idx:  Determines the purpose of the corresponding parameter
                                               in the graph_body. This parameter will be used as
                                               an iteration counter. Optional.
-    @return: The new node which performs Loop.
+    :return: The new node which performs Loop.
     """
     attributes = {
         "body": graph_body.serialize(),
diff --git a/src/bindings/python/src/compatibility/ngraph/opset6/ops.py b/src/bindings/python/src/compatibility/ngraph/opset6/ops.py
index 415ba6b5db2..df8cbce0ead 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset6/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset6/ops.py
@@ -57,11 +57,11 @@ def ctc_greedy_decoder_seq_len(
 ) -> Node:
     """Return a node which performs CTCGreedyDecoderSeqLen.
 
-    @param data:            The input 3D tensor. Shape: [batch_size, seq_length, num_classes]
-    @param sequence_length: Input 1D tensor with sequence length. Shape: [batch_size]
-    @param blank_index:     Scalar or 1D tensor with specifies the class index to use for the blank class.
+    :param data:            The input 3D tensor. Shape: [batch_size, seq_length, num_classes]
+    :param sequence_length: Input 1D tensor with sequence length. Shape: [batch_size]
+    :param blank_index:     Scalar or 1D tensor with specifies the class index to use for the blank class.
                             Optional parameter. Default value is num_classes-1.
-    @return:                The new node which performs CTCGreedyDecoderSeqLen.
+    :return:                The new node which performs CTCGreedyDecoderSeqLen.
     """
     if blank_index is not None:
         inputs = as_nodes(data, sequence_length, blank_index)
@@ -86,10 +86,10 @@ def gather_elements(
 ) -> Node:
     """Return a node which performs GatherElements.
 
-    @param data:       N-D tensor with data for gathering
-    @param indices:    N-D tensor with indices by which data is gathered
-    @param axis:       axis along which elements are gathered
-    @return:           The new node which performs GatherElements
+    :param data:       N-D tensor with data for gathering
+    :param indices:    N-D tensor with indices by which data is gathered
+    :param axis:       axis along which elements are gathered
+    :return:           The new node which performs GatherElements
     """
     inputs = as_nodes(data, indices)
 
@@ -111,14 +111,14 @@ def mvn(
 ) -> Node:
     """Return a node which performs MeanVarianceNormalization (MVN).
 
-    @param data: The node with data tensor.
-    @param axes: The node with axes to reduce on.
-    @param normalize_variance: Denotes whether to perform variance normalization.
-    @param eps: The number added to the variance to avoid division by zero
+    :param data: The node with data tensor.
+    :param axes: The node with axes to reduce on.
+    :param normalize_variance: Denotes whether to perform variance normalization.
+    :param eps: The number added to the variance to avoid division by zero
                when normalizing the value. Scalar value.
-    @param eps_mode: how eps is applied (`inside_sqrt` or `outside_sqrt`)
-    @param name: Optional output node name.
-    @return The new node performing a MVN operation on input tensor.
+    :param eps_mode: how eps is applied (`inside_sqrt` or `outside_sqrt`)
+    :param name: Optional output node name.
+    :return: The new node performing a MVN operation on input tensor.
     """
     inputs = as_nodes(data, axes)
 
@@ -135,10 +135,10 @@ def mvn(
 def assign(new_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node:
     """Return a node which produces the Assign operation.
 
-    @param new_value:    Node producing a value to be assigned to a variable.
-    @param variable_id:  Id of a variable to be updated.
-    @param name:         Optional name for output node.
-    @return Assign node
+    :param new_value:    Node producing a value to be assigned to a variable.
+    :param variable_id:  Id of a variable to be updated.
+    :param name:         Optional name for output node.
+    :return: Assign node
     """
     return _get_node_factory_opset6().create(
         "Assign",
@@ -151,10 +151,10 @@ def assign(new_value: NodeInput, variable_id: str, name: Optional[str] = None) -
 def read_value(init_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node:
     """Return a node which produces the Assign operation.
 
-    @param init_value:   Node producing a value to be returned instead of an unassigned variable.
-    @param variable_id:  Id of a variable to be read.
-    @param name:         Optional name for output node.
-    @return ReadValue node
+    :param init_value:   Node producing a value to be returned instead of an unassigned variable.
+    :param variable_id:  Id of a variable to be read.
+    :param name:         Optional name for output node.
+    :return: ReadValue node
     """
     return _get_node_factory_opset6().create(
         "ReadValue",
diff --git a/src/bindings/python/src/compatibility/ngraph/opset7/ops.py b/src/bindings/python/src/compatibility/ngraph/opset7/ops.py
index d8b4325adcd..680ed771a85 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset7/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset7/ops.py
@@ -52,9 +52,9 @@ def einsum(
 ) -> Node:
     """Return a node which performs Einsum operation.
 
-    @param inputs: The list of input nodes
-    @param equation: Einsum equation
-    @return: The new node performing Einsum operation on the inputs
+    :param inputs: The list of input nodes
+    :param equation: Einsum equation
+    :return: The new node performing Einsum operation on the inputs
     """
     attributes = {
         "equation": equation
@@ -71,10 +71,10 @@ def gelu(
 ) -> Node:
     """Return a node which performs Gelu activation function.
 
-    @param data: The node with data tensor.
-    @param approximation_mode: defines which approximation to use ('tanh' or 'erf')
-    @param name: Optional output node name.
-    @return The new node performing a Gelu activation with the input tensor.
+    :param data: The node with data tensor.
+    :param approximation_mode: defines which approximation to use ('tanh' or 'erf')
+    :param name: Optional output node name.
+    :return: The new node performing a Gelu activation with the input tensor.
     """
     inputs = as_nodes(data)
 
@@ -93,10 +93,10 @@ def roll(
 ) -> Node:
     """Return a node which performs Roll operation.
 
-    @param data: The node with data tensor.
-    @param shift: The node with the tensor with numbers of places by which elements are shifted.
-    @param axes: The node with the tensor with axes along which elements are shifted.
-    @return The new node performing a Roll operation on the input tensor.
+    :param data: The node with data tensor.
+    :param shift: The node with the tensor with numbers of places by which elements are shifted.
+    :param axes: The node with the tensor with axes along which elements are shifted.
+    :return: The new node performing a Roll operation on the input tensor.
     """
     inputs = as_nodes(data, shift, axes)
 
@@ -112,11 +112,11 @@ def gather(
 ) -> Node:
     """Return a node which performs Gather.
 
-    @param data:         N-D tensor with data for gathering
-    @param indices:      N-D tensor with indices by which data is gathered
-    @param axis:         axis along which elements are gathered
-    @param batch_dims:   number of batch dimensions
-    @return:             The new node which performs Gather
+    :param data:         N-D tensor with data for gathering
+    :param indices:      N-D tensor with indices by which data is gathered
+    :param axis:         axis along which elements are gathered
+    :param batch_dims:   number of batch dimensions
+    :return:             The new node which performs Gather
     """
     inputs = as_nodes(data, indices, axis)
     attributes = {
@@ -132,10 +132,10 @@ def dft(
 ) -> Node:
     """Return a node which performs DFT operation.
 
-    @param data: Tensor with transformed data.
-    @param axes: Tensor with axes to transform.
-    @param signal_size: Tensor specifying signal size with respect to axes from the input 'axes'.
-    @return: The new node which performs DFT operation on the input data tensor.
+    :param data: Tensor with transformed data.
+    :param axes: Tensor with axes to transform.
+    :param signal_size: Tensor specifying signal size with respect to axes from the input 'axes'.
+    :return: The new node which performs DFT operation on the input data tensor.
     """
     if signal_size is None:
         inputs = as_nodes(data, axes)
@@ -153,10 +153,10 @@ def idft(
 ) -> Node:
     """Return a node which performs IDFT operation.
 
-    @param data: Tensor with transformed data.
-    @param axes: Tensor with axes to transform.
-    @param signal_size: Tensor specifying signal size with respect to axes from the input 'axes'.
-    @return: The new node which performs IDFT operation on the input data tensor.
+    :param data: Tensor with transformed data.
+    :param axes: Tensor with axes to transform.
+    :param signal_size: Tensor specifying signal size with respect to axes from the input 'axes'.
+    :return: The new node which performs IDFT operation on the input data tensor.
     """
     if signal_size is None:
         inputs = as_nodes(data, axes)
diff --git a/src/bindings/python/src/compatibility/ngraph/utils/broadcasting.py b/src/bindings/python/src/compatibility/ngraph/utils/broadcasting.py
index db9ab7e9b9f..3f9ef1685ad 100644
--- a/src/bindings/python/src/compatibility/ngraph/utils/broadcasting.py
+++ b/src/bindings/python/src/compatibility/ngraph/utils/broadcasting.py
@@ -20,10 +20,10 @@ def get_broadcast_axes(
     replicating elements from the input tensor as needed to fill the new dimensions.
     Function calculate which of the output axes are added in this way.
 
-    @param output_shape: The new shape for the output tensor.
-    @param input_shape: The shape of input tensor.
-    @param axis: The axis along which we want to replicate elements.
-    @return The indices of added axes.
+    :param output_shape: The new shape for the output tensor.
+    :param input_shape: The shape of input tensor.
+    :param axis: The axis along which we want to replicate elements.
+    :return: The indices of added axes.
     """
     axes_indexes = list(range(0, len(output_shape)))
     if axis is None:
diff --git a/src/bindings/python/src/compatibility/ngraph/utils/input_validation.py b/src/bindings/python/src/compatibility/ngraph/utils/input_validation.py
index 6a39335d16c..82f938392a9 100644
--- a/src/bindings/python/src/compatibility/ngraph/utils/input_validation.py
+++ b/src/bindings/python/src/compatibility/ngraph/utils/input_validation.py
@@ -28,14 +28,14 @@ def _check_value(op_name, attr_key, value, val_type, cond=None):
     # type: (str, str, Any, Type, Optional[Callable[[Any], bool]]) -> bool
     """Check whether provided value satisfies specified criteria.
 
-    @param      op_name:        The operator name which attributes are checked.
-    @param      attr_key:       The attribute name.
-    @param      value:          The value to check.
-    @param      val_type:       Required value type.
-    @param      cond:           The optional function running additional checks.
+    :param      op_name:        The operator name which attributes are checked.
+    :param      attr_key:       The attribute name.
+    :param      value:          The value to check.
+    :param      val_type:       Required value type.
+    :param      cond:           The optional function running additional checks.
 
     :raises     UserInputError:
-    @return    True if attribute satisfies all criterias. Otherwise False.
+    :return:    True if attribute satisfies all criterias. Otherwise False.
     """
     if not np.issubdtype(type(value), val_type):
         raise UserInputError(
@@ -56,17 +56,17 @@ def check_valid_attribute(op_name, attr_dict, attr_key, val_type, cond=None, req
     # type: (str, dict, str, Type, Optional[Callable[[Any], bool]], Optional[bool]) -> bool
     """Check whether specified attribute satisfies given criteria.
 
-    @param  op_name:    The operator name which attributes are checked.
-    @param attr_dict:   Dictionary containing key-value attributes to check.
-    @param attr_key:    Key value for validated attribute.
-    @param val_type:    Value type for validated attribute.
-    @param cond:        Any callable wich accept attribute value and returns True or False.
-    @param required:    Whether provided attribute key is not required. This mean it may be missing
+    :param  op_name:    The operator name which attributes are checked.
+    :param attr_dict:   Dictionary containing key-value attributes to check.
+    :param attr_key:    Key value for validated attribute.
+    :param val_type:    Value type for validated attribute.
+    :param cond:        Any callable wich accept attribute value and returns True or False.
+    :param required:    Whether provided attribute key is not required. This mean it may be missing
                         from provided dictionary.
 
     :raises     UserInputError:
 
-    @return True if attribute satisfies all criterias. Otherwise False.
+    :return: True if attribute satisfies all criterias. Otherwise False.
     """
     result = True
 
@@ -99,9 +99,9 @@ def check_valid_attributes(
     # type: (...) -> bool
     """Perform attributes validation according to specified type, value criteria.
 
-    @param  op_name:        The operator name which attributes are checked.
-    @param  attributes:     The dictionary with user provided attributes to check.
-    @param  requirements:   The list of tuples describing attributes' requirements. The tuple should
+    :param  op_name:        The operator name which attributes are checked.
+    :param  attributes:     The dictionary with user provided attributes to check.
+    :param  requirements:   The list of tuples describing attributes' requirements. The tuple should
                             contain following values:
                             (attr_name: str,
                              is_required: bool,
@@ -109,7 +109,7 @@ def check_valid_attributes(
                              value_condition: Callable)
 
     :raises     UserInputError:
-    @return True if all attributes satisfies criterias. Otherwise False.
+    :return: True if all attributes satisfies criterias. Otherwise False.
     """
     for attr, required, val_type, cond in requirements:
         check_valid_attribute(op_name, attributes, attr, val_type, cond, required)
@@ -119,9 +119,9 @@ def check_valid_attributes(
 def is_positive_value(x):  # type: (Any) -> bool
     """Determine whether the specified x is positive value.
 
-    @param      x:    The value to check.
+    :param      x:    The value to check.
 
-    @return   True if the specified x is positive value, False otherwise.
+    :return:   True if the specified x is positive value, False otherwise.
     """
     return x > 0
 
@@ -129,8 +129,8 @@ def is_positive_value(x):  # type: (Any) -> bool
 def is_non_negative_value(x):  # type: (Any) -> bool
     """Determine whether the specified x is non-negative value.
 
-    @param      x:    The value to check.
+    :param      x:    The value to check.
 
-    @return   True if the specified x is non-negative value, False otherwise.
+    :return:   True if the specified x is non-negative value, False otherwise.
     """
     return x >= 0
diff --git a/src/bindings/python/src/compatibility/ngraph/utils/reduction.py b/src/bindings/python/src/compatibility/ngraph/utils/reduction.py
index 85bbd1f20a0..9c1caeecbad 100644
--- a/src/bindings/python/src/compatibility/ngraph/utils/reduction.py
+++ b/src/bindings/python/src/compatibility/ngraph/utils/reduction.py
@@ -11,9 +11,9 @@ def get_reduction_axes(node: Node, reduction_axes: Optional[Iterable[int]]) -> I
 
     If reduction_axes is None we default to reduce all axes.
 
-    @param node: The node we fill reduction axes for.
-    @param reduction_axes: The collection of indices of axes to reduce. May be None.
-    @return Set filled with indices of axes we want to reduce.
+    :param node: The node we fill reduction axes for.
+    :param reduction_axes: The collection of indices of axes to reduce. May be None.
+    :return: Set filled with indices of axes we want to reduce.
     """
     if reduction_axes is None:
         reduction_axes = set(range(len(node.shape)))
diff --git a/src/bindings/python/src/openvino/runtime/utils/node_factory.py b/src/bindings/python/src/openvino/runtime/utils/node_factory.py
index ecd6e627ed1..2c95ad27fc6 100644
--- a/src/bindings/python/src/openvino/runtime/utils/node_factory.py
+++ b/src/bindings/python/src/openvino/runtime/utils/node_factory.py
@@ -21,7 +21,7 @@ class NodeFactory(object):
     def __init__(self, opset_version: str = DEFAULT_OPSET) -> None:
         """Create the NodeFactory object.
 
-        @param      opset_version:  The opset version the factory will use to produce ops from.
+        :param      opset_version:  The opset version the factory will use to produce ops from.
         """
         self.factory = _NodeFactory(opset_version)
 
@@ -35,11 +35,11 @@ class NodeFactory(object):
 
         The user does not have to provide all node's attributes, but only required ones.
 
-        @param      op_type_name:  The operator type name.
-        @param      arguments:     The operator arguments.
-        @param      attributes:    The operator attributes.
+        :param      op_type_name:  The operator type name.
+        :param      arguments:     The operator arguments.
+        :param      attributes:    The operator attributes.
 
-        @return   Node object representing requested operator with attributes set.
+        :return:   Node object representing requested operator with attributes set.
         """
         if arguments is None and attributes is None:
             node = self.factory.create(op_type_name)
@@ -111,10 +111,10 @@ class NodeFactory(object):
     def _normalize_attr_name(attr_name: str, prefix: str) -> str:
         """Normalize attribute name.
 
-        @param      attr_name:  The attribute name.
-        @param      prefix:     The prefix to attach to attribute name.
+        :param      attr_name:  The attribute name.
+        :param      prefix:     The prefix to attach to attribute name.
 
-        @return   The modified attribute name.
+        :return:   The modified attribute name.
         """
         # Trim first part of the name if there is only one level of attribute hierarchy.
         if attr_name.count(".") == 1:
@@ -125,9 +125,9 @@ class NodeFactory(object):
     def _normalize_attr_name_getter(cls, attr_name: str) -> str:
         """Normalize atr name to be suitable for getter function name.
 
-        @param      attr_name:  The attribute name to normalize
+        :param      attr_name:  The attribute name to normalize
 
-        @return   The appropriate getter function name.
+        :return:   The appropriate getter function name.
         """
         return cls._normalize_attr_name(attr_name, "get_")
 
@@ -135,9 +135,9 @@ class NodeFactory(object):
     def _normalize_attr_name_setter(cls, attr_name: str) -> str:
         """Normalize attribute name to be suitable for setter function name.
 
-        @param      attr_name:  The attribute name to normalize
+        :param      attr_name:  The attribute name to normalize
 
-        @return   The appropriate setter function name.
+        :return:   The appropriate setter function name.
         """
         return cls._normalize_attr_name(attr_name, "set_")
 
@@ -145,10 +145,10 @@ class NodeFactory(object):
     def _get_node_attr_value(node: Node, attr_name: str) -> Any:
         """Get provided node attribute value.
 
-        @param      node:       The node we retrieve attribute value from.
-        @param      attr_name:  The attribute name.
+        :param      node:       The node we retrieve attribute value from.
+        :param      attr_name:  The attribute name.
 
-        @return   The node attribute value.
+        :return:   The node attribute value.
         """
         if not node._attr_cache_valid:
             node._attr_cache = node.get_attributes()
@@ -159,9 +159,9 @@ class NodeFactory(object):
     def _set_node_attr_value(node: Node, attr_name: str, value: Any) -> None:
         """Set the node attribute value.
 
-        @param      node:       The node we change attribute value for.
-        @param      attr_name:  The attribute name.
-        @param      value:      The new attribute value.
+        :param      node:       The node we change attribute value for.
+        :param      attr_name:  The attribute name.
+        :param      value:      The new attribute value.
         """
         node.set_attribute(attr_name, value)
         node._attr_cache[attr_name] = value
diff --git a/src/common/itt/include/openvino/itt.hpp b/src/common/itt/include/openvino/itt.hpp
index ee276fa1feb..fdfa73fcafd 100644
--- a/src/common/itt/include/openvino/itt.hpp
+++ b/src/common/itt/include/openvino/itt.hpp
@@ -13,6 +13,9 @@
 #include <string>
 #include <utility>
 
+/** @ingroup ie_dev_profiling
+  * @brief openvino namespace
+  */
 namespace openvino
 {
     namespace itt
diff --git a/src/common/transformations/include/transformations_visibility.hpp b/src/common/transformations/include/transformations_visibility.hpp
index 692447a19d4..6f7b3bb7cec 100644
--- a/src/common/transformations/include/transformations_visibility.hpp
+++ b/src/common/transformations/include/transformations_visibility.hpp
@@ -12,6 +12,7 @@
  */
 
 /**
+ * @ingroup ie_cpp_api
  * @defgroup ie_transformation_api Inference Engine Transformation API
  * @brief Defines Inference Engine Transformations API which is used to transform ngraph::Function
  *
diff --git a/src/core/include/ngraph/ngraph.hpp b/src/core/include/ngraph/ngraph.hpp
index 45108e821d9..82f4fb57172 100644
--- a/src/core/include/ngraph/ngraph.hpp
+++ b/src/core/include/ngraph/ngraph.hpp
@@ -6,6 +6,11 @@
 // The public API for ngraph++
 //
 
+/**
+ *  @defgroup ngraph_cpp_api nGraph C++ API
+ *  nGraph C++ API
+ */
+
 #pragma once
 
 #include <string>
diff --git a/src/frontends/onnx/onnx_common/include/onnx_common/parser.hpp b/src/frontends/onnx/onnx_common/include/onnx_common/parser.hpp
index 1e98ce21702..867e5287de6 100644
--- a/src/frontends/onnx/onnx_common/include/onnx_common/parser.hpp
+++ b/src/frontends/onnx/onnx_common/include/onnx_common/parser.hpp
@@ -6,6 +6,7 @@
 #include <fstream>
 #include <string>
 
+/// \ingroup ngraph_cpp_api
 namespace ONNX_NAMESPACE {
 class ModelProto;
 }
diff --git a/src/inference/dev_api/exec_graph_info.hpp b/src/inference/dev_api/exec_graph_info.hpp
index 5352e50bdf9..140a4890b91 100644
--- a/src/inference/dev_api/exec_graph_info.hpp
+++ b/src/inference/dev_api/exec_graph_info.hpp
@@ -18,7 +18,7 @@
 
 /**
  * @brief A namespace with const values for Execution Graph parameters names.
- *
+ * @ingroup ie_dev_exec_graph
  * Executable Graph Info is represented in CNNNetwork format with general ExecutionNode nodes inside
  * including connections between the nodes. Each node describes an executable hardware-specific
  * primitive and stores its parameters within ExecutionNode::get_rt_info map.
diff --git a/src/inference/dev_api/file_utils.h b/src/inference/dev_api/file_utils.h
index c1bf5bbab77..12c934772ce 100644
--- a/src/inference/dev_api/file_utils.h
+++ b/src/inference/dev_api/file_utils.h
@@ -16,6 +16,7 @@
 #include "ie_api.h"
 #include "openvino/util/file_util.hpp"
 
+/// @ingroup ie_dev_api_file_utils
 namespace FileUtils {
 
 /**
diff --git a/src/inference/include/ie/ie_api.h b/src/inference/include/ie/ie_api.h
index f6dcc7fec40..eac00269b4d 100644
--- a/src/inference/include/ie/ie_api.h
+++ b/src/inference/include/ie/ie_api.h
@@ -2,6 +2,11 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+/**
+ *  @defgroup ie_cpp_api Inference Engine C++ API
+ *  Inference Engine C++ API
+ */
+
 /**
  * @brief The macro defines a symbol import/export mechanism essential for Microsoft Windows(R) OS.
  *
diff --git a/src/inference/include/ie/ie_core.hpp b/src/inference/include/ie/ie_core.hpp
index 3bcd321e9cc..d41e85b8e2d 100644
--- a/src/inference/include/ie/ie_core.hpp
+++ b/src/inference/include/ie/ie_core.hpp
@@ -24,6 +24,7 @@
 namespace InferenceEngine {
 
 /**
+ * @ingroup ie_cpp
  * @brief This class represents Inference Engine Core entity.
  *
  * It can throw exceptions safely for the application, where it is properly handled.
diff --git a/src/inference/include/ie/ie_version.hpp b/src/inference/include/ie/ie_version.hpp
index 55806697c1e..7ca91972240 100644
--- a/src/inference/include/ie/ie_version.hpp
+++ b/src/inference/include/ie/ie_version.hpp
@@ -27,6 +27,7 @@
 #include "ie_api.h"
 
 /**
+ * @ingroup ie_cpp_api
  * @brief Inference Engine C++ API
  */
 namespace InferenceEngine {
diff --git a/tools/benchmark_tool/README.md b/tools/benchmark_tool/README.md
index 681a1d0e66a..e317c25ba0a 100644
--- a/tools/benchmark_tool/README.md
+++ b/tools/benchmark_tool/README.md
@@ -3,7 +3,7 @@
 This topic demonstrates how to run the Benchmark Python* Tool, which performs inference using convolutional networks.
 Performance can be measured for two inference modes: latency- and throughput-oriented.
 
-> **NOTE:** This topic describes usage of Python implementation of the Benchmark Tool. For the C++ implementation, refer to [Benchmark C++ Tool](../../samples/cpp/benchmark_app/README.md).
+> **NOTE**: This topic describes usage of Python implementation of the Benchmark Tool. For the C++ implementation, refer to [Benchmark C++ Tool](../../samples/cpp/benchmark_app/README.md).
 
 > **TIP**: You can quick start with the Benchmark Tool inside the OpenVINO™ [Deep Learning Workbench](@ref openvino_docs_get_started_get_started_dl_workbench) (DL Workbench).
 > [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI you to 
@@ -19,7 +19,7 @@ usually, the resulting values are not very performance-portable,
 so the values from one machine or device are not necessarily optimal for another.
 From this perspective, the most portable way is experimenting only the performance hints. To learn more, refer to the section below.
 
-> **NOTE**: By default, Inference Engine samples, tools and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
+> **NOTE**: By default, Inference Engine samples, tools and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model to Intermediate Representation (IR)](../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
 
 ### Latency and Throughput-focused Inference Modes
 In many cases the primary performance metric is the time (in milliseconds) for an individual inference request.
@@ -173,21 +173,18 @@ To run the tool, you can use [public](@ref omz_models_group_public) or [Intel's]
 
 This section provides step-by-step instructions on how to run the Benchmark Tool with the `googlenet-v1` public model on CPU or GPU devices. As an input, the `car.png` file from the `<INSTALL_DIR>/samples/scripts/` directory is used.
 
-> **NOTE:** The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
+> **NOTE**: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
 
-1. Download the model. Go to the the Model Downloader directory and run the `downloader.py` script with specifying the model name and directory to download the model to:
+1. Download the model. Go to the the Model Downloader directory and run the `downloader.py` script with the model name and directory to download the model to:
    ```sh
    cd <INSTALL_DIR>/extras/open_model_zoo/tools/downloader
    ```
    ```sh
    python3 downloader.py --name googlenet-v1 -o <models_dir>
    ```
-2. Convert the model to the Inference Engine IR format. Go to the Model Optimizer directory and run the `mo.py` script with specifying the path to the model, model format (which must be FP32 for CPU and FPG) and output directory to generate the IR files:
+2. Convert the model to the Inference Engine IR format. Run Model Optimizer with the path to the model, model format (which must be FP32 for CPU and FPG) and output directory to generate the IR files:
    ```sh
-   cd <INSTALL_DIR>/tools/model_optimizer
-   ```
-   ```sh
-   python3 mo.py --input_model <models_dir>/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP32 --output_dir <ir_dir>
+   mo --input_model <models_dir>/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP32 --output_dir <ir_dir>
    ```
 3. Run the tool with specifying the `<INSTALL_DIR>/samples/scripts/car.png` file as an input image, the IR of the `googlenet-v1` model and a device to perform inference on. The following commands demonstrate running the Benchmark Tool in the asynchronous mode on CPU and GPU devices:
 

From 3b74aa0d30d016c6e1e9ce2ebf580c779d4f00d5 Mon Sep 17 00:00:00 2001
From: Mikhail Ryzhov <mikhail.ryzhov@intel.com>
Date: Tue, 21 Dec 2021 20:44:32 +0300
Subject: [PATCH 079/160] Store function inputs/outputs tensor names (#8961)

* Refactoring of input/output structures

* Added tensor names

* Updated outputs/inputs before export
---
 .../intel_gna/descriptions/gna_desc.hpp       | 151 ++++++++
 .../intel_gna/descriptions/gna_input_desc.cpp |  59 ---
 .../intel_gna/descriptions/gna_input_desc.hpp |  32 --
 .../descriptions/gna_output_desc.hpp          |  20 -
 .../intel_gna/frontend/model_quantizer.hpp    |  48 ++-
 .../intel_gna/gna_executable_network.hpp      |  21 +-
 src/plugins/intel_gna/gna_graph_compiler.cpp  |  43 ++-
 src/plugins/intel_gna/gna_graph_compiler.hpp  |   6 +-
 src/plugins/intel_gna/gna_model_serial.cpp    | 350 ++++++++----------
 src/plugins/intel_gna/gna_model_serial.hpp    | 118 ++----
 src/plugins/intel_gna/gna_plugin.cpp          | 332 ++++++++++-------
 src/plugins/intel_gna/gna_plugin.hpp          |  41 +-
 .../serial/headers/2dot7/gna_model_header.hpp |   2 +-
 .../serial/headers/2dot8/gna_model_header.hpp | 212 +++++++++++
 .../headers/latest/gna_model_header.hpp       |   6 +-
 .../ov_executable_network/exec_graph_info.cpp |  28 ++
 .../skip_tests_config.cpp                     |   4 +-
 src/tests/unit/gna/gna_wait_test.cpp          |  10 +-
 .../unit/engines/gna/gna_matcher.cpp          |   4 +-
 19 files changed, 877 insertions(+), 610 deletions(-)
 create mode 100644 src/plugins/intel_gna/descriptions/gna_desc.hpp
 delete mode 100644 src/plugins/intel_gna/descriptions/gna_input_desc.cpp
 delete mode 100644 src/plugins/intel_gna/descriptions/gna_input_desc.hpp
 delete mode 100644 src/plugins/intel_gna/descriptions/gna_output_desc.hpp
 create mode 100644 src/plugins/intel_gna/serial/headers/2dot8/gna_model_header.hpp
 create mode 100644 src/tests/functional/plugin/gna/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp

diff --git a/src/plugins/intel_gna/descriptions/gna_desc.hpp b/src/plugins/intel_gna/descriptions/gna_desc.hpp
new file mode 100644
index 00000000000..e7f103dd8c9
--- /dev/null
+++ b/src/plugins/intel_gna/descriptions/gna_desc.hpp
@@ -0,0 +1,151 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <vector>
+#include <numeric>
+#include <functional>
+#include <unordered_set>
+
+#include "ie_precision.hpp"
+#include "ie_input_info.hpp"
+#include "ie_algorithm.hpp"
+
+#include "backend/dnn_types.h"
+#include "gna_plugin_config.hpp"
+
+namespace GNAPluginNS {
+
+/*
+ * This base structure accumulates all required information for network inputs and outputs
+ */
+struct GnaDesc {
+    // common OV properties
+    std::string name = "";
+    std::unordered_set<std::string> tensor_names = {};
+    InferenceEngine::Layout model_layout = InferenceEngine::Layout::ANY;
+    InferenceEngine::SizeVector dims = {};
+    InferenceEngine::Precision model_precision  = InferenceEngine::Precision::UNSPECIFIED;
+    InferenceEngine::Precision tensor_precision = InferenceEngine::Precision::UNSPECIFIED;
+
+    // gna specific properties
+    double scale_factor = GNAPluginNS::kScaleFactorDefault;
+    intel_dnn_orientation_t orientation = kDnnUnknownOrientation;
+    uint32_t num_bytes_per_element = 0;
+    uint32_t num_elements = 0;
+    uint32_t allocated_size = 0;
+    std::vector<void *> ptrs = {};  // ptr per each infer request
+
+    // help methods
+    uint32_t get_required_size() {
+        return num_elements * num_bytes_per_element;
+    }
+
+    uint32_t get_allocated_size() {
+        return allocated_size;
+    }
+
+    void set_precision(InferenceEngine::Precision precision) {
+        this->tensor_precision = precision;
+        this->num_bytes_per_element = precision.size();
+    }
+
+    InferenceEngine::DataPtr to_ie_data() {
+        return std::make_shared<InferenceEngine::Data>(name, InferenceEngine::TensorDesc(model_precision, dims, model_layout));
+    }
+};
+
+/*
+ * This structure accumulates all required information for one the network input
+ */
+struct InputDesc : GnaDesc {
+    InputDesc(const std::string &name) { this->name = name; }
+
+    void Update(const InferenceEngine::InputInfo::Ptr inputInfo) {
+        this->model_precision = inputInfo->getPrecision();
+        this->tensor_precision = inputInfo->getPrecision();
+        this->model_layout = inputInfo->getLayout();
+        this->dims = inputInfo->getTensorDesc().getDims();
+        this->name = inputInfo->name();
+        this->num_bytes_per_element = tensor_precision.size();
+        this->num_elements = InferenceEngine::details::product(dims.begin(), dims.end());
+    }
+
+    InferenceEngine::InputInfo::Ptr ToIEInputInfo() {
+        InferenceEngine::InputInfo::Ptr input_info = std::make_shared<InferenceEngine::InputInfo>();
+        input_info->setInputData(this->to_ie_data());
+        return input_info;
+    }
+};
+
+/*
+ * This structure accumulates all required information for one network output
+ */
+struct OutputDesc : GnaDesc {
+    OutputDesc(const std::string &name) { this->name = name; }
+
+    void Update(const InferenceEngine::DataPtr outputData) {
+        this->model_precision = outputData->getPrecision();
+        this->tensor_precision = outputData->getPrecision();
+        this->model_layout = outputData->getLayout();
+        this->dims = outputData->getTensorDesc().getDims();
+        this->name = outputData->getName();
+        this->num_bytes_per_element = tensor_precision.size();
+        this->num_elements = InferenceEngine::details::product(dims.begin(), dims.end());
+    }
+};
+
+/**
+ * Wraps vectors of input/output structure to keep their order and simplify usage.
+ * @tparam T - InputDesc/OutputDesc
+ */
+template <class T>
+class GnaNetworkInfo {
+private:
+    std::vector<T> infos_;
+
+public:
+    GnaNetworkInfo(): infos_({}) { }
+
+    const T& at(const std::string &key) const {
+        if (key.empty()) {
+            throw std::invalid_argument("The key cannot be empty");
+        }
+        auto desc_it = std::find_if(infos_.begin(), infos_.end(), [&key](const T& desc){return desc.name == key;});
+        if (desc_it == infos_.end()) {
+            throw std::out_of_range("The key cannot be found");
+        }
+        return *desc_it;
+    }
+
+    T& at(const std::string &key) {
+      return const_cast<T&>( static_cast<const GnaNetworkInfo&>(*this).at(key) );
+    }
+
+    T& operator[](const std::string &key) {
+        if (key.empty()) {
+            throw std::invalid_argument("The key cannot be empty");
+        }
+        auto desc_it = std::find_if(infos_.begin(), infos_.end(), [&key](const T& desc){return desc.name == key;});
+        if (desc_it == infos_.end()) {
+            infos_.push_back(T(key));
+            return infos_.back();
+        }
+        return *desc_it;
+    }
+
+    size_t size() const { return infos_.size(); }
+
+    bool empty() const { return infos_.empty(); }
+
+    const std::vector<T>& Get() const { return infos_; }
+
+    std::vector<T>& Get() { return infos_; }
+};
+
+typedef GnaNetworkInfo<InputDesc> GnaInputs;
+typedef GnaNetworkInfo<OutputDesc> GnaOutputs;
+
+}  // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/descriptions/gna_input_desc.cpp b/src/plugins/intel_gna/descriptions/gna_input_desc.cpp
deleted file mode 100644
index 8095d9cf4dd..00000000000
--- a/src/plugins/intel_gna/descriptions/gna_input_desc.cpp
+++ /dev/null
@@ -1,59 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <vector>
-#include <string>
-#include <frontend/quantized_layer_params.hpp>
-#include <legacy/layer_transform.hpp>
-#include <layers/gna_layer_info.hpp>
-
-#include "gna_input_desc.hpp"
-#include "gna_plugin_log.hpp"
-
-using namespace InferenceEngine;
-using namespace GNAPluginNS;
-
-size_t InputDesc::minBytesRequiredForStoreInput(CNNLayerPtr layer) {
-    auto quantized = getInjectedData<QuantizedLayerParams>(layer);
-    size_t precision_bytes;
-    if (quantized) {
-        if (quantized->lowPrecision) {
-            precision_bytes = 1;
-        } else {
-            precision_bytes = 2;
-        }
-    } else {
-        precision_bytes = 4;
-    }
-    if (!LayerInfo(layer).isInput()) {
-        THROW_GNA_LAYER_EXCEPTION(layer) << "minBytesRequiredForStoreInput expect to worn on \"Input\" layer";
-    }
-    if (layer->outData.size() != 1) {
-        THROW_GNA_LAYER_EXCEPTION(layer) << "minBytesRequiredForStoreInput invalid outData for the layer";
-    }
-    auto dims = layer->outData.front()->getTensorDesc().getDims();
-    return details::product(dims.begin(), dims.end()) * precision_bytes;
-}
-
-std::vector<void *>& InputDesc::getPtrInputsGlobal(const std::string& name) {
-    if (ptr_inputs_global_id.find(name) == ptr_inputs_global_id.end()) {
-        ptr_inputs_global_storage.push_front({});
-        ptr_inputs_global_id[name] = ptr_inputs_global_storage.begin();
-    }
-    return *ptr_inputs_global_id[name];
-}
-
-intel_dnn_orientation_t InputDesc::getOrientation(const std::string& name) {
-    if (orientation_in.find(name) == orientation_in.end()) {
-        THROW_GNA_EXCEPTION << "Can't find orientation for input name '" << name << "'";
-    }
-    return orientation_in[name];
-}
-
-float InputDesc::getScaleFactor(const std::size_t index) {
-    if (index >= inputScaleFactors.size()) {
-        THROW_GNA_EXCEPTION << "Can't find scale factor for index = " << index;
-    }
-    return inputScaleFactors[index];
-}
diff --git a/src/plugins/intel_gna/descriptions/gna_input_desc.hpp b/src/plugins/intel_gna/descriptions/gna_input_desc.hpp
deleted file mode 100644
index 41f08f0bdde..00000000000
--- a/src/plugins/intel_gna/descriptions/gna_input_desc.hpp
+++ /dev/null
@@ -1,32 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <vector>
-#include <map>
-#include <list>
-#include <unordered_map>
-#include <string>
-#include <legacy/ie_layers.h>
-
-#include "backend/dnn_types.h"
-
-namespace GNAPluginNS {
-struct InputDesc {
-    std::unordered_map<std::string, intel_dnn_orientation_t> orientation_in;
-    /// order of scale factors matches inputs order in original topology
-    std::vector<float> inputScaleFactors;
-    std::vector<uint8_t> inputPrecisions;
-    std::map<std::string, int> bytes_allocated_for_input;
-    size_t minBytesRequiredForStoreInput(InferenceEngine::CNNLayerPtr);
-
-    std::unordered_map<std::string, std::list<std::vector<void *>>::iterator> ptr_inputs_global_id;
-    std::list<std::vector<void *>> ptr_inputs_global_storage;
-
-    std::vector<void *>& getPtrInputsGlobal(const std::string& name);
-    intel_dnn_orientation_t getOrientation(const std::string& name);
-    float getScaleFactor(std::size_t index);
-};
-}  // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/descriptions/gna_output_desc.hpp b/src/plugins/intel_gna/descriptions/gna_output_desc.hpp
deleted file mode 100644
index f9902004eaa..00000000000
--- a/src/plugins/intel_gna/descriptions/gna_output_desc.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <vector>
-
-#include "backend/dnn_types.h"
-
-namespace GNAPluginNS {
-struct OutputDesc {
-    uint8_t precision = InferenceEngine::Precision::UNSPECIFIED;
-    double scale_factor = 1.0;
-    uint32_t num_bytes_per_element = 0;
-    uint32_t num_elements = 0;
-    std::vector<void *> ptrs;  // ptr per each infer request
-    intel_dnn_orientation_t orientation = kDnnUnknownOrientation;
-};
-}  // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/frontend/model_quantizer.hpp b/src/plugins/intel_gna/frontend/model_quantizer.hpp
index f4337f03f7e..fad86ff72e3 100644
--- a/src/plugins/intel_gna/frontend/model_quantizer.hpp
+++ b/src/plugins/intel_gna/frontend/model_quantizer.hpp
@@ -9,13 +9,15 @@
 #include <string>
 #include <type_traits>
 
-#include <legacy/layer_transform.hpp>
-#include "gna_graph_tools.hpp"
 #include <legacy/details/ie_cnn_network_tools.h>
+#include <legacy/layer_transform.hpp>
+
+#include "gna_graph_tools.hpp"
 #include "layer_quantizer.hpp"
 #include "scale_factor_calc.hpp"
 #include "weights_converter.hpp"
 #include "gna_itt.hpp"
+#include "descriptions/gna_desc.hpp"
 
 namespace GNAPluginNS {
 
@@ -27,7 +29,7 @@ template<class T>
 class ModelQuantizer {
  public:
     InferenceEngine::CNNNetwork quantize(const InferenceEngine::CNNNetwork &model, float scaleFactor) const {
-        return quantize(model, [](const InferenceEngine::CNNNetwork &, bool runBeforeCopy, bool lowPrecision){}, std::vector<float>({scaleFactor}));
+        return quantize(model, [](const InferenceEngine::CNNNetwork &, bool runBeforeCopy, bool lowPrecision){}, scaleFactor);
     }
 
     template <class PreQuantisationCb>
@@ -35,12 +37,30 @@ class ModelQuantizer {
         return quantize(model, cb, std::vector<float>({scaleFactor}));
     }
 
-    InferenceEngine::CNNNetwork quantize(const InferenceEngine::CNNNetwork &model, std::vector<float> scaleFactor) const {
-        return quantize(model, [](InferenceEngine::CNNNetwork &, bool runBeforeCopy, bool lowPrecision){}, scaleFactor);
+    InferenceEngine::CNNNetwork quantize(const InferenceEngine::CNNNetwork &model, std::vector<float> scaleFactors) const {
+        return quantize(model, [](InferenceEngine::CNNNetwork &, bool runBeforeCopy, bool lowPrecision){}, scaleFactors);
     }
 
     template <class PreQuantisationCb>
-    InferenceEngine::CNNNetwork quantize(const InferenceEngine::CNNNetwork &model, const PreQuantisationCb &cb, std::vector<float> scaleFactor) const {
+    InferenceEngine::CNNNetwork quantize(const InferenceEngine::CNNNetwork &model,  const PreQuantisationCb &cb, std::vector<float> scaleFactors) const {
+        GNAPluginNS::GnaInputs inputs;
+        InferenceEngine::InputsDataMap inputs_map = model.getInputsInfo();
+        int sf_id = 0;
+        for (auto &&input_data : inputs_map) {
+            auto input_layer = getCreatorLayer(input_data.second->getInputData()).lock();
+            if (scaleFactors.size() <= sf_id) {
+                THROW_GNA_EXCEPTION << "Scale factors are not set for some of the inputs";
+            }
+            inputs[input_layer->name].scale_factor = scaleFactors[sf_id++];
+        }
+
+        return quantize(model, cb, inputs);
+    }
+
+    template <class PreQuantisationCb>
+    InferenceEngine::CNNNetwork quantize(const InferenceEngine::CNNNetwork &model,
+                                         const PreQuantisationCb &cb,
+                                         const GNAPluginNS::GnaInputs &inputs) const {
         OV_ITT_SCOPED_TASK(itt::domains::GNA_LT, "ModelQuantizer::quantize");
         auto visitor = [&](InferenceEngine::CNNLayerPtr lp) {
             auto newLayer = InferenceEngine::injectData<QuantizedLayerParams>(lp);
@@ -57,11 +77,14 @@ class ModelQuantizer {
         // another preprocessing
         cb(copiedNet, false, lowPrecision);
 
-        if (scaleFactor.empty()) {
-            THROW_GNA_EXCEPTION << "Scale factor is empty";
+        float lc_sf = GNAPluginNS::kScaleFactorDefault;
+        if (inputs.empty()) {
+            gnawarn() << "Inputs structure is empty, will be used default scale factor: " << lc_sf << std::endl;
+        } else {
+            lc_sf = inputs.Get().begin()->scale_factor;
         }
 
-        LayersQuantizer<T> lc(*scaleFactor.begin());
+        LayersQuantizer<T> lc(lc_sf);
         auto sortedNewNet = InferenceEngine::details::CNNNetSortTopologically(copiedNet);
         gnalog() << "Sorted layers: " << std::endl;
         for (auto &&layer : sortedNewNet) {
@@ -71,16 +94,11 @@ class ModelQuantizer {
         }
         /// filling scale factors for input layers, memory layers will have scaleFactor of 1.0 by default
         InferenceEngine::InputsDataMap dm = copiedNet.getInputsInfo();
-        int scaleIndex = 0;
         for (auto &&inputData : dm) {
             auto inputLayer = getCreatorLayer(inputData.second->getInputData()).lock();
             auto quantData = InferenceEngine::getInjectedData<QuantizedLayerParams>(inputLayer);
-            if (scaleFactor.size() <= scaleIndex) {
-                THROW_GNA_EXCEPTION << "Scale factors are not set for some of the inputs";
-            }
             IE_ASSERT(quantData != nullptr);
-            quantData->_src_quant.SetScale(scaleFactor[scaleIndex]);
-            scaleIndex++;
+            quantData->_src_quant.SetScale(inputs.at(inputLayer->name).scale_factor);
         }
 
         propagateScaleFactor(sortedNewNet);
diff --git a/src/plugins/intel_gna/gna_executable_network.hpp b/src/plugins/intel_gna/gna_executable_network.hpp
index 923fcc92f5a..94741d64b9b 100644
--- a/src/plugins/intel_gna/gna_executable_network.hpp
+++ b/src/plugins/intel_gna/gna_executable_network.hpp
@@ -27,17 +27,24 @@ class GNAExecutableNetwork : public InferenceEngine::IExecutableNetworkInternal
          if (inputStream.fail()) {
              THROW_GNA_EXCEPTION << "Cannot open file to import model: " << aotFileName;
          }
-
          plg->ImportNetwork(inputStream);
-         _networkInputs = plg->GetInputs();
-         _networkOutputs = plg->GetOutputs();
+         // old API
+         setNetworkInputs(plg->GetNetworkInputs());
+         setNetworkOutputs(plg->GetNetworkOutputs());
+         // new API
+         setInputs(plg->GetInputs());
+         setOutputs(plg->GetOutputs());
      }
 
     GNAExecutableNetwork(std::istream& networkModel, std::shared_ptr<GNAPlugin> plg)
         : plg(plg) {
         plg->ImportNetwork(networkModel);
-        _networkInputs = plg->GetInputs();
-        _networkOutputs = plg->GetOutputs();
+        // old API
+        setNetworkInputs(plg->GetNetworkInputs());
+        setNetworkOutputs(plg->GetNetworkOutputs());
+        // new API
+        setInputs(plg->GetInputs());
+        setOutputs(plg->GetOutputs());
     }
 
     GNAExecutableNetwork(InferenceEngine::CNNNetwork &network, std::shared_ptr<GNAPlugin> plg)
@@ -68,10 +75,14 @@ class GNAExecutableNetwork : public InferenceEngine::IExecutableNetworkInternal
     }
 
     void Export(const std::string &modelFileName) override {
+        plg->UpdateInputs(getInputs());
+        plg->UpdateOutputs(getOutputs());
         plg->Export(modelFileName);
     }
 
     void Export(std::ostream& modelStream) override {
+        plg->UpdateInputs(getInputs());
+        plg->UpdateOutputs(getOutputs());
         plg->Export(modelStream);
     }
 
diff --git a/src/plugins/intel_gna/gna_graph_compiler.cpp b/src/plugins/intel_gna/gna_graph_compiler.cpp
index 29223f2d581..54596c63b08 100644
--- a/src/plugins/intel_gna/gna_graph_compiler.cpp
+++ b/src/plugins/intel_gna/gna_graph_compiler.cpp
@@ -36,6 +36,7 @@
 #include "round_float_define.hpp"
 #include "gna_groups.hpp"
 #include "backend/gna_limitations.hpp"
+#include "descriptions/gna_desc.hpp"
 
 using namespace InferenceEngine;
 using namespace std;
@@ -53,8 +54,8 @@ void GNAGraphCompiler::setDNNPtr(std::shared_ptr<GNAPluginNS::backend::AMIntelDN
     this->dnn = std::move(dnnPtr);
 }
 
-void GNAGraphCompiler::setInputDescPtr(std::shared_ptr<GNAPluginNS::InputDesc> inputDescPtr) {
-    this->inputDesc = std::move(inputDescPtr);
+void GNAGraphCompiler::setInputsPtr(std::shared_ptr<GNAPluginNS::GnaInputs> inputsPtr) {
+    this->inputs_ptr_ = std::move(inputsPtr);
 }
 
 void GNAGraphCompiler::setGNAFlagsPtr(std::shared_ptr<GNAPluginNS::GNAFlags> gnaFlagsPtr) {
@@ -2286,8 +2287,10 @@ void GNAGraphCompiler::connectOutput(InferenceEngine::CNNLayerPtr layer,
                     if (included == concat_connection.end()) {
                         gnamem->reserve_ptr(layer, &concatLayerInfoItem.gna_ptr, ALIGN64(concatLayerInfoItem.reserved_size), 64);
 
-                        std::function<void(GNAConcatLayer, GNAPluginNS::InputDesc&, ConcatConnection&)> allocate_input_recursively =
-                            [&allocate_input_recursively](GNAConcatLayer clayer, GNAPluginNS::InputDesc& inputDesc, ConcatConnection& concat_connection) {
+                        std::function<void(GNAConcatLayer, GNAPluginNS::GnaInputs&, ConcatConnection&)> allocate_input_recursively =
+                            [&allocate_input_recursively](GNAConcatLayer clayer,
+                                                          GNAPluginNS::GnaInputs &inputs,
+                                                          ConcatConnection& concat_connection) {
                             size_t concatInputIdx = 0;
                             for (auto &&inputLayer : clayer.concatInputLayers) {
                                 // skipping non functional and reshape layer, as in that case input might be not connected to anything
@@ -2295,20 +2298,20 @@ void GNAGraphCompiler::connectOutput(InferenceEngine::CNNLayerPtr layer,
                                     return !LayerInfo(l).isNonFunctional() && !LayerInfo(l).isSplit();
                                     }, concatInputIdx++);
 
-                                for (auto rInput :  realConcatInputs) {
+                                for (auto rInput : realConcatInputs) {
                                     if (LayerInfo(rInput.first).isInput()) {
-                                        inputDesc.bytes_allocated_for_input[rInput.first->name] += inputLayer.tensorSize + inputLayer.offset;
+                                        inputs[rInput.first->name].allocated_size += inputLayer.tensorSize + inputLayer.offset;
                                     }
                                     if (LayerInfo(rInput.first).isConcat()) {
                                         auto concatLayerInfo = concat_connection.find(rInput.first->name);
-                                        allocate_input_recursively(concatLayerInfo->second, inputDesc, concat_connection);
+                                        allocate_input_recursively(concatLayerInfo->second, inputs, concat_connection);
                                     }
                                 }
                             }
                             clayer.input_allocated = true;
                         };
 
-                        allocate_input_recursively(concatLayerInfoItem, *inputDesc, concat_connection);
+                        allocate_input_recursively(concatLayerInfoItem, *inputs_ptr_, concat_connection);
                     }
                     concatLayerInfo->second.output_allocation_flag = true;
                 }
@@ -2345,9 +2348,17 @@ GNAPluginNS::ConnectionDetails GNAGraphCompiler::connectInput(CNNLayerPtr layer,
 
     // real input not a memory input
     if (LayerInfo(prevLayer).isInput()) {
-        if (0 == inputDesc->bytes_allocated_for_input[prevLayer->name]) {
+        auto quantized = getInjectedData<QuantizedLayerParams>(prevLayer);
+        if (quantized) {
+            if (quantized->lowPrecision) {
+                inputs_ptr_->at(prevLayer->name).set_precision(Precision::I8);
+            } else {
+              inputs_ptr_->at(prevLayer->name).set_precision(Precision::I16);
+            }
+        }
+        if (0 == inputs_ptr_->at(prevLayer->name).get_allocated_size()) {
             // if request for allocation less that realTensorInput - we need to extend request
-            auto minInput = inputDesc->minBytesRequiredForStoreInput(prevLayer);
+            auto minInput = inputs_ptr_->at(prevLayer->name).get_required_size();
             if (num_data_bytes_in < minInput) {
                 const uint32_t noOfInputsDivisor = gnaFlags->input_low_precision ?
                     GNALimitations::noOfInputsLowPrecDivisor : GNALimitations::noOfInputsDivisor;
@@ -2362,25 +2373,25 @@ GNAPluginNS::ConnectionDetails GNAGraphCompiler::connectInput(CNNLayerPtr layer,
                                    num_data_bytes_in,
                                    64);
             } else {
-                gnamem->push_value(nullptr, &inputDesc->getPtrInputsGlobal(prevLayer->name).front(),
+                gnamem->push_value(nullptr, &inputs_ptr_->at(prevLayer->name).ptrs.front(),
                                    static_cast<uint8_t>(0),
                                    num_data_bytes_in,
                                    64);
             }
-            inputDesc->bytes_allocated_for_input[prevLayer->name] = num_data_bytes_in;
+            inputs_ptr_->at(prevLayer->name).allocated_size = num_data_bytes_in;
         }
-        if (ALIGN(num_data_bytes_in, 64) > ALIGN(inputDesc->bytes_allocated_for_input[prevLayer->name], 64)) {
+        if (ALIGN(num_data_bytes_in, 64) > ALIGN(inputs_ptr_->at(prevLayer->name).get_allocated_size(), 64)) {
             THROW_GNA_EXCEPTION
                     << "Layer: " << layer->name
                     << " Cannot bind pointer to already allocated input(" << prevLayer->name
-                    << "), due to size_allocated=" << inputDesc->bytes_allocated_for_input[prevLayer->name]
+                    << "), due to size_allocated=" << inputs_ptr_->at(prevLayer->name).get_allocated_size()
                     << ", and size_requested=" << num_data_bytes_in;
         }
 
         if (connectTo) {
-            gnamem->bind_ptr(nullptr, ptr, &inputDesc->getPtrInputsGlobal(prevLayer->name).front(), offset, ALIGN(num_data_bytes_in, 64));
+            gnamem->bind_ptr(nullptr, ptr, &inputs_ptr_->at(prevLayer->name).ptrs.front(), offset, ALIGN(num_data_bytes_in, 64));
         } else {
-            gnamem->bind_ptr(nullptr, &inputDesc->getPtrInputsGlobal(prevLayer->name).front(), ptr, offset, ALIGN(num_data_bytes_in, 64));
+            gnamem->bind_ptr(nullptr, &inputs_ptr_->at(prevLayer->name).ptrs.front(), ptr, offset, ALIGN(num_data_bytes_in, 64));
         }
 
         return prevLayer;
diff --git a/src/plugins/intel_gna/gna_graph_compiler.hpp b/src/plugins/intel_gna/gna_graph_compiler.hpp
index 5aebc3aa158..131f16da674 100644
--- a/src/plugins/intel_gna/gna_graph_compiler.hpp
+++ b/src/plugins/intel_gna/gna_graph_compiler.hpp
@@ -12,7 +12,7 @@
 #include <vector>
 
 #include <legacy/ie_layers.h>
-#include "descriptions/gna_input_desc.hpp"
+#include "descriptions/gna_desc.hpp"
 #include "descriptions/gna_flags.hpp"
 #include "connection_details.hpp"
 #include "backend/dnn.hpp"
@@ -33,8 +33,8 @@ class GNAGraphCompiler {
 private:
     std::shared_ptr<GNAPluginNS::backend::AMIntelDNN> dnn;
     std::shared_ptr<GNAPluginNS::gna_memory_type> gnamem;
-    std::shared_ptr<GNAPluginNS::InputDesc> inputDesc;
     std::shared_ptr<GNAPluginNS::GNAFlags> gnaFlags;
+    std::shared_ptr<GNAPluginNS::GnaInputs> inputs_ptr_;
 
     // layers with extra storage for connections and additional
     // non trivial processing
@@ -60,8 +60,8 @@ public:
 
     void setGNAMemoryPtr(std::shared_ptr<GNAPluginNS::gna_memory_type> gnaMemPtr);
     void setDNNPtr(std::shared_ptr<GNAPluginNS::backend::AMIntelDNN> dnnPtr);
-    void setInputDescPtr(std::shared_ptr<GNAPluginNS::InputDesc> inputDescPtr);
     void setGNAFlagsPtr(std::shared_ptr<GNAPluginNS::GNAFlags> gnaFlagsPtr);
+    void setInputsPtr(std::shared_ptr<GNAPluginNS::GnaInputs> inputsPtr);
 
     void fillMemoryConnections(std::unordered_map<std::string,
             std::vector<InferenceEngine::CNNLayerPtr>> &memoryPairs);
diff --git a/src/plugins/intel_gna/gna_model_serial.cpp b/src/plugins/intel_gna/gna_model_serial.cpp
index bc31eba3359..8c23810364b 100644
--- a/src/plugins/intel_gna/gna_model_serial.cpp
+++ b/src/plugins/intel_gna/gna_model_serial.cpp
@@ -17,7 +17,8 @@
 #include <mm_malloc.h>
 #include <serial/headers/2dot2/gna_model_header.hpp>
 #include <serial/headers/2dot5/gna_model_header.hpp>
-#include <serial/headers/2dot6/gna_model_header.hpp>
+#include <serial/headers/2dot7/gna_model_header.hpp>
+#include <serial/headers/2dot8/gna_model_header.hpp>
 
 #endif
 
@@ -36,6 +37,13 @@ inline void writeBits(const T & obj, std::ostream & os) {
     os.write(reinterpret_cast<const char *>(&obj), sizeof(T));
 }
 
+inline void writeString(const std::string &str, std::ostream &os) {
+    const char *c_str = str.c_str();
+    const size_t str_len = strlen(c_str) + 1;
+    writeBits(static_cast<uint32_t>(str_len), os);
+    writeNBytes(c_str, str_len, os);
+}
+
 template <class T>
 inline void readBits(T & obj, std::istream & is) {
     is.read(reinterpret_cast<char *>(&obj), sizeof(T));
@@ -53,6 +61,14 @@ inline void readNBits(T & obj, std::istream & is) {
     obj = * reinterpret_cast<T*>(&tmp.front());
 }
 
+inline std::string readString(std::istream &is) {
+    uint32_t str_len = 0;
+    readNBits<32>(str_len, is);
+    std::string str(str_len, '\0');
+    readNBytes(&str[0], str_len, is);
+    return str.substr(0, str_len - 1);
+}
+
 inline void * offsetToPointer(void * const base, uint64_t offset) {
     return reinterpret_cast<uint8_t *>(base) + offset;
 }
@@ -135,10 +151,11 @@ GNAPluginNS::HeaderLatest::ModelHeader GNAModelSerial::ReadHeader(std::istream &
                 case 5:
                 case 6:
                 case 7:
+                case 8:
                     readNBytes(&header, sizeof(HeaderLatest::ModelHeader), is);
                     break;
                 default:
-                    THROW_GNA_EXCEPTION << "Imported file unsupported. minor version should have values in range 1 to 7 and is: " << header.version.minor;
+                    THROW_GNA_EXCEPTION << "Imported file unsupported. minor version should have values in range 1 to 8 and is: " << header.version.minor;
             }
             break;
         default:
@@ -160,9 +177,9 @@ GNAPluginNS::HeaderLatest::RuntimeEndPoint GNAModelSerial::ReadEndPoint(std::ist
     is.exceptions(std::istream::failbit);
 
     HeaderLatest::RuntimeEndPoint endPoint;
-    switch (modelHeader.version.major) {
+    switch (model_header_.version.major) {
         case 2:
-            switch (modelHeader.version.minor) {
+            switch (model_header_.version.minor) {
                 case 1:
                 case 2:
                 case 3:
@@ -172,19 +189,27 @@ GNAPluginNS::HeaderLatest::RuntimeEndPoint GNAModelSerial::ReadEndPoint(std::ist
                 {
                     Header2dot6::RuntimeEndPoint tempEndPoint2dot6;
                     readBits(tempEndPoint2dot6, is);
-                    endPoint = HeaderLatest::RuntimeEndPoint(tempEndPoint2dot6, modelHeader.nGroup);
+                    endPoint = HeaderLatest::RuntimeEndPoint(tempEndPoint2dot6, model_header_.nGroup);
                     break;
                 }
                 case 7:
+                {
+                    Header2dot7::RuntimeEndPoint tempEndPoint2dot7;
+                    readBits(tempEndPoint2dot7, is);
+                    endPoint = HeaderLatest::RuntimeEndPoint(tempEndPoint2dot7);
+                    break;
+                }
+                case 8:
                     readNBytes(&endPoint, sizeof(HeaderLatest::RuntimeEndPoint), is);
                     break;
                 default:
-                    THROW_GNA_EXCEPTION << "Imported file unsupported. minor version should have values in range 1 to 7 and is: " << modelHeader.version.minor;
+                    THROW_GNA_EXCEPTION << "Imported file unsupported. minor version should have values in range 1 to 8 and is: "
+                                        << model_header_.version.minor;
             }
             break;
         default:
             THROW_GNA_EXCEPTION << "Imported file unsupported. Import for files with major version equal to: "
-            << modelHeader.version.major << " is not implemented";
+            << model_header_.version.major << " is not implemented";
     }
 
     return endPoint;
@@ -218,33 +243,28 @@ static const std::map<Gna2OperationType, std::vector<uint32_t>> GnaParamSize{
 
 void GNAModelSerial::Import(void *basePointer,
         size_t gnaGraphSize,
-        std::istream & is,
-        std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
-        std::vector<GNAPluginNS::OutputDesc> &desc,
-        InferenceEngine::InputsDataMap& inputsDataMap,
-        InferenceEngine::OutputsDataMap& outputsDataMap,
-        TranspositionInfoMap& inputsTranspositionInfo,
-        TranspositionInfoMap& outputsTranspositionInfo) {
+        std::istream &is,
+        GNAPluginNS::GnaInputs &inputs,
+        GNAPluginNS::GnaOutputs &outputs,
+        TranspositionInfoMap &inputsTranspositionInfo,
+        TranspositionInfoMap &outputsTranspositionInfo) {
     is.exceptions(std::istream::failbit);
-
-    if (modelHeader.version.major == 2) {
-        if (modelHeader.version.minor >= 3) {
-            for (auto inputIndex = 0; inputIndex < modelHeader.nInputs; inputIndex++) {
-                uint32_t nameSize = 0;
-                readNBits<32>(nameSize, is);
-                std::string inName(nameSize, '\0');
-                readNBytes(&inName[0], nameSize, is);
-                inputNames.push_back(inName.substr(0, nameSize - 1));
-            }
+    // 2. Read inputs names
+    if (model_header_.version.major == 2) {
+        for (auto inputIndex = 0; inputIndex < model_header_.nInputs; inputIndex++) {
+            std::string name = (model_header_.version.minor >= 3) ? readString(is) : std::string("input" + std::to_string(inputIndex));
+            inputs[name] = GNAPluginNS::InputDesc(name);
         }
-        if (modelHeader.version.minor >= 5) {
-            for (int inputIx = 0; inputIx < modelHeader.nTransposeInputs; ++inputIx) {
+        if (model_header_.version.minor >= 5) {
+            // 3. Read transposition input info
+            for (int inputIx = 0; inputIx < model_header_.nTransposeInputs; ++inputIx) {
                 std::string inputName;
                 std::vector<TranspositionInfo> transpositionInfo;
                 ImportTranspositionInfo(is, inputName, transpositionInfo);
                 inputsTranspositionInfo[inputName] = transpositionInfo;
             }
-            for (int outputIx = 0; outputIx < modelHeader.nTransposeOutputs; ++outputIx) {
+            // 4. Read transposition output info
+            for (int outputIx = 0; outputIx < model_header_.nTransposeOutputs; ++outputIx) {
                 std::string outputName;
                 std::vector<TranspositionInfo> transpositionInfo;
                 ImportTranspositionInfo(is, outputName, transpositionInfo);
@@ -252,22 +272,19 @@ void GNAModelSerial::Import(void *basePointer,
             }
         }
     }
-    ImportInputs(is, basePointer, inputsDesc, inputsDataMap);
-
-    if (modelHeader.version.major == 2) {
-        if (modelHeader.version.minor >= 3) {
-            for (auto inputIndex = 0; inputIndex < modelHeader.nOutputs; inputIndex++) {
-                uint32_t nameSize = 0;
-                readNBits<32>(nameSize, is);
-                std::string outName(nameSize, '\0');
-                readNBytes(&outName[0], nameSize, is);
-                outputNames.push_back(outName.substr(0, nameSize - 1));
-            }
+    // 5. Read Inputs endpoints
+    ImportInputs(is, basePointer, inputs);
+    // 6. Read output names
+    if (model_header_.version.major == 2) {
+        for (auto outputIndex = 0; outputIndex < model_header_.nOutputs; outputIndex++) {
+            std::string name = (model_header_.version.minor >= 3) ? readString(is) : std::string("output" + std::to_string(outputIndex));
+            outputs[name] = GNAPluginNS::OutputDesc(name);
         }
     }
-    ImportOutputs(is, basePointer, desc, outputsDataMap);
+    // 7. Read outputs
+    ImportOutputs(is, basePointer, outputs);
 
-    for (auto operation = gna2Model->Operations; operation != gna2Model->Operations + gna2Model->NumberOfOperations; ++operation) {
+    for (auto operation = gna2model_->Operations; operation != gna2model_->Operations + gna2model_->NumberOfOperations; ++operation) {
         readNBits<32>(operation->Type, is);
         readBits(operation->NumberOfOperands, is);
         operation->Operands = static_cast<Gna2Tensor const **>(gnaUserAllocator(sizeof(Gna2Tensor*) * operation->NumberOfOperands));
@@ -324,19 +341,19 @@ void GNAModelSerial::Import(void *basePointer,
     // writing memory information
     uint32_t nStates = 0;
     readBits(nStates, is);
-    if (pstates != nullptr) {
-        pstates->resize(nStates);
+    if (pstates_ != nullptr) {
+        pstates_->resize(nStates);
     }
 
     for (int i = 0; i != nStates; i++) {
         void *pSegment;
-        if ( modelHeader.version.major == 2 ) {
-            if ( modelHeader.version.minor < 6 ) {
+        if ( model_header_.version.major == 2 ) {
+            if ( model_header_.version.minor < 6 ) {
                 readOffset(pSegment, basePointer, is);
                 uint32_t segmentSz = 0;
                 readBits(segmentSz, is);
-                if (pstates) {
-                    (*pstates)[i] = std::make_tuple( pSegment, segmentSz, "noname", 1.0f );
+                if (pstates_) {
+                    (*pstates_)[i] = std::make_tuple( pSegment, segmentSz, "noname", 1.0f );
                 }
             } else {
                 readOffset(pSegment, basePointer, is);
@@ -348,8 +365,8 @@ void GNAModelSerial::Import(void *basePointer,
                 readNBytes(&inName[0], nameSize, is);
                 float scale_factor = 1.0f;
                 readBits(scale_factor, is);
-                if (pstates) {
-                    (*pstates)[i] = std::make_tuple( pSegment, segmentSz, inName.substr(0, nameSize - 1), scale_factor);
+                if (pstates_) {
+                    (*pstates_)[i] = std::make_tuple( pSegment, segmentSz, inName.substr(0, nameSize - 1), scale_factor);
                 }
             }
         }
@@ -364,7 +381,7 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
     os.exceptions(std::ostream::failbit);
 
     const std::vector<Gna2Operation>
-        layers(gna2Model->Operations, gna2Model->Operations + gna2Model->NumberOfOperations);
+        layers(gna2model_->Operations, gna2model_->Operations + gna2model_->NumberOfOperations);
 
 
     // all offsets will be from this pointer
@@ -384,17 +401,22 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
         return out;
     };
 
-    auto convert_to_serial = [getOffsetFromBase](const HeaderLatest::RuntimeEndPoint& ep) {
-        HeaderLatest::RuntimeEndPoint out;
-        out.elements_count = ep.elements_count;
-        out.descriptor_offset = offsetFromBase(ep.descriptor_ptr);
-        out.scaleFactor = ep.scaleFactor;
-        out.element_size = ep.element_size;
-        out.shape = ep.shape;
-        out.layout = ep.layout;
-        out.precision = ep.precision;
-        out.orientation = ep.orientation;
-        return out;
+    auto convert_to_serial = [getOffsetFromBase](const GNAPluginNS::GnaDesc &desc) {
+        HeaderLatest::RuntimeEndPoint ep;
+        ep.elements_count = desc.num_elements;
+        ep.scaleFactor = desc.scale_factor;
+        ep.element_size = desc.num_bytes_per_element;
+        ep.layout = desc.model_layout;
+        ep.precision = desc.model_precision;
+        ep.orientation = desc.orientation;
+        ep.tensor_names_count = static_cast<uint8_t>(desc.tensor_names.size());
+        ep.descriptor_offset = offsetFromBase(*desc.ptrs.begin());
+        // shape
+        ep.shape.NumberOfDimensions = desc.dims.size();
+        for (size_t i=0; i < ep.shape.NumberOfDimensions; ++i) {
+            ep.shape.Dimensions[i] = desc.dims[i];
+        }
+        return ep;
     };
 
     /**
@@ -409,32 +431,45 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
     header.gnaMemSize = gnaGraphSize;
     header.layersCount = layers.size();
     header.nGroup = 1; // just to support the old models
-    header.nInputs = inputs.size();
-    header.nOutputs = outputs.size();
-    header.nTransposeInputs = transposeInputsInfo.size();
-    header.nTransposeOutputs = transposeOutputsInfo.size();
-
+    header.nInputs = inputs_.size();
+    header.nOutputs = outputs_.size();
+    header.nTransposeInputs = inputs_transpose_info_.size();
+    header.nTransposeOutputs = outputs_transpose_info_.size();
+    // 1. Write header
     writeBits(header, os);
-
-    for (auto &name : inputNames) {
-        const auto nameSize = strlen(name.c_str()) + 1;
-        writeBits(static_cast<uint32_t>(nameSize), os);
-        writeNBytes(name.c_str(), nameSize , os);
+    // 2. Write input names
+    for (const auto &input : inputs_.Get()) {
+        // Write the input name
+        writeString(input.name, os);
     }
-    ExportTranspositionInfo(os, transposeInputsInfo);
-    ExportTranspositionInfo(os, transposeOutputsInfo);
-    for (const auto &input : inputs) {
+    // 3. Write transposition input info
+    ExportTranspositionInfo(os, inputs_transpose_info_);
+    // 4. Write transposition output info
+    ExportTranspositionInfo(os, outputs_transpose_info_);
+    // 5. Write input endpoints and tensor names
+    for (const auto &input : inputs_.Get()) {
+        // write RuntimeEndPoint
         writeBits(convert_to_serial(input), os);
+        // write the input tensor names
+        for (const auto &tname : input.tensor_names) {
+            writeString(tname, os);
+        }
     }
-    for (auto &name : outputNames) {
-        const auto nameSize = strlen(name.c_str()) + 1;
-        writeBits(static_cast<uint32_t>(nameSize), os);
-        writeNBytes(name.c_str(), nameSize, os);
+    // 6. Write outputs names
+    for (auto &output : outputs_.Get()) {
+        // write the output name
+        writeString(output.name, os);
     }
-    for (const auto &output : outputs) {
+    // 7. Write outputs endpoints and tensor names
+    for (auto &output : outputs_.Get()) {
+        // write RuntimeEndPoint
         writeBits(convert_to_serial(output), os);
+        // write the output tensor names
+        for (auto &tname : output.tensor_names) {
+            writeString(tname, os);
+        }
     }
-
+    // 8. Write layers
     for (const auto & layer : layers) {
         writeBits(static_cast<uint32_t>(layer.Type), os);
         writeBits(layer.NumberOfOperands, os);
@@ -817,130 +852,57 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
 
 #endif
 
-std::vector<HeaderLatest::RuntimeEndPoint> GNAModelSerial::serializeOutputs(const InferenceEngine::OutputsDataMap& outputsDataMap,
-        const std::vector<GNAPluginNS::OutputDesc>& outputsDesc) {
-    std::vector<HeaderLatest::RuntimeEndPoint> endPoints;
-    std::size_t outputIndex = 0;
-    for (auto const &output : outputsDataMap) {
-        auto outputName = output.first;
-        auto outputDims = output.second->getTensorDesc().getDims();
-        HeaderLatest::RuntimeEndPoint::Shape outputShape;
-        outputShape.NumberOfDimensions = outputDims.size();
-        for (size_t i=0; i < outputShape.NumberOfDimensions; ++i) {
-            outputShape.Dimensions[i] = static_cast<uint32_t>(outputDims[i]);
-        }
-        uint32_t elementsCount = static_cast<uint32_t>(InferenceEngine::details::product(outputDims.begin(), outputDims.end()));
-        InferenceEngine::Layout outputLayout = output.second->getLayout();
-        HeaderLatest::RuntimeEndPoint endPoint(outputsDesc[outputIndex].scale_factor,
-                                                 outputsDesc[outputIndex].ptrs[0],
-                                                 outputsDesc[outputIndex].num_bytes_per_element,
-                                                 elementsCount,
-                                                 outputShape,
-                                                 outputLayout,
-                                                 outputsDesc[outputIndex].precision,
-                                                 outputsDesc[outputIndex].orientation);
-        endPoints.push_back(endPoint);
-        outputIndex++;
-    }
-    return endPoints;
-}
+void GNAModelSerial::ImportInputs(std::istream &is, void* basePtr, GNAPluginNS::GnaInputs &inputs) {
+    for (auto &input : inputs.Get()) {
+        HeaderLatest::RuntimeEndPoint ep = ReadEndPoint(is);
 
-std::vector<HeaderLatest::RuntimeEndPoint> GNAModelSerial::serializeInputs(const InferenceEngine::InputsDataMap& inputsDataMap,
-                                                                             std::shared_ptr<GNAPluginNS::InputDesc> inputDesc) {
-    std::vector<HeaderLatest::RuntimeEndPoint> endPoints;
-
-    std::size_t inputIndex = 0;
-    for (auto const& input : inputsDataMap) {
-        auto inputName = input.first;
-        auto inputDims = input.second->getTensorDesc().getDims();
-        HeaderLatest::RuntimeEndPoint::Shape inputShape;
-        inputShape.NumberOfDimensions = inputDims.size();
-        for (size_t i=0; i < inputShape.NumberOfDimensions; ++i) {
-            inputShape.Dimensions[i] = static_cast<uint32_t>(inputDims[i]);
-        }
-        double scaleFactor = inputDesc->getScaleFactor(inputIndex);
-        std::vector<void *> descriptor_ptr = inputDesc->getPtrInputsGlobal(inputName);
-        IE_ASSERT(descriptor_ptr.size() > 0);
-        uint32_t element_size = 2u;
-        uint32_t elementsCount = static_cast<uint32_t>(InferenceEngine::details::product(inputDims.begin(), inputDims.end()));
-        intel_dnn_orientation_t orientation = inputDesc->getOrientation(inputName);
-        InferenceEngine::Layout inputLayout = input.second->getLayout();
-        uint8_t inputPrecision = inputDesc->inputPrecisions.at(inputIndex);
-        HeaderLatest::RuntimeEndPoint endPoint(scaleFactor,
-                                                 descriptor_ptr[0],
-                                                 element_size,
-                                                 elementsCount,
-                                                 inputShape,
-                                                 inputLayout,
-                                                 inputPrecision,
-                                                 orientation);
-        endPoints.push_back(endPoint);
-        inputIndex++;
-    }
-    return endPoints;
-}
-
-void GNAModelSerial::ImportInputs(std::istream &is,
-        void* basePtr,
-        std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
-        InferenceEngine::InputsDataMap& dataMap) {
-    dataMap.clear();
-    for (uint32_t inputIndex = 0; inputIndex < modelHeader.nInputs; inputIndex++) {
-        const std::string& name = (modelHeader.version.major == 2 && modelHeader.version.minor >= 3)
-                ? inputNames.at(inputIndex) : std::string("input" + std::to_string(inputIndex));
-
-        HeaderLatest::RuntimeEndPoint input = ReadEndPoint(is);
-        inputsDesc->getPtrInputsGlobal(name).push_back(reinterpret_cast<float*>(reinterpret_cast<uint8_t *> (basePtr) + input.descriptor_offset));
-        inputsDesc->orientation_in[name] = input.orientation;
-        inputsDesc->bytes_allocated_for_input[name] = input.element_size * input.elements_count;
+        input.ptrs.push_back(reinterpret_cast<float*>(reinterpret_cast<uint8_t *> (basePtr) + ep.descriptor_offset));
+        input.orientation = ep.orientation;
+        input.num_elements = ep.elements_count;
+        input.num_bytes_per_element = ep.element_size;
+        input.scale_factor = ep.scaleFactor;
+        input.model_precision = InferenceEngine::Precision(static_cast<InferenceEngine::Precision::ePrecision>(ep.precision));
+        input.tensor_precision = InferenceEngine::Precision(static_cast<InferenceEngine::Precision::ePrecision>(ep.precision));
+        input.model_layout = static_cast<InferenceEngine::Layout>(ep.layout);
+        input.allocated_size = input.get_required_size();
 
         auto inputDims = InferenceEngine::SizeVector();
-        for (auto i = 0; i < input.shape.NumberOfDimensions; ++i) {
-            inputDims.push_back(input.shape.Dimensions[i]);
+        for (auto i = 0; i < ep.shape.NumberOfDimensions; ++i) {
+            inputDims.push_back(ep.shape.Dimensions[i]);
+        }
+        input.dims = inputDims;
+
+        // read tensor names
+        for (uint8_t tId = 0; tId < ep.tensor_names_count; ++tId) {
+            input.tensor_names.insert(readString(is));
         }
-        InferenceEngine::Layout inputLayout = static_cast<InferenceEngine::Layout>(input.layout);
-        InferenceEngine::Precision inputPresicion = InferenceEngine::Precision(static_cast<InferenceEngine::Precision::ePrecision>(input.precision));
-        dataMap[name] = std::make_shared<InferenceEngine::InputInfo>();
-        dataMap[name]->setInputData(std::make_shared<InferenceEngine::Data>(name,
-                                                            InferenceEngine::TensorDesc(
-                                                                    inputPresicion,
-                                                                    inputDims,
-                                                                    inputLayout)));
-        inputsDesc->inputScaleFactors.push_back(input.scaleFactor);
     }
 }
 
-void GNAModelSerial::ImportOutputs(std::istream &is,
-        void* basePtr,
-        std::vector<GNAPluginNS::OutputDesc> &desc,
-        InferenceEngine::OutputsDataMap& dataMap) {
-    desc.clear();
-    dataMap.clear();
-    desc.resize(modelHeader.nOutputs);
+void GNAModelSerial::ImportOutputs(std::istream &is, void* basePtr, GNAPluginNS::GnaOutputs &outputs) {
+    for (auto &output : outputs.Get()) {
+        HeaderLatest::RuntimeEndPoint ep = ReadEndPoint(is);
 
-    for (uint32_t outputIndex = 0; outputIndex < modelHeader.nOutputs; outputIndex++) {
-        const std::string& name = (modelHeader.version.major == 2 && modelHeader.version.minor >= 3)
-                                  ? outputNames.at(outputIndex) : std::string("output" + std::to_string(outputIndex));
-
-        HeaderLatest::RuntimeEndPoint output = ReadEndPoint(is);
-        OutputDesc description;
-        description.ptrs.push_back(reinterpret_cast<float*>(reinterpret_cast<uint8_t *> (basePtr) + output.descriptor_offset));
-        description.orientation = output.orientation;
-        description.num_bytes_per_element = output.element_size;
-        description.scale_factor = output.scaleFactor;
+        output.ptrs.push_back(reinterpret_cast<float*>(reinterpret_cast<uint8_t *> (basePtr) + ep.descriptor_offset));
+        output.orientation = ep.orientation;
+        output.num_elements = ep.elements_count;
+        output.num_bytes_per_element = ep.element_size;
+        output.scale_factor = ep.scaleFactor;
+        output.model_precision = InferenceEngine::Precision(static_cast<InferenceEngine::Precision::ePrecision>(ep.precision));
+        output.tensor_precision = InferenceEngine::Precision(static_cast<InferenceEngine::Precision::ePrecision>(ep.precision));
+        output.model_layout = static_cast<InferenceEngine::Layout>(ep.layout);
+        output.allocated_size = output.get_required_size();
 
         auto outputDims = InferenceEngine::SizeVector();
-        for (auto i = 0; i < output.shape.NumberOfDimensions; ++i) {
-            outputDims.push_back(output.shape.Dimensions[i]);
+        for (auto i = 0; i < ep.shape.NumberOfDimensions; ++i) {
+            outputDims.push_back(ep.shape.Dimensions[i]);
+        }
+        output.dims = outputDims;
+
+        // read tensor names
+        for (uint8_t tId = 0; tId < ep.tensor_names_count; ++tId) {
+            output.tensor_names.insert(readString(is));
         }
-        InferenceEngine::Layout outputLayout = static_cast<InferenceEngine::Layout>(output.layout);
-        InferenceEngine::Precision outputPresicion =  InferenceEngine::Precision(static_cast<InferenceEngine::Precision::ePrecision>(output.precision));
-        dataMap[name] = std::make_shared<InferenceEngine::Data>(name,
-                                                 InferenceEngine::TensorDesc(
-                                                         outputPresicion,
-                                                         outputDims,
-                                                         outputLayout));
-        desc.at(outputIndex) = description;
     }
 }
 
@@ -973,7 +935,3 @@ void GNAModelSerial::ExportTranspositionInfo(std::ostream &os,
         }
     }
 }
-
-void GNAModelSerial::setHeader(HeaderLatest::ModelHeader header) {
-    modelHeader = header;
-}
diff --git a/src/plugins/intel_gna/gna_model_serial.hpp b/src/plugins/intel_gna/gna_model_serial.hpp
index f5310d826c4..1173fa3368b 100644
--- a/src/plugins/intel_gna/gna_model_serial.hpp
+++ b/src/plugins/intel_gna/gna_model_serial.hpp
@@ -9,8 +9,7 @@
 #include <utility>
 #include <ie_input_info.hpp>
 
-#include "descriptions/gna_input_desc.hpp"
-#include "descriptions/gna_output_desc.hpp"
+#include "descriptions/gna_desc.hpp"
 #include "gna_plugin_log.hpp"
 #include "serial/headers/latest/gna_model_header.hpp"
 #if GNA_LIB_VER == 2
@@ -19,103 +18,53 @@
 
 
 /**
- * @brief implements serialisation tasks for GNAGraph
+ * @brief implements serialization tasks for GNAGraph
  */
 class GNAModelSerial {
- public:
+public:
     using MemoryType = std::vector<std::tuple<void*, uint32_t, std::string, float>>;
 
 private:
 #if GNA_LIB_VER == 2
-    Gna2Model * gna2Model;
+    Gna2Model * gna2model_;
+    MemoryType states, *pstates_ = nullptr;
 #else
-    intel_nnet_type_t *ptr_nnet;
+    intel_nnet_type_t *ptr_nnet_;
 #endif
-    std::vector<GNAPluginNS::HeaderLatest::RuntimeEndPoint> inputs;
-    std::vector<GNAPluginNS::HeaderLatest::RuntimeEndPoint> outputs;
-    std::vector<std::string> inputNames;
-    std::vector<std::string> outputNames;
-    TranspositionInfoMap transposeInputsInfo;
-    TranspositionInfoMap transposeOutputsInfo;
+    GNAPluginNS::GnaInputs inputs_;
+    GNAPluginNS::GnaOutputs outputs_;
+    TranspositionInfoMap inputs_transpose_info_;
+    TranspositionInfoMap outputs_transpose_info_;
+    GNAPluginNS::HeaderLatest::ModelHeader model_header_;
 
-    MemoryType states, *pstates = nullptr;
-    GNAPluginNS::HeaderLatest::ModelHeader modelHeader;
+    void ImportInputs(std::istream &is, void* basePtr, GNAPluginNS::GnaInputs &inputs);
 
-    void ImportInputs(std::istream &is,
-            void* basePtr,
-            std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
-            InferenceEngine::InputsDataMap& dataMap);
+    void ImportOutputs(std::istream &is, void* basePtr, GNAPluginNS::GnaOutputs &outputs);
 
-    void ImportOutputs(std::istream &is,
-            void* basePtr,
-            std::vector<GNAPluginNS::OutputDesc> &desc,
-            InferenceEngine::OutputsDataMap& dataMap);
+    void ImportTranspositionInfo(std::istream &is, std::string &name, std::vector<TranspositionInfo> &transpositionInfo);
 
-    void ImportTranspositionInfo(std::istream &is,
-            std::string &name,
-            std::vector<TranspositionInfo> &transpositionInfo);
-
-    void ExportTranspositionInfo(std::ostream &os,
-            const TranspositionInfoMap &transpositionInfoMap) const;
+    void ExportTranspositionInfo(std::ostream &os, const TranspositionInfoMap &transpositionInfoMap) const;
 
  public:
-#if GNA_LIB_VER == 2
     GNAModelSerial(Gna2Model * model, MemoryType & states_holder)
-        : gna2Model(model), pstates(&states_holder) {
+        : gna2model_(model), pstates_(&states_holder) {
     }
 
-    GNAModelSerial(
-        Gna2Model * model,
-        const std::shared_ptr<GNAPluginNS::InputDesc> inputDesc,
-        const std::vector<GNAPluginNS::OutputDesc>& outputsDesc,
-        const InferenceEngine::InputsDataMap& inputsDataMap,
-        const InferenceEngine::OutputsDataMap& outputsDataMap) : gna2Model(model),
-            inputs(serializeInputs(inputsDataMap, inputDesc)),
-            outputs(serializeOutputs(outputsDataMap, outputsDesc)) {
-        for (auto const& input : inputsDataMap) {
-            inputNames.push_back(input.first);
-        }
-
-        for (auto const& input : outputsDataMap) {
-            outputNames.push_back(input.first);
-        }
+    GNAModelSerial(Gna2Model * model, GNAPluginNS::GnaInputs &inputs, GNAPluginNS::GnaOutputs &outputs)
+        : gna2model_(model), inputs_(inputs), outputs_(outputs) {
     }
 
-#else
-     /**
-  *
-  * @brief Used for import/export
-  * @param ptr_nnet
-  * @param inputScale  - in/out parameter representing input scale factor
-  * @param outputScale - in/out parameter representing output scale factor
-  */
-     GNAModelSerial(intel_nnet_type_t *ptr_nnet, MemoryType &states_holder)
-         : ptr_nnet(ptr_nnet), pstates(&states_holder) {
-     }
+    void setHeader(GNAPluginNS::HeaderLatest::ModelHeader header) {
+        model_header_ = header;
+    }
 
-     /**
-      * @brief used for export only since runtime params are not passed by pointer
-      * @param ptr_nnet
-      * @param runtime
-      */
-     GNAModelSerial(
-         intel_nnet_type_t *ptr_nnet,
-         const std::shared_ptr<GNAPluginNS::InputDesc> inputDesc,
-         const std::vector<GNAPluginNS::OutputDesc>& outputsDesc,
-         const InferenceEngine::InputsDataMap& inputsDataMap,
-         const InferenceEngine::OutputsDataMap& outputsDataMap) : ptr_nnet(ptr_nnet),
-                                                                  inputs(serializeInputs(inputsDataMap, inputDesc)),
-                                                                  outputs(serializeOutputs(outputsDataMap, outputsDesc)) {
-     }
-#endif
-
-    GNAModelSerial & SetInputRotation(const TranspositionInfoMap &transposeInputsInfo) {
-      this->transposeInputsInfo = transposeInputsInfo;
+    GNAModelSerial & SetInputRotation(const TranspositionInfoMap &transpose_inputs_info) {
+      inputs_transpose_info_ = transpose_inputs_info;
       return *this;
     }
 
-    GNAModelSerial& SetOutputRotation(const TranspositionInfoMap &transposeOutputsInfo) {
-        this->transposeOutputsInfo = transposeOutputsInfo;
+    GNAModelSerial & SetOutputRotation(const TranspositionInfoMap &transpose_outputs_info) {
+        outputs_transpose_info_ = transpose_outputs_info;
         return *this;
     }
 
@@ -149,11 +98,9 @@ private:
      */
     void Import(void *basePointer,
                 size_t gnaGraphSize,
-                std::istream & is,
-                std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
-                std::vector<GNAPluginNS::OutputDesc> &desc,
-                InferenceEngine::InputsDataMap& inputsDataMap,
-                InferenceEngine::OutputsDataMap& outputsDataMap,
+                std::istream &is,
+                GNAPluginNS::GnaInputs &inputs,
+                GNAPluginNS::GnaOutputs &outputs,
                 TranspositionInfoMap& inputstranspositionInfo,
                 TranspositionInfoMap& outputstranspositionInfo);
 
@@ -166,13 +113,4 @@ private:
     void Export(void *basePtr,
                 size_t gnaGraphSize,
                 std::ostream &os) const;
-
-    static std::vector<GNAPluginNS::HeaderLatest::RuntimeEndPoint> serializeOutputs(const InferenceEngine::OutputsDataMap& outputsDataMap,
-            const std::vector<GNAPluginNS::OutputDesc>& outputsDesc);
-
-
-    static std::vector<GNAPluginNS::HeaderLatest::RuntimeEndPoint> serializeInputs(const InferenceEngine::InputsDataMap& inputsDataMap,
-                                                                        const std::shared_ptr<GNAPluginNS::InputDesc>);
-
-    void setHeader(GNAPluginNS::HeaderLatest::ModelHeader header);
 };
diff --git a/src/plugins/intel_gna/gna_plugin.cpp b/src/plugins/intel_gna/gna_plugin.cpp
index 6fae93ffac7..d3bcb691fc6 100644
--- a/src/plugins/intel_gna/gna_plugin.cpp
+++ b/src/plugins/intel_gna/gna_plugin.cpp
@@ -181,40 +181,6 @@ void GNAPlugin::copyInputData(T *dst,
     }
 }
 
-template <typename T, typename U>
-void GNAPlugin::copyInputDataWithSplit(T *const dst,
-                const U *src,
-                const GNASplitLayer& splitInfo,
-                size_t precision_size,
-                int idx) {
-    if (!dst || !src) {
-        return;
-    }
-    T *dst_ptr = dst;
-    const U *src_ptr = src;
-    precision_size = sizeof(T);
-    // we found split/slice layer connected to Input
-    for (auto&& outputLayer : splitInfo.splitOutputLayers) {
-        uint32_t begin = outputLayer.offset / precision_size;
-        uint32_t end = (outputLayer.offset + outputLayer.pure_size)/precision_size;
-        if (dst_ptr - dst >= end) {
-            // output layer with bind pointer as previous one. Skip
-            continue;
-        }
-        for (uint32_t i = begin; i < end; ++i) {
-            if (!std::is_same<T, U>::value) {
-                *(dst_ptr++) = GNAPluginNS::ConvertFloatToInt16(*(src_ptr++) * inputsDesc->getScaleFactor(idx));
-            } else {
-                *(dst_ptr++) = *(src_ptr++);
-            }
-        }
-        begin = end;
-        end = (outputLayer.offset + ALIGN64(outputLayer.pure_size))/precision_size;
-        std::memset(dst_ptr, 0, (end - begin )* sizeof(uint16_t));
-        dst_ptr += end - begin;
-    }
-}
-
 void GNAPlugin::ExportScores(void *ptr_dst,
                   const void *ptr_src,
                   intel_dnn_orientation_t orientation,
@@ -404,12 +370,13 @@ GNAPlugin::GNAPlugin(const std::map<std::string, std::string>& configMap) {
 void GNAPlugin::Init() {
     OV_ITT_SCOPED_TASK(itt::domains::GNAPlugin, "Init");
     dnn = std::make_shared<backend::AMIntelDNN>(backend::AMIntelDNN());
-    inputsDesc = std::make_shared<GNAPluginNS::InputDesc>(GNAPluginNS::InputDesc());
     gnaFlags = std::make_shared<GNAPluginNS::GNAFlags>(GNAPluginNS::GNAFlags());
+    inputs_ptr_ = std::make_shared<GNAPluginNS::GnaInputs>(GNAPluginNS::GnaInputs());
+    outputs_ = GNAPluginNS::GnaOutputs();
 
     graphCompiler.setDNNPtr(dnn);
-    graphCompiler.setInputDescPtr(inputsDesc);
     graphCompiler.setGNAFlagsPtr(gnaFlags);
+    graphCompiler.setInputsPtr(inputs_ptr_);
 }
 
 void GNAPlugin::InitGNADevice() {
@@ -465,20 +432,19 @@ void GNAPlugin::UpdateInputScaleFromNetwork(InferenceEngine::CNNNetwork& network
             auto scaleInput = frontend::CalculateScaleFactorFromStats(levels, inputRange.first[0], inputRange.second[0]);
 
             IE_ASSERT(config.inputScaleFactors.size() > inputIdx);
-            IE_ASSERT(inputsDesc->inputScaleFactors.size() > inputIdx);
 
             if (!config.inputScaleFactors.empty()) {
-                gnalog() << "Scale factor calculated during model quantization (" << scaleInput
-                    << ") will be used instead of user input (" << inputsDesc->inputScaleFactors[inputIdx] << ").\n";
-                if (inputsDesc->inputScaleFactors[inputIdx] < scaleInput) {
-                    gnawarn() << "WARNING: Scale factor calculated based on input values (" << inputsDesc->inputScaleFactors[inputIdx]
+                gnawarn() << "WARNING: Scale factor calculated during model quantization (" << scaleInput
+                    << ") will be used instead of user input (" << (*inputs_ptr_)[input.first].scale_factor << ").\n";
+                if ((*inputs_ptr_)[input.first].scale_factor < scaleInput) {
+                    gnawarn() << "WARNING: Scale factor calculated based on input values (" << (*inputs_ptr_)[input.first].scale_factor
                         << ") is smaller than scale factor used to quantize model (" << scaleInput << "). "
                         << "Input values will be clamped.\n";
                 }
             }
 
             config.inputScaleFactors[inputIdx] = scaleInput;
-            inputsDesc->inputScaleFactors[inputIdx] = scaleInput;
+            (*inputs_ptr_)[input.first].scale_factor = scaleInput;
         }
 
         inputIdx++;
@@ -490,42 +456,82 @@ void GNAPlugin::UpdateInputsAndOutputsInfoFromNetwork(InferenceEngine::CNNNetwor
 
     // update inputs
     {
-        InputsDataMap inputs = network.getInputsInfo();
-        if (inputsDesc->inputPrecisions.size() != 0) {
-            inputsDesc->inputPrecisions.clear();
-        }
-        for (const auto input : inputs) {
-            inputsDesc->inputPrecisions.push_back(input.second->getPrecision().getPrecVal());
+        InputsDataMap network_inputs = network.getInputsInfo();
+
+        size_t id = 0;
+        for (const auto input : network_inputs) {
+            (*inputs_ptr_)[input.first].Update(input.second);
+
+            // update scale factor from config
+            if (id < config.inputScaleFactors.size()) {
+                (*inputs_ptr_)[input.first].scale_factor = config.inputScaleFactors[id];
+            }
+
+            id++;
         }
     }
-
     // update outputs
     {
         OutputsDataMap outputs = network.getOutputsInfo();
-        outputsDesc.resize(outputs.size());
-
-        size_t outputIdx = 0;
         for (const auto output : outputs) {
-            outputsDesc[outputIdx].precision = output.second->getPrecision().getPrecVal();
-            ++outputIdx;
+            outputs_[output.first].Update(output.second);
         }
     }
 }
 
-bool GNAPlugin::TryToInitOutput(int portId, InferenceEngine::CNNLayerPtr layer) {
-    auto initOutput = [this, portId, layer]
+void GNAPlugin::UpdateInputs(const std::vector<std::shared_ptr<const ov::Node>>& params) {
+    OV_ITT_SCOPED_TASK(itt::domains::GNA_LT, "UpdateInputs");
+    for (const auto& param : params) {
+        const std::string ie_name = param->get_friendly_name();
+        (*inputs_ptr_)[ie_name].name = param->get_friendly_name();
+        (*inputs_ptr_)[ie_name].tensor_names = param->get_output_tensor(0).get_names();
+    }
+}
+
+void GNAPlugin::UpdateOutputs(const std::vector<std::shared_ptr<const ov::Node>>& results) {
+    OV_ITT_SCOPED_TASK(itt::domains::GNA_LT, "UpdateOutputs");
+    for (const auto& result : results) {
+        const std::string ie_name = ngraph::op::util::create_ie_output_name(result->input_value(0));
+        outputs_[ie_name].name = ie_name;
+        outputs_[ie_name].tensor_names = result->get_output_tensor(0).get_names();
+    }
+}
+
+void GNAPlugin::UpdateInputsAndOutputsInfoFromModel(const std::shared_ptr<ov::Model> &model) {
+    OV_ITT_SCOPED_TASK(itt::domains::GNA_LT, "UpdateInputsAndOutputsInfoFromFModel");
+
+    // update inputs
+    {
+        std::vector<std::shared_ptr<const ov::Node>> node_vector;
+        for (auto& param : model->get_parameters()) {
+            node_vector.emplace_back(param);
+        }
+        UpdateInputs(node_vector);
+    }
+
+    // update outputs
+    {
+        std::vector<std::shared_ptr<const ov::Node>> node_vector;
+        for (auto& result : model->get_results()) {
+            node_vector.emplace_back(result);
+        }
+        UpdateOutputs(node_vector);
+    }
+}
+
+bool GNAPlugin::TryToInitOutput(const std::string &portName, InferenceEngine::CNNLayerPtr layer) {
+    auto initOutput = [this, portName, layer]
             (intel_dnn_orientation_t orientation, size_t numBytesPerElem, size_t numElem, void* outputPtr) {
-        auto & desc = outputsDesc[portId];
         auto quantized = InferenceEngine::getInjectedData<QuantizedLayerParams>(layer);
 
-        desc.ptrs.resize(gnaFlags->gna_lib_async_threads_num);
-        desc.orientation = orientation;
-        desc.num_bytes_per_element = numBytesPerElem;
-        desc.scale_factor = quantized != nullptr ? quantized->_dst_quant.GetScale() : 1.0f;
-        desc.num_elements = numElem;
+        outputs_.at(portName).ptrs.resize(gnaFlags->gna_lib_async_threads_num);
+        outputs_.at(portName).orientation = orientation;
+        outputs_.at(portName).num_bytes_per_element = numBytesPerElem;
+        outputs_.at(portName).scale_factor = quantized != nullptr ? quantized->_dst_quant.GetScale() : GNAPluginNS::kScaleFactorDefault;
+        outputs_.at(portName).num_elements = numElem;
 
         // binding ptr for first infer request - then others will be setup during relocation
-        gnamem->bind_ptr(layer, &desc.ptrs.front(), outputPtr);
+        gnamem->bind_ptr(layer, &outputs_.at(portName).ptrs.front(), outputPtr);
     };
 
     // probing gna_primitives
@@ -765,13 +771,18 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         THROW_GNA_EXCEPTION << error.c_str();
     }
 
-    if (fake_quantized) {
-        UpdateInputScaleFromNetwork(network);
+    // Set input and output information from ngraph function
+    if (_network.getFunction()) {
+        UpdateInputsAndOutputsInfoFromModel(_network.getFunction());
     }
 
     // Set input and output information from orginal network
     UpdateInputsAndOutputsInfoFromNetwork(network);
 
+    if (fake_quantized) {
+        UpdateInputScaleFromNetwork(network);
+    }
+
     if (MustBeConvertedFromNCHWToNHWC(details::CNNNetSortTopologically(network))) {
         FillInputsAndOutputsTranspositionInfo(network);
     }
@@ -823,6 +834,7 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
     };
 
     InferenceEngine::CNNNetwork newNet;
+
     if (gnaFlags->sw_fp32) {
         auto visitor = [&](InferenceEngine::CNNLayerPtr lp) {
             transformLayer(lp, WeightsConverter());
@@ -834,20 +846,20 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         run_passes(newNet, false, gnaFlags->input_low_precision);
     } else if (fake_quantized) {
         ModelQuantizer<FakeQuant> modelQuantizer;
-        newNet = modelQuantizer.quantize(network, run_passes, inputsDesc->inputScaleFactors);
+        newNet = modelQuantizer.quantize(network, run_passes, *inputs_ptr_);
     } else {
         switch (config.gnaPrecision) {
             case Precision::I16:
                 ModelQuantizer<QuantI16> q16;
-                newNet = q16.quantize(network, run_passes, inputsDesc->inputScaleFactors);
+                newNet = q16.quantize(network, run_passes, *inputs_ptr_);
                 break;
             case Precision::I8:
                 if (gnaFlags->input_low_precision == false) {
                     ModelQuantizer<QuantI8> q8;
-                    newNet = q8.quantize(network, run_passes, inputsDesc->inputScaleFactors);
+                    newNet = q8.quantize(network, run_passes, *inputs_ptr_);
                 } else {
                     ModelQuantizer<QuantI8_I8> q8_8;
-                    newNet = q8_8.quantize(network, run_passes, inputsDesc->inputScaleFactors);
+                    newNet = q8_8.quantize(network, run_passes, *inputs_ptr_);
                 }
                 break;
             default:
@@ -912,19 +924,19 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
     }
 
     // keep inputs information and create input primitives
-    inputsDataMap = newNet.getInputsInfo();
-    if (inputsDataMap.empty()) {
+    inputs_data_map_ = newNet.getInputsInfo();
+    if (inputs_data_map_.empty()) {
         gnawarn() << "No inputs for the topology\n";
     }
 
     // keep output dims
-    outputsDataMap = newNet.getOutputsInfo();
-    if (outputsDataMap.empty()) {
+    outputs_data_map_ = newNet.getOutputsInfo();
+    if (outputs_data_map_.empty()) {
         THROW_GNA_EXCEPTION << "No outputs for the topology";
     }
 
-    for (auto && input : inputsDataMap) {
-        inputsDesc->getPtrInputsGlobal(input.first).resize(gnaFlags->gna_lib_async_threads_num);
+    for (auto && input : inputs_data_map_) {
+        inputs_ptr_->at(input.first).ptrs.resize(gnaFlags->gna_lib_async_threads_num);
     }
 
     // Creating Layer primitives
@@ -938,8 +950,8 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
 
     for (auto& inputLayer : inputLayers) {
         auto layerInfo = LayerInfo(inputLayer);
-        if (layerInfo.isInput() && 0 == inputsDesc->bytes_allocated_for_input[inputLayer->name]) {
-            graphCompiler.connectOutput(inputLayer, &inputsDesc->getPtrInputsGlobal(inputLayer->name).front(), 0);
+        if (layerInfo.isInput() && 0 == inputs_ptr_->at(inputLayer->name).get_allocated_size()) {
+            graphCompiler.connectOutput(inputLayer, &inputs_ptr_->at(inputLayer->name).ptrs.front(), 0);
         }
     }
 
@@ -950,7 +962,7 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
 
     /// setting-up output layers information
     int portId = 0;
-    for (auto && outPort : outputsDataMap) {
+    for (auto && outPort : outputs_data_map_) {
         // gets output layer pointer in original topology not in cloned
         auto outLayer = getCreatorLayer(outPort.second).lock();
 
@@ -971,9 +983,9 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         gnalog() << "[UFS] searching for : "<< outPort.first << " representation in GNA\n";
         bool stopSearching = false;
 
-        CNNNetDFS(outLayer, [this, &outPort, portId, &stopSearching](CNNLayerPtr layer) {
+        CNNNetDFS(outLayer, [this, &outPort, &stopSearching](CNNLayerPtr layer) {
             gnalog() << "[UFS] from : "<< outPort.first <<" reached: " << layer->name << "\n";
-            stopSearching = TryToInitOutput(portId, layer);
+            stopSearching = TryToInitOutput(outPort.first, layer);
         }, true, [&stopSearching](InferenceEngine::CNNLayer* from) {
             return make_upstream_order(!stopSearching ? from : nullptr);
         });
@@ -986,8 +998,7 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
 
     // TODO: how active list will work in multioutput case
     // make room for active list
-    gnamem->reserve_ptr(nullptr, nullptr,
-        ALIGN64(outputsDesc.front().num_bytes_per_element * outputsDesc.front().num_elements), 64);
+    gnamem->reserve_ptr(nullptr, nullptr, ALIGN64(outputs_.Get().begin()->get_required_size()), 64);
 
     void *pParallelExecutionData  = nullptr;
 
@@ -1050,13 +1061,13 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
             }
         };
 
-        for (auto &&input : inputsDesc->ptr_inputs_global_storage) {
-            relocate(input[i], input[0]);
+        for (auto &input : inputs_ptr_->Get()) {
+            relocate(input.ptrs[i], input.ptrs[0]);
         }
 
         // relocating all output pointers
-        for (int j = 0; j < outputsDesc.size(); ++j) {
-            relocate(outputsDesc[j].ptrs[i], outputsDesc[j].ptrs[0]);
+        for (auto &output : outputs_.Get()) {
+            relocate(output.ptrs[i], output.ptrs[0]);
         }
 
 #if GNA_LIB_VER == 2
@@ -1114,11 +1125,11 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
 
             if (orientations.empty()) {
                 // in this case orientation doesn't make a sense
-                inputsDesc->orientation_in[inputLayer->name] = kDnnNonInterleavedOrientation;
+                inputs_ptr_->at(inputLayer->name).orientation = kDnnNonInterleavedOrientation;
             } else if (std::adjacent_find(orientations.begin(), orientations.end(),
                            std::not_equal_to<intel_dnn_orientation_t>()) == orientations.end()) {
                 // all orientations are equal
-                inputsDesc->orientation_in[inputLayer->name] = orientations.front();
+                inputs_ptr_->at(inputLayer->name).orientation = orientations.front();
             } else {
                 // unsupported case: orientations are different and they are important for these components
                 THROW_GNA_EXCEPTION << "orientation for input layer: " << inputLayer->name << " cannot be calculated";
@@ -1126,7 +1137,9 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         }
     } else {
         for (auto &inputLayer : inputLayers) {
-            inputsDesc->orientation_in[inputLayer->name] = kDnnInterleavedOrientation;
+            if (LayerInfo(inputLayer).isInput()) {
+                inputs_ptr_->at(inputLayer->name).orientation = kDnnInterleavedOrientation;
+            }
         }
     }
 
@@ -1185,19 +1198,18 @@ void GNAPlugin::DumpXNNToFile() const {
         THROW_GNA_EXCEPTION << "Wrong GNA version for embedded model dump: " << config.dumpXNNGeneration;
     auto dump = gnadevice->dumpXnn(&std::get<0>(nnets.front())->obj, ptr_active_indices, num_active_indices);
     dump.header.rw_region_size = gnamem->getRWBytes();
-    dump.header.input_scaling_factor = inputsDesc->inputScaleFactors.front();
-    dump.header.output_scaling_factor = outputsDesc.front().scale_factor;
+    dump.header.input_scaling_factor = _inputs.begin()->second.scale_factor;
+    dump.header.output_scaling_factor =  outputs_.begin()->second.scale_factor;
     dumpStream.write(reinterpret_cast<char*>(&dump.header), sizeof(intel_gna_model_header));
     dumpStream.write(reinterpret_cast<char*>(dump.model.get()), dump.header.model_size);
 #else
     auto const modelId = gnadevice->createModel(std::get<0>(gnaModels.front())->obj);
     auto dump = gnadevice->dumpXnn(modelId);
     dump.header.RwRegionSize = gnamem->getRWBytes();
-    dump.header.InputScalingFactor = inputsDesc->inputScaleFactors.front();
-    dump.header.OutputScalingFactor = outputsDesc.front().scale_factor;
+    dump.header.InputScalingFactor = inputs_ptr_->Get().begin()->scale_factor;
+    dump.header.OutputScalingFactor = outputs_.Get().begin()->scale_factor;
     dumpStream.write(reinterpret_cast<char*>(&dump.header), sizeof(Gna2ModelSueCreekHeader));
     dumpStream.write(reinterpret_cast<char*>(dump.model.get()), dump.header.ModelSize);
-
     gnadevice->releaseModel(modelId);
 #endif
 }
@@ -1241,24 +1253,24 @@ uint32_t GNAPlugin::QueueInference(const InferenceEngine::BlobMap &inputs, Infer
         auto is1D = input.second->getTensorDesc().getLayout() == Layout::C;
         auto is3D = input.second->getTensorDesc().getLayout() == Layout::CHW;
 
-        if (!inputsDesc->ptr_inputs_global_id.count(input.first)) {
+        if (inputs_ptr_->at(input.first).ptrs.empty()) {
             // should not happen in user code however might happen if there any non executable network based integration of GNAPlugin instance
             THROW_GNA_EXCEPTION << "network not loaded : input pointer for " << input.first << " not set";
         }
 
-        if (inputsDesc->getPtrInputsGlobal(input.first)[idx] == nullptr) {
+        if (inputs_ptr_->at(input.first).ptrs[idx] == nullptr) {
             // should not happen in user code however might happen if there any non executable network based integration of GNAPlugin instance
             THROW_GNA_EXCEPTION << "network not loaded : input pointer for (" << input.first << " at inferRequest #"
                                 << idx << " not set";
         }
-        const auto inputOrientation = inputsDesc->getOrientation(input.first);
+        const auto inputOrientation = inputs_ptr_->at(input.first).orientation;
         if (inputOrientation == kDnnUnknownOrientation) {
             // should not happen in user code however might happen if there any non executable network based integration of GNAPlugin instance
             THROW_GNA_EXCEPTION << "network not loaded : input orientation for " << input.first << " not set";
         }
 
-        for (auto& outputDesc : outputsDesc) {
-            if (outputDesc.orientation == kDnnUnknownOrientation) {
+        for (auto& output : outputs_.Get()) {
+            if (output.orientation == kDnnUnknownOrientation) {
                 // should not happen in user code however might happen if there any non executable network based integration of GNAPlugin instance
                 THROW_GNA_EXCEPTION << "network not loaded : output orientation not set";
             }
@@ -1272,16 +1284,16 @@ uint32_t GNAPlugin::QueueInference(const InferenceEngine::BlobMap &inputs, Infer
         auto  importedElementSizeBytes = gnaFlags->sw_fp32 ? 4 : (gnaFlags->input_low_precision ? 1 : 2);
         auto  importedBytes = importedElements * importedFrames * importedElementSizeBytes;
 
-        if (inputsDesc->bytes_allocated_for_input[input.first] < importedBytes) {
+        if (inputs_ptr_->at(input.first).get_required_size() < importedBytes) {
             THROW_GNA_EXCEPTION << "Cannot import input frames for :" << input.first
-                                  << ", allocated size: " << inputsDesc->bytes_allocated_for_input[input.first]
+                                  << ", allocated size: " << inputs_ptr_->at(input.first).get_required_size()
                                   << ", but input blob size: " << importedBytes;
         }
 
-        ImportFrames(inputsDesc->getPtrInputsGlobal(input.first)[idx],
+        ImportFrames(inputs_ptr_->at(input.first).ptrs[idx],
                      input.second->cbuffer().as<float *>(),
                      input.second->getTensorDesc().getPrecision(),
-                     gnaFlags->sw_fp32 ? 1.0f : inputsDesc->getScaleFactor(inputNum),
+                     gnaFlags->sw_fp32 ? 1.0f : inputs_ptr_->at(input.first).scale_factor,
                      inputOrientation,
                      importedFrames,
                      targetGroups,
@@ -1300,7 +1312,7 @@ uint32_t GNAPlugin::QueueInference(const InferenceEngine::BlobMap &inputs, Infer
                 THROW_GNA_EXCEPTION << "Transposed data size (" << transposed_data_size
                                     << ") do not match input buffer length of " << elementsPerBatch;
             }
-            auto input_ptr = reinterpret_cast<uint8_t *>(inputsDesc->getPtrInputsGlobal(input.first)[idx]);
+            auto input_ptr = reinterpret_cast<uint8_t *>(inputs_ptr_->at(input.first).ptrs[idx]);
             ConvertTensorFromNCHWToNHWC(gnadevice ? 2 : 4, batchSize, elementsPerBatch, input_ptr, true, transpose_info->second);
         }
         ++inputNum;
@@ -1377,7 +1389,7 @@ GnaWaitStatus GNAPlugin::WaitFor(uint32_t request_idx, int64_t millisTimeout) {
     int output_idx = 0;
     for (auto && outputBlobIt : request) {
         auto & outputBlob = outputBlobIt.second;
-        auto & outputDesc = outputsDesc[output_idx];
+        auto & outputDesc = outputs_.at(outputBlobIt.first);
         if (outputBlob->getTensorDesc().getLayout() != Layout::C && outputBlob->getTensorDesc().getLayout() != Layout::NC &&
             outputBlob->getTensorDesc().getLayout() != Layout::CN && outputBlob->getTensorDesc().getLayout() != Layout::NCHW &&
             outputBlob->getTensorDesc().getLayout() != Layout::CHW && outputBlob->getTensorDesc().getLayout() != Layout::SCALAR) {
@@ -1478,14 +1490,14 @@ void GNAPlugin::Reset() {
 bool GNAPlugin::Infer(const InferenceEngine::Blob &input, InferenceEngine::Blob &output) {
     BlobMap bmInput;
     BlobMap bmOutput;
-    if (inputsDataMap.size() != 1) {
-        THROW_GNA_EXCEPTION << "cannot infer using Infer(Blob&, Blob&)"<< "model accepts " << inputsDataMap.size() << " inputs";
+    if (inputs_data_map_.size() != 1) {
+        THROW_GNA_EXCEPTION << "cannot infer using Infer(Blob&, Blob&)"<< "model accepts " << inputs_data_map_.size() << " inputs";
     }
 
-    IE_ASSERT(!inputsDataMap.empty());
-    bmInput[inputsDataMap.begin()->first] = std::shared_ptr<Blob>(const_cast<Blob*>(&input), [](Blob*){});
-    IE_ASSERT(!outputsDataMap.empty());
-    bmOutput[outputsDataMap.begin()->first] = std::shared_ptr<Blob>(&output, [](Blob*){});
+    IE_ASSERT(!inputs_data_map_.empty());
+    bmInput[inputs_data_map_.begin()->first] = std::shared_ptr<Blob>(const_cast<Blob*>(&input), [](Blob*){});
+    IE_ASSERT(!outputs_data_map_.empty());
+    bmOutput[outputs_data_map_.begin()->first] = std::shared_ptr<Blob>(&output, [](Blob*){});
     return Infer(bmInput, bmOutput);
 }
 
@@ -1508,8 +1520,8 @@ static InferenceEngine::Layout GetLayoutForDims(const InferenceEngine::SizeVecto
 Blob::Ptr GNAPlugin::GetOutputBlob(const std::string& name, InferenceEngine::Precision precision) {
     // need to have intermediate blob for interleave conversion
     InferenceEngine::Blob::Ptr outputBlob;
-    auto outputDataIt = outputsDataMap.find(name);
-    if (outputDataIt == std::end(outputsDataMap)) {
+    auto outputDataIt = outputs_data_map_.find(name);
+    if (outputDataIt == std::end(outputs_data_map_)) {
         THROW_GNA_EXCEPTION << "Output " << name << " isn't found";
     }
     auto outputDims = outputDataIt->second->getTensorDesc().getDims();
@@ -1522,8 +1534,8 @@ Blob::Ptr GNAPlugin::GetInputBlob(const std::string& name, InferenceEngine::Prec
     InferenceEngine::Blob::Ptr inputBlob;
     // need to have intermediate blob for interleave conversion
     // TODO: NCHW format support is experimental = c++ MO did insert reshape, while TF mo - not
-    auto inputDataIt = inputsDataMap.find(name);
-    if (inputDataIt == std::end(inputsDataMap)) {
+    auto inputDataIt = inputs_data_map_.find(name);
+    if (inputDataIt == std::end(inputs_data_map_)) {
         THROW_GNA_EXCEPTION << "Input " << name << " isn't found";
     }
     auto inputDims = inputDataIt->second->getTensorDesc().getDims();
@@ -1573,31 +1585,27 @@ InferenceEngine::IExecutableNetworkInternal::Ptr GNAPlugin::ImportNetwork(std::i
     auto serial = GNAModelSerial(&std::get<0>(nnets.back())->obj, mt);
 #endif
 
-    if (!inputsDesc->inputScaleFactors.empty()) {
-        gnalog() << "[Import Network] Clearing input scale factors defined in configuration. "
-                 << "Scale factors provided in imported model will be used\n";
-        inputsDesc->inputScaleFactors.clear();
-    }
-
     serial.setHeader(header);
     serial.Import(basePtr,
             header.gnaMemSize,
             networkModel,
-            inputsDesc,
-            outputsDesc,
-            inputsDataMap,
-            outputsDataMap,
+            *(inputs_ptr_),
+            outputs_,
             transpose_inputs_info,
             transpose_outputs_info);
 
+    SetNetworkInputs();
+    SetNetworkOutputs();
+
     // If scale factors are defined in configuration we still need to use them instead of imported values,
     // for example to change the scale factors for the old models.
     if (!config.inputScaleFactors.empty()) {
-        IE_ASSERT(config.inputScaleFactors.size() <= inputsDesc->inputScaleFactors.size());
-        for (size_t i = 0; i < config.inputScaleFactors.size(); ++i) {
-            if (config.inputScaleFactors[i] != GNAPluginNS::kScaleFactorDefault) {
-                gnalog() << "[Import Network] Using input scale factor defined in configuration for input " << i << std::endl;
-                inputsDesc->inputScaleFactors[i] = config.inputScaleFactors[i];
+        IE_ASSERT(config.inputScaleFactors.size() <= inputs_ptr_->size());
+        // TODO: config should  use the map of inputs as well
+        for (size_t id = 0; id < config.inputScaleFactors.size(); ++id) {
+            if (id < inputs_ptr_->size() && config.inputScaleFactors[id] != GNAPluginNS::kScaleFactorDefault) {
+                gnalog() << "[Import Network] Using input scale factor defined in configuration for input " << id << std::endl;
+                inputs_ptr_->Get().at(id).scale_factor = config.inputScaleFactors[id];
             }
         }
     }
@@ -1616,17 +1624,17 @@ InferenceEngine::IExecutableNetworkInternal::Ptr GNAPlugin::ImportNetwork(std::i
 #endif
 
 #if GNA_LIB_VER == 1
-    inputsDesc->orientation_in["input"] = getOrientation(std::get<0>(nnets.back())->obj.pLayers[0]);
+    (*inputs_ptr_)["input"].orientation = getOrientation(std::get<0>(nnets.back())->obj.pLayers[0]);
     outputsDesc[0].orientation = getOrientation(std::get<0>(nnets.back())->obj.pLayers[std::get<0>(nnets.back())->obj.nLayers - 1]);
 #endif
 
     if (header.doRotateInput) {
-        for (auto && input : inputsDataMap) {
+        for (auto && input : inputs_data_map_) {
             transpose_inputs_info.insert({input.first, {{header.doRotateInput, header.nRotateRows, header.nRotateColumns}}});
         }
     }
     if (header.doRotateOutput) {
-        for (auto && output : outputsDataMap) {
+        for (auto && output : outputs_data_map_) {
             transpose_outputs_info.insert({output.first, {{header.doRotateOutput, header.nRotateOutputRows, header.nRotateOutputColumns}}});
         }
     }
@@ -1658,19 +1666,19 @@ void GNAPlugin::Export(const std::string &fileName) {
 }
 
 void GNAPlugin::Export(std::ostream &outStream) {
-    if (inputsDesc->ptr_inputs_global_id.empty() || outputsDesc.empty()) {
+    if (inputs_ptr_->empty() || outputs_.empty()) {
         THROW_GNA_EXCEPTION << " network not loaded";
     }
 
 #if GNA_LIB_VER == 1
-    if (inputsDesc->ptr_inputs_global_id.size() != 1) {
+    if (_inputs.size() != 1) {
         THROW_GNA_EXCEPTION << " exporting network with multiple inputs not supported";
     }
 #endif
 
     // TODO: nnet group parameter looks only used in application - so can we move this line into load network.
-    IE_ASSERT(!inputsDataMap.empty());
-    auto inputDims = inputsDataMap.begin()->second->getTensorDesc().getDims();
+    IE_ASSERT(!inputs_data_map_.empty());
+    auto inputDims = inputs_data_map_.begin()->second->getTensorDesc().getDims();
     if (inputDims.size() == 2) {
 #if GNA_LIB_VER == 1
         std::get<0>(nnets.front())->obj.nGroup = inputDims[0];
@@ -1682,10 +1690,8 @@ void GNAPlugin::Export(std::ostream &outStream) {
     intel_nnet_type_t* modelToSerial = &std::get<0>(nnets.front())->obj;
 #endif
     auto serial = GNAModelSerial(modelToSerial,
-                                 inputsDesc,
-                                 outputsDesc,
-                                 inputsDataMap,
-                                 outputsDataMap)
+                                 *(inputs_ptr_),
+                                 outputs_)
                     .SetInputRotation(transpose_inputs_info)
                     .SetOutputRotation(transpose_outputs_info);
 
@@ -1716,10 +1722,52 @@ void GNAPlugin::SetConfig(const std::map<std::string, std::string> &config_map)
 }
 
 void GNAPlugin::UpdateFieldsFromConfig() {
-    inputsDesc->inputScaleFactors = config.inputScaleFactors;
     *gnaFlags = config.gnaFlags;
 }
 
+void GNAPlugin::SetNetworkInputs() {
+    inputs_data_map_.clear();
+    for (auto & input : inputs_ptr_->Get()) {
+        inputs_data_map_[input.name] = input.ToIEInputInfo();
+    }
+}
+
+void GNAPlugin::SetNetworkOutputs() {
+    outputs_data_map_.clear();
+    for (auto & output : outputs_.Get()) {
+        outputs_data_map_[output.name] = output.to_ie_data();
+    }
+}
+
+std::vector<std::shared_ptr<const ov::Node>> GNAPlugin::GetInputs() {
+    std::vector<std::shared_ptr<const ov::Node>> params;
+    params.reserve(inputs_ptr_->size());
+    for (auto&& input : inputs_ptr_->Get()) {
+        auto param = std::make_shared<ov::op::v0::Parameter>(
+            convertPrecision(input.model_precision),
+            ov::PartialShape(input.dims));
+        param->set_friendly_name(input.name);
+        param->get_output_tensor(0).add_names(input.tensor_names);
+        params.emplace_back(move(param));
+    }
+    return params;
+}
+
+std::vector<std::shared_ptr<const ov::Node>> GNAPlugin::GetOutputs() {
+    std::vector<std::shared_ptr<const ov::Node>> results;
+    results.reserve(outputs_.size());
+    for (auto&& output : outputs_.Get()) {
+        auto param = std::make_shared<ov::op::v0::Parameter>(
+            convertPrecision(output.model_precision),
+            ov::PartialShape(output.dims));
+        param->set_friendly_name(output.name);
+        auto result = std::make_shared<ov::op::v0::Result>(param);
+        result->get_output_tensor(0).add_names(output.tensor_names);
+        results.emplace_back(std::move(result));
+    }
+    return results;
+}
+
 InferenceEngine::QueryNetworkResult GNAPlugin::QueryNetwork(const InferenceEngine::CNNNetwork& network,
                                                             const std::map<std::string, std::string>& config) const {
     InferenceEngine::QueryNetworkResult res;
diff --git a/src/plugins/intel_gna/gna_plugin.hpp b/src/plugins/intel_gna/gna_plugin.hpp
index d85a712cc5c..a757ecab201 100644
--- a/src/plugins/intel_gna/gna_plugin.hpp
+++ b/src/plugins/intel_gna/gna_plugin.hpp
@@ -16,8 +16,7 @@
 #include <cpp_interfaces/interface/ie_iexecutable_network_internal.hpp>
 #include "cpp_interfaces/interface/ie_ivariable_state_internal.hpp"
 #include "descriptions/gna_flags.hpp"
-#include "descriptions/gna_input_desc.hpp"
-#include "descriptions/gna_output_desc.hpp"
+#include "descriptions/gna_desc.hpp"
 #include "backend/am_intel_dnn.hpp"
 #include "gna_data_types.hpp"
 #include "gna_graph_compiler.hpp"
@@ -38,7 +37,8 @@ class GNAPlugin : public InferenceEngine::IInferencePlugin {
     std::shared_ptr<GNAPluginNS::backend::AMIntelDNN> dnn;
     std::shared_ptr<GNAPluginNS::GNAFlags> gnaFlags;
     std::shared_ptr<GNAPluginNS::gna_memory_type> gnamem;
-    std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc;
+    std::shared_ptr<GNAPluginNS::GnaInputs> inputs_ptr_;
+    GNAPluginNS::GnaOutputs outputs_;
 
     GNAPluginNS::GNAGraphCompiler graphCompiler;
 
@@ -62,10 +62,6 @@ class GNAPlugin : public InferenceEngine::IInferencePlugin {
     uint32_t num_active_indices = 0;
     uint32_t num_group_in = 0;
     uint32_t dnn_dump_write_index = 0;
-
-    // index matches iterating order of cnnnetwork outputs info
-    std::vector<GNAPluginNS::OutputDesc> outputsDesc = std::vector<OutputDesc>();
-
     intel_dnn_number_type_t output_type = kDnnInt;
 
 #if GNA_LIB_VER == 2
@@ -80,8 +76,9 @@ class GNAPlugin : public InferenceEngine::IInferencePlugin {
      */
     uint32_t rwSegmentSize = 0;
 
-    InferenceEngine::InputsDataMap inputsDataMap;
-    InferenceEngine::OutputsDataMap outputsDataMap;
+    InferenceEngine::InputsDataMap inputs_data_map_;    //!< Holds information about network inputs info
+    InferenceEngine::OutputsDataMap outputs_data_map_;  //!< Holds information about network outputs data
+
     std::vector<InferenceEngine::IVariableStateInternal::Ptr> memoryStates;
     bool trivialTopology = false;
 
@@ -150,8 +147,20 @@ class GNAPlugin : public InferenceEngine::IInferencePlugin {
     /**
      * helpers to provide inputs info on AOT network
      */
-    InferenceEngine::InputsDataMap GetInputs() {return inputsDataMap;}
-    InferenceEngine::OutputsDataMap GetOutputs() {return outputsDataMap;}
+    InferenceEngine::InputsDataMap GetNetworkInputs() {return inputs_data_map_;}
+    InferenceEngine::OutputsDataMap GetNetworkOutputs() {return outputs_data_map_;}
+    std::vector<std::shared_ptr<const ov::Node>> GetOutputs();
+    std::vector<std::shared_ptr<const ov::Node>> GetInputs();
+    /**
+     * helpers to set inputs/output info on AOT network
+     */
+    void SetNetworkInputs();
+    void SetNetworkOutputs();
+    /**
+     * helpers to update internal inputs/output descriptions from loaded network
+     */
+    void UpdateInputs(const std::vector<std::shared_ptr<const ov::Node>>& params);
+    void UpdateOutputs(const std::vector<std::shared_ptr<const ov::Node>>& results);
     /**
      * QueryState API
      * @return
@@ -203,23 +212,17 @@ class GNAPlugin : public InferenceEngine::IInferencePlugin {
                     intel_dnn_orientation_t orientation,
                     float scaleFactor);
 
-    template <typename T, typename U>
-    void copyInputDataWithSplit(T *const dst,
-                    const U *src,
-                    const GNASplitLayer& splitInfo,
-                    size_t precision_size,
-                    int idx = 0);
-
     void UpdateFieldsFromConfig();
     void UpdateInputScaleFromNetwork(InferenceEngine::CNNNetwork& network);
     void UpdateInputsAndOutputsInfoFromNetwork(InferenceEngine::CNNNetwork &);
+    void UpdateInputsAndOutputsInfoFromModel(const std::shared_ptr<ov::Model> &model);
     /**
      * @brief Tries to init an output on the base of a layer data
      * @param portId output port identificator
      * @param layer layer pointer
      * @return true if the output is initiated, false otherwise
     */
-    bool TryToInitOutput(int portId, InferenceEngine::CNNLayerPtr layer);
+    bool TryToInitOutput(const std::string &portName, InferenceEngine::CNNLayerPtr layer);
 
     /**
      * @brief Fills inputs and outputs transposition info for model convertion from NCHW to NHWC.
diff --git a/src/plugins/intel_gna/serial/headers/2dot7/gna_model_header.hpp b/src/plugins/intel_gna/serial/headers/2dot7/gna_model_header.hpp
index 14badf3adcf..6192a00ba0c 100644
--- a/src/plugins/intel_gna/serial/headers/2dot7/gna_model_header.hpp
+++ b/src/plugins/intel_gna/serial/headers/2dot7/gna_model_header.hpp
@@ -9,7 +9,6 @@
 #include "backend/dnn_types.h"
 #include "serial/headers/2dot4/gna_model_header.hpp"
 #include "serial/headers/2dot6/gna_model_header.hpp"
-#include "serial/headers/latest/gna_model_header.hpp"
 #include "gna_data_types.hpp"
 
 #pragma pack(push, 1)
@@ -193,5 +192,6 @@ struct RuntimeEndPoint {
                                                            precision(precision),
                                                            orientation(orientation) { }
 };
+
 } // namespace Header2dot7
 } // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/serial/headers/2dot8/gna_model_header.hpp b/src/plugins/intel_gna/serial/headers/2dot8/gna_model_header.hpp
new file mode 100644
index 00000000000..ca364dd2d7c
--- /dev/null
+++ b/src/plugins/intel_gna/serial/headers/2dot8/gna_model_header.hpp
@@ -0,0 +1,212 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <cstdint>
+#include <map>
+#include "backend/dnn_types.h"
+#include "serial/headers/2dot7/gna_model_header.hpp"
+#include "gna_data_types.hpp"
+
+#pragma pack(push, 1)
+
+namespace GNAPluginNS {
+namespace Header2dot8 {
+
+/**
+ Maximal number of supported shape dimensions.
+ */
+#define GNA_SHAPE_MAXIMUM_NUMBER_OF_DIMENSIONS 8
+
+/**
+ * @brief Header version 2.8
+ */
+struct ModelHeader {
+    /**
+     *@brief MagicNumber – GNAM in ascii table, equals to hex 0x474e414d
+     */
+    char gnam[4] = {};
+    /**
+     * @brief if header size is not equal to sizeof ModelHeader - some reserved data append in the end of header
+     * usually it is an indicator of working with version of model different that is current export function produce
+     */
+    uint32_t headerSize = 0u;
+    struct Version {
+        /**
+         * @details Version of format Major – unsigned int, ex: 0x0001
+         * every change in the header or in the layers definition should be reflected in version change
+         * for backward compatibility new parsers can read old versions of model with certain restrictions
+         */
+        uint16_t major = 2u;
+        /**
+         * @details Version of Format Minor – unsigned int,  corresponding to build revision for example
+         * changes in minor version are not affected layout of model
+         */
+        uint32_t minor = 8u;
+    } version;
+    /**
+     * @brief Memory required to be allocated using GNAAlloc()
+     */
+    uint64_t gnaMemSize = 0ull;
+    /**
+     * @brief Number of GNA Layers
+     */
+    uint64_t layersCount = 0ull;
+    /**
+     * @brief Grouping level
+     * This is depricted field and used for old models only (<=2.6)
+     */
+    uint32_t nGroup = 0u;
+
+    /**
+     * Convolution related setting - they are affecting input transformation
+     */
+    uint32_t nRotateRows = 0u;
+    uint32_t nRotateColumns = 0u;
+    bool doRotateInput = false;
+
+    uint32_t nInputs = 0u;
+    uint32_t nOutputs = 0u;
+
+    /**
+     * Convolution related setting - they are affecting output transformation
+     */
+    uint32_t nRotateOutputRows = 0u;
+    uint32_t nRotateOutputColumns = 0u;
+    bool doRotateOutput = false;
+
+    uint32_t nTransposeInputs = 0u;
+    uint32_t nTransposeOutputs = 0u;
+
+    /**
+     * Reserved Data might be here
+     */
+
+    ModelHeader() = default;
+
+    ModelHeader(GNAPluginNS::Header2dot1::ModelHeader const &old) {
+        gnaMemSize = old.gnaMemSize;
+        layersCount = old.layersCount;
+        nGroup = old.nGroup;
+        nRotateRows = old.nRotateRows;
+        nRotateColumns = old.nRotateColumns;
+        nInputs = old.nInputs;
+        nOutputs = old.nOutputs;
+        version.minor = old.version.minor;
+    }
+
+    ModelHeader(GNAPluginNS::Header2dot4::ModelHeader const &old) {
+        gnaMemSize = old.gnaMemSize;
+        layersCount = old.layersCount;
+        nGroup = old.nGroup;
+        nRotateRows = old.nRotateRows;
+        nRotateColumns = old.nRotateColumns;
+        nInputs = old.nInputs;
+        nOutputs = old.nOutputs;
+        nRotateOutputRows = old.nRotateOutputRows;
+        nRotateOutputColumns = old.nRotateOutputColumns;
+        doRotateOutput = old.doRotateOutput;
+        version.minor = old.version.minor;
+    }
+};
+#pragma pack(pop)
+
+/*
+ * In runtime endpoint mostly same as in serial version, except of descriptor field
+ */
+struct RuntimeEndPoint {
+    /**
+     * if scale factor is different then pased into infer , network might need to be requantized
+     */
+    float scaleFactor = 0;
+    /**
+     * Pointer descriptor
+     */
+    void* descriptor_ptr = nullptr;
+    /**
+     * Endpoint resolution in bytes.
+     */
+    uint32_t element_size = 0;
+    /**
+     * Number of elements
+     */
+    uint32_t elements_count = 0;
+    /**
+     * Offset in bytes of pointer descriptor
+    */
+    uint64_t descriptor_offset = 0ull;
+    /**
+     Shape specifying dimension values.
+    */
+    struct Shape {
+        /**
+         Number of dimensions or rank or order.
+        */
+        uint32_t NumberOfDimensions = 0;
+        /**
+         array specifying value of each dimension.
+        Set all zeros for scalars.
+        */
+        uint32_t Dimensions[GNA_SHAPE_MAXIMUM_NUMBER_OF_DIMENSIONS] = {0};
+    } shape;
+    /**
+     * Blob layout
+     */
+    uint8_t layout = InferenceEngine::Layout::NC;
+    /**
+     * Blob precision
+     */
+    uint8_t precision = InferenceEngine::Precision::FP32;
+    /**
+     * Number of tensor names
+     */
+    uint8_t tensor_names_count = 0;
+
+    intel_dnn_orientation_t orientation = kDnnUnknownOrientation;
+
+    RuntimeEndPoint() = default;
+
+    // support of previous versions
+    RuntimeEndPoint(const GNAPluginNS::Header2dot6::RuntimeEndPoint &old, uint32_t ngroup) {
+        GNAPluginNS::Header2dot7::RuntimeEndPoint ep_v7 = GNAPluginNS::Header2dot7::RuntimeEndPoint(old, ngroup);
+        *this = GNAPluginNS::Header2dot8::RuntimeEndPoint(ep_v7);
+    }
+
+    RuntimeEndPoint(GNAPluginNS::Header2dot7::RuntimeEndPoint &old) {
+        scaleFactor = old.scaleFactor;
+        descriptor_ptr = old.descriptor_ptr;
+        element_size = old.element_size;
+        elements_count = old.elements_count;
+        orientation = old.orientation;
+        layout = old.layout;
+        precision = old.precision;
+        descriptor_offset = old.descriptor_offset;
+        shape.NumberOfDimensions = old.shape.NumberOfDimensions;
+        for (auto i = 0; i < shape.NumberOfDimensions; i++) {
+            shape.Dimensions[i] = old.shape.Dimensions[i];
+        }
+        tensor_names_count = 0;
+    }
+
+    RuntimeEndPoint(double scaleFactor,
+                    void* descriptor_ptr,
+                    uint32_t element_size,
+                    uint32_t elements_count,
+                    Shape shape,
+                    uint8_t layout,
+                    uint8_t precision,
+                    uint8_t tensor_names_count,
+                    intel_dnn_orientation_t orientation) : scaleFactor(scaleFactor),
+                                                           descriptor_ptr(descriptor_ptr),
+                                                           element_size(element_size),
+                                                           elements_count(elements_count),
+                                                           shape(shape),
+                                                           layout(layout),
+                                                           precision(precision),
+                                                           tensor_names_count(tensor_names_count),
+                                                           orientation(orientation) { }
+};
+} // namespace Header2dot8
+} // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/serial/headers/latest/gna_model_header.hpp b/src/plugins/intel_gna/serial/headers/latest/gna_model_header.hpp
index 7ec27b2caed..db1cdeb015e 100644
--- a/src/plugins/intel_gna/serial/headers/latest/gna_model_header.hpp
+++ b/src/plugins/intel_gna/serial/headers/latest/gna_model_header.hpp
@@ -4,11 +4,11 @@
 
 #pragma once
 
-#include "serial/headers/2dot7/gna_model_header.hpp"
+#include "serial/headers/2dot8/gna_model_header.hpp"
 
 namespace GNAPluginNS {
 namespace HeaderLatest {
-using ModelHeader = GNAPluginNS::Header2dot7::ModelHeader;
-using RuntimeEndPoint = GNAPluginNS::Header2dot7::RuntimeEndPoint;
+using ModelHeader = GNAPluginNS::Header2dot8::ModelHeader;
+using RuntimeEndPoint = GNAPluginNS::Header2dot8::RuntimeEndPoint;
 }
 }
diff --git a/src/tests/functional/plugin/gna/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp b/src/tests/functional/plugin/gna/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp
new file mode 100644
index 00000000000..12740bb9ea0
--- /dev/null
+++ b/src/tests/functional/plugin/gna/shared_tests_instances/behavior/ov_executable_network/exec_graph_info.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "behavior/ov_executable_network/exec_graph_info.hpp"
+
+#include "ie_plugin_config.hpp"
+#include <common_test_utils/test_constants.hpp>
+
+using namespace ov::test::behavior;
+namespace {
+const std::vector<ov::element::Type_t> netPrecisions = {
+        ov::element::i16,
+        ov::element::u8,
+        ov::element::f32
+};
+const std::vector<std::map<std::string, std::string>> configs = {
+    {{"GNA_DEVICE_MODE", "GNA_SW_EXACT"}}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests,
+                         OVExecGraphImportExportTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_GNA),
+                                 ::testing::ValuesIn(configs)),
+                         OVExecGraphImportExportTest::getTestCaseName);
+
+}  // namespace
\ No newline at end of file
diff --git a/src/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp
index 9b805022d36..37b3d1fd2e8 100644
--- a/src/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp
@@ -68,10 +68,10 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*OVExecutableNetworkBaseTest.*getOutputsFromSplitFunctionWithSeveralOutputs.*)",
         R"(.*OVClassHeteroExecutableNetworkGetMetricTest_TARGET_FALLBACK.*GetMetricNoThrow.*)",
         R"(.*Behavior.*OVExecutableNetworkBaseTest.*get(Inputs|Outputs)FromFunctionWithSeveral(Inputs|Outputs).*)",
+        // TODO: temporary disabled. Need to be enabled when PR 9282 is merged
+        R"(.*OVExecGraphImportExportTest.*readFromV10IR.*)",
         // TODO: Issue: 29577
         R"(.*QueryNetwork.*)",
-        // TODO: GNA plugin does not support ExecGraph
-        R"(.*ExecGraphTests.*)",
         // Issue connected with OV2.0
         R"(.*EltwiseLayerTest.*NetType=f16.*)",
         // TODO: Issue: 69639
diff --git a/src/tests/unit/gna/gna_wait_test.cpp b/src/tests/unit/gna/gna_wait_test.cpp
index 0621da16c18..6da18d29551 100644
--- a/src/tests/unit/gna/gna_wait_test.cpp
+++ b/src/tests/unit/gna/gna_wait_test.cpp
@@ -29,8 +29,8 @@ class GNAPluginForGNAWaitTest : public GNAPlugin {
         auto fakeInfo = std::make_shared<InferenceEngine::InputInfo>();
         auto fakePtr = std::make_shared<InferenceEngine::Data>("fakeName", td);
         fakeInfo->setInputData(fakePtr);
-        outputsDataMap["fakeOut"] = fakePtr;
-        inputsDataMap["fakeIn"] = fakeInfo;
+        outputs_data_map_["fakeOut"] = fakePtr;
+        inputs_data_map_["fakeIn"] = fakeInfo;
         gnaRequestConfigToRequestIdMap.push_back(std::tuple<uint32_t, int64_t, InferenceEngine::BlobMap>{ 0, 0, {} });
         InitGNADevice();
     }
@@ -39,10 +39,10 @@ class GNAPluginForGNAWaitTest : public GNAPlugin {
 class GNAInferRequestForGNAWaitTest : public GNAInferRequest {
  public:
     // Prepare underlining object to enable Wait() working
-    GNAInferRequestForGNAWaitTest(std::shared_ptr<GNAPlugin> plugin) : GNAInferRequest{
+    GNAInferRequestForGNAWaitTest(std::shared_ptr<GNAPlugin> plugin) : GNAInferRequest {
                                             plugin,
-                                            plugin->GetInputs(),
-                                            plugin->GetOutputs() } {
+                                            plugin->GetNetworkInputs(),
+                                            plugin->GetNetworkOutputs() } {
         inferRequestIdx = 0;
     }
 };
diff --git a/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp b/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp
index 15cf4951c58..7d6105ad47e 100644
--- a/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp
+++ b/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp
@@ -200,8 +200,8 @@ void GNAPropagateMatcher :: match() {
             }
 
             auto sp = plugin.ImportNetwork(inputStream);
-            inputsInfo = plugin.GetInputs();
-            outputsInfo = plugin.GetOutputs();
+            inputsInfo = plugin.GetNetworkInputs();
+            outputsInfo = plugin.GetNetworkOutputs();
         };
 
         std::map<std::string, Blob::Ptr> input;

From 63121e28ca6539f0d372e34ce6f7596ac4a71a6d Mon Sep 17 00:00:00 2001
From: Svetlana Dolinina <svetlana.a.dolinina@intel.com>
Date: Wed, 22 Dec 2021 01:41:49 +0300
Subject: [PATCH 080/160] fixed function that changes parameter but don't
 return it back; (#9177)

fixed return of results from transformation for case of not 1 added result (last node with several out ports)
modified test to cover case with several output ports for last node
---
 .../tools/mo/back/add_outputs_recursive.py    | 20 +++++++++++--------
 .../mo/back/add_outputs_recursive_test.py     | 16 ++++++++++-----
 2 files changed, 23 insertions(+), 13 deletions(-)

diff --git a/tools/mo/openvino/tools/mo/back/add_outputs_recursive.py b/tools/mo/openvino/tools/mo/back/add_outputs_recursive.py
index aa05bd3c157..fb70d560919 100644
--- a/tools/mo/openvino/tools/mo/back/add_outputs_recursive.py
+++ b/tools/mo/openvino/tools/mo/back/add_outputs_recursive.py
@@ -32,7 +32,6 @@ def get_iterations_count_from_output_record(output_rec):
     def check_field(record, field):
         return field in record and record[field] is not None
 
-    iterations_count = 1
     # 1. check if we need to concatenate iteration results for given output
     if not check_field(output_rec, 'axis'):
         # in this case we do not concatenate outputs, so iterations count is not needed really
@@ -122,7 +121,7 @@ def add_output_in_body(node, port_num, cur_graph, cur_max_layer_id, tracks, trac
     cur_max_layer_id += 1
     tracks.insert(track_index, {'node': res_node, 'graph': cur_graph})
 
-    return res_node
+    return res_node, tracks, cur_max_layer_id
 
 
 class AddOutputRecursive(BackReplacementPattern):
@@ -154,8 +153,9 @@ class AddOutputRecursive(BackReplacementPattern):
             if cur_loop_node.op is not 'If':
                 # add Unsqueeze and Result for TensorIterator and Loop and update output_port_map
                 for p_num in ports_to_add_nodes:
-                    res_node = add_output_in_body(step_node, p_num, cur_graph, cur_max_layer_id,
-                                                  graphs_nodes_path, i)
+                    res_node, graphs_nodes_path, cur_max_layer_id = add_output_in_body(step_node, p_num, cur_graph,
+                                                                                       cur_max_layer_id,
+                                                                                       graphs_nodes_path, i)
 
                     # IR reader fix output port map for Loop, but have not change for TensorIterator
                     new_port_id = len(cur_loop_node.out_ports())
@@ -173,8 +173,10 @@ class AddOutputRecursive(BackReplacementPattern):
             else:
                 # add Result nodes for If and update output_id
                 for p_num in ports_to_add_nodes:
-                    res_node = add_output_in_body(step_node, p_num, cur_graph, cur_max_layer_id, graphs_nodes_path, i,
-                                                  add_unsqueeze=False)
+                    res_node, graphs_nodes_path, cur_max_layer_id = add_output_in_body(step_node, p_num, cur_graph,
+                                                                                       cur_max_layer_id,
+                                                                                       graphs_nodes_path, i,
+                                                                                       add_unsqueeze=False)
 
                     if cur_loop_node.then_graph == cur_graph:
                         new_port_id = len(cur_loop_node.out_ports())
@@ -314,7 +316,9 @@ class AddOutputRecursive(BackReplacementPattern):
         new_nodes = []
         for i in range(len(paths_nodes_graphs)):
             # new Result added to main graph should be on last place
-            if paths_nodes_graphs_old[i][0]['node'] != paths_nodes_graphs[i][0]['node']:
-                new_nodes.append(paths_nodes_graphs[i][0]['node'])
+            k = 0
+            while paths_nodes_graphs_old[i][0]['node'] != paths_nodes_graphs[i][k]['node']:
+                new_nodes.append(paths_nodes_graphs[i][k]['node'])
+                k += 1
 
         return new_nodes
diff --git a/tools/mo/unit_tests/mo/back/add_outputs_recursive_test.py b/tools/mo/unit_tests/mo/back/add_outputs_recursive_test.py
index 4c021ea4442..362116a6b5e 100644
--- a/tools/mo/unit_tests/mo/back/add_outputs_recursive_test.py
+++ b/tools/mo/unit_tests/mo/back/add_outputs_recursive_test.py
@@ -65,9 +65,10 @@ sub_graph_1_nodes = {
 
 sub_graph_2_nodes = {
     **shaped_parameter('cond_2_int', [1, 4, 64, 54], {'internal_layer_id': 0}),
-    **result("cond_2_int_out"),
+    **regular_op_with_empty_data("cond_2_int_out",
+                                 {'op': 'Result', 'type': 'Result', 'infer': lambda x: None, 'internal_layer_id': 8}),
     **shaped_parameter('in_2_int', [1, 4, 64, 54], {'internal_layer_id': 1}),
-    **shaped_const_with_data('ones', int64_array([1, 4, 64, 54])),
+    **shaped_const_with_data('ones', int64_array([1, 4, 64, 54]), {'internal_layer_id': 9}),
     **regular_op_with_shaped_data('OUT_2', int64_array([1, 4, 64, 54]), {'op': "Add", 'infer': copy_shape_infer}),
     **regular_op_with_empty_data('OUT_2_out',
                                  {'op': 'Result', 'type': 'Result', 'infer': lambda x: None, 'internal_layer_id': 7}),
@@ -153,16 +154,21 @@ class AddOutputRecursiveTest(unittest.TestCase):
         loop_node = Node(main_graph, 'Loop')
         loop_node.body = sub_graph_1
         main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+        loop_node_1['out_ports_count'] = 2
+        loop_node_1.add_output_port(1)
+        loop_node_1['output_port_map'].append({'external_port_id': 1, 'internal_layer_id': 8, 'axis': None})
+
         loop_node_output_port_map_len = len(loop_node.output_port_map)
         loop_node_out_ports_len = len(loop_node.out_ports())
         loop_2_out_ports_len = len(loop_node_1.out_ports())
         max_layer_id = 5
 
-        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        results = AddOutputRecursive().find_and_replace_pattern(main_graph)
 
+        self.assertEqual(len(results), 2)
         loop_node = Node(main_graph, 'Loop')
-        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
-        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 2)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 2)
         self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
         self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() == int64_array([5, 10, 4, 64, 54])))
         last_node = Node(sub_graph_1, 'Loop_2')

From 7bcca1b82dc34ac53bf1fac3f210cf98aab42762 Mon Sep 17 00:00:00 2001
From: Jade Cho <jade.cho@intel.com>
Date: Wed, 22 Dec 2021 11:43:04 +0900
Subject: [PATCH 081/160] [GPU] Fix some bugs of cldnn eltwise kernel at
 bs_fs_yx_bsv32_fsv16 format. (#9198)

* Fix some bugs of cldnn eltwise kernel at bs_fs_yx_bsv32_fsv16 format.

+ Add a condition so that the eltwise_simple_vload8 kernel is not selected when the tensor is not aligned as bsv32_fsv16 or bsv32_fsv32.
+ Optimize gws/lws of eltwise_ref kernel for bsv32_fsv16 format.

* Check feature align of b_fs_yx_fsv32 for eltwise vload8 kernel

+ Minor fix for OV_GPU_Help option
---
 .../eltwise/eltwise_kernel_base.cpp           | 10 +++---
 .../eltwise/eltwise_kernel_vload8.cpp         | 33 ++++++++++++-------
 .../core/common/kernel_selector_utils.cpp     |  4 ++-
 .../clDNN/runtime/debug_configuration.cpp     |  2 +-
 4 files changed, 31 insertions(+), 18 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp
index 9874507ae0b..af1a19b96e6 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp
@@ -624,11 +624,11 @@ EltwiseKernelBase::DispatchData EltwiseKernelBase::SetDefault(const eltwise_para
         }
     } else if (params.output.GetLayout() == DataLayout::bs_fs_yx_bsv32_fsv16 &&
                 (params.output.Feature().v % 16 != 0 || dispatchData.gws[1] % 16 != 0)) {
-            auto bs_fsv16_local = GetLimitedOptimalLocalWorkGroupSizes({dispatchData.gws[1], dispatchData.gws[2], dispatchData.gws[0]},
-                                                                        params.engineInfo, {16, 32, params.engineInfo.maxWorkGroupSize});
-            dispatchData.lws[0] = bs_fsv16_local[2];
-            dispatchData.lws[1] = bs_fsv16_local[0];
-            dispatchData.lws[2] = bs_fsv16_local[1];
+            auto bs_fsv16_local = GetLimitedOptimalLocalWorkGroupSizes({dispatchData.gws[2], dispatchData.gws[0], dispatchData.gws[1]},
+                                                                        params.engineInfo, {32 * 16, 1024, 1024});
+            dispatchData.lws[0] = bs_fsv16_local[1];
+            dispatchData.lws[1] = bs_fsv16_local[2];
+            dispatchData.lws[2] = bs_fsv16_local[0];
     } else {
         dispatchData.lws[0] = local[0];
         dispatchData.lws[1] = local[1];
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp
index b5f6b40c5fc..b89ba4e0fce 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp
@@ -32,18 +32,29 @@ bool EltwiseKernel_vload8::Validate(const Params& params, const optional_params&
 
     const auto& ewParams = static_cast<const eltwise_params&>(params);
 
-    for (size_t i = 0; i < ewParams.inputs.size(); i++) {
-        if ((ewParams.inputs[i].GetLayout() == DataLayout::b_fs_yx_fsv16 && ewParams.inputs[i].Feature().v % 16 != 0) ||
-            (ewParams.inputs[i].GetLayout() == DataLayout::b_fs_zyx_fsv16 && ewParams.inputs[i].Feature().v % 16 != 0) ||
-            (ewParams.inputs[i].GetLayout() == DataLayout::b_fs_yx_fsv4 && ewParams.inputs[i].Feature().v % 8 != 0) ||
-            ewParams.inputs[i].GetLayout() == DataLayout::fs_b_yx_fsv32)
+        for (size_t i = 0; i < ewParams.inputs.size(); i++) {
+            const auto input_layout = ewParams.inputs[i].GetLayout();
+            const auto batch_size = ewParams.inputs[i].Batch().v;
+            const auto feature_size = ewParams.inputs[i].Feature().v;
+            if ((input_layout == DataLayout::b_fs_yx_fsv16 && feature_size % 16 != 0) ||
+                (input_layout == DataLayout::b_fs_yx_fsv32 && feature_size % 32 != 0) ||
+                (input_layout == DataLayout::b_fs_zyx_fsv16 && feature_size % 16 != 0) ||
+                (input_layout == DataLayout::b_fs_yx_fsv4 && feature_size % 8 != 0) ||
+                input_layout == DataLayout::fs_b_yx_fsv32 ||
+                (input_layout == DataLayout::bs_fs_yx_bsv32_fsv16 && (feature_size % 16 != 0 || batch_size % 32 != 0)) ||
+                (input_layout == DataLayout::bs_fs_yx_bsv32_fsv32 && (feature_size % 32 != 0 || batch_size % 32 != 0)))
+                return false;
+        }
+        if ((ewParams.output.GetLayout() == DataLayout::b_fs_yx_fsv16 && ewParams.output.Feature().v % 16 != 0) ||
+            (ewParams.output.GetLayout() == DataLayout::b_fs_yx_fsv32 && ewParams.output.Feature().v % 32 != 0) ||
+            (ewParams.output.GetLayout() == DataLayout::b_fs_zyx_fsv16 && ewParams.output.Feature().v % 16 != 0) ||
+            (ewParams.output.GetLayout() == DataLayout::b_fs_yx_fsv4 && ewParams.output.Feature().v % 8 != 0) ||
+            ewParams.output.GetLayout() == DataLayout::fs_b_yx_fsv32 ||
+            (ewParams.output.GetLayout() == DataLayout::bs_fs_yx_bsv32_fsv16 &&
+                (ewParams.output.Feature().v % 16 != 0 || ewParams.output.Batch().v % 32 != 0)) ||
+            (ewParams.output.GetLayout() == DataLayout::bs_fs_yx_bsv32_fsv32 &&
+                (ewParams.output.Feature().v % 32 != 0 || ewParams.output.Batch().v % 32 != 0)))
             return false;
-    }
-    if ((ewParams.output.GetLayout() == DataLayout::b_fs_yx_fsv16 && ewParams.output.Feature().v % 16 != 0) ||
-        (ewParams.output.GetLayout() == DataLayout::b_fs_zyx_fsv16 && ewParams.output.Feature().v % 16 != 0) ||
-        (ewParams.output.GetLayout() == DataLayout::b_fs_yx_fsv4 && ewParams.output.Feature().v % 8 != 0) ||
-        ewParams.output.GetLayout() == DataLayout::fs_b_yx_fsv32)
-        return false;
 
     const auto& output = ewParams.output;
     const auto count = output.PhysicalSize();
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp
index d230508ce91..82ef6112177 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp
@@ -425,7 +425,9 @@ bool CheckInputsOutputNoPitchSameDims(const base_params& params) {
         {DataLayout::bs_f_bsv16__af8,        {16, 8}},
         {DataLayout::b_fs_yx_fsv4,           {1, 4}},
         {DataLayout::fs_b_yx_fsv32,          {1, 32}},
-        {DataLayout::b_fs_yx_32fp,           {1, 32}}
+        {DataLayout::b_fs_yx_32fp,           {1, 32}},
+        {DataLayout::bs_fs_yx_bsv32_fsv16,   {32, 16}},
+        {DataLayout::bs_fs_yx_bsv32_fsv32,   {32, 32}}
     };
 
     if (params.inputs.size()) {
diff --git a/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp b/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
index 2a387b39515..39053475511 100644
--- a/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
@@ -123,7 +123,7 @@ static void print_help_messages() {
 
     GPU_DEBUG_COUT << "Supported environment variables for debugging" << std::endl;
     for (auto& p : message_list) {
-        GPU_DEBUG_COUT << " - " << std::left << std::setw(name_width) << p.first + ": " << p.second << std::endl;
+        GPU_DEBUG_COUT << " - " << std::left << std::setw(name_width) << p.first + "  " << p.second << std::endl;
     }
 }
 

From b8e6b6368c0bc976bd5407299b8d2c1c23b83a39 Mon Sep 17 00:00:00 2001
From: Tingqian Li <tingqian.li@intel.com>
Date: Wed, 22 Dec 2021 11:54:12 +0800
Subject: [PATCH 082/160] [Shape inference]
 Pad_1/Topk_3/Split_1/VariadicSplit_1/ExperimentalDetectronROIFeatureExtractor_6/Bucketize_3/EmbeddingBagOffsetsSum_3/EmbeddingSegmentsSum_3/Range_4/RegionYolo_0/ReorgYolo_0
 (#8413)

* [shape_infer] add shape_infer for ExperimentalDetectronROIFeatureExtractor op

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* add test

* Use compatible & merge for intersection checks

* Update

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* Add perf_test

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* Initial commit

* fix compile issue

* Add test

* fix clang format issue

* support for pads_begin/pads_end with different sizes

* fix bug in EDGE mode checking

* fix padding mode checks

* fix according to jane's review comment

* fix const reference

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* Initial commit

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* fix bugs

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* Switch to use single generic code with small helper template

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* Initial commit on Split

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* Convolution update

* Adds pragma once

* Reductions shape infer

* Shape nodes

* style

* Update

* add exp detectron roi feature

* Update

Signed-off-by: Li, Tingqian <tingqian.li@intel.com>

* Use get_data_as_int64 + constant_data

* Add test

* Add utils.hpp into cpuUnit shape inference test

* avoid using friend template function

* fix topk axis bug

* Add bucketize

* Add embeddingbag offsets sum

* Add embedding segments sum

* fix code style issue

* Add Range_4

* Update tests

* Add range

* Add region Yolo

* Add reorg

* fix according to Globev's comment

* call shape_infer in evaluate_variadic_split()

* fix CI issue

* fix CI issue

* fix CI issue, topk change revert

* fix flake8 E302

* fix myriad smoke test issue

* fix according to Vladislav's second round review

* fix format

* Add StridedSlice & Einsum

* fix pad_test.cpp build issue

* fix according to review comment

* insert directly into output shape

* revert infer_slice_shape() change since vpux compiler uses this function

* move tests

Co-authored-by: Stepyreva, Evgenya <evgenya.stepyreva@intel.com>
---
 .../utils/shape_inference/shape_inference.cpp |  44 ++++
 src/bindings/python/tests/__init__.py         |   1 -
 .../python/tests/test_onnx/test_backend.py    |   7 -
 .../tests/test_onnx/test_ops_reshape.py       |   5 +-
 .../python/tests_compatibility/__init__.py    |   1 -
 .../test_onnx/test_backend.py                 |   7 -
 .../test_onnx/test_ops_reshape.py             |   5 +-
 src/core/include/openvino/op/einsum.hpp       |   2 +-
 .../op/experimental_detectron_roi_feature.hpp |   5 +
 src/core/include/openvino/op/range.hpp        |   5 +
 src/core/include/openvino/op/region_yolo.hpp  |   3 +
 src/core/include/openvino/op/split.hpp        |   1 +
 .../include/bucketize_shape_inference.hpp     |  30 +++
 .../include/einsum_shape_inference.hpp        | 112 +++++++++
 ...embedding_segments_sum_shape_inference.hpp |  71 ++++++
 .../embeddingbag_offsets_shape_inference.hpp  |  58 +++++
 ..._detectron_roi_feature_shape_inference.hpp |  89 +++++++
 .../include/pad_shape_inference.hpp           | 148 +++++++++++
 .../include/range_shape_inference.hpp         | 152 +++++++++++
 .../include/region_yolo_shape_inference.hpp   |  59 +++++
 .../include/reorg_yolo_shape_inference.hpp    |  58 +++++
 .../include/split_shape_inference.hpp         |  85 +++++++
 .../include/strided_slice_shape_inference.hpp | 236 ++++++++++++++++++
 .../include/topk_shape_inference.hpp          |  93 +++++++
 .../variadic_split_shape_inference.hpp        | 131 ++++++++++
 src/core/src/op/bucketize.cpp                 |  10 +-
 src/core/src/op/einsum.cpp                    |  93 +------
 src/core/src/op/embedding_segments_sum.cpp    |  56 +----
 .../op/experimental_detectron_roi_feature.cpp |  62 +----
 src/core/src/op/pad.cpp                       |  88 +------
 src/core/src/op/range.cpp                     | 142 ++---------
 src/core/src/op/region_yolo.cpp               |  40 +--
 src/core/src/op/reorg_yolo.cpp                |  29 +--
 src/core/src/op/split.cpp                     |  50 +---
 src/core/src/op/strided_slice.cpp             |  46 +---
 src/core/src/op/topk.cpp                      |  42 +---
 .../src/op/util/embeddingbag_offsets_base.cpp |  40 +--
 src/core/src/op/variadic_split.cpp            | 110 ++------
 .../shape_inference_test/bucketize_test.cpp   |  18 ++
 .../cpu/shape_inference_test/einsum_test.cpp  |  50 ++++
 .../embedding_segments_sum_test.cpp           |  34 +++
 .../embeddingbag_offsets_sum_test.cpp         |  26 ++
 ...mental_detectron_roi_feature_extractor.cpp |  44 ++++
 .../cpu/shape_inference_test/pad_test.cpp     |  26 ++
 .../cpu/shape_inference_test/range_test.cpp   |  36 +++
 .../shape_inference_test/region_yolo_test.cpp |  27 ++
 .../shape_inference_test/reorg_yolo_test.cpp  |  19 ++
 .../cpu/shape_inference_test/split_tests.cpp  |  41 +++
 .../strided_slice_test.cpp                    |  97 +++++++
 .../cpu/shape_inference_test/topk_test.cpp    |  43 ++++
 .../unit/cpu/shape_inference_test/utils.hpp   |  84 +++++++
 .../variadic_split_tests.cpp                  |  50 ++++
 52 files changed, 2097 insertions(+), 714 deletions(-)
 create mode 100644 src/core/shape_inference/include/bucketize_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/einsum_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/embeddingbag_offsets_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/experimental_detectron_roi_feature_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/pad_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/range_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/region_yolo_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/reorg_yolo_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/split_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/strided_slice_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/topk_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/variadic_split_shape_inference.hpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/bucketize_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/einsum_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/embedding_segments_sum_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/embeddingbag_offsets_sum_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/experimental_detectron_roi_feature_extractor.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/pad_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/range_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/region_yolo_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/reorg_yolo_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/split_tests.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/strided_slice_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/topk_test.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/utils.hpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/variadic_split_tests.cpp

diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
index 1026524e75b..e9a10d89b67 100644
--- a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
+++ b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
@@ -10,6 +10,7 @@
 #include <openvino/opsets/opset4.hpp>
 #include <openvino/opsets/opset5.hpp>
 #include <openvino/opsets/opset6.hpp>
+#include <openvino/opsets/opset7.hpp>
 #include <openvino/opsets/opset8.hpp>
 
 #include "assign_shape_inference.hpp"
@@ -30,6 +31,21 @@
 #include "scatter_nd_base_shape_inference.hpp"
 #include "shape_inference.hpp"
 #include "shape_nodes.hpp"
+#include "fake_quantize.hpp"
+#include "experimental_detectron_detection_output_shape_inference.hpp"
+#include "bucketize_shape_inference.hpp"
+#include "embedding_segments_sum_shape_inference.hpp"
+#include "embeddingbag_offsets_shape_inference.hpp"
+#include "experimental_detectron_roi_feature_shape_inference.hpp"
+#include "pad_shape_inference.hpp"
+#include "range_shape_inference.hpp"
+#include "region_yolo_shape_inference.hpp"
+#include "reorg_yolo_shape_inference.hpp"
+#include "split_shape_inference.hpp"
+#include "topk_shape_inference.hpp"
+#include "variadic_split_shape_inference.hpp"
+#include "einsum_shape_inference.hpp"
+#include "strided_slice_shape_inference.hpp"
 #include "static_shape.hpp"
 #include "tile_shape_inference.hpp"
 #include "utils.hpp"
@@ -104,6 +120,34 @@ void shape_inference(ov::Node* op,
         shape_infer(node, input_shapes, output_shapes);
     } else if (auto node = ov::as_type<ov::opset6::ExperimentalDetectronDetectionOutput>(op)) {
         shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset3::TopK>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset3::Bucketize>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset3::EmbeddingSegmentsSum>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset3::EmbeddingBagOffsetsSum>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset6::ExperimentalDetectronROIFeatureExtractor>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset1::Pad>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset4::Range>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset1::Range>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset1::RegionYolo>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset2::ReorgYolo>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset1::Split>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset1::VariadicSplit>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset7::Einsum>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset1::StridedSlice>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
     } else if (auto node = ov::as_type<ov::opset3::Assign>(op)) {
         shape_infer(node, input_shapes, output_shapes);
     } else if (auto node = ov::as_type<ov::opset6::Assign>(op)) {
diff --git a/src/bindings/python/tests/__init__.py b/src/bindings/python/tests/__init__.py
index 08eb6fdd7b5..fc682409113 100644
--- a/src/bindings/python/tests/__init__.py
+++ b/src/bindings/python/tests/__init__.py
@@ -77,7 +77,6 @@ xfail_issue_38735 = xfail_test(reason="RuntimeError: nGraph does not support the
 xfail_issue_48052 = xfail_test(reason="Dropout op is not supported in traning mode")
 xfail_issue_45180 = xfail_test(reason="RuntimeError: Unsupported dynamic op: ReduceSum")
 xfail_issue_44851 = xfail_test(reason="Expected: Unsupported dynamic op: Broadcast")
-xfail_issue_44854 = xfail_test(reason="Expected: Unsupported dynamic op: VariadicSplit")
 xfail_issue_44858 = xfail_test(reason="Expected: Unsupported dynamic op: Unsqueeze")
 xfail_issue_44956 = xfail_test(reason="Expected: Unsupported dynamic op: Loop")
 xfail_issue_44957 = xfail_test(reason="Expected: Unsupported dynamic op: NonZero")
diff --git a/src/bindings/python/tests/test_onnx/test_backend.py b/src/bindings/python/tests/test_onnx/test_backend.py
index 1f7ab1d76c4..bc13cd18068 100644
--- a/src/bindings/python/tests/test_onnx/test_backend.py
+++ b/src/bindings/python/tests/test_onnx/test_backend.py
@@ -27,7 +27,6 @@ from tests import (
     xfail_issue_38735,
     xfail_issue_39658,
     xfail_issue_39662,
-    xfail_issue_44854,
     xfail_issue_44858,
     xfail_issue_44956,
     xfail_issue_44965,
@@ -264,12 +263,6 @@ tests_expected_to_fail = [
         "OnnxBackendNodeModelTest.test_reduce_sum_do_not_keepdims_example_cpu",
         "OnnxBackendNodeModelTest.test_reduce_sum_do_not_keepdims_random_cpu",
     ),
-    (
-        xfail_issue_44854,
-        "OnnxBackendNodeModelTest.test_split_variable_parts_1d_cpu",
-        "OnnxBackendNodeModelTest.test_split_variable_parts_2d_cpu",
-        "OnnxBackendNodeModelTest.test_split_variable_parts_default_axis_cpu",
-    ),
     (
         xfail_issue_44858,
         "OnnxBackendNodeModelTest.test_unsqueeze_axis_0_cpu",
diff --git a/src/bindings/python/tests/test_onnx/test_ops_reshape.py b/src/bindings/python/tests/test_onnx/test_ops_reshape.py
index e6fa1972e7f..356df399c0d 100644
--- a/src/bindings/python/tests/test_onnx/test_ops_reshape.py
+++ b/src/bindings/python/tests/test_onnx/test_ops_reshape.py
@@ -15,7 +15,6 @@ from tests.test_onnx.utils import (
     run_node,
 )
 from tests import (xfail_issue_35927,
-                   xfail_issue_44854,
                    xfail_issue_44858,
                    xfail_issue_44968)
 
@@ -306,7 +305,6 @@ def test_split_2d(node, expected_output):
     assert all_arrays_equal(ng_results, expected_output)
 
 
-@xfail_issue_44854
 def test_split_2d_splits_input():
     data = np.arange(8, dtype=np.int32).reshape(2, 4)
     splits = np.array([3, 1]).astype(np.int64)
@@ -321,7 +319,6 @@ def test_split_2d_splits_input():
     assert all_arrays_equal(ng_results, expected_outputs)
 
 
-@xfail_issue_44854
 def test_split_1d():
     # 1D
     data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
@@ -360,7 +357,7 @@ def test_split_1d():
 
     splits = np.array([2, 4]).astype(np.int64)
     node = onnx.helper.make_node(
-        "Split", inputs=["input", "splits"], outputs=["y", "z"], split=[2, 4]
+        "Split", inputs=["input", "splits"], outputs=["y", "z"]
     )
     expected_outputs = [
         np.array([1.0, 2.0]).astype(np.float32),
diff --git a/src/bindings/python/tests_compatibility/__init__.py b/src/bindings/python/tests_compatibility/__init__.py
index 23983b5e105..8b1a7baf257 100644
--- a/src/bindings/python/tests_compatibility/__init__.py
+++ b/src/bindings/python/tests_compatibility/__init__.py
@@ -88,7 +88,6 @@ xfail_issue_38735 = xfail_test(reason="RuntimeError: nGraph does not support the
 xfail_issue_48052 = xfail_test(reason="Dropout op is not supported in traning mode")
 xfail_issue_45180 = xfail_test(reason="RuntimeError: Unsupported dynamic op: ReduceSum")
 xfail_issue_44851 = xfail_test(reason="Expected: Unsupported dynamic op: Broadcast")
-xfail_issue_44854 = xfail_test(reason="Expected: Unsupported dynamic op: VariadicSplit")
 xfail_issue_44858 = xfail_test(reason="Expected: Unsupported dynamic op: Unsqueeze")
 xfail_issue_44956 = xfail_test(reason="Expected: Unsupported dynamic op: Loop")
 xfail_issue_44957 = xfail_test(reason="Expected: Unsupported dynamic op: NonZero")
diff --git a/src/bindings/python/tests_compatibility/test_onnx/test_backend.py b/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
index 58d36643606..2ba57b3a320 100644
--- a/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
+++ b/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
@@ -26,7 +26,6 @@ from tests_compatibility import (
     xfail_issue_38735,
     xfail_issue_39658,
     xfail_issue_39662,
-    xfail_issue_44854,
     xfail_issue_44858,
     xfail_issue_44956,
     xfail_issue_44965,
@@ -256,12 +255,6 @@ tests_expected_to_fail = [
         "OnnxBackendNodeModelTest.test_reduce_sum_do_not_keepdims_example_cpu",
         "OnnxBackendNodeModelTest.test_reduce_sum_do_not_keepdims_random_cpu",
     ),
-    (
-        xfail_issue_44854,
-        "OnnxBackendNodeModelTest.test_split_variable_parts_1d_cpu",
-        "OnnxBackendNodeModelTest.test_split_variable_parts_2d_cpu",
-        "OnnxBackendNodeModelTest.test_split_variable_parts_default_axis_cpu",
-    ),
     (
         xfail_issue_44858,
         "OnnxBackendNodeModelTest.test_unsqueeze_axis_0_cpu",
diff --git a/src/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py
index 28be8a7a5be..42187ad3c24 100644
--- a/src/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py
+++ b/src/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py
@@ -15,7 +15,6 @@ from tests_compatibility.test_onnx.utils import (
     run_node,
 )
 from tests_compatibility import (xfail_issue_35927,
-                                 xfail_issue_44854,
                                  xfail_issue_44858,
                                  xfail_issue_44968)
 
@@ -306,7 +305,6 @@ def test_split_2d(node, expected_output):
     assert all_arrays_equal(ng_results, expected_output)
 
 
-@xfail_issue_44854
 def test_split_2d_splits_input():
     data = np.arange(8, dtype=np.int32).reshape(2, 4)
     splits = np.array([3, 1]).astype(np.int64)
@@ -321,7 +319,6 @@ def test_split_2d_splits_input():
     assert all_arrays_equal(ng_results, expected_outputs)
 
 
-@xfail_issue_44854
 def test_split_1d():
     # 1D
     data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
@@ -360,7 +357,7 @@ def test_split_1d():
 
     splits = np.array([2, 4]).astype(np.int64)
     node = onnx.helper.make_node(
-        "Split", inputs=["input", "splits"], outputs=["y", "z"], split=[2, 4]
+        "Split", inputs=["input", "splits"], outputs=["y", "z"]
     )
     expected_outputs = [
         np.array([1.0, 2.0]).astype(np.float32),
diff --git a/src/core/include/openvino/op/einsum.hpp b/src/core/include/openvino/op/einsum.hpp
index d4060a6b54f..f0f18d191b3 100644
--- a/src/core/include/openvino/op/einsum.hpp
+++ b/src/core/include/openvino/op/einsum.hpp
@@ -37,7 +37,7 @@ public:
     ///
     /// \return     Einsum equation
     ///
-    std::string get_equation() const {
+    const std::string& get_equation() const {
         return m_equation;
     }
 
diff --git a/src/core/include/openvino/op/experimental_detectron_roi_feature.hpp b/src/core/include/openvino/op/experimental_detectron_roi_feature.hpp
index b9740d2d999..74ea29a8b9e 100644
--- a/src/core/include/openvino/op/experimental_detectron_roi_feature.hpp
+++ b/src/core/include/openvino/op/experimental_detectron_roi_feature.hpp
@@ -54,6 +54,11 @@ public:
 
 private:
     Attributes m_attrs;
+
+    template <class T>
+    friend void shape_infer(const ExperimentalDetectronROIFeatureExtractor* op,
+                            const std::vector<T>& input_shapes,
+                            std::vector<T>& output_shapes);
 };
 }  // namespace v6
 }  // namespace op
diff --git a/src/core/include/openvino/op/range.hpp b/src/core/include/openvino/op/range.hpp
index da605b0140c..a93f354b410 100644
--- a/src/core/include/openvino/op/range.hpp
+++ b/src/core/include/openvino/op/range.hpp
@@ -39,6 +39,11 @@ public:
     void set_output_type(element::Type output_type) {
         m_output_type = output_type;
     }
+
+    const element::Type& get_output_type() const {
+        return m_output_type;
+    }
+
     // Overload collision with method on Node
     using Node::set_output_type;
 
diff --git a/src/core/include/openvino/op/region_yolo.hpp b/src/core/include/openvino/op/region_yolo.hpp
index 092f82753e3..52b67ba7626 100644
--- a/src/core/include/openvino/op/region_yolo.hpp
+++ b/src/core/include/openvino/op/region_yolo.hpp
@@ -79,6 +79,9 @@ private:
     std::vector<float> m_anchors{};
     int m_axis;
     int m_end_axis;
+
+    template <class T>
+    friend void shape_infer(const RegionYolo* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes);
 };
 }  // namespace v0
 }  // namespace op
diff --git a/src/core/include/openvino/op/split.hpp b/src/core/include/openvino/op/split.hpp
index b7383c8ecac..ac2fa1c1092 100644
--- a/src/core/include/openvino/op/split.hpp
+++ b/src/core/include/openvino/op/split.hpp
@@ -12,6 +12,7 @@
 namespace ov {
 namespace op {
 namespace v1 {
+
 /// \brief Splits the input tensor into a list of equal sized tensors
 class OPENVINO_API Split : public Op {
 public:
diff --git a/src/core/shape_inference/include/bucketize_shape_inference.hpp b/src/core/shape_inference/include/bucketize_shape_inference.hpp
new file mode 100644
index 00000000000..755c4d9d247
--- /dev/null
+++ b/src/core/shape_inference/include/bucketize_shape_inference.hpp
@@ -0,0 +1,30 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/bucketize.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v3 {
+
+template <class T>
+void shape_infer(const Bucketize* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 2) && output_shapes.size() == 1);
+
+    const auto& data_shape = input_shapes[0];
+    const auto& buckets_shape = input_shapes[1];
+
+    NODE_VALIDATION_CHECK(op,
+                          buckets_shape.rank().compatible(1),
+                          "Buckets input must be a 1D tensor. Got: ",
+                          buckets_shape);
+    output_shapes[0] = data_shape;
+}
+}  // namespace v3
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/einsum_shape_inference.hpp b/src/core/shape_inference/include/einsum_shape_inference.hpp
new file mode 100644
index 00000000000..58342148444
--- /dev/null
+++ b/src/core/shape_inference/include/einsum_shape_inference.hpp
@@ -0,0 +1,112 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/einsum.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v7 {
+
+template <class T>
+void shape_infer(const Einsum* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+
+    // check that equation has correct format and extract input and output subscripts
+    std::vector<std::string> input_subscripts;
+    std::string output_subscript;
+    Einsum::parse_equation(op->get_equation(), input_subscripts, output_subscript);
+
+    // a number of input subscripts must match with a number of input tensors
+    NODE_VALIDATION_CHECK(op,
+                          input_subscripts.size() == input_shapes.size(),
+                          "Equation must contain a number of subscripts equal to a number of Einsum inputs.");
+    NODE_VALIDATION_CHECK(op, output_shapes.size() == 1);
+
+    // create a dictionary with dimension sizes (or ranges in case of dynamic shapes) for each label
+    // and check their compatibility in case of repeating labels
+    std::unordered_map<std::string, T> label_to_shape;
+
+    for (size_t input_idx = 0; input_idx < input_shapes.size(); ++input_idx) {
+        const auto& pshape = input_shapes[input_idx];
+        std::vector<std::string> labels;
+        labels = Einsum::extract_labels(input_subscripts[input_idx]);
+
+        if (pshape.rank().is_static()) {
+            size_t input_rank = pshape.size();
+            // check that a rank is greater or equal to a number of labels
+            // these numbers are always equal if there is no ellipsis in the subscript
+            NODE_VALIDATION_CHECK(op,
+                                  input_rank >= labels.size(),
+                                  "Input rank must be greater or equal to a number of labels in the "
+                                  "corresponding input subscript.");
+
+            for (size_t label_ind = 0, dim_ind = 0; label_ind < labels.size() && dim_ind < input_rank; ++label_ind) {
+                auto const& label = labels[label_ind];
+                if (label.compare("...") == 0) {
+                    size_t num_broadcasted_dims = input_rank - labels.size() + 1;
+                    auto current_sub_pshape = T(std::vector<DimType>(pshape.begin() + dim_ind,
+                                                                     pshape.begin() + dim_ind + num_broadcasted_dims));
+                    if (label_to_shape.find(label) == label_to_shape.end()) {
+                        label_to_shape[label] = current_sub_pshape;
+                    } else {
+                        bool is_broadcast_success = T::broadcast_merge_into(label_to_shape[label],
+                                                                            current_sub_pshape,
+                                                                            op::AutoBroadcastType::NUMPY);
+                        NODE_VALIDATION_CHECK(op,
+                                              is_broadcast_success,
+                                              "Input dimensions labeled with ellipsis for Einsum "
+                                              "must be broadcastable.");
+                    }
+                    dim_ind += num_broadcasted_dims;
+                } else {
+                    if (label_to_shape.find(label) == label_to_shape.end()) {
+                        label_to_shape[label] = T{pshape[dim_ind]};
+                    } else {
+                        NODE_VALIDATION_CHECK(op,
+                                              label_to_shape[label].compatible(T{pshape[label_ind]}),
+                                              "Different input dimensions indicated by the same labels for Einsum "
+                                              "must be compatible.");
+                        T::merge_into(label_to_shape[label], T{pshape[dim_ind]});
+                    }
+                    ++dim_ind;
+                }
+            }
+        } else {
+            for (auto const& label : labels) {
+                NODE_VALIDATION_CHECK(op,
+                                      label != "...",
+                                      "The subscript corresponding to a dynamic rank input must "
+                                      "not contain ellipsis.");
+
+                if (label_to_shape.find(label) == label_to_shape.end()) {
+                    label_to_shape[label] = ov::PartialShape{Dimension::dynamic()};
+                }
+            }
+        }
+    }
+
+    // compute the output shape
+    std::vector<std::string> output_labels;
+    output_labels = Einsum::extract_labels(output_subscript);
+    auto& output_shape = output_shapes[0];
+
+    output_shape.resize(0);
+
+    for (auto const& output_label : output_labels) {
+        NODE_VALIDATION_CHECK(op,
+                              label_to_shape.find(output_label) != label_to_shape.end(),
+                              "Label in output subscript of Einsum equation must enter at least "
+                              "one input subscript.");
+        output_shape.insert(output_shape.end(),
+                            label_to_shape[output_label].begin(),
+                            label_to_shape[output_label].end());
+    }
+}
+}  // namespace v7
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp b/src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp
new file mode 100644
index 00000000000..8d05ebf172c
--- /dev/null
+++ b/src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp
@@ -0,0 +1,71 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <bitset>
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/embeddingbag_offsets_sum.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v3 {
+
+template <class T>
+void shape_infer(const EmbeddingSegmentsSum* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    const auto input_size = input_shapes.size();
+
+    NODE_VALIDATION_CHECK(op, (input_size >= 4 && input_size <= 6) && output_shapes.size() == 1);
+
+    static constexpr int EMB_TABLE = 0;
+    static constexpr int INDICES = 1;
+    static constexpr int SEGMENT_IDS = 2;
+    static constexpr int NUM_SEGMENTS = 3;
+    static constexpr int DEFAULT_INDEX = 4;
+    static constexpr int PER_SAMPLE_WEIGHTS = 5;
+
+    NODE_VALIDATION_CHECK(op, input_shapes[INDICES].rank().compatible(1), "INDICES must be 1D");
+    NODE_VALIDATION_CHECK(op, input_shapes[SEGMENT_IDS].rank().compatible(1), "SEGMENT_IDS must be 1D");
+    NODE_VALIDATION_CHECK(op,
+                          input_shapes[INDICES].compatible(input_shapes[SEGMENT_IDS]),
+                          "INDICES and SEGMENT_IDS shape must be same");
+
+    NODE_VALIDATION_CHECK(op, input_shapes[NUM_SEGMENTS].compatible(T{}), "NUM_SEGMENTS must be a scalar");
+
+    if (input_size >= 5) {
+        NODE_VALIDATION_CHECK(op, input_shapes[DEFAULT_INDEX].compatible(T{}), "DEFAULT_INDEX must be a scalar");
+    }
+
+    if (input_size == 6) {
+        NODE_VALIDATION_CHECK(op,
+                              input_shapes[PER_SAMPLE_WEIGHTS].rank().compatible(1),
+                              "PER_SAMPLE_WEIGHTS must be 1D");
+
+        NODE_VALIDATION_CHECK(op,
+                              input_shapes[INDICES].compatible(input_shapes[PER_SAMPLE_WEIGHTS]),
+                              "INDICES and PER_SAMPLE_WEIGHTS shape must be same");
+    }
+
+    const auto& emb_table_shape = input_shapes[EMB_TABLE];
+
+    auto& result_shape = output_shapes[0];
+    if (emb_table_shape.rank().is_static()) {
+        result_shape = emb_table_shape;
+        std::vector<int64_t> segments_value;
+        if (get_data_as_int64<T>(NUM_SEGMENTS, op, segments_value, constant_data)) {
+            result_shape[0] = segments_value[0];
+        } else {
+            result_shape[0] = Dimension::dynamic();
+        }
+    } else {
+        result_shape = ov::PartialShape::dynamic();
+    }
+}
+}  // namespace v3
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/embeddingbag_offsets_shape_inference.hpp b/src/core/shape_inference/include/embeddingbag_offsets_shape_inference.hpp
new file mode 100644
index 00000000000..292557a207e
--- /dev/null
+++ b/src/core/shape_inference/include/embeddingbag_offsets_shape_inference.hpp
@@ -0,0 +1,58 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/embeddingbag_offsets_sum.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace util {
+
+template <class T>
+void shape_infer(const ov::op::util::EmbeddingBagOffsetsBase* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes) {
+    const auto input_size = input_shapes.size();
+
+    NODE_VALIDATION_CHECK(op, (input_size >= 3 && input_size <= 5) && output_shapes.size() == 1);
+
+    static constexpr int EMB_TABLE = 0;
+    static constexpr int INDICES = 1;
+    static constexpr int OFFSETS = 2;
+    static constexpr int DEFAULT_INDEX = 3;
+    static constexpr int PER_SAMPLE_WEIGHTS = 4;
+
+    NODE_VALIDATION_CHECK(op, input_shapes[INDICES].rank().compatible(1), "INDICES must be 1D");
+    NODE_VALIDATION_CHECK(op, input_shapes[OFFSETS].rank().compatible(1), "OFFSETS must be 1D");
+
+    if (input_size >= 4) {
+        NODE_VALIDATION_CHECK(op, input_shapes[DEFAULT_INDEX].rank().compatible(0), "DEFAULT_INDEX must be a scalar");
+    }
+
+    if (input_size == 5) {
+        NODE_VALIDATION_CHECK(op,
+                              input_shapes[PER_SAMPLE_WEIGHTS].rank().compatible(1),
+                              "PER_SAMPLE_WEIGHTS must be 1D");
+
+        NODE_VALIDATION_CHECK(op,
+                              input_shapes[INDICES].compatible(input_shapes[PER_SAMPLE_WEIGHTS]),
+                              "INDICES and PER_SAMPLE_WEIGHTS shape must be same");
+    }
+
+    const auto& emb_table_shape = input_shapes[EMB_TABLE];
+    const auto& offsets_shape = input_shapes[OFFSETS];
+
+    if (emb_table_shape.rank().is_static()) {
+        output_shapes[0] = emb_table_shape;
+        output_shapes[0][0] = offsets_shape.rank().is_static() ? offsets_shape[0] : Dimension::dynamic();
+    } else {
+        output_shapes[0] = PartialShape::dynamic();
+    }
+}
+}  // namespace util
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/experimental_detectron_roi_feature_shape_inference.hpp b/src/core/shape_inference/include/experimental_detectron_roi_feature_shape_inference.hpp
new file mode 100644
index 00000000000..f68f4d46772
--- /dev/null
+++ b/src/core/shape_inference/include/experimental_detectron_roi_feature_shape_inference.hpp
@@ -0,0 +1,89 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/op/experimental_detectron_roi_feature.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v6 {
+// by definition:
+// inputs:
+//          1.    [number_of_ROIs, 4]
+//          2..L  [1, number_of_channels, layer_size[l], layer_size[l]]
+// outputs:
+//          1.  out_shape = [number_of_ROIs, number_of_channels, output_size, output_size]
+//          2.  out_rois_shape = [number_of_ROIs, 4]
+template <class T>
+void shape_infer(const ExperimentalDetectronROIFeatureExtractor* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+
+    NODE_VALIDATION_CHECK(op, input_shapes.size() >= 2 && output_shapes.size() == 2);
+
+    const auto& rois_shape = input_shapes[0];
+    auto& out_shape = output_shapes[0];
+    auto& out_rois_shape = output_shapes[1];
+
+    // all dimensions is initialized by-default as dynamic
+    out_shape.resize(4);
+    out_rois_shape.resize(2);
+
+    // infer static dimensions
+    out_shape[2] = op->get_attrs().output_size;
+    out_shape[3] = op->get_attrs().output_size;
+    out_rois_shape[1] = 4;
+
+    // infer number_of_ROIs (which may be dynamic/static)
+    auto rois_shape_rank = rois_shape.rank();
+    NODE_VALIDATION_CHECK(op, rois_shape_rank.compatible(2), "Input rois rank must be equal to 2.");
+
+    if (rois_shape_rank.is_static()) {
+        NODE_VALIDATION_CHECK(op,
+                              rois_shape[1].compatible(4),
+                              "The last dimension of the 'input_rois' input must be equal to 4. "
+                              "Got: ",
+                              rois_shape[1]);
+
+        out_shape[0] = rois_shape[0];
+        out_rois_shape[0] = rois_shape[0];
+    }
+
+    // infer number_of_channels;
+    // by definition, all shapes starting from input 2 must have same number_of_channels
+    DimType channels_intersection;
+    bool channels_intersection_initialized = false;
+    for (size_t i = 1; i < input_shapes.size(); i++) {
+        const auto& current_shape = input_shapes[i];
+        auto current_rank = current_shape.rank();
+
+        NODE_VALIDATION_CHECK(op,
+                              current_rank.compatible(4),
+                              "Rank of each element of the pyramid must be equal to 4. Got: ",
+                              current_rank);
+
+        if (current_rank.is_static()) {
+            NODE_VALIDATION_CHECK(op,
+                                  current_shape[0].compatible(1),
+                                  "The first dimension of each pyramid element must be equal to 1. "
+                                  "Got: ",
+                                  current_shape[0]);
+
+            if (channels_intersection_initialized) {
+                NODE_VALIDATION_CHECK(op,
+                                      DimType::merge(channels_intersection, channels_intersection, current_shape[1]),
+                                      "The number of channels must be the same for all layers of the pyramid.");
+            } else {
+                channels_intersection = current_shape[1];
+                channels_intersection_initialized = true;
+            }
+        }
+    }
+
+    out_shape[1] = channels_intersection;
+}
+}  // namespace v6
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/pad_shape_inference.hpp b/src/core/shape_inference/include/pad_shape_inference.hpp
new file mode 100644
index 00000000000..aaf90fcd96a
--- /dev/null
+++ b/src/core/shape_inference/include/pad_shape_inference.hpp
@@ -0,0 +1,148 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/pad.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v1 {
+
+template <class T>
+void shape_infer(const Pad* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    constexpr bool is_dynamic_shape = std::is_base_of<ov::PartialShape, T>::value;
+
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 3 || input_shapes.size() == 4) && output_shapes.size() == 1);
+
+    auto& output_shape = output_shapes[0];
+    auto pad_mode = op->get_pad_mode();
+
+    // Check the shape of pad_value
+    if (pad_mode == PadMode::CONSTANT && input_shapes.size() == 4) {
+        const auto& pad_value_shape = input_shapes[3];
+        NODE_VALIDATION_CHECK(op,
+                              pad_value_shape.rank().compatible(0),
+                              "Argument for padding value is not a scalar (shape: ",
+                              pad_value_shape,
+                              ").");
+    }
+    const auto& pads_begin_shape = input_shapes[1];
+    const auto& pads_begin_rank = pads_begin_shape.rank();
+
+    NODE_VALIDATION_CHECK(op,
+                          pads_begin_rank.compatible(1),
+                          "Argument for pads_begin is not 1D (shape: ",
+                          pads_begin_rank,
+                          ").");
+
+    const auto& pads_end_shape = input_shapes[2];
+    const auto& pads_end_rank = pads_end_shape.rank();
+    NODE_VALIDATION_CHECK(op,
+                          pads_end_rank.compatible(1),
+                          "Argument for pads_end is not 1D (shape: ",
+                          pads_end_rank,
+                          ").");
+
+    const auto& arg_shape = input_shapes[0];
+    const auto& arg_shape_rank = arg_shape.rank();
+
+    if (arg_shape_rank.is_static()) {
+        if (pads_begin_shape.is_static()) {
+            NODE_VALIDATION_CHECK(op,
+                                  pads_begin_shape[0].get_length() <= arg_shape_rank.get_length(),
+                                  "Number of elements of pads_begin must be >= 0 and <= arg rank "
+                                  "(pads_begin_shape[0]: ",
+                                  pads_begin_shape[0],
+                                  ").");
+        }
+        if (pads_end_shape.is_static()) {
+            NODE_VALIDATION_CHECK(op,
+                                  pads_end_shape[0].get_length() <= arg_shape_rank.get_length(),
+                                  "Number of elements of pads_end must be >= 0 and <= arg rank (pads_end_shape[0]: ",
+                                  pads_end_shape[0],
+                                  ").");
+        }
+
+        output_shape.resize(arg_shape_rank.get_length());
+
+        std::vector<int64_t> pads_begin_coord;
+        std::vector<int64_t> pads_end_coord;
+
+        get_data_as_int64<T>(1, op, pads_begin_coord, constant_data);
+        get_data_as_int64<T>(2, op, pads_end_coord, constant_data);
+
+        // special check for static shape inference
+        NODE_VALIDATION_CHECK(op,
+                              is_dynamic_shape || (!pads_begin_coord.empty()),
+                              "Cannot determined static output shape when pads_begin is not determined.");
+
+        NODE_VALIDATION_CHECK(op,
+                              is_dynamic_shape || (!pads_end_coord.empty()),
+                              "Cannot determined static output shape when pads_begin is not determined.");
+
+        if (!pads_begin_coord.empty() && !pads_end_coord.empty()) {
+            NODE_VALIDATION_CHECK(op,
+                                  (output_shape.size() == pads_begin_coord.size()),
+                                  "length of pads_begin mismatches with rank of input, expect ",
+                                  output_shape.size(),
+                                  ", but got ",
+                                  pads_begin_coord.size());
+
+            NODE_VALIDATION_CHECK(op,
+                                  (output_shape.size() == pads_end_coord.size()),
+                                  "length of pads_end mismatches with rank of input, expect ",
+                                  output_shape.size(),
+                                  ", but got ",
+                                  pads_end_coord.size());
+
+            for (size_t i = 0; i < output_shape.size(); i++) {
+                ptrdiff_t begin = pads_begin_coord[i];
+                ptrdiff_t end = pads_end_coord[i];
+
+                if (arg_shape[i].is_static()) {
+                    const auto& dim = arg_shape[i].get_length();
+                    output_shape[i] = static_cast<size_t>(begin + dim + end);
+
+                    if (i > 1) {
+                        NODE_VALIDATION_CHECK(op,
+                                              pad_mode != op::PadMode::EDGE || arg_shape[i].get_length() >= 1,
+                                              "EDGE padding mode requires an input of dimension of "
+                                              "at least 1 at each "
+                                              "spatial axis.");
+                        NODE_VALIDATION_CHECK(op,
+                                              pad_mode != op::PadMode::REFLECT || arg_shape[i].get_length() >= 2,
+                                              "REFLECT padding mode requires an input of dimension "
+                                              "of at least 2 at each "
+                                              "spatial axis.");
+                    }
+                    NODE_VALIDATION_CHECK(op,
+                                          pad_mode != op::PadMode::REFLECT || (begin < dim && end < dim),
+                                          "REFLECT padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
+                                          "must be not greater than 'data_shape[D] - 1'.");
+                    NODE_VALIDATION_CHECK(op,
+                                          pad_mode != op::PadMode::SYMMETRIC || (begin <= dim && end <= dim),
+                                          "SYMMETRIC padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
+                                          "must be not greater than 'data_shape[D]'.");
+                } else {
+                    output_shape[i] = arg_shape[i] + (begin + end);
+                }
+            }
+        } else {
+            output_shape = ov::PartialShape::dynamic(arg_shape_rank);
+        }
+    } else {
+        output_shape = ov::PartialShape::dynamic(arg_shape_rank);
+    }
+}
+
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/range_shape_inference.hpp b/src/core/shape_inference/include/range_shape_inference.hpp
new file mode 100644
index 00000000000..13d0486aa30
--- /dev/null
+++ b/src/core/shape_inference/include/range_shape_inference.hpp
@@ -0,0 +1,152 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/range.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+
+namespace ShapeInferRange {
+
+template <class T>
+inline bool get_data_as_double(
+    size_t idx,
+    const ov::Node* op,
+    std::vector<double>& axes_value,
+    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    if (constant_data.count(idx)) {
+        axes_value = ov::opset1::Constant(constant_data.at(idx)).cast_vector<double>();
+    } else {
+        const auto& constant = ov::as_type_ptr<ov::opset1::Constant>(op->get_input_node_shared_ptr(idx));
+        NODE_VALIDATION_CHECK(op, constant != nullptr, "Static shape inference lacks constant data on port ", idx);
+        axes_value = constant->cast_vector<double>();
+    }
+    return true;
+}
+
+template <>
+inline bool get_data_as_double<ov::PartialShape>(
+    size_t idx,
+    const ov::Node* op,
+    std::vector<double>& axes_value,
+    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data) {
+    if (constant_data.count(idx)) {
+        axes_value = ov::opset1::Constant(constant_data.at(idx)).cast_vector<double>();
+    } else if (const auto& constant = ov::get_constant_from_source(op->input_value(idx))) {
+        axes_value = constant->cast_vector<double>();
+    } else {
+        return false;
+    }
+    return true;
+}
+
+template <class T>
+void range_shape_infer(const Node* op,
+                       const std::vector<T>& input_shapes,
+                       std::vector<T>& output_shapes,
+                       bool output_is_integral,
+                       bool step_allows_zero,
+                       const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data) {
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 3) && output_shapes.size() == 1);
+
+    NODE_VALIDATION_CHECK(op, input_shapes[0].rank().compatible(0), "'start' input is not a scalar");
+    NODE_VALIDATION_CHECK(op, input_shapes[1].rank().compatible(0), "'stop' input is not a scalar");
+    NODE_VALIDATION_CHECK(op, input_shapes[2].rank().compatible(0), "'step' input is not a scalar");
+
+    std::vector<double> start_val;
+    std::vector<double> stop_val;
+    std::vector<double> step_val;
+
+    double start = 0;
+    double stop = 0;
+    double step = 0;
+
+    if (get_data_as_double<T>(0, op, start_val, constant_data)) {
+        NODE_VALIDATION_CHECK(op, start_val.size() == 1);
+        start = start_val[0];
+        NODE_VALIDATION_CHECK(op, std::isfinite(start) && !std::isnan(start), "'start' cannot be nan or infinite.");
+    }
+
+    if (get_data_as_double<T>(1, op, stop_val, constant_data)) {
+        NODE_VALIDATION_CHECK(op, stop_val.size() == 1);
+        stop = stop_val[0];
+        NODE_VALIDATION_CHECK(op, std::isfinite(stop) && !std::isnan(stop), "'stop' cannot be nan or infinite.");
+    }
+
+    if (get_data_as_double<T>(2, op, step_val, constant_data)) {
+        NODE_VALIDATION_CHECK(op, step_val.size() == 1);
+        step = step_val[0];
+        if (step_allows_zero)
+            NODE_VALIDATION_CHECK(op, std::isfinite(step) && !std::isnan(step), "'step' cannot be nan or infinite.");
+        else
+            NODE_VALIDATION_CHECK(op,
+                                  std::isfinite(step) && !std::isnan(step) && step != 0,
+                                  "'step' cannot be zero, nan, or infinite.");
+    }
+
+    if (start_val.size() == 1 && stop_val.size() == 1 && step_val.size() == 1) {
+        // all inputs must be casted to output_type before
+        // the rounding for casting values are done towards zero
+        if (output_is_integral) {
+            start = std::trunc(start);
+            stop = std::trunc(stop);
+            step = std::trunc(step);
+        }
+
+        // the number of elements is: max(ceil((stop − start) / step), 0)
+        double span;
+        if ((step > 0 && start >= stop) || (step < 0 && start <= stop)) {
+            span = 0;
+        } else {
+            span = stop - start;
+        }
+
+        double strided = ceil(fabs(span) / fabs(step));
+
+        output_shapes[0] = T{static_cast<uint32_t>(strided)};
+    } else {
+        output_shapes[0] = ov::PartialShape::dynamic(1);
+    }
+}
+}  // namespace ShapeInferRange
+
+namespace v0 {
+
+template <class T>
+void shape_infer(const Range* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    ShapeInferRange::range_shape_infer(op,
+                                       input_shapes,
+                                       output_shapes,
+                                       op->get_input_element_type(0).is_integral_number(),
+                                       false,
+                                       constant_data);
+}
+
+}  // namespace v0
+
+namespace v4 {
+
+template <class T>
+void shape_infer(const Range* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    ShapeInferRange::range_shape_infer(op,
+                                       input_shapes,
+                                       output_shapes,
+                                       op->get_output_type().is_integral_number(),
+                                       true,
+                                       constant_data);
+}
+
+}  // namespace v4
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/region_yolo_shape_inference.hpp b/src/core/shape_inference/include/region_yolo_shape_inference.hpp
new file mode 100644
index 00000000000..fb429ca7e09
--- /dev/null
+++ b/src/core/shape_inference/include/region_yolo_shape_inference.hpp
@@ -0,0 +1,59 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/region_yolo.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v0 {
+
+template <class T>
+void shape_infer(const RegionYolo* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 1) && output_shapes.size() == 1);
+
+    const auto& input_shape = input_shapes[0];
+    const auto& input_rank = input_shape.rank();
+    auto& output_shape = output_shapes[0];
+
+    NODE_VALIDATION_CHECK(op, input_rank.compatible(4), "Input must be a tensor of rank 4, but got ", input_rank);
+
+    if (input_rank.is_static()) {
+        int end_axis = op->m_end_axis;
+        if (end_axis < 0) {
+            end_axis += input_shape.size();
+        }
+
+        if (op->m_do_softmax) {
+            output_shape.resize(0);
+            auto axis = ov::normalize_axis(op, op->m_axis, input_rank);
+            DimType flat_dim = 1;
+            for (int64_t i = 0; i < axis; i++) {
+                output_shape.push_back(input_shape[i]);
+            }
+            for (int64_t i = axis; i < end_axis + 1; i++) {
+                flat_dim *= input_shape[i];
+            }
+            output_shape.push_back(flat_dim);
+            for (size_t i = end_axis + 1; i < input_shape.size(); i++) {
+                output_shape.push_back(input_shape[i]);
+            }
+        } else {
+            output_shape = T({input_shape[0],
+                              static_cast<typename DimType::value_type>(
+                                  (op->get_num_classes() + op->get_num_coords() + 1) * op->get_mask().size()),
+                              input_shape[2],
+                              input_shape[3]});
+        }
+    } else {
+        output_shape = ov::PartialShape::dynamic(ov::Rank(1, 4));
+    }
+}
+}  // namespace v0
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/reorg_yolo_shape_inference.hpp b/src/core/shape_inference/include/reorg_yolo_shape_inference.hpp
new file mode 100644
index 00000000000..83d04a47357
--- /dev/null
+++ b/src/core/shape_inference/include/reorg_yolo_shape_inference.hpp
@@ -0,0 +1,58 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/reorg_yolo.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v0 {
+
+template <class T>
+void shape_infer(const ReorgYolo* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 1) && output_shapes.size() == 1);
+    const auto& input_shape = input_shapes[0];
+    auto& output_shape = output_shapes[0];
+    const auto & strides = op->get_strides();
+    if (input_shape.rank().is_static()) {
+        NODE_VALIDATION_CHECK(op, input_shape.size() == 4, "[N, C, H, W] input shape is required.");
+
+        NODE_VALIDATION_CHECK(op,
+                              input_shape[2].is_dynamic() || (input_shape[2].get_length() % strides[0]) == 0,
+                              "For [N, C, H, W] input shape, H should be divisible by stride.");
+
+        NODE_VALIDATION_CHECK(op,
+                              input_shape[3].is_dynamic() || (input_shape[3].get_length() % strides[0]) == 0,
+                              "For [N, C, H, W] input shape, W should be divisible by stride.");
+
+        NODE_VALIDATION_CHECK(op,
+                              input_shape[1].is_dynamic() || input_shape[1].get_length() >= (strides[0] * strides[0]),
+                              "For [N, C, H, W] input shape, C >= (stride*stride) is required.");
+
+        output_shape = T({input_shape[0], input_shape[1]});
+
+        for (size_t i = 2; i < input_shape.size(); i++) {
+            if (input_shape[i].is_static())
+                output_shape.push_back(input_shape[i].get_length() / strides[0]);
+            else {
+                const auto& interval = input_shape[i].get_interval();
+                if (interval.has_upper_bound()) {
+                    output_shape.push_back(
+                        ov::Dimension(interval.get_max_val() / strides[0], interval.get_min_val() / strides[0]));
+                } else {
+                    output_shape.push_back(ov::Dimension::dynamic());
+                }
+            }
+            output_shape[1] *= strides[0];
+        }
+    } else {
+        output_shape = ov::PartialShape::dynamic(input_shape.rank());
+    }
+}
+}  // namespace v0
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/split_shape_inference.hpp b/src/core/shape_inference/include/split_shape_inference.hpp
new file mode 100644
index 00000000000..e658c09b3eb
--- /dev/null
+++ b/src/core/shape_inference/include/split_shape_inference.hpp
@@ -0,0 +1,85 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/split.hpp>
+
+#include "utils.hpp"
+
+namespace ov {
+namespace op {
+namespace v1 {
+
+template <typename T>
+void shape_infer(const Split* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 2));
+
+    output_shapes.clear();
+
+    const auto& data_ps = input_shapes[0];
+    const auto& axis_ps = input_shapes[1];
+
+    NODE_VALIDATION_CHECK(op, axis_ps.rank().compatible(0), "'axis' input must be a scalar. Got: ", axis_ps);
+
+    auto each_output_shape = data_ps;
+    const auto data_rank = data_ps.rank();
+
+    std::vector<int64_t> axes_values;
+    const auto & num_splits = op->get_num_splits();
+    if (get_data_as_int64<T>(1, op, axes_values, constant_data) && data_rank.is_static()) {
+        NODE_VALIDATION_CHECK(op,
+                              axes_values.size() == 1,
+                              "a scalar axis value is expected. Got: ",
+                              axes_values.size(),
+                              " axes");
+
+        auto axis = ov::normalize_axis(op, axes_values[0], data_rank);
+
+        if (data_ps[axis].is_static()) {
+            const auto dimension_at_axis = data_ps[axis].get_length();
+
+            NODE_VALIDATION_CHECK(op,
+                                  dimension_at_axis % num_splits == 0,
+                                  "Dimension of data input shape along 'axis': ",
+                                  dimension_at_axis,
+                                  " must be evenly divisible by 'num_splits' attribute value: ",
+                                  num_splits);
+
+            each_output_shape[axis] = dimension_at_axis / num_splits;
+        } else {
+            const auto dim_interval_at_axis = data_ps[axis].get_interval();
+            NODE_VALIDATION_CHECK(op,
+                                  dim_interval_at_axis.get_max_val() >= static_cast<int64_t>(num_splits),
+                                  "The interval maximum of the dimension for data "
+                                  "input shape along 'axis' must be "
+                                  "greater or equal to 'num_splits' attribute. Got: ",
+                                  dim_interval_at_axis,
+                                  " and ",
+                                  num_splits);
+
+            auto dim_interval_at_axis_min =
+                static_cast<int64_t>(dim_interval_at_axis.get_min_val() * (1.0f / num_splits));
+            auto dim_interval_at_axis_max = dim_interval_at_axis.get_max_val();
+            if (dim_interval_at_axis.has_upper_bound()) {
+                dim_interval_at_axis_max = static_cast<int64_t>(dim_interval_at_axis_max * (1.0f / num_splits));
+            }
+            each_output_shape[axis] = Dimension(dim_interval_at_axis_min, dim_interval_at_axis_max);
+        }
+    } else {
+        each_output_shape = ov::PartialShape::dynamic(data_ps.rank());
+    }
+
+    for (size_t i = 0; i < num_splits; ++i)
+        output_shapes.push_back(each_output_shape);
+}
+
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/strided_slice_shape_inference.hpp b/src/core/shape_inference/include/strided_slice_shape_inference.hpp
new file mode 100644
index 00000000000..7df56f24129
--- /dev/null
+++ b/src/core/shape_inference/include/strided_slice_shape_inference.hpp
@@ -0,0 +1,236 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/strided_slice.hpp>
+
+#include "utils.hpp"
+namespace ov {
+namespace op {
+namespace v1 {
+
+template <class T>
+void shape_infer(const StridedSlice* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 3 || input_shapes.size() == 4) && output_shapes.size() == 1);
+
+    const auto& input_shape = input_shapes[0];
+    const auto& begin_shape = input_shapes[1];
+    NODE_VALIDATION_CHECK(op,
+                          begin_shape.rank().compatible(1),
+                          "Begin input must be 1D (begin rank: ",
+                          begin_shape.rank(),
+                          ").");
+
+    const auto& end_shape = input_shapes[2];
+    NODE_VALIDATION_CHECK(op,
+                          end_shape.rank().compatible(1),
+                          "End input must be 1D (end rank: ",
+                          end_shape.rank(),
+                          ").");
+
+    std::vector<int64_t> begin;
+    std::vector<int64_t> end;
+    std::vector<int64_t> strides;
+
+    bool got_begin = get_data_as_int64<T>(1, op, begin, constant_data);
+    bool got_end = get_data_as_int64<T>(2, op, end, constant_data);
+    bool got_strides = false;
+
+    if (input_shapes.size() > 3) {
+        got_strides = get_data_as_int64<T>(3, op, strides, constant_data);
+    } else if (got_begin) {
+        // generate default strides
+        strides.resize(begin.size(), 1);
+        got_strides = true;
+    }
+
+    if (got_begin && got_end && got_strides) {
+        if (begin.size() && end.size()) {
+            NODE_VALIDATION_CHECK(op,
+                                  begin.size() == end.size(),
+                                  "Lower bounds and Upper bounds needs to have same number of values");
+        }
+        if (begin.size() && strides.size()) {
+            NODE_VALIDATION_CHECK(op,
+                                  begin.size() == strides.size(),
+                                  "Lower bounds and strides needs to have same number of values");
+        }
+        if (end.size() && strides.size()) {
+            NODE_VALIDATION_CHECK(op,
+                                  end.size() == strides.size(),
+                                  "Upper bounds and strides needs to have same number of values");
+        }
+
+        auto convert_mask_to_axis_set = [](const std::vector<int64_t>& mask) {
+            AxisSet axis_set{};
+            for (size_t i = 0; i < mask.size(); ++i) {
+                if (mask[i] == 1) {
+                    axis_set.emplace(i);
+                }
+            }
+            return axis_set;
+        };
+
+        AxisSet ellipsis_mask = convert_mask_to_axis_set(op->get_ellipsis_mask());
+        NODE_VALIDATION_CHECK(op, ellipsis_mask.size() <= 1, "At most one ellipsis is allowed.");
+
+        if (input_shape.rank().is_dynamic()) {
+            output_shapes[0] = ov::PartialShape::dynamic();
+            return;
+        }
+
+        auto input_rank = input_shape.size();
+
+        AxisSet new_axis_mask = convert_mask_to_axis_set(op->get_new_axis_mask());
+
+        NODE_VALIDATION_CHECK(op,
+                              input_rank + new_axis_mask.size() >= begin.size(),
+                              "Input rank plus number of new axis has to be at least the size of Lower "
+                              "and Upper bounds vector.");
+
+        AxisSet begin_mask = convert_mask_to_axis_set(op->get_begin_mask());
+        AxisSet end_mask = convert_mask_to_axis_set(op->get_end_mask());
+        AxisSet shrink_axis_mask = convert_mask_to_axis_set(op->get_shrink_axis_mask());
+
+        std::vector<DimType> dim;
+
+        int64_t input_shape_idx = 0;
+        for (size_t axis = 0; axis < begin.size(); ++axis) {
+            // add all dimensions hidden under the ellipsis mask if ellipsis mask is set
+            if (ellipsis_mask.count(axis)) {
+                // only one bit in ellipsis mask is allowed
+                int num_new_axis_after_ellipses = 0;
+                int num_input_axis_before_ellipses = 0;
+                for (size_t i = 0; i < axis; ++i) {
+                    if (!new_axis_mask.count(i)) {
+                        num_input_axis_before_ellipses++;
+                    }
+                }
+                for (size_t i = axis + 1; i < begin.size(); ++i) {
+                    if (new_axis_mask.count(i)) {
+                        num_new_axis_after_ellipses++;
+                    }
+                }
+
+                int64_t num_input_axis_after_ellipses =
+                    (begin.size() - axis - num_new_axis_after_ellipses - 1);  // -1 because it's a position of ellipses
+                int64_t num_of_hidden_dims =
+                    input_rank - num_input_axis_after_ellipses - num_input_axis_before_ellipses;
+                for (int64_t i = 0; i < num_of_hidden_dims; ++i) {
+                    dim.emplace_back(input_shape[input_shape_idx]);
+                    input_shape_idx++;
+                }
+            } else {
+                // add new single dimension if new_axis_mask is set
+                if (new_axis_mask.count(axis)) {
+                    dim.emplace_back(1);
+                }
+                // skip this dimension if shrink_axis_mask is set
+                else if (shrink_axis_mask.count(axis)) {
+                    input_shape_idx++;
+                }
+                // calculating dimension (begin, end, begin_mask, end_mask, stride)
+                else {
+                    const int64_t lb0 = begin[axis];
+                    const int64_t ub0 = end[axis];
+                    // set default value for stride or use given value
+                    int64_t stride = 1;
+                    if (strides.size() > axis) {
+                        stride = strides[axis];
+                    }
+                    NODE_VALIDATION_CHECK(op, stride != 0, "Stride must be non-zero");
+
+                    auto get_output_dim = [&](int64_t input_dim) {
+                        // make a mutable copy
+                        auto lb = lb0;
+                        auto ub = ub0;
+                        // convert negative indexes to positive
+                        // take max for this case: if abs(lb) > input_shape[input_shape_idx],then after
+                        // conversion lb < 0
+                        // so according to tensorflow and numpy we just get 0
+                        if (lb < 0) {
+                            lb = std::max(input_dim + lb, int64_t(0));
+                        }
+
+                        if (ub < 0) {
+                            ub = std::max(input_dim + ub, stride > 0 ? int64_t(0) : int64_t(-1));
+                        }
+
+                        // apply restrictions when begin or end values more than max possible values.
+                        lb = std::min(input_dim, lb);
+                        ub = std::min(input_dim, ub);
+
+                        int64_t dimension = 0;
+                        if (stride < 0) {
+                            // apply masks
+                            if (begin_mask.count(axis)) {
+                                lb = input_dim - 1;
+                            }
+                            if (end_mask.count(axis)) {
+                                ub = -1;
+                            }
+
+                            lb = std::min(lb, input_dim - 1);
+                            lb -= 1;  // we always get 1st element, so we need decrease range
+                            if (ub <= lb) {
+                                dimension = (ub - lb) / stride + 1;
+                            }
+                        } else {
+                            // apply masks
+                            if (begin_mask.count(axis)) {
+                                lb = 0;
+                            }
+                            if (end_mask.count(axis)) {
+                                ub = input_dim;
+                            }
+
+                            lb += 1;  // we always get 1st element, so we need decrease range
+                            if (ub >= lb) {
+                                dimension = (ub - lb) / stride + 1;
+                            }
+                        }
+                        return dimension;
+                    };
+
+                    if (input_shape[input_shape_idx].is_dynamic()) {
+                        // the relationship between input and output length is monotonically increasing
+                        // so we repeat the dimension inference twice to infer dynamic dimension
+                        const Interval& interval = input_shape[input_shape_idx].get_interval();
+                        int64_t odim_min = get_output_dim(interval.get_min_val());
+                        int64_t odim_max;
+                        if (interval.has_upper_bound())
+                            odim_max = get_output_dim(interval.get_max_val());
+                        else
+                            odim_max = -1;
+
+                        dim.emplace_back(ov::Dimension(odim_min, odim_max));
+                    } else {
+                        int64_t dimension = get_output_dim(input_shape[input_shape_idx].get_length());
+                        dim.emplace_back(dimension);
+                    }
+
+                    input_shape_idx++;
+                }
+            }
+        }
+        // get remaining values
+        for (; input_shape_idx < input_shape.rank().get_length(); ++input_shape_idx) {
+            dim.emplace_back(input_shape[input_shape_idx]);
+        }
+
+        output_shapes[0] = T(dim);
+    } else {
+        output_shapes[0] = ov::PartialShape::dynamic(input_shape.rank());
+    }
+}
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/topk_shape_inference.hpp b/src/core/shape_inference/include/topk_shape_inference.hpp
new file mode 100644
index 00000000000..ead34092a8b
--- /dev/null
+++ b/src/core/shape_inference/include/topk_shape_inference.hpp
@@ -0,0 +1,93 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/topk.hpp>
+
+#include "utils.hpp"
+
+namespace ov {
+namespace op {
+namespace v1 {
+
+template <typename T>
+void shape_infer(const TopK* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    constexpr bool is_dynamic_shape = std::is_base_of<ov::PartialShape, T>::value;
+
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 2 && output_shapes.size() == 2));
+    const auto& input_shape = input_shapes[0];
+    const auto input_rank = input_shape.rank();
+
+    NODE_VALIDATION_CHECK(op,
+                          input_rank.is_dynamic() || input_rank.get_length() > 0,
+                          "Input rank must be greater than 0.");
+
+    const auto& k_shape = input_shapes[1];
+    NODE_VALIDATION_CHECK(op, k_shape.rank().compatible(0), "The 'K' input must be a scalar.");
+
+    auto output_shape = input_shape;
+    if (input_shape.rank().is_static()) {
+        ov::PartialShape k_as_shape;
+        auto input_rank = static_cast<int64_t>(input_shape.size());
+        auto normalized_axis = ov::normalize_axis(op, op->get_provided_axis(), input_rank, -input_rank, input_rank - 1);
+        auto& dim_axis = output_shape[normalized_axis];
+
+        if (!is_dynamic_shape) {
+            std::vector<int64_t> k_val;
+            NODE_VALIDATION_CHECK(op,
+                                  get_data_as_int64<T>(1, op, k_val, constant_data),
+                                  "determined k is required to infer static shape");
+
+            NODE_VALIDATION_CHECK(op,
+                                  k_val.size() == 1,
+                                  "Only one value (scalar) should be provided as the 'K' input to TopK",
+                                  " (got ",
+                                  k_val.size(),
+                                  " elements).");
+            dim_axis = k_val[0];
+        } else if (ov::evaluate_as_partial_shape(op->input_value(1), k_as_shape)) {
+            NODE_VALIDATION_CHECK(op,
+                                  k_as_shape.size() == 1,
+                                  "Only one value (scalar) should be provided as the 'K' input to TopK",
+                                  " (got ",
+                                  k_as_shape.size(),
+                                  " elements).");
+            if (k_as_shape[0].is_static()) {
+                NODE_VALIDATION_CHECK(op,
+                                      k_as_shape[0].get_max_length() >= 0,
+                                      "The value of 'K' must not be a negative number.",
+                                      " (got ",
+                                      k_as_shape[0].get_max_length(),
+                                      ").");
+                dim_axis = k_as_shape[0].get_length();
+            } else {
+                // in this dynamic branch we are sure of dim_axis's type
+                const auto in_min = dim_axis.get_min_length();
+                const auto in_max = dim_axis.get_max_length();
+
+                const auto k_min = k_as_shape[0].get_min_length();
+                const auto k_max = k_as_shape[0].get_max_length();
+
+                const auto lower = std::min<Dimension::value_type>(in_min, k_min);
+                const auto upper =
+                    in_max < 0 ? Dimension::dynamic().get_max_length() : std::max<Dimension::value_type>(in_max, k_max);
+                dim_axis = Dimension(lower, upper);
+            }
+        } else {
+            dim_axis = Dimension(0, dim_axis.get_max_length());
+        }
+    }
+
+    output_shapes[0] = output_shape;
+    output_shapes[1] = output_shape;
+}  // namespace
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/variadic_split_shape_inference.hpp b/src/core/shape_inference/include/variadic_split_shape_inference.hpp
new file mode 100644
index 00000000000..11cb5d918c4
--- /dev/null
+++ b/src/core/shape_inference/include/variadic_split_shape_inference.hpp
@@ -0,0 +1,131 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/variadic_split.hpp>
+
+#include "utils.hpp"
+
+namespace ov {
+namespace op {
+namespace v1 {
+
+template <typename T>
+void shape_infer(const VariadicSplit* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    constexpr bool is_dynamic_shape = std::is_base_of<ov::PartialShape, T>::value;
+
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 3));
+
+    output_shapes.clear();
+    auto axis_pshape = input_shapes[1];
+    auto split_lengths_pshape = input_shapes[2];
+
+    NODE_VALIDATION_CHECK(op,
+                          axis_pshape.rank().compatible(0) || axis_pshape.compatible({1}),
+                          "Axis should be a scalar or of shape [1]. Got ",
+                          axis_pshape,
+                          " instead.");
+
+    if (split_lengths_pshape.is_static()) {
+        NODE_VALIDATION_CHECK(op,
+                              split_lengths_pshape.size() == 1,
+                              "Split lengths should be a 1-D tensor. Got ",
+                              split_lengths_pshape.size(),
+                              " instead.");
+
+        const auto num_outputs = split_lengths_pshape[0].get_length();
+        const auto& data_shape = input_shapes[0];
+
+        std::vector<int64_t> axis_values;
+        std::vector<int64_t> split_lengths;
+        if (data_shape.rank().is_static() && get_data_as_int64<T>(1, op, axis_values, constant_data)) {
+            NODE_VALIDATION_CHECK(op,
+                                  axis_values.size() == 1,
+                                  "a scalar axis value is expected. Got: ",
+                                  axis_values.size(),
+                                  " axes");
+            const auto axis_val = axis_values[0];
+            // Adjust split axis in case of negatives
+            const int64_t axis = ov::normalize_axis(op, axis_val, data_shape.rank());
+
+            if (get_data_as_int64<T>(2, op, split_lengths, constant_data)) {
+                // Adjust split lengths in case of negatives
+                int64_t sum_of_splits = 0;
+                int64_t negative_one_idx = -1;
+                for (size_t i = 0; i < split_lengths.size(); i++) {
+                    NODE_VALIDATION_CHECK(op,
+                                          split_lengths[i] >= -1,
+                                          "Invalid value ",
+                                          split_lengths[i],
+                                          " in split lengths input. Should be >= -1.");
+
+                    if (split_lengths[i] == -1) {
+                        NODE_VALIDATION_CHECK(op,
+                                              negative_one_idx == -1,
+                                              "Cannot infer split with multiple -1 values at ",
+                                              negative_one_idx,
+                                              " and ",
+                                              i);
+                        negative_one_idx = i;
+                    } else {
+                        sum_of_splits += split_lengths[i];
+                    }
+                }
+                const auto dimension_at_axis = data_shape[axis];
+
+                if (negative_one_idx >= 0 && dimension_at_axis.is_static()) {
+                    split_lengths[negative_one_idx] = dimension_at_axis.get_length() - sum_of_splits;
+                    sum_of_splits += split_lengths[negative_one_idx];
+                }
+                if (data_shape[axis].is_static()) {
+                    NODE_VALIDATION_CHECK(op,
+                                          sum_of_splits == data_shape[axis].get_length(),
+                                          "Total length of splits: ",
+                                          sum_of_splits,
+                                          " must match the length of the chosen axis: ",
+                                          data_shape[axis]);
+                }
+
+                for (int64_t output = 0; output < num_outputs; ++output) {
+                    if (split_lengths.at(output) == -1) {
+                        auto out_shape = data_shape;
+                        out_shape[axis] = Dimension::dynamic();
+                        output_shapes.push_back(out_shape);
+                    } else {
+                        auto out_shape = data_shape;
+                        out_shape[axis] = split_lengths.at(output);
+                        output_shapes.push_back(out_shape);
+                    }
+                }
+            } else {
+                // we know num_outputs & axis but split_lengths, pass other dimensions besides axis in dynamic shape
+                // case
+                NODE_VALIDATION_CHECK(op, is_dynamic_shape, "Cannot infer static shape due to lack of split_lengths.");
+
+                auto out_shape = data_shape;
+                out_shape[axis] = Dimension::dynamic();
+                for (int64_t output = 0; output < num_outputs; ++output)
+                    output_shapes.push_back(out_shape);
+            }
+        } else {
+            // we only know num_outputs, only predict the rank
+            auto out_shape = ov::PartialShape::dynamic(data_shape.rank());
+            for (int64_t output = 0; output < num_outputs; ++output)
+                output_shapes.push_back(out_shape);
+        }
+    } else {
+        // we don't even known the number of outputs in this case.
+        // just leave output_shapes as empty.
+    }
+}
+
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/bucketize.cpp b/src/core/src/op/bucketize.cpp
index f13db68953c..bf05333dbb0 100644
--- a/src/core/src/op/bucketize.cpp
+++ b/src/core/src/op/bucketize.cpp
@@ -4,6 +4,7 @@
 
 #include "ngraph/op/bucketize.hpp"
 
+#include "bucketize_shape_inference.hpp"
 #include "itt.hpp"
 
 using namespace ngraph;
@@ -51,17 +52,16 @@ void op::v3::Bucketize::validate_and_infer_types() {
                           "Output type must be i32 or i64. Got: ",
                           m_output_type);
 
-    NODE_VALIDATION_CHECK(this,
-                          buckets_pshape.rank().compatible(1),
-                          "Buckets input must be a 1D tensor. Got: ",
-                          buckets_pshape);
+    std::vector<ov::PartialShape> input_shapes = {data_pshape, buckets_pshape};
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape::dynamic()};
+    shape_infer(this, input_shapes, output_shapes);
 
     if (data_pshape.is_dynamic()) {
         set_input_is_relevant_to_shape(0);
     }
 
     set_output_size(1);
-    set_output_type(0, m_output_type, data_pshape);
+    set_output_type(0, m_output_type, output_shapes[0]);
 }
 
 shared_ptr<Node> op::v3::Bucketize::clone_with_new_inputs(const OutputVector& inputs) const {
diff --git a/src/core/src/op/einsum.cpp b/src/core/src/op/einsum.cpp
index 62246d35221..3f34eefc97f 100644
--- a/src/core/src/op/einsum.cpp
+++ b/src/core/src/op/einsum.cpp
@@ -10,6 +10,7 @@
 #include <string>
 #include <unordered_map>
 
+#include "einsum_shape_inference.hpp"
 #include "itt.hpp"
 
 using namespace std;
@@ -180,97 +181,15 @@ void op::v7::Einsum::validate_and_infer_types() {
                               "Inputs to Einsum operation must have the same type.");
     }
 
-    // check that equation has correct format and extract input and output subscripts
-    std::vector<std::string> input_subscripts;
-    std::string output_subscript;
-    parse_equation(m_equation, input_subscripts, output_subscript);
-
-    // a number of input subscripts must match with a number of input tensors
-    NODE_VALIDATION_CHECK(this,
-                          input_subscripts.size() == num_inputs,
-                          "Equation must contain a number of subscripts equal to a number of Einsum inputs.");
-
-    // create a dictionary with dimension sizes (or ranges in case dynamic shapes) for each label
-    // and check their compatibility in case repeating labels
-    unordered_map<string, ov::PartialShape> label_to_shape;
-    label_to_shape.clear();
-
+    std::vector<ov::PartialShape> input_shapes;
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape::dynamic()};
     for (size_t input_idx = 0; input_idx < num_inputs; ++input_idx) {
-        const auto& pshape = get_input_partial_shape(input_idx);
-        std::vector<std::string> labels;
-        labels = extract_labels(input_subscripts[input_idx]);
-
-        if (pshape.rank().is_static()) {
-            size_t input_rank = pshape.rank().get_length();
-            // check that a rank is greater or equal to a number of labels
-            // these numbers are always equal if there is no ellipsis in the subscript
-            NODE_VALIDATION_CHECK(this,
-                                  input_rank >= labels.size(),
-                                  "Input rank must be greater or equal to a number of labels in the "
-                                  "corresponding input subscript.");
-
-            for (size_t label_ind = 0, dim_ind = 0; label_ind < labels.size() && dim_ind < input_rank; ++label_ind) {
-                auto const& label = labels[label_ind];
-                if (label.compare("...") == 0) {
-                    size_t num_broadcasted_dims = input_rank - labels.size() + 1;
-                    auto current_sub_pshape =
-                        ov::PartialShape(std::vector<Dimension>(pshape.begin() + dim_ind,
-                                                                pshape.begin() + dim_ind + num_broadcasted_dims));
-                    if (label_to_shape.find(label) == label_to_shape.end()) {
-                        label_to_shape[label] = current_sub_pshape;
-                    } else {
-                        bool is_broadcast_success =
-                            ov::PartialShape::broadcast_merge_into(label_to_shape[label],
-                                                                   current_sub_pshape,
-                                                                   op::AutoBroadcastType::NUMPY);
-                        NODE_VALIDATION_CHECK(this,
-                                              is_broadcast_success,
-                                              "Input dimensions labeled with ellipsis for Einsum "
-                                              "must be broadcastable.");
-                    }
-                    dim_ind += num_broadcasted_dims;
-                } else {
-                    if (label_to_shape.find(label) == label_to_shape.end()) {
-                        label_to_shape[label] = ov::PartialShape{pshape[dim_ind]};
-                    } else {
-                        NODE_VALIDATION_CHECK(this,
-                                              label_to_shape[label].compatible(ov::PartialShape{pshape[label_ind]}),
-                                              "Different input dimensions indicated by the same labels for Einsum "
-                                              "must be compatible.");
-                        ov::PartialShape::merge_into(label_to_shape[label], ov::PartialShape{pshape[dim_ind]});
-                    }
-                    ++dim_ind;
-                }
-            }
-        } else {
-            for (auto const& label : labels) {
-                NODE_VALIDATION_CHECK(this,
-                                      label != "...",
-                                      "The subscript corresponding to a dynamic rank input must "
-                                      "not contain ellipsis.");
-
-                if (label_to_shape.find(label) == label_to_shape.end()) {
-                    label_to_shape[label] = ov::PartialShape{Dimension::dynamic()};
-                }
-            }
-        }
+        input_shapes.push_back(get_input_partial_shape(input_idx));
     }
 
-    // compute the output shape
-    std::vector<std::string> output_labels;
-    output_labels = extract_labels(output_subscript);
-    std::vector<Dimension> output_pshape_vector;
+    shape_infer(this, input_shapes, output_shapes);
 
-    for (auto const& output_label : output_labels) {
-        NODE_VALIDATION_CHECK(this,
-                              label_to_shape.find(output_label) != label_to_shape.end(),
-                              "Label in output subscript of Einsum equation must enter at least "
-                              "one input subscript.");
-        output_pshape_vector.insert(output_pshape_vector.end(),
-                                    label_to_shape[output_label].begin(),
-                                    label_to_shape[output_label].end());
-    }
-    set_output_type(0, input_type_0, ov::PartialShape(output_pshape_vector));
+    set_output_type(0, input_type_0, output_shapes[0]);
 }
 
 bool op::v7::Einsum::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/embedding_segments_sum.cpp b/src/core/src/op/embedding_segments_sum.cpp
index de453d17c67..3603d8f3b8e 100644
--- a/src/core/src/op/embedding_segments_sum.cpp
+++ b/src/core/src/op/embedding_segments_sum.cpp
@@ -6,6 +6,7 @@
 
 #include <ngraph/validation_util.hpp>
 
+#include "embedding_segments_sum_shape_inference.hpp"
 #include "itt.hpp"
 #include "ngraph/op/constant.hpp"
 #include "ngraph/opsets/opset3.hpp"
@@ -75,24 +76,6 @@ void op::v3::EmbeddingSegmentsSum::validate_and_infer_types() {
                           get_input_element_type(SEGMENT_IDS),
                           ")");
 
-    NODE_VALIDATION_CHECK(
-        this,
-        get_input_partial_shape(INDICES).is_dynamic() || get_input_partial_shape(INDICES).to_shape().size() == 1,
-        "INDICES must be 1D");
-
-    NODE_VALIDATION_CHECK(this,
-                          get_input_partial_shape(SEGMENT_IDS).is_dynamic() ||
-                              get_input_partial_shape(SEGMENT_IDS).to_shape().size() == 1,
-                          "SEGMENT_IDS must be 1D");
-
-    NODE_VALIDATION_CHECK(this,
-                          get_input_partial_shape(INDICES).compatible(get_input_partial_shape(SEGMENT_IDS)),
-                          "INDICES and SEGMENT_IDS shape must be same");
-
-    NODE_VALIDATION_CHECK(this,
-                          get_input_partial_shape(NUM_SEGMENTS).compatible(ov::PartialShape{}),
-                          "NUM_SEGMENTS must be a scalar");
-
     if (get_input_size() >= 5) {
         NODE_VALIDATION_CHECK(this,
                               get_input_element_type(DEFAULT_INDEX) == element::i64 ||
@@ -106,10 +89,6 @@ void op::v3::EmbeddingSegmentsSum::validate_and_infer_types() {
                               ") must match indices element type (",
                               get_input_element_type(INDICES),
                               ")");
-
-        NODE_VALIDATION_CHECK(this,
-                              get_input_partial_shape(DEFAULT_INDEX).compatible(ov::PartialShape{}),
-                              "DEFAULT_INDEX must be a scalar");
     }
 
     if (get_input_size() == 6) {
@@ -120,36 +99,21 @@ void op::v3::EmbeddingSegmentsSum::validate_and_infer_types() {
                               ") must match embedding table element type (",
                               get_input_element_type(EMB_TABLE),
                               ")");
-
-        NODE_VALIDATION_CHECK(this,
-                              get_input_partial_shape(PER_SAMPLE_WEIGHTS).is_dynamic() ||
-                                  get_input_partial_shape(PER_SAMPLE_WEIGHTS).to_shape().size() == 1,
-                              "PER_SAMPLE_WEIGHTS must be 1D");
-
-        NODE_VALIDATION_CHECK(this,
-                              get_input_partial_shape(INDICES).compatible(get_input_partial_shape(PER_SAMPLE_WEIGHTS)),
-                              "INDICES and PER_SAMPLE_WEIGHTS shape must be same");
     }
 
     element::Type result_et = get_input_element_type(EMB_TABLE);
 
-    const ov::PartialShape& emb_table_shape = get_input_partial_shape(EMB_TABLE);
+    std::vector<PartialShape> result_shapes = {PartialShape::dynamic()};
+    std::vector<PartialShape> input_shapes;
+    for (int i = 0; i < get_input_size(); i++)
+        input_shapes.push_back(get_input_partial_shape(i));
 
-    ov::PartialShape result_shape;
-    if (emb_table_shape.rank().is_static()) {
-        result_shape = emb_table_shape;
-        if (const auto& num_segments_const = get_constant_from_source(input_value(NUM_SEGMENTS))) {
-            result_shape[0] = num_segments_const->cast_vector<int64_t>()[0];
-        } else {
-            result_shape[0] = Dimension::dynamic();
-            set_input_is_relevant_to_shape(NUM_SEGMENTS);
-        }
-    } else {
-        result_shape = ov::PartialShape::dynamic();
-        set_input_is_relevant_to_shape(NUM_SEGMENTS);
+    shape_infer(this, input_shapes, result_shapes);
+
+    if (result_shapes[EMB_TABLE].rank().is_dynamic() || result_shapes[EMB_TABLE][0].is_dynamic()) {
+        set_input_is_relevant_to_shape(NUM_SEGMENTS, true);
     }
-
-    set_output_type(0, result_et, result_shape);
+    set_output_type(0, result_et, result_shapes[0]);
 }
 
 shared_ptr<Node> op::v3::EmbeddingSegmentsSum::clone_with_new_inputs(const OutputVector& new_args) const {
diff --git a/src/core/src/op/experimental_detectron_roi_feature.cpp b/src/core/src/op/experimental_detectron_roi_feature.cpp
index 64eef50b5e6..9d0a2d597f2 100644
--- a/src/core/src/op/experimental_detectron_roi_feature.cpp
+++ b/src/core/src/op/experimental_detectron_roi_feature.cpp
@@ -5,6 +5,7 @@
 #include "ngraph/op/experimental_detectron_roi_feature.hpp"
 
 #include <algorithm>
+#include <experimental_detectron_roi_feature_shape_inference.hpp>
 #include <memory>
 #include <utility>
 
@@ -40,59 +41,18 @@ void op::v6::ExperimentalDetectronROIFeatureExtractor::validate_and_infer_types(
     NGRAPH_OP_SCOPE(v6_ExperimentalDetectronROIFeatureExtractor_validate_and_infer_types);
     NODE_VALIDATION_CHECK(this, get_input_size() >= 2, "At least two argument required.");
 
-    auto rois_shape = get_input_partial_shape(0);
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}, ov::PartialShape{}};
+    std::vector<ov::PartialShape> input_shapes;
+    for (size_t i = 0; i < get_input_size(); i++)
+        input_shapes.push_back(get_input_partial_shape(i));
+
+    shape_infer(this, input_shapes, output_shapes);
+
     auto input_et = get_input_element_type(0);
 
-    ov::PartialShape out_shape = {Dimension::dynamic(), Dimension::dynamic(), m_attrs.output_size, m_attrs.output_size};
-
-    ov::PartialShape out_rois_shape = {Dimension::dynamic(), 4};
-    if (rois_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this, rois_shape.rank().get_length() == 2, "Input rois rank must be equal to 2.");
-
-        auto input_rois_last_dim_intersection_with_4 = rois_shape[1] & Dimension(4);
-        NODE_VALIDATION_CHECK(this,
-                              !input_rois_last_dim_intersection_with_4.get_interval().empty(),
-                              "The last dimension of the 'input_rois' input must be equal to 4. "
-                              "Got: ",
-                              rois_shape[1]);
-
-        out_shape[0] = rois_shape[0];
-        out_rois_shape[0] = rois_shape[0];
-    }
-
-    size_t num_of_inputs = get_input_size();
-    Dimension channels_intersection;
-
-    for (size_t i = 1; i < num_of_inputs; i++) {
-        auto current_shape = get_input_partial_shape(i);
-        auto current_rank = current_shape.rank();
-
-        if (current_rank.is_static()) {
-            NODE_VALIDATION_CHECK(this,
-                                  current_rank.get_length() == 4,
-                                  "Rank of each element of the pyramid must be equal to 4. Got: ",
-                                  current_rank);
-
-            auto first_dim_intersection_with_1 = current_shape[0] & Dimension(1);
-            NODE_VALIDATION_CHECK(this,
-                                  !first_dim_intersection_with_1.get_interval().empty(),
-                                  "The first dimension of each pyramid element must be equal to 1. "
-                                  "Got: ",
-                                  current_shape[0]);
-
-            channels_intersection &= current_shape[1];
-        }
-    }
-
-    NODE_VALIDATION_CHECK(this,
-                          !channels_intersection.get_interval().empty(),
-                          "The number of channels must be the same for all layers of the pyramid.");
-
-    out_shape[1] = channels_intersection;
-
-    set_output_size(2);
-    set_output_type(0, input_et, out_shape);
-    set_output_type(1, input_et, out_rois_shape);
+    set_output_size(output_shapes.size());
+    for (size_t i = 0; i < output_shapes.size(); i++)
+        set_output_type(i, input_et, output_shapes[i]);
 }
 
 shared_ptr<Node> op::v6::ExperimentalDetectronROIFeatureExtractor::clone_with_new_inputs(
diff --git a/src/core/src/op/pad.cpp b/src/core/src/op/pad.cpp
index 87d43449e2f..c39e4cc6639 100644
--- a/src/core/src/op/pad.cpp
+++ b/src/core/src/op/pad.cpp
@@ -14,6 +14,7 @@
 #include "ngraph/op/util/op_types.hpp"
 #include "ngraph/runtime/reference/pad.hpp"
 #include "openvino/op/util/precision_sensitive_attribute.hpp"
+#include "pad_shape_inference.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -75,7 +76,6 @@ void op::v1::Pad::validate_and_infer_types() {
 
     if (m_pad_mode == PadMode::CONSTANT && get_input_size() == 4) {
         const auto& arg_pad_element_type = get_input_element_type(3);
-        const auto& arg_pad_shape = get_input_partial_shape(3);
         NODE_VALIDATION_CHECK(this,
                               element::Type::merge(result_et, arg_element_type, arg_pad_element_type),
                               "Argument element types do not match (input arg element type: ",
@@ -83,12 +83,6 @@ void op::v1::Pad::validate_and_infer_types() {
                               ", arg_pad element type: ",
                               arg_pad_element_type,
                               ").");
-
-        NODE_VALIDATION_CHECK(this,
-                              arg_pad_shape.compatible(ov::PartialShape{}),
-                              "Argument for padding value is not a scalar (shape: ",
-                              arg_pad_shape,
-                              ").");
     }
 
     NODE_VALIDATION_CHECK(this,
@@ -103,80 +97,12 @@ void op::v1::Pad::validate_and_infer_types() {
                           pads_end_element_type,
                           ").");
 
-    const auto& pads_begin_shape = get_input_partial_shape(1);
-    const auto& pads_begin_rank = pads_begin_shape.rank();
-
-    NODE_VALIDATION_CHECK(this,
-                          pads_begin_rank.compatible(1),
-                          "Argument for pads_begin is not 1D (shape: ",
-                          pads_begin_rank,
-                          ").");
-
-    const auto& pads_end_shape = get_input_partial_shape(2);
-    const auto& pads_end_rank = pads_end_shape.rank();
-    NODE_VALIDATION_CHECK(this,
-                          pads_end_rank.compatible(1),
-                          "Argument for pads_end is not 1D (shape: ",
-                          pads_end_rank,
-                          ").");
-
-    const auto& arg_shape = get_input_partial_shape(0);
-    const auto& arg_shape_rank = arg_shape.rank();
-    if (arg_shape_rank.is_static() && pads_begin_shape.is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              pads_begin_shape[0].get_length() <= arg_shape_rank.get_length(),
-                              "Number of elements of pads_begin must be >= 0 and <= arg rank "
-                              "(pads_begin_shape[0]: ",
-                              pads_begin_shape[0],
-                              ").");
-    }
-    if (arg_shape_rank.is_static() && pads_end_shape.is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              pads_end_shape[0].get_length() <= arg_shape_rank.get_length(),
-                              "Number of elements of pads_end must be >= 0 and <= arg rank (pads_end_shape[0]: ",
-                              pads_end_shape[0],
-                              ").");
-    }
-    const auto& pads_begin_coord = get_pads_begin();
-    const auto& pads_end_coord = get_pads_end();
-
-    if (arg_shape_rank.is_static() && !pads_begin_coord.empty() && !pads_end_coord.empty()) {
-        const auto implied_rank = pads_begin_coord.size();
-        std::vector<Dimension> result_dims(implied_rank, Dimension::dynamic());
-        for (size_t i = 0; i < implied_rank; i++) {
-            if (arg_shape[i].is_static()) {
-                ptrdiff_t result_dim = pads_begin_coord[i] + arg_shape[i].get_length() + pads_end_coord[i];
-                result_dims[i] = static_cast<size_t>(result_dim);
-                if (i > 1) {
-                    NODE_VALIDATION_CHECK(this,
-                                          m_pad_mode != op::PadMode::EDGE || arg_shape[i].get_length() >= 1,
-                                          "EDGE padding mode requires an input of dimension of "
-                                          "at least 1 at each "
-                                          "spatial axis.");
-                    NODE_VALIDATION_CHECK(this,
-                                          m_pad_mode != op::PadMode::REFLECT || arg_shape[i].get_length() >= 2,
-                                          "REFLECT padding mode requires an input of dimension "
-                                          "of at least 2 at each "
-                                          "spatial axis.");
-                }
-                NODE_VALIDATION_CHECK(
-                    this,
-                    m_pad_mode != op::PadMode::REFLECT || (pads_begin_coord[i] < arg_shape[i].get_length() &&
-                                                           pads_end_coord[i] < arg_shape[i].get_length()),
-                    "REFLECT padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
-                    "must be not greater than 'data_shape[D] - 1'.");
-                NODE_VALIDATION_CHECK(
-                    this,
-                    m_pad_mode != op::PadMode::SYMMETRIC || (pads_begin_coord[i] <= arg_shape[i].get_length() &&
-                                                             pads_end_coord[i] <= arg_shape[i].get_length()),
-                    "SYMMETRIC padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
-                    "must be not greater than 'data_shape[D]'.");
-            }
-        }
-        set_output_type(0, get_input_element_type(0), result_dims);
-    } else {
-        set_output_type(0, get_input_element_type(0), ov::PartialShape::dynamic(arg_shape_rank));
-    }
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape::dynamic()};
+    std::vector<ov::PartialShape> input_shapes;
+    for (size_t i = 0; i < get_input_size(); i++)
+        input_shapes.push_back(get_input_partial_shape(i));
+    shape_infer(this, input_shapes, output_shapes);
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
 
 shared_ptr<Node> op::v1::Pad::clone_with_new_inputs(const OutputVector& new_args) const {
diff --git a/src/core/src/op/range.cpp b/src/core/src/op/range.cpp
index 670ebe07faf..bfe456624ba 100644
--- a/src/core/src/op/range.cpp
+++ b/src/core/src/op/range.cpp
@@ -12,6 +12,7 @@
 #include "ngraph/runtime/host_tensor.hpp"
 #include "ngraph/runtime/reference/range.hpp"
 #include "ngraph/type/element_type_traits.hpp"
+#include "range_shape_inference.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -71,10 +72,6 @@ void op::v4::Range::validate_and_infer_types() {
     set_input_is_relevant_to_shape(1);
     set_input_is_relevant_to_shape(2);
 
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(0).compatible(ov::Shape{}), "'start' input is not a scalar");
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(1).compatible(ov::Shape{}), "'stop' input is not a scalar");
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(2).compatible(ov::Shape{}), "'step' input is not a scalar");
-
     NODE_VALIDATION_CHECK(this,
                           get_input_element_type(0).is_integral_number() || get_input_element_type(0).is_real(),
                           "'start' input scalar should be a numeric type. Got: ",
@@ -88,63 +85,14 @@ void op::v4::Range::validate_and_infer_types() {
                           "'step' input scalar should be a numeric type. Got: ",
                           get_input_element_type(2));
 
-    auto const_start = get_constant_from_source(input_value(0));
-    auto const_stop = get_constant_from_source(input_value(1));
-    auto const_step = get_constant_from_source(input_value(2));
+    std::vector<PartialShape> result_shapes = {PartialShape::dynamic()};
+    std::vector<PartialShape> input_shapes;
+    for (int i = 0; i < get_input_size(); i++)
+        input_shapes.push_back(get_input_partial_shape(i));
 
-    double start = 0;
-    double stop = 0;
-    double step = 0;
+    op::v4::shape_infer(this, input_shapes, result_shapes);
 
-    if (const_start != nullptr) {
-        std::vector<double> start_val = const_start->cast_vector<double>();
-        NODE_VALIDATION_CHECK(this, start_val.size() == 1);
-        start = start_val[0];
-        NODE_VALIDATION_CHECK(this, std::isfinite(start) && !std::isnan(start), "'start' cannot be nan or infinite.");
-    }
-
-    if (const_stop != nullptr) {
-        std::vector<double> stop_val = const_stop->cast_vector<double>();
-        NODE_VALIDATION_CHECK(this, stop_val.size() == 1);
-        stop = stop_val[0];
-        NODE_VALIDATION_CHECK(this, std::isfinite(stop) && !std::isnan(stop), "'stop' cannot be nan or infinite.");
-    }
-
-    if (const_step != nullptr) {
-        std::vector<double> step_val = const_step->cast_vector<double>();
-        NODE_VALIDATION_CHECK(this, step_val.size() == 1);
-        step = step_val[0];
-        NODE_VALIDATION_CHECK(this, std::isfinite(step) && !std::isnan(step), "'step' cannot be nan or infinite.");
-    }
-
-    ov::PartialShape result{ov::PartialShape::dynamic(1)};
-
-    if (const_start != nullptr && const_stop != nullptr && const_step != nullptr) {
-        // all inputs must be casted to output_type before
-        // the rounding for casting values are done towards zero
-        if (m_output_type.is_integral_number() && get_input_element_type(0).is_real()) {
-            start = std::trunc(start);
-        }
-        if (m_output_type.is_integral_number() && get_input_element_type(1).is_real()) {
-            stop = std::trunc(stop);
-        }
-        if (m_output_type.is_integral_number() && get_input_element_type(2).is_real()) {
-            step = std::trunc(step);
-        }
-
-        // the number of elements is: max(ceil((stop − start) / step), 0)
-        double span;
-        if ((step > 0 && start >= stop) || (step < 0 && start <= stop)) {
-            span = 0;
-        } else {
-            span = stop - start;
-        }
-
-        double strided = ceil(fabs(span) / fabs(step));
-
-        result = ov::PartialShape{Dimension(static_cast<int64_t>(strided))};
-    }
-    set_output_type(0, m_output_type, result);
+    set_output_type(0, m_output_type, result_shapes[0]);
 }
 
 shared_ptr<Node> op::v4::Range::clone_with_new_inputs(const OutputVector& new_args) const {
@@ -401,70 +349,24 @@ void op::v0::Range::validate_and_infer_types() {
                           result_et != element::boolean,
                           "Element type for start, stop, and step, must not be boolean.");
 
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(0).compatible(ov::Shape{}), "'start' input is not a scalar");
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(1).compatible(ov::Shape{}), "'stop' input is not a scalar");
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(2).compatible(ov::Shape{}), "'step' input is not a scalar");
+    NODE_VALIDATION_CHECK(this,
+                          result_et != element::Type_t::u1 && result_et != element::Type_t::i4 &&
+                              result_et != element::Type_t::u4 && result_et != element::Type_t::undefined,
+                          "Internal nGraph error: unsupported element type: ",
+                          result_et);
 
-    ov::PartialShape result_shape;
+    if (result_et == element::Type_t::dynamic) {
+        set_output_type(0, result_et, ov::PartialShape::dynamic(1));
+    } else {
+        std::vector<PartialShape> result_shapes = {PartialShape::dynamic()};
+        std::vector<PartialShape> input_shapes;
+        for (int i = 0; i < get_input_size(); i++)
+            input_shapes.push_back(get_input_partial_shape(i));
 
-#if defined(__GNUC__) && !(__GNUC__ == 4 && __GNUC_MINOR__ == 8)
-#    pragma GCC diagnostic push
-#    pragma GCC diagnostic error "-Wswitch"
-#    pragma GCC diagnostic error "-Wswitch-enum"
-#endif
-    switch (result_et) {
-    case element::Type_t::bf16:
-        result_shape = infer_output_shape<bfloat16>(this, result_et);
-        break;
-    case element::Type_t::f16:
-        result_shape = infer_output_shape<float16>(this, result_et);
-        break;
-    case element::Type_t::f32:
-        result_shape = infer_output_shape<float>(this, result_et);
-        break;
-    case element::Type_t::f64:
-        result_shape = infer_output_shape<double>(this, result_et);
-        break;
-    case element::Type_t::i8:
-        result_shape = infer_output_shape<int8_t>(this, result_et);
-        break;
-    case element::Type_t::i16:
-        result_shape = infer_output_shape<int16_t>(this, result_et);
-        break;
-    case element::Type_t::i32:
-        result_shape = infer_output_shape<int32_t>(this, result_et);
-        break;
-    case element::Type_t::i64:
-        result_shape = infer_output_shape<int64_t>(this, result_et);
-        break;
-    case element::Type_t::u8:
-        result_shape = infer_output_shape<uint8_t>(this, result_et);
-        break;
-    case element::Type_t::u16:
-        result_shape = infer_output_shape<uint16_t>(this, result_et);
-        break;
-    case element::Type_t::u32:
-        result_shape = infer_output_shape<uint32_t>(this, result_et);
-        break;
-    case element::Type_t::u64:
-        result_shape = infer_output_shape<uint64_t>(this, result_et);
-        break;
-    case element::Type_t::dynamic:
-        result_shape = ov::PartialShape::dynamic(1);
-        break;
-    case element::Type_t::u1:
-    case element::Type_t::i4:
-    case element::Type_t::u4:
-    case element::Type_t::undefined:
-    case element::Type_t::boolean:
-        NODE_VALIDATION_CHECK(this, false, "Internal nGraph error: unsupported element type: ", result_et);
-        break;
+        op::v0::shape_infer(this, input_shapes, result_shapes);
+
+        set_output_type(0, result_et, result_shapes[0]);
     }
-#if defined(__GNUC__) && !(__GNUC__ == 4 && __GNUC_MINOR__ == 8)
-#    pragma GCC diagnostic pop
-#endif
-
-    set_output_type(0, result_et, result_shape);
 }
 
 shared_ptr<Node> op::v0::Range::clone_with_new_inputs(const OutputVector& new_args) const {
diff --git a/src/core/src/op/region_yolo.cpp b/src/core/src/op/region_yolo.cpp
index 1718e690c4c..4d4dcc9357c 100644
--- a/src/core/src/op/region_yolo.cpp
+++ b/src/core/src/op/region_yolo.cpp
@@ -6,6 +6,7 @@
 
 #include "itt.hpp"
 #include "ngraph/attribute_visitor.hpp"
+#include "region_yolo_shape_inference.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -54,42 +55,11 @@ void op::RegionYolo::validate_and_infer_types() {
                           input_et.is_real(),
                           "Type of input is expected to be a floating point type. Got: ",
                           input_et);
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0)};
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+    shape_infer(this, input_shapes, output_shapes);
 
-    const auto& input_partial_shape = get_input_partial_shape(0);
-    if (input_partial_shape.rank().is_static()) {
-        ov::PartialShape input_shape = get_input_partial_shape(0);
-        ov::PartialShape output_shape;
-        int end_axis = m_end_axis;
-        if (m_end_axis < 0) {
-            m_end_axis += input_shape.size();
-        }
-
-        if (m_do_softmax) {
-            size_t flat_dim = 1;
-            for (int64_t i = 0; i < m_axis; i++) {
-                output_shape.push_back(input_shape[i]);
-            }
-            for (int64_t i = m_axis; i < end_axis + 1; i++) {
-                if (input_shape[i].is_dynamic()) {
-                    flat_dim = -1;
-                    break;
-                }
-                flat_dim *= input_shape[i].get_length();
-            }
-            output_shape.push_back(flat_dim);
-            for (size_t i = end_axis + 1; i < input_shape.size(); i++) {
-                output_shape.push_back(input_shape[i]);
-            }
-        } else {
-            output_shape = {input_shape[0],
-                            ov::Dimension((m_num_classes + m_num_coords + 1) * m_mask.size()),
-                            input_shape[2],
-                            input_shape[3]};
-        }
-        set_output_type(0, input_et, output_shape);
-    } else {
-        set_output_type(0, input_et, ov::PartialShape::dynamic());
-    }
+    set_output_type(0, input_et, output_shapes[0]);
 }
 
 shared_ptr<Node> op::RegionYolo::clone_with_new_inputs(const OutputVector& new_args) const {
diff --git a/src/core/src/op/reorg_yolo.cpp b/src/core/src/op/reorg_yolo.cpp
index 56739f3820f..53781422f2f 100644
--- a/src/core/src/op/reorg_yolo.cpp
+++ b/src/core/src/op/reorg_yolo.cpp
@@ -6,6 +6,7 @@
 
 #include "itt.hpp"
 #include "ngraph/runtime/reference/reorg_yolo.hpp"
+#include "reorg_yolo_shape_inference.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -27,32 +28,12 @@ void op::ReorgYolo::validate_and_infer_types() {
     NODE_VALIDATION_CHECK(this, !m_strides.empty(), "Stride attribute is required.");
 
     auto input_et = get_input_element_type(0);
-    if (get_input_partial_shape(0).is_static()) {
-        auto input_shape = get_input_partial_shape(0).to_shape();
-        NODE_VALIDATION_CHECK(this, input_shape.size() == 4, "[N, C, H, W] input shape is required.");
 
-        NODE_VALIDATION_CHECK(this,
-                              (input_shape[2] % m_strides[0]) == 0,
-                              "For [N, C, H, W] input shape, H should be divisible by stride.");
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0)};
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+    shape_infer(this, input_shapes, output_shapes);
 
-        NODE_VALIDATION_CHECK(this,
-                              (input_shape[3] % m_strides[0]) == 0,
-                              "For [N, C, H, W] input shape, W should be divisible by stride.");
-
-        NODE_VALIDATION_CHECK(this,
-                              input_shape[1] >= (m_strides[0] * m_strides[0]),
-                              "For [N, C, H, W] input shape, C >= (stride*stride) is required.");
-
-        ov::Shape output_shape{input_shape[0], input_shape[1]};
-        for (size_t i = 2; i < input_shape.size(); i++) {
-            output_shape.push_back(input_shape[i] / m_strides[0]);
-            output_shape[1] *= m_strides[0];
-        }
-        set_output_type(0, input_et, output_shape);
-    } else {
-        auto input_shape = get_input_partial_shape(0);
-        set_output_type(0, input_et, ov::PartialShape::dynamic(input_shape.rank()));
-    }
+    set_output_type(0, input_et, output_shapes[0]);
 }
 
 shared_ptr<Node> op::ReorgYolo::clone_with_new_inputs(const OutputVector& new_args) const {
diff --git a/src/core/src/op/split.cpp b/src/core/src/op/split.cpp
index d23971d207a..6ad9dae9e85 100644
--- a/src/core/src/op/split.cpp
+++ b/src/core/src/op/split.cpp
@@ -5,6 +5,7 @@
 #include "ngraph/runtime/reference/split.hpp"
 
 #include <numeric>
+#include <split_shape_inference.hpp>
 
 #include "itt.hpp"
 #include "ngraph/attribute_visitor.hpp"
@@ -34,12 +35,8 @@ bool ngraph::op::v1::Split::visit_attributes(AttributeVisitor& visitor) {
 
 void op::v1::Split::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v1_Split_validate_and_infer_types);
-    const ov::PartialShape& data_ps = get_input_partial_shape(0);
-    const ov::PartialShape& axis_ps = get_input_partial_shape(1);
     const element::Type& axis_et = get_input_element_type(1);
 
-    NODE_VALIDATION_CHECK(this, axis_ps.rank().compatible(0), "'axis' input must be a scalar. Got: ", axis_ps);
-
     NODE_VALIDATION_CHECK(this,
                           axis_et.is_integral_number(),
                           "Element type of 'axis' input must be integer. Got: ",
@@ -50,48 +47,13 @@ void op::v1::Split::validate_and_infer_types() {
                           "Attribute 'num_splits' must be greater than zero. Got: ",
                           m_num_splits);
 
-    ov::PartialShape each_output_shape{data_ps};
-    const Rank data_rank = data_ps.rank();
-    const auto axis_input = get_constant_from_source(input_value(1));
-    if (axis_input && data_rank.is_static()) {
-        auto axis = axis_input->cast_vector<int64_t>()[0];
-        axis = ngraph::normalize_axis(this, axis, data_rank);
-
-        if (data_ps[axis].is_static()) {
-            const auto dimension_at_axis = data_ps[axis].get_length();
-
-            NODE_VALIDATION_CHECK(this,
-                                  dimension_at_axis % m_num_splits == 0,
-                                  "Dimension of data input shape along 'axis': ",
-                                  dimension_at_axis,
-                                  " must be evenly divisible by 'num_splits' attribute value: ",
-                                  m_num_splits);
-
-            each_output_shape[axis] = dimension_at_axis / m_num_splits;
-        } else {
-            const auto dim_interval_at_axis = data_ps[axis].get_interval();
-            NODE_VALIDATION_CHECK(this,
-                                  dim_interval_at_axis.get_max_val() >= static_cast<int64_t>(m_num_splits),
-                                  "The interval maximum of the dimension for data input shape along 'axis' must be "
-                                  "greater or equal to 'num_splits' attribute. Got: ",
-                                  dim_interval_at_axis,
-                                  " and ",
-                                  m_num_splits);
-
-            auto dim_interval_at_axis_min =
-                static_cast<int64_t>(dim_interval_at_axis.get_min_val() * (1.0f / m_num_splits));
-            auto dim_interval_at_axis_max = dim_interval_at_axis.get_max_val();
-            if (dim_interval_at_axis.has_upper_bound()) {
-                dim_interval_at_axis_max = static_cast<int64_t>(dim_interval_at_axis_max * (1.0f / m_num_splits));
-            }
-            each_output_shape[axis] = Dimension(dim_interval_at_axis_min, dim_interval_at_axis_max);
-        }
-    } else {
-        each_output_shape = ov::PartialShape::dynamic(data_ps.rank());
-    }
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0), get_input_partial_shape(1)};
+    std::vector<ov::PartialShape> output_shapes;
+    shape_infer(this, input_shapes, output_shapes);
 
+    set_output_size(m_num_splits);
     for (size_t i = 0; i < m_num_splits; ++i) {
-        set_output_type(i, get_input_element_type(0), each_output_shape);
+        set_output_type(i, get_input_element_type(0), output_shapes[i]);
     }
 
     set_input_is_relevant_to_shape(0);
diff --git a/src/core/src/op/strided_slice.cpp b/src/core/src/op/strided_slice.cpp
index 1cb0e93ee20..8d0946ca447 100644
--- a/src/core/src/op/strided_slice.cpp
+++ b/src/core/src/op/strided_slice.cpp
@@ -20,6 +20,7 @@
 #include "ngraph/util.hpp"
 #include "ngraph/validation_util.hpp"
 #include "openvino/op/util/precision_sensitive_attribute.hpp"
+#include "strided_slice_shape_inference.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -132,24 +133,6 @@ void op::v1::StridedSlice::validate_and_infer_types() {
     });
     NODE_VALIDATION_CHECK(this, are_attr_sizes_eq, "All masks of StridedSlice must have the same size");
 
-    const auto& data_rank = get_input_partial_shape(0).rank();
-    const auto& begin_shape = get_input_partial_shape(1);
-    if (begin_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              begin_shape.rank().get_length() == 1,
-                              "Begin input must be 1D (begin rank: ",
-                              begin_shape.rank(),
-                              ").");
-    }
-    const auto& end_shape = get_input_partial_shape(2);
-    if (end_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              end_shape.rank().get_length() == 1,
-                              "End input must be 1D (end rank: ",
-                              end_shape.rank(),
-                              ").");
-    }
-
     // Fill up strides input with default strides if not set by this point.
     if (get_input_size() < 4) {
         set_argument(3, calculate_default_strides(get_input_node_ptr(1)->output(0), get_input_node_ptr(2)->output(0)));
@@ -159,26 +142,15 @@ void op::v1::StridedSlice::validate_and_infer_types() {
     set_input_is_relevant_to_shape(2);
     set_input_is_relevant_to_shape(3);
 
-    auto begin_const = get_constant_from_source(input_value(1));
-    auto end_const = get_constant_from_source(input_value(2));
-    auto strides = get_constant_from_source(input_value(3));
-
-    if (begin_const && end_const && strides) {
-        set_output_type(0,
-                        get_input_element_type(0),
-                        infer_slice_shape(this,
-                                          get_input_partial_shape(0),
-                                          begin_const->cast_vector<int64_t>(),
-                                          end_const->cast_vector<int64_t>(),
-                                          strides->cast_vector<int64_t>(),
-                                          convert_mask_to_axis_set(get_begin_mask()),
-                                          convert_mask_to_axis_set(get_end_mask()),
-                                          convert_mask_to_axis_set(get_new_axis_mask()),
-                                          convert_mask_to_axis_set(get_shrink_axis_mask()),
-                                          convert_mask_to_axis_set(get_ellipsis_mask())));
-    } else {
-        set_output_type(0, get_input_element_type(0), ov::PartialShape::dynamic(data_rank));
+    std::vector<ov::PartialShape> input_shapes;
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape::dynamic()};
+    for (size_t input_idx = 0; input_idx < get_input_size(); ++input_idx) {
+        input_shapes.push_back(get_input_partial_shape(input_idx));
     }
+
+    shape_infer(this, input_shapes, output_shapes);
+
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
 
 AxisSet op::v1::StridedSlice::convert_mask_to_axis_set(const std::vector<int64_t>& mask) const {
diff --git a/src/core/src/op/topk.cpp b/src/core/src/op/topk.cpp
index ad95d9703df..a3fe31d07f3 100644
--- a/src/core/src/op/topk.cpp
+++ b/src/core/src/op/topk.cpp
@@ -5,6 +5,7 @@
 #include "ngraph/op/topk.hpp"
 
 #include <memory>
+#include <topk_shape_inference.hpp>
 
 #include "itt.hpp"
 #include "ngraph/attribute_visitor.hpp"
@@ -186,15 +187,6 @@ bool ngraph::op::v1::TopK::visit_attributes(AttributeVisitor& visitor) {
 
 void op::v1::TopK::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v1_TopK_validate_and_infer_types);
-    const auto& input_partial_shape = get_input_partial_shape(0);
-    const auto input_rank = input_partial_shape.rank();
-
-    NODE_VALIDATION_CHECK(this,
-                          input_rank.is_dynamic() || input_rank.get_length() > 0,
-                          "Input rank must be greater than 0.");
-
-    const auto& k_partial_shape = get_input_partial_shape(1);
-    NODE_VALIDATION_CHECK(this, k_partial_shape.rank().compatible(0), "The 'K' input must be a scalar.");
 
     NODE_VALIDATION_CHECK(this,
                           m_index_element_type == element::i32 || m_index_element_type == element::i64,
@@ -206,35 +198,15 @@ void op::v1::TopK::validate_and_infer_types() {
         read_k_from_constant_node(input_value(1).get_node_shared_ptr(), get_input_element_type(1));
     }
 
-    ov::PartialShape output_shape{input_partial_shape};
+    set_axis(get_input_partial_shape(0).rank(), get_provided_axis());
 
-    if (output_shape.rank().is_static()) {
-        m_normalized_axis = ngraph::normalize_axis(this, m_axis, output_shape.rank());
-
-        ov::PartialShape k_as_shape;
-        if (evaluate_as_partial_shape(input_value(1), k_as_shape)) {
-            if (k_as_shape.is_static()) {
-                output_shape[m_normalized_axis] = k_as_shape[0];
-            } else {
-                const auto in_min = output_shape[m_normalized_axis].get_min_length();
-                const auto in_max = output_shape[m_normalized_axis].get_max_length();
-
-                const auto k_min = k_as_shape[0].get_min_length();
-                const auto k_max = k_as_shape[0].get_max_length();
-
-                const auto lower = std::min<Dimension::value_type>(in_min, k_min);
-                const auto upper =
-                    in_max < 0 ? Dimension::dynamic().get_max_length() : std::max<Dimension::value_type>(in_max, k_max);
-                output_shape[m_normalized_axis] = Dimension(lower, upper);
-            }
-        } else {
-            output_shape[m_normalized_axis] = Dimension(0, input_partial_shape[m_normalized_axis].get_max_length());
-        }
-    }
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}, ov::PartialShape{}};
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0), get_input_partial_shape(1)};
+    shape_infer(this, input_shapes, output_shapes);
 
     set_output_size(2);
-    set_output_type(0, get_input_element_type(0), output_shape);
-    set_output_type(1, m_index_element_type, output_shape);
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
+    set_output_type(1, m_index_element_type, output_shapes[1]);
 }
 
 ov::Shape op::v1::TopK::compute_output_shape(const std::string& node_description,
diff --git a/src/core/src/op/util/embeddingbag_offsets_base.cpp b/src/core/src/op/util/embeddingbag_offsets_base.cpp
index 0ed6c9a9dcc..13848bfce60 100644
--- a/src/core/src/op/util/embeddingbag_offsets_base.cpp
+++ b/src/core/src/op/util/embeddingbag_offsets_base.cpp
@@ -4,6 +4,7 @@
 
 #include "ngraph/op/util/embeddingbag_offsets_base.hpp"
 
+#include "embeddingbag_offsets_shape_inference.hpp"
 #include "itt.hpp"
 #include "ngraph/op/constant.hpp"
 
@@ -55,16 +56,6 @@ void ov::op::util::EmbeddingBagOffsetsBase::validate_and_infer_types() {
                           get_input_element_type(INDICES),
                           ")");
 
-    NODE_VALIDATION_CHECK(
-        this,
-        get_input_partial_shape(INDICES).is_dynamic() || get_input_partial_shape(INDICES).to_shape().size() == 1,
-        "INDICES must be 1D");
-
-    NODE_VALIDATION_CHECK(
-        this,
-        get_input_partial_shape(OFFSETS).is_dynamic() || get_input_partial_shape(OFFSETS).to_shape().size() == 1,
-        "OFFSETS must be 1D");
-
     if (get_input_size() >= 4) {
         NODE_VALIDATION_CHECK(this,
                               get_input_element_type(DEFAULT_INDEX) == element::i64 ||
@@ -78,10 +69,6 @@ void ov::op::util::EmbeddingBagOffsetsBase::validate_and_infer_types() {
                               ") must match indices element type (",
                               get_input_element_type(INDICES),
                               ")");
-
-        NODE_VALIDATION_CHECK(this,
-                              get_input_partial_shape(DEFAULT_INDEX).compatible(PartialShape{}),
-                              "DEFAULT_INDEX must be a scalar");
     }
 
     if (get_input_size() == 5) {
@@ -92,31 +79,18 @@ void ov::op::util::EmbeddingBagOffsetsBase::validate_and_infer_types() {
                               ") must match embedding table element type (",
                               get_input_element_type(EMB_TABLE),
                               ")");
-
-        NODE_VALIDATION_CHECK(this,
-                              get_input_partial_shape(PER_SAMPLE_WEIGHTS).is_dynamic() ||
-                                  get_input_partial_shape(PER_SAMPLE_WEIGHTS).to_shape().size() == 1,
-                              "PER_SAMPLE_WEIGHTS must be 1D");
-
-        NODE_VALIDATION_CHECK(this,
-                              get_input_partial_shape(INDICES).compatible(get_input_partial_shape(PER_SAMPLE_WEIGHTS)),
-                              "INDICES and PER_SAMPLE_WEIGHTS shape must be same");
     }
 
     element::Type result_et = get_input_element_type(EMB_TABLE);
 
-    const PartialShape& emb_table_shape = get_input_partial_shape(EMB_TABLE);
-    const PartialShape& offsets_shape = get_input_partial_shape(OFFSETS);
+    std::vector<PartialShape> result_shapes = {PartialShape::dynamic()};
+    std::vector<PartialShape> input_shapes;
+    for (int i = 0; i < get_input_size(); i++)
+        input_shapes.push_back(get_input_partial_shape(i));
 
-    PartialShape result_shape;
-    if (emb_table_shape.rank().is_static()) {
-        result_shape = emb_table_shape;
-        result_shape[0] = offsets_shape.rank().is_static() ? offsets_shape[0] : Dimension::dynamic();
-    } else {
-        result_shape = PartialShape::dynamic();
-    }
+    shape_infer(this, input_shapes, result_shapes);
 
-    set_output_type(0, result_et, result_shape);
+    set_output_type(0, result_et, result_shapes[0]);
 }
 
 bool ov::op::util::EmbeddingBagOffsetsBase::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/variadic_split.cpp b/src/core/src/op/variadic_split.cpp
index ff0ace0096b..a12b466f6bc 100644
--- a/src/core/src/op/variadic_split.cpp
+++ b/src/core/src/op/variadic_split.cpp
@@ -9,6 +9,7 @@
 #include "itt.hpp"
 #include "ngraph/runtime/reference/slice.hpp"
 #include "ngraph/validation_util.hpp"
+#include "variadic_split_shape_inference.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -33,81 +34,15 @@ void ngraph::op::v1::VariadicSplit::validate_and_infer_types() {
     set_input_is_relevant_to_value(1);
     set_input_is_relevant_to_value(2);
 
-    auto split_lengths_pshape = get_input_partial_shape(2);
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0),
+                                                  get_input_partial_shape(1),
+                                                  get_input_partial_shape(2)};
+    std::vector<ov::PartialShape> output_shapes;
+    shape_infer(this, input_shapes, output_shapes);
 
-    if (split_lengths_pshape.is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              split_lengths_pshape.rank().get_length() == 1,
-                              "Split lengths should be a 1-D tensor. Got ",
-                              split_lengths_pshape.rank(),
-                              " instead.");
-
-        const auto num_outputs = split_lengths_pshape[0].get_length();
-        const auto data = input_value(0);
-        const auto axis_source = input_value(1);
-        const auto split_lengths_source = input_value(2);
-        const auto data_shape = data.get_partial_shape();
-        const auto& data_type = data.get_element_type();
-
-        set_output_size(num_outputs);
-        const auto& axis_input_constant = get_constant_from_source(axis_source);
-        const auto& split_lengths_constant = get_constant_from_source(split_lengths_source);
-        if (data_shape.rank().is_static() && axis_input_constant && split_lengths_constant) {
-            const auto axis_val = axis_input_constant->cast_vector<int64_t>()[0];
-            // Adjust split axis in case of negatives
-            const int64_t axis = ngraph::normalize_axis(this, axis_val, data_shape.rank());
-
-            auto split_lengths = split_lengths_constant->cast_vector<int64_t>();
-            // Adjust split lengths in case of negatives
-            int64_t sum_of_splits = 0;
-            int64_t negative_one = -1;
-            for (size_t i = 0; i < split_lengths.size(); i++) {
-                NODE_VALIDATION_CHECK(this,
-                                      split_lengths[i] >= -1,
-                                      "Invalid value ",
-                                      split_lengths[i],
-                                      " in split lengths input. Should be >= -1.");
-
-                if (split_lengths[i] == -1) {
-                    NODE_VALIDATION_CHECK(this,
-                                          negative_one == -1,
-                                          "Cannot infer split with multiple -1 values at ",
-                                          negative_one,
-                                          " and ",
-                                          i);
-                    negative_one = i;
-                } else {
-                    sum_of_splits += split_lengths[i];
-                }
-            }
-            const auto data_shape_dims = vector<Dimension>{data.get_partial_shape()};
-            const auto dimension_at_axis = data_shape_dims.at(axis);
-
-            if (negative_one >= 0 && dimension_at_axis.is_static()) {
-                split_lengths[negative_one] = dimension_at_axis.get_length() - sum_of_splits;
-                sum_of_splits += split_lengths[negative_one];
-            }
-            if (data_shape[axis].is_static()) {
-                NODE_VALIDATION_CHECK(this,
-                                      sum_of_splits == data_shape[axis].get_length(),
-                                      "Total length of splits: ",
-                                      sum_of_splits,
-                                      " must match the length of the chosen axis: ",
-                                      data_shape[axis]);
-            }
-
-            for (int64_t output{0}; output < num_outputs; ++output) {
-                const auto output_split_dim =
-                    split_lengths.at(output) == -1 ? Dimension::dynamic() : split_lengths.at(output);
-                auto tmp_shape = data_shape_dims;
-                tmp_shape.at(axis) = output_split_dim;
-                set_output_type(output, data_type, ov::PartialShape{tmp_shape});
-            }
-        } else {
-            for (int64_t output{0}; output < num_outputs; ++output) {
-                set_output_type(output, data_type, ov::PartialShape::dynamic());
-            }
-        }
+    const auto& data_type = get_input_element_type(0);
+    for (size_t i = 0; i < output_shapes.size(); ++i) {
+        set_output_type(i, data_type, output_shapes[i]);
     }
 }
 
@@ -143,37 +78,30 @@ bool op::v1::VariadicSplit::evaluate_variadic_split(const HostTensorVector& inpu
     const auto& axis_tensor = inputs[1];
     const auto& split_lengths_tensor = inputs[2];
     NGRAPH_CHECK(axis_tensor->get_element_type().is_integral_number(), "axis element type is not integral data type");
+    NGRAPH_CHECK(split_lengths_tensor->get_element_type().is_integral_number(),
+                 "split_lengths element type is not integral data type");
 
     int64_t axis = host_tensor_2_vector<int64_t>(axis_tensor)[0];
-
     axis = ngraph::normalize_axis(this, axis, data_tensor->get_partial_shape().rank());
 
-    NGRAPH_CHECK(split_lengths_tensor->get_element_type().is_integral_number(),
-                 "axis element type is not integral data type");
-
-    std::vector<int64_t> split_lengths = host_tensor_2_vector<int64_t>(split_lengths_tensor);
+    std::vector<ov::PartialShape> input_shapes = {data_tensor->get_partial_shape(),
+                                                  axis_tensor->get_partial_shape(),
+                                                  split_lengths_tensor->get_partial_shape()};
+    std::vector<ov::PartialShape> output_shapes;
+    shape_infer(this, input_shapes, output_shapes, {{1, axis_tensor}, {2, split_lengths_tensor}});
 
     const auto data_shape = data_tensor->get_shape();
-    const auto neg_one = std::find(std::begin(split_lengths), std::end(split_lengths), -1);
-    if (neg_one != std::end(split_lengths))  // negative length set
-    {
-        const auto sum_of_known_splits = std::accumulate(std::begin(split_lengths), std::end(split_lengths), 0) + 1;
-        split_lengths[std::distance(std::begin(split_lengths), neg_one)] = data_shape[axis] - sum_of_known_splits;
-    }
-
-    ov::Shape output_shape = data_shape;
     std::vector<size_t> lower_bounds(data_shape.size(), 0);
     std::vector<size_t> upper_bounds = data_shape;
-    upper_bounds.at(axis) = split_lengths[0];
+    upper_bounds[axis] = 0;
 
     size_t split_pos = 0;
     for (const auto& output : outputs) {
-        output_shape.at(axis) = split_lengths[split_pos++];
+        ov::Shape output_shape = output_shapes[split_pos++].get_shape();
+        upper_bounds[axis] += output_shape[axis];
         output->set_shape(output_shape);
         variadic_split::evaluate(data_tensor, output, lower_bounds, upper_bounds);
         lower_bounds.at(axis) = upper_bounds.at(axis);
-        if (split_pos < split_lengths.size())
-            upper_bounds.at(axis) += split_lengths[split_pos];
     }
 
     return true;
diff --git a/src/tests/unit/cpu/shape_inference_test/bucketize_test.cpp b/src/tests/unit/cpu/shape_inference_test/bucketize_test.cpp
new file mode 100644
index 00000000000..15f6ea0f69a
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/bucketize_test.cpp
@@ -0,0 +1,18 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <bucketize_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+using namespace std;
+
+TEST(StaticShapeInferenceTest, BucketizeV3) {
+    auto data = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{-1, -1, -1});
+    auto buckets = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{-1});
+    auto bucketize = make_shared<op::v3::Bucketize>(data, buckets);
+
+    check_static_shape(bucketize.get(), {ov::StaticShape{2, 3, 2}, ov::StaticShape{4}}, {ov::StaticShape{2, 3, 2}});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/einsum_test.cpp b/src/tests/unit/cpu/shape_inference_test/einsum_test.cpp
new file mode 100644
index 00000000000..56c873aeb7e
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/einsum_test.cpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <einsum_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, Einsum1) {
+    auto I1 = std::make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+    auto I2 = std::make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+    auto O = std::make_shared<op::v7::Einsum>(OutputVector{I1, I2}, "i,i->");
+
+    check_static_shape(O.get(), {ov::StaticShape{3}, ov::StaticShape{3}}, {ov::StaticShape{}});
+}
+
+TEST(StaticShapeInferenceTest, Einsum2) {
+    auto I1 = std::make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+    auto I2 = std::make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+    auto O = std::make_shared<op::v7::Einsum>(OutputVector{I1, I2}, "ab,bc->ac");
+
+    check_static_shape(O.get(), {ov::StaticShape{2, 3}, ov::StaticShape{3, 4}}, {ov::StaticShape{2, 4}});
+}
+
+TEST(StaticShapeInferenceTest, Einsum3) {
+    auto I1 = std::make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+    auto O = std::make_shared<op::v7::Einsum>(OutputVector{I1}, "kii->k");
+
+    check_static_shape(O.get(), {ov::StaticShape{2, 3, 3}}, {ov::StaticShape{2}});
+}
+
+TEST(StaticShapeInferenceTest, Einsum4) {
+    auto I1 = std::make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+    auto O = std::make_shared<op::v7::Einsum>(OutputVector{I1}, "ijk->kij");
+
+    check_static_shape(O.get(), {ov::StaticShape{1, 2, 3}}, {ov::StaticShape{3, 1, 2}});
+}
+
+TEST(StaticShapeInferenceTest, Einsum5) {
+    auto I1 = std::make_shared<op::v0::Parameter>(element::i32, ov::PartialShape::dynamic());
+    auto I2 = std::make_shared<op::v0::Parameter>(element::i32, ov::PartialShape::dynamic());
+    auto I3 = std::make_shared<op::v0::Parameter>(element::i32, ov::PartialShape::dynamic());
+    auto O = std::make_shared<op::v7::Einsum>(OutputVector{I1, I2, I3}, "ab,bcd,bc->ca");
+
+    check_static_shape(O.get(),
+                       {ov::StaticShape{2, 5}, ov::StaticShape{5, 3, 6}, ov::StaticShape{5, 3}},
+                       {ov::StaticShape{3, 2}});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/embedding_segments_sum_test.cpp b/src/tests/unit/cpu/shape_inference_test/embedding_segments_sum_test.cpp
new file mode 100644
index 00000000000..254d6a1492c
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/embedding_segments_sum_test.cpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <embedding_segments_sum_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+using namespace std;
+
+TEST(StaticShapeInferenceTest, EmbeddingSegmentsSum) {
+    auto emb_table = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{-1, -1});
+    auto indices = make_shared<op::v0::Parameter>(element::i64, ov::PartialShape{-1});
+    auto segment_ids = make_shared<op::v0::Parameter>(element::i64, ov::PartialShape{-1});
+    auto num_segments = op::v0::Constant::create(element::i64, ov::Shape{}, {3});
+    auto default_index = make_shared<op::v0::Parameter>(element::i64, ov::PartialShape{});
+    auto per_sample_weights = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{-1});
+
+    auto ess = make_shared<op::v3::EmbeddingSegmentsSum>(emb_table,
+                                                         indices,
+                                                         segment_ids,
+                                                         num_segments,
+                                                         default_index,
+                                                         per_sample_weights);
+    check_static_shape(
+        ess.get(),
+        {StaticShape{5, 2}, StaticShape{4}, StaticShape{4}, StaticShape{}, StaticShape{}, StaticShape{4}},
+        {StaticShape{3, 2}});
+
+    check_static_shape(ess.get(),
+                       {StaticShape{5, 2}, StaticShape{4}, StaticShape{4}, 8, StaticShape{}, StaticShape{4}},
+                       {StaticShape{8, 2}});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/embeddingbag_offsets_sum_test.cpp b/src/tests/unit/cpu/shape_inference_test/embeddingbag_offsets_sum_test.cpp
new file mode 100644
index 00000000000..14f1935ecb9
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/embeddingbag_offsets_sum_test.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <embeddingbag_offsets_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+using namespace std;
+
+TEST(StaticShapeInferenceTest, EmbeddingBagOffsetsSumV3) {
+    auto emb_table = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+    auto indices = make_shared<op::v0::Parameter>(element::i64, ov::PartialShape::dynamic());
+    auto offsets = make_shared<op::v0::Parameter>(element::i64, ov::PartialShape::dynamic());
+    auto default_index = make_shared<op::v0::Parameter>(element::i64, ov::PartialShape::dynamic());
+    auto per_sample_weights = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape::dynamic());
+
+    auto ebos =
+        make_shared<op::v3::EmbeddingBagOffsetsSum>(emb_table, indices, offsets, default_index, per_sample_weights);
+
+    check_static_shape(
+        ebos.get(),
+        {ov::StaticShape{5, 2}, ov::StaticShape{4}, ov::StaticShape{3}, ov::StaticShape{}, ov::StaticShape{4}},
+        {ov::StaticShape{3, 2}});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/experimental_detectron_roi_feature_extractor.cpp b/src/tests/unit/cpu/shape_inference_test/experimental_detectron_roi_feature_extractor.cpp
new file mode 100644
index 00000000000..a943c62d0a0
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/experimental_detectron_roi_feature_extractor.cpp
@@ -0,0 +1,44 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <convolution_shape_inference.hpp>
+#include <experimental_detectron_roi_feature_shape_inference.hpp>
+#include <openvino/op/ops.hpp>
+#include <openvino/op/parameter.hpp>
+
+#include "utils/shape_inference/shape_inference.hpp"
+#include "utils/shape_inference/static_shape.hpp"
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, ExperimentalDetectronROIFeatureExtractor) {
+    op::v6::ExperimentalDetectronROIFeatureExtractor::Attributes attrs;
+    attrs.aligned = false;
+    attrs.output_size = 14;
+    attrs.sampling_ratio = 2;
+    attrs.pyramid_scales = {4, 8, 16, 32};
+
+    auto input = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1});
+    auto pyramid_layer0 = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{1, -1, -1, -1});
+    auto pyramid_layer1 = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{1, -1, -1, -1});
+    auto pyramid_layer2 = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{1, -1, -1, -1});
+    auto pyramid_layer3 = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{1, -1, -1, -1});
+
+    auto roi = std::make_shared<op::v6::ExperimentalDetectronROIFeatureExtractor>(
+        NodeVector{input, pyramid_layer0, pyramid_layer1, pyramid_layer2, pyramid_layer3},
+        attrs);
+
+    std::vector<StaticShape> input_shapes = {StaticShape{1000, 4},
+                                             StaticShape{1, 256, 200, 336},
+                                             StaticShape{1, 256, 100, 168},
+                                             StaticShape{1, 256, 50, 84},
+                                             StaticShape{1, 256, 25, 42}};
+    std::vector<StaticShape> output_shapes = {StaticShape{}, StaticShape{}};
+    shape_inference(roi.get(), input_shapes, output_shapes);
+
+    EXPECT_EQ(output_shapes[0], (StaticShape{1000, 256, 14, 14}));
+    EXPECT_EQ(output_shapes[1], (StaticShape{1000, 4}));
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/pad_test.cpp b/src/tests/unit/cpu/shape_inference_test/pad_test.cpp
new file mode 100644
index 00000000000..379d0b1dbd9
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/pad_test.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <pad_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, Padv1) {
+    const auto data = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
+
+    const auto pads_begin = ov::op::v0::Constant::create(element::i64, ov::Shape{4}, {3, 2, 1, 0});
+    const auto pads_end = ov::op::v0::Constant::create(element::i64, ov::Shape{4}, {0, 1, 2, 3});
+    const auto pad_val = ov::op::v0::Constant::create(element::f32, ov::Shape{}, {2112});
+
+    const auto pad = std::make_shared<ov::op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT);
+
+    check_static_shape(pad.get(),
+                       {ov::StaticShape{3, 6, 5, 5},
+                        ov::StaticShape{4},
+                        ov::StaticShape{4},
+                        ov::StaticShape()},
+                       {ov::StaticShape({6, 9, 8, 8})});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/range_test.cpp b/src/tests/unit/cpu/shape_inference_test/range_test.cpp
new file mode 100644
index 00000000000..bd9ad7954f8
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/range_test.cpp
@@ -0,0 +1,36 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <range_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+using namespace std;
+
+TEST(StaticShapeInferenceTest, Rangev4_i32) {
+    auto start = make_shared<op::v0::Parameter>(element::i32, ov::PartialShape{});
+    auto stop = make_shared<op::v0::Parameter>(element::i32, ov::PartialShape{});
+    auto step = make_shared<op::v0::Parameter>(element::i32, ov::PartialShape{});
+
+    auto range = make_shared<op::v4::Range>(start, stop, step, element::i32);
+
+    check_static_shape(range.get(), {2, 0, -2}, {ov::StaticShape{1}});
+    check_static_shape(range.get(), {2, 0, -1}, {ov::StaticShape{2}});
+    check_static_shape(range.get(), {-19, 19, 1}, {ov::StaticShape{38}});
+    check_static_shape(range.get(), {-19, 19, 3}, {ov::StaticShape{13}});
+    check_static_shape(range.get(), {20, -19, 1}, {ov::StaticShape{0}});
+}
+
+TEST(StaticShapeInferenceTest, Rangev4_f32) {
+    auto start = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{});
+    auto stop = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{});
+    auto step = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{});
+
+    auto range = make_shared<op::v4::Range>(start, stop, step, element::f32);
+
+    check_static_shape(range.get(), {0., 1., 0.25}, {ov::StaticShape{4}});
+    check_static_shape(range.get(), {-1., 1., 0.25}, {ov::StaticShape{8}});
+    check_static_shape(range.get(), {-1., 0.875, 0.25}, {ov::StaticShape{8}});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/region_yolo_test.cpp b/src/tests/unit/cpu/shape_inference_test/region_yolo_test.cpp
new file mode 100644
index 00000000000..421d46167fc
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/region_yolo_test.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <region_yolo_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+using namespace std;
+
+TEST(StaticShapeInferenceTest, RegionYoloV0) {
+    auto inputs = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{-1, -1, -1, -1});
+    auto op = make_shared<op::v0::RegionYolo>(inputs, 0, 0, 0, true, std::vector<int64_t>{}, 0, 1);
+
+    check_static_shape(op.get(), {ov::StaticShape{1, 125, 13, 13}}, {ov::StaticShape{1 * 125, 13, 13}});
+}
+
+TEST(StaticShapeInferenceTest, RegionYoloV0Dynamic) {
+    auto inputs = make_shared<op::v0::Parameter>(element::f32,
+                                                 ov::PartialShape{{1, 11}, {2, 12}, ov::Dimension::dynamic(), {4, 14}});
+    auto op = make_shared<op::v0::RegionYolo>(inputs, 4, 80, 5, true, std::vector<int64_t>{}, 1, 3);
+
+    EXPECT_EQ(op->get_output_partial_shape(0), ov::PartialShape({{1, 11}, ov::Dimension::dynamic()}));
+
+    check_static_shape(op.get(), {ov::StaticShape{10, 125, 13, 13}}, {ov::StaticShape{10, 125 * 13 * 13}});
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/reorg_yolo_test.cpp b/src/tests/unit/cpu/shape_inference_test/reorg_yolo_test.cpp
new file mode 100644
index 00000000000..ec9743c586c
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/reorg_yolo_test.cpp
@@ -0,0 +1,19 @@
+
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <reorg_yolo_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+using namespace std;
+
+TEST(StaticShapeInferenceTest, ReorgYoloV0) {
+    size_t stride = 2;
+    auto data_param = make_shared<op::v0::Parameter>(element::f32, ov::PartialShape{-1, -1, -1, -1});
+    auto op = make_shared<op::v0::ReorgYolo>(data_param, stride);
+
+    check_static_shape(op.get(), {ov::StaticShape{1, 64, 26, 26}}, {ov::StaticShape{1, 256, 13, 13}});
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/split_tests.cpp b/src/tests/unit/cpu/shape_inference_test/split_tests.cpp
new file mode 100644
index 00000000000..4a95a2a5166
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/split_tests.cpp
@@ -0,0 +1,41 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <split_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+
+static std::shared_ptr<op::v1::Split> build_split(PartialShape data_shape,
+                                                  std::initializer_list<int64_t> axis_value,
+                                                  size_t num_splits) {
+    std::shared_ptr<ov::Node> axis;
+    const auto data = std::make_shared<op::v0::Parameter>(element::f32, data_shape);
+    if (axis_value.size())
+        axis = op::v0::Constant::create(element::i64, ov::Shape{}, {*axis_value.begin()});
+    else
+        axis = std::make_shared<op::v0::Parameter>(element::i64, ov::PartialShape{});
+
+    return std::make_shared<op::v1::Split>(data, axis, num_splits);
+}
+
+TEST(StaticShapeInferenceTest, SplitV1) {
+    const auto op = build_split(PartialShape{-1, -1, -1}, {}, 3);
+    check_static_shape(op.get(), {StaticShape{2, 3, 4}, 1}, {{2, 1, 4}, {2, 1, 4}, {2, 1, 4}});
+}
+
+TEST(StaticShapeInferenceTest, SplitV1_Dynamic) {
+    check_output_shape(build_split(PartialShape({2, 8, 4}), {}, 4).get(),
+                       {ov::PartialShape::dynamic(ov::Rank(3)),
+                        ov::PartialShape::dynamic(ov::Rank(3)),
+                        ov::PartialShape::dynamic(ov::Rank(3)),
+                        ov::PartialShape::dynamic(ov::Rank(3))});
+}
+
+TEST(StaticShapeInferenceTest, SplitV1_StaticWithConstMap) {
+    check_static_shape(build_split(PartialShape({-1, -1, -1}), {}, 4).get(),
+                       {StaticShape{2, 8, 4}, 2},
+                       {{2, 8, 1}, {2, 8, 1}, {2, 8, 1}, {2, 8, 1}});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/strided_slice_test.cpp b/src/tests/unit/cpu/shape_inference_test/strided_slice_test.cpp
new file mode 100644
index 00000000000..7b24a7d8075
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/strided_slice_test.cpp
@@ -0,0 +1,97 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <strided_slice_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, StridedSlice1) {
+    auto data = std::make_shared<op::v0::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+    auto begin = op::v0::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {100});
+    auto end = op::v0::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {-100});
+    auto stride = op::v0::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {-1});
+
+    std::vector<int64_t> begin_mask = {0, 0, 0, 0};
+    std::vector<int64_t> end_mask = {0, 0, 0, 0};
+
+    auto ss = std::make_shared<op::v1::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+    check_static_shape(ss.get(),
+                       {ov::StaticShape{3, 4, 5}, ov::StaticShape{3}, ov::StaticShape{3}, ov::StaticShape{3}},
+                       {ov::StaticShape{3, 4, 5}});
+}
+
+TEST(StaticShapeInferenceTest, StridedSlice2) {
+    auto data = std::make_shared<op::v0::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+    auto begin = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto end = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto stride = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+
+    std::vector<int64_t> begin_mask(3, 0);
+    std::vector<int64_t> end_mask(3, 0);
+
+    auto ss = std::make_shared<op::v1::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+    check_static_shape(ss.get(),
+                       {ov::StaticShape{3, 2, 3}, {1, 0, 0}, {2, 1, 3}, {1, 1, 1}},
+                       {ov::StaticShape{1, 1, 3}});
+
+    check_static_shape(ss.get(),
+                       {ov::StaticShape{3, 2, 3}, {1, 0, 0}, {2, 2, 3}, {1, 1, 1}},
+                       {ov::StaticShape{1, 2, 3}});
+
+    check_static_shape(ss.get(),
+                       {ov::StaticShape{3, 2, 3}, {2, 0, 0}, {3, 2, 3}, {1, 1, 2}},
+                       {ov::StaticShape{1, 2, 2}});
+}
+
+TEST(StaticShapeInferenceTest, StridedSlice3) {
+    auto data = std::make_shared<op::v0::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+    auto begin = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto end = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto stride = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+
+    std::vector<int64_t> begin_mask{0, 1, 1};
+    std::vector<int64_t> end_mask{1, 1, 1};
+
+    auto ss = std::make_shared<op::v1::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+    check_static_shape(ss.get(),
+                       {ov::StaticShape{3, 2, 3}, {1, 0, 0}, {0, 0, 0}, {1, 1, 1}},
+                       {ov::StaticShape{2, 2, 3}});
+}
+
+TEST(StaticShapeInferenceTest, StridedSlice4) {
+    auto data = std::make_shared<op::v0::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+    auto begin = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto end = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto stride = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+
+    std::vector<int64_t> begin_mask{1, 0, 1};
+    std::vector<int64_t> end_mask{0, 1, 1};
+
+    auto ss = std::make_shared<op::v1::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+    check_static_shape(ss.get(),
+                       {ov::StaticShape{3, 2, 3}, {0, 1, 0}, {2, 0, 0}, {1, 1, 2}},
+                       {ov::StaticShape{2, 1, 2}});
+}
+
+TEST(StaticShapeInferenceTest, StridedSlice5) {
+    auto data = std::make_shared<op::v0::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+    auto begin = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto end = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+    auto stride = std::make_shared<op::v0::Parameter>(ngraph::element::i64, ngraph::Shape{3});
+
+    std::vector<int64_t> begin_mask{0, 1, 1};
+    std::vector<int64_t> end_mask{0, 1, 1};
+
+    auto ss = std::make_shared<op::v1::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+    check_static_shape(ss.get(),
+                       {ov::StaticShape{3, 2, 3}, {0, 0, 0}, {1, 0, 0}, {1, 1, -1}},
+                       {ov::StaticShape{1, 2, 3}});
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/topk_test.cpp b/src/tests/unit/cpu/shape_inference_test/topk_test.cpp
new file mode 100644
index 00000000000..83e31c37bc5
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/topk_test.cpp
@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <topk_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+
+static std::shared_ptr<op::v3::TopK> build_topk(PartialShape data_shape = PartialShape::dynamic(),
+                                                int64_t axis = 1,
+                                                int k_value = -1) {
+    std::shared_ptr<ov::Node> k;
+    const auto data = std::make_shared<op::v0::Parameter>(element::f32, data_shape);
+    if (k_value >= 0)
+        k = op::v0::Constant::create(element::i64, ov::Shape{}, {2});
+    else
+        k = std::make_shared<op::v0::Parameter>(element::i64, ov::PartialShape{});
+    return std::make_shared<op::v3::TopK>(data, k, axis, "max", "value");
+}
+
+TEST(StaticShapeInferenceTest, TopKv3) {
+    const auto topk = build_topk(PartialShape::dynamic(), 1, 2);
+
+    check_static_shape(topk.get(),
+                       {StaticShape{1, 10, 100}, StaticShape{}},
+                       {StaticShape({1, 2, 100}), StaticShape({1, 2, 100})});
+}
+
+TEST(StaticShapeInferenceTest, TopKv3_StaticNoConstMap) {
+    const auto topk = build_topk();
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 10, 100}, StaticShape{}};
+    std::vector<StaticShape> static_output_shapes = {StaticShape{}, StaticShape{}};
+    EXPECT_THROW(shape_inference(topk.get(), static_input_shapes, static_output_shapes), NodeValidationFailure);
+}
+
+TEST(StaticShapeInferenceTest, TopKv3_StaticWithConstMap) {
+    const auto topk = build_topk();
+
+    check_static_shape(topk.get(), {StaticShape{1, 10, 100}, 2}, {StaticShape{1, 2, 100}, StaticShape{1, 2, 100}});
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/utils.hpp b/src/tests/unit/cpu/shape_inference_test/utils.hpp
new file mode 100644
index 00000000000..bd44d25fc0a
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/utils.hpp
@@ -0,0 +1,84 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <openvino/op/ops.hpp>
+#include <openvino/op/parameter.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+#pragma once
+
+struct TestTensor {
+    std::shared_ptr<ngraph::runtime::HostTensor> tensor;
+    ov::StaticShape static_shape;
+
+    template <typename T>
+    TestTensor(std::initializer_list<T> values) : TestTensor(ov::StaticShape({values.size()}), values) {}
+
+    template <typename T>
+    TestTensor(T scalar) : TestTensor(ov::StaticShape({}), {scalar}) {}
+
+    TestTensor(ov::StaticShape shape) : static_shape(shape) {}
+
+    template <typename T>
+    TestTensor(ov::StaticShape shape, std::initializer_list<T> values) {
+        static_shape = shape;
+
+        ov::Shape s;
+        for (auto dim : shape)
+            s.push_back(dim.get_length());
+
+        if (values.size() > 0) {
+            tensor = std::make_shared<ngraph::runtime::HostTensor>(ov::element::from<T>(), s);
+            T* ptr = tensor->get_data_ptr<T>();
+            int i = 0;
+            for (auto& v : values)
+                ptr[i++] = v;
+        }
+    }
+};
+
+// TestTensor can be constructed from initializer_list<T>/int64_t/Shape/Shape+initializer_list
+// so each element of inputs can be:
+//      {1,2,3,4}                   tensor of shape [4] and values (1,2,3,4)
+//      2                           tensor of scalar with value 2
+//      Shape{2,2}                  tensor of shape [2,2] and value unknown
+//      {Shape{2,2}, {1,2,3,4}}     tensor of shape [2,2] and values (1,2,3,4)
+static void check_static_shape(ov::Node* op,
+                               std::initializer_list<TestTensor> inputs,
+                               std::initializer_list<ov::StaticShape> expect_shapes) {
+    std::vector<ov::StaticShape> output_shapes;
+    std::vector<ov::StaticShape> input_shapes;
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constData;
+
+    int index = 0;
+    std::for_each(inputs.begin(), inputs.end(), [&](TestTensor t) {
+        input_shapes.push_back(t.static_shape);
+        if (t.tensor)
+            constData[index] = t.tensor;
+        index++;
+    });
+
+    output_shapes.resize(expect_shapes.size(), ov::StaticShape{});
+
+    shape_inference(op, input_shapes, output_shapes, constData);
+
+    EXPECT_EQ(output_shapes.size(), expect_shapes.size());
+    int id = 0;
+    for (auto& shape : expect_shapes) {
+        EXPECT_EQ(output_shapes[id], shape);
+        id++;
+    }
+}
+
+static void check_output_shape(ov::Node* op, std::initializer_list<ov::PartialShape> expect_shapes) {
+    int id = 0;
+    EXPECT_EQ(op->outputs().size(), expect_shapes.size());
+    for (auto& shape : expect_shapes) {
+        EXPECT_EQ(op->get_output_partial_shape(id), shape);
+        id++;
+    }
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/variadic_split_tests.cpp b/src/tests/unit/cpu/shape_inference_test/variadic_split_tests.cpp
new file mode 100644
index 00000000000..169c0d9f701
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/variadic_split_tests.cpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <variadic_split_shape_inference.hpp>
+
+#include "utils.hpp"
+
+using namespace ov;
+
+static std::shared_ptr<op::v1::VariadicSplit> build_variadic_split(PartialShape data_shape,
+                                                                   std::initializer_list<int64_t> axis_value,
+                                                                   std::initializer_list<int64_t> splits) {
+    std::shared_ptr<ov::Node> axis;
+    std::shared_ptr<ov::Node> splits_len;
+
+    const auto data = std::make_shared<op::v0::Parameter>(element::i32, data_shape);
+    if (axis_value.size())
+        axis = op::v0::Constant::create(element::i64, ov::Shape{}, {*axis_value.begin()});
+    else
+        axis = std::make_shared<op::v0::Parameter>(element::i64, ov::PartialShape::dynamic(ov::Rank(0)));
+
+    if (splits.size())
+        splits_len = op::v0::Constant::create(element::i64, ov::Shape{splits.size()}, splits);
+    else
+        splits_len = std::make_shared<op::v0::Parameter>(element::i64, ov::PartialShape::dynamic(ov::Rank(1)));
+
+    return std::make_shared<op::v1::VariadicSplit>(data, axis, splits_len);
+}
+
+TEST(StaticShapeInferenceTest, VariadicSplitV1) {
+    const auto split = build_variadic_split(ov::PartialShape::dynamic(), {}, {});
+
+    check_static_shape(split.get(),
+                       {StaticShape{12, 6}, {-2}, {7, -1, 2}},
+                       {{7, 6}, {3, 6}, {2, 6}});
+    check_static_shape(split.get(),
+                       {StaticShape{12, 6}, {-2}, {-1, 7, 2}},
+                       {{3, 6}, {7, 6}, {2, 6}});
+    check_static_shape(split.get(),
+                       {StaticShape{12, 1, 6}, {2}, {3, 1, 2}},
+                       {{12, 1, 3}, {12, 1, 1}, {12, 1, 2}});
+    check_static_shape(split.get(), {StaticShape{12, 6}, {1}, {6, 0}}, {{12, 6}, {12, 0}});
+}
+
+TEST(StaticShapeInferenceTest, VariadicSplitV1_StaticWithConstMap) {
+    check_static_shape(build_variadic_split(ov::PartialShape{-1, -1}, {}, {}).get(),
+                       {StaticShape{12, 6}, {-2}, {7, -1, 2}},
+                       {{7, 6}, {3, 6}, {2, 6}});
+}
\ No newline at end of file

From 5fada94504926120bbf8420067676d66d5401be4 Mon Sep 17 00:00:00 2001
From: Mang Guo <mang.guo@intel.com>
Date: Wed, 22 Dec 2021 12:47:44 +0800
Subject: [PATCH 083/160] Mang/shape inference (#8412)

* Implement DFT and IDFT shape inference

* Implement CTCLoss shape inference

* Fix error message.

* Refactor test case.

* Apply review comments

* Apply review comments

* Fix clang format error

* Fix merge error

* Remove axes_vector.
---
 .../utils/shape_inference/shape_inference.cpp |   8 +
 .../include/openvino/op/util/fft_base.hpp     |   2 -
 .../include/ctc_loss_shape_inference.hpp      | 133 ++++++++++++++
 .../include/fft_base_shape_inference.hpp      | 117 +++++++++++++
 src/core/src/op/ctc_loss.cpp                  | 113 +-----------
 src/core/src/op/util/fft_base.cpp             | 165 ++----------------
 .../ctc_loss_shape_inference.cpp              |  34 ++++
 .../fft_base_shape_inference.cpp              | 145 +++++++++++++++
 8 files changed, 460 insertions(+), 257 deletions(-)
 create mode 100644 src/core/shape_inference/include/ctc_loss_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/fft_base_shape_inference.hpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/ctc_loss_shape_inference.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/fft_base_shape_inference.cpp

diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
index e9a10d89b67..be2c0bb0bc5 100644
--- a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
+++ b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
@@ -29,6 +29,8 @@
 #include "reduce_shape_inference.hpp"
 #include "scatter_elements_update_shape_inference.hpp"
 #include "scatter_nd_base_shape_inference.hpp"
+#include "ctc_loss_shape_inference.hpp"
+#include "fft_base_shape_inference.hpp"
 #include "shape_inference.hpp"
 #include "shape_nodes.hpp"
 #include "fake_quantize.hpp"
@@ -184,6 +186,12 @@ void shape_inference(ov::Node* op,
         shape_infer(node, input_shapes, output_shapes);
     } else if (auto node = ov::as_type<ov::opset1::OneHot>(op)) {
         shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset4::CTCLoss>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset7::DFT>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset7::IDFT>(op)) {
+        shape_infer(node, input_shapes, output_shapes, constant_data);
     } else {
         ngraph::OutputVector new_inputs;
         for (size_t i = 0; i < op->get_input_size(); ++i) {
diff --git a/src/core/include/openvino/op/util/fft_base.hpp b/src/core/include/openvino/op/util/fft_base.hpp
index 3a14001936a..083f043c9d1 100644
--- a/src/core/include/openvino/op/util/fft_base.hpp
+++ b/src/core/include/openvino/op/util/fft_base.hpp
@@ -33,8 +33,6 @@ protected:
     /// \param axes Axes to perform FFT
     /// \param signal_size Signal sizes for 'axes'
     FFTBase(const Output<Node>& data, const Output<Node>& axes, const Output<Node>& signal_size);
-
-    void validate();
 };
 }  // namespace util
 }  // namespace op
diff --git a/src/core/shape_inference/include/ctc_loss_shape_inference.hpp b/src/core/shape_inference/include/ctc_loss_shape_inference.hpp
new file mode 100644
index 00000000000..9861cbc4d92
--- /dev/null
+++ b/src/core/shape_inference/include/ctc_loss_shape_inference.hpp
@@ -0,0 +1,133 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/op/ctc_loss.hpp>
+
+namespace ov {
+namespace op {
+namespace v4 {
+
+template <class T>
+void shape_infer(const CTCLoss* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 4 || input_shapes.size() == 5) && output_shapes.size() == 1);
+
+    // check ranks of input tensors
+    const auto& logits_pshape = input_shapes[0];
+    const auto& logit_length_pshape = input_shapes[1];
+    const auto& labels_pshape = input_shapes[2];
+    const auto& label_length_pshape = input_shapes[3];
+
+    NODE_VALIDATION_CHECK(op,
+                          logits_pshape.rank().compatible(3),
+                          "Expected a 3D tensor for logits. Got: ",
+                          logits_pshape);
+
+    NODE_VALIDATION_CHECK(op,
+                          logit_length_pshape.rank().compatible(1),
+                          "Expected a 1D tensor for logit length. Got: ",
+                          logit_length_pshape);
+
+    NODE_VALIDATION_CHECK(op,
+                          labels_pshape.rank().compatible(2),
+                          "Expected a 2D tensor for labels. Got: ",
+                          labels_pshape);
+
+    NODE_VALIDATION_CHECK(op,
+                          label_length_pshape.rank().compatible(1),
+                          "Expected a 1D tensor for label length. Got: ",
+                          label_length_pshape);
+
+    // check optional input shape: blank index
+    if (input_shapes.size() == 5) {
+        const auto& blank_index_pshape = input_shapes[4];
+        NODE_VALIDATION_CHECK(op,
+                              blank_index_pshape.rank().compatible(0),
+                              "Expected a scalar for blank index. Got: ",
+                              blank_index_pshape);
+    }
+
+    // check shapes of input tensors
+    DimType batch_size = 1;
+    bool is_batch_size_set = false;
+    DimType time_steps = 1;
+    bool is_time_steps_set = false;
+
+    if (logits_pshape.rank().is_static()) {
+        batch_size = logits_pshape[0];
+        is_batch_size_set = true;
+        time_steps = logits_pshape[1];
+        is_time_steps_set = true;
+    }
+
+    if (logit_length_pshape.rank().is_static()) {
+        if (is_batch_size_set) {
+            NODE_VALIDATION_CHECK(op,
+                                  DimType::merge(batch_size, batch_size, logit_length_pshape[0]),
+                                  "The first dimension of logit length must be equal to the first dimension ",
+                                  "of the logits. Got: ",
+                                  logit_length_pshape[0],
+                                  " and: ",
+                                  batch_size);
+        } else {
+            batch_size = logit_length_pshape[0];
+            is_batch_size_set = true;
+        }
+    }
+
+    if (labels_pshape.rank().is_static()) {
+        if (is_batch_size_set) {
+            NODE_VALIDATION_CHECK(op,
+                                  DimType::merge(batch_size, batch_size, labels_pshape[0]),
+                                  "The first dimension of labels must be equal to the first dimension ",
+                                  "of the logits and the logit length. Got: ",
+                                  labels_pshape[0],
+                                  " and: ",
+                                  batch_size);
+        } else {
+            batch_size = labels_pshape[0];
+            is_batch_size_set = true;
+        }
+
+        if (is_time_steps_set) {
+            NODE_VALIDATION_CHECK(op,
+                                  DimType::merge(time_steps, time_steps, labels_pshape[1]),
+                                  "The second dimension of labels must be equal to the second dimension ",
+                                  "of logits. Got: ",
+                                  labels_pshape[1],
+                                  " and: ",
+                                  time_steps);
+        }
+    }
+
+    if (label_length_pshape.rank().is_static()) {
+        if (is_batch_size_set) {
+            NODE_VALIDATION_CHECK(op,
+                                  DimType::merge(batch_size, batch_size, label_length_pshape[0]),
+                                  "The first dimension of label length must be equal to the first dimension ",
+                                  "of the logits, the logit length and labels. Got: ",
+                                  label_length_pshape[0],
+                                  " and: ",
+                                  batch_size);
+        } else {
+            batch_size = label_length_pshape[0];
+            is_batch_size_set = true;
+        }
+    }
+
+    auto& output_shape = output_shapes[0];
+    output_shape.resize(1);
+
+    if (is_batch_size_set) {
+        output_shape[0] = batch_size;
+    } else {
+        output_shape[0] = Dimension::dynamic();
+    }
+}
+
+}  // namespace v4
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/fft_base_shape_inference.hpp b/src/core/shape_inference/include/fft_base_shape_inference.hpp
new file mode 100644
index 00000000000..c148fd3ff84
--- /dev/null
+++ b/src/core/shape_inference/include/fft_base_shape_inference.hpp
@@ -0,0 +1,117 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include <openvino/op/util/fft_base.hpp>
+
+#include "openvino/core/axis_vector.hpp"
+#include "utils.hpp"
+
+template <class T>
+void shape_infer(const ov::op::util::FFTBase* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 2 || input_shapes.size() == 3) && output_shapes.size() == 1);
+
+    const auto& input_shape = input_shapes[0];
+    const auto& axes_shape = input_shapes[1];
+    auto& output_shape = output_shapes[0];
+    std::vector<int64_t> axes;
+    bool axes_are_known = get_data_as_int64<T>(1, op, axes, constant_data);
+
+    if (input_shape.rank().is_static()) {
+        const auto input_rank = input_shape.size();
+        NODE_VALIDATION_CHECK(op,
+                              input_rank >= 2,
+                              "The input rank must be greater or equal to 2. Got input rank: ",
+                              input_rank);
+
+        NODE_VALIDATION_CHECK(op,
+                              input_shape[input_rank - 1].compatible(2),
+                              "The last dimension of input data must be 2. Got: ",
+                              input_shape[input_rank - 1]);
+
+        if (axes_shape.is_static()) {
+            NODE_VALIDATION_CHECK(op,
+                                  input_rank >= static_cast<int64_t>(axes_shape[0].get_length() + 1),
+                                  "The input rank must be greater than number of FFT op axes. Got "
+                                  "input rank: ",
+                                  input_rank,
+                                  ", number of axes: ",
+                                  axes_shape[0].get_length());
+        }
+
+        // FFT operation supports for negative axes to transform. More precisely, according to
+        // the FFT operation specification, axes should be integers from -(r - 1) to (r - 2)
+        // inclusively, where r = rank(data). A negative axis 'a' is interpreted as an axis
+        // 'r - 1 + a'. The reason is the following: real input tensor of the shape
+        // [n_0, ..., n_{r - 1}, 2] is interpreted as a complex tensor with the shape
+        // [n_0, ..., n_{r - 1}].
+        if (axes_shape.rank().is_static() && axes_are_known) {
+            for (int64_t& axis : axes) {
+                if (axis < 0) {
+                    axis += input_rank - 1;
+                }
+            }
+
+            ov::AxisSet axes_set;
+            for (const auto& axis : axes) {
+                axes_set.insert(static_cast<size_t>(axis));
+            }
+
+            NODE_VALIDATION_CHECK(op, axes.size() == axes_set.size(), "FFT op axes must be unique.");
+
+            NODE_VALIDATION_CHECK(op,
+                                  std::find(axes.begin(), axes.end(), input_rank - 1) == axes.end(),
+                                  "FFT op axes cannot contain the last axis.");
+        }
+    }
+
+    NODE_VALIDATION_CHECK(op, axes_shape.rank().compatible(1), "FFT op axes input must be 1D tensor.");
+
+    if (input_shapes.size() == 3) {
+        const auto& signal_size_shape = input_shapes[2];
+        NODE_VALIDATION_CHECK(op,
+                              signal_size_shape.rank().compatible(1),
+                              "FFT op signal size input must be 1D tensor. Got signal: ",
+                              signal_size_shape);
+
+        if (axes_shape.is_static() && signal_size_shape.is_static()) {
+            NODE_VALIDATION_CHECK(op,
+                                  axes_shape[0].compatible(signal_size_shape[0]),
+                                  "Sizes of inputs 'axes' and 'signal_size' must be equal. Got "
+                                  "size of 'axes': ",
+                                  axes_shape[0],
+                                  "size of 'signal_size': ",
+                                  signal_size_shape[0]);
+        }
+    }
+
+    output_shape = input_shape;
+    if (input_shape.rank().is_static() && axes_shape.rank().is_static() && input_shapes.size() == 3 && axes_are_known) {
+        const auto& signal_size_shape = input_shapes[2];
+        std::vector<int64_t> signal_size;
+        bool status_signal_size = get_data_as_int64<T>(2, op, signal_size, constant_data);
+
+        if (signal_size_shape.rank().is_static() && status_signal_size) {
+            size_t num_of_axes = axes.size();
+            for (size_t i = 0; i < num_of_axes; ++i) {
+                if (signal_size[i] == -1) {
+                    continue;
+                }
+                output_shape[axes[i]] = DimType(signal_size[i]);
+            }
+        } else if (signal_size_shape.rank().is_static()) {
+            for (int64_t& axis : axes) {
+                output_shape[axis] = ov::Dimension::dynamic();
+            }
+        }
+    } else if (input_shape.rank().is_static() && (axes_shape.rank().is_dynamic() || !axes_are_known)) {
+        const auto input_rank = input_shape.size();
+        for (int64_t i = 0; i < input_rank - 1; ++i) {
+            output_shape[i] = ov::Dimension::dynamic();
+        }
+    }
+}
\ No newline at end of file
diff --git a/src/core/src/op/ctc_loss.cpp b/src/core/src/op/ctc_loss.cpp
index 1819316aaf9..1ade3c7614c 100644
--- a/src/core/src/op/ctc_loss.cpp
+++ b/src/core/src/op/ctc_loss.cpp
@@ -4,6 +4,8 @@
 
 #include "ngraph/op/ctc_loss.hpp"
 
+#include <ctc_loss_shape_inference.hpp>
+
 #include "itt.hpp"
 
 using namespace std;
@@ -77,119 +79,22 @@ void op::v4::CTCLoss::validate_and_infer_types() {
                               blank_index_type);
     }
 
-    // check ranks of input tensors
     const auto& logits_pshape = get_input_partial_shape(0);
     const auto& logit_length_pshape = get_input_partial_shape(1);
     const auto& labels_pshape = get_input_partial_shape(2);
     const auto& label_length_pshape = get_input_partial_shape(3);
 
-    NODE_VALIDATION_CHECK(this,
-                          logits_pshape.rank().compatible(3),
-                          "Expected a 3D tensor for logits. Got: ",
-                          logits_pshape);
-
-    NODE_VALIDATION_CHECK(this,
-                          logit_length_pshape.rank().compatible(1),
-                          "Expected a 1D tensor for logit length. Got: ",
-                          logit_length_pshape);
-
-    NODE_VALIDATION_CHECK(this,
-                          labels_pshape.rank().compatible(2),
-                          "Expected a 2D tensor for labels. Got: ",
-                          labels_pshape);
-
-    NODE_VALIDATION_CHECK(this,
-                          label_length_pshape.rank().compatible(1),
-                          "Expected a 1D tensor for label length. Got: ",
-                          label_length_pshape);
-
-    // check optional input shape: blank index
+    std::vector<ov::PartialShape> input_shapes;
     if (get_input_size() == 5) {
         const auto& blank_index_pshape = get_input_partial_shape(4);
-        NODE_VALIDATION_CHECK(this,
-                              blank_index_pshape.rank().compatible(0),
-                              "Expected a scalar for blank index. Got: ",
-                              blank_index_pshape);
-    }
-
-    // check shapes of input tensors
-    size_t batch_size = 1;
-    bool is_batch_size_set = false;
-    size_t time_steps = 1;
-    bool is_time_steps_set = false;
-
-    if (logits_pshape.rank().is_static()) {
-        if (logits_pshape[0].is_static()) {
-            batch_size = logits_pshape[0].get_length();
-            is_batch_size_set = true;
-        }
-        if (logits_pshape[1].is_static()) {
-            time_steps = logits_pshape[1].get_length();
-            is_time_steps_set = true;
-        }
-    }
-
-    if (logit_length_pshape.is_static()) {
-        if (is_batch_size_set) {
-            NODE_VALIDATION_CHECK(this,
-                                  logit_length_pshape[0].compatible(batch_size),
-                                  "The first dimension of logit length must be equal to the first dimension ",
-                                  "of the logits. Got: ",
-                                  logit_length_pshape[0],
-                                  " and: ",
-                                  batch_size);
-        } else if (logit_length_pshape[0].is_static()) {
-            batch_size = logit_length_pshape[0].get_length();
-            is_batch_size_set = true;
-        }
-    }
-
-    if (labels_pshape.is_static()) {
-        if (is_batch_size_set) {
-            NODE_VALIDATION_CHECK(this,
-                                  labels_pshape[0].compatible(batch_size),
-                                  "The first dimension of labels must be equal to the first dimension ",
-                                  "of the logits and the logit length. Got: ",
-                                  labels_pshape[0],
-                                  " and: ",
-                                  batch_size);
-        } else if (labels_pshape[0].is_static()) {
-            batch_size = labels_pshape[0].get_length();
-            is_batch_size_set = true;
-        }
-
-        if (is_time_steps_set) {
-            NODE_VALIDATION_CHECK(this,
-                                  labels_pshape[1].compatible(time_steps),
-                                  "The second dimension of labels must be equal to the second dimension ",
-                                  "of logits. Got: ",
-                                  labels_pshape[1],
-                                  " and: ",
-                                  time_steps);
-        }
-    }
-
-    if (label_length_pshape.is_static()) {
-        if (!is_batch_size_set && label_length_pshape[0].is_static()) {
-            batch_size = label_length_pshape[0].get_length();
-            is_batch_size_set = true;
-        }
-        NODE_VALIDATION_CHECK(this,
-                              label_length_pshape[0].compatible(batch_size),
-                              "The first dimension of label length must be equal to the first dimension ",
-                              "of the logits, the logit length and labels. Got: ",
-                              label_length_pshape[0],
-                              " and: ",
-                              batch_size);
-    }
-
-    // set output shape
-    set_output_size(1);
-    if (is_batch_size_set) {
-        set_output_type(0, logits_type, ov::Shape{batch_size});
+        input_shapes = {logits_pshape, logit_length_pshape, labels_pshape, label_length_pshape, blank_index_pshape};
     } else {
-        set_output_type(0, logits_type, ov::PartialShape{Dimension::dynamic()});
+        input_shapes = {logits_pshape, logit_length_pshape, labels_pshape, label_length_pshape};
     }
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+
+    shape_infer(this, input_shapes, output_shapes);
+    set_output_type(0, logits_type, output_shapes[0]);
 }
 
 bool op::v4::CTCLoss::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/util/fft_base.cpp b/src/core/src/op/util/fft_base.cpp
index 29930914373..6e93011e004 100644
--- a/src/core/src/op/util/fft_base.cpp
+++ b/src/core/src/op/util/fft_base.cpp
@@ -4,6 +4,7 @@
 
 #include "ngraph/op/util/fft_base.hpp"
 
+#include <fft_base_shape_inference.hpp>
 #include <ngraph/validation_util.hpp>
 
 #include "itt.hpp"
@@ -23,9 +24,10 @@ bool ov::op::util::FFTBase::visit_attributes(AttributeVisitor& visitor) {
     return true;
 }
 
-void ov::op::util::FFTBase::validate() {
-    size_t num_of_inputs = get_input_size();
+void ov::op::util::FFTBase::validate_and_infer_types() {
+    NGRAPH_OP_SCOPE(util_FFTBase_validate_and_infer_types);
 
+    size_t num_of_inputs = get_input_size();
     NODE_VALIDATION_CHECK(this, num_of_inputs == 2 || num_of_inputs == 3, "FFT op must have 2 or 3 inputs.");
 
     element::Type input_et = get_input_element_type(0);
@@ -38,164 +40,25 @@ void ov::op::util::FFTBase::validate() {
                           axes_et == element::i64 || axes_et == element::i32,
                           "FFT op axes element type must be i32 or i64");
 
-    const auto& input_shape = PartialShape(get_input_partial_shape(0));
-    if (input_shape.rank().is_static()) {
-        const auto input_rank = input_shape.rank().get_length();
-        NODE_VALIDATION_CHECK(this,
-                              input_rank >= 2,
-                              "The input rank must be greater or equal to 2. Got input rank: ",
-                              input_rank);
-
-        auto last_dim_with_two = input_shape[input_rank - 1] & Dimension(2);
-        NODE_VALIDATION_CHECK(this,
-                              !last_dim_with_two.get_interval().empty(),
-                              "The last dimension of input data must be 2. Got: ",
-                              input_shape[input_rank - 1]);
-    }
-
-    const auto& axes_shape = PartialShape(get_input_partial_shape(1));
-    if (axes_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              axes_shape.rank().get_length() == 1,
-                              "FFT op axes input must be 1D tensor. Got axes input rank: ",
-                              axes_shape.rank().get_length());
-    }
-
-    if (input_shape.rank().is_static() && axes_shape.is_static()) {
-        const auto input_rank = input_shape.rank().get_length();
-        NODE_VALIDATION_CHECK(this,
-                              input_rank >= static_cast<int64_t>(axes_shape.to_shape()[0] + 1),
-                              "The input rank must be greater than number of FFT op axes. Got "
-                              "input rank: ",
-                              input_rank,
-                              ", number of axes: ",
-                              axes_shape.to_shape()[0]);
-    }
-
-    if (input_shape.rank().is_static() && ov::is_type<ngraph::op::Constant>(input_value(1).get_node())) {
-        const auto input_rank = input_shape.rank().get_length();
-        const auto& const_axes = get_constant_from_source(input_value(1));
-        auto axes = const_axes->cast_vector<int64_t>();
-
-        // FFT operation supports for negative axes to transform. More precisely, according to
-        // the FFT operation specification, axes should be integers from -(r - 1) to (r - 2)
-        // inclusively, where r = rank(data). A negative axis 'a' is interpreted as an axis
-        // 'r - 1 + a'. The reason is the following: real input tensor of the shape
-        // [n_0, ..., n_{r - 1}, 2] is interpreted as a complex tensor with the shape
-        // [n_0, ..., n_{r - 1}].
-        for (int64_t& axis : axes) {
-            if (axis < 0) {
-                axis += input_rank - 1;
-            }
-        }
-
-        AxisVector axes_vector;
-        AxisSet axes_set;
-        for (const int64_t axis : axes) {
-            axes_vector.push_back(static_cast<size_t>(axis));
-            axes_set.insert(static_cast<size_t>(axis));
-        }
-
-        NODE_VALIDATION_CHECK(this, axes.size() == axes_set.size(), "FFT op axes must be unique. Got: ", axes_vector);
-
-        NODE_VALIDATION_CHECK(this,
-                              std::find(axes.begin(), axes.end(), input_rank - 1) == axes.end(),
-                              "FFT op axes cannot contain the last axis. Got axes: ",
-                              axes_vector);
-    }
-
     if (num_of_inputs == 3) {
         element::Type signal_size_et = get_input_element_type(2);
         NODE_VALIDATION_CHECK(this,
                               signal_size_et == element::i64 || signal_size_et == element::i32,
                               "FFT op signal_size element type must be i32 or i64");
-
-        const auto& signal_size_shape = PartialShape(get_input_partial_shape(2));
-        if (signal_size_shape.rank().is_static()) {
-            NODE_VALIDATION_CHECK(this,
-                                  signal_size_shape.rank().get_length() == 1,
-                                  "FFT op signal size input must be 1D tensor. Got signal size "
-                                  "input rank: ",
-                                  signal_size_shape.rank().get_length());
-        }
-
-        if (axes_shape.is_static() && signal_size_shape.is_static()) {
-            NODE_VALIDATION_CHECK(this,
-                                  axes_shape.to_shape()[0] == signal_size_shape.to_shape()[0],
-                                  "Sizes of inputs 'axes' and 'signal_size' must be equal. Got "
-                                  "size of 'axes': ",
-                                  axes_shape.to_shape()[0],
-                                  "size of 'signal_size': ",
-                                  signal_size_shape.to_shape()[0]);
-        }
-    }
-}
-
-void ov::op::util::FFTBase::validate_and_infer_types() {
-    NGRAPH_OP_SCOPE(util_FFTBase_validate_and_infer_types);
-    validate();
-
-    const auto& input_shape = PartialShape(get_input_partial_shape(0));
-    const auto& axes_shape = PartialShape(get_input_partial_shape(1));
-    PartialShape output_shape = input_shape;
-    if (input_shape.rank().is_dynamic()) {
-        set_output_type(0, get_input_element_type(0), output_shape);
-        return;
     }
 
-    const auto input_rank = input_shape.rank().get_length();
-
-    if (axes_shape.rank().is_dynamic() || !ov::is_type<ngraph::op::Constant>(input_value(1).get_node())) {
-        for (int64_t i = 0; i < input_rank - 1; ++i) {
-            output_shape[i] = Dimension::dynamic();
-        }
-        set_output_type(0, get_input_element_type(0), output_shape);
-        return;
-    }
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape()};
+    std::vector<ov::PartialShape> input_shapes;
 
+    const auto& data = get_input_partial_shape(0);
+    const auto& axes = get_input_partial_shape(1);
     if (input_values().size() == 2) {
-        set_output_type(0, get_input_element_type(0), output_shape);
-        return;
+        input_shapes = {data, axes};
+    } else {
+        const auto& signal_size = get_input_partial_shape(2);
+        input_shapes = {data, axes, signal_size};
     }
 
-    const auto& signal_size_shape = PartialShape(get_input_partial_shape(2));
-    if (signal_size_shape.rank().is_dynamic()) {
-        set_output_type(0, get_input_element_type(0), output_shape);
-        return;
-    }
-
-    const auto& const_axes = get_constant_from_source(input_value(1));
-    auto axes = const_axes->cast_vector<int64_t>();
-    // FFT operation supports for negative axes to transform. More precisely, according to
-    // the FFT operation specification, axes should be integers from -(r - 1) to (r - 2)
-    // inclusively, where r = rank(data). A negative axis 'a' is interpreted as an axis
-    // 'r - 1 + a'. The reason is the following: real input tensor of the shape
-    // [n_0, ..., n_{r - 1}, 2] is interpreted as a complex tensor with the shape
-    // [n_0, ..., n_{r - 1}].
-    for (int64_t& axis : axes) {
-        if (axis < 0) {
-            axis += input_rank - 1;
-        }
-    }
-
-    if (!ov::is_type<ngraph::op::Constant>(input_value(2).get_node())) {
-        for (int64_t axis : axes) {
-            output_shape[axis] = Dimension::dynamic();
-        }
-        set_output_type(0, get_input_element_type(0), output_shape);
-        return;
-    }
-
-    const auto& const_signal_size = get_constant_from_source(input_value(2));
-    const auto signal_size = const_signal_size->cast_vector<int64_t>();
-
-    size_t num_of_axes = axes.size();
-    for (size_t i = 0; i < num_of_axes; ++i) {
-        if (signal_size[i] == -1) {
-            continue;
-        }
-        output_shape[axes[i]] = Dimension(signal_size[i]);
-    }
-
-    set_output_type(0, get_input_element_type(0), output_shape);
+    shape_infer(this, input_shapes, output_shapes);
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
diff --git a/src/tests/unit/cpu/shape_inference_test/ctc_loss_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/ctc_loss_shape_inference.cpp
new file mode 100644
index 00000000000..4854878a568
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/ctc_loss_shape_inference.cpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <ctc_loss_shape_inference.hpp>
+#include <openvino/op/ctc_loss.hpp>
+#include <openvino/op/parameter.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, CTCLossTest) {
+    const auto& logits = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1});
+    const auto& logit_length = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{-1});
+    const auto& labels = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{-1, -1});
+    const auto& label_length = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{-1});
+    const auto& blank_index = std::make_shared<ov::op::v0::Parameter>(element::i32, ov::Shape{});
+
+    // create CTCLoss node
+    auto ctc_loss = std::make_shared<op::v4::CTCLoss>(logits, logit_length, labels, label_length, blank_index);
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{10, 120, 28},
+                                                    StaticShape{10},
+                                                    StaticShape{10, 120},
+                                                    StaticShape{10},
+                                                    ov::Shape{}},
+                             static_output_shapes = {StaticShape{}};
+    shape_inference(ctc_loss.get(), static_input_shapes, static_output_shapes);
+
+    ASSERT_EQ(static_output_shapes[0], StaticShape({10}));
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/fft_base_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/fft_base_shape_inference.cpp
new file mode 100644
index 00000000000..b55b89b5db7
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/fft_base_shape_inference.cpp
@@ -0,0 +1,145 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <fft_base_shape_inference.hpp>
+#include <openvino/op/dft.hpp>
+#include <openvino/op/idft.hpp>
+#include <openvino/op/ops.hpp>
+#include <openvino/op/parameter.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+static std::shared_ptr<op::v7::DFT> build_dft() {
+    auto input_shape = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto axes = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
+    auto DFT = std::make_shared<ov::op::v7::DFT>(input_shape, axes);
+    return DFT;
+}
+
+static std::shared_ptr<op::v7::DFT> build_dft_signal() {
+    auto input_shape = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto axes = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
+    auto signal = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
+    auto DFT_signal = std::make_shared<ov::op::v7::DFT>(input_shape, axes, signal);
+    return DFT_signal;
+}
+
+static std::shared_ptr<op::v7::DFT> build_dft_constant() {
+    auto input_shape = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto axes = std::make_shared<ov::op::v0::Constant>(element::i32, Shape{2}, std::vector<int32_t>{1, 2});
+    auto signal = std::make_shared<ov::op::v0::Constant>(element::i32, Shape{2}, std::vector<int32_t>{512, 100});
+    auto DFT_signal = std::make_shared<ov::op::v7::DFT>(input_shape, axes, signal);
+    return DFT_signal;
+}
+
+static std::shared_ptr<op::v7::IDFT> build_idft() {
+    auto input_shape = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto axes = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
+    auto IDFT = std::make_shared<ov::op::v7::IDFT>(input_shape, axes);
+    return IDFT;
+}
+
+static std::shared_ptr<op::v7::IDFT> build_idft_signal() {
+    auto input_shape = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto axes = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
+    auto signal = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
+    auto IDFT_signal = std::make_shared<ov::op::v7::IDFT>(input_shape, axes, signal);
+    return IDFT_signal;
+}
+
+TEST(StaticShapeInferenceTest, DFTTest) {
+    auto DFT = build_dft();
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
+    int32_t axes_val[] = {1, 2};
+    constant_data[1] = std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, axes_val);
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 320, 320, 2}, StaticShape{2}},
+                             static_output_shapes = {StaticShape{}};
+
+    shape_inference(DFT.get(), static_input_shapes, static_output_shapes, constant_data);
+    ASSERT_EQ(static_output_shapes[0], StaticShape({1, 320, 320, 2}));
+}
+
+TEST(StaticShapeInferenceTest, DFTSignalTest) {
+    auto DFT = build_dft_signal();
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
+    int32_t axes_val[] = {1, 2};
+    int32_t signal_val[] = {512, 100};
+    constant_data[1] = std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, axes_val);
+    constant_data[2] =
+        std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, signal_val);
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 320, 320, 2}, StaticShape{2}, StaticShape{2}},
+                             static_output_shapes = {StaticShape{}};
+
+    shape_inference(DFT.get(), static_input_shapes, static_output_shapes, constant_data);
+    ASSERT_EQ(static_output_shapes[0], StaticShape({1, 512, 100, 2}));
+}
+
+TEST(StaticShapeInferenceTest, DFTConstantTest) {
+    auto DFT = build_dft_constant();
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 320, 320, 2}, StaticShape{2}, StaticShape{2}},
+                             static_output_shapes = {StaticShape{}};
+
+    shape_inference(DFT.get(), static_input_shapes, static_output_shapes);
+    ASSERT_EQ(static_output_shapes[0], StaticShape({1, 512, 100, 2}));
+}
+
+TEST(StaticShapeInferenceTest, DFTSignalMissingConstDataTest) {
+    auto DFT = build_dft_signal();
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
+    int32_t axes_val[] = {1, 2};
+    constant_data[1] = std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, axes_val);
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 320, 320, 2}, StaticShape{2}, StaticShape{2}},
+                             static_output_shapes = {StaticShape{}};
+    EXPECT_THROW(shape_inference(DFT.get(), static_input_shapes, static_output_shapes, constant_data),
+                 NodeValidationFailure);
+}
+
+TEST(StaticShapeInferenceTest, IDFTTest) {
+    auto IDFT = build_idft();
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
+    int32_t axes_val[] = {1, 2};
+    constant_data[1] = std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, axes_val);
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 320, 320, 2}, StaticShape{2}},
+                             static_output_shapes = {StaticShape{}};
+
+    shape_inference(IDFT.get(), static_input_shapes, static_output_shapes, constant_data);
+    ASSERT_EQ(static_output_shapes[0], StaticShape({1, 320, 320, 2}));
+}
+
+TEST(StaticShapeInferenceTest, IDFTSignalTest) {
+    auto IDFT = build_idft_signal();
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
+    int32_t axes_val[] = {1, 2};
+    int32_t signal_val[] = {512, 100};
+    constant_data[1] = std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, axes_val);
+    constant_data[2] =
+        std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, signal_val);
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 320, 320, 2}, StaticShape{2}, StaticShape{2}},
+                             static_output_shapes = {StaticShape{}};
+
+    shape_inference(IDFT.get(), static_input_shapes, static_output_shapes, constant_data);
+    ASSERT_EQ(static_output_shapes[0], StaticShape({1, 512, 100, 2}));
+}
+
+TEST(StaticShapeInferenceTest, IDFTSignalMissingConstDataTest) {
+    auto IDFT = build_idft_signal();
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
+    int32_t axes_val[] = {1, 2};
+    constant_data[1] = std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, axes_val);
+
+    std::vector<StaticShape> static_input_shapes = {StaticShape{1, 320, 320, 2}, StaticShape{2}, StaticShape{2}},
+                             static_output_shapes = {StaticShape{}};
+    EXPECT_THROW(shape_inference(IDFT.get(), static_input_shapes, static_output_shapes, constant_data),
+                 NodeValidationFailure);
+}
\ No newline at end of file

From 8a1fd76124efae2070dd863a89e2f5c95b829ac1 Mon Sep 17 00:00:00 2001
From: Bartek Szmelczynski <bartosz.szmelczynski@intel.com>
Date: Wed, 22 Dec 2021 08:55:57 +0100
Subject: [PATCH 084/160] [PYTHON] Expose get_rt_info method for input/output
 nodes (#9211)

* expose get_rt_info method for input/output nodes

* expose rt_info

* add tests with rf_info update

* remvoe redundant import

* rename PyRTMap to RTMap
---
 .../python/src/openvino/runtime/__init__.py   |   1 +
 .../src/pyopenvino/graph/node_input.cpp       |  20 +++
 .../src/pyopenvino/graph/node_output.hpp      |  24 ++++
 .../python/src/pyopenvino/graph/rt_map.cpp    |   4 +-
 .../test_inference_engine/test_input_node.py  | 123 ++++++++++++++++++
 .../test_output_const_node.py                 |  33 ++++-
 6 files changed, 202 insertions(+), 3 deletions(-)
 create mode 100644 src/bindings/python/tests/test_inference_engine/test_input_node.py

diff --git a/src/bindings/python/src/openvino/runtime/__init__.py b/src/bindings/python/src/openvino/runtime/__init__.py
index b9af9ca9403..850d55bc9bb 100644
--- a/src/bindings/python/src/openvino/runtime/__init__.py
+++ b/src/bindings/python/src/openvino/runtime/__init__.py
@@ -60,6 +60,7 @@ from openvino.pyopenvino import Layout
 from openvino.pyopenvino import ConstOutput
 from openvino.pyopenvino import util
 from openvino.pyopenvino import layout_helpers
+from openvino.pyopenvino import RTMap
 
 from openvino.runtime.ie_api import Core
 from openvino.runtime.ie_api import CompiledModel
diff --git a/src/bindings/python/src/pyopenvino/graph/node_input.cpp b/src/bindings/python/src/pyopenvino/graph/node_input.cpp
index 4ae56c1427b..008f941ec9a 100644
--- a/src/bindings/python/src/pyopenvino/graph/node_input.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/node_input.cpp
@@ -11,6 +11,10 @@
 
 namespace py = pybind11;
 
+using PyRTMap = ov::Node::RTMap;
+
+PYBIND11_MAKE_OPAQUE(PyRTMap);
+
 void regclass_graph_Input(py::module m) {
     py::class_<ov::Input<ov::Node>, std::shared_ptr<ov::Input<ov::Node>>> input(m, "Input", py::dynamic_attr());
     input.doc() = "openvino.runtime.Input wraps ov::Input<Node>";
@@ -75,4 +79,20 @@ void regclass_graph_Input(py::module m) {
                 get_source_output : Output
                     Output that is connected to the input.
               )");
+
+    input.def("get_rt_info",
+              (ov::RTMap & (ov::Input<ov::Node>::*)()) & ov::Input<ov::Node>::get_rt_info,
+              py::return_value_policy::reference_internal,
+              R"(
+                Returns RTMap which is a dictionary of user defined runtime info.
+
+                Returns
+                ----------
+                get_rt_info : RTMap
+                    A dictionary of user defined data.
+             )");
+    input.def_property_readonly("rt_info", (ov::RTMap & (ov::Input<ov::Node>::*)()) & ov::Input<ov::Node>::get_rt_info);
+    input.def_property_readonly("rt_info",
+                                (const ov::RTMap& (ov::Input<ov::Node>::*)() const) & ov::Input<ov::Node>::get_rt_info,
+                                py::return_value_policy::reference_internal);
 }
diff --git a/src/bindings/python/src/pyopenvino/graph/node_output.hpp b/src/bindings/python/src/pyopenvino/graph/node_output.hpp
index c4db5c96781..43eba05305a 100644
--- a/src/bindings/python/src/pyopenvino/graph/node_output.hpp
+++ b/src/bindings/python/src/pyopenvino/graph/node_output.hpp
@@ -11,6 +11,10 @@
 
 namespace py = pybind11;
 
+using PyRTMap = ov::Node::RTMap;
+
+PYBIND11_MAKE_OPAQUE(PyRTMap);
+
 template <typename VT>
 void regclass_graph_Output(py::module m, std::string typestring)
 {
@@ -115,6 +119,18 @@ void regclass_graph_Output(py::module m, std::string typestring)
                 get_tensor : descriptor.Tensor
                     Tensor of the output.
                )");
+    output.def("get_rt_info",
+             (ov::RTMap & (ov::Output<VT>::*)()) &  ov::Output<VT>::get_rt_info,
+             py::return_value_policy::reference_internal,
+             R"(
+                Returns RTMap which is a dictionary of user defined runtime info.
+
+                Returns
+                ----------
+                get_rt_info : RTMap
+                    A dictionary of user defined data.
+             )");
+
 
     output.def_property_readonly("node", &ov::Output<VT>::get_node);
     output.def_property_readonly("index", &ov::Output<VT>::get_index);
@@ -125,4 +141,12 @@ void regclass_graph_Output(py::module m, std::string typestring)
     output.def_property_readonly("partial_shape", &ov::Output<VT>::get_partial_shape);
     output.def_property_readonly("target_inputs", &ov::Output<VT>::get_target_inputs);
     output.def_property_readonly("tensor", &ov::Output<VT>::get_tensor);
+    output.def_property_readonly("rt_info",
+                                (ov::RTMap&(ov::Output<VT>::*)()) &
+                                ov::Output<VT>::get_rt_info,
+                                py::return_value_policy::reference_internal);
+    output.def_property_readonly("rt_info",
+                                (const ov::RTMap&(ov::Output<VT>::*)() const) &
+                                ov::Output<VT>::get_rt_info,
+                                py::return_value_policy::reference_internal);
 }
diff --git a/src/bindings/python/src/pyopenvino/graph/rt_map.cpp b/src/bindings/python/src/pyopenvino/graph/rt_map.cpp
index bf56a66aa13..cbfcb8b3d55 100644
--- a/src/bindings/python/src/pyopenvino/graph/rt_map.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/rt_map.cpp
@@ -26,8 +26,8 @@ using PyRTMap = ov::RTMap;
 PYBIND11_MAKE_OPAQUE(PyRTMap);
 
 void regclass_graph_PyRTMap(py::module m) {
-    auto py_map = py::class_<PyRTMap>(m, "PyRTMap");
-    py_map.doc() = "openvino.runtime.PyRTMap makes bindings for std::map<std::string, "
+    auto py_map = py::class_<PyRTMap>(m, "RTMap");
+    py_map.doc() = "openvino.runtime.RTMap makes bindings for std::map<std::string, "
                    "ov::Any, which can later be used as ov::Node::RTMap";
 
     py_map.def("__setitem__", [](PyRTMap& m, const std::string& k, const std::string v) {
diff --git a/src/bindings/python/tests/test_inference_engine/test_input_node.py b/src/bindings/python/tests/test_inference_engine/test_input_node.py
new file mode 100644
index 00000000000..59a6704adeb
--- /dev/null
+++ b/src/bindings/python/tests/test_inference_engine/test_input_node.py
@@ -0,0 +1,123 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+from ..conftest import model_path
+from openvino.runtime import Input, Shape, PartialShape, Type, Parameter, \
+    RTMap
+
+from openvino.runtime import Core
+
+is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
+test_net_xml, test_net_bin = model_path(is_myriad)
+
+
+def model_path(is_myriad=False):
+    path_to_repo = os.environ["MODELS_PATH"]
+    if not is_myriad:
+        test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
+        test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
+    else:
+        test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
+        test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
+    return (test_xml, test_bin)
+
+
+def test_input_type(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    assert isinstance(input_node, Input)
+
+
+def test_const_output_docs(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    exptected_string = "openvino.runtime.Input wraps ov::Input<Node>"
+    assert input_node.__doc__ == exptected_string
+
+
+def test_input_get_index(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    assert input_node.get_index() == 0
+
+
+def test_input_element_type(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    assert input_node.get_element_type() == Type.f32
+
+
+def test_input_get_shape(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    assert str(input_node.get_shape()) == str(Shape([1, 10]))
+
+
+def test_input_get_partial_shape(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    expected_partial_shape = PartialShape([1, 10])
+    assert input_node.get_partial_shape() == expected_partial_shape
+
+
+def test_input_get_source_output(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    name = input_node.get_source_output().get_node().get_friendly_name()
+    assert name == "fc_out"
+
+
+def test_input_get_rt_info(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    rt_info = input_node.get_rt_info()
+    assert isinstance(rt_info, RTMap)
+
+
+def test_input_rt_info(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    rt_info = input_node.rt_info
+    assert isinstance(rt_info, RTMap)
+
+
+def test_input_update_rt_info(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.output(0)
+    input_node = input.get_node().inputs()[0]
+    rt = input_node.get_rt_info()
+    rt["test12345"] = "test"
+    for k, v in input_node.get_rt_info().items():
+        assert k == "test12345"
+        assert isinstance(v, Parameter)
diff --git a/src/bindings/python/tests/test_inference_engine/test_output_const_node.py b/src/bindings/python/tests/test_inference_engine/test_output_const_node.py
index 05b5a45b52d..a2f0e3d7ab7 100644
--- a/src/bindings/python/tests/test_inference_engine/test_output_const_node.py
+++ b/src/bindings/python/tests/test_inference_engine/test_output_const_node.py
@@ -4,7 +4,9 @@
 import os
 
 from ..conftest import model_path
-from openvino.runtime import ConstOutput, Shape, PartialShape, Type
+import openvino.runtime.opset8 as ops
+from openvino.runtime import ConstOutput, Shape, PartialShape, Type, \
+    Output, Parameter, RTMap
 
 from openvino.runtime import Core
 
@@ -100,3 +102,32 @@ def test_const_output_get_names(device):
     assert node.names == expected_names
     assert node.get_any_name() == input_name
     assert node.any_name == input_name
+
+
+def test_const_get_rf_info(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    output_node = exec_net.output(0)
+    rt_info = output_node.get_rt_info()
+    assert isinstance(rt_info, RTMap)
+
+
+def test_const_output_runtime_info(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input_name = "data"
+    output_node = exec_net.input(input_name)
+    rt_info = output_node.rt_info
+    assert isinstance(rt_info, RTMap)
+
+
+def test_update_rt_info(device):
+    relu = ops.relu(5)
+    output_node = Output._from_node(relu)
+    rt = output_node.get_rt_info()
+    rt["test12345"] = "test"
+    for k, v in output_node.get_rt_info().items():
+        assert k == "test12345"
+        assert isinstance(v, Parameter)

From 2daafec53df3b7e2c0609b8a748f50744e3f9acd Mon Sep 17 00:00:00 2001
From: Gleb Kazantaev <gleb.kazantaev@intel.com>
Date: Wed, 22 Dec 2021 11:08:24 +0300
Subject: [PATCH 085/160] Update RIC Fusion for known Gathers (#9354)

* RIC Fusion for dynamic case

* Code style
---
 .../transformations/rt_info/attributes.hpp    |  1 +
 .../rt_info/reverse_input_channels.hpp        | 27 +++++++++
 .../common_optimizations/ric_fusion.cpp       | 56 ++++++++++++++-----
 .../transformations/rt_info/attributes.cpp    |  1 +
 src/core/CMakeLists.txt                       |  1 +
 .../pass/rt_info/reverse_input_channels.cpp   | 15 +++++
 .../src/preprocess/preprocess_steps_impl.cpp  | 11 +++-
 .../transformations/ric_fusion_tests.cpp      | 43 ++++++++++++--
 8 files changed, 135 insertions(+), 20 deletions(-)
 create mode 100644 src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp
 create mode 100644 src/core/src/pass/rt_info/reverse_input_channels.cpp

diff --git a/src/common/transformations/include/transformations/rt_info/attributes.hpp b/src/common/transformations/include/transformations/rt_info/attributes.hpp
index 424a8104e86..29ec755d21e 100644
--- a/src/common/transformations/include/transformations/rt_info/attributes.hpp
+++ b/src/common/transformations/include/transformations/rt_info/attributes.hpp
@@ -20,6 +20,7 @@
 #include <transformations/rt_info/old_api_map_order_attribute.hpp>
 #include <transformations/rt_info/primitives_priority_attribute.hpp>
 #include <transformations/rt_info/strides_property.hpp>
+#include <transformations/rt_info/reverse_input_channels.hpp>
 #include <transformations/rt_info/decompression.hpp>
 #include <transformations_visibility.hpp>
 #include <utility>
diff --git a/src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp b/src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp
new file mode 100644
index 00000000000..2fd8f9426a9
--- /dev/null
+++ b/src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/node.hpp>
+#include <ngraph/variant.hpp>
+#include <openvino/core/rtti.hpp>
+#include <openvino/core/runtime_attribute.hpp>
+
+namespace ov {
+NGRAPH_API bool is_reverse_input_channels(const std::shared_ptr<ngraph::Node>& node);
+
+NGRAPH_API void set_is_reverse_input_channels(std::shared_ptr<ngraph::Node> node);
+
+/*
+ * ReverseInputChannels attribute indicates that operation can be fused
+ * by ReverseInputChannels fusion transformation for cases when shape is dynamic.
+ */
+class NGRAPH_API ReverseInputChannels : public ov::RuntimeAttribute {
+public:
+    OPENVINO_RTTI("reverse_input_channels", "0");
+    ReverseInputChannels() = default;
+    bool visit_attributes(AttributeVisitor& visitor) override { return true; };
+};
+} // namespace ov
diff --git a/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp
index 8f6272617ea..85b071ef735 100644
--- a/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp
@@ -77,7 +77,7 @@ public:
     // we have to merge RIC attrs from all inputs. To check that given attr be merged with
     // current we check the order and axis which must be the same.
     bool can_be_merged_with(const Attribute & other) {
-        return m_order == other.m_order && m_axis == other.m_axis;
+        return (m_order.empty() || other.m_order.empty() || m_order == other.m_order) && m_axis == other.m_axis;
     }
 
     // When merging two and more attrs for further propagation we have to keep can_be_fused references
@@ -101,6 +101,8 @@ public:
     bool is_initial() const { return m_is_initial; }
 
 private:
+    // empty order means that the order is default and must be n, n-1, ..., 0
+    // according to the dimension values specified by m_axis
     std::vector<int64_t> m_order;
     int64_t m_axis;
 
@@ -227,19 +229,24 @@ public:
             const auto & pattern_map = m.get_pattern_value_map();
             const auto & output = pattern_map.at(pattern_root);
 
-            auto order = ov::get_constant_from_source(pattern_map.at(indices_p));
             auto axis = ov::get_constant_from_source(pattern_map.at(axis_p));
-            if (!order || !axis) {
-                return false;
+            if (!axis) return false;
+
+            const auto axis_value = axis->cast_vector<int64_t>().at(0);
+
+            if (ov::is_reverse_input_channels(output.get_node_shared_ptr())) {
+                ric_attr::init(output, {}, axis_value);
+                return true;
             }
 
+            auto order = ov::get_constant_from_source(pattern_map.at(indices_p));
+            if (!order) return false;
+
             // Avoid cases with two consecutive Gathers
             if (ric_attr::has(pattern_map.at(input_p))) {
                 return false;
             }
 
-            const auto axis_value = axis->cast_vector<int64_t>().at(0);
-
             // This constraint helps to avoid detection of other Gathers that do not perform RIC
             const auto & data_shape = m.get_match_root()->input(0).get_partial_shape();
             if (shape_size(order->get_shape()) == 1 ||
@@ -333,7 +340,8 @@ public:
                 }
 
                 const int64_t & new_axis = ric.get_axis() - (data_rank - shape_rank);
-                if (shape[new_axis] == 1) {
+                const auto & axis_dim = shape[new_axis];
+                if (axis_dim == 1) {
                     // we don't have to insert RIC for constant, so we keep propagating
                     ric_attr::set(m.get_match_value(), ric);
                     continue;
@@ -343,9 +351,15 @@ public:
                 auto ric_const = ric;
                 ric_const.set_axis(new_axis);
                 ric_const.set_is_final(true);
-                ric_const.set_callback([](Input<Node> input, const ric_attr::Attribute & attr) {
+                ric_const.set_callback([axis_dim](Input<Node> input, const ric_attr::Attribute & attr) {
                     auto output = input.get_source_output();
-                    auto gather = std::make_shared<opset8::Gather>(output, create_const(attr.get_order()),
+                    // Handle case when the RIC order is default
+                    auto order = attr.get_order();
+                    if (order.empty()) {
+                        order.resize(axis_dim);
+                        std::iota(order.rbegin(), order.rend(), 0);
+                    }
+                    auto gather = std::make_shared<opset8::Gather>(output, create_const(order),
                                                                    create_const({attr.get_axis()}));
                     input.replace_source_output(gather);
                     // TODO: copy runtime info from RIC sub-graph
@@ -372,7 +386,7 @@ public:
         auto input_p = pattern::any_input(ric_attr::has<Output<Node>>);
         auto pattern_root = pattern::wrap_type<opset8::Convolution>({input_p,
                                                                      pattern::wrap_type<opset8::Constant,
-                                                                                        opset8::FakeQuantize>()});
+                                                                                        opset8::FakeQuantize>(pattern::has_static_dim(1/*output channel*/))});
         auto callback = [=](pattern::Matcher& m) {
             auto conv = m.get_match_root();
             auto ric = ric_attr::get(conv->input_value(0)).propagate();
@@ -380,9 +394,16 @@ public:
 
             ric.set_is_final(true);
             ric.set_callback([](Input<Node> input, const ric_attr::Attribute & attr) {
+                const auto output_channel_index = 1;
+                auto order = attr.get_order();
+                // Handle case when the RIC order is default
+                if (order.empty()) {
+                    order.resize(input.get_partial_shape()[output_channel_index].get_length());
+                    std::iota(order.rbegin(), order.rend(), 0);
+                }
                 auto weights = input.get_source_output();
-                auto gather = std::make_shared<opset8::Gather>(weights, create_const(attr.get_order()),
-                                                               create_const({1} /* output channel */));
+                auto gather = std::make_shared<opset8::Gather>(weights, create_const(order),
+                                                               create_const({output_channel_index}));
                 input.replace_source_output(gather);
                 // TODO: copy runtime info from RIC sub-graph
             });
@@ -419,9 +440,18 @@ public:
             const auto & weights_shape = conv->input_value(1).get_shape();
             const int64_t & group = static_cast<int64_t>(weights_shape.at(0));
             const int64_t & channels = static_cast<int64_t>(weights_shape.at(1));
+            const int64_t & in_channels = static_cast<int64_t>(weights_shape.at(2));
 
             auto ric = ric_attr::get(conv->input_value(0)).propagate();
-            if (ric.get_order().size() != static_cast<size_t>(group) || ric.get_axis() != 1) {
+            auto order = ric.get_order();
+            // Handle case when the RIC order is default
+            if (order.empty()) {
+                order.resize(group);
+                std::iota(order.rbegin(), order.rend(), 0);
+                ric.set_order(order);
+            }
+
+            if (in_channels != 1 || ric.get_order().size() != static_cast<size_t>(group) || ric.get_axis() != 1) {
                 // TODO: insert RIC when group == 1
                 return false;
             }
diff --git a/src/common/transformations/src/transformations/rt_info/attributes.cpp b/src/common/transformations/src/transformations/rt_info/attributes.cpp
index 821c959932f..17f2a512c13 100644
--- a/src/common/transformations/src/transformations/rt_info/attributes.cpp
+++ b/src/common/transformations/src/transformations/rt_info/attributes.cpp
@@ -16,6 +16,7 @@ ov::pass::Attributes::Attributes() {
     register_factory<Decompression>();
     register_factory<ov::preprocess::TensorInfoMemoryType>();
     register_factory<StridesPropagation>();
+    register_factory<ReverseInputChannels>();
 }
 
 ov::Any ov::pass::Attributes::create_by_type_info(const ov::DiscreteTypeInfo& type_info) {
diff --git a/src/core/CMakeLists.txt b/src/core/CMakeLists.txt
index df7360a2a94..1ebda071d61 100644
--- a/src/core/CMakeLists.txt
+++ b/src/core/CMakeLists.txt
@@ -97,6 +97,7 @@ set_source_files_properties("${CMAKE_CURRENT_SOURCE_DIR}/src/pass/convert_precis
                             "${CMAKE_CURRENT_SOURCE_DIR}/src/pass/init_node_info.cpp"
                             "${CMAKE_CURRENT_SOURCE_DIR}/src/pass/serialize.cpp"
                             "${CMAKE_CURRENT_SOURCE_DIR}/src/op/type_relaxed.cpp"
+                            "${CMAKE_CURRENT_SOURCE_DIR}/src/preprocess/preprocess_steps_impl.cpp"
                             "${CMAKE_CURRENT_SOURCE_DIR}/src/model.cpp" # for SmartReshape
                             ${smart_reshape_srcs} ${rt_info_srcs}
         PROPERTIES INCLUDE_DIRECTORIES $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>)
diff --git a/src/core/src/pass/rt_info/reverse_input_channels.cpp b/src/core/src/pass/rt_info/reverse_input_channels.cpp
new file mode 100644
index 00000000000..770f591cf0c
--- /dev/null
+++ b/src/core/src/pass/rt_info/reverse_input_channels.cpp
@@ -0,0 +1,15 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/rt_info/reverse_input_channels.hpp"
+
+#include "ngraph/node.hpp"
+
+bool ov::is_reverse_input_channels(const std::shared_ptr<ngraph::Node>& node) {
+    return node->get_rt_info().count(ReverseInputChannels::get_type_info_static());
+}
+
+void ov::set_is_reverse_input_channels(std::shared_ptr<ngraph::Node> node) {
+    node->get_rt_info().emplace(ReverseInputChannels::get_type_info_static(), ReverseInputChannels{});
+}
\ No newline at end of file
diff --git a/src/core/src/preprocess/preprocess_steps_impl.cpp b/src/core/src/preprocess/preprocess_steps_impl.cpp
index 7be7b4bb9fd..2672d60d482 100644
--- a/src/core/src/preprocess/preprocess_steps_impl.cpp
+++ b/src/core/src/preprocess/preprocess_steps_impl.cpp
@@ -11,6 +11,7 @@
 #include "openvino/op/nv12_to_bgr.hpp"
 #include "openvino/op/nv12_to_rgb.hpp"
 #include "openvino/opsets/opset8.hpp"
+#include "transformations/rt_info/reverse_input_channels.hpp"
 
 namespace ov {
 namespace preprocess {
@@ -362,7 +363,11 @@ void PreStepsList::add_reverse_channels() {
     m_actions.emplace_back([](const std::vector<Output<Node>>& nodes,
                               const std::shared_ptr<Model>& function,
                               PreprocessingContext& context) {
-        return reverse_channels(nodes, function, context);
+        auto resp = reverse_channels(nodes, function, context);
+        auto outputs = std::get<0>(resp);
+        OPENVINO_ASSERT(outputs.size() == 1, "Internal error: reverse_channels returned unexpected number of outputs");
+        set_is_reverse_input_channels(outputs.at(0).get_node_shared_ptr());
+        return resp;
     });
 }
 
@@ -413,8 +418,8 @@ std::tuple<std::vector<Output<Node>>, bool> PreStepsList::reverse_channels(const
 
     // Gather slices in reverse order (indexes are specified by 'range' operation)
     auto constant_axis = op::v0::Constant::create(element::i32, {1}, {channels_idx});
-    auto convert = std::make_shared<op::v8::Gather>(nodes[0], range, constant_axis);
-    return std::make_tuple(std::vector<Output<Node>>{convert}, false);
+    auto gather = std::make_shared<op::v8::Gather>(nodes[0], range, constant_axis);
+    return std::make_tuple(std::vector<Output<Node>>{gather}, false);
 }
 
 std::tuple<std::vector<Output<Node>>, bool> PreStepsList::cut_last_channel(const std::vector<Output<Node>>& nodes,
diff --git a/src/tests/functional/inference_engine/transformations/ric_fusion_tests.cpp b/src/tests/functional/inference_engine/transformations/ric_fusion_tests.cpp
index cf56e34439e..6fe7848e9ff 100644
--- a/src/tests/functional/inference_engine/transformations/ric_fusion_tests.cpp
+++ b/src/tests/functional/inference_engine/transformations/ric_fusion_tests.cpp
@@ -124,7 +124,7 @@ TEST_F(TransformationTestsF, RICFusionSimple) {
 
 TEST_F(TransformationTestsF, RICFusionHard) {
     {
-        auto input = create_param({ -1, 3, -1, -1 });
+        auto input = create_param({ -1, -1, -1, -1 });
         auto relu = std::make_shared<Relu>(input);
 
         auto input2 = create_param({ -1, 3, -1, -1 });
@@ -148,11 +148,10 @@ TEST_F(TransformationTestsF, RICFusionHard) {
 
         apply_reverse_input_channels(function, {{0, "NCHW"}});
 
-        manager.register_pass<pass::ConstantFolding>();
         manager.register_pass<pass::ReverseInputChannelsFusion>();
     }
     {
-        auto input = create_param({ -1, 3, -1, -1 });
+        auto input = create_param({ -1, -1, -1, -1 });
         auto relu = std::make_shared<Relu>(input);
 
         auto input2 = create_param({ -1, 3, -1, -1 });
@@ -180,6 +179,30 @@ TEST_F(TransformationTestsF, RICFusionHard) {
     enable_accuracy_check();
 }
 
+TEST_F(TransformationTestsF, RICFusionDynamic) {
+    {
+        auto input = create_param({ -1, -1, -1, -1 });
+        auto relu = std::make_shared<Relu>(input);
+        auto conv = create_conv(relu, {6, 3, 3, 3});
+
+        function = std::make_shared<Function>(NodeVector{ conv }, ParameterVector{ input });
+        apply_reverse_input_channels(function, {{0, "NCHW"}});
+
+        manager.register_pass<pass::ReverseInputChannelsFusion>();
+    }
+
+    {
+        auto input = create_param({ -1, -1, -1, -1 });
+        auto relu = std::make_shared<Relu>(input);
+        auto conv = create_conv_with_gather(relu, {6, 3, 3, 3}, {2, 1, 0});
+        function_ref = std::make_shared<Function>(NodeVector{ conv }, ParameterVector{ input });
+    }
+
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    disable_rt_info_check();
+}
+
 TEST_F(TransformationTestsF, RICFusionEltwise1) {
     {
         auto input = create_param({ 1, 3, 64, 64 });
@@ -322,7 +345,7 @@ TEST_F(TransformationTestsF, RICFusionEltwiseNegative) {
     }
 }
 
-TEST_F(TransformationTestsF, RICFusionEltwiseNegative2) {
+TEST_F(TransformationTestsF, RICFusionEltwiseTwoRIC) {
     {
         auto input = create_param({ 1, 3, 64, 64 });
         auto input2 = create_param({ 1, 1, 64, 64 });
@@ -334,6 +357,18 @@ TEST_F(TransformationTestsF, RICFusionEltwiseNegative2) {
 
         manager.register_pass<pass::ReverseInputChannelsFusion>();
     }
+    {
+        auto input = create_param({ 1, 3, 64, 64 });
+        auto input2 = create_param({ 1, 1, 64, 64 });
+        auto add = std::make_shared<Add>(input, input2);
+        auto conv = create_conv_with_gather(add, {6, 3, 3, 3}, {2, 1, 0});
+
+        function_ref = std::make_shared<Function>(NodeVector{ conv }, ParameterVector{ input, input2 });
+    }
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    disable_rt_info_check();
+    enable_accuracy_check();
 }
 
 TEST_F(TransformationTestsF, RICFusionEltwiseNegative3) {

From 0bbda24186887a23dead8e3ff0816654aa51f9bb Mon Sep 17 00:00:00 2001
From: Ivan Tikhonov <ivan.tikhonov@intel.com>
Date: Wed, 22 Dec 2021 11:20:54 +0300
Subject: [PATCH 086/160] Paddle FrontEnd Refactoring (#9157)

* Refactor PaddlePaddle FrontEnd

* Codestyle

* FrontEnd,InputModel,Place base classes -> abstract, renamed model file

* Fix unit tests

* fix unit tests

* ngraph:: to ov::

* Rename frontends dir to frontend

* Rename paddlepaddle to paddle; pdpd to paddle

* add missing file

* codestyle

* Remove local change

* paddlepaddle -> paddle for azure configs and .md files

* fix package name, fix config files

* Fix win build

* Revert Broadcast/AutoBroadcast changes

* codestyle

* fix FronEnd class

* fix ngraph_cpp_api.config

* codestyle

* merge master

* fix build

* refactoring; revert broadcast/autobroadcast changes

* codestyle

* fix MacOS config
---
 .ci/azure/linux.yml                           |   8 +-
 .ci/azure/windows.yml                         |   8 +-
 cmake/developer_package/plugins/plugins.cmake |   4 +-
 cmake/features.cmake                          |   4 +-
 cmake/templates/OpenVINOConfig.cmake.in       |  18 +-
 cmake/templates/ngraphConfig.cmake.in         |  14 +-
 cmake/test_model_zoo.cmake                    |   4 +-
 docs/IE_DG/inference_engine_intro.md          |   4 +-
 docs/doxygen/ngraph_cpp_api.config            |  39 ++++
 src/core/tests/frontend/CMakeLists.txt        |   4 +-
 .../{paddlepaddle => paddle}/CMakeLists.txt   |  12 +-
 .../{paddlepaddle => paddle}/basic_api.cpp    |   4 +-
 .../convert_model.cpp                         |   4 +-
 .../convert_unsupported.cpp                   |   0
 .../cut_specific_model.cpp                    |   4 +-
 .../incorrect_cut_model.cpp                   |   0
 .../json_config_extension.cpp                 |   4 +-
 .../{paddlepaddle => paddle}/load_from.cpp    |   4 +-
 .../{paddlepaddle => paddle}/main.cpp         |   0
 .../{paddlepaddle => paddle}/op_fuzzy.cpp     |   8 +-
 .../{paddlepaddle => paddle}/paddle_utils.hpp |   0
 .../partial_shape.cpp                         |   4 +-
 .../{paddlepaddle => paddle}/places.cpp       |  26 +--
 .../requirements_dev.txt                      |   0
 .../set_element_type.cpp                      |   4 +-
 .../{paddlepaddle => paddle}/telemetry.cpp    |   4 +-
 .../gen_scripts/generate_2in_2out.py          |   2 +-
 .../gen_scripts/generate_2in_2out_dynbatch.py |   2 +-
 .../gen_scripts/generate_argmax.py            |  60 +++++
 .../gen_scripts/generate_assign_value.py      |  28 +--
 .../gen_scripts/generate_batch_norm.py        |  46 ++--
 .../test_models/gen_scripts/generate_bmm.py   |  22 +-
 .../test_models/gen_scripts/generate_clip.py  |  16 +-
 .../gen_scripts/generate_conv2d.py            |   2 +-
 .../generate_conv2d_combinations.py           |  26 +--
 .../gen_scripts/generate_conv2d_relu.py       |   2 +-
 .../gen_scripts/generate_conv2d_s.py          |   2 +-
 .../gen_scripts/generate_conv2d_transpose.py  |  26 +--
 .../gen_scripts/generate_cumsum.py            |  18 +-
 .../gen_scripts/generate_dropout.py           |  47 ++++
 .../gen_scripts/generate_dynamic_pool2d.py    |   0
 .../gen_scripts/generate_elementwise_ops.py   | 126 +++++------
 .../gen_scripts/generate_embedding.py         |   8 +-
 .../test_models/gen_scripts/generate_equal.py |  20 +-
 .../test_models/gen_scripts/generate_exp.py   |  16 +-
 .../gen_scripts/generate_expand_v2.py         |  36 +--
 .../gen_scripts/generate_fill_any_like.py     |  18 +-
 .../gen_scripts/generate_fill_constant.py     |  97 +++++++++
 .../generate_fill_constant_batch_size_like.py |  16 +-
 .../generate_flatten_contiguous_range.py      |  16 +-
 .../test_models/gen_scripts/generate_gelu.py  |  16 +-
 .../gen_scripts/generate_greater_equal.py     |  20 +-
 .../gen_scripts/generate_hard_sigmoid.py      |  16 +-
 .../gen_scripts/generate_hard_swish.py        |  16 +-
 .../gen_scripts/generate_interpolate.py       | 134 ++++++------
 .../gen_scripts/generate_layer_norm.py        |  20 +-
 .../gen_scripts/generate_leaky_relu.py        |  16 +-
 .../test_models/gen_scripts/generate_log.py   |  16 +-
 .../gen_scripts/generate_logical_not.py       |  22 +-
 .../gen_scripts/generate_lower_version.py     |   0
 .../gen_scripts/generate_matmul_v2.py         |  20 +-
 .../test_models/gen_scripts/generate_mul.py   |  28 +--
 .../generate_multi_tensor_split.py            |   4 +-
 .../test_models/gen_scripts/generate_pad3d.py |  18 +-
 .../gen_scripts/generate_place_test_model.py  |  53 +++++
 .../gen_scripts/generate_pool2d.py            |  80 +++----
 .../test_models/gen_scripts/generate_pow.py   |  46 ++--
 .../gen_scripts/generate_prior_box.py         |  20 +-
 .../test_models/gen_scripts/generate_range.py |  26 +--
 .../test_models/gen_scripts/generate_relu.py  |  14 +-
 .../test_models/gen_scripts/generate_relu6.py |  16 +-
 .../gen_scripts/generate_reshape.py           |  40 ++--
 .../gen_scripts/generate_rnn_lstm.py          |  28 +--
 .../test_models/gen_scripts/generate_scale.py |  93 ++++++++
 .../test_models/gen_scripts/generate_shape.py |  22 +-
 .../gen_scripts/generate_sigmoid.py           |  16 +-
 .../test_models/gen_scripts/generate_slice.py |  42 ++--
 .../gen_scripts/generate_softmax.py           |  14 +-
 .../gen_scripts/generate_softplus.py          |  14 +-
 .../test_models/gen_scripts/generate_split.py |  56 ++---
 .../gen_scripts/generate_squeeze.py           |  16 +-
 .../test_models/gen_scripts/generate_stack.py |  22 +-
 .../test_models/gen_scripts/generate_tanh.py  |  16 +-
 .../gen_scripts/generate_unsqueeze.py         |  16 +-
 .../gen_scripts/generate_unsupported_relu.py  |  20 +-
 .../gen_scripts/generate_yolo_box.py          |  42 ++--
 .../test_models/gen_scripts/save_model.py     |  12 +-
 .../test_models/gen_wrapper.py                |   0
 .../throw_in_conversion.cpp                   |   0
 .../gen_scripts/generate_argmax.py            |  60 -----
 .../gen_scripts/generate_dropout.py           |  47 ----
 .../gen_scripts/generate_fill_constant.py     |  97 ---------
 .../gen_scripts/generate_place_test_model.py  |  53 -----
 .../test_models/gen_scripts/generate_scale.py |  93 --------
 src/frontends/CMakeLists.txt                  |   4 +-
 src/frontends/common/src/manager.cpp          |   9 +-
 src/frontends/common/src/plugin_loader.cpp    |   2 +-
 .../{paddlepaddle => paddle}/CMakeLists.txt   |   4 +-
 .../openvino/frontend/paddle}/frontend.hpp    |  20 +-
 .../openvino/frontend/paddle/visibility.hpp   |  20 ++
 .../{paddlepaddle => paddle}/src/decoder.cpp  |  35 +--
 .../{paddlepaddle => paddle}/src/decoder.hpp  |  18 +-
 .../src/default_opset.hpp                     |   4 +-
 .../src/exceptions.cpp}                       |   7 +-
 src/frontends/paddle/src/exceptions.hpp       |  37 ++++
 .../{paddlepaddle => paddle}/src/frontend.cpp | 139 ++++++------
 .../model.cpp => paddle/src/input_model.cpp}  | 206 +++++++++---------
 .../model.hpp => paddle/src/input_model.hpp}  |  35 +--
 .../src/node_context.hpp                      |  22 +-
 .../src/op/argmax.cpp                         |   4 +-
 .../src/op/assign_value.cpp                   |   6 +-
 .../src/op/batch_norm.cpp                     |   6 +-
 .../{paddlepaddle => paddle}/src/op/cast.cpp  |   4 +-
 .../{paddlepaddle => paddle}/src/op/clip.cpp  |   6 +-
 .../src/op/concat.cpp                         |   4 +-
 .../src/op/conv2d.cpp                         |   4 +-
 .../src/op/conv2d_transpose.cpp               |   4 +-
 .../src/op/conv2d_utils.cpp                   |   6 +-
 .../src/op/conv2d_utils.hpp                   |   6 +-
 .../src/op/cumsum.cpp                         |   4 +-
 .../src/op/deformable_conv.cpp                |   4 +-
 .../src/op/dropout.cpp                        |  11 +-
 .../src/op/elementwise_ops.cpp                |  11 +-
 .../src/op/embedding.cpp                      |   4 +-
 .../{paddlepaddle => paddle}/src/op/exp.cpp   |   4 +-
 .../src/op/expand_v2.cpp                      |   5 +-
 .../src/op/fill_any_like.cpp                  |   6 +-
 .../src/op/fill_constant.cpp                  |  11 +-
 .../src/op/fill_constant_batch_size_like.cpp  |   5 +-
 .../src/op/flatten_contiguous_range.cpp       |   4 +-
 .../{paddlepaddle => paddle}/src/op/gelu.cpp  |   4 +-
 .../src/op/hard_sigmoid.cpp                   |   5 +-
 .../src/op/hard_swish.cpp                     |  13 +-
 .../src/op/interp.cpp                         |   4 +-
 .../src/op/layer_norm.cpp                     |   8 +-
 .../src/op/leakyrelu.cpp                      |   4 +-
 .../{paddlepaddle => paddle}/src/op/log.cpp   |   5 +-
 .../src/op/logical_not.cpp                    |   4 +-
 .../{paddlepaddle => paddle}/src/op/lstm.cpp  |   9 +-
 .../src/op/matmul.cpp                         |   4 +-
 .../src/op/matmul_v2.cpp                      |   4 +-
 .../src/op/matrix_nms.cpp                     |  15 +-
 .../src/op/multiclass_nms.cpp                 |  12 +-
 .../{paddlepaddle => paddle}/src/op/pad3d.cpp |  14 +-
 .../src/op/pool2d.cpp                         |  12 +-
 .../{paddlepaddle => paddle}/src/op/pow.cpp   |   5 +-
 .../src/op/prior_box.cpp                      |   4 +-
 .../{paddlepaddle => paddle}/src/op/range.cpp |   4 +-
 .../{paddlepaddle => paddle}/src/op/relu.cpp  |   4 +-
 .../{paddlepaddle => paddle}/src/op/relu6.cpp |   5 +-
 .../src/op/reshape2.cpp                       |   5 +-
 .../{paddlepaddle => paddle}/src/op/rnn.cpp   |  14 +-
 .../{paddlepaddle => paddle}/src/op/scale.cpp |   6 +-
 .../{paddlepaddle => paddle}/src/op/shape.cpp |   4 +-
 .../src/op/sigmoid.cpp                        |   4 +-
 .../{paddlepaddle => paddle}/src/op/slice.cpp |  10 +-
 .../src/op/softmax.cpp                        |   6 +-
 .../src/op/softplus.cpp                       |   6 +-
 .../{paddlepaddle => paddle}/src/op/split.cpp |  14 +-
 .../src/op/squeeze.cpp                        |   4 +-
 .../{paddlepaddle => paddle}/src/op/stack.cpp |  17 +-
 .../{paddlepaddle => paddle}/src/op/tanh.cpp  |   4 +-
 .../src/op/transpose2.cpp                     |   4 +-
 .../src/op/unsqueeze.cpp                      |   4 +-
 .../src/op/yolo_box.cpp                       |   6 +-
 .../{paddlepaddle => paddle}/src/op_table.cpp |   4 +-
 .../{paddlepaddle => paddle}/src/op_table.hpp |   4 +-
 .../src/paddle_fw_node.cpp}                   |  13 +-
 .../src/paddle_fw_node.hpp}                   |  18 +-
 .../src/paddle_utils.cpp}                     |   6 +-
 .../src/paddle_utils.hpp}                     |   4 +-
 .../{paddlepaddle => paddle}/src/place.cpp    | 157 ++++++-------
 .../src}/place.hpp                            | 101 ++++-----
 .../src/proto/framework.proto                 |   0
 .../include/paddlepaddle_frontend/utility.hpp |  30 ---
 src/frontends/paddlepaddle/src/exceptions.hpp |  36 ---
 .../inference_engine/CMakeLists.txt           |   6 +-
 .../paddle_reader/read_paddle_model_test.cpp  |   8 +-
 tests/fuzz/scripts/init_corpus.py             |   4 +-
 thirdparty/CMakeLists.txt                     |   2 +-
 tools/deployment_manager/configs/darwin.json  |   2 +-
 tools/deployment_manager/configs/linux.json   |   2 +-
 tools/deployment_manager/configs/windows.json |   2 +-
 183 files changed, 1816 insertions(+), 1768 deletions(-)
 create mode 100644 docs/doxygen/ngraph_cpp_api.config
 rename src/core/tests/frontend/{paddlepaddle => paddle}/CMakeLists.txt (85%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/basic_api.cpp (91%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/convert_model.cpp (90%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/convert_unsupported.cpp (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/cut_specific_model.cpp (92%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/incorrect_cut_model.cpp (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/json_config_extension.cpp (85%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/load_from.cpp (90%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/main.cpp (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/op_fuzzy.cpp (99%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/paddle_utils.hpp (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/partial_shape.cpp (96%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/places.cpp (96%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/requirements_dev.txt (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/set_element_type.cpp (87%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/telemetry.cpp (90%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_2in_2out.py (94%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_2in_2out_dynbatch.py (94%)
 create mode 100644 src/core/tests/frontend/paddle/test_models/gen_scripts/generate_argmax.py
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_assign_value.py (57%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_batch_norm.py (61%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_bmm.py (62%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_clip.py (57%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_conv2d.py (88%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_conv2d_combinations.py (87%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_conv2d_relu.py (90%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_conv2d_s.py (90%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_conv2d_transpose.py (87%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_cumsum.py (64%)
 create mode 100644 src/core/tests/frontend/paddle/test_models/gen_scripts/generate_dropout.py
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_dynamic_pool2d.py (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_elementwise_ops.py (52%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_embedding.py (96%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_equal.py (61%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_exp.py (60%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_expand_v2.py (54%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_fill_any_like.py (66%)
 create mode 100644 src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_constant.py
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_fill_constant_batch_size_like.py (65%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_flatten_contiguous_range.py (65%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_gelu.py (58%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_greater_equal.py (68%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_hard_sigmoid.py (61%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_hard_swish.py (60%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_interpolate.py (74%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_layer_norm.py (66%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_leaky_relu.py (60%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_log.py (60%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_logical_not.py (60%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_lower_version.py (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_matmul_v2.py (74%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_mul.py (51%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_multi_tensor_split.py (90%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_pad3d.py (78%)
 create mode 100644 src/core/tests/frontend/paddle/test_models/gen_scripts/generate_place_test_model.py
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_pool2d.py (79%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_pow.py (58%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_prior_box.py (89%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_range.py (52%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_relu.py (64%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_relu6.py (61%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_reshape.py (55%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_rnn_lstm.py (80%)
 create mode 100644 src/core/tests/frontend/paddle/test_models/gen_scripts/generate_scale.py
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_shape.py (50%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_sigmoid.py (60%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_slice.py (68%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_softmax.py (71%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_softplus.py (70%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_split.py (60%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_squeeze.py (59%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_stack.py (82%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_tanh.py (59%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_unsqueeze.py (57%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_unsupported_relu.py (84%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/generate_yolo_box.py (70%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_scripts/save_model.py (87%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/test_models/gen_wrapper.py (100%)
 rename src/core/tests/frontend/{paddlepaddle => paddle}/throw_in_conversion.cpp (100%)
 delete mode 100644 src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py
 delete mode 100644 src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py
 delete mode 100644 src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_constant.py
 delete mode 100644 src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_place_test_model.py
 delete mode 100644 src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_scale.py
 rename src/frontends/{paddlepaddle => paddle}/CMakeLists.txt (66%)
 rename src/frontends/{paddlepaddle/include/paddlepaddle_frontend => paddle/include/openvino/frontend/paddle}/frontend.hpp (87%)
 create mode 100644 src/frontends/paddle/include/openvino/frontend/paddle/visibility.hpp
 rename src/frontends/{paddlepaddle => paddle}/src/decoder.cpp (78%)
 rename src/frontends/{paddlepaddle => paddle}/src/decoder.hpp (66%)
 rename src/frontends/{paddlepaddle => paddle}/src/default_opset.hpp (86%)
 rename src/frontends/{paddlepaddle/src/exception.cpp => paddle/src/exceptions.cpp} (70%)
 create mode 100644 src/frontends/paddle/src/exceptions.hpp
 rename src/frontends/{paddlepaddle => paddle}/src/frontend.cpp (72%)
 rename src/frontends/{paddlepaddle/src/model.cpp => paddle/src/input_model.cpp} (62%)
 rename src/frontends/{paddlepaddle/include/paddlepaddle_frontend/model.hpp => paddle/src/input_model.hpp} (59%)
 rename src/frontends/{paddlepaddle => paddle}/src/node_context.hpp (88%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/argmax.cpp (97%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/assign_value.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/batch_norm.cpp (93%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/cast.cpp (92%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/clip.cpp (80%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/concat.cpp (92%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/conv2d.cpp (90%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/conv2d_transpose.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/conv2d_utils.cpp (96%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/conv2d_utils.hpp (95%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/cumsum.cpp (96%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/deformable_conv.cpp (98%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/dropout.cpp (79%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/elementwise_ops.cpp (85%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/embedding.cpp (98%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/exp.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/expand_v2.cpp (96%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/fill_any_like.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/fill_constant.cpp (84%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/fill_constant_batch_size_like.cpp (98%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/flatten_contiguous_range.cpp (98%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/gelu.cpp (93%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/hard_sigmoid.cpp (90%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/hard_swish.cpp (64%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/interp.cpp (99%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/layer_norm.cpp (90%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/leakyrelu.cpp (93%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/log.cpp (84%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/logical_not.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/lstm.cpp (98%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/matmul.cpp (95%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/matmul_v2.cpp (94%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/matrix_nms.cpp (85%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/multiclass_nms.cpp (84%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/pad3d.cpp (84%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/pool2d.cpp (94%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/pow.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/prior_box.cpp (98%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/range.cpp (95%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/relu.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/relu6.cpp (86%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/reshape2.cpp (94%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/rnn.cpp (62%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/scale.cpp (93%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/shape.cpp (92%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/sigmoid.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/slice.cpp (95%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/softmax.cpp (80%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/softplus.cpp (85%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/split.cpp (83%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/squeeze.cpp (95%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/stack.cpp (68%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/tanh.cpp (91%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/transpose2.cpp (93%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/unsqueeze.cpp (95%)
 rename src/frontends/{paddlepaddle => paddle}/src/op/yolo_box.cpp (99%)
 rename src/frontends/{paddlepaddle => paddle}/src/op_table.cpp (99%)
 rename src/frontends/{paddlepaddle => paddle}/src/op_table.hpp (90%)
 rename src/frontends/{paddlepaddle/src/pdpd_fw_node.cpp => paddle/src/paddle_fw_node.cpp} (72%)
 rename src/frontends/{paddlepaddle/src/pdpd_fw_node.hpp => paddle/src/paddle_fw_node.hpp} (63%)
 rename src/frontends/{paddlepaddle/src/pdpd_utils.cpp => paddle/src/paddle_utils.cpp} (66%)
 rename src/frontends/{paddlepaddle/src/pdpd_utils.hpp => paddle/src/paddle_utils.hpp} (96%)
 rename src/frontends/{paddlepaddle => paddle}/src/place.cpp (60%)
 rename src/frontends/{paddlepaddle/include/paddlepaddle_frontend => paddle/src}/place.hpp (59%)
 rename src/frontends/{paddlepaddle => paddle}/src/proto/framework.proto (100%)
 delete mode 100644 src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
 delete mode 100644 src/frontends/paddlepaddle/src/exceptions.hpp

diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
index 90c9aa11d17..383a22feb07 100644
--- a/.ci/azure/linux.yml
+++ b/.ci/azure/linux.yml
@@ -116,8 +116,8 @@ jobs:
       python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
       # For running Python API tests
       python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/src/requirements-dev.txt
-      # For running PaddlePaddle frontend unit tests
-      python3 -m pip install -r $(REPO_DIR)/src/core/tests/frontend/paddlepaddle/requirements_dev.txt
+      # For running Paddle frontend unit tests
+      python3 -m pip install -r $(REPO_DIR)/src/core/tests/frontend/paddle/requirements_dev.txt
       # For running ONNX frontend unit tests
       python3 -m pip install -r $(REPO_DIR)/src/core/tests/requirements_test_onnx.txt
       # For MO unit tests
@@ -248,8 +248,8 @@ jobs:
     displayName: 'OV Core UT'
     continueOnError: false
 
-  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/paddlepaddle_tests --gtest_print_time=1 --gtest_output=xml:TEST-PaddlePaddle.xml
-    displayName: 'PaddlePaddle Frontend UT'
+  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/paddle_tests --gtest_print_time=1 --gtest_output=xml:TEST-Paddle.xml
+    displayName: 'Paddle Frontend UT'
     continueOnError: false
 
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/tensorflow_tests --gtest_print_time=1 --gtest_output=xml:TEST-Tensorflow.xml
diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index 81e2b47a622..7363d058c0f 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -109,8 +109,8 @@ jobs:
       python -m pip install --upgrade pip
       rem For running Python API tests
       python -m pip install -r $(REPO_DIR)\inference-engine\ie_bridges\python\src\requirements-dev.txt
-      rem For running PaddlePaddle frontend unit tests
-      python -m pip install -r $(REPO_DIR)\src\core\tests\frontend\paddlepaddle\requirements_dev.txt
+      rem For running Paddle frontend unit tests
+      python -m pip install -r $(REPO_DIR)\src\core\tests\frontend\paddle\requirements_dev.txt
       rem For running ONNX frontend unit tests
       python -m pip install -r $(REPO_DIR)\src\core\tests\requirements_test_onnx.txt
       rem For MO unit tests
@@ -207,8 +207,8 @@ jobs:
     displayName: 'OV Core UT'
     continueOnError: false
 
-  - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\paddlepaddle_tests --gtest_print_time=1 --gtest_output=xml:TEST-PaddlePaddle.xml
-    displayName: 'PaddlePaddle Frontend UT'
+  - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\paddle_tests --gtest_print_time=1 --gtest_output=xml:TEST-Paddle.xml
+    displayName: 'Paddle Frontend UT'
     continueOnError: false
 
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\tensorflow_tests --gtest_print_time=1 --gtest_output=xml:TEST-Tensorflow.xml
diff --git a/cmake/developer_package/plugins/plugins.cmake b/cmake/developer_package/plugins/plugins.cmake
index e1bdb66ce1d..6068d4e5af0 100644
--- a/cmake/developer_package/plugins/plugins.cmake
+++ b/cmake/developer_package/plugins/plugins.cmake
@@ -126,8 +126,8 @@ function(ie_add_plugin)
             if(TARGET ov_onnx_frontend)
                 add_dependencies(${IE_PLUGIN_NAME} ov_onnx_frontend)
             endif()
-            if(TARGET ov_paddlepaddle_frontend)
-                add_dependencies(${IE_PLUGIN_NAME} ov_paddlepaddle_frontend)
+            if(TARGET ov_paddle_frontend)
+                add_dependencies(${IE_PLUGIN_NAME} ov_paddle_frontend)
             endif()
             if(TARGET ov_tensorflow_frontend)
                 add_dependencies(${IE_PLUGIN_NAME} ov_tensorflow_frontend)
diff --git a/cmake/features.cmake b/cmake/features.cmake
index 1d0353f9fbd..8ceefe75d85 100644
--- a/cmake/features.cmake
+++ b/cmake/features.cmake
@@ -163,11 +163,11 @@ else()
 endif()
 
 ie_dependent_option(ENABLE_OV_ONNX_FRONTEND "Enable ONNX FrontEnd" ON "protoc_available" OFF)
-ie_dependent_option(ENABLE_OV_PDPD_FRONTEND "Enable PaddlePaddle FrontEnd" ON "protoc_available" OFF)
+ie_dependent_option(ENABLE_OV_PADDLE_FRONTEND "Enable PaddlePaddle FrontEnd" ON "protoc_available" OFF)
 ie_option(ENABLE_OV_IR_FRONTEND "Enable IR FrontEnd" ON)
 ie_dependent_option(ENABLE_OV_TF_FRONTEND "Enable TensorFlow FrontEnd" ON "protoc_available" OFF)
 ie_dependent_option(ENABLE_SYSTEM_PROTOBUF "Use system protobuf" OFF
-    "ENABLE_OV_ONNX_FRONTEND OR ENABLE_OV_PDPD_FRONTEND OR ENABLE_OV_TF_FRONTEND;BUILD_SHARED_LIBS" OFF)
+    "ENABLE_OV_ONNX_FRONTEND OR ENABLE_OV_PADDLE_FRONTEND OR ENABLE_OV_TF_FRONTEND;BUILD_SHARED_LIBS" OFF)
 ie_dependent_option(ENABLE_OV_CORE_UNIT_TESTS "Enables OpenVINO core unit tests" ON "ENABLE_TESTS;NOT ANDROID" OFF)
 ie_dependent_option(ENABLE_OV_CORE_BACKEND_UNIT_TESTS "Control the building of unit tests using backends" ON
     "ENABLE_OV_CORE_UNIT_TESTS" OFF)
diff --git a/cmake/templates/OpenVINOConfig.cmake.in b/cmake/templates/OpenVINOConfig.cmake.in
index fd6ad5d66a0..5ab40ad9153 100644
--- a/cmake/templates/OpenVINOConfig.cmake.in
+++ b/cmake/templates/OpenVINOConfig.cmake.in
@@ -11,7 +11,7 @@
 #
 #  * `Runtime`: OpenVINO C++ and C Core & Inference Runtime, frontend common
 #  * `ONNX`: OpenVINO ONNX frontend
-#  * `PaddlePaddle`: OpenVINO PaddlePaddle frontend
+#  * `Paddle`: OpenVINO Paddle frontend
 #
 # If no components are specified, `Runtime` component is provided:
 #
@@ -43,8 +43,8 @@
 #   `openvino::frontend::onnx`
 #   ONNX FrontEnd target (optional)
 #
-#   `openvino::frontend::paddlepaddle`
-#   PaddlePaddle FrontEnd target (optional)
+#   `openvino::frontend::paddle`
+#   Paddle FrontEnd target (optional)
 #
 #   `openvino::frontend::tensorflow`
 #   TensorFlow FrontEnd target (optional)
@@ -63,8 +63,8 @@
 #   `OpenVINO_Frontend_ONNX_FOUND`
 #   OpenVINO ONNX frontend is available
 #
-#   `OpenVINO_Frontend_PaddlePaddle_FOUND`
-#   OpenVINO PaddlePaddle frontend is available
+#   `OpenVINO_Frontend_Paddle_FOUND`
+#   OpenVINO Paddle frontend is available
 #
 #   `OpenVINO_Frontend_TensorFlow_FOUND`
 #   OpenVINO TensorFlow frontend is available
@@ -193,12 +193,12 @@ endif()
 set(${CMAKE_FIND_PACKAGE_NAME}_Runtime_FOUND ON)
 
 set(${CMAKE_FIND_PACKAGE_NAME}_ONNX_FOUND @ENABLE_OV_ONNX_FRONTEND@)
-set(${CMAKE_FIND_PACKAGE_NAME}_PaddlePaddle_FOUND @ENABLE_OV_PDPD_FRONTEND@)
+set(${CMAKE_FIND_PACKAGE_NAME}_Paddle_FOUND @ENABLE_OV_PADDLE_FRONTEND@)
 set(${CMAKE_FIND_PACKAGE_NAME}_TensorFlow_FOUND @ENABLE_OV_TF_FRONTEND@)
 set(${CMAKE_FIND_PACKAGE_NAME}_IR_FOUND @ENABLE_OV_IR_FRONTEND@)
 
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_ONNX_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_ONNX_FOUND})
-set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_PaddlePaddle_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_PaddlePaddle_FOUND})
+set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_Paddle_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_Paddle_FOUND})
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_TensorFlow_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_TensorFlow_FOUND})
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_IR_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_IR_FOUND})
 
@@ -212,7 +212,7 @@ endif()
 #
 
 foreach(target openvino::runtime openvino::runtime::c
-               openvino::frontend::onnx openvino::frontend::paddlepaddle openvino::frontend::tensorflow)
+               openvino::frontend::onnx openvino::frontend::paddle openvino::frontend::tensorflow)
     if(TARGET ${target} AND _ov_as_external_package)
         _ov_target_no_deprecation_error(${target})
     endif()
@@ -230,6 +230,6 @@ if(_need_package_name_reset)
     unset(_need_package_name_reset)
 endif()
 
-unset(${CMAKE_FIND_PACKAGE_NAME}_PaddlePaddle_FOUND)
+unset(${CMAKE_FIND_PACKAGE_NAME}_Paddle_FOUND)
 unset(${CMAKE_FIND_PACKAGE_NAME}_ONNX_FOUND)
 unset(${CMAKE_FIND_PACKAGE_NAME}_TensorFlow_FOUND)
diff --git a/cmake/templates/ngraphConfig.cmake.in b/cmake/templates/ngraphConfig.cmake.in
index a67c3f3c473..f9fd12cb0f7 100644
--- a/cmake/templates/ngraphConfig.cmake.in
+++ b/cmake/templates/ngraphConfig.cmake.in
@@ -29,8 +29,8 @@
 #   ngraph_ov_onnx_frontend_FOUND          - True if the system has ov_onnx_frontend library
 #   ngraph::ov_onnx_frontend               - ONNX FrontEnd target (optional)
 #
-#   ngraph_paddlepaddle_frontend_FOUND     - True if the system has PDPD frontend
-#   ngraph::ov_paddlepaddle_frontend       - nGraph PDPD frontend (optional)
+#   ngraph_paddle_frontend_FOUND     - True if the system has Paddle frontend
+#   ngraph::ov_paddle_frontend       - nGraph Paddle frontend (optional)
 #
 
 @PACKAGE_INIT@
@@ -56,10 +56,10 @@ if(TARGET openvino::frontend::onnx AND NOT TARGET ngraph::ov_onnx_frontend)
         INTERFACE_LINK_LIBRARIES openvino::frontend::onnx)
 endif()
 
-if(TARGET openvino::frontend::paddlepaddle AND NOT TARGET ngraph::ov_paddlepaddle_frontend)
-    add_library(ngraph::ov_paddlepaddle_frontend INTERFACE IMPORTED)
-    set_target_properties(ngraph::ov_paddlepaddle_frontend PROPERTIES
-        INTERFACE_LINK_LIBRARIES openvino::frontend::paddlepaddle)
+if(TARGET openvino::frontend::paddle AND NOT TARGET ngraph::ov_paddle_frontend)
+    add_library(ngraph::ov_paddle_frontend INTERFACE IMPORTED)
+    set_target_properties(ngraph::ov_paddle_frontend PROPERTIES
+        INTERFACE_LINK_LIBRARIES openvino::frontend::paddle)
 endif()
 
 if(TARGET openvino::frontend::tensorflow AND NOT TARGET ngraph::ov_tensorflow_frontend)
@@ -85,7 +85,7 @@ if(ngraph_onnx_importer_FOUND)
     endif()
 endif()
 
-set(ngraph_paddlepaddle_frontend_FOUND ${OpenVINO_Frontend_PaddlePaddle_FOUND})
+set(ngraph_paddle_frontend_FOUND ${OpenVINO_Frontend_Paddle_FOUND})
 set(ngraph_tensorflow_frontend_FOUND ${OpenVINO_Frontend_TensorFlow_FOUND})
 set(ngraph_ir_frontend_FOUND ${OpenVINO_Frontend_IR_FOUND})
 
diff --git a/cmake/test_model_zoo.cmake b/cmake/test_model_zoo.cmake
index de29223baa7..23353762279 100644
--- a/cmake/test_model_zoo.cmake
+++ b/cmake/test_model_zoo.cmake
@@ -136,8 +136,8 @@ if(ENABLE_TESTS)
         add_dependencies(test_model_zoo test_pip_prerequsites)
     endif()
 
-    if (ENABLE_OV_PDPD_FRONTEND AND ENABLE_OV_CORE_UNIT_TESTS)
-        add_dependencies(test_model_zoo paddlepaddle_test_models)
+    if (ENABLE_OV_PADDLE_FRONTEND AND ENABLE_OV_CORE_UNIT_TESTS)
+        add_dependencies(test_model_zoo paddle_test_models)
     endif()
 
     install(DIRECTORY "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo"
diff --git a/docs/IE_DG/inference_engine_intro.md b/docs/IE_DG/inference_engine_intro.md
index e48b7578211..9692fabe72d 100644
--- a/docs/IE_DG/inference_engine_intro.md
+++ b/docs/IE_DG/inference_engine_intro.md
@@ -46,11 +46,11 @@ This library contains the classes to:
 Starting from 2022.1 release, OpenVINO Runtime introduced a concept of frontend plugins. Such plugins can be automatically dynamically loaded by OpenVINO Runtime dynamically depending on file format:
 * Unix* OS:
     - `libov_ir_frontend.so` to read a network from IR
-    - `libov_paddlepaddle_frontend.so` to read a network from PaddlePaddle model format
+    - `libov_paddle_frontend.so` to read a network from PaddlePaddle model format
     - `libov_onnx_frontend.so` to read a network from ONNX model format
 * Windows* OS:
     - `ov_ir_frontend.dll` to read a network from IR
-    - `ov_paddlepaddle_frontend.dll` to read a network from PaddlePaddle model format
+    - `ov_paddle_frontend.dll` to read a network from PaddlePaddle model format
     - `ov_onnx_frontend.dll` to read a network from ONNX model format
 
 ### Device-specific Plugin Libraries ###
diff --git a/docs/doxygen/ngraph_cpp_api.config b/docs/doxygen/ngraph_cpp_api.config
new file mode 100644
index 00000000000..56ab7dd7ebc
--- /dev/null
+++ b/docs/doxygen/ngraph_cpp_api.config
@@ -0,0 +1,39 @@
+# Doxyfile 1.8.18
+
+# This file describes the settings to be used by the documentation system
+# doxygen (www.doxygen.org) for a project.
+#
+# All text after a double hash (##) is considered a comment and is placed in
+# front of the TAG it is preceding.
+#
+# All text after a single hash (#) is considered a comment and will be ignored.
+# The format is:
+# TAG = value [value, ...]
+# For lists, items can also be appended using:
+# TAG += value [value, ...]
+# Values that contain spaces should be placed between quotes (\" \").
+
+@INCLUDE = "@IE_CONFIG_BUILD@"
+
+EXCLUDE_SYMBOLS        = 
+
+PREDEFINED             =
+
+EXCLUDE_PATTERNS       = */python/*
+
+FILE_PATTERNS          = *.cpp \
+                         *.c \
+                         *.hpp \
+                         *.h
+
+LAYOUT_FILE            = "@NGRAPH_CPP_LAYOUT_BUILD@"
+
+INPUT                  = "@CORE_DIR@/core/include/" \
+                         "@FRONTENDS_DIR@/onnx/frontend/include/" \
+                         "@FRONTENDS_DIR@/paddle/frontend/include/"
+
+HTML_OUTPUT            = "@NGRAPH_CPP_OUTPUT@"
+
+GENERATE_TAGFILE       = "@DOCS_BUILD_DIR@/ngraph_cpp_api.tag"
+
+WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ngraph_cpp_api.log"
diff --git a/src/core/tests/frontend/CMakeLists.txt b/src/core/tests/frontend/CMakeLists.txt
index 723776d0407..971b6533119 100644
--- a/src/core/tests/frontend/CMakeLists.txt
+++ b/src/core/tests/frontend/CMakeLists.txt
@@ -3,8 +3,8 @@
 #
 add_subdirectory(shared)
 
-if (ENABLE_OV_PDPD_FRONTEND)
-    add_subdirectory(paddlepaddle)
+if (ENABLE_OV_PADDLE_FRONTEND)
+    add_subdirectory(paddle)
 endif()
 
 if (ENABLE_OV_ONNX_FRONTEND)
diff --git a/src/core/tests/frontend/paddlepaddle/CMakeLists.txt b/src/core/tests/frontend/paddle/CMakeLists.txt
similarity index 85%
rename from src/core/tests/frontend/paddlepaddle/CMakeLists.txt
rename to src/core/tests/frontend/paddle/CMakeLists.txt
index 1f759130daa..998be1684ef 100644
--- a/src/core/tests/frontend/paddlepaddle/CMakeLists.txt
+++ b/src/core/tests/frontend/paddle/CMakeLists.txt
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME "paddlepaddle_tests")
+set(TARGET_NAME "paddle_tests")
 
 file(GLOB_RECURSE SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
@@ -18,7 +18,7 @@ install(TARGETS ${TARGET_NAME}
         EXCLUDE_FROM_ALL)
 
 # Test model generating
-ie_check_pip_package(paddlepaddle WARNING)
+ie_check_pip_package(paddlepaddle WARNING paddle)
 
 set(TEST_PADDLE_MODELS_DIRNAME test_model_zoo/paddle_test_models)
 target_compile_definitions(${TARGET_NAME} PRIVATE -D TEST_PADDLE_MODELS_DIRNAME=\"${TEST_PADDLE_MODELS_DIRNAME}/\")
@@ -39,7 +39,7 @@ if (paddlepaddle_FOUND)
             ${TEST_PADDLE_MODELS}
             DEPENDS ${PADDLE_ALL_SCRIPTS}
             )
-    add_custom_target(paddlepaddle_test_models DEPENDS ${OUT_FILE})
+    add_custom_target(paddle_test_models DEPENDS ${OUT_FILE})
 
     install(DIRECTORY ${TEST_PADDLE_MODELS}
             DESTINATION tests/${TEST_PADDLE_MODELS_DIRNAME}
@@ -51,11 +51,11 @@ else()
             COMMAND ${CMAKE_COMMAND}
             -E cmake_echo_color --red "Warning: Unable to generate PaddlePaddle test models. Running '${TARGET_NAME}' will likely fail"
             )
-    add_custom_target(paddlepaddle_test_models DEPENDS unable_build_paddle_models.txt)
+    add_custom_target(paddle_test_models DEPENDS unable_build_paddle_models.txt)
 endif()
 
-add_dependencies(${TARGET_NAME} paddlepaddle_test_models)
-add_dependencies(${TARGET_NAME} ov_paddlepaddle_frontend)
+add_dependencies(${TARGET_NAME} paddle_test_models)
+add_dependencies(${TARGET_NAME} ov_paddle_frontend)
 
 # Fuzzy tests for PaddlePaddle use IE_CPU engine
 if (ENABLE_MKL_DNN)
diff --git a/src/core/tests/frontend/paddlepaddle/basic_api.cpp b/src/core/tests/frontend/paddle/basic_api.cpp
similarity index 91%
rename from src/core/tests/frontend/paddlepaddle/basic_api.cpp
rename to src/core/tests/frontend/paddle/basic_api.cpp
index f01ff120c10..605f1df2bfb 100644
--- a/src/core/tests/frontend/paddlepaddle/basic_api.cpp
+++ b/src/core/tests/frontend/paddle/basic_api.cpp
@@ -9,7 +9,7 @@
 using namespace ngraph;
 using namespace ov::frontend;
 
-using PDPDBasicTest = FrontEndBasicTest;
+using PaddleBasicTest = FrontEndBasicTest;
 
 static const std::vector<std::string> models{
     std::string("conv2d"),
@@ -20,7 +20,7 @@ static const std::vector<std::string> models{
     std::string("2in_2out_dynbatch/2in_2out_dynbatch.pdmodel"),
 };
 
-INSTANTIATE_TEST_SUITE_P(PDPDBasicTest,
+INSTANTIATE_TEST_SUITE_P(PaddleBasicTest,
                          FrontEndBasicTest,
                          ::testing::Combine(::testing::Values(PADDLE_FE),
                                             ::testing::Values(std::string(TEST_PADDLE_MODELS_DIRNAME)),
diff --git a/src/core/tests/frontend/paddlepaddle/convert_model.cpp b/src/core/tests/frontend/paddle/convert_model.cpp
similarity index 90%
rename from src/core/tests/frontend/paddlepaddle/convert_model.cpp
rename to src/core/tests/frontend/paddle/convert_model.cpp
index 3dbb8e0ecf8..e3290e3e681 100644
--- a/src/core/tests/frontend/paddlepaddle/convert_model.cpp
+++ b/src/core/tests/frontend/paddle/convert_model.cpp
@@ -9,7 +9,7 @@
 using namespace ov;
 using namespace ov::frontend;
 
-using PDPDConvertModelTest = FrontEndConvertModelTest;
+using PaddleConvertModelTest = FrontEndConvertModelTest;
 
 static const std::vector<std::string> models{
     std::string("conv2d"),
@@ -21,7 +21,7 @@ static const std::vector<std::string> models{
     std::string("pool2d_dyn_hw/pool2d_dyn_hw.pdmodel"),
 };
 
-INSTANTIATE_TEST_SUITE_P(PDPDConvertModelTest,
+INSTANTIATE_TEST_SUITE_P(PaddleConvertModelTest,
                          FrontEndConvertModelTest,
                          ::testing::Combine(::testing::Values(PADDLE_FE),
                                             ::testing::Values(std::string(TEST_PADDLE_MODELS_DIRNAME)),
diff --git a/src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp b/src/core/tests/frontend/paddle/convert_unsupported.cpp
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp
rename to src/core/tests/frontend/paddle/convert_unsupported.cpp
diff --git a/src/core/tests/frontend/paddlepaddle/cut_specific_model.cpp b/src/core/tests/frontend/paddle/cut_specific_model.cpp
similarity index 92%
rename from src/core/tests/frontend/paddlepaddle/cut_specific_model.cpp
rename to src/core/tests/frontend/paddle/cut_specific_model.cpp
index cacb420770a..7c52a0b439b 100644
--- a/src/core/tests/frontend/paddlepaddle/cut_specific_model.cpp
+++ b/src/core/tests/frontend/paddle/cut_specific_model.cpp
@@ -9,7 +9,7 @@
 using namespace ngraph;
 using namespace ov::frontend;
 
-using PDPDCutTest = FrontEndCutModelTest;
+using PaddleCutTest = FrontEndCutModelTest;
 
 static CutModelParam getTestData_2in_2out() {
     CutModelParam res;
@@ -26,7 +26,7 @@ static CutModelParam getTestData_2in_2out() {
     return res;
 }
 
-INSTANTIATE_TEST_SUITE_P(PDPDCutTest,
+INSTANTIATE_TEST_SUITE_P(PaddleCutTest,
                          FrontEndCutModelTest,
                          ::testing::Values(getTestData_2in_2out()),
                          FrontEndCutModelTest::getTestCaseName);
diff --git a/src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp b/src/core/tests/frontend/paddle/incorrect_cut_model.cpp
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp
rename to src/core/tests/frontend/paddle/incorrect_cut_model.cpp
diff --git a/src/core/tests/frontend/paddlepaddle/json_config_extension.cpp b/src/core/tests/frontend/paddle/json_config_extension.cpp
similarity index 85%
rename from src/core/tests/frontend/paddlepaddle/json_config_extension.cpp
rename to src/core/tests/frontend/paddle/json_config_extension.cpp
index 507a3f9db23..415ca76367f 100644
--- a/src/core/tests/frontend/paddlepaddle/json_config_extension.cpp
+++ b/src/core/tests/frontend/paddle/json_config_extension.cpp
@@ -7,7 +7,7 @@
 
 using namespace ov::frontend;
 
-using PDPDJsonConfigTest = FrontEndJsonConfigTest;
+using PaddleJsonConfigTest = FrontEndJsonConfigTest;
 
 static JsonConfigFEParam getTestData() {
     JsonConfigFEParam res;
@@ -17,7 +17,7 @@ static JsonConfigFEParam getTestData() {
     return res;
 }
 
-INSTANTIATE_TEST_SUITE_P(PDPDJsonConfigTest,
+INSTANTIATE_TEST_SUITE_P(PaddleJsonConfigTest,
                          FrontEndJsonConfigTest,
                          ::testing::Values(getTestData()),
                          FrontEndJsonConfigTest::getTestCaseName);
diff --git a/src/core/tests/frontend/paddlepaddle/load_from.cpp b/src/core/tests/frontend/paddle/load_from.cpp
similarity index 90%
rename from src/core/tests/frontend/paddlepaddle/load_from.cpp
rename to src/core/tests/frontend/paddle/load_from.cpp
index ac3dfd51ba5..5b3e1845151 100644
--- a/src/core/tests/frontend/paddlepaddle/load_from.cpp
+++ b/src/core/tests/frontend/paddle/load_from.cpp
@@ -9,7 +9,7 @@
 using namespace ngraph;
 using namespace ov::frontend;
 
-using PDPDCutTest = FrontEndLoadFromTest;
+using PaddleCutTest = FrontEndLoadFromTest;
 
 static LoadFromFEParam getTestData() {
     LoadFromFEParam res;
@@ -22,7 +22,7 @@ static LoadFromFEParam getTestData() {
     return res;
 }
 
-INSTANTIATE_TEST_SUITE_P(PDPDCutTest,
+INSTANTIATE_TEST_SUITE_P(PaddleCutTest,
                          FrontEndLoadFromTest,
                          ::testing::Values(getTestData()),
                          FrontEndLoadFromTest::getTestCaseName);
diff --git a/src/core/tests/frontend/paddlepaddle/main.cpp b/src/core/tests/frontend/paddle/main.cpp
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/main.cpp
rename to src/core/tests/frontend/paddle/main.cpp
diff --git a/src/core/tests/frontend/paddlepaddle/op_fuzzy.cpp b/src/core/tests/frontend/paddle/op_fuzzy.cpp
similarity index 99%
rename from src/core/tests/frontend/paddlepaddle/op_fuzzy.cpp
rename to src/core/tests/frontend/paddle/op_fuzzy.cpp
index 6ae5f3a659e..f7082035140 100644
--- a/src/core/tests/frontend/paddlepaddle/op_fuzzy.cpp
+++ b/src/core/tests/frontend/paddle/op_fuzzy.cpp
@@ -18,7 +18,7 @@ using namespace InferenceEngine;
 using namespace ngraph;
 using namespace ov::frontend;
 
-using PDPDFuzzyOpTest = FrontEndFuzzyOpTest;
+using PaddleFuzzyOpTest = FrontEndFuzzyOpTest;
 
 static const std::vector<std::string> models{std::string("argmax"),
                                              std::string("argmax1"),
@@ -64,7 +64,7 @@ static const std::vector<std::string> models{std::string("argmax"),
                                              std::string("conv2d_strides_no_padding"),
                                              std::string("conv2d_strides_padding"),
                                              std::string("conv2d_transpose_dilation_assymetric_pads_strides"),
-                                             // conv2d_transpose_SAME_padding(PDPD outputs wrong results),
+                                             // conv2d_transpose_SAME_padding(Paddle outputs wrong results),
                                              std::string("conv2d_transpose_strides_assymetric_padding"),
                                              std::string("conv2d_transpose_strides_no_padding"),
                                              std::string("conv2d_transpose_strides_padding"),
@@ -261,9 +261,9 @@ static const std::vector<std::string> models{std::string("argmax"),
                                              // std::string("yolo_box_scale_xy"),
                                              std::string("yolo_box_uneven_wh")};
 
-INSTANTIATE_TEST_SUITE_P(PDPDFuzzyOpTest,
+INSTANTIATE_TEST_SUITE_P(PaddleFuzzyOpTest,
                          FrontEndFuzzyOpTest,
                          ::testing::Combine(::testing::Values(PADDLE_FE),
                                             ::testing::Values(std::string(TEST_PADDLE_MODELS_DIRNAME)),
                                             ::testing::ValuesIn(models)),
-                         PDPDFuzzyOpTest::getTestCaseName);
+                         PaddleFuzzyOpTest::getTestCaseName);
diff --git a/src/core/tests/frontend/paddlepaddle/paddle_utils.hpp b/src/core/tests/frontend/paddle/paddle_utils.hpp
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/paddle_utils.hpp
rename to src/core/tests/frontend/paddle/paddle_utils.hpp
diff --git a/src/core/tests/frontend/paddlepaddle/partial_shape.cpp b/src/core/tests/frontend/paddle/partial_shape.cpp
similarity index 96%
rename from src/core/tests/frontend/paddlepaddle/partial_shape.cpp
rename to src/core/tests/frontend/paddle/partial_shape.cpp
index 525c4684813..80a995c4264 100644
--- a/src/core/tests/frontend/paddlepaddle/partial_shape.cpp
+++ b/src/core/tests/frontend/paddle/partial_shape.cpp
@@ -9,7 +9,7 @@
 using namespace ngraph;
 using namespace ov::frontend;
 
-using PDPDPartialShapeTest = FrontEndPartialShapeTest;
+using PaddlePartialShapeTest = FrontEndPartialShapeTest;
 
 static PartShape getTestShape_2in_2out() {
     PartShape res;
@@ -57,7 +57,7 @@ static PartShape getTestShape_conv2d_relu() {
 }
 
 INSTANTIATE_TEST_SUITE_P(
-    PDPDPartialShapeTest,
+    PaddlePartialShapeTest,
     FrontEndPartialShapeTest,
     ::testing::Combine(::testing::Values(BaseFEParam{PADDLE_FE, std::string(TEST_PADDLE_MODELS_DIRNAME)}),
                        ::testing::ValuesIn(std::vector<PartShape>{getTestShape_2in_2out(),
diff --git a/src/core/tests/frontend/paddlepaddle/places.cpp b/src/core/tests/frontend/paddle/places.cpp
similarity index 96%
rename from src/core/tests/frontend/paddlepaddle/places.cpp
rename to src/core/tests/frontend/paddle/places.cpp
index 2092920cbee..df74eb66ab1 100644
--- a/src/core/tests/frontend/paddlepaddle/places.cpp
+++ b/src/core/tests/frontend/paddle/places.cpp
@@ -53,7 +53,7 @@ std::vector<std::string> tensor_names = {
     "save_infer_model/scale_5.tmp_1",
 };
 
-TEST(PDPD_Places, check_tensor_names) {
+TEST(Paddle_Places, check_tensor_names) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -67,7 +67,7 @@ TEST(PDPD_Places, check_tensor_names) {
     }
 }
 
-TEST(PDPD_Places, check_input_outputs) {
+TEST(Paddle_Places, check_input_outputs) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -101,7 +101,7 @@ TEST(PDPD_Places, check_input_outputs) {
 }
 
 // all existed in the model ops have "Out" port
-TEST(PDPD_Places, check_out_port_of_all_ops) {
+TEST(Paddle_Places, check_out_port_of_all_ops) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -124,7 +124,7 @@ TEST(PDPD_Places, check_out_port_of_all_ops) {
     }
 }
 
-TEST(PDPD_Places, check_in_out_ports_of_model_outputs) {
+TEST(Paddle_Places, check_in_out_ports_of_model_outputs) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -159,7 +159,7 @@ TEST(PDPD_Places, check_in_out_ports_of_model_outputs) {
     }
 }
 
-TEST(PDPD_Places, check_source_target_tensors_of_model_outputs) {
+TEST(Paddle_Places, check_source_target_tensors_of_model_outputs) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -194,7 +194,7 @@ TEST(PDPD_Places, check_source_target_tensors_of_model_outputs) {
     }
 }
 
-TEST(PDPD_Places, check_producing_consuming_ops_of_model_outputs) {
+TEST(Paddle_Places, check_producing_consuming_ops_of_model_outputs) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -230,7 +230,7 @@ TEST(PDPD_Places, check_producing_consuming_ops_of_model_outputs) {
 }
 
 // check data flow [ output port -> tensor -> input port ]
-TEST(PDPD_Places, check_data_flow) {
+TEST(Paddle_Places, check_data_flow) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -270,7 +270,7 @@ TEST(PDPD_Places, check_data_flow) {
 //                -> input_port_2
 //                -> input_port_N]
 // input_port, input_port_2, ... input_port_N are equal data
-TEST(PDPD_Places, check_tensor_to_multiple_ports) {
+TEST(Paddle_Places, check_tensor_to_multiple_ports) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -297,7 +297,7 @@ TEST(PDPD_Places, check_tensor_to_multiple_ports) {
 }
 
 // consuming ops should be equal for tensor place and producing output port
-TEST(PDPD_Places, check_consuming_ops) {
+TEST(Paddle_Places, check_consuming_ops) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -339,7 +339,7 @@ TEST(PDPD_Places, check_consuming_ops) {
     }
 }
 
-TEST(PDPD_Places, check_consuming_ops_2) {
+TEST(Paddle_Places, check_consuming_ops_2) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -379,7 +379,7 @@ TEST(PDPD_Places, check_consuming_ops_2) {
     }
 }
 
-TEST(PDPD_Places, check_producing_ops) {
+TEST(Paddle_Places, check_producing_ops) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -402,7 +402,7 @@ TEST(PDPD_Places, check_producing_ops) {
     }
 }
 
-TEST(PDPD_Places, check_input_output_ports_dy_idx) {
+TEST(Paddle_Places, check_input_output_ports_dy_idx) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
@@ -429,7 +429,7 @@ TEST(PDPD_Places, check_input_output_ports_dy_idx) {
     }
 }
 
-TEST(PDPD_Places, check_ops_tensors_by_idx) {
+TEST(Paddle_Places, check_ops_tensors_by_idx) {
     FrontEndTestUtils::setupTestEnv();
     auto fem = FrontEndManager();
     FrontEnd::Ptr frontend;
diff --git a/src/core/tests/frontend/paddlepaddle/requirements_dev.txt b/src/core/tests/frontend/paddle/requirements_dev.txt
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/requirements_dev.txt
rename to src/core/tests/frontend/paddle/requirements_dev.txt
diff --git a/src/core/tests/frontend/paddlepaddle/set_element_type.cpp b/src/core/tests/frontend/paddle/set_element_type.cpp
similarity index 87%
rename from src/core/tests/frontend/paddlepaddle/set_element_type.cpp
rename to src/core/tests/frontend/paddle/set_element_type.cpp
index f9decce7316..a1f3d2b7b4c 100644
--- a/src/core/tests/frontend/paddlepaddle/set_element_type.cpp
+++ b/src/core/tests/frontend/paddle/set_element_type.cpp
@@ -9,7 +9,7 @@
 using namespace ngraph;
 using namespace ov::frontend;
 
-using PDPDCutTest = FrontEndElementTypeTest;
+using PaddleCutTest = FrontEndElementTypeTest;
 
 static SetTypeFEParam getTestData_relu() {
     SetTypeFEParam res;
@@ -19,7 +19,7 @@ static SetTypeFEParam getTestData_relu() {
     return res;
 }
 
-INSTANTIATE_TEST_SUITE_P(PDPDCutTest,
+INSTANTIATE_TEST_SUITE_P(PaddleCutTest,
                          FrontEndElementTypeTest,
                          ::testing::Values(getTestData_relu()),
                          FrontEndElementTypeTest::getTestCaseName);
diff --git a/src/core/tests/frontend/paddlepaddle/telemetry.cpp b/src/core/tests/frontend/paddle/telemetry.cpp
similarity index 90%
rename from src/core/tests/frontend/paddlepaddle/telemetry.cpp
rename to src/core/tests/frontend/paddle/telemetry.cpp
index 9a7f22f6567..1b2980d5610 100644
--- a/src/core/tests/frontend/paddlepaddle/telemetry.cpp
+++ b/src/core/tests/frontend/paddle/telemetry.cpp
@@ -8,7 +8,7 @@
 
 using namespace ov::frontend;
 
-using PDPDTelemetryTest = FrontEndTelemetryTest;
+using PaddleTelemetryTest = FrontEndTelemetryTest;
 
 static TelemetryFEParam getTestData() {
     TelemetryFEParam res;
@@ -22,7 +22,7 @@ static TelemetryFEParam getTestData() {
     return res;
 }
 
-INSTANTIATE_TEST_SUITE_P(PDPDTelemetryTest,
+INSTANTIATE_TEST_SUITE_P(PaddleTelemetryTest,
                          FrontEndTelemetryTest,
                          ::testing::Values(getTestData()),
                          FrontEndTelemetryTest::getTestCaseName);
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_2in_2out.py
similarity index 94%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_2in_2out.py
index b8bb0a7cb84..8a7aa75bdc4 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_2in_2out.py
@@ -33,7 +33,7 @@ exe = fluid.Executor(fluid.CPUPlace())
 exe.run(fluid.default_startup_program())
 inp_dict = {'inputX1': inp_blob1, 'inputX2': inp_blob2}
 var = [relu3a, relu3b]
-res_pdpd = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
+res_paddle = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
 
 fluid.io.save_inference_model(os.path.join(sys.argv[1], "2in_2out"), list(inp_dict.keys()), var, exe,
                               model_filename="2in_2out.pdmodel", params_filename="2in_2out.pdiparams")
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
similarity index 94%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
index 3453189b3b7..eb68446a46d 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
@@ -33,7 +33,7 @@ exe = fluid.Executor(fluid.CPUPlace())
 exe.run(fluid.default_startup_program())
 inp_dict = {'inputX1': inp_blob1, 'inputX2': inp_blob2}
 var = [relu3a, relu3b]
-res_pdpd = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
+res_paddle = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
 
 fluid.io.save_inference_model(os.path.join(sys.argv[1], "2in_2out_dynbatch"), list(inp_dict.keys()), var, exe,
                               model_filename="2in_2out_dynbatch.pdmodel", params_filename="2in_2out_dynbatch.pdiparams")
diff --git a/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_argmax.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_argmax.py
new file mode 100644
index 00000000000..92f57c72e93
--- /dev/null
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_argmax.py
@@ -0,0 +1,60 @@
+#
+# pool2d paddle model generator
+#
+import numpy as np
+from save_model import saveModel
+import sys
+data_type = 'float32'
+
+
+def paddle_argmax(name : str, x, axis):
+    import paddle
+    paddle.enable_static()
+    
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+        out = paddle.argmax(x=node_x, axis=axis)
+        out = paddle.cast(out, np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            feed={'x': x},
+            fetch_list=[out])             
+
+        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+def paddle_argmax1(name : str, x):
+    import paddle
+    paddle.enable_static()
+    
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+        out = paddle.argmax(x=node_x)
+        out = paddle.cast(out, np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            feed={'x': x},
+            fetch_list=[out])             
+
+        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+def main():
+    data = np.random.random([3,5,7,2]).astype("float32")
+    axis = 0
+    paddle_argmax("argmax", data, axis)
+    paddle_argmax1("argmax1", data)
+
+
+if __name__ == "__main__":
+    main()     
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_assign_value.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_assign_value.py
similarity index 57%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_assign_value.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_assign_value.py
index 10e5d5ea904..3554ed67b72 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_assign_value.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_assign_value.py
@@ -3,20 +3,20 @@ from save_model import saveModel
 import sys
 
 
-def pdpd_assign_value(name, test_x):
-    import paddle as pdpd
-    pdpd.enable_static()
-    main_program = pdpd.static.Program()
-    startup_program = pdpd.static.Program()
-    with pdpd.static.program_guard(main_program, startup_program):
-        node_x = pdpd.static.data(name='x', shape=test_x.shape, dtype=test_x.dtype if test_x.dtype != np.bool else np.int32)
-        node_x = pdpd.cast(node_x, dtype=test_x.dtype)
-        const_value = pdpd.assign(test_x, output=None)
-        result = pdpd.cast(pdpd.concat([node_x, const_value], 0), dtype=np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+def paddle_assign_value(name, test_x):
+    import paddle
+    paddle.enable_static()
+    main_program = paddle.static.Program()
+    startup_program = paddle.static.Program()
+    with paddle.static.program_guard(main_program, startup_program):
+        node_x = paddle.static.data(name='x', shape=test_x.shape, dtype=test_x.dtype if test_x.dtype != np.bool else np.int32)
+        node_x = paddle.cast(node_x, dtype=test_x.dtype)
+        const_value = paddle.assign(test_x, output=None)
+        result = paddle.cast(paddle.concat([node_x, const_value], 0), dtype=np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         if test_x.dtype == np.bool:
             test_x = test_x.astype(np.int32)
 
@@ -49,7 +49,7 @@ def compare():
         }
     ]
     for test in test_cases:
-        pdpd_assign_value(test['name'], test['input'])
+        paddle_assign_value(test['name'], test['input'])
 
 
 if __name__ == "__main__":
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_batch_norm.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_batch_norm.py
similarity index 61%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_batch_norm.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_batch_norm.py
index fbbba99160c..a9c8d405e5b 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_batch_norm.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_batch_norm.py
@@ -7,14 +7,14 @@ import sys
 
 
 def batch_norm1(name : str, x, scale, bias, mean, var, data_layout):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    scale_attr = pdpd.ParamAttr(name="scale1", initializer=pdpd.nn.initializer.Assign(scale))
-    bias_attr = pdpd.ParamAttr(name="bias1", initializer=pdpd.nn.initializer.Assign(bias))
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    scale_attr = paddle.ParamAttr(name="scale1", initializer=paddle.nn.initializer.Assign(scale))
+    bias_attr = paddle.ParamAttr(name="bias1", initializer=paddle.nn.initializer.Assign(bias))
 
-    out = pdpd.static.nn.batch_norm(node_x, epsilon=1e-5,
+    out = paddle.static.nn.batch_norm(node_x, epsilon=1e-5,
                                     param_attr=scale_attr,
                                     bias_attr=bias_attr,
                                     moving_mean_name="bn_mean1",
@@ -22,12 +22,12 @@ def batch_norm1(name : str, x, scale, bias, mean, var, data_layout):
                                     use_global_stats=True,
                                     data_layout=data_layout)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
-    pdpd.static.global_scope().var("bn_mean1").get_tensor().set(mean, pdpd.CPUPlace())
-    pdpd.static.global_scope().var("bn_variance1").get_tensor().set(var, pdpd.CPUPlace())
+    exe.run(paddle.static.default_startup_program())
+    paddle.static.global_scope().var("bn_mean1").get_tensor().set(mean, paddle.CPUPlace())
+    paddle.static.global_scope().var("bn_variance1").get_tensor().set(var, paddle.CPUPlace())
 
     outs = exe.run(
         feed={'x': x},
@@ -38,14 +38,14 @@ def batch_norm1(name : str, x, scale, bias, mean, var, data_layout):
     return outs[0]
 
 def batch_norm2(name : str, x, scale, bias, mean, var, data_layout):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    scale_attr = pdpd.ParamAttr(name="scale2", initializer=pdpd.nn.initializer.Assign(scale))
-    bias_attr = pdpd.ParamAttr(name="bias2", initializer=pdpd.nn.initializer.Assign(bias))
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    scale_attr = paddle.ParamAttr(name="scale2", initializer=paddle.nn.initializer.Assign(scale))
+    bias_attr = paddle.ParamAttr(name="bias2", initializer=paddle.nn.initializer.Assign(bias))
 
-    out = pdpd.static.nn.batch_norm(node_x, epsilon=1e-5,
+    out = paddle.static.nn.batch_norm(node_x, epsilon=1e-5,
                                     param_attr=scale_attr,
                                     bias_attr=bias_attr,
                                     moving_mean_name="bn_mean2",
@@ -53,12 +53,12 @@ def batch_norm2(name : str, x, scale, bias, mean, var, data_layout):
                                     use_global_stats=True,
                                     data_layout=data_layout)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
-    pdpd.static.global_scope().var("bn_mean2").get_tensor().set(mean, pdpd.CPUPlace())
-    pdpd.static.global_scope().var("bn_variance2").get_tensor().set(var, pdpd.CPUPlace())
+    exe.run(paddle.static.default_startup_program())
+    paddle.static.global_scope().var("bn_mean2").get_tensor().set(mean, paddle.CPUPlace())
+    paddle.static.global_scope().var("bn_variance2").get_tensor().set(var, paddle.CPUPlace())
 
     outs = exe.run(
         feed={'x': x},
@@ -69,7 +69,7 @@ def batch_norm2(name : str, x, scale, bias, mean, var, data_layout):
     return outs[0]
 
 def main():
-    import paddle as pdpd
+    import paddle
     data = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
     # data layout is NCHW
     scale = np.array([1.0, 1.5]).astype(np.float32)
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_bmm.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_bmm.py
similarity index 62%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_bmm.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_bmm.py
index 92a468db917..016e89634ed 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_bmm.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_bmm.py
@@ -3,19 +3,19 @@ from save_model import saveModel
 import sys
 
 
-def pdpd_bmm(x1, x2):
-    import paddle as pdpd
+def paddle_bmm(x1, x2):
+    import paddle
 
-    pdpd.enable_static()
-    node_x1 = pdpd.static.data(name='x1', shape=x1.shape, dtype=x1.dtype)
-    node_x2 = pdpd.static.data(name='x2', shape=x2.shape, dtype=x2.dtype)
-    bmm_node = pdpd.bmm(node_x1, node_x2)
-    result = pdpd.static.nn.batch_norm(bmm_node, use_global_stats=True)
+    paddle.enable_static()
+    node_x1 = paddle.static.data(name='x1', shape=x1.shape, dtype=x1.dtype)
+    node_x2 = paddle.static.data(name='x2', shape=x2.shape, dtype=x2.dtype)
+    bmm_node = paddle.bmm(node_x1, node_x2)
+    result = paddle.static.nn.batch_norm(bmm_node, use_global_stats=True)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
+    exe.run(paddle.static.default_startup_program())
 
     outs = exe.run(
         feed={'x1': x1, 'x2': x2},
@@ -36,4 +36,4 @@ if __name__ == "__main__":
                         [30., 31., 32., 33., 34.,]]]).astype(np.float32)
 
     input2 = np.ones([1, 5, 7]).astype('float32')
-    pdpd_result = pdpd_bmm(input1, input2)
+    paddle_result = paddle_bmm(input1, input2)
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_clip.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_clip.py
similarity index 57%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_clip.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_clip.py
index 55edd6c62dd..64868b5f95a 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_clip.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_clip.py
@@ -6,17 +6,17 @@ from save_model import saveModel
 import sys
 
 def clip(name: str, x, min, max):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-        out = pdpd.fluid.layers.clip(node_x, min=min, max=max)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+        out = paddle.fluid.layers.clip(node_x, min=min, max=max)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d.py
similarity index 88%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d.py
index b3dc2c4aab2..7b7deebc09a 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d.py
@@ -17,6 +17,6 @@ exe = fluid.Executor(fluid.CPUPlace())
 exe.run(fluid.default_startup_program())
 inp_dict = {'x': inp_blob}
 var = [test_layer]
-res_pdpd = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
+res_paddle = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
 
 fluid.io.save_inference_model(os.path.join(sys.argv[1], "conv2d"), list(inp_dict.keys()), var, exe)
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_combinations.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_combinations.py
similarity index 87%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_combinations.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_combinations.py
index e54643decfe..348ed4dd97f 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_combinations.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_combinations.py
@@ -1,31 +1,31 @@
 from save_model import saveModel
 import numpy as np
-import paddle as pdpd
+import paddle
 import sys
-pdpd.enable_static()
+paddle.enable_static()
 
 
 def run_and_save_model(input_x, name, feed, fetch_list, main_prog, start_prog):
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     exe.run(start_prog)
     outs = exe.run(
         feed={'x': input_x},
         fetch_list=fetch_list,
         program=main_prog)
 
-    with pdpd.static.program_guard(main_prog, start_prog):
+    with paddle.static.program_guard(main_prog, start_prog):
         saveModel(name, exe, feedkeys=['x'], fetchlist=fetch_list, inputs=[input_x],
                   outputs=[outs[0]], target_dir=sys.argv[1])
 
 
-def pdpd_conv2d(input_x, name, input_shape, kernel, dilation, padding, stride, groups=1, use_cudnn=True):
-    main_program = pdpd.static.Program()
-    startup_program = pdpd.static.Program()
-    with pdpd.static.program_guard(main_program, startup_program):
-        data = pdpd.static.data(name='x', shape=input_shape, dtype='float32')
-        weight_attr = pdpd.ParamAttr(name="conv2d_weight", initializer=pdpd.nn.initializer.Assign(kernel))
-        conv2d = pdpd.static.nn.conv2d(input=data, num_filters=kernel.shape[0], filter_size=kernel.shape[2:4],
+def paddle_conv2d(input_x, name, input_shape, kernel, dilation, padding, stride, groups=1, use_cudnn=True):
+    main_program = paddle.static.Program()
+    startup_program = paddle.static.Program()
+    with paddle.static.program_guard(main_program, startup_program):
+        data = paddle.static.data(name='x', shape=input_shape, dtype='float32')
+        weight_attr = paddle.ParamAttr(name="conv2d_weight", initializer=paddle.nn.initializer.Assign(kernel))
+        conv2d = paddle.static.nn.conv2d(input=data, num_filters=kernel.shape[0], filter_size=kernel.shape[2:4],
                                        padding=padding, param_attr=weight_attr, dilation=dilation, stride=stride, groups=groups, use_cudnn=use_cudnn)
     run_and_save_model(input_x, name, data, conv2d, main_program, startup_program)
 
@@ -135,7 +135,7 @@ if __name__ == "__main__":
     ]
     for test in test_cases:
 
-        pdpd_conv2d(test['input_x'], test['name'], test["input_shape"],
+        paddle_conv2d(test['input_x'], test['name'], test["input_shape"],
                     test['kernel'], test['dilation'],
                     test['padding'],
                     test['stride'],
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_relu.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_relu.py
similarity index 90%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_relu.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_relu.py
index 28e818d5d6f..c42b8c143f1 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_relu.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_relu.py
@@ -19,7 +19,7 @@ exe = fluid.Executor(fluid.CPUPlace())
 exe.run(fluid.default_startup_program())
 inp_dict = {'xxx': inp_blob}
 var = [relu]
-res_pdpd = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
+res_paddle = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
 
 fluid.io.save_inference_model(os.path.join(sys.argv[1], "conv2d_relu"), list(inp_dict.keys()), var, exe,
                               model_filename="conv2d_relu.pdmodel", params_filename="conv2d_relu.pdiparams")
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_s.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_s.py
similarity index 90%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_s.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_s.py
index fae73f3ee84..14fe9e24ae4 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_s.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_s.py
@@ -16,7 +16,7 @@ exe = fluid.Executor(fluid.CPUPlace())
 exe.run(fluid.default_startup_program())
 inp_dict = {'x': inp_blob}
 var = [test_layer]
-res_pdpd = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
+res_paddle = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
 
 fluid.io.save_inference_model(os.path.join(sys.argv[1], "conv2d_s"), list(inp_dict.keys()), var, exe,
                               model_filename="conv2d.pdmodel", params_filename="conv2d.pdiparams")
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_transpose.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_transpose.py
similarity index 87%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_transpose.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_transpose.py
index 79608128bc6..de92d9a377e 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_transpose.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_conv2d_transpose.py
@@ -1,30 +1,30 @@
 import numpy as np
-import paddle as pdpd
-pdpd.enable_static()
+import paddle
+paddle.enable_static()
 from save_model import saveModel
 import sys
 
 
 def run_and_save_model(input_x, name, feed, fetch_list, main_prog, start_prog):
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     exe.run(start_prog)
     outs = exe.run(
         feed={'x': input_x},
         fetch_list=fetch_list,
         program=main_prog)
-    with pdpd.static.program_guard(main_prog, start_prog):
+    with paddle.static.program_guard(main_prog, start_prog):
         saveModel(name, exe, feedkeys=['x'], fetchlist=fetch_list, inputs=[input_x],
                   outputs=[outs[0]], target_dir=sys.argv[1])
 
 
-def pdpd_conv2d_transpose(input_x, name, input_shape, kernel, dilation, padding, stride, groups=1, use_cudnn=True):
-    main_program = pdpd.static.Program()
-    startup_program = pdpd.static.Program()
-    with pdpd.static.program_guard(main_program, startup_program):
-        data = pdpd.static.data(name='x', shape=input_shape, dtype='float32')
-        weight_attr = pdpd.ParamAttr(name="conv2d_weight", initializer=pdpd.nn.initializer.Assign(kernel))
-        conv2d = pdpd.static.nn.conv2d_transpose(input=data, num_filters=kernel.shape[0], filter_size=kernel.shape[2:4],
+def paddle_conv2d_transpose(input_x, name, input_shape, kernel, dilation, padding, stride, groups=1, use_cudnn=True):
+    main_program = paddle.static.Program()
+    startup_program = paddle.static.Program()
+    with paddle.static.program_guard(main_program, startup_program):
+        data = paddle.static.data(name='x', shape=input_shape, dtype='float32')
+        weight_attr = paddle.ParamAttr(name="conv2d_weight", initializer=paddle.nn.initializer.Assign(kernel))
+        conv2d = paddle.static.nn.conv2d_transpose(input=data, num_filters=kernel.shape[0], filter_size=kernel.shape[2:4],
                                        padding=padding, param_attr=weight_attr, dilation=dilation, stride=stride, groups=groups, use_cudnn=use_cudnn)
     run_and_save_model(input_x, name, data, conv2d, main_program, startup_program)
 
@@ -134,7 +134,7 @@ if __name__ == "__main__":
     ]
     for test in test_cases:
 
-        pdpd_conv2d_transpose(test['input_x'], test['name'], test["input_shape"],
+        paddle_conv2d_transpose(test['input_x'], test['name'], test["input_shape"],
                     test['kernel'], test['dilation'],
                     test['padding'],
                     test['stride'],
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_cumsum.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_cumsum.py
similarity index 64%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_cumsum.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_cumsum.py
index 6d612477892..b7651152326 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_cumsum.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_cumsum.py
@@ -3,23 +3,23 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def cumsum(name:str, x, axis, dtype=None):
-    pdpd.enable_static()
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        data = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.cumsum(data, axis, dtype=dtype)
-        out = pdpd.cast(out, np.float32)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        data = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.cumsum(data, axis, dtype=dtype)
+        out = paddle.cast(out, np.float32)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_dropout.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_dropout.py
new file mode 100644
index 00000000000..2880eb3c2b0
--- /dev/null
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_dropout.py
@@ -0,0 +1,47 @@
+#
+# pool2d paddle model generator
+#
+import numpy as np
+from save_model import saveModel
+import sys
+
+
+def paddle_dropout(name : str, x, p, paddle_attrs):
+    import paddle
+    paddle.enable_static()
+    
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+        out = paddle.nn.functional.dropout(x=node_x, p=p, training=paddle_attrs['training'], mode=paddle_attrs['mode'])
+
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            feed={'x': x},
+            fetch_list=[out])             
+
+        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x],
+                  outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def main():
+    p=0.5
+    data = np.random.random(size=(3, 10, 3, 7)).astype('float32')
+    paddle_attrs = {
+    'training' : False,
+    'mode' : "downscale_in_infer"
+    }
+    paddle_attrs2 = {
+        'training' : False,
+        'mode' : "upscale_in_train"
+    }
+    paddle_dropout("dropout", data, p, paddle_attrs)
+    paddle_dropout("dropout_upscale_in_train", data, p, paddle_attrs2)
+
+if __name__ == "__main__":
+    main()     
\ No newline at end of file
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dynamic_pool2d.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_dynamic_pool2d.py
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dynamic_pool2d.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_dynamic_pool2d.py
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_elementwise_ops.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_elementwise_ops.py
similarity index 52%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_elementwise_ops.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_elementwise_ops.py
index f1592434631..e3b8e47b83d 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_elementwise_ops.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_elementwise_ops.py
@@ -7,19 +7,19 @@ from save_model import saveModel
 
 
 def elementwise_add(name : str, x, y, axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=in_dtype)
-        node_y = pdpd.static.data(name='y', shape=y.shape, dtype=in_dtype)
-        out = pdpd.fluid.layers.nn.elementwise_add(node_x, node_y, axis=axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=in_dtype)
+        node_y = paddle.static.data(name='y', shape=y.shape, dtype=in_dtype)
+        out = paddle.fluid.layers.nn.elementwise_add(node_x, node_y, axis=axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
         feed={'x': x, 'y': y},
         fetch_list=[out])
@@ -29,19 +29,19 @@ def elementwise_add(name : str, x, y, axis, in_dtype):
 
 
 def elementwise_sub(name : str, x, y, axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=in_dtype)
-        node_y = pdpd.static.data(name='y', shape=y.shape, dtype=in_dtype)
-        out = pdpd.fluid.layers.nn.elementwise_sub(node_x, node_y, axis=axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=in_dtype)
+        node_y = paddle.static.data(name='y', shape=y.shape, dtype=in_dtype)
+        out = paddle.fluid.layers.nn.elementwise_sub(node_x, node_y, axis=axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
         feed={'x': x, 'y': y},
         fetch_list=[out])
@@ -51,19 +51,19 @@ def elementwise_sub(name : str, x, y, axis, in_dtype):
 
 
 def elementwise_div(name : str, x, y, axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
-        node_y = pdpd.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
-        out = pdpd.fluid.layers.nn.elementwise_div(node_x, node_y, axis=axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
+        node_y = paddle.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
+        out = paddle.fluid.layers.nn.elementwise_div(node_x, node_y, axis=axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
         feed={'x': x, 'y': y},
         fetch_list=[out])
@@ -73,19 +73,19 @@ def elementwise_div(name : str, x, y, axis, in_dtype):
 
 
 def elementwise_mul(name : str, x, y, axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
-        node_y = pdpd.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
-        out = pdpd.fluid.layers.nn.elementwise_mul(node_x, node_y, axis=axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
+        node_y = paddle.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
+        out = paddle.fluid.layers.nn.elementwise_mul(node_x, node_y, axis=axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
         feed={'x': x, 'y': y},
         fetch_list=[out])
@@ -95,19 +95,19 @@ def elementwise_mul(name : str, x, y, axis, in_dtype):
 
 
 def elementwise_min(name : str, x, y, axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
-        node_y = pdpd.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
-        out = pdpd.fluid.layers.nn.elementwise_min(node_x, node_y, axis=axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
+        node_y = paddle.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
+        out = paddle.fluid.layers.nn.elementwise_min(node_x, node_y, axis=axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
             feed={'x': x, 'y': y},
             fetch_list=[out])
@@ -117,19 +117,19 @@ def elementwise_min(name : str, x, y, axis, in_dtype):
 
 
 def elementwise_max(name : str, x, y, axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
-        node_y = pdpd.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
-        out = pdpd.fluid.layers.nn.elementwise_max(node_x, node_y, axis=axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
+        node_y = paddle.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
+        out = paddle.fluid.layers.nn.elementwise_max(node_x, node_y, axis=axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
             feed={'x': x, 'y': y},
             fetch_list=[out])
@@ -139,19 +139,19 @@ def elementwise_max(name : str, x, y, axis, in_dtype):
 
 
 def elementwise_pow(name : str, x, y, axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
-        node_y = pdpd.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
-        out = pdpd.fluid.layers.nn.elementwise_pow(node_x, node_y, axis=axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
+        node_y = paddle.static.data(name = 'y', shape = y.shape, dtype = in_dtype)
+        out = paddle.fluid.layers.nn.elementwise_pow(node_x, node_y, axis=axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
             feed={'x': x, 'y': y},
             fetch_list=[out])
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_embedding.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_embedding.py
similarity index 96%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_embedding.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_embedding.py
index d20b2d891d1..09e0b985406 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_embedding.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_embedding.py
@@ -97,10 +97,10 @@ def embedding(name : str, ids, vocab_size, embedding_dim, padding_idx=None, spar
         ng_result = ngraph_embedding(ids, vocab_embeddings, vocab_size, embedding_dim, padding_idx, sparse)
 
         ng_result = list(ng_result.values())[0]
-        pdpd_result = list(outputs.values())[0]
+        paddle_result = list(outputs.values())[0]
     
         match = np.all(np.isclose(
-            pdpd_result, ng_result, rtol=1e-4, atol=1e-5))
+            paddle_result, ng_result, rtol=1e-4, atol=1e-5))
 
         prefix_color = '\n\033[92m' if match else '\n\033[91m'
         print(prefix_color +
@@ -111,11 +111,11 @@ def embedding(name : str, ids, vocab_size, embedding_dim, padding_idx=None, spar
             np.set_printoptions(suppress=True)
 
             print(prefix_color +
-                  'pdpd_result: {}'.format(pdpd_result) + '\033[0m\n')
+                  'paddle_result: {}'.format(paddle_result) + '\033[0m\n')
             print(prefix_color +
                   'ng_result: {}'.format(ng_result) + '\033[0m\n')
 
-            raise ValueError(name + ': OV result does not match PDPD!')
+            raise ValueError(name + ': OV result does not match paddle!')
 
     return outputs
     
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_equal.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_equal.py
similarity index 61%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_equal.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_equal.py
index b311bf89e89..953c3419990 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_equal.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_equal.py
@@ -7,19 +7,19 @@ import sys
 
 
 def equal(name : str, x, y):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    node_y = pdpd.static.data(name='y', shape=y.shape, dtype='float32')
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    node_y = paddle.static.data(name='y', shape=y.shape, dtype='float32')
 
-    out = pdpd.equal(node_x, node_y)
-    out = pdpd.cast(out, np.float32)
+    out = paddle.equal(node_x, node_y)
+    out = paddle.cast(out, np.float32)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
+    exe.run(paddle.static.default_startup_program())
 
     outs = exe.run(
         feed={'x': x, 'y': y},
@@ -32,7 +32,7 @@ def equal(name : str, x, y):
 
 
 def main():
-    import paddle as pdpd
+    import paddle
     data_x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
     data_y = np.array([[[[2, 0, 3]], [[3, 1, 4]]]]).astype(np.float32)
 
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_exp.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_exp.py
similarity index 60%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_exp.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_exp.py
index 69496a2dab2..97e3b22a083 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_exp.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_exp.py
@@ -7,16 +7,16 @@ import sys
 
 
 def exp(name: str, x):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=x.dtype)
-        out = pdpd.fluid.layers.exp(x=node_x)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=x.dtype)
+        out = paddle.fluid.layers.exp(x=node_x)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_expand_v2.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_expand_v2.py
similarity index 54%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_expand_v2.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_expand_v2.py
index ab5aacaacec..b1afcecb80e 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_expand_v2.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_expand_v2.py
@@ -3,23 +3,23 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 
 def expand_v2(name:str, x, shape:list):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.expand(node_x, shape=shape, name='expand_v2')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.expand(node_x, shape=shape, name='expand_v2')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -32,22 +32,22 @@ def expand_v2(name:str, x, shape:list):
 
 
 def expand_v2_tensor(name:str, x, out_shape, use_tensor_in_list):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
         if use_tensor_in_list:
-            out_shape[0] = pdpd.assign(np.array((out_shape[0],)).astype('int32'))
-            out = pdpd.expand(node_x, shape=out_shape, name='expand_v2')
+            out_shape[0] = paddle.assign(np.array((out_shape[0],)).astype('int32'))
+            out = paddle.expand(node_x, shape=out_shape, name='expand_v2')
         else:
             out_shape = np.array(out_shape).astype('int32')
-            node_shape = pdpd.assign(out_shape, output=None)
-            out = pdpd.expand(node_x, shape=node_shape, name='expand_v2')
+            node_shape = paddle.assign(out_shape, output=None)
+            out = paddle.expand(node_x, shape=node_shape, name='expand_v2')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_any_like.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_any_like.py
similarity index 66%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_any_like.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_any_like.py
index 9d74dfcdb72..a9daebd745b 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_any_like.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_any_like.py
@@ -3,23 +3,23 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def fill_any_like(name:str, x, value, dtype=None):
-    pdpd.enable_static()
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        data = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.full_like(data, value, dtype=dtype)
-        out = pdpd.cast(out, np.float32)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        data = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.full_like(data, value, dtype=dtype)
+        out = paddle.cast(out, np.float32)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_constant.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_constant.py
new file mode 100644
index 00000000000..e8afc96c48c
--- /dev/null
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_constant.py
@@ -0,0 +1,97 @@
+#
+# fill_const paddle model generator
+#
+import numpy as np
+from save_model import saveModel
+import paddle
+import sys
+
+
+def fill_constant(name : str, shape : list, dtype, value):
+    paddle.enable_static()
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        x1 = paddle.fluid.layers.fill_constant(shape=shape, value=value, dtype=dtype, name='fill_constant')
+        x2 = paddle.fluid.layers.fill_constant(shape=shape, value=value, dtype=dtype, name='fill_constant')
+        out = paddle.add(paddle.cast(x1, np.float32), paddle.cast(x2, np.float32))
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            fetch_list=[out])             
+
+        saveModel(name, exe, feedkeys=[], fetchlist=[out], inputs=[], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def fill_constant_tensor(name : str, shape : list, dtype, value):
+    paddle.enable_static()
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_value = paddle.static.data(name='value', shape=[1], dtype=dtype)
+        x1 = paddle.fluid.layers.fill_constant(shape=shape, value=node_value, dtype=dtype, name='fill_constant1')
+        out = paddle.cast(x1, np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            feed={"value": value},
+            fetch_list=[out])
+
+        saveModel(name, exe, feedkeys=["value"], fetchlist=[out], inputs=[np.array([value]).astype(dtype)], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def fill_constant_shape_tensor(name : str, shape, dtype, value):
+    paddle.enable_static()
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_shape = paddle.fluid.layers.fill_constant(shape=[2], value=shape, dtype='int32', name='shape')
+        x1 = paddle.fluid.layers.fill_constant(shape=node_shape, value=value, dtype=dtype, name='fill_constant')
+        out = paddle.cast(x1, np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            fetch_list=[out])
+
+        saveModel(name, exe, feedkeys=[], fetchlist=[out], inputs=[], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def fill_constant_shape_tensor_list(name : str, shape: list, dtype, value):
+    paddle.enable_static()
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_shape = paddle.fluid.layers.fill_constant(shape=[1], value=shape, dtype='int32', name='shape')
+        x1 = paddle.fluid.layers.fill_constant(shape=[2, node_shape], value=value, dtype=dtype, name='fill_constant')
+        out = paddle.cast(x1, np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            fetch_list=[out])
+
+        saveModel(name, exe, feedkeys=[], fetchlist=[out], inputs=[], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def main():
+    fill_constant("fill_constant", [2, 3, 4], 'float32', 0.03)
+    fill_constant("fill_constant_int32", [2, 3, 4], "int32", 2)
+    fill_constant("fill_constant_int64", [2, 3, 4], "int64", 4)
+    fill_constant_tensor("fill_constant_tensor", [2, 3, 4], 'float32', 0.05)
+    fill_constant_shape_tensor("fill_constant_shape_tensor", 2, 'float32', 0.05)
+    fill_constant_shape_tensor_list("fill_constant_shape_tensor_list", 2, 'float32', 0.05)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_constant_batch_size_like.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_constant_batch_size_like.py
similarity index 65%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_constant_batch_size_like.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_constant_batch_size_like.py
index 25bde96ad59..e80b5106d3e 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_constant_batch_size_like.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_fill_constant_batch_size_like.py
@@ -3,24 +3,24 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def fill_constant_batch_size_like(name : str, x, shape, dtype, value, input_dim_idx=0, output_dim_idx=0):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        like = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.fluid.layers.fill_constant_batch_size_like(input=like, shape=shape, \
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        like = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.fluid.layers.fill_constant_batch_size_like(input=like, shape=shape, \
             value=value, dtype=dtype, \
             output_dim_idx=output_dim_idx, input_dim_idx=input_dim_idx)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_flatten_contiguous_range.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_flatten_contiguous_range.py
similarity index 65%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_flatten_contiguous_range.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_flatten_contiguous_range.py
index 5d6274587f5..ba7488a3e62 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_flatten_contiguous_range.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_flatten_contiguous_range.py
@@ -6,18 +6,18 @@ from save_model import saveModel
 import sys
 
 def generate_flatten_contiguous_range(name : str, x, start_axis, stop_axis, in_dtype):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
-        out = pdpd.flatten(node_x, start_axis, stop_axis)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
+        out = paddle.flatten(node_x, start_axis, stop_axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
         feed={'x': x},
         fetch_list=[out])
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_gelu.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_gelu.py
similarity index 58%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_gelu.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_gelu.py
index 3ac50226ed6..211dbd33765 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_gelu.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_gelu.py
@@ -3,22 +3,22 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def gelu(name:str, x, approximate=False):
-    pdpd.enable_static()
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        data = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.fluid.layers.gelu(data, approximate=approximate)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        data = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.fluid.layers.gelu(data, approximate=approximate)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_greater_equal.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_greater_equal.py
similarity index 68%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_greater_equal.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_greater_equal.py
index 506e9ccb214..8b73fb0ec8b 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_greater_equal.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_greater_equal.py
@@ -3,27 +3,27 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
 def greater_equal(name : str, x, y, data_type, cast_to_fp32=False):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='input_x', shape=x.shape, dtype=data_type)
-        node_y = pdpd.static.data(name='input_y', shape=y.shape, dtype=data_type)
-        out = pdpd.fluid.layers.greater_equal(x=node_x, y=node_y, name='greater_equal')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='input_x', shape=x.shape, dtype=data_type)
+        node_y = paddle.static.data(name='input_y', shape=y.shape, dtype=data_type)
+        out = paddle.fluid.layers.greater_equal(x=node_x, y=node_y, name='greater_equal')
         # FuzzyTest framework doesn't support boolean so cast to fp32/int32
 
         if cast_to_fp32:
             data_type = "float32"
 
-        out = pdpd.cast(out, data_type)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        out = paddle.cast(out, data_type)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'input_x': x, 'input_y': y},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_hard_sigmoid.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_hard_sigmoid.py
similarity index 61%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_hard_sigmoid.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_hard_sigmoid.py
index 2a9d8e55842..e7691f50e38 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_hard_sigmoid.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_hard_sigmoid.py
@@ -3,21 +3,21 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
 def hard_sigmoid(name: str, x, slope: float = 0.2, offset: float = 0.5, data_type='float32'):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.fluid.layers.hard_sigmoid(node_x, slope=slope, offset=offset, name='hard_sigmoid')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.fluid.layers.hard_sigmoid(node_x, slope=slope, offset=offset, name='hard_sigmoid')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_hard_swish.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_hard_swish.py
similarity index 60%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_hard_swish.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_hard_swish.py
index bc1bec52f5b..329abb07346 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_hard_swish.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_hard_swish.py
@@ -3,21 +3,21 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
 def hard_swish(name: str, x, threshold=6.0, scale=6.0, offset=3.0, data_type='float32'):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.hard_swish(node_x, threshold=threshold, scale=scale, offset=offset, name='hard_swish')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.hard_swish(node_x, threshold=threshold, scale=scale, offset=offset, name='hard_swish')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_interpolate.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_interpolate.py
similarity index 74%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_interpolate.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_interpolate.py
index 3a2fc9e042b..2a3cd62b82e 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_interpolate.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_interpolate.py
@@ -1,38 +1,38 @@
 import numpy as np
-import paddle as pdpd
+import paddle
 from paddle.nn.functional import interpolate
 from save_model import saveModel
 import sys
-pdpd.enable_static()
+paddle.enable_static()
 
 
 def run_and_save_model(input_x, name, feed, fetch_list, main_prog, start_prog):
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     exe.run(start_prog)
     outs = exe.run(
         feed={'x': input_x},
         fetch_list=fetch_list,
         program=main_prog)
 
-    with pdpd.static.program_guard(main_prog, start_prog):
+    with paddle.static.program_guard(main_prog, start_prog):
         saveModel(name, exe, feedkeys=['x'], fetchlist=fetch_list, inputs=[input_x],
                   outputs=[outs[0]], target_dir=sys.argv[1])
 
     return outs
 
 
-def pdpd_interpolate(x, sizes=None, scale_factor=None, mode='nearest', align_corners=True,
+def paddle_interpolate(x, sizes=None, scale_factor=None, mode='nearest', align_corners=True,
                      align_mode=0, data_format='NCHW', name=None):
-    pdpd.enable_static()
-    main_program = pdpd.static.Program()
-    startup_program = pdpd.static.Program()
-    with pdpd.static.program_guard(main_program, startup_program):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
+    paddle.enable_static()
+    main_program = paddle.static.Program()
+    startup_program = paddle.static.Program()
+    with paddle.static.program_guard(main_program, startup_program):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
         interp = interpolate(node_x, size=sizes, scale_factor=scale_factor,
                              mode=mode, align_corners=align_corners, align_mode=align_mode,
                              data_format=data_format, name=name)
-        out = pdpd.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
+        out = paddle.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
     outs = run_and_save_model(x, name, node_x, out, main_program, startup_program)
     return outs[0]
 
@@ -50,7 +50,7 @@ def resize_upsample_bilinear():
                  {'name': 'bilinear_upsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, [64, 64], None, mode='bilinear', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, [64, 64], None, mode='bilinear', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 
@@ -67,7 +67,7 @@ def resize_downsample_bilinear():
                  {'name': 'bilinear_downsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data_28, [2, 4], None, mode='bilinear', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data_28, [2, 4], None, mode='bilinear', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 def resize_upsample_nearest():
@@ -84,7 +84,7 @@ def resize_upsample_nearest():
     ]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, test['size'], None, mode='nearest', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, test['size'], None, mode='nearest', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 
@@ -97,7 +97,7 @@ def resize_downsample_nearest():
     ]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data_64, test['size'], None, mode='nearest', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data_64, test['size'], None, mode='nearest', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 
@@ -109,20 +109,20 @@ def nearest_upsample_tensor_size():
         [13, 14, 15, 16]
     ]]], dtype=np.float32)
     sizes = np.array([8, 8], dtype=np.int32)
-    pdpd.enable_static()
+    paddle.enable_static()
     test_case = [{'name': 'nearest_upsample_tensor_size', 'align_corners': False, 'align_mode': 0}]
     for test in test_case:
-        main_program = pdpd.static.Program()
-        startup_program = pdpd.static.Program()
-        with pdpd.static.program_guard(main_program, startup_program):
-            node_x = pdpd.static.data(name='x', shape=data.shape, dtype='float32')
-            node_sizes = pdpd.static.data(name='sizes', shape=sizes.shape, dtype='int32')
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            node_x = paddle.static.data(name='x', shape=data.shape, dtype='float32')
+            node_sizes = paddle.static.data(name='sizes', shape=sizes.shape, dtype='int32')
             interp = interpolate(node_x, size=node_sizes, scale_factor=None,
                                  mode='nearest', align_corners=test['align_corners'], align_mode=test['align_mode'],
                                  data_format='NCHW', name=test['name'])
-            out = pdpd.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
-            cpu = pdpd.static.cpu_places(1)
-            exe = pdpd.static.Executor(cpu[0])
+            out = paddle.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
+            cpu = paddle.static.cpu_places(1)
+            exe = paddle.static.Executor(cpu[0])
             exe.run(startup_program)
             outs = exe.run(
                 feed={'x': data, 'sizes': sizes},
@@ -143,17 +143,17 @@ def bilinear_upsample_tensor_size():
     test_case = [{'name': 'bilinear_upsample_tensor_size', 'align_corners': False, 'align_mode': 1}]
 
     for test in test_case:
-        main_program = pdpd.static.Program()
-        startup_program = pdpd.static.Program()
-        with pdpd.static.program_guard(main_program, startup_program):
-            node_x = pdpd.static.data(name='x', shape=data.shape, dtype='float32')
-            node_sizes = pdpd.static.data(name='sizes', shape=sizes.shape, dtype='int32')
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            node_x = paddle.static.data(name='x', shape=data.shape, dtype='float32')
+            node_sizes = paddle.static.data(name='sizes', shape=sizes.shape, dtype='int32')
             interp = interpolate(node_x, size=node_sizes, scale_factor=None,
                                  mode='bilinear', align_corners=test['align_corners'], align_mode=test['align_mode'],
                                  data_format='NCHW', name=test['name'])
-            out = pdpd.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
-            cpu = pdpd.static.cpu_places(1)
-            exe = pdpd.static.Executor(cpu[0])
+            out = paddle.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
+            cpu = paddle.static.cpu_places(1)
+            exe = paddle.static.Executor(cpu[0])
             exe.run(startup_program)
             outs = exe.run(
                 feed={'x': data, 'sizes': sizes},
@@ -174,7 +174,7 @@ def bilinear_upsample_scales():
                  {'name': 'bilinear_upsample_scales2', 'align_corners': False, 'align_mode': 1, "scales": [2, 2]}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, None, 2, mode='bilinear', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, None, 2, mode='bilinear', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 # trilinear
@@ -196,7 +196,7 @@ def resize_upsample_trilinear():
                  {'name': 'trilinear_upsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, [4, 64, 64], None, mode='TRILINEAR', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, [4, 64, 64], None, mode='TRILINEAR', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCDHW', name=test['name'])
 
 
@@ -218,7 +218,7 @@ def resize_downsample_trilinear():
                  {'name': 'trilinear_downsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data_28, [2, 2, 4], None, mode='TRILINEAR', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data_28, [2, 2, 4], None, mode='TRILINEAR', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCDHW', name=test['name'])
 
 def trilinear_upsample_tensor_size():
@@ -233,17 +233,17 @@ def trilinear_upsample_tensor_size():
     test_case = [{'name': 'trilinear_upsample_tensor_size', 'align_corners': False, 'align_mode': 1}]
 
     for test in test_case:
-        main_program = pdpd.static.Program()
-        startup_program = pdpd.static.Program()
-        with pdpd.static.program_guard(main_program, startup_program):
-            node_x = pdpd.static.data(name='x', shape=data.shape, dtype='float32')
-            node_sizes = pdpd.static.data(name='sizes', shape=sizes.shape, dtype='int32')
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            node_x = paddle.static.data(name='x', shape=data.shape, dtype='float32')
+            node_sizes = paddle.static.data(name='sizes', shape=sizes.shape, dtype='int32')
             interp = interpolate(node_x, size=node_sizes, scale_factor=None,
                                  mode='TRILINEAR', align_corners=test['align_corners'], align_mode=test['align_mode'],
                                  data_format='NCDHW', name=test['name'])
-            out = pdpd.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
-            cpu = pdpd.static.cpu_places(1)
-            exe = pdpd.static.Executor(cpu[0])
+            out = paddle.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
+            cpu = paddle.static.cpu_places(1)
+            exe = paddle.static.Executor(cpu[0])
             exe.run(startup_program)
             outs = exe.run(
                 feed={'x': data, 'sizes': sizes},
@@ -263,7 +263,7 @@ def trilinear_upsample_scales():
                  {'name': 'trilinear_upsample_scales2', 'align_corners': False, 'align_mode': 1, "scales": [1, 2, 2]}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, None, 3, mode='TRILINEAR', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, None, 3, mode='TRILINEAR', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCDHW', name=test['name'])
 
 
@@ -280,7 +280,7 @@ def resize_upsample_bicubic():
                  {'name': 'bicubic_upsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, [6, 6], None, mode='bicubic', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, [6, 6], None, mode='bicubic', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 
@@ -297,7 +297,7 @@ def resize_downsample_bicubic():
                  {'name': 'bicubic_downsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data_28, [2, 4], None, mode='bicubic', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data_28, [2, 4], None, mode='bicubic', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 def bicubic_upsample_tensor_size():
@@ -312,17 +312,17 @@ def bicubic_upsample_tensor_size():
     test_case = [{'name': 'bicubic_upsample_tensor_size', 'align_corners': False, 'align_mode': 1}]
 
     for test in test_case:
-        main_program = pdpd.static.Program()
-        startup_program = pdpd.static.Program()
-        with pdpd.static.program_guard(main_program, startup_program):
-            node_x = pdpd.static.data(name='x', shape=data.shape, dtype='float32')
-            node_sizes = pdpd.static.data(name='sizes', shape=sizes.shape, dtype='int32')
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            node_x = paddle.static.data(name='x', shape=data.shape, dtype='float32')
+            node_sizes = paddle.static.data(name='sizes', shape=sizes.shape, dtype='int32')
             interp = interpolate(node_x, size=node_sizes, scale_factor=None,
                                  mode='bicubic', align_corners=test['align_corners'], align_mode=test['align_mode'],
                                  data_format='NCHW', name=test['name'])
-            out = pdpd.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
-            cpu = pdpd.static.cpu_places(1)
-            exe = pdpd.static.Executor(cpu[0])
+            out = paddle.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
+            cpu = paddle.static.cpu_places(1)
+            exe = paddle.static.Executor(cpu[0])
             exe.run(startup_program)
             outs = exe.run(
                 feed={'x': data, 'sizes': sizes},
@@ -342,7 +342,7 @@ def bicubic_upsample_scales():
                  {'name': 'bicubic_upsample_scales2', 'align_corners': False, 'align_mode': 1, "scales": [2, 2]}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, None, 2, mode='bicubic', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, None, 2, mode='bicubic', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCHW', name=test['name'])
 
 # linear
@@ -356,7 +356,7 @@ def resize_upsample_linear():
                  {'name': 'linear_upsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, [6,], None, mode='linear', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, [6,], None, mode='linear', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCW', name=test['name'])
 
 
@@ -371,7 +371,7 @@ def resize_downsample_linear():
                  {'name': 'linear_downsample_true_0', 'align_corners': True, 'align_mode': 0}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data_28, [4,], None, mode='linear', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data_28, [4,], None, mode='linear', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCW', name=test['name'])
 
 def linear_upsample_tensor_size():
@@ -383,17 +383,17 @@ def linear_upsample_tensor_size():
     test_case = [{'name': 'linear_upsample_tensor_size', 'align_corners': False, 'align_mode': 1}]
 
     for test in test_case:
-        main_program = pdpd.static.Program()
-        startup_program = pdpd.static.Program()
-        with pdpd.static.program_guard(main_program, startup_program):
-            node_x = pdpd.static.data(name='x', shape=data.shape, dtype='float32')
-            node_sizes = pdpd.static.data(name='sizes', shape=sizes.shape, dtype='int32')
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            node_x = paddle.static.data(name='x', shape=data.shape, dtype='float32')
+            node_sizes = paddle.static.data(name='sizes', shape=sizes.shape, dtype='int32')
             interp = interpolate(node_x, size=node_sizes, scale_factor=None,
                                  mode='linear', align_corners=test['align_corners'], align_mode=test['align_mode'],
                                  data_format='NCW', name=test['name'])
-            out = pdpd.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
-            cpu = pdpd.static.cpu_places(1)
-            exe = pdpd.static.Executor(cpu[0])
+            out = paddle.static.nn.batch_norm(interp, use_global_stats=True, epsilon=0)
+            cpu = paddle.static.cpu_places(1)
+            exe = paddle.static.Executor(cpu[0])
             exe.run(startup_program)
             outs = exe.run(
                 feed={'x': data, 'sizes': sizes},
@@ -410,7 +410,7 @@ def linear_upsample_scales():
                  {'name': 'linear_upsample_scales2', 'align_corners': False, 'align_mode': 1, "scales": [2, 2]}]
 
     for test in test_case:
-        pdpd_result = pdpd_interpolate(data, None, 2, mode='linear', align_corners=test['align_corners'],
+        paddle_result = paddle_interpolate(data, None, 2, mode='linear', align_corners=test['align_corners'],
                                        align_mode=test['align_mode'], data_format='NCW', name=test['name'])
 
 if __name__ == "__main__":
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_layer_norm.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_layer_norm.py
similarity index 66%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_layer_norm.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_layer_norm.py
index 5cd60061aad..d9e8a28a334 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_layer_norm.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_layer_norm.py
@@ -4,23 +4,23 @@
 import numpy as np
 from paddle.fluid import param_attr
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def layer_norm(name:str, x, begin_norm_axis, scale=True, shift=True, param_attr=None, bias_attr=None):
-    pdpd.enable_static()
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        data = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.static.nn.layer_norm(input=data, scale=scale, shift=shift,\
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        data = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.static.nn.layer_norm(input=data, scale=scale, shift=shift,\
             begin_norm_axis=begin_norm_axis, param_attr=param_attr, bias_attr=bias_attr)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -33,8 +33,8 @@ def layer_norm(name:str, x, begin_norm_axis, scale=True, shift=True, param_attr=
 def main():
     x = np.random.rand(8, 24, 32).astype(data_type)
     random_data = np.random.rand(24 * 32).astype(data_type)
-    attr = pdpd.ParamAttr(
-              initializer=pdpd.fluid.initializer.NumpyArrayInitializer(random_data))
+    attr = paddle.ParamAttr(
+              initializer=paddle.fluid.initializer.NumpyArrayInitializer(random_data))
     layer_norm("layer_norm", x, begin_norm_axis=1, param_attr=attr, bias_attr=attr)
     layer_norm("layer_norm_noscale", x, scale=False, begin_norm_axis=2)
     layer_norm("layer_norm_noshift", x, shift=False, begin_norm_axis=1)
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_leaky_relu.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_leaky_relu.py
similarity index 60%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_leaky_relu.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_leaky_relu.py
index bc2a54741d7..c9da226a38b 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_leaky_relu.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_leaky_relu.py
@@ -3,21 +3,21 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
 def leaky_relu(name: str, x, alpha: float = 0.02, data_type='float32'):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.fluid.layers.leaky_relu(node_x, alpha=alpha, name='leaky_relu')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.fluid.layers.leaky_relu(node_x, alpha=alpha, name='leaky_relu')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_log.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_log.py
similarity index 60%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_log.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_log.py
index 6e269e03ed5..a5909e1739c 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_log.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_log.py
@@ -3,21 +3,21 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
 def log(name: str, x, data_type='float32'):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.log(node_x, name='log')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.log(node_x, name='log')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_logical_not.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_logical_not.py
similarity index 60%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_logical_not.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_logical_not.py
index 6e638738134..74dd66b6aa3 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_logical_not.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_logical_not.py
@@ -7,20 +7,20 @@ import sys
 
 
 def equal_logical_not(name : str, x, y):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    node_y = pdpd.static.data(name='y', shape=y.shape, dtype='float32')
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    node_y = paddle.static.data(name='y', shape=y.shape, dtype='float32')
 
-    out = pdpd.equal(node_x, node_y)
-    out = pdpd.logical_not(out)
-    out = pdpd.cast(out, np.float32)
+    out = paddle.equal(node_x, node_y)
+    out = paddle.logical_not(out)
+    out = paddle.cast(out, np.float32)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
+    exe.run(paddle.static.default_startup_program())
 
     outs = exe.run(
         feed={'x': x, 'y': y},
@@ -33,7 +33,7 @@ def equal_logical_not(name : str, x, y):
 
 
 def main():
-    import paddle as pdpd
+    import paddle
     data_x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
     data_y = np.array([[[[2, 0, 3]], [[3, 1, 4]]]]).astype(np.float32)
 
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_lower_version.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_lower_version.py
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_lower_version.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_lower_version.py
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_matmul_v2.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_matmul_v2.py
similarity index 74%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_matmul_v2.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_matmul_v2.py
index 08a5eab50dd..dee36612a8b 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_matmul_v2.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_matmul_v2.py
@@ -3,19 +3,19 @@ from save_model import saveModel
 import sys
 
 def matmul(name, x1, x2, x_transpose=False, y_transpose=False):
-    import paddle as pdpd
+    import paddle
 
-    pdpd.enable_static()
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x1 = pdpd.static.data(name='x1', shape=x1.shape, dtype=x1.dtype)
-        node_x2 = pdpd.static.data(name='x2', shape=x2.shape, dtype=x2.dtype)
-        result = pdpd.matmul(node_x1, node_x2, x_transpose, y_transpose)
-        #result = pdpd.static.nn.batch_norm(mul_node, use_global_stats=True)
+    paddle.enable_static()
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x1 = paddle.static.data(name='x1', shape=x1.shape, dtype=x1.dtype)
+        node_x2 = paddle.static.data(name='x2', shape=x2.shape, dtype=x2.dtype)
+        result = paddle.matmul(node_x1, node_x2, x_transpose, y_transpose)
+        #result = paddle.static.nn.batch_norm(mul_node, use_global_stats=True)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x1': x1, 'x2': x2},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_mul.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_mul.py
similarity index 51%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_mul.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_mul.py
index 02ce1d6d9ee..fa9efa7f251 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_mul.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_mul.py
@@ -2,20 +2,20 @@ import numpy as np
 from save_model import saveModel
 import sys
 
-def pdpd_matmul(name, x1, x2, x_transpose=False, y_transpose=False):
-    import paddle as pdpd
+def paddle_matmul(name, x1, x2, x_transpose=False, y_transpose=False):
+    import paddle
 
-    pdpd.enable_static()
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x1 = pdpd.static.data(name='x1', shape=x1.shape, dtype=x1.dtype)
-        node_x2 = pdpd.static.data(name='x2', shape=x2.shape, dtype=x2.dtype)
-        mul_node = pdpd.fluid.layers.matmul(node_x1, node_x2, x_transpose, y_transpose)
-        result = pdpd.static.nn.batch_norm(mul_node, use_global_stats=True)
+    paddle.enable_static()
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x1 = paddle.static.data(name='x1', shape=x1.shape, dtype=x1.dtype)
+        node_x2 = paddle.static.data(name='x2', shape=x2.shape, dtype=x2.dtype)
+        mul_node = paddle.fluid.layers.matmul(node_x1, node_x2, x_transpose, y_transpose)
+        result = paddle.static.nn.batch_norm(mul_node, use_global_stats=True)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x1': x1, 'x2': x2},
@@ -44,6 +44,6 @@ if __name__ == "__main__":
     input_2x3 = np.array([[1, 2, 3],
                           [4, 5, 6]]).astype(np.float32)
 
-    pdpd_matmul("matmul_xt", input_2x5, input_2x3, x_transpose=True, y_transpose=False)
-    pdpd_matmul("matmul_yt", input_2x3, input_5x3, x_transpose=False, y_transpose=True)
-    pdpd_matmul("matmul_xt_yt", input_2x5, input_5x2, x_transpose=True, y_transpose=True)
+    paddle_matmul("matmul_xt", input_2x5, input_2x3, x_transpose=True, y_transpose=False)
+    paddle_matmul("matmul_yt", input_2x3, input_5x3, x_transpose=False, y_transpose=True)
+    paddle_matmul("matmul_xt_yt", input_2x5, input_5x2, x_transpose=True, y_transpose=True)
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_multi_tensor_split.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_multi_tensor_split.py
similarity index 90%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_multi_tensor_split.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_multi_tensor_split.py
index 231dedde46e..f8ce53f6390 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_multi_tensor_split.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_multi_tensor_split.py
@@ -12,7 +12,7 @@ import os
 def create_multi_output_model():
     paddle.enable_static()
 
-    # PDPD model creation and inference
+    # paddle model creation and inference
     num_splits = 20
     inp_blob_1 = np.random.randn(2, num_splits, 4, 4).astype(np.float32)
 
@@ -27,7 +27,7 @@ def create_multi_output_model():
     exe = fluid.Executor(fluid.CPUPlace())
     exe.run(fluid.default_startup_program())
     inp_dict = {'x': inp_blob_1}
-    res_pdpd = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
+    res_paddle = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
 
     fluid.io.save_inference_model(os.path.join(sys.argv[1], "multi_tensor_split"),
                                   list(inp_dict.keys()), var, exe,
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pad3d.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pad3d.py
similarity index 78%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pad3d.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pad3d.py
index 5a711f6f83d..e13b797e89c 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pad3d.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pad3d.py
@@ -6,24 +6,24 @@ from save_model import saveModel
 import sys
 
 def pad3d(name : str, x, in_dtype, pad, data_format, mode, value = 0):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name = 'x', shape = x.shape, dtype = in_dtype)
 
         if mode == 'constant':
-            pad_constant = pdpd.nn.Pad3D(padding=pad, mode=mode, value=value, data_format=data_format)
+            pad_constant = paddle.nn.Pad3D(padding=pad, mode=mode, value=value, data_format=data_format)
             out = pad_constant(node_x)
         else:
-            pad_other_mode = pdpd.nn.Pad3D(padding=pad, mode=mode, data_format=data_format)
+            pad_other_mode = paddle.nn.Pad3D(padding=pad, mode=mode, data_format=data_format)
             out = pad_other_mode(node_x)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
 
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
         outs = exe.run(
         feed={'x': x},
         fetch_list=[out])
diff --git a/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_place_test_model.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_place_test_model.py
new file mode 100644
index 00000000000..072d21c2e2e
--- /dev/null
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_place_test_model.py
@@ -0,0 +1,53 @@
+import numpy as np
+from save_model import saveModel
+import sys
+
+
+def paddle_rnn_lstm(input_size, hidden_size, layers, direction):
+    import paddle
+    paddle.enable_static()
+    main_program = paddle.static.Program()
+    startup_program = paddle.static.Program()
+
+    num_of_directions = 1 if direction == 'forward' else 2
+    with paddle.static.program_guard(main_program, startup_program):
+
+        rnn = paddle.nn.LSTM(input_size, hidden_size, layers, direction, name="lstm")
+
+        data = paddle.static.data(name='x', shape=[4, 3, input_size], dtype='float32')
+        prev_h = paddle.ones(shape=[layers * num_of_directions, 4, hidden_size], dtype=np.float32, name="const_1")
+        prev_c = paddle.ones(shape=[layers * num_of_directions, 4, hidden_size], dtype=np.float32, name="const_2")
+
+        y, (h, c) = rnn(data, (prev_h, prev_c))
+        relu_1 = paddle.nn.functional.relu(c, name="relu_1")
+        relu_2 = paddle.nn.functional.relu(c, name="relu_2")
+        relu_3 = paddle.nn.functional.relu(c, name="relu_3")
+
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        exe.run(startup_program)
+
+        outs = exe.run(
+            feed={'x': np.ones([4, 3, input_size]).astype(np.float32)},
+            fetch_list=[y, h, c],
+            program=main_program)
+        saveModel("place_test_model", exe, feedkeys=['x'],
+                  fetchlist=[y, h, c, relu_1, relu_2, relu_3],
+                  inputs=[np.ones([4, 3, input_size]).astype(np.float32)],
+                  outputs=[outs[0], outs[1], outs[2]], target_dir=sys.argv[1])
+    return outs[0]
+
+
+if __name__ == "__main__":
+
+    testCases = [
+        {
+            'input_size': 2,
+            'hidden_size': 2,
+            'layers': 1,
+            'direction': 'forward',
+        },
+    ]
+
+    for test in testCases:
+        paddle_rnn_lstm(test['input_size'], test['hidden_size'], test['layers'], test['direction'])
\ No newline at end of file
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pool2d.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pool2d.py
similarity index 79%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pool2d.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pool2d.py
index 1f6c32e242b..e1709b1a979 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pool2d.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pool2d.py
@@ -8,12 +8,12 @@ from save_model import saveModel
 data_type = 'float32'
 
 def pool2d(name : str, x, attrs : dict):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.pool2d(node_x,
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.pool2d(node_x,
                                        pool_size=attrs['pool_size'],
                                        pool_type=attrs['pool_type'],
                                        pool_stride=attrs['pool_stride'],
@@ -23,10 +23,10 @@ def pool2d(name : str, x, attrs : dict):
                                        exclusive=attrs['exclusive'],
                                        data_format=attrs['data_format'])
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -37,21 +37,21 @@ def pool2d(name : str, x, attrs : dict):
     return outs[0]
 
 def adaptive_pool2d(name : str, x, attrs : dict):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.adaptive_pool2d(
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.adaptive_pool2d(
             input=node_x,
             pool_size=attrs['pool_size'],
             pool_type=attrs['pool_type'],
             require_index=attrs['require_index'])
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -74,7 +74,7 @@ def main():
     for i, pooling_type in enumerate(pooling_types):
         # example 1:
         # ceil_mode = False
-        pdpd_attrs = {
+        paddle_attrs = {
             # input=data_NCHW, # shape: [2, 3, 8, 8]
             'pool_size' : [3,3],
             'pool_type' : pooling_type,
@@ -86,12 +86,12 @@ def main():
             'data_format' : "NCHW"
         }
         # shape of out_1: [2, 3, 4, 3]
-        pool2d(pooling_type+'Pool_test1', data_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test1', data_NCHW, paddle_attrs)
 
         # Cecilia: there is a bug of PaddlePaddle in this case.
         # example 2:
         # ceil_mode = True (different from example 1)
-        pdpd_attrs = {
+        paddle_attrs = {
             #input=data_NCHW,
             'pool_size':[3,3],
             'pool_type' : pooling_type,
@@ -103,11 +103,11 @@ def main():
             'data_format':"NCHW"
         }
         # shape of out_2: [2, 3, 4, 4] which is different from out_1
-        pool2d(pooling_type+'Pool_test2', data_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test2', data_NCHW, paddle_attrs)
 
         # example 3:
         # pool_padding = "SAME" (different from example 1)
-        pdpd_attrs = {
+        paddle_attrs = {
             #input=data_NCHW,
             'pool_size':[3,3],
             'pool_type' : pooling_type,
@@ -119,11 +119,11 @@ def main():
             'data_format':"NCHW"
         }
         # shape of out_3: [2, 3, 3, 3] which is different from out_1
-        pool2d(pooling_type+'Pool_test3', data_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test3', data_NCHW, paddle_attrs)
 
         # example 4:
         # pool_padding = "VALID" (different from example 1)
-        pdpd_attrs = {
+        paddle_attrs = {
             #input=data_NCHW,
             'pool_size':[3,3],
             'pool_type' : pooling_type,
@@ -135,12 +135,12 @@ def main():
             'data_format':"NCHW"
         }
         # shape of out_4: [2, 3, 2, 2] which is different from out_1
-        pool2d(pooling_type+'Pool_test4', data_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test4', data_NCHW, paddle_attrs)
 
         # example 5:
         # global_pooling = True (different from example 1)
         # It will be set pool_size = [8,8] and pool_padding = [0,0] actually.
-        pdpd_attrs = {
+        paddle_attrs = {
             #input=data_NCHW,
             'pool_size':[3,3],
             'pool_type' : pooling_type,
@@ -152,11 +152,11 @@ def main():
             'data_format':"NCHW"
         }
         # shape of out_5: [2, 3, 1, 1] which is different from out_1
-        pool2d(pooling_type+'Pool_test5', data_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test5', data_NCHW, paddle_attrs)
 
         # example 6:
         # data_format = "NHWC" (different from example 1)
-        pdpd_attrs = {
+        paddle_attrs = {
             #input=data_NHWC, # shape: [2, 8, 8, 3]
             'pool_size':[3,3],
             'pool_type' : pooling_type,
@@ -168,11 +168,11 @@ def main():
             'data_format':"NHWC"
         }
         # shape of out_6: [2, 4, 3, 3] which is different from out_1
-        pool2d(pooling_type+'Pool_test6', data_NHWC, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test6', data_NHWC, paddle_attrs)
 
         # example 7:
         # pool_size is [9, 9]
-        pdpd_attrs = {
+        paddle_attrs = {
             #input=data_NCHW,
             'pool_size':[9,9],
             'pool_type' : pooling_type,
@@ -183,11 +183,11 @@ def main():
             'exclusive':True,
             'data_format':"NCHW"
         }
-        pool2d(pooling_type+'Pool_test7', data_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test7', data_NCHW, paddle_attrs)
 
         # example 8:
         # pool_padding size is 1
-        pdpd_attrs = {
+        paddle_attrs = {
             'pool_size':[3,3],
             'pool_type' : pooling_type,
             'pool_stride' : [3,3],
@@ -197,7 +197,7 @@ def main():
             'exclusive':True,
             'data_format':"NCHW"
         }
-        pool2d(pooling_type+'Pool_test8', data_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test8', data_NCHW, paddle_attrs)
 
         #input data for test9 and test10
         N_data1, C_data1, H_data1, W_data1 = 2, 3, 8, 8
@@ -205,7 +205,7 @@ def main():
         data1_NCHW = data1.reshape(N_data1, C_data1, H_data1, W_data1)
         # example 9:
         # pool_padding size is 4: [pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]
-        pdpd_attrs = {
+        paddle_attrs = {
             'pool_size':[3,3],
             'pool_type' : pooling_type,
             'pool_stride' : [3,3],
@@ -215,11 +215,11 @@ def main():
             'exclusive':True,
             'data_format':"NCHW"
         }
-        pool2d(pooling_type+'Pool_test9', data1_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test9', data1_NCHW, paddle_attrs)
 
         # example 10:
         # input=data_NCHW and pool_padding is [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]
-        pdpd_attrs = {
+        paddle_attrs = {
             'pool_size':[3,3],
             'pool_type' : pooling_type,
             'pool_stride' : [3,3],
@@ -229,11 +229,11 @@ def main():
             'exclusive':True,
             'data_format':"NCHW"
         }
-        pool2d(pooling_type+'Pool_test10', data1_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test10', data1_NCHW, paddle_attrs)
 
         # example 11:
         # input=data_NCHW and poolsize is the multiply by width & height. pool_padding is [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]
-        pdpd_attrs = {
+        paddle_attrs = {
             'pool_size': 9,
             'pool_type' : pooling_type,
             'pool_stride' : [3,3],
@@ -243,17 +243,17 @@ def main():
             'exclusive':True,
             'data_format':"NCHW"
         }
-        pool2d(pooling_type+'Pool_test11', data1_NCHW, pdpd_attrs)
+        pool2d(pooling_type+'Pool_test11', data1_NCHW, paddle_attrs)
 
 
     # adaptive_pool2d
     for i, pooling_type in enumerate(pooling_types):
-        pdpd_attrs = {
+        paddle_attrs = {
             'pool_size': [3,3],
             'pool_type': pooling_type,
             'require_index': False
         }
-        adaptive_pool2d(pooling_type+'AdaptivePool2D_test1', data_NCHW, pdpd_attrs)
+        adaptive_pool2d(pooling_type+'AdaptivePool2D_test1', data_NCHW, paddle_attrs)
 
 
 if __name__ == "__main__":
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pow.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pow.py
similarity index 58%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pow.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pow.py
index 97e1df3ae72..c71d6f6e89c 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_pow.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_pow.py
@@ -3,24 +3,24 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
-def pdpd_pow(name : str, x, y, data_type):
-    pdpd.enable_static()
+def paddle_pow(name : str, x, y, data_type):
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.pow(node_x, y, name='pow')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.pow(node_x, y, name='pow')
         #FuzzyTest supports int32 & float32
         if data_type == "int64":
-            out = pdpd.cast(out, "float32")
-        out = pdpd.cast(out, "float32")
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+            out = paddle.cast(out, "float32")
+        out = paddle.cast(out, "float32")
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -31,19 +31,19 @@ def pdpd_pow(name : str, x, y, data_type):
     return outs[0]
 
 
-def pdpd_pow_tensor(name : str, x, y, data_type):
-    pdpd.enable_static()
+def paddle_pow_tensor(name : str, x, y, data_type):
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        node_y = pdpd.static.data(name='y', shape=y.shape, dtype=data_type)
-        out = pdpd.fluid.layers.pow(node_x, node_y, name='pow')
-        out = pdpd.cast(out, "float32")
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        node_y = paddle.static.data(name='y', shape=y.shape, dtype=data_type)
+        out = paddle.fluid.layers.pow(node_x, node_y, name='pow')
+        out = paddle.cast(out, "float32")
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x, 'y': y},
@@ -84,11 +84,11 @@ def main():
     ]
 
     for test in test_cases:
-        pdpd_pow("pow_" + test['name'], test['x'], test['y'], test['dtype'])
+        paddle_pow("pow_" + test['name'], test['x'], test['y'], test['dtype'])
 
     x = np.array([0, 1, 2, -10]).astype("float32")
     y = np.array([2.0]).astype("float32")
-    pdpd_pow_tensor("pow_y_tensor", x, y, 'float32')
+    paddle_pow_tensor("pow_y_tensor", x, y, 'float32')
 
 
 if __name__ == "__main__":
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_prior_box.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_prior_box.py
similarity index 89%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_prior_box.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_prior_box.py
index 12e9ddd80fe..41541fa17dc 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_prior_box.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_prior_box.py
@@ -7,16 +7,16 @@ import sys
 
 
 def prior_box(name: str, input_data, image_data, attrs: dict):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        Input = pdpd.static.data(
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        Input = paddle.static.data(
             name='Input', shape=input_data.shape, dtype=input_data.dtype)
-        Image = pdpd.static.data(
+        Image = paddle.static.data(
             name='Image', shape=image_data.shape, dtype=image_data.dtype)
 
-        box, var = pdpd.fluid.layers.prior_box(Input,
+        box, var = paddle.fluid.layers.prior_box(Input,
                                                Image,
                                                min_sizes=attrs['min_sizes'],
                                                max_sizes=attrs['max_sizes'],
@@ -29,10 +29,10 @@ def prior_box(name: str, input_data, image_data, attrs: dict):
                                                name=None,
                                                min_max_aspect_ratios_order=attrs['min_max_aspect_ratios_order'])
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'Input': input_data, 'Image': image_data},
@@ -121,4 +121,4 @@ if __name__ == "__main__":
          image_h)).astype('float32')
 
     for item in prior_box_attrs_list:
-        pred_pdpd = prior_box(item['name'], input_data, image_data, item)
+        pred_paddle = prior_box(item['name'], input_data, image_data, item)
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_range.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_range.py
similarity index 52%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_range.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_range.py
index c2d7a0b5715..e6f0bfc37a5 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_range.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_range.py
@@ -6,21 +6,21 @@ from save_model import saveModel
 import sys
 
 
-def pdpd_range(name : str, x, start, end, step, out_type):
-    import paddle as pdpd
-    pdpd.enable_static()
+def paddle_range(name : str, x, start, end, step, out_type):
+    import paddle
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
         # Range op only support fill_constant input, since dynamic op is not supported in ov
-        out = pdpd.fluid.layers.range(start, end, step, out_type)
-        out = pdpd.cast(out, np.float32)
-        out = pdpd.add(node_x, out)
-        #out = pdpd.cast(out, np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        out = paddle.fluid.layers.range(start, end, step, out_type)
+        out = paddle.cast(out, np.float32)
+        out = paddle.add(node_x, out)
+        #out = paddle.cast(out, np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
                 feed={'x': x},
@@ -38,7 +38,7 @@ def main():
     data = np.random.random([1, 5]).astype("float32")
     out_type = ["float32", "int32", "int64"]
     for i, dtype in enumerate(out_type):
-        pdpd_range("range"+str(i), data, start, end, step, dtype)
+        paddle_range("range"+str(i), data, start, end, step, dtype)
 
 
 if __name__ == "__main__":
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_relu.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_relu.py
similarity index 64%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_relu.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_relu.py
index 6952bd27cd8..4fe1045f44a 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_relu.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_relu.py
@@ -7,16 +7,16 @@ import sys
 
 
 def relu(name: str, x):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    out = pdpd.nn.functional.relu(node_x)
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    out = paddle.nn.functional.relu(node_x)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
+    exe.run(paddle.static.default_startup_program())
 
     outs = exe.run(
         feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_relu6.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_relu6.py
similarity index 61%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_relu6.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_relu6.py
index af96e5e690e..7fc646b74de 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_relu6.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_relu6.py
@@ -3,21 +3,21 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
 def relu6(name: str, x, threshold: float = 6.0, data_type='float32'):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.relu6(node_x, threshold=threshold, name='relu6')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.relu6(node_x, threshold=threshold, name='relu6')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_reshape.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_reshape.py
similarity index 55%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_reshape.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_reshape.py
index 3c2a7dd8f67..6b6ad2b9c3a 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_reshape.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_reshape.py
@@ -9,17 +9,17 @@ data_type = 'float32'
 
 
 def reshape(name : str, x, out_shape):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.reshape(x=node_x, shape=out_shape)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.reshape(x=node_x, shape=out_shape)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -32,24 +32,24 @@ def reshape(name : str, x, out_shape):
 
 
 def reshape_tensor(name : str, x, out_shape, use_tensor_in_list):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
         if use_tensor_in_list:
-            out_shape[0] = pdpd.assign(np.array((out_shape[0],)).astype('int32'))
-            out = pdpd.fluid.layers.reshape(x=node_x, shape=out_shape)
+            out_shape[0] = paddle.assign(np.array((out_shape[0],)).astype('int32'))
+            out = paddle.fluid.layers.reshape(x=node_x, shape=out_shape)
         else:
             out_shape = np.array(out_shape).astype('int32')
-            node_shape = pdpd.assign(out_shape)
-            out = pdpd.fluid.layers.reshape(x=node_x, shape=node_shape)
+            node_shape = paddle.assign(out_shape)
+            out = paddle.fluid.layers.reshape(x=node_x, shape=node_shape)
 
-        out = pdpd.pow(out, 1)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        out = paddle.pow(out, 1)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_rnn_lstm.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_rnn_lstm.py
similarity index 80%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_rnn_lstm.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_rnn_lstm.py
index c5e62b75bc2..6bb894b460d 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_rnn_lstm.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_rnn_lstm.py
@@ -3,32 +3,32 @@ from save_model import saveModel
 import sys
 
 
-def pdpd_rnn_lstm(input_size, hidden_size, layers, direction, seq_len):
-    import paddle as pdpd
-    pdpd.enable_static()
-    main_program = pdpd.static.Program()
-    startup_program = pdpd.static.Program()
+def paddle_rnn_lstm(input_size, hidden_size, layers, direction, seq_len):
+    import paddle
+    paddle.enable_static()
+    main_program = paddle.static.Program()
+    startup_program = paddle.static.Program()
 
     num_of_directions = 1 if direction == 'forward' else 2
-    with pdpd.static.program_guard(main_program, startup_program):
+    with paddle.static.program_guard(main_program, startup_program):
 
-        rnn = pdpd.nn.LSTM(input_size, hidden_size, layers, direction)
+        rnn = paddle.nn.LSTM(input_size, hidden_size, layers, direction)
 
-        data = pdpd.static.data(
+        data = paddle.static.data(
             name='x', shape=[4, 3, input_size], dtype='float32')
-        prev_h = pdpd.ones(
+        prev_h = paddle.ones(
             shape=[layers * num_of_directions, 4, hidden_size], dtype=np.float32)
-        prev_c = pdpd.ones(
+        prev_c = paddle.ones(
             shape=[layers * num_of_directions, 4, hidden_size], dtype=np.float32)
 
         if seq_len:
-            seq_lengths = pdpd.static.data(name='sl', shape=[4], dtype='int32')
+            seq_lengths = paddle.static.data(name='sl', shape=[4], dtype='int32')
             y, (h, c) = rnn(data, (prev_h, prev_c), seq_lengths)
         else:
             y, (h, c) = rnn(data, (prev_h, prev_c))
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         exe.run(startup_program)
 
         if seq_len:
@@ -99,5 +99,5 @@ if __name__ == "__main__":
     ]
 
     for test in testCases:
-        pdpd_rnn_lstm(test['input_size'], test['hidden_size'],
+        paddle_rnn_lstm(test['input_size'], test['hidden_size'],
                       test['layers'], test['direction'], test['seq_len'])
diff --git a/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_scale.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_scale.py
new file mode 100644
index 00000000000..7f8303e7d80
--- /dev/null
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_scale.py
@@ -0,0 +1,93 @@
+#
+# pool2d paddle model generator
+#
+import numpy as np
+import sys
+from save_model import saveModel
+
+
+def paddle_scale(name : str, x, scale, bias, attrs : dict, data_type):
+    import paddle
+    paddle.enable_static()
+    
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.scale(x=node_x, scale=scale, bias=bias,
+                         bias_after_scale=attrs['bias_after_scale'])
+        #FuzzyTest only support FP32 now, so cast result to fp32
+        out = paddle.cast(out, "float32")
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            feed={'x': x},
+            fetch_list=[out])             
+
+        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def paddle_scale_tensor(name : str, x, scale, bias, attrs : dict, data_type):
+    import paddle as paddle
+    paddle.enable_static()
+
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        node_scale = paddle.static.data(name='scale', shape=[1], dtype='float32')
+        out = paddle.scale(x=node_x, scale=node_scale, bias=bias,
+                         bias_after_scale=attrs['bias_after_scale'])
+        #FuzzyTest only support FP32 now, so cast result to fp32
+        out = paddle.cast(out, "float32")
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+
+        outs = exe.run(
+            feed={'x': x, 'scale': scale},
+            fetch_list=[out])
+
+        saveModel(name, exe, feedkeys=['x', 'scale'], fetchlist=[out], inputs=[x, np.array([scale]).astype('float32')], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+def main():
+    scale = 2.0
+    bias = 1.0
+    data = np.random.random([2, 3]).astype("float32")
+
+    test_cases = [
+        "float32",
+        "int32",
+        "int64"
+    ]
+
+    paddle_attrs = {
+        'bias_after_scale': True,
+    }
+    paddle_scale_tensor("scale_tensor_bias_after", data, scale, bias, paddle_attrs, 'float32')
+
+    paddle_attrs = {
+        'bias_after_scale': False,
+    }
+    paddle_scale_tensor("scale_tensor_bias_before", data, scale, bias, paddle_attrs, 'float32')
+
+    for test in test_cases:
+        data = np.random.random([2, 3]).astype(test)
+        paddle_attrs = {
+            'bias_after_scale': True,
+        }
+        paddle_scale("scale_bias_after_" + test, data, scale, bias, paddle_attrs, test)
+
+        paddle_attrs = {
+            'bias_after_scale': False,
+        }
+        paddle_scale("scale_bias_before_" + test, data, scale, bias, paddle_attrs, test)
+
+
+
+if __name__ == "__main__":
+    main()     
\ No newline at end of file
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_shape.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_shape.py
similarity index 50%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_shape.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_shape.py
index 35241487bba..e7413760790 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_shape.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_shape.py
@@ -6,18 +6,18 @@ from save_model import saveModel
 import sys
 
 
-def pdpd_shape(name : str, x):
-    import paddle as pdpd
-    pdpd.enable_static()
+def paddle_shape(name : str, x):
+    import paddle
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-        out = pdpd.shape(node_x)
-        out = pdpd.cast(out, np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+        out = paddle.shape(node_x)
+        out = paddle.cast(out, np.float32)
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -31,7 +31,7 @@ def pdpd_shape(name : str, x):
 def main():
 
     data = np.random.random(size=(2, 3)).astype('float32')
-    pdpd_shape("shape", data)
+    paddle_shape("shape", data)
 
 
 if __name__ == "__main__":
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_sigmoid.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_sigmoid.py
similarity index 60%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_sigmoid.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_sigmoid.py
index 815a016907d..e94d8ed40ef 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_sigmoid.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_sigmoid.py
@@ -3,21 +3,21 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 
 def sigmoid(name: str, x, data_type):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.fluid.layers.sigmoid(node_x, name='sigmoid')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=data_type)
+        out = paddle.fluid.layers.sigmoid(node_x, name='sigmoid')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_slice.py
similarity index 68%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_slice.py
index 75328e48fca..1aec3fc7228 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_slice.py
@@ -5,7 +5,7 @@ import sys
 import os
 
 import numpy as np
-import paddle as pdpd
+import paddle
 
 from save_model import exportModel
 from save_model import saveModel
@@ -13,16 +13,16 @@ from save_model import saveModel
 data_type = 'float32'
 
 def slice(name : str, x, axes : list, start : list, end : list):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.fluid.layers.slice(node_x, axes = axes, starts = start, ends = end)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.fluid.layers.slice(node_x, axes = axes, starts = start, ends = end)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -34,14 +34,14 @@ def slice(name : str, x, axes : list, start : list, end : list):
 
 
 def slice_dyn(test_shape=[2,8,10,10]):
-    pdpd.disable_static()
+    paddle.disable_static()
 
-    data = pdpd.rand(shape=test_shape, dtype='float32')
+    data = paddle.rand(shape=test_shape, dtype='float32')
 
     '''
     slice w/ decrease_axis
     '''
-    @pdpd.jit.to_static
+    @paddle.jit.to_static
     def test_slice_decrease_axis(x):
         return x[0, 1:3, :, 5]
     exportModel('slice_decrease_axis', test_slice_decrease_axis, [data], target_dir=sys.argv[1]) # output shape (2, 10)
@@ -49,15 +49,15 @@ def slice_dyn(test_shape=[2,8,10,10]):
     '''
     slice w/o decrease_axis
     '''
-    @pdpd.jit.to_static
+    @paddle.jit.to_static
     def test_slice(x):
-        return pdpd.slice(x, axes=[0,1,3], starts=[0,1,5], ends=[1,3,6])
+        return paddle.slice(x, axes=[0,1,3], starts=[0,1,5], ends=[1,3,6])
     # exportModel('slice_dyn', test_slice, [data], target_dir=sys.argv[1]) # output shape (1, 2, 10, 1)  # disable it by default as this kind of test model already there. It's for comparsion only.
 
     '''
     slice w/ decrease_axis of all dims
     '''
-    @pdpd.jit.to_static
+    @paddle.jit.to_static
     def test_slice_decrease_axis_all(x):
         return x[0, 0, 0, 0]
     exportModel('slice_decrease_axis_all', test_slice_decrease_axis_all, [data], target_dir=sys.argv[1]) # output shape (1,)
@@ -65,23 +65,23 @@ def slice_dyn(test_shape=[2,8,10,10]):
     '''
     slice w/o decrease_axis of all dims
     '''
-    @pdpd.jit.to_static
+    @paddle.jit.to_static
     def test_slice_alldim(x):
-        return pdpd.slice(x, axes=[0,1,2,3], starts=[0,0,0,0], ends=[1,1,1,1])
+        return paddle.slice(x, axes=[0,1,2,3], starts=[0,0,0,0], ends=[1,1,1,1])
     # exportModel('slice_alldim', test_slice_alldim, [data], target_dir=sys.argv[1]) # output shape (1, 1, 1, 1) # disable it by default as this kind of test model already there. It's for comparsion only.
 
 '''
 a test case simulating the last reshape2 of ocrnet which accepts slice (with decrease_axes in all dims) as its parents.
 '''
 def slice_reshape(B=1, C=256, H=16, W=32):
-    pdpd.disable_static()
+    paddle.disable_static()
 
-    data = pdpd.rand(shape=[B, C, H*W], dtype='float32')
+    data = paddle.rand(shape=[B, C, H*W], dtype='float32')
 
-    @pdpd.jit.to_static
+    @paddle.jit.to_static
     def test_model(x):
-        x2 = pdpd.assign([-1, -1, 16, 32]).astype('int32')
-        node_reshape = pdpd.reshape(x, [0, 256, x2[2], x2[3]])
+        x2 = paddle.assign([-1, -1, 16, 32]).astype('int32')
+        node_reshape = paddle.reshape(x, [0, 256, x2[2], x2[3]])
         return node_reshape
     exportModel('slice_reshape', test_model, [data], target_dir=sys.argv[1])
 
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_softmax.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_softmax.py
similarity index 71%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_softmax.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_softmax.py
index f797a048606..0e6e25b3ffe 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_softmax.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_softmax.py
@@ -7,16 +7,16 @@ from save_model import saveModel
 
 
 def softmax(name: str, x, axis):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    out = pdpd.nn.functional.softmax(x=node_x, axis=axis)
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    out = paddle.nn.functional.softmax(x=node_x, axis=axis)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
+    exe.run(paddle.static.default_startup_program())
 
     outs = exe.run(
         feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_softplus.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_softplus.py
similarity index 70%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_softplus.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_softplus.py
index f5d31f27fe3..48d0e5431c4 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_softplus.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_softplus.py
@@ -7,16 +7,16 @@ from save_model import saveModel
 
 
 def softplus(name: str, x, beta, threshold):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    out = pdpd.nn.functional.softplus(x=node_x, beta=beta, threshold=threshold)
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    out = paddle.nn.functional.softplus(x=node_x, beta=beta, threshold=threshold)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
+    exe.run(paddle.static.default_startup_program())
 
     outs = exe.run(
         feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_split.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_split.py
similarity index 60%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_split.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_split.py
index 8ddb4790126..769c155a9dd 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_split.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_split.py
@@ -7,17 +7,17 @@ import sys
 
 
 def split(name : str, x, attrs : dict):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=x.dtype)
-        out = pdpd.fluid.layers.split(node_x, num_or_sections=attrs['num_or_sections'], dim=attrs['axis'])
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=x.dtype)
+        out = paddle.fluid.layers.split(node_x, num_or_sections=attrs['num_or_sections'], dim=attrs['axis'])
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -31,18 +31,18 @@ def split(name : str, x, attrs : dict):
 
 
 def split_dim_tensor(name : str, x, attrs : dict, dim):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=x.dtype)
-        dim_node = pdpd.assign(dim)
-        out = pdpd.fluid.layers.split(node_x, num_or_sections=attrs['num_or_sections'], dim=dim_node)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=x.dtype)
+        dim_node = paddle.assign(dim)
+        out = paddle.fluid.layers.split(node_x, num_or_sections=attrs['num_or_sections'], dim=dim_node)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -56,19 +56,19 @@ def split_dim_tensor(name : str, x, attrs : dict, dim):
 
 
 def split_test_list_tensor(name : str, x, attrs : dict):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=x.dtype)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=x.dtype)
         section = attrs['num_or_sections']
-        section[0] = pdpd.assign(np.array((section[0],)).astype('int32'))
-        out = pdpd.fluid.layers.split(node_x, num_or_sections=section, dim=attrs['axis'])
+        section[0] = paddle.assign(np.array((section[0],)).astype('int32'))
+        out = paddle.fluid.layers.split(node_x, num_or_sections=section, dim=attrs['axis'])
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
@@ -91,12 +91,12 @@ def main():
     for t in data_types:
         for s in num_or_sections:
             for i in axes:
-                pdpd_attrs = {
+                paddle_attrs = {
                     'num_or_sections': s,
                     'axis': i
                 }
                 data_NCHW = np.random.rand(3,9,5).astype(t)
-                split("split_test{}".format(idx), data_NCHW, pdpd_attrs)
+                split("split_test{}".format(idx), data_NCHW, paddle_attrs)
                 idx+=1
 
     split("split_test_list", data_NCHW, {
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_squeeze.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_squeeze.py
similarity index 59%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_squeeze.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_squeeze.py
index 04eae5cf0b1..4c2f2dd1520 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_squeeze.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_squeeze.py
@@ -3,22 +3,22 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def squeeze(name : str, x, axes : list):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.fluid.layers.squeeze(node_x, axes=axes, name='squeeze')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.fluid.layers.squeeze(node_x, axes=axes, name='squeeze')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_stack.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_stack.py
similarity index 82%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_stack.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_stack.py
index 04a6917d4fb..021e2c3b218 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_stack.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_stack.py
@@ -7,26 +7,26 @@ import sys
 
 
 def stack(axis, input1, input2, input3):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        data1 = pdpd.static.data(
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        data1 = paddle.static.data(
             'data1', shape=input1.shape, dtype=input1.dtype)
-        data2 = pdpd.static.data(
+        data2 = paddle.static.data(
             'data2', shape=input2.shape, dtype=input2.dtype)
-        data3 = pdpd.static.data(
+        data3 = paddle.static.data(
             'data3', shape=input3.shape, dtype=input3.dtype)
 
         if (axis == None):
-            out = pdpd.paddle.stack([data1, data2, data3])
+            out = paddle.paddle.stack([data1, data2, data3])
         else:
-            out = pdpd.paddle.stack([data1, data2, data3], axis)
+            out = paddle.paddle.stack([data1, data2, data3], axis)
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={"data1": input1,
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_tanh.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_tanh.py
similarity index 59%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_tanh.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_tanh.py
index 40847a7417d..c819c7a8503 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_tanh.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_tanh.py
@@ -3,22 +3,22 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def tanh(name:str, x):
-    pdpd.enable_static()
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        data = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.tanh(data)
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        data = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.tanh(data)
         
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_unsqueeze.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_unsqueeze.py
similarity index 57%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_unsqueeze.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_unsqueeze.py
index e2fee0e97f5..b2b11c5b05a 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_unsqueeze.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_unsqueeze.py
@@ -3,22 +3,22 @@
 #
 import numpy as np
 from save_model import saveModel
-import paddle as pdpd
+import paddle
 import sys
 
 data_type = 'float32'
 
 def unsqueeze(name : str, x, axes : list):
-    pdpd.enable_static()
+    paddle.enable_static()
 
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = pdpd.fluid.layers.unsqueeze(node_x, axes = axes, name = 'unsqueeze')
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
+        out = paddle.fluid.layers.unsqueeze(node_x, axes = axes, name = 'unsqueeze')
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_unsupported_relu.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_unsupported_relu.py
similarity index 84%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_unsupported_relu.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_unsupported_relu.py
index ef70895ed38..6c738a67808 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_unsupported_relu.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_unsupported_relu.py
@@ -7,7 +7,7 @@ import sys
 
 import os
 import numpy as np
-import paddle as pdpd
+import paddle
 
 
 # print numpy array like C structure
@@ -60,22 +60,22 @@ def saveModel(name, exe, feedkeys: list, fetchlist: list, inputs: list, outputs:
         np.save(os.path.join(model_dir, "output{}".format(i)), output)
 
         # composited model + scattered model
-    pdpd.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe)
-    pdpd.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe, model_filename=name + ".pdmodel",
+    paddle.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe)
+    paddle.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe, model_filename=name + ".pdmodel",
                                        params_filename=name + ".pdiparams")
 
 
 def relu(name: str, x):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
 
-    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-    out = pdpd.nn.functional.relu(node_x)
+    node_x = paddle.static.data(name='x', shape=x.shape, dtype='float32')
+    out = paddle.nn.functional.relu(node_x)
 
-    cpu = pdpd.static.cpu_places(1)
-    exe = pdpd.static.Executor(cpu[0])
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
     # startup program will call initializer to initialize the parameters.
-    exe.run(pdpd.static.default_startup_program())
+    exe.run(paddle.static.default_startup_program())
 
     outs = exe.run(
         feed={'x': x},
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_yolo_box.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_yolo_box.py
similarity index 70%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_yolo_box.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/generate_yolo_box.py
index f737068faf5..64daf67c78c 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_yolo_box.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/generate_yolo_box.py
@@ -6,13 +6,13 @@ from save_model import saveModel
 import sys
 
 def yolo_box(name : str, x, img_size, attrs : dict):
-    import paddle as pdpd
-    pdpd.enable_static()
+    import paddle
+    paddle.enable_static()
     
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=x.dtype)
-        node_img_size = pdpd.static.data(name='img_size', shape=img_size.shape, dtype=img_size.dtype)
-        boxes, scores = pdpd.vision.ops.yolo_box(node_x,
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=x.dtype)
+        node_img_size = paddle.static.data(name='img_size', shape=img_size.shape, dtype=img_size.dtype)
+        boxes, scores = paddle.vision.ops.yolo_box(node_x,
                                                 node_img_size,
                                                 anchors=attrs['anchors'],
                                                 class_num=attrs['class_num'],
@@ -22,10 +22,10 @@ def yolo_box(name : str, x, img_size, attrs : dict):
                                                 name=None, 
                                                 scale_x_y=attrs['scale_x_y'])
 
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
         # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
+        exe.run(paddle.static.default_startup_program())
 
         outs = exe.run(
             feed={'x': x, 'img_size': img_size},
@@ -41,7 +41,7 @@ def yolo_box(name : str, x, img_size, attrs : dict):
 
 def TEST1():
     # yolo_box
-    pdpd_attrs = {
+    paddle_attrs = {
             'name': "yolo_box_default",
             'anchors': [10, 13, 16, 30, 33, 23],
             'class_num': 2,
@@ -51,7 +51,7 @@ def TEST1():
             'scale_x_y': 1.0
     }
 
-    pdpd_attrs_clip_box = {
+    paddle_attrs_clip_box = {
         'name': "yolo_box_clip_box",
         'anchors': [10, 13, 16, 30, 33, 23],
         'class_num': 2,
@@ -61,7 +61,7 @@ def TEST1():
         'scale_x_y': 1.0
     }
 
-    pdpd_attrs_scale_xy = {
+    paddle_attrs_scale_xy = {
         'name': "yolo_box_scale_xy",
         'anchors': [10, 13, 16, 30, 33, 23],
         'class_num': 2,
@@ -71,23 +71,23 @@ def TEST1():
         'scale_x_y': 1.2
     }
 
-    pdpd_attrs_list = [pdpd_attrs, pdpd_attrs_clip_box, pdpd_attrs_scale_xy]
+    paddle_attrs_list = [paddle_attrs, paddle_attrs_clip_box, paddle_attrs_scale_xy]
     
     N = 32
-    num_anchors = int(len(pdpd_attrs['anchors'])//2)
-    x_shape = (N, num_anchors * (5 + pdpd_attrs['class_num']), 13, 13)
+    num_anchors = int(len(paddle_attrs['anchors'])//2)
+    x_shape = (N, num_anchors * (5 + paddle_attrs['class_num']), 13, 13)
     imgsize_shape = (N, 2)
 
     data = np.random.random(x_shape).astype('float32')
     data_ImSize = np.random.randint(10, 20, imgsize_shape).astype('int32') 
 
-    for item in pdpd_attrs_list:
-        pred_pdpd = yolo_box(item['name'], data, data_ImSize, item)
+    for item in paddle_attrs_list:
+        pred_paddle = yolo_box(item['name'], data, data_ImSize, item)
 
 
 def TEST2():
     # yolo_box uneven spatial width and height
-    pdpd_attrs = {
+    paddle_attrs = {
             'name': "yolo_box_uneven_wh",
             'anchors': [10, 13, 16, 30, 33, 23],
             'class_num': 2,
@@ -100,14 +100,14 @@ def TEST2():
     N = 16
     SPATIAL_WIDTH = 13
     SPATIAL_HEIGHT = 9
-    num_anchors = int(len(pdpd_attrs['anchors'])//2)
-    x_shape = (N, num_anchors * (5 + pdpd_attrs['class_num']), SPATIAL_HEIGHT, SPATIAL_WIDTH)
+    num_anchors = int(len(paddle_attrs['anchors'])//2)
+    x_shape = (N, num_anchors * (5 + paddle_attrs['class_num']), SPATIAL_HEIGHT, SPATIAL_WIDTH)
     imgsize_shape = (N, 2)
 
     data = np.random.random(x_shape).astype('float32')
     data_ImSize = np.random.randint(10, 20, imgsize_shape).astype('int32')
     
-    pred_pdpd = yolo_box(pdpd_attrs['name'], data, data_ImSize, pdpd_attrs)
+    pred_paddle = yolo_box(paddle_attrs['name'], data, data_ImSize, paddle_attrs)
 
 if __name__ == "__main__":
     TEST1()
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/save_model.py b/src/core/tests/frontend/paddle/test_models/gen_scripts/save_model.py
similarity index 87%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/save_model.py
rename to src/core/tests/frontend/paddle/test_models/gen_scripts/save_model.py
index 3512fb2fd14..14429089e7a 100644
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/save_model.py
+++ b/src/core/tests/frontend/paddle/test_models/gen_scripts/save_model.py
@@ -1,7 +1,7 @@
 import os
 import sys
 import numpy as np
-import paddle as pdpd
+import paddle
 
 
 #print numpy array like C structure
@@ -52,8 +52,8 @@ def saveModel(name, exe, feedkeys:list, fetchlist:list, inputs:list, outputs:lis
         np.save(os.path.join(model_dir, "output{}".format(i)), output)     
 
     # composited model + scattered model
-    pdpd.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe)
-    pdpd.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe, model_filename=name+".pdmodel", params_filename=name+".pdiparams")   
+    paddle.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe)
+    paddle.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe, model_filename=name+".pdmodel", params_filename=name+".pdiparams")
 
 
 '''
@@ -70,17 +70,17 @@ def exportModel(name, dyn_func, input_data:list, target_dir:str):
     for idx, data in enumerate(input_data):
         input_name = 'input{}'.format(idx)
         input_specs.append(
-            pdpd.static.InputSpec(shape=data.shape, dtype=data.dtype, name=input_name)
+            paddle.static.InputSpec(shape=data.shape, dtype=data.dtype, name=input_name)
         )
 
         # dump input
         np.save(os.path.join(model_dir, "input{}".format(idx)), data)        
 
-    pdpd.jit.save(dyn_func, save_path, input_specs)
+    paddle.jit.save(dyn_func, save_path, input_specs)
     print('saved exported model to {}'.format(save_path))
 
     # infer
-    model = pdpd.jit.load(save_path)
+    model = paddle.jit.load(save_path)
 
     result = model(*[input[:] for input in input_data])
    
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_wrapper.py b/src/core/tests/frontend/paddle/test_models/gen_wrapper.py
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/test_models/gen_wrapper.py
rename to src/core/tests/frontend/paddle/test_models/gen_wrapper.py
diff --git a/src/core/tests/frontend/paddlepaddle/throw_in_conversion.cpp b/src/core/tests/frontend/paddle/throw_in_conversion.cpp
similarity index 100%
rename from src/core/tests/frontend/paddlepaddle/throw_in_conversion.cpp
rename to src/core/tests/frontend/paddle/throw_in_conversion.cpp
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py
deleted file mode 100644
index 54b24364b2d..00000000000
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#
-# pool2d paddle model generator
-#
-import numpy as np
-from save_model import saveModel
-import sys
-data_type = 'float32'
-
-
-def pdpd_argmax(name : str, x, axis):
-    import paddle as pdpd
-    pdpd.enable_static()
-    
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-        out = pdpd.argmax(x=node_x, axis=axis)
-        out = pdpd.cast(out, np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            feed={'x': x},
-            fetch_list=[out])             
-
-        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-def pdpd_argmax1(name : str, x):
-    import paddle as pdpd
-    pdpd.enable_static()
-    
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-        out = pdpd.argmax(x=node_x)
-        out = pdpd.cast(out, np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            feed={'x': x},
-            fetch_list=[out])             
-
-        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-def main():
-    data = np.random.random([3,5,7,2]).astype("float32")
-    axis = 0
-    pdpd_argmax("argmax", data, axis)
-    pdpd_argmax1("argmax1", data)
-
-
-if __name__ == "__main__":
-    main()     
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py
deleted file mode 100644
index 6f40afdb1f2..00000000000
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py
+++ /dev/null
@@ -1,47 +0,0 @@
-#
-# pool2d paddle model generator
-#
-import numpy as np
-from save_model import saveModel
-import sys
-
-
-def pdpd_dropout(name : str, x, p, pdpd_attrs):
-    import paddle as pdpd
-    pdpd.enable_static()
-    
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
-        out = pdpd.nn.functional.dropout(x=node_x, p=p, training=pdpd_attrs['training'], mode=pdpd_attrs['mode'])
-
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            feed={'x': x},
-            fetch_list=[out])             
-
-        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x],
-                  outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-
-def main():
-    p=0.5
-    data = np.random.random(size=(3, 10, 3, 7)).astype('float32')
-    pdpd_attrs = {
-    'training' : False,
-    'mode' : "downscale_in_infer"
-    }
-    pdpd_attrs2 = {
-        'training' : False,
-        'mode' : "upscale_in_train"
-    }
-    pdpd_dropout("dropout", data, p, pdpd_attrs)
-    pdpd_dropout("dropout_upscale_in_train", data, p, pdpd_attrs2)
-
-if __name__ == "__main__":
-    main()     
\ No newline at end of file
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_constant.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_constant.py
deleted file mode 100644
index feb8ce40300..00000000000
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_fill_constant.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# fill_const paddle model generator
-#
-import numpy as np
-from save_model import saveModel
-import paddle as pdpd
-import sys
-
-
-def fill_constant(name : str, shape : list, dtype, value):
-    pdpd.enable_static()
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        x1 = pdpd.fluid.layers.fill_constant(shape=shape, value=value, dtype=dtype, name='fill_constant')
-        x2 = pdpd.fluid.layers.fill_constant(shape=shape, value=value, dtype=dtype, name='fill_constant')
-        out = pdpd.add(pdpd.cast(x1, np.float32), pdpd.cast(x2, np.float32))
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            fetch_list=[out])             
-
-        saveModel(name, exe, feedkeys=[], fetchlist=[out], inputs=[], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-
-def fill_constant_tensor(name : str, shape : list, dtype, value):
-    pdpd.enable_static()
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_value = pdpd.static.data(name='value', shape=[1], dtype=dtype)
-        x1 = pdpd.fluid.layers.fill_constant(shape=shape, value=node_value, dtype=dtype, name='fill_constant1')
-        out = pdpd.cast(x1, np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            feed={"value": value},
-            fetch_list=[out])
-
-        saveModel(name, exe, feedkeys=["value"], fetchlist=[out], inputs=[np.array([value]).astype(dtype)], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-
-def fill_constant_shape_tensor(name : str, shape, dtype, value):
-    pdpd.enable_static()
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_shape = pdpd.fluid.layers.fill_constant(shape=[2], value=shape, dtype='int32', name='shape')
-        x1 = pdpd.fluid.layers.fill_constant(shape=node_shape, value=value, dtype=dtype, name='fill_constant')
-        out = pdpd.cast(x1, np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            fetch_list=[out])
-
-        saveModel(name, exe, feedkeys=[], fetchlist=[out], inputs=[], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-
-def fill_constant_shape_tensor_list(name : str, shape: list, dtype, value):
-    pdpd.enable_static()
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_shape = pdpd.fluid.layers.fill_constant(shape=[1], value=shape, dtype='int32', name='shape')
-        x1 = pdpd.fluid.layers.fill_constant(shape=[2, node_shape], value=value, dtype=dtype, name='fill_constant')
-        out = pdpd.cast(x1, np.float32)
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            fetch_list=[out])
-
-        saveModel(name, exe, feedkeys=[], fetchlist=[out], inputs=[], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-
-def main():
-    fill_constant("fill_constant", [2, 3, 4], 'float32', 0.03)
-    fill_constant("fill_constant_int32", [2, 3, 4], "int32", 2)
-    fill_constant("fill_constant_int64", [2, 3, 4], "int64", 4)
-    fill_constant_tensor("fill_constant_tensor", [2, 3, 4], 'float32', 0.05)
-    fill_constant_shape_tensor("fill_constant_shape_tensor", 2, 'float32', 0.05)
-    fill_constant_shape_tensor_list("fill_constant_shape_tensor_list", 2, 'float32', 0.05)
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_place_test_model.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_place_test_model.py
deleted file mode 100644
index ef8dd32f0b3..00000000000
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_place_test_model.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import numpy as np
-from save_model import saveModel
-import sys
-
-
-def pdpd_rnn_lstm(input_size, hidden_size, layers, direction):
-    import paddle as pdpd
-    pdpd.enable_static()
-    main_program = pdpd.static.Program()
-    startup_program = pdpd.static.Program()
-
-    num_of_directions = 1 if direction == 'forward' else 2
-    with pdpd.static.program_guard(main_program, startup_program):
-
-        rnn = pdpd.nn.LSTM(input_size, hidden_size, layers, direction, name="lstm")
-
-        data = pdpd.static.data(name='x', shape=[4, 3, input_size], dtype='float32')
-        prev_h = pdpd.ones(shape=[layers * num_of_directions, 4, hidden_size], dtype=np.float32, name="const_1")
-        prev_c = pdpd.ones(shape=[layers * num_of_directions, 4, hidden_size], dtype=np.float32, name="const_2")
-
-        y, (h, c) = rnn(data, (prev_h, prev_c))
-        relu_1 = pdpd.nn.functional.relu(c, name="relu_1")
-        relu_2 = pdpd.nn.functional.relu(c, name="relu_2")
-        relu_3 = pdpd.nn.functional.relu(c, name="relu_3")
-
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        exe.run(startup_program)
-
-        outs = exe.run(
-            feed={'x': np.ones([4, 3, input_size]).astype(np.float32)},
-            fetch_list=[y, h, c],
-            program=main_program)
-        saveModel("place_test_model", exe, feedkeys=['x'],
-                  fetchlist=[y, h, c, relu_1, relu_2, relu_3],
-                  inputs=[np.ones([4, 3, input_size]).astype(np.float32)],
-                  outputs=[outs[0], outs[1], outs[2]], target_dir=sys.argv[1])
-    return outs[0]
-
-
-if __name__ == "__main__":
-
-    testCases = [
-        {
-            'input_size': 2,
-            'hidden_size': 2,
-            'layers': 1,
-            'direction': 'forward',
-        },
-    ]
-
-    for test in testCases:
-        pdpd_rnn_lstm(test['input_size'], test['hidden_size'], test['layers'], test['direction'])
\ No newline at end of file
diff --git a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_scale.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_scale.py
deleted file mode 100644
index 7ceee084fa6..00000000000
--- a/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_scale.py
+++ /dev/null
@@ -1,93 +0,0 @@
-#
-# pool2d paddle model generator
-#
-import numpy as np
-import sys
-from save_model import saveModel
-
-
-def pdpd_scale(name : str, x, scale, bias, attrs : dict, data_type):
-    import paddle as pdpd
-    pdpd.enable_static()
-    
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        out = pdpd.scale(x=node_x, scale=scale, bias=bias, 
-                         bias_after_scale=attrs['bias_after_scale'])
-        #FuzzyTest only support FP32 now, so cast result to fp32
-        out = pdpd.cast(out, "float32")
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            feed={'x': x},
-            fetch_list=[out])             
-
-        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-
-def pdpd_scale_tensor(name : str, x, scale, bias, attrs : dict, data_type):
-    import paddle as pdpd
-    pdpd.enable_static()
-
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x = pdpd.static.data(name='x', shape=x.shape, dtype=data_type)
-        node_scale = pdpd.static.data(name='scale', shape=[1], dtype='float32')
-        out = pdpd.scale(x=node_x, scale=node_scale, bias=bias,
-                         bias_after_scale=attrs['bias_after_scale'])
-        #FuzzyTest only support FP32 now, so cast result to fp32
-        out = pdpd.cast(out, "float32")
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            feed={'x': x, 'scale': scale},
-            fetch_list=[out])
-
-        saveModel(name, exe, feedkeys=['x', 'scale'], fetchlist=[out], inputs=[x, np.array([scale]).astype('float32')], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-def main():
-    scale = 2.0
-    bias = 1.0
-    data = np.random.random([2, 3]).astype("float32")
-
-    test_cases = [
-        "float32",
-        "int32",
-        "int64"
-    ]
-
-    pdpd_attrs = {
-        'bias_after_scale': True,
-    }
-    pdpd_scale_tensor("scale_tensor_bias_after", data, scale, bias, pdpd_attrs, 'float32')
-
-    pdpd_attrs = {
-        'bias_after_scale': False,
-    }
-    pdpd_scale_tensor("scale_tensor_bias_before", data, scale, bias, pdpd_attrs, 'float32')
-
-    for test in test_cases:
-        data = np.random.random([2, 3]).astype(test)
-        pdpd_attrs = {
-            'bias_after_scale': True,
-        }
-        pdpd_scale("scale_bias_after_" + test, data, scale, bias, pdpd_attrs, test)
-
-        pdpd_attrs = {
-            'bias_after_scale': False,
-        }
-        pdpd_scale("scale_bias_before_" + test, data, scale, bias, pdpd_attrs, test)
-
-
-
-if __name__ == "__main__":
-    main()     
\ No newline at end of file
diff --git a/src/frontends/CMakeLists.txt b/src/frontends/CMakeLists.txt
index b60c8156aff..1e5d6790ffa 100644
--- a/src/frontends/CMakeLists.txt
+++ b/src/frontends/CMakeLists.txt
@@ -12,8 +12,8 @@ if(ENABLE_OV_ONNX_FRONTEND)
     add_subdirectory(onnx)
 endif()
 
-if(ENABLE_OV_PDPD_FRONTEND)
-    add_subdirectory(paddlepaddle)
+if(ENABLE_OV_PADDLE_FRONTEND)
+    add_subdirectory(paddle)
 endif()
 
 if(ENABLE_OV_IR_FRONTEND)
diff --git a/src/frontends/common/src/manager.cpp b/src/frontends/common/src/manager.cpp
index 4a27068a8c7..8d5b2c1b1df 100644
--- a/src/frontends/common/src/manager.cpp
+++ b/src/frontends/common/src/manager.cpp
@@ -7,8 +7,9 @@
 #include <openvino/util/env_util.hpp>
 #include <openvino/util/file_util.hpp>
 
+#include "ngraph/except.hpp"
 #include "openvino/frontend/exception.hpp"
-#include "openvino/frontend/place.hpp"
+#include "openvino/util/env_util.hpp"
 #include "plugin_loader.hpp"
 #include "utils.hpp"
 
@@ -32,7 +33,7 @@ public:
             {"ir", "ir"},
             {"onnx", "onnx"},
             {"tf", "tensorflow"},
-            {"paddle", "paddlepaddle"},
+            {"paddle", "paddle"},
         };
         auto it = predefined_frontends.find(framework);
         std::lock_guard<std::mutex> guard(m_loading_mutex);
@@ -119,7 +120,7 @@ private:
             {".xml", {"ir", "ir"}},
             {".onnx", {"onnx", "onnx"}},
             {".pb", {"tf", "tensorflow"}},
-            {".pdmodel", {"paddle", "paddlepaddle"}},
+            {".pdmodel", {"paddle", "paddle"}},
         };
 
         // List of prioritized frontends.
@@ -127,7 +128,7 @@ private:
             {"ir", "ir"},
             {"onnx", "onnx"},
             {"tf", "tensorflow"},
-            {"paddle", "paddlepaddle"},
+            {"paddle", "paddle"},
         };
         if (variants.empty()) {
             return nullptr;
diff --git a/src/frontends/common/src/plugin_loader.cpp b/src/frontends/common/src/plugin_loader.cpp
index e7233ec2542..abe4da11f6e 100644
--- a/src/frontends/common/src/plugin_loader.cpp
+++ b/src/frontends/common/src/plugin_loader.cpp
@@ -45,7 +45,7 @@ void load_static_plugins(std::vector<PluginInfo>& res) {
             {"ir", "ir"},
             {"onnx", "onnx"},
             {"tf", "tensorflow"},
-            {"paddle", "paddlepaddle"},
+            {"paddle", "paddle"},
         };
         auto it = predefined_frontends.find(factory.m_name);
         if (it != predefined_frontends.end()) {
diff --git a/src/frontends/paddlepaddle/CMakeLists.txt b/src/frontends/paddle/CMakeLists.txt
similarity index 66%
rename from src/frontends/paddlepaddle/CMakeLists.txt
rename to src/frontends/paddle/CMakeLists.txt
index d6c5e4999f5..c42ead4cf8f 100644
--- a/src/frontends/paddlepaddle/CMakeLists.txt
+++ b/src/frontends/paddle/CMakeLists.txt
@@ -2,8 +2,8 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-# TODO: Add LINKABLE_FRONTEND option when tensorflow frontend directory is moved to openvino folder
-ov_add_frontend(NAME paddlepaddle
+ov_add_frontend(NAME paddle
+                LINKABLE_FRONTEND
                 PROTOBUF_LITE
                 FILEDESCRIPTION "FrontEnd to load and convert PaddlePaddle file format"
                 LINK_LIBRARIES inference_engine_transformations)
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp b/src/frontends/paddle/include/openvino/frontend/paddle/frontend.hpp
similarity index 87%
rename from src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
rename to src/frontends/paddle/include/openvino/frontend/paddle/frontend.hpp
index fd705b2913e..26b60fd96dc 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
+++ b/src/frontends/paddle/include/openvino/frontend/paddle/frontend.hpp
@@ -6,18 +6,21 @@
 
 #include <openvino/frontend/extension/decoder_transformation.hpp>
 #include <openvino/frontend/extension/telemetry.hpp>
-#include <openvino/frontend/manager.hpp>
+#include <openvino/frontend/frontend.hpp>
+#include <openvino/frontend/input_model.hpp>
 
 #include "exceptions.hpp"
-#include "model.hpp"
+#include "openvino/frontend/paddle/visibility.hpp"
 
 namespace ov {
 namespace frontend {
-class OpPlacePDPD;
+namespace paddle {
 
-class PDPD_API FrontEndPDPD : public FrontEnd {
+class OpPlace;
+
+class PADDLE_API FrontEnd : public ov::frontend::FrontEnd {
 public:
-    FrontEndPDPD() = default;
+    FrontEnd() = default;
 
     /// \brief Completely convert the remaining, not converted part of a function.
     /// \param partiallyConverted partially converted OV Model
@@ -52,7 +55,7 @@ public:
     void add_extension(const std::shared_ptr<ov::Extension>& extension) override;
 
 protected:
-    /// \brief Check if FrontEndPDPD can recognize model from given parts
+    /// \brief Check if FrontEnd can recognize model from given parts
     /// \param params Can be path to folder which contains __model__ file or path to
     /// .pdmodel file
     /// \return InputModel::Ptr
@@ -67,12 +70,13 @@ protected:
 
 private:
     static std::shared_ptr<Model> convert_each_node(
-        const std::shared_ptr<InputModelPDPD>& model,
+        const std::shared_ptr<InputModel>& frontend_model,
         std::function<std::map<std::string, OutputVector>(const std::map<std::string, Output<Node>>&,
-                                                          const std::shared_ptr<OpPlacePDPD>&)> func);
+                                                          const std::shared_ptr<OpPlace>&)> func);
     std::shared_ptr<TelemetryExtension> m_telemetry;
     std::vector<std::shared_ptr<DecoderTransformationExtension>> m_transformation_extensions;
 };
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddle/include/openvino/frontend/paddle/visibility.hpp b/src/frontends/paddle/include/openvino/frontend/paddle/visibility.hpp
new file mode 100644
index 00000000000..9107fe2a60f
--- /dev/null
+++ b/src/frontends/paddle/include/openvino/frontend/paddle/visibility.hpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/exception.hpp"
+
+#ifdef OPENVINO_STATIC_LIBRARY
+#    define PADDLE_API
+#    define PADDLE_C_API
+#else
+#    ifdef ov_paddle_frontend_EXPORTS
+#        define PADDLE_API   OPENVINO_CORE_EXPORTS
+#        define PADDLE_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#    else
+#        define PADDLE_API   OPENVINO_CORE_IMPORTS
+#        define PADDLE_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
+#    endif  // ov_paddle_frontend_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/paddlepaddle/src/decoder.cpp b/src/frontends/paddle/src/decoder.cpp
similarity index 78%
rename from src/frontends/paddlepaddle/src/decoder.cpp
rename to src/frontends/paddle/src/decoder.cpp
index 816d7864fcd..432957d8d88 100644
--- a/src/frontends/paddlepaddle/src/decoder.cpp
+++ b/src/frontends/paddle/src/decoder.cpp
@@ -17,9 +17,11 @@
 
 namespace ov {
 namespace frontend {
-using namespace paddle::framework;
+namespace paddle {
 
-std::map<paddle::framework::proto::VarType_Type, ov::element::Type> TYPE_MAP{
+using namespace ::paddle::framework;
+
+std::map<::paddle::framework::proto::VarType_Type, ov::element::Type> TYPE_MAP{
     {proto::VarType_Type::VarType_Type_BOOL, ov::element::boolean},
     {proto::VarType_Type::VarType_Type_INT16, ov::element::i16},
     {proto::VarType_Type::VarType_Type_INT32, ov::element::i32},
@@ -31,7 +33,7 @@ std::map<paddle::framework::proto::VarType_Type, ov::element::Type> TYPE_MAP{
     {proto::VarType_Type::VarType_Type_INT8, ov::element::i8},
     {proto::VarType_Type::VarType_Type_BF16, ov::element::bf16}};
 
-ov::Any DecoderPDPDProto::get_attribute(const std::string& name, const std::type_info& type_info) const {
+ov::Any DecoderProto::get_attribute(const std::string& name, const std::type_info& type_info) const {
     auto attrs = decode_attribute_helper(name);
     if (attrs.empty()) {
         return {};
@@ -51,8 +53,8 @@ ov::Any DecoderPDPDProto::get_attribute(const std::string& name, const std::type
         return attrs[0].f();
     } else if (type_info == typeid(std::vector<float>)) {
         return std::vector<float>(attrs[0].floats().begin(), attrs[0].floats().end());
-    } else if (type_info == typeid(ngraph::element::Type)) {
-        return TYPE_MAP[static_cast<paddle::framework::proto::VarType_Type>(attrs[0].i())];
+    } else if (type_info == typeid(ov::element::Type)) {
+        return TYPE_MAP[static_cast<::paddle::framework::proto::VarType_Type>(attrs[0].i())];
     } else if (type_info == typeid(bool)) {
         return attrs[0].b();
     }
@@ -61,7 +63,7 @@ ov::Any DecoderPDPDProto::get_attribute(const std::string& name, const std::type
     return {};
 }
 
-std::vector<pdpd::OutPortName> DecoderPDPDProto::get_output_names() const {
+std::vector<paddle::OutPortName> DecoderProto::get_output_names() const {
     std::vector<std::string> output_names;
     for (const auto& output : op_place->get_desc().outputs()) {
         output_names.push_back(output.parameter());
@@ -69,7 +71,7 @@ std::vector<pdpd::OutPortName> DecoderPDPDProto::get_output_names() const {
     return output_names;
 }
 
-size_t DecoderPDPDProto::get_output_size() const {
+size_t DecoderProto::get_output_size() const {
     size_t res = 0;
     for (const auto& output : op_place->get_desc().outputs()) {
         res += output.arguments().size();
@@ -77,20 +79,20 @@ size_t DecoderPDPDProto::get_output_size() const {
     return res;
 }
 
-std::map<std::string, std::vector<ov::element::Type>> DecoderPDPDProto::get_output_type_map() const {
+std::map<std::string, std::vector<ov::element::Type>> DecoderProto::get_output_type_map() const {
     std::map<std::string, std::vector<ov::element::Type>> output_types;
     for (const auto& out_port_pair : op_place->get_output_ports()) {
         for (const auto& p_place : out_port_pair.second) {
-            output_types[out_port_pair.first].push_back(p_place->get_target_tensor_pdpd()->get_element_type());
+            output_types[out_port_pair.first].push_back(p_place->get_target_tensor_paddle()->get_element_type());
         }
     }
     return output_types;
 }
 
-ov::element::Type DecoderPDPDProto::get_out_port_type(const std::string& port_name) const {
+ov::element::Type DecoderProto::get_out_port_type(const std::string& port_name) const {
     std::vector<ov::element::Type> output_types;
     for (const auto& out_port : op_place->get_output_ports().at(port_name)) {
-        output_types.push_back(out_port->get_target_tensor_pdpd()->get_element_type());
+        output_types.push_back(out_port->get_target_tensor_paddle()->get_element_type());
     }
     FRONT_END_GENERAL_CHECK(output_types.size() > 0, "Port has no tensors connected.");
     FRONT_END_GENERAL_CHECK(std::equal(output_types.begin() + 1, output_types.end(), output_types.begin()),
@@ -98,11 +100,11 @@ ov::element::Type DecoderPDPDProto::get_out_port_type(const std::string& port_na
     return output_types[0];
 }
 
-std::string DecoderPDPDProto::get_op_type() const {
+std::string DecoderProto::get_op_type() const {
     return op_place->get_desc().type();
 }
 
-std::vector<proto::OpDesc_Attr> DecoderPDPDProto::decode_attribute_helper(const std::string& name) const {
+std::vector<proto::OpDesc_Attr> DecoderProto::decode_attribute_helper(const std::string& name) const {
     std::vector<proto::OpDesc_Attr> attrs;
     for (const auto& attr : op_place->get_desc().attrs()) {
         if (attr.name() == name)
@@ -121,7 +123,7 @@ std::vector<proto::OpDesc_Attr> DecoderPDPDProto::decode_attribute_helper(const
 
 namespace {
 inline std::map<std::string, OutputVector> map_for_each_input_impl(
-    const google::protobuf::RepeatedPtrField<paddle::framework::proto::OpDesc_Var>& c,
+    const google::protobuf::RepeatedPtrField<::paddle::framework::proto::OpDesc_Var>& c,
     const std::function<Output<Node>(const std::string&, size_t)>& func) {
     size_t idx = 0;
     std::map<std::string, OutputVector> res;
@@ -137,15 +139,16 @@ inline std::map<std::string, OutputVector> map_for_each_input_impl(
 }
 }  // namespace
 
-std::map<std::string, OutputVector> DecoderPDPDProto::map_for_each_input(
+std::map<std::string, OutputVector> DecoderProto::map_for_each_input(
     const std::function<Output<Node>(const std::string&, size_t)>& func) const {
     return map_for_each_input_impl(op_place->get_desc().inputs(), func);
 }
 
-std::map<std::string, OutputVector> DecoderPDPDProto::map_for_each_output(
+std::map<std::string, OutputVector> DecoderProto::map_for_each_output(
     const std::function<Output<Node>(const std::string&, size_t)>& func) const {
     return map_for_each_input_impl(op_place->get_desc().outputs(), func);
 }
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/decoder.hpp b/src/frontends/paddle/src/decoder.hpp
similarity index 66%
rename from src/frontends/paddlepaddle/src/decoder.hpp
rename to src/frontends/paddle/src/decoder.hpp
index b56ed58116e..82e4d7a37bf 100644
--- a/src/frontends/paddlepaddle/src/decoder.hpp
+++ b/src/frontends/paddle/src/decoder.hpp
@@ -16,20 +16,21 @@
 
 #include "framework.pb.h"
 #include "node_context.hpp"
-#include "paddlepaddle_frontend/frontend.hpp"
-#include "paddlepaddle_frontend/place.hpp"
+#include "openvino/frontend/paddle/frontend.hpp"
+#include "place.hpp"
 
 namespace ov {
 namespace frontend {
-extern std::map<paddle::framework::proto::VarType_Type, ov::element::Type> TYPE_MAP;
+namespace paddle {
+extern std::map<::paddle::framework::proto::VarType_Type, ov::element::Type> TYPE_MAP;
 
-class DecoderPDPDProto : public pdpd::DecoderBase {
+class DecoderProto : public paddle::DecoderBase {
 public:
-    explicit DecoderPDPDProto(const std::shared_ptr<OpPlacePDPD>& op) : op_place(op) {}
+    explicit DecoderProto(const std::shared_ptr<OpPlace>& op) : op_place(op) {}
 
     ov::Any get_attribute(const std::string& name, const std::type_info& type_info) const override;
 
-    std::vector<pdpd::OutPortName> get_output_names() const override;
+    std::vector<paddle::OutPortName> get_output_names() const override;
 
     size_t get_output_size() const override;
 
@@ -46,9 +47,10 @@ public:
         const std::function<Output<Node>(const std::string&, size_t)>& func) const;
 
 private:
-    std::vector<paddle::framework::proto::OpDesc_Attr> decode_attribute_helper(const std::string& name) const;
-    std::shared_ptr<OpPlacePDPD> op_place;
+    std::vector<::paddle::framework::proto::OpDesc_Attr> decode_attribute_helper(const std::string& name) const;
+    std::shared_ptr<OpPlace> op_place;
 };
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/default_opset.hpp b/src/frontends/paddle/src/default_opset.hpp
similarity index 86%
rename from src/frontends/paddlepaddle/src/default_opset.hpp
rename to src/frontends/paddle/src/default_opset.hpp
index 852018f88a1..413d7036f06 100644
--- a/src/frontends/paddlepaddle/src/default_opset.hpp
+++ b/src/frontends/paddle/src/default_opset.hpp
@@ -6,11 +6,11 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 namespace default_opset = ov::opset8;
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/exception.cpp b/src/frontends/paddle/src/exceptions.cpp
similarity index 70%
rename from src/frontends/paddlepaddle/src/exception.cpp
rename to src/frontends/paddle/src/exceptions.cpp
index ddd097ba5ef..f571183b61f 100644
--- a/src/frontends/paddlepaddle/src/exception.cpp
+++ b/src/frontends/paddle/src/exceptions.cpp
@@ -3,16 +3,17 @@
 //
 
 #include "exceptions.hpp"
+
 #include "node_context.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
-std::string OpValidationFailurePDPD::get_error_msg_prefix_pdpd(const pdpd::NodeContext& node) {
+namespace paddle {
+std::string OpValidationFailure::get_error_msg_prefix_paddle(const paddle::NodeContext& node) {
     std::stringstream ss;
     ss << "While validating node '" << node.get_op_type() << '\'';
     return ss.str();
 }
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddle/src/exceptions.hpp b/src/frontends/paddle/src/exceptions.hpp
new file mode 100644
index 00000000000..7d8254cbca5
--- /dev/null
+++ b/src/frontends/paddle/src/exceptions.hpp
@@ -0,0 +1,37 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/frontend/exception.hpp>
+
+namespace ov {
+namespace frontend {
+namespace paddle {
+
+class NodeContext;
+
+class OpValidationFailure : public ov::frontend::OpValidationFailure {
+public:
+    OpValidationFailure(const CheckLocInfo& check_loc_info,
+                        const paddle::NodeContext& node,
+                        const std::string& explanation)
+        : ov::frontend::OpValidationFailure(check_loc_info, get_error_msg_prefix_paddle(node), explanation) {}
+
+private:
+    static std::string get_error_msg_prefix_paddle(const paddle::NodeContext& node);
+};
+}  // namespace paddle
+}  // namespace frontend
+
+/// \brief Macro to check whether a boolean condition holds.
+/// \param node_context Object of NodeContext class
+/// \param cond Condition to check
+/// \param ... Additional error message info to be added to the error message via the `<<`
+///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
+///            i.e., only if the `cond` evalutes to `false`.
+/// \throws ::ov::OpValidationFailure if `cond` is false.
+#define PADDLE_OP_CHECK(node_context, ...) \
+    OPENVINO_ASSERT_HELPER(::ov::frontend::paddle::OpValidationFailure, (node_context), __VA_ARGS__)
+}  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/frontend.cpp b/src/frontends/paddle/src/frontend.cpp
similarity index 72%
rename from src/frontends/paddlepaddle/src/frontend.cpp
rename to src/frontends/paddle/src/frontend.cpp
index c6e41123bdd..6e8cdc0c9c7 100644
--- a/src/frontends/paddlepaddle/src/frontend.cpp
+++ b/src/frontends/paddle/src/frontend.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "paddlepaddle_frontend/frontend.hpp"
+#include "openvino/frontend/paddle/frontend.hpp"
 
 #include <fstream>
 #include <map>
@@ -10,15 +10,14 @@
 #include <vector>
 
 #include "decoder.hpp"
-#include "exceptions.hpp"
 #include "framework.pb.h"
+#include "input_model.hpp"
 #include "node_context.hpp"
 #include "op_table.hpp"
 #include "openvino/opsets/opset7.hpp"
-#include "paddlepaddle_frontend/model.hpp"
-#include "paddlepaddle_frontend/place.hpp"
-#include "pdpd_fw_node.hpp"
-#include "pdpd_utils.hpp"
+#include "paddle_fw_node.hpp"
+#include "paddle_utils.hpp"
+#include "place.hpp"
 
 using namespace ov::opset7;
 using namespace ov;
@@ -26,10 +25,11 @@ using namespace ov::frontend;
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace {
-NamedOutputs make_ng_node(const std::map<pdpd::TensorName, Output<Node>>& nodes,
-                          const std::shared_ptr<OpPlacePDPD>& op_place,
+
+NamedOutputs make_ng_node(const std::map<paddle::TensorName, Output<Node>>& nodes,
+                          const std::shared_ptr<OpPlace>& op_place,
                           const std::map<std::string, CreatorFunction>& CREATORS_MAP) {
     const auto& op_desc = op_place->get_desc();
 
@@ -52,7 +52,7 @@ NamedOutputs make_ng_node(const std::map<pdpd::TensorName, Output<Node>>& nodes,
     NamedOutputs outputs;
     // In case the conversion function throws exception
     try {
-        outputs = creator_it->second(NodeContext(DecoderPDPDProto(op_place), named_inputs));
+        outputs = creator_it->second(NodeContext(DecoderProto(op_place), named_inputs));
     } catch (std::exception& ex) {
         FRONT_END_OP_CONVERSION_CHECK(false, "Fail to convert " + op_desc.type() + " Exception " + ex.what());
     }
@@ -60,8 +60,8 @@ NamedOutputs make_ng_node(const std::map<pdpd::TensorName, Output<Node>>& nodes,
     return outputs;
 }
 
-NamedOutputs make_framework_node(const std::map<pdpd::TensorName, Output<Node>>& nodes,
-                                 const std::shared_ptr<OpPlacePDPD>& op_place) {
+NamedOutputs make_framework_node(const std::map<paddle::TensorName, Output<Node>>& nodes,
+                                 const std::shared_ptr<OpPlace>& op_place) {
     const auto& op_desc = op_place->get_desc();
 
     OutputVector inputs_vector;
@@ -82,13 +82,12 @@ NamedOutputs make_framework_node(const std::map<pdpd::TensorName, Output<Node>>&
         }
     }
 
-    auto node =
-        std::make_shared<ov::frontend::PDPDFrameworkNode>(DecoderPDPDProto(op_place), inputs_vector, inputs_names);
+    auto node = std::make_shared<FrameworkNode>(DecoderProto(op_place), inputs_vector, inputs_names);
 
     return node->return_named_outputs();
 }
 
-bool normalize_framework_node(const std::shared_ptr<PDPDFrameworkNode>& node,
+bool normalize_framework_node(const std::shared_ptr<FrameworkNode>& node,
                               const std::map<std::string, CreatorFunction>& CREATORS_MAP) {
     auto type = node->get_op_type();
     auto creator_it = CREATORS_MAP.find(type);
@@ -132,18 +131,19 @@ std::istream* variant_to_stream_ptr(const ov::Any& variant, std::ifstream& ext_s
     return &ext_stream;
 }
 }  // namespace
-}  // namespace pdpd
 
-std::shared_ptr<ngraph::Function> FrontEndPDPD::convert_each_node(
-    const std::shared_ptr<InputModelPDPD>& model,
+std::shared_ptr<ov::Model> FrontEnd::convert_each_node(
+    const std::shared_ptr<ov::frontend::InputModel>& frontend_model,
     std::function<std::map<std::string, OutputVector>(const std::map<std::string, Output<Node>>&,
-                                                      const std::shared_ptr<OpPlacePDPD>&)> func) {
+                                                      const std::shared_ptr<OpPlace>&)> func) {
+    auto model = std::dynamic_pointer_cast<InputModel>(frontend_model);
+    FRONT_END_GENERAL_CHECK(model, "Invalid input model");
     auto nodes_dict(model->get_tensor_values());
     ParameterVector parameter_nodes;
     ResultVector result_nodes;
 
     for (const auto& _inp_place : model->get_inputs()) {
-        const auto& inp_place = std::dynamic_pointer_cast<TensorPlacePDPD>(_inp_place);
+        const auto& inp_place = std::dynamic_pointer_cast<TensorPlace>(_inp_place);
         const auto& var = inp_place->get_desc();
         const auto& shape = inp_place->get_partial_shape();
         const auto& type = inp_place->get_element_type();
@@ -161,10 +161,10 @@ std::shared_ptr<ngraph::Function> FrontEndPDPD::convert_each_node(
             // inputs and outputs are stored in the model already
             continue;
         } else {
-            pdpd::NamedOutputs named_outputs = func(nodes_dict, op_place);
+            paddle::NamedOutputs named_outputs = func(nodes_dict, op_place);
 
             if (!named_outputs.empty()) {
-                if (op_desc.outputs().begin()->arguments().size() > 0) {
+                if (!op_desc.outputs().begin()->arguments().empty()) {
                     const auto& tensor_name = op_desc.outputs().begin()->arguments()[0];
                     auto node = named_outputs.begin()->second[0].get_node_shared_ptr();
                     node->set_friendly_name(tensor_name);
@@ -193,7 +193,7 @@ std::shared_ptr<ngraph::Function> FrontEndPDPD::convert_each_node(
     }
 
     for (const auto& _outp_place : model->get_outputs()) {
-        const auto& outp_place = std::dynamic_pointer_cast<TensorPlacePDPD>(_outp_place);
+        const auto& outp_place = std::dynamic_pointer_cast<TensorPlace>(_outp_place);
         auto var = outp_place->get_desc();
         auto input_var_name = var.name();
         auto result = std::make_shared<Result>(nodes_dict.at(input_var_name));
@@ -204,8 +204,8 @@ std::shared_ptr<ngraph::Function> FrontEndPDPD::convert_each_node(
     return std::make_shared<ov::Model>(result_nodes, parameter_nodes);
 }
 
-bool FrontEndPDPD::supported_impl(const std::vector<ov::Any>& variants) const {
-    // FrontEndPDPD can only load model specified by one path, one file or two files.
+bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
+    // FrontEnd can only load model specified by one path, one file or two files.
     if (variants.empty() || variants.size() > 2)
         return false;
 
@@ -213,8 +213,8 @@ bool FrontEndPDPD::supported_impl(const std::vector<ov::Any>& variants) const {
     if (variants[0].is<std::string>()) {
         std::string suffix = ".pdmodel";
         std::string model_path = variants[0].as<std::string>();
-        if (!pdpd::endsWith(model_path, suffix)) {
-            model_path += pdpd::get_path_sep<char>() + "__model__";
+        if (!paddle::endsWith(model_path, suffix)) {
+            model_path += paddle::get_path_sep<char>() + "__model__";
         }
         std::ifstream model_str(model_path, std::ios::in | std::ifstream::binary);
         // It is possible to validate here that protobuf can read model from the stream,
@@ -225,8 +225,8 @@ bool FrontEndPDPD::supported_impl(const std::vector<ov::Any>& variants) const {
     else if (variants[0].is<std::wstring>()) {
         std::wstring suffix = L".pdmodel";
         std::wstring model_path = variants[0].as<std::wstring>();
-        if (!pdpd::endsWith(model_path, suffix)) {
-            model_path += pdpd::get_path_sep<wchar_t>() + L"__model__";
+        if (!paddle::endsWith(model_path, suffix)) {
+            model_path += paddle::get_path_sep<wchar_t>() + L"__model__";
         }
         std::ifstream model_str(model_path, std::ios::in | std::ifstream::binary);
         // It is possible to validate here that protobuf can read model from the stream,
@@ -237,48 +237,48 @@ bool FrontEndPDPD::supported_impl(const std::vector<ov::Any>& variants) const {
     else if (variants[0].is<std::istream*>()) {
         // Validating first stream, it must contain a model
         auto p_model_stream = variants[0].as<std::istream*>();
-        paddle::framework::proto::ProgramDesc fw;
+        ::paddle::framework::proto::ProgramDesc fw;
         return fw.ParseFromIstream(p_model_stream);
     }
     return false;
 }
 
-InputModel::Ptr FrontEndPDPD::load_impl(const std::vector<ov::Any>& variants) const {
+InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const {
     if (variants.size() == 1) {
         // The case when folder with __model__ and weight files is provided or .pdmodel file
         if (variants[0].is<std::string>()) {
             std::string m_path = variants[0].as<std::string>();
-            return std::make_shared<InputModelPDPD>(m_path, m_telemetry);
+            return std::make_shared<InputModel>(m_path, m_telemetry);
         }
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
         else if (variants[0].is<std::wstring>()) {
             std::wstring m_path = variants[0].as<std::wstring>();
-            return std::make_shared<InputModelPDPD>(m_path, m_telemetry);
+            return std::make_shared<InputModel>(m_path, m_telemetry);
         }
 #endif
         // The case with only model stream provided and no weights. This means model has
         // no learnable weights
         else if (variants[0].is<std::istream*>()) {
             auto p_model_stream = variants[0].as<std::istream*>();
-            return std::make_shared<InputModelPDPD>(std::vector<std::istream*>{p_model_stream}, m_telemetry);
+            return std::make_shared<InputModel>(std::vector<std::istream*>{p_model_stream}, m_telemetry);
         }
     } else if (variants.size() == 2) {
         // The case when .pdmodel and .pdparams files are provided
         std::ifstream model_stream;
         std::ifstream weights_stream;
-        std::istream* p_model_stream = pdpd::variant_to_stream_ptr(variants[0], model_stream);
-        std::istream* p_weights_stream = pdpd::variant_to_stream_ptr(variants[1], weights_stream);
+        std::istream* p_model_stream = paddle::variant_to_stream_ptr(variants[0], model_stream);
+        std::istream* p_weights_stream = paddle::variant_to_stream_ptr(variants[1], weights_stream);
         if (p_model_stream && p_weights_stream) {
-            return std::make_shared<InputModelPDPD>(std::vector<std::istream*>{p_model_stream, p_weights_stream},
-                                                    m_telemetry);
+            return std::make_shared<InputModel>(std::vector<std::istream*>{p_model_stream, p_weights_stream},
+                                                m_telemetry);
         }
     }
-    PDPD_THROW("Model can be loaded either from 1 or 2 files/streams");
+    FRONT_END_THROW("Model can be loaded either from 1 or 2 files/streams");
 }
 
-std::shared_ptr<ov::Model> FrontEndPDPD::convert(const InputModel::Ptr& model) const {
-    auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
-    FRONT_END_GENERAL_CHECK(pdpd_model != nullptr, "Invalid input model");
+std::shared_ptr<ov::Model> FrontEnd::convert(const InputModel::Ptr& model) const {
+    auto paddle_model = std::dynamic_pointer_cast<InputModel>(model);
+    FRONT_END_GENERAL_CHECK(paddle_model != nullptr, "Invalid input model");
 
     if (!m_transformation_extensions.empty()) {
         auto function = decode(model);
@@ -292,20 +292,20 @@ std::shared_ptr<ov::Model> FrontEndPDPD::convert(const InputModel::Ptr& model) c
         return function;
     }
 
-    std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
+    std::map<std::string, paddle::CreatorFunction> CREATORS_MAP = paddle::get_supported_ops();
     auto f = convert_each_node(
-        pdpd_model,
-        [&](const std::map<std::string, Output<Node>>& nodes_dict, const std::shared_ptr<OpPlacePDPD>& op_place) {
-            return pdpd::make_ng_node(nodes_dict, op_place, CREATORS_MAP);
+        paddle_model,
+        [&](const std::map<std::string, Output<Node>>& nodes_dict, const std::shared_ptr<OpPlace>& op_place) {
+            return paddle::make_ng_node(nodes_dict, op_place, CREATORS_MAP);
         });
     return f;
 }
 
-void FrontEndPDPD::convert(const std::shared_ptr<ov::Model>& partiallyConverted) const {
+void FrontEnd::convert(const std::shared_ptr<ov::Model>& partiallyConverted) const {
     for (const auto& node : partiallyConverted->get_ordered_ops()) {
-        if (ov::is_type<PDPDFrameworkNode>(node)) {
-            pdpd::normalize_framework_node(std::dynamic_pointer_cast<PDPDFrameworkNode>(node),
-                                           pdpd::get_supported_ops());
+        if (ov::is_type<FrameworkNode>(node)) {
+            paddle::normalize_framework_node(std::dynamic_pointer_cast<FrameworkNode>(node),
+                                             paddle::get_supported_ops());
         }
     }
     for (const auto& result : partiallyConverted->get_results()) {
@@ -313,9 +313,9 @@ void FrontEndPDPD::convert(const std::shared_ptr<ov::Model>& partiallyConverted)
     }
 }
 
-std::shared_ptr<ov::Model> FrontEndPDPD::convert_partially(const InputModel::Ptr& model) const {
-    auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
-    FRONT_END_GENERAL_CHECK(pdpd_model != nullptr, "Invalid input model");
+std::shared_ptr<ov::Model> FrontEnd::convert_partially(const InputModel::Ptr& model) const {
+    auto paddle_model = std::dynamic_pointer_cast<InputModel>(model);
+    FRONT_END_GENERAL_CHECK(paddle_model != nullptr, "Invalid input model");
 
     if (!m_transformation_extensions.empty()) {
         auto function = decode(model);
@@ -329,35 +329,35 @@ std::shared_ptr<ov::Model> FrontEndPDPD::convert_partially(const InputModel::Ptr
         return function;
     }
 
-    std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
+    std::map<std::string, paddle::CreatorFunction> CREATORS_MAP = paddle::get_supported_ops();
     auto f = convert_each_node(
-        pdpd_model,
-        [&](const std::map<std::string, Output<Node>>& nodes_dict, const std::shared_ptr<OpPlacePDPD>& op_place) {
-            pdpd::NamedOutputs named_outputs;
+        paddle_model,
+        [&](const std::map<std::string, Output<Node>>& nodes_dict, const std::shared_ptr<OpPlace>& op_place) {
+            paddle::NamedOutputs named_outputs;
             try {
-                named_outputs = pdpd::make_ng_node(nodes_dict, op_place, CREATORS_MAP);
+                named_outputs = paddle::make_ng_node(nodes_dict, op_place, CREATORS_MAP);
             } catch (const OpConversionFailure&) {
-                named_outputs = pdpd::make_framework_node(nodes_dict, op_place);
+                named_outputs = paddle::make_framework_node(nodes_dict, op_place);
             }
             return named_outputs;
         });
     return f;
 }
 
-std::shared_ptr<ov::Model> FrontEndPDPD::decode(const InputModel::Ptr& model) const {
-    auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
-    FRONT_END_GENERAL_CHECK(pdpd_model != nullptr, "Invalid input model");
+std::shared_ptr<ov::Model> FrontEnd::decode(const InputModel::Ptr& model) const {
+    auto paddle_model = std::dynamic_pointer_cast<InputModel>(model);
+    FRONT_END_GENERAL_CHECK(paddle_model != nullptr, "Invalid input model");
 
-    std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
-    auto f = convert_each_node(pdpd_model, pdpd::make_framework_node);
+    std::map<std::string, paddle::CreatorFunction> CREATORS_MAP = paddle::get_supported_ops();
+    auto f = convert_each_node(paddle_model, paddle::make_framework_node);
     return f;
 }
 
-std::string FrontEndPDPD::get_name() const {
+std::string FrontEnd::get_name() const {
     return "paddle";
 }
 
-void FrontEndPDPD::add_extension(const std::shared_ptr<ov::Extension>& extension) {
+void FrontEnd::add_extension(const std::shared_ptr<ov::Extension>& extension) {
     if (auto telemetry = std::dynamic_pointer_cast<TelemetryExtension>(extension)) {
         m_telemetry = telemetry;
     } else if (auto transformation = std::dynamic_pointer_cast<DecoderTransformationExtension>(extension)) {
@@ -365,18 +365,19 @@ void FrontEndPDPD::add_extension(const std::shared_ptr<ov::Extension>& extension
     }
 }
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
 
-PDPD_C_API FrontEndVersion GetAPIVersion() {
+PADDLE_C_API FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
-PDPD_C_API void* GetFrontEndData() {
+PADDLE_C_API void* GetFrontEndData() {
     FrontEndPluginInfo* res = new FrontEndPluginInfo();
     res->m_name = "paddle";
     res->m_creator = []() {
-        return std::make_shared<FrontEndPDPD>();
+        return std::make_shared<ov::frontend::paddle::FrontEnd>();
     };
     return res;
 }
diff --git a/src/frontends/paddlepaddle/src/model.cpp b/src/frontends/paddle/src/input_model.cpp
similarity index 62%
rename from src/frontends/paddlepaddle/src/model.cpp
rename to src/frontends/paddle/src/input_model.cpp
index b499aedd719..84cb362b787 100644
--- a/src/frontends/paddlepaddle/src/model.cpp
+++ b/src/frontends/paddle/src/input_model.cpp
@@ -2,18 +2,18 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "paddlepaddle_frontend/model.hpp"
+#include "input_model.hpp"
 
 #include <fstream>
 #include <queue>
 
 #include "decoder.hpp"
-#include "exceptions.hpp"
 #include "framework.pb.h"
+#include "input_model.hpp"
 #include "node_context.hpp"
 #include "openvino/opsets/opset7.hpp"
-#include "paddlepaddle_frontend/place.hpp"
-#include "pdpd_utils.hpp"
+#include "paddle_utils.hpp"
+#include "place.hpp"
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
 #    include <codecvt>
@@ -22,17 +22,19 @@
 
 namespace ov {
 namespace frontend {
-using namespace paddle::framework::proto;
+namespace paddle {
 
-class InputModelPDPD::InputModelPDPDImpl {
+using namespace ::paddle::framework::proto;
+
+class InputModel::InputModelImpl {
 public:
     template <typename T>
-    InputModelPDPDImpl(const std::basic_string<T>& path,
-                       const InputModel& input_model,
-                       const std::shared_ptr<TelemetryExtension>& telemetry);
-    InputModelPDPDImpl(const std::vector<std::istream*>& streams,
-                       const InputModel& input_model,
-                       const std::shared_ptr<TelemetryExtension>& telemetry);
+    InputModelImpl(const std::basic_string<T>& path,
+                   const InputModel& input_model,
+                   const std::shared_ptr<TelemetryExtension>& telemetry);
+    InputModelImpl(const std::vector<std::istream*>& streams,
+                   const InputModel& input_model,
+                   const std::shared_ptr<TelemetryExtension>& telemetry);
     std::vector<Place::Ptr> getInputs() const;
     std::vector<Place::Ptr> getOutputs() const;
     Place::Ptr getPlaceByTensorName(const std::string& tensorName) const;
@@ -45,11 +47,11 @@ public:
     void setElementType(Place::Ptr place, const ov::element::Type&);
     void setTensorValue(Place::Ptr place, const void* value);
 
-    std::vector<std::shared_ptr<OpPlacePDPD>> get_op_places() const;
-    std::map<std::string, std::shared_ptr<TensorPlacePDPD>> get_var_places() const {
+    std::vector<std::shared_ptr<OpPlace>> get_op_places() const;
+    std::map<std::string, std::shared_ptr<TensorPlace>> get_var_places() const {
         return m_var_places;
     }
-    std::map<pdpd::TensorName, Output<Node>> get_tensor_values() const {
+    std::map<paddle::TensorName, Output<Node>> get_tensor_values() const {
         return m_tensor_values;
     };
 
@@ -57,15 +59,15 @@ private:
     void loadPlaces();
     template <typename T>
     void loadConsts(const std::basic_string<T>& folder_with_weights, std::istream* weight_stream);
-    std::vector<std::shared_ptr<OpPlacePDPD>> determine_cut_nodes() const;
+    std::vector<std::shared_ptr<OpPlace>> determine_cut_nodes() const;
 
-    std::vector<std::shared_ptr<OpPlacePDPD>> m_op_places;
-    std::map<std::string, std::shared_ptr<TensorPlacePDPD>> m_var_places;
+    std::vector<std::shared_ptr<OpPlace>> m_op_places;
+    std::map<std::string, std::shared_ptr<TensorPlace>> m_var_places;
     std::shared_ptr<ProgramDesc> m_fw_ptr;
     const InputModel& m_input_model;
     std::vector<Place::Ptr> m_inputs;
     std::vector<Place::Ptr> m_outputs;
-    std::map<pdpd::TensorName, Output<Node>> m_tensor_values;
+    std::map<paddle::TensorName, Output<Node>> m_tensor_values;
 
     std::shared_ptr<TelemetryExtension> m_telemetry;
 
@@ -73,7 +75,7 @@ private:
     bool m_graph_changed = false;
 };
 
-void InputModelPDPD::InputModelPDPDImpl::loadPlaces() {
+void InputModel::InputModelImpl::loadPlaces() {
     const int cnt_of_blocks = m_fw_ptr->blocks_size();
     const auto& blocks = m_fw_ptr->blocks();
     std::map<std::string, uint64_t> op_statistics;
@@ -82,11 +84,11 @@ void InputModelPDPD::InputModelPDPDImpl::loadPlaces() {
         const auto& block = blocks[block_idx];
 
         for (const auto& var : block.vars()) {
-            m_var_places[var.name()] = std::make_shared<TensorPlacePDPD>(m_input_model, var);
+            m_var_places[var.name()] = std::make_shared<TensorPlace>(m_input_model, var);
         }
 
         for (const auto& op : block.ops()) {
-            auto op_place = std::make_shared<OpPlacePDPD>(m_input_model, op);
+            auto op_place = std::make_shared<OpPlace>(m_input_model, op);
 
             if (m_telemetry) {
                 op_statistics[op.type()]++;
@@ -96,7 +98,7 @@ void InputModelPDPD::InputModelPDPDImpl::loadPlaces() {
 
             for (const auto& output : op.outputs()) {
                 for (const auto& var_name : output.arguments()) {
-                    auto out_port = std::make_shared<OutPortPlacePDPD>(m_input_model);
+                    auto out_port = std::make_shared<OutPortPlace>(m_input_model);
 
                     // connect out_port and tensor
                     const auto& tensor = m_var_places.at(var_name);
@@ -111,7 +113,7 @@ void InputModelPDPD::InputModelPDPDImpl::loadPlaces() {
 
             for (const auto& input : op.inputs()) {
                 for (const auto& var_name : input.arguments()) {
-                    auto in_port = std::make_shared<InPortPlacePDPD>(m_input_model);
+                    auto in_port = std::make_shared<InPortPlace>(m_input_model);
 
                     // connect in_port and tensor
                     const auto& tensor = m_var_places.at(var_name);
@@ -126,8 +128,8 @@ void InputModelPDPD::InputModelPDPDImpl::loadPlaces() {
 
             // Determine outputs and inputs
             if (op.type() == "feed") {
-                const auto& place = op_place->get_output_port_pdpd("Out", 0);
-                const auto& var_place = std::dynamic_pointer_cast<TensorPlacePDPD>(place->get_target_tensor_pdpd());
+                const auto& place = op_place->get_output_port_paddle("Out", 0);
+                const auto& var_place = std::dynamic_pointer_cast<TensorPlace>(place->get_target_tensor_paddle());
                 const auto& tensor_desc = var_place->get_desc().type().lod_tensor().tensor();
                 const auto& dims = tensor_desc.dims();
 
@@ -135,8 +137,8 @@ void InputModelPDPD::InputModelPDPDImpl::loadPlaces() {
                 var_place->set_partial_shape(PartialShape(std::vector<Dimension>(dims.begin(), dims.end())));
                 m_inputs.push_back(var_place);
             } else if (op.type() == "fetch") {
-                auto place = op_place->get_input_port_pdpd("X", 0);
-                m_outputs.push_back(place->get_source_tensor_pdpd());
+                auto place = op_place->get_input_port_paddle("X", 0);
+                m_outputs.push_back(place->get_source_tensor_paddle());
             }
         }
     }
@@ -163,7 +165,7 @@ bool read_tensor(std::istream& is, char* data, size_t len) {
 
 template <typename T>
 std::basic_string<T> get_const_path(const std::basic_string<T>& folder_with_weights, const std::string& name) {
-    return folder_with_weights + pdpd::get_path_sep<T>() + name;
+    return folder_with_weights + paddle::get_path_sep<T>() + name;
 }
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
@@ -171,7 +173,7 @@ template <>
 std::basic_string<wchar_t> get_const_path(const std::basic_string<wchar_t>& folder, const std::string& name) {
     std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
     std::wstring _name = converter.from_bytes(name);
-    return folder + pdpd::get_path_sep<wchar_t>() + _name;
+    return folder + paddle::get_path_sep<wchar_t>() + _name;
 }
 #endif
 
@@ -179,7 +181,7 @@ template <typename T>
 std::basic_string<T> get_model_path(const std::basic_string<T>& path, std::ifstream* weights_stream) {
     std::string model_file{path};
     std::string ext = ".pdmodel";
-    if (pdpd::endsWith(model_file, ext)) {
+    if (paddle::endsWith(model_file, ext)) {
         std::string params_ext = ".pdiparams";
         std::string weights_file{path};
         weights_file.replace(weights_file.size() - ext.size(), ext.size(), params_ext);
@@ -187,7 +189,7 @@ std::basic_string<T> get_model_path(const std::basic_string<T>& path, std::ifstr
         // Don't throw error if file isn't opened
         // It may mean that model don't have constants
     } else {
-        model_file += pdpd::get_path_sep<T>() + "__model__";
+        model_file += paddle::get_path_sep<T>() + "__model__";
     }
     return model_file;
 }
@@ -197,7 +199,7 @@ template <>
 std::basic_string<wchar_t> get_model_path(const std::basic_string<wchar_t>& path, std::ifstream* weights_stream) {
     std::wstring model_file{path};
     std::wstring ext = L".pdmodel";
-    if (pdpd::endsWith(model_file, ext)) {
+    if (paddle::endsWith(model_file, ext)) {
         std::wstring params_ext = L".pdiparams";
         std::wstring weights_file{path};
         weights_file.replace(weights_file.size() - ext.size(), ext.size(), params_ext);
@@ -205,34 +207,34 @@ std::basic_string<wchar_t> get_model_path(const std::basic_string<wchar_t>& path
         // Don't throw error if file isn't opened
         // It may mean that model don't have constants
     } else {
-        model_file += pdpd::get_path_sep<wchar_t>() + L"__model__";
+        model_file += paddle::get_path_sep<wchar_t>() + L"__model__";
     }
     return model_file;
 }
 #endif
 }  // namespace
 
-std::vector<std::shared_ptr<OpPlacePDPD>> InputModelPDPD::InputModelPDPDImpl::get_op_places() const {
+std::vector<std::shared_ptr<OpPlace>> InputModel::InputModelImpl::get_op_places() const {
     if (m_graph_changed) {
         return determine_cut_nodes();
     }
     return m_op_places;
 }
 
-std::vector<std::shared_ptr<OpPlacePDPD>> InputModelPDPD::InputModelPDPDImpl::determine_cut_nodes() const {
-    std::queue<OpPlacePDPD*> q;
-    std::unordered_set<OpPlacePDPD*> visited;
-    std::vector<std::shared_ptr<OpPlacePDPD>> new_op_places;
+std::vector<std::shared_ptr<OpPlace>> InputModel::InputModelImpl::determine_cut_nodes() const {
+    std::queue<OpPlace*> q;
+    std::unordered_set<OpPlace*> visited;
+    std::vector<std::shared_ptr<OpPlace>> new_op_places;
     new_op_places.reserve(m_op_places.size());
     // Marking nodes from outputs to inputs/constants
     for (const auto& output : getOutputs()) {
         if (!output->is_input()) {
-            auto pdpd_output_op = std::dynamic_pointer_cast<OpPlacePDPD>(output->get_producing_operation());
-            PDPD_ASSERT(pdpd_output_op != nullptr, "Output doesn't have producing operation");
-            if (!visited.count(pdpd_output_op.get())) {
-                visited.insert(pdpd_output_op.get());
-                q.push(pdpd_output_op.get());
-                new_op_places.push_back(pdpd_output_op);
+            auto paddle_output_op = std::dynamic_pointer_cast<OpPlace>(output->get_producing_operation());
+            FRONT_END_GENERAL_CHECK(paddle_output_op != nullptr, "Output doesn't have producing operation");
+            if (!visited.count(paddle_output_op.get())) {
+                visited.insert(paddle_output_op.get());
+                q.push(paddle_output_op.get());
+                new_op_places.push_back(paddle_output_op);
             }
         }
     }
@@ -243,12 +245,12 @@ std::vector<std::shared_ptr<OpPlacePDPD>> InputModelPDPD::InputModelPDPDImpl::de
             for (const auto& port : map_pair.second) {
                 auto tensor = port->get_source_tensor();
                 if (tensor && !tensor->is_input() && !m_tensor_values.count(tensor->get_names()[0])) {
-                    std::shared_ptr<OpPlacePDPD> pdpd_op =
-                        std::dynamic_pointer_cast<OpPlacePDPD>(tensor->get_producing_operation());
-                    if (pdpd_op && !visited.count(pdpd_op.get())) {
-                        visited.insert(pdpd_op.get());
-                        q.push(pdpd_op.get());
-                        new_op_places.push_back(pdpd_op);
+                    std::shared_ptr<OpPlace> paddle_op =
+                        std::dynamic_pointer_cast<OpPlace>(tensor->get_producing_operation());
+                    if (paddle_op && !visited.count(paddle_op.get())) {
+                        visited.insert(paddle_op.get());
+                        q.push(paddle_op.get());
+                        new_op_places.push_back(paddle_op);
                     }
                 }
             }
@@ -259,17 +261,17 @@ std::vector<std::shared_ptr<OpPlacePDPD>> InputModelPDPD::InputModelPDPDImpl::de
 }
 
 template <typename T>
-void InputModelPDPD::InputModelPDPDImpl::loadConsts(const std::basic_string<T>& folder_with_weights,
-                                                    std::istream* weight_stream) {
+void InputModel::InputModelImpl::loadConsts(const std::basic_string<T>& folder_with_weights,
+                                            std::istream* weight_stream) {
     for (const auto& item : m_var_places) {
         const auto& var_desc = item.second->get_desc();
         const auto& name = item.first;
-        if (pdpd::endsWith(name, std::string{"feed"}) || pdpd::endsWith(name, std::string{"fetch"}))
+        if (paddle::endsWith(name, std::string{"feed"}) || paddle::endsWith(name, std::string{"fetch"}))
             continue;
         if (!var_desc.persistable())
             continue;
 
-        FRONT_END_GENERAL_CHECK(var_desc.type().type() == paddle::framework::proto::VarType::LOD_TENSOR);
+        FRONT_END_GENERAL_CHECK(var_desc.type().type() == ::paddle::framework::proto::VarType::LOD_TENSOR);
         const auto& tensor = var_desc.type().lod_tensor().tensor();
         Shape shape(tensor.dims().cbegin(), tensor.dims().cend());
         const auto& type = TYPE_MAP[tensor.data_type()];
@@ -298,9 +300,9 @@ void InputModelPDPD::InputModelPDPDImpl::loadConsts(const std::basic_string<T>&
 }
 
 template <typename T>
-InputModelPDPD::InputModelPDPDImpl::InputModelPDPDImpl(const std::basic_string<T>& path,
-                                                       const InputModel& input_model,
-                                                       const std::shared_ptr<TelemetryExtension>& telemetry)
+InputModel::InputModelImpl::InputModelImpl(const std::basic_string<T>& path,
+                                           const InputModel& input_model,
+                                           const std::shared_ptr<TelemetryExtension>& telemetry)
     : m_fw_ptr{std::make_shared<ProgramDesc>()},
       m_input_model(input_model),
       m_telemetry(telemetry) {
@@ -312,8 +314,8 @@ InputModelPDPD::InputModelPDPDImpl::InputModelPDPDImpl(const std::basic_string<T
     FRONT_END_GENERAL_CHECK(m_fw_ptr->ParseFromIstream(&pb_stream), "Model can't be parsed");
     // According to Paddle, the saved model has the framework version
     // For example Paddle 2.1.0 is encoded as 2001000. 0 means the latest framework.
-    // https://github.com/PaddlePaddle/Paddle/blob/develop/cmake/version.cmake
-    // https://github.com/PaddlePaddle/Paddle/blob/2100816c5190693cc7dee181e96af72e9f0fbd1d/paddle/fluid/framework/program_desc.cc#L52
+    // https://github.com/paddle/Paddle/blob/develop/cmake/version.cmake
+    // https://github.com/paddle/Paddle/blob/2100816c5190693cc7dee181e96af72e9f0fbd1d/paddle/fluid/framework/program_desc.cc#L52
     int64_t version = m_fw_ptr->version().version();
     FRONT_END_GENERAL_CHECK(
         version >= 2000000 || version == 0,
@@ -326,9 +328,9 @@ InputModelPDPD::InputModelPDPDImpl::InputModelPDPDImpl(const std::basic_string<T
     }
 }
 
-InputModelPDPD::InputModelPDPDImpl::InputModelPDPDImpl(const std::vector<std::istream*>& streams,
-                                                       const InputModel& input_model,
-                                                       const std::shared_ptr<TelemetryExtension>& telemetry)
+InputModel::InputModelImpl::InputModelImpl(const std::vector<std::istream*>& streams,
+                                           const InputModel& input_model,
+                                           const std::shared_ptr<TelemetryExtension>& telemetry)
     : m_fw_ptr{std::make_shared<ProgramDesc>()},
       m_input_model(input_model),
       m_telemetry(telemetry) {
@@ -346,35 +348,35 @@ InputModelPDPD::InputModelPDPDImpl::InputModelPDPDImpl(const std::vector<std::is
         loadConsts(std::string(), streams[1]);
 }
 
-std::vector<Place::Ptr> InputModelPDPD::InputModelPDPDImpl::getInputs() const {
+std::vector<Place::Ptr> InputModel::InputModelImpl::getInputs() const {
     return m_inputs;
 }
 
-std::vector<Place::Ptr> InputModelPDPD::InputModelPDPDImpl::getOutputs() const {
+std::vector<Place::Ptr> InputModel::InputModelImpl::getOutputs() const {
     return m_outputs;
 }
 
-Place::Ptr InputModelPDPD::InputModelPDPDImpl::getPlaceByTensorName(const std::string& tensorName) const {
+Place::Ptr InputModel::InputModelImpl::getPlaceByTensorName(const std::string& tensorName) const {
     if (m_var_places.count(tensorName))
         return m_var_places.at(tensorName);
     return nullptr;
 }
 
 namespace {
-std::shared_ptr<TensorPlacePDPD> castToTensorPlace(const Place::Ptr& place) {
-    if (auto var_place = std::dynamic_pointer_cast<TensorPlacePDPD>(place)) {
+std::shared_ptr<TensorPlace> castToTensorPlace(const Place::Ptr& place) {
+    if (auto var_place = std::dynamic_pointer_cast<TensorPlace>(place)) {
         return var_place;
-    } else if (auto in_port_place = std::dynamic_pointer_cast<InPortPlacePDPD>(place)) {
-        return in_port_place->get_source_tensor_pdpd();
-    } else if (auto out_port_place = std::dynamic_pointer_cast<OutPortPlacePDPD>(place)) {
-        return out_port_place->get_target_tensor_pdpd();
+    } else if (auto in_port_place = std::dynamic_pointer_cast<InPortPlace>(place)) {
+        return in_port_place->get_source_tensor_paddle();
+    } else if (auto out_port_place = std::dynamic_pointer_cast<OutPortPlace>(place)) {
+        return out_port_place->get_target_tensor_paddle();
     }
-    FRONT_END_GENERAL_CHECK(false, "Cannot cast this Place to TensorPlacePDPD.");
+    FRONT_END_GENERAL_CHECK(false, "Cannot cast this Place to TensorPlacepaddle.");
 }
 
 }  // namespace
 
-void InputModelPDPD::InputModelPDPDImpl::overrideAllInputs(const std::vector<Place::Ptr>& inputs) {
+void InputModel::InputModelImpl::overrideAllInputs(const std::vector<Place::Ptr>& inputs) {
     m_graph_changed = true;
     m_inputs.clear();
     for (const auto& inp : inputs) {
@@ -382,7 +384,7 @@ void InputModelPDPD::InputModelPDPDImpl::overrideAllInputs(const std::vector<Pla
     }
 }
 
-void InputModelPDPD::InputModelPDPDImpl::overrideAllOutputs(const std::vector<Place::Ptr>& outputs) {
+void InputModel::InputModelImpl::overrideAllOutputs(const std::vector<Place::Ptr>& outputs) {
     m_graph_changed = true;
     m_outputs.clear();
     for (const auto& outp : outputs) {
@@ -390,30 +392,30 @@ void InputModelPDPD::InputModelPDPDImpl::overrideAllOutputs(const std::vector<Pl
     }
 }
 
-void InputModelPDPD::InputModelPDPDImpl::extractSubgraph(const std::vector<Place::Ptr>& inputs,
-                                                         const std::vector<Place::Ptr>& outputs) {
+void InputModel::InputModelImpl::extractSubgraph(const std::vector<Place::Ptr>& inputs,
+                                                 const std::vector<Place::Ptr>& outputs) {
     m_graph_changed = true;
     overrideAllInputs(inputs);
     overrideAllOutputs(outputs);
 }
 
-void InputModelPDPD::InputModelPDPDImpl::setDefaultShape(Place::Ptr place, const ov::Shape& shape) {
+void InputModel::InputModelImpl::setDefaultShape(Place::Ptr place, const ov::Shape& shape) {
     FRONT_END_NOT_IMPLEMENTED("setDefaultShape");
 }
 
-void InputModelPDPD::InputModelPDPDImpl::setPartialShape(Place::Ptr place, const ov::PartialShape& p_shape) {
+void InputModel::InputModelImpl::setPartialShape(Place::Ptr place, const ov::PartialShape& p_shape) {
     castToTensorPlace(place)->set_partial_shape(p_shape);
 }
 
-ov::PartialShape InputModelPDPD::InputModelPDPDImpl::getPartialShape(Place::Ptr place) const {
+ov::PartialShape InputModel::InputModelImpl::getPartialShape(Place::Ptr place) const {
     return castToTensorPlace(place)->get_partial_shape();
 }
 
-void InputModelPDPD::InputModelPDPDImpl::setElementType(Place::Ptr place, const ov::element::Type& type) {
+void InputModel::InputModelImpl::setElementType(Place::Ptr place, const ov::element::Type& type) {
     castToTensorPlace(place)->set_element_type(type);
 }
 
-void InputModelPDPD::InputModelPDPDImpl::setTensorValue(Place::Ptr place, const void* value) {
+void InputModel::InputModelImpl::setTensorValue(Place::Ptr place, const void* value) {
     m_graph_changed = true;
     auto tensor_place = castToTensorPlace(place);
     auto p_shape = tensor_place->get_partial_shape();
@@ -424,69 +426,69 @@ void InputModelPDPD::InputModelPDPDImpl::setTensorValue(Place::Ptr place, const
     m_tensor_values[name] = constant;
 }
 
-InputModelPDPD::InputModelPDPD(const std::string& path, const std::shared_ptr<TelemetryExtension>& telemetry)
-    : _impl{std::make_shared<InputModelPDPDImpl>(path, *this, telemetry)} {}
+InputModel::InputModel(const std::string& path, const std::shared_ptr<TelemetryExtension>& telemetry)
+    : _impl{std::make_shared<InputModelImpl>(path, *this, telemetry)} {}
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-InputModelPDPD::InputModelPDPD(const std::wstring& path, const std::shared_ptr<TelemetryExtension>& telemetry)
-    : _impl{std::make_shared<InputModelPDPDImpl>(path, *this, telemetry)} {}
+InputModel::InputModel(const std::wstring& path, const std::shared_ptr<TelemetryExtension>& telemetry)
+    : _impl{std::make_shared<InputModelImpl>(path, *this, telemetry)} {}
 #endif
 
-InputModelPDPD::InputModelPDPD(const std::vector<std::istream*>& streams,
-                               const std::shared_ptr<TelemetryExtension>& telemetry)
-    : _impl{std::make_shared<InputModelPDPDImpl>(streams, *this, telemetry)} {}
+InputModel::InputModel(const std::vector<std::istream*>& streams, const std::shared_ptr<TelemetryExtension>& telemetry)
+    : _impl{std::make_shared<InputModelImpl>(streams, *this, telemetry)} {}
 
-std::vector<std::shared_ptr<OpPlacePDPD>> InputModelPDPD::get_op_places() const {
+std::vector<std::shared_ptr<OpPlace>> InputModel::get_op_places() const {
     return _impl->get_op_places();
 }
 
-std::map<std::string, std::shared_ptr<TensorPlacePDPD>> InputModelPDPD::get_var_places() const {
+std::map<std::string, std::shared_ptr<TensorPlace>> InputModel::get_var_places() const {
     return _impl->get_var_places();
 }
 
-std::map<pdpd::TensorName, Output<Node>> InputModelPDPD::get_tensor_values() const {
+std::map<paddle::TensorName, Output<Node>> InputModel::get_tensor_values() const {
     return _impl->get_tensor_values();
 }
 
-std::vector<Place::Ptr> InputModelPDPD::get_inputs() const {
+std::vector<Place::Ptr> InputModel::get_inputs() const {
     return _impl->getInputs();
 }
 
-std::vector<Place::Ptr> InputModelPDPD::get_outputs() const {
+std::vector<Place::Ptr> InputModel::get_outputs() const {
     return _impl->getOutputs();
 }
 
-Place::Ptr InputModelPDPD::get_place_by_tensor_name(const std::string& tensorName) const {
+Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensorName) const {
     return _impl->getPlaceByTensorName(tensorName);
 }
 
-void InputModelPDPD::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
+void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
     _impl->overrideAllOutputs(outputs);
 }
 
-void InputModelPDPD::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
+void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
     _impl->overrideAllInputs(inputs);
 }
 
-void InputModelPDPD::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
+void InputModel::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
     _impl->extractSubgraph(inputs, outputs);
 }
 
-void InputModelPDPD::set_partial_shape(const Place::Ptr& place, const ov::PartialShape& p_shape) {
+void InputModel::set_partial_shape(const Place::Ptr& place, const ov::PartialShape& p_shape) {
     _impl->setPartialShape(place, p_shape);
 }
 
-ov::PartialShape InputModelPDPD::get_partial_shape(const Place::Ptr& place) const {
+ov::PartialShape InputModel::get_partial_shape(const Place::Ptr& place) const {
     return _impl->getPartialShape(place);
 }
 
-void InputModelPDPD::set_element_type(const Place::Ptr& place, const ov::element::Type& type) {
+void InputModel::set_element_type(const Place::Ptr& place, const ov::element::Type& type) {
     _impl->setElementType(place, type);
 }
 
-void InputModelPDPD::set_tensor_value(const Place::Ptr& place, const void* value) {
+void InputModel::set_tensor_value(const Place::Ptr& place, const void* value) {
     _impl->setTensorValue(place, value);
 }
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp b/src/frontends/paddle/src/input_model.hpp
similarity index 59%
rename from src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
rename to src/frontends/paddle/src/input_model.hpp
index 491af81b3ba..6a9beb2505d 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/model.hpp
+++ b/src/frontends/paddle/src/input_model.hpp
@@ -5,30 +5,23 @@
 #pragma once
 
 #include <openvino/frontend/extension/telemetry.hpp>
-#include <openvino/frontend/manager.hpp>
-
-#include "paddlepaddle_frontend/utility.hpp"
+#include <openvino/frontend/paddle/frontend.hpp>
 
 namespace ov {
 namespace frontend {
-class OpPlacePDPD;
-class TensorPlacePDPD;
-class PDPD_API InputModelPDPD : public InputModel {
-    friend class FrontEndPDPD;
-    class InputModelPDPDImpl;
-    std::shared_ptr<InputModelPDPDImpl> _impl;
+namespace paddle {
 
-    std::vector<std::shared_ptr<OpPlacePDPD>> get_op_places() const;
-    std::map<std::string, std::shared_ptr<TensorPlacePDPD>> get_var_places() const;
-    std::map<std::string, Output<Node>> get_tensor_values() const;
+class OpPlace;
+class TensorPlace;
 
+class InputModel : public ov::frontend::InputModel {
 public:
-    explicit InputModelPDPD(const std::string& path, const std::shared_ptr<TelemetryExtension>& telemetry = {});
+    explicit InputModel(const std::string& path, const std::shared_ptr<TelemetryExtension>& telemetry = {});
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-    explicit InputModelPDPD(const std::wstring& path, const std::shared_ptr<TelemetryExtension>& telemetry = {});
+    explicit InputModel(const std::wstring& path, const std::shared_ptr<TelemetryExtension>& telemetry = {});
 #endif
-    explicit InputModelPDPD(const std::vector<std::istream*>& streams,
-                            const std::shared_ptr<TelemetryExtension>& telemetry = {});
+    explicit InputModel(const std::vector<std::istream*>& streams,
+                        const std::shared_ptr<TelemetryExtension>& telemetry = {});
     std::vector<Place::Ptr> get_inputs() const override;
     std::vector<Place::Ptr> get_outputs() const override;
     Place::Ptr get_place_by_tensor_name(const std::string& tensorName) const override;
@@ -39,7 +32,17 @@ public:
     ov::PartialShape get_partial_shape(const Place::Ptr& place) const override;
     void set_element_type(const Place::Ptr& place, const ov::element::Type&) override;
     void set_tensor_value(const Place::Ptr& place, const void* value) override;
+
+private:
+    friend class ov::frontend::paddle::FrontEnd;
+    class InputModelImpl;
+    std::shared_ptr<InputModelImpl> _impl;
+
+    std::vector<std::shared_ptr<OpPlace>> get_op_places() const;
+    std::map<std::string, std::shared_ptr<TensorPlace>> get_var_places() const;
+    std::map<std::string, Output<Node>> get_tensor_values() const;
 };
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/node_context.hpp b/src/frontends/paddle/src/node_context.hpp
similarity index 88%
rename from src/frontends/paddlepaddle/src/node_context.hpp
rename to src/frontends/paddle/src/node_context.hpp
index 6dfe727f6ee..4525ea948c2 100644
--- a/src/frontends/paddlepaddle/src/node_context.hpp
+++ b/src/frontends/paddle/src/node_context.hpp
@@ -4,13 +4,13 @@
 
 #pragma once
 #include "exceptions.hpp"
-#include "ngraph/compatibility.hpp"
 #include "openvino/core/any.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
+#include "openvino/core/node.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
+
 using InPortName = std::string;
 using OutPortName = std::string;
 using TensorName = std::string;
@@ -119,24 +119,24 @@ public:
     }
 
     NamedOutputs default_single_output_mapping(const std::shared_ptr<Node>& node,
-                                               const std::vector<OutPortName>& required_pdpd_out_names) const;
+                                               const std::vector<OutPortName>& required_paddle_out_names) const;
 };
 
 inline NamedOutputs NodeContext::default_single_output_mapping(
     const std::shared_ptr<Node>& node,
-    const std::vector<OutPortName>& required_pdpd_out_names) const {
+    const std::vector<OutPortName>& required_paddle_out_names) const {
     NamedOutputs named_outputs;
     const auto& outputs = node->outputs();
-    const auto& pdpd_op_output_names = this->get_output_names();
+    const auto& paddle_op_output_names = this->get_output_names();
     FRONT_END_GENERAL_CHECK(outputs.size() == 1, "OV node must have exactly one output");
-    for (const auto& pdpd_name : pdpd_op_output_names) {
-        if (std::find(required_pdpd_out_names.begin(), required_pdpd_out_names.end(), pdpd_name) !=
-            required_pdpd_out_names.end())
-            named_outputs[pdpd_name] = {outputs[0]};
+    for (const auto& paddle_name : paddle_op_output_names) {
+        if (std::find(required_paddle_out_names.begin(), required_paddle_out_names.end(), paddle_name) !=
+            required_paddle_out_names.end())
+            named_outputs[paddle_name] = {outputs[0]};
     }
     return named_outputs;
 }
 
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/argmax.cpp b/src/frontends/paddle/src/op/argmax.cpp
similarity index 97%
rename from src/frontends/paddlepaddle/src/op/argmax.cpp
rename to src/frontends/paddle/src/op/argmax.cpp
index 265c12ad879..637f80013ce 100644
--- a/src/frontends/paddlepaddle/src/op/argmax.cpp
+++ b/src/frontends/paddle/src/op/argmax.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs argmax(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -36,6 +36,6 @@ NamedOutputs argmax(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/assign_value.cpp b/src/frontends/paddle/src/op/assign_value.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/assign_value.cpp
rename to src/frontends/paddle/src/op/assign_value.cpp
index 93b2fdfb08e..41fa114037b 100644
--- a/src/frontends/paddlepaddle/src/op/assign_value.cpp
+++ b/src/frontends/paddle/src/op/assign_value.cpp
@@ -7,7 +7,7 @@
 #include "openvino/opsets/opset6.hpp"
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs assign_value(const NodeContext& node) {
     std::vector<int32_t> shape = node.get_attribute<std::vector<int32_t>>("shape");
@@ -36,7 +36,7 @@ NamedOutputs assign_value(const NodeContext& node) {
         break;
     }
     default: {
-        PDPD_OP_VALIDATION_CHECK(node, false, "assign_value only supports int32, int64, float32, bool");
+        PADDLE_OP_CHECK(node, false, "assign_value only supports int32, int64, float32, bool");
         break;
     }
     }
@@ -45,6 +45,6 @@ NamedOutputs assign_value(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/batch_norm.cpp b/src/frontends/paddle/src/op/batch_norm.cpp
similarity index 93%
rename from src/frontends/paddlepaddle/src/op/batch_norm.cpp
rename to src/frontends/paddle/src/op/batch_norm.cpp
index 86f4cea41fc..e608b1da86e 100644
--- a/src/frontends/paddlepaddle/src/op/batch_norm.cpp
+++ b/src/frontends/paddle/src/op/batch_norm.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs batch_norm(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -18,7 +18,7 @@ NamedOutputs batch_norm(const NodeContext& node) {
     auto variance = node.get_ng_input("Variance");
     auto data_layout = node.get_attribute<std::string>("data_layout");
 
-    PDPD_ASSERT((data_layout == "NCHW" || data_layout == "NHWC"), "Not supported input data layout!");
+    PADDLE_OP_CHECK(node, (data_layout == "NCHW" || data_layout == "NHWC"), "Not supported input data layout!");
     if (data_layout == "NCHW") {
         return node.default_single_output_mapping(
             {std::make_shared<ov::opset6::BatchNormInference>(data,
@@ -45,6 +45,6 @@ NamedOutputs batch_norm(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/cast.cpp b/src/frontends/paddle/src/op/cast.cpp
similarity index 92%
rename from src/frontends/paddlepaddle/src/op/cast.cpp
rename to src/frontends/paddle/src/op/cast.cpp
index 4530705483a..9a5dac28e8a 100644
--- a/src/frontends/paddlepaddle/src/op/cast.cpp
+++ b/src/frontends/paddle/src/op/cast.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs cast(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -18,6 +18,6 @@ NamedOutputs cast(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/clip.cpp b/src/frontends/paddle/src/op/clip.cpp
similarity index 80%
rename from src/frontends/paddlepaddle/src/op/clip.cpp
rename to src/frontends/paddle/src/op/clip.cpp
index c34806a1d53..e22a8368a5c 100644
--- a/src/frontends/paddlepaddle/src/op/clip.cpp
+++ b/src/frontends/paddle/src/op/clip.cpp
@@ -8,18 +8,18 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs clip(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     auto min = node.get_attribute<float>("min");
     auto max = node.get_attribute<float>("max");
-    PDPD_OP_VALIDATION_CHECK(node, max >= min, "clip: max value must greater than min value!");
+    PADDLE_OP_CHECK(node, max >= min, "clip: max value must greater than min value!");
 
     return node.default_single_output_mapping({std::make_shared<ov::opset6::Clamp>(data, min, max)}, {"Out"});
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/concat.cpp b/src/frontends/paddle/src/op/concat.cpp
similarity index 92%
rename from src/frontends/paddlepaddle/src/op/concat.cpp
rename to src/frontends/paddle/src/op/concat.cpp
index 512df6cc806..df744e8ae99 100644
--- a/src/frontends/paddlepaddle/src/op/concat.cpp
+++ b/src/frontends/paddle/src/op/concat.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs concat(const NodeContext& node) {
     auto data = node.get_ng_inputs("X");
@@ -17,6 +17,6 @@ NamedOutputs concat(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/conv2d.cpp b/src/frontends/paddle/src/op/conv2d.cpp
similarity index 90%
rename from src/frontends/paddlepaddle/src/op/conv2d.cpp
rename to src/frontends/paddle/src/op/conv2d.cpp
index cd71b250fcd..5c2ff481024 100644
--- a/src/frontends/paddlepaddle/src/op/conv2d.cpp
+++ b/src/frontends/paddle/src/op/conv2d.cpp
@@ -7,13 +7,13 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs conv2d(const NodeContext& node) {
     return conv2d_base<opset6::GroupConvolution, opset6::Convolution>(node);
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/conv2d_transpose.cpp b/src/frontends/paddle/src/op/conv2d_transpose.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/conv2d_transpose.cpp
rename to src/frontends/paddle/src/op/conv2d_transpose.cpp
index e0f8db7655f..3ddf75a7d72 100644
--- a/src/frontends/paddlepaddle/src/op/conv2d_transpose.cpp
+++ b/src/frontends/paddle/src/op/conv2d_transpose.cpp
@@ -9,13 +9,13 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs conv2d_transpose(const NodeContext& node) {
     return conv2d_base<opset6::GroupConvolutionBackpropData, opset6::ConvolutionBackpropData>(node);
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/conv2d_utils.cpp b/src/frontends/paddle/src/op/conv2d_utils.cpp
similarity index 96%
rename from src/frontends/paddlepaddle/src/op/conv2d_utils.cpp
rename to src/frontends/paddle/src/op/conv2d_utils.cpp
index 7155b896514..e22bd823a28 100644
--- a/src/frontends/paddlepaddle/src/op/conv2d_utils.cpp
+++ b/src/frontends/paddle/src/op/conv2d_utils.cpp
@@ -10,7 +10,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 ov::op::PadType get_auto_pad(const NodeContext& node) {
     // Default value means use explicitly provided padding values.
@@ -59,7 +59,7 @@ std::pair<CoordinateDiff, CoordinateDiff> get_pads(const NodeContext& node, cons
 
 std::pair<CoordinateDiff, CoordinateDiff> get_pads(const NodeContext& node) {
     const auto data_rank = node.get_ng_input("Input").get_partial_shape().rank();
-    PDPD_ASSERT(data_rank.get_length() > 2, "the rank of conv input must > 2");
+    PADDLE_OP_CHECK(node, data_rank.get_length() > 2, "the rank of conv input must > 2");
     const auto data_spatial_dims = data_rank.get_length() - 2;
 
     return get_pads(node, data_spatial_dims);
@@ -84,6 +84,6 @@ std::shared_ptr<Node> get_reshaped_filter(const Output<Node>& filters, const int
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/conv2d_utils.hpp b/src/frontends/paddle/src/op/conv2d_utils.hpp
similarity index 95%
rename from src/frontends/paddlepaddle/src/op/conv2d_utils.hpp
rename to src/frontends/paddle/src/op/conv2d_utils.hpp
index 6ed034231f5..5cf6c553c76 100644
--- a/src/frontends/paddlepaddle/src/op/conv2d_utils.hpp
+++ b/src/frontends/paddle/src/op/conv2d_utils.hpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 ov::op::PadType get_auto_pad(const NodeContext& node);
 std::pair<CoordinateDiff, CoordinateDiff> get_pads(const NodeContext& node);
@@ -28,7 +28,7 @@ NamedOutputs conv2d_base(const NodeContext& node) {
     const auto groups = node.get_attribute<int32_t>("groups");
     const auto data_format = node.get_attribute<std::string>("data_format");
     // TODO Support Other data layout #55423
-    PDPD_ASSERT(data_format == "NCHW", "conv2d only supports NCHW now");
+    PADDLE_OP_CHECK(node, data_format == "NCHW", "conv2d only supports NCHW now");
 
     if (groups > 1) {
         const auto reshaped_filters = get_reshaped_filter(filters, groups);
@@ -54,6 +54,6 @@ NamedOutputs conv2d_base(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/cumsum.cpp b/src/frontends/paddle/src/op/cumsum.cpp
similarity index 96%
rename from src/frontends/paddlepaddle/src/op/cumsum.cpp
rename to src/frontends/paddle/src/op/cumsum.cpp
index a56724f0bf5..4d55bdb567c 100644
--- a/src/frontends/paddlepaddle/src/op/cumsum.cpp
+++ b/src/frontends/paddle/src/op/cumsum.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs cumsum(const NodeContext& node) {
     const auto x = node.get_ng_input("X");
@@ -32,6 +32,6 @@ NamedOutputs cumsum(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/deformable_conv.cpp b/src/frontends/paddle/src/op/deformable_conv.cpp
similarity index 98%
rename from src/frontends/paddlepaddle/src/op/deformable_conv.cpp
rename to src/frontends/paddle/src/op/deformable_conv.cpp
index 93b03e9d9ec..ea569d002ac 100644
--- a/src/frontends/paddlepaddle/src/op/deformable_conv.cpp
+++ b/src/frontends/paddle/src/op/deformable_conv.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs deformable_conv(const NodeContext& node) {
     auto input = node.get_ng_input("Input");
@@ -62,6 +62,6 @@ NamedOutputs deformable_conv(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/dropout.cpp b/src/frontends/paddle/src/op/dropout.cpp
similarity index 79%
rename from src/frontends/paddlepaddle/src/op/dropout.cpp
rename to src/frontends/paddle/src/op/dropout.cpp
index fd4d5546184..40206acadf2 100644
--- a/src/frontends/paddlepaddle/src/op/dropout.cpp
+++ b/src/frontends/paddle/src/op/dropout.cpp
@@ -8,15 +8,14 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs dropout(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     auto dropout_implementation = node.get_attribute<std::string>("dropout_implementation");
-    PDPD_OP_VALIDATION_CHECK(
-        node,
-        (dropout_implementation == "downgrade_in_infer" || dropout_implementation == "upscale_in_train"),
-        "Unsupported dropout mode!");
+    PADDLE_OP_CHECK(node,
+                    (dropout_implementation == "downgrade_in_infer" || dropout_implementation == "upscale_in_train"),
+                    "Unsupported dropout mode!");
     if (dropout_implementation == "downgrade_in_infer") {
         auto dropout_prob =
             ov::opset6::Constant::create(ov::element::f32, {1}, {1 - node.get_attribute<float>("dropout_prob")});
@@ -28,6 +27,6 @@ NamedOutputs dropout(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/elementwise_ops.cpp b/src/frontends/paddle/src/op/elementwise_ops.cpp
similarity index 85%
rename from src/frontends/paddlepaddle/src/op/elementwise_ops.cpp
rename to src/frontends/paddle/src/op/elementwise_ops.cpp
index d62707771fd..948089a33c3 100644
--- a/src/frontends/paddlepaddle/src/op/elementwise_ops.cpp
+++ b/src/frontends/paddle/src/op/elementwise_ops.cpp
@@ -9,7 +9,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 template <typename T>
 NamedOutputs elementwise_ops(const NodeContext& node) {
@@ -18,8 +18,8 @@ NamedOutputs elementwise_ops(const NodeContext& node) {
 
     auto axis = node.get_attribute<int>("axis");
 
-    PDPD_OP_VALIDATION_CHECK(node, x.get_partial_shape().rank().is_static(), "elementwise_ops: X rank must be static!");
-    PDPD_OP_VALIDATION_CHECK(node, y.get_partial_shape().rank().is_static(), "elementwise_ops: Y rank must be static!");
+    PADDLE_OP_CHECK(node, x.get_partial_shape().rank().is_static(), "elementwise_ops: X rank must be static!");
+    PADDLE_OP_CHECK(node, y.get_partial_shape().rank().is_static(), "elementwise_ops: Y rank must be static!");
     int64_t x_rank = x.get_partial_shape().rank().get_length();
     int64_t y_rank = y.get_partial_shape().rank().get_length();
 
@@ -32,8 +32,7 @@ NamedOutputs elementwise_ops(const NodeContext& node) {
         for (int64_t i = y_rank + axis; i < x_rank; i++)
             indices.push_back(i);
 
-        auto indices_node =
-            default_opset::Constant::create(ngraph::element::i64, ngraph::Shape{indices.size()}, indices);
+        auto indices_node = default_opset::Constant::create(ov::element::i64, ov::Shape{indices.size()}, indices);
         auto y_node = std::make_shared<default_opset::Unsqueeze>(y, indices_node);
         return node.default_single_output_mapping({std::make_shared<T>(x, y_node)}, {"Out"});
     }
@@ -77,6 +76,6 @@ NamedOutputs elementwise_greater_equal(const NodeContext& node_context) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/embedding.cpp b/src/frontends/paddle/src/op/embedding.cpp
similarity index 98%
rename from src/frontends/paddlepaddle/src/op/embedding.cpp
rename to src/frontends/paddle/src/op/embedding.cpp
index e8cfe77f4e8..180ed514e78 100644
--- a/src/frontends/paddlepaddle/src/op/embedding.cpp
+++ b/src/frontends/paddle/src/op/embedding.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 using namespace opset8;
 using namespace element;
@@ -51,6 +51,6 @@ NamedOutputs embedding(const NodeContext& node) {
     return node.default_single_output_mapping({node_embedding}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/exp.cpp b/src/frontends/paddle/src/op/exp.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/exp.cpp
rename to src/frontends/paddle/src/op/exp.cpp
index 4af6f32a6b1..0ab58108185 100644
--- a/src/frontends/paddlepaddle/src/op/exp.cpp
+++ b/src/frontends/paddle/src/op/exp.cpp
@@ -8,13 +8,13 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs exp(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     return node.default_single_output_mapping({std::make_shared<default_opset::Exp>(data)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/expand_v2.cpp b/src/frontends/paddle/src/op/expand_v2.cpp
similarity index 96%
rename from src/frontends/paddlepaddle/src/op/expand_v2.cpp
rename to src/frontends/paddle/src/op/expand_v2.cpp
index 3cc59b41778..1a00ea2f5b1 100644
--- a/src/frontends/paddlepaddle/src/op/expand_v2.cpp
+++ b/src/frontends/paddle/src/op/expand_v2.cpp
@@ -5,11 +5,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs expand_v2(const NodeContext& node) {
     auto x = node.get_ng_input("X");
@@ -46,6 +45,6 @@ NamedOutputs expand_v2(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/fill_any_like.cpp b/src/frontends/paddle/src/op/fill_any_like.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/fill_any_like.cpp
rename to src/frontends/paddle/src/op/fill_any_like.cpp
index af52a2581b4..bdd1bec34dd 100644
--- a/src/frontends/paddlepaddle/src/op/fill_any_like.cpp
+++ b/src/frontends/paddle/src/op/fill_any_like.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs fill_any_like(const NodeContext& node) {
     const auto x = node.get_ng_input("X");
@@ -27,7 +27,7 @@ NamedOutputs fill_any_like(const NodeContext& node) {
         std::any_of(supported_type.begin(), supported_type.end(), [dtype](const element::Type& type) {
             return dtype == type;
         });
-    PDPD_ASSERT(valid_type, "fill_any_like only supports i32, i64, f16, f32, f64");
+    PADDLE_OP_CHECK(node, valid_type, "fill_any_like only supports i32, i64, f16, f32, f64");
     const auto value_node = default_opset::Constant::create(dtype, {1}, {value});
     const auto shape_node = std::make_shared<default_opset::ShapeOf>(x);
 
@@ -36,6 +36,6 @@ NamedOutputs fill_any_like(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/fill_constant.cpp b/src/frontends/paddle/src/op/fill_constant.cpp
similarity index 84%
rename from src/frontends/paddlepaddle/src/op/fill_constant.cpp
rename to src/frontends/paddle/src/op/fill_constant.cpp
index 0983bbabe41..450d8d902c7 100644
--- a/src/frontends/paddlepaddle/src/op/fill_constant.cpp
+++ b/src/frontends/paddle/src/op/fill_constant.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs fill_constant(const NodeContext& node) {
     auto shape = node.get_attribute<std::vector<int64_t>>("shape");
@@ -27,11 +27,12 @@ NamedOutputs fill_constant(const NodeContext& node) {
         int64_t value = static_cast<int64_t>(node.get_attribute<float>("value"));
         value_node = opset6::Constant::create(dtype, {1}, {value});
     } else {
-        PDPD_ASSERT(false, "fill_constant only supports i32, f32, i64");
+        PADDLE_OP_CHECK(node, false, "fill_constant only supports i32, f32, i64");
     }
 
-    PDPD_ASSERT(shape.size() > 0 || node.has_ng_input("ShapeTensor") || node.has_ng_input("ShapeTensorList"),
-                "fill_constant shape not set");
+    PADDLE_OP_CHECK(node,
+                    shape.size() > 0 || node.has_ng_input("ShapeTensor") || node.has_ng_input("ShapeTensorList"),
+                    "fill_constant shape not set");
 
     if (node.has_ng_input("ShapeTensor")) {
         shape_node = node.get_ng_input("ShapeTensor");
@@ -47,6 +48,6 @@ NamedOutputs fill_constant(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/fill_constant_batch_size_like.cpp b/src/frontends/paddle/src/op/fill_constant_batch_size_like.cpp
similarity index 98%
rename from src/frontends/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
rename to src/frontends/paddle/src/op/fill_constant_batch_size_like.cpp
index e0f4c5728b0..8cc5592ccbc 100644
--- a/src/frontends/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
+++ b/src/frontends/paddle/src/op/fill_constant_batch_size_like.cpp
@@ -7,11 +7,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 static std::shared_ptr<Node> get_val(int32_t idx, const Output<Node>& data) {
     auto startsNode = ov::opset6::Constant::create(element::i32, {1}, {idx});
@@ -120,6 +119,6 @@ NamedOutputs fill_constant_batch_size_like(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/flatten_contiguous_range.cpp b/src/frontends/paddle/src/op/flatten_contiguous_range.cpp
similarity index 98%
rename from src/frontends/paddlepaddle/src/op/flatten_contiguous_range.cpp
rename to src/frontends/paddle/src/op/flatten_contiguous_range.cpp
index 7a395248b1d..a079944f424 100644
--- a/src/frontends/paddlepaddle/src/op/flatten_contiguous_range.cpp
+++ b/src/frontends/paddle/src/op/flatten_contiguous_range.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs flatten_contiguous_range(const NodeContext& node) {
     auto x_node = node.get_ng_input("X");
@@ -42,6 +42,6 @@ NamedOutputs flatten_contiguous_range(const NodeContext& node) {
                                               {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/gelu.cpp b/src/frontends/paddle/src/op/gelu.cpp
similarity index 93%
rename from src/frontends/paddlepaddle/src/op/gelu.cpp
rename to src/frontends/paddle/src/op/gelu.cpp
index a5ed25ec636..de8e3ff0fa0 100644
--- a/src/frontends/paddlepaddle/src/op/gelu.cpp
+++ b/src/frontends/paddle/src/op/gelu.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs gelu(const NodeContext& node) {
     const auto data = node.get_ng_input("X");
@@ -18,6 +18,6 @@ NamedOutputs gelu(const NodeContext& node) {
     return node.default_single_output_mapping({std::make_shared<default_opset::Gelu>(data, mode)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/hard_sigmoid.cpp b/src/frontends/paddle/src/op/hard_sigmoid.cpp
similarity index 90%
rename from src/frontends/paddlepaddle/src/op/hard_sigmoid.cpp
rename to src/frontends/paddle/src/op/hard_sigmoid.cpp
index fa993e5e444..29dabfb23ef 100644
--- a/src/frontends/paddlepaddle/src/op/hard_sigmoid.cpp
+++ b/src/frontends/paddle/src/op/hard_sigmoid.cpp
@@ -5,11 +5,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs hard_sigmoid(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -22,6 +21,6 @@ NamedOutputs hard_sigmoid(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/hard_swish.cpp b/src/frontends/paddle/src/op/hard_swish.cpp
similarity index 64%
rename from src/frontends/paddlepaddle/src/op/hard_swish.cpp
rename to src/frontends/paddle/src/op/hard_swish.cpp
index 196705e0496..6261c66d950 100644
--- a/src/frontends/paddlepaddle/src/op/hard_swish.cpp
+++ b/src/frontends/paddle/src/op/hard_swish.cpp
@@ -5,30 +5,31 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs hard_swish(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     if (node.has_attribute<float>("threshold")) {
         auto threshold = node.get_attribute<float>("threshold");
-        PDPD_ASSERT(std::abs(threshold - 6.0) < 0.001, "hard_swish: Only threshold = 6.0 is currently supported");
+        PADDLE_OP_CHECK(node,
+                        std::abs(threshold - 6.0) < 0.001,
+                        "hard_swish: Only threshold = 6.0 is currently supported");
     }
     if (node.has_attribute<float>("scale")) {
         auto scale = node.get_attribute<float>("scale");
-        PDPD_ASSERT(std::abs(scale - 6.0) < 0.001, "hard_swish: Only scale = 6.0 is currently supported");
+        PADDLE_OP_CHECK(node, std::abs(scale - 6.0) < 0.001, "hard_swish: Only scale = 6.0 is currently supported");
     }
     if (node.has_attribute<float>("offset")) {
         auto offset = node.get_attribute<float>("offset");
-        PDPD_ASSERT(std::abs(offset - 3.0) < 0.001, "hard_swish: Only offset = 3.0 is currently supported");
+        PADDLE_OP_CHECK(node, std::abs(offset - 3.0) < 0.001, "hard_swish: Only offset = 3.0 is currently supported");
     }
     return node.default_single_output_mapping({std::make_shared<ov::opset6::HSwish>(data)}, {"Out"});
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/interp.cpp b/src/frontends/paddle/src/op/interp.cpp
similarity index 99%
rename from src/frontends/paddlepaddle/src/op/interp.cpp
rename to src/frontends/paddle/src/op/interp.cpp
index 7831ce06389..a8dd145bea4 100644
--- a/src/frontends/paddlepaddle/src/op/interp.cpp
+++ b/src/frontends/paddle/src/op/interp.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 using namespace default_opset;
 
@@ -173,6 +173,6 @@ NamedOutputs bicubic_interp_v2(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/layer_norm.cpp b/src/frontends/paddle/src/op/layer_norm.cpp
similarity index 90%
rename from src/frontends/paddlepaddle/src/op/layer_norm.cpp
rename to src/frontends/paddle/src/op/layer_norm.cpp
index 12599ad99bc..a4cdc6c25bd 100644
--- a/src/frontends/paddlepaddle/src/op/layer_norm.cpp
+++ b/src/frontends/paddle/src/op/layer_norm.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs layer_norm(const NodeContext& node) {
     using namespace default_opset;
@@ -16,8 +16,8 @@ NamedOutputs layer_norm(const NodeContext& node) {
     const auto epsilon = node.get_attribute<float>("epsilon", 1e-05);
     const auto begin_norm_axis = node.get_attribute<int32_t>("begin_norm_axis", 1);
     // The limitation from:
-    // https://github.com/PaddlePaddle/Paddle/blob/cec36ea6ff16fda90c1a004c6e043cd9b2096a2a/paddle/fluid/operators/layer_norm_op.cc#L176
-    PDPD_ASSERT(begin_norm_axis > 0, "begin_norm_axis should be greater than 0");
+    // https://github.com/paddle/Paddle/blob/cec36ea6ff16fda90c1a004c6e043cd9b2096a2a/paddle/fluid/operators/layer_norm_op.cc#L176
+    PADDLE_OP_CHECK(node, begin_norm_axis > 0, "begin_norm_axis should be greater than 0");
 
     // shape of input
     const auto shape_of_node = std::make_shared<ShapeOf>(data);
@@ -54,6 +54,6 @@ NamedOutputs layer_norm(const NodeContext& node) {
     return node.default_single_output_mapping({result}, {"Y"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/leakyrelu.cpp b/src/frontends/paddle/src/op/leakyrelu.cpp
similarity index 93%
rename from src/frontends/paddlepaddle/src/op/leakyrelu.cpp
rename to src/frontends/paddle/src/op/leakyrelu.cpp
index 4a54ccb5173..641360c40da 100644
--- a/src/frontends/paddlepaddle/src/op/leakyrelu.cpp
+++ b/src/frontends/paddle/src/op/leakyrelu.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs leaky_relu(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -16,6 +16,6 @@ NamedOutputs leaky_relu(const NodeContext& node) {
     return node.default_single_output_mapping({std::make_shared<ov::opset6::PRelu>(data, alpha)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/log.cpp b/src/frontends/paddle/src/op/log.cpp
similarity index 84%
rename from src/frontends/paddlepaddle/src/op/log.cpp
rename to src/frontends/paddle/src/op/log.cpp
index a38e802cb5a..305a60bb7cf 100644
--- a/src/frontends/paddlepaddle/src/op/log.cpp
+++ b/src/frontends/paddle/src/op/log.cpp
@@ -5,17 +5,16 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs log(const NodeContext& node) {
     auto x = node.get_ng_input("X");
     return node.default_single_output_mapping({std::make_shared<ov::opset6::Log>(x)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/logical_not.cpp b/src/frontends/paddle/src/op/logical_not.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/logical_not.cpp
rename to src/frontends/paddle/src/op/logical_not.cpp
index f182be450ec..473f5a1ea55 100644
--- a/src/frontends/paddlepaddle/src/op/logical_not.cpp
+++ b/src/frontends/paddle/src/op/logical_not.cpp
@@ -8,13 +8,13 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs logical_not(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     return node.default_single_output_mapping({std::make_shared<ov::opset6::LogicalNot>(data)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/lstm.cpp b/src/frontends/paddle/src/op/lstm.cpp
similarity index 98%
rename from src/frontends/paddlepaddle/src/op/lstm.cpp
rename to src/frontends/paddle/src/op/lstm.cpp
index da8c93a2cff..02184a28b79 100644
--- a/src/frontends/paddlepaddle/src/op/lstm.cpp
+++ b/src/frontends/paddle/src/op/lstm.cpp
@@ -5,12 +5,11 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
-#include "pdpd_utils.hpp"
+#include "paddle_utils.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 namespace {
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ INPUT NODES PARSING ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -154,7 +153,7 @@ struct LSTMAttributes {
 }  // namespace
 NamedOutputs lstm(const NodeContext& node) {
     auto mode = node.get_attribute<std::string>("mode");
-    PDPD_ASSERT(mode == "LSTM", "RNN only support LSTM now");
+    PADDLE_OP_CHECK(node, mode == "LSTM", "RNN only support LSTM now");
     auto prev_inputs = node.get_ng_inputs("Input");
     Output<Node> prev_output = prev_inputs[0];
     LSTMAttributes attrs(node);
@@ -188,6 +187,6 @@ NamedOutputs lstm(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/matmul.cpp b/src/frontends/paddle/src/op/matmul.cpp
similarity index 95%
rename from src/frontends/paddlepaddle/src/op/matmul.cpp
rename to src/frontends/paddle/src/op/matmul.cpp
index 37a4a69eeb5..f5638bd5d51 100644
--- a/src/frontends/paddlepaddle/src/op/matmul.cpp
+++ b/src/frontends/paddle/src/op/matmul.cpp
@@ -7,7 +7,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs matmul(const NodeContext& node) {
     auto x = node.get_ng_input("X");
@@ -25,6 +25,6 @@ NamedOutputs matmul(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/matmul_v2.cpp b/src/frontends/paddle/src/op/matmul_v2.cpp
similarity index 94%
rename from src/frontends/paddlepaddle/src/op/matmul_v2.cpp
rename to src/frontends/paddle/src/op/matmul_v2.cpp
index f4e414a2d0c..1f88ec1bda4 100644
--- a/src/frontends/paddlepaddle/src/op/matmul_v2.cpp
+++ b/src/frontends/paddle/src/op/matmul_v2.cpp
@@ -7,7 +7,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs matmul_v2(const NodeContext& node) {
     const auto x = node.get_ng_input("X");
@@ -19,6 +19,6 @@ NamedOutputs matmul_v2(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/matrix_nms.cpp b/src/frontends/paddle/src/op/matrix_nms.cpp
similarity index 85%
rename from src/frontends/paddlepaddle/src/op/matrix_nms.cpp
rename to src/frontends/paddle/src/op/matrix_nms.cpp
index 12a7df7cd1d..246722f306e 100644
--- a/src/frontends/paddlepaddle/src/op/matrix_nms.cpp
+++ b/src/frontends/paddle/src/op/matrix_nms.cpp
@@ -4,11 +4,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset8.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs matrix_nms(const NodeContext& node) {
     using namespace opset8;
@@ -31,8 +30,9 @@ NamedOutputs matrix_nms(const NodeContext& node) {
     }
 
     auto out_names = node.get_output_names();
-    PDPD_ASSERT(out_names.size() == 3 || out_names.size() == 2,
-                "Unexpected number of outputs of MatrixNMS: " + std::to_string(out_names.size()));
+    PADDLE_OP_CHECK(node,
+                    out_names.size() == 3 || out_names.size() == 2,
+                    "Unexpected number of outputs of MatrixNMS: " + std::to_string(out_names.size()));
 
     element::Type type_num = i32;
     bool return_rois_num = true;
@@ -44,8 +44,9 @@ NamedOutputs matrix_nms(const NodeContext& node) {
     }
 
     auto type_index = node.get_out_port_type("Index");
-    PDPD_ASSERT((type_index == i32 || type_index == i64) && (type_num == i32 || type_num == i64),
-                "Unexpected data type of outputs of MatrixNMS");
+    PADDLE_OP_CHECK(node,
+                    (type_index == i32 || type_index == i64) && (type_num == i32 || type_num == i64),
+                    "Unexpected data type of outputs of MatrixNMS");
 
     auto normalized = node.get_attribute<bool>("normalized");
 
@@ -82,6 +83,6 @@ NamedOutputs matrix_nms(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/multiclass_nms.cpp b/src/frontends/paddle/src/op/multiclass_nms.cpp
similarity index 84%
rename from src/frontends/paddlepaddle/src/op/multiclass_nms.cpp
rename to src/frontends/paddle/src/op/multiclass_nms.cpp
index 88871e680ea..b5a45ae721e 100644
--- a/src/frontends/paddlepaddle/src/op/multiclass_nms.cpp
+++ b/src/frontends/paddle/src/op/multiclass_nms.cpp
@@ -4,11 +4,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset8.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs multiclass_nms(const NodeContext& node) {
     using namespace opset8;
@@ -25,12 +24,13 @@ NamedOutputs multiclass_nms(const NodeContext& node) {
     auto nms_eta = node.get_attribute<float>("nms_eta");
 
     auto out_names = node.get_output_names();
-    PDPD_ASSERT(out_names.size() == 3, "Unexpected number of outputs of MulticlassNMS");
+    PADDLE_OP_CHECK(node, out_names.size() == 3, "Unexpected number of outputs of MulticlassNMS");
 
     auto type_index = node.get_out_port_type("Index");
     auto type_num = node.get_out_port_type("NmsRoisNum");
-    PDPD_ASSERT((type_index == i32 || type_index == i64) && (type_num == i32 || type_num == i64),
-                "Unexpected data type of outputs of MulticlassNMS: " + std::to_string(out_names.size()));
+    PADDLE_OP_CHECK(node,
+                    (type_index == i32 || type_index == i64) && (type_num == i32 || type_num == i64),
+                    "Unexpected data type of outputs of MulticlassNMS: " + std::to_string(out_names.size()));
 
     auto normalized = node.get_attribute<bool>("normalized");
 
@@ -64,6 +64,6 @@ NamedOutputs multiclass_nms(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/pad3d.cpp b/src/frontends/paddle/src/op/pad3d.cpp
similarity index 84%
rename from src/frontends/paddlepaddle/src/op/pad3d.cpp
rename to src/frontends/paddle/src/op/pad3d.cpp
index 8a2a8f1b555..22a07ecac61 100644
--- a/src/frontends/paddlepaddle/src/op/pad3d.cpp
+++ b/src/frontends/paddle/src/op/pad3d.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs pad3d(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -18,18 +18,18 @@ NamedOutputs pad3d(const NodeContext& node) {
 
     auto paddings = std::vector<int32_t>(6, 0);
 
-    // padding of type int feature only supported by PaddlePaddle 'develop'
+    // padding of type int feature only supported by paddle 'develop'
     // version(>=2.1.0)
     if (node.has_attribute<std::vector<int32_t>>("paddings")) {
         auto paddings_vector = node.get_attribute<std::vector<int32_t>>("paddings");
-        PDPD_OP_VALIDATION_CHECK(node, paddings_vector.size() == 6, "paddings Params size should be 6 in pad3d!");
+        PADDLE_OP_CHECK(node, paddings_vector.size() == 6, "paddings Params size should be 6 in pad3d!");
         paddings = paddings_vector;
     } else if (node.has_attribute<int32_t>("paddings")) {
         auto padding_int = node.get_attribute<int32_t>("paddings");
         for (int i = 0; i < 6; i++)
             paddings[i] = padding_int;
     } else {
-        PDPD_OP_VALIDATION_CHECK(node, false, "Unsupported paddings attribute!");
+        PADDLE_OP_CHECK(node, false, "Unsupported paddings attribute!");
     }
 
     auto pads_begin = std::vector<int32_t>(5, 0);
@@ -49,7 +49,7 @@ NamedOutputs pad3d(const NodeContext& node) {
     } else if (mode == "replicate") {
         pad_mode = ov::op::PadMode::EDGE;
     } else {
-        PDPD_OP_VALIDATION_CHECK(node, false, "Unsupported 3d paddings mode: [" + mode + "]");
+        PADDLE_OP_CHECK(node, false, "Unsupported 3d paddings mode: [" + mode + "]");
     }
 
     if (data_format == "NCDHW") {
@@ -67,7 +67,7 @@ NamedOutputs pad3d(const NodeContext& node) {
         pads_begin[1] = paddings[4];  // front
         pads_end[1] = paddings[5];    // back
     } else {
-        PDPD_OP_VALIDATION_CHECK(node, false, "Unsupported 3d paddings data_format: [" + data_format + "]");
+        PADDLE_OP_CHECK(node, false, "Unsupported 3d paddings data_format: [" + data_format + "]");
     }
 
     padding_begin = ov::opset6::Constant::create(element::i32, ov::Shape{pads_begin.size()}, pads_begin);
@@ -83,6 +83,6 @@ NamedOutputs pad3d(const NodeContext& node) {
             {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/pool2d.cpp b/src/frontends/paddle/src/op/pool2d.cpp
similarity index 94%
rename from src/frontends/paddlepaddle/src/op/pool2d.cpp
rename to src/frontends/paddle/src/op/pool2d.cpp
index d7606aa85bb..23b3efa363e 100644
--- a/src/frontends/paddlepaddle/src/op/pool2d.cpp
+++ b/src/frontends/paddle/src/op/pool2d.cpp
@@ -11,7 +11,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 // helper func - get pad_begin and pad_end
 static void get_paddings(const NodeContext& node, ov::Shape& pad_begin, ov::Shape& pad_end, ov::op::PadType& auto_pad) {
@@ -73,13 +73,13 @@ NamedOutputs pool2d(const NodeContext& node) {
         pooling_type = "max";
     }
 
-    PDPD_ASSERT((pooling_type == "max") || (pooling_type == "avg"), "pool2d: not supported pooling type !");
-    PDPD_ASSERT(kernel_shape.size() == 1 || kernel_shape.size() == 2, "pool2d: ksize must be 1 or 2!");
+    PADDLE_OP_CHECK(node, (pooling_type == "max") || (pooling_type == "avg"), "pool2d: not supported pooling type !");
+    PADDLE_OP_CHECK(node, kernel_shape.size() == 1 || kernel_shape.size() == 2, "pool2d: ksize must be 1 or 2!");
 
     PartialShape input_shape = data.get_partial_shape();
 
     int32_t input_rank = input_shape.rank().get_length();
-    PDPD_ASSERT(input_rank >= 2, "input tensor rank must be greater than 2");
+    PADDLE_OP_CHECK(node, input_rank >= 2, "input tensor rank must be greater than 2");
 
     auto auto_pad = ov::op::PadType::EXPLICIT;
     ov::Shape pad_begin, pad_end;
@@ -139,7 +139,7 @@ NamedOutputs pool2d(const NodeContext& node) {
             kernel_w = kernel_shape[1];
         }
 
-        PDPD_ASSERT(kernel_h > 0 && kernel_w > 0, "pool2d kernel shape must be greater than 0");
+        PADDLE_OP_CHECK(node, kernel_h > 0 && kernel_w > 0, "pool2d kernel shape must be greater than 0");
 
         // Note: this shape check is only valid when the spatial dim of input_shape
         // is static.
@@ -181,6 +181,6 @@ NamedOutputs pool2d(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/pow.cpp b/src/frontends/paddle/src/op/pow.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/pow.cpp
rename to src/frontends/paddle/src/op/pow.cpp
index 22cbc91461e..5784005d2c3 100644
--- a/src/frontends/paddlepaddle/src/op/pow.cpp
+++ b/src/frontends/paddle/src/op/pow.cpp
@@ -4,11 +4,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs pow(const NodeContext& node) {
     auto x = node.get_ng_input("X");
@@ -26,6 +25,6 @@ NamedOutputs pow(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/prior_box.cpp b/src/frontends/paddle/src/op/prior_box.cpp
similarity index 98%
rename from src/frontends/paddlepaddle/src/op/prior_box.cpp
rename to src/frontends/paddle/src/op/prior_box.cpp
index 1801f8fd59f..4d3302ae926 100644
--- a/src/frontends/paddlepaddle/src/op/prior_box.cpp
+++ b/src/frontends/paddle/src/op/prior_box.cpp
@@ -10,7 +10,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 using namespace default_opset;
 using namespace element;
@@ -65,6 +65,6 @@ NamedOutputs prior_box(const NodeContext& node) {
     return outputs;
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/range.cpp b/src/frontends/paddle/src/op/range.cpp
similarity index 95%
rename from src/frontends/paddlepaddle/src/op/range.cpp
rename to src/frontends/paddle/src/op/range.cpp
index 9ebeff92d06..741e08cc8b7 100644
--- a/src/frontends/paddlepaddle/src/op/range.cpp
+++ b/src/frontends/paddle/src/op/range.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs range(const NodeContext& node) {
     auto start = node.get_ng_input("Start");
@@ -27,6 +27,6 @@ NamedOutputs range(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/relu.cpp b/src/frontends/paddle/src/op/relu.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/relu.cpp
rename to src/frontends/paddle/src/op/relu.cpp
index 9277594cc9a..108db9d4435 100644
--- a/src/frontends/paddlepaddle/src/op/relu.cpp
+++ b/src/frontends/paddle/src/op/relu.cpp
@@ -8,13 +8,13 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs relu(const NodeContext& node) {
     return node.default_single_output_mapping({std::make_shared<ov::opset6::Relu>(node.get_ng_input("X"))}, {"Out"});
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/relu6.cpp b/src/frontends/paddle/src/op/relu6.cpp
similarity index 86%
rename from src/frontends/paddlepaddle/src/op/relu6.cpp
rename to src/frontends/paddle/src/op/relu6.cpp
index 2c37f0ac81f..d24660c951d 100644
--- a/src/frontends/paddlepaddle/src/op/relu6.cpp
+++ b/src/frontends/paddle/src/op/relu6.cpp
@@ -5,11 +5,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs relu6(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -18,6 +17,6 @@ NamedOutputs relu6(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/reshape2.cpp b/src/frontends/paddle/src/op/reshape2.cpp
similarity index 94%
rename from src/frontends/paddlepaddle/src/op/reshape2.cpp
rename to src/frontends/paddle/src/op/reshape2.cpp
index 20e2bccea10..fcdcf8086b2 100644
--- a/src/frontends/paddlepaddle/src/op/reshape2.cpp
+++ b/src/frontends/paddle/src/op/reshape2.cpp
@@ -5,11 +5,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs reshape2(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -38,6 +37,6 @@ NamedOutputs reshape2(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/rnn.cpp b/src/frontends/paddle/src/op/rnn.cpp
similarity index 62%
rename from src/frontends/paddlepaddle/src/op/rnn.cpp
rename to src/frontends/paddle/src/op/rnn.cpp
index 0e779c168de..ebb552ca623 100644
--- a/src/frontends/paddlepaddle/src/op/rnn.cpp
+++ b/src/frontends/paddle/src/op/rnn.cpp
@@ -5,23 +5,23 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset6.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs lstm(const NodeContext& node);
 NamedOutputs rnn(const NodeContext& node) {
     auto mode = node.get_attribute<std::string>("mode");
-    PDPD_ASSERT(mode == "LSTM",
-                "[Paddle Frontend]RNN Only Supports LSTM Ops Conversion now, don't "
-                "support " +
-                    mode);
+    PADDLE_OP_CHECK(node,
+                    mode == "LSTM",
+                    "[Paddle Frontend]RNN Only Supports LSTM Ops Conversion now, don't "
+                    "support " +
+                        mode);
     return lstm(node);
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/scale.cpp b/src/frontends/paddle/src/op/scale.cpp
similarity index 93%
rename from src/frontends/paddlepaddle/src/op/scale.cpp
rename to src/frontends/paddle/src/op/scale.cpp
index da8dd865178..6098e1b60fd 100644
--- a/src/frontends/paddlepaddle/src/op/scale.cpp
+++ b/src/frontends/paddle/src/op/scale.cpp
@@ -8,12 +8,12 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs scale(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     auto dtype = data.get_element_type();
-    // Note: PDPD Scale output data_type is the same with input
+    // Note: paddle Scale output data_type is the same with input
     Output<Node> scale;
     Output<Node> bias;
 
@@ -45,6 +45,6 @@ NamedOutputs scale(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/shape.cpp b/src/frontends/paddle/src/op/shape.cpp
similarity index 92%
rename from src/frontends/paddlepaddle/src/op/shape.cpp
rename to src/frontends/paddle/src/op/shape.cpp
index b2594edcba3..98e5d518c5e 100644
--- a/src/frontends/paddlepaddle/src/op/shape.cpp
+++ b/src/frontends/paddle/src/op/shape.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs shape(const NodeContext& node) {
     auto data = node.get_ng_input("Input");
@@ -17,6 +17,6 @@ NamedOutputs shape(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/sigmoid.cpp b/src/frontends/paddle/src/op/sigmoid.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/sigmoid.cpp
rename to src/frontends/paddle/src/op/sigmoid.cpp
index b4145c68695..b11154143ef 100644
--- a/src/frontends/paddlepaddle/src/op/sigmoid.cpp
+++ b/src/frontends/paddle/src/op/sigmoid.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs sigmoid(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -16,6 +16,6 @@ NamedOutputs sigmoid(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/slice.cpp b/src/frontends/paddle/src/op/slice.cpp
similarity index 95%
rename from src/frontends/paddlepaddle/src/op/slice.cpp
rename to src/frontends/paddle/src/op/slice.cpp
index 873695a8fc8..e739a96be79 100644
--- a/src/frontends/paddlepaddle/src/op/slice.cpp
+++ b/src/frontends/paddle/src/op/slice.cpp
@@ -10,7 +10,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 using namespace default_opset;
 NamedOutputs slice(const NodeContext& node) {
@@ -80,9 +80,9 @@ NamedOutputs slice(const NodeContext& node) {
         // according to paddle slice_op, when all axes are decreased, output shape is [1], instead of scalar.
         // Ref: paddle/fluid/operators/slice_op.h
         PartialShape input_shape = data.get_partial_shape();
-        PDPD_OP_VALIDATION_CHECK(node,
-                                 input_shape.rank().is_static(),
-                                 "input rank of slice must be static when decrease_axis is set.");
+        PADDLE_OP_CHECK(node,
+                        input_shape.rank().is_static(),
+                        "input rank of slice must be static when decrease_axis is set.");
 
         auto squeeze_index_node = Constant::create(element::i32, {decrease_axis.size()}, decrease_axis);
         auto decreased_node = std::make_shared<Squeeze>(stride_slice_node, squeeze_index_node);
@@ -101,6 +101,6 @@ NamedOutputs slice(const NodeContext& node) {
     return node.default_single_output_mapping({stride_slice_node}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/softmax.cpp b/src/frontends/paddle/src/op/softmax.cpp
similarity index 80%
rename from src/frontends/paddlepaddle/src/op/softmax.cpp
rename to src/frontends/paddle/src/op/softmax.cpp
index b70dd6971d9..bb19d209900 100644
--- a/src/frontends/paddlepaddle/src/op/softmax.cpp
+++ b/src/frontends/paddle/src/op/softmax.cpp
@@ -8,19 +8,19 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs softmax(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     auto axis = node.get_attribute<int32_t>("axis");
     if (axis < 0) {
-        PDPD_OP_VALIDATION_CHECK(node, data.get_partial_shape().rank().is_static(), "Softmax rank must be static");
+        PADDLE_OP_CHECK(node, data.get_partial_shape().rank().is_static(), "Softmax rank must be static");
         auto data_rank = data.get_partial_shape().rank().get_length();
         axis = data_rank + axis;
     }
     return node.default_single_output_mapping({std::make_shared<ov::opset6::Softmax>(data, axis)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/softplus.cpp b/src/frontends/paddle/src/op/softplus.cpp
similarity index 85%
rename from src/frontends/paddlepaddle/src/op/softplus.cpp
rename to src/frontends/paddle/src/op/softplus.cpp
index 35e2ec3f132..0d7c5b85cc2 100644
--- a/src/frontends/paddlepaddle/src/op/softplus.cpp
+++ b/src/frontends/paddle/src/op/softplus.cpp
@@ -7,7 +7,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs softplus(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -18,11 +18,11 @@ NamedOutputs softplus(const NodeContext& node) {
     const float EPSINON = 1e-6;
 
     if (!(abs(beta - supported_beta) <= EPSINON) || !(abs(threshold - supported_threshold) <= EPSINON)) {
-        PDPD_OP_VALIDATION_CHECK(node, false, "only support beta==1.0 && threshold==20.0");
+        PADDLE_OP_CHECK(node, false, "only support beta==1.0 && threshold==20.0");
     }
     return node.default_single_output_mapping({std::make_shared<default_opset::SoftPlus>(data)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/split.cpp b/src/frontends/paddle/src/op/split.cpp
similarity index 83%
rename from src/frontends/paddlepaddle/src/op/split.cpp
rename to src/frontends/paddle/src/op/split.cpp
index 843f2bb6df5..9364399e091 100644
--- a/src/frontends/paddlepaddle/src/op/split.cpp
+++ b/src/frontends/paddle/src/op/split.cpp
@@ -5,11 +5,10 @@
 #include <node_context.hpp>
 
 #include "openvino/opsets/opset7.hpp"
-#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs split(const NodeContext& node) {
     using namespace opset7;
@@ -35,8 +34,9 @@ NamedOutputs split(const NodeContext& node) {
             auto inputs = node.get_ng_inputs("SectionsTensorList");
             sections_node = std::make_shared<ov::opset7::Concat>(inputs, 0);
         } else {
-            PDPD_ASSERT(node.has_attribute<std::vector<int32_t>>("sections"),
-                        "split: num==0 && no sections is invalid.");
+            PADDLE_OP_CHECK(node,
+                            node.has_attribute<std::vector<int32_t>>("sections"),
+                            "split: num==0 && no sections is invalid.");
             auto sections = node.get_attribute<std::vector<int32_t>>("sections");
             sections_node = Constant::create(element::i32, {sections.size()}, sections);
         }
@@ -46,16 +46,16 @@ NamedOutputs split(const NodeContext& node) {
     }
 
     auto out_names = node.get_output_names();
-    PDPD_ASSERT(out_names.size() == 1, "Unexpected number of outputs");
+    PADDLE_OP_CHECK(node, out_names.size() == 1, "Unexpected number of outputs");
 
     auto it = std::find(out_names.begin(), out_names.end(), "Out");
-    PDPD_ASSERT(it != out_names.end(), "Expected output not found");
+    PADDLE_OP_CHECK(node, it != out_names.end(), "Expected output not found");
     for (const auto& split_output : split_outputs) {
         named_outputs[*it].push_back(split_output);
     }
     return named_outputs;
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/squeeze.cpp b/src/frontends/paddle/src/op/squeeze.cpp
similarity index 95%
rename from src/frontends/paddlepaddle/src/op/squeeze.cpp
rename to src/frontends/paddle/src/op/squeeze.cpp
index 2c3472df019..af83f9575e3 100644
--- a/src/frontends/paddlepaddle/src/op/squeeze.cpp
+++ b/src/frontends/paddle/src/op/squeeze.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs squeeze(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -27,6 +27,6 @@ NamedOutputs squeeze(const NodeContext& node) {
     return node.default_single_output_mapping(out, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/stack.cpp b/src/frontends/paddle/src/op/stack.cpp
similarity index 68%
rename from src/frontends/paddlepaddle/src/op/stack.cpp
rename to src/frontends/paddle/src/op/stack.cpp
index 1b11e3715f6..92535d46728 100644
--- a/src/frontends/paddlepaddle/src/op/stack.cpp
+++ b/src/frontends/paddle/src/op/stack.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 using namespace default_opset;
 NamedOutputs stack(const NodeContext& node) {
@@ -20,15 +20,14 @@ NamedOutputs stack(const NodeContext& node) {
 
     auto axis_const = std::make_shared<Constant>(element::i64, Shape{}, axis);
     if (data_shape.rank().is_static())
-        PDPD_OP_VALIDATION_CHECK(
-            node,
-            (axis >= -(data_shape.rank().get_length() + 1)) && axis < (data_shape.rank().get_length() + 1),
-            "axis range is [-(R+1), R+1)!");
+        PADDLE_OP_CHECK(node,
+                        (axis >= -(data_shape.rank().get_length() + 1)) && axis < (data_shape.rank().get_length() + 1),
+                        "axis range is [-(R+1), R+1)!");
 
     for (const auto& data : datas) {
-        PDPD_OP_VALIDATION_CHECK(node,
-                                 data_type == data.get_element_type(),
-                                 "stack input tensor must have the same data types!");
+        PADDLE_OP_CHECK(node,
+                        data_type == data.get_element_type(),
+                        "stack input tensor must have the same data types!");
 
         node_datas_reshape.push_back(std::make_shared<Unsqueeze>(data, axis_const));
     }
@@ -36,6 +35,6 @@ NamedOutputs stack(const NodeContext& node) {
     return node.default_single_output_mapping({std::make_shared<Concat>(node_datas_reshape, axis)}, {"Y"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/tanh.cpp b/src/frontends/paddle/src/op/tanh.cpp
similarity index 91%
rename from src/frontends/paddlepaddle/src/op/tanh.cpp
rename to src/frontends/paddle/src/op/tanh.cpp
index 96ead42fa1b..2b90b5340a3 100644
--- a/src/frontends/paddlepaddle/src/op/tanh.cpp
+++ b/src/frontends/paddle/src/op/tanh.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs tanh(const NodeContext& node) {
     const auto x = node.get_ng_input("X");
@@ -17,6 +17,6 @@ NamedOutputs tanh(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/transpose2.cpp b/src/frontends/paddle/src/op/transpose2.cpp
similarity index 93%
rename from src/frontends/paddlepaddle/src/op/transpose2.cpp
rename to src/frontends/paddle/src/op/transpose2.cpp
index 2c2464fd01b..051e0c95bcd 100644
--- a/src/frontends/paddlepaddle/src/op/transpose2.cpp
+++ b/src/frontends/paddle/src/op/transpose2.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs transpose2(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -18,6 +18,6 @@ NamedOutputs transpose2(const NodeContext& node) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/unsqueeze.cpp b/src/frontends/paddle/src/op/unsqueeze.cpp
similarity index 95%
rename from src/frontends/paddlepaddle/src/op/unsqueeze.cpp
rename to src/frontends/paddle/src/op/unsqueeze.cpp
index 0d4a583b5a8..0791aa0e349 100644
--- a/src/frontends/paddlepaddle/src/op/unsqueeze.cpp
+++ b/src/frontends/paddle/src/op/unsqueeze.cpp
@@ -8,7 +8,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 NamedOutputs unsqueeze(const NodeContext& node) {
     auto data = node.get_ng_input("X");
@@ -25,6 +25,6 @@ NamedOutputs unsqueeze(const NodeContext& node) {
     return node.default_single_output_mapping({std::make_shared<ov::opset6::Unsqueeze>(data, axesNode)}, {"Out"});
 }
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op/yolo_box.cpp b/src/frontends/paddle/src/op/yolo_box.cpp
similarity index 99%
rename from src/frontends/paddlepaddle/src/op/yolo_box.cpp
rename to src/frontends/paddle/src/op/yolo_box.cpp
index 4393304ef99..2b63bf47586 100644
--- a/src/frontends/paddlepaddle/src/op/yolo_box.cpp
+++ b/src/frontends/paddle/src/op/yolo_box.cpp
@@ -9,7 +9,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 using namespace opset6;
 using namespace element;
@@ -36,7 +36,7 @@ NamedOutputs yolo_box(const NodeContext& node_context) {
     int32_t class_num = node_context.get_attribute<int32_t>("class_num");
     auto const_class_num = Constant::create<int64_t>(i64, {1}, {class_num});
 
-    // PDPD anchors attribute is of type int32. Convert to float for computing
+    // Paddle anchors attribute is of type int32. Convert to float for computing
     // convinient.
     auto _anchors = node_context.get_attribute<std::vector<int32_t>>("anchors");
     std::vector<float> anchors(_anchors.begin(), _anchors.end());
@@ -324,6 +324,6 @@ NamedOutputs yolo_box(const NodeContext& node_context) {
 }
 
 }  // namespace op
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op_table.cpp b/src/frontends/paddle/src/op_table.cpp
similarity index 99%
rename from src/frontends/paddlepaddle/src/op_table.cpp
rename to src/frontends/paddle/src/op_table.cpp
index e5c508c3237..aecdcbf4fc0 100644
--- a/src/frontends/paddlepaddle/src/op_table.cpp
+++ b/src/frontends/paddle/src/op_table.cpp
@@ -5,7 +5,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 namespace op {
 #define OP_CONVERTER(op) NamedOutputs op(const NodeContext& node)
 OP_CONVERTER(argmax);
@@ -150,6 +150,6 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"yolo_box", op::yolo_box}};
 };
 
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/op_table.hpp b/src/frontends/paddle/src/op_table.hpp
similarity index 90%
rename from src/frontends/paddlepaddle/src/op_table.hpp
rename to src/frontends/paddle/src/op_table.hpp
index b24f2f94180..7654ed6521e 100644
--- a/src/frontends/paddlepaddle/src/op_table.hpp
+++ b/src/frontends/paddle/src/op_table.hpp
@@ -12,11 +12,11 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 using CreatorFunction = std::function<NamedOutputs(const NodeContext&)>;
 
 std::map<std::string, CreatorFunction> get_supported_ops();
 
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/pdpd_fw_node.cpp b/src/frontends/paddle/src/paddle_fw_node.cpp
similarity index 72%
rename from src/frontends/paddlepaddle/src/pdpd_fw_node.cpp
rename to src/frontends/paddle/src/paddle_fw_node.cpp
index b4e43718790..4d3fc17f3d1 100644
--- a/src/frontends/paddlepaddle/src/pdpd_fw_node.cpp
+++ b/src/frontends/paddle/src/paddle_fw_node.cpp
@@ -2,13 +2,13 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <pdpd_fw_node.hpp>
+#include <paddle_fw_node.hpp>
 
 namespace ov {
 namespace frontend {
-
-void PDPDFrameworkNode::validate_and_infer_types() {
-    FrameworkNode::validate_and_infer_types();
+namespace paddle {
+void FrameworkNode::validate_and_infer_types() {
+    ov::op::util::FrameworkNode::validate_and_infer_types();
     size_t idx = 0;
     for (const auto& port_pair : m_decoder.get_output_type_map()) {
         for (const auto& p_type : port_pair.second) {
@@ -17,7 +17,7 @@ void PDPDFrameworkNode::validate_and_infer_types() {
     }
 }
 
-std::map<std::string, OutputVector> PDPDFrameworkNode::get_named_inputs() const {
+std::map<std::string, OutputVector> FrameworkNode::get_named_inputs() const {
     return m_decoder.map_for_each_input([&](const std::string& name, size_t) {
         auto it = std::find(m_inputs_names.begin(), m_inputs_names.end(), name);
         if (it != m_inputs_names.end()) {
@@ -28,11 +28,12 @@ std::map<std::string, OutputVector> PDPDFrameworkNode::get_named_inputs() const
     });
 }
 
-std::map<std::string, OutputVector> PDPDFrameworkNode::return_named_outputs() {
+std::map<std::string, OutputVector> FrameworkNode::return_named_outputs() {
     return m_decoder.map_for_each_output([&](const std::string&, size_t idx) {
         return output(idx);
     });
 }
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/pdpd_fw_node.hpp b/src/frontends/paddle/src/paddle_fw_node.hpp
similarity index 63%
rename from src/frontends/paddlepaddle/src/pdpd_fw_node.hpp
rename to src/frontends/paddle/src/paddle_fw_node.hpp
index 81ede7fa33e..859402ab9e3 100644
--- a/src/frontends/paddlepaddle/src/pdpd_fw_node.hpp
+++ b/src/frontends/paddle/src/paddle_fw_node.hpp
@@ -10,14 +10,13 @@
 
 namespace ov {
 namespace frontend {
-class PDPDFrameworkNode : public ov::op::util::FrameworkNode {
+namespace paddle {
+class FrameworkNode : public ov::op::util::FrameworkNode {
 public:
-    OPENVINO_OP("PDPDFrameworkNode", "util", ov::op::util::FrameworkNode);
+    OPENVINO_OP("FrameworkNode", "util", ov::op::util::FrameworkNode);
 
-    PDPDFrameworkNode(const DecoderPDPDProto& decoder,
-                      const OutputVector& inputs,
-                      const std::vector<std::string>& inputs_names)
-        : FrameworkNode(inputs, decoder.get_output_size()),
+    FrameworkNode(const DecoderProto& decoder, const OutputVector& inputs, const std::vector<std::string>& inputs_names)
+        : ov::op::util::FrameworkNode(inputs, decoder.get_output_size()),
           m_decoder{decoder},
           m_inputs_names{inputs_names} {
         ov::op::util::FrameworkNodeAttrs attrs;
@@ -30,14 +29,14 @@ public:
     void validate_and_infer_types() override;
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
-        return std::make_shared<PDPDFrameworkNode>(m_decoder, inputs, m_inputs_names);
+        return std::make_shared<FrameworkNode>(m_decoder, inputs, m_inputs_names);
     }
 
     std::string get_op_type() const {
         return m_decoder.get_op_type();
     }
 
-    const DecoderPDPDProto& get_decoder() const {
+    const DecoderProto& get_decoder() const {
         return m_decoder;
     }
 
@@ -46,8 +45,9 @@ public:
     std::map<std::string, OutputVector> return_named_outputs();
 
 private:
-    const DecoderPDPDProto m_decoder;
+    const DecoderProto m_decoder;
     std::vector<std::string> m_inputs_names;
 };
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/pdpd_utils.cpp b/src/frontends/paddle/src/paddle_utils.cpp
similarity index 66%
rename from src/frontends/paddlepaddle/src/pdpd_utils.cpp
rename to src/frontends/paddle/src/paddle_utils.cpp
index 5545de213bb..68508799b1e 100644
--- a/src/frontends/paddlepaddle/src/pdpd_utils.cpp
+++ b/src/frontends/paddle/src/paddle_utils.cpp
@@ -2,10 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "pdpd_utils.hpp"
+#include "paddle_utils.hpp"
 
-std::shared_ptr<ov::Node> ov::frontend::pdpd::reorder_axes(const ov::Output<ov::Node>& value,
-                                                           std::vector<size_t> axes_order) {
+std::shared_ptr<ov::Node> ov::frontend::paddle::reorder_axes(const ov::Output<ov::Node>& value,
+                                                             std::vector<size_t> axes_order) {
     const auto axes_order_const =
         std::make_shared<opset6::Constant>(element::i64,
                                            Shape{axes_order.size()},
diff --git a/src/frontends/paddlepaddle/src/pdpd_utils.hpp b/src/frontends/paddle/src/paddle_utils.hpp
similarity index 96%
rename from src/frontends/paddlepaddle/src/pdpd_utils.hpp
rename to src/frontends/paddle/src/paddle_utils.hpp
index 5fcaaa6c188..e098425a778 100644
--- a/src/frontends/paddlepaddle/src/pdpd_utils.hpp
+++ b/src/frontends/paddle/src/paddle_utils.hpp
@@ -9,7 +9,7 @@
 
 namespace ov {
 namespace frontend {
-namespace pdpd {
+namespace paddle {
 #ifdef _WIN32
 const char PATH_SEPARATOR = '\\';
 #    if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT)
@@ -40,6 +40,6 @@ bool endsWith(const std::basic_string<T>& str, const std::basic_string<T>& suffi
 }
 
 std::shared_ptr<Node> reorder_axes(const Output<Node>& value, std::vector<size_t> axes_order);
-}  // namespace pdpd
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/place.cpp b/src/frontends/paddle/src/place.cpp
similarity index 60%
rename from src/frontends/paddlepaddle/src/place.cpp
rename to src/frontends/paddle/src/place.cpp
index 59a3e571ee2..62b71986dc1 100644
--- a/src/frontends/paddlepaddle/src/place.cpp
+++ b/src/frontends/paddle/src/place.cpp
@@ -2,15 +2,16 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "paddlepaddle_frontend/place.hpp"
+#include "place.hpp"
 
 #include "decoder.hpp"
 #include "framework.pb.h"
 
-using namespace ov;
-using namespace frontend;
+namespace ov {
+namespace frontend {
+namespace paddle {
 
-bool PlacePDPD::is_input() const {
+bool Place::is_input() const {
     const auto& model_ins = m_input_model.get_inputs();
 
     const auto cmp = [this](const Place::Ptr& p) {
@@ -19,7 +20,7 @@ bool PlacePDPD::is_input() const {
     return std::find_if(model_ins.begin(), model_ins.end(), cmp) != model_ins.end();
 }
 
-bool PlacePDPD::is_output() const {
+bool Place::is_output() const {
     const auto& model_outs = m_input_model.get_outputs();
     const auto cmp = [this](const Place::Ptr& p) {
         return p.get() == this;
@@ -27,81 +28,80 @@ bool PlacePDPD::is_output() const {
     return std::find_if(model_outs.begin(), model_outs.end(), cmp) != model_outs.end();
 }
 
-OpPlacePDPD::OpPlacePDPD(const InputModel& input_model,
-                         const paddle::framework::proto::OpDesc& op_desc,
-                         const std::vector<std::string>& names)
-    : PlacePDPD(input_model, names),
+OpPlace::OpPlace(const ov::frontend::InputModel& input_model,
+                 const ::paddle::framework::proto::OpDesc& op_desc,
+                 const std::vector<std::string>& names)
+    : Place(input_model, names),
       m_op_desc(op_desc) {}
 
-OpPlacePDPD::OpPlacePDPD(const InputModel& input_model, const paddle::framework::proto::OpDesc& op_desc)
-    : OpPlacePDPD(input_model, op_desc, {}) {}
+OpPlace::OpPlace(const ov::frontend::InputModel& input_model, const ::paddle::framework::proto::OpDesc& op_desc)
+    : OpPlace(input_model, op_desc, {}) {}
 
-const std::map<std::string, std::vector<std::shared_ptr<OutPortPlacePDPD>>>& OpPlacePDPD::get_output_ports() const {
+const std::map<std::string, std::vector<std::shared_ptr<OutPortPlace>>>& OpPlace::get_output_ports() const {
     return m_output_ports;
 }
 
-const std::map<std::string, std::vector<std::shared_ptr<InPortPlacePDPD>>>& OpPlacePDPD::get_input_ports() const {
+const std::map<std::string, std::vector<std::shared_ptr<InPortPlace>>>& OpPlace::get_input_ports() const {
     return m_input_ports;
 }
 
-std::shared_ptr<OutPortPlacePDPD> OpPlacePDPD::get_output_port_pdpd(const std::string& outputName,
-                                                                    int outputPortIndex) const {
+std::shared_ptr<OutPortPlace> OpPlace::get_output_port_paddle(const std::string& outputName,
+                                                              int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(outputPortIndex <= m_output_ports.at(outputName).size(),
                             "outputPortIndex is out of bounds.");
     return m_output_ports.at(outputName)[outputPortIndex];
 }
 
-std::shared_ptr<InPortPlacePDPD> OpPlacePDPD::get_input_port_pdpd(const std::string& inputName,
-                                                                  int inputPortIndex) const {
+std::shared_ptr<InPortPlace> OpPlace::get_input_port_paddle(const std::string& inputName, int inputPortIndex) const {
     FRONT_END_GENERAL_CHECK(inputPortIndex <= m_input_ports.at(inputName).size(), "inputPortIndex is out of bounds.");
     return m_input_ports.at(inputName)[inputPortIndex];
 }
 
-const paddle::framework::proto::OpDesc& OpPlacePDPD::get_desc() const {
+const ::paddle::framework::proto::OpDesc& OpPlace::get_desc() const {
     return m_op_desc;
 }
 
-void OpPlacePDPD::add_out_port(const std::shared_ptr<OutPortPlacePDPD>& output, const std::string& name) {
+void OpPlace::add_out_port(const std::shared_ptr<OutPortPlace>& output, const std::string& name) {
     m_output_ports[name].push_back(output);
 }
 
-void OpPlacePDPD::add_in_port(const std::shared_ptr<InPortPlacePDPD>& input, const std::string& name) {
+void OpPlace::add_in_port(const std::shared_ptr<InPortPlace>& input, const std::string& name) {
     m_input_ports[name].push_back(input);
 }
 
-Place::Ptr OpPlacePDPD::get_output_port(const std::string& name) const {
+Place::Ptr OpPlace::get_output_port(const std::string& name) const {
     FRONT_END_GENERAL_CHECK(m_output_ports.at(name).size() == 1, "Only one output port should exist.");
     return m_output_ports.at(name)[0];
 }
 
-Place::Ptr OpPlacePDPD::get_input_port(const std::string& name) const {
+Place::Ptr OpPlace::get_input_port(const std::string& name) const {
     FRONT_END_GENERAL_CHECK(m_input_ports.at(name).size() == 1, "Only one input port should exist.");
     return m_input_ports.at(name)[0];
 }
 
-Place::Ptr OpPlacePDPD::get_input_port(int outputPortIndex) const {
+Place::Ptr OpPlace::get_input_port(int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(m_input_ports.size() == 1, "Only one named input port should exist.");
     return m_input_ports.begin()->second[outputPortIndex];
 }
 
-Place::Ptr OpPlacePDPD::get_output_port(int outputPortIndex) const {
+Place::Ptr OpPlace::get_output_port(int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(m_output_ports.size() == 1, "Only one named output port should exist.");
     return m_output_ports.begin()->second[outputPortIndex];
 }
 
-Place::Ptr OpPlacePDPD::get_output_port() const {
+Place::Ptr OpPlace::get_output_port() const {
     FRONT_END_GENERAL_CHECK(m_output_ports.size() == 1 && m_output_ports.begin()->second.size() == 1,
                             "Only one output port should exist.");
     return m_output_ports.begin()->second[0];
 }
 
-Place::Ptr OpPlacePDPD::get_input_port() const {
+Place::Ptr OpPlace::get_input_port() const {
     FRONT_END_GENERAL_CHECK(m_input_ports.size() == 1 && m_input_ports.begin()->second.size() == 1,
                             "Only one input port should exist.");
     return m_input_ports.begin()->second[0];
 }
 
-std::vector<Place::Ptr> OpPlacePDPD::get_consuming_operations() const {
+std::vector<Place::Ptr> OpPlace::get_consuming_operations() const {
     std::vector<Place::Ptr> consuming_ops;
     for (const auto& out_port : m_output_ports) {
         for (const auto& out_port_place : out_port.second) {
@@ -112,20 +112,20 @@ std::vector<Place::Ptr> OpPlacePDPD::get_consuming_operations() const {
     return consuming_ops;
 }
 
-std::vector<Place::Ptr> OpPlacePDPD::get_consuming_operations(const std::string& outputPortName,
-                                                              int outputPortIndex) const {
+std::vector<Place::Ptr> OpPlace::get_consuming_operations(const std::string& outputPortName,
+                                                          int outputPortIndex) const {
     return get_output_port(outputPortName, outputPortIndex)->get_consuming_operations();
 }
 
-std::vector<Place::Ptr> OpPlacePDPD::get_consuming_operations(int outputPortIndex) const {
+std::vector<Place::Ptr> OpPlace::get_consuming_operations(int outputPortIndex) const {
     return get_output_port(outputPortIndex)->get_consuming_operations();
 }
 
-std::vector<Place::Ptr> OpPlacePDPD::get_consuming_operations(const std::string& outputPortName) const {
+std::vector<Place::Ptr> OpPlace::get_consuming_operations(const std::string& outputPortName) const {
     return get_output_port(outputPortName)->get_consuming_operations();
 }
 
-std::vector<Place::Ptr> OpPlacePDPD::get_consuming_ports() const {
+std::vector<Place::Ptr> OpPlace::get_consuming_ports() const {
     std::vector<Place::Ptr> consuming_ports;
     for (const auto& out_port : m_output_ports) {
         for (const auto& out_port_place : out_port.second) {
@@ -136,82 +136,83 @@ std::vector<Place::Ptr> OpPlacePDPD::get_consuming_ports() const {
     return consuming_ports;
 }
 
-Place::Ptr OpPlacePDPD::get_output_port(const std::string& outputName, int outputPortIndex) const {
+Place::Ptr OpPlace::get_output_port(const std::string& outputName, int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(outputPortIndex <= m_output_ports.at(outputName).size(),
                             "outputPortIndex is Out of bounds.");
     return m_output_ports.at(outputName)[outputPortIndex];
 }
 
-Place::Ptr OpPlacePDPD::get_input_port(const std::string& inputName, int inputPortIndex) const {
+Place::Ptr OpPlace::get_input_port(const std::string& inputName, int inputPortIndex) const {
     FRONT_END_GENERAL_CHECK(inputPortIndex <= m_input_ports.at(inputName).size(), "inputPortIndex is out of bounds.");
     return m_input_ports.at(inputName)[inputPortIndex];
 }
 
-Place::Ptr OpPlacePDPD::get_source_tensor() const {
+Place::Ptr OpPlace::get_source_tensor() const {
     return get_input_port()->get_source_tensor();
 }
 
-Place::Ptr OpPlacePDPD::get_source_tensor(const std::string& inputName) const {
+Place::Ptr OpPlace::get_source_tensor(const std::string& inputName) const {
     return get_input_port(inputName)->get_source_tensor();
 }
 
-Place::Ptr OpPlacePDPD::get_source_tensor(int inputPortIndex) const {
+Place::Ptr OpPlace::get_source_tensor(int inputPortIndex) const {
     return get_input_port(inputPortIndex)->get_source_tensor();
 }
 
-Place::Ptr OpPlacePDPD::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
+Place::Ptr OpPlace::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
     return get_input_port(inputName, inputPortIndex)->get_source_tensor();
 }
 
-Place::Ptr OpPlacePDPD::get_target_tensor() const {
+Place::Ptr OpPlace::get_target_tensor() const {
     return get_output_port()->get_target_tensor();
 }
 
-Place::Ptr OpPlacePDPD::get_target_tensor(const std::string& outputName) const {
+Place::Ptr OpPlace::get_target_tensor(const std::string& outputName) const {
     return get_output_port(outputName)->get_target_tensor();
 }
 
-Place::Ptr OpPlacePDPD::get_target_tensor(const std::string& outputName, int outputPortIndex) const {
+Place::Ptr OpPlace::get_target_tensor(const std::string& outputName, int outputPortIndex) const {
     return get_output_port(outputName, outputPortIndex)->get_target_tensor();
 }
 
-Place::Ptr OpPlacePDPD::get_producing_operation(const std::string& inputName) const {
+Place::Ptr OpPlace::get_producing_operation(const std::string& inputName) const {
     return get_input_port(inputName)->get_producing_operation();
 }
 
-Place::Ptr OpPlacePDPD::get_producing_operation(const std::string& inputName, int inputPortIndex) const {
+Place::Ptr OpPlace::get_producing_operation(const std::string& inputName, int inputPortIndex) const {
     return get_input_port(inputName, inputPortIndex)->get_producing_operation();
 }
 
-Place::Ptr OpPlacePDPD::get_producing_operation() const {
+Place::Ptr OpPlace::get_producing_operation() const {
     return get_input_port()->get_producing_operation();
 }
 
-Place::Ptr OpPlacePDPD::get_producing_operation(int inputPortIndex) const {
+Place::Ptr OpPlace::get_producing_operation(int inputPortIndex) const {
     return get_input_port(inputPortIndex)->get_producing_operation();
 }
 
-Place::Ptr OpPlacePDPD::get_target_tensor(int outputPortIndex) const {
+Place::Ptr OpPlace::get_target_tensor(int outputPortIndex) const {
     return get_output_port(outputPortIndex)->get_target_tensor();
 }
 
-TensorPlacePDPD::TensorPlacePDPD(const InputModel& input_model,
-                                 const std::vector<std::string>& names,
-                                 const paddle::framework::proto::VarDesc& var_desc)
-    : PlacePDPD(input_model, names),
+TensorPlace::TensorPlace(const ov::frontend::InputModel& input_model,
+                         const std::vector<std::string>& names,
+                         const ::paddle::framework::proto::VarDesc& var_desc)
+    : Place(input_model, names),
       m_var_desc(var_desc) {
     const auto& var_type = var_desc.type();
-    if (var_type.type() == paddle::framework::proto::VarType::LOD_TENSOR) {
+    if (var_type.type() == ::paddle::framework::proto::VarType::LOD_TENSOR) {
         const auto& tensor_desc = var_type.lod_tensor().tensor();
         m_type = TYPE_MAP[tensor_desc.data_type()];
         m_pshape = PartialShape(std::vector<Dimension>(tensor_desc.dims().begin(), tensor_desc.dims().end()));
     }
 }
 
-TensorPlacePDPD::TensorPlacePDPD(const InputModel& input_model, const paddle::framework::proto::VarDesc& var_desc)
-    : TensorPlacePDPD(input_model, {var_desc.name()}, var_desc) {}
+TensorPlace::TensorPlace(const ov::frontend::InputModel& input_model,
+                         const ::paddle::framework::proto::VarDesc& var_desc)
+    : TensorPlace(input_model, {var_desc.name()}, var_desc) {}
 
-std::vector<Place::Ptr> TensorPlacePDPD::get_consuming_ports() const {
+std::vector<Place::Ptr> TensorPlace::get_consuming_ports() const {
     std::vector<Place::Ptr> consuming_ports;
     for (const auto& consuming_port : m_consuming_ports) {
         if (const auto& locked = consuming_port.lock()) {
@@ -223,7 +224,7 @@ std::vector<Place::Ptr> TensorPlacePDPD::get_consuming_ports() const {
     return consuming_ports;
 }
 
-Place::Ptr TensorPlacePDPD::get_producing_port() const {
+Place::Ptr TensorPlace::get_producing_port() const {
     FRONT_END_GENERAL_CHECK(m_producing_ports.size() == 1, "Only one producing port is supported.");
     if (const auto& producing_port = m_producing_ports[0].lock()) {
         return producing_port;
@@ -231,19 +232,19 @@ Place::Ptr TensorPlacePDPD::get_producing_port() const {
     FRONT_END_THROW("Producing Port has expired.");
 }
 
-void TensorPlacePDPD::add_producing_port(const std::shared_ptr<OutPortPlacePDPD>& out_port) {
+void TensorPlace::add_producing_port(const std::shared_ptr<OutPortPlace>& out_port) {
     m_producing_ports.push_back(out_port);
 }
 
-void TensorPlacePDPD::add_consuming_port(const std::shared_ptr<InPortPlacePDPD>& in_port) {
+void TensorPlace::add_consuming_port(const std::shared_ptr<InPortPlace>& in_port) {
     m_consuming_ports.push_back(in_port);
 }
 
-const paddle::framework::proto::VarDesc& TensorPlacePDPD::get_desc() const {
+const ::paddle::framework::proto::VarDesc& TensorPlace::get_desc() const {
     return m_var_desc;
 }
 
-std::vector<Place::Ptr> TensorPlacePDPD::get_consuming_operations() const {
+std::vector<Place::Ptr> TensorPlace::get_consuming_operations() const {
     std::vector<Place::Ptr> consuming_ops;
     for (const auto& consuming_port : m_consuming_ports) {
         if (auto port_ptr = consuming_port.lock()) {
@@ -256,7 +257,7 @@ std::vector<Place::Ptr> TensorPlacePDPD::get_consuming_operations() const {
     return consuming_ops;
 }
 
-bool TensorPlacePDPD::is_equal_data(const Place::Ptr& another) const {
+bool TensorPlace::is_equal_data(const Place::Ptr& another) const {
     auto consuming_ports = get_consuming_ports();
     bool eq_to_consuming_port =
         std::any_of(consuming_ports.begin(), consuming_ports.end(), [&another](const Ptr& place) {
@@ -265,93 +266,97 @@ bool TensorPlacePDPD::is_equal_data(const Place::Ptr& another) const {
     return is_equal(another) || get_producing_port()->is_equal(another) || eq_to_consuming_port;
 }
 
-Place::Ptr TensorPlacePDPD::get_producing_operation() const {
+Place::Ptr TensorPlace::get_producing_operation() const {
     return get_producing_port()->get_producing_operation();
 }
 
-std::shared_ptr<TensorPlacePDPD> InPortPlacePDPD::get_source_tensor_pdpd() const {
+std::shared_ptr<TensorPlace> InPortPlace::get_source_tensor_paddle() const {
     if (const auto& tensor = m_source_tensor.lock()) {
         return tensor;
     }
     FRONT_END_THROW("Source Tensor has expired.");
 }
 
-std::shared_ptr<OpPlacePDPD> InPortPlacePDPD::get_op() {
+std::shared_ptr<OpPlace> InPortPlace::get_op() {
     if (const auto& op = m_op.lock()) {
         return op;
     }
     FRONT_END_THROW("Operation has expired.");
 }
 
-void InPortPlacePDPD::set_source_tensor(const std::weak_ptr<TensorPlacePDPD>& source_tensor) {
+void InPortPlace::set_source_tensor(const std::weak_ptr<TensorPlace>& source_tensor) {
     m_source_tensor = source_tensor;
 }
 
-std::vector<Place::Ptr> InPortPlacePDPD::get_consuming_operations() const {
+std::vector<Place::Ptr> InPortPlace::get_consuming_operations() const {
     if (const auto& consuming_op = m_op.lock()) {
         return {consuming_op};
     }
     FRONT_END_THROW("Operation has expired.");
 }
 
-Place::Ptr InPortPlacePDPD::get_source_tensor() const {
+Place::Ptr InPortPlace::get_source_tensor() const {
     if (const auto& tensor = m_source_tensor.lock()) {
         return tensor;
     }
     FRONT_END_THROW("Source Tensor has expired.");
 }
 
-Place::Ptr InPortPlacePDPD::get_producing_port() const {
+Place::Ptr InPortPlace::get_producing_port() const {
     return get_source_tensor()->get_producing_port();
 }
 
-bool InPortPlacePDPD::is_equal_data(const Place::Ptr& another) const {
+bool InPortPlace::is_equal_data(const Place::Ptr& another) const {
     return get_source_tensor()->is_equal_data(another);
 }
 
-Place::Ptr InPortPlacePDPD::get_producing_operation() const {
+Place::Ptr InPortPlace::get_producing_operation() const {
     return get_producing_port()->get_producing_operation();
 }
 
-std::shared_ptr<TensorPlacePDPD> OutPortPlacePDPD::get_target_tensor_pdpd() const {
+std::shared_ptr<TensorPlace> OutPortPlace::get_target_tensor_paddle() const {
     if (const auto& target_tensor = m_target_tensor.lock()) {
         return target_tensor;
     }
     FRONT_END_THROW("Target Tensor has expired.");
 }
 
-std::vector<Place::Ptr> OutPortPlacePDPD::get_consuming_operations() const {
+std::vector<Place::Ptr> OutPortPlace::get_consuming_operations() const {
     if (auto tensor_ptr = m_target_tensor.lock()) {
         return tensor_ptr->get_consuming_operations();
     }
     FRONT_END_THROW("Tensor has expired.");
 }
 
-void OutPortPlacePDPD::set_target_tensor(const std::weak_ptr<TensorPlacePDPD>& target_tensor) {
+void OutPortPlace::set_target_tensor(const std::weak_ptr<TensorPlace>& target_tensor) {
     m_target_tensor = target_tensor;
 }
 
-std::vector<Place::Ptr> OutPortPlacePDPD::get_consuming_ports() const {
+std::vector<Place::Ptr> OutPortPlace::get_consuming_ports() const {
     if (auto tensor_ptr = m_target_tensor.lock()) {
         return tensor_ptr->get_consuming_ports();
     }
     FRONT_END_THROW("Tensor has expired.");
 }
 
-bool OutPortPlacePDPD::is_equal_data(const Place::Ptr& another) const {
+bool OutPortPlace::is_equal_data(const Place::Ptr& another) const {
     return get_target_tensor()->is_equal_data(another);
 }
 
-Place::Ptr OutPortPlacePDPD::get_target_tensor() const {
+Place::Ptr OutPortPlace::get_target_tensor() const {
     if (const auto& target_tensor = m_target_tensor.lock()) {
         return target_tensor;
     }
     FRONT_END_THROW("Target Tensor has expired.");
 }
 
-Place::Ptr OutPortPlacePDPD::get_producing_operation() const {
+Place::Ptr OutPortPlace::get_producing_operation() const {
     if (auto op = m_op.lock()) {
         return op;
     }
     FRONT_END_THROW("Operation has expired.");
 }
+
+}  // namespace paddle
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp b/src/frontends/paddle/src/place.hpp
similarity index 59%
rename from src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
rename to src/frontends/paddle/src/place.hpp
index 4fdd245d005..48c49b0602f 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/place.hpp
+++ b/src/frontends/paddle/src/place.hpp
@@ -6,7 +6,7 @@
 
 #include <openvino/frontend/manager.hpp>
 
-#include "exceptions.hpp"
+#include "input_model.hpp"
 
 namespace paddle {
 namespace framework {
@@ -20,18 +20,20 @@ class VarDesc;
 
 namespace ov {
 namespace frontend {
-class TensorPlacePDPD;
-class OpPlacePDPD;
+namespace paddle {
 
-class PlacePDPD : public Place {
+class TensorPlace;
+class OpPlace;
+
+class Place : public ov::frontend::Place {
 public:
-    PlacePDPD(const InputModel& input_model, const std::vector<std::string>& names)
+    Place(const ov::frontend::InputModel& input_model, const std::vector<std::string>& names)
         : m_input_model(input_model),
           m_names(names) {}
 
-    explicit PlacePDPD(const InputModel& input_model) : PlacePDPD(input_model, std::vector<std::string>{}) {}
+    explicit Place(const ov::frontend::InputModel& input_model) : Place(input_model, std::vector<std::string>{}) {}
 
-    ~PlacePDPD() override = default;
+    ~Place() override = default;
 
     bool is_input() const override;
     bool is_output() const override;
@@ -44,22 +46,22 @@ public:
     }
 
 private:
-    const InputModel& m_input_model;
+    const ov::frontend::InputModel& m_input_model;
     std::vector<std::string> m_names;
 };
 
-class InPortPlacePDPD : public PlacePDPD {
+class InPortPlace : public Place {
 public:
-    explicit InPortPlacePDPD(const InputModel& input_model) : PlacePDPD(input_model) {}
+    explicit InPortPlace(const ov::frontend::InputModel& input_model) : Place(input_model) {}
 
-    void set_op(const std::weak_ptr<OpPlacePDPD>& op) {
+    void set_op(const std::weak_ptr<OpPlace>& op) {
         m_op = op;
     }
-    void set_source_tensor(const std::weak_ptr<TensorPlacePDPD>& source_tensor);
+    void set_source_tensor(const std::weak_ptr<TensorPlace>& source_tensor);
 
     // Internal usage
-    std::shared_ptr<TensorPlacePDPD> get_source_tensor_pdpd() const;
-    std::shared_ptr<OpPlacePDPD> get_op();
+    std::shared_ptr<TensorPlace> get_source_tensor_paddle() const;
+    std::shared_ptr<OpPlace> get_op();
 
     // External usage
     std::vector<Ptr> get_consuming_operations() const override;
@@ -70,20 +72,20 @@ public:
     bool is_equal_data(const Ptr& another) const override;
 
 private:
-    std::weak_ptr<TensorPlacePDPD> m_source_tensor;
-    std::weak_ptr<OpPlacePDPD> m_op;
+    std::weak_ptr<TensorPlace> m_source_tensor;
+    std::weak_ptr<OpPlace> m_op;
 };
 
-class OutPortPlacePDPD : public PlacePDPD {
+class OutPortPlace : public Place {
 public:
-    explicit OutPortPlacePDPD(const InputModel& input_model) : PlacePDPD(input_model) {}
+    explicit OutPortPlace(const ov::frontend::InputModel& input_model) : Place(input_model) {}
 
-    void set_op(const std::weak_ptr<OpPlacePDPD>& op) {
+    void set_op(const std::weak_ptr<OpPlace>& op) {
         m_op = op;
     }
-    void set_target_tensor(const std::weak_ptr<TensorPlacePDPD>& target_tensor);
+    void set_target_tensor(const std::weak_ptr<TensorPlace>& target_tensor);
 
-    std::shared_ptr<TensorPlacePDPD> get_target_tensor_pdpd() const;
+    std::shared_ptr<TensorPlace> get_target_tensor_paddle() const;
 
     // External usage
     std::vector<Ptr> get_consuming_operations() const override;
@@ -93,27 +95,27 @@ public:
     bool is_equal_data(const Ptr& another) const override;
 
 private:
-    std::weak_ptr<OpPlacePDPD> m_op;
-    std::weak_ptr<TensorPlacePDPD> m_target_tensor;
+    std::weak_ptr<OpPlace> m_op;
+    std::weak_ptr<TensorPlace> m_target_tensor;
 };
 
-class OpPlacePDPD : public PlacePDPD {
+class OpPlace : public Place {
 public:
-    OpPlacePDPD(const InputModel& input_model,
-                const paddle::framework::proto::OpDesc& op_desc,
-                const std::vector<std::string>& names);
+    OpPlace(const ov::frontend::InputModel& input_model,
+            const ::paddle::framework::proto::OpDesc& op_desc,
+            const std::vector<std::string>& names);
 
-    OpPlacePDPD(const InputModel& input_model, const paddle::framework::proto::OpDesc& op_desc);
+    OpPlace(const ov::frontend::InputModel& input_model, const ::paddle::framework::proto::OpDesc& op_desc);
 
-    void add_in_port(const std::shared_ptr<InPortPlacePDPD>& input, const std::string& name);
-    void add_out_port(const std::shared_ptr<OutPortPlacePDPD>& output, const std::string& name);
+    void add_in_port(const std::shared_ptr<InPortPlace>& input, const std::string& name);
+    void add_out_port(const std::shared_ptr<OutPortPlace>& output, const std::string& name);
 
     // Internal usage
-    const std::map<std::string, std::vector<std::shared_ptr<OutPortPlacePDPD>>>& get_output_ports() const;
-    const std::map<std::string, std::vector<std::shared_ptr<InPortPlacePDPD>>>& get_input_ports() const;
-    std::shared_ptr<OutPortPlacePDPD> get_output_port_pdpd(const std::string& outputName, int outputPortIndex) const;
-    std::shared_ptr<InPortPlacePDPD> get_input_port_pdpd(const std::string& inputName, int inputPortIndex) const;
-    const paddle::framework::proto::OpDesc& get_desc() const;
+    const std::map<std::string, std::vector<std::shared_ptr<OutPortPlace>>>& get_output_ports() const;
+    const std::map<std::string, std::vector<std::shared_ptr<InPortPlace>>>& get_input_ports() const;
+    std::shared_ptr<OutPortPlace> get_output_port_paddle(const std::string& outputName, int outputPortIndex) const;
+    std::shared_ptr<InPortPlace> get_input_port_paddle(const std::string& inputName, int inputPortIndex) const;
+    const ::paddle::framework::proto::OpDesc& get_desc() const;
 
     // External API methods
     std::vector<Place::Ptr> get_consuming_ports() const override;
@@ -149,21 +151,21 @@ public:
     Ptr get_target_tensor(const std::string& outputName, int outputPortIndex) const override;
 
 private:
-    const paddle::framework::proto::OpDesc& m_op_desc;
-    std::map<std::string, std::vector<std::shared_ptr<InPortPlacePDPD>>> m_input_ports;
-    std::map<std::string, std::vector<std::shared_ptr<OutPortPlacePDPD>>> m_output_ports;
+    const ::paddle::framework::proto::OpDesc& m_op_desc;
+    std::map<std::string, std::vector<std::shared_ptr<InPortPlace>>> m_input_ports;
+    std::map<std::string, std::vector<std::shared_ptr<OutPortPlace>>> m_output_ports;
 };
 
-class TensorPlacePDPD : public PlacePDPD {
+class TensorPlace : public Place {
 public:
-    TensorPlacePDPD(const InputModel& input_model,
-                    const std::vector<std::string>& names,
-                    const paddle::framework::proto::VarDesc& var_desc);
+    TensorPlace(const ov::frontend::InputModel& input_model,
+                const std::vector<std::string>& names,
+                const ::paddle::framework::proto::VarDesc& var_desc);
 
-    TensorPlacePDPD(const InputModel& input_model, const paddle::framework::proto::VarDesc& var_desc);
+    TensorPlace(const ov::frontend::InputModel& input_model, const ::paddle::framework::proto::VarDesc& var_desc);
 
-    void add_producing_port(const std::shared_ptr<OutPortPlacePDPD>& out_port);
-    void add_consuming_port(const std::shared_ptr<InPortPlacePDPD>& in_port);
+    void add_producing_port(const std::shared_ptr<OutPortPlace>& out_port);
+    void add_consuming_port(const std::shared_ptr<InPortPlace>& in_port);
 
     // Internal usage
     const PartialShape& get_partial_shape() const {
@@ -178,7 +180,7 @@ public:
     void set_element_type(const element::Type& type) {
         m_type = type;
     }
-    const paddle::framework::proto::VarDesc& get_desc() const;
+    const ::paddle::framework::proto::VarDesc& get_desc() const;
 
     // External usage
     Ptr get_producing_operation() const override;
@@ -188,13 +190,14 @@ public:
     bool is_equal_data(const Ptr& another) const override;
 
 private:
-    const paddle::framework::proto::VarDesc& m_var_desc;
+    const ::paddle::framework::proto::VarDesc& m_var_desc;
     PartialShape m_pshape;
     element::Type m_type;
 
-    std::vector<std::weak_ptr<OutPortPlacePDPD>> m_producing_ports;
-    std::vector<std::weak_ptr<InPortPlacePDPD>> m_consuming_ports;
+    std::vector<std::weak_ptr<OutPortPlace>> m_producing_ports;
+    std::vector<std::weak_ptr<InPortPlace>> m_consuming_ports;
 };
 
+}  // namespace paddle
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/paddlepaddle/src/proto/framework.proto b/src/frontends/paddle/src/proto/framework.proto
similarity index 100%
rename from src/frontends/paddlepaddle/src/proto/framework.proto
rename to src/frontends/paddle/src/proto/framework.proto
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
deleted file mode 100644
index 9ab066dc759..00000000000
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
+++ /dev/null
@@ -1,30 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <openvino/frontend/manager.hpp>
-
-#ifdef OPENVINO_STATIC_LIBRARY
-#    define PDPD_API
-#    define PDPD_C_API
-#else
-#    ifdef ov_paddlepaddle_frontend_EXPORTS
-#        define PDPD_API   OPENVINO_CORE_EXPORTS
-#        define PDPD_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
-#    else
-#        define PDPD_API   OPENVINO_CORE_IMPORTS
-#        define PDPD_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
-#    endif  // ov_paddlepaddle_frontend_EXPORTS
-#endif      // OPENVINO_STATIC_LIBRARY
-
-#define PDPD_ASSERT(ex, msg)               \
-    {                                      \
-        if (!(ex))                         \
-            throw std::runtime_error(msg); \
-    }
-
-#define PDPD_THROW(msg) throw std::runtime_error(std::string("ERROR: ") + msg)
-
-#define NOT_IMPLEMENTED(msg) throw std::runtime_error(std::string(msg) + " is not implemented")
diff --git a/src/frontends/paddlepaddle/src/exceptions.hpp b/src/frontends/paddlepaddle/src/exceptions.hpp
deleted file mode 100644
index 78ce6937d83..00000000000
--- a/src/frontends/paddlepaddle/src/exceptions.hpp
+++ /dev/null
@@ -1,36 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <openvino/frontend/exception.hpp>
-
-namespace ov {
-namespace frontend {
-namespace pdpd {
-class NodeContext;
-
-class OpValidationFailurePDPD : public OpValidationFailure {
-public:
-    OpValidationFailurePDPD(const CheckLocInfo& check_loc_info,
-                            const pdpd::NodeContext& node,
-                            const std::string& explanation)
-        : OpValidationFailure(check_loc_info, get_error_msg_prefix_pdpd(node), explanation) {}
-
-private:
-    static std::string get_error_msg_prefix_pdpd(const pdpd::NodeContext& node);
-};
-}  // namespace pdpd
-}  // namespace frontend
-
-/// \brief Macro to check whether a boolean condition holds.
-/// \param node_context Object of NodeContext class
-/// \param cond Condition to check
-/// \param ... Additional error message info to be added to the error message via the `<<`
-///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
-///            i.e., only if the `cond` evalutes to `false`.
-/// \throws ::ov::OpValidationFailurePDPD if `cond` is false.
-#define PDPD_OP_VALIDATION_CHECK(node_context, ...) \
-    OPENVINO_ASSERT_HELPER(::ov::frontend::pdpd::OpValidationFailurePDPD, (node_context), __VA_ARGS__)
-}  // namespace ov
diff --git a/src/tests/functional/inference_engine/CMakeLists.txt b/src/tests/functional/inference_engine/CMakeLists.txt
index 0cb94729dbc..0e8eae35322 100644
--- a/src/tests/functional/inference_engine/CMakeLists.txt
+++ b/src/tests/functional/inference_engine/CMakeLists.txt
@@ -52,7 +52,7 @@ if (NOT ENABLE_OV_ONNX_FRONTEND)
     list(APPEND EXCLUDED_SOURCE_PATHS "${CMAKE_CURRENT_SOURCE_DIR}/onnx_reader")
 endif()
 
-if (NOT ENABLE_OV_PDPD_FRONTEND)
+if (NOT ENABLE_OV_PADDLE_FRONTEND)
     list(APPEND EXCLUDED_SOURCE_PATHS "${CMAKE_CURRENT_SOURCE_DIR}/paddle_reader")
 endif()
 
@@ -77,10 +77,10 @@ if(ENABLE_OV_ONNX_FRONTEND)
     add_dependencies(${TARGET_NAME} ov_onnx_frontend)
 endif()
 
-if(ENABLE_OV_PDPD_FRONTEND)
+if(ENABLE_OV_PADDLE_FRONTEND)
     target_compile_definitions(${TARGET_NAME} PRIVATE
             PADDLE_TEST_MODELS="${CMAKE_CURRENT_SOURCE_DIR}/paddle_reader/models/")
-    add_dependencies(${TARGET_NAME} ov_paddlepaddle_frontend)
+    add_dependencies(${TARGET_NAME} ov_paddle_frontend)
 endif()
 
 ie_faster_build(${TARGET_NAME}
diff --git a/src/tests/functional/inference_engine/paddle_reader/read_paddle_model_test.cpp b/src/tests/functional/inference_engine/paddle_reader/read_paddle_model_test.cpp
index 8bb7b222bc1..dfa0fe053b8 100644
--- a/src/tests/functional/inference_engine/paddle_reader/read_paddle_model_test.cpp
+++ b/src/tests/functional/inference_engine/paddle_reader/read_paddle_model_test.cpp
@@ -15,7 +15,7 @@
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "common_test_utils/unicode_utils.hpp"
 
-TEST(PDPD_Reader_Tests, ImportBasicModelToCore) {
+TEST(Paddle_Reader_Tests, ImportBasicModelToCore) {
     auto model = std::string(PADDLE_TEST_MODELS) + "relu.pdmodel";
     InferenceEngine::Core ie;
     auto cnnNetwork = ie.ReadNetwork(model);
@@ -41,14 +41,14 @@ TEST(PDPD_Reader_Tests, ImportBasicModelToCore) {
     const auto reference = std::make_shared<ngraph::Function>(
         ngraph::NodeVector{ result },
         ngraph::ParameterVector{ data },
-        "RefPDPDFunction");
+        "RefPaddleFunction");
     const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::NAMES);
     const FunctionsComparator::Result res = func_comparator(function, reference);
     ASSERT_TRUE(res.valid) << res.message;
 }
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-TEST(PDPD_Reader_Tests, ImportBasicModelToCoreWstring) {
+TEST(Paddle_Reader_Tests, ImportBasicModelToCoreWstring) {
     std::string win_dir_path{ PADDLE_TEST_MODELS "relu.pdmodel" };
     std::wstring wmodel = CommonTestUtils::addUnicodePostfixToPath(win_dir_path,
         CommonTestUtils::test_unicode_postfix_vector[0]);
@@ -82,7 +82,7 @@ TEST(PDPD_Reader_Tests, ImportBasicModelToCoreWstring) {
     const auto reference = std::make_shared<ngraph::Function>(
         ngraph::NodeVector{ result },
         ngraph::ParameterVector{ data },
-        "RefPDPDFunction");
+        "RefPaddleFunction");
     const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::NAMES);
     const FunctionsComparator::Result res = func_comparator(function, reference);
     ASSERT_TRUE(res.valid) << res.message;
diff --git a/tests/fuzz/scripts/init_corpus.py b/tests/fuzz/scripts/init_corpus.py
index e7a6ff66011..b4a0c872567 100755
--- a/tests/fuzz/scripts/init_corpus.py
+++ b/tests/fuzz/scripts/init_corpus.py
@@ -3,8 +3,8 @@
 # SPDX-License-Identifier: Apache-2.0
 
 # Sample usage:
-#   ./scripts/init_corpus.py ./pdpd_layer_models/**/*.pdmodel --join pdiparams
-#   mkdir -p corpus && find ./pdpd_layer_models/ -name "*.fuzz" -exec cp \{\} .//import_pdpd-corpus \;
+#   ./scripts/init_corpus.py ./paddle_layer_models/**/*.pdmodel --join pdiparams
+#   mkdir -p corpus && find ./paddle_layer_models/ -name "*.fuzz" -exec cp \{\} .//import_paddle-corpus \;
 
 import argparse
 import glob
diff --git a/thirdparty/CMakeLists.txt b/thirdparty/CMakeLists.txt
index 2e34ec79e53..84c10f8b044 100644
--- a/thirdparty/CMakeLists.txt
+++ b/thirdparty/CMakeLists.txt
@@ -82,7 +82,7 @@ endif()
 # Protobuf
 #
 
-if(ENABLE_OV_PDPD_FRONTEND OR ENABLE_OV_ONNX_FRONTEND OR ENABLE_OV_TF_FRONTEND)
+if(ENABLE_OV_PADDLE_FRONTEND OR ENABLE_OV_ONNX_FRONTEND OR ENABLE_OV_TF_FRONTEND)
     if(ENABLE_SYSTEM_PROTOBUF)
         set(Protobuf_USE_STATIC_LIBS ON)
         if(VERBOSE_BUILD)
diff --git a/tools/deployment_manager/configs/darwin.json b/tools/deployment_manager/configs/darwin.json
index a8b07728099..575fa4d8bc5 100644
--- a/tools/deployment_manager/configs/darwin.json
+++ b/tools/deployment_manager/configs/darwin.json
@@ -23,7 +23,7 @@
         "runtime/lib/intel64/libov_auto_plugin.so",
         "runtime/lib/intel64/libov_ir_frontend.dylib",
         "runtime/lib/intel64/libov_onnx_frontend.dylib",
-        "runtime/lib/intel64/libov_paddlepaddle_frontend.dylib",
+        "runtime/lib/intel64/libov_paddle_frontend.dylib",
         "runtime/lib/intel64/plugins.xml",
         "runtime/3rdparty/tbb"
       ]
diff --git a/tools/deployment_manager/configs/linux.json b/tools/deployment_manager/configs/linux.json
index 2d17f1a788f..05e8ee9c983 100644
--- a/tools/deployment_manager/configs/linux.json
+++ b/tools/deployment_manager/configs/linux.json
@@ -29,7 +29,7 @@
         "runtime/lib/intel64/libov_auto_plugin.so",
         "runtime/lib/intel64/libov_ir_frontend.so",
         "runtime/lib/intel64/libov_onnx_frontend.so",
-        "runtime/lib/intel64/libov_paddlepaddle_frontend.so",
+        "runtime/lib/intel64/libov_paddle_frontend.so",
         "runtime/lib/intel64/plugins.xml",
         "runtime/3rdparty/tbb"
       ]
diff --git a/tools/deployment_manager/configs/windows.json b/tools/deployment_manager/configs/windows.json
index 9cb8d68b379..729f2496ea8 100644
--- a/tools/deployment_manager/configs/windows.json
+++ b/tools/deployment_manager/configs/windows.json
@@ -23,7 +23,7 @@
         "runtime/bin/intel64/Release/ov_auto_plugin.dll",
         "runtime/bin/intel64/Release/ov_ir_frontend.dll",
         "runtime/bin/intel64/Release/ov_onnx_frontend.dll",
-        "runtime/bin/intel64/Release/ov_paddlepaddle_frontend.dll",
+        "runtime/bin/intel64/Release/ov_paddle_frontend.dll",
         "runtime/bin/intel64/Release/plugins.xml",
         "runtime/3rdparty/tbb"
       ]

From 0e3c4cc103caf43745603ec068187e3683acf2f4 Mon Sep 17 00:00:00 2001
From: "Min, Byungil" <byungil.min@intel.com>
Date: Wed, 22 Dec 2021 18:17:08 +0900
Subject: [PATCH 087/160] Modify padding checking logic to enable new model
 (#9310)

+ Modified a way to add padding in prepare_padding
+ Changed condition of assertion for onednn padding

Signed-off-by: Min, Byungil <byungil.min@intel.com>
---
 .../clDNN/src/graph_optimizer/prepare_padding.cpp          | 7 ++++++-
 .../src/graph_optimizer/remove_redundant_reorders.cpp      | 4 ++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_padding.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_padding.cpp
index d7aff78742e..190a32824dd 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_padding.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_padding.cpp
@@ -30,7 +30,12 @@ void prepare_padding::run(program& p) {
             auto add_required_padding = [&p](program_node& node, padding& needed_padding) {
                 // Add extra reorder for cldnn primitive to handle required padding if needed
                 auto& input = node.get_dependency(0);
-                if (input.get_preferred_impl_type() == impl_types::onednn &&
+                bool is_usr_onednn = false;
+                for (auto& input_usr : input.get_users())
+                    if (input_usr->get_preferred_impl_type() == impl_types::onednn)
+                        is_usr_onednn = true;
+
+                if ((input.get_preferred_impl_type() == impl_types::onednn || is_usr_onednn) &&
                     node.get_preferred_impl_type() == impl_types::ocl &&
                     static_cast<bool>(needed_padding)) {
                     auto new_reorder = std::make_shared<reorder>(node.id() + "_padding_reorder_for_" + input.id(), input.id(), input.get_output_layout());
diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/remove_redundant_reorders.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/remove_redundant_reorders.cpp
index dad1f44d94e..6437620a048 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/remove_redundant_reorders.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/remove_redundant_reorders.cpp
@@ -544,8 +544,8 @@ void remove_redundant_reorders::run(program& p) {
             n->set_preferred_impl_type(preferred_impl);
         }
 
-        // Validate fused layout when onednn is enable
-        if (n->get_preferred_impl_type() == impl_types::onednn && !lo.are_layouts_suitable_for_onednn(*n)) {
+        // Validate fused layout when onednn is enable in post_optimize_graph
+        if (!enable_reorder_fusing && n->get_preferred_impl_type() == impl_types::onednn && !lo.are_layouts_suitable_for_onednn(*n)) {
             throw std::runtime_error("Onednn doesnot support padded input or output");
         }
     }

From 8c746fd52c4231e3aabef4558aeeb8062ccac7c9 Mon Sep 17 00:00:00 2001
From: "Wang, Yang" <yang4.wang@intel.com>
Date: Wed, 22 Dec 2021 17:21:08 +0800
Subject: [PATCH 088/160] Align AUTO/MULTI/HETERO wording (#8884)

* Align AUTO's behavior to parent Epic.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* update wording.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Update.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Update wprding in hetero plugin.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Update.

Signed-off-by: Wang, Yang <yang4.wang@intel.com>

* Change "metric" to  "metric key".

Signed-off-by: Wang, Yang <yang4.wang@intel.com>
---
 src/plugins/auto/executable_network.cpp   | 11 ++++++-----
 src/plugins/auto/plugin.cpp               |  4 ++--
 src/plugins/hetero/executable_network.cpp | 10 +++++-----
 src/plugins/hetero/plugin.cpp             | 10 +++++-----
 4 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/src/plugins/auto/executable_network.cpp b/src/plugins/auto/executable_network.cpp
index 9bcfb868038..0f63b63f114 100644
--- a/src/plugins/auto/executable_network.cpp
+++ b/src/plugins/auto/executable_network.cpp
@@ -158,11 +158,11 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
                                                            , _context(context)
                                                            , _workModeIsAUTO(true) {
     if (_multiPlugin->GetCore() == nullptr) {
-        IE_THROW() << "Please, work with MULTI device via InferencEngine::Core object";
+        IE_THROW() << "Please, work with " << _multiPlugin->GetName() << " device via InferencEngine::Core object";
     }
 
     if (modelPath.empty() && network.getFunction() == nullptr) {
-        IE_THROW() << "MULTI device supports just ngraph network representation";
+        IE_THROW() << "MULTI " << _multiPlugin->GetName() << " device supports just ngraph network representation";
     }
 
     _core = _multiPlugin->GetCore(); // shared_ptr that holds the Core
@@ -413,7 +413,7 @@ void MultiDeviceExecutableNetwork::ScheduleToWorkerInferRequest(Task inferPipeli
             WaitActualNetworkReady();
             // the preferred_device should be the selected device in AUTO work mode
             if (preferred_device != _loadContext[ACTUALDEVICE].deviceInfo.deviceName) {
-                IE_THROW(NotFound) << "The preferred_device should be the selected device";
+                IE_THROW(NotFound) << "The preferred device should be the selected device";
             }
             devices.push_back(_loadContext[ACTUALDEVICE].deviceInfo);
         } else {
@@ -515,7 +515,7 @@ std::shared_ptr<InferenceEngine::RemoteContext> MultiDeviceExecutableNetwork::Ge
             return n->GetContext();
         } catch (const NotImplemented&) {}
     }
-    IE_THROW(NotImplemented) << "None of the devices in the MULTI has an associated remote context."
+    IE_THROW(NotImplemented) << "None of the devices in the MULTI device has an associated remote context."
                              << " Current list of devices allowed via the DEVICE_PRIORITIES config: " << devices_names;
 }
 
@@ -654,6 +654,7 @@ InferenceEngine::Parameter MultiDeviceExecutableNetwork::GetConfig(const std::st
                 return execNetwork->GetConfig(configKey);
             }
         }
+        IE_THROW() << "Unsupported ExecutableNetwork config key: " << name;
     }
     IE_THROW(NotFound) << name <<" not found in the ExecutableNetwork config";
 }
@@ -708,7 +709,7 @@ InferenceEngine::Parameter MultiDeviceExecutableNetwork::GetMetric(const std::st
         std::vector<std::string> configKeys = { MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES };
         IE_SET_METRIC_RETURN(SUPPORTED_CONFIG_KEYS, configKeys);
     } else {
-        IE_THROW() << "Unsupported Network metric: " << name;
+        IE_THROW() << "Unsupported ExecutableNetwork metric key: " << name;
     }
 }
 }  // namespace MultiDevicePlugin
diff --git a/src/plugins/auto/plugin.cpp b/src/plugins/auto/plugin.cpp
index c1bdb8953c6..0588142b2a3 100644
--- a/src/plugins/auto/plugin.cpp
+++ b/src/plugins/auto/plugin.cpp
@@ -217,7 +217,7 @@ InferenceEngine::Parameter MultiDeviceInferencePlugin::GetMetric(const std::stri
     } else if (name == METRIC_KEY(SUPPORTED_CONFIG_KEYS)) {
         IE_SET_METRIC_RETURN(SUPPORTED_CONFIG_KEYS, supported_configKeys);
     } else {
-        IE_THROW() << "Unsupported metric key " << name;
+        IE_THROW() << "Unsupported metric key: " << name;
     }
 }
 
@@ -271,7 +271,7 @@ IExecutableNetworkInternal::Ptr MultiDeviceInferencePlugin::LoadNetworkImpl(cons
         auto metaDevices = ParseMetaDevices(strDevices, fullConfig);
         auto supportDevicesByConfig = FilterDevice(metaDevices, filterConfig);
         if (supportDevicesByConfig.size() == 0) {
-             IE_THROW() << "there is no device support the configure";
+             IE_THROW() << "There is no device support the configure";
         }
         auto supportDevices = FilterDeviceByNetwork(supportDevicesByConfig, network);
         // replace the configure with configure that auto want to pass to device
diff --git a/src/plugins/hetero/executable_network.cpp b/src/plugins/hetero/executable_network.cpp
index 51cffb1dd6d..53b5d54cec6 100644
--- a/src/plugins/hetero/executable_network.cpp
+++ b/src/plugins/hetero/executable_network.cpp
@@ -93,7 +93,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
         if (it != _config.end()) {
             queryNetworkResult = _heteroPlugin->QueryNetwork(network, _config);
         } else {
-            IE_THROW() << "The 'TARGET_FALLBACK' option was not defined for heterogeneous plugin";
+            IE_THROW() << "The 'TARGET_FALLBACK' option was not defined for heterogeneous device";
         }
     }
 
@@ -131,7 +131,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
             affinities[node.get()] = itAffinity->second;
             devices.emplace(itAffinity->second);
         } else if (allEmpty) {
-            IE_THROW() << "Hetero plugin used default fallback policy, but some layers eg: \n(Name:"
+            IE_THROW() << "Hetero device used default fallback policy, but some layers eg: \n(Name:"
                        << node->get_friendly_name() << ", Type: " << node->get_type_name()
                        << ") were not able to be assigned on any pointed device.\n"
                        << "It happened because these layers are not supported in plugins by default.\n"
@@ -501,7 +501,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream& heteroModel,
     pugi::xml_parse_result res = heteroXmlDoc.load_string(heteroXmlStr.c_str());
 
     if (res.status != pugi::status_ok) {
-        IE_THROW(NetworkNotRead) << "Error reading HETERO plugin xml header";
+        IE_THROW(NetworkNotRead) << "Error reading HETERO device xml header";
     }
 
     using namespace XMLParseUtils;
@@ -751,7 +751,7 @@ void HeteroExecutableNetwork::Export(std::ostream& heteroModel) {
         } else {
             auto subnet = subnetwork._clonedNetwork;
             if (!subnet.getFunction()) {
-                IE_THROW() << "Hetero plugin supports only ngraph function representation";
+                IE_THROW() << "Hetero device supports only ngraph function representation";
             }
 
             // Note: custom ngraph extensions are not supported
@@ -937,6 +937,6 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string&
             }
         }
 
-        IE_THROW() << "Unsupported ExecutableNetwork metric: " << name;
+        IE_THROW() << "Unsupported ExecutableNetwork metric key: " << name;
     }
 }
diff --git a/src/plugins/hetero/plugin.cpp b/src/plugins/hetero/plugin.cpp
index 8ff68515290..6503e8dabcd 100644
--- a/src/plugins/hetero/plugin.cpp
+++ b/src/plugins/hetero/plugin.cpp
@@ -55,13 +55,13 @@ InferenceEngine::IExecutableNetworkInternal::Ptr Engine::LoadExeNetworkImpl(cons
     auto tconfig = mergeConfigs(_config, config);
     auto it = tconfig.find("TARGET_FALLBACK");
     if (it == tconfig.end()) {
-        IE_THROW() << "The 'TARGET_FALLBACK' option was not defined for heterogeneous plugin";
+        IE_THROW() << "The 'TARGET_FALLBACK' option was not defined for heterogeneous device";
     }
     DeviceMetaInformationMap metaDevices = GetDevicePlugins(it->second, tconfig);
 
     auto function = network.getFunction();
     if (function == nullptr) {
-        IE_THROW() << "HETERO plugin supports just ngraph network representation";
+        IE_THROW() << "HETERO device supports just ngraph network representation";
     }
 
     return std::make_shared<HeteroExecutableNetwork>(network, mergeConfigs(_config, config), this);
@@ -133,7 +133,7 @@ QueryNetworkResult Engine::QueryNetwork(const CNNNetwork& network, const Configs
     auto tconfig = mergeConfigs(_config, config);
     auto it = tconfig.find("TARGET_FALLBACK");
     if (it == tconfig.end()) {
-        IE_THROW() << "The 'TARGET_FALLBACK' option was not defined for heterogeneous plugin";
+        IE_THROW() << "The 'TARGET_FALLBACK' option was not defined for heterogeneous device";
     }
 
     std::string fallbackDevicesStr = it->second;
@@ -141,7 +141,7 @@ QueryNetworkResult Engine::QueryNetwork(const CNNNetwork& network, const Configs
 
     auto function = network.getFunction();
     if (function == nullptr) {
-        IE_THROW() << "HETERO plugin supports just ngraph network representation";
+        IE_THROW() << "HETERO device supports just ngraph network representation";
     }
 
     std::map<std::string, QueryNetworkResult> queryResults;
@@ -189,7 +189,7 @@ Parameter Engine::GetMetric(const std::string& name, const std::map<std::string,
         }
         IE_SET_METRIC_RETURN(DEVICE_ARCHITECTURE, DeviceArchitecture(targetFallback));
     } else {
-        IE_THROW() << "Unsupported Plugin metric: " << name;
+        IE_THROW() << "Unsupported metric key: " << name;
     }
 }
 std::string Engine::DeviceArchitecture(const std::string& targetFallback) const {

From 2ec04f9fafafba2dc8f1db14cd20093232258882 Mon Sep 17 00:00:00 2001
From: Dmitrii Khurtin <dmitrii.khurtin@intel.com>
Date: Wed, 22 Dec 2021 13:31:01 +0300
Subject: [PATCH 089/160] AddressSanitizer: heap-buffer-overflow in PwlApply32
 (#9256)

* fixed bug related to AddressSanitizer

* changed the way of calculation of input bytes of pooling layer if a previous layer is convolution
---
 src/plugins/intel_gna/gna_graph_compiler.cpp | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/src/plugins/intel_gna/gna_graph_compiler.cpp b/src/plugins/intel_gna/gna_graph_compiler.cpp
index 54596c63b08..1b94d2ade84 100644
--- a/src/plugins/intel_gna/gna_graph_compiler.cpp
+++ b/src/plugins/intel_gna/gna_graph_compiler.cpp
@@ -883,6 +883,18 @@ void GNAGraphCompiler::PoolingPrimitive(InferenceEngine::CNNLayerPtr layer) {
     uint32_t num_padding = ALIGN(hw_in, 8) - hw_in;
     size_t num_data_bytes_in = c_dim_in * (hw_in + num_padding) * inputs->getPrecision().size();
 
+    if (dnn->new_num_conv_columns) {
+        uint32_t num_rows = 1;
+        uint32_t num_columns = c_dim_in * w_dim_in + (ALIGN(c_dim_in * w_dim_in, 8) - c_dim_in * w_dim_in);
+        if (dnn->new_num_conv_columns % num_columns == 0) {
+            num_rows = dnn->new_num_conv_columns / num_columns;
+        } else {
+            num_columns = dnn->new_num_conv_columns;
+        }
+        dnn->new_num_conv_columns = 0;
+        num_data_bytes_in = num_rows * num_columns * inputs->getPrecision().size();
+    }
+
     connectInput(layer, ptr_inputs, num_data_bytes_in);
     connectOutput(layer, ptr_outputs, num_data_bytes_out);
 }
@@ -2459,7 +2471,7 @@ GNAPluginNS::ConnectionDetails GNAGraphCompiler::connectInput(CNNLayerPtr layer,
 
     // check for generic prev layer
     if (prevDnnLayer != nullptr) {
-        gnamem->bind_ptr(layer, ptr, &prevDnnLayer->ptr_outputs, offset);
+        gnamem->bind_ptr(layer, ptr, &prevDnnLayer->ptr_outputs, offset, num_data_bytes_in);
         return prevLayer;
     }
 

From b7750f8f0d769126a673f851867c7433a6cd9cba Mon Sep 17 00:00:00 2001
From: Katarzyna Mitrus <katarzyna.mitrus@intel.com>
Date: Wed, 22 Dec 2021 12:23:50 +0100
Subject: [PATCH 090/160] [ONNX][PYTHON API] FLOAT_LIKE_xfail - Enable bfloat16
 onnx tests (#9291)

---
 src/bindings/python/tests/__init__.py         |  1 -
 src/bindings/python/tests/runtime.py          | 20 +++++++++++++++++--
 .../python/tests/test_onnx/test_backend.py    |  8 --------
 3 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/src/bindings/python/tests/__init__.py b/src/bindings/python/tests/__init__.py
index fc682409113..0513d7dfb95 100644
--- a/src/bindings/python/tests/__init__.py
+++ b/src/bindings/python/tests/__init__.py
@@ -24,7 +24,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 
 skip_segfault = pytest.mark.skip(reason="Segmentation fault error")
 xfail_accuracy = xfail_test(reason="Accuracy")
-xfail_issue_FLOAT_LIKE = xfail_test(reason="Use of bfloat16 or float16")
 xfail_issue_69444 = xfail_test(reason="ONNX Resize - AssertionError: Mismatched elements.")
 skip_issue_67415 = pytest.mark.skip(reason="RuntimeError: Unsupported data type for when filling blob!")
 xfail_issue_67415 = xfail_test(reason="RuntimeError: Unsupported data type for when filling blob!")
diff --git a/src/bindings/python/tests/runtime.py b/src/bindings/python/tests/runtime.py
index 2cfad917ee7..b509058cd5d 100644
--- a/src/bindings/python/tests/runtime.py
+++ b/src/bindings/python/tests/runtime.py
@@ -11,7 +11,7 @@ import numpy as np
 from openvino.runtime import Core
 
 from openvino.runtime.exceptions import UserInputError
-from openvino.runtime import Model, Node, PartialShape, Type
+from openvino.runtime import Model, Node, PartialShape, Tensor, Type
 from openvino.runtime.utils.types import NumericData, get_shape, get_dtype
 
 import tests
@@ -87,11 +87,23 @@ class Computation(object):
             target_dtype = target_dtypes[i]
             # custom conversion for bf16
             if self.results[i].get_output_element_type(0) == Type.bf16:
-                converted_buffers.append((source_buffers[k].view(np.uint32) >> 16).astype(np.uint16))
+                converted_buffers.append((source_buffers[k].view(target_dtype)).astype(target_dtype))
             else:
                 converted_buffers.append(source_buffers[k].astype(target_dtype))
         return converted_buffers
 
+    def convert_to_tensors(self, input_values):
+        input_tensors = []
+        for parameter, input in zip(self.parameters, input_values):
+            if not isinstance(input, (np.ndarray)):
+                input = np.ndarray([], type(input), np.array(input))
+            if parameter.get_output_element_type(0) == Type.bf16:
+                input_tensors.append(Tensor(Type.bf16, input.shape))
+                input_tensors[-1].data[:] = input.view(np.float16)
+            else:
+                input_tensors.append(Tensor(input))
+        return input_tensors
+
     def __call__(self, *input_values: NumericData) -> List[NumericData]:
         """Run computation on input values and return result."""
         # Input validation
@@ -123,6 +135,10 @@ class Computation(object):
                     parameter_shape,
                 )
 
+        is_bfloat16 = any(parameter.get_output_element_type(0) == Type.bf16 for parameter in self.parameters)
+        if is_bfloat16:
+            input_values = self.convert_to_tensors(input_values)
+
         request = executable_network.create_infer_request()
         result_buffers = request.infer(dict(zip(param_names, input_values)))
         # # Note: other methods to get result_buffers from request
diff --git a/src/bindings/python/tests/test_onnx/test_backend.py b/src/bindings/python/tests/test_onnx/test_backend.py
index bc13cd18068..9ac78a48b92 100644
--- a/src/bindings/python/tests/test_onnx/test_backend.py
+++ b/src/bindings/python/tests/test_onnx/test_backend.py
@@ -6,7 +6,6 @@ import logging
 import onnx.backend.test
 from tests import (
     BACKEND_NAME,
-    xfail_issue_FLOAT_LIKE,
     skip_rng_tests,
     xfail_issue_33488,
     xfail_issue_33538,
@@ -97,13 +96,6 @@ OnnxBackendPyTorchConvertedModelTest = None
 globals().update(backend_test.enable_report().test_cases)
 
 tests_expected_to_fail = [
-    (
-        xfail_issue_FLOAT_LIKE,
-        "OnnxBackendNodeModelTest.test_cast_BFLOAT16_to_FLOAT_cpu",
-        "OnnxBackendNodeModelTest.test_cast_FLOAT_to_BFLOAT16_cpu",
-        "OnnxBackendNodeModelTest.test_castlike_BFLOAT16_to_FLOAT_expanded_cpu",
-        "OnnxBackendNodeModelTest.test_castlike_FLOAT_to_BFLOAT16_expanded_cpu",
-    ),
     (
         xfail_issue_49207,
         "OnnxBackendNodeModelTest.test_rnn_seq_length_cpu",

From eca9ff698195befdd089a4d2ef5f655fa842dac8 Mon Sep 17 00:00:00 2001
From: Ilya Znamenskiy <ilya.znamenskiy@intel.com>
Date: Wed, 22 Dec 2021 14:38:24 +0300
Subject: [PATCH 091/160] [GPU] Fixed gemm_tiled_opt fusing error and code
 refactoring in fusing tests (#9303)

---
 .../gemm/gemm_kernel_tiled_opt.cpp            |     2 +-
 .../core/cl_kernels/gemm_tiled_opt.cl         |    15 +-
 .../kernel_selector/core/common/jitter.cpp    |     4 +-
 .../tests/test_cases/fusings_gpu_test.cpp     | 10481 ++++++++--------
 4 files changed, 5325 insertions(+), 5177 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_tiled_opt.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_tiled_opt.cpp
index 9f77050b46d..fb18b8f8d2c 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_tiled_opt.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_tiled_opt.cpp
@@ -160,7 +160,7 @@ JitConstants GemmKernelTiledOpt::GetJitConstants(const gemm_params& params) cons
                                                "dequantized",
                                                input_dt,
                                                1,
-                                               LoadType::LT_ALIGNED_READ,
+                                               LoadType::LT_UNALIGNED,
                                                BoundaryCheck::ENABLED,
                                                IndexType::TENSOR_COORD,
                                                Tensor::DataChannelName::Y };
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gemm_tiled_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gemm_tiled_opt.cl
index cba34cdcf8c..0afe7b0bc7c 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gemm_tiled_opt.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gemm_tiled_opt.cl
@@ -85,11 +85,12 @@ KERNEL(gemm_tiled_opt)(
     const uint sglid = (uint)get_sub_group_local_id();
 
     // Setting x and y for fusings indexing
-#if B_VEC_SIZE == 1
+    // TODO: investigate how we can use only TILE_N_NOT_DIVISIBLE here for getting stable results in fusings
+#if TILE_N_NOT_DIVISIBLE || B_VEC_SIZE == 1
     const uint x = (uint)get_global_id(0);
-#else // B_VEC_SIZE == 1
+#else // TILE_N_NOT_DIVISIBLE || B_VEC_SIZE == 1
     const uint x = tile_n_num * SIMD_WIDTH * B_VEC_SIZE;
-#endif // B_VEC_SIZE == 1
+#endif // TILE_N_NOT_DIVISIBLE || B_VEC_SIZE == 1
     uint y = tile_m_offset;
 
 #if TILE_M_NOT_DIVISIBLE
@@ -282,7 +283,7 @@ KERNEL(gemm_tiled_opt)(
 #endif // TILE_K_NOT_DIVISIBLE
 
 #if HAS_FUSED_OPS && FUSED_OPS_CAN_USE_PRELOAD
-#if TILE_N_NOT_DIVISIBLE
+#if TILE_N_NOT_DIVISIBLE || B_VEC_SIZE == 1
     FUSED_OPS_PRELOAD_SCALAR;
 #else // TILE_N_NOT_DIVISIBLE
     FUSED_OPS_PRELOAD_VEC;
@@ -291,7 +292,7 @@ KERNEL(gemm_tiled_opt)(
 
     // Writing result in the global memory
     unroll_for (uint write_id = 0; write_id < tile_m_iterations; write_id++) {
-#if TILE_N_NOT_DIVISIBLE
+#if TILE_N_NOT_DIVISIBLE || B_VEC_SIZE == 1
         if (b_raw_global_id < N) {
 #ifdef INPUT2_TYPE
             ACCUMULATOR_TYPE dequantized = TO_ACCUMULATOR_TYPE(ALPHA) * c_tile[write_id] + TO_ACCUMULATOR_TYPE(BETA) * c_ptr[sglid];
@@ -312,7 +313,7 @@ KERNEL(gemm_tiled_opt)(
 #endif // HAS_FUSED_OPS
         }
 
-#else // TILE_N_NOT_DIVISIBLE
+#else // TILE_N_NOT_DIVISIBLE || B_VEC_SIZE == 1
 
 #ifdef INPUT2_TYPE
         B_FLOATN c_val = BLOCK_READ_B(c_ptr, 0);
@@ -333,7 +334,7 @@ KERNEL(gemm_tiled_opt)(
         BLOCK_WRITE_C(d_ptr, 0, dequantized);
 #endif // HAS_FUSED_OPS
 
-#endif // TILE_N_NOT_DIVISIBLE
+#endif // TILE_N_NOT_DIVISIBLE || B_VEC_SIZE == 1
         d_ptr += N;
 #ifdef INPUT2_TYPE
         c_ptr += N;
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp
index 73f164b3659..f2de7e3e3ad 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp
@@ -1868,10 +1868,10 @@ std::string FusedOpsCodeGenerator::GetJitLoad(const FusedOpsConfiguration& conf,
                 return block_read;
             } else if (input_tensor.LogicalSize() > 1) {
                 // Currently we assume that in such scenario we can safely load sub_group_size elements from the pointer
-                return Broadcast(block_read, input_dt, conf.vec_size);
+                return Broadcast(block_read, input_dt, vec_size);
             } else {
                 // Input has only one element, so broadcast it for the whole vector size
-                return Broadcast(GetInputPtrName(input_id) + "[" + index_func_call + "]", input_dt, conf.vec_size);
+                return Broadcast(GetInputPtrName(input_id) + "[" + index_func_call + "]", input_dt, vec_size);
             }
         } else {
             if (vec_size > 1) {
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
index 48c42e4eae5..bc1f60587f5 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
@@ -309,32 +309,30 @@ public:
             weights_tensor = cldnn::tensor(group(p.groups), batch(p.out_shape.feature[0] / p.groups), feature(p.in_shape.feature[0] / p.groups),
                                            spatial(p.kernel.spatial[0], p.kernel.spatial[1], p.kernel.spatial[2]));
         }
-        return layout{p.weights_type, p.weights_format, weights_tensor};
+        return layout{ p.weights_type, p.weights_format, weights_tensor };
     }
 
     layout get_weights_layout(T& p, const int32_t /* split */, cldnn::format f) {
         cldnn::tensor weights_tensor;
         weights_tensor = cldnn::tensor(batch(p.out_shape.feature[0]), feature(static_cast<int32_t>(p.in_shape.feature[0] / p.groups)),
                                        spatial(p.kernel.spatial[0], p.kernel.spatial[1], p.kernel.spatial[2]));
-        return layout{p.weights_type, f, weights_tensor};
+        return layout{ p.weights_type, f, weights_tensor };
     }
 
     layout get_bias_layout(T& p) {
-        return layout{ p.default_type, format::bfyx, tensor{1, p.out_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, format::bfyx, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
     }
 
     layout get_weights_zp_layout(T& p) {
-        return layout{ p.weights_type, p.default_format, tensor{p.out_shape.feature[0], 1, 1, 1} };
+        return layout{ p.weights_type, p.default_format, tensor{ p.out_shape.feature[0], 1, 1, 1 } };
     }
 
     layout get_activations_zp_layout(T& p) {
-        return layout{ p.data_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1} };
+        return layout{ p.data_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
     }
 
-
-
     layout get_single_element_layout(T& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, 1, 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, 1, 1, 1 } };
     }
 
     template <class... Args>
@@ -361,11 +359,11 @@ public:
     layout get_input_layout(T& p) {
         auto pad = p.pad;
         std::vector<int> pad_ = { 0, 0, pad.spatial[0], pad.spatial[1] };
-        return layout{ p.data_type, p.input_format, p.in_shape, padding{pad_} };
+        return layout{ p.data_type, p.input_format, p.in_shape, padding{ pad_ } };
     }
 
     layout get_per_channel_layout(T& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
     }
 
     size_t get_fc_output_dim_size(bc_test_params& p) {
@@ -385,18 +383,18 @@ public:
         }
         else {
             weights_tensor = cldnn::tensor(batch(p.out_shape.feature[0]), feature(p.in_shape.feature[0]),
-                   spatial(p.kernel.spatial[0], p.kernel.spatial[1], p.kernel.spatial[2]));
+                                           spatial(p.kernel.spatial[0], p.kernel.spatial[1], p.kernel.spatial[2]));
         }
-        return layout{p.weights_type, p.weights_format, weights_tensor};
+        return layout{ p.weights_type, p.weights_format, weights_tensor };
     }
 
     layout get_fc_bias_layout(T& p) {
         if (p.out_shape.spatial[1] > 1) {
             // 3d case
-            return layout{ p.default_type, format::bfyx, tensor{1, p.out_shape.spatial[1], 1, 1} };
+            return layout{ p.default_type, format::bfyx, tensor{ 1, p.out_shape.spatial[1], 1, 1 } };
         }
         else {
-            return layout{ p.default_type, format::bfyx, tensor{1, p.out_shape.feature[0], 1, 1} };
+            return layout{ p.default_type, format::bfyx, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
         }
     }
 };
@@ -419,7 +417,7 @@ public:
     }
 
     layout get_per_channel_layout(resample_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -449,15 +447,15 @@ public:
         auto pad = p.pad;
         std::vector<int> pad_ = { 0, 0, pad.spatial[0], pad.spatial[1] };
         if (in_no == 0)
-            return layout{ p.data_type_in0, p.input_format, p.in_shapes.at(0), padding{pad_} };
+            return layout{ p.data_type_in0, p.input_format, p.in_shapes.at(0), padding{ pad_ } };
         else if (in_no == 1)
-            return layout{ p.data_type_in1, p.input_format, p.in_shapes.at(1), padding{pad_} };
+            return layout{ p.data_type_in1, p.input_format, p.in_shapes.at(1), padding{ pad_ } };
         else
-            return layout{ p.data_type_in2, p.input_format, p.in_shapes.at(2), padding{pad_} };
+            return layout{ p.data_type_in2, p.input_format, p.in_shapes.at(2), padding{ pad_ } };
     }
 
     layout get_per_channel_layout(gemm_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.in_shapes.at(0).feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.in_shapes.at(0).feature[0], 1, 1 } };
     }
 
     layout get_output_layout(gemm_test_params& p) {
@@ -492,130 +490,131 @@ public:
     layout get_input_layout(conv_eltw_test_params& p) {
         auto pad = p.pad;
         std::vector<int> pad_ = { 0, 0, pad.spatial[0], pad.spatial[1] };
-        return layout{ p.data_type, p.input_format, p.in_shape, padding{pad_} };
+        return layout{ p.data_type, p.input_format, p.in_shape, padding{ pad_ } };
     }
 
     layout get_per_channel_layout(conv_eltw_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
     }
 };
 
 // in_shape; out_shape; kernel; stride; pad; dilation; groups; data_type; input_format; weights_type; weights_format; default_type; default_format;
-#define CASE_CONV_FP32_1 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
-#define CASE_CONV_FP32_2 {1, 16, 4, 5}, {1, 32, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_CONV_FP32_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_CONV_FP32_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
-#define CASE_CONV_FP32_5 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_FP32_6 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_7 {1, 16, 4, 5, 4}, {1, 32, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_8 {1, 32, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_9 {1, 32, 4, 5, 4}, {1, 32, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_10 {32, 16, 4, 5, 4}, {32, 32, 4, 5, 4}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_11 {1, 32, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_12 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_13 {1, 16, 18, 5, 4}, {1, 16, 16, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_FP32_14 {1, 3, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_FP32_1 { 1, 15, 4, 5 }, { 1, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define CASE_CONV_FP32_2 { 1, 16, 4, 5 }, { 1, 32, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_CONV_FP32_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_CONV_FP32_4 { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
+#define CASE_CONV_FP32_5 { 1, 15, 4, 5 }, { 1, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_FP32_6 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_7 { 1, 16, 4, 5, 4 }, { 1, 32, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_8 { 1, 32, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_9 { 1, 32, 4, 5, 4 }, { 1, 32, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_10 { 32, 16, 4, 5, 4 }, { 32, 32, 4, 5, 4 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_11 { 1, 32, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_12 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_13 { 1, 16, 18, 5, 4 }, { 1, 16, 16, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_FP32_14 { 1, 3, 4, 5 }, { 1, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::bfyx, data_types::f32, format::bfyx
 
 
-#define CASE_CONV_FP16_1 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfyx, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_CONV_FP16_2 {1, 16, 4, 5}, {1, 32, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::os_is_yx_isv16_osv16, data_types::f16, format::bfyx
-#define CASE_CONV_FP16_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::os_is_yx_isv16_osv16, data_types::f16, format::bfyx
-#define CASE_CONV_FP16_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16,  format::gs_oiyx_gsv16, data_types::f16, format::bfyx
-#define CASE_CONV_FP16_5 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfyx, data_types::i8, format::bfyx, data_types::f16, format::bfyx
-#define CASE_CONV_FP16_6 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_CONV_FP16_7 {1, 16, 4, 5, 4}, {1, 32, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_CONV_FP16_8 {1, 32, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_CONV_FP16_9 {1, 32, 4, 5, 4}, {1, 32, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_CONV_FP16_10 {32, 16, 4, 5, 4}, {32, 32, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_CONV_FP16_11 {1, 32, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_CONV_FP16_12 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_CONV_FP16_13 {16, 32, 4, 5}, {16, 64, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::fs_b_yx_fsv32, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_CONV_FP16_1 { 1, 15, 4, 5 }, { 1, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bfyx, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_CONV_FP16_2 { 1, 16, 4, 5 }, { 1, 32, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::os_is_yx_isv16_osv16, data_types::f16, format::bfyx
+#define CASE_CONV_FP16_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::os_is_yx_isv16_osv16, data_types::f16, format::bfyx
+#define CASE_CONV_FP16_4 { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16,  format::gs_oiyx_gsv16, data_types::f16, format::bfyx
+#define CASE_CONV_FP16_5 { 1, 15, 4, 5 }, { 1, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bfyx, data_types::i8, format::bfyx, data_types::f16, format::bfyx
+#define CASE_CONV_FP16_6 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_CONV_FP16_7 { 1, 16, 4, 5, 4 }, { 1, 32, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_CONV_FP16_8 { 1, 32, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_CONV_FP16_9 { 1, 32, 4, 5, 4 }, { 1, 32, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_CONV_FP16_10 { 32, 16, 4, 5, 4 }, { 32, 32, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_CONV_FP16_11 { 1, 32, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_CONV_FP16_12 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::g_os_is_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_CONV_FP16_13 { 16, 32, 4, 5 }, { 16, 64, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::fs_b_yx_fsv32, data_types::f16, format::bfyx, data_types::f16, format::bfyx
 
-#define CASE_CONV_U8S8_1 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_2 {1, 15, 5, 5}, {1, 30, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_4 {1, 17, 4, 5}, {1, 17, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 17, data_types::u8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_5 {1, 16, 5, 5}, {1, 32, 5, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_6 {1, 17, 4, 5}, {1, 17, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 17, data_types::u8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_7 {1, 64, 7, 7}, {1, 32, 7, 7}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_8 {1, 3, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_9 {16, 32, 5, 5}, {16, 32, 3, 3}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_10 {16, 32, 5, 5}, {16, 32, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_11 {32, 15, 4, 5}, {32, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_12 {32, 15, 5, 5}, {32, 30, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_13 {32, 16, 4, 5}, {32, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_14 {32, 17, 4, 5}, {32, 17, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 17, data_types::u8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
-#define CASE_CONV_U8S8_15 {1, 15, 2, 2}, {1, 30, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_1 { 1, 15, 4, 5 }, { 1, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_2 { 1, 15, 5, 5 }, { 1, 30, 3, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_4 { 1, 17, 4, 5 }, { 1, 17, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 17, data_types::u8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_5 { 1, 16, 5, 5 }, { 1, 32, 5, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_6 { 1, 17, 4, 5 }, { 1, 17, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 17, data_types::u8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_7 { 1, 64, 7, 7 }, { 1, 32, 7, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_8 { 1, 3, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_9 { 16, 32, 5, 5 }, { 16, 32, 3, 3 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_10 { 16, 32, 5, 5 }, { 16, 32, 3, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_11 { 32, 15, 4, 5 }, { 32, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_12 { 32, 15, 5, 5 }, { 32, 30, 3, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_13 { 32, 16, 4, 5 }, { 32, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_14 { 32, 17, 4, 5 }, { 32, 17, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 17, data_types::u8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_15 { 1, 15, 2, 2 }, { 1, 30, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_CONV_S8S8_1 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_2 {1, 15, 5, 5}, {1, 30, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_4 {1, 17, 4, 5}, {1, 17, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 17, data_types::i8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_5 {1, 16, 5, 5}, {1, 32, 5, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_6 {1, 17, 4, 5}, {1, 17, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 17, data_types::i8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_7  {1, 64, 7, 7}, {1, 32, 7, 7}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_8 {1, 3, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_9 {16, 32, 5, 5}, {16, 32, 3, 3}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_10 {16, 32, 5, 5}, {16, 32, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_11 {1, 4, 1280, 720}, {1, 4, 1280, 720}, {1, 1, 5, 5}, tensor{1}, tensor{{0, 0, 2, 2}, 0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv4, data_types::i8, format::os_is_yx_osv16_isv4, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_12 {32, 15, 4, 5}, {32, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_13 {32, 15, 5, 5}, {32, 30, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_14 {32, 16, 4, 5}, {32, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONV_S8S8_15 {32, 17, 4, 5}, {32, 17, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 17, data_types::i8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_1 { 1, 15, 4, 5 }, { 1, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_2 { 1, 15, 5, 5 }, { 1, 30, 3, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_4 { 1, 17, 4, 5 }, { 1, 17, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 17, data_types::i8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_5 { 1, 16, 5, 5 }, { 1, 32, 5, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_6 { 1, 17, 4, 5 }, { 1, 17, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 17, data_types::i8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_7  { 1, 64, 7, 7 }, { 1, 32, 7, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_8 { 1, 3, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_9 { 16, 32, 5, 5 }, { 16, 32, 3, 3 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_10 { 16, 32, 5, 5 }, { 16, 32, 3, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bs_fs_yx_bsv16_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_11 { 1, 4, 1280, 720 }, { 1, 4, 1280, 720 }, { 1, 1, 5, 5 }, tensor{ 1 }, tensor{ { 0, 0, 2, 2 }, 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv4, data_types::i8, format::os_is_yx_osv16_isv4, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_12 { 32, 15, 4, 5 }, { 32, 30, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_13 { 32, 15, 5, 5 }, { 32, 30, 3, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_14 { 32, 16, 4, 5 }, { 32, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONV_S8S8_15 { 32, 17, 4, 5 }, { 32, 17, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 17, data_types::i8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
 
-#define CASE_CONV3D_U8S8_1 {1, 15, 5, 4, 5}, {1, 30, 3, 2, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_U8S8_2 {1, 15, 5, 5, 5}, {1, 30, 3, 3, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_U8S8_3 {1, 16, 5, 4, 5}, {1, 32, 5, 4, 5}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_U8S8_4 {1, 17, 5, 4, 5}, {1, 17, 5, 4, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 17, data_types::u8, format::bfzyx, data_types::i8, format::goizyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_U8S8_5 {1, 3, 5, 4, 5},  {1, 32, 5, 4, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_U8S8_1 { 1, 15, 5, 4, 5 }, { 1, 30, 3, 2, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_U8S8_2 { 1, 15, 5, 5, 5 }, { 1, 30, 3, 3, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_U8S8_3 { 1, 16, 5, 4, 5 }, { 1, 32, 5, 4, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_U8S8_4 { 1, 17, 5, 4, 5 }, { 1, 17, 5, 4, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 17, data_types::u8, format::bfzyx, data_types::i8, format::goizyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_U8S8_5 { 1, 3, 5, 4, 5 },  { 1, 32, 5, 4, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 1, data_types::u8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
 
-#define CASE_CONV3D_S8S8_1 {1, 15, 5, 4, 5}, {1, 30, 3, 2, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_S8S8_2 {1, 15, 5, 5, 5}, {1, 30, 3, 3, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_S8S8_3 {1, 16, 5, 4, 5}, {1, 32, 5, 4, 5}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_S8S8_4 {1, 17, 5, 4, 5}, {1, 17, 5, 4, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 17, data_types::i8, format::bfzyx, data_types::i8, format::goizyx, data_types::f32, format::bfzyx
-#define CASE_CONV3D_S8S8_5 {1, 3, 5, 4, 5},  {1, 18, 5, 4, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_S8S8_1 { 1, 15, 5, 4, 5 }, { 1, 30, 3, 2, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_S8S8_2 { 1, 15, 5, 5, 5 }, { 1, 30, 3, 3, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_S8S8_3 { 1, 16, 5, 4, 5 }, { 1, 32, 5, 4, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_S8S8_4 { 1, 17, 5, 4, 5 }, { 1, 17, 5, 4, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 17, data_types::i8, format::bfzyx, data_types::i8, format::goizyx, data_types::f32, format::bfzyx
+#define CASE_CONV3D_S8S8_5 { 1, 3, 5, 4, 5 },  { 1, 18, 5, 4, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
 
 // in_shape; out_shape; eltw_shape; kernel; stride; pad; dilation; groups; data_type; input_format; weights_type; weights_format; default_type; default_format;
-#define CASE_CONV_ELTW_FP32_1 {1, 16, 4, 5}, {1, 32, 2, 3}, {1, 32, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::oiyx, data_types::f32, format::bfyx
-#define CASE_CONV_ELTW_FP32_2 {1, 16, 4, 5}, {1, 32, 2, 3}, {1, 1, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_CONV_ELTW_FP32_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_CONV_ELTW_FP32_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 32, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
-#define CASE_CONV_ELTW_FP32_5 {1, 32, 4, 5, 4}, {1, 32, 2, 3, 2}, {1, 32, 2, 1, 1}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_ELTW_FP32_6 {1, 32, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 16, 2, 1, 1}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_CONV_ELTW_FP32_7 {1, 16, 3, 5}, {1, 32, 1, 3}, {1, 32, 3, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_CONV_ELTW_FP32_8 {1, 32, 3, 5, 4}, {1, 16, 1, 3, 2}, {1, 1, 2, 1, 1}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_ELTW_FP32_1 { 1, 16, 4, 5 }, { 1, 32, 2, 3 }, { 1, 32, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define CASE_CONV_ELTW_FP32_2 { 1, 16, 4, 5 }, { 1, 32, 2, 3 }, { 1, 1, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_CONV_ELTW_FP32_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_CONV_ELTW_FP32_4 { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 32, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
+#define CASE_CONV_ELTW_FP32_5 { 1, 32, 4, 5, 4 }, { 1, 32, 2, 3, 2 }, { 1, 32, 2, 1, 1 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_ELTW_FP32_6 { 1, 32, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, { 1, 16, 2, 1, 1 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_CONV_ELTW_FP32_7 { 1, 16, 3, 5 }, { 1, 32, 1, 3 }, { 1, 32, 3, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_CONV_ELTW_FP32_8 { 1, 32, 3, 5, 4 }, { 1, 16, 1, 3, 2 }, { 1, 1, 2, 1, 1 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::g_os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
 
-#define CASE_CONV_ELTW_i8_1 {1, 16, 3, 5}, {1, 32, 1, 3}, {1, 32, 3, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_CONV_ELTW_i8_2 {1, 16, 3, 5, 3}, {1, 32, 2, 4, 2}, {1, 1, 2, 4, 2}, {1, 1, 2, 2, 2}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
-#define CASE_CONV_ELTW_i8_3 {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
-#define CASE_CONV_ELTW_i8_4 {1, 16, 1, 4}, {1, 16, 1, 2}, {1, 16, 1, 1}, {1, 1, 1, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_CONV_ELTW_i8_5 {1, 16, 1, 4, 1}, {1, 16, 1, 2, 1}, {1, 16, 2, 1, 1}, {1, 1, 1, 3, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
+#define CASE_CONV_ELTW_i8_1 { 1, 16, 3, 5 }, { 1, 32, 1, 3 }, { 1, 32, 3, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_CONV_ELTW_i8_2 { 1, 16, 3, 5, 3 }, { 1, 32, 2, 4, 2 }, { 1, 1, 2, 4, 2 }, { 1, 1, 2, 2, 2 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
+#define CASE_CONV_ELTW_i8_3 { 1, 1, 1, 1, 1 }, { 1, 1, 1, 1, 1 }, { 1, 1, 1, 1, 1 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
+#define CASE_CONV_ELTW_i8_4 { 1, 16, 1, 4 }, { 1, 16, 1, 2 }, { 1, 16, 1, 1 }, { 1, 1, 1, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_CONV_ELTW_i8_5 { 1, 16, 1, 4, 1 }, { 1, 16, 1, 2, 1 }, { 1, 16, 2, 1, 1 }, { 1, 1, 1, 3, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
 
-#define CASE_BIN_CONV1 {1, 16, 4, 5}, {1, 16, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 1, data_types::bin, format::b_fs_yx_32fp, data_types::bin, format::os_is_yx_osv32_isv32p, data_types::f32, format::bfyx
-#define CASE_BIN_CONV2 {1, 16, 4, 5}, {1, 30, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::bin, format::b_fs_yx_32fp, data_types::bin, format::os_is_yx_osv32_isv32p, data_types::f32, format::bfyx
-#define CASE_BIN_CONV3 {1, 184, 12, 21}, {1, 224, 12, 21}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::bin, format::b_fs_yx_32fp, data_types::bin, format::os_is_yx_osv32_isv32p, data_types::f32, format::bfyx
+#define CASE_BIN_CONV1 { 1, 16, 4, 5 }, { 1, 16, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 1, data_types::bin, format::b_fs_yx_32fp, data_types::bin, format::os_is_yx_osv32_isv32p, data_types::f32, format::bfyx
+#define CASE_BIN_CONV2 { 1, 16, 4, 5 }, { 1, 30, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::bin, format::b_fs_yx_32fp, data_types::bin, format::os_is_yx_osv32_isv32p, data_types::f32, format::bfyx
+#define CASE_BIN_CONV3 { 1, 184, 12, 21 }, { 1, 224, 12, 21 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::bin, format::b_fs_yx_32fp, data_types::bin, format::os_is_yx_osv32_isv32p, data_types::f32, format::bfyx
 
-#define CASE_FC_FP32_1 {1, 1, 3, 1}, {1, 4, 1, 1}, {4, 1, 3, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_FP32_2 {2, 1, 3, 1}, {2, 4, 1, 1}, {4, 1, 3, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::yxfb, data_types::f32, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_FP32_3 {2, 32, 1, 1}, {2, 16, 1, 1}, {16, 32, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_FP32_3D_1 {5, 3, 1, 3}, {5, 3, 1, 5}, {5, 3, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::os_iyx_osv16, data_types::f32, format::bfyx
-#define CASE_FC_FP32_3D_2 {2, 1, 1, 1}, {2, 1, 1, 32}, {32, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::os_iyx_osv16, data_types::f32, format::bfyx
-#define CASE_FC_FP32_3D_3 {2, 32, 1, 32}, {2, 32, 1, 16}, {16, 32, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::os_iyx_osv16, data_types::f32, format::bfyx
+#define CASE_FC_FP32_1 { 1, 1, 3, 1 }, { 1, 4, 1, 1 }, { 4, 1, 3, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_FP32_2 { 2, 1, 3, 1 }, { 2, 4, 1, 1 }, { 4, 1, 3, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::yxfb, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_FP32_3 { 2, 32, 1, 1 }, { 2, 16, 1, 1 }, { 16, 32, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_FP32_3D_1 { 5, 3, 1, 3 }, { 5, 3, 1, 5 }, { 5, 3, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::os_iyx_osv16, data_types::f32, format::bfyx
+#define CASE_FC_FP32_3D_2 { 2, 1, 1, 1 }, { 2, 1, 1, 32 }, { 32, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::os_iyx_osv16, data_types::f32, format::bfyx
+#define CASE_FC_FP32_3D_3 { 2, 32, 1, 32 }, { 2, 32, 1, 16 }, { 16, 32, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::os_iyx_osv16, data_types::f32, format::bfyx
 
-#define CASE_FC_U8S8_1 {1, 1, 3, 1}, {1, 4, 1, 1}, {4, 1, 3, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_U8S8_2 {2, 1, 3, 1}, {2, 4, 1, 1}, {4, 1, 3, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv4, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_U8S8_3 {2, 32, 1, 1}, {2, 16, 1, 1}, {16, 32, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv4, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_U8S8_3D_1 {2, 32, 1, 3}, {2, 32, 1, 16}, {16, 3, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_U8S8_3D_2 {1, 1, 1, 3}, {1, 1, 1, 32}, {32, 3, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_U8S8_3D_3 {2, 3, 1, 1}, {2, 3, 1, 15}, {15, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_FC_U8S8_3D_4 {1, 512, 1, 1024}, {1, 384, 1, 1024}, {1024, 1024, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_1 { 1, 1, 3, 1 }, { 1, 4, 1, 1 }, { 4, 1, 3, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_2 { 2, 1, 3, 1 }, { 2, 4, 1, 1 }, { 4, 1, 3, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_yx_fsv4, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_3 { 2, 32, 1, 1 }, { 2, 16, 1, 1 }, { 16, 32, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_yx_fsv4, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_3D_1 { 2, 32, 1, 3 }, { 2, 32, 1, 16 }, { 16, 3, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_3D_2 { 1, 1, 1, 3 }, { 1, 1, 1, 32 }, { 32, 3, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_3D_3 { 2, 3, 1, 1 }, { 2, 3, 1, 15 }, { 15, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_FC_U8S8_3D_4 { 1, 512, 1, 1024 }, { 1, 384, 1, 1024 }, { 1024, 1024, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
 
-#define CASE_NORMALIZE_I8_1 {1, 2, 3, 3}, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_NORMALIZE_I8_1 { 1, 2, 3, 3 }, data_types::u8, format::bfyx, data_types::f32, format::bfyx
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- FP32 convolution cases ------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
 /* ----------- NOTE: A part of tests is disabled until all FP kernels don't support fusings ------------ */
+
 class ConvFusingTest : public WeightsPrimitiveFusingTest<bc_test_params> {
 public:
     void execute(bc_test_params& p) {
@@ -642,7 +641,8 @@ public:
 class conv_fp32_reorder_fsv16_to_bfyx : public ConvFusingTest {};
 TEST_P(conv_fp32_reorder_fsv16_to_bfyx, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("weights", get_mem(get_weights_layout(p))),
         reorder("reorder_fsv16", "input", format::b_fs_yx_fsv16, data_types::f32),
         convolution("conv_prim", "reorder_fsv16", { "weights" }, p.groups, p.stride, p.pad, p.dilation),
@@ -653,19 +653,19 @@ TEST_P(conv_fp32_reorder_fsv16_to_bfyx, basic) {
 }
 
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_reorder_fsv16_to_bfyx, ::testing::ValuesIn(std::vector<bc_test_params>{
-    bc_test_params{ CASE_CONV_FP32_1, 2, 2},
-    bc_test_params{ CASE_CONV_FP32_2, 2, 2},
-    bc_test_params{ CASE_CONV_FP32_3, 2, 2},
+    bc_test_params{ CASE_CONV_FP32_1, 2, 2 },
+    bc_test_params{ CASE_CONV_FP32_2, 2, 2 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 2 },
     bc_test_params{ CASE_CONV_FP32_4, 2, 2 },
     bc_test_params{ CASE_CONV_FP32_5, 2, 2 },
     bc_test_params{ CASE_CONV_FP32_14, 2, 2 },
 
-    bc_test_params{ CASE_CONV_FP16_1, 2, 2},
-    bc_test_params{ CASE_CONV_FP16_2, 2, 2},
-    bc_test_params{ CASE_CONV_FP16_3, 2, 2},
+    bc_test_params{ CASE_CONV_FP16_1, 2, 2 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 2 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 2 },
     bc_test_params{ CASE_CONV_FP16_4, 2, 2 },
     bc_test_params{ CASE_CONV_FP16_5, 2, 2 },
-    bc_test_params{ CASE_CONV_FP16_13, 2, 2}
+    bc_test_params{ CASE_CONV_FP16_13, 2, 2 }
 }));
 
 class conv_fp32_reorder_fsv16_to_bfyx_conv : public ConvFusingTest {};
@@ -676,7 +676,8 @@ TEST_P(conv_fp32_reorder_fsv16_to_bfyx_conv, basic) {
     auto dw_weights_layout = layout{ p.default_type, format::goiyx, dw_tensor };
     auto dw_stride = tensor{ 0, 0, 1, 1 };
 
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("weights", get_mem(get_weights_layout(p), -127, 127)),
         data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
         reorder("reorder_fsv16", "input", format::b_fs_yx_fsv16, data_types::f32),
@@ -710,126 +711,128 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_reorder_fsv16_to_bfyx_conv, ::te
 class conv_fp32_activation : public ConvFusingTest {};
 TEST_P(conv_fp32_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::abs),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::abs),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     execute(p);
 }
 
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation, ::testing::ValuesIn(std::vector<bc_test_params>{
-                                                                           bc_test_params{CASE_CONV_FP32_1, 2, 3},
-                                                                           bc_test_params{CASE_CONV_FP32_2, 2, 3},
-                                                                           bc_test_params{CASE_CONV_FP32_3, 2, 3},
-                                                                           bc_test_params{CASE_CONV_FP32_4, 2, 3},
+    bc_test_params{ CASE_CONV_FP32_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 3 },
 
-                                                                           bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                                                                           bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                                                                           bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                                                                           bc_test_params{CASE_CONV_FP16_4, 2, 3},
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
 }));
 
 
 class conv_fp32_scale : public ConvFusingTest {};
 TEST_P(conv_fp32_scale, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             // bc_test_params{CASE_CONV_FP32_1, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_2, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_3, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_4, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_10, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // bc_test_params{ CASE_CONV_FP32_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_10, 2, 3 },
 
-                                             // bc_test_params{CASE_CONV_FP16_1, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_10, 2, 3},
-                                             }));
+    // bc_test_params{ CASE_CONV_FP16_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_10, 2, 3 },
+}));
 
 class conv_fp32_bias : public ConvFusingTest {};
 TEST_P(conv_fp32_bias, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, std::vector<primitive_id>{}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("add_bias", {"conv_prim", "bias"}, eltwise_mode::sum),
-                 reorder("reorder_bfyx", "add_bias", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, std::vector<primitive_id>{}, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("add_bias", { "conv_prim", "bias" }, eltwise_mode::sum),
+        reorder("reorder_bfyx", "add_bias", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_bias,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_1, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_2, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_3, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_4, 2, 3},
-                                             bc_test_params{CASE_CONV_FP32_10, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_bias, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_10, 2, 3 },
 
-                                             bc_test_params{CASE_CONV_FP16_1, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                                             bc_test_params{CASE_CONV_FP16_10, 2, 3},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_10, 2, 3 },
+}));
 
 class conv_fp32_double_bias : public ConvFusingTest {};
 TEST_P(conv_fp32_double_bias, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias1", get_mem(get_bias_layout(p))),
-                 data("bias2", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, std::vector<primitive_id>{}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("add_bias1", {"conv_prim", "bias1"}, eltwise_mode::sum),
-                 eltwise("add_bias2", {"add_bias1", "bias2"}, eltwise_mode::sum),
-                 reorder("reorder_bfyx", "add_bias2", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias1", get_mem(get_bias_layout(p))),
+        data("bias2", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, std::vector<primitive_id>{}, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("add_bias1", { "conv_prim", "bias1" }, eltwise_mode::sum),
+        eltwise("add_bias2", { "add_bias1", "bias2" }, eltwise_mode::sum),
+        reorder("reorder_bfyx", "add_bias2", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_double_bias,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                            bc_test_params{CASE_CONV_U8S8_1, 2, 4},
-                                            bc_test_params{CASE_CONV_S8S8_1, 2, 4},
-                                            }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_double_bias, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 4 },
+}));
 
 class conv_fp32_prelu_eltwise : public ConvFusingTest {};
 TEST_P(conv_fp32_prelu_eltwise, basic_sum) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -838,15 +841,16 @@ TEST_P(conv_fp32_prelu_eltwise, basic_sum) {
 
 TEST_P(conv_fp32_prelu_eltwise, basic_prod) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::prod),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::prod),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -855,16 +859,17 @@ TEST_P(conv_fp32_prelu_eltwise, basic_prod) {
 
 TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_sum) {
     auto p = GetParam();
-    tensor eltw_shape = p.default_format.spatial_num() == 2 ? tensor{1, 1, 1, 1} : tensor{1, 1, 1, 1, 1};
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, eltw_shape })),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    tensor eltw_shape = p.default_format.spatial_num() == 2 ? tensor{ 1, 1, 1, 1 } : tensor{ 1, 1, 1, 1, 1 };
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, eltw_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -873,16 +878,17 @@ TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_sum) {
 
 TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_prod) {
     auto p = GetParam();
-    tensor eltw_shape = p.default_format.spatial_num() == 2 ? tensor{1, 1, 1, 1} : tensor{1, 1, 1, 1, 1};
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, eltw_shape })),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::prod),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    tensor eltw_shape = p.default_format.spatial_num() == 2 ? tensor{ 1, 1, 1, 1 } : tensor{ 1, 1, 1, 1, 1 };
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, eltw_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::prod),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -891,19 +897,20 @@ TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_prod) {
 
 TEST_P(conv_fp32_prelu_eltwise, vector_ops) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
@@ -912,36 +919,36 @@ TEST_P(conv_fp32_prelu_eltwise, vector_ops) {
 TEST_P(conv_fp32_prelu_eltwise, vector_ops_mixed_types) {
     auto p = GetParam();
     auto slope_type = p.default_type == data_types::f32 ? data_types::f16 : data_types::f32;
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(layout{ slope_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} })),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(layout{ slope_type, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } })),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_prelu_eltwise,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             // bc_test_params{CASE_CONV_FP32_1, 2, 4},
-                                             bc_test_params{CASE_CONV_FP32_2, 2, 4},
-                                             bc_test_params{CASE_CONV_FP32_3, 2, 4},
-                                             bc_test_params{CASE_CONV_FP32_4, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_prelu_eltwise, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // bc_test_params{ CASE_CONV_FP32_1, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 4 },
 
-                                             // bc_test_params{CASE_CONV_FP32_1, 2, 4},
-                                             bc_test_params{CASE_CONV_FP16_2, 2, 4},
-                                             bc_test_params{CASE_CONV_FP16_3, 2, 4},
-                                             bc_test_params{CASE_CONV_FP16_4, 2, 4},
-                                             }));
+    // bc_test_params{ CASE_CONV_FP32_1, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 4 },
+}));
 
 class conv_fp32_multi_eltwise_2 : public ConvFusingTest {};
 TEST_P(conv_fp32_multi_eltwise_2, basic) {
@@ -950,32 +957,32 @@ TEST_P(conv_fp32_multi_eltwise_2, basic) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise_data", get_mem(get_output_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1", "conv_prim", "eltwise_data", eltwise_mode::sum),
         eltwise("eltwise2", "eltwise1", "conv_prim", eltwise_mode::prod),
         reorder("reorder_bfyx", "eltwise2", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_2,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_2, 2, 4},
-                                             bc_test_params{CASE_CONV_FP32_3, 2, 4},
-                                             bc_test_params{CASE_CONV_FP32_4, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_2, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 4 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 2, 4},
-                                             bc_test_params{CASE_CONV_FP16_3, 2, 4},
-                                             bc_test_params{CASE_CONV_FP16_4, 2, 4},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 4 },
+}));
 
 
 class conv_fp32_multi_eltwise_2_clamp : public ConvFusingTest {};
@@ -985,33 +992,33 @@ TEST_P(conv_fp32_multi_eltwise_2_clamp, basic) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise1_data", get_mem(get_output_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1", "conv_prim", "eltwise1_data", eltwise_mode::sum),
-        activation("activation", "eltwise1", activation_func::clamp, {0.5f, 2.5f}),
+        activation("activation", "eltwise1", activation_func::clamp, { 0.5f, 2.5f }),
         eltwise("eltwise2", "activation", "conv_prim", eltwise_mode::prod),
         reorder("reorder_bfyx", "eltwise2", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_2_clamp,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_2, 2, 5},
-                                             bc_test_params{CASE_CONV_FP32_3, 2, 5},
-                                             bc_test_params{CASE_CONV_FP32_4, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_2_clamp, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 2, 5 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 5 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 5 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 2, 5},
-                                             bc_test_params{CASE_CONV_FP16_3, 2, 5},
-                                             bc_test_params{CASE_CONV_FP16_4, 2, 5},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 5 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 5 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 5 },
+}));
 
 
 class conv_fp32_multi_eltwise_4_clamp : public ConvFusingTest {};
@@ -1021,37 +1028,37 @@ TEST_P(conv_fp32_multi_eltwise_4_clamp, basic) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise1_data", get_mem(get_output_layout(p))),
         data("eltwise2_data", get_mem(get_output_layout(p))),
         data("eltwise4_data", get_mem(get_output_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1_add", "conv_prim", "eltwise1_data", eltwise_mode::sum),
-        activation("activation", "eltwise1_add", activation_func::clamp, {0.5f, 2.5f}),
+        activation("activation", "eltwise1_add", activation_func::clamp, { 0.5f, 2.5f }),
         eltwise("eltwise2_mul", "activation", "conv_prim", eltwise_mode::prod),
         eltwise("eltwise3_div", "eltwise2_mul", "eltwise2_data", eltwise_mode::prod),
         eltwise("eltwise4_add", "eltwise3_div", "eltwise4_data", eltwise_mode::sum),
         reorder("reorder_bfyx", "eltwise4_add", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_4_clamp,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_2, 2, 7},
-                                             bc_test_params{CASE_CONV_FP32_3, 2, 7},
-                                             bc_test_params{CASE_CONV_FP32_4, 2, 7},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_4_clamp, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 2, 7 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 7 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 7 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 2, 7},
-                                             bc_test_params{CASE_CONV_FP16_3, 2, 7},
-                                             bc_test_params{CASE_CONV_FP16_4, 2, 7},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 7 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 7 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 7 },
+}));
 
 class conv_fp32_eltwise_fusing_extend_ops : public ConvFusingTest {};
 TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern01_simple_sub) {
@@ -1060,13 +1067,14 @@ TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern01_simple_sub) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise_data1", get_mem(get_output_layout(p))),
         data("eltwise_data2", get_mem(get_output_layout(p))),
         data("eltwise_data4", get_mem(get_output_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1_sum", "conv_prim", "eltwise_data1", eltwise_mode::sum),
         eltwise("eltwise2_sub", "conv_prim", "eltwise_data2", eltwise_mode::sub),
         eltwise("eltwise3_prod", "eltwise1_sum", "eltwise2_sub", eltwise_mode::prod),
@@ -1074,8 +1082,8 @@ TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern01_simple_sub) {
         concatenation("concat", { "eltwise4_sum", "eltwise4_sum" }, cldnn::concatenation::along_f),
         reorder("reorder_bfyx", "concat", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
@@ -1087,13 +1095,14 @@ TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern02_sub_scale) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise_data1", get_mem(get_output_layout(p))),
         data("eltwise_data2", get_mem(get_output_layout(p))),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1_sum", "conv_prim", "eltwise_data1", eltwise_mode::sum),
         eltwise("eltwise2_sub", "conv_prim", "eltwise1_sum", eltwise_mode::sub),
         eltwise("eltwise3_prod", "eltwise2_sub", "eltwise_data2", eltwise_mode::prod),
@@ -1101,8 +1110,8 @@ TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern02_sub_scale) {
         concatenation("concat", { "scale", "scale" }, cldnn::concatenation::along_f),
         reorder("reorder_bfyx", "concat", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
@@ -1114,14 +1123,15 @@ TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern03_sub_div) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise_data1", get_mem(get_output_layout(p))),
         data("eltwise_data2", get_mem(get_output_layout(p), 1.0f)),
         data("eltwise_data3", get_mem(get_output_layout(p))),
         data("eltwise_data4", get_mem(get_output_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1_sum", "conv_prim", "eltwise_data1", eltwise_mode::sum),
         eltwise("eltwise2_div", "eltwise1_sum", "eltwise_data2", eltwise_mode::div),
         eltwise("eltwise3_prod", "eltwise2_div", "eltwise_data3", eltwise_mode::prod),
@@ -1129,23 +1139,22 @@ TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern03_sub_div) {
         concatenation("concat", { "eltwise4_sum", "eltwise4_sum" }, cldnn::concatenation::along_f),
         reorder("reorder_bfyx", "concat", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_eltwise_fusing_extend_ops,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_2, 3, 7},
-                                             bc_test_params{CASE_CONV_FP32_3, 3, 7},
-                                             bc_test_params{CASE_CONV_FP32_4, 3, 7},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_eltwise_fusing_extend_ops, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 3, 7 },
+    bc_test_params{ CASE_CONV_FP32_3, 3, 7 },
+    bc_test_params{ CASE_CONV_FP32_4, 3, 7 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 3, 7},
-                                             bc_test_params{CASE_CONV_FP16_3, 3, 7},
-                                             bc_test_params{CASE_CONV_FP16_4, 3, 7},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 3, 7 },
+    bc_test_params{ CASE_CONV_FP16_3, 3, 7 },
+    bc_test_params{ CASE_CONV_FP16_4, 3, 7 },
+}));
 
 class conv_fp32_eltwise_fusing_2conv : public ConvFusingTest {};
 TEST_P(conv_fp32_eltwise_fusing_2conv, basic) {
@@ -1154,36 +1163,36 @@ TEST_P(conv_fp32_eltwise_fusing_2conv, basic) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("bias0", get_mem(get_bias_layout(p))),
         data("weights0", get_mem(get_weights_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim0", "input", { "weights0" }, {"bias0"}, p.groups, p.stride, p.pad, p.dilation),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim0", "input", { "weights0" }, { "bias0" }, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1", "conv_prim0", "conv_prim", eltwise_mode::sum),
         eltwise("eltwise2", "conv_prim0", "conv_prim", eltwise_mode::sum),
         eltwise("eltwise3", "eltwise1", "eltwise2", eltwise_mode::prod),
         concatenation("concat", { "eltwise3", "eltwise3" }, cldnn::concatenation::along_f),
         reorder("reorder_bfyx", "concat", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim0", conv_impl}, {"conv_prim", conv_impl}  }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim0", conv_impl }, { "conv_prim", conv_impl }  }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_eltwise_fusing_2conv,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_2, 4, 7},
-                                             bc_test_params{CASE_CONV_FP32_3, 4, 7},
-                                             bc_test_params{CASE_CONV_FP32_4, 4, 7},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_eltwise_fusing_2conv, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 4, 7 },
+    bc_test_params{ CASE_CONV_FP32_3, 4, 7 },
+    bc_test_params{ CASE_CONV_FP32_4, 4, 7 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 4, 7},
-                                             bc_test_params{CASE_CONV_FP16_3, 4, 7},
-                                             bc_test_params{CASE_CONV_FP16_4, 4, 7},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 4, 7 },
+    bc_test_params{ CASE_CONV_FP16_3, 4, 7 },
+    bc_test_params{ CASE_CONV_FP16_4, 4, 7 },
+}));
 
 
 class conv_fp32_multi_eltwise_3_fusing : public ConvFusingTest {};
@@ -1193,122 +1202,123 @@ TEST_P(conv_fp32_multi_eltwise_3_fusing, basic) {
     }
 
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise_data1", get_mem(get_output_layout(p))),
         data("eltwise_data2", get_mem(get_output_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1", "conv_prim", "eltwise_data1", eltwise_mode::sum),
         eltwise("eltwise2", "conv_prim", "eltwise_data2", eltwise_mode::sum),
         eltwise("eltwise3", "eltwise1", "eltwise2", eltwise_mode::prod),
         reorder("reorder_bfyx", "eltwise3", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_3_fusing,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_2, 2, 5},
-                                             bc_test_params{CASE_CONV_FP32_3, 2, 5},
-                                             bc_test_params{CASE_CONV_FP32_4, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_3_fusing, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 2, 5 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 5 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 5 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 2, 5},
-                                             bc_test_params{CASE_CONV_FP16_3, 2, 5},
-                                             bc_test_params{CASE_CONV_FP16_4, 2, 5},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 5 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 5 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 5 },
+}));
 
 
 
 class conv_fp32_multi_eltwise_quantization : public ConvFusingTest {};
 TEST_P(conv_fp32_multi_eltwise_quantization, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                        data("weights", get_mem(get_weights_layout(p))),
-                        data("bias", get_mem(get_bias_layout(p))),
-                        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                        data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                        data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                        data("eltwise_data1", get_mem(get_output_layout(p))),
-                        convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                        eltwise("eltwise1", "conv_prim", "eltwise_data1", eltwise_mode::sum),
-                        eltwise("eltwise2", "eltwise1", "quantize", eltwise_mode::prod),
-                        reorder("reorder_bfyx", "eltwise2", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("eltwise_data1", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        eltwise("eltwise1", "conv_prim", "eltwise_data1", eltwise_mode::sum),
+        eltwise("eltwise2", "eltwise1", "quantize", eltwise_mode::prod),
+        reorder("reorder_bfyx", "eltwise2", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_quantization,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                            //  bc_test_params{CASE_CONV_FP32_2, 4, 5},
-                                             bc_test_params{CASE_CONV_FP32_4, 4, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_quantization, ::testing::ValuesIn(std::vector<bc_test_params>{
+//  bc_test_params{ CASE_CONV_FP32_2, 4, 5 },
+    bc_test_params{ CASE_CONV_FP32_4, 4, 5 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 4, 5},
-                                             bc_test_params{CASE_CONV_FP16_3, 4, 5},
-                                             bc_test_params{CASE_CONV_FP16_4, 4, 5},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 4, 5 },
+    bc_test_params{ CASE_CONV_FP16_3, 4, 5 },
+    bc_test_params{ CASE_CONV_FP16_4, 4, 5 },
+}));
 
 
 class conv_fp32_multi_eltwise_concat : public ConvFusingTest {};
 TEST_P(conv_fp32_multi_eltwise_concat, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("eltwise_data1", get_mem(get_output_layout(p))),
         data("eltwise_data2", get_mem(get_output_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         data("weights", get_mem(get_weights_layout(p))),
-        convolution("conv_prim", "input", { "weights" }, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
         eltwise("eltwise1", "conv_prim", "eltwise_data1", eltwise_mode::sum),
         eltwise("eltwise2", "conv_prim", "eltwise_data2", eltwise_mode::sum),
         concatenation("concat",
-            {"eltwise1", "eltwise2"},
+            { "eltwise1", "eltwise2" },
             concatenation::concatenation_axis::along_f,
             data_types::i8,
             "",
-            padding{{0, 0, 0, 0}, 0}),
+            padding{ { 0, 0, 0, 0 }, 0 }),
         reorder("reorder_bfyx", "concat", p.default_format, data_types::f32)
     );
-    implementation_desc conv_impl = { format::b_fs_yx_fsv16, ""};
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_concat,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                             bc_test_params{CASE_CONV_FP32_2, 5, 5},
-                                             bc_test_params{CASE_CONV_FP32_3, 5, 5},
-                                             bc_test_params{CASE_CONV_FP32_4, 5, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_multi_eltwise_concat, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 5, 5 },
+    bc_test_params{ CASE_CONV_FP32_3, 5, 5 },
+    bc_test_params{ CASE_CONV_FP32_4, 5, 5 },
 
-                                             bc_test_params{CASE_CONV_FP16_2, 5, 5},
-                                             bc_test_params{CASE_CONV_FP16_3, 5, 5},
-                                             bc_test_params{CASE_CONV_FP16_4, 5, 5},
-                                             }));
+    bc_test_params{ CASE_CONV_FP16_2, 5, 5 },
+    bc_test_params{ CASE_CONV_FP16_3, 5, 5 },
+    bc_test_params{ CASE_CONV_FP16_4, 5, 5 },
+}));
 
 class conv_fp32_eltwise_b_fs_zyx_fsv16 : public ConvFusingTest {};
 
 TEST_P(conv_fp32_eltwise_b_fs_zyx_fsv16, vector_ops) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("eltwise", "conv_prim", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("eltwise", "conv_prim", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::b_fs_zyx_fsv16, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
@@ -1317,31 +1327,31 @@ TEST_P(conv_fp32_eltwise_b_fs_zyx_fsv16, vector_ops) {
 class conv_fp32_swish : public ConvFusingTest {};
 TEST_P(conv_fp32_swish, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("sigmoid", "conv_prim", activation_func::logistic),
-                 eltwise("mul", {"conv_prim", "sigmoid"}, eltwise_mode::prod),
-                 reorder("reorder_bfyx", "mul", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("sigmoid", "conv_prim", activation_func::logistic),
+        eltwise("mul", { "conv_prim", "sigmoid" }, eltwise_mode::prod),
+        reorder("reorder_bfyx", "mul", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_swish,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // bc_test_params{CASE_CONV_FP32_1, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_2, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_4, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_swish, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // bc_test_params{ CASE_CONV_FP32_1, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 4 },
 
-                                // bc_test_params{CASE_CONV_FP32_1, 2, 4},
-                                bc_test_params{CASE_CONV_FP16_2, 2, 4},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 4},
-                                bc_test_params{CASE_CONV_FP16_4, 2, 4},
-                        }));
+    // bc_test_params{ CASE_CONV_FP32_1, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 4 },
+}));
 
 TEST_P(conv_fp32_eltwise_b_fs_zyx_fsv16, splitted_vector_ops) {
     auto p = GetParam();
@@ -1352,108 +1362,109 @@ TEST_P(conv_fp32_eltwise_b_fs_zyx_fsv16, splitted_vector_ops) {
         weights_idx.push_back(("weights" + std::to_string(w)));
     }
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", weights_idx, {}, 1, p.stride, p.pad, p.dilation),
-                 eltwise("eltwise", "conv_prim", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", weights_idx, {}, 1, p.stride, p.pad, p.dilation),
+        eltwise("eltwise", "conv_prim", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::b_fs_zyx_fsv16, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1e-5f;
     //  commented because split mode is disabled
     //  execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_eltwise_b_fs_zyx_fsv16,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP32_6, 2, 3},
-                                bc_test_params{CASE_CONV_FP32_7, 2, 3},
-                                bc_test_params{CASE_CONV_FP32_8, 2, 3},
-                                bc_test_params{CASE_CONV_FP32_9, 2, 3},
-                                bc_test_params{CASE_CONV_FP32_11, 2, 3},
-                                bc_test_params{CASE_CONV_FP32_12, 2, 3},
-                                // bc_test_params{CASE_CONV_FP32_13, 2, 3}, - leads to mvn_scale_activation_quantize_i8_eltwise_fp32_quantize_i8.basic/11 test failure
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_eltwise_b_fs_zyx_fsv16, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_6, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_7, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_8, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_9, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_11, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_12, 2, 3 },
+    // bc_test_params{ CASE_CONV_FP32_13, 2, 3 }, - leads to mvn_scale_activation_quantize_i8_eltwise_fp32_quantize_i8.basic/11 test failure
 
-                                bc_test_params{CASE_CONV_FP16_6, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_7, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_8, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_9, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_11, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_12, 2, 3},
-                        }));
+    bc_test_params{ CASE_CONV_FP16_6, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_7, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_8, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_9, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_11, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_12, 2, 3 },
+}));
 
 class conv_fp32_quantize_u8_first_conv : public ConvFusingTest {};
 TEST_P(conv_fp32_quantize_u8_first_conv, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 reorder("reordered_input", "input", format::b_fs_yx_fsv16, p.data_type),
-                 convolution("conv_prim", "reordered_input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        reorder("reordered_input", "input", format::b_fs_yx_fsv16, p.data_type),
+        convolution("conv_prim", "reordered_input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_quantize_u8_first_conv,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP32_14, 2, 3},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_quantize_u8_first_conv, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_14, 2, 3 },
+}));
 
 class conv_fp32_quantize_u8 : public ConvFusingTest {};
 TEST_P(conv_fp32_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_quantize_u8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // For now only b_fs_yx_fsv16 supports this case
-                                bc_test_params{CASE_CONV_FP32_2, 2, 3},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_quantize_u8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // For now only b_fs_yx_fsv16 supports this case
+    bc_test_params{ CASE_CONV_FP32_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 3 },
 
-                                bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                        }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+}));
 
 class conv_fp32_scale_quantize_i8 : public ConvFusingTest {};
 TEST_P(conv_fp32_scale_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
     // Output elements are in range [-127, 127]
     // 1.0f difference is allowed, since quantize can return different values in ref and scale_shift kernels
@@ -1462,240 +1473,245 @@ TEST_P(conv_fp32_scale_quantize_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_quantize_i8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // For now only b_fs_yx_fsv16 supports this case
-                                bc_test_params{CASE_CONV_FP32_2, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // For now only b_fs_yx_fsv16 supports this case
+    bc_test_params{ CASE_CONV_FP32_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 4 },
 
-                                bc_test_params{CASE_CONV_FP16_2, 2, 4},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 4},
-                        }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 4 },
+}));
 
 class conv_fp32_scale_activation_quantize_i8 : public ConvFusingTest {};
 TEST_P(conv_fp32_scale_activation_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", activation_func::exp),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", activation_func::exp),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_i8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // For now only b_fs_yx_fsv16 supports this case
-                                bc_test_params{CASE_CONV_FP32_2, 2, 5},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // For now only b_fs_yx_fsv16 supports this case
+    bc_test_params{ CASE_CONV_FP32_2, 2, 5 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 5 },
 
-                                bc_test_params{CASE_CONV_FP16_2, 2, 5},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 5},
-                        }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 5 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 5 },
+}));
 
 class conv_fp32_scale_activation_quantize_u8_eltwise_fp32 : public ConvFusingTest {};
 TEST_P(conv_fp32_scale_activation_quantize_u8_eltwise_fp32, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", activation_func::exp),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 eltwise("sum", { "quantize", "eltwise_data"}, eltwise_mode::sum,  p.default_type),
-                 reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", activation_func::exp),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        eltwise("sum", { "quantize", "eltwise_data" }, eltwise_mode::sum,  p.default_type),
+        reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
+
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_u8_eltwise_fp32,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // For now only b_fs_yx_fsv16 supports this case
-                                bc_test_params{CASE_CONV_FP32_2, 2, 6},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 6},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_u8_eltwise_fp32, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // For now only b_fs_yx_fsv16 supports this case
+    bc_test_params{ CASE_CONV_FP32_2, 2, 6 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 6 },
 
-                                bc_test_params{CASE_CONV_FP16_2, 2, 6},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 6},
-                        }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 6 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 6 },
+}));
 
 class conv_fp32_scale_activation_quantize_i8_activation : public ConvFusingTest {};
 TEST_P(conv_fp32_scale_activation_quantize_i8_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 activation("activation_quantize", "quantize", activation_func::relu),
-                 reorder("reorder_bfyx", "activation_quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        activation("activation_quantize", "quantize", activation_func::relu),
+        reorder("reorder_bfyx", "activation_quantize", p.default_format, data_types::f32)
     );
+
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_i8_activation,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP32_2, 2, 6},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 6},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_i8_activation, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 2, 6 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 6 },
 
-                                bc_test_params{CASE_CONV_FP16_2, 2, 6},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 6},
-                        }));
+    bc_test_params{ CASE_CONV_FP16_2, 2, 6 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 6 },
+}));
 
 
 class conv_fp32_scale_activation_quantize_i8_eltwise_fp32_quantize_i8 : public ConvFusingTest {};
 TEST_P(conv_fp32_scale_activation_quantize_i8_eltwise_fp32_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_lo1", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("out_hi1", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("eltwise_data", get_mem(layout{data_types::i8, p.input_format, p.out_shape})),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", activation_func::exp),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 eltwise("sum", { "quantize", "eltwise_data"}, eltwise_mode::sum, data_types::f32),
-                 quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_lo1", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("out_hi1", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("eltwise_data", get_mem(layout{ data_types::i8, p.input_format, p.out_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", activation_func::exp),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        eltwise("sum", { "quantize", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_i8_eltwise_fp32_quantize_i8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP32_2, 2, 7},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 7},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_scale_activation_quantize_i8_eltwise_fp32_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP32_2, 2, 7 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 7 },
+}));
 
 class conv_fp32_activation_eltwise_in_u8_fp32 : public WeightsPrimitiveFusingTest<bc_test_params> {};
 TEST_P(conv_fp32_activation_eltwise_in_u8_fp32, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(layout{ data_types::i8, p.input_format, p.out_shape })),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::relu_negative_slope),
-                 eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
-                 reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(layout{ data_types::i8, p.input_format, p.out_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::relu_negative_slope),
+        eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_eltwise_in_u8_fp32,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // bc_test_params{CASE_CONV_FP32_1, 2, 4}, - eltwise fusing not supported
-                                bc_test_params{CASE_CONV_FP32_2, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_3, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_4, 2, 4},
-                                // bc_test_params{CASE_CONV_FP32_5, 2, 4}, - eltwise fusing not supported
-                                bc_test_params{CASE_CONV_FP32_6, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_7, 2, 4},
-                                // bc_test_params{CASE_CONV_FP32_8, 2, 4}, - unknown bug
-                                bc_test_params{CASE_CONV_FP32_9, 2, 4},
-                                bc_test_params{CASE_CONV_FP32_10, 2, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_eltwise_in_u8_fp32, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // bc_test_params{ CASE_CONV_FP32_1, 2, 4 }, - eltwise fusing not supported
+    bc_test_params{ CASE_CONV_FP32_2, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_3, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_4, 2, 4 },
+    // bc_test_params{ CASE_CONV_FP32_5, 2, 4 }, - eltwise fusing not supported
+    bc_test_params{ CASE_CONV_FP32_6, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_7, 2, 4 },
+    // bc_test_params{ CASE_CONV_FP32_8, 2, 4 }, - unknown bug
+    bc_test_params{ CASE_CONV_FP32_9, 2, 4 },
+    bc_test_params{ CASE_CONV_FP32_10, 2, 4 },
+}));
 
 class conv_fp32_activation_eltwise_diff_sizes : public ConvEltwTest {};
 TEST_P(conv_fp32_activation_eltwise_diff_sizes, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::relu_negative_slope),
-                 eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
-                 reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::relu_negative_slope),
+        eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_eltwise_diff_sizes,
-                        ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_1, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_2, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_3, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_4, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_5, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_6, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_7, 3, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_8, 3, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_eltwise_diff_sizes, ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_1, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_2, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_3, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_4, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_5, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_6, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_7, 3, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_8, 3, 4 },
+}));
 
 class conv_scale_activation_eltwise_fp32_quantize_i8 : public ConvEltwTest {};
 TEST_P(conv_scale_activation_eltwise_fp32_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 data("scale_data", get_mem(get_per_channel_layout(p))),
-                 scale("scale", "conv", "scale_data"),
-                 activation("activation", "scale", activation_func::hyperbolic_tan),
-                 data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
-                 eltwise("eltw", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
-                 data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_low", get_mem(get_single_element_layout(p), -127, 127)),
-                 data("out_high", get_mem(get_single_element_layout(p), -127, 127)),
-                 quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        data("scale_data", get_mem(get_per_channel_layout(p))),
+        scale("scale", "conv", "scale_data"),
+        activation("activation", "scale", activation_func::hyperbolic_tan),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
+        eltwise("eltw", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -127, 127)),
+        data("out_high", get_mem(get_single_element_layout(p), -127, 127)),
+        quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_scale_activation_eltwise_fp32_quantize_i8,
-                        ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_1, 2, 6},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_2, 2, 6},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_3, 2, 6},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_4, 2, 6},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_5, 3, 6},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_6, 3, 6},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_7, 3, 6},
-                                conv_eltw_test_params{CASE_CONV_ELTW_FP32_8, 3, 6},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_scale_activation_eltwise_fp32_quantize_i8, ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_1, 2, 6 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_2, 2, 6 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_3, 2, 6 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_4, 2, 6 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_5, 3, 6 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_6, 3, 6 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_7, 3, 6 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_FP32_8, 3, 6 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* -------------------------------------- binary convolution cases ------------------------------------- */
@@ -1704,57 +1720,60 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_scale_activation_eltwise_fp32_quantiz
 class conv_bin_activation : public ConvFusingTest {};
 TEST_P(conv_bin_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 activation("activation", "bin_conv_prim", activation_func::relu),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        activation("activation", "bin_conv_prim", activation_func::relu),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_activation,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                            bc_test_params{CASE_BIN_CONV1, 2, 3},
-                                            }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_activation, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_BIN_CONV1, 2, 3 },
+}));
 
 class conv_bin_scale_activation : public ConvFusingTest {};
 TEST_P(conv_bin_scale_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 scale("scale", "bin_conv_prim", "scale_data"),
-                 activation("activation", "scale", activation_func::relu),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        scale("scale", "bin_conv_prim", "scale_data"),
+        activation("activation", "scale", activation_func::relu),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_scale_activation,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                            bc_test_params{CASE_BIN_CONV1, 2, 4},
-                            bc_test_params{CASE_BIN_CONV2, 2, 4},
-                                            }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_scale_activation, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_BIN_CONV1, 2, 4 },
+    bc_test_params{ CASE_BIN_CONV2, 2, 4 },
+}));
 
 class conv_bin_quantize_bin : public ConvFusingTest {};
 TEST_P(conv_bin_quantize_bin, channel_wise_quantize) {
     auto p = GetParam();
     auto in_thresh = get_mem(get_per_channel_layout(p), min_random, max_random);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 data("in_lo", in_thresh),
-                 data("in_hi", in_thresh),
-                 data("out_lo", get_mem(get_per_channel_layout(p), -1)),
-                 data("out_hi", get_mem(get_per_channel_layout(p),  1)),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 quantize("quantize_data", "bin_conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 2, data_types::bin),
-                 reorder("reorder_bfyx", "quantize_data", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        data("in_lo", in_thresh),
+        data("in_hi", in_thresh),
+        data("out_lo", get_mem(get_per_channel_layout(p), -1)),
+        data("out_hi", get_mem(get_per_channel_layout(p),  1)),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        quantize("quantize_data", "bin_conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 2, data_types::bin),
+        reorder("reorder_bfyx", "quantize_data", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
@@ -1762,42 +1781,45 @@ TEST_P(conv_bin_quantize_bin, channel_wise_quantize) {
 TEST_P(conv_bin_quantize_bin, blob_wise_quantize) {
     auto p = GetParam();
     auto in_thresh = get_mem(get_single_element_layout(p), min_random, max_random);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 data("in_lo", in_thresh),
-                 data("in_hi", in_thresh),
-                 data("out_lo", get_mem(get_single_element_layout(p), -1)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 1)),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 quantize("quantize_data", "bin_conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 2, data_types::bin),
-                 reorder("reorder_bfyx", "quantize_data", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        data("in_lo", in_thresh),
+        data("in_hi", in_thresh),
+        data("out_lo", get_mem(get_single_element_layout(p), -1)),
+        data("out_hi", get_mem(get_single_element_layout(p), 1)),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        quantize("quantize_data", "bin_conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 2, data_types::bin),
+        reorder("reorder_bfyx", "quantize_data", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_quantize_bin,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                            bc_test_params{CASE_BIN_CONV1, 2, 3},
-                            bc_test_params{CASE_BIN_CONV2, 2, 3},
-                                            }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_quantize_bin, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_BIN_CONV1, 2, 3 },
+    bc_test_params{ CASE_BIN_CONV2, 2, 3 },
+}));
 
 class conv_bin_scale_conv_dw : public ConvFusingTest {};
 TEST_P(conv_bin_scale_conv_dw, dw_kernel_3x3_stride2) {
     auto p = GetParam();
     auto dw_tensor = cldnn::tensor(group(p.out_shape.feature[0]), batch(1), feature(1), spatial(3, 3));
-    auto dw_weights_layout = layout{p.default_type, format::goiyx, dw_tensor};
+    auto dw_weights_layout = layout{ p.default_type, format::goiyx, dw_tensor };
 
-    auto dw_stride = tensor{1, 1, 2, 2};
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 scale("scale", "bin_conv_prim", "scale_data"),
-                 convolution("conv_dw", "scale", {"weights_dw"}, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
-                 reorder("reorder_bfyx", "conv_dw", p.default_format, data_types::f32)
+    auto dw_stride = tensor{ 1, 1, 2, 2 };
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        scale("scale", "bin_conv_prim", "scale_data"),
+        convolution("conv_dw", "scale", { "weights_dw" }, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
+        reorder("reorder_bfyx", "conv_dw", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
@@ -1805,47 +1827,50 @@ TEST_P(conv_bin_scale_conv_dw, dw_kernel_3x3_stride2) {
 TEST_P(conv_bin_scale_conv_dw, dw_kernel_3x3_stride1) {
     auto p = GetParam();
     auto dw_tensor = cldnn::tensor(group(p.out_shape.feature[0]), batch(1), feature(1), spatial(3, 3));
-    auto dw_weights_layout = layout{p.default_type, format::goiyx, dw_tensor};
+    auto dw_weights_layout = layout{ p.default_type, format::goiyx, dw_tensor };
 
-    auto dw_stride = tensor{1, 1, 1, 1};
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 scale("scale", "bin_conv_prim", "scale_data"),
-                 convolution("conv_dw", "scale", {"weights_dw"}, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
-                 reorder("reorder_bfyx", "conv_dw", p.default_format, data_types::f32)
+    auto dw_stride = tensor{ 1, 1, 1, 1 };
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        scale("scale", "bin_conv_prim", "scale_data"),
+        convolution("conv_dw", "scale", { "weights_dw" }, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
+        reorder("reorder_bfyx", "conv_dw", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_scale_conv_dw,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                            bc_test_params{CASE_BIN_CONV2, 3, 4},
-                            bc_test_params{CASE_BIN_CONV3, 3, 4},
-                                            }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_scale_conv_dw, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_BIN_CONV2, 3, 4 },
+    bc_test_params{ CASE_BIN_CONV3, 3, 4 },
+}));
 
 class conv_bin_scale_conv_dw_prelu : public ConvFusingTest {};
 TEST_P(conv_bin_scale_conv_dw_prelu, dw_kernel_3x3_stride2) {
     auto p = GetParam();
     auto dw_tensor = cldnn::tensor(group(p.out_shape.feature[0]), batch(1), feature(1), spatial(3, 3));
-    auto dw_weights_layout = layout{p.default_type, format::goiyx, dw_tensor};
+    auto dw_weights_layout = layout{ p.default_type, format::goiyx, dw_tensor };
 
-    auto dw_stride = tensor{1, 1, 2, 2};
+    auto dw_stride = tensor{ 1, 1, 2, 2 };
     auto in_thresh = get_mem(get_per_channel_layout(p), min_random, max_random);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 scale("scale", "bin_conv_prim", "scale_data"),
-                 convolution("conv_dw", "scale", {"weights_dw"}, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 activation("activation", "conv_dw", "slope_data", activation_func::relu_negative_slope),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        scale("scale", "bin_conv_prim", "scale_data"),
+        convolution("conv_dw", "scale", { "weights_dw" }, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        activation("activation", "conv_dw", "slope_data", activation_func::relu_negative_slope),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
@@ -1853,44 +1878,47 @@ TEST_P(conv_bin_scale_conv_dw_prelu, dw_kernel_3x3_stride2) {
 TEST_P(conv_bin_scale_conv_dw_prelu, dw_kernel_3x3_stride1) {
     auto p = GetParam();
     auto dw_tensor = cldnn::tensor(group(p.out_shape.feature[0]), batch(1), feature(1), spatial(3, 3));
-    auto dw_weights_layout = layout{p.default_type, format::goiyx, dw_tensor};
+    auto dw_weights_layout = layout{ p.default_type, format::goiyx, dw_tensor };
 
-    auto dw_stride = tensor{1, 1, 1, 1};
+    auto dw_stride = tensor{ 1, 1, 1, 1 };
     auto in_thresh = get_mem(get_per_channel_layout(p), min_random, max_random);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -127, 127)),
-                 data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
-                 binary_convolution("bin_conv_prim", "input", {"weights"}, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
-                 scale("scale", "bin_conv_prim", "scale_data"),
-                 convolution("conv_dw", "scale", {"weights_dw"}, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 activation("activation", "conv_dw", "slope_data", activation_func::relu_negative_slope),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -127, 127)),
+        data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1e-1f)),
+        binary_convolution("bin_conv_prim", "input", { "weights" }, p.stride, p.pad, p.dilation, p.out_shape, p.groups),
+        scale("scale", "bin_conv_prim", "scale_data"),
+        convolution("conv_dw", "scale", { "weights_dw" }, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        activation("activation", "conv_dw", "slope_data", activation_func::relu_negative_slope),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_scale_conv_dw_prelu,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                            bc_test_params{CASE_BIN_CONV2, 3, 5},
-                            bc_test_params{CASE_BIN_CONV3, 3, 5},
-                                            }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_bin_scale_conv_dw_prelu, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_BIN_CONV2, 3, 5 },
+    bc_test_params{ CASE_BIN_CONV3, 3, 5 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- INT8 convolution cases ------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 class conv_int8_scale : public ConvFusingTest {};
 TEST_P(conv_int8_scale, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -1899,138 +1927,139 @@ TEST_P(conv_int8_scale, basic) {
 
 TEST_P(conv_int8_scale, fp16_scale_out) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data", optional_data_type{data_types::f16}),
-                 reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data", optional_data_type{ data_types::f16 }),
+        reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 3 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 3},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 3 },
+}));
 
 class conv_int8_eltwise : public ConvFusingTest {};
 TEST_P(conv_int8_eltwise, fp16_eltwise_out) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("scale", {"conv_prim", "scale_data"}, eltwise_mode::prod, data_types::f16),
-                 reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("scale", { "conv_prim", "scale_data" }, eltwise_mode::prod, data_types::f16),
+        reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 3 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 3},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 3 },
+}));
 
 class conv_int8_scale_shift_swish : public ConvFusingTest {};
 TEST_P(conv_int8_scale_shift_swish, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 data("shift_data", get_mem(get_per_channel_layout(p), 1)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("scale0", {"conv_prim", "scale_data"}, eltwise_mode::prod),
-                 eltwise("scale1", {"conv_prim", "scale_data"}, eltwise_mode::prod),
-                 eltwise("shift0", {"scale0", "shift_data"}, eltwise_mode::sum),
-                 eltwise("shift1", {"scale1", "shift_data"}, eltwise_mode::sum),
-                 activation("sigmoid", "shift0", activation_func::logistic),
-                 eltwise("mul", {"shift1", "sigmoid"}, eltwise_mode::prod),
-                 reorder("reorder_bfyx", "mul", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        data("shift_data", get_mem(get_per_channel_layout(p), 1)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("scale0", { "conv_prim", "scale_data" }, eltwise_mode::prod),
+        eltwise("scale1", { "conv_prim", "scale_data" }, eltwise_mode::prod),
+        eltwise("shift0", { "scale0", "shift_data" }, eltwise_mode::sum),
+        eltwise("shift1", { "scale1", "shift_data" }, eltwise_mode::sum),
+        activation("sigmoid", "shift0", activation_func::logistic),
+        eltwise("mul", { "shift1", "sigmoid" }, eltwise_mode::prod),
+        reorder("reorder_bfyx", "mul", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-3f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_shift_swish,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 8},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 8},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 8},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 8},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 8},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 8},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 8},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 8},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_shift_swish, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 8 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 8 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 8 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 8 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 8 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 8 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 8 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 8 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 8},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 8},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 8},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 8},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 8},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 8},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 8},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 8},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 8},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 8},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 8 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 8 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 8 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 8 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 8 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 8 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 8 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 8 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 8 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 8 },
+}));
 
 class conv_int8_prelu_eltwise : public ConvFusingTest {};
 TEST_P(conv_int8_prelu_eltwise, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -2039,20 +2068,21 @@ TEST_P(conv_int8_prelu_eltwise, basic) {
 
 TEST_P(conv_int8_prelu_eltwise, fsv16) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     if (p.default_format.dimension() == 4) {
         implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
-        bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+        bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
     } else {
         // TODO Add 5D int8 optimized convolution implementations
         return;
@@ -2062,54 +2092,54 @@ TEST_P(conv_int8_prelu_eltwise, fsv16) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_prelu_eltwise,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_7, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_8, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_7, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_8, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_prelu_eltwise, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_7, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_8, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_7, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_8, 2, 4 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 4},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 4 },
+}));
 
 class conv_int8_activation_eltwise_quantize : public ConvFusingTest {};
 TEST_P(conv_int8_activation_eltwise_quantize, fsv16) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::negative),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::negative),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     if (p.default_format.dimension() == 4) {
         implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
-        bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+        bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
     } else {
         // TODO Add 5D int8 optimized convolution implementations
         return;
@@ -2121,24 +2151,25 @@ TEST_P(conv_int8_activation_eltwise_quantize, fsv16) {
 
 TEST_P(conv_int8_activation_eltwise_quantize, fsv32) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::negative),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::negative),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     if (p.default_format.dimension() == 4) {
         implementation_desc conv_impl = { format::b_fs_yx_fsv32, "" };
-        bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+        bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
     } else {
         // TODO Add 5D int8 optimized convolution implementations
         return;
@@ -2148,38 +2179,38 @@ TEST_P(conv_int8_activation_eltwise_quantize, fsv32) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_activation_eltwise_quantize,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_7, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_8, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_7, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_8, 2, 5},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_activation_eltwise_quantize, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_7, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_8, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_7, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_8, 2, 5 },
+}));
 
 class conv_int8_activation_eltwise : public ConvFusingTest {};
 TEST_P(conv_int8_activation_eltwise, fsv16) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::negative),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::negative),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     if (p.default_format.dimension() == 4) {
         implementation_desc conv_impl = { format::b_fs_yx_fsv16, "" };
-        bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+        bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
     } else {
         // TODO Add 5D int8 optimized convolution implementations
         return;
@@ -2191,19 +2222,20 @@ TEST_P(conv_int8_activation_eltwise, fsv16) {
 
 TEST_P(conv_int8_activation_eltwise, fsv32) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::negative),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::negative),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
     if (p.default_format.dimension() == 4) {
         implementation_desc conv_impl = { format::b_fs_yx_fsv32, "" };
-        bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+        bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
     } else {
         // TODO Add 5D int8 optimized convolution implementations
         return;
@@ -2213,35 +2245,35 @@ TEST_P(conv_int8_activation_eltwise, fsv32) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_activation_eltwise,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_7, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_8, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_7, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_8, 2, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_activation_eltwise, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_7, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_8, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_7, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_8, 2, 4 },
+}));
 
 class conv_int8_quantize_u8 : public ConvFusingTest {};
 TEST_P(conv_int8_quantize_u8, per_channel) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.0f;
@@ -2250,61 +2282,62 @@ TEST_P(conv_int8_quantize_u8, per_channel) {
 
 TEST_P(conv_int8_quantize_u8, per_tensor) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_single_element_layout(p), -10)),
-                 data("in_hi", get_mem(get_single_element_layout(p), 10)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_single_element_layout(p), -10)),
+        data("in_hi", get_mem(get_single_element_layout(p), 10)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_quantize_u8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_8, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_quantize_u8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_8, 2, 3 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 3},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 3 },
+}));
 
 class conv_int8_scale_quantize_i8 : public ConvFusingTest {};
 TEST_P(conv_int8_scale_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
     // Output elements are in range [-127, 127]
     // 1.0f difference is allowed, since quantize can return different values in ref and scale_shift kernels
@@ -2313,75 +2346,75 @@ TEST_P(conv_int8_scale_quantize_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_quantize_i8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_9, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_9, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_9, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_9, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 4 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 4},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 4 },
+}));
 
 class conv_int8_scale_quantize_i8_conv_b_fs_yx_fsv4_int8 : public ConvFusingTest {};
 TEST_P(conv_int8_scale_quantize_i8_conv_b_fs_yx_fsv4_int8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / p.kernel.count() / 255)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / p.kernel.count() / 255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
     implementation_desc conv_impl = { format::b_fs_yx_fsv4, "convolution_gpu_b_fs_yx_fsv4_int8" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_quantize_i8_conv_b_fs_yx_fsv4_int8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{ CASE_CONV_S8S8_11, 2, 4 },
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_quantize_i8_conv_b_fs_yx_fsv4_int8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_S8S8_11, 2, 4 },
+}));
 
 class conv_int8_relu_quantize : public ConvFusingTest {};
 TEST_P(conv_int8_relu_quantize, i8) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("relu", "conv_prim", activation_func::relu),
-                 quantize("quantize", "relu", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("relu", "conv_prim", activation_func::relu),
+        quantize("quantize", "relu", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
     // Output elements are in range [-127, 127]
     // 1.0f difference is allowed, since quantize can return different values in ref and scale_shift kernels
@@ -2392,263 +2425,268 @@ TEST_P(conv_int8_relu_quantize, i8) {
 
 TEST_P(conv_int8_relu_quantize, u8) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("relu", "conv_prim", activation_func::relu),
-                 quantize("quantize", "relu", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("relu", "conv_prim", activation_func::relu),
+        quantize("quantize", "relu", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
+
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_relu_quantize,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_relu_quantize, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 4 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 4},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 4 },
+}));
 
 class conv_int8_scale_activation_quantize_i8 : public ConvFusingTest {};
 TEST_P(conv_int8_scale_activation_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", activation_func::exp),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", activation_func::exp),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 2.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 5 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 5},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 5},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 5},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 5},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 5 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 5 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 5 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 5 },
+}));
 
 class conv_int8_scale_activation_quantize_i8_eltwise_fp32 : public ConvFusingTest {};
 TEST_P(conv_int8_scale_activation_quantize_i8_eltwise_fp32, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", activation_func::exp),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 eltwise("sum", { "quantize", "eltwise_data"}, eltwise_mode::sum,  data_types::f32),
-                 reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", activation_func::exp),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        eltwise("sum", { "quantize", "eltwise_data" }, eltwise_mode::sum,  data_types::f32),
+        reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8_eltwise_fp32,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 6},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8_eltwise_fp32, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 6 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 6},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 6 },
+}));
 
 class conv_int8_scale_activation_quantize_i8_activation : public ConvFusingTest {};
 TEST_P(conv_int8_scale_activation_quantize_i8_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 activation("activation_quantize", "quantize", activation_func::relu),
-                 reorder("reorder_bfyx", "activation_quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        activation("activation_quantize", "quantize", activation_func::relu),
+        reorder("reorder_bfyx", "activation_quantize", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8_activation,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 6},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8_activation, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 6 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 6},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 6},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 6},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 6 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 6 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 6 },
+}));
 
 
 class conv_int8_scale_activation_quantize_i8_eltwise_fp32_quantize_i8 : public ConvFusingTest {};
 // With some input values accuracy error might be = 2, so the test is disabled.
 TEST_P(conv_int8_scale_activation_quantize_i8_eltwise_fp32_quantize_i8, DISABLED_basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_lo1", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("out_hi1", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("eltwise_data", get_mem(layout{data_types::i8, p.input_format, p.out_shape})),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", activation_func::exp),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 eltwise("sum", { "quantize", "eltwise_data"}, eltwise_mode::sum, data_types::f32),
-                 quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_lo1", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("out_hi1", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("eltwise_data", get_mem(layout{ data_types::i8, p.input_format, p.out_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", activation_func::exp),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        eltwise("sum", { "quantize", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8_eltwise_fp32_quantize_i8,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 7},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 7},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 7},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 7},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_activation_quantize_i8_eltwise_fp32_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 7 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 7 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 7 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 7 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 7},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 7},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 7},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 7},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 7},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 7},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 7},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 7},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 7},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 7},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 7 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 7 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 7 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 7 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 7 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 7 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 7 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 7 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 7 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 7 },
+}));
 
 class conv_int8_scale_prelu_quantize_i8_eltwise_fp32_quantize_i8_vec : public ConvFusingTest {};
 TEST_P(conv_int8_scale_prelu_quantize_i8_eltwise_fp32_quantize_i8_vec, vector_ops) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_lo1", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("out_hi1", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("slope_data", get_mem(get_per_channel_layout(p))),
-                 data("eltwise_data", get_mem(layout{data_types::i8, format::b_fs_yx_fsv4, p.out_shape})),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 eltwise("sum", { "quantize", "eltwise_data"}, eltwise_mode::sum, data_types::f32),
-                 quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_lo1", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("out_hi1", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("slope_data", get_mem(get_per_channel_layout(p))),
+        data("eltwise_data", get_mem(layout{ data_types::i8, format::b_fs_yx_fsv4, p.out_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        eltwise("sum", { "quantize", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::b_fs_yx_fsv4, "convolution_gpu_b_fs_yx_fsv4_1x1" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1.f;
     execute(p);
@@ -2656,43 +2694,43 @@ TEST_P(conv_int8_scale_prelu_quantize_i8_eltwise_fp32_quantize_i8_vec, vector_op
 
 TEST_P(conv_int8_scale_prelu_quantize_i8_eltwise_fp32_quantize_i8_vec, vector_ops_mixed_types) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_lo1", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("out_hi1", get_mem(get_single_element_layout(p), 127)),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
-                 data("slope_data", get_mem(layout{ data_types::f16, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} })),
-                 data("eltwise_data", get_mem(layout{data_types::u8, format::b_fs_yx_fsv4, p.out_shape})),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
-                 quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 eltwise("sum", { "quantize", "eltwise_data"}, eltwise_mode::sum, data_types::f32),
-                 quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_lo1", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("in_hi1", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_lo1", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("out_hi1", get_mem(get_single_element_layout(p), 127)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()/255)),
+        data("slope_data", get_mem(layout{ data_types::f16, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } })),
+        data("eltwise_data", get_mem(layout{ data_types::u8, format::b_fs_yx_fsv4, p.out_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        activation("activation_scale", "scale", "slope_data", activation_func::relu_negative_slope),
+        quantize("quantize", "activation_scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        eltwise("sum", { "quantize", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        quantize("quantize_1", "sum", "in_lo1", "in_hi1", "out_lo1", "out_hi1", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize_1", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::b_fs_yx_fsv4, "convolution_gpu_b_fs_yx_fsv4_1x1" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_prelu_quantize_i8_eltwise_fp32_quantize_i8_vec,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 7},
-                                bc_test_params{CASE_CONV_U8S8_5, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_5, 2, 7},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_prelu_quantize_i8_eltwise_fp32_quantize_i8_vec, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 7 },
+    bc_test_params{ CASE_CONV_U8S8_5, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_5, 2, 7 },
+}));
 
 class conv_int8_asymmetric_weights : public ConvFusingTest {};
 TEST_P(conv_int8_asymmetric_weights, basic) {
@@ -2700,14 +2738,16 @@ TEST_P(conv_int8_asymmetric_weights, basic) {
     auto weights_format = (p.weights_format == format::goiyx) ? format::bfyx : format::bfzyx;
     auto weights_layout = (p.groups > 1) ? get_weights_layout(p, 1, weights_format) :
                                            get_weights_layout(p);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(weights_layout)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("w_zp", get_mem(get_weights_zp_layout(p), 1, 127)),
-                 eltwise("w_sub", {"weights", "w_zp"}, eltwise_mode::sub, data_types::f32),
-                 convolution("conv_prim", "input", {"w_sub"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
-                 reorder("reorder_bfyx", "conv_prim", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(weights_layout)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("w_zp", get_mem(get_weights_zp_layout(p), 1, 127)),
+        eltwise("w_sub", { "weights", "w_zp" }, eltwise_mode::sub, data_types::f32),
+        convolution("conv_prim", "input", { "w_sub" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
+        reorder("reorder_bfyx", "conv_prim", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
 
     auto input_prim = get_mem(get_input_layout(p));
@@ -2740,26 +2780,25 @@ TEST_P(conv_int8_asymmetric_weights, basic) {
     compare(network_not_fused, network_fused, p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_asymmetric_weights,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 2},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 2},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 2},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 2},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 2},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 2},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 2},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 2},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_asymmetric_weights, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 2 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 2 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 2 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 2 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 2 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 2 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 2 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 2 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 2},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 2},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 2},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 2},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 2},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 2},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 2},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 2},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 2 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 2 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 2 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 2 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 2 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 2 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 2 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 2 },
+}));
 
 class conv_int8_asymmetric_data : public ConvFusingTest {};
 TEST_P(conv_int8_asymmetric_data, basic) {
@@ -2767,14 +2806,16 @@ TEST_P(conv_int8_asymmetric_data, basic) {
     auto weights_format = (p.weights_format == format::goiyx) ? format::bfyx : format::bfzyx;
     auto weights_layout = (p.groups > 1) ? get_weights_layout(p, 1, weights_format) :
                           get_weights_layout(p);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(weights_layout)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("a_zp", get_mem(get_activations_zp_layout(p), 1, 127)),
-                 eltwise("a_sub", {"input", "a_zp"}, eltwise_mode::sub, data_types::f32),
-                 convolution("conv_prim", "a_sub", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
-                 reorder("reorder_bfyx", "conv_prim", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(weights_layout)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("a_zp", get_mem(get_activations_zp_layout(p), 1, 127)),
+        eltwise("a_sub", { "input", "a_zp" }, eltwise_mode::sub, data_types::f32),
+        convolution("conv_prim", "a_sub", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
+        reorder("reorder_bfyx", "conv_prim", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
 
     auto input_prim = get_mem(get_input_layout(p));
@@ -2807,28 +2848,27 @@ TEST_P(conv_int8_asymmetric_data, basic) {
     compare(network_not_fused, network_fused, p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_asymmetric_data,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_asymmetric_data, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 3 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 3},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 3 },
+}));
 
 class conv_int8_asymmetric_data_and_weights : public ConvFusingTest {};
 TEST_P(conv_int8_asymmetric_data_and_weights, basic) {
@@ -2836,16 +2876,18 @@ TEST_P(conv_int8_asymmetric_data_and_weights, basic) {
     auto weights_format = (p.weights_format == format::goiyx) ? format::bfyx : format::bfzyx;
     auto weights_layout = (p.groups > 1) ? get_weights_layout(p, 1, weights_format) :
                           get_weights_layout(p);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(weights_layout)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("a_zp", get_mem(get_activations_zp_layout(p), 1, 127)),
-                 data("w_zp", get_mem(get_weights_zp_layout(p), 1, 127)),
-                 eltwise("a_sub", {"input", "a_zp"}, eltwise_mode::sub, data_types::f32),
-                 eltwise("w_sub", {"weights", "w_zp"}, eltwise_mode::sub, data_types::f32),
-                 convolution("conv_prim", "a_sub", {"w_sub"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
-                 reorder("reorder_bfyx", "conv_prim", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(weights_layout)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("a_zp", get_mem(get_activations_zp_layout(p), 1, 127)),
+        data("w_zp", get_mem(get_weights_zp_layout(p), 1, 127)),
+        eltwise("a_sub", { "input", "a_zp" }, eltwise_mode::sub, data_types::f32),
+        eltwise("w_sub", { "weights", "w_zp" }, eltwise_mode::sub, data_types::f32),
+        convolution("conv_prim", "a_sub", { "w_sub" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
+        reorder("reorder_bfyx", "conv_prim", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
 
     auto input_prim = get_mem(get_input_layout(p));
@@ -2878,66 +2920,67 @@ TEST_P(conv_int8_asymmetric_data_and_weights, basic) {
     compare(network_not_fused, network_fused, p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_asymmetric_data_and_weights,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_asymmetric_data_and_weights, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 3 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_4, 2, 3},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 2, 3},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_4, 2, 3 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 2, 3 },
+}));
 
 
 class conv_i8_activation_eltwise_diff_sizes : public ConvEltwTest {};
 TEST_P(conv_i8_activation_eltwise_diff_sizes, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::abs),
-                 eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
-                 reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::abs),
+        eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, data_types::f32),
+        reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_i8_activation_eltwise_diff_sizes,
-                        ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
-                                conv_eltw_test_params{CASE_CONV_ELTW_i8_1, 3, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_i8_2, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_i8_3, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_i8_4, 2, 4},
-                                conv_eltw_test_params{CASE_CONV_ELTW_i8_5, 3, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_i8_activation_eltwise_diff_sizes, ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
+    conv_eltw_test_params{ CASE_CONV_ELTW_i8_1, 3, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_i8_2, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_i8_3, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_i8_4, 2, 4 },
+    conv_eltw_test_params{ CASE_CONV_ELTW_i8_5, 3, 4 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ----------------------------------- Force convolution kernel cases ---------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 class ConvFusingForceKernelTest : public ::WeightsPrimitiveFusingTest<bc_force_kernel_params> {
     public:
     void execute(bc_force_kernel_params& p) {
         auto input_prim = get_mem(get_input_layout(p));
         build_options options;
         options.set_option(build_option::optimize_data(true));
-        implementation_desc conv_impl = {p.input_format, p.kernel_name};
-        options.set_option(build_option::force_implementations({{"conv_prim", conv_impl}}));
+        implementation_desc conv_impl = { p.input_format, p.kernel_name };
+        options.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
         network network_not_fused(this->engine, this->topology_non_fused, bo_not_fused);
         network network_fused(this->engine, this->topology_fused, options);
@@ -2961,56 +3004,59 @@ class ConvFusingForceKernelTest : public ::WeightsPrimitiveFusingTest<bc_force_k
 class conv_fp16_activation : public ConvFusingForceKernelTest {};
 TEST_P(conv_fp16_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::abs),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::abs),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     execute(p);
 }
 
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp16_activation, ::testing::ValuesIn(std::vector<bc_force_kernel_params>{
-                                                                           bc_force_kernel_params{CASE_CONV_FP16_13, 2, 3, "convolution_gpu_fs_byx_fsv32"},
-                                                                           }));
+    bc_force_kernel_params{ CASE_CONV_FP16_13, 2, 3, "convolution_gpu_fs_byx_fsv32" },
+}));
 
 
 class conv_fp16_scale : public ConvFusingForceKernelTest {};
 TEST_P(conv_fp16_scale, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 scale("scale", "conv_prim", "scale_data"),
-                 reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        scale("scale", "conv_prim", "scale_data"),
+        reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp16_scale,
-                        ::testing::ValuesIn(std::vector<bc_force_kernel_params>{
-                                             bc_force_kernel_params{CASE_CONV_FP16_13, 2, 3, "convolution_gpu_fs_byx_fsv32"},
-                                             }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp16_scale, ::testing::ValuesIn(std::vector<bc_force_kernel_params>{
+    bc_force_kernel_params{ CASE_CONV_FP16_13, 2, 3, "convolution_gpu_fs_byx_fsv32" },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- FC cases --------------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 class FCFusingTest : public WeightsPrimitiveFusingTest<bc_test_params> {};
 class fc_fp32_activation : public FCFusingTest {};
 TEST_P(fc_fp32_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                data("weights", get_mem(get_fc_weights_layout(p))),
-                data("bias", get_mem(get_fc_bias_layout(p))),
-                fully_connected("fc_prim", "input", "weights", "bias", "", padding(), get_fc_output_dim_size(p)),
-                activation("activation", "fc_prim", activation_func::abs),
-                reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_fc_weights_layout(p))),
+        data("bias", get_mem(get_fc_bias_layout(p))),
+        fully_connected("fc_prim", "input", "weights", "bias", "", padding(), get_fc_output_dim_size(p)),
+        activation("activation", "fc_prim", activation_func::abs),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -3018,23 +3064,24 @@ TEST_P(fc_fp32_activation, basic) {
 }
 
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_fp32_activation, ::testing::ValuesIn(std::vector<bc_test_params>{
-                                                                            bc_test_params{ CASE_FC_FP32_1, 2, 3 },
-                                                                            bc_test_params{ CASE_FC_FP32_2, 2, 3 },
-                                                                            bc_test_params{ CASE_FC_FP32_3, 2, 3 },
-                                                                            bc_test_params{ CASE_FC_FP32_3D_1, 2, 3 },
-                                                                            bc_test_params{ CASE_FC_FP32_3D_2, 2, 3 },
-                                                                            bc_test_params{ CASE_FC_FP32_3D_3, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_1, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_2, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_3, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_3D_1, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_3D_2, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_3D_3, 2, 3 },
 }));
 
 class fc_fp32_bias : public FCFusingTest {};
 TEST_P(fc_fp32_bias, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                data("weights", get_mem(get_fc_weights_layout(p))),
-                data("bias", get_mem(get_fc_bias_layout(p))),
-                fully_connected("fc_prim", "input", "weights", ""),
-                eltwise("bias_add", {"fc_prim", "bias"}, eltwise_mode::sum),
-                reorder("reorder_bfyx", "bias_add", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_fc_weights_layout(p))),
+        data("bias", get_mem(get_fc_bias_layout(p))),
+        fully_connected("fc_prim", "input", "weights", ""),
+        eltwise("bias_add", { "fc_prim", "bias" }, eltwise_mode::sum),
+        reorder("reorder_bfyx", "bias_add", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -3042,15 +3089,16 @@ TEST_P(fc_fp32_bias, basic) {
 }
 
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_fp32_bias, ::testing::ValuesIn(std::vector<bc_test_params>{
-                                                                            bc_test_params{ CASE_FC_FP32_1, 2, 3 },
-                                                                            bc_test_params{ CASE_FC_FP32_2, 2, 3 },
-                                                                            bc_test_params{ CASE_FC_FP32_3, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_1, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_2, 2, 3 },
+    bc_test_params{ CASE_FC_FP32_3, 2, 3 },
 }));
 
 class fc_int8_scale : public FCFusingTest {};
 TEST_P(fc_int8_scale, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("weights", get_mem(get_fc_weights_layout(p))),
         data("bias", get_mem(get_fc_bias_layout(p))),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / p.kernel.count())),
@@ -3065,12 +3113,13 @@ TEST_P(fc_int8_scale, basic) {
 
 TEST_P(fc_int8_scale, fp16_scale_out) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("weights", get_mem(get_fc_weights_layout(p))),
         data("bias", get_mem(get_fc_bias_layout(p))),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / p.kernel.count())),
         fully_connected("fc_prim", "input", "weights", "bias", data_types::f32, "", padding(), get_fc_output_dim_size(p)),
-        scale("scale", "fc_prim", "scale_data", optional_data_type{data_types::f16}),
+        scale("scale", "fc_prim", "scale_data", optional_data_type{ data_types::f16 }),
         reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
 
@@ -3078,20 +3127,20 @@ TEST_P(fc_int8_scale, fp16_scale_out) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_scale,
-    ::testing::ValuesIn(std::vector<bc_test_params>{
-                        bc_test_params{ CASE_FC_U8S8_1, 2, 3 },
-                        bc_test_params{ CASE_FC_U8S8_2, 2, 3 },
-                        bc_test_params{ CASE_FC_U8S8_3, 2, 3 },
-                        bc_test_params{ CASE_FC_U8S8_3D_1, 2, 3 },
-                        bc_test_params{ CASE_FC_U8S8_3D_2, 2, 3 },
-                        bc_test_params{ CASE_FC_U8S8_3D_3, 2, 3 },
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_scale, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_FC_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3D_1, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3D_2, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3D_3, 2, 3 },
+}));
 
 class fc_int8_quantize_u8 : public FCFusingTest {};
 TEST_P(fc_int8_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("weights", get_mem(get_fc_weights_layout(p))),
         data("bias", get_mem(get_fc_bias_layout(p))),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
@@ -3107,20 +3156,20 @@ TEST_P(fc_int8_quantize_u8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu_fc, fc_int8_quantize_u8,
-    ::testing::ValuesIn(std::vector<bc_test_params>{
-        bc_test_params{CASE_FC_U8S8_1, 2, 3},
-        bc_test_params{CASE_FC_U8S8_2, 2, 3},
-        bc_test_params{CASE_FC_U8S8_3, 2, 3},
-        bc_test_params{ CASE_FC_U8S8_3D_1, 2, 3 },
-        bc_test_params{ CASE_FC_U8S8_3D_2, 2, 3 },
-        bc_test_params{ CASE_FC_U8S8_3D_3, 2, 3 },
-        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu_fc, fc_int8_quantize_u8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_FC_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3D_1, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3D_2, 2, 3 },
+    bc_test_params{ CASE_FC_U8S8_3D_3, 2, 3 },
+}));
 
 class fc_int8_scale_quantize_i8 : public FCFusingTest {};
 TEST_P(fc_int8_scale_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("weights", get_mem(get_fc_weights_layout(p))),
         data("bias", get_mem(get_fc_bias_layout(p))),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
@@ -3133,26 +3182,27 @@ TEST_P(fc_int8_scale_quantize_i8, basic) {
         quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
         reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_scale_quantize_i8,
-    ::testing::ValuesIn(std::vector<bc_test_params>{
-        bc_test_params{CASE_FC_U8S8_1, 2, 4},
-        bc_test_params{CASE_FC_U8S8_2, 2, 4},
-        bc_test_params{CASE_FC_U8S8_3, 2, 4},
-        bc_test_params{ CASE_FC_U8S8_3D_1, 2, 4 },
-        bc_test_params{ CASE_FC_U8S8_3D_2, 2, 4 },
-        bc_test_params{ CASE_FC_U8S8_3D_3, 2, 4 },
-        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_scale_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_FC_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_FC_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_FC_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_FC_U8S8_3D_1, 2, 4 },
+    bc_test_params{ CASE_FC_U8S8_3D_2, 2, 4 },
+    bc_test_params{ CASE_FC_U8S8_3D_3, 2, 4 },
+}));
 
 
 
 class fc_int8_scale_activation_quantize_i8 : public FCFusingTest {};
 TEST_P(fc_int8_scale_activation_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("weights", get_mem(get_fc_weights_layout(p))),
         data("bias", get_mem(get_fc_bias_layout(p))),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
@@ -3171,49 +3221,62 @@ TEST_P(fc_int8_scale_activation_quantize_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_scale_activation_quantize_i8,
-    ::testing::ValuesIn(std::vector<bc_test_params>{
-        bc_test_params{CASE_FC_U8S8_1, 2, 5},
-        bc_test_params{CASE_FC_U8S8_2, 2, 5},
-        bc_test_params{CASE_FC_U8S8_3, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_scale_activation_quantize_i8, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_FC_U8S8_1, 2, 5 },
+    bc_test_params{ CASE_FC_U8S8_2, 2, 5 },
+    bc_test_params{ CASE_FC_U8S8_3, 2, 5 },
 
-        bc_test_params{ CASE_FC_U8S8_3D_1, 2, 5 },
-        bc_test_params{ CASE_FC_U8S8_3D_2, 2, 5 },
-        bc_test_params{ CASE_FC_U8S8_3D_3, 2, 5 },
+    bc_test_params{ CASE_FC_U8S8_3D_1, 2, 5 },
+    bc_test_params{ CASE_FC_U8S8_3D_2, 2, 5 },
+    bc_test_params{ CASE_FC_U8S8_3D_3, 2, 5 },
 
-        bc_test_params{ CASE_FC_FP32_3D_1, 3, 5 },
-        bc_test_params{ CASE_FC_FP32_3D_2, 3, 5 },
-        bc_test_params{ CASE_FC_FP32_3D_3, 3, 5 },
-        }));
+    bc_test_params{ CASE_FC_FP32_3D_1, 3, 5 },
+    bc_test_params{ CASE_FC_FP32_3D_2, 3, 5 },
+    bc_test_params{ CASE_FC_FP32_3D_3, 3, 5 },
+}));
 
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- Gemm cases ------------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
-#define CASE_GEMM_3IN_FP32_1 {{1, 1, 2, 2}, {1, 1, 2, 2}, {1, 1, 2, 2}}, {1, 1, 2, 2}, tensor{1}, tensor{0}, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_3IN_FP16_1 {{1, 1, 2, 2}, {1, 1, 2, 2}, {1, 1, 2, 2}}, {1, 1, 2, 2}, tensor{1}, tensor{0}, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_GEMM_3IN_S8S8_1 {{1, 1, 2, 2}, {1, 1, 2, 2}, {1, 1, 2, 2}}, {1, 1, 2, 2}, tensor{1}, tensor{0}, data_types::i8, data_types::i8, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_3IN_S8S8_2 {{1, 2, 64, 128}, {1, 2, 256, 64}, {1, 2, 256, 128}}, {1, 2, 256, 128}, tensor{1}, tensor{0}, data_types::i8, data_types::i8, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_3IN_S8S8_3 {{1, 1, 8, 16}, {1, 1, 32, 8}, {1, 1, 32, 16}}, {1, 1, 32, 16}, tensor{1}, tensor{0}, data_types::i8, data_types::i8, data_types::i8, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_GEMM_2IN_FP32_1 {{1, 1, 2, 2}, {1, 1, 2, 2}}, {1, 1, 2, 2}, tensor{1}, tensor{0}, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_2IN_FP16_1 {{1, 1, 2, 2}, {1, 1, 2, 2}}, {1, 1, 2, 2}, tensor{1}, tensor{0}, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_GEMM_2IN_U8U8_1 {{1, 1, 2, 2}, {1, 1, 2, 2}}, {1, 1, 2, 2}, tensor{1}, tensor{0}, data_types::u8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_2IN_U8U8_2 {{1, 2, 64, 128}, {1, 2, 256, 64}}, {1, 2, 256, 128}, tensor{1}, tensor{0}, data_types::u8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_2IN_U8U8_3 {{1, 1, 16, 32}, {1, 1, 32, 16}}, {1, 1, 32, 32}, tensor{1}, tensor{0}, data_types::u8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_3IN_FP32_1 { { 1, 1, 2, 2 }, { 1, 1, 2, 2 }, { 1, 1, 2, 2 } }, { 1, 1, 2, 2 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_3IN_FP32_2 { { 1, 1, 63, 63 }, { 1, 1, 63, 63 }, { 1, 1, 63, 63 } }, { 1, 1, 63, 63 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_3IN_FP32_3 { { 1, 1, 128, 128 }, { 1, 1, 128, 128 }, { 1, 1, 128, 128 } }, { 1, 1, 128, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_3IN_FP32_4 { { 1, 2, 64, 128 }, { 1, 2, 256, 64 }, { 1, 2, 256, 128 } }, { 1, 2, 256, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_3IN_FP16_1 { { 1, 1, 2, 2 }, { 1, 1, 2, 2 }, { 1, 1, 2, 2 } }, { 1, 1, 2, 2 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_3IN_FP16_2 { { 1, 1, 31, 31 }, { 1, 1, 31, 31 }, { 1, 1, 31, 31 } }, { 1, 1, 31, 31 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_3IN_FP16_3 { { 1, 1, 64, 64 }, { 1, 1, 64, 64 }, { 1, 1, 64, 64 } }, { 1, 1, 64, 64 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_3IN_FP16_4 { { 1, 2, 64, 128 }, { 1, 2, 256, 64 }, { 1, 2, 256, 128 } }, { 1, 2, 256, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_3IN_S8S8_1 { { 1, 1, 2, 2 }, { 1, 1, 2, 2 }, { 1, 1, 2, 2 } }, { 1, 1, 2, 2 }, tensor{ 1 }, tensor{ 0 }, data_types::i8, data_types::i8, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_3IN_S8S8_2 { { 1, 2, 64, 128 }, { 1, 2, 256, 64 }, { 1, 2, 256, 128 } }, { 1, 2, 256, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::i8, data_types::i8, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_3IN_S8S8_3 { { 1, 1, 8, 16 }, { 1, 1, 32, 8 }, { 1, 1, 32, 16 } }, { 1, 1, 32, 16 }, tensor{ 1 }, tensor{ 0 }, data_types::i8, data_types::i8, data_types::i8, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_GEMM_2IN_U8S8_1 {{1, 1, 4, 2}, {1, 1, 8, 4}}, {1, 1, 8, 4}, tensor{1}, tensor{0}, data_types::u8, data_types::i8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_2IN_S8U8_1 {{1, 2, 64, 128}, {1, 2, 256, 64}}, {1, 2, 256, 128}, tensor{1}, tensor{0}, data_types::i8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_FP32_1 { { 1, 1, 2, 2 }, { 1, 1, 2, 2 } }, { 1, 1, 2, 2 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_FP32_2 { { 1, 1, 63, 63 }, { 1, 1, 63, 63 } }, { 1, 1, 63, 63 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_FP32_3 { { 1, 1, 128, 128 }, { 1, 1, 128, 128 } }, { 1, 1, 128, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_FP32_4 { { 1, 2, 64, 128 }, { 1, 2, 256, 64 } }, { 1, 2, 256, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_FP16_1 { { 1, 1, 2, 2 }, { 1, 1, 2, 2 } }, { 1, 1, 2, 2 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_2IN_FP16_2 { { 1, 1, 31, 31 }, { 1, 1, 31, 31 } }, { 1, 1, 31, 31 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_2IN_FP16_3 { { 1, 1, 64, 64 }, { 1, 1, 64, 64 } }, { 1, 1, 64, 64 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_2IN_FP16_4 { { 1, 2, 64, 128 }, { 1, 2, 256, 64 } }, { 1, 2, 256, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_2IN_U8U8_1 { { 1, 1, 2, 2 }, { 1, 1, 2, 2 } }, { 1, 1, 2, 2 }, tensor{ 1 }, tensor{ 0 }, data_types::u8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_U8U8_2 { { 1, 2, 64, 128 }, { 1, 2, 256, 64 } }, { 1, 2, 256, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::u8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_U8U8_3 { { 1, 1, 16, 32 }, { 1, 1, 32, 16 } }, { 1, 1, 32, 32 }, tensor{ 1 }, tensor{ 0 }, data_types::u8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_GEMM_ELTWISE_2IN_FP32_1 {{1, 1, 4, 4}, {1, 1, 4, 4}}, {1, 1, 4, 4}, tensor{1}, tensor{0}, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_ELTWISE_2IN_FP16_1 {{1, 1, 32, 32}, {1, 1, 32, 32}}, {1, 1, 32, 32}, tensor{1}, tensor{0}, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_GEMM_ELTWISE_2IN_U8S8_1 {{1, 1, 4, 4}, {1, 1, 4, 4}}, {1, 1, 4, 4}, tensor{1}, tensor{0}, data_types::u8, data_types::i8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GEMM_ELTWISE_2IN_S8U8_1 {{1, 1, 32, 32}, {1, 1, 32, 32}}, {1, 1, 32, 32}, tensor{1}, tensor{0}, data_types::i8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_U8S8_1 { { 1, 1, 4, 2 }, { 1, 1, 8, 4 } }, { 1, 1, 8, 4 }, tensor{ 1 }, tensor{ 0 }, data_types::u8, data_types::i8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_2IN_S8U8_1 { { 1, 2, 64, 128 }, { 1, 2, 256, 64 } }, { 1, 2, 256, 128 }, tensor{ 1 }, tensor{ 0 }, data_types::i8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+
+#define CASE_GEMM_ELTWISE_2IN_FP32_1 { { 1, 1, 4, 4 }, { 1, 1, 4, 4 } }, { 1, 1, 4, 4 }, tensor{ 1 }, tensor{ 0 }, data_types::f32, data_types::f32, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_ELTWISE_2IN_FP16_1 { { 1, 1, 32, 32 }, { 1, 1, 32, 32 } }, { 1, 1, 32, 32 }, tensor{ 1 }, tensor{ 0 }, data_types::f16, data_types::f16, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GEMM_ELTWISE_2IN_U8S8_1 { { 1, 1, 4, 4 }, { 1, 1, 4, 4 } }, { 1, 1, 4, 4 }, tensor{ 1 }, tensor{ 0 }, data_types::u8, data_types::i8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GEMM_ELTWISE_2IN_S8U8_1 { { 1, 1, 32, 32 }, { 1, 1, 32, 32 } }, { 1, 1, 32, 32 }, tensor{ 1 }, tensor{ 0 }, data_types::i8, data_types::u8, data_types::u8, format::bfyx, data_types::f32, format::bfyx
 
 class gemm_3in_quantize_i8 : public GemmFusingTest {};
 TEST_P(gemm_3in_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         input_layout("input2", get_input_layout(p, 2)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
@@ -3229,19 +3292,25 @@ TEST_P(gemm_3in_quantize_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_3in_quantize_i8,
-    ::testing::ValuesIn(std::vector<gemm_test_params>{
-                        gemm_test_params{ CASE_GEMM_3IN_FP16_1, 4, 5 },
-                        gemm_test_params{ CASE_GEMM_3IN_S8S8_1, 4, 5 },
-                        gemm_test_params{ CASE_GEMM_3IN_S8S8_2, 4, 5 },
-                        gemm_test_params{ CASE_GEMM_3IN_S8S8_3, 4, 5 },
-                      //gemm_test_params{ CASE_GEMM_3IN_FP32_1, 4, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_3in_quantize_i8, ::testing::ValuesIn(std::vector<gemm_test_params>{
+    gemm_test_params{ CASE_GEMM_3IN_FP16_1, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_FP16_2, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_FP16_3, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_FP16_4, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_FP32_1, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_FP32_2, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_FP32_3, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_FP32_4, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_S8S8_1, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_S8S8_2, 4, 5 },
+    gemm_test_params{ CASE_GEMM_3IN_S8S8_3, 4, 5 },
 }));
 
 class gemm_2in_quantize_u8 : public GemmFusingTest {};
 TEST_P(gemm_2in_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("in_lo", get_mem(get_per_channel_layout(p), 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
@@ -3256,19 +3325,25 @@ TEST_P(gemm_2in_quantize_u8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_quantize_u8,
-    ::testing::ValuesIn(std::vector<gemm_test_params>{
-                        gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_U8U8_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_U8U8_2, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_U8U8_3, 3, 4 },
-                      //gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 4 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_quantize_u8, ::testing::ValuesIn(std::vector<gemm_test_params>{
+    gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_3, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_4, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_3, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_4, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_U8U8_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_U8U8_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_U8U8_3, 3, 4 },
 }));
 
 class gemm_2in_quantize_float_in : public GemmFusingTest {};
 TEST_P(gemm_2in_quantize_float_in, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("in_lo", get_mem(get_per_channel_layout(p), 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
@@ -3280,24 +3355,30 @@ TEST_P(gemm_2in_quantize_float_in, basic) {
     );
 
     implementation_desc gemm_impl = { format::bfyx, "gemm_tiled_opt" };
-    bo_fused.set_option(build_option::force_implementations({ {"gemm_prim", gemm_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "gemm_prim", gemm_impl } }));
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_quantize_float_in,
-    ::testing::ValuesIn(std::vector<gemm_test_params>{
-                        gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP16_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP32_1, 3, 4 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_quantize_float_in, ::testing::ValuesIn(std::vector<gemm_test_params>{
+    gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_3, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_4, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_3, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_4, 3, 4 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP16_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP32_1, 3, 4 },
 }));
 
 class gemm_2in_scale : public GemmFusingTest {};
 TEST_P(gemm_2in_scale, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
         gemm("gemm_prim", { "input0", "input1" }, data_types::f32),
@@ -3311,11 +3392,12 @@ TEST_P(gemm_2in_scale, basic) {
 
 TEST_P(gemm_2in_scale, fp16_scale_out) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
         gemm("gemm_prim", { "input0", "input1" }, data_types::f32),
-        scale("scale", "gemm_prim", "scale_data", optional_data_type{data_types::f16}),
+        scale("scale", "gemm_prim", "scale_data", optional_data_type{ data_types::f16 }),
         reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
 
@@ -3323,19 +3405,25 @@ TEST_P(gemm_2in_scale, fp16_scale_out) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_scale,
-    ::testing::ValuesIn(std::vector<gemm_test_params>{
-                        gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_U8U8_1, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_U8U8_2, 3, 4 },
-                        gemm_test_params{ CASE_GEMM_2IN_U8U8_3, 3, 4 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_scale, ::testing::ValuesIn(std::vector<gemm_test_params>{
+    gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_3, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_4, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_3, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_4, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_U8U8_1, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_U8U8_2, 3, 4 },
+    gemm_test_params{ CASE_GEMM_2IN_U8U8_3, 3, 4 },
 }));
 
 class gemm_2in_act_scale_quantize_i8 : public GemmFusingTest {};
 TEST_P(gemm_2in_act_scale_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
@@ -3353,18 +3441,24 @@ TEST_P(gemm_2in_act_scale_quantize_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_act_scale_quantize_i8,
-    ::testing::ValuesIn(std::vector<gemm_test_params>{
-                        gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 6 },
-                        gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 6 },
-                        gemm_test_params{ CASE_GEMM_2IN_U8S8_1, 3, 6 },
-                        gemm_test_params{ CASE_GEMM_2IN_S8U8_1, 3, 6 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_act_scale_quantize_i8, ::testing::ValuesIn(std::vector<gemm_test_params>{
+    gemm_test_params{ CASE_GEMM_2IN_FP32_1, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_2, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_3, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_FP32_4, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_1, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_2, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_3, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_FP16_4, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_U8S8_1, 3, 6 },
+    gemm_test_params{ CASE_GEMM_2IN_S8U8_1, 3, 6 },
 }));
 
 class gemm_2in_act_scale_quantize_eltwise_i8 : public GemmFusingTest {};
 TEST_P(gemm_2in_act_scale_quantize_eltwise_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
@@ -3376,7 +3470,7 @@ TEST_P(gemm_2in_act_scale_quantize_eltwise_i8, basic) {
         activation("activation", "gemm_prim", activation_func::exp),
         scale("scale", "activation", "scale_data"),
         quantize("quantize", "scale", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-        eltwise("sum", { "quantize", "eltwise_data"}, eltwise_mode::sum,  data_types::f32),
+        eltwise("sum", { "quantize", "eltwise_data" }, eltwise_mode::sum,  data_types::f32),
         reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
 
@@ -3384,25 +3478,25 @@ TEST_P(gemm_2in_act_scale_quantize_eltwise_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_act_scale_quantize_eltwise_i8,
-    ::testing::ValuesIn(std::vector<gemm_test_params>{
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP32_1, 3, 7 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP16_1, 3, 7 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_U8S8_1, 3, 7 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_S8U8_1, 3, 7 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_act_scale_quantize_eltwise_i8, ::testing::ValuesIn(std::vector<gemm_test_params>{
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP32_1, 3, 7 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP16_1, 3, 7 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_U8S8_1, 3, 7 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_S8U8_1, 3, 7 },
 }));
 
 class gemm_2in_act_scale_eltwise : public GemmFusingTest {};
 TEST_P(gemm_2in_act_scale_eltwise, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / p.kernel.count() / 255)),
         data("eltwise_data", get_mem(get_output_layout(p))),
         gemm("gemm_prim", { "input0", "input1" }, data_types::f32),
         scale("scale", "gemm_prim", "scale_data"),
         activation("activation", "scale", activation_func::negative),
-        eltwise("sum", { "activation", "eltwise_data"}, eltwise_mode::sum,  data_types::f32),
+        eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum,  data_types::f32),
         reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
 
@@ -3412,14 +3506,15 @@ TEST_P(gemm_2in_act_scale_eltwise, basic) {
 
 TEST_P(gemm_2in_act_scale_eltwise, broadcast_eltwise) {
     auto p = GetParam();
-    create_topologies(input_layout("input0", get_input_layout(p, 0)),
+    create_topologies(
+        input_layout("input0", get_input_layout(p, 0)),
         input_layout("input1", get_input_layout(p, 1)),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / p.kernel.count() / 255)),
         data("eltwise_data", get_mem(get_single_element_layout(p))),
         gemm("gemm_prim", { "input0", "input1" }, data_types::f32),
         scale("scale", "gemm_prim", "scale_data"),
         activation("activation", "scale", activation_func::negative),
-        eltwise("sum", { "activation", "eltwise_data"}, eltwise_mode::sum,  data_types::f32),
+        eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum,  data_types::f32),
         reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
 
@@ -3427,57 +3522,58 @@ TEST_P(gemm_2in_act_scale_eltwise, broadcast_eltwise) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_act_scale_eltwise,
-    ::testing::ValuesIn(std::vector<gemm_test_params>{
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP32_1, 3, 6 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP16_1, 3, 6 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_U8S8_1, 3, 6 },
-                        gemm_test_params{ CASE_GEMM_ELTWISE_2IN_S8U8_1, 3, 6 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gemm_2in_act_scale_eltwise, ::testing::ValuesIn(std::vector<gemm_test_params>{
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP32_1, 3, 6 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_FP16_1, 3, 6 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_U8S8_1, 3, 6 },
+    gemm_test_params{ CASE_GEMM_ELTWISE_2IN_S8U8_1, 3, 6 },
 }));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- Resample cases --------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
-#define CASE_RESAMPLE_FP32_1 {1, 15, 4, 5}, {1, 15, 2, 3}, data_types::f32, format::bfyx, resample_type::nearest, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_FP32_2 {1, 15, 4, 5}, {1, 15, 2, 3}, data_types::f32, format::bfyx, resample_type::bilinear, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_FP32_3 {1, 15, 4, 5}, {1, 15, 2, 3}, data_types::f32, format::bfyx, resample_type::caffe_bilinear, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_FP32_4 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f32, format::bfyx, resample_type::nearest, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_FP32_5 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f32, format::bfyx, resample_type::bilinear, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_FP32_6 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f32, format::bfyx, resample_type::caffe_bilinear, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_FP32_7 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, data_types::f32, format::bfzyx, resample_type::nearest, data_types::f32, format::bfzyx
-#define CASE_RESAMPLE_FP32_8 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, data_types::f32, format::bfzyx, resample_type::caffe_bilinear, data_types::f32, format::bfzyx
-#define CASE_RESAMPLE_FP32_9 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f32, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_FP32_10 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f32, format::b_fs_yx_fsv16, resample_type::caffe_bilinear, data_types::f32, format::bfyx
 
-#define CASE_RESAMPLE_FP16_1 {1, 15, 4, 5}, {1, 15, 2, 3}, data_types::f16, format::bfyx, resample_type::nearest, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_2 {1, 15, 4, 5}, {1, 15, 2, 3}, data_types::f16, format::bfyx, resample_type::bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_3 {1, 15, 4, 5}, {1, 15, 2, 3}, data_types::f16, format::bfyx, resample_type::caffe_bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_4 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f16, format::bfyx, resample_type::nearest, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_5 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f16, format::bfyx, resample_type::bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_6 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f16, format::bfyx, resample_type::caffe_bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_7 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, data_types::f16, format::bfzyx, resample_type::nearest, data_types::f16, format::bfzyx
-#define CASE_RESAMPLE_FP16_8 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, data_types::f16, format::bfzyx, resample_type::caffe_bilinear, data_types::f16, format::bfzyx
-#define CASE_RESAMPLE_FP16_9 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f16, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_10 {2, 32, 4, 5}, {2, 32, 7, 8}, data_types::f16, format::fs_b_yx_fsv32, resample_type::bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_11 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f16, format::b_fs_yx_fsv16, resample_type::caffe_bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_12 {2, 32, 4, 5}, {2, 32, 7, 8}, data_types::f16, format::fs_b_yx_fsv32, resample_type::caffe_bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_13 {1, 16, 4, 5}, {1, 16, 7, 8}, data_types::f16, format::b_fs_yx_fsv16, resample_type::caffe_bilinear, data_types::f16, format::bfyx
-#define CASE_RESAMPLE_FP16_14 {1, 32, 4, 5}, {1, 32, 2, 3}, data_types::f16, format::fs_b_yx_fsv32, resample_type::caffe_bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP32_1 { 1, 15, 4, 5 }, { 1, 15, 2, 3 }, data_types::f32, format::bfyx, resample_type::nearest, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP32_2 { 1, 15, 4, 5 }, { 1, 15, 2, 3 }, data_types::f32, format::bfyx, resample_type::bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP32_3 { 1, 15, 4, 5 }, { 1, 15, 2, 3 }, data_types::f32, format::bfyx, resample_type::caffe_bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP32_4 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f32, format::bfyx, resample_type::nearest, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP32_5 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f32, format::bfyx, resample_type::bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP32_6 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f32, format::bfyx, resample_type::caffe_bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP32_7 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, data_types::f32, format::bfzyx, resample_type::nearest, data_types::f32, format::bfzyx
+#define CASE_RESAMPLE_FP32_8 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, data_types::f32, format::bfzyx, resample_type::caffe_bilinear, data_types::f32, format::bfzyx
+#define CASE_RESAMPLE_FP32_9 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f32, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP32_10 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f32, format::b_fs_yx_fsv16, resample_type::caffe_bilinear, data_types::f32, format::bfyx
 
-#define CASE_RESAMPLE_I8_1 {1, 16, 4, 5}, {1, 16, 2, 3}, data_types::i8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_I8_2 {2, 32, 4, 5}, {2, 32, 2, 3}, data_types::i8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_I8_3 {1, 16, 4, 5}, {1, 16, 2, 3}, data_types::i8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_I8_4 {2, 32, 4, 5}, {2, 32, 2, 3}, data_types::i8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_FP16_1 { 1, 15, 4, 5 }, { 1, 15, 2, 3 }, data_types::f16, format::bfyx, resample_type::nearest, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_2 { 1, 15, 4, 5 }, { 1, 15, 2, 3 }, data_types::f16, format::bfyx, resample_type::bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_3 { 1, 15, 4, 5 }, { 1, 15, 2, 3 }, data_types::f16, format::bfyx, resample_type::caffe_bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_4 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f16, format::bfyx, resample_type::nearest, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_5 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f16, format::bfyx, resample_type::bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_6 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f16, format::bfyx, resample_type::caffe_bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_7 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, data_types::f16, format::bfzyx, resample_type::nearest, data_types::f16, format::bfzyx
+#define CASE_RESAMPLE_FP16_8 { 1, 16, 4, 5, 4 }, { 1, 16, 2, 3, 2 }, data_types::f16, format::bfzyx, resample_type::caffe_bilinear, data_types::f16, format::bfzyx
+#define CASE_RESAMPLE_FP16_9 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f16, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_10 { 2, 32, 4, 5 }, { 2, 32, 7, 8 }, data_types::f16, format::fs_b_yx_fsv32, resample_type::bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_11 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f16, format::b_fs_yx_fsv16, resample_type::caffe_bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_12 { 2, 32, 4, 5 }, { 2, 32, 7, 8 }, data_types::f16, format::fs_b_yx_fsv32, resample_type::caffe_bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_13 { 1, 16, 4, 5 }, { 1, 16, 7, 8 }, data_types::f16, format::b_fs_yx_fsv16, resample_type::caffe_bilinear, data_types::f16, format::bfyx
+#define CASE_RESAMPLE_FP16_14 { 1, 32, 4, 5 }, { 1, 32, 2, 3 }, data_types::f16, format::fs_b_yx_fsv32, resample_type::caffe_bilinear, data_types::f16, format::bfyx
 
-#define CASE_RESAMPLE_U8_1 {1, 16, 4, 5}, {1, 16, 2, 3}, data_types::u8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_U8_2 {2, 32, 4, 5}, {2, 32, 2, 3}, data_types::u8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_U8_3 {1, 16, 4, 5}, {1, 16, 2, 3}, data_types::u8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
-#define CASE_RESAMPLE_U8_4 {2, 32, 4, 5}, {2, 32, 2, 3}, data_types::u8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_I8_1 { 1, 16, 4, 5 }, { 1, 16, 2, 3 }, data_types::i8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_I8_2 { 2, 32, 4, 5 }, { 2, 32, 2, 3 }, data_types::i8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_I8_3 { 1, 16, 4, 5 }, { 1, 16, 2, 3 }, data_types::i8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_I8_4 { 2, 32, 4, 5 }, { 2, 32, 2, 3 }, data_types::i8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
+
+#define CASE_RESAMPLE_U8_1 { 1, 16, 4, 5 }, { 1, 16, 2, 3 }, data_types::u8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_U8_2 { 2, 32, 4, 5 }, { 2, 32, 2, 3 }, data_types::u8, format::b_fs_yx_fsv16, resample_type::nearest, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_U8_3 { 1, 16, 4, 5 }, { 1, 16, 2, 3 }, data_types::u8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
+#define CASE_RESAMPLE_U8_4 { 2, 32, 4, 5 }, { 2, 32, 2, 3 }, data_types::u8, format::b_fs_yx_fsv16, resample_type::bilinear, data_types::f32, format::bfyx
 
 class resample_quantize : public ResamplePrimitiveFusingTest {};
 TEST_P(resample_quantize, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
         data("out_lo", get_mem(get_single_element_layout(p), -127)),
@@ -3491,41 +3587,41 @@ TEST_P(resample_quantize, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_quantize,
-    ::testing::ValuesIn(std::vector<resample_test_params>{
-                        resample_test_params{ CASE_RESAMPLE_FP32_1, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_2, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_3, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_4, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_5, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_6, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_7, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_8, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_9, 2, 3 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_10, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_quantize, ::testing::ValuesIn(std::vector<resample_test_params>{
+    resample_test_params{ CASE_RESAMPLE_FP32_1, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_2, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_3, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_4, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_5, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_6, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_7, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_8, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_9, 2, 3 },
+    resample_test_params{ CASE_RESAMPLE_FP32_10, 2, 3 },
 
-                        // FQ can't be fused to FP16 primitive for now
-                        // resample_test_params{ CASE_RESAMPLE_FP16_1, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_2, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_3, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_4, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_5, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_6, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_7, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_8, 2, 3 },
-                        // resample_test_params{ CASE_RESAMPLE_FP16_9, 2, 3 },
+    // FQ can't be fused to FP16 primitive for now
+    // resample_test_params{ CASE_RESAMPLE_FP16_1, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_2, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_3, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_4, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_5, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_6, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_7, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_8, 2, 3 },
+    // resample_test_params{ CASE_RESAMPLE_FP16_9, 2, 3 },
 }));
 
 class resample_scale_activation_eltwise : public ResamplePrimitiveFusingTest {};
 TEST_P(resample_scale_activation_eltwise, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
         data("eltwise_data", get_mem(get_output_layout(p), -10, 10)),
         resample("resample_prim", "input", p.out_shape, p.in_shape.feature[0], p.type),
         scale("scale", "resample_prim", "scale_data"),
         activation("activation", "scale", activation_func::abs),
-        eltwise("eltwise", { "activation", "eltwise_data"}, eltwise_mode::sum),
+        eltwise("eltwise", { "activation", "eltwise_data" }, eltwise_mode::sum),
         reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
 
@@ -3533,43 +3629,42 @@ TEST_P(resample_scale_activation_eltwise, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_scale_activation_eltwise,
-    ::testing::ValuesIn(std::vector<resample_test_params>{
-                        resample_test_params{ CASE_RESAMPLE_FP32_1, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_2, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_3, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_4, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_5, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_6, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_7, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_8, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_9, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_10, 2, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_scale_activation_eltwise, ::testing::ValuesIn(std::vector<resample_test_params>{
+    resample_test_params{ CASE_RESAMPLE_FP32_1, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_2, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_3, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_4, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_5, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_6, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_7, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_8, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_9, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP32_10, 2, 5 },
 
-                        resample_test_params{ CASE_RESAMPLE_FP16_1, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_2, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_3, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_4, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_5, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_6, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_7, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_8, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_9, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_10, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_11, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_12, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_13, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_14, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_1, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_2, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_3, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_4, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_5, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_6, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_7, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_8, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_9, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_10, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_11, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_12, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_13, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_FP16_14, 2, 5 },
 
-                        resample_test_params{ CASE_RESAMPLE_I8_1, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_I8_2, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_I8_3, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_I8_4, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_I8_1, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_I8_2, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_I8_3, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_I8_4, 2, 5 },
 
-                        resample_test_params{ CASE_RESAMPLE_U8_1, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_U8_2, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_U8_3, 2, 5 },
-                        resample_test_params{ CASE_RESAMPLE_U8_4, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_U8_1, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_U8_2, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_U8_3, 2, 5 },
+    resample_test_params{ CASE_RESAMPLE_U8_4, 2, 5 },
 }));
 
 class resample_quantize_concat : public ResamplePrimitiveFusingTest {};
@@ -3597,39 +3692,38 @@ TEST_P(resample_quantize_concat, along_f) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_quantize_concat,
-    ::testing::ValuesIn(std::vector<resample_test_params>{
-                        resample_test_params{ CASE_RESAMPLE_FP32_1, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_2, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_3, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_4, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_5, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_6, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_7, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_8, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_9, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_10, 3, 6 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_quantize_concat, ::testing::ValuesIn(std::vector<resample_test_params>{
+    resample_test_params{ CASE_RESAMPLE_FP32_1, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_2, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_4, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_5, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_6, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_7, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_8, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_9, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_10, 3, 6 },
 
-                        resample_test_params{ CASE_RESAMPLE_FP16_1, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_2, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_3, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_4, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_5, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_6, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_7, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_8, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_9, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_10, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_11, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_12, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_13, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_14, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_1, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_2, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_4, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_5, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_6, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_7, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_8, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_9, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_10, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_11, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_12, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_13, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_14, 3, 6 },
 
-                        resample_test_params{ CASE_RESAMPLE_I8_3, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_I8_4, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_I8_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_I8_4, 3, 6 },
 
-                        resample_test_params{ CASE_RESAMPLE_U8_3, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_U8_4, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_U8_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_U8_4, 3, 6 },
 }));
 
 class resample_scale_concat : public ResamplePrimitiveFusingTest {};
@@ -3653,48 +3747,48 @@ TEST_P(resample_scale_concat, along_f) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_scale_concat,
-    ::testing::ValuesIn(std::vector<resample_test_params>{
-                        resample_test_params{ CASE_RESAMPLE_FP32_1, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_2, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_3, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_4, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_5, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_6, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_7, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_8, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_9, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP32_10, 3, 6 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, resample_scale_concat, ::testing::ValuesIn(std::vector<resample_test_params>{
+    resample_test_params{ CASE_RESAMPLE_FP32_1, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_2, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_4, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_5, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_6, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_7, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_8, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_9, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP32_10, 3, 6 },
 
-                        resample_test_params{ CASE_RESAMPLE_FP16_1, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_2, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_3, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_4, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_5, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_6, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_7, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_8, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_9, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_10, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_11, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_12, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_13, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_FP16_14, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_1, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_2, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_4, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_5, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_6, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_7, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_8, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_9, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_10, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_11, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_12, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_13, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_FP16_14, 3, 6 },
 
-                        resample_test_params{ CASE_RESAMPLE_I8_1, 3, 6},
-                        resample_test_params{ CASE_RESAMPLE_I8_2, 3, 6},
-                        resample_test_params{ CASE_RESAMPLE_I8_3, 3, 6},
-                        resample_test_params{ CASE_RESAMPLE_I8_4, 3, 6},
+    resample_test_params{ CASE_RESAMPLE_I8_1, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_I8_2, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_I8_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_I8_4, 3, 6 },
 
-                        resample_test_params{ CASE_RESAMPLE_U8_1, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_U8_2, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_U8_3, 3, 6 },
-                        resample_test_params{ CASE_RESAMPLE_U8_4, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_U8_1, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_U8_2, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_U8_3, 3, 6 },
+    resample_test_params{ CASE_RESAMPLE_U8_4, 3, 6 },
 }));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* --------------------------------------- MVN cases --------------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct mvn_test_params {
     tensor input_size;
     tensor elwise_size;
@@ -3708,39 +3802,39 @@ struct mvn_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_MVN_F32_1      {1, 16, 8, 8},    {1, 16, 8, 8},    data_types::f32, format::bfyx, false, true, data_types::f32, format::bfyx
-#define CASE_MVN_F32_2      {2, 16, 8, 8},    {2, 16, 8, 8},    data_types::f32, format::bfyx, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_3D_F32_1   {1, 16, 8, 8, 8}, {1, 16, 8, 8, 8}, data_types::f32, format::bfzyx, false, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_F32_2   {2, 16, 8, 8, 8}, {2, 16, 8, 8, 8}, data_types::f32, format::bfzyx, true, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_F32_3   {2, 8, 4, 4, 4},  {2, 8, 1, 1, 1},  data_types::f32, format::bfzyx, true, true, data_types::f32, format::bfzyx
-#define CASE_MVN_F16_1      {1, 16, 8, 8},    {1, 16, 8, 8},    data_types::f16, format::bfyx, false, true, data_types::f16, format::bfyx
-#define CASE_MVN_F16_2      {2, 16, 8, 8},    {2, 16, 8, 8},    data_types::f16, format::bfyx, true, true, data_types::f16, format::bfyx
-#define CASE_MVN_3D_F16_1   {1, 16, 8, 8, 8}, {1, 16, 8, 8, 8}, data_types::f16, format::bfzyx, false, true, data_types::f16, format::bfzyx
-#define CASE_MVN_3D_F16_2   {2, 16, 8, 8, 8}, {2, 16, 8, 8, 8}, data_types::f16, format::bfzyx, true, true, data_types::f16, format::bfzyx
-#define CASE_MVN_I8_1       {1, 16, 8, 8},    {1, 16, 8, 8},    data_types::i8, format::bfyx, false, true, data_types::f32, format::bfyx
-#define CASE_MVN_I8_2       {2, 16, 8, 8},    {2, 16, 8, 8},    data_types::i8, format::bfyx, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_I8_3       {1, 16, 8, 8},    {1, 16, 8, 8},    data_types::i8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
-#define CASE_MVN_I8_4       {2, 16, 8, 8},    {2, 16, 8, 8},    data_types::i8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_I8_5       {2, 16, 8, 8},    {1, 1, 1, 8},     data_types::i8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
-#define CASE_MVN_I8_6       {2, 16, 8, 8},    {1, 1, 1, 1},     data_types::i8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_I8_7       {2, 16, 1, 8},    {1, 1, 8, 1},     data_types::i8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_3D_I8_1    {1, 16, 8, 8, 8}, {1, 16, 8, 8, 8}, data_types::i8, format::bfzyx, false, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_I8_2    {2, 16, 8, 8, 8}, {2, 16, 8, 8, 8}, data_types::i8, format::bfzyx, true, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_I8_3    {2, 16, 8, 8, 8}, {2, 1, 8, 8, 1},  data_types::i8, format::bfzyx, true, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_I8_4    {2, 16, 8, 8, 8}, {2, 16, 8, 1, 8}, data_types::i8, format::bfzyx, false, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_I8_5    {2, 2, 1, 2, 1},  {2, 2, 2, 2, 2},  data_types::i8, format::bfzyx, false, true, data_types::f32, format::bfzyx
-#define CASE_MVN_U8_1       {1, 16, 8, 8},    {1, 16, 8, 8},    data_types::u8, format::bfyx, false, true, data_types::f32, format::bfyx
-#define CASE_MVN_U8_2       {2, 16, 8, 8},    {2, 16, 8, 8},    data_types::u8, format::bfyx, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_U8_3       {1, 16, 8, 8},    {1, 16, 8, 8},    data_types::u8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
-#define CASE_MVN_U8_4       {2, 16, 8, 8},    {2, 16, 8, 8},    data_types::u8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_U8_5       {2, 16, 8, 8},    {2, 1, 8, 8},     data_types::u8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
-#define CASE_MVN_U8_6       {2, 16, 8, 8},    {1, 1, 1, 8},     data_types::u8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_U8_7       {1, 16, 16, 1},   {1, 16, 1, 16},   data_types::u8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
-#define CASE_MVN_3D_U8_1    {1, 16, 8, 8, 8}, {1, 16, 8, 8, 8}, data_types::u8, format::bfzyx, false, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_U8_2    {2, 16, 8, 8, 8}, {2, 16, 8, 8, 8}, data_types::u8, format::bfzyx, true, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_U8_3    {2, 16, 8, 8, 8}, {2, 1, 1, 1, 1},  data_types::u8, format::bfzyx, true, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_U8_4    {2, 16, 8, 8, 8}, {1, 1, 1, 1, 1},  data_types::u8, format::bfzyx, false, true, data_types::f32, format::bfzyx
-#define CASE_MVN_3D_U8_5    {2, 16, 1, 8, 8}, {1, 1, 8, 1, 1},  data_types::u8, format::bfzyx, false, true, data_types::f32, format::bfzyx
+#define CASE_MVN_F32_1      { 1, 16, 8, 8 },    { 1, 16, 8, 8 },    data_types::f32, format::bfyx, false, true, data_types::f32, format::bfyx
+#define CASE_MVN_F32_2      { 2, 16, 8, 8 },    { 2, 16, 8, 8 },    data_types::f32, format::bfyx, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_3D_F32_1   { 1, 16, 8, 8, 8 }, { 1, 16, 8, 8, 8 }, data_types::f32, format::bfzyx, false, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_F32_2   { 2, 16, 8, 8, 8 }, { 2, 16, 8, 8, 8 }, data_types::f32, format::bfzyx, true, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_F32_3   { 2, 8, 4, 4, 4 },  { 2, 8, 1, 1, 1 },  data_types::f32, format::bfzyx, true, true, data_types::f32, format::bfzyx
+#define CASE_MVN_F16_1      { 1, 16, 8, 8 },    { 1, 16, 8, 8 },    data_types::f16, format::bfyx, false, true, data_types::f16, format::bfyx
+#define CASE_MVN_F16_2      { 2, 16, 8, 8 },    { 2, 16, 8, 8 },    data_types::f16, format::bfyx, true, true, data_types::f16, format::bfyx
+#define CASE_MVN_3D_F16_1   { 1, 16, 8, 8, 8 }, { 1, 16, 8, 8, 8 }, data_types::f16, format::bfzyx, false, true, data_types::f16, format::bfzyx
+#define CASE_MVN_3D_F16_2   { 2, 16, 8, 8, 8 }, { 2, 16, 8, 8, 8 }, data_types::f16, format::bfzyx, true, true, data_types::f16, format::bfzyx
+#define CASE_MVN_I8_1       { 1, 16, 8, 8 },    { 1, 16, 8, 8 },    data_types::i8, format::bfyx, false, true, data_types::f32, format::bfyx
+#define CASE_MVN_I8_2       { 2, 16, 8, 8 },    { 2, 16, 8, 8 },    data_types::i8, format::bfyx, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_I8_3       { 1, 16, 8, 8 },    { 1, 16, 8, 8 },    data_types::i8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
+#define CASE_MVN_I8_4       { 2, 16, 8, 8 },    { 2, 16, 8, 8 },    data_types::i8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_I8_5       { 2, 16, 8, 8 },    { 1, 1, 1, 8 },     data_types::i8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
+#define CASE_MVN_I8_6       { 2, 16, 8, 8 },    { 1, 1, 1, 1 },     data_types::i8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_I8_7       { 2, 16, 1, 8 },    { 1, 1, 8, 1 },     data_types::i8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_3D_I8_1    { 1, 16, 8, 8, 8 }, { 1, 16, 8, 8, 8 }, data_types::i8, format::bfzyx, false, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_I8_2    { 2, 16, 8, 8, 8 }, { 2, 16, 8, 8, 8 }, data_types::i8, format::bfzyx, true, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_I8_3    { 2, 16, 8, 8, 8 }, { 2, 1, 8, 8, 1 },  data_types::i8, format::bfzyx, true, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_I8_4    { 2, 16, 8, 8, 8 }, { 2, 16, 8, 1, 8 }, data_types::i8, format::bfzyx, false, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_I8_5    { 2, 2, 1, 2, 1 },  { 2, 2, 2, 2, 2 },  data_types::i8, format::bfzyx, false, true, data_types::f32, format::bfzyx
+#define CASE_MVN_U8_1       { 1, 16, 8, 8 },    { 1, 16, 8, 8 },    data_types::u8, format::bfyx, false, true, data_types::f32, format::bfyx
+#define CASE_MVN_U8_2       { 2, 16, 8, 8 },    { 2, 16, 8, 8 },    data_types::u8, format::bfyx, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_U8_3       { 1, 16, 8, 8 },    { 1, 16, 8, 8 },    data_types::u8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
+#define CASE_MVN_U8_4       { 2, 16, 8, 8 },    { 2, 16, 8, 8 },    data_types::u8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_U8_5       { 2, 16, 8, 8 },    { 2, 1, 8, 8 },     data_types::u8, format::b_fs_yx_fsv16, false, true, data_types::f32, format::bfyx
+#define CASE_MVN_U8_6       { 2, 16, 8, 8 },    { 1, 1, 1, 8 },     data_types::u8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_U8_7       { 1, 16, 16, 1 },   { 1, 16, 1, 16 },   data_types::u8, format::b_fs_yx_fsv16, true, true, data_types::f32, format::bfyx
+#define CASE_MVN_3D_U8_1    { 1, 16, 8, 8, 8 }, { 1, 16, 8, 8, 8 }, data_types::u8, format::bfzyx, false, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_U8_2    { 2, 16, 8, 8, 8 }, { 2, 16, 8, 8, 8 }, data_types::u8, format::bfzyx, true, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_U8_3    { 2, 16, 8, 8, 8 }, { 2, 1, 1, 1, 1 },  data_types::u8, format::bfzyx, true, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_U8_4    { 2, 16, 8, 8, 8 }, { 1, 1, 1, 1, 1 },  data_types::u8, format::bfzyx, false, true, data_types::f32, format::bfzyx
+#define CASE_MVN_3D_U8_5    { 2, 16, 1, 8, 8 }, { 1, 1, 8, 1, 1 },  data_types::u8, format::bfzyx, false, true, data_types::f32, format::bfzyx
 
 class MVNFusingTest : public ::BaseFusingTest<mvn_test_params> {
 public:
@@ -3761,7 +3855,7 @@ public:
     }
 
     layout get_per_channel_layout(mvn_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.input_size.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.input_size.feature[0], 1, 1 } };
     }
 };
 
@@ -3779,28 +3873,27 @@ TEST_P(mvn_activation, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_activation,
-    ::testing::ValuesIn(std::vector<mvn_test_params>{
-                        mvn_test_params{ CASE_MVN_F32_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_F32_2, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_F32_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_F32_2, 2, 3 },
-                        mvn_test_params{ CASE_MVN_F16_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_F16_2, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_F16_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_F16_2, 2, 3 },
-                        mvn_test_params{ CASE_MVN_I8_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_I8_2, 2, 3 },
-                        mvn_test_params{ CASE_MVN_I8_3, 2, 3 },
-                        mvn_test_params{ CASE_MVN_I8_4, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_I8_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_I8_2, 2, 3 },
-                        mvn_test_params{ CASE_MVN_U8_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_U8_2, 2, 3 },
-                        mvn_test_params{ CASE_MVN_U8_3, 2, 3 },
-                        mvn_test_params{ CASE_MVN_U8_4, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_U8_1, 2, 3 },
-                        mvn_test_params{ CASE_MVN_3D_U8_2, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_activation, ::testing::ValuesIn(std::vector<mvn_test_params>{
+    mvn_test_params{ CASE_MVN_F32_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_F32_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_F32_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_F32_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_F16_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_F16_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_F16_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_F16_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_I8_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_I8_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_I8_3, 2, 3 },
+    mvn_test_params{ CASE_MVN_I8_4, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_I8_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_I8_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_3, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_4, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_U8_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_U8_2, 2, 3 },
 }));
 
 class mvn_scale_quantize_i8 : public MVNFusingTest {};
@@ -3823,29 +3916,28 @@ TEST_P(mvn_scale_quantize_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_scale_quantize_i8,
-    ::testing::ValuesIn(std::vector<mvn_test_params>{
-        // Full fusing for fp input not supported yet, it may lead to output padding and non-optimal kernel
-        // mvn_test_params{ CASE_MVN_F32_1, 2, 4 },
-        // mvn_test_params{ CASE_MVN_F32_2, 2, 4 },
-        // mvn_test_params{ CASE_MVN_3D_F32_1, 2, 4 },
-        // mvn_test_params{ CASE_MVN_3D_F32_2, 2, 4 },
-        // mvn_test_params{ CASE_MVN_F16_1, 2, 4 },
-        // mvn_test_params{ CASE_MVN_F16_2, 2, 4 },
-        // mvn_test_params{ CASE_MVN_3D_F16_1, 2, 4 },
-        // mvn_test_params{ CASE_MVN_3D_F16_2, 2, 4 },
-        mvn_test_params{ CASE_MVN_I8_1, 2, 4 },
-        mvn_test_params{ CASE_MVN_I8_2, 2, 4 },
-        mvn_test_params{ CASE_MVN_I8_3, 2, 4 },
-        mvn_test_params{ CASE_MVN_I8_4, 2, 4 },
-        mvn_test_params{ CASE_MVN_3D_I8_1, 2, 4 },
-        mvn_test_params{ CASE_MVN_3D_I8_2, 2, 4 },
-        mvn_test_params{ CASE_MVN_U8_1, 2, 4 },
-        mvn_test_params{ CASE_MVN_U8_2, 2, 4 },
-        mvn_test_params{ CASE_MVN_U8_3, 2, 4 },
-        mvn_test_params{ CASE_MVN_U8_4, 2, 4 },
-        mvn_test_params{ CASE_MVN_3D_U8_1, 2, 4 },
-        mvn_test_params{ CASE_MVN_3D_U8_2, 2, 4 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_scale_quantize_i8, ::testing::ValuesIn(std::vector<mvn_test_params>{
+    // Full fusing for fp input not supported yet, it may lead to output padding and non-optimal kernel
+    // mvn_test_params{ CASE_MVN_F32_1, 2, 4 },
+    // mvn_test_params{ CASE_MVN_F32_2, 2, 4 },
+    // mvn_test_params{ CASE_MVN_3D_F32_1, 2, 4 },
+    // mvn_test_params{ CASE_MVN_3D_F32_2, 2, 4 },
+    // mvn_test_params{ CASE_MVN_F16_1, 2, 4 },
+    // mvn_test_params{ CASE_MVN_F16_2, 2, 4 },
+    // mvn_test_params{ CASE_MVN_3D_F16_1, 2, 4 },
+    // mvn_test_params{ CASE_MVN_3D_F16_2, 2, 4 },
+    mvn_test_params{ CASE_MVN_I8_1, 2, 4 },
+    mvn_test_params{ CASE_MVN_I8_2, 2, 4 },
+    mvn_test_params{ CASE_MVN_I8_3, 2, 4 },
+    mvn_test_params{ CASE_MVN_I8_4, 2, 4 },
+    mvn_test_params{ CASE_MVN_3D_I8_1, 2, 4 },
+    mvn_test_params{ CASE_MVN_3D_I8_2, 2, 4 },
+    mvn_test_params{ CASE_MVN_U8_1, 2, 4 },
+    mvn_test_params{ CASE_MVN_U8_2, 2, 4 },
+    mvn_test_params{ CASE_MVN_U8_3, 2, 4 },
+    mvn_test_params{ CASE_MVN_U8_4, 2, 4 },
+    mvn_test_params{ CASE_MVN_3D_U8_1, 2, 4 },
+    mvn_test_params{ CASE_MVN_3D_U8_2, 2, 4 },
 }));
 
 class mvn_scale_activation_eltwise_fp32_quantize_i8 : public MVNFusingTest {};
@@ -3858,7 +3950,7 @@ TEST_P(mvn_scale_activation_eltwise_fp32_quantize_i8, basic) {
         scale("scale", "mvn", "scale_data"),
         activation("act", "scale", activation_func::hyperbolic_tan),
         data("eltw_data", get_mem(layout{ p.input_type, p.default_format, p.elwise_size })),
-        eltwise("eltw", {"act", "eltw_data"}, eltwise_mode::sum, data_types::f32),
+        eltwise("eltw", { "act", "eltw_data" }, eltwise_mode::sum, data_types::f32),
         data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
         data("out_low", get_mem(get_single_element_layout(p), -128)),
@@ -3871,102 +3963,102 @@ TEST_P(mvn_scale_activation_eltwise_fp32_quantize_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_scale_activation_eltwise_fp32_quantize_i8,
-    ::testing::ValuesIn(std::vector<mvn_test_params>{
-        // Full using for fp input not supported yet, it may lead to output padding and non-optimal kernel
-        // mvn_test_params{ CASE_MVN_F32_1, 2, 7 },
-        // mvn_test_params{ CASE_MVN_F32_2, 2, 7 },
-        // mvn_test_params{ CASE_MVN_3D_F32_1, 2, 7 },
-        // mvn_test_params{ CASE_MVN_3D_F32_2, 2, 7 },
-        // mvn_test_params{ CASE_MVN_F16_1, 2, 7 },
-        // mvn_test_params{ CASE_MVN_F16_2, 2, 7 },
-        // mvn_test_params{ CASE_MVN_3D_F16_1, 2, 7 },
-        // mvn_test_params{ CASE_MVN_3D_F16_2, 2, 7 },
-        mvn_test_params{ CASE_MVN_I8_1, 2, 6 },
-        mvn_test_params{ CASE_MVN_I8_2, 2, 6 },
-        mvn_test_params{ CASE_MVN_I8_3, 2, 6 },
-        mvn_test_params{ CASE_MVN_I8_4, 2, 6 },
-        mvn_test_params{ CASE_MVN_I8_5, 2, 6 },
-        mvn_test_params{ CASE_MVN_I8_6, 2, 6 },
-        mvn_test_params{ CASE_MVN_I8_7, 3, 6 },
-        mvn_test_params{ CASE_MVN_3D_I8_1, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_I8_2, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_I8_3, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_I8_4, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_I8_5, 3, 6 },
-        mvn_test_params{ CASE_MVN_U8_1, 2, 6 },
-        mvn_test_params{ CASE_MVN_U8_2, 2, 6 },
-        mvn_test_params{ CASE_MVN_U8_3, 2, 6 },
-        mvn_test_params{ CASE_MVN_U8_4, 2, 6 },
-        mvn_test_params{ CASE_MVN_U8_5, 2, 6 },
-        mvn_test_params{ CASE_MVN_U8_6, 2, 6 },
-        mvn_test_params{ CASE_MVN_U8_7, 3, 6 },
-        mvn_test_params{ CASE_MVN_3D_U8_1, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_U8_2, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_U8_3, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_U8_4, 2, 6 },
-        mvn_test_params{ CASE_MVN_3D_U8_5, 3, 6 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_scale_activation_eltwise_fp32_quantize_i8, ::testing::ValuesIn(std::vector<mvn_test_params>{
+    // Full using for fp input not supported yet, it may lead to output padding and non-optimal kernel
+    // mvn_test_params{ CASE_MVN_F32_1, 2, 7 },
+    // mvn_test_params{ CASE_MVN_F32_2, 2, 7 },
+    // mvn_test_params{ CASE_MVN_3D_F32_1, 2, 7 },
+    // mvn_test_params{ CASE_MVN_3D_F32_2, 2, 7 },
+    // mvn_test_params{ CASE_MVN_F16_1, 2, 7 },
+    // mvn_test_params{ CASE_MVN_F16_2, 2, 7 },
+    // mvn_test_params{ CASE_MVN_3D_F16_1, 2, 7 },
+    // mvn_test_params{ CASE_MVN_3D_F16_2, 2, 7 },
+    mvn_test_params{ CASE_MVN_I8_1, 2, 6 },
+    mvn_test_params{ CASE_MVN_I8_2, 2, 6 },
+    mvn_test_params{ CASE_MVN_I8_3, 2, 6 },
+    mvn_test_params{ CASE_MVN_I8_4, 2, 6 },
+    mvn_test_params{ CASE_MVN_I8_5, 2, 6 },
+    mvn_test_params{ CASE_MVN_I8_6, 2, 6 },
+    mvn_test_params{ CASE_MVN_I8_7, 3, 6 },
+    mvn_test_params{ CASE_MVN_3D_I8_1, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_I8_2, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_I8_3, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_I8_4, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_I8_5, 3, 6 },
+    mvn_test_params{ CASE_MVN_U8_1, 2, 6 },
+    mvn_test_params{ CASE_MVN_U8_2, 2, 6 },
+    mvn_test_params{ CASE_MVN_U8_3, 2, 6 },
+    mvn_test_params{ CASE_MVN_U8_4, 2, 6 },
+    mvn_test_params{ CASE_MVN_U8_5, 2, 6 },
+    mvn_test_params{ CASE_MVN_U8_6, 2, 6 },
+    mvn_test_params{ CASE_MVN_U8_7, 3, 6 },
+    mvn_test_params{ CASE_MVN_3D_U8_1, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_U8_2, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_U8_3, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_U8_4, 2, 6 },
+    mvn_test_params{ CASE_MVN_3D_U8_5, 3, 6 },
 }));
 
 class mvn_eltwise : public MVNFusingTest {};
 TEST_P(mvn_eltwise, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", layout{ p.input_type, p.input_format, p.input_size }),
-                 mvn("mvn", "input", p.normalize_variance, 1e-10f, false, false),
-                 data("eltw_data", get_mem(layout{ p.input_type, p.default_format, p.elwise_size })),
-                 eltwise("eltw", {"mvn", "eltw_data"}, eltwise_mode::sum, data_types::f32),
-                 reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", layout{ p.input_type, p.input_format, p.input_size }),
+        mvn("mvn", "input", p.normalize_variance, 1e-10f, false, false),
+        data("eltw_data", get_mem(layout{ p.input_type, p.default_format, p.elwise_size })),
+        eltwise("eltw", { "mvn", "eltw_data" }, eltwise_mode::sum, data_types::f32),
+        reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_eltwise,
-    ::testing::ValuesIn(std::vector<mvn_test_params>{
-        mvn_test_params{ CASE_MVN_I8_5, 2, 3 },
-        mvn_test_params{ CASE_MVN_I8_6, 2, 3 },
-        mvn_test_params{ CASE_MVN_I8_7, 3, 3 },
-        mvn_test_params{ CASE_MVN_3D_I8_3, 2, 3 },
-        mvn_test_params{ CASE_MVN_3D_I8_4, 2, 3 },
-        mvn_test_params{ CASE_MVN_3D_I8_5, 3, 3 },
-        mvn_test_params{ CASE_MVN_U8_1, 2, 3 },
-        mvn_test_params{ CASE_MVN_U8_2, 2, 3 },
-        mvn_test_params{ CASE_MVN_U8_3, 2, 3 },
-        mvn_test_params{ CASE_MVN_U8_4, 2, 3 },
-        mvn_test_params{ CASE_MVN_U8_5, 2, 3 },
-        mvn_test_params{ CASE_MVN_U8_6, 2, 3 },
-        mvn_test_params{ CASE_MVN_U8_7, 3, 3 },
-        mvn_test_params{ CASE_MVN_3D_U8_1, 2, 3 },
-        mvn_test_params{ CASE_MVN_3D_U8_2, 2, 3 },
-        mvn_test_params{ CASE_MVN_3D_U8_3, 2, 3 },
-        mvn_test_params{ CASE_MVN_3D_U8_4, 2, 3 },
-        mvn_test_params{ CASE_MVN_3D_U8_5, 3, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_eltwise, ::testing::ValuesIn(std::vector<mvn_test_params>{
+    mvn_test_params{ CASE_MVN_I8_5, 2, 3 },
+    mvn_test_params{ CASE_MVN_I8_6, 2, 3 },
+    mvn_test_params{ CASE_MVN_I8_7, 3, 3 },
+    mvn_test_params{ CASE_MVN_3D_I8_3, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_I8_4, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_I8_5, 3, 3 },
+    mvn_test_params{ CASE_MVN_U8_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_3, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_4, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_5, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_6, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_7, 3, 3 },
+    mvn_test_params{ CASE_MVN_3D_U8_1, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_U8_2, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_U8_3, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_U8_4, 2, 3 },
+    mvn_test_params{ CASE_MVN_3D_U8_5, 3, 3 },
 }));
 
 class mvn_eltwise_f16 : public MVNFusingTest {};
 TEST_P(mvn_eltwise_f16, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", layout{ p.input_type, p.input_format, p.input_size }),
-                 mvn("mvn", "input", p.normalize_variance, 1e-10f, false, false),
-                 data("eltw_data", get_mem(layout{ p.input_type, p.default_format, p.elwise_size })),
-                 eltwise("eltw", {"mvn", "eltw_data"}, eltwise_mode::sum, data_types::f16),
-                 reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", layout{ p.input_type, p.input_format, p.input_size }),
+        mvn("mvn", "input", p.normalize_variance, 1e-10f, false, false),
+        data("eltw_data", get_mem(layout{ p.input_type, p.default_format, p.elwise_size })),
+        eltwise("eltw", { "mvn", "eltw_data" }, eltwise_mode::sum, data_types::f16),
+        reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32)
     );
 
     tolerance = 0.1f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_eltwise_f16,
-    ::testing::ValuesIn(std::vector<mvn_test_params>{
-        mvn_test_params{ CASE_MVN_I8_6, 2, 3 },
-        mvn_test_params{ CASE_MVN_U8_2, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, mvn_eltwise_f16, ::testing::ValuesIn(std::vector<mvn_test_params>{
+    mvn_test_params{ CASE_MVN_I8_6, 2, 3 },
+    mvn_test_params{ CASE_MVN_U8_2, 2, 3 },
 }));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- LRN cases -------------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct lrn_test_params {
     tensor in_shape;
     data_types data_type;
@@ -3979,23 +4071,23 @@ struct lrn_test_params {
     std::string kernel_name;
 };
 
-#define CASE_LRN_FP32_1 {2, 16, 4, 4}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_LRN_FP32_2 {8, 16, 4, 4}, data_types::f32, format::yxfb, data_types::f32, format::yxfb
-#define CASE_LRN_FP32_3 {2, 16, 4, 4}, data_types::f32, format::byxf, data_types::f32, format::byxf
-#define CASE_LRN_FP32_4 {2, 16, 4, 4}, data_types::f32, format::b_fs_yx_fsv4, data_types::f32, format::bfyx
-#define CASE_LRN_FP32_5 {2, 16, 4, 4}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_LRN_FP32_1 { 2, 16, 4, 4 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_LRN_FP32_2 { 8, 16, 4, 4 }, data_types::f32, format::yxfb, data_types::f32, format::yxfb
+#define CASE_LRN_FP32_3 { 2, 16, 4, 4 }, data_types::f32, format::byxf, data_types::f32, format::byxf
+#define CASE_LRN_FP32_4 { 2, 16, 4, 4 }, data_types::f32, format::b_fs_yx_fsv4, data_types::f32, format::bfyx
+#define CASE_LRN_FP32_5 { 2, 16, 4, 4 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
 
-#define CASE_LRN_FP32_TO_FP16_1 {2, 16, 5, 5}, data_types::f32, format::bfyx, data_types::f16, format::bfyx
-#define CASE_LRN_FP32_TO_FP16_2 {2, 16, 5, 5}, data_types::f32, format::byxf, data_types::f16, format::byxf
-#define CASE_LRN_FP32_TO_FP16_3 {8, 16, 4, 4}, data_types::f32, format::yxfb, data_types::f16, format::byxf
-#define CASE_LRN_FP32_TO_FP16_4 {2, 16, 4, 4}, data_types::f32, format::b_fs_yx_fsv4, data_types::f16, format::bfyx
-#define CASE_LRN_FP32_TO_FP16_5 {2, 16, 4, 4}, data_types::f32, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
+#define CASE_LRN_FP32_TO_FP16_1 { 2, 16, 5, 5 }, data_types::f32, format::bfyx, data_types::f16, format::bfyx
+#define CASE_LRN_FP32_TO_FP16_2 { 2, 16, 5, 5 }, data_types::f32, format::byxf, data_types::f16, format::byxf
+#define CASE_LRN_FP32_TO_FP16_3 { 8, 16, 4, 4 }, data_types::f32, format::yxfb, data_types::f16, format::byxf
+#define CASE_LRN_FP32_TO_FP16_4 { 2, 16, 4, 4 }, data_types::f32, format::b_fs_yx_fsv4, data_types::f16, format::bfyx
+#define CASE_LRN_FP32_TO_FP16_5 { 2, 16, 4, 4 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
 
-#define CASE_LRN_FP16_1 {2, 16, 4, 4}, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_LRN_FP16_2 {8, 16, 4, 4}, data_types::f16, format::yxfb, data_types::f16, format::yxfb
-#define CASE_LRN_FP16_3 {2, 16, 4, 4}, data_types::f16, format::byxf, data_types::f16, format::byxf
-#define CASE_LRN_FP16_4 {2, 16, 4, 4}, data_types::f16, format::b_fs_yx_fsv4, data_types::f16, format::bfyx
-#define CASE_LRN_FP16_5 {2, 16, 4, 4}, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
+#define CASE_LRN_FP16_1 { 2, 16, 4, 4 }, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_LRN_FP16_2 { 8, 16, 4, 4 }, data_types::f16, format::yxfb, data_types::f16, format::yxfb
+#define CASE_LRN_FP16_3 { 2, 16, 4, 4 }, data_types::f16, format::byxf, data_types::f16, format::byxf
+#define CASE_LRN_FP16_4 { 2, 16, 4, 4 }, data_types::f16, format::b_fs_yx_fsv4, data_types::f16, format::bfyx
+#define CASE_LRN_FP16_5 { 2, 16, 4, 4 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
 
 class LrnFusingTest : public ::BaseFusingTest<lrn_test_params> {
 public:
@@ -4003,9 +4095,9 @@ public:
         auto input_prim = get_mem(get_input_layout(p));
 
         build_options options;
-        implementation_desc lrn_impl = {p.input_format, p.kernel_name};
+        implementation_desc lrn_impl = { p.input_format, p.kernel_name };
         options.set_option(build_option::optimize_data(true));
-        options.set_option(build_option::force_implementations({{"lrn_norm", lrn_impl}}));
+        options.set_option(build_option::force_implementations({ { "lrn_norm", lrn_impl } }));
         network network_fused(this->engine, this->topology_fused, options);
         network network_not_fused(this->engine, this->topology_non_fused, this->bo_not_fused);
 
@@ -4032,10 +4124,12 @@ public:
         compare(network_not_fused, network_fused, p);
     }
 
-    layout get_input_layout(lrn_test_params& p) { return layout{p.data_type, p.input_format, p.in_shape}; }
+    layout get_input_layout(lrn_test_params& p) {
+        return layout{ p.data_type, p.input_format, p.in_shape };
+    }
 
     layout get_per_channel_layout(lrn_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -4048,17 +4142,19 @@ TEST_P(lrn_fp32_quantize_u8_scale_activation, basic) {
     float alpha = (float)9.9e-05;
     float beta = 0.75;
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                      data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
-                      lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
-                      quantize("quantize", "lrn_norm", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                      scale("scale", "quantize", "scale_data"),
-                      activation("activation", "scale", activation_func::exp),
-                      reorder("reorder", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
+        lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
+        quantize("quantize", "lrn_norm", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        scale("scale", "quantize", "scale_data"),
+        activation("activation", "scale", activation_func::exp),
+        reorder("reorder", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
@@ -4072,47 +4168,46 @@ TEST_P(lrn_fp32_quantize_u8_scale_activation, per_channel) {
     float alpha = (float)9.9e-05;
     float beta = 0.75;
 
-   create_topologies(input_layout("input", get_input_layout(p)),
-                     data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                     data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                     data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                     data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                     data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / 255)),
-                     lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
-                     quantize("quantize", "lrn_norm", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                     scale("scale", "quantize", "scale_data"),
-                     activation("activation", "scale", activation_func::exp),
-                     reorder("reorder", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / 255)),
+        lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
+        quantize("quantize", "lrn_norm", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        scale("scale", "quantize", "scale_data"),
+        activation("activation", "scale", activation_func::exp),
+        reorder("reorder", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        lrn_fp32_quantize_u8_scale_activation,
-                        ::testing::ValuesIn(std::vector<lrn_test_params>{
-                            // InputDataType = FP32   OutputDataType = FP32
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_2, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt"},
-                            lrn_test_params{CASE_LRN_FP32_3, 2, 5, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt"},
-                            lrn_test_params{CASE_LRN_FP32_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, lrn_fp32_quantize_u8_scale_activation, ::testing::ValuesIn(std::vector<lrn_test_params>{
+    // InputDataType = FP32   OutputDataType = FP32
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_2, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt" },
+    lrn_test_params{ CASE_LRN_FP32_3, 2, 5, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt" },
+    lrn_test_params{ CASE_LRN_FP32_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16" },
 
-                            // InputDataType = FP32   OutputDataType = FP16
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref"},
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref"},
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt"},
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel"},
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_3, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt"},
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_TO_FP16_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16"},
-
-                        }));
+    // InputDataType = FP32   OutputDataType = FP16
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref" },
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref" },
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt" },
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel" },
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_3, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt" },
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_TO_FP16_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16" },
+}));
 
 class lrn_fp32_quantize_i8_scale_activation : public LrnFusingTest {};
 TEST_P(lrn_fp32_quantize_i8_scale_activation, basic) {
@@ -4123,39 +4218,39 @@ TEST_P(lrn_fp32_quantize_i8_scale_activation, basic) {
     float alpha = (float)9.9e-05;
     float beta = 0.75;
 
-   create_topologies(input_layout("input", get_input_layout(p)),
-                     data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
-                     data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
-                     data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                     data("out_hi", get_mem(get_single_element_layout(p),  127)),
-                     data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
-                     lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
-                     scale("scale", "lrn_norm", "scale_data"),
-                     activation("activation", "scale", activation_func::exp),
-                     quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                     reorder("reorder", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p),  127)),
+        data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
+        lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
+        scale("scale", "lrn_norm", "scale_data"),
+        activation("activation", "scale", activation_func::exp),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        lrn_fp32_quantize_i8_scale_activation,
-                        ::testing::ValuesIn(std::vector<lrn_test_params>{
-                            // InputDataType = FP32   OutputDataType = INT8
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref"},
-                            lrn_test_params{CASE_LRN_FP32_2, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt"},
-                            lrn_test_params{CASE_LRN_FP32_3, 2, 5, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt"},
-                            lrn_test_params{CASE_LRN_FP32_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, lrn_fp32_quantize_i8_scale_activation, ::testing::ValuesIn(std::vector<lrn_test_params>{
+    // InputDataType = FP32   OutputDataType = INT8
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref" },
+    lrn_test_params{ CASE_LRN_FP32_2, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt" },
+    lrn_test_params{ CASE_LRN_FP32_3, 2, 5, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt" },
+    lrn_test_params{ CASE_LRN_FP32_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16" },
 
-                            // InputDataType = FP16   OutputDataType = INT8/UINT8 can't be tested for now, because quantize
-                            // primitive doesn't support input type FP16 while fusing (prepare_quantization.cpp :114 -> prepare_primitive_fusing.cpp :474)
-                        }));
+    // InputDataType = FP16   OutputDataType = INT8/UINT8 can't be tested for now, because quantize
+    // primitive doesn't support input type FP16 while fusing (prepare_quantization.cpp :114 -> prepare_primitive_fusing.cpp :474)
+}));
 
 class lrn_fp32_scale_activation_quantize_u8 : public LrnFusingTest {};
 TEST_P(lrn_fp32_scale_activation_quantize_u8, basic) {
@@ -4166,36 +4261,36 @@ TEST_P(lrn_fp32_scale_activation_quantize_u8, basic) {
     float alpha = (float)9.9e-05;
     float beta = 0.75;
 
-   create_topologies(input_layout("input", get_input_layout(p)),
-                     data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
-                     data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
-                     data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                     data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                     data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
-                     lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
-                     scale("scale", "lrn_norm", "scale_data"),
-                     activation("activation", "scale", activation_func::exp),
-                     quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                     reorder("reorder", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
+        lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
+        scale("scale", "lrn_norm", "scale_data"),
+        activation("activation", "scale", activation_func::exp),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        lrn_fp32_scale_activation_quantize_u8,
-                        ::testing::ValuesIn(std::vector<lrn_test_params>{
-                            // InputDataType = FP32   OutputDataType = UINT8
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref"},
-                            lrn_test_params{CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_2, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt"},
-                            lrn_test_params{CASE_LRN_FP32_3, 2, 5, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt"},
-                            lrn_test_params{CASE_LRN_FP32_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP32_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16"},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, lrn_fp32_scale_activation_quantize_u8, ::testing::ValuesIn(std::vector<lrn_test_params>{
+    // InputDataType = FP32   OutputDataType = UINT8
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_within_channel, "lrn_gpu_within_channel" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_ref" },
+    lrn_test_params{ CASE_LRN_FP32_1, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_2, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_yxfb_b8_opt" },
+    lrn_test_params{ CASE_LRN_FP32_3, 2, 5, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt" },
+    lrn_test_params{ CASE_LRN_FP32_4, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP32_5, 2, 5, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16" },
+}));
 
 class lrn_fp16_scale_activation : public LrnFusingTest {};
 TEST_P(lrn_fp16_scale_activation, basic) {
@@ -4206,34 +4301,35 @@ TEST_P(lrn_fp16_scale_activation, basic) {
     float alpha = (float)9.9e-05;
     float beta = 0.75;
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
-                      lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
-                      scale("scale", "lrn_norm", "scale_data"),
-                      activation("activation", "scale", activation_func::exp),
-                      reorder("reorder", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
+        lrn("lrn_norm", "input", size, k, alpha, beta, p.lrn_type),
+        scale("scale", "lrn_norm", "scale_data"),
+        activation("activation", "scale", activation_func::exp),
+        reorder("reorder", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        lrn_fp16_scale_activation,
-                        ::testing::ValuesIn(std::vector<lrn_test_params>{
-                            // InputDataType = FP16   OutputDataType = FP16
-                            lrn_test_params{CASE_LRN_FP16_1, 2, 4, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt"},
-                            lrn_test_params{CASE_LRN_FP16_1, 2, 4, lrn_norm_region_within_channel, "lrn_gpu_within_channel"},
-                            lrn_test_params{CASE_LRN_FP16_1, 2, 4, lrn_norm_region_across_channel, "lrn_ref"},
-                            lrn_test_params{CASE_LRN_FP16_1, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP16_1, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref"},
-                            lrn_test_params{CASE_LRN_FP16_3, 2, 4, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt"},
-                            lrn_test_params{CASE_LRN_FP16_4, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"},
-                            lrn_test_params{CASE_LRN_FP16_5, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16"},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, lrn_fp16_scale_activation, ::testing::ValuesIn(std::vector<lrn_test_params>{
+    // InputDataType = FP16   OutputDataType = FP16
+    lrn_test_params{ CASE_LRN_FP16_1, 2, 4, lrn_norm_region_within_channel, "lrn_gpu_within_channel_opt" },
+    lrn_test_params{ CASE_LRN_FP16_1, 2, 4, lrn_norm_region_within_channel, "lrn_gpu_within_channel" },
+    lrn_test_params{ CASE_LRN_FP16_1, 2, 4, lrn_norm_region_across_channel, "lrn_ref" },
+    lrn_test_params{ CASE_LRN_FP16_1, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP16_1, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_ref" },
+    lrn_test_params{ CASE_LRN_FP16_3, 2, 4, lrn_norm_region_within_channel, "lrn_within_channel_byxf_opt" },
+    lrn_test_params{ CASE_LRN_FP16_4, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features" },
+    lrn_test_params{ CASE_LRN_FP16_5, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features_fsv16" },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* -------------------------------- Activation cases --------------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct activation_test_params {
     tensor input_size;
     data_types input_type;
@@ -4245,41 +4341,41 @@ struct activation_test_params {
     std::string kernel_name;
 };
 
-#define CASE_ACTIVATION_F32_0 {7, 32, 3, 3}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F32_1 {1, 16, 8, 8}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F32_2 {7, 3, 7, 7}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F32_3 {1, 14, 8, 8}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F32_4 {1, 17, 31, 29}, data_types::f32, format::yxfb, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F32_5 {1, 17, 31, 29}, data_types::f32, format::b_fs_yx_fsv4, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F32_6 {1, 17, 31, 29}, data_types::f32, format::b_fs_yx_fsv32, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F32_7 {1, 17, 31, 29}, data_types::f32, format::fyxb, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_3D_F32_0 {3, 16, 13, 13, 13}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F32_1 {2, 16, 8, 8, 8}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F32_2 {1, 16, 7, 7, 7}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F32_3 {1, 17, 7, 7, 7}, data_types::f32, format::b_fs_zyx_fsv32, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F32_4 {1, 17, 7, 7, 7}, data_types::f32, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F32_5 {1, 17, 7, 7, 7}, data_types::f32, format::fs_b_yx_fsv32, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_F32_0 { 7, 32, 3, 3 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F32_1 { 1, 16, 8, 8 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F32_2 { 7, 3, 7, 7 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F32_3 { 1, 14, 8, 8 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F32_4 { 1, 17, 31, 29 }, data_types::f32, format::yxfb, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F32_5 { 1, 17, 31, 29 }, data_types::f32, format::b_fs_yx_fsv4, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F32_6 { 1, 17, 31, 29 }, data_types::f32, format::b_fs_yx_fsv32, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F32_7 { 1, 17, 31, 29 }, data_types::f32, format::fyxb, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_3D_F32_0 { 3, 16, 13, 13, 13 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F32_1 { 2, 16, 8, 8, 8 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F32_2 { 1, 16, 7, 7, 7 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F32_3 { 1, 17, 7, 7, 7 }, data_types::f32, format::b_fs_zyx_fsv32, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F32_4 { 1, 17, 7, 7, 7 }, data_types::f32, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F32_5 { 1, 17, 7, 7, 7 }, data_types::f32, format::fs_b_yx_fsv32, data_types::f32, format::bfzyx
 
-#define CASE_ACTIVATION_F16_0 {7, 32, 5, 5}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F16_1 {1, 16, 8, 8}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F16_2 {7, 16, 7, 7}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F16_3 {1, 14, 8, 8}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F16_4 {1, 17, 31, 29}, data_types::f16, format::yxfb, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F16_5 {1, 17, 31, 29}, data_types::f16, format::b_fs_yx_fsv4, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F16_6 {1, 17, 31, 29}, data_types::f16, format::b_fs_yx_fsv32, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_F16_7 {1, 17, 31, 29}, data_types::f16, format::fyxb, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_3D_F16_0 {3, 16, 13, 13, 13}, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F16_1 {2, 16, 8, 8, 8}, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F16_2 {1, 16, 7, 7, 7}, data_types::f16, format::b_fs_zyx_fsv16, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F16_3 {1, 17, 7, 7, 7}, data_types::f16, format::b_fs_zyx_fsv32, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F16_4 {1, 17, 7, 7, 7}, data_types::f16, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfzyx
-#define CASE_ACTIVATION_3D_F16_5 {1, 17, 7, 7, 7}, data_types::f16, format::fs_b_yx_fsv32, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_F16_0 { 7, 32, 5, 5 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F16_1 { 1, 16, 8, 8 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F16_2 { 7, 16, 7, 7 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F16_3 { 1, 14, 8, 8 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F16_4 { 1, 17, 31, 29 }, data_types::f16, format::yxfb, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F16_5 { 1, 17, 31, 29 }, data_types::f16, format::b_fs_yx_fsv4, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F16_6 { 1, 17, 31, 29 }, data_types::f16, format::b_fs_yx_fsv32, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_F16_7 { 1, 17, 31, 29 }, data_types::f16, format::fyxb, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_3D_F16_0 { 3, 16, 13, 13, 13 }, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F16_1 { 2, 16, 8, 8, 8 }, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F16_2 { 1, 16, 7, 7, 7 }, data_types::f16, format::b_fs_zyx_fsv16, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F16_3 { 1, 17, 7, 7, 7 }, data_types::f16, format::b_fs_zyx_fsv32, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F16_4 { 1, 17, 7, 7, 7 }, data_types::f16, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_3D_F16_5 { 1, 17, 7, 7, 7 }, data_types::f16, format::fs_b_yx_fsv32, data_types::f32, format::bfzyx
 
-#define CASE_ACTIVATION_U8_1 {1, 16, 8, 8}, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_U8_2 {1, 12, 8, 8}, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_I8_1 {1, 16, 8, 8}, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_I8_2 {1, 14, 8, 8}, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_ACTIVATION_3D_I8_1 {1, 17, 8, 8, 8}, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_ACTIVATION_U8_1 { 1, 16, 8, 8 }, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_U8_2 { 1, 12, 8, 8 }, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_I8_1 { 1, 16, 8, 8 }, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_I8_2 { 1, 14, 8, 8 }, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_ACTIVATION_3D_I8_1 { 1, 17, 8, 8, 8 }, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
 
 class ActivationFusingTest : public ::BaseFusingTest<activation_test_params> {
 public:
@@ -4287,9 +4383,9 @@ public:
         auto input_prim = get_mem(get_input_layout(p));
 
         build_options options;
-        implementation_desc activation_impl = {p.input_format, p.kernel_name};
+        implementation_desc activation_impl = { p.input_format, p.kernel_name };
         options.set_option(build_option::optimize_data(true));
-        options.set_option(build_option::force_implementations({{"act", activation_impl}}));
+        options.set_option(build_option::force_implementations({ { "act", activation_impl } }));
         network network_fused(this->engine, this->topology_fused, options);
         network network_not_fused(this->engine, this->topology_non_fused, bo_not_fused);
 
@@ -4299,10 +4395,12 @@ public:
         compare(network_not_fused, network_fused, p);
     }
 
-    layout get_input_layout(activation_test_params& p) { return layout{p.input_type, p.input_format, p.input_size}; }
+    layout get_input_layout(activation_test_params& p) {
+        return layout{ p.input_type, p.input_format, p.input_size };
+    }
 
     layout get_per_channel_layout(activation_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.input_size.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.input_size.feature[0], 1, 1 } };
     }
 
     format get_input_format(activation_test_params &p) { return p.input_format; }
@@ -4311,14 +4409,16 @@ public:
 class activation_quantize_i8 : public ActivationFusingTest {};
 TEST_P(activation_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      activation("act", "input", activation_func::relu),
-                      data("in_low", get_mem(get_single_element_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_single_element_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -127, 0)),
-                      data("out_high", get_mem(get_single_element_layout(p), 0, 127)),
-                      quantize("quant", "act", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        activation("act", "input", activation_func::relu),
+        data("in_low", get_mem(get_single_element_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -127, 0)),
+        data("out_high", get_mem(get_single_element_layout(p), 0, 127)),
+        quantize("quant", "act", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
@@ -4326,64 +4426,62 @@ TEST_P(activation_quantize_i8, basic) {
 
 TEST_P(activation_quantize_i8, per_channel) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      activation("act", "input", activation_func::relu),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -127, 0)),
-                      data("out_high", get_mem(get_single_element_layout(p), 0, 127)),
-                      quantize("quant", "act", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        activation("act", "input", activation_func::relu),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -127, 0)),
+        data("out_high", get_mem(get_single_element_layout(p), 0, 127)),
+        quantize("quant", "act", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    activation_quantize_i8,
-    ::testing::ValuesIn(std::vector<activation_test_params>{
-        // InputDataType = FP32
-        activation_test_params{CASE_ACTIVATION_F32_0, 2, 3, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_F32_1, 2, 3, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_0, 2, 3, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 3, "activation_opt"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, activation_quantize_i8, ::testing::ValuesIn(std::vector<activation_test_params>{
+    // InputDataType = FP32
+    activation_test_params{ CASE_ACTIVATION_F32_0, 2, 3, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_F32_1, 2, 3, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_0, 2, 3, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_1, 2, 3, "activation_opt" },
 
-        activation_test_params{CASE_ACTIVATION_F32_0, 2, 3, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_1, 2, 3, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_2, 2, 3, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_3, 2, 3, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_4, 2, 3, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_0, 2, 3, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 3, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_2, 2, 3, "activation_ref"},
-    }));
+    activation_test_params{ CASE_ACTIVATION_F32_0, 2, 3, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_1, 2, 3, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_2, 2, 3, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_3, 2, 3, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_4, 2, 3, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_0, 2, 3, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_1, 2, 3, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_2, 2, 3, "activation_ref" },
+}));
 
-INSTANTIATE_TEST_SUITE_P(
-    DISABLED_fusings_gpu,
-    activation_quantize_i8,
-    ::testing::ValuesIn(std::vector<activation_test_params>{
-        activation_test_params{CASE_ACTIVATION_F32_5, 2, 3, "activation_ref"},     // FIXME - accuracy bug
-        activation_test_params{CASE_ACTIVATION_F32_6, 2, 3, "activation_ref"},     // FIXME - accuracy bug
-        activation_test_params{CASE_ACTIVATION_F32_7, 2, 3, "activation_ref"},     // FIXME - accuracy bug
-        activation_test_params{CASE_ACTIVATION_3D_F32_3, 2, 3, "activation_ref"},  // FIXME - accuracy bug
-        activation_test_params{CASE_ACTIVATION_3D_F32_5, 2, 3, "activation_ref"},  // FIXME - accuracy bug
-    }));
+INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu, activation_quantize_i8, ::testing::ValuesIn(std::vector<activation_test_params>{
+    activation_test_params{ CASE_ACTIVATION_F32_5, 2, 3, "activation_ref" },     // FIXME - accuracy bug
+    activation_test_params{ CASE_ACTIVATION_F32_6, 2, 3, "activation_ref" },     // FIXME - accuracy bug
+    activation_test_params{ CASE_ACTIVATION_F32_7, 2, 3, "activation_ref" },     // FIXME - accuracy bug
+    activation_test_params{ CASE_ACTIVATION_3D_F32_3, 2, 3, "activation_ref" },  // FIXME - accuracy bug
+    activation_test_params{ CASE_ACTIVATION_3D_F32_5, 2, 3, "activation_ref" },  // FIXME - accuracy bug
+}));
 
 class activation_scale_activation_quantize_u8 : public ActivationFusingTest {};
 TEST_P(activation_scale_activation_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      activation("act", "input", activation_func::relu),
-                      data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
-                      data("in_low", get_mem(get_single_element_layout(p), 0)),
-                      data("in_high", get_mem(get_single_element_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -127)),
-                      data("out_high", get_mem(get_single_element_layout(p), 127)),
-                      scale("scale", "act", "scale_data"),
-                      activation("act2", "scale", activation_func::softsign),
-                      quantize("quant", "act2", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        activation("act", "input", activation_func::relu),
+        data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
+        data("in_low", get_mem(get_single_element_layout(p), 0)),
+        data("in_high", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -127)),
+        data("out_high", get_mem(get_single_element_layout(p), 127)),
+        scale("scale", "act", "scale_data"),
+        activation("act2", "scale", activation_func::softsign),
+        quantize("quant", "act2", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
@@ -4391,226 +4489,219 @@ TEST_P(activation_scale_activation_quantize_u8, basic) {
 
 TEST_P(activation_scale_activation_quantize_u8, per_channel) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      activation("act", "input", activation_func::relu),
-                      data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
-                      data("in_low", get_mem(get_per_channel_layout(p), 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -127)),
-                      data("out_high", get_mem(get_single_element_layout(p), 127)),
-                      scale("scale", "act", "scale_data"),
-                      activation("act2", "scale", activation_func::softsign),
-                      quantize("quant", "act2", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        activation("act", "input", activation_func::relu),
+        data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
+        data("in_low", get_mem(get_per_channel_layout(p), 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -127)),
+        data("out_high", get_mem(get_single_element_layout(p), 127)),
+        scale("scale", "act", "scale_data"),
+        activation("act2", "scale", activation_func::softsign),
+        quantize("quant", "act2", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    activation_scale_activation_quantize_u8,
-    ::testing::ValuesIn(std::vector<activation_test_params>{
-        // InputDataType = FP32
-        activation_test_params{CASE_ACTIVATION_F32_0, 2, 5, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_F32_1, 2, 5, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_0, 2, 5, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 5, "activation_opt"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, activation_scale_activation_quantize_u8, ::testing::ValuesIn(std::vector<activation_test_params>{
+    // InputDataType = FP32
+    activation_test_params{ CASE_ACTIVATION_F32_0, 2, 5, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_F32_1, 2, 5, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_0, 2, 5, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_1, 2, 5, "activation_opt" },
 
-        activation_test_params{CASE_ACTIVATION_F32_0, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_1, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_2, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_3, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_4, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_5, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_6, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_7, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_0, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 5, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_2, 2, 5, "activation_ref"},
-    }));
+    activation_test_params{ CASE_ACTIVATION_F32_0, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_1, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_2, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_3, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_4, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_5, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_6, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_7, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_0, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_1, 2, 5, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_2, 2, 5, "activation_ref" },
+}));
 
-INSTANTIATE_TEST_SUITE_P(
-    DISABLED_fusings_gpu,
-    activation_scale_activation_quantize_u8,
-    ::testing::ValuesIn(std::vector<activation_test_params>{
-        activation_test_params{CASE_ACTIVATION_3D_F32_5, 2, 5, "activation_ref"},  // FIXME - accuracy bug
-    }));
+INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu, activation_scale_activation_quantize_u8, ::testing::ValuesIn(std::vector<activation_test_params>{
+    activation_test_params{ CASE_ACTIVATION_3D_F32_5, 2, 5, "activation_ref" },  // FIXME - accuracy bug
+}));
 
 class activation_scale_activation : public ActivationFusingTest {};
 TEST_P(activation_scale_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      activation("act", "input", activation_func::relu),
-                      data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
-                      scale("scale", "act", "scale_data"),
-                      activation("act2", "scale", activation_func::exp),
-                      reorder("reorder_bfyx", "act2", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        activation("act", "input", activation_func::relu),
+        data("scale_data", get_mem(get_single_element_layout(p), 1.0f / 255)),
+        scale("scale", "act", "scale_data"),
+        activation("act2", "scale", activation_func::exp),
+        reorder("reorder_bfyx", "act2", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    activation_scale_activation,
-    ::testing::ValuesIn(std::vector<activation_test_params>{
-        // InputDataType = FP32
-        activation_test_params{CASE_ACTIVATION_F32_0, 2, 4, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_F32_1, 2, 4, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_0, 2, 4, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 4, "activation_opt"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, activation_scale_activation, ::testing::ValuesIn(std::vector<activation_test_params>{
+    // InputDataType = FP32
+    activation_test_params{ CASE_ACTIVATION_F32_0, 2, 4, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_F32_1, 2, 4, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_0, 2, 4, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_1, 2, 4, "activation_opt" },
 
-        activation_test_params{CASE_ACTIVATION_F32_0, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_1, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_2, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_3, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_4, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_5, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_6, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F32_7, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_0, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F32_2, 2, 4, "activation_ref"},
+    activation_test_params{ CASE_ACTIVATION_F32_0, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_1, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_2, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_3, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_4, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_5, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_6, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F32_7, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_0, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_1, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F32_2, 2, 4, "activation_ref" },
 
-        // InputDataType = FP16
-        activation_test_params{CASE_ACTIVATION_F16_0, 2, 4, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_F16_1, 2, 4, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F16_0, 2, 4, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_F16_1, 2, 4, "activation_opt"},
+    // InputDataType = FP16
+    activation_test_params{ CASE_ACTIVATION_F16_0, 2, 4, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_F16_1, 2, 4, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F16_0, 2, 4, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_F16_1, 2, 4, "activation_opt" },
 
-        activation_test_params{CASE_ACTIVATION_F16_0, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F16_1, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F16_2, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F16_3, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F16_4, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F16_5, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F16_6, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_F16_7, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F16_0, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F16_1, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F16_2, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F16_3, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_F16_4, 2, 4, "activation_ref"},
+    activation_test_params{ CASE_ACTIVATION_F16_0, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F16_1, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F16_2, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F16_3, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F16_4, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F16_5, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F16_6, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_F16_7, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F16_0, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F16_1, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F16_2, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F16_3, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_F16_4, 2, 4, "activation_ref" },
 
-        // InputDataType = UINT8
-        activation_test_params{CASE_ACTIVATION_U8_1, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_U8_2, 2, 4, "activation_ref"},
+    // InputDataType = UINT8
+    activation_test_params{ CASE_ACTIVATION_U8_1, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_U8_2, 2, 4, "activation_ref" },
 
-        // InputDataType = INT8
-        activation_test_params{CASE_ACTIVATION_I8_1, 2, 4, "activation_opt"},
-        activation_test_params{CASE_ACTIVATION_3D_I8_1, 2, 4, "activation_opt"},
+    // InputDataType = INT8
+    activation_test_params{ CASE_ACTIVATION_I8_1, 2, 4, "activation_opt" },
+    activation_test_params{ CASE_ACTIVATION_3D_I8_1, 2, 4, "activation_opt" },
 
-        activation_test_params{CASE_ACTIVATION_I8_1, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_I8_2, 2, 4, "activation_ref"},
-        activation_test_params{CASE_ACTIVATION_3D_I8_1, 2, 4, "activation_ref"}
-    }));
+    activation_test_params{ CASE_ACTIVATION_I8_1, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_I8_2, 2, 4, "activation_ref" },
+    activation_test_params{ CASE_ACTIVATION_3D_I8_1, 2, 4, "activation_ref" }
+}));
 
-INSTANTIATE_TEST_SUITE_P(
-    DISABLED_fusings_gpu,
-    activation_scale_activation,
-    ::testing::ValuesIn(std::vector<activation_test_params>{
-        activation_test_params{CASE_ACTIVATION_3D_F32_4, 2, 4, "activation_ref"},  // FIXME - accuracy bug
-        activation_test_params{CASE_ACTIVATION_3D_F32_5, 2, 4, "activation_ref"},  // FIXME - accuracy bug
-    }));
+INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu, activation_scale_activation, ::testing::ValuesIn(std::vector<activation_test_params>{
+    activation_test_params{ CASE_ACTIVATION_3D_F32_4, 2, 4, "activation_ref" },  // FIXME - accuracy bug
+    activation_test_params{ CASE_ACTIVATION_3D_F32_5, 2, 4, "activation_ref" },  // FIXME - accuracy bug
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* --------------------------------------- Deconvolution cases ----------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 using deconv_test_params = bc_test_params;
 
 // in_shape; out_shape; kernel; stride; pad; dilation; groups; data_type; input_format; weights_type; weights_format; default_type; default_format;
-#define CASE_DECONV_FP32_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_FP32_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_DECONV_FP32_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_DECONV_FP32_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
-#define CASE_DECONV_FP32_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_FP32_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_DECONV_FP32_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_DECONV_FP32_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_1 { 1, 15, 4, 5 }, { 1, 30, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_2 { 1, 16, 4, 5 }, { 1, 32, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_4 { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_5 { 1, 15, 4, 5 }, { 1, 30, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_6 { 1, 16, 4, 5 }, { 1, 32, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_7 { 1, 16, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_DECONV_FP32_8 { 1, 32, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
 
-#define CASE_DECONV_FP16_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfyx, data_types::f16, format::oiyx, data_types::f16, format::bfyx
-#define CASE_DECONV_FP16_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
-#define CASE_DECONV_FP16_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
-#define CASE_DECONV_FP16_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16,  format::gs_oiyx_gsv16, data_types::f16, format::bfyx
-#define CASE_DECONV_FP16_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfyx, data_types::f16, format::oiyx, data_types::f16, format::bfyx
-#define CASE_DECONV_FP16_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
-#define CASE_DECONV_FP16_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
-#define CASE_DECONV_FP16_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16,  format::gs_oiyx_gsv16, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_1 { 1, 15, 4, 5 }, { 1, 30, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bfyx, data_types::f16, format::oiyx, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_2 { 1, 16, 4, 5 }, { 1, 32, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_4 { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16,  format::gs_oiyx_gsv16, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_5 { 1, 15, 4, 5 }, { 1, 30, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bfyx, data_types::f16, format::oiyx, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_6 { 1, 16, 4, 5 }, { 1, 32, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_7 { 1, 16, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_isv16_osv16, data_types::f16, format::bfyx
+#define CASE_DECONV_FP16_8 { 1, 32, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16,  format::gs_oiyx_gsv16, data_types::f16, format::bfyx
 
-#define CASE_DECONV_S8S8_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_S8S8_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_S8S8_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_S8S8_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1}, 0}, tensor{1}, 32, data_types::i8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_S8S8_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_S8S8_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_S8S8_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_S8S8_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::i8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_1 { 1, 15, 4, 5 }, { 1, 30, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_2 { 1, 16, 4, 5 }, { 1, 32, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_4 { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::i8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_5 { 1, 15, 4, 5 }, { 1, 30, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_6 { 1, 16, 4, 5 }, { 1, 32, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_7 { 1, 16, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_S8S8_8 { 1, 32, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::i8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
 
-#define CASE_DECONV_U8S8_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_U8S8_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_U8S8_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_U8S8_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1}, 0}, tensor{1}, 32, data_types::u8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_U8S8_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_U8S8_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_U8S8_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_U8S8_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::u8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_1 { 1, 15, 4, 5 }, { 1, 30, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_2 { 1, 16, 4, 5 }, { 1, 32, 6, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_4 { 1, 32, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::u8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_5 { 1, 15, 4, 5 }, { 1, 30, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_6 { 1, 16, 4, 5 }, { 1, 32, 9, 11 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_7 { 1, 16, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_U8S8_8 { 1, 32, 4, 5 }, { 1, 32, 7, 9 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::u8, format::b_fs_yx_fsv16, data_types::i8,  format::goiyx, data_types::f32, format::bfyx
 
 
 // 3D
 // in_shape; out_shape; kernel; stride; pad; dilation; groups; data_type; input_format; weights_type; weights_format; default_type; default_format;
-#define CASE_DECONV_FP32_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32,  format::gs_oizyx_gsv16, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32,  format::gs_oizyx_gsv16, data_types::f32, format::bfzyx
-#define CASE_DECONV_FP32_3D_9 {16, 16, 4, 5, 3}, {16, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_1 { 1, 15, 4, 5, 3 }, { 1, 30, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_2 { 1, 16, 4, 5, 3 }, { 1, 32, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_3 { 1, 16, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_4 { 1, 32, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32,  format::gs_oizyx_gsv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_5 { 1, 15, 4, 5, 3 }, { 1, 30, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_6 { 1, 16, 4, 5, 3 }, { 1, 32, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_7 { 1, 16, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_8 { 1, 32, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32,  format::gs_oizyx_gsv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_FP32_3D_9 { 16, 16, 4, 5, 3 }, { 16, 32, 7, 9, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::is_os_zyx_isv16_osv16, data_types::f32, format::bfzyx
 
-#define CASE_DECONV_FP16_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfzyx, data_types::f16, format::oizyx, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16,  format::gs_oizyx_gsv16, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfzyx, data_types::f16, format::oizyx, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16,  format::gs_oizyx_gsv16, data_types::f16, format::bfzyx
-#define CASE_DECONV_FP16_3D_9 {16, 16, 4, 5, 3}, {16, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_1 { 1, 15, 4, 5, 3 }, { 1, 30, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bfzyx, data_types::f16, format::oizyx, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_2 { 1, 16, 4, 5, 3 }, { 1, 32, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_3 { 1, 16, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_4 { 1, 32, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16,  format::gs_oizyx_gsv16, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_5 { 1, 15, 4, 5, 3 }, { 1, 30, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bfzyx, data_types::f16, format::oizyx, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_6 { 1, 16, 4, 5, 3 }, { 1, 32, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_7 { 1, 16, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_8 { 1, 32, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16,  format::gs_oizyx_gsv16, data_types::f16, format::bfzyx
+#define CASE_DECONV_FP16_3D_9 { 16, 16, 4, 5, 3 }, { 16, 32, 7, 9, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f16, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::is_os_zyx_isv16_osv16, data_types::f16, format::bfzyx
 
-#define CASE_DECONV_S8S8_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_S8S8_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_S8S8_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_S8S8_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_S8S8_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_S8S8_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_S8S8_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_S8S8_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_1 { 1, 15, 4, 5, 3 }, { 1, 30, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_2 { 1, 16, 4, 5, 3 }, { 1, 32, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_3 { 1, 16, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_4 { 1, 32, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_5 { 1, 15, 4, 5, 3 }, { 1, 30, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_6 { 1, 16, 4, 5, 3 }, { 1, 32, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_7 { 1, 16, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_S8S8_3D_8 { 1, 32, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
 
-#define CASE_DECONV_U8S8_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_U8S8_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_U8S8_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_U8S8_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_U8S8_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_U8S8_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_U8S8_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_U8S8_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_1 { 1, 15, 4, 5, 3 }, { 1, 30, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_2 { 1, 16, 4, 5, 3 }, { 1, 32, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_3 { 1, 16, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_4 { 1, 32, 4, 5, 3 }, { 1, 32, 4, 5, 3 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_5 { 1, 15, 4, 5, 3 }, { 1, 30, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_6 { 1, 16, 4, 5, 3 }, { 1, 32, 9, 11, 7 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_7 { 1, 16, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 1, 1, 1 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_U8S8_3D_8 { 1, 32, 4, 5, 3 }, { 1, 32, 7, 9, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
 
-#define CASE_DECONV_ELTW_FP32_1 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 32, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::oiyx, data_types::f32, format::bfyx
-#define CASE_DECONV_ELTW_FP32_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_DECONV_ELTW_FP32_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_DECONV_ELTW_FP32_4 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_ELTW_FP32_5 {1, 15, 4, 5, 4}, {1, 30, 6, 7, 6}, {1, 30, 6, 1, 6}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_ELTW_FP32_6 {1, 32, 2, 2, 2}, {1, 16, 4, 4, 4}, {1, 16, 1, 4, 1}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
-#define CASE_DECONV_ELTW_FP32_7 {1, 16, 3, 5}, {1, 32, 5, 7}, {1, 32, 1, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
-#define CASE_DECONV_ELTW_FP32_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 32, 1, 1}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{{0, 0, 1, 1, 0, 0}, 0}, tensor{1}, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_FP32_1 { 1, 16, 4, 5 }, { 1, 32, 6, 7 }, { 1, 32, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_FP32_2 { 1, 16, 4, 5 }, { 1, 32, 6, 7 }, { 1, 1, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_FP32_3 { 1, 16, 4, 5 }, { 1, 32, 4, 5 }, { 1, 1, 1, 1 }, { 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_FP32_4 { 1, 15, 4, 5, 3 }, { 1, 30, 6, 7, 5 }, { 1, 1, 6, 7, 5 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_ELTW_FP32_5 { 1, 15, 4, 5, 4 }, { 1, 30, 6, 7, 6 }, { 1, 30, 6, 1, 6 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_ELTW_FP32_6 { 1, 32, 2, 2, 2 }, { 1, 16, 4, 4, 4 }, { 1, 16, 1, 4, 1 }, { 1, 1, 3, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx
+#define CASE_DECONV_ELTW_FP32_7 { 1, 16, 3, 5 }, { 1, 32, 5, 7 }, { 1, 32, 1, 7 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::os_is_yx_isv16_osv16, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_FP32_8 { 1, 32, 4, 5 }, { 1, 32, 7, 9 }, { 1, 32, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1, 1, 2, 2 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }, tensor{ 1 }, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32,  format::gs_oiyx_gsv16, data_types::f32, format::bfyx
 
-#define CASE_DECONV_ELTW_i8_1 {1, 16, 3, 5}, {1, 32, 5, 7}, {1, 32, 5, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_DECONV_ELTW_i8_2 {1, 32, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 32, 1, 1, 1}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{{0, 0, 1, 1, 1}, 0}, tensor{1}, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_ELTW_i8_3 {1, 5, 5, 5, 5}, {1, 5, 5, 5, 5}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
-#define CASE_DECONV_ELTW_i8_4 {1, 16, 1, 4}, {1, 16, 1, 6}, {1, 16, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
-#define CASE_DECONV_ELTW_i8_5 {1, 16, 2, 4}, {1, 16, 4, 6}, {1, 16, 4, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_i8_1 { 1, 16, 3, 5 }, { 1, 32, 5, 7 }, { 1, 32, 5, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_i8_2 { 1, 32, 4, 5, 3 }, { 1, 32, 6, 7, 5 }, { 1, 32, 1, 1, 1 }, { 1, 1, 3, 3, 3 }, tensor{ 1, 1, 2, 2, 2 }, tensor{ { 0, 0, 1, 1, 1 }, 0 }, tensor{ 1 }, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8,  format::goizyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_ELTW_i8_3 { 1, 5, 5, 5, 5 }, { 1, 5, 5, 5, 5 }, { 1, 1, 1, 1, 1 }, { 1, 1, 1, 1, 1 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::bfzyx, data_types::i8, format::oiyx, data_types::f32, format::bfzyx
+#define CASE_DECONV_ELTW_i8_4 { 1, 16, 1, 4 }, { 1, 16, 1, 6 }, { 1, 16, 1, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
+#define CASE_DECONV_ELTW_i8_5 { 1, 16, 2, 4 }, { 1, 16, 4, 6 }, { 1, 16, 4, 1 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::os_is_yx_osv16_isv16, data_types::f32, format::bfyx
 
 
 class DeconvolutionFusingTest : public ::WeightsPrimitiveFusingTest<bc_test_params> {
@@ -4651,82 +4742,81 @@ TEST_P(deconv_actv, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_actv,
-    ::testing::ValuesIn(std::vector<deconv_test_params>{
-        deconv_test_params{ CASE_DECONV_FP32_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_8, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_actv, ::testing::ValuesIn(std::vector<deconv_test_params>{
+    deconv_test_params{ CASE_DECONV_FP32_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_FP16_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3, 2, 3 },
-        // Here and below this test case and CASE_DECONV_S8S8_4 are commented because they fail for z_pad=0 which is unexpected
-        // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3, 2, 3 },
+    // Here and below this test case and CASE_DECONV_S8S8_4 are commented because they fail for z_pad=0 which is unexpected
+    // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3, 2, 3 },
-        // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3, 2, 3 },
+    // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_9, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_9, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_9, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_9, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 3 },
 }));
 
 
@@ -4738,47 +4828,47 @@ TEST_P(deconv_bias, basic) {
         data("weights", get_mem(get_weights_layout(p))),
         data("bias", get_mem(get_bias_layout(p))),
         deconvolution("deconv", "input", { "weights" }, p.groups, p.stride, p.pad),
-        eltwise("bias_add", {"deconv", "bias"}, eltwise_mode::sum),
+        eltwise("bias_add", { "deconv", "bias" }, eltwise_mode::sum),
         reorder("out", "bias_add", p.default_format, data_types::f32)
     );
+
     // Need much higher tolerance because of deconvolution -> convolution optimization
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_bias,
-    ::testing::ValuesIn(std::vector<deconv_test_params>{
-        deconv_test_params{ CASE_DECONV_FP32_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_bias, ::testing::ValuesIn(std::vector<deconv_test_params>{
+    deconv_test_params{ CASE_DECONV_FP32_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_FP16_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 3 },
 }));
 
 class deconv_scale : public DeconvolutionFusingTest {};
@@ -4792,6 +4882,7 @@ TEST_P(deconv_scale, basic) {
         scale("scale", "deconv", "scale_data"),
         reorder("out", "scale", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
@@ -4803,50 +4894,50 @@ TEST_P(deconv_scale, fp16_scale_out) {
         data("weights", get_mem(get_weights_layout(p))),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
         deconvolution("deconv", "input", { "weights" }, p.groups, p.stride, p.pad),
-        scale("scale", "deconv", "scale_data", optional_data_type{data_types::f16}),
+        scale("scale", "deconv", "scale_data", optional_data_type{ data_types::f16 }),
         reorder("out", "scale", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale,
-    ::testing::ValuesIn(std::vector<deconv_test_params>{
-        deconv_test_params{ CASE_DECONV_U8S8_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3, 2, 3 },
-        // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_8, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale, ::testing::ValuesIn(std::vector<deconv_test_params>{
+    deconv_test_params{ CASE_DECONV_U8S8_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3, 2, 3 },
+    // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3, 2, 3 },
-        // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3, 2, 3 },
+    // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 3 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 3 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 3 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 3 },
 }));
 
 class deconv_actv_eltw_actv : public DeconvolutionFusingTest {};
@@ -4858,91 +4949,91 @@ TEST_P(deconv_actv_eltw_actv, basic) {
         data("eltw_data", get_mem(get_output_layout(p))),
         deconvolution("deconv", "input", { "weights" }, p.groups, p.stride, p.pad),
         activation("act1", "deconv", activation_func::relu),
-        eltwise("eltw", {"act1", "eltw_data"}, eltwise_mode::sum),
+        eltwise("eltw", { "act1", "eltw_data" }, eltwise_mode::sum),
         activation("act2", "eltw", activation_func::relu),
         reorder("out", "act2", p.default_format, data_types::f32)
     );
+
     // Need much higher tolerance because of deconvolution -> convolution optimization
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_actv_eltw_actv,
-    ::testing::ValuesIn(std::vector<deconv_test_params>{
-        // Some fusings disabled under deconvolution -> convolution optimization
-        deconv_test_params{ CASE_DECONV_FP32_1, 3, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_8, 2, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_actv_eltw_actv, ::testing::ValuesIn(std::vector<deconv_test_params>{
+    // Some fusings disabled under deconvolution -> convolution optimization
+    deconv_test_params{ CASE_DECONV_FP32_1, 3, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_FP16_1, 3, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_1, 3, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3, 2, 5 },
-        // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3, 2, 5 },
+    // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3, 2, 5 },
-        // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3, 2, 5 },
+    // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_9, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_9, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_9, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_9, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 5 },
 }));
 
 class deconv_scale_actv_quant_i8 : public DeconvolutionFusingTest {};
@@ -4967,83 +5058,82 @@ TEST_P(deconv_scale_actv_quant_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale_actv_quant_i8,
-    ::testing::ValuesIn(std::vector<deconv_test_params>{
-        deconv_test_params{ CASE_DECONV_FP32_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_8, 2, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale_actv_quant_i8, ::testing::ValuesIn(std::vector<deconv_test_params>{
+    deconv_test_params{ CASE_DECONV_FP32_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_FP16_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3, 2, 5 },
-        // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3, 2, 5 },
+    // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3, 2, 5 },
-        // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3, 2, 5 },
+    // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 5 },
-        // FIXME no quantize implementation for bs_fs_yx_bsv16_fsv16 format AND add_required_reorders pass completely ruins data types
-        // add_required_reorders pass tries to reorder everything to output type if no format exists, this ruins fp32 -> int8 quantize
-        //deconv_test_params{ CASE_DECONV_FP32_3D_9, 3, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 5 },
+    // FIXME no quantize implementation for bs_fs_yx_bsv16_fsv16 format AND add_required_reorders pass completely ruins data types
+    // add_required_reorders pass tries to reorder everything to output type if no format exists, this ruins fp32 -> int8 quantize
+    //deconv_test_params{ CASE_DECONV_FP32_3D_9, 3, 5 },
 
-        deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 5 },
-        //deconv_test_params{ CASE_DECONV_FP16_3D_9, 3, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 5 },
+    //deconv_test_params{ CASE_DECONV_FP16_3D_9, 3, 5 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 5 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 5 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 5 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 5 },
 }));
 
 class deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8 : public DeconvolutionFusingTest {};
@@ -5067,7 +5157,7 @@ TEST_P(deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8, basic) {
         scale("scale1", "deconv", "scale1_data"),
         activation("actv1", "scale1", activation_func::relu),
         quantize("quant1", "actv1", "in1_lo", "in1_hi", "out1_lo", "out1_hi", 256, data_types::u8),
-        eltwise("eltw", {"quant1", "eltw_data"}, eltwise_mode::sum, p.default_type),
+        eltwise("eltw", { "quant1", "eltw_data" }, eltwise_mode::sum, p.default_type),
         scale("scale2", "eltw", "scale2_data"),
         activation("actv2", "scale2", activation_func::relu),
         quantize("quant2", "actv2", "in2_lo", "in2_hi", "out2_lo", "out2_hi", 255, data_types::i8),
@@ -5078,168 +5168,168 @@ TEST_P(deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8,
-    ::testing::ValuesIn(std::vector<deconv_test_params>{
-        deconv_test_params{ CASE_DECONV_FP32_1, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_2, 2, 9 },
-        // deconv_test_params{ CASE_DECONV_FP32_3, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_6, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_8, 2, 9 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8, ::testing::ValuesIn(std::vector<deconv_test_params>{
+    deconv_test_params{ CASE_DECONV_FP32_1, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_2, 2, 9 },
+    // deconv_test_params{ CASE_DECONV_FP32_3, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_6, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_8, 2, 9 },
 
-        deconv_test_params{ CASE_DECONV_FP16_1, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_2, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_6, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_8, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_1, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_2, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_6, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_8, 2, 9 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_1, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_2, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3, 2, 9 },
-        // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_6, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_8, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_1, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_2, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3, 2, 9 },
+    // deconv_test_params{ CASE_DECONV_U8S8_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_6, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_8, 2, 9 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_1, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_2, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3, 2, 9 },
-        // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_6, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_8, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_1, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_2, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3, 2, 9 },
+    // deconv_test_params{ CASE_DECONV_S8S8_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_6, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_8, 2, 9 },
 
-        deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 9 },
-        // deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 9 },
-        // deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 9 },
-        // deconv_test_params{ CASE_DECONV_FP32_3D_9, 6, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 9 },
+    // deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 9 },
+    // deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 9 },
+    // deconv_test_params{ CASE_DECONV_FP32_3D_9, 6, 9 },
 
-        deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 9 },
-        // deconv_test_params{ CASE_DECONV_FP16_3D_9, 6, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 9 },
+    // deconv_test_params{ CASE_DECONV_FP16_3D_9, 6, 9 },
 
-        deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 9 },
 
-        deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 9 },
-        deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 9 },
+    deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 9 },
 }));
 
 class deconv_scale_activation_quantize_i8_eltwise_quantize_u8 : public ConvEltwTest {};
 TEST_P(deconv_scale_activation_quantize_i8_eltwise_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 deconvolution("deconv_prim", "input", { "weights" }, p.groups, p.stride, p.pad),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.f / p.kernel.count())),
-                 scale("scale", "deconv_prim", "scale_data"),
-                 activation("activation", "scale", activation_func::relu),
-                 data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_low", get_mem(get_single_element_layout(p), -127)),
-                 data("out_high", get_mem(get_single_element_layout(p), 127)),
-                 quantize("quant", "activation", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
-                 data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
-                 eltwise("eltw", { "quant", "eltwise_data" }, eltwise_mode::sum, p.default_type),
-                 data("in_low2", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_high2", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_low2", get_mem(get_single_element_layout(p), 0)),
-                 data("out_high2", get_mem(get_single_element_layout(p), 255)),
-                 quantize("quant2", "eltw", "in_low2", "in_high2", "out_low2", "out_high2", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quant2", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        deconvolution("deconv_prim", "input", { "weights" }, p.groups, p.stride, p.pad),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.f / p.kernel.count())),
+        scale("scale", "deconv_prim", "scale_data"),
+        activation("activation", "scale", activation_func::relu),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -127)),
+        data("out_high", get_mem(get_single_element_layout(p), 127)),
+        quantize("quant", "activation", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
+        eltwise("eltw", { "quant", "eltwise_data" }, eltwise_mode::sum, p.default_type),
+        data("in_low2", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high2", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low2", get_mem(get_single_element_layout(p), 0)),
+        data("out_high2", get_mem(get_single_element_layout(p), 255)),
+        quantize("quant2", "eltw", "in_low2", "in_high2", "out_low2", "out_high2", 256, data_types::u8),
+        reorder("reorder_bfyx", "quant2", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale_activation_quantize_i8_eltwise_quantize_u8,
-                        ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_1, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_2, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_3, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_4, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_5, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_6, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_7, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_8, 2, 7},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_scale_activation_quantize_i8_eltwise_quantize_u8, ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_1, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_2, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_3, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_4, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_5, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_6, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_7, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_8, 2, 7 },
 
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_1, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_2, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_3, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_4, 2, 7},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_5, 2, 7},
-
-                        }));
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_1, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_2, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_3, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_4, 2, 7 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_5, 2, 7 },
 
+}));
 
 class deconv_activation_eltwise_diff_sizes : public ConvEltwTest {};
 TEST_P(deconv_activation_eltwise_diff_sizes, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
-                 deconvolution("deconv_prim", "input", { "weights" }, p.groups, p.stride, p.pad),
-                 activation("activation", "deconv_prim", activation_func::relu),
-                 eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, p.default_type),
-                 reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.eltw_shape })),
+        deconvolution("deconv_prim", "input", { "weights" }, p.groups, p.stride, p.pad),
+        activation("activation", "deconv_prim", activation_func::relu),
+        eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum, p.default_type),
+        reorder("reorder_bfyx", "sum", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_activation_eltwise_diff_sizes,
-                        ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_1, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_2, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_3, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_4, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_5, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_6, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_7, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_FP32_8, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, deconv_activation_eltwise_diff_sizes, ::testing::ValuesIn(std::vector<conv_eltw_test_params>{
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_1, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_2, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_3, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_4, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_5, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_6, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_7, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_FP32_8, 2, 4 },
 
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_1, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_2, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_3, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_4, 2, 4},
-                                conv_eltw_test_params{CASE_DECONV_ELTW_i8_5, 2, 4},
-                        }));
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_1, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_2, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_3, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_4, 2, 4 },
+    conv_eltw_test_params{ CASE_DECONV_ELTW_i8_5, 2, 4 },
+}));
 
 #ifdef ENABLE_ONEDNN_FOR_GPU
 /* ----------------------------------------------------------------------------------------------------- */
 /* --------------------------------------- Concat cases ------------------------------------------------ */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct concat_test_params {
     tensor in_shape;
     data_types data_type;
@@ -5251,8 +5341,8 @@ struct concat_test_params {
     std::string kernel_name;
 };
 
-#define CASE_CONCAT_F32_1 {1, 8, 4, 4}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_CONCAT_F16_1 {1, 8, 4, 4}, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_CONCAT_F32_1 { 1, 8, 4, 4 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_CONCAT_F16_1 { 1, 8, 4, 4 }, data_types::f16, format::bfyx, data_types::f16, format::bfyx
 
 class ConcatOneDNNFusingTest : public ::BaseFusingTest<concat_test_params> {
 public:
@@ -5270,10 +5360,10 @@ public:
         onednn_options.set_option(build_option::optimize_data(true));
         cldnn_options.set_option(build_option::optimize_data(true));
 
-        implementation_desc onednn_impl = {p.input_format, "", impl_types::onednn};
-        implementation_desc cldnn_impl = {p.input_format, "", impl_types::ocl};
-        onednn_options.set_option(build_option::force_implementations({{"concat", onednn_impl}}));
-        cldnn_options.set_option(build_option::force_implementations({{"concat", cldnn_impl}}));
+        implementation_desc onednn_impl = { p.input_format, "", impl_types::onednn };
+        implementation_desc cldnn_impl = { p.input_format, "", impl_types::ocl };
+        onednn_options.set_option(build_option::force_implementations({ { "concat", onednn_impl } }));
+        cldnn_options.set_option(build_option::force_implementations({ { "concat", cldnn_impl } }));
 
         // for onednn fusing test, topology_non_fused means cldnn, topology_fused is onednn
         network network_fused_cldnn(this->engine, this->topology_non_fused, cldnn_options);
@@ -5304,9 +5394,12 @@ public:
         compare(network_fused_cldnn, network_fused_onednn, p);
     }
 
-    layout get_input_layout(concat_test_params& p) { return layout{p.data_type, p.input_format, p.in_shape}; }
+    layout get_input_layout(concat_test_params& p) {
+        return layout{ p.data_type, p.input_format, p.in_shape };
+    }
+
     layout get_per_channel_layout(concat_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -5317,11 +5410,11 @@ TEST_P(concat_onednn_activation, along_f) {
         input_layout("input0", get_input_layout(p)),
         input_layout("input1", get_input_layout(p)),
         concatenation("concat",
-                        { "input0", "input1" },
-                        concatenation::concatenation_axis::along_f,
-                        data_types::f16,
-                        "",
-                        padding{ { 0, 0, 0, 0 }, 0 }),
+                      { "input0", "input1" },
+                      concatenation::concatenation_axis::along_f,
+                      data_types::f16,
+                      "",
+                      padding{ { 0, 0, 0, 0 }, 0 }),
         activation("act", "concat", activation_func::relu),
         reorder("reorder_bfyx", "act", cldnn::format::bfyx, p.default_type)
     );
@@ -5333,19 +5426,19 @@ TEST_P(concat_onednn_activation, along_f) {
 class concat_onednn_eltwise : public ConcatOneDNNFusingTest {};
 TEST_P(concat_onednn_eltwise, along_f) {
     auto p = GetParam();
-    layout data_layout(p.default_type, p.default_format, tensor{1, p.in_shape.feature[0]*2, 1, 1});
+    layout data_layout(p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0]*2, 1, 1 });
 
     create_topologies(
         input_layout("input0", get_input_layout(p)),
         input_layout("input1", get_input_layout(p)),
-        data("scale_data", get_mem(data_layout, 1.0f / tensor{1, 1, 4, 4}.count())),
+        data("scale_data", get_mem(data_layout, 1.0f / tensor{ 1, 1, 4, 4 }.count())),
         concatenation("concat",
-                        { "input0", "input1" },
-                        concatenation::concatenation_axis::along_f,
-                        data_types::f16,
-                        "",
-                        padding{ { 0, 0, 0, 0 }, 0 }),
-        eltwise("scale", {"concat", "scale_data"}, eltwise_mode::prod, p.default_type),
+                      { "input0", "input1" },
+                      concatenation::concatenation_axis::along_f,
+                      data_types::f16,
+                      "",
+                      padding{ { 0, 0, 0, 0 }, 0 }),
+        eltwise("scale", { "concat", "scale_data" }, eltwise_mode::prod, p.default_type),
         reorder("reorder_bfyx", "scale", cldnn::format::bfyx, p.default_type)
     );
 
@@ -5353,23 +5446,20 @@ TEST_P(concat_onednn_eltwise, along_f) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        concat_onednn_activation,
-                        ::testing::ValuesIn(std::vector<concat_test_params>{
-                            concat_test_params{CASE_CONCAT_F16_1, 3, 3, ""},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, concat_onednn_activation, ::testing::ValuesIn(std::vector<concat_test_params>{
+    concat_test_params{ CASE_CONCAT_F16_1, 3, 3, "" },
+}));
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        concat_onednn_eltwise,
-                        ::testing::ValuesIn(std::vector<concat_test_params>{
-                            concat_test_params{CASE_CONCAT_F32_1, 4, 4, ""},
-                            concat_test_params{CASE_CONCAT_F16_1, 4, 4, ""},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, concat_onednn_eltwise, ::testing::ValuesIn(std::vector<concat_test_params>{
+    concat_test_params{ CASE_CONCAT_F32_1, 4, 4, "" },
+    concat_test_params{ CASE_CONCAT_F16_1, 4, 4, "" },
+}));
 #endif
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* --------------------------------------- Pooling cases ----------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct pooling_test_params {
     tensor in_shape;
     data_types data_type;
@@ -5382,65 +5472,65 @@ struct pooling_test_params {
     std::string kernel_name;
 };
 
-#define CASE_POOLING_F32_1 {1, 16, 8, 8}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_2 {2, 16, 8, 8}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_3 {1, 32, 10, 10}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_4 {1, 32, 10, 10}, data_types::f32, format::fs_b_yx_fsv32, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_5 {1, 32, 10, 10}, data_types::f32, format::byxf, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_6 {1, 32, 40, 40}, data_types::f32, format::byxf, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_7 {16, 32, 10, 10}, data_types::f32, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_8 {16, 32, 10, 10}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_9 {16, 32, 10, 10}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_10 {16, 32, 10, 10, 10}, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_F32_11 {1, 1, 3, 3}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_1 { 1, 16, 8, 8 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_2 { 2, 16, 8, 8 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_3 { 1, 32, 10, 10 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_4 { 1, 32, 10, 10 }, data_types::f32, format::fs_b_yx_fsv32, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_5 { 1, 32, 10, 10 }, data_types::f32, format::byxf, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_6 { 1, 32, 40, 40 }, data_types::f32, format::byxf, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_7 { 16, 32, 10, 10 }, data_types::f32, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_8 { 16, 32, 10, 10 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_9 { 16, 32, 10, 10 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_10 { 16, 32, 10, 10, 10 }, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F32_11 { 1, 1, 3, 3 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_POOLING_F32_F16_1 {1, 16, 8, 8}, data_types::f32, format::bfyx, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_2 {2, 16, 8, 8}, data_types::f32, format::bfyx, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_3 {1, 32, 10, 10}, data_types::f32, format::bfyx, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_4 {1, 32, 10, 10}, data_types::f32, format::fs_b_yx_fsv32, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_5 {1, 32, 10, 10}, data_types::f32, format::byxf, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_6 {1, 32, 40, 40}, data_types::f32, format::byxf, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_7 {16, 32, 10, 10}, data_types::f32, format::bs_fs_yx_bsv16_fsv16, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_8 {16, 32, 10, 10}, data_types::f32, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_9 {16, 32, 10, 10}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f16, format::bfyx
-#define CASE_POOLING_F32_F16_10 {16, 32, 10, 10, 10}, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_1 { 1, 16, 8, 8 }, data_types::f32, format::bfyx, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_2 { 2, 16, 8, 8 }, data_types::f32, format::bfyx, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_3 { 1, 32, 10, 10 }, data_types::f32, format::bfyx, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_4 { 1, 32, 10, 10 }, data_types::f32, format::fs_b_yx_fsv32, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_5 { 1, 32, 10, 10 }, data_types::f32, format::byxf, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_6 { 1, 32, 40, 40 }, data_types::f32, format::byxf, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_7 { 16, 32, 10, 10 }, data_types::f32, format::bs_fs_yx_bsv16_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_8 { 16, 32, 10, 10 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_9 { 16, 32, 10, 10 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F32_F16_10 { 16, 32, 10, 10, 10 }, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::bfyx
 
-#define CASE_POOLING_F16_1 {1, 16, 8, 8}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_3 {1, 32, 10, 10}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_4 {1, 32, 10, 10}, data_types::f16, format::fs_b_yx_fsv32, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_5 {1, 32, 10, 10}, data_types::f16, format::byxf, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_6 {1, 32, 40, 40}, data_types::f16, format::byxf, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_7 {16, 32, 10, 10}, data_types::f16, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_8 {16, 32, 10, 10}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_9 {16, 32, 10, 10, 10}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_F16_10 {16, 32, 10, 10, 10}, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_1 { 1, 16, 8, 8 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_3 { 1, 32, 10, 10 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_4 { 1, 32, 10, 10 }, data_types::f16, format::fs_b_yx_fsv32, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_5 { 1, 32, 10, 10 }, data_types::f16, format::byxf, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_6 { 1, 32, 40, 40 }, data_types::f16, format::byxf, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_7 { 16, 32, 10, 10 }, data_types::f16, format::bs_fs_yx_bsv16_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_8 { 16, 32, 10, 10 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_9 { 16, 32, 10, 10, 10 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_F16_10 { 16, 32, 10, 10, 10 }, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::bfyx
 
-#define CASE_POOLING_F16_FP16_1 {1, 32, 10, 10}, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_POOLING_F16_FP16_2 {1, 32, 10, 10}, data_types::f16, format::fs_b_yx_fsv32, data_types::f16, format::bfyx
-#define CASE_POOLING_F16_FP16_3 {1, 32, 10, 10}, data_types::f16, format::byxf, data_types::f16, format::bfyx
-#define CASE_POOLING_F16_FP16_4 {1, 32, 40, 40}, data_types::f16, format::byxf, data_types::f16, format::bfyx
-#define CASE_POOLING_F16_FP16_5 {16, 32, 10, 10}, data_types::f16, format::bs_fs_yx_bsv16_fsv16, data_types::f16, format::bfyx
-#define CASE_POOLING_F16_FP16_6 {16, 32, 10, 10}, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
-#define CASE_POOLING_F16_FP16_7 {16, 32, 10, 10, 10}, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::bfyx
-#define CASE_POOLING_F16_FP16_8 {16, 32, 10, 10, 10}, data_types::f16, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_1 { 1, 32, 10, 10 }, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_2 { 1, 32, 10, 10 }, data_types::f16, format::fs_b_yx_fsv32, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_3 { 1, 32, 10, 10 }, data_types::f16, format::byxf, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_4 { 1, 32, 40, 40 }, data_types::f16, format::byxf, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_5 { 16, 32, 10, 10 }, data_types::f16, format::bs_fs_yx_bsv16_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_6 { 16, 32, 10, 10 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_7 { 16, 32, 10, 10, 10 }, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::bfyx
+#define CASE_POOLING_F16_FP16_8 { 16, 32, 10, 10, 10 }, data_types::f16, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::bfyx
 
-#define CASE_POOLING_U8_1 {1, 16, 8, 8}, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_POOLING_U8_2 {2, 16, 8, 8}, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_U8_3 {1, 32, 10, 10}, data_types::u8, format::b_fs_yx_fsv4, data_types::f32, format::b_fs_yx_fsv4
-#define CASE_POOLING_U8_5 {16, 32, 10, 10, 10}, data_types::u8, format::b_fs_zyx_fsv32, data_types::f32, format::bfyx
-#define CASE_POOLING_U8_6 {16, 32, 10, 10, 10}, data_types::u8, format::b_fs_zyx_fsv32, data_types::f32, format::bfyx
+#define CASE_POOLING_U8_1 { 1, 16, 8, 8 }, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_U8_2 { 2, 16, 8, 8 }, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_U8_3 { 1, 32, 10, 10 }, data_types::u8, format::b_fs_yx_fsv4, data_types::f32, format::b_fs_yx_fsv4
+#define CASE_POOLING_U8_5 { 16, 32, 10, 10, 10 }, data_types::u8, format::b_fs_zyx_fsv32, data_types::f32, format::bfyx
+#define CASE_POOLING_U8_6 { 16, 32, 10, 10, 10 }, data_types::u8, format::b_fs_zyx_fsv32, data_types::f32, format::bfyx
 
-#define CASE_POOLING_U8_FP16_3 {1, 32, 10, 10}, data_types::u8, format::b_fs_yx_fsv4, data_types::f16, format::b_fs_yx_fsv4
-#define CASE_POOLING_U8_FP16_5 {16, 32, 10, 10, 10}, data_types::u8, format::b_fs_zyx_fsv32, data_types::f16, format::bfyx
-#define CASE_POOLING_U8_FP16_6 {16, 32, 10, 10, 10}, data_types::u8, format::b_fs_zyx_fsv32, data_types::f16, format::bfyx
+#define CASE_POOLING_U8_FP16_3 { 1, 32, 10, 10 }, data_types::u8, format::b_fs_yx_fsv4, data_types::f16, format::b_fs_yx_fsv4
+#define CASE_POOLING_U8_FP16_5 { 16, 32, 10, 10, 10 }, data_types::u8, format::b_fs_zyx_fsv32, data_types::f16, format::bfyx
+#define CASE_POOLING_U8_FP16_6 { 16, 32, 10, 10, 10 }, data_types::u8, format::b_fs_zyx_fsv32, data_types::f16, format::bfyx
 
-#define CASE_POOLING_I8_1 {1, 16, 8, 8}, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_POOLING_I8_2 {2, 16, 8, 8}, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_POOLING_I8_5 {1, 32, 10, 10}, data_types::i8, format::b_fs_yx_fsv4, data_types::f32, format::b_fs_yx_fsv4
-#define CASE_POOLING_I8_6 {16, 32, 10, 10, 10}, data_types::i8, format::b_fs_zyx_fsv32, data_types::f32, format::bfyx
+#define CASE_POOLING_I8_1 { 1, 16, 8, 8 }, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_POOLING_I8_2 { 2, 16, 8, 8 }, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_POOLING_I8_5 { 1, 32, 10, 10 }, data_types::i8, format::b_fs_yx_fsv4, data_types::f32, format::b_fs_yx_fsv4
+#define CASE_POOLING_I8_6 { 16, 32, 10, 10, 10 }, data_types::i8, format::b_fs_zyx_fsv32, data_types::f32, format::bfyx
 
-#define CASE_POOLING_I8_FP16_5 {1, 32, 10, 10}, data_types::i8, format::b_fs_yx_fsv4, data_types::f16, format::b_fs_yx_fsv4
-#define CASE_POOLING_I8_FP16_6 {16, 32, 10, 10, 10}, data_types::i8, format::b_fs_zyx_fsv32, data_types::f16, format::bfyx
+#define CASE_POOLING_I8_FP16_5 { 1, 32, 10, 10 }, data_types::i8, format::b_fs_yx_fsv4, data_types::f16, format::b_fs_yx_fsv4
+#define CASE_POOLING_I8_FP16_6 { 16, 32, 10, 10, 10 }, data_types::i8, format::b_fs_zyx_fsv32, data_types::f16, format::bfyx
 
 class PoolingFusingTest : public ::BaseFusingTest<pooling_test_params> {
 public:
@@ -5449,8 +5539,8 @@ public:
         build_options options;
         options.set_option(build_option::optimize_data(true));
         if (!p.kernel_name.empty()) {
-            implementation_desc impl = {p.input_format, p.kernel_name};
-            options.set_option(build_option::force_implementations({{"pooling", impl}}));
+            implementation_desc impl = { p.input_format, p.kernel_name };
+            options.set_option(build_option::force_implementations({ { "pooling", impl } }));
         }
         network network_not_fused(this->engine, this->topology_non_fused, bo_not_fused);
         network network_fused(this->engine, this->topology_fused, options);
@@ -5478,9 +5568,12 @@ public:
         compare(network_not_fused, network_fused, p);
     }
 
-    layout get_input_layout(pooling_test_params& p) { return layout{p.data_type, p.input_format, p.in_shape}; }
+    layout get_input_layout(pooling_test_params& p) {
+        return layout{ p.data_type, p.input_format, p.in_shape };
+    }
+
     layout get_per_channel_layout(pooling_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -5489,42 +5582,42 @@ TEST_P(pooling_f32_activation, basic) {
     auto p = GetParam();
     create_topologies(
         input_layout("input", get_input_layout(p)),
-        pooling("pooling", "input", p.pool_mode, tensor{1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}),
+        pooling("pooling", "input", p.pool_mode, tensor{ 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }),
         activation("act", "pooling", activation_func::relu),
-        reorder("output_reorder", "act", format::bfyx, data_types::f32));
+        reorder("output_reorder", "act", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        pooling_f32_activation,
-                        ::testing::ValuesIn(std::vector<pooling_test_params>{
-                            pooling_test_params{CASE_POOLING_F32_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_F32_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_F16_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_F16_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_U8_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_U8_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_U8_2, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_U8_2, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_I8_2, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_2, 2, 3, pooling_mode::average, ""},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, pooling_f32_activation, ::testing::ValuesIn(std::vector<pooling_test_params>{
+    pooling_test_params{ CASE_POOLING_F32_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_F32_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_F16_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_F16_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_U8_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_U8_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_U8_2, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_U8_2, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_I8_2, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_2, 2, 3, pooling_mode::average, "" },
+}));
 
 class pooling_f32_scale : public PoolingFusingTest {};
 TEST_P(pooling_f32_scale, basic) {
     auto p = GetParam();
     create_topologies(
         input_layout("input", get_input_layout(p)),
-        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{1, 1, 3, 3}.count())),
-        pooling("pooling", "input", p.pool_mode, tensor{1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{ 1, 1, 3, 3 }.count())),
+        pooling("pooling", "input", p.pool_mode, tensor{ 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }),
         scale("scale", "pooling", "scale_data"),
-        reorder("output_reorder", "scale", format::bfyx, data_types::f32));
+        reorder("output_reorder", "scale", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
@@ -5534,47 +5627,48 @@ TEST_P(pooling_f32_scale, fp16_scale_out) {
     auto p = GetParam();
     create_topologies(
         input_layout("input", get_input_layout(p)),
-        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{1, 1, 3, 3}.count())),
-        pooling("pooling", "input", p.pool_mode, tensor{1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}),
-        scale("scale", "pooling", "scale_data", optional_data_type{data_types::f16}),
-        reorder("output_reorder", "scale", format::bfyx, data_types::f32));
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{ 1, 1, 3, 3 }.count())),
+        pooling("pooling", "input", p.pool_mode, tensor{ 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }),
+        scale("scale", "pooling", "scale_data", optional_data_type{ data_types::f16 }),
+        reorder("output_reorder", "scale", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        pooling_f32_scale,
-                        ::testing::ValuesIn(std::vector<pooling_test_params>{
-                            pooling_test_params{CASE_POOLING_F32_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_F32_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_F16_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_F16_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_U8_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_U8_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_U8_2, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_U8_2, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 3, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_I8_2, 2, 3, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_2, 2, 3, pooling_mode::average, ""},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, pooling_f32_scale, ::testing::ValuesIn(std::vector<pooling_test_params>{
+    pooling_test_params{ CASE_POOLING_F32_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_F32_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_F16_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_F16_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_U8_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_U8_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_U8_2, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_U8_2, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 3, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_I8_2, 2, 3, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_2, 2, 3, pooling_mode::average, "" },
+}));
 
 class pooling_scale_activation_quantize : public PoolingFusingTest {};
 TEST_P(pooling_scale_activation_quantize, basic) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{1, 1, 4, 4}.count())),
-                      pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
-                      scale("scale", "pooling", "scale_data"),
-                      activation("activation", "scale", activation_func::relu),
-                      quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::u8),
-                      reorder("output_reorder", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{ 1, 1, 4, 4 }.count())),
+        pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
+        scale("scale", "pooling", "scale_data"),
+        activation("activation", "scale", activation_func::relu),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::u8),
+        reorder("output_reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
@@ -5583,17 +5677,19 @@ TEST_P(pooling_scale_activation_quantize, basic) {
 TEST_P(pooling_scale_activation_quantize, i8_output_data_type) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), -127, 127)),
-                      data("out_hi", get_mem(get_single_element_layout(p), -127, 127)),
-                      data("scale_data",  get_mem(get_per_channel_layout(p), 1.0f / tensor{1, 1, 4, 4}.count())),
-                      pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
-                      scale("scale", "pooling", "scale_data"),
-                      activation("activation", "scale", activation_func::relu),
-                      quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                      reorder("output_reorder", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127, 127)),
+        data("out_hi", get_mem(get_single_element_layout(p), -127, 127)),
+        data("scale_data",  get_mem(get_per_channel_layout(p), 1.0f / tensor{ 1, 1, 4, 4 }.count())),
+        pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
+        scale("scale", "pooling", "scale_data"),
+        activation("activation", "scale", activation_func::relu),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("output_reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
@@ -5602,78 +5698,78 @@ TEST_P(pooling_scale_activation_quantize, i8_output_data_type) {
 TEST_P(pooling_scale_activation_quantize, per_channel) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{1, 1, 4, 4}.count())),
-                      pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
-                      scale("scale", "pooling", "scale_data"),
-                      activation("activation", "scale", activation_func::atan),
-                      quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::u8),
-                      reorder("output_reorder", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{ 1, 1, 4, 4 }.count())),
+        pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
+        scale("scale", "pooling", "scale_data"),
+        activation("activation", "scale", activation_func::atan),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::u8),
+        reorder("output_reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                         pooling_scale_activation_quantize,
-                         ::testing::ValuesIn(std::vector<pooling_test_params>{
-                            // Input type: FP32
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 5, pooling_mode::average, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 5, pooling_mode::max, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 5, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 5, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_4, 2, 5, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F32_4, 2, 5, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F32_5, 2, 5, pooling_mode::average, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F32_5, 2, 5, pooling_mode::max, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F32_6, 2, 5, pooling_mode::average, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F32_6, 2, 5, pooling_mode::max, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F32_7, 2, 5, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_7, 2, 5, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_8, 2, 5, pooling_mode::average, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F32_8, 2, 5, pooling_mode::max, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F32_9, 2, 5, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_9, 2, 5, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_10, 2, 5, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_10, 2, 5, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, pooling_scale_activation_quantize, ::testing::ValuesIn(std::vector<pooling_test_params>{
+    // Input type: FP32
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 5, pooling_mode::average, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 5, pooling_mode::max, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 5, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 5, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_4, 2, 5, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F32_4, 2, 5, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F32_5, 2, 5, pooling_mode::average, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F32_5, 2, 5, pooling_mode::max, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F32_6, 2, 5, pooling_mode::average, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F32_6, 2, 5, pooling_mode::max, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F32_7, 2, 5, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_7, 2, 5, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_8, 2, 5, pooling_mode::average, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F32_8, 2, 5, pooling_mode::max, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F32_9, 2, 5, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_9, 2, 5, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_10, 2, 5, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_10, 2, 5, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
 
-                            // Input type: INT8
-                            pooling_test_params{CASE_POOLING_I8_5, 2, 5, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_I8_5, 2, 5, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_I8_6, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_I8_6, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref"},
+    // Input type: INT8
+    pooling_test_params{ CASE_POOLING_I8_5, 2, 5, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_I8_5, 2, 5, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_I8_6, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_I8_6, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref" },
 
-                            // Input type: UINT8
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 5, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 5, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_U8_5, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_5, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_6, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_6, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref"},
-                        }));
+    // Input type: UINT8
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 5, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 5, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_U8_5, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_5, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_6, 2, 5, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_6, 2, 5, pooling_mode::max, "pooling_gpu_int8_ref" },
+}));
 
-INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu,
-                         pooling_scale_activation_quantize,
-                         ::testing::ValuesIn(std::vector<pooling_test_params>{
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 5, pooling_mode::average, "pooling_gpu_average_opt"},  //currently not enabled, fusing not upported
-                        }));
+INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu, pooling_scale_activation_quantize, ::testing::ValuesIn(std::vector<pooling_test_params>{
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 5, pooling_mode::average, "pooling_gpu_average_opt" },  //currently not enabled, fusing not upported
+}));
 
 class pooling_scale_activation : public PoolingFusingTest {};
 TEST_P(pooling_scale_activation, basic) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{1, 1, 4, 4}.count())),
-                      pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
-                      scale("scale", "pooling", "scale_data"),
-                      activation("activation", "scale", activation_func::relu),
-                      reorder("output_reorder", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{ 1, 1, 4, 4 }.count())),
+        pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
+        scale("scale", "pooling", "scale_data"),
+        activation("activation", "scale", activation_func::relu),
+        reorder("output_reorder", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
@@ -5682,132 +5778,132 @@ TEST_P(pooling_scale_activation, basic) {
 TEST_P(pooling_scale_activation, eltwise_mul) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{1, 1, 4, 4}.count())),
-                      pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
-                      eltwise("scale", {"pooling", "scale_data"}, eltwise_mode::prod, p.default_type),
-                      activation("activation", "scale", activation_func::relu),
-                      reorder("output_reorder", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / tensor{ 1, 1, 4, 4 }.count())),
+        pooling("pooling", "input", "", p.pool_mode, tensor(1, 1, 4, 4), tensor(1, 1, 2, 2)),
+        eltwise("scale", { "pooling", "scale_data" }, eltwise_mode::prod, p.default_type),
+        activation("activation", "scale", activation_func::relu),
+        reorder("output_reorder", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        pooling_scale_activation,
-                        ::testing::ValuesIn(std::vector<pooling_test_params>{
-                            // Input type: F32
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_3, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_4, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F32_4, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F32_5, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F32_5, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F32_6, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F32_6, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F32_7, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_7, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_8, 2, 4, pooling_mode::average, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F32_8, 2, 4, pooling_mode::max, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F32_9, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_9, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_10, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_10, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, pooling_scale_activation, ::testing::ValuesIn(std::vector<pooling_test_params>{
+    // Input type: F32
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_3, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_4, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F32_4, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F32_5, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F32_5, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F32_6, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F32_6, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F32_7, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_7, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_8, 2, 4, pooling_mode::average, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F32_8, 2, 4, pooling_mode::max, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F32_9, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_9, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_10, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_10, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
 
-                            // Input type: INT8
-                            pooling_test_params{CASE_POOLING_I8_5, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_I8_5, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_I8_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_I8_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
+    // Input type: INT8
+    pooling_test_params{ CASE_POOLING_I8_5, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_I8_5, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_I8_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_I8_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
 
-                            // Input type: UINT8
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_U8_3, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_U8_5, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_5, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
+    // Input type: UINT8
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_U8_3, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_U8_5, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_5, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
 
-                            // Input type: FP16  Output type: F32
-                            pooling_test_params{CASE_POOLING_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_4, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F16_4, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F16_5, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F16_5, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F16_6, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F16_6, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F16_7, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F16_7, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F16_8, 2, 4, pooling_mode::average, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F16_8, 2, 4, pooling_mode::max, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F16_9, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_9, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_10, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F16_10, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
+    // Input type: FP16  Output type: F32
+    pooling_test_params{ CASE_POOLING_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_4, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F16_4, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F16_5, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F16_5, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F16_6, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F16_6, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F16_7, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F16_7, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F16_8, 2, 4, pooling_mode::average, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F16_8, 2, 4, pooling_mode::max, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F16_9, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_9, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_10, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F16_10, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
 
-                            // Input type: FP16
-                            pooling_test_params{CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_2, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_2, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_3, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_3, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_4, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_4, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_5, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_5, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_6, 2, 4, pooling_mode::average, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_6, 2, 4, pooling_mode::max, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_7, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_7, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_8, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F16_FP16_8, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
+    // Input type: FP16
+    pooling_test_params{ CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_1, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_2, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_2, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_3, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_3, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_4, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_4, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_5, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_5, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_6, 2, 4, pooling_mode::average, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_6, 2, 4, pooling_mode::max, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_7, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_7, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_8, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F16_FP16_8, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
 
-                            // Input type: FP32
-                            pooling_test_params{CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt"},
-                            pooling_test_params{CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_F16_4, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F32_F16_4, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32"},
-                            pooling_test_params{CASE_POOLING_F32_F16_5, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F32_F16_5, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt"},
-                            pooling_test_params{CASE_POOLING_F32_F16_6, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F32_F16_6, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt"},
-                            pooling_test_params{CASE_POOLING_F32_F16_7, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_F16_7, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_F16_8, 2, 4, pooling_mode::average, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F32_F16_8, 2, 4, pooling_mode::max, "pooling_gpu_blocked"},
-                            pooling_test_params{CASE_POOLING_F32_F16_9, 2, 4, pooling_mode::average, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_F16_9, 2, 4, pooling_mode::max, "pooling_gpu_ref"},
-                            pooling_test_params{CASE_POOLING_F32_F16_10, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16"},
-                            pooling_test_params{CASE_POOLING_F32_F16_10, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16"},
+    // Input type: FP32
+    pooling_test_params{ CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_bfyx_block_opt" },
+    pooling_test_params{ CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_F16_3, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_F16_4, 2, 4, pooling_mode::average, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F32_F16_4, 2, 4, pooling_mode::max, "pooling_gpu_fs_b_yx_fsv32" },
+    pooling_test_params{ CASE_POOLING_F32_F16_5, 2, 4, pooling_mode::average, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F32_F16_5, 2, 4, pooling_mode::max, "pooling_gpu_byxf_padding_opt" },
+    pooling_test_params{ CASE_POOLING_F32_F16_6, 2, 4, pooling_mode::average, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F32_F16_6, 2, 4, pooling_mode::max, "pooling_gpu_byxf_opt" },
+    pooling_test_params{ CASE_POOLING_F32_F16_7, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_F16_7, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_F16_8, 2, 4, pooling_mode::average, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F32_F16_8, 2, 4, pooling_mode::max, "pooling_gpu_blocked" },
+    pooling_test_params{ CASE_POOLING_F32_F16_9, 2, 4, pooling_mode::average, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_F16_9, 2, 4, pooling_mode::max, "pooling_gpu_ref" },
+    pooling_test_params{ CASE_POOLING_F32_F16_10, 2, 4, pooling_mode::average, "pooling_gpu_bsv16_fsv16" },
+    pooling_test_params{ CASE_POOLING_F32_F16_10, 2, 4, pooling_mode::max, "pooling_gpu_bsv16_fsv16" },
 
-                            // Input type: INT8
-                            pooling_test_params{CASE_POOLING_I8_FP16_5, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_I8_FP16_5, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_I8_FP16_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_I8_FP16_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
+    // Input type: INT8
+    pooling_test_params{ CASE_POOLING_I8_FP16_5, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_I8_FP16_5, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_I8_FP16_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_I8_FP16_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
 
-                            // Input type: UINT8
-                            pooling_test_params{CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4"},
-                            pooling_test_params{CASE_POOLING_U8_FP16_5, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_FP16_5, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_FP16_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref"},
-                            pooling_test_params{CASE_POOLING_U8_FP16_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref"},
-                     }));
+    // Input type: UINT8
+    pooling_test_params{ CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::average, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_U8_FP16_3, 2, 4, pooling_mode::max, "pooling_gpu_b_fs_yx_fsv4" },
+    pooling_test_params{ CASE_POOLING_U8_FP16_5, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_FP16_5, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_FP16_6, 2, 4, pooling_mode::average, "pooling_gpu_int8_ref" },
+    pooling_test_params{ CASE_POOLING_U8_FP16_6, 2, 4, pooling_mode::max, "pooling_gpu_int8_ref" },
+}));
 
 #ifdef ENABLE_ONEDNN_FOR_GPU
 class PoolingOneDNNFusingTest : public ::BaseFusingTest<pooling_test_params> {
@@ -5825,10 +5921,10 @@ public:
         onednn_options.set_option(build_option::optimize_data(true));
         cldnn_options.set_option(build_option::optimize_data(true));
 
-        implementation_desc onednn_impl = {p.input_format, "", impl_types::onednn};
-        implementation_desc cldnn_impl = {p.input_format, "", impl_types::ocl};
-        onednn_options.set_option(build_option::force_implementations({{"pooling", onednn_impl}}));
-        cldnn_options.set_option(build_option::force_implementations({{"pooling", cldnn_impl}}));
+        implementation_desc onednn_impl = { p.input_format, "", impl_types::onednn };
+        implementation_desc cldnn_impl = { p.input_format, "", impl_types::ocl };
+        onednn_options.set_option(build_option::force_implementations({ { "pooling", onednn_impl } }));
+        cldnn_options.set_option(build_option::force_implementations({ { "pooling", cldnn_impl } }));
 
         // for onednn fusing test, topology_non_fused means cldnn, topology_fused is onednn
         network network_fused_cldnn(this->engine, this->topology_non_fused, cldnn_options);
@@ -5857,9 +5953,12 @@ public:
         compare(network_fused_cldnn, network_fused_onednn, p);
     }
 
-    layout get_input_layout(pooling_test_params& p) { return layout{p.data_type, p.input_format, p.in_shape}; }
+    layout get_input_layout(pooling_test_params& p) {
+        return layout{ p.data_type, p.input_format, p.in_shape };
+    }
+
     layout get_per_channel_layout(pooling_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -5868,9 +5967,10 @@ TEST_P(pooling_onednn_activation1, basic) {
     auto p = GetParam();
     create_topologies(
         input_layout("input", get_input_layout(p)),
-        pooling("pooling", "input", p.pool_mode, tensor{1, 1, 3, 3}, tensor{1}, tensor{{0, 0, 1, 1, 0, 0}, 0}),
+        pooling("pooling", "input", p.pool_mode, tensor{ 1, 1, 3, 3 }, tensor{ 1 }, tensor{ { 0, 0, 1, 1, 0, 0 }, 0 }),
         activation("act", "pooling", activation_func::relu),
-        reorder("output_reorder", "act", format::bfyx, data_types::f32));
+        reorder("output_reorder", "act", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
@@ -5883,36 +5983,34 @@ TEST_P(pooling_onednn_activation2, basic) {
         input_layout("input", get_input_layout(p)),
         pooling("pooling", "input", p.pool_mode, { 1, 1, 3, 3 }, { 1, 1, 1, 1 }),
         activation("act", "pooling", activation_func::relu),
-        reorder("output_reorder", "act", format::bfyx, data_types::f32));
+        reorder("output_reorder", "act", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        pooling_onednn_activation1,
-                        ::testing::ValuesIn(std::vector<pooling_test_params>{
-                            // pooling_test_params{CASE_POOLING_F32_1, 2, 2, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_F16_1, 2, 2, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 2, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_U8_1, 2, 2, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_U8_2, 2, 2, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_1, 2, 2, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_I8_2, 2, 2, pooling_mode::max, ""},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, pooling_onednn_activation1, ::testing::ValuesIn(std::vector<pooling_test_params>{
+    // pooling_test_params{ CASE_POOLING_F32_1, 2, 2, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_F16_1, 2, 2, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 2, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_U8_1, 2, 2, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_U8_2, 2, 2, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_1, 2, 2, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_I8_2, 2, 2, pooling_mode::max, "" },
+}));
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        pooling_onednn_activation2,
-                        ::testing::ValuesIn(std::vector<pooling_test_params>{
-                            pooling_test_params{CASE_POOLING_F32_11, 2, 2, pooling_mode::max, ""},
-                            pooling_test_params{CASE_POOLING_F32_11, 2, 2, pooling_mode::average, ""},
-                            pooling_test_params{CASE_POOLING_F32_11, 2, 2, pooling_mode::average_no_padding, ""},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, pooling_onednn_activation2, ::testing::ValuesIn(std::vector<pooling_test_params>{
+    pooling_test_params{ CASE_POOLING_F32_11, 2, 2, pooling_mode::max, "" },
+    pooling_test_params{ CASE_POOLING_F32_11, 2, 2, pooling_mode::average, "" },
+    pooling_test_params{ CASE_POOLING_F32_11, 2, 2, pooling_mode::average_no_padding, "" },
+}));
 #endif
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* -------------------------------- DepthToSpace cases ------------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct depth_to_space_test_params {
     tensor input_size;
     tensor output_size;
@@ -5926,14 +6024,14 @@ struct depth_to_space_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_DEPTH_TO_SPACE_F32_1 {1, 16, 8, 10}, {1, 4, 16, 20}, depth_to_space_mode::blocks_first, data_types::f32, format::bfyx, 2, data_types::f32, format::bfyx
-#define CASE_DEPTH_TO_SPACE_F32_2 {1, 32, 8, 8},  {1, 2, 32, 32}, depth_to_space_mode::blocks_first, data_types::f32, format::b_fs_yx_fsv16, 4, data_types::f32, format::bfyx
-#define CASE_DEPTH_TO_SPACE_F16_1 {1, 12, 8, 8},  {1, 3, 16, 16}, depth_to_space_mode::blocks_first, data_types::f16, format::bfyx, 2, data_types::f32, format::bfyx
-#define CASE_DEPTH_TO_SPACE_F16_2 {1, 16, 9, 8},  {1, 1, 36, 32}, depth_to_space_mode::blocks_first, data_types::f16, format::b_fs_yx_fsv16, 4, data_types::f32, format::bfyx
-#define CASE_DEPTH_TO_SPACE_U8_1  {1, 128, 8, 8}, {1, 2, 64, 64}, depth_to_space_mode::blocks_first, data_types::u8, format::bfyx, 8, data_types::f32, format::bfyx
-#define CASE_DEPTH_TO_SPACE_U8_2  {1, 128, 4, 8}, {1, 8, 16, 32}, depth_to_space_mode::blocks_first, data_types::u8, format::b_fs_yx_fsv16, 4, data_types::f32, format::bfyx
-#define CASE_DEPTH_TO_SPACE_I8_1  {1, 16, 8, 8},  {1, 4, 16, 16}, depth_to_space_mode::blocks_first, data_types::i8, format::bfyx, 2, data_types::f32, format::bfyx
-#define CASE_DEPTH_TO_SPACE_I8_2  {1, 256, 8, 8}, {1, 4, 64, 64}, depth_to_space_mode::blocks_first, data_types::i8, format::b_fs_yx_fsv16, 8, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_F32_1 { 1, 16, 8, 10 }, { 1, 4, 16, 20 }, depth_to_space_mode::blocks_first, data_types::f32, format::bfyx, 2, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_F32_2 { 1, 32, 8, 8 },  { 1, 2, 32, 32 }, depth_to_space_mode::blocks_first, data_types::f32, format::b_fs_yx_fsv16, 4, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_F16_1 { 1, 12, 8, 8 },  { 1, 3, 16, 16 }, depth_to_space_mode::blocks_first, data_types::f16, format::bfyx, 2, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_F16_2 { 1, 16, 9, 8 },  { 1, 1, 36, 32 }, depth_to_space_mode::blocks_first, data_types::f16, format::b_fs_yx_fsv16, 4, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_U8_1  { 1, 128, 8, 8 }, { 1, 2, 64, 64 }, depth_to_space_mode::blocks_first, data_types::u8, format::bfyx, 8, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_U8_2  { 1, 128, 4, 8 }, { 1, 8, 16, 32 }, depth_to_space_mode::blocks_first, data_types::u8, format::b_fs_yx_fsv16, 4, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_I8_1  { 1, 16, 8, 8 },  { 1, 4, 16, 16 }, depth_to_space_mode::blocks_first, data_types::i8, format::bfyx, 2, data_types::f32, format::bfyx
+#define CASE_DEPTH_TO_SPACE_I8_2  { 1, 256, 8, 8 }, { 1, 4, 64, 64 }, depth_to_space_mode::blocks_first, data_types::i8, format::b_fs_yx_fsv16, 8, data_types::f32, format::bfyx
 
 class DepthToSpaceFusingsTest : public ::BaseFusingTest<depth_to_space_test_params> {
 public:
@@ -5949,109 +6047,112 @@ public:
         compare(network_not_fused, network_fused, p);
     }
 
-    layout get_input_layout(depth_to_space_test_params& p) { return layout{p.input_type, p.input_format, p.input_size}; }
+    layout get_input_layout(depth_to_space_test_params& p) {
+        return layout{ p.input_type, p.input_format, p.input_size };
+    }
 
     layout get_per_channel_layout(depth_to_space_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.output_size.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.output_size.feature[0], 1, 1 } };
+    }
+
+    format get_input_format(depth_to_space_test_params &p) {
+        return p.input_format;
     }
-    format get_input_format(depth_to_space_test_params &p) { return p.input_format; }
 };
 
 class depth_to_space_quantize_i8 : public DepthToSpaceFusingsTest {};
 TEST_P(depth_to_space_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      depth_to_space("depth_to_space", "input", p.block_size, p.mode),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -128)),
-                      data("out_high", get_mem(get_single_element_layout(p), 127)),
-                      quantize("quant", "depth_to_space", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
-                      reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        depth_to_space("depth_to_space", "input", p.block_size, p.mode),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -128)),
+        data("out_high", get_mem(get_single_element_layout(p), 127)),
+        quantize("quant", "depth_to_space", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
+        reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    depth_to_space_quantize_i8,
-    ::testing::ValuesIn(std::vector<depth_to_space_test_params>{
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F32_1, 2, 3},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F32_2, 2, 3},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F16_1, 2, 3},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F16_2, 2, 3},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, depth_to_space_quantize_i8, ::testing::ValuesIn(std::vector<depth_to_space_test_params>{
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F32_1, 2, 3 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F32_2, 2, 3 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F16_1, 2, 3 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F16_2, 2, 3 },
+}));
 
 class depth_to_space_scale_act_eltwise_quantize_u8 : public DepthToSpaceFusingsTest {};
 TEST_P(depth_to_space_scale_act_eltwise_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      depth_to_space("depth_to_space", "input", p.block_size, p.mode),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "depth_to_space", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), 0)),
-                      data("out_high", get_mem(get_single_element_layout(p), 255)),
-                      quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
-                      reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        depth_to_space("depth_to_space", "input", p.block_size, p.mode),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "depth_to_space", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), 0)),
+        data("out_high", get_mem(get_single_element_layout(p), 255)),
+        quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
+        reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    depth_to_space_scale_act_eltwise_quantize_u8,
-    ::testing::ValuesIn(std::vector<depth_to_space_test_params>{
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F32_1, 2, 6},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F32_2, 2, 6},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F16_1, 2, 6},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F16_2, 2, 6},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_U8_1, 2, 6},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_U8_2, 2, 6},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_I8_1, 2, 6},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_I8_2, 2, 6},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, depth_to_space_scale_act_eltwise_quantize_u8, ::testing::ValuesIn(std::vector<depth_to_space_test_params>{
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F32_1, 2, 6 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F32_2, 2, 6 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F16_1, 2, 6 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F16_2, 2, 6 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_U8_1, 2, 6 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_U8_2, 2, 6 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_I8_1, 2, 6 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_I8_2, 2, 6 },
+}));
 
 
 class depth_to_space_scale_act_eltw : public DepthToSpaceFusingsTest {};
 TEST_P(depth_to_space_scale_act_eltw, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      depth_to_space("depth_to_space", "input", p.block_size, p.mode),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "depth_to_space", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      reorder("reorder_bfyx", "eltw", format::bfyx, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        depth_to_space("depth_to_space", "input", p.block_size, p.mode),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "depth_to_space", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        reorder("reorder_bfyx", "eltw", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    depth_to_space_scale_act_eltw,
-    ::testing::ValuesIn(std::vector<depth_to_space_test_params>{
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F32_1, 2, 5},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F32_2, 2, 5},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F16_1, 2, 5},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_F16_2, 2, 5},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_U8_1, 2, 5},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_U8_2, 2, 5},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_I8_1, 2, 5},
-        depth_to_space_test_params{CASE_DEPTH_TO_SPACE_I8_2, 2, 5},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, depth_to_space_scale_act_eltw, ::testing::ValuesIn(std::vector<depth_to_space_test_params>{
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F32_1, 2, 5 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F32_2, 2, 5 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F16_1, 2, 5 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_F16_2, 2, 5 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_U8_1, 2, 5 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_U8_2, 2, 5 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_I8_1, 2, 5 },
+    depth_to_space_test_params{ CASE_DEPTH_TO_SPACE_I8_2, 2, 5 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* -------------------------------- SpaceToDepth cases ------------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct space_to_depth_params {
     tensor input_size;
     tensor output_size;
@@ -6065,14 +6166,14 @@ struct space_to_depth_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_SPACE_TO_DEPTH_F32_1 {2, 2, 8, 10}, {2, 8, 4, 5}, space_to_depth::depth_mode::blocks_first, data_types::f32, format::bfyx, 2, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_DEPTH_F32_2 {1, 2, 6, 6, 6},  {1, 54, 2, 2, 2}, space_to_depth::depth_mode::depth_first, data_types::f32, format::bfzyx, 3, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_DEPTH_F16_1 {1, 3, 6, 6},  {1, 12, 3, 3}, space_to_depth::depth_mode::blocks_first, data_types::f16, format::bfyx, 2, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_DEPTH_F16_2 {2, 1, 3, 3},  {2, 9, 1, 1}, space_to_depth::depth_mode::blocks_first, data_types::f16, format::b_fs_yx_fsv16, 3, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_DEPTH_U8_1  {2, 2, 8, 10}, {2, 8, 4, 5}, space_to_depth::depth_mode::blocks_first, data_types::u8, format::bfyx, 2, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_DEPTH_U8_2  {1, 2, 6, 6, 6},  {1, 54, 2, 2, 2}, space_to_depth::depth_mode::depth_first, data_types::u8, format::bfzyx, 3, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_DEPTH_I8_1  {1, 3, 6, 6},  {1, 12, 3, 3}, space_to_depth::depth_mode::blocks_first, data_types::i8, format::bfyx, 2, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_DEPTH_I8_2  {2, 1, 3, 3},  {2, 9, 1, 1}, space_to_depth::depth_mode::blocks_first, data_types::i8, format::b_fs_yx_fsv16, 3, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_F32_1 { 2, 2, 8, 10 }, { 2, 8, 4, 5 }, space_to_depth::depth_mode::blocks_first, data_types::f32, format::bfyx, 2, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_F32_2 { 1, 2, 6, 6, 6 },  { 1, 54, 2, 2, 2 }, space_to_depth::depth_mode::depth_first, data_types::f32, format::bfzyx, 3, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_F16_1 { 1, 3, 6, 6 },  { 1, 12, 3, 3 }, space_to_depth::depth_mode::blocks_first, data_types::f16, format::bfyx, 2, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_F16_2 { 2, 1, 3, 3 },  { 2, 9, 1, 1 }, space_to_depth::depth_mode::blocks_first, data_types::f16, format::b_fs_yx_fsv16, 3, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_U8_1  { 2, 2, 8, 10 }, { 2, 8, 4, 5 }, space_to_depth::depth_mode::blocks_first, data_types::u8, format::bfyx, 2, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_U8_2  { 1, 2, 6, 6, 6 },  { 1, 54, 2, 2, 2 }, space_to_depth::depth_mode::depth_first, data_types::u8, format::bfzyx, 3, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_I8_1  { 1, 3, 6, 6 },  { 1, 12, 3, 3 }, space_to_depth::depth_mode::blocks_first, data_types::i8, format::bfyx, 2, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_DEPTH_I8_2  { 2, 1, 3, 3 },  { 2, 9, 1, 1 }, space_to_depth::depth_mode::blocks_first, data_types::i8, format::b_fs_yx_fsv16, 3, data_types::f32, format::bfyx
 
 class SpaceToDepthFusingsTest : public ::BaseFusingTest<space_to_depth_params> {
 public:
@@ -6088,109 +6189,112 @@ public:
         compare(network_not_fused, network_fused, p);
     }
 
-    layout get_input_layout(space_to_depth_params& p) { return layout{p.input_type, p.input_format, p.input_size}; }
+    layout get_input_layout(space_to_depth_params& p) {
+        return layout{ p.input_type, p.input_format, p.input_size };
+    }
 
     layout get_per_channel_layout(space_to_depth_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.output_size.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.output_size.feature[0], 1, 1 } };
+    }
+
+    format get_input_format(space_to_depth_params &p) {
+        return p.input_format;
     }
-    format get_input_format(space_to_depth_params &p) { return p.input_format; }
 };
 
 class space_to_depth_quantize_i8 : public SpaceToDepthFusingsTest {};
 TEST_P(space_to_depth_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      space_to_depth("space_to_depth", "input", p.mode, p.block_size),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -128)),
-                      data("out_high", get_mem(get_single_element_layout(p), 127)),
-                      quantize("quant", "space_to_depth", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
-                      reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        space_to_depth("space_to_depth", "input", p.mode, p.block_size),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -128)),
+        data("out_high", get_mem(get_single_element_layout(p), 127)),
+        quantize("quant", "space_to_depth", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
+        reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    space_to_depth_quantize_i8,
-    ::testing::ValuesIn(std::vector<space_to_depth_params>{
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F32_1, 2, 3},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F32_2, 2, 3},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F16_1, 2, 3},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F16_2, 2, 3},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, space_to_depth_quantize_i8, ::testing::ValuesIn(std::vector<space_to_depth_params>{
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F32_1, 2, 3 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F32_2, 2, 3 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F16_1, 2, 3 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F16_2, 2, 3 },
+}));
 
 class space_to_depth_scale_act_eltwise_quantize_u8 : public SpaceToDepthFusingsTest {};
 TEST_P(space_to_depth_scale_act_eltwise_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      space_to_depth("space_to_depth", "input", p.mode, p.block_size),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "space_to_depth", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), 0)),
-                      data("out_high", get_mem(get_single_element_layout(p), 255)),
-                      quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
-                      reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        space_to_depth("space_to_depth", "input", p.mode, p.block_size),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "space_to_depth", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), 0)),
+        data("out_high", get_mem(get_single_element_layout(p), 255)),
+        quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
+        reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    space_to_depth_scale_act_eltwise_quantize_u8,
-    ::testing::ValuesIn(std::vector<space_to_depth_params>{
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F32_1, 2, 6},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F32_2, 2, 6},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F16_1, 2, 6},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F16_2, 2, 6},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_U8_1, 2, 6},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_U8_2, 2, 6},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_I8_1, 2, 6},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_I8_2, 2, 6},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, space_to_depth_scale_act_eltwise_quantize_u8, ::testing::ValuesIn(std::vector<space_to_depth_params>{
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F32_1, 2, 6 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F32_2, 2, 6 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F16_1, 2, 6 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F16_2, 2, 6 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_U8_1, 2, 6 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_U8_2, 2, 6 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_I8_1, 2, 6 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_I8_2, 2, 6 },
+}));
 
 
 class space_to_depth_scale_act_eltw : public SpaceToDepthFusingsTest {};
 TEST_P(space_to_depth_scale_act_eltw, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      space_to_depth("space_to_depth", "input", p.mode, p.block_size),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "space_to_depth", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      reorder("reorder_bfyx", "eltw", format::bfyx, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        space_to_depth("space_to_depth", "input", p.mode, p.block_size),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "space_to_depth", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        reorder("reorder_bfyx", "eltw", format::bfyx, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    space_to_depth_scale_act_eltw,
-    ::testing::ValuesIn(std::vector<space_to_depth_params>{
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F32_1, 2, 5},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F32_2, 2, 5},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F16_1, 2, 5},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_F16_2, 2, 5},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_U8_1, 2, 5},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_U8_2, 2, 5},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_I8_1, 2, 5},
-        space_to_depth_params{CASE_SPACE_TO_DEPTH_I8_2, 2, 5},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, space_to_depth_scale_act_eltw, ::testing::ValuesIn(std::vector<space_to_depth_params>{
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F32_1, 2, 5 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F32_2, 2, 5 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F16_1, 2, 5 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_F16_2, 2, 5 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_U8_1, 2, 5 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_U8_2, 2, 5 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_I8_1, 2, 5 },
+    space_to_depth_params{ CASE_SPACE_TO_DEPTH_I8_2, 2, 5 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ------------------------------------------ Gather cases --------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct gather_test_params {
     tensor dictionary_shape;
     tensor indices_shape;
@@ -6205,29 +6309,29 @@ struct gather_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_GATHER_FP32_1 {2, 3, 1, 4}, {4, 1, 1, 1}, {4, 3, 1, 4}, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GATHER_FP32_2 {3, 2, 1, 2}, {2, 3, 1, 1}, {2, 3, 2, 2}, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GATHER_FP32_3 {3, 1, 1, 2}, {2, 1, 1, 1}, {3, 2, 1, 2}, format::bfyx, cldnn::gather::gather_axis::along_f, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GATHER_FP32_4 {5, 3, 2, 2}, {3, 1, 1, 1}, {5, 2, 2, 3}, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_GATHER_FP32_5 {2, 3, 1, 2}, {1, 3, 1, 1}, {2, 3, 3, 1}, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GATHER_FP32_1 { 2, 3, 1, 4 }, { 4, 1, 1, 1 }, { 4, 3, 1, 4 }, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GATHER_FP32_2 { 3, 2, 1, 2 }, { 2, 3, 1, 1 }, { 2, 3, 2, 2 }, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GATHER_FP32_3 { 3, 1, 1, 2 }, { 2, 1, 1, 1 }, { 3, 2, 1, 2 }, format::bfyx, cldnn::gather::gather_axis::along_f, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GATHER_FP32_4 { 5, 3, 2, 2 }, { 3, 1, 1, 1 }, { 5, 2, 2, 3 }, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_GATHER_FP32_5 { 2, 3, 1, 2 }, { 1, 3, 1, 1 }, { 2, 3, 3, 1 }, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_GATHER_FP16_1 {2, 3, 1, 4}, {4, 1, 1, 1}, {4, 3, 1, 4}, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_GATHER_FP16_2 {3, 2, 1, 2}, {2, 3, 1, 1}, {2, 3, 2, 2}, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_GATHER_FP16_3 {3, 1, 1, 2}, {2, 1, 1, 1}, {3, 2, 1, 2}, format::bfyx, cldnn::gather::gather_axis::along_f, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_GATHER_FP16_4 {5, 3, 2, 2}, {3, 1, 1, 1}, {5, 2, 2, 3}, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_GATHER_FP16_5 {2, 3, 1, 2}, {1, 3, 1, 1}, {2, 3, 3, 1}, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GATHER_FP16_1 { 2, 3, 1, 4 }, { 4, 1, 1, 1 }, { 4, 3, 1, 4 }, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GATHER_FP16_2 { 3, 2, 1, 2 }, { 2, 3, 1, 1 }, { 2, 3, 2, 2 }, format::bfyx, cldnn::gather::gather_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GATHER_FP16_3 { 3, 1, 1, 2 }, { 2, 1, 1, 1 }, { 3, 2, 1, 2 }, format::bfyx, cldnn::gather::gather_axis::along_f, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GATHER_FP16_4 { 5, 3, 2, 2 }, { 3, 1, 1, 1 }, { 5, 2, 2, 3 }, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_GATHER_FP16_5 { 2, 3, 1, 2 }, { 1, 3, 1, 1 }, { 2, 3, 3, 1 }, format::bfyx, cldnn::gather::gather_axis::along_y, data_types::f16, format::bfyx, data_types::f16, format::bfyx
 
-#define CASE_GATHER_5D_FP32_1 {2, 3, 1, 4, 1}, {4, 1, 1, 1}, {4, 3, 1, 4, 1}, format::bfzyx, cldnn::gather::gather_axis::along_b, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_GATHER_5D_FP32_2 {2, 3, 2, 2, 2}, {2, 1, 1, 1}, {2, 2, 2, 2, 2}, format::bfzyx, cldnn::gather::gather_axis::along_f, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_GATHER_5D_FP32_3 {5, 3, 2, 2, 2}, {3, 1, 1, 1}, {5, 3, 2, 3, 2}, format::bfzyx, cldnn::gather::gather_axis::along_y, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_GATHER_5D_FP32_4 {2, 3, 1, 4, 4}, {2, 1, 1, 1}, {2, 3, 1, 4, 2}, format::bfzyx, cldnn::gather::gather_axis::along_z, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_GATHER_5D_FP32_5 {3, 1, 5, 2, 1}, {2, 1, 1, 1}, {3, 1, 2, 2, 1}, format::bfzyx, cldnn::gather::gather_axis::along_x, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_GATHER_5D_FP32_1 { 2, 3, 1, 4, 1 }, { 4, 1, 1, 1 }, { 4, 3, 1, 4, 1 }, format::bfzyx, cldnn::gather::gather_axis::along_b, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_GATHER_5D_FP32_2 { 2, 3, 2, 2, 2 }, { 2, 1, 1, 1 }, { 2, 2, 2, 2, 2 }, format::bfzyx, cldnn::gather::gather_axis::along_f, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_GATHER_5D_FP32_3 { 5, 3, 2, 2, 2 }, { 3, 1, 1, 1 }, { 5, 3, 2, 3, 2 }, format::bfzyx, cldnn::gather::gather_axis::along_y, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_GATHER_5D_FP32_4 { 2, 3, 1, 4, 4 }, { 2, 1, 1, 1 }, { 2, 3, 1, 4, 2 }, format::bfzyx, cldnn::gather::gather_axis::along_z, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_GATHER_5D_FP32_5 { 3, 1, 5, 2, 1 }, { 2, 1, 1, 1 }, { 3, 1, 2, 2, 1 }, format::bfzyx, cldnn::gather::gather_axis::along_x, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
 
-#define CASE_GATHER_5D_FP16_1 {3, 2, 1, 2, 1}, {2, 1, 1, 1}, {2, 2, 2, 2, 1}, format::bfzyx, cldnn::gather::gather_axis::along_b, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_GATHER_5D_FP16_2 {1, 3, 1, 2, 1}, {2, 1, 1, 1}, {1, 2, 1, 2, 1}, format::bfzyx, cldnn::gather::gather_axis::along_f, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_GATHER_5D_FP16_3 {2, 3, 1, 3, 3}, {1, 2, 1, 1}, {2, 3, 1, 2, 3}, format::bfzyx, cldnn::gather::gather_axis::along_y, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_GATHER_5D_FP16_4 {3, 2, 2, 2, 2}, {2, 1, 1, 1}, {3, 2, 2, 2, 2}, format::bfzyx, cldnn::gather::gather_axis::along_z, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_GATHER_5D_FP16_5 {1, 1, 2, 1, 1}, {3, 1, 1, 1}, {1, 1, 3, 1, 1}, format::bfzyx, cldnn::gather::gather_axis::along_x, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_GATHER_5D_FP16_1 { 3, 2, 1, 2, 1 }, { 2, 1, 1, 1 }, { 2, 2, 2, 2, 1 }, format::bfzyx, cldnn::gather::gather_axis::along_b, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_GATHER_5D_FP16_2 { 1, 3, 1, 2, 1 }, { 2, 1, 1, 1 }, { 1, 2, 1, 2, 1 }, format::bfzyx, cldnn::gather::gather_axis::along_f, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_GATHER_5D_FP16_3 { 2, 3, 1, 3, 3 }, { 1, 2, 1, 1 }, { 2, 3, 1, 2, 3 }, format::bfzyx, cldnn::gather::gather_axis::along_y, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_GATHER_5D_FP16_4 { 3, 2, 2, 2, 2 }, { 2, 1, 1, 1 }, { 3, 2, 2, 2, 2 }, format::bfzyx, cldnn::gather::gather_axis::along_z, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_GATHER_5D_FP16_5 { 1, 1, 2, 1, 1 }, { 3, 1, 1, 1 }, { 1, 1, 3, 1, 1 }, format::bfzyx, cldnn::gather::gather_axis::along_x, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
 
 class GatherPrimitiveFusingTest : public ::BaseFusingTest<gather_test_params> {
 public:
@@ -6269,14 +6373,15 @@ public:
     }
 
     layout get_per_channel_layout(gather_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
     }
 };
 
 class gather_quantize : public GatherPrimitiveFusingTest {};
 TEST_P(gather_quantize, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("gather_indices", get_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p)))),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
@@ -6291,37 +6396,37 @@ TEST_P(gather_quantize, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_quantize,
-    ::testing::ValuesIn(std::vector<gather_test_params>{
-                        gather_test_params{ CASE_GATHER_FP32_1, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP32_2, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP32_3, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP32_4, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP32_5, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_quantize, ::testing::ValuesIn(std::vector<gather_test_params>{
+    gather_test_params{ CASE_GATHER_FP32_1, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP32_2, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP32_3, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP32_4, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP32_5, 2, 3 },
 
-                        gather_test_params{ CASE_GATHER_FP16_1, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP16_2, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP16_3, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP16_4, 2, 3 },
-                        gather_test_params{ CASE_GATHER_FP16_5, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP16_1, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP16_2, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP16_3, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP16_4, 2, 3 },
+    gather_test_params{ CASE_GATHER_FP16_5, 2, 3 },
 
-                        gather_test_params{ CASE_GATHER_5D_FP32_1, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_2, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_3, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_4, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_5, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP32_1, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP32_2, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP32_3, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP32_4, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP32_5, 2, 3 },
 
-                        gather_test_params{ CASE_GATHER_5D_FP16_1, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_2, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_3, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_4, 2, 3 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_5, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP16_1, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP16_2, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP16_3, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP16_4, 2, 3 },
+    gather_test_params{ CASE_GATHER_5D_FP16_5, 2, 3 },
 }));
 
 class gather_scale_activation : public GatherPrimitiveFusingTest {};
 TEST_P(gather_scale_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("gather_indices", get_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p)))),
         data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
         gather("gather_prim", "input", "gather_indices", p.axis, p.out_format, p.out_shape),
@@ -6334,36 +6439,35 @@ TEST_P(gather_scale_activation, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_scale_activation,
-    ::testing::ValuesIn(std::vector<gather_test_params>{
-                        gather_test_params{ CASE_GATHER_FP32_1, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP32_2, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP32_3, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP32_4, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP32_5, 2, 4 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_scale_activation, ::testing::ValuesIn(std::vector<gather_test_params>{
+    gather_test_params{ CASE_GATHER_FP32_1, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP32_2, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP32_3, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP32_4, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP32_5, 2, 4 },
 
-                        gather_test_params{ CASE_GATHER_FP16_1, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP16_2, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP16_3, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP16_4, 2, 4 },
-                        gather_test_params{ CASE_GATHER_FP16_5, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP16_1, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP16_2, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP16_3, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP16_4, 2, 4 },
+    gather_test_params{ CASE_GATHER_FP16_5, 2, 4 },
 
-                        gather_test_params{ CASE_GATHER_5D_FP32_1, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_2, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_3, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_4, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP32_5, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP32_1, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP32_2, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP32_3, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP32_4, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP32_5, 2, 4 },
 
-                        gather_test_params{ CASE_GATHER_5D_FP16_1, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_2, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_3, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_4, 2, 4 },
-                        gather_test_params{ CASE_GATHER_5D_FP16_5, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP16_1, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP16_2, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP16_3, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP16_4, 2, 4 },
+    gather_test_params{ CASE_GATHER_5D_FP16_5, 2, 4 },
 }));
 
 
 /* ----------------------------------------------------------------------------------------------------- */
-/* ------------------------------------------ ScatterUpdate cases --------------------------------------------- */
+/* ------------------------------------------ ScatterUpdate cases -------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
 
 struct scatter_update_test_params {
@@ -6379,29 +6483,29 @@ struct scatter_update_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_SCATTER_UPDATE_FP32_1 {2, 4, 1, 1}, {2, 1, 1, 1}, {2, 4, 1, 1}, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_UPDATE_FP32_2 {8, 1, 1, 1}, {4, 1, 1, 1}, {4, 1, 1, 1}, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_UPDATE_FP32_3 {4, 3, 1, 1}, {2, 2, 1, 1}, {2, 2, 1, 3}, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_UPDATE_FP32_4 {2, 5, 1, 2}, {2, 2, 1, 1}, {2, 2, 2, 2}, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_UPDATE_FP32_5 {2, 2, 1, 4}, {2, 2, 1, 1}, {2, 2, 2, 2}, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_UPDATE_FP32_1 { 2, 4, 1, 1 }, { 2, 1, 1, 1 }, { 2, 4, 1, 1 }, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_UPDATE_FP32_2 { 8, 1, 1, 1 }, { 4, 1, 1, 1 }, { 4, 1, 1, 1 }, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_UPDATE_FP32_3 { 4, 3, 1, 1 }, { 2, 2, 1, 1 }, { 2, 2, 1, 3 }, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_UPDATE_FP32_4 { 2, 5, 1, 2 }, { 2, 2, 1, 1 }, { 2, 2, 2, 2 }, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_UPDATE_FP32_5 { 2, 2, 1, 4 }, { 2, 2, 1, 1 }, { 2, 2, 2, 2 }, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_SCATTER_UPDATE_FP16_1 {2, 4, 1, 1}, {1, 1, 1, 2}, {2, 1, 2, 1}, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_UPDATE_FP16_2 {8, 2, 1, 20}, {2, 3, 1, 1}, {2, 3, 20, 2}, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_UPDATE_FP16_3 {2, 2, 4, 1}, {3, 1, 1, 1}, {2, 2, 3, 1}, cldnn::scatter_update::scatter_update_axis::along_x, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_UPDATE_FP16_4 {6, 2, 1, 1}, {1, 2, 1, 2}, {1, 2, 2, 2}, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_UPDATE_FP16_5 {3, 1, 1, 5}, {2, 2, 1, 1}, {3, 1, 2, 2}, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_UPDATE_FP16_1 { 2, 4, 1, 1 }, { 1, 1, 1, 2 }, { 2, 1, 2, 1 }, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_UPDATE_FP16_2 { 8, 2, 1, 20 }, { 2, 3, 1, 1 }, { 2, 3, 20, 2 }, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_UPDATE_FP16_3 { 2, 2, 4, 1 }, { 3, 1, 1, 1 }, { 2, 2, 3, 1 }, cldnn::scatter_update::scatter_update_axis::along_x, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_UPDATE_FP16_4 { 6, 2, 1, 1 }, { 1, 2, 1, 2 }, { 1, 2, 2, 2 }, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_UPDATE_FP16_5 { 3, 1, 1, 5 }, { 2, 2, 1, 1 }, { 3, 1, 2, 2 }, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f16, format::bfyx, data_types::f16, format::bfyx
 
-#define CASE_SCATTER_UPDATE_5D_FP32_1 {4, 3, 1, 4, 1}, {4, 1, 1, 1}, {4, 3, 1, 4, 1}, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP32_2 {2, 3, 2, 2, 2}, {2, 1, 1, 1}, {2, 2, 2, 2, 2}, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP32_3 {5, 3, 2, 4, 2}, {3, 1, 1, 1}, {5, 3, 2, 3, 2}, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP32_4 {2, 3, 1, 4, 4}, {2, 1, 1, 1}, {2, 3, 1, 4, 2}, cldnn::scatter_update::scatter_update_axis::along_z, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP32_5 {3, 1, 5, 2, 1}, {2, 1, 1, 1}, {3, 1, 2, 2, 1}, cldnn::scatter_update::scatter_update_axis::along_x, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP32_1 { 4, 3, 1, 4, 1 }, { 4, 1, 1, 1 }, { 4, 3, 1, 4, 1 }, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP32_2 { 2, 3, 2, 2, 2 }, { 2, 1, 1, 1 }, { 2, 2, 2, 2, 2 }, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP32_3 { 5, 3, 2, 4, 2 }, { 3, 1, 1, 1 }, { 5, 3, 2, 3, 2 }, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP32_4 { 2, 3, 1, 4, 4 }, { 2, 1, 1, 1 }, { 2, 3, 1, 4, 2 }, cldnn::scatter_update::scatter_update_axis::along_z, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP32_5 { 3, 1, 5, 2, 1 }, { 2, 1, 1, 1 }, { 3, 1, 2, 2, 1 }, cldnn::scatter_update::scatter_update_axis::along_x, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
 
-#define CASE_SCATTER_UPDATE_5D_FP16_1 {3, 2, 1, 2, 1}, {2, 1, 1, 1}, {2, 2, 2, 2, 1}, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP16_2 {1, 3, 1, 2, 1}, {2, 1, 1, 1}, {1, 2, 1, 2, 1}, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP16_3 {2, 3, 1, 3, 3}, {1, 2, 1, 1}, {2, 3, 1, 2, 3}, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP16_4 {3, 2, 2, 2, 2}, {2, 1, 1, 1}, {3, 2, 2, 2, 2}, cldnn::scatter_update::scatter_update_axis::along_z, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_SCATTER_UPDATE_5D_FP16_5 {1, 1, 4, 1, 1}, {3, 1, 1, 1}, {1, 1, 3, 1, 1}, cldnn::scatter_update::scatter_update_axis::along_x, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP16_1 { 3, 2, 1, 2, 1 }, { 2, 1, 1, 1 }, { 2, 2, 2, 2, 1 }, cldnn::scatter_update::scatter_update_axis::along_b, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP16_2 { 1, 3, 1, 2, 1 }, { 2, 1, 1, 1 }, { 1, 2, 1, 2, 1 }, cldnn::scatter_update::scatter_update_axis::along_f, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP16_3 { 2, 3, 1, 3, 3 }, { 1, 2, 1, 1 }, { 2, 3, 1, 2, 3 }, cldnn::scatter_update::scatter_update_axis::along_y, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP16_4 { 3, 2, 2, 2, 2 }, { 2, 1, 1, 1 }, { 3, 2, 2, 2, 2 }, cldnn::scatter_update::scatter_update_axis::along_z, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_UPDATE_5D_FP16_5 { 1, 1, 4, 1, 1 }, { 3, 1, 1, 1 }, { 1, 1, 3, 1, 1 }, cldnn::scatter_update::scatter_update_axis::along_x, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
 
 class ScatterUpdatePrimitiveFusingTest : public ::BaseFusingTest<scatter_update_test_params> {
 public:
@@ -6446,14 +6550,15 @@ public:
     }
 
     layout get_per_channel_layout(scatter_update_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.dictionary_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.dictionary_shape.feature[0], 1, 1 } };
     }
 };
 
 class scatter_update_quantize : public ScatterUpdatePrimitiveFusingTest {};
 TEST_P(scatter_update_quantize, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scatter_update_indices", get_repeatless_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p)) - 1)),
         data("scatter_update_updates", get_mem(get_updates_layout(p), 0, 1000)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
@@ -6464,42 +6569,42 @@ TEST_P(scatter_update_quantize, basic) {
         quantize("quantize", "scatter_update_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
         reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_update_quantize,
-    ::testing::ValuesIn(std::vector<scatter_update_test_params>{
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_1, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_2, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_3, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_4, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_5, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_update_quantize, ::testing::ValuesIn(std::vector<scatter_update_test_params>{
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_1, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_2, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_3, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_4, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_5, 2, 3 },
 
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_1, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_2, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_3, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_4, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_5, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_1, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_2, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_3, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_4, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_5, 2, 3 },
 
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_1, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_2, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_3, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_4, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_5, 2, 3 },
 
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_1, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_2, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_3, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_4, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_5, 2, 3 },
-
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_1, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_2, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_3, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_4, 2, 3 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_5, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_1, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_2, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_3, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_4, 2, 3 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_5, 2, 3 },
 }));
 
 class scatter_update_scale_activation : public ScatterUpdatePrimitiveFusingTest {};
 TEST_P(scatter_update_scale_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scatter_update_indices", get_repeatless_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p)) - 1)),
         data("scatter_update_updates", get_mem(get_updates_layout(p), 0, 1000)),
         data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
@@ -6508,49 +6613,49 @@ TEST_P(scatter_update_scale_activation, basic) {
         scale("scale", "activation", "scale_data"),
         reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_update_scale_activation,
-    ::testing::ValuesIn(std::vector<scatter_update_test_params>{
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_1, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_2, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_3, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_4, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_5, 2, 4 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_update_scale_activation, ::testing::ValuesIn(std::vector<scatter_update_test_params>{
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_1, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_2, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_3, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_4, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_5, 2, 4 },
 
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_1, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_2, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_3, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_4, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_5, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_1, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_2, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_3, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_4, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_5, 2, 4 },
 
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_1, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_2, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_3, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_4, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_5, 2, 4 },
 
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_1, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_2, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_3, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_4, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_5, 2, 4 },
-
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_1, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_2, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_3, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_4, 2, 4 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_5, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_1, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_2, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_3, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_4, 2, 4 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_5, 2, 4 },
 }));
 
 class scatter_update_scale_activation_eltwise : public ScatterUpdatePrimitiveFusingTest {};
 TEST_P(scatter_update_scale_activation_eltwise, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scatter_update_indices", get_repeatless_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p)) - 1)),
         data("scatter_update_updates", get_mem(get_updates_layout(p), 0, 1000)),
         data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
         data("eltw_data", get_mem(layout(p.default_type, p.default_format, p.dictionary_shape))),
         scatter_update("scatter_update_prim", "input", "scatter_update_indices", "scatter_update_updates", p.axis),
         activation("activation", "scatter_update_prim", activation_func::abs),
-        eltwise("eltw", {"activation", "eltw_data"}, eltwise_mode::sum, p.default_type),
+        eltwise("eltw", { "activation", "eltw_data" }, eltwise_mode::sum, p.default_type),
         scale("scale", "eltw", "scale_data"),
         reorder("reorder_bfyx", "scale", p.default_format, data_types::f32)
     );
@@ -6558,37 +6663,34 @@ TEST_P(scatter_update_scale_activation_eltwise, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_update_scale_activation_eltwise,
-                        ::testing::ValuesIn(std::vector<scatter_update_test_params> {
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_1, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_2, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_3, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_4, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_5, 3, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_update_scale_activation_eltwise, ::testing::ValuesIn(std::vector<scatter_update_test_params>{
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_1, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_2, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_3, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_4, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP32_5, 3, 5 },
 
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_1, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_2, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_3, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_4, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_5, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_1, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_2, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_3, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_4, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_FP16_5, 3, 5 },
 
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_1, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_2, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_3, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_4, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_5, 3, 5 },
 
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_1, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_2, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_3, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_4, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP32_5, 3, 5 },
-
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_1, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_2, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_3, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_4, 3, 5 },
-                        scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_5, 3, 5 },
-
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_1, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_2, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_3, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_4, 3, 5 },
+    scatter_update_test_params{ CASE_SCATTER_UPDATE_5D_FP16_5, 3, 5 },
 }));
 
 /* ----------------------------------------------------------------------------------------------------- */
-/* ------------------------------------------ ScatterElementsUpdate cases --------------------------------------------- */
+/* ------------------------------------------ ScatterElementsUpdate cases ------------------------------ */
 /* ----------------------------------------------------------------------------------------------------- */
 
 struct scatter_elements_update_test_params {
@@ -6605,21 +6707,21 @@ struct scatter_elements_update_test_params {
 
 // input shape along the update axis should be larger than the total number of elements in the update tensor.
 // This is not a limitation of operation itself, but a limitation of test implementation.
-#define CASE_SCATTER_ELEMENTS_UPDATE_FP32_1 {8, 4, 1, 1}, {2, 4, 1, 1}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ELEMENTS_UPDATE_FP32_2 {2, 8, 1, 2}, {2, 2, 1, 2}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_f, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ELEMENTS_UPDATE_FP32_3 {2, 3, 10, 10}, {2, 2, 1, 2}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_FP32_1 { 8, 4, 1, 1 }, { 2, 4, 1, 1 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_b, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_FP32_2 { 2, 8, 1, 2 }, { 2, 2, 1, 2 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_f, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_FP32_3 { 2, 3, 10, 10 }, { 2, 2, 1, 2 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_y, data_types::f32, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_SCATTER_ELEMENTS_UPDATE_FP16_1 {2, 2, 14, 12}, {2, 2, 3, 1}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_x, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_FP16_1 { 2, 2, 14, 12 }, { 2, 2, 3, 1 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_x, data_types::f16, format::bfyx, data_types::f16, format::bfyx
 
-#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_1 {24, 3, 1, 4, 1}, {4, 3, 1, 2, 1}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_b, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_2 {2, 17, 2, 2, 2}, {1, 2, 2, 2, 2}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_f, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_3 {5, 3, 2, 20, 22}, {5, 1, 1, 2, 2}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_y, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_1 { 24, 3, 1, 4, 1 }, { 4, 3, 1, 2, 1 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_b, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_2 { 2, 17, 2, 2, 2 }, { 1, 2, 2, 2, 2 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_f, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_3 { 5, 3, 2, 20, 22 }, { 5, 1, 1, 2, 2 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_y, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
 
-#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_1 {13, 2, 1, 2, 1}, {2, 2, 1, 2, 1}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_b, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_2 {1, 13, 1, 2, 1}, {1, 2, 1, 2, 1}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_f, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
-#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_3 {2, 3, 1, 13, 13}, {2, 3, 1, 2, 1}, cldnn::scatter_elements_update::scatter_elements_update_axis::along_y, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_1 { 13, 2, 1, 2, 1 }, { 2, 2, 1, 2, 1 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_b, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_2 { 1, 13, 1, 2, 1 }, { 1, 2, 1, 2, 1 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_f, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
+#define CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_3 { 2, 3, 1, 13, 13 }, { 2, 3, 1, 2, 1 }, cldnn::scatter_elements_update::scatter_elements_update_axis::along_y, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
 
-class ScatterElementsUpdatePrimitiveFusingTest : public ::BaseFusingTest<scatter_elements_update_test_params> {
+class ScatterElementsUpdatePrimitiveFusingTest : public ::BaseFusingTest<scatter_elements_update_test_params>{
 public:
     void execute(scatter_elements_update_test_params& p) {
 
@@ -6663,7 +6765,7 @@ public:
     }
 
     layout get_per_channel_layout(scatter_elements_update_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.input_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.input_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -6673,7 +6775,8 @@ TEST_P(scatter_elements_update_quantize, basic) {
     const auto &seu = scatter_elements_update("scatter_elements_update_prim", "input", "scatter_elements_update_indices", "scatter_elements_update_updates", p.axis);
     const auto &q = quantize("quantize", "scatter_elements_update_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8);
     const auto &r = reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32);
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scatter_elements_update_indices", get_repeatless_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p)) - 1)),
         data("scatter_elements_update_updates", get_mem(get_updates_layout(p), 0, 100)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
@@ -6684,65 +6787,67 @@ TEST_P(scatter_elements_update_quantize, basic) {
         q,
         r
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_elements_update_quantize,
-    ::testing::ValuesIn(std::vector<scatter_elements_update_test_params>{
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_1, 2, 3 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_2, 2, 3 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_3, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_elements_update_quantize, ::testing::ValuesIn(std::vector<scatter_elements_update_test_params>{
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_1, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_2, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_3, 2, 3 },
 
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP16_1, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP16_1, 2, 3 },
 
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_1, 2, 3 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_2, 2, 3 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_3, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_1, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_2, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_3, 2, 3 },
 
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_1, 2, 3 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_2, 2, 3 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_3, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_1, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_2, 2, 3 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_3, 2, 3 },
 }));
 
 class scatter_elements_update_scale_activation_eltwise : public ScatterElementsUpdatePrimitiveFusingTest {};
 TEST_P(scatter_elements_update_scale_activation_eltwise, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scatter_elements_update_indices", get_repeatless_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p)) - 1)),
         data("scatter_elements_update_updates", get_mem(get_updates_layout(p), 0, 5)),
         data("scale_data", get_mem(get_per_channel_layout(p), -1, 1)),
-        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.input_shape})),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.input_shape })),
         scatter_elements_update("scatter_elements_update_prim", "input", "scatter_elements_update_indices", "scatter_elements_update_updates", p.axis),
         activation("activation", "scatter_elements_update_prim", activation_func::abs),
         scale("scale", "activation", "scale_data"),
-        eltwise("eltwise", {"scale", "eltwise_data"}, eltwise_mode::sum, p.data_type),
+        eltwise("eltwise", { "scale", "eltwise_data" }, eltwise_mode::sum, p.data_type),
         reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
     );
+
     tolerance = 1e-2f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_elements_update_scale_activation_eltwise,
-    ::testing::ValuesIn(std::vector<scatter_elements_update_test_params>{
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_1, 2, 5 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_2, 2, 5 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_3, 2, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_elements_update_scale_activation_eltwise, ::testing::ValuesIn(std::vector<scatter_elements_update_test_params>{
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_1, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_2, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP32_3, 2, 5 },
 
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP16_1, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_FP16_1, 2, 5 },
 
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_1, 2, 5 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_2, 2, 5 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_3, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_1, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_2, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP32_3, 2, 5 },
 
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_1, 2, 5 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_2, 2, 5 },
-                        scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_3, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_1, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_2, 2, 5 },
+    scatter_elements_update_test_params{ CASE_SCATTER_ELEMENTS_UPDATE_5D_FP16_3, 2, 5 },
 }));
 
 /* ------------------------------------------------------------------------------------------------------------ */
-/* ---------------------------------------- PERMUTE FUSE cases -------------------------------------------------- */
+/* ---------------------------------------- PERMUTE FUSE cases ------------------------------------------------ */
 /* ------------------------------------------------------------------------------------------------------------ */
+
 struct permute_params {
     tensor in_shape;
     tensor out_shape;
@@ -6756,77 +6861,77 @@ struct permute_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_PERMUTE_F32_0 {1, 16, 2, 2}, {1, 16, 2, 2}, {0, 1, 2, 3}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F32_1 {1, 15, 16, 16}, {1, 15, 16, 16}, {0, 1, 2, 3}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F32_2 {1, 8, 16, 16}, {16, 16, 8, 1}, {3, 2, 1, 0}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F32_3 {1, 1, 3, 4}, {1, 3, 4, 1}, {1, 2, 3, 0}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F32_4 {2, 16, 16, 16}, {2, 16, 16, 16}, {0, 1, 2, 3}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F32_5 {1, 32, 4, 5}, {32, 4, 5, 1}, {1, 2, 3, 0}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F32_6 {1, 16, 4, 5}, {5, 16, 4, 1}, {3, 1, 2, 0}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F32_7 {1, 16, 1, 1}, {1, 1, 1, 16}, {2, 3, 0, 1}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_0 { 1, 16, 2, 2 }, { 1, 16, 2, 2 }, { 0, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_1 { 1, 15, 16, 16 }, { 1, 15, 16, 16 }, { 0, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_2 { 1, 8, 16, 16 }, { 16, 16, 8, 1 }, { 3, 2, 1, 0 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_3 { 1, 1, 3, 4 }, { 1, 3, 4, 1 }, { 1, 2, 3, 0 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_4 { 2, 16, 16, 16 }, { 2, 16, 16, 16 }, { 0, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_5 { 1, 32, 4, 5 }, { 32, 4, 5, 1 }, { 1, 2, 3, 0 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_6 { 1, 16, 4, 5 }, { 5, 16, 4, 1 }, { 3, 1, 2, 0 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F32_7 { 1, 16, 1, 1 }, { 1, 1, 1, 16 }, { 2, 3, 0, 1 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
 
-#define CASE_PERMUTE_F16_0 {1, 16, 4, 5}, {1, 16, 4, 5}, {0, 1, 2, 3}, tensor{0}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F16_1 {2, 16, 4, 5}, {16, 4, 5, 2}, {1, 2, 3, 0}, tensor{0}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F16_2 {1, 32, 2, 3}, {2, 3, 32, 1}, {2, 3, 1, 0}, tensor{0}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F16_3 {3, 16, 1, 1}, {1, 1, 16, 3}, {3, 2, 1, 0}, tensor{0}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F16_4 {2, 15, 4, 5}, {4, 2, 5, 15}, {2, 0, 3, 1}, tensor{0}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F16_5 {1, 15, 1, 2}, {15, 2, 1, 1}, {1, 3, 2, 0}, tensor{0}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_F16_6 {1, 15, 4, 4}, {4, 4, 1, 15}, {2, 3, 0, 1}, tensor{0}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F16_0 { 1, 16, 4, 5 }, { 1, 16, 4, 5 }, { 0, 1, 2, 3 }, tensor{ 0 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F16_1 { 2, 16, 4, 5 }, { 16, 4, 5, 2 }, { 1, 2, 3, 0 }, tensor{ 0 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F16_2 { 1, 32, 2, 3 }, { 2, 3, 32, 1 }, { 2, 3, 1, 0 }, tensor{ 0 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F16_3 { 3, 16, 1, 1 }, { 1, 1, 16, 3 }, { 3, 2, 1, 0 }, tensor{ 0 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F16_4 { 2, 15, 4, 5 }, { 4, 2, 5, 15 }, { 2, 0, 3, 1 }, tensor{ 0 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F16_5 { 1, 15, 1, 2 }, { 15, 2, 1, 1 }, { 1, 3, 2, 0 }, tensor{ 0 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_F16_6 { 1, 15, 4, 4 }, { 4, 4, 1, 15 }, { 2, 3, 0, 1 }, tensor{ 0 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_PERMUTE_S8_0 {1, 15, 4, 5}, {1, 15, 4, 5}, {0, 1, 2, 3}, tensor{0}, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_S8_1 {1, 15, 4, 5}, {5, 4, 15, 1}, {3, 2, 1, 0}, tensor{0}, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_S8_2 {1, 16, 1, 2}, {1, 1, 16, 2}, {2, 0, 1, 3}, tensor{0}, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_S8_3 {1, 16, 2, 2}, {2, 2, 16, 1}, {2, 3, 1, 0}, tensor{0}, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_U8_0 {1, 15, 4, 5}, {15, 5, 1, 4}, {1, 3, 0, 2}, tensor{0}, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_U8_1 {1, 15, 16, 16}, {15, 16, 1, 16}, {1, 2, 0, 3}, tensor{0}, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_U8_2 {1, 32, 5, 4}, {1, 32, 5, 4}, {0, 1, 2, 3}, tensor{0}, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_PERMUTE_U8_3 {1, 16, 4, 5}, {5, 4, 16, 1}, {3, 2, 1, 0}, tensor{0}, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_S8_0 { 1, 15, 4, 5 }, { 1, 15, 4, 5 }, { 0, 1, 2, 3 }, tensor{ 0 }, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_S8_1 { 1, 15, 4, 5 }, { 5, 4, 15, 1 }, { 3, 2, 1, 0 }, tensor{ 0 }, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_S8_2 { 1, 16, 1, 2 }, { 1, 1, 16, 2 }, { 2, 0, 1, 3 }, tensor{ 0 }, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_S8_3 { 1, 16, 2, 2 }, { 2, 2, 16, 1 }, { 2, 3, 1, 0 }, tensor{ 0 }, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_U8_0 { 1, 15, 4, 5 }, { 15, 5, 1, 4 }, { 1, 3, 0, 2 }, tensor{ 0 }, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_U8_1 { 1, 15, 16, 16 }, { 15, 16, 1, 16 }, { 1, 2, 0, 3 }, tensor{ 0 }, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_U8_2 { 1, 32, 5, 4 }, { 1, 32, 5, 4 }, { 0, 1, 2, 3 }, tensor{ 0 }, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_PERMUTE_U8_3 { 1, 16, 4, 5 }, { 5, 4, 16, 1 }, { 3, 2, 1, 0 }, tensor{ 0 }, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
 
 // 3d
-#define CASE_PERMUTE_F32_3D_0 {1, 15, 4, 4, 5}, {1, 15, 4, 4, 5}, {0, 1, 2, 3, 4}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F32_3D_1 {2, 15, 2, 3, 4}, {15, 2, 3, 4, 2}, {1, 2, 3, 4, 0}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F32_3D_2 {2, 16, 4, 4, 5}, {4, 2, 4, 5, 16}, {3, 0, 2, 4, 1}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F32_3D_3 {1, 32, 4, 2, 2}, {2, 2, 32, 1, 4}, {4, 3, 1, 0, 2}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F32_3D_4 {1, 16, 1, 1, 1}, {1, 1, 1, 16, 1}, {2, 4, 0, 1, 3}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F32_3D_0 { 1, 15, 4, 4, 5 }, { 1, 15, 4, 4, 5 }, { 0, 1, 2, 3, 4 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F32_3D_1 { 2, 15, 2, 3, 4 }, { 15, 2, 3, 4, 2 }, { 1, 2, 3, 4, 0 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F32_3D_2 { 2, 16, 4, 4, 5 }, { 4, 2, 4, 5, 16 }, { 3, 0, 2, 4, 1 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F32_3D_3 { 1, 32, 4, 2, 2 }, { 2, 2, 32, 1, 4 }, { 4, 3, 1, 0, 2 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F32_3D_4 { 1, 16, 1, 1, 1 }, { 1, 1, 1, 16, 1 }, { 2, 4, 0, 1, 3 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
 
-#define CASE_PERMUTE_F16_3D_0 {1, 15, 4, 4, 5}, {1, 15, 4, 4, 5}, {0, 1, 2, 3, 4}, tensor{0}, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F16_3D_1 {2, 15, 4, 3, 4}, {4, 4, 2, 15, 3}, {2, 4, 0, 1, 3}, tensor{0}, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F16_3D_2 {2, 16, 4, 4, 3}, {2, 4, 3, 16, 4}, {0, 3, 4, 1, 2}, tensor{0}, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F16_3D_3 {1, 32, 4, 2, 1}, {2, 32, 4, 1, 1}, {3, 1, 2, 4, 0}, tensor{0}, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_F16_3D_4 {16, 16, 1, 1, 1},{1, 16, 1, 1, 16},{4, 0, 3, 2, 1}, tensor{0}, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F16_3D_0 { 1, 15, 4, 4, 5 }, { 1, 15, 4, 4, 5 }, { 0, 1, 2, 3, 4 }, tensor{ 0 }, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F16_3D_1 { 2, 15, 4, 3, 4 }, { 4, 4, 2, 15, 3 }, { 2, 4, 0, 1, 3 }, tensor{ 0 }, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F16_3D_2 { 2, 16, 4, 4, 3 }, { 2, 4, 3, 16, 4 }, { 0, 3, 4, 1, 2 }, tensor{ 0 }, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F16_3D_3 { 1, 32, 4, 2, 1 }, { 2, 32, 4, 1, 1 }, { 3, 1, 2, 4, 0 }, tensor{ 0 }, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_F16_3D_4 { 16, 16, 1, 1, 1 },{ 1, 16, 1, 1, 16 },{ 4, 0, 3, 2, 1 }, tensor{ 0 }, data_types::f16, format::bfzyx, data_types::f32, format::bfzyx
 
-#define CASE_PERMUTE_S8_3D_0 {1, 15, 4, 4, 5}, {1, 15, 4, 4, 5}, {0, 1, 2, 3, 4}, tensor{0}, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_S8_3D_1 {2, 15, 4, 3, 4}, {4, 4, 15, 2, 3}, {4, 2, 1, 0, 3}, tensor{0}, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_S8_3D_2 {2, 16, 4, 4, 3}, {2, 4, 3, 16, 4}, {0, 3, 4, 1, 2}, tensor{0}, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_S8_3D_3 {1, 32, 4, 2, 1}, {2, 32, 4, 1, 1}, {3, 1, 2, 4, 0}, tensor{0}, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_U8_3D_0 {16, 16, 1, 1, 1}, {1, 1, 16, 16, 1}, {2, 4, 0, 1, 3}, tensor{0}, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_U8_3D_1 {16, 16, 1, 1, 1}, {1, 1, 1, 16, 16}, {4, 3, 2, 1, 0}, tensor{0}, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_U8_3D_2 {2, 16, 4, 4, 3}, {4, 2, 4, 3, 16}, {3, 0, 2, 4, 1}, tensor{0}, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_U8_3D_3 {1, 32, 4, 2, 1}, {1, 2, 32, 1, 4}, {4, 3, 1, 0, 2}, tensor{0}, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_S8_3D_0 { 1, 15, 4, 4, 5 }, { 1, 15, 4, 4, 5 }, { 0, 1, 2, 3, 4 }, tensor{ 0 }, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_S8_3D_1 { 2, 15, 4, 3, 4 }, { 4, 4, 15, 2, 3 }, { 4, 2, 1, 0, 3 }, tensor{ 0 }, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_S8_3D_2 { 2, 16, 4, 4, 3 }, { 2, 4, 3, 16, 4 }, { 0, 3, 4, 1, 2 }, tensor{ 0 }, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_S8_3D_3 { 1, 32, 4, 2, 1 }, { 2, 32, 4, 1, 1 }, { 3, 1, 2, 4, 0 }, tensor{ 0 }, data_types::i8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_U8_3D_0 { 16, 16, 1, 1, 1 }, { 1, 1, 16, 16, 1 }, { 2, 4, 0, 1, 3 }, tensor{ 0 }, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_U8_3D_1 { 16, 16, 1, 1, 1 }, { 1, 1, 1, 16, 16 }, { 4, 3, 2, 1, 0 }, tensor{ 0 }, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_U8_3D_2 { 2, 16, 4, 4, 3 }, { 4, 2, 4, 3, 16 }, { 3, 0, 2, 4, 1 }, tensor{ 0 }, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_U8_3D_3 { 1, 32, 4, 2, 1 }, { 1, 2, 32, 1, 4 }, { 4, 3, 1, 0, 2 }, tensor{ 0 }, data_types::u8, format::bfzyx, data_types::f32, format::bfzyx
 
 // permute_tile_8x8_4x4
-#define CASE_PERMUTE_TILE_8x8_4x4_4D_0 {1, 8, 8, 2}, {1, 2, 8, 8}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_TILE_8x8_4x4_4D_1 {1, 5, 8, 2}, {1, 2, 5, 8}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_TILE_8x8_4x4_4D_2 {1, 8, 5, 2}, {1, 2, 8, 5}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_TILE_8x8_4x4_4D_3 {1, 5, 5, 2}, {1, 2, 5, 5}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_PERMUTE_TILE_8x8_4x4_5D_0 {1, 8, 8, 2, 2}, {1, 2, 8, 8, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_TILE_8x8_4x4_5D_1 {1, 5, 8, 2, 2}, {1, 2, 5, 8, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_TILE_8x8_4x4_5D_2 {1, 8, 5, 2, 2}, {1, 2, 8, 5, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_TILE_8x8_4x4_5D_3 {1, 5, 5, 2, 2}, {1, 2, 5, 5, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
-#define CASE_PERMUTE_TILE_8x8_4x4_6D_0 {1, 8, 8, 2, 2, 2}, {1, 2, 8, 8, 2, 2}, {0, 5, 1, 2, 3, 4}, tensor{0}, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
-#define CASE_PERMUTE_TILE_8x8_4x4_6D_1 {1, 5, 8, 2, 2, 2}, {1, 2, 5, 8, 2, 2}, {0, 5, 1, 2, 3, 4}, tensor{0}, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
-#define CASE_PERMUTE_TILE_8x8_4x4_6D_2 {1, 8, 5, 2, 2, 2}, {1, 2, 8, 5, 2, 2}, {0, 5, 1, 2, 3, 4}, tensor{0}, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
-#define CASE_PERMUTE_TILE_8x8_4x4_6D_3 {1, 5, 5, 2, 2, 2}, {1, 2, 5, 5, 2, 2}, {0, 5, 1, 2, 3, 4}, tensor{0}, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_4D_0 { 1, 8, 8, 2 }, { 1, 2, 8, 8 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_TILE_8x8_4x4_4D_1 { 1, 5, 8, 2 }, { 1, 2, 5, 8 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_TILE_8x8_4x4_4D_2 { 1, 8, 5, 2 }, { 1, 2, 8, 5 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_TILE_8x8_4x4_4D_3 { 1, 5, 5, 2 }, { 1, 2, 5, 5 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_PERMUTE_TILE_8x8_4x4_5D_0 { 1, 8, 8, 2, 2 }, { 1, 2, 8, 8, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_5D_1 { 1, 5, 8, 2, 2 }, { 1, 2, 5, 8, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_5D_2 { 1, 8, 5, 2, 2 }, { 1, 2, 8, 5, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_5D_3 { 1, 5, 5, 2, 2 }, { 1, 2, 5, 5, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_6D_0 { 1, 8, 8, 2, 2, 2 }, { 1, 2, 8, 8, 2, 2 }, { 0, 5, 1, 2, 3, 4 }, tensor{ 0 }, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_6D_1 { 1, 5, 8, 2, 2, 2 }, { 1, 2, 5, 8, 2, 2 }, { 0, 5, 1, 2, 3, 4 }, tensor{ 0 }, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_6D_2 { 1, 8, 5, 2, 2, 2 }, { 1, 2, 8, 5, 2, 2 }, { 0, 5, 1, 2, 3, 4 }, tensor{ 0 }, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
+#define CASE_PERMUTE_TILE_8x8_4x4_6D_3 { 1, 5, 5, 2, 2, 2 }, { 1, 2, 5, 5, 2, 2 }, { 0, 5, 1, 2, 3, 4 }, tensor{ 0 }, data_types::f32, format::bfwzyx, data_types::f32, format::bfwzyx
 
 // permute_tile_8x8_4x4_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_0 {1, 16, 16, 2}, {1, 2, 16, 16}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_1 {1, 15, 16, 2}, {1, 2, 15, 16}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_2 {1, 16,  3, 2}, {1, 2, 16,  3}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_3 {1,  5,  7, 2}, {1, 2,  5,  7}, {0, 3, 1, 2}, tensor{0}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_0 {1, 16, 16, 2, 2}, {1, 2, 16, 16, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_1 {1, 15, 16, 2, 2}, {1, 2, 15, 16, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_2 {1, 16,  3, 2, 2}, {1, 2, 16,  3, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
-#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_3 {1,  5,  7, 2, 2}, {1, 2,  5,  7, 2}, {0, 4, 1, 2, 3}, tensor{0}, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_0 { 1, 16, 16, 2 }, { 1, 2, 16, 16 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_1 { 1, 15, 16, 2 }, { 1, 2, 15, 16 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_2 { 1, 16,  3, 2 }, { 1, 2, 16,  3 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_3 { 1,  5,  7, 2 }, { 1, 2,  5,  7 }, { 0, 3, 1, 2 }, tensor{ 0 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::b_fs_yx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_0 { 1, 16, 16, 2, 2 }, { 1, 2, 16, 16, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_1 { 1, 15, 16, 2, 2 }, { 1, 2, 15, 16, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_2 { 1, 16,  3, 2, 2 }, { 1, 2, 16,  3, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
+#define CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_3 { 1,  5,  7, 2, 2 }, { 1, 2,  5,  7, 2 }, { 0, 4, 1, 2, 3 }, tensor{ 0 }, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::b_fs_zyx_fsv16
 
 class PermuteFusingTest : public ::BaseFusingTest<permute_params> {
 public:
@@ -6846,109 +6951,108 @@ public:
     }
 
     layout get_per_channel_layout(permute_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.out_shape.feature[0], 1, 1 } };
     }
 };
 
 class permute_activation_scale_eltwise: public PermuteFusingTest {};
 TEST_P(permute_activation_scale_eltwise, basic) {
-        auto p = GetParam();
+    auto p = GetParam();
 
-        create_topologies(
-            input_layout("input", get_input_layout(p)),
-            data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.out_shape})),
-            data("scale_data", get_mem(get_per_channel_layout(p), 5e-1f)),
-            permute("permute", "input", p.permute_order),
-            scale("scale", "permute", "scale_data"),
-            activation("actv", "scale", activation_func::relu),
-            eltwise("eltwise", {"actv", "eltwise_data"}, eltwise_mode::sum, p.data_type),
-            reorder("reorder_bfyx", "eltwise", p.default_format, p.default_type)
-        );
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.out_shape })),
+        data("scale_data", get_mem(get_per_channel_layout(p), 5e-1f)),
+        permute("permute", "input", p.permute_order),
+        scale("scale", "permute", "scale_data"),
+        activation("actv", "scale", activation_func::relu),
+        eltwise("eltwise", { "actv", "eltwise_data" }, eltwise_mode::sum, p.data_type),
+        reorder("reorder_bfyx", "eltwise", p.default_format, p.default_type)
+    );
 
-        tolerance = 1e-5f;
-        execute(p);
+    tolerance = 1e-5f;
+    execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_activation_scale_eltwise,
-                        ::testing::ValuesIn(std::vector<permute_params> {
-                            permute_params{CASE_PERMUTE_F32_0, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_1, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_2, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_3, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_4, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_5, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_6, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_7, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_activation_scale_eltwise, ::testing::ValuesIn(std::vector<permute_params>{
+    permute_params{ CASE_PERMUTE_F32_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_3, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_4, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_5, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_6, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_7, 2, 5 },
 
-                            permute_params{CASE_PERMUTE_F16_0, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_1, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_2, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_3, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_4, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_5, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_6, 2, 5},
+    permute_params{ CASE_PERMUTE_F16_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_3, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_4, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_5, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_6, 2, 5 },
 
-                            permute_params{CASE_PERMUTE_S8_0, 2, 5},
-                            permute_params{CASE_PERMUTE_S8_1, 2, 5},
-                            permute_params{CASE_PERMUTE_S8_2, 2, 5},
-                            permute_params{CASE_PERMUTE_S8_3, 2, 5},
+    permute_params{ CASE_PERMUTE_S8_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_S8_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_S8_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_S8_3, 2, 5 },
 
-                            permute_params{CASE_PERMUTE_U8_0, 2, 5},
-                            permute_params{CASE_PERMUTE_U8_1, 2, 5},
-                            permute_params{CASE_PERMUTE_U8_2, 2, 5},
-                            permute_params{CASE_PERMUTE_U8_3, 2, 5},
+    permute_params{ CASE_PERMUTE_U8_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_U8_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_U8_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_U8_3, 2, 5 },
 
-                            permute_params{CASE_PERMUTE_F32_3D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_3D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_3D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_3D_3, 2, 5},
-                            permute_params{CASE_PERMUTE_F32_3D_4, 2, 5},
+    permute_params{ CASE_PERMUTE_F32_3D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_3D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_3D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_3D_3, 2, 5 },
+    permute_params{ CASE_PERMUTE_F32_3D_4, 2, 5 },
 
-                            permute_params{CASE_PERMUTE_F16_3D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_3D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_3D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_3D_3, 2, 5},
-                            permute_params{CASE_PERMUTE_F16_3D_4, 2, 5},
+    permute_params{ CASE_PERMUTE_F16_3D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_3D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_3D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_3D_3, 2, 5 },
+    permute_params{ CASE_PERMUTE_F16_3D_4, 2, 5 },
 
-                            permute_params{CASE_PERMUTE_S8_3D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_S8_3D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_S8_3D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_S8_3D_3, 2, 5},
+    permute_params{ CASE_PERMUTE_S8_3D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_S8_3D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_S8_3D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_S8_3D_3, 2, 5 },
 
-                            permute_params{CASE_PERMUTE_U8_3D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_U8_3D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_U8_3D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_U8_3D_3, 2, 5},
+    permute_params{ CASE_PERMUTE_U8_3D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_U8_3D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_U8_3D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_U8_3D_3, 2, 5 },
 
-                            // Fusing tests for permute_tile_8x8_4x4
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_3, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_3, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_3, 2, 5},
+    // Fusing tests for permute_tile_8x8_4x4
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_3, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_3, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_3, 2, 5 },
 
-                            // Fusing tests for permute_tile_8x8_4x4_fsv16
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_3, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_0, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_1, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_2, 2, 5},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_3, 2, 5},
-                        }));
+    // Fusing tests for permute_tile_8x8_4x4_fsv16
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_3, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_0, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_1, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_2, 2, 5 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_3, 2, 5 },
+}));
 
 class permute_quant_u8: public PermuteFusingTest {};
 TEST_P(permute_quant_u8, basic) {
-        auto p = GetParam();
-        create_topologies(
+    auto p = GetParam();
+    create_topologies(
         input_layout("input", get_input_layout(p)),
         data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
@@ -6957,20 +7061,19 @@ TEST_P(permute_quant_u8, basic) {
         permute("permute", "input", p.permute_order),
         quantize("quant", "permute", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
         reorder("reorder_bfyx", "quant", p.default_format, p.default_type)
-        );
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_quant_u8,
-                        ::testing::ValuesIn(std::vector<permute_params> {
-                            permute_params{CASE_PERMUTE_F32_0, 2, 3},
-                            permute_params{CASE_PERMUTE_F32_1, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_quant_u8, ::testing::ValuesIn(std::vector<permute_params>{
+    permute_params{ CASE_PERMUTE_F32_0, 2, 3 },
+    permute_params{ CASE_PERMUTE_F32_1, 2, 3 },
 
-                            permute_params{CASE_PERMUTE_F16_0, 2, 3},
-                            permute_params{CASE_PERMUTE_F16_1, 2, 3},
-                        }));
+    permute_params{ CASE_PERMUTE_F16_0, 2, 3 },
+    permute_params{ CASE_PERMUTE_F16_1, 2, 3 },
+}));
 
 class permute_scale_actv_eltw_scale_actv_quant_i8: public PermuteFusingTest {};
 TEST_P(permute_scale_actv_eltw_scale_actv_quant_i8, basic) {
@@ -6987,7 +7090,7 @@ TEST_P(permute_scale_actv_eltw_scale_actv_quant_i8, basic) {
         permute("permute", "input", p.permute_order),
         scale("scale1", "permute", "scale1_data"),
         activation("actv1", "scale1", activation_func::relu),
-        eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.data_type),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.data_type),
         scale("scale2", "eltw", "scale2_data"),
         activation("actv2", "scale2", activation_func::relu),
         quantize("quant", "actv2", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
@@ -6998,155 +7101,153 @@ TEST_P(permute_scale_actv_eltw_scale_actv_quant_i8, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_scale_actv_eltw_scale_actv_quant_i8,
-                        ::testing::ValuesIn(std::vector<permute_params> {
-                            permute_params{CASE_PERMUTE_F32_0, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_1, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_2, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_3, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_4, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_5, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_6, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_7, 2, 8},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_scale_actv_eltw_scale_actv_quant_i8, ::testing::ValuesIn(std::vector<permute_params>{
+    permute_params{ CASE_PERMUTE_F32_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_3, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_4, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_5, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_6, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_7, 2, 8 },
 
-                            permute_params{CASE_PERMUTE_F16_0, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_1, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_2, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_3, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_4, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_5, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_6, 2, 8},
+    permute_params{ CASE_PERMUTE_F16_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_3, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_4, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_5, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_6, 2, 8 },
 
-                            permute_params{CASE_PERMUTE_S8_0, 2, 8},
-                            permute_params{CASE_PERMUTE_S8_1, 2, 8},
-                            permute_params{CASE_PERMUTE_S8_2, 2, 8},
-                            permute_params{CASE_PERMUTE_S8_3, 2, 8},
+    permute_params{ CASE_PERMUTE_S8_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_S8_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_S8_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_S8_3, 2, 8 },
 
-                            permute_params{CASE_PERMUTE_U8_0, 2, 8},
-                            permute_params{CASE_PERMUTE_U8_1, 2, 8},
-                            permute_params{CASE_PERMUTE_U8_2, 2, 8},
-                            permute_params{CASE_PERMUTE_U8_3, 2, 8},
+    permute_params{ CASE_PERMUTE_U8_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_U8_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_U8_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_U8_3, 2, 8 },
 
-                            permute_params{CASE_PERMUTE_F32_3D_0, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_3D_1, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_3D_2, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_3D_3, 2, 8},
-                            permute_params{CASE_PERMUTE_F32_3D_4, 2, 8},
+    permute_params{ CASE_PERMUTE_F32_3D_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_3D_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_3D_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_3D_3, 2, 8 },
+    permute_params{ CASE_PERMUTE_F32_3D_4, 2, 8 },
 
-                            permute_params{CASE_PERMUTE_F16_3D_0, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_3D_1, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_3D_2, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_3D_3, 2, 8},
-                            permute_params{CASE_PERMUTE_F16_3D_4, 2, 8},
+    permute_params{ CASE_PERMUTE_F16_3D_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_3D_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_3D_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_3D_3, 2, 8 },
+    permute_params{ CASE_PERMUTE_F16_3D_4, 2, 8 },
 
-                            permute_params{CASE_PERMUTE_S8_3D_0, 2, 8},
-                            permute_params{CASE_PERMUTE_S8_3D_1, 2, 8},
-                            permute_params{CASE_PERMUTE_S8_3D_2, 2, 8},
-                            permute_params{CASE_PERMUTE_S8_3D_3, 2, 8},
+    permute_params{ CASE_PERMUTE_S8_3D_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_S8_3D_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_S8_3D_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_S8_3D_3, 2, 8 },
 
-                            permute_params{CASE_PERMUTE_U8_3D_0, 2, 8},
-                            permute_params{CASE_PERMUTE_U8_3D_1, 2, 8},
-                            permute_params{CASE_PERMUTE_U8_3D_2, 2, 8},
-                            permute_params{CASE_PERMUTE_U8_3D_3, 2, 8},
-                        }));
+    permute_params{ CASE_PERMUTE_U8_3D_0, 2, 8 },
+    permute_params{ CASE_PERMUTE_U8_3D_1, 2, 8 },
+    permute_params{ CASE_PERMUTE_U8_3D_2, 2, 8 },
+    permute_params{ CASE_PERMUTE_U8_3D_3, 2, 8 },
+}));
 
 class permute_scale_eltwise_actv_scale_actv: public PermuteFusingTest {};
 TEST_P(permute_scale_eltwise_actv_scale_actv, basic) {
     auto p = GetParam();
 
-        create_topologies(
-            input_layout("input", get_input_layout(p)),
-            data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.out_shape})),
-            data("scale_data1", get_mem(get_per_channel_layout(p), 1e-1f)),
-            data("scale_data2", get_mem(get_per_channel_layout(p), 1e-1f)),
-            permute("permute", "input", p.permute_order),
-            scale("scale1", "permute", "scale_data1"),
-            activation("actv1", "scale1", activation_func::relu),
-            eltwise("eltwise", {"actv1", "eltwise_data"}, eltwise_mode::sum, p.default_type),
-            scale("scale2", "eltwise", "scale_data2"),
-            activation("actv2", "scale2", activation_func::relu),
-            reorder("reorder_bfyx", "actv2", p.default_format, p.default_type)
-        );
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("eltwise_data", get_mem(layout{ p.data_type, p.input_format, p.out_shape })),
+        data("scale_data1", get_mem(get_per_channel_layout(p), 1e-1f)),
+        data("scale_data2", get_mem(get_per_channel_layout(p), 1e-1f)),
+        permute("permute", "input", p.permute_order),
+        scale("scale1", "permute", "scale_data1"),
+        activation("actv1", "scale1", activation_func::relu),
+        eltwise("eltwise", { "actv1", "eltwise_data" }, eltwise_mode::sum, p.default_type),
+        scale("scale2", "eltwise", "scale_data2"),
+        activation("actv2", "scale2", activation_func::relu),
+        reorder("reorder_bfyx", "actv2", p.default_format, p.default_type)
+    );
 
-        tolerance = 1e-5f;
-        execute(p);
+    tolerance = 1e-5f;
+    execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_scale_eltwise_actv_scale_actv,
-                        ::testing::ValuesIn(std::vector<permute_params> {
-                            permute_params{CASE_PERMUTE_F32_0, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_1, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_2, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_3, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_4, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_5, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_6, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_7, 2, 7},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_scale_eltwise_actv_scale_actv, ::testing::ValuesIn(std::vector<permute_params>{
+    permute_params{ CASE_PERMUTE_F32_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_3, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_4, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_5, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_6, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_7, 2, 7 },
 
-                            permute_params{CASE_PERMUTE_F16_0, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_1, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_2, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_3, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_4, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_5, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_6, 2, 7},
+    permute_params{ CASE_PERMUTE_F16_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_3, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_4, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_5, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_6, 2, 7 },
 
-                            permute_params{CASE_PERMUTE_S8_0, 2, 7},
-                            permute_params{CASE_PERMUTE_S8_1, 2, 7},
-                            permute_params{CASE_PERMUTE_S8_2, 2, 7},
-                            permute_params{CASE_PERMUTE_S8_3, 2, 7},
+    permute_params{ CASE_PERMUTE_S8_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_S8_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_S8_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_S8_3, 2, 7 },
 
-                            permute_params{CASE_PERMUTE_U8_0, 2, 7},
-                            permute_params{CASE_PERMUTE_U8_1, 2, 7},
-                            permute_params{CASE_PERMUTE_U8_2, 2, 7},
-                            permute_params{CASE_PERMUTE_U8_3, 2, 7},
+    permute_params{ CASE_PERMUTE_U8_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_U8_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_U8_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_U8_3, 2, 7 },
 
-                            permute_params{CASE_PERMUTE_F32_3D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_3D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_3D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_3D_3, 2, 7},
-                            permute_params{CASE_PERMUTE_F32_3D_4, 2, 7},
+    permute_params{ CASE_PERMUTE_F32_3D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_3D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_3D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_3D_3, 2, 7 },
+    permute_params{ CASE_PERMUTE_F32_3D_4, 2, 7 },
 
-                            permute_params{CASE_PERMUTE_F16_3D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_3D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_3D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_3D_3, 2, 7},
-                            permute_params{CASE_PERMUTE_F16_3D_4, 2, 7},
+    permute_params{ CASE_PERMUTE_F16_3D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_3D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_3D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_3D_3, 2, 7 },
+    permute_params{ CASE_PERMUTE_F16_3D_4, 2, 7 },
 
-                            permute_params{CASE_PERMUTE_S8_3D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_S8_3D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_S8_3D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_S8_3D_3, 2, 7},
+    permute_params{ CASE_PERMUTE_S8_3D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_S8_3D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_S8_3D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_S8_3D_3, 2, 7 },
 
-                            permute_params{CASE_PERMUTE_U8_3D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_U8_3D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_U8_3D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_U8_3D_3, 2, 7},
+    permute_params{ CASE_PERMUTE_U8_3D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_U8_3D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_U8_3D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_U8_3D_3, 2, 7 },
 
-                            // Fusing tests for permute_tile_8x8_4x4
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_4D_3, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_5D_3, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_6D_3, 2, 7},
+    // Fusing tests for permute_tile_8x8_4x4
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_4D_3, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_5D_3, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_6D_3, 2, 7 },
 
-                            // Fusing tests for permute_tile_8x8_4x4_fsv16
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_3, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_0, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_1, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_2, 2, 7},
-                            permute_params{CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_3, 2, 7},
-                        }));
+    // Fusing tests for permute_tile_8x8_4x4_fsv16
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_4D_3, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_0, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_1, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_2, 2, 7 },
+    permute_params{ CASE_PERMUTE_TILE_8x8_4x4_FSV16_5D_3, 2, 7 },
+}));
 
 /* ------------------------------------------------------------------------------------------------------------ */
 /* ---------------------------- PERMUTE FUSE REDUNDANT REORDER cases ------------------------------------------ */
@@ -7164,49 +7265,49 @@ struct permute_reorder_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_PERMUTE_REORDER_F32_0 {1, 16, 32, 2},   {0, 3, 2, 1},    {0, 3, 2, 1},    data_types::f32, data_types::f32, format::b_fs_yx_fsv16,  format::bfyx
-#define CASE_PERMUTE_REORDER_F32_1 {2, 7, 9, 27},  {0, 3, 2, 1},    {0, 3, 2, 1},    data_types::f32, data_types::f32, format::b_fs_yx_fsv4,   format::bfyx
-#define CASE_PERMUTE_REORDER_F32_2 {1, 16, 4, 5, 16}, {0, 3, 2, 1, 4}, {0, 3, 2, 1, 4}, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfzyx
-#define CASE_PERMUTE_REORDER_F16_0 {1, 16, 2, 4},     {0, 3, 2, 1},    {0, 3, 2, 1},    data_types::f16, data_types::f16, format::b_fs_yx_fsv16,  format::bfyx
-#define CASE_PERMUTE_REORDER_F16_1 {1, 16, 4, 5, 16}, {0, 4, 2, 3, 1}, {0, 1, 2, 3, 4}, data_types::f16, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
-#define CASE_PERMUTE_REORDER_F16_2 {1, 5, 1, 2, 14},  {0, 3, 2, 1, 4}, {0, 3, 2, 1, 4}, data_types::f16, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_F32_0 { 1, 16, 32, 2 },   { 0, 3, 2, 1 },    { 0, 3, 2, 1 },    data_types::f32, data_types::f32, format::b_fs_yx_fsv16,  format::bfyx
+#define CASE_PERMUTE_REORDER_F32_1 { 2, 7, 9, 27 },  { 0, 3, 2, 1 },    { 0, 3, 2, 1 },    data_types::f32, data_types::f32, format::b_fs_yx_fsv4,   format::bfyx
+#define CASE_PERMUTE_REORDER_F32_2 { 1, 16, 4, 5, 16 }, { 0, 3, 2, 1, 4 }, { 0, 3, 2, 1, 4 }, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_F16_0 { 1, 16, 2, 4 },     { 0, 3, 2, 1 },    { 0, 3, 2, 1 },    data_types::f16, data_types::f16, format::b_fs_yx_fsv16,  format::bfyx
+#define CASE_PERMUTE_REORDER_F16_1 { 1, 16, 4, 5, 16 }, { 0, 4, 2, 3, 1 }, { 0, 1, 2, 3, 4 }, data_types::f16, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_F16_2 { 1, 5, 1, 2, 14 },  { 0, 3, 2, 1, 4 }, { 0, 3, 2, 1, 4 }, data_types::f16, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
 
 // type change
-#define CASE_PERMUTE_REORDER_S8_TO_F32_0 {1, 15, 4, 5},    {0, 3, 2, 1},    {0, 3, 2, 1},    data_types::i8, data_types::f32, format::b_fs_yx_fsv4,   format::bfyx
-#define CASE_PERMUTE_REORDER_S8_TO_F32_1 {1, 2, 15, 4, 5}, {0, 3, 2, 1, 4}, {0, 3, 2, 1, 4}, data_types::i8, data_types::f32, format::b_fs_zyx_fsv16, format::bfzyx
-#define CASE_PERMUTE_REORDER_F32_TO_F16_0 {1, 5, 1, 2, 14}, {0, 4, 2, 3, 1}, {0, 1, 2, 3, 4}, data_types::f32, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
-#define CASE_PERMUTE_REORDER_U8_TO_F16_0 {1, 17, 1, 2, 7},  {0, 4, 2, 3, 1}, {0, 1, 2, 3, 4}, data_types::u8, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_S8_TO_F32_0 { 1, 15, 4, 5 },    { 0, 3, 2, 1 },    { 0, 3, 2, 1 },    data_types::i8, data_types::f32, format::b_fs_yx_fsv4,   format::bfyx
+#define CASE_PERMUTE_REORDER_S8_TO_F32_1 { 1, 2, 15, 4, 5 }, { 0, 3, 2, 1, 4 }, { 0, 3, 2, 1, 4 }, data_types::i8, data_types::f32, format::b_fs_zyx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_F32_TO_F16_0 { 1, 5, 1, 2, 14 }, { 0, 4, 2, 3, 1 }, { 0, 1, 2, 3, 4 }, data_types::f32, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_U8_TO_F16_0 { 1, 17, 1, 2, 7 },  { 0, 4, 2, 3, 1 }, { 0, 1, 2, 3, 4 }, data_types::u8, data_types::f16, format::b_fs_zyx_fsv16, format::bfzyx
 
 // dim change
-#define CASE_PERMUTE_REORDER_4D_TO_5D_F32_0 {1, 16, 8, 16}, {1, 2, 0, 3}, {0, 3, 1, 4, 2}, data_types::f32, data_types::f32, format::bfyx, format::bfzyx
-#define CASE_PERMUTE_REORDER_4D_TO_6D_F32_1 {1, 16, 8, 16}, {0, 2, 3, 1}, {0, 3, 5, 4, 1, 2}, data_types::f32, data_types::f32, format::bfyx, format::bfwzyx
-#define CASE_PERMUTE_REORDER_5D_TO_4D_F32_0 {1, 16, 4, 5, 18},{0, 4, 2, 3, 1}, {0, 3, 1, 2}, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfyx
-#define CASE_PERMUTE_REORDER_5D_TO_4D_F32_1 {1, 16, 4, 5, 16},{0, 2, 3, 4, 1}, {0, 3, 1, 2}, data_types::f32, data_types::f32, format::bfzyx, format::bfyx
-#define CASE_PERMUTE_REORDER_5D_TO_6D_F32_2 {1, 16, 8, 4, 16}, {0, 4, 2, 3, 1}, {0, 3, 5, 4, 1, 2}, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfwzyx
-#define CASE_PERMUTE_REORDER_6D_TO_4D_F32_0 {1, 16, 4, 5, 4, 16}, {0, 2, 5, 3, 4, 1}, {0, 3, 1, 2}, data_types::f32, data_types::f32, format::bfwzyx, format::bfyx
-#define CASE_PERMUTE_REORDER_6D_TO_5D_F32_1 {1, 16, 4, 5, 4, 16}, {0, 2, 5, 3, 4, 1}, {0, 3, 4, 1, 2}, data_types::f32, data_types::f32, format::bfwzyx, format::bfzyx
+#define CASE_PERMUTE_REORDER_4D_TO_5D_F32_0 { 1, 16, 8, 16 }, { 1, 2, 0, 3 }, { 0, 3, 1, 4, 2 }, data_types::f32, data_types::f32, format::bfyx, format::bfzyx
+#define CASE_PERMUTE_REORDER_4D_TO_6D_F32_1 { 1, 16, 8, 16 }, { 0, 2, 3, 1 }, { 0, 3, 5, 4, 1, 2 }, data_types::f32, data_types::f32, format::bfyx, format::bfwzyx
+#define CASE_PERMUTE_REORDER_5D_TO_4D_F32_0 { 1, 16, 4, 5, 18 },{ 0, 4, 2, 3, 1 }, { 0, 3, 1, 2 }, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfyx
+#define CASE_PERMUTE_REORDER_5D_TO_4D_F32_1 { 1, 16, 4, 5, 16 },{ 0, 2, 3, 4, 1 }, { 0, 3, 1, 2 }, data_types::f32, data_types::f32, format::bfzyx, format::bfyx
+#define CASE_PERMUTE_REORDER_5D_TO_6D_F32_2 { 1, 16, 8, 4, 16 }, { 0, 4, 2, 3, 1 }, { 0, 3, 5, 4, 1, 2 }, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfwzyx
+#define CASE_PERMUTE_REORDER_6D_TO_4D_F32_0 { 1, 16, 4, 5, 4, 16 }, { 0, 2, 5, 3, 4, 1 }, { 0, 3, 1, 2 }, data_types::f32, data_types::f32, format::bfwzyx, format::bfyx
+#define CASE_PERMUTE_REORDER_6D_TO_5D_F32_1 { 1, 16, 4, 5, 4, 16 }, { 0, 2, 5, 3, 4, 1 }, { 0, 3, 4, 1, 2 }, data_types::f32, data_types::f32, format::bfwzyx, format::bfzyx
 
 // permute_opt for blocked format
-#define CASE_PERMUTE_REORDER_TILED_F32_0 {1, 256, 2, 64}, {0, 3, 1, 2}, {0, 2, 3, 1},  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F32_1 {1, 78, 2, 259}, {0, 3, 1, 2}, {0, 2, 3, 1},  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F32_2 {1, 48, 1, 3, 259}, {0, 4, 1, 2, 3}, {0, 2, 3, 4, 1},  data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_TILED_F32_0 { 1, 256, 2, 64 }, { 0, 3, 1, 2 }, { 0, 2, 3, 1 },  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F32_1 { 1, 78, 2, 259 }, { 0, 3, 1, 2 }, { 0, 2, 3, 1 },  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F32_2 { 1, 48, 1, 3, 259 }, { 0, 4, 1, 2, 3 }, { 0, 2, 3, 4, 1 },  data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfzyx
 
 // permute_opt for blocked format => reorder to differnt dim
-#define CASE_PERMUTE_REORDER_TILED_F32_3 {1, 45, 1, 3, 259}, {0, 4, 1, 2, 3}, {0, 2, 3, 1},  data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F32_4 {2, 273, 19, 19}, {0, 3, 1, 2}, {0, 2, 3, 1},  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F32_5 {2, 546, 2, 2}, {0, 3, 1, 2}, {0, 2, 3, 1},  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F32_3 { 1, 45, 1, 3, 259 }, { 0, 4, 1, 2, 3 }, { 0, 2, 3, 1 },  data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F32_4 { 2, 273, 19, 19 }, { 0, 3, 1, 2 }, { 0, 2, 3, 1 },  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F32_5 { 2, 546, 2, 2 }, { 0, 3, 1, 2 }, { 0, 2, 3, 1 },  data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::bfyx
 
 // permute opt for blocked format => reorder to different dim/type
-#define CASE_PERMUTE_REORDER_TILED_I8_4 {1, 45, 1, 3, 259}, {0, 4, 1, 2, 3}, {0, 2, 3, 1},  data_types::i8, data_types::f32, format::b_fs_zyx_fsv16, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F16_5 {1, 48, 3, 256}, {0, 3, 1, 2}, {0, 2, 4, 3, 1},  data_types::f16, data_types::f32, format::b_fs_yx_fsv16, format::bfzyx
-#define CASE_PERMUTE_REORDER_TILED_F16_6 {1, 48, 2, 3, 256}, {0, 4, 1, 2, 3}, {0, 2, 5, 4, 3, 1},  data_types::f16, data_types::f32, format::b_fs_zyx_fsv16, format::bfwzyx
+#define CASE_PERMUTE_REORDER_TILED_I8_4 { 1, 45, 1, 3, 259 }, { 0, 4, 1, 2, 3 }, { 0, 2, 3, 1 },  data_types::i8, data_types::f32, format::b_fs_zyx_fsv16, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F16_5 { 1, 48, 3, 256 }, { 0, 3, 1, 2 }, { 0, 2, 4, 3, 1 },  data_types::f16, data_types::f32, format::b_fs_yx_fsv16, format::bfzyx
+#define CASE_PERMUTE_REORDER_TILED_F16_6 { 1, 48, 2, 3, 256 }, { 0, 4, 1, 2, 3 }, { 0, 2, 5, 4, 3, 1 },  data_types::f16, data_types::f32, format::b_fs_zyx_fsv16, format::bfwzyx
 
 // permute opt for non_blocked format => reorder to differnt dim/type
-#define CASE_PERMUTE_REORDER_TILED_F16_7 {1, 48, 2, 3, 256}, {0, 4, 1, 2, 3}, {0, 2, 3, 1},  data_types::f16, data_types::f32, format::bfzyx, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F16_8 {1, 28, 2, 2, 3, 256}, {0, 5, 1, 2, 3, 4}, {0, 2, 3, 1},  data_types::f16, data_types::f32, format::bfwzyx, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F16_9 {1, 24, 2, 3, 256}, {0, 4, 1, 2, 3}, {0, 2, 3, 1},  data_types::f16, data_types::f32, format::bfzyx, format::bfyx
-#define CASE_PERMUTE_REORDER_TILED_F16_10 {1, 35, 3, 253}, {0, 3, 1, 2}, {0, 2, 4, 3, 1},  data_types::f16, data_types::f32, format::bfyx, format::bfzyx
-#define CASE_PERMUTE_REORDER_TILED_F16_11 {1, 32, 3, 253}, {0, 3, 1, 2}, {0, 2, 4, 5, 3, 1},  data_types::f16, data_types::f32, format::bfyx, format::bfwzyx
+#define CASE_PERMUTE_REORDER_TILED_F16_7 { 1, 48, 2, 3, 256 }, { 0, 4, 1, 2, 3 }, { 0, 2, 3, 1 },  data_types::f16, data_types::f32, format::bfzyx, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F16_8 { 1, 28, 2, 2, 3, 256 }, { 0, 5, 1, 2, 3, 4 }, { 0, 2, 3, 1 },  data_types::f16, data_types::f32, format::bfwzyx, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F16_9 { 1, 24, 2, 3, 256 }, { 0, 4, 1, 2, 3 }, { 0, 2, 3, 1 },  data_types::f16, data_types::f32, format::bfzyx, format::bfyx
+#define CASE_PERMUTE_REORDER_TILED_F16_10 { 1, 35, 3, 253 }, { 0, 3, 1, 2 }, { 0, 2, 4, 3, 1 },  data_types::f16, data_types::f32, format::bfyx, format::bfzyx
+#define CASE_PERMUTE_REORDER_TILED_F16_11 { 1, 32, 3, 253 }, { 0, 3, 1, 2 }, { 0, 2, 4, 5, 3, 1 },  data_types::f16, data_types::f32, format::bfyx, format::bfwzyx
 
 class PermuteReorderFusingTest : public ::BaseFusingTest<permute_reorder_params> {
 public:
@@ -7235,43 +7336,43 @@ TEST_P(permute_redundant_reorder, basic) {
         reorder("reorder1", "permute1", p.output_format, p.output_type), // to be fused
         permute("permute2", "reorder1", p.permute_order2)                  // dummy last op to make reorder fused
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_redundant_reorder,
-                            ::testing::ValuesIn(std::vector<permute_reorder_params> {
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F32_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F32_1, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F32_2, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F16_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F16_1, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F16_2, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_S8_TO_F32_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_S8_TO_F32_1, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F32_TO_F16_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_U8_TO_F16_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_4D_TO_5D_F32_0, 3, 3},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_4D_TO_6D_F32_1, 3, 3},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_5D_TO_4D_F32_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_5D_TO_4D_F32_1, 3, 3},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_5D_TO_6D_F32_2, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_6D_TO_4D_F32_0, 3, 3},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_6D_TO_5D_F32_1, 3, 3},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_1, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_2, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_3, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_4, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_I8_4, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_5, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_6, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_7, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_8, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_9, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_10, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_11, 3, 4},
-                                }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_redundant_reorder, ::testing::ValuesIn(std::vector<permute_reorder_params>{
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F32_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F32_1, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F32_2, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F16_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F16_1, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F16_2, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_S8_TO_F32_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_S8_TO_F32_1, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F32_TO_F16_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_U8_TO_F16_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_4D_TO_5D_F32_0, 3, 3 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_4D_TO_6D_F32_1, 3, 3 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_5D_TO_4D_F32_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_5D_TO_4D_F32_1, 3, 3 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_5D_TO_6D_F32_2, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_6D_TO_4D_F32_0, 3, 3 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_6D_TO_5D_F32_1, 3, 3 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_1, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_2, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_3, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_4, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_I8_4, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_5, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_6, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_7, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_8, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_9, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_10, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_11, 3, 4 },
+}));
 
 class permute_act_reorder : public PermuteReorderFusingTest {};
 
@@ -7284,37 +7385,37 @@ TEST_P(permute_act_reorder, basic) {
         reorder("reorder1", "activation", p.output_format, p.output_type),    // to be fused
         permute("permute2", "reorder1", p.permute_order2)                // dummy last op to make reorder fused
     );
+
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_act_reorder,
-                            ::testing::ValuesIn(std::vector<permute_reorder_params> {
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F32_0, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F32_1, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F32_2, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F16_0, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F16_1, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_F16_2, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_4D_TO_5D_F32_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_4D_TO_6D_F32_1, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_5D_TO_4D_F32_0, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_5D_TO_4D_F32_1, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_5D_TO_6D_F32_2, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_6D_TO_4D_F32_0, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_6D_TO_5D_F32_1, 3, 4},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_0, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_1, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_2, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F32_3, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_5, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_6, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_7, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_8, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_9, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_10, 3, 5},
-                                permute_reorder_params{CASE_PERMUTE_REORDER_TILED_F16_11, 3, 5},
-                                }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, permute_act_reorder, ::testing::ValuesIn(std::vector<permute_reorder_params>{
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F32_0, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F32_1, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F32_2, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F16_0, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F16_1, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_F16_2, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_4D_TO_5D_F32_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_4D_TO_6D_F32_1, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_5D_TO_4D_F32_0, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_5D_TO_4D_F32_1, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_5D_TO_6D_F32_2, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_6D_TO_4D_F32_0, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_6D_TO_5D_F32_1, 3, 4 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_0, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_1, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_2, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F32_3, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_5, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_6, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_7, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_8, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_9, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_10, 3, 5 },
+    permute_reorder_params{ CASE_PERMUTE_REORDER_TILED_F16_11, 3, 5 },
+}));
 
 class NormalizeFusingTest : public ::BaseFusingTest<normalize_test_params> {
 public:
@@ -7327,11 +7428,18 @@ public:
 
         compare(network_not_fused, network_fused, p);
     }
-    layout get_input_layout(normalize_test_params& p) { return layout{p.data_type, p.input_format, p.in_shape}; }
-    layout get_per_channel_layout(normalize_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1}};
+
+    layout get_input_layout(normalize_test_params& p) {
+        return layout{ p.data_type, p.input_format, p.in_shape };
+    }
+
+    layout get_per_channel_layout(normalize_test_params& p) {
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
+    }
+
+    layout get_weights_layout(normalize_test_params& p) {
+        return layout { p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
     }
-    layout get_weights_layout(normalize_test_params& p) { return layout {p.default_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1}}; }
 };
 
 class normalize_i8_quantize : public NormalizeFusingTest {};
@@ -7346,18 +7454,17 @@ TEST_P(normalize_i8_quantize, basic) {
         data("out_hi", get_mem(get_single_element_layout(p), 255)),
         normalize("normalizel2", "input", "weights", p.across_spatial),
         quantize("quantize", "normalizel2", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::u8),
-        reorder("output_reorder", "quantize", p.default_format, data_types::f32));
+        reorder("output_reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        normalize_i8_quantize,
-                        ::testing::ValuesIn(std::vector<normalize_test_params>{
-                            normalize_test_params{CASE_NORMALIZE_I8_1, false, 2, 3},
-                            normalize_test_params{CASE_NORMALIZE_I8_1, true, 2, 3},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, normalize_i8_quantize, ::testing::ValuesIn(std::vector<normalize_test_params>{
+    normalize_test_params{ CASE_NORMALIZE_I8_1, false, 2, 3 },
+    normalize_test_params{ CASE_NORMALIZE_I8_1, true, 2, 3 },
+}));
 
 class normalize_i8_float : public NormalizeFusingTest {};
 TEST_P(normalize_i8_float, basic) {
@@ -7369,22 +7476,22 @@ TEST_P(normalize_i8_float, basic) {
         normalize("normalizel2", "input", "weights", p.across_spatial),
         scale("scale", "normalizel2", "scale_data"),
         activation("activation", "scale", activation_func::abs),
-        reorder("output_reorder", "activation", p.default_format, data_types::f32));
+        reorder("output_reorder", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-05f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        normalize_i8_float,
-                        ::testing::ValuesIn(std::vector<normalize_test_params>{
-                            normalize_test_params{CASE_NORMALIZE_I8_1, false, 2, 4},
-                            normalize_test_params{CASE_NORMALIZE_I8_1, true, 2, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, normalize_i8_float, ::testing::ValuesIn(std::vector<normalize_test_params>{
+    normalize_test_params{ CASE_NORMALIZE_I8_1, false, 2, 4 },
+    normalize_test_params{ CASE_NORMALIZE_I8_1, true, 2, 4 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- BatchToSpace cases ----------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct batch_to_space_test_params {
     tensor input_size;
     tensor output_size;
@@ -7399,14 +7506,14 @@ struct batch_to_space_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_BATCH_TO_SPACE_F32_1 {8,  1,  1, 1}, {2, 1,   2,  2}, data_types::f32, format::bfyx,          {1, 1, 2,  2}, {0, 0, 0, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_BATCH_TO_SPACE_F32_2 {64, 16, 2, 2}, {2, 112, 4,  4}, data_types::f32, format::b_fs_yx_fsv16, {1, 8, 2,  2}, {0, 8, 0, 0}, {0, 8, 0, 0}, data_types::f32, format::bfyx
-#define CASE_BATCH_TO_SPACE_F16_1 {16, 4,  1, 2}, {2, 12,  1,  2}, data_types::f16, format::bfyx,          {1, 4, 2,  1}, {0, 2, 1, 0}, {0, 2, 0, 0}, data_types::f32, format::bfyx
-#define CASE_BATCH_TO_SPACE_F16_2 {32, 16, 2, 1}, {1, 16,  32, 2}, data_types::f16, format::b_fs_yx_fsv16, {1, 1, 16, 2}, {0, 0, 0, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_BATCH_TO_SPACE_U8_1  {30, 12, 4, 6}, {1, 52,  8,  9}, data_types::u8,  format::bfyx,          {1, 5, 2,  3}, {0, 8, 0, 9}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_BATCH_TO_SPACE_U8_2  {24, 32, 4, 5}, {2, 64,  12, 8}, data_types::u8,  format::b_fs_yx_fsv16, {1, 2, 3,  2}, {0, 0, 0, 2}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_BATCH_TO_SPACE_I8_1  {32, 1,  3, 4}, {1, 8,   6,  8}, data_types::i8,  format::bfyx,          {1, 8, 2,  2}, {0, 0, 0, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_BATCH_TO_SPACE_I8_2  {16, 16, 2, 1}, {2, 32,  4,  2}, data_types::i8,  format::b_fs_yx_fsv16, {1, 2, 2,  2}, {0, 0, 0, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_F32_1 { 8,  1,  1, 1 }, { 2, 1,   2,  2 }, data_types::f32, format::bfyx,          { 1, 1, 2,  2 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_F32_2 { 64, 16, 2, 2 }, { 2, 112, 4,  4 }, data_types::f32, format::b_fs_yx_fsv16, { 1, 8, 2,  2 }, { 0, 8, 0, 0 }, { 0, 8, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_F16_1 { 16, 4,  1, 2 }, { 2, 12,  1,  2 }, data_types::f16, format::bfyx,          { 1, 4, 2,  1 }, { 0, 2, 1, 0 }, { 0, 2, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_F16_2 { 32, 16, 2, 1 }, { 1, 16,  32, 2 }, data_types::f16, format::b_fs_yx_fsv16, { 1, 1, 16, 2 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_U8_1  { 30, 12, 4, 6 }, { 1, 52,  8,  9 }, data_types::u8,  format::bfyx,          { 1, 5, 2,  3 }, { 0, 8, 0, 9 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_U8_2  { 24, 32, 4, 5 }, { 2, 64,  12, 8 }, data_types::u8,  format::b_fs_yx_fsv16, { 1, 2, 3,  2 }, { 0, 0, 0, 2 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_I8_1  { 32, 1,  3, 4 }, { 1, 8,   6,  8 }, data_types::i8,  format::bfyx,          { 1, 8, 2,  2 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_BATCH_TO_SPACE_I8_2  { 16, 16, 2, 1 }, { 2, 32,  4,  2 }, data_types::i8,  format::b_fs_yx_fsv16, { 1, 2, 2,  2 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
 
 class BatchToSpaceFusingsTest : public ::BaseFusingTest<batch_to_space_test_params> {
 public:
@@ -7423,104 +7530,101 @@ public:
     }
 
     layout get_input_layout(batch_to_space_test_params& p) {
-        return layout{p.input_type, p.input_format, p.input_size};
+        return layout{ p.input_type, p.input_format, p.input_size };
     }
 
     layout get_per_channel_layout(batch_to_space_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.output_size.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.output_size.feature[0], 1, 1 } };
     }
 };
 
 class batch_to_space_quantize_i8 : public BatchToSpaceFusingsTest {};
 TEST_P(batch_to_space_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      batch_to_space("batch_to_space", "input", p.block_shape, p.crops_begin, p.crops_end, p.output_size),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -128)),
-                      data("out_high", get_mem(get_single_element_layout(p), 127)),
-                      quantize("quant", "batch_to_space", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        batch_to_space("batch_to_space", "input", p.block_shape, p.crops_begin, p.crops_end, p.output_size),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -128)),
+        data("out_high", get_mem(get_single_element_layout(p), 127)),
+        quantize("quant", "batch_to_space", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    batch_to_space_quantize_i8,
-    ::testing::ValuesIn(std::vector<batch_to_space_test_params> {
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F32_1, 2, 3},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F32_2, 2, 3},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F16_1, 2, 3},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F16_2, 2, 3},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, batch_to_space_quantize_i8, ::testing::ValuesIn(std::vector<batch_to_space_test_params>{
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F32_1, 2, 3 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F32_2, 2, 3 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F16_1, 2, 3 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F16_2, 2, 3 },
+}));
 
 class batch_to_space_scale_act_eltwise_quantize_u8 : public BatchToSpaceFusingsTest {};
 TEST_P(batch_to_space_scale_act_eltwise_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      batch_to_space("batch_to_space", "input", p.block_shape, p.crops_begin, p.crops_end, p.output_size),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "batch_to_space", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), 0)),
-                      data("out_high", get_mem(get_single_element_layout(p), 255)),
-                      quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        batch_to_space("batch_to_space", "input", p.block_shape, p.crops_begin, p.crops_end, p.output_size),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "batch_to_space", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), 0)),
+        data("out_high", get_mem(get_single_element_layout(p), 255)),
+        quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    batch_to_space_scale_act_eltwise_quantize_u8,
-    ::testing::ValuesIn(std::vector<batch_to_space_test_params> {
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F32_1, 2, 6},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F32_2, 2, 6},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F16_1, 2, 6},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F16_2, 2, 6},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_U8_1, 2, 6},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_U8_2, 2, 6},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_I8_1, 2, 6},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_I8_2, 2, 6},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, batch_to_space_scale_act_eltwise_quantize_u8, ::testing::ValuesIn(std::vector<batch_to_space_test_params>{
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F32_1, 2, 6 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F32_2, 2, 6 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F16_1, 2, 6 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F16_2, 2, 6 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_U8_1, 2, 6 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_U8_2, 2, 6 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_I8_1, 2, 6 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_I8_2, 2, 6 },
+}));
 
 class batch_to_space_scale_act_eltw : public BatchToSpaceFusingsTest {};
 TEST_P(batch_to_space_scale_act_eltw, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      batch_to_space("batch_to_space", "input", p.block_shape, p.crops_begin, p.crops_end, p.output_size),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "batch_to_space", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        batch_to_space("batch_to_space", "input", p.block_shape, p.crops_begin, p.crops_end, p.output_size),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "batch_to_space", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    batch_to_space_scale_act_eltw,
-    ::testing::ValuesIn(std::vector<batch_to_space_test_params> {
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F32_1, 2, 5},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F32_2, 2, 5},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F16_1, 2, 5},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_F16_2, 2, 5},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_U8_1, 2, 5},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_U8_2, 2, 5},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_I8_1, 2, 5},
-        batch_to_space_test_params{CASE_BATCH_TO_SPACE_I8_2, 2, 5},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, batch_to_space_scale_act_eltw, ::testing::ValuesIn(std::vector<batch_to_space_test_params>{
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F32_1, 2, 5 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F32_2, 2, 5 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F16_1, 2, 5 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_F16_2, 2, 5 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_U8_1, 2, 5 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_U8_2, 2, 5 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_I8_1, 2, 5 },
+    batch_to_space_test_params{ CASE_BATCH_TO_SPACE_I8_2, 2, 5 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- SpaceToBatch cases ----------------------------------------- */
@@ -7540,14 +7644,14 @@ struct space_to_batch_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_SPACE_TO_BATCH_F32_1 {1, 4,  8, 8}, {16, 2, 3, 8}, data_types::f32, format::bfyx,          {1, 2,  4, 1}, {0, 0, 4, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_BATCH_F32_2 {2, 16, 4, 6}, {24, 4, 4, 3}, data_types::f32, format::b_fs_yx_fsv16, {1, 4,  1, 3}, {0, 0, 0, 0}, {0, 0, 0, 3}, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_BATCH_F16_1 {1, 1,  6, 8}, {48, 1, 1, 1}, data_types::f16, format::bfyx,          {1, 1,  6, 8}, {0, 0, 0, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_BATCH_F16_2 {1, 32, 1, 5}, {20, 4, 1, 4}, data_types::f16, format::b_fs_yx_fsv16, {1, 10, 1, 2}, {0, 8, 0, 0}, {0, 0, 0, 3}, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_BATCH_U8_1  {3, 12, 4, 8}, {48, 6, 2, 3}, data_types::u8,  format::bfyx,          {1, 2,  2, 4}, {0, 0, 0, 4}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_BATCH_U8_2  {2, 16, 3, 6}, {30, 4, 1, 6}, data_types::u8,  format::b_fs_yx_fsv16, {1, 5,  3, 1}, {0, 4, 0, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_BATCH_I8_1  {1, 2,  8, 1}, {4,  2, 2, 1}, data_types::i8,  format::bfyx,          {1, 1,  4, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, data_types::f32, format::bfyx
-#define CASE_SPACE_TO_BATCH_I8_2  {1, 32, 4, 8}, {48, 2, 6, 3}, data_types::i8,  format::b_fs_yx_fsv16, {1, 16, 1, 3}, {0, 0, 2, 0}, {0, 0, 0, 1}, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_F32_1 { 1, 4,  8, 8 }, { 16, 2, 3, 8 }, data_types::f32, format::bfyx,          { 1, 2,  4, 1 }, { 0, 0, 4, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_F32_2 { 2, 16, 4, 6 }, { 24, 4, 4, 3 }, data_types::f32, format::b_fs_yx_fsv16, { 1, 4,  1, 3 }, { 0, 0, 0, 0 }, { 0, 0, 0, 3 }, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_F16_1 { 1, 1,  6, 8 }, { 48, 1, 1, 1 }, data_types::f16, format::bfyx,          { 1, 1,  6, 8 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_F16_2 { 1, 32, 1, 5 }, { 20, 4, 1, 4 }, data_types::f16, format::b_fs_yx_fsv16, { 1, 10, 1, 2 }, { 0, 8, 0, 0 }, { 0, 0, 0, 3 }, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_U8_1  { 3, 12, 4, 8 }, { 48, 6, 2, 3 }, data_types::u8,  format::bfyx,          { 1, 2,  2, 4 }, { 0, 0, 0, 4 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_U8_2  { 2, 16, 3, 6 }, { 30, 4, 1, 6 }, data_types::u8,  format::b_fs_yx_fsv16, { 1, 5,  3, 1 }, { 0, 4, 0, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_I8_1  { 1, 2,  8, 1 }, { 4,  2, 2, 1 }, data_types::i8,  format::bfyx,          { 1, 1,  4, 1 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, data_types::f32, format::bfyx
+#define CASE_SPACE_TO_BATCH_I8_2  { 1, 32, 4, 8 }, { 48, 2, 6, 3 }, data_types::i8,  format::b_fs_yx_fsv16, { 1, 16, 1, 3 }, { 0, 0, 2, 0 }, { 0, 0, 0, 1 }, data_types::f32, format::bfyx
 
 class SpaceToBatchFusingsTest : public ::BaseFusingTest<space_to_batch_test_params> {
 public:
@@ -7564,109 +7668,107 @@ public:
     }
 
     layout get_input_layout(space_to_batch_test_params& p) {
-        return layout{p.input_type, p.input_format, p.input_size};
+        return layout{ p.input_type, p.input_format, p.input_size };
     }
 
     layout get_per_channel_layout(space_to_batch_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.output_size.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.output_size.feature[0], 1, 1 } };
     }
 };
 
 class space_to_batch_quantize_i8 : public SpaceToBatchFusingsTest {};
 TEST_P(space_to_batch_quantize_i8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      space_to_batch("space_to_batch", "input", p.block_shape, p.pads_begin, p.pads_end, p.output_size),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), -128)),
-                      data("out_high", get_mem(get_single_element_layout(p), 127)),
-                      quantize("quant", "space_to_batch", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        space_to_batch("space_to_batch", "input", p.block_shape, p.pads_begin, p.pads_end, p.output_size),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), -128)),
+        data("out_high", get_mem(get_single_element_layout(p), 127)),
+        quantize("quant", "space_to_batch", "in_low", "in_high", "out_low", "out_high", 256, data_types::i8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    space_to_batch_quantize_i8,
-    ::testing::ValuesIn(std::vector<space_to_batch_test_params> {
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F32_1, 2, 3},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F32_2, 2, 3},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F16_1, 2, 3},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F16_2, 2, 3},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, space_to_batch_quantize_i8, ::testing::ValuesIn(std::vector<space_to_batch_test_params>{
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F32_1, 2, 3 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F32_2, 2, 3 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F16_1, 2, 3 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F16_2, 2, 3 },
+}));
 
 class space_to_batch_scale_act_eltwise_quantize_u8 : public SpaceToBatchFusingsTest {};
 TEST_P(space_to_batch_scale_act_eltwise_quantize_u8, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      space_to_batch("space_to_batch", "input", p.block_shape, p.pads_begin, p.pads_end, p.output_size),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "space_to_batch", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_low", get_mem(get_single_element_layout(p), 0)),
-                      data("out_high", get_mem(get_single_element_layout(p), 255)),
-                      quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
-                      reorder("reorder_bfyx", "quant", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        space_to_batch("space_to_batch", "input", p.block_shape, p.pads_begin, p.pads_end, p.output_size),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "space_to_batch", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_low", get_mem(get_single_element_layout(p), 0)),
+        data("out_high", get_mem(get_single_element_layout(p), 255)),
+        quantize("quant", "eltw", "in_low", "in_high", "out_low", "out_high", 256, data_types::u8),
+        reorder("reorder_bfyx", "quant", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    space_to_batch_scale_act_eltwise_quantize_u8,
-    ::testing::ValuesIn(std::vector<space_to_batch_test_params> {
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F32_1, 2, 6},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F32_2, 2, 6},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F16_1, 2, 6},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F16_2, 2, 6},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_U8_1, 2, 6},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_U8_2, 2, 6},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_I8_1, 2, 6},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_I8_2, 2, 6},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, space_to_batch_scale_act_eltwise_quantize_u8, ::testing::ValuesIn(std::vector<space_to_batch_test_params>{
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F32_1, 2, 6 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F32_2, 2, 6 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F16_1, 2, 6 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F16_2, 2, 6 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_U8_1, 2, 6 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_U8_2, 2, 6 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_I8_1, 2, 6 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_I8_2, 2, 6 },
+}));
 
 
 class space_to_batch_scale_act_eltw : public SpaceToBatchFusingsTest {};
 TEST_P(space_to_batch_scale_act_eltw, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      space_to_batch("space_to_batch", "input", p.block_shape, p.pads_begin, p.pads_end, p.output_size),
-                      data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      scale("scale1", "space_to_batch", "scale1_data"),
-                      activation("actv1", "scale1", activation_func::relu),
-                      data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
-                      eltwise("eltw", {"actv1", "eltw_data"}, eltwise_mode::sum, p.default_type),
-                      reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        space_to_batch("space_to_batch", "input", p.block_shape, p.pads_begin, p.pads_end, p.output_size),
+        data("scale1_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        scale("scale1", "space_to_batch", "scale1_data"),
+        activation("actv1", "scale1", activation_func::relu),
+        data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.output_size))),
+        eltwise("eltw", { "actv1", "eltw_data" }, eltwise_mode::sum, p.default_type),
+        reorder("reorder_bfyx", "eltw", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(
-    fusings_gpu,
-    space_to_batch_scale_act_eltw,
-    ::testing::ValuesIn(std::vector<space_to_batch_test_params> {
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F32_1, 2, 5},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F32_2, 2, 5},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F16_1, 2, 5},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_F16_2, 2, 5},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_U8_1, 2, 5},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_U8_2, 2, 5},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_I8_1, 2, 5},
-        space_to_batch_test_params{CASE_SPACE_TO_BATCH_I8_2, 2, 5},
-    }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, space_to_batch_scale_act_eltw, ::testing::ValuesIn(std::vector<space_to_batch_test_params>{
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F32_1, 2, 5 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F32_2, 2, 5 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F16_1, 2, 5 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_F16_2, 2, 5 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_U8_1, 2, 5 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_U8_2, 2, 5 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_I8_1, 2, 5 },
+    space_to_batch_test_params{ CASE_SPACE_TO_BATCH_I8_2, 2, 5 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
-/* ---------------------------------------- Eltwise cases -------------------------------------------------- */
+/* ---------------------------------------- Eltwise cases ---------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct eltwise_test_params {
     tensor input_size;
     data_types input_type;
@@ -7679,45 +7781,45 @@ struct eltwise_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_ELTWISE_FP32_1         {2, 16, 4, 4}, data_types::f32, data_types::f32, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_FP32_2         {2, 16, 4, 4}, data_types::f32, data_types::f32, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_FP32_3         {2, 32, 4, 8}, data_types::f32, data_types::f32, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_FP32_4         {2, 16, 4, 4}, data_types::f32, data_types::f32, format::bfwzyx,         data_types::f32,  format::bfwzyx,           eltwise_mode::sum
-#define CASE_ELTWISE_FP16_1         {2, 16, 4, 4}, data_types::f16, data_types::f16, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_FP16_2         {2, 16, 4, 4}, data_types::f16, data_types::f16, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_FP16_3         {2, 32, 4, 8}, data_types::f16, data_types::f16, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_FP16_4         {3, 32, 4, 4}, data_types::f16, data_types::f16, format::fs_b_yx_fsv32,  data_types::f16,  format::fs_b_yx_fsv32,    eltwise_mode::sum
-#define CASE_ELTWISE_I8_1           {2, 16, 4, 4}, data_types::i8,  data_types::i8,  format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_I8_2           {2, 16, 4, 4}, data_types::i8,  data_types::i8,  format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_I8_3           {2, 16, 4, 4}, data_types::i8,  data_types::i8,  format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_U8_1           {2, 16, 4, 4}, data_types::u8,  data_types::u8,  format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_U8_2           {2, 16, 4, 4}, data_types::u8,  data_types::u8,  format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_U8_3           {2, 16, 4, 4}, data_types::u8,  data_types::u8,  format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_FP32_FP16_1    {2, 16, 4, 4}, data_types::f32, data_types::f16, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_FP32_FP16_2    {2, 16, 4, 4}, data_types::f32, data_types::f16, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_FP32_FP16_3    {2, 32, 4, 4}, data_types::f32, data_types::f16, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_FP16_FP32_1    {2, 16, 4, 4}, data_types::f16, data_types::f32, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_FP16_FP32_2    {2, 16, 4, 4}, data_types::f16, data_types::f32, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_FP16_FP32_3    {2, 32, 4, 4}, data_types::f16, data_types::f32, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_I8_FP16_1      {2, 16, 4, 4}, data_types::i8,  data_types::f16, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_I8_FP16_2      {2, 16, 4, 4}, data_types::i8,  data_types::f16, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_I8_FP16_3      {2, 32, 4, 4}, data_types::i8,  data_types::f16, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_I8_FP32_1      {2, 16, 4, 4}, data_types::i8,  data_types::f32, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_I8_FP32_2      {2, 16, 4, 4}, data_types::i8,  data_types::f32, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_I8_FP32_3      {2, 32, 4, 4}, data_types::i8,  data_types::f32, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_U8_FP16_1      {2, 16, 4, 4}, data_types::u8,  data_types::f16, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_U8_FP16_2      {2, 16, 4, 4}, data_types::u8,  data_types::f16, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_U8_FP16_3      {2, 32, 4, 4}, data_types::u8,  data_types::f16, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
-#define CASE_ELTWISE_U8_FP32_1      {2, 16, 4, 4}, data_types::u8,  data_types::f32, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
-#define CASE_ELTWISE_U8_FP32_2      {2, 16, 4, 4}, data_types::u8,  data_types::f32, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
-#define CASE_ELTWISE_U8_FP32_3      {2, 32, 4, 4}, data_types::u8,  data_types::f32, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_FP32_1         { 2, 16, 4, 4 }, data_types::f32, data_types::f32, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_FP32_2         { 2, 16, 4, 4 }, data_types::f32, data_types::f32, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_FP32_3         { 2, 32, 4, 8 }, data_types::f32, data_types::f32, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_FP32_4         { 2, 16, 4, 4 }, data_types::f32, data_types::f32, format::bfwzyx,         data_types::f32,  format::bfwzyx,           eltwise_mode::sum
+#define CASE_ELTWISE_FP16_1         { 2, 16, 4, 4 }, data_types::f16, data_types::f16, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_FP16_2         { 2, 16, 4, 4 }, data_types::f16, data_types::f16, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_FP16_3         { 2, 32, 4, 8 }, data_types::f16, data_types::f16, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_FP16_4         { 3, 32, 4, 4 }, data_types::f16, data_types::f16, format::fs_b_yx_fsv32,  data_types::f16,  format::fs_b_yx_fsv32,    eltwise_mode::sum
+#define CASE_ELTWISE_I8_1           { 2, 16, 4, 4 }, data_types::i8,  data_types::i8,  format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_I8_2           { 2, 16, 4, 4 }, data_types::i8,  data_types::i8,  format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_I8_3           { 2, 16, 4, 4 }, data_types::i8,  data_types::i8,  format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_U8_1           { 2, 16, 4, 4 }, data_types::u8,  data_types::u8,  format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_U8_2           { 2, 16, 4, 4 }, data_types::u8,  data_types::u8,  format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_U8_3           { 2, 16, 4, 4 }, data_types::u8,  data_types::u8,  format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_FP32_FP16_1    { 2, 16, 4, 4 }, data_types::f32, data_types::f16, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_FP32_FP16_2    { 2, 16, 4, 4 }, data_types::f32, data_types::f16, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_FP32_FP16_3    { 2, 32, 4, 4 }, data_types::f32, data_types::f16, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_FP16_FP32_1    { 2, 16, 4, 4 }, data_types::f16, data_types::f32, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_FP16_FP32_2    { 2, 16, 4, 4 }, data_types::f16, data_types::f32, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_FP16_FP32_3    { 2, 32, 4, 4 }, data_types::f16, data_types::f32, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_I8_FP16_1      { 2, 16, 4, 4 }, data_types::i8,  data_types::f16, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_I8_FP16_2      { 2, 16, 4, 4 }, data_types::i8,  data_types::f16, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_I8_FP16_3      { 2, 32, 4, 4 }, data_types::i8,  data_types::f16, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_I8_FP32_1      { 2, 16, 4, 4 }, data_types::i8,  data_types::f32, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_I8_FP32_2      { 2, 16, 4, 4 }, data_types::i8,  data_types::f32, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_I8_FP32_3      { 2, 32, 4, 4 }, data_types::i8,  data_types::f32, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_U8_FP16_1      { 2, 16, 4, 4 }, data_types::u8,  data_types::f16, format::bfyx,           data_types::f32,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_U8_FP16_2      { 2, 16, 4, 4 }, data_types::u8,  data_types::f16, format::bfzyx,          data_types::f32,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_U8_FP16_3      { 2, 32, 4, 4 }, data_types::u8,  data_types::f16, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16,    eltwise_mode::sum
+#define CASE_ELTWISE_U8_FP32_1      { 2, 16, 4, 4 }, data_types::u8,  data_types::f32, format::bfyx,           data_types::f16,  format::bfyx,             eltwise_mode::sum
+#define CASE_ELTWISE_U8_FP32_2      { 2, 16, 4, 4 }, data_types::u8,  data_types::f32, format::bfzyx,          data_types::f16,  format::bfzyx,            eltwise_mode::sum
+#define CASE_ELTWISE_U8_FP32_3      { 2, 32, 4, 4 }, data_types::u8,  data_types::f32, format::b_fs_yx_fsv16,  data_types::f16,  format::b_fs_yx_fsv16,    eltwise_mode::sum
 
-#define CASE_ELTWISE_FP32_5         {1,  5, 4, 4}, data_types::f32, data_types::f32, format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
-#define CASE_ELTWISE_FP32_6         {2, 32, 4, 8}, data_types::f32, data_types::f32, format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
-#define CASE_ELTWISE_FP16_5         {2, 32, 4, 8}, data_types::f16, data_types::f16, format::b_fs_yx_fsv4,  data_types::f16,  format::b_fs_yx_fsv4,    eltwise_mode::sum
-#define CASE_ELTWISE_FP16_6         {1, 32, 4, 8}, data_types::f16, data_types::f16, format::byxf,          data_types::f16,  format::byxf,            eltwise_mode::sum
-#define CASE_ELTWISE_I8_4           {2, 16, 4, 4}, data_types::i8,  data_types::i8,  format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
-#define CASE_ELTWISE_U8_4           {2, 16, 4, 4}, data_types::u8,  data_types::u8,  format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
+#define CASE_ELTWISE_FP32_5         { 1,  5, 4, 4 }, data_types::f32, data_types::f32, format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
+#define CASE_ELTWISE_FP32_6         { 2, 32, 4, 8 }, data_types::f32, data_types::f32, format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
+#define CASE_ELTWISE_FP16_5         { 2, 32, 4, 8 }, data_types::f16, data_types::f16, format::b_fs_yx_fsv4,  data_types::f16,  format::b_fs_yx_fsv4,    eltwise_mode::sum
+#define CASE_ELTWISE_FP16_6         { 1, 32, 4, 8 }, data_types::f16, data_types::f16, format::byxf,          data_types::f16,  format::byxf,            eltwise_mode::sum
+#define CASE_ELTWISE_I8_4           { 2, 16, 4, 4 }, data_types::i8,  data_types::i8,  format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
+#define CASE_ELTWISE_U8_4           { 2, 16, 4, 4 }, data_types::u8,  data_types::u8,  format::b_fs_yx_fsv4,  data_types::f32,  format::b_fs_yx_fsv4,    eltwise_mode::sum
 
 
 class EltwiseFusingTest : public ::BaseFusingTest<eltwise_test_params> {
@@ -7740,26 +7842,33 @@ public:
         compare(network_not_fused, network_fused, p);
     }
 
-    layout get_input_layout(eltwise_test_params& p) { return layout{p.input_type, p.input_format, p.input_size}; }
-    layout get_input_layout2(eltwise_test_params& p) { return layout{p.input_type2, p.input_format, p.input_size}; }
+    layout get_input_layout(eltwise_test_params& p) {
+        return layout{ p.input_type, p.input_format, p.input_size };
+    }
+
+    layout get_input_layout2(eltwise_test_params& p) {
+        return layout{ p.input_type2, p.input_format, p.input_size };
+    }
 
     layout get_per_channel_layout(eltwise_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.input_size.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.input_size.feature[0], 1, 1 } };
     }
 };
 
 class eltwise_quantize : public EltwiseFusingTest {};
 TEST_P(eltwise_quantize, u8) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                      quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                      reorder("out", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("out", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
@@ -7767,94 +7876,96 @@ TEST_P(eltwise_quantize, u8) {
 
 TEST_P(eltwise_quantize, i8_per_channel) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), -128)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                      quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                      reorder("out", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -128)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("out", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_quantize,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP16_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP32_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP32_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP32_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP32_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP32_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP32_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP16_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP16_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP16_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP16_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP16_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP16_3, 3, 4},
-                            // fsv4
-                            eltwise_test_params{CASE_ELTWISE_FP16_5, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_5, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_6, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_I8_4, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_U8_4, 3, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_quantize, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP32_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP32_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP32_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP32_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP32_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP32_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP16_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP16_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP16_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP16_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP16_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP16_3, 3, 4 },
+    // fsv4
+    eltwise_test_params{ CASE_ELTWISE_FP16_5, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_5, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_6, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_I8_4, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_U8_4, 3, 4 },
+}));
 
 class eltwise_const_path : public EltwiseFusingTest {};
 TEST_P(eltwise_const_path, not_fuse_to_const_eltwise) {
     auto p = GetParam();
-    create_topologies(data("const1", get_mem(get_input_layout2(p), -10, 10)),
-                      data("const2", get_mem(get_input_layout2(p), -10, 10)),
-                      input_layout("input", get_input_layout2(p)),
-                      eltwise("eltwise", {"const1", "const2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "input"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        data("const1", get_mem(get_input_layout2(p), -10, 10)),
+        data("const2", get_mem(get_input_layout2(p), -10, 10)),
+        input_layout("input", get_input_layout2(p)),
+        eltwise("eltwise", { "const1", "const2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "input" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_const_path,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP16_3, 2, 3},
-                            eltwise_test_params{CASE_ELTWISE_FP32_3, 2, 3},
-                            eltwise_test_params{CASE_ELTWISE_FP32_5, 2, 3},
-                            eltwise_test_params{CASE_ELTWISE_FP32_6, 2, 3},
-                            eltwise_test_params{CASE_ELTWISE_I8_4, 2, 3},
-                            eltwise_test_params{CASE_ELTWISE_U8_4, 2, 3},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_const_path, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_3, 2, 3 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_3, 2, 3 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_5, 2, 3 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_6, 2, 3 },
+    eltwise_test_params{ CASE_ELTWISE_I8_4, 2, 3 },
+    eltwise_test_params{ CASE_ELTWISE_U8_4, 2, 3 },
+}));
 
 class eltwise_fp32_fsv16 : public EltwiseFusingTest {};
 TEST_P(eltwise_fp32_fsv16, add) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "add_data"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "add_data" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     implementation_desc eltw_impl = { format::b_fs_yx_fsv16, "eltwise_b_fs_yx_fsv16" };
-    bo_fused.set_option(build_option::force_implementations({ {"eltwise", eltw_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "eltwise", eltw_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
@@ -7862,41 +7973,43 @@ TEST_P(eltwise_fp32_fsv16, add) {
 
 TEST_P(eltwise_fp32_fsv16, add_per_element) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("add_data", get_mem(get_input_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "add_data"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("add_data", get_mem(get_input_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "add_data" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     implementation_desc eltw_impl = { format::b_fs_yx_fsv16, "eltwise_b_fs_yx_fsv16" };
-    bo_fused.set_option(build_option::force_implementations({ {"eltwise", eltw_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "eltwise", eltw_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_fp32_fsv16,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP16_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_3, 3, 5},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_fp32_fsv16, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_3, 3, 5 },
+}));
 
 class eltwise_fp32_fsv32 : public EltwiseFusingTest {};
 TEST_P(eltwise_fp32_fsv32, add) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "add_data"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "add_data" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     implementation_desc eltw_impl = { format::fs_b_yx_fsv32, "eltwise_fs_b_yx_fsv32" };
-    bo_fused.set_option(build_option::force_implementations({ {"eltwise", eltw_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "eltwise", eltw_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
@@ -7904,42 +8017,44 @@ TEST_P(eltwise_fp32_fsv32, add) {
 
 TEST_P(eltwise_fp32_fsv32, add_per_element) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("add_data", get_mem(get_input_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "add_data"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("add_data", get_mem(get_input_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "add_data" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     implementation_desc eltw_impl = { format::fs_b_yx_fsv32, "eltwise_fs_b_yx_fsv32" };
-    bo_fused.set_option(build_option::force_implementations({ {"eltwise", eltw_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "eltwise", eltw_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_fp32_fsv32,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            // There's no optimized eltwise kernel yet for fsv32 layout that supports fused_ops
-                            // So only activation is fused via legacy mechanism
-                            eltwise_test_params{CASE_ELTWISE_FP16_4, 4, 5},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_fp32_fsv32, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    // There's no optimized eltwise kernel yet for fsv32 layout that supports fused_ops
+    // So only activation is fused via legacy mechanism
+    eltwise_test_params{ CASE_ELTWISE_FP16_4, 4, 5 },
+}));
 
 class eltwise_fp32_fsv4 : public EltwiseFusingTest {};
 TEST_P(eltwise_fp32_fsv4, add) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "add_data"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "add_data" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     implementation_desc eltw_impl = { format::b_fs_yx_fsv4, "eltwise_b_fs_yx_fsv4" };
-    bo_fused.set_option(build_option::force_implementations({ {"eltwise", eltw_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "eltwise", eltw_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
@@ -7947,41 +8062,42 @@ TEST_P(eltwise_fp32_fsv4, add) {
 
 TEST_P(eltwise_fp32_fsv4, add_per_element) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("add_data", get_mem(get_input_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "add_data"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("add_data", get_mem(get_input_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "add_data" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     implementation_desc eltw_impl = { format::b_fs_yx_fsv4, "eltwise_b_fs_yx_fsv4" };
-    bo_fused.set_option(build_option::force_implementations({ {"eltwise", eltw_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "eltwise", eltw_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_fp32_fsv4,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP32_5, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_6, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_4,   3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_4,   3, 5},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_fp32_fsv4, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP32_5, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_6, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_4,   3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_4,   3, 5 },
+}));
 
-//
 class eltwise_fp32_fused_prims : public EltwiseFusingTest {};
 TEST_P(eltwise_fp32_fused_prims, scale_activation) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      scale("scale", "eltwise", "scale_data"),
-                      activation("activation", "scale", activation_func::abs),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        scale("scale", "eltwise", "scale_data"),
+        activation("activation", "scale", activation_func::abs),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
@@ -7989,13 +8105,15 @@ TEST_P(eltwise_fp32_fused_prims, scale_activation) {
 
 TEST_P(eltwise_fp32_fused_prims, eltwise_activation) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("eltwise_data", get_mem(get_input_layout2(p), -10, 10)),
-                      eltwise("eltwise1", {"input", "input2"}, p.mode, data_types::f32),
-                      eltwise("eltwise2", {"eltwise1", "eltwise_data"}, eltwise_mode::prod, p.default_type),
-                      activation("activation", "eltwise2", activation_func::abs),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("eltwise_data", get_mem(get_input_layout2(p), -10, 10)),
+        eltwise("eltwise1", { "input", "input2" }, p.mode, data_types::f32),
+        eltwise("eltwise2", { "eltwise1", "eltwise_data" }, eltwise_mode::prod, p.default_type),
+        activation("activation", "eltwise2", activation_func::abs),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
@@ -8003,99 +8121,100 @@ TEST_P(eltwise_fp32_fused_prims, eltwise_activation) {
 
 TEST_P(eltwise_fp32_fused_prims, eltwise_activation_with_broadcast) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("eltwise_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise1", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("eltwise2", {"eltwise1", "eltwise_data"}, eltwise_mode::prod, p.default_type),
-                      activation("activation", "eltwise2", activation_func::abs),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("eltwise_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise1", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("eltwise2", { "eltwise1", "eltwise_data" }, eltwise_mode::prod, p.default_type),
+        activation("activation", "eltwise2", activation_func::abs),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_fp32_fused_prims,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP16_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP16_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP16_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP32_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP32_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP32_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP32_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP32_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP32_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP16_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP16_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_FP16_3, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP16_1, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP16_2, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_FP16_3, 3, 5},
-                            // fsv4
-                            eltwise_test_params{CASE_ELTWISE_FP32_5, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_FP32_6, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_I8_4, 3, 5},
-                            eltwise_test_params{CASE_ELTWISE_U8_4, 3, 5},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_fp32_fused_prims, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP32_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP32_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP32_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP32_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP32_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP32_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP16_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP16_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_FP16_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP16_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP16_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_FP16_3, 3, 5 },
+    // fsv4
+    eltwise_test_params{ CASE_ELTWISE_FP32_5, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_6, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_I8_4, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_U8_4, 3, 5 },
+}));
 
 class eltwise_fp32_scale : public EltwiseFusingTest {};
 TEST_P(eltwise_fp32_scale, 6d) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      scale("scale", "eltwise", "scale_data"),
-                      reorder("out", "scale", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        scale("scale", "eltwise", "scale_data"),
+        reorder("out", "scale", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_fp32_scale,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP32_4, 3, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_fp32_scale, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP32_4, 3, 4 },
+}));
 
 class eltwise_fp16_byxf : public EltwiseFusingTest {};
 TEST_P(eltwise_fp16_byxf, add) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      eltwise("add", {"eltwise", "add_data"}, eltwise_mode::sum),
-                      activation("activation", "add", activation_func::negative),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        data("add_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        eltwise("add", { "eltwise", "add_data" }, eltwise_mode::sum),
+        activation("activation", "add", activation_func::negative),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     implementation_desc eltw_impl = { format::byxf, "generic_eltwise_ref" };
-    bo_fused.set_option(build_option::force_implementations({ {"eltwise", eltw_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "eltwise", eltw_impl } }));
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_fp16_byxf,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP16_6, 3, 5}
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_fp16_byxf, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_6, 3, 5 }
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- Scale cases ------------------------------------------------ */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct scale_test_params {
     tensor input_size;
     data_types input_type;
@@ -8109,9 +8228,9 @@ struct scale_test_params {
 // Scale uses the same kernel as eltwise primitive, so the kernel is well covered by the eltwise tests above
 // So here we can just check that fused scale kernel is constructed correctly (inputs are set correctly, fused precision is propagated, etc)
 // and fusing conditions in the graph are correct
-#define CASE_SCALE_FP32_1         {2, 16, 4, 4}, data_types::f32, format::bfyx,           data_types::f32,  format::bfyx
-#define CASE_SCALE_FP32_2         {2, 16, 4, 4}, data_types::f32, format::bfzyx,          data_types::f32,  format::bfzyx
-#define CASE_SCALE_FP32_3         {2, 16, 4, 4}, data_types::f32, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16
+#define CASE_SCALE_FP32_1         { 2, 16, 4, 4 }, data_types::f32, format::bfyx,           data_types::f32,  format::bfyx
+#define CASE_SCALE_FP32_2         { 2, 16, 4, 4 }, data_types::f32, format::bfzyx,          data_types::f32,  format::bfzyx
+#define CASE_SCALE_FP32_3         { 2, 16, 4, 4 }, data_types::f32, format::b_fs_yx_fsv16,  data_types::f32,  format::b_fs_yx_fsv16
 
 class ScaleFusingTest : public ::BaseFusingTest<scale_test_params> {
 public:
@@ -8127,23 +8246,27 @@ public:
         compare(network_not_fused, network_fused, p);
     }
 
-    layout get_input_layout(scale_test_params& p) { return layout{p.input_type, p.input_format, p.input_size}; }
+    layout get_input_layout(scale_test_params& p) {
+        return layout{ p.input_type, p.input_format, p.input_size };
+    }
 
     layout get_per_channel_layout(scale_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.input_size.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.input_size.feature[0], 1, 1 } };
     }
 };
 
 class scale_basic : public ScaleFusingTest {};
 TEST_P(scale_basic, no_bias_act_eltwise) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      scale("scale", "input", "scale_data"),
-                      activation("activation", "scale", activation_func::negative),
-                      data("eltwise_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"activation", "eltwise_data"}, eltwise_mode::prod, p.default_type),
-                      reorder("out", "eltwise", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        scale("scale", "input", "scale_data"),
+        activation("activation", "scale", activation_func::negative),
+        data("eltwise_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "activation", "eltwise_data" }, eltwise_mode::prod, p.default_type),
+        reorder("out", "eltwise", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
@@ -8151,14 +8274,16 @@ TEST_P(scale_basic, no_bias_act_eltwise) {
 
 TEST_P(scale_basic, bias_act_eltwise) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      data("bias_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      scale("scale", "input", "scale_data", "bias_data"),
-                      activation("activation", "scale", activation_func::negative),
-                      data("eltwise_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      eltwise("eltwise", {"activation", "eltwise_data"}, eltwise_mode::prod, p.default_type),
-                      reorder("out", "eltwise", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        data("bias_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        scale("scale", "input", "scale_data", "bias_data"),
+        activation("activation", "scale", activation_func::negative),
+        data("eltwise_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        eltwise("eltwise", { "activation", "eltwise_data" }, eltwise_mode::prod, p.default_type),
+        reorder("out", "eltwise", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
@@ -8166,14 +8291,16 @@ TEST_P(scale_basic, bias_act_eltwise) {
 
 TEST_P(scale_basic, bias_act_scale) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      data("bias_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      scale("scale", "input", "scale_data", "bias_data"),
-                      activation("activation", "scale", activation_func::negative),
-                      data("scale_data2", get_mem(get_per_channel_layout(p), -10, 10)),
-                      scale("scale2", "activation", "scale_data2"),
-                      reorder("out", "scale2", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        data("bias_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        scale("scale", "input", "scale_data", "bias_data"),
+        activation("activation", "scale", activation_func::negative),
+        data("scale_data2", get_mem(get_per_channel_layout(p), -10, 10)),
+        scale("scale2", "activation", "scale_data2"),
+        reorder("out", "scale2", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
@@ -8181,66 +8308,68 @@ TEST_P(scale_basic, bias_act_scale) {
 
 TEST_P(scale_basic, bias_act_quantize) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      data("bias_data", get_mem(get_per_channel_layout(p), -10, 10)),
-                      scale("scale", "input", "scale_data", "bias_data"),
-                      activation("activation", "scale", activation_func::negative),
-                      data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), -128)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                      quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                      reorder("out", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        data("bias_data", get_mem(get_per_channel_layout(p), -10, 10)),
+        scale("scale", "input", "scale_data", "bias_data"),
+        activation("activation", "scale", activation_func::negative),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -128)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("out", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        scale_basic,
-                        ::testing::ValuesIn(std::vector<scale_test_params>{
-                            scale_test_params{CASE_SCALE_FP32_1, 2, 4},
-                            scale_test_params{CASE_SCALE_FP32_2, 2, 4},
-                            scale_test_params{CASE_SCALE_FP32_3, 2, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scale_basic, ::testing::ValuesIn(std::vector<scale_test_params>{
+    scale_test_params{ CASE_SCALE_FP32_1, 2, 4 },
+    scale_test_params{ CASE_SCALE_FP32_2, 2, 4 },
+    scale_test_params{ CASE_SCALE_FP32_3, 2, 4 },
+}));
 
 class eltwise_no_pitches_same_dims_quantize : public EltwiseFusingTest {};
 TEST_P(eltwise_no_pitches_same_dims_quantize, quantize_f32_output) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), -128)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                      quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 256, p.input_type),
-                      reorder("out", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -128)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 256, p.input_type),
+        reorder("out", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_no_pitches_same_dims_quantize,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP16_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_3, 3, 4},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_no_pitches_same_dims_quantize, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_3, 3, 4 },
+}));
 
 class eltwise_activation : public EltwiseFusingTest {};
 TEST_P(eltwise_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, p.default_type),
-                      activation("activation", "eltwise", activation_func::relu, {6.0f, 0.0f}),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, p.default_type),
+        activation("activation", "eltwise", activation_func::relu, { 6.0f, 0.0f }),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
@@ -8248,36 +8377,37 @@ TEST_P(eltwise_activation, basic) {
 
 TEST_P(eltwise_activation, fp16_out) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      input_layout("input2", get_input_layout2(p)),
-                      eltwise("eltwise", {"input", "input2"}, p.mode, data_types::f16),
-                      activation("activation", "eltwise", activation_func::relu, {6.0f, 0.0f}),
-                      reorder("out", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        input_layout("input2", get_input_layout2(p)),
+        eltwise("eltwise", { "input", "input2" }, p.mode, data_types::f16),
+        activation("activation", "eltwise", activation_func::relu, { 6.0f, 0.0f }),
+        reorder("out", "activation", p.default_format, data_types::f32)
+    );
 
     tolerance = 1e-5f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        eltwise_activation,
-                        ::testing::ValuesIn(std::vector<eltwise_test_params>{
-                            eltwise_test_params{CASE_ELTWISE_FP16_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP32_FP16_3, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_1, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_2, 3, 4},
-                            eltwise_test_params{CASE_ELTWISE_FP16_FP32_3, 3, 4}
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_activation, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_3, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_1, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_2, 3, 4 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_3, 3, 4 }
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- Reduce cases ----------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct reduce_test_params {
     cldnn::tensor in_shape;
     cldnn::tensor out_shape;
@@ -8293,35 +8423,35 @@ struct reduce_test_params {
     std::string kernel_name;
 };
 
-#define CASE_REDUCE_F32_0 {3, 7, 5, 7}, {3, 7, 5, 7}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_REDUCE_F32_1 {3, 7, 5, 7}, {3, 7, 5, 7}, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_F32_2 {2, 4, 8, 4, 4}, {2, 4, 8, 4, 4}, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_F32_3 {16, 16, 16, 8, 8, 8}, {16, 16, 16, 8, 8, 8}, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_F32_4 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_F32_0 { 3, 7, 5, 7 }, { 3, 7, 5, 7 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_F32_1 { 3, 7, 5, 7 }, { 3, 7, 5, 7 }, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_F32_2 { 2, 4, 8, 4, 4 }, { 2, 4, 8, 4, 4 }, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_F32_3 { 16, 16, 16, 8, 8, 8 }, { 16, 16, 16, 8, 8, 8 }, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_F32_4 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
 
-#define CASE_REDUCE_F16_0 {3, 7, 5, 7}, {3, 7, 5, 7}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_REDUCE_F16_1 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::f16, format::bfyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_F16_2 {2, 4, 8, 4, 4}, {2, 4, 8, 4, 4}, data_types::f16, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_F16_3 {3, 5, 3, 5, 7, 7}, {3, 5, 3, 5, 7, 7}, data_types::f16, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_F16_4 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_F16_0 { 3, 7, 5, 7 }, { 3, 7, 5, 7 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_F16_1 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::f16, format::bfyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_F16_2 { 2, 4, 8, 4, 4 }, { 2, 4, 8, 4, 4 }, data_types::f16, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_F16_3 { 3, 5, 3, 5, 7, 7 }, { 3, 5, 3, 5, 7, 7 }, data_types::f16, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_F16_4 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::f16, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
 
-#define CASE_REDUCE_I32_0 {3, 7, 5, 7}, {3, 7, 5, 7}, data_types::i32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_REDUCE_I32_1 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::i32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_I32_2 {2, 4, 8, 4, 4}, {2, 4, 8, 4, 4}, data_types::i32, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_I32_3 {3, 5, 3, 5, 7, 7}, {3, 5, 3, 5, 7, 7}, data_types::i32, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_I32_4 {3, 5, 3, 5, 7, 7}, {3, 5, 3, 5, 7, 7}, data_types::i32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_I32_0 { 3, 7, 5, 7 }, { 3, 7, 5, 7 }, data_types::i32, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_I32_1 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::i32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_I32_2 { 2, 4, 8, 4, 4 }, { 2, 4, 8, 4, 4 }, data_types::i32, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_I32_3 { 3, 5, 3, 5, 7, 7 }, { 3, 5, 3, 5, 7, 7 }, data_types::i32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_I32_4 { 3, 5, 3, 5, 7, 7 }, { 3, 5, 3, 5, 7, 7 }, data_types::i32, format::bfwzyx, data_types::f32, format::bfyx
 
-#define CASE_REDUCE_I8_0 {3, 7, 5, 7}, {3, 7, 5, 7}, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_REDUCE_I8_1 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::i8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_I8_2 {2, 4, 8, 4, 4}, {2, 4, 8, 4, 4}, data_types::i8, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_I8_3 {3, 5, 3, 5, 7, 7}, {3, 5, 3, 5, 7, 7}, data_types::i8, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_I8_4 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_I8_0 { 3, 7, 5, 7 }, { 3, 7, 5, 7 }, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_I8_1 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_I8_2 { 2, 4, 8, 4, 4 }, { 2, 4, 8, 4, 4 }, data_types::i8, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_I8_3 { 3, 5, 3, 5, 7, 7 }, { 3, 5, 3, 5, 7, 7 }, data_types::i8, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_I8_4 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::i8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
 
-#define CASE_REDUCE_U8_0 {3, 7, 5, 7}, {3, 7, 5, 7},data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
-#define CASE_REDUCE_U8_1 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::u8, format::bfyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_U8_2 {2, 4, 8, 4, 4}, {2, 4, 8, 4, 4}, data_types::u8, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_U8_3 {3, 5, 3, 5, 7, 7}, {3, 5, 3, 5, 7, 7}, data_types::u8, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_REDUCE_U8_4 {2, 8, 4, 4}, {2, 8, 4, 4}, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_U8_0 { 3, 7, 5, 7 }, { 3, 7, 5, 7 },data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
+#define CASE_REDUCE_U8_1 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::u8, format::bfyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_U8_2 { 2, 4, 8, 4, 4 }, { 2, 4, 8, 4, 4 }, data_types::u8, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_U8_3 { 3, 5, 3, 5, 7, 7 }, { 3, 5, 3, 5, 7, 7 }, data_types::u8, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_REDUCE_U8_4 { 2, 8, 4, 4 }, { 2, 8, 4, 4 }, data_types::u8, format::b_fs_yx_fsv16, data_types::f32, format::bfyx
 
 
 class ReduceFusingTest : public ::BaseFusingTest<reduce_test_params> {
@@ -8363,9 +8493,12 @@ public:
         }
     }
 
-    layout get_input_layout(reduce_test_params& p) { return layout{p.data_type, p.input_format, p.in_shape}; }
+    layout get_input_layout(reduce_test_params& p) {
+        return layout{ p.data_type, p.input_format, p.in_shape };
+    }
+
     layout get_per_channel_layout(reduce_test_params& p) {
-        return layout{p.default_type, p.default_format, tensor{1, p.in_shape.feature[0], 1, 1}};
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.in_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -8373,17 +8506,19 @@ class reduce_eltwise_activation_quantize : public ReduceFusingTest {};
 TEST_P(reduce_eltwise_activation_quantize, basic) {
     auto p = GetParam();
     update_out_shape(p);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), -128)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                      data("eltwise_data", get_mem(get_output_layout(p))),
-                      reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
-                      eltwise("eltwise", {"reduce", "eltwise_data"}, eltwise_mode::sum, p.default_type),
-                      activation("activation", "eltwise", activation_func::relu),
-                      quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                      reorder("output_reorder", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_single_element_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_single_element_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -128)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
+        eltwise("eltwise", { "reduce", "eltwise_data" }, eltwise_mode::sum, p.default_type),
+        activation("activation", "eltwise", activation_func::relu),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("output_reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
@@ -8392,155 +8527,158 @@ TEST_P(reduce_eltwise_activation_quantize, basic) {
 TEST_P(reduce_eltwise_activation_quantize, per_channel) {
     auto p = GetParam();
     update_out_shape(p);
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                      data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                      data("out_lo", get_mem(get_single_element_layout(p), -128)),
-                      data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                      data("eltwise_data", get_mem(get_output_layout(p))),
-                      reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
-                      eltwise("eltwise", {"reduce", "eltwise_data"}, eltwise_mode::sum, p.default_type),
-                      activation("activation", "eltwise", activation_func::relu),
-                      quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                      reorder("output_reorder", "quantize", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -128)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
+        eltwise("eltwise", { "reduce", "eltwise_data" }, eltwise_mode::sum, p.default_type),
+        activation("activation", "eltwise", activation_func::relu),
+        quantize("quantize", "activation", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("output_reorder", "quantize", p.default_format, data_types::f32)
+    );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        reduce_eltwise_activation_quantize,
-                        ::testing::ValuesIn(std::vector<reduce_test_params>{
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 5, reduce_mode::mean, {reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_f, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_f, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 5, reduce_mode::min, {reduce::along_x, reduce::along_y, reduce::along_f}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_1, 2, 5, reduce_mode::sum, {reduce::along_f, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_2, 2, 5, reduce_mode::mean, {reduce::along_f, reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_1, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_2, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, {reduce::along_y}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 5, reduce_mode::min, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_1, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_2, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_1, 2, 5, reduce_mode::mean, {reduce::along_x}, true, "reduce_ref"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, reduce_eltwise_activation_quantize, ::testing::ValuesIn(std::vector<reduce_test_params>{
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 5, reduce_mode::mean, { reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_f, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_f, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 5, reduce_mode::min, { reduce::along_x, reduce::along_y, reduce::along_f }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_1, 2, 5, reduce_mode::sum, { reduce::along_f, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_2, 2, 5, reduce_mode::mean, { reduce::along_f, reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_1, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_2, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_4, 2, 5, reduce_mode::sum, { reduce::along_y }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 5, reduce_mode::min, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_1, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_2, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_1, 2, 5, reduce_mode::mean, { reduce::along_x }, true, "reduce_ref" },
 
-                            reduce_test_params{CASE_REDUCE_F16_1, 2, 5, reduce_mode::mean, {reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_2, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_f, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_1, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_f, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_2, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_1, 2, 5, reduce_mode::min, {reduce::along_x, reduce::along_y, reduce::along_f}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_0, 2, 5, reduce_mode::sum, {reduce::along_f, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_4, 2, 5, reduce_mode::mean, {reduce::along_f, reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_0, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_4, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_1, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_2, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_1, 2, 5, reduce_mode::sum, {reduce::along_y}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_2, 2, 5, reduce_mode::min, {reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_4, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_0, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_4, 2, 5, reduce_mode::mean, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
+    reduce_test_params{ CASE_REDUCE_F16_1, 2, 5, reduce_mode::mean, { reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_2, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_f, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_1, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_f, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_2, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_1, 2, 5, reduce_mode::min, { reduce::along_x, reduce::along_y, reduce::along_f }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_0, 2, 5, reduce_mode::sum, { reduce::along_f, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_4, 2, 5, reduce_mode::mean, { reduce::along_f, reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_0, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_4, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_1, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_2, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_1, 2, 5, reduce_mode::sum, { reduce::along_y }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_2, 2, 5, reduce_mode::min, { reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_4, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_0, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_4, 2, 5, reduce_mode::mean, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
 
-                            reduce_test_params{CASE_REDUCE_I8_0, 2, 5, reduce_mode::mean, {reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_f, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_0, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_f, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_0, 2, 5, reduce_mode::min, {reduce::along_x, reduce::along_y, reduce::along_f}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_1, 2, 5, reduce_mode::sum, {reduce::along_f, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I8_2, 2, 5, reduce_mode::mean, {reduce::along_f, reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I8_1, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I8_2, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_0, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, {reduce::along_y}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_0, 2, 5, reduce_mode::min, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_I8_1, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I8_2, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I8_1, 2, 5, reduce_mode::mean, {reduce::along_x}, true, "reduce_ref"},
+    reduce_test_params{ CASE_REDUCE_I8_0, 2, 5, reduce_mode::mean, { reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_f, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_0, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_f, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_0, 2, 5, reduce_mode::min, { reduce::along_x, reduce::along_y, reduce::along_f }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_1, 2, 5, reduce_mode::sum, { reduce::along_f, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I8_2, 2, 5, reduce_mode::mean, { reduce::along_f, reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I8_1, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I8_2, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_0, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_4, 2, 5, reduce_mode::sum, { reduce::along_y }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_0, 2, 5, reduce_mode::min, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_I8_1, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I8_2, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I8_1, 2, 5, reduce_mode::mean, { reduce::along_x }, true, "reduce_ref" },
 
-                            reduce_test_params{CASE_REDUCE_U8_1, 2, 5, reduce_mode::mean, {reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_2, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_f, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_1, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_f, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_2, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_1, 2, 5, reduce_mode::min, {reduce::along_x, reduce::along_y, reduce::along_f}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_0, 2, 5, reduce_mode::sum, {reduce::along_f, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_U8_4, 2, 5, reduce_mode::mean, {reduce::along_f, reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_U8_0, 2, 5, reduce_mode::max, {reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_U8_4, 2, 5, reduce_mode::sum, {reduce::along_x, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_U8_1, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_2, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_1, 2, 5, reduce_mode::sum, {reduce::along_y}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_2, 2, 5, reduce_mode::min, {reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_4, 2, 5, reduce_mode::sum, {reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_U8_0, 2, 5, reduce_mode::max, {reduce::along_f}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_U8_4, 2, 5, reduce_mode::mean, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"}
-                        }));
+    reduce_test_params{ CASE_REDUCE_U8_1, 2, 5, reduce_mode::mean, { reduce::along_x, reduce::along_f, reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_2, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_f, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_1, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_f, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_2, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_1, 2, 5, reduce_mode::min, { reduce::along_x, reduce::along_y, reduce::along_f }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_0, 2, 5, reduce_mode::sum, { reduce::along_f, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_U8_4, 2, 5, reduce_mode::mean, { reduce::along_f, reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_U8_0, 2, 5, reduce_mode::max, { reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_U8_4, 2, 5, reduce_mode::sum, { reduce::along_x, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_U8_1, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_2, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_1, 2, 5, reduce_mode::sum, { reduce::along_y }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_2, 2, 5, reduce_mode::min, { reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_4, 2, 5, reduce_mode::sum, { reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_U8_0, 2, 5, reduce_mode::max, { reduce::along_f }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_U8_4, 2, 5, reduce_mode::mean, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" }
+}));
 
 class reduce_scale_activation : public ReduceFusingTest {};
 TEST_P(reduce_scale_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_single_element_layout(p), -0.125f)),
-                      reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
-                      scale("scale", "reduce", "scale_data"),
-                      activation("activation", "scale", activation_func::cos),
-                      reorder("output_reorder", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_single_element_layout(p), -0.125f)),
+        reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
+        scale("scale", "reduce", "scale_data"),
+        activation("activation", "scale", activation_func::cos),
+        reorder("output_reorder", "activation", p.default_format, data_types::f32)
+    );
+
     tolerance = 1e-02f;
     execute(p);
 }
 
 TEST_P(reduce_scale_activation, per_channel) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                      data("scale_data", get_mem(get_per_channel_layout(p), -0.125f)),
-                      reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
-                      scale("scale", "reduce", "scale_data"),
-                      activation("activation", "scale", activation_func::cos),
-                      reorder("output_reorder", "activation", p.default_format, data_types::f32));
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("scale_data", get_mem(get_per_channel_layout(p), -0.125f)),
+        reduce("reduce", "input", p.reduce_mode, p.reduce_axes, p.keep_dims),
+        scale("scale", "reduce", "scale_data"),
+        activation("activation", "scale", activation_func::cos),
+        reorder("output_reorder", "activation", p.default_format, data_types::f32)
+    );
+
     tolerance = 1e-02f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu,
-                        reduce_scale_activation,
-                        ::testing::ValuesIn(std::vector<reduce_test_params>{
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 4, reduce_mode::max, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_1, 2, 4, reduce_mode::sum, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 4, reduce_mode::min, {reduce::along_x, reduce::along_y}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_2, 2, 4, reduce_mode::mean, {reduce::along_x, reduce::along_y}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 4, reduce_mode::l1, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 4, reduce_mode::l1, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 4, reduce_mode::min, {reduce::along_y}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F32_0, 2, 4, reduce_mode::sum, {reduce::along_y}, true, "reduce_gpu_b_fs_yx_fsv16"},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, reduce_scale_activation, ::testing::ValuesIn(std::vector<reduce_test_params>{
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 4, reduce_mode::max, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_1, 2, 4, reduce_mode::sum, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 4, reduce_mode::min, { reduce::along_x, reduce::along_y }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_2, 2, 4, reduce_mode::mean, { reduce::along_x, reduce::along_y }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 4, reduce_mode::l1, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 4, reduce_mode::l1, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 4, reduce_mode::min, { reduce::along_y }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F32_0, 2, 4, reduce_mode::sum, { reduce::along_y }, true, "reduce_gpu_b_fs_yx_fsv16" },
 
-                            reduce_test_params{CASE_REDUCE_F16_0, 2, 4, reduce_mode::max, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_1, 2, 4, reduce_mode::sum, {reduce::along_x, reduce::along_y, reduce::along_b}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_0, 2, 4, reduce_mode::min, {reduce::along_x, reduce::along_y}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_2, 2, 4, reduce_mode::mean, {reduce::along_x, reduce::along_y}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_0, 2, 4, reduce_mode::min, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                            reduce_test_params{CASE_REDUCE_F16_0, 2, 4, reduce_mode::sum, {reduce::along_x}, true, "reduce_gpu_b_fs_yx_fsv16"},
-                        }));
+    reduce_test_params{ CASE_REDUCE_F16_0, 2, 4, reduce_mode::max, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_1, 2, 4, reduce_mode::sum, { reduce::along_x, reduce::along_y, reduce::along_b }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_0, 2, 4, reduce_mode::min, { reduce::along_x, reduce::along_y }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_2, 2, 4, reduce_mode::mean, { reduce::along_x, reduce::along_y }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_0, 2, 4, reduce_mode::min, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+    reduce_test_params{ CASE_REDUCE_F16_0, 2, 4, reduce_mode::sum, { reduce::along_x }, true, "reduce_gpu_b_fs_yx_fsv16" },
+}));
 
-INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu,
-                        reduce_eltwise_activation_quantize,
-                        ::testing::ValuesIn(std::vector<reduce_test_params>{
-                            // No layout format available for quantize/scale
-                            reduce_test_params{CASE_REDUCE_F32_3, 2, 4, reduce_mode::l1, {reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_F16_3, 2, 4, reduce_mode::min, {reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I32_2, 2, 4, reduce_mode::max, {reduce::along_x, reduce::along_y}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I32_3, 2, 4, reduce_mode::sum, {reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_I8_3, 2, 4, reduce_mode::mean, {reduce::along_x}, true, "reduce_ref"},
-                            reduce_test_params{CASE_REDUCE_U8_3, 2, 4, reduce_mode::l2, {reduce::along_x}, true, "reduce_ref"}
-                        }));
+INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu, reduce_eltwise_activation_quantize, ::testing::ValuesIn(std::vector<reduce_test_params>{
+    // No layout format available for quantize/scale
+    reduce_test_params{ CASE_REDUCE_F32_3, 2, 4, reduce_mode::l1, { reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_F16_3, 2, 4, reduce_mode::min, { reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I32_2, 2, 4, reduce_mode::max, { reduce::along_x, reduce::along_y }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I32_3, 2, 4, reduce_mode::sum, { reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_I8_3, 2, 4, reduce_mode::mean, { reduce::along_x }, true, "reduce_ref" },
+    reduce_test_params{ CASE_REDUCE_U8_3, 2, 4, reduce_mode::l2, { reduce::along_x }, true, "reduce_ref" }
+}));
 
 
-/* ----------------------------------------------------------------------------------------------------- */
-/* ------------------------------------------ ScatterNDUpdate cases ------------------------------ */
-/* ----------------------------------------------------------------------------------------------------- */
+/* ----------------------------------------------------------------------------------------------- */
+/* ------------------------------------ ScatterNDUpdate cases ------------------------------------ */
+/* ----------------------------------------------------------------------------------------------- */
+
 struct scatter_nd_update_test_params {
     tensor input_shape;
     tensor indices_shape;
@@ -8554,49 +8692,49 @@ struct scatter_nd_update_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_SCATTER_ND_UPDATE_FP16_4D_1 {6, 1, 1, 1}, {3, 1, 1, 1}, {3, 1, 1, 1}, 1, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_4D_2 {6, 6, 1, 1}, {3, 2, 1, 1}, {3, 1, 1, 1}, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_4D_3 {6, 7, 8, 9}, {5, 1, 1, 1}, {5, 7, 8, 9}, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_4D_4 {6, 7, 8, 9}, {5, 1, 1, 1}, {5, 7, 8, 9}, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_4D_5 {6, 7, 8, 9}, {6, 2, 1, 1}, {6, 9, 1, 8}, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_4D_6 {6, 7, 8, 9}, {6, 3, 1, 1}, {6, 8, 1, 1}, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_4D_1 { 6, 1, 1, 1 }, { 3, 1, 1, 1 }, { 3, 1, 1, 1 }, 1, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_4D_2 { 6, 6, 1, 1 }, { 3, 2, 1, 1 }, { 3, 1, 1, 1 }, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_4D_3 { 6, 7, 8, 9 }, { 5, 1, 1, 1 }, { 5, 7, 8, 9 }, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_4D_4 { 6, 7, 8, 9 }, { 5, 1, 1, 1 }, { 5, 7, 8, 9 }, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_4D_5 { 6, 7, 8, 9 }, { 6, 2, 1, 1 }, { 6, 9, 1, 8 }, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_4D_6 { 6, 7, 8, 9 }, { 6, 3, 1, 1 }, { 6, 8, 1, 1 }, 2, data_types::f16, format::bfyx, data_types::f16, format::bfyx
 
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_1 {6, 7, 8, 9, 10}, {5, 1, 1, 1}, {5, 7, 8, 9, 10}, 1, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_2 {6, 7, 8, 9, 10}, {5, 2, 1, 1}, {5, 10, 1, 8, 9}, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_3 {6, 7, 8, 9, 10}, {5, 3, 1, 1}, {5, 9, 1, 1, 8}, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_4 {6, 7, 8, 9, 10}, {5, 4, 1, 1}, {5, 8, 1, 1, 1}, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_5 {6, 7, 8, 9, 10}, {5, 5, 1, 1}, {5, 1, 1, 1, 1}, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_6 {6, 7, 8, 9, 10}, {5, 2, 1, 2}, {5, 2, 8, 9, 10}, 3, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_7 {6, 7, 8, 9, 10}, {5, 2, 1, 3}, {5, 2, 1, 8, 9}, 3, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_8 {6, 7, 8, 9, 10}, {5, 2, 4, 3}, {5, 2, 1, 8, 3}, 4, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_5D_9 {6, 7, 8, 9, 10}, {5, 2, 3, 3}, {5, 2, 8, 9, 3}, 4, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_1 { 6, 7, 8, 9, 10 }, { 5, 1, 1, 1 }, { 5, 7, 8, 9, 10 }, 1, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_2 { 6, 7, 8, 9, 10 }, { 5, 2, 1, 1 }, { 5, 10, 1, 8, 9 }, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_3 { 6, 7, 8, 9, 10 }, { 5, 3, 1, 1 }, { 5, 9, 1, 1, 8 }, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_4 { 6, 7, 8, 9, 10 }, { 5, 4, 1, 1 }, { 5, 8, 1, 1, 1 }, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_5 { 6, 7, 8, 9, 10 }, { 5, 5, 1, 1 }, { 5, 1, 1, 1, 1 }, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_6 { 6, 7, 8, 9, 10 }, { 5, 2, 1, 2 }, { 5, 2, 8, 9, 10 }, 3, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_7 { 6, 7, 8, 9, 10 }, { 5, 2, 1, 3 }, { 5, 2, 1, 8, 9 }, 3, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_8 { 6, 7, 8, 9, 10 }, { 5, 2, 4, 3 }, { 5, 2, 1, 8, 3 }, 4, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_5D_9 { 6, 7, 8, 9, 10 }, { 5, 2, 3, 3 }, { 5, 2, 8, 9, 3 }, 4, data_types::f16, format::bfzyx, data_types::f16, format::bfyx
 
-#define CASE_SCATTER_ND_UPDATE_FP16_6D_1 {6, 7, 8, 9, 10, 11}, {5, 1, 1, 1}, {5, 7, 8, 9, 10, 11}, 1, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_6D_2 {6, 7, 8, 9, 10, 11}, {5, 2, 1, 1}, {5, 11, 1, 8, 9, 10}, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_6D_3 {6, 7, 8, 9, 10, 11}, {5, 3, 1, 1}, {5, 10, 1, 1, 8, 9}, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_6D_4 {6, 7, 8, 9, 10, 11}, {5, 4, 1, 1}, {5, 9, 1, 1, 1, 8}, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_6D_5 {6, 7, 8, 9, 2, 2}, {5, 5, 1, 1}, {5, 8, 1, 1, 1, 1}, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP16_6D_6 {6, 7, 8, 9, 2, 2}, {5, 6, 1, 1}, {5, 1, 1, 1, 1, 1}, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_6D_1 { 6, 7, 8, 9, 10, 11 }, { 5, 1, 1, 1 }, { 5, 7, 8, 9, 10, 11 }, 1, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_6D_2 { 6, 7, 8, 9, 10, 11 }, { 5, 2, 1, 1 }, { 5, 11, 1, 8, 9, 10 }, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_6D_3 { 6, 7, 8, 9, 10, 11 }, { 5, 3, 1, 1 }, { 5, 10, 1, 1, 8, 9 }, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_6D_4 { 6, 7, 8, 9, 10, 11 }, { 5, 4, 1, 1 }, { 5, 9, 1, 1, 1, 8 }, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_6D_5 { 6, 7, 8, 9, 2, 2 }, { 5, 5, 1, 1 }, { 5, 8, 1, 1, 1, 1 }, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP16_6D_6 { 6, 7, 8, 9, 2, 2 }, { 5, 6, 1, 1 }, { 5, 1, 1, 1, 1, 1 }, 2, data_types::f16, format::bfwzyx, data_types::f16, format::bfyx
 
-#define CASE_SCATTER_ND_UPDATE_FP32_4D_1 {6, 1, 1, 1}, {3, 1, 1, 1}, {3, 1, 1, 1}, 1, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_4D_2 {6, 6, 1, 1}, {3, 2, 1, 1}, {3, 1, 1, 1}, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_4D_3 {6, 7, 8, 1}, {5, 1, 1, 1}, {5, 7, 8, 1}, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_4D_4 {6, 7, 8, 9}, {5, 1, 1, 1}, {5, 7, 8, 9}, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_4D_5 {6, 7, 8, 9}, {6, 2, 1, 1}, {6, 9, 1, 8}, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_4D_6 {6, 7, 8, 9}, {6, 3, 1, 1}, {6, 8, 1, 1}, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_4D_1 { 6, 1, 1, 1 }, { 3, 1, 1, 1 }, { 3, 1, 1, 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_4D_2 { 6, 6, 1, 1 }, { 3, 2, 1, 1 }, { 3, 1, 1, 1 }, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_4D_3 { 6, 7, 8, 1 }, { 5, 1, 1, 1 }, { 5, 7, 8, 1 }, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_4D_4 { 6, 7, 8, 9 }, { 5, 1, 1, 1 }, { 5, 7, 8, 9 }, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_4D_5 { 6, 7, 8, 9 }, { 6, 2, 1, 1 }, { 6, 9, 1, 8 }, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_4D_6 { 6, 7, 8, 9 }, { 6, 3, 1, 1 }, { 6, 8, 1, 1 }, 2, data_types::f32, format::bfyx, data_types::f32, format::bfyx
 
-#define CASE_SCATTER_ND_UPDATE_FP32_5D_1 {6, 7, 8, 9, 10}, {5, 1, 1, 1}, {5, 7, 8, 9, 10}, 1, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_5D_2 {6, 7, 8, 9, 10}, {5, 2, 1, 1}, {5, 10, 1, 8, 9}, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_5D_3 {6, 7, 8, 9, 10}, {5, 3, 1, 1}, {5, 9, 1, 1, 8}, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_5D_4 {6, 7, 8, 9, 10}, {5, 4, 1, 1}, {5, 8, 1, 1, 1}, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_5D_5 {6, 7, 8, 9, 10}, {5, 5, 1, 1}, {5, 1, 1, 1, 1}, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_5D_1 { 6, 7, 8, 9, 10 }, { 5, 1, 1, 1 }, { 5, 7, 8, 9, 10 }, 1, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_5D_2 { 6, 7, 8, 9, 10 }, { 5, 2, 1, 1 }, { 5, 10, 1, 8, 9 }, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_5D_3 { 6, 7, 8, 9, 10 }, { 5, 3, 1, 1 }, { 5, 9, 1, 1, 8 }, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_5D_4 { 6, 7, 8, 9, 10 }, { 5, 4, 1, 1 }, { 5, 8, 1, 1, 1 }, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_5D_5 { 6, 7, 8, 9, 10 }, { 5, 5, 1, 1 }, { 5, 1, 1, 1, 1 }, 2, data_types::f32, format::bfzyx, data_types::f32, format::bfyx
 
-#define CASE_SCATTER_ND_UPDATE_FP32_6D_1 {6, 7, 8, 9, 10, 11}, {5, 1, 1, 1}, {5, 7, 8, 9, 10, 11}, 1, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_6D_2 {6, 7, 8, 9, 10, 11}, {5, 2, 1, 1}, {5, 11, 1, 8, 9, 10}, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_6D_3 {6, 7, 8, 9, 10, 11}, {5, 3, 1, 1}, {5, 10, 1, 1, 8, 9}, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_6D_4 {6, 7, 8, 9, 10, 11}, {5, 4, 1, 1}, {5, 9, 1, 1, 1, 8}, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_6D_5 {6, 7, 8, 9, 2, 2}, {5, 5, 1, 1}, {5, 8, 1, 1, 1, 1}, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
-#define CASE_SCATTER_ND_UPDATE_FP32_6D_6 {6, 7, 8, 9, 2, 2}, {5, 6, 1, 1}, {5, 1, 1, 1, 1, 1}, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_6D_1 { 6, 7, 8, 9, 10, 11 }, { 5, 1, 1, 1 }, { 5, 7, 8, 9, 10, 11 }, 1, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_6D_2 { 6, 7, 8, 9, 10, 11 }, { 5, 2, 1, 1 }, { 5, 11, 1, 8, 9, 10 }, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_6D_3 { 6, 7, 8, 9, 10, 11 }, { 5, 3, 1, 1 }, { 5, 10, 1, 1, 8, 9 }, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_6D_4 { 6, 7, 8, 9, 10, 11 }, { 5, 4, 1, 1 }, { 5, 9, 1, 1, 1, 8 }, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_6D_5 { 6, 7, 8, 9, 2, 2 }, { 5, 5, 1, 1 }, { 5, 8, 1, 1, 1, 1 }, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
+#define CASE_SCATTER_ND_UPDATE_FP32_6D_6 { 6, 7, 8, 9, 2, 2 }, { 5, 6, 1, 1 }, { 5, 1, 1, 1, 1, 1 }, 2, data_types::f32, format::bfwzyx, data_types::f32, format::bfyx
 
 class ScatterNDUpdatePrimitiveFusingTest : public ::BaseFusingTest<scatter_nd_update_test_params> {
 public:
@@ -8622,7 +8760,7 @@ public:
     }
 
     layout get_per_channel_layout(scatter_nd_update_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.input_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.input_shape.feature[0], 1, 1 } };
     }
 
     format get_default_format(int rank = 4) {
@@ -8696,7 +8834,8 @@ public:
 class scatter_nd_update_quantize : public ScatterNDUpdatePrimitiveFusingTest {};
 TEST_P(scatter_nd_update_quantize, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scatter_nd_update_indices", get_indices_mem(p)),
         data("scatter_nd_update_updates", get_mem(get_updates_layout(p), 0, 100)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
@@ -8707,59 +8846,60 @@ TEST_P(scatter_nd_update_quantize, basic) {
         quantize("quantize", "scatter_nd_update_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
         reorder("reorder_bfyx", "quantize", p.input_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_nd_update_quantize,
-    ::testing::ValuesIn(std::vector<scatter_nd_update_test_params>{
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_1, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_2, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_3, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_4, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_5, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_6, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_nd_update_quantize, ::testing::ValuesIn(std::vector<scatter_nd_update_test_params>{
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_1, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_2, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_3, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_4, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_5, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_6, 2, 3 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_1, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_2, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_3, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_4, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_5, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_7, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_9, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_1, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_2, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_3, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_4, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_5, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_7, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_9, 2, 3 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_1, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_2, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_3, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_4, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_5, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_6, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_1, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_2, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_3, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_4, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_5, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_6, 2, 3 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_1, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_2, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_3, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_4, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_5, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_6, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_1, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_2, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_3, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_4, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_5, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_6, 2, 3 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_1, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_2, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_3, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_4, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_5, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_1, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_2, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_3, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_4, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_5, 2, 3 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_1, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_2, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_3, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_4, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_5, 2, 3 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_6, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_1, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_2, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_3, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_4, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_5, 2, 3 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_6, 2, 3 },
 }));
 
 class scatter_nd_update_scale_activation_eltwise : public ScatterNDUpdatePrimitiveFusingTest {};
 TEST_P(scatter_nd_update_scale_activation_eltwise, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("scatter_nd_update_indices", get_indices_mem(p)),
         data("scatter_nd_update_updates", get_mem(get_updates_layout(p), 0, 100)),
         data("scale_data", get_mem(get_per_channel_layout(p), -1, 1)),
@@ -8775,54 +8915,54 @@ TEST_P(scatter_nd_update_scale_activation_eltwise, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_nd_update_scale_activation_eltwise,
-    ::testing::ValuesIn(std::vector<scatter_nd_update_test_params>{
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_1, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_2, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_3, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_4, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_5, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_6, 2, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, scatter_nd_update_scale_activation_eltwise, ::testing::ValuesIn(std::vector<scatter_nd_update_test_params>{
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_1, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_2, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_3, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_4, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_5, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_4D_6, 2, 5 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_1, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_2, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_3, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_4, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_5, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_6, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_1, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_2, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_3, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_4, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_5, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_5D_6, 2, 5 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_1, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_2, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_3, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_4, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_5, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_6, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_1, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_2, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_3, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_4, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_5, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP16_6D_6, 2, 5 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_1, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_2, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_3, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_4, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_5, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_6, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_1, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_2, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_3, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_4, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_5, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_4D_6, 2, 5 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_1, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_2, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_3, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_4, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_5, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_1, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_2, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_3, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_4, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_5D_5, 2, 5 },
 
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_1, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_2, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_3, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_4, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_5, 2, 5 },
-        scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_6, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_1, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_2, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_3, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_4, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_5, 2, 5 },
+    scatter_nd_update_test_params{ CASE_SCATTER_ND_UPDATE_FP32_6D_6, 2, 5 },
 }));
 
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ------------------------------------------ GatherND cases ------------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct gather_nd_test_params {
     data_types data_type;
 
@@ -8846,37 +8986,37 @@ struct gather_nd_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_GATHER_ND_FP16_4D_1 data_types::f16, format::bfyx, {6, 7, 9, 8}, format::bfyx, {3, 1, 1, 1}, format::bfyx, {3, 7, 9, 8}, 6, 2, 0, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_4D_2 data_types::f16, format::bfyx, {6, 7, 9, 8}, format::bfyx, {6, 1, 1, 1}, format::bfyx, {6, 8, 1, 9}, 6, 2, 1, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_4D_3 data_types::f16, format::bfyx, {5, 4, 7, 2}, format::bfyx, {5, 4, 1, 2}, format::bfyx, {40, 1, 1, 1}, 6, 4, 3, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_4D_1 data_types::f16, format::bfyx, { 6, 7, 9, 8 }, format::bfyx, { 3, 1, 1, 1 }, format::bfyx, { 3, 7, 9, 8 }, 6, 2, 0, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_4D_2 data_types::f16, format::bfyx, { 6, 7, 9, 8 }, format::bfyx, { 6, 1, 1, 1 }, format::bfyx, { 6, 8, 1, 9 }, 6, 2, 1, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_4D_3 data_types::f16, format::bfyx, { 5, 4, 7, 2 }, format::bfyx, { 5, 4, 1, 2 }, format::bfyx, { 40, 1, 1, 1 }, 6, 4, 3, data_types::f16, format::bfyx
 
-#define CASE_GATHER_ND_FP16_5D_1 data_types::f16, format::bfzyx, {5, 6, 7, 8, 5}, format::bfyx, {5, 1, 1, 1}, format::bfzyx, {5, 6, 7, 8, 5}, 5, 2, 0, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_5D_2 data_types::f16, format::bfzyx, {5, 6, 7, 8, 5}, format::bfyx, {5, 1, 1, 1}, format::bfyx, {5, 5, 7, 8}, 5, 2, 1, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_5D_3 data_types::f16, format::bfzyx, {5, 4, 7, 8, 5}, format::bfyx, {5, 4, 1, 3}, format::bfyx, {20, 1, 1, 1}, 4, 3, 2, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_5D_4 data_types::f16, format::bfzyx, {5, 4, 7, 8, 3}, format::bfyx, {5, 4, 1, 3}, format::bfyx, {60, 7, 1, 1}, 4, 4, 3, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_5D_5 data_types::f16, format::bfzyx, {5, 4, 7, 2, 3}, format::bfzyx, {5, 4, 1, 2, 3}, format::bfyx, {120, 1, 1, 1}, 4, 5, 4, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_5D_6 data_types::f16, format::bfzyx, {5, 4, 7, 4, 4}, format::bfzyx, {5, 4, 1, 1, 3}, format::bfzyx, {20, 3, 7, 4, 1}, 4, 5, 2, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_5D_1 data_types::f16, format::bfzyx, { 5, 6, 7, 8, 5 }, format::bfyx, { 5, 1, 1, 1 }, format::bfzyx, { 5, 6, 7, 8, 5 }, 5, 2, 0, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_5D_2 data_types::f16, format::bfzyx, { 5, 6, 7, 8, 5 }, format::bfyx, { 5, 1, 1, 1 }, format::bfyx, { 5, 5, 7, 8 }, 5, 2, 1, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_5D_3 data_types::f16, format::bfzyx, { 5, 4, 7, 8, 5 }, format::bfyx, { 5, 4, 1, 3 }, format::bfyx, { 20, 1, 1, 1 }, 4, 3, 2, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_5D_4 data_types::f16, format::bfzyx, { 5, 4, 7, 8, 3 }, format::bfyx, { 5, 4, 1, 3 }, format::bfyx, { 60, 7, 1, 1 }, 4, 4, 3, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_5D_5 data_types::f16, format::bfzyx, { 5, 4, 7, 2, 3 }, format::bfzyx, { 5, 4, 1, 2, 3 }, format::bfyx, { 120, 1, 1, 1 }, 4, 5, 4, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_5D_6 data_types::f16, format::bfzyx, { 5, 4, 7, 4, 4 }, format::bfzyx, { 5, 4, 1, 1, 3 }, format::bfzyx, { 20, 3, 7, 4, 1 }, 4, 5, 2, data_types::f16, format::bfyx
 
-#define CASE_GATHER_ND_FP16_6D_1 data_types::f16, format::bfwzyx, {5, 4, 6, 7, 8, 5}, format::bfyx, {5, 4, 2, 2}, format::bfyx, {20, 2, 6, 7}, 5, 4, 2, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_6D_2 data_types::f16, format::bfwzyx, {5, 4, 6, 7, 8, 2}, format::bfyx, {5, 4, 2, 2}, format::bfyx, {40, 6, 1, 1}, 5, 4, 3, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_6D_3 data_types::f16, format::bfwzyx, {5, 4, 6, 7, 2, 2}, format::bfzyx, {5, 4, 1, 2, 2}, format::bfyx, {80, 6, 1, 1}, 5, 5, 4, data_types::f16, format::bfyx
-#define CASE_GATHER_ND_FP16_6D_4 data_types::f16, format::bfwzyx, {5, 4, 6, 3, 2, 2}, format::bfwzyx, {5, 4, 1, 3, 2, 2}, format::bfyx, {240, 1, 1, 1}, 5, 6, 5, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_6D_1 data_types::f16, format::bfwzyx, { 5, 4, 6, 7, 8, 5 }, format::bfyx, { 5, 4, 2, 2 }, format::bfyx, { 20, 2, 6, 7 }, 5, 4, 2, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_6D_2 data_types::f16, format::bfwzyx, { 5, 4, 6, 7, 8, 2 }, format::bfyx, { 5, 4, 2, 2 }, format::bfyx, { 40, 6, 1, 1 }, 5, 4, 3, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_6D_3 data_types::f16, format::bfwzyx, { 5, 4, 6, 7, 2, 2 }, format::bfzyx, { 5, 4, 1, 2, 2 }, format::bfyx, { 80, 6, 1, 1 }, 5, 5, 4, data_types::f16, format::bfyx
+#define CASE_GATHER_ND_FP16_6D_4 data_types::f16, format::bfwzyx, { 5, 4, 6, 3, 2, 2 }, format::bfwzyx, { 5, 4, 1, 3, 2, 2 }, format::bfyx, { 240, 1, 1, 1 }, 5, 6, 5, data_types::f16, format::bfyx
 
-#define CASE_GATHER_ND_FP32_4D_1 data_types::f32, format::bfyx, {6, 7, 9, 8}, format::bfyx, {3, 1, 1, 1}, format::bfyx, {3, 7, 9, 8}, 6, 2, 0, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_4D_2 data_types::f32, format::bfyx, {6, 7, 9, 8}, format::bfyx, {6, 1, 1, 1}, format::bfyx, {6, 8, 1, 9}, 6, 2, 1, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_4D_3 data_types::f32, format::bfyx, {5, 4, 7, 2}, format::bfyx, {5, 4, 1, 2}, format::bfyx, {40, 1, 1, 1}, 6, 4, 3, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_4D_1 data_types::f32, format::bfyx, { 6, 7, 9, 8 }, format::bfyx, { 3, 1, 1, 1 }, format::bfyx, { 3, 7, 9, 8 }, 6, 2, 0, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_4D_2 data_types::f32, format::bfyx, { 6, 7, 9, 8 }, format::bfyx, { 6, 1, 1, 1 }, format::bfyx, { 6, 8, 1, 9 }, 6, 2, 1, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_4D_3 data_types::f32, format::bfyx, { 5, 4, 7, 2 }, format::bfyx, { 5, 4, 1, 2 }, format::bfyx, { 40, 1, 1, 1 }, 6, 4, 3, data_types::f32, format::bfyx
 
-#define CASE_GATHER_ND_FP32_5D_1 data_types::f32, format::bfzyx, {5, 6, 7, 8, 5}, format::bfyx, {5, 1, 1, 1}, format::bfzyx, {5, 6, 7, 8, 5}, 5, 2, 0, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_5D_2 data_types::f32, format::bfzyx, {5, 6, 7, 8, 5}, format::bfyx, {5, 1, 1, 1}, format::bfyx, {5, 5, 7, 8}, 5, 2, 1, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_5D_3 data_types::f32, format::bfzyx, {5, 4, 7, 8, 5}, format::bfyx, {5, 4, 1, 3}, format::bfyx, {20, 1, 1, 1}, 4, 3, 2, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_5D_4 data_types::f32, format::bfzyx, {5, 4, 7, 8, 3}, format::bfyx, {5, 4, 1, 3}, format::bfyx, {60, 7, 1, 1}, 4, 4, 3, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_5D_5 data_types::f32, format::bfzyx, {5, 4, 7, 2, 3}, format::bfzyx, {5, 4, 1, 2, 3}, format::bfyx, {120, 1, 1, 1}, 4, 5, 4, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_5D_6 data_types::f32, format::bfzyx, {5, 4, 7, 4, 4}, format::bfzyx, {5, 4, 1, 1, 3}, format::bfzyx, {20, 3, 7, 4, 1}, 4, 5, 2, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_5D_1 data_types::f32, format::bfzyx, { 5, 6, 7, 8, 5 }, format::bfyx, { 5, 1, 1, 1 }, format::bfzyx, { 5, 6, 7, 8, 5 }, 5, 2, 0, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_5D_2 data_types::f32, format::bfzyx, { 5, 6, 7, 8, 5 }, format::bfyx, { 5, 1, 1, 1 }, format::bfyx, { 5, 5, 7, 8 }, 5, 2, 1, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_5D_3 data_types::f32, format::bfzyx, { 5, 4, 7, 8, 5 }, format::bfyx, { 5, 4, 1, 3 }, format::bfyx, { 20, 1, 1, 1 }, 4, 3, 2, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_5D_4 data_types::f32, format::bfzyx, { 5, 4, 7, 8, 3 }, format::bfyx, { 5, 4, 1, 3 }, format::bfyx, { 60, 7, 1, 1 }, 4, 4, 3, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_5D_5 data_types::f32, format::bfzyx, { 5, 4, 7, 2, 3 }, format::bfzyx, { 5, 4, 1, 2, 3 }, format::bfyx, { 120, 1, 1, 1 }, 4, 5, 4, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_5D_6 data_types::f32, format::bfzyx, { 5, 4, 7, 4, 4 }, format::bfzyx, { 5, 4, 1, 1, 3 }, format::bfzyx, { 20, 3, 7, 4, 1 }, 4, 5, 2, data_types::f32, format::bfyx
 
-#define CASE_GATHER_ND_FP32_6D_1 data_types::f32, format::bfwzyx, {5, 4, 6, 7, 8, 5}, format::bfyx, {5, 4, 2, 2}, format::bfyx, {20, 2, 6, 7}, 5, 4, 2, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_6D_2 data_types::f32, format::bfwzyx, {5, 4, 6, 7, 8, 2}, format::bfyx, {5, 4, 2, 2}, format::bfyx, {40, 6, 1, 1}, 5, 4, 3, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_6D_3 data_types::f32, format::bfwzyx, {5, 4, 6, 7, 2, 2}, format::bfzyx, {5, 4, 1, 2, 2}, format::bfyx, {80, 6, 1, 1}, 5, 5, 4, data_types::f32, format::bfyx
-#define CASE_GATHER_ND_FP32_6D_4 data_types::f32, format::bfwzyx, {5, 4, 6, 3, 2, 2}, format::bfwzyx, {5, 4, 1, 3, 2, 2}, format::bfyx, {240, 1, 1, 1}, 5, 6, 5, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_6D_1 data_types::f32, format::bfwzyx, { 5, 4, 6, 7, 8, 5 }, format::bfyx, { 5, 4, 2, 2 }, format::bfyx, { 20, 2, 6, 7 }, 5, 4, 2, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_6D_2 data_types::f32, format::bfwzyx, { 5, 4, 6, 7, 8, 2 }, format::bfyx, { 5, 4, 2, 2 }, format::bfyx, { 40, 6, 1, 1 }, 5, 4, 3, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_6D_3 data_types::f32, format::bfwzyx, { 5, 4, 6, 7, 2, 2 }, format::bfzyx, { 5, 4, 1, 2, 2 }, format::bfyx, { 80, 6, 1, 1 }, 5, 5, 4, data_types::f32, format::bfyx
+#define CASE_GATHER_ND_FP32_6D_4 data_types::f32, format::bfwzyx, { 5, 4, 6, 3, 2, 2 }, format::bfwzyx, { 5, 4, 1, 3, 2, 2 }, format::bfyx, { 240, 1, 1, 1 }, 5, 6, 5, data_types::f32, format::bfyx
 
 
 
@@ -8904,7 +9044,7 @@ public:
     }
 
     layout get_per_channel_layout(gather_nd_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.output_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.output_shape.feature[0], 1, 1 } };
     }
 };
 
@@ -8921,7 +9061,8 @@ TEST_P(gather_nd_quantize, basic) {
         input_rank = 6;
     }
 
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("gather_nd_indices", get_mem(get_indices_layout(p), 0, p.max_number_in_indices - 1)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
@@ -8931,43 +9072,43 @@ TEST_P(gather_nd_quantize, basic) {
         quantize("quantize", "gather_nd_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
         reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_nd_quantize,
-    ::testing::ValuesIn(std::vector<gather_nd_test_params>{
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_1, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_2, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_3, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_nd_quantize, ::testing::ValuesIn(std::vector<gather_nd_test_params>{
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_1, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_2, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_3, 2, 3 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_1, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_2, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_3, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_4, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_5, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_6, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_1, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_2, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_3, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_4, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_5, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_6, 2, 3 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_1, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_2, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_3, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_4, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_1, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_2, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_3, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_4, 2, 3 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_1, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_2, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_3, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_1, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_2, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_3, 2, 3 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_1, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_2, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_3, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_4, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_5, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_6, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_1, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_2, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_3, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_4, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_5, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_6, 2, 3 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_1, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_2, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_3, 2, 3 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_4, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_1, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_2, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_3, 2, 3 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_4, 2, 3 },
 }));
 
 class gather_nd_activation_scale_eltwise : public GatherNDPrimitiveFusingTest {};
@@ -8983,7 +9124,8 @@ TEST_P(gather_nd_activation_scale_eltwise, basic) {
         input_rank = 6;
     }
 
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("gather_nd_indices", get_mem(get_indices_layout(p), 0, p.max_number_in_indices - 1)),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / 255)),
         data("eltwise_data", get_mem(get_output_layout(p))),
@@ -8998,39 +9140,38 @@ TEST_P(gather_nd_activation_scale_eltwise, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_nd_activation_scale_eltwise,
-    ::testing::ValuesIn(std::vector<gather_nd_test_params>{
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_1, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_2, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_3, 2, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_nd_activation_scale_eltwise, ::testing::ValuesIn(std::vector<gather_nd_test_params>{
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_1, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_2, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_4D_3, 2, 5 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_1, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_2, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_3, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_4, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_5, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_6, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_1, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_2, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_3, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_4, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_5, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_5D_6, 2, 5 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_1, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_2, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_3, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_4, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_1, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_2, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_3, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP16_6D_4, 2, 5 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_1, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_2, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_3, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_1, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_2, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_4D_3, 2, 5 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_1, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_2, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_3, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_4, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_5, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_6, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_1, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_2, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_3, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_4, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_5, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_5D_6, 2, 5 },
 
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_1, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_2, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_3, 2, 5 },
-        gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_4, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_1, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_2, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_3, 2, 5 },
+    gather_nd_test_params{ CASE_GATHER_ND_FP32_6D_4, 2, 5 },
 }));
 
 
@@ -9038,6 +9179,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_nd_activation_scale_eltwise,
 /* ----------------------------------------------------------------------------------------------------- */
 /* ------------------------------------------ GatherElements cases ------------------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
+
 struct gather_elements_test_params {
     data_types data_type;
 
@@ -9059,28 +9201,28 @@ struct gather_elements_test_params {
     size_t expected_not_fused_primitives;
 };
 
-#define CASE_GATHER_ELEMENTS_FP16_4D_1 data_types::f16, format::bfyx, {3, 7, 9, 8}, format::bfyx, {3, 7, 9, 8}, format::bfyx, {3, 7, 9, 8}, cldnn::gather_elements::gather_elements_axis::along_y, data_types::f16, format::bfyx
-#define CASE_GATHER_ELEMENTS_FP16_4D_2 data_types::f16, format::bfyx, {3, 2, 8, 3}, format::bfyx, {2, 2, 8, 3}, format::bfyx, {2, 2, 8, 3}, cldnn::gather_elements::gather_elements_axis::along_b, data_types::f16, format::bfyx
-#define CASE_GATHER_ELEMENTS_FP16_4D_3 data_types::f16, format::bfyx, {1, 3, 2, 9}, format::bfyx, {1, 3, 5, 9}, format::bfyx, {1, 3, 5, 9}, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f16, format::bfyx
+#define CASE_GATHER_ELEMENTS_FP16_4D_1 data_types::f16, format::bfyx, { 3, 7, 9, 8 }, format::bfyx, { 3, 7, 9, 8 }, format::bfyx, { 3, 7, 9, 8 }, cldnn::gather_elements::gather_elements_axis::along_y, data_types::f16, format::bfyx
+#define CASE_GATHER_ELEMENTS_FP16_4D_2 data_types::f16, format::bfyx, { 3, 2, 8, 3 }, format::bfyx, { 2, 2, 8, 3 }, format::bfyx, { 2, 2, 8, 3 }, cldnn::gather_elements::gather_elements_axis::along_b, data_types::f16, format::bfyx
+#define CASE_GATHER_ELEMENTS_FP16_4D_3 data_types::f16, format::bfyx, { 1, 3, 2, 9 }, format::bfyx, { 1, 3, 5, 9 }, format::bfyx, { 1, 3, 5, 9 }, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f16, format::bfyx
 
-#define CASE_GATHER_ELEMENTS_FP16_5D_1 data_types::f16, format::bfzyx, {3, 2, 5, 2, 3}, format::bfzyx, {3, 2, 2, 2, 3}, format::bfzyx, {3, 2, 2, 2, 3}, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f16, format::bfzyx
-#define CASE_GATHER_ELEMENTS_FP16_5D_2 data_types::f16, format::bfzyx, {5, 4, 7, 4, 4}, format::bfzyx, {5, 4, 7, 4, 3}, format::bfzyx, {5, 4, 7, 4, 3}, cldnn::gather_elements::gather_elements_axis::along_z, data_types::f16, format::bfzyx
+#define CASE_GATHER_ELEMENTS_FP16_5D_1 data_types::f16, format::bfzyx, { 3, 2, 5, 2, 3 }, format::bfzyx, { 3, 2, 2, 2, 3 }, format::bfzyx, { 3, 2, 2, 2, 3 }, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f16, format::bfzyx
+#define CASE_GATHER_ELEMENTS_FP16_5D_2 data_types::f16, format::bfzyx, { 5, 4, 7, 4, 4 }, format::bfzyx, { 5, 4, 7, 4, 3 }, format::bfzyx, { 5, 4, 7, 4, 3 }, cldnn::gather_elements::gather_elements_axis::along_z, data_types::f16, format::bfzyx
 
-#define CASE_GATHER_ELEMENTS_FP16_6D_1 data_types::f16, format::bfwzyx, {5, 4, 6, 7, 8, 2}, format::bfwzyx, {5, 2, 6, 7, 8, 2}, format::bfwzyx, {5, 2, 6, 7, 8, 2}, cldnn::gather_elements::gather_elements_axis::along_f, data_types::f16, format::bfwzyx
-#define CASE_GATHER_ELEMENTS_FP16_6D_2 data_types::f16, format::bfwzyx, {2, 1, 2, 3, 2, 1}, format::bfwzyx, {2, 1, 2, 3, 2, 3}, format::bfwzyx, {2, 1, 2, 3, 2, 3}, cldnn::gather_elements::gather_elements_axis::along_w, data_types::f16, format::bfwzyx
-#define CASE_GATHER_ELEMENTS_FP16_6D_3 data_types::f16, format::bfwzyx, {2, 2, 3, 4, 4, 2}, format::bfwzyx, {2, 2, 6, 4, 4, 2}, format::bfwzyx, {2, 2, 6, 4, 4, 2}, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f16, format::bfwzyx
+#define CASE_GATHER_ELEMENTS_FP16_6D_1 data_types::f16, format::bfwzyx, { 5, 4, 6, 7, 8, 2 }, format::bfwzyx, { 5, 2, 6, 7, 8, 2 }, format::bfwzyx, { 5, 2, 6, 7, 8, 2 }, cldnn::gather_elements::gather_elements_axis::along_f, data_types::f16, format::bfwzyx
+#define CASE_GATHER_ELEMENTS_FP16_6D_2 data_types::f16, format::bfwzyx, { 2, 1, 2, 3, 2, 1 }, format::bfwzyx, { 2, 1, 2, 3, 2, 3 }, format::bfwzyx, { 2, 1, 2, 3, 2, 3 }, cldnn::gather_elements::gather_elements_axis::along_w, data_types::f16, format::bfwzyx
+#define CASE_GATHER_ELEMENTS_FP16_6D_3 data_types::f16, format::bfwzyx, { 2, 2, 3, 4, 4, 2 }, format::bfwzyx, { 2, 2, 6, 4, 4, 2 }, format::bfwzyx, { 2, 2, 6, 4, 4, 2 }, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f16, format::bfwzyx
 
 
-#define CASE_GATHER_ELEMENTS_FP32_4D_1 data_types::f32, format::bfyx, {3, 7, 9, 8}, format::bfyx, {3, 7, 9, 8}, format::bfyx, {3, 7, 9, 8}, cldnn::gather_elements::gather_elements_axis::along_y, data_types::f32, format::bfyx
-#define CASE_GATHER_ELEMENTS_FP32_4D_2 data_types::f32, format::bfyx, {3, 2, 8, 3}, format::bfyx, {2, 2, 8, 3}, format::bfyx, {2, 2, 8, 3}, cldnn::gather_elements::gather_elements_axis::along_b, data_types::f32, format::bfyx
-#define CASE_GATHER_ELEMENTS_FP32_4D_3 data_types::f32, format::bfyx, {1, 3, 2, 9}, format::bfyx, {1, 3, 5, 9}, format::bfyx, {1, 3, 5, 9}, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f32, format::bfyx
+#define CASE_GATHER_ELEMENTS_FP32_4D_1 data_types::f32, format::bfyx, { 3, 7, 9, 8 }, format::bfyx, { 3, 7, 9, 8 }, format::bfyx, { 3, 7, 9, 8 }, cldnn::gather_elements::gather_elements_axis::along_y, data_types::f32, format::bfyx
+#define CASE_GATHER_ELEMENTS_FP32_4D_2 data_types::f32, format::bfyx, { 3, 2, 8, 3 }, format::bfyx, { 2, 2, 8, 3 }, format::bfyx, { 2, 2, 8, 3 }, cldnn::gather_elements::gather_elements_axis::along_b, data_types::f32, format::bfyx
+#define CASE_GATHER_ELEMENTS_FP32_4D_3 data_types::f32, format::bfyx, { 1, 3, 2, 9 }, format::bfyx, { 1, 3, 5, 9 }, format::bfyx, { 1, 3, 5, 9 }, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f32, format::bfyx
 
-#define CASE_GATHER_ELEMENTS_FP32_5D_1 data_types::f32, format::bfzyx, {3, 2, 5, 2, 3}, format::bfzyx, {3, 2, 2, 2, 3}, format::bfzyx, {3, 2, 2, 2, 3}, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f32, format::bfzyx
-#define CASE_GATHER_ELEMENTS_FP32_5D_2 data_types::f32, format::bfzyx, {5, 4, 7, 4, 4}, format::bfzyx, {5, 4, 7, 4, 3}, format::bfzyx, {5, 4, 7, 4, 3}, cldnn::gather_elements::gather_elements_axis::along_z, data_types::f32, format::bfzyx
+#define CASE_GATHER_ELEMENTS_FP32_5D_1 data_types::f32, format::bfzyx, { 3, 2, 5, 2, 3 }, format::bfzyx, { 3, 2, 2, 2, 3 }, format::bfzyx, { 3, 2, 2, 2, 3 }, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f32, format::bfzyx
+#define CASE_GATHER_ELEMENTS_FP32_5D_2 data_types::f32, format::bfzyx, { 5, 4, 7, 4, 4 }, format::bfzyx, { 5, 4, 7, 4, 3 }, format::bfzyx, { 5, 4, 7, 4, 3 }, cldnn::gather_elements::gather_elements_axis::along_z, data_types::f32, format::bfzyx
 
-#define CASE_GATHER_ELEMENTS_FP32_6D_1 data_types::f32, format::bfwzyx, {5, 4, 6, 7, 8, 2}, format::bfwzyx, {5, 2, 6, 7, 8, 2}, format::bfwzyx, {5, 2, 6, 7, 8, 2}, cldnn::gather_elements::gather_elements_axis::along_f, data_types::f32, format::bfwzyx
-#define CASE_GATHER_ELEMENTS_FP32_6D_2 data_types::f32, format::bfwzyx, {2, 1, 2, 3, 2, 1}, format::bfwzyx, {2, 1, 2, 3, 2, 3}, format::bfwzyx, {2, 1, 2, 3, 2, 3}, cldnn::gather_elements::gather_elements_axis::along_w, data_types::f32, format::bfwzyx
-#define CASE_GATHER_ELEMENTS_FP32_6D_3 data_types::f32, format::bfwzyx, {2, 2, 3, 4, 4, 2}, format::bfwzyx, {2, 2, 6, 4, 4, 2}, format::bfwzyx, {2, 2, 6, 4, 4, 2}, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f32, format::bfwzyx
+#define CASE_GATHER_ELEMENTS_FP32_6D_1 data_types::f32, format::bfwzyx, { 5, 4, 6, 7, 8, 2 }, format::bfwzyx, { 5, 2, 6, 7, 8, 2 }, format::bfwzyx, { 5, 2, 6, 7, 8, 2 }, cldnn::gather_elements::gather_elements_axis::along_f, data_types::f32, format::bfwzyx
+#define CASE_GATHER_ELEMENTS_FP32_6D_2 data_types::f32, format::bfwzyx, { 2, 1, 2, 3, 2, 1 }, format::bfwzyx, { 2, 1, 2, 3, 2, 3 }, format::bfwzyx, { 2, 1, 2, 3, 2, 3 }, cldnn::gather_elements::gather_elements_axis::along_w, data_types::f32, format::bfwzyx
+#define CASE_GATHER_ELEMENTS_FP32_6D_3 data_types::f32, format::bfwzyx, { 2, 2, 3, 4, 4, 2 }, format::bfwzyx, { 2, 2, 6, 4, 4, 2 }, format::bfwzyx, { 2, 2, 6, 4, 4, 2 }, cldnn::gather_elements::gather_elements_axis::along_x, data_types::f32, format::bfwzyx
 
 class GatherElementsPrimitiveFusingTest : public ::BaseFusingTest<gather_elements_test_params> {
 public:
@@ -9125,14 +9267,15 @@ public:
     }
 
     layout get_per_channel_layout(gather_elements_test_params& p) {
-        return layout{ p.default_type, p.default_format, tensor{1, p.output_shape.feature[0], 1, 1} };
+        return layout{ p.default_type, p.default_format, tensor{ 1, p.output_shape.feature[0], 1, 1 } };
     }
 };
 
 class gather_elements_quantize : public GatherElementsPrimitiveFusingTest {};
 TEST_P(gather_elements_quantize, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("gather_elements_indices", get_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p))-1)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
@@ -9142,40 +9285,41 @@ TEST_P(gather_elements_quantize, basic) {
         quantize("quantize", "gather_elements_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
         reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
+
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_elements_quantize,
-    ::testing::ValuesIn(std::vector<gather_elements_test_params>{
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_1, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_2, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_3, 2, 3 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_elements_quantize, ::testing::ValuesIn(std::vector<gather_elements_test_params>{
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_1, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_2, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_3, 2, 3 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_1, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_2, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_1, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_2, 2, 3 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_1, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_2, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_3, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_1, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_2, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_3, 2, 3 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_1, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_2, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_3, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_1, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_2, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_3, 2, 3 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_1, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_2, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_1, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_2, 2, 3 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_1, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_2, 2, 3 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_3, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_1, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_2, 2, 3 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_3, 2, 3 },
 }));
 
 
 class gather_elements_scale_activation : public GatherElementsPrimitiveFusingTest {};
 TEST_P(gather_elements_scale_activation, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("gather_elements_indices", get_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p))-1)),
         data("scale_data", get_mem(get_per_channel_layout(p), -10, 10)),
         gather_elements("gather_elements_prim", "input", "gather_elements_indices", p.output_format, p.output_shape, p.axis),
@@ -9188,29 +9332,28 @@ TEST_P(gather_elements_scale_activation, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_elements_scale_activation,
-    ::testing::ValuesIn(std::vector<gather_elements_test_params>{
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_1, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_2, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_3, 2, 4 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_elements_scale_activation, ::testing::ValuesIn(std::vector<gather_elements_test_params>{
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_1, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_2, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_3, 2, 4 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_1, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_2, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_1, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_2, 2, 4 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_1, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_2, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_3, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_1, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_2, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_3, 2, 4 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_1, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_2, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_3, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_1, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_2, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_3, 2, 4 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_1, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_2, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_1, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_2, 2, 4 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_1, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_2, 2, 4 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_3, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_1, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_2, 2, 4 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_3, 2, 4 },
 }));
 
 
@@ -9218,7 +9361,8 @@ class gather_elements_activation_scale_eltwise : public GatherElementsPrimitiveF
 TEST_P(gather_elements_activation_scale_eltwise, basic) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
         data("gather_elements_indices", get_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p))-1)),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / 255)),
         data("eltwise_data", get_mem(get_output_layout(p))),
@@ -9233,29 +9377,28 @@ TEST_P(gather_elements_activation_scale_eltwise, basic) {
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_elements_activation_scale_eltwise,
-    ::testing::ValuesIn(std::vector<gather_elements_test_params>{
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_1, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_2, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_3, 2, 5 },
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_elements_activation_scale_eltwise, ::testing::ValuesIn(std::vector<gather_elements_test_params>{
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_1, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_2, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_4D_3, 2, 5 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_1, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_2, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_1, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_5D_2, 2, 5 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_1, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_2, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_3, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_1, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_2, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP16_6D_3, 2, 5 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_1, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_2, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_3, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_1, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_2, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_4D_3, 2, 5 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_1, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_2, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_1, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_5D_2, 2, 5 },
 
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_1, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_2, 2, 5 },
-        gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_3, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_1, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_2, 2, 5 },
+    gather_elements_test_params{ CASE_GATHER_ELEMENTS_FP32_6D_3, 2, 5 },
 }));
 
 #ifdef ENABLE_ONEDNN_FOR_GPU
@@ -9279,7 +9422,7 @@ public:
         }
 
         implementation_desc conv_impl = { forcing_format, "", impl_types::onednn };
-        bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+        bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
         network network_not_fused(this->engine, this->topology_non_fused, bo_not_fused);
         network network_fused(this->engine, this->topology_fused, bo_fused);
@@ -9307,15 +9450,16 @@ TEST_P(conv_int8_eltwise_onednn, u8_eltwise_sum_out) {
     auto shift_layout = get_output_layout(p);
     shift_layout.data_type = data_types::f32;
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), 0, 2)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("shift_data", get_mem(shift_layout)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("shift", {"conv_prim", "shift_data"}, eltwise_mode::sum, data_types::f32),
-                 // Add 'not fusable' primitive to be able to test full size tensor sum
-                 crop("crop", "shift", get_output_layout(p).size, {0, 0, 0, 0}),
-                 reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), 0, 2)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("shift_data", get_mem(shift_layout)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("shift", { "conv_prim", "shift_data" }, eltwise_mode::sum, data_types::f32),
+        // Add 'not fusable' primitive to be able to test full size tensor sum
+        crop("crop", "shift", get_output_layout(p).size, { 0, 0, 0, 0 }),
+        reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
@@ -9325,174 +9469,175 @@ TEST_P(conv_int8_eltwise_onednn, u8_eltwise_sum_out) {
 TEST_P(conv_int8_eltwise_onednn, u8_eltwise_prod_out) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -2, 2)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()) ),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("scale", {"conv_prim", "scale_data"}, eltwise_mode::prod, data_types::u8),
-                 crop("crop", "scale", get_output_layout(p).size, {0, 0, 0, 0}),
-                 reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -2, 2)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count()) ),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("scale", { "conv_prim", "scale_data" }, eltwise_mode::prod, data_types::u8),
+        crop("crop", "scale", get_output_layout(p).size, { 0, 0, 0, 0 }),
+        reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 4 },
 
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 4},
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 4 },
 
-                                bc_test_params{CASE_CONV3D_U8S8_1, 3, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_2, 3, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_3, 3, 4},
-                                bc_test_params{CASE_CONV3D_U8S8_5, 3, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_1, 3, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_2, 3, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_3, 3, 4},
-                                bc_test_params{CASE_CONV3D_S8S8_5, 3, 4},
-                        }));
+    bc_test_params{ CASE_CONV3D_U8S8_1, 3, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_2, 3, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_3, 3, 4 },
+    bc_test_params{ CASE_CONV3D_U8S8_5, 3, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_1, 3, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_2, 3, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_3, 3, 4 },
+    bc_test_params{ CASE_CONV3D_S8S8_5, 3, 4 },
+}));
 
 class conv_fp32_activation_abs_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_abs_onednn, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::abs),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::abs),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-2f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_abs_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP16_1, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                          }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_abs_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP16_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+}));
 
 class conv_fp32_activation_mish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_mish_onednn, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::mish),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::mish),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-2f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_mish_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP16_1, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                          }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_mish_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP16_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+}));
 
 class conv_fp32_activation_swish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_swish_onednn, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::swish),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::swish),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-2f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_swish_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP16_1, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                          }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_swish_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP16_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+}));
 
 class conv_fp32_activation_hswish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_hswish_onednn, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::hswish),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::hswish),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-2f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_hswish_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP16_1, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                          }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_hswish_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP16_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+}));
 
 class conv_fp32_activation_exp_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_fp32_activation_exp_onednn, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::exp),
-                 reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::exp),
+        reorder("reorder_bfyx", "activation", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-2f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_exp_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_FP16_1, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_2, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_3, 2, 3},
-                                bc_test_params{CASE_CONV_FP16_4, 2, 3},
-                          }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation_exp_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_FP16_1, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_2, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_3, 2, 3 },
+    bc_test_params{ CASE_CONV_FP16_4, 2, 3 },
+}));
 
 class conv_int8_quantize_u8_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_quantize_u8_onednn, per_channel) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -2, 2)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), -10, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 0, 10)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -2, 2)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), -10, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 0, 10)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.0f;
@@ -9501,137 +9646,138 @@ TEST_P(conv_int8_quantize_u8_onednn, per_channel) {
 
 TEST_P(conv_int8_quantize_u8_onednn, per_tensor) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -2, 2)),
-                 data("bias", get_mem(get_bias_layout(p), 0)),
-                 data("in_lo", get_mem(get_single_element_layout(p), -10)),
-                 data("in_hi", get_mem(get_single_element_layout(p), 10)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 255)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -2, 2)),
+        data("bias", get_mem(get_bias_layout(p), 0)),
+        data("in_lo", get_mem(get_single_element_layout(p), -10)),
+        data("in_hi", get_mem(get_single_element_layout(p), 10)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 255)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.0f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_quantize_u8_onednn,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_quantize_u8_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
+}));
 
 class conv_int8_activation_eltwise_quantize_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_activation_eltwise_quantize_onednn, bsv32_fsv32) {
     auto p = GetParam();
     layout eltwise_layout = get_output_layout(p);
     eltwise_layout.format = format::bs_fs_yx_bsv32_fsv32;
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -1, 1)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(eltwise_layout, -0.5, 0.5)),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::abs),
-                 eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
-                 quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -1, 1)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(eltwise_layout, -0.5, 0.5)),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::abs),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::bs_fs_yx_bsv32_fsv32, "", impl_types::onednn };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_activation_eltwise_quantize_onednn,
-                        ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_4, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_7, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_8, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_14, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_activation_eltwise_quantize_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_4, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_7, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_8, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_14, 2, 5 },
 
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_4, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_7, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_8, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_15, 2, 5},
-                        }));
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_4, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_7, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_8, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_15, 2, 5 },
+}));
 
 class conv_int8_scale_shift_swish_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_scale_shift_swish_onednn, bsv32_fsv32) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -1, 1)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 data("shift_data", get_mem(get_per_channel_layout(p), 1)),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("scale0", {"conv_prim", "scale_data"}, eltwise_mode::sum),
-                 eltwise("shift0", {"scale0", "shift_data"}, eltwise_mode::sum),
-                 activation("sigmoid", "shift0", activation_func::swish),
-                 eltwise("scale1", {"sigmoid", "scale_data"}, eltwise_mode::sum),
-                 eltwise("shift1", {"scale1", "shift_data"}, eltwise_mode::sum),
-                 reorder("reorder_bfyx", "shift1", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -1, 1)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        data("shift_data", get_mem(get_per_channel_layout(p), 1)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("scale0", { "conv_prim", "scale_data" }, eltwise_mode::sum),
+        eltwise("shift0", { "scale0", "shift_data" }, eltwise_mode::sum),
+        activation("sigmoid", "shift0", activation_func::swish),
+        eltwise("scale1", { "sigmoid", "scale_data" }, eltwise_mode::sum),
+        eltwise("shift1", { "scale1", "shift_data" }, eltwise_mode::sum),
+        reorder("reorder_bfyx", "shift1", p.default_format, data_types::f32)
     );
 
     implementation_desc conv_impl = { format::bs_fs_yx_bsv32_fsv32, "", impl_types::onednn };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_shift_swish_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 7},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 7},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_shift_swish_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 7 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 7 },
 
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 7},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 7},
-                                bc_test_params{CASE_CONV_U8S8_14, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 7},
-                                bc_test_params{CASE_CONV_S8S8_15, 2, 7},
-                        }));
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 7 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 7 },
+    bc_test_params{ CASE_CONV_U8S8_14, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 7 },
+    bc_test_params{ CASE_CONV_S8S8_15, 2, 7 },
+}));
 
 class conv_int8_eltwise_scale_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_eltwise_scale_onednn, u8_eltwise_prod_out_reuse) {
     auto p = GetParam();
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p), -2, 2)),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("sum_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
-                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
-                 eltwise("sum", {"conv_prim", "sum_data"}, eltwise_mode::sum, data_types::f32),
-                 eltwise("scale", {"sum", "scale_data"}, eltwise_mode::prod, data_types::f32),
-                 crop("crop", "scale", get_output_layout(p).size, {0, 0, 0, 0}),
-                 reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p), -2, 2)),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("sum_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
+        eltwise("sum", { "conv_prim", "sum_data" }, eltwise_mode::sum, data_types::f32),
+        eltwise("scale", { "sum", "scale_data" }, eltwise_mode::prod, data_types::f32),
+        crop("crop", "scale", get_output_layout(p).size, { 0, 0, 0, 0 }),
+        reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
@@ -9640,7 +9786,7 @@ TEST_P(conv_int8_eltwise_scale_onednn, u8_eltwise_prod_out_reuse) {
 
     auto forcing_format = p.input_format;
     implementation_desc conv_impl = { forcing_format, "", impl_types::onednn };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_prim", conv_impl } }));
 
     network network_not_fused(this->engine, this->topology_non_fused, bo_not_fused);
     network network_fused(this->engine, this->topology_fused, bo_fused);
@@ -9654,10 +9800,9 @@ TEST_P(conv_int8_eltwise_scale_onednn, u8_eltwise_prod_out_reuse) {
     compare(network_not_fused, network_fused, p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_scale_onednn,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                bc_test_params{CASE_CONV_U8S8_15, 2, 5},
-                        }));
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_scale_onednn, ::testing::ValuesIn(std::vector<bc_test_params>{
+    bc_test_params{ CASE_CONV_U8S8_15, 2, 5 },
+}));
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ------------------------------ OneDNN post-ops cases with optimizations ----------------------------- */
@@ -9671,48 +9816,48 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_scale_onednn,
 class post_ops_optimizations_onednn_eltw_linear_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_eltw_linear_eltw_linear, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_single_element_layout(p), -10)),
-                 data("in_hi", get_mem(get_single_element_layout(p), 10)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -128)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_single_element_layout(p), -10)),
+        data("in_hi", get_mem(get_single_element_layout(p), 10)),
+        data("out_lo", get_mem(get_single_element_layout(p), -128)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_linear_eltw_linear,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // cases with batch = 1
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_linear_eltw_linear, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // cases with batch = 1
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
 
-                                // cases with batch = 16
-                                bc_test_params{CASE_CONV_U8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 3},
+    // cases with batch = 16
+    bc_test_params{ CASE_CONV_U8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 3 },
 
-                                // cases with batch = 32
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_15, 2, 3},
-                        }));
+    // cases with batch = 32
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_15, 2, 3 },
+}));
 
 // Before optimization: eltw_non_linear + eltw_linear
 // After optimization: eltw_non_linear
@@ -9722,48 +9867,48 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_linear_
 class post_ops_optimizations_onednn_eltw_non_linear_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_eltw_non_linear_eltw_linear, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_single_element_layout(p), -10)),
-                 data("in_hi", get_mem(get_single_element_layout(p), 10)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 512)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::f32),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_single_element_layout(p), -10)),
+        data("in_hi", get_mem(get_single_element_layout(p), 10)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 512)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::f32),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_non_linear_eltw_linear,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // cases with batch = 1
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_non_linear_eltw_linear, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // cases with batch = 1
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
 
-                                // cases with batch = 16
-                                bc_test_params{CASE_CONV_U8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 3},
+    // cases with batch = 16
+    bc_test_params{ CASE_CONV_U8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 3 },
 
-                                // cases with batch = 32
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_15, 2, 3},
-                        }));
+    // cases with batch = 32
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_15, 2, 3 },
+}));
 
 // Before optimization: binary_add + eltw_linear
 // After optimization: binary_add
@@ -9773,48 +9918,48 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_non_lin
 class post_ops_optimizations_onednn_binary_add_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_binary_add_eltw_linear, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), -127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), -127)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_add_eltw_linear,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // cases with batch = 1
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_add_eltw_linear, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // cases with batch = 1
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
 
-                                // cases with batch = 16
-                                bc_test_params{CASE_CONV_U8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 3},
+    // cases with batch = 16
+    bc_test_params{ CASE_CONV_U8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 3 },
 
-                                // cases with batch = 32
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_15, 2, 3},
-                        }));
+    // cases with batch = 32
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_15, 2, 3 },
+}));
 
 // Before optimization: binary_mul + eltw_linear
 // After optimization: binary_mul
@@ -9824,48 +9969,48 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_add_e
 class post_ops_optimizations_onednn_binary_mul_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_binary_mul_eltw_linear, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("eltwise_data", get_mem(get_per_channel_layout(p), -1, 1)),
-                 data("in_lo", get_mem(get_per_channel_layout(p), 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 512)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 eltwise("eltwise", { "conv_prim", "eltwise_data" }, eltwise_mode::prod),
-                 quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("eltwise_data", get_mem(get_per_channel_layout(p), -1, 1)),
+        data("in_lo", get_mem(get_per_channel_layout(p), 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 512)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        eltwise("eltwise", { "conv_prim", "eltwise_data" }, eltwise_mode::prod),
+        quantize("quantize", "eltwise", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_mul_eltw_linear,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // cases with batch = 1
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 4},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_mul_eltw_linear, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // cases with batch = 1
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 4 },
 
-                                // cases with batch = 16
-                                bc_test_params{CASE_CONV_U8S8_9, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_9, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 4},
+    // cases with batch = 16
+    bc_test_params{ CASE_CONV_U8S8_9, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_9, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 4 },
 
-                                // cases with batch = 32
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 4},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 4},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 4},
-                        }));
+    // cases with batch = 32
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 4 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 4 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 4 },
+}));
 
 // Before optimization: o_scale + eltw_linear
 // After optimization: o_scale
@@ -9875,46 +10020,46 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_binary_mul_e
 class post_ops_optimizations_onednn_oscale_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_oscale_eltw_linear, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_per_channel_layout(p), 0)),
-                 data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 512)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_per_channel_layout(p), 0)),
+        data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 512)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_oscale_eltw_linear,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // cases with batch = 1
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_oscale_eltw_linear, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // cases with batch = 1
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
 
-                                // cases with batch = 16
-                                bc_test_params{CASE_CONV_U8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 3},
+    // cases with batch = 16
+    bc_test_params{ CASE_CONV_U8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 3 },
 
-                                // cases with batch = 32
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 3},
-                        }));
+    // cases with batch = 32
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 3 },
+}));
 
 // Before optimization: eltw_any + sum + eltw_linear
 // After optimization: eltw_any + sum
@@ -9924,49 +10069,49 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_oscale_eltw_
 class post_ops_optimizations_onednn_eltw_any_sum_eltw_linear : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_onednn_eltw_any_sum_eltw_linear, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("in_hi", get_mem(get_single_element_layout(p), 10)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 0)),
-                 data("out_hi", get_mem(get_single_element_layout(p), 127)),
-                 data("eltwise_data", get_mem(get_output_layout(p))),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 activation("activation", "conv_prim", activation_func::relu_negative_slope),
-                 eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum),
-                 quantize("quantize", "sum", "in_lo", "in_hi", "out_lo", "out_hi", 128, data_types::u8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_single_element_layout(p), 0)),
+        data("in_hi", get_mem(get_single_element_layout(p), 10)),
+        data("out_lo", get_mem(get_single_element_layout(p), 0)),
+        data("out_hi", get_mem(get_single_element_layout(p), 127)),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", activation_func::relu_negative_slope),
+        eltwise("sum", { "activation", "eltwise_data" }, eltwise_mode::sum),
+        quantize("quantize", "sum", "in_lo", "in_hi", "out_lo", "out_hi", 128, data_types::u8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_any_sum_eltw_linear,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // cases with batch = 1
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 5},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_any_sum_eltw_linear, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // cases with batch = 1
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 5 },
 
-                                // cases with batch = 16
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 5},
+    // cases with batch = 16
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 5 },
 
-                                // cases with batch = 32
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 5},
-                                bc_test_params{CASE_CONV_U8S8_14, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 5},
-                                bc_test_params{CASE_CONV_S8S8_15, 2, 5},
-                        }));
+    // cases with batch = 32
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 5 },
+    bc_test_params{ CASE_CONV_U8S8_14, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 5 },
+    bc_test_params{ CASE_CONV_S8S8_15, 2, 5 },
+}));
 
 // Input range uses in 2 cases: not per-tensor output range or out_lo > out_hi
 // Here's out_lo > out_hi and no optimizations
@@ -9974,62 +10119,65 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_onednn_eltw_any_sum
 class post_ops_optimizations_input_range : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(post_ops_optimizations_input_range, basic) {
     auto p = GetParam();
-    create_topologies(input_layout("input", get_input_layout(p)),
-                 data("weights", get_mem(get_weights_layout(p))),
-                 data("bias", get_mem(get_bias_layout(p))),
-                 data("in_lo", get_mem(get_single_element_layout(p), -10)),
-                 data("in_hi", get_mem(get_single_element_layout(p), 10)),
-                 data("out_lo", get_mem(get_single_element_layout(p), 127)),
-                 data("out_hi", get_mem(get_single_element_layout(p), -128)),
-                 convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
-                 quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
-                 reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("in_lo", get_mem(get_single_element_layout(p), -10)),
+        data("in_hi", get_mem(get_single_element_layout(p), 10)),
+        data("out_lo", get_mem(get_single_element_layout(p), 127)),
+        data("out_hi", get_mem(get_single_element_layout(p), -128)),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        quantize("quantize", "conv_prim", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8),
+        reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
 
     tolerance = 1.f;
     execute(p);
 }
 
-INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_input_range,
-                         ::testing::ValuesIn(std::vector<bc_test_params>{
-                                // cases with batch = 1
-                                bc_test_params{CASE_CONV_U8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_3, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_1, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_2, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_3, 2, 3},
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, post_ops_optimizations_input_range, ::testing::ValuesIn(std::vector<bc_test_params>{
+    // cases with batch = 1
+    bc_test_params{ CASE_CONV_U8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_3, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_1, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_2, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_3, 2, 3 },
 
-                                // cases with batch = 16
-                                bc_test_params{CASE_CONV_U8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_10, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_9, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_10, 2, 3},
+    // cases with batch = 16
+    bc_test_params{ CASE_CONV_U8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_10, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_9, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_10, 2, 3 },
 
-                                // cases with batch = 32
-                                bc_test_params{CASE_CONV_U8S8_11, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_U8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_12, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_13, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_14, 2, 3},
-                                bc_test_params{CASE_CONV_S8S8_15, 2, 3},
-                        }));
+    // cases with batch = 32
+    bc_test_params{ CASE_CONV_U8S8_11, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_U8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_12, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_13, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_14, 2, 3 },
+    bc_test_params{ CASE_CONV_S8S8_15, 2, 3 },
+}));
 
+
+// FC onednn sum case
 class fc_int8_inputs_fused_fp32_sum : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(fc_int8_inputs_fused_fp32_sum, basic) {
     auto p = GetParam();
-    auto shift_layout = layout{ p.default_type, p.default_format, tensor{1, 1, 1, p.kernel.batch[0]} };
+    auto shift_layout = layout{ p.default_type, p.default_format, tensor{ 1, 1, 1, p.kernel.batch[0] } };
 
-    create_topologies(input_layout("input", get_input_layout(p)),
-                data("weights", get_mem(get_fc_weights_layout(p))),
-                data("bias", get_mem(get_fc_bias_layout(p))),
-                data("shift_data", get_mem(shift_layout, 1)),
-                fully_connected("fc_prim", "input", "weights", "bias", cldnn::data_types::f32, "", padding(), get_fc_output_dim_size(p)),
-                eltwise("shift", {"fc_prim", "shift_data"}, eltwise_mode::sum, cldnn::data_types::f32),
-                crop("crop", "shift", get_output_layout(p).size, {0, 0, 0, 0}),
-                reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_fc_weights_layout(p))),
+        data("bias", get_mem(get_fc_bias_layout(p))),
+        data("shift_data", get_mem(shift_layout, 1)),
+        fully_connected("fc_prim", "input", "weights", "bias", cldnn::data_types::f32, "", padding(), get_fc_output_dim_size(p)),
+        eltwise("shift", { "fc_prim", "shift_data" }, eltwise_mode::sum, cldnn::data_types::f32),
+        crop("crop", "shift", get_output_layout(p).size, { 0, 0, 0, 0 }),
+        reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
     );
 
     tolerance = 1e-5f;
@@ -10037,17 +10185,16 @@ TEST_P(fc_int8_inputs_fused_fp32_sum, basic) {
 }
 
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_int8_inputs_fused_fp32_sum, ::testing::ValuesIn(std::vector<bc_test_params>{
-                                                                            // OneDNN has issue with small shapes - ticket 7064
-                                                                            // bc_test_params{ CASE_FC_U8S8_3D_1, 2, 4 },
-                                                                            // bc_test_params{ CASE_FC_U8S8_3D_2, 2, 4 },
-                                                                            bc_test_params{ CASE_FC_U8S8_3D_4, 2, 4 },
-
+    // OneDNN has issue with small shapes - ticket 7064
+    // bc_test_params{ CASE_FC_U8S8_3D_1, 2, 4 },
+    // bc_test_params{ CASE_FC_U8S8_3D_2, 2, 4 },
+    bc_test_params{ CASE_FC_U8S8_3D_4, 2, 4 },
 }));
 #endif
 
 
 // reorder(bfyx to fs_b_yx_fsv32) + conv
-#define FSV32_CASE_CONV_FP32_1 {1, 32, 4, 5}, {1, 32, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
+#define FSV32_CASE_CONV_FP32_1 { 1, 32, 4, 5 }, { 1, 32, 2, 3 }, { 1, 1, 3, 3 }, tensor{ 1 }, tensor{ 0 }, tensor{ 1 }, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx
 
 class conv_fp32_reorder_bfyx_to_fsv32_conv_basic : public ConvFusingTest {};
 TEST_P(conv_fp32_reorder_bfyx_to_fsv32_conv_basic, basic) {
@@ -10062,7 +10209,7 @@ TEST_P(conv_fp32_reorder_bfyx_to_fsv32_conv_basic, basic) {
     );
 
     implementation_desc conv_impl = { format::fs_b_yx_fsv32, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_output", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_output", conv_impl } }));
 
     execute(p);
 }
@@ -10087,7 +10234,7 @@ TEST_P(conv_fp32_reorder_bfyx_to_fsv32_conv_mean, have_mean) {
     );
 
     implementation_desc conv_impl = { format::fs_b_yx_fsv32, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_output", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_output", conv_impl } }));
 
     execute(p);
 }
@@ -10121,7 +10268,7 @@ TEST_P(conv_fp32_reorder_bfyx_to_fsv32_conv_subtract, have_subtract_per_feature)
     );
 
     implementation_desc conv_impl = { format::fs_b_yx_fsv32, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_output", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_output", conv_impl } }));
 
     execute(p);
 }
@@ -10150,7 +10297,7 @@ TEST_P(conv_fp32_reorder_bfyx_to_fsv32_conv_fused_activation, have_fused_activat
     );
 
     implementation_desc conv_impl = { format::fs_b_yx_fsv32, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_output", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_output", conv_impl } }));
 
     execute(p);
 }
@@ -10172,7 +10319,7 @@ TEST_P(conv_fp32_reorder_bfyx_to_fsv32_conv_data_padding, have_data_padding) {
         data("weights", get_mem(get_weights_layout(p), -127, 127)),
         data("weights_dw", get_mem(dw_weights_layout, -127, 127)),
         convolution("conv_prim", "input", { "weights" }, p.groups, p.stride, p.pad, p.dilation),
-        reorder("reorder_fsv32", "conv_prim", layout(data_types::f32, format::fs_b_yx_fsv32, dw_tensor, padding{ {0, 0, 1, 1}, 0 })),
+        reorder("reorder_fsv32", "conv_prim", layout(data_types::f32, format::fs_b_yx_fsv32, dw_tensor, padding{ { 0, 0, 1, 1 }, 0 })),
         convolution("conv_output", "reorder_fsv32", { "weights_dw" }, p.out_shape.feature[0], dw_stride, p.pad, p.dilation),
         activation("activation", "conv_output", activation_func::abs),
         activation("activation2", "conv_prim", activation_func::abs),
@@ -10180,7 +10327,7 @@ TEST_P(conv_fp32_reorder_bfyx_to_fsv32_conv_data_padding, have_data_padding) {
     );
 
     implementation_desc conv_impl = { format::fs_b_yx_fsv32, "" };
-    bo_fused.set_option(build_option::force_implementations({ {"conv_output", conv_impl} }));
+    bo_fused.set_option(build_option::force_implementations({ { "conv_output", conv_impl } }));
 
     execute(p);
 }

From e20a97acbcb05359c893b5278e5c5baed6470fdb Mon Sep 17 00:00:00 2001
From: Liubov Talamanova <piccione-mail@yandex.ru>
Date: Wed, 22 Dec 2021 14:55:48 +0300
Subject: [PATCH 092/160] Support old import system in POT (#9331)

* Support old import system in POT

* Fixed comments

* Update imports in samples
---
 tools/pot/compression/__init__.py             | 21 ++++++++++++++++++
 tools/pot/openvino/tools/pot/__init__.py      |  8 +++++++
 tools/pot/openvino/tools/pot/api/README.md    | 22 +++++++++----------
 .../3d_segmentation/3d_segmentation_sample.py |  7 ++----
 .../classification/classification_sample.py   |  7 ++----
 .../face_detection/face_detection_sample.py   |  8 +++----
 .../samples/object_detection/data_loader.py   |  2 +-
 .../api/samples/object_detection/metric.py    |  2 +-
 .../obejct_detection_sample.py                |  5 +----
 .../segmentation/segmentation_sample.py       |  7 ++----
 .../pot/api/samples/speech/data_loader.py     |  2 +-
 .../pot/api/samples/speech/gna_sample.py      |  3 +--
 12 files changed, 54 insertions(+), 40 deletions(-)
 create mode 100644 tools/pot/compression/__init__.py

diff --git a/tools/pot/compression/__init__.py b/tools/pot/compression/__init__.py
new file mode 100644
index 00000000000..b9a5ad5a80c
--- /dev/null
+++ b/tools/pot/compression/__init__.py
@@ -0,0 +1,21 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+import warnings
+
+import openvino.tools.pot.api
+import openvino.tools.pot.engines
+import openvino.tools.pot.graph
+import openvino.tools.pot.pipeline
+
+
+warnings.warn('Import compression is deprecated. Please use openvino.tools.pot instead', DeprecationWarning)
+
+sys.modules["compression.api"] = openvino.tools.pot.api
+sys.modules["compression.engines"] = openvino.tools.pot.engines
+sys.modules["compression.engines.ie_engine"] = openvino.tools.pot.engines.ie_engine
+sys.modules["compression.graph"] = openvino.tools.pot.graph
+sys.modules["compression.graph.model_utils"] = openvino.tools.pot.graph.model_utils
+sys.modules["compression.pipeline"] = openvino.tools.pot.pipeline
+sys.modules["compression.pipeline.initializer"] = openvino.tools.pot.pipeline.initializer
diff --git a/tools/pot/openvino/tools/pot/__init__.py b/tools/pot/openvino/tools/pot/__init__.py
index 3bd6d0d967b..4ccc1a407d4 100644
--- a/tools/pot/openvino/tools/pot/__init__.py
+++ b/tools/pot/openvino/tools/pot/__init__.py
@@ -33,6 +33,14 @@ from .algorithms.sparsity.default.base_algorithm import BaseWeightSparsity
 from .algorithms.quantization.overflow_correction.algorithm import OverflowCorrection
 from .algorithms.quantization.ranger.algorithm import Ranger
 
+from .api.data_loader import DataLoader
+from .api.metric import Metric
+from .api.engine import Engine
+from .engines.ie_engine import IEEngine
+from .graph import load_model, save_model
+from .graph.model_utils import compress_model_weights
+from .pipeline.initializer import create_pipeline
+
 QUANTIZATION_ALGORITHMS = [
     'MinMaxQuantization',
     'RangeOptimization',
diff --git a/tools/pot/openvino/tools/pot/api/README.md b/tools/pot/openvino/tools/pot/api/README.md
index 0a73d707558..4054f206917 100644
--- a/tools/pot/openvino/tools/pot/api/README.md
+++ b/tools/pot/openvino/tools/pot/api/README.md
@@ -51,7 +51,7 @@ pipeline.
 ### DataLoader
 
 ```
-class compression.api.DataLoader(config)
+class openvino.tools.pot.api.DataLoader(config)
 ```
 The base class for all DataLoaders.
 
@@ -64,7 +64,7 @@ which supports integer indexing in range of 0 to `len(self)`
 ### Metric
 
 ```
-class compression.api.Metric()
+class openvino.tools.pot.api.Metric()
 ```
 An abstract class representing an accuracy metric.
 
@@ -87,7 +87,7 @@ All subclasses should override the following methods:
 ### Engine
 
 ```
-class compression.api.Engine(config, data_loader=None, metric=None)
+class openvino.tools.pot.api.Engine(config, data_loader=None, metric=None)
 ```
 Base class for all Engines.
 
@@ -153,7 +153,7 @@ describe internal representation of the DL model and how to work with it.
 ### IEEngine
 
 ```
-class compression.engines.IEEngine(config, data_loader=None, metric=None)
+class openvino.tools.pot.engines.ie_engine.IEEngine(config, data_loader=None, metric=None)
 ```
 IEEngine is a helper which implements Engine class based on [OpenVINO&trade; Inference Engine Python* API](@ref openvino_inference_engine_ie_bridges_python_docs_api_overview).
 This class support inference in synchronous and asynchronous modes and can be reused as-is in the custom pipeline or 
@@ -196,7 +196,7 @@ The Python* POT API provides the `NXModel` class as one interface for working wi
 It is used to load, save and access the model, in case of the cascaded model, access each model of the cascaded model.
 
 ```
-class compression.graph.nx_wrapper.NXModel(**kwargs)
+class openvino.tools.pot.graph.nx_model.NXModel(**kwargs)
 ```
 The NXModel class provides a representation of the DL model. A single model and cascaded model can be 
 represented as an instance of this class. The cascaded model is stored as a list of models.
@@ -209,7 +209,7 @@ represented as an instance of this class. The cascaded model is stored as a list
 
 The Python* POT API provides the utility function to load model from the OpenVINO&trade; Intermediate Representation (IR):
 ```
-compression.graph.model_utils.load_model(model_config)
+openvino.tools.pot.graph.model_utils.load_model(model_config)
 ```
 *Parameters*
 - `model_config` - dictionary describing a model that includes the following attributes:
@@ -255,7 +255,7 @@ compression.graph.model_utils.load_model(model_config)
 #### Saving model to IR
 The Python* POT API provides the utility function to save model in the OpenVINO&trade; Intermediate Representation (IR):
 ```
-compression.graph.model_utils.save_model(model, save_path, model_name=None, for_stat_collection=False)
+openvino.tools.pot.graph.model_utils.save_model(model, save_path, model_name=None, for_stat_collection=False)
 ```
 *Parameters*
 - `model` - `NXModel` instance.
@@ -280,7 +280,7 @@ compression.graph.model_utils.save_model(model, save_path, model_name=None, for_
 ### Sampler
 
 ```
-class compression.samplers.Sampler(data_loader=None, batch_size=1, subset_indices=None)
+class openvino.tools.pot.samplers.Sampler(data_loader=None, batch_size=1, subset_indices=None)
 ```
 Base class for all Samplers.
 
@@ -298,7 +298,7 @@ that returns the length of the returned iterators.
 ### BatchSampler
 
 ```
-class  compression.samplers.batch_sampler.BatchSampler(data_loader, batch_size=1, subset_indices=None):
+class openvino.tools.pot.samplers.batch_sampler.BatchSampler(data_loader, batch_size=1, subset_indices=None):
 ```
 Sampler provides an iterable over the dataset subset if `subset_indices` is specified or over the whole dataset with 
 given `batch_size`. Returns a list of data items.
@@ -306,7 +306,7 @@ given `batch_size`. Returns a list of data items.
 ## Pipeline
 
 ```
-class compression.pipeline.pipeline.Pipeline(engine)
+class openvino.tools.pot.pipeline.pipeline.Pipeline(engine)
 ```
 Pipeline class represents the optimization pipeline.
 
@@ -319,7 +319,7 @@ The pipeline can be applied to the DL model by calling `run(model)` method where
 
 The POT Python* API provides the utility function to create and configure the pipeline:
 ```
-compression.pipeline.initializer.create_pipeline(algo_config, engine)
+openvino.tools.pot.pipeline.initializer.create_pipeline(algo_config, engine)
 ```
 *Parameters* 
 - `algo_config` - a list defining optimization algorithms and their parameters included in the optimization pipeline. 
diff --git a/tools/pot/openvino/tools/pot/api/samples/3d_segmentation/3d_segmentation_sample.py b/tools/pot/openvino/tools/pot/api/samples/3d_segmentation/3d_segmentation_sample.py
index 03ef2226a6b..7c967b62956 100644
--- a/tools/pot/openvino/tools/pot/api/samples/3d_segmentation/3d_segmentation_sample.py
+++ b/tools/pot/openvino/tools/pot/api/samples/3d_segmentation/3d_segmentation_sample.py
@@ -8,11 +8,8 @@ import numpy as np
 from scipy.ndimage import interpolation
 from addict import Dict
 
-from openvino.tools.pot.api import Metric, DataLoader
-from openvino.tools.pot.engines.ie_engine import IEEngine
-from openvino.tools.pot.graph import load_model, save_model
-from openvino.tools.pot.graph.model_utils import compress_model_weights
-from openvino.tools.pot.pipeline.initializer import create_pipeline
+from openvino.tools.pot import Metric, DataLoader, IEEngine, \
+    load_model, save_model, compress_model_weights, create_pipeline
 from openvino.tools.pot.utils.logger import init_logger
 from openvino.tools.pot.api.samples.utils.argument_parser import get_common_argparser
 
diff --git a/tools/pot/openvino/tools/pot/api/samples/classification/classification_sample.py b/tools/pot/openvino/tools/pot/api/samples/classification/classification_sample.py
index 8a61b382e68..622402458da 100644
--- a/tools/pot/openvino/tools/pot/api/samples/classification/classification_sample.py
+++ b/tools/pot/openvino/tools/pot/api/samples/classification/classification_sample.py
@@ -6,11 +6,8 @@ import numpy as np
 from addict import Dict
 from cv2 import imread, resize as cv2_resize
 
-from openvino.tools.pot.api import Metric, DataLoader
-from openvino.tools.pot.graph import load_model, save_model
-from openvino.tools.pot.graph.model_utils import compress_model_weights
-from openvino.tools.pot.engines.ie_engine import IEEngine
-from openvino.tools.pot.pipeline.initializer import create_pipeline
+from openvino.tools.pot import Metric, DataLoader, IEEngine, \
+    load_model, save_model, compress_model_weights, create_pipeline
 from openvino.tools.pot.utils.logger import init_logger
 from openvino.tools.pot.api.samples.utils.argument_parser import get_common_argparser
 
diff --git a/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py b/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py
index 20b894b42a5..b3ba49b7000 100644
--- a/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py
+++ b/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py
@@ -10,11 +10,9 @@ from addict import Dict
 import cv2
 import numpy as np
 
-from openvino.tools.pot.api import Metric, DataLoader
-from openvino.tools.pot.engines.ie_engine import IEEngine
-from openvino.tools.pot.graph import load_model
-from openvino.tools.pot.graph.model_utils import compress_model_weights, add_outputs
-from openvino.tools.pot.pipeline.initializer import create_pipeline
+from openvino.tools.pot import Metric, DataLoader, IEEngine, \
+    load_model, compress_model_weights, create_pipeline
+from openvino.tools.pot.graph.model_utils import add_outputs
 from openvino.tools.pot.utils.logger import init_logger, get_logger
 from openvino.tools.pot.api.samples.face_detection import utils
 
diff --git a/tools/pot/openvino/tools/pot/api/samples/object_detection/data_loader.py b/tools/pot/openvino/tools/pot/api/samples/object_detection/data_loader.py
index 5313603ec99..5778bb5112b 100644
--- a/tools/pot/openvino/tools/pot/api/samples/object_detection/data_loader.py
+++ b/tools/pot/openvino/tools/pot/api/samples/object_detection/data_loader.py
@@ -6,7 +6,7 @@ import json
 import cv2
 import numpy as np
 
-from openvino.tools.pot.api import DataLoader
+from openvino.tools.pot import DataLoader
 
 
 class COCOLoader(DataLoader):
diff --git a/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py b/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py
index e708ecd523c..f677765cb0f 100644
--- a/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py
+++ b/tools/pot/openvino/tools/pot/api/samples/object_detection/metric.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from openvino.tools.pot.api import Metric
+from openvino.tools.pot import Metric
 
 
 class MAP(Metric):
diff --git a/tools/pot/openvino/tools/pot/api/samples/object_detection/obejct_detection_sample.py b/tools/pot/openvino/tools/pot/api/samples/object_detection/obejct_detection_sample.py
index 3c65b847d10..53d7a2240af 100644
--- a/tools/pot/openvino/tools/pot/api/samples/object_detection/obejct_detection_sample.py
+++ b/tools/pot/openvino/tools/pot/api/samples/object_detection/obejct_detection_sample.py
@@ -5,10 +5,7 @@ import os
 
 from addict import Dict
 
-from openvino.tools.pot.engines.ie_engine import IEEngine
-from openvino.tools.pot.graph import load_model, save_model
-from openvino.tools.pot.graph.model_utils import compress_model_weights
-from openvino.tools.pot.pipeline.initializer import create_pipeline
+from openvino.tools.pot import IEEngine, load_model, save_model, compress_model_weights, create_pipeline
 from openvino.tools.pot.utils.logger import init_logger
 from openvino.tools.pot.api.samples.utils.argument_parser import get_common_argparser
 from openvino.tools.pot.api.samples.object_detection.metric import MAP
diff --git a/tools/pot/openvino/tools/pot/api/samples/segmentation/segmentation_sample.py b/tools/pot/openvino/tools/pot/api/samples/segmentation/segmentation_sample.py
index cdb639ff548..24ba1235a56 100644
--- a/tools/pot/openvino/tools/pot/api/samples/segmentation/segmentation_sample.py
+++ b/tools/pot/openvino/tools/pot/api/samples/segmentation/segmentation_sample.py
@@ -8,11 +8,8 @@ import cv2
 import numpy as np
 from addict import Dict
 
-from openvino.tools.pot.api import Metric, DataLoader
-from openvino.tools.pot.engines.ie_engine import IEEngine
-from openvino.tools.pot.graph import load_model, save_model
-from openvino.tools.pot.graph.model_utils import compress_model_weights
-from openvino.tools.pot.pipeline.initializer import create_pipeline
+from openvino.tools.pot import Metric, DataLoader, IEEngine, \
+    load_model, save_model, compress_model_weights, create_pipeline
 from openvino.tools.pot.utils.logger import init_logger
 from openvino.tools.pot.api.samples.utils.argument_parser import get_common_argparser
 
diff --git a/tools/pot/openvino/tools/pot/api/samples/speech/data_loader.py b/tools/pot/openvino/tools/pot/api/samples/speech/data_loader.py
index db638b3844c..b09a9bc3139 100644
--- a/tools/pot/openvino/tools/pot/api/samples/speech/data_loader.py
+++ b/tools/pot/openvino/tools/pot/api/samples/speech/data_loader.py
@@ -5,7 +5,7 @@ import os
 import struct
 import numpy as np
 
-from openvino.tools.pot.api.data_loader import DataLoader
+from openvino.tools.pot import DataLoader
 
 
 class ArkDataLoader(DataLoader):
diff --git a/tools/pot/openvino/tools/pot/api/samples/speech/gna_sample.py b/tools/pot/openvino/tools/pot/api/samples/speech/gna_sample.py
index 90ec9220a63..a82b0f7c1d4 100644
--- a/tools/pot/openvino/tools/pot/api/samples/speech/gna_sample.py
+++ b/tools/pot/openvino/tools/pot/api/samples/speech/gna_sample.py
@@ -5,8 +5,7 @@ import json
 import os
 from addict import Dict
 
-from openvino.tools.pot.graph import load_model, save_model
-from openvino.tools.pot.pipeline.initializer import create_pipeline
+from openvino.tools.pot import load_model, save_model, create_pipeline
 from openvino.tools.pot.utils.logger import init_logger
 from openvino.tools.pot.api.samples.utils.argument_parser import get_common_argparser
 from openvino.tools.pot.api.samples.speech.data_loader import ArkDataLoader

From 186f6016993dd88ef6fc6f980dae39e2d159d48e Mon Sep 17 00:00:00 2001
From: Artur Kulikowski <artur.kulikowski@intel.com>
Date: Wed, 22 Dec 2021 13:14:48 +0100
Subject: [PATCH 093/160] For the ONNX frontend find nodes by tensor name
 (#9345)

---
 .../tools/mo/moc_frontend/extractor.py        | 38 +++++++++++--------
 .../tools/mo/moc_frontend/pipeline.py         |  6 +--
 2 files changed, 25 insertions(+), 19 deletions(-)

diff --git a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
index 92b5df2ecac..80c82d87df9 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/extractor.py
@@ -11,7 +11,7 @@ from openvino.frontend import InputModel  # pylint: disable=no-name-in-module,im
 import numpy as np
 
 
-def decode_name_with_port(input_model: InputModel, node_name: str):
+def decode_name_with_port(input_model: InputModel, node_name: str, framework=""):
     """
     Decode name with optional port specification w/o traversing all the nodes in the graph
     TODO: in future node_name can specify input/output port groups as well as indices (58562)
@@ -27,10 +27,22 @@ def decode_name_with_port(input_model: InputModel, node_name: str):
         found_node_names.append('Tensor:' + node_name)
         found_nodes.append(node)
 
+
+    def try_get_node(model, name, framework):
+        node = model.get_place_by_operation_name(name)
+        if node:
+            return node
+        if framework == "onnx":
+            tensor = model.get_place_by_tensor_name(name)
+            if tensor:
+                return tensor.get_producing_operation()
+        return None
+
+
     regexp_post = r'(.+):(\d+)'
     match_post = re.search(regexp_post, node_name)
     if match_post:
-        node_post = input_model.get_place_by_operation_name(match_post.group(1))
+        node_post = try_get_node(input_model, match_post.group(1), framework)
         if node_post:
             node_post = node_post.get_output_port(output_port_index=int(match_post.group(2)))
             if node_post:
@@ -40,7 +52,7 @@ def decode_name_with_port(input_model: InputModel, node_name: str):
     regexp_pre = r'(\d+):(.+)'
     match_pre = re.search(regexp_pre, node_name)
     if match_pre:
-        node_pre = input_model.get_place_by_operation_name(match_pre.group(2))
+        node_pre = try_get_node(input_model, match_pre.group(2), framework)
         if node_pre:
             node_pre = node_pre.get_input_port(input_port_index=int(match_pre.group(1)))
             if node_pre:
@@ -54,19 +66,13 @@ def decode_name_with_port(input_model: InputModel, node_name: str):
     if not all([n.is_equal_data(found_nodes[0]) for n in found_nodes]):
         raise_node_name_collision(node_name, found_node_names)
 
-    # TODO: ONNX specific (59408)
-    # To comply with legacy behavior, for ONNX-only there shall be considered additional 2 possibilities
-    # 1) "abc:1" - get_place_by_tensor_name("abc").get_producing_operation().get_output_port(1)
-    # 2) "1:abc" - get_place_by_tensor_name("abc").get_producing_operation().get_input_port(1)
-    # This logic is not going to work with other frontends
-
     # TODO: Add support for input/output group name and port index here (58562)
     # For new frontends logic shall be extended to additionally support input and output group names
     return found_nodes[0]
 
 
 def fe_input_user_data_repack(input_model: InputModel, input_user_shapes: [None, list, dict, np.ndarray],
-                              freeze_placeholder: dict, input_user_data_types=dict()):
+                              freeze_placeholder: dict, framework: str, input_user_data_types=dict(), ):
     """
     Restructures user input cutting request. Splits ports out of node names.
         Transforms node names to node ids.
@@ -107,7 +113,7 @@ def fe_input_user_data_repack(input_model: InputModel, input_user_shapes: [None,
     _input_shapes = []
     if isinstance(input_user_shapes, list) or isinstance(input_user_shapes, dict):
         for input_name in input_user_shapes:
-            node = decode_name_with_port(input_model, input_name)
+            node = decode_name_with_port(input_model, input_name, framework)
             if node is None:
                 raise Error('Cannot find location {} in the input model'.format(input_name))
             shape = None if isinstance(input_user_shapes, list) else input_user_shapes[input_name]
@@ -126,7 +132,7 @@ def fe_input_user_data_repack(input_model: InputModel, input_user_shapes: [None,
     return _input_shapes, dict()
 
 
-def fe_output_user_data_repack(input_model: InputModel, outputs: list):
+def fe_output_user_data_repack(input_model: InputModel, outputs: list, framework: str):
     """
 
     :param input_model: Input Model to operate on
@@ -153,7 +159,7 @@ def fe_output_user_data_repack(input_model: InputModel, outputs: list):
     _outputs = []
     if outputs is not None:
         for output in outputs:
-            node = decode_name_with_port(input_model, output)
+            node = decode_name_with_port(input_model, output, framework)
             if node is None:
                 raise Error('Cannot find location {} in the graph'.format(output))
             _outputs.append({'node': node})
@@ -161,7 +167,7 @@ def fe_output_user_data_repack(input_model: InputModel, outputs: list):
 
 
 def fe_user_data_repack(input_model: InputModel, input_user_shapes: [None, list, dict, np.array],
-                        input_user_data_types: dict, outputs: list, freeze_placeholder: dict):
+                        input_user_data_types: dict, outputs: list, freeze_placeholder: dict, framework: str):
     """
     :param input_model: Input Model to operate on
     :param input_user_shapes: data structure representing user input cutting request
@@ -171,7 +177,7 @@ def fe_user_data_repack(input_model: InputModel, input_user_shapes: [None, list,
     :return: restructured input, output and freeze placeholder dictionaries or None values
     """
     _input_shapes, _freeze_placeholder = fe_input_user_data_repack(
-        input_model, input_user_shapes, freeze_placeholder, input_user_data_types=input_user_data_types)
-    _outputs = fe_output_user_data_repack(input_model, outputs)
+        input_model, input_user_shapes, freeze_placeholder,  framework, input_user_data_types=input_user_data_types,)
+    _outputs = fe_output_user_data_repack(input_model, outputs, framework)
 
     return _input_shapes, _outputs, _freeze_placeholder
diff --git a/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py b/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
index a393fd8742a..ba2ba640e01 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/pipeline.py
@@ -26,7 +26,7 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
 
     user_shapes, outputs, freeze_placeholder = fe_user_data_repack(
         input_model, argv.placeholder_shapes, argv.placeholder_data_types,
-        argv.output, argv.freeze_placeholder_with_value)
+        argv.output, argv.freeze_placeholder_with_value, moc_front_end.get_name())
 
     def check_places_are_same(places_original: List[Place], places_new: List[Place]):
         """
@@ -74,7 +74,7 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
         if user_shapes:
             user_shapes, outputs, freeze_placeholder = fe_user_data_repack(
                 input_model, argv.placeholder_shapes, argv.placeholder_data_types,
-                argv.output, argv.freeze_placeholder_with_value)
+                argv.output, argv.freeze_placeholder_with_value, moc_front_end.get_name())
     elif not inputs_equal:
         log.debug('Using override_all_inputs')
         add_names_to_tensors(input_model, user_shapes)
@@ -84,7 +84,7 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
         if user_shapes:
             user_shapes, outputs, freeze_placeholder = fe_user_data_repack(
                 input_model, argv.placeholder_shapes, argv.placeholder_data_types,
-                argv.output, argv.freeze_placeholder_with_value)
+                argv.output, argv.freeze_placeholder_with_value, moc_front_end.get_name())
     elif not outputs_equal:
         log.debug('Using override_all_outputs')
         add_names_to_tensors(input_model, user_shapes)

From fc689699eb29475ae73b51ae88c3e8d61002eb03 Mon Sep 17 00:00:00 2001
From: Ivan Tikhonov <ivan.tikhonov@intel.com>
Date: Wed, 22 Dec 2021 16:23:20 +0300
Subject: [PATCH 094/160] Delete ngraph doxygen file (#9365)

---
 docs/doxygen/ngraph_cpp_api.config | 39 ------------------------------
 1 file changed, 39 deletions(-)
 delete mode 100644 docs/doxygen/ngraph_cpp_api.config

diff --git a/docs/doxygen/ngraph_cpp_api.config b/docs/doxygen/ngraph_cpp_api.config
deleted file mode 100644
index 56ab7dd7ebc..00000000000
--- a/docs/doxygen/ngraph_cpp_api.config
+++ /dev/null
@@ -1,39 +0,0 @@
-# Doxyfile 1.8.18
-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
-#
-# All text after a double hash (##) is considered a comment and is placed in
-# front of the TAG it is preceding.
-#
-# All text after a single hash (#) is considered a comment and will be ignored.
-# The format is:
-# TAG = value [value, ...]
-# For lists, items can also be appended using:
-# TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (\" \").
-
-@INCLUDE = "@IE_CONFIG_BUILD@"
-
-EXCLUDE_SYMBOLS        = 
-
-PREDEFINED             =
-
-EXCLUDE_PATTERNS       = */python/*
-
-FILE_PATTERNS          = *.cpp \
-                         *.c \
-                         *.hpp \
-                         *.h
-
-LAYOUT_FILE            = "@NGRAPH_CPP_LAYOUT_BUILD@"
-
-INPUT                  = "@CORE_DIR@/core/include/" \
-                         "@FRONTENDS_DIR@/onnx/frontend/include/" \
-                         "@FRONTENDS_DIR@/paddle/frontend/include/"
-
-HTML_OUTPUT            = "@NGRAPH_CPP_OUTPUT@"
-
-GENERATE_TAGFILE       = "@DOCS_BUILD_DIR@/ngraph_cpp_api.tag"
-
-WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ngraph_cpp_api.log"

From eb90272331508d3a1f1803a65a9161225e3835bf Mon Sep 17 00:00:00 2001
From: Milana Shhanukova <milana.shhanukova@intel.com>
Date: Wed, 22 Dec 2021 17:45:37 +0400
Subject: [PATCH 095/160] [POT] Remove gitlab mention (#9079)

* change in installation

* develop mode

* change in install guide

* style change

* change declare
---
 tools/pot/README_dev.md | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/tools/pot/README_dev.md b/tools/pot/README_dev.md
index f8b849181ad..df92dff54da 100644
--- a/tools/pot/README_dev.md
+++ b/tools/pot/README_dev.md
@@ -24,23 +24,22 @@ Post-Training Optimization Tool includes standalone command-line tool and Python
 - OpenVINO
 
 ### Installation (Temporary)
-1) Clone compression tool repo: `git clone git@gitlab-icv.inn.intel.com:algo/post-training-compression-tool.git`
+1) Clone DLDT repo: `git clone https://github.com/openvinotoolkit/openvino`
 2) Download submodules:
    ```
    git submodule init
    git submodule update
    ```
-3) Clone DLDT repo: `git clone https://github.com/openvinotoolkit/openvino` (Not into the post-training-compression-tool)
-4) Switch dldt to required branch: `feature/low_precision/develop_fp_v10`
-5) Build inference engine (Instruction can be found in dldt repo)
-6) Switch dldt to _mkaglins/poc_ branch (Inference engine is built from _feature/low_precision/develop_fp_v10_ branch to support `FakeQuantize` layers. ModelOptimizer is used from _mkaglins/poc_ branch. So stay on _mkaglins/poc_ branch as you've built IE and don't build it from there again)
-7) Set _PYTHONPATH_ variable: `export PYTHONPATH=<path to DLDT bins>/bin/intel64/Release/lib/python_api/python3.6:<path to DLDT>/dldt/model-optimizer`
-8) Install requirements for accuracy checker:
+3) Setup Model Optimizer. 
+    You can setup Model Optimizer that needs for POT purposed with the two ways:
+    1. Install Model Optimizer with pip using "python setup.py install" at the mo folder (`<openvino_path>/tools/mo/setup.py`)
+    2. Setup Model Optimizer for Python using PYTHONPATH environment variable. Add the the following `<openvino_path>/tools/mo` into PYTHONPATH.
+4) Install requirements for accuracy checker:
     - From POT root: `cd ./thirdparty/open_model_zoo/tools/accuracy_checker`
     - Call setup script: `python3 setup.py install`
     - Get back to root POT dir: `cd <PATH_TO_POT_DIR>`
-9) Install requirements for the tool:
-    - Call setup script: `python3 setup.py install`
+5) Install requirements for the tool:
+    - Call setup script: `python3 setup.py develop`
 
 ### Run
 1) Prepare configuration file for the tool based on the examples in the `configs` folder
@@ -48,7 +47,7 @@ Post-Training Optimization Tool includes standalone command-line tool and Python
 3) Launch the tool running the following command:
     `python3 main.py -c <path to config file> -e`
 
-To test the tool you can use PyTorch Mobilenet_v2 model from `tests/data/models/mobilenetv2/mobilenetv2.onnx`
+To test the tool you can use PyTorch Mobilenet_v2 model from `tests/data/models/mobilenetv2_example/mobilenetv2_example.onnx`
 
- - If there're some errors with imports in ModelOptimizer first of all make the following steps:
-    - Checkout _mkaglins/poc_ branch in DLDT (It's important!)
+- If there're some errors with imports in ModelOptimizer, first of all make the following steps:
+    - If you've installed ModelOptimizer with setting _PYTHONPATH_ variable, checkout the path. It should be as following `<openvino_path>/tools/mo.` The whole command can be found in step 3 Installation (Temporary) guide above.

From 529ab2b0993d5b0d6ed30e4f8c2c99012b3dba00 Mon Sep 17 00:00:00 2001
From: Zhang Yi <yi3.zhang@intel.com>
Date: Wed, 22 Dec 2021 23:14:37 +0800
Subject: [PATCH 096/160] Yi3/shape infer 2nd batch (#8420)

* [ShapeInfer]shape infer 2nd batch

* [ShapeInfer]Impl Reverse Sequence

* [ShapeInfer]Fix typo

* [ShapeInfer]fix error

* [ShapeInfer]remove useless code

* [ShapeInfer]fix code style

* [ShapeInfer]enable shape_infer in mkldnn

* [ShapeInfer]use shape_inference in tests

* [ShapeInfer]add partialshape for tests

* [ShapeInfer]revise test cases

* [ShapeInfer]fix review comments

* [ShapeInfer]remove debug logs

* [ShapeInfer]fix ci build

* [ShapeInfer]Fix errors

* [ShapeInfer]fix build

* [ShapeInfer]fix bug

* [ShapeInfer]remove useless check

* [ShapeInfer]Fix vpu tests

* [ShapeInfer]Fix extract_image

* [ShapeInfer]apply reviews
---
 .../utils/shape_inference/shape_inference.cpp | 14 ++-
 .../openvino/op/extractimagepatches.hpp       |  4 +
 .../include/openvino/op/reverse_sequence.hpp  | 14 +--
 ...greedy_decoder_seq_len_shape_inference.hpp | 57 +++++++++++
 .../ctc_greedy_decoder_shape_inference.hpp    | 51 ++++++++++
 .../extract_image_patches_shape_inference.hpp | 94 +++++++++++++++++++
 .../reverse_sequence_shape_inference.hpp      | 50 ++++++++++
 src/core/src/op/ctc_greedy_decoder.cpp        | 55 ++---------
 .../src/op/ctc_greedy_decoder_seq_len.cpp     | 61 ++----------
 src/core/src/op/extractimagepatches.cpp       | 88 ++---------------
 src/core/src/op/reverse_sequence.cpp          | 67 ++++++-------
 .../tests/type_prop/extractimagepatches.cpp   | 12 +++
 ...xtract_image_patches_to_reorg_yolo_vpu.cpp |  3 +-
 ...greedy_decoder_seq_len_shape_inference.cpp | 25 +++++
 .../ctc_greedy_decoder_shape_inference.cpp    | 24 +++++
 .../extract_image_patches_shape_inference.cpp | 28 ++++++
 .../reverse_sequence_shape_inference.cpp      | 23 +++++
 17 files changed, 442 insertions(+), 228 deletions(-)
 create mode 100644 src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/ctc_greedy_decoder_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/extract_image_patches_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/reverse_sequence_shape_inference.hpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_seq_len_shape_inference.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_shape_inference.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/extract_image_patches_shape_inference.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/reverse_sequence_shape_inference.cpp

diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
index be2c0bb0bc5..a597e406977 100644
--- a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
+++ b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
@@ -15,9 +15,12 @@
 
 #include "assign_shape_inference.hpp"
 #include "convolution_shape_inference.hpp"
+#include "ctc_greedy_decoder_seq_len_shape_inference.hpp"
+#include "ctc_greedy_decoder_shape_inference.hpp"
 #include "experimental_detectron_detection_output_shape_inference.hpp"
 #include "experimental_detectron_prior_grid_generator_shape_inference.hpp"
 #include "experimental_detectron_topkrois_shape_inference.hpp"
+#include "extract_image_patches_shape_inference.hpp"
 #include "fake_quantize.hpp"
 #include "gather_elements_shape_inference.hpp"
 #include "gather_shape_inference.hpp"
@@ -27,6 +30,7 @@
 #include "one_hot_shape_inference.hpp"
 #include "read_value_shape_inference.hpp"
 #include "reduce_shape_inference.hpp"
+#include "reverse_sequence_shape_inference.hpp"
 #include "scatter_elements_update_shape_inference.hpp"
 #include "scatter_nd_base_shape_inference.hpp"
 #include "ctc_loss_shape_inference.hpp"
@@ -178,7 +182,7 @@ void shape_inference(ov::Node* op,
         shape_infer(node, input_shapes, output_shapes, constant_data);
     } else if (auto node = ov::as_type<ov::opset4::ScatterNDUpdate>(op)) {
         shape_infer(node, input_shapes, output_shapes);
-        } else if (auto node = ov::as_type<ov::opset6::GatherElements>(op)) {
+    } else if (auto node = ov::as_type<ov::opset6::GatherElements>(op)) {
         shape_infer(node, input_shapes, output_shapes);
     } else if (auto node = ov::as_type<ov::op::util::GatherBase>(op)) {
         shape_infer(node, input_shapes, output_shapes, constant_data);
@@ -192,6 +196,14 @@ void shape_inference(ov::Node* op,
         shape_infer(node, input_shapes, output_shapes, constant_data);
     } else if (auto node = ov::as_type<ov::opset7::IDFT>(op)) {
         shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset6::CTCGreedyDecoderSeqLen>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset6::CTCGreedyDecoder>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset3::ExtractImagePatches>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset1::ReverseSequence>(op)) {
+        shape_infer(node, input_shapes, output_shapes);
     } else {
         ngraph::OutputVector new_inputs;
         for (size_t i = 0; i < op->get_input_size(); ++i) {
diff --git a/src/core/include/openvino/op/extractimagepatches.hpp b/src/core/include/openvino/op/extractimagepatches.hpp
index fbf7fb12c6e..20ed6681dbb 100644
--- a/src/core/include/openvino/op/extractimagepatches.hpp
+++ b/src/core/include/openvino/op/extractimagepatches.hpp
@@ -65,6 +65,10 @@ private:
     Strides m_patch_movement_strides;
     Shape m_patch_selection_rates;
     PadType m_padding;
+    template <class T>
+    friend void shape_infer(const ExtractImagePatches* op,
+                            const std::vector<T>& input_shapes,
+                            std::vector<T>& output_shapes);
 };
 }  // namespace v3
 }  // namespace op
diff --git a/src/core/include/openvino/op/reverse_sequence.hpp b/src/core/include/openvino/op/reverse_sequence.hpp
index b7e65181e6b..9bff578ee8c 100644
--- a/src/core/include/openvino/op/reverse_sequence.hpp
+++ b/src/core/include/openvino/op/reverse_sequence.hpp
@@ -38,24 +38,26 @@ public:
     int64_t get_origin_batch_axis() const {
         return m_batch_axis;
     }
-    void set_batch_axis(int64_t batch_axis) {
-        m_batch_axis = batch_axis;
-    }
+    void set_batch_axis(int64_t batch_axis);
     size_t get_sequence_axis() const {
         return m_normalized_seq_axis;
     }
     int64_t get_origin_sequence_axis() const {
         return m_seq_axis;
     }
-    void set_sequence_axis(int64_t sequence_axis) {
-        m_seq_axis = sequence_axis;
-    }
+    void set_sequence_axis(int64_t sequence_axis);
 
 private:
     int64_t m_batch_axis;
     int64_t m_seq_axis = 1;
     size_t m_normalized_batch_axis;
     size_t m_normalized_seq_axis;
+    template <class T>
+    friend void shape_infer(const ReverseSequence* op,
+                            const std::vector<T>& input_shapes,
+                            std::vector<T>& output_shapes);
+
+    void normalize_axes(ReverseSequence* op);
 };
 }  // namespace v0
 }  // namespace op
diff --git a/src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp b/src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp
new file mode 100644
index 00000000000..0f21b1ef9bb
--- /dev/null
+++ b/src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp
@@ -0,0 +1,57 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include <openvino/op/ctc_greedy_decoder_seq_len.hpp>
+
+namespace ov {
+namespace op {
+namespace v6 {
+
+template <class T>
+void shape_infer(const CTCGreedyDecoderSeqLen* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, (input_shapes.size() == 2 || input_shapes.size() == 3) && output_shapes.size() == 2);
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    const auto& logits_shape = input_shapes[0];
+    const auto& seq_len_shape = input_shapes[1];
+    const bool logits_is_static_rank = logits_shape.rank().is_static();
+    const bool seq_len_is_static_rank = seq_len_shape.rank().is_static();
+    auto& decoded_shape = output_shapes[0];
+    auto& seq_shape = output_shapes[1];
+    decoded_shape.resize(2);
+    seq_shape.resize(1);
+    if (input_shapes.size() == 3) {
+        const auto& blank_shape = input_shapes[2];
+        const auto& blank_rank = blank_shape.rank();
+        if (blank_shape.is_static()) {
+            const auto blank_is_scalar = blank_rank.get_length() == 0;
+            const auto blank_has_one_elem = blank_rank.get_length() == 1 && blank_shape[0].get_length() == 1;
+            NODE_VALIDATION_CHECK(op,
+                                  blank_is_scalar || blank_has_one_elem,
+                                  "Expected 0D or 1D tensor for the 'blank_index' input. Got: ",
+                                  blank_shape);
+        }
+    }
+    auto& batch_size = decoded_shape[0];
+    auto& time_size = decoded_shape[1];
+
+    // check ranks of input tensors
+    if (logits_is_static_rank) {
+        NODE_VALIDATION_CHECK(op, logits_shape.rank().compatible(3), "The rank of logits tensor must be equal to 3.");
+        batch_size = logits_shape[0];
+        time_size = logits_shape[1];
+    }
+    if (seq_len_is_static_rank) {
+        NODE_VALIDATION_CHECK(op,
+                              seq_len_shape.rank().compatible(1),
+                              "The rank of sequence len tensor must be equal to 1.");
+        NODE_VALIDATION_CHECK(op,
+                              DimType::merge(batch_size, batch_size, seq_len_shape[0]),
+                              "The first dimensions of input tensors must match.");
+    }
+
+    seq_shape[0] = batch_size;
+}
+}  // namespace v6
+}  // namespace op
+}  // namespace ov
\ No newline at end of file
diff --git a/src/core/shape_inference/include/ctc_greedy_decoder_shape_inference.hpp b/src/core/shape_inference/include/ctc_greedy_decoder_shape_inference.hpp
new file mode 100644
index 00000000000..eb80900f21b
--- /dev/null
+++ b/src/core/shape_inference/include/ctc_greedy_decoder_shape_inference.hpp
@@ -0,0 +1,51 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include <openvino/op/ctc_greedy_decoder.hpp>
+
+namespace ov {
+namespace op {
+namespace v0 {
+
+template <class T>
+void shape_infer(const CTCGreedyDecoder* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 2 && output_shapes.size() == 1);
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    // output dynamic rank tensor if all inputs are of dynamic rank
+    const auto& logits_pshape = input_shapes[0];
+    const auto& seq_mask_pshape = input_shapes[1];
+    auto& output_shape = output_shapes[0];
+    output_shape.resize(4);
+    output_shape[2] = 1;
+    output_shape[3] = 1;
+
+    if (logits_pshape.rank().is_dynamic() && seq_mask_pshape.rank().is_dynamic()) {
+        return;
+    }
+
+    // validate input shapes and compute output shape
+    auto& batch_size = output_shape[0];
+    auto& time_size = output_shape[1];
+    // check ranks of input tensors
+    if (logits_pshape.rank().is_static()) {
+        NODE_VALIDATION_CHECK(op, logits_pshape.rank().compatible(3), "The rank of logits tensor must be equal to 3.");
+        time_size = logits_pshape[0];
+        batch_size = logits_pshape[1];
+    }
+
+    if (seq_mask_pshape.rank().is_static()) {
+        NODE_VALIDATION_CHECK(op,
+                              seq_mask_pshape.rank().compatible(2),
+                              "The rank of sequence mask tensor must be equal to 2.");
+        NODE_VALIDATION_CHECK(op,
+                              DimType::merge(time_size, time_size, seq_mask_pshape[0]),
+                              "The first dimensions of input tensors must match.");
+        NODE_VALIDATION_CHECK(op,
+                              DimType::merge(batch_size, batch_size, seq_mask_pshape[1]),
+                              "The second dimensions of input tensors must match.");
+    }
+}
+}  // namespace v0
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/extract_image_patches_shape_inference.hpp b/src/core/shape_inference/include/extract_image_patches_shape_inference.hpp
new file mode 100644
index 00000000000..357b8790261
--- /dev/null
+++ b/src/core/shape_inference/include/extract_image_patches_shape_inference.hpp
@@ -0,0 +1,94 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include <openvino/op/extractimagepatches.hpp>
+
+namespace ov {
+namespace op {
+namespace v3 {
+int32_t inline calc_shape_padding(const int32_t input,
+                                  const int32_t rate,
+                                  const int32_t stride,
+                                  const int32_t patch_size,
+                                  const PadType type) {
+    int32_t out = 0;
+    if (type == PadType::VALID) {
+        out = (input - rate * (patch_size - 1) - 1) / stride + 1;
+    } else {
+        out = 1 + (input - 1) / stride;
+    }
+    return out < 0 ? 0 : out;
+}
+
+template <class T>
+void shape_infer(const ExtractImagePatches* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 1 && output_shapes.size() == 1);
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    const auto& input_shape = input_shapes[0];
+    auto& output_shape = output_shapes[0];
+    output_shape.resize(4);
+    NODE_VALIDATION_CHECK(op, input_shape.rank().compatible(4), "input tensor must be 4D tensor.");
+
+    NODE_VALIDATION_CHECK(op,
+                          op->m_patch_sizes.size() == 2,
+                          "Attribute sizes should be in [size_rows, size_cols] format.");
+
+    NODE_VALIDATION_CHECK(op,
+                          op->m_patch_movement_strides.size() == 2,
+                          "Attribute strides should be in [stride_rows, stride_cols] format.");
+
+    NODE_VALIDATION_CHECK(op,
+                          op->m_patch_movement_strides[0] > 0 && op->m_patch_movement_strides[1] > 0,
+                          "Attribute strides should be strictly greater than zeros in values.");
+
+    NODE_VALIDATION_CHECK(op,
+                          op->m_patch_selection_rates.size() == 2,
+                          "Attribute rates should be in [rate_rows, rate_cols] format.");
+
+    NODE_VALIDATION_CHECK(op,
+                          op->m_patch_selection_rates[0] > 0 && op->m_patch_selection_rates[1] > 0,
+                          "Attribute rates should be strictly greater than zeros in values.");
+
+    NODE_VALIDATION_CHECK(
+        op,
+        op->m_padding == PadType::VALID || op->m_padding == PadType::SAME_LOWER || op->m_padding == PadType::SAME_UPPER,
+        "Attribute padding should be in either valid or same_lower or same_upper.");
+
+    if (input_shape.rank().is_static()) {
+        // Determine batch & output_depth
+        output_shape[0] = input_shape[0];
+        output_shape[1] = input_shape[1] * op->m_patch_sizes[0] * op->m_patch_sizes[1];
+        // Determine spatial shape
+        if (input_shape[2].is_static() && input_shape[3].is_static()) {
+            int32_t input_rows = input_shape[2].get_length();
+            int32_t input_cols = input_shape[3].get_length();
+            int32_t out_rows(0);
+            int32_t out_cols(0);
+            if (input_rows == 0 || input_cols == 0) {
+                output_shape = input_shape;
+                return;
+            }
+
+            out_rows = calc_shape_padding(input_rows,
+                                          op->m_patch_selection_rates[0],
+                                          op->m_patch_movement_strides[0],
+                                          op->m_patch_sizes[0],
+                                          op->m_padding);
+            out_cols = calc_shape_padding(input_cols,
+                                          op->m_patch_selection_rates[1],
+                                          op->m_patch_movement_strides[1],
+                                          op->m_patch_sizes[1],
+                                          op->m_padding);
+
+            auto out_rows_cast = static_cast<typename DimType::value_type>(out_rows);
+            auto out_cols_cast = static_cast<typename DimType::value_type>(out_cols);
+
+            output_shape[2] = out_rows_cast;
+            output_shape[3] = out_cols_cast;
+        }
+    }
+}
+}  // namespace v3
+}  // namespace op
+}  // namespace ov
\ No newline at end of file
diff --git a/src/core/shape_inference/include/reverse_sequence_shape_inference.hpp b/src/core/shape_inference/include/reverse_sequence_shape_inference.hpp
new file mode 100644
index 00000000000..ba727a58f53
--- /dev/null
+++ b/src/core/shape_inference/include/reverse_sequence_shape_inference.hpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+
+#include <openvino/core/validation_util.hpp>
+#include <openvino/op/reverse_sequence.hpp>
+
+namespace ov {
+namespace op {
+namespace v0 {
+template <class T>
+void shape_infer(const ReverseSequence* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 2 && output_shapes.size() == 1);
+    const auto& data_pshape = input_shapes[0];
+    const auto& data_rank = data_pshape.rank();
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    const auto& seq_lengths_pshape = input_shapes[1];
+    const auto& seq_lengths_rank = seq_lengths_pshape.rank();
+    NODE_VALIDATION_CHECK(op,
+                          data_rank.is_dynamic() || data_rank.get_length() >= 2,
+                          "Data input rank should be equal or greater than 2. Got: ",
+                          data_pshape);
+
+    NODE_VALIDATION_CHECK(op,
+                          seq_lengths_rank.compatible(1),
+                          "Sequence lengths rank must be equal to 1. Got: ",
+                          seq_lengths_pshape);
+    auto& output_pshape = output_shapes[0];
+
+    output_pshape = data_pshape;
+    if (data_rank.is_static() && seq_lengths_rank.is_static()) {
+        DimType merged_sequence_length;
+        NODE_VALIDATION_CHECK(op,
+                              DimType::merge(merged_sequence_length, data_pshape[op->m_normalized_batch_axis], seq_lengths_pshape[0]),
+                              "Sequence lengths input size (",
+                              seq_lengths_pshape[0],
+                              ") is not equal to batch axis dimension of data input (",
+                              data_pshape[op->m_normalized_batch_axis],
+                              ") (argument shape: ",
+                              data_pshape,
+                              ", sequence indices shape: ",
+                              seq_lengths_pshape,
+                              ").");
+        output_pshape[op->m_normalized_batch_axis] = merged_sequence_length;
+    }
+}
+}  // namespace v0
+}  // namespace op
+}  // namespace ov
\ No newline at end of file
diff --git a/src/core/src/op/ctc_greedy_decoder.cpp b/src/core/src/op/ctc_greedy_decoder.cpp
index ca92dbbfe9e..b76e233ee4f 100644
--- a/src/core/src/op/ctc_greedy_decoder.cpp
+++ b/src/core/src/op/ctc_greedy_decoder.cpp
@@ -4,6 +4,8 @@
 
 #include "ngraph/op/ctc_greedy_decoder.hpp"
 
+#include <ctc_greedy_decoder_shape_inference.hpp>
+
 #include "itt.hpp"
 
 using namespace std;
@@ -23,55 +25,12 @@ void op::CTCGreedyDecoder::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v0_CTCGreedyDecoder_validate_and_infer_types);
     const auto& logits_pshape = get_input_partial_shape(0);
     const auto& seq_mask_pshape = get_input_partial_shape(1);
-    auto input_et = get_input_element_type(0);
+    const auto& input_et = get_input_element_type(0);
 
-    // output dynamic rank tensor if all inputs are of dynamic rank
-    if (logits_pshape.rank().is_dynamic() && seq_mask_pshape.rank().is_dynamic()) {
-        set_output_type(0, input_et, ov::PartialShape{Dimension::dynamic(), Dimension::dynamic(), 1, 1});
-    }
-
-    // check ranks of input tensors
-    if (logits_pshape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              logits_pshape.rank().get_length() == 3,
-                              "The rank of logits tensor must be equal to 3.");
-    }
-    if (seq_mask_pshape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              seq_mask_pshape.rank().get_length() == 2,
-                              "The rank of sequence mask tensor must be equal to 2.");
-    }
-
-    // validate input shapes and compute output shape
-    ngraph::Dimension batch_size = Dimension::dynamic();
-    ngraph::Dimension time_size = Dimension::dynamic();
-    if (logits_pshape.rank().is_static()) {
-        if (logits_pshape[0].is_static()) {
-            time_size = logits_pshape[0];
-        }
-        if (logits_pshape[1].is_static()) {
-            batch_size = logits_pshape[1];
-        }
-    }
-    if (seq_mask_pshape.rank().is_static()) {
-        if (seq_mask_pshape[0].is_static()) {
-            if (time_size != Dimension::dynamic()) {
-                NODE_VALIDATION_CHECK(this,
-                                      seq_mask_pshape[0] == time_size,
-                                      "The first dimensions of input tensors must match.");
-            }
-            time_size = seq_mask_pshape[0];
-        }
-        if (seq_mask_pshape[1].is_static()) {
-            if (batch_size != Dimension::dynamic()) {
-                NODE_VALIDATION_CHECK(this,
-                                      seq_mask_pshape[1] == batch_size,
-                                      "The second dimensions of input tensors must match.");
-            }
-            batch_size = seq_mask_pshape[1];
-        }
-    }
-    set_output_type(0, input_et, ov::PartialShape{batch_size, time_size, 1, 1});
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+    std::vector<ov::PartialShape> input_shapes = {logits_pshape, seq_mask_pshape};
+    shape_infer(this, input_shapes, output_shapes);
+    set_output_type(0, input_et, output_shapes[0]);
 }
 
 bool op::CTCGreedyDecoder::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/ctc_greedy_decoder_seq_len.cpp b/src/core/src/op/ctc_greedy_decoder_seq_len.cpp
index 740d4762c3d..8767dffdfc8 100644
--- a/src/core/src/op/ctc_greedy_decoder_seq_len.cpp
+++ b/src/core/src/op/ctc_greedy_decoder_seq_len.cpp
@@ -4,6 +4,8 @@
 
 #include "ngraph/op/ctc_greedy_decoder_seq_len.hpp"
 
+#include <ctc_greedy_decoder_seq_len_shape_inference.hpp>
+
 #include "itt.hpp"
 
 using namespace std;
@@ -40,21 +42,7 @@ void op::v6::CTCGreedyDecoderSeqLen::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v6_CTCGreedyDecoderSeqLen_validate_and_infer_types);
     const auto& logits_pshape = get_input_partial_shape(0);
     const auto& seq_len_pshape = get_input_partial_shape(1);
-    const bool logits_is_static_rank = logits_pshape.rank().is_static();
-    const bool seq_len_is_static_rank = seq_len_pshape.rank().is_static();
-
-    // check ranks of input tensors
-    if (logits_is_static_rank) {
-        NODE_VALIDATION_CHECK(this,
-                              logits_pshape.rank().get_length() == 3,
-                              "The rank of logits tensor must be equal to 3.");
-    }
-    if (seq_len_is_static_rank) {
-        NODE_VALIDATION_CHECK(this,
-                              seq_len_pshape.rank().get_length() == 1,
-                              "The rank of sequence len tensor must be equal to 1.");
-    }
-
+    std::vector<ov::PartialShape> input_shapes = {logits_pshape, seq_len_pshape};
     // check optional input type: blank index
     if (get_input_size() == 3) {
         const auto& blank_index_type = get_input_element_type(2);
@@ -62,46 +50,13 @@ void op::v6::CTCGreedyDecoderSeqLen::validate_and_infer_types() {
                               blank_index_type.is_integral_number(),
                               "The blank index type is expected to be an integer type. Got: ",
                               blank_index_type);
-
-        const auto& blank_index_partial_shape = get_input_partial_shape(2);
-        if (blank_index_partial_shape.is_static()) {
-            ov::Shape blank_index_shape = blank_index_partial_shape.to_shape();
-            NODE_VALIDATION_CHECK(
-                this,
-                ngraph::is_scalar(blank_index_shape) || (is_vector(blank_index_shape) && (blank_index_shape[0] == 1)),
-                "Expected 0D or 1D tensor for the 'blank_index' input. Got: ",
-                blank_index_shape);
-        }
+        input_shapes.push_back(get_input_partial_shape(2));
     }
 
-    // validate input shapes and compute output shape
-    ngraph::Dimension batch_size = Dimension::dynamic();
-    ngraph::Dimension time_size = Dimension::dynamic();
-
-    if (logits_is_static_rank) {
-        if (logits_pshape[0].is_static()) {
-            batch_size = logits_pshape[0];
-        }
-        if (logits_pshape[1].is_static()) {
-            time_size = logits_pshape[1];
-        }
-    }
-
-    if (seq_len_is_static_rank && seq_len_pshape[0].is_static()) {
-        if (batch_size != Dimension::dynamic()) {
-            NODE_VALIDATION_CHECK(this,
-                                  seq_len_pshape[0] == batch_size,
-                                  "The first dimensions of input tensors must match.");
-        }
-        batch_size = seq_len_pshape[0];
-    }
-
-    if (logits_is_static_rank && seq_len_is_static_rank) {
-        batch_size = seq_len_pshape[0] & logits_pshape[0];
-    }
-
-    set_output_type(0, m_classes_index_type, ov::PartialShape{batch_size, time_size});
-    set_output_type(1, m_sequence_length_type, ov::PartialShape{batch_size});
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}, ov::PartialShape{}};
+    shape_infer(this, input_shapes, output_shapes);
+    set_output_type(0, m_classes_index_type, output_shapes[0]);
+    set_output_type(1, m_sequence_length_type, output_shapes[1]);
 }
 
 bool op::v6::CTCGreedyDecoderSeqLen::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/extractimagepatches.cpp b/src/core/src/op/extractimagepatches.cpp
index 30aed5ad1cf..c938042b27e 100644
--- a/src/core/src/op/extractimagepatches.cpp
+++ b/src/core/src/op/extractimagepatches.cpp
@@ -4,6 +4,8 @@
 
 #include "ngraph/op/extractimagepatches.hpp"
 
+#include <extract_image_patches_shape_inference.hpp>
+
 #include "itt.hpp"
 #include "ngraph/attribute_visitor.hpp"
 
@@ -29,89 +31,11 @@ op::v3::ExtractImagePatches::ExtractImagePatches(const Output<Node>& image,
 
 void op::v3::ExtractImagePatches::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v3_ExtractImagePatches_validate_and_infer_types);
-    const ov::PartialShape input_pshape = get_input_partial_shape(0);
-
-    NODE_VALIDATION_CHECK(this, input_pshape.rank() == 4, "input tensor must be 4D tensor.");
-
-    NODE_VALIDATION_CHECK(this,
-                          m_patch_sizes.size() == 2,
-                          "Attribute sizes should be in [size_rows, size_cols] format.");
-
-    NODE_VALIDATION_CHECK(this,
-                          m_patch_movement_strides.size() == 2,
-                          "Attribute strides should be in [stride_rows, stride_cols] format.");
-
-    NODE_VALIDATION_CHECK(this,
-                          m_patch_movement_strides[0] > 0 && m_patch_movement_strides[1] > 0,
-                          "Attribute strides should be strictly greater than zeros in values.");
-
-    NODE_VALIDATION_CHECK(this,
-                          m_patch_selection_rates.size() == 2,
-                          "Attribute rates should be in [rate_rows, rate_cols] format.");
-
-    NODE_VALIDATION_CHECK(this,
-                          m_patch_selection_rates[0] > 0 && m_patch_selection_rates[1] > 0,
-                          "Attribute rates should be strictly greater than zeros in values.");
-
-    NODE_VALIDATION_CHECK(
-        this,
-        m_padding == PadType::VALID || m_padding == PadType::SAME_LOWER || m_padding == PadType::SAME_UPPER,
-        "Attribute padding should be in either valid or same_lower or same_upper.");
-
-    if (input_pshape[1].is_dynamic() || input_pshape[2].is_dynamic() || input_pshape[3].is_dynamic()) {
+    std::vector<PartialShape> input_shapes = {get_input_partial_shape(0)}, output_shapes = {PartialShape{}};
+    shape_infer(this, input_shapes, output_shapes);
+    if (output_shapes[0].is_dynamic())
         set_input_is_relevant_to_shape(0);
-        auto output_pshape = ov::PartialShape::dynamic(4);
-        set_output_type(0, get_input_element_type(0), output_pshape);
-    } else {
-        int32_t input_depth = input_pshape[1].get_length();
-        int32_t input_rows = input_pshape[2].get_length();
-        int32_t input_cols = input_pshape[3].get_length();
-        int32_t out_rows(0);
-        int32_t out_cols(0);
-
-        if (input_rows == 0 || input_cols == 0) {
-            out_rows = 0;
-            out_cols = 0;
-        } else if (m_padding == PadType::VALID) {
-            out_rows =
-                (((input_rows) -
-                  static_cast<int32_t>(m_patch_selection_rates[0]) * (static_cast<int32_t>(m_patch_sizes[0]) - 1) - 1) /
-                 m_patch_movement_strides[0]) +
-                1;
-            out_cols =
-                (((input_cols) -
-                  static_cast<int32_t>(m_patch_selection_rates[1]) * (static_cast<int32_t>(m_patch_sizes[1]) - 1) - 1) /
-                 m_patch_movement_strides[1]) +
-                1;
-        } else {
-            out_rows = 1 + (((input_rows)-1) / m_patch_movement_strides[0]);
-            out_cols = 1 + (((input_cols)-1) / m_patch_movement_strides[1]);
-        }
-
-        if (out_rows < 0)
-            out_rows = 0;
-        if (out_cols < 0)
-            out_cols = 0;
-
-        auto out_depth_cast =
-            static_cast<ngraph::Dimension::value_type>(input_depth * m_patch_sizes[0] * m_patch_sizes[1]);
-        auto out_rows_cast = static_cast<ngraph::Dimension::value_type>(out_rows);
-        auto out_cols_cast = static_cast<ngraph::Dimension::value_type>(out_cols);
-
-        ov::PartialShape output_pshape;
-        if (input_pshape[0].is_dynamic()) {
-            output_pshape = ov::PartialShape{input_pshape[0], out_depth_cast, out_rows_cast, out_cols_cast};
-        } else {
-            auto input_batch_cast = static_cast<ngraph::Dimension::value_type>(input_pshape[0].get_length());
-            output_pshape = ov::PartialShape{input_batch_cast, out_depth_cast, out_rows_cast, out_cols_cast};
-        }
-
-        if (input_rows == 0 || input_cols == 0) {
-            output_pshape = input_pshape;
-        }
-
-        set_output_type(0, get_input_element_type(0), output_pshape);
-    }
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
 
 bool op::v3::ExtractImagePatches::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/reverse_sequence.cpp b/src/core/src/op/reverse_sequence.cpp
index 5c8bb7ee104..8f28cdf071e 100644
--- a/src/core/src/op/reverse_sequence.cpp
+++ b/src/core/src/op/reverse_sequence.cpp
@@ -6,6 +6,7 @@
 
 #include <algorithm>
 #include <memory>
+#include <reverse_sequence_shape_inference.hpp>
 
 #include "itt.hpp"
 #include "ngraph/attribute_visitor.hpp"
@@ -39,48 +40,17 @@ bool ngraph::op::v0::ReverseSequence::visit_attributes(AttributeVisitor& visitor
 void op::ReverseSequence::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v0_ReverseSequence_validate_and_infer_types);
     const auto& data_pshape = get_input_partial_shape(0);
-    const auto& data_rank = data_pshape.rank();
-
-    NODE_VALIDATION_CHECK(this,
-                          data_rank.is_dynamic() || data_rank.get_length() >= 2,
-                          "Data input rank should be equal or greater than 2. Got: ",
-                          data_pshape);
-
-    m_normalized_batch_axis = ngraph::normalize_axis(this, m_batch_axis, data_rank);
-    m_normalized_seq_axis = ngraph::normalize_axis(this, m_seq_axis, data_rank);
-
     const auto& seq_lengths_et = get_input_element_type(1);
     const auto& seq_lengths_pshape = get_input_partial_shape(1);
-    const auto& seq_lengths_rank = seq_lengths_pshape.rank();
-
+    normalize_axes(this);
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+    std::vector<ov::PartialShape> input_shapes = {data_pshape, seq_lengths_pshape};
+    shape_infer(this, input_shapes, output_shapes);
     NODE_VALIDATION_CHECK(this,
                           seq_lengths_et.is_real() || seq_lengths_et.is_integral_number(),
                           "Sequence lengths element type must be numeric type. Got: ",
                           seq_lengths_et);
-
-    NODE_VALIDATION_CHECK(this,
-                          seq_lengths_rank.compatible(1),
-                          "Sequence lengths rank must be equal to 1. Got: ",
-                          seq_lengths_pshape);
-
-    ov::PartialShape output_pshape{data_pshape};
-    if (data_rank.is_static() && seq_lengths_rank.is_static()) {
-        Dimension merged_sequence_length;
-        NODE_VALIDATION_CHECK(
-            this,
-            Dimension::merge(merged_sequence_length, data_pshape[m_normalized_batch_axis], seq_lengths_pshape[0]),
-            "Sequence lengths input size (",
-            seq_lengths_pshape[0],
-            ") is not equal to batch axis dimension of data input (",
-            data_pshape[m_normalized_batch_axis],
-            ") (argument shape: ",
-            data_pshape,
-            ", sequence indices shape: ",
-            seq_lengths_pshape,
-            ").");
-        output_pshape[m_normalized_batch_axis] = merged_sequence_length;
-    }
-    set_output_type(0, get_input_element_type(0), output_pshape);
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
 
 shared_ptr<Node> op::ReverseSequence::clone_with_new_inputs(const OutputVector& new_args) const {
@@ -88,3 +58,28 @@ shared_ptr<Node> op::ReverseSequence::clone_with_new_inputs(const OutputVector&
     check_new_args_count(this, new_args);
     return make_shared<ReverseSequence>(new_args.at(0), new_args.at(1), m_batch_axis, m_seq_axis);
 }
+
+void op::ReverseSequence::set_batch_axis(int64_t batch_axis) {
+    m_batch_axis = batch_axis;
+    const auto& data_pshape = get_input_partial_shape(0);
+    const auto& data_rank = data_pshape.rank();
+    m_normalized_batch_axis = ov::normalize_axis(this, m_batch_axis, data_rank);
+}
+
+void op::ReverseSequence::set_sequence_axis(int64_t sequence_axis) {
+    m_seq_axis = sequence_axis;
+    const auto& data_pshape = get_input_partial_shape(0);
+    const auto& data_rank = data_pshape.rank();
+    m_normalized_seq_axis = ov::normalize_axis(this, m_seq_axis, data_rank);
+}
+
+void op::ReverseSequence::normalize_axes(ReverseSequence* op) {
+    const auto& data_pshape = op->get_input_partial_shape(0);
+    const auto& data_rank = data_pshape.rank();
+    NODE_VALIDATION_CHECK(op,
+                          data_rank.is_dynamic() || data_rank.get_length() >= 2,
+                          "Data input rank should be equal or greater than 2. Got: ",
+                          data_pshape);
+    op->m_normalized_batch_axis = ov::normalize_axis(op, op->m_batch_axis, data_rank);
+    op->m_normalized_seq_axis = ov::normalize_axis(op, op->m_seq_axis, data_rank);
+}
diff --git a/src/core/tests/type_prop/extractimagepatches.cpp b/src/core/tests/type_prop/extractimagepatches.cpp
index 5d65294b7cd..8f6b816c4d7 100644
--- a/src/core/tests/type_prop/extractimagepatches.cpp
+++ b/src/core/tests/type_prop/extractimagepatches.cpp
@@ -164,3 +164,15 @@ TEST(type_prop, extractimagepatches_large_stride_same_padding) {
     EXPECT_EQ(extractimagepatches->get_output_element_type(0), element::i32);
     EXPECT_EQ(extractimagepatches->get_output_shape(0), (Shape{64, 27, 1, 1}));
 }
+
+TEST(type_prop, extractimagepatches_dyn) {
+    auto data = make_shared<op::Parameter>(element::i64, PartialShape::dynamic());
+    auto sizes = Shape{3, 3};
+    auto strides = Strides{5, 5};
+    auto rates = Shape{1, 1};
+    auto padtype_padding = op::PadType::VALID;
+    auto extractimagepatches = make_shared<op::v3::ExtractImagePatches>(data, sizes, strides, rates, padtype_padding);
+
+    EXPECT_EQ(extractimagepatches->get_output_element_type(0), element::i64);
+    EXPECT_TRUE(extractimagepatches->get_output_partial_shape(0).same_scheme(PartialShape::dynamic(4)));
+}
\ No newline at end of file
diff --git a/src/tests/functional/plugin/myriad/ngraph/transformations/convert_extract_image_patches_to_reorg_yolo_vpu.cpp b/src/tests/functional/plugin/myriad/ngraph/transformations/convert_extract_image_patches_to_reorg_yolo_vpu.cpp
index f3bb5bc2d63..2451ed54da9 100644
--- a/src/tests/functional/plugin/myriad/ngraph/transformations/convert_extract_image_patches_to_reorg_yolo_vpu.cpp
+++ b/src/tests/functional/plugin/myriad/ngraph/transformations/convert_extract_image_patches_to_reorg_yolo_vpu.cpp
@@ -130,8 +130,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_NGraph, DoNotConvertEIPToReorgYoloOnNot4DInput, t
         testing::Values(ngraph::PartialShape{1, 1, 64, 500, 500},
                         ngraph::PartialShape{64, 500, 500},
                         ngraph::PartialShape{500, 500},
-                        ngraph::PartialShape{500},
-                        ngraph::PartialShape::dynamic()),
+                        ngraph::PartialShape{500}),
         testing::Values(ngraph::Shape{5, 5}),
         testing::Values(ngraph::Strides{5, 5}),
         testing::Values(ngraph::Shape{1, 1}),
diff --git a/src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_seq_len_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_seq_len_shape_inference.cpp
new file mode 100644
index 00000000000..6611abfa0c9
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_seq_len_shape_inference.cpp
@@ -0,0 +1,25 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <ctc_greedy_decoder_seq_len_shape_inference.hpp>
+#include <openvino/op/ops.hpp>
+#include <openvino/op/parameter.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, CtcGreedyDecoderSeqLenTest) {
+    auto P = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1});
+    auto I = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{-1});
+    auto G = std::make_shared<op::v6::CTCGreedyDecoderSeqLen>(P, I);
+    // Test StaticShape
+    std::vector<StaticShape> static_input_shapes = {StaticShape{3, 100, 1200}, StaticShape{3}},
+                             static_output_shapes = {StaticShape{}, StaticShape{}};
+    shape_inference(G.get(), static_input_shapes, static_output_shapes);
+    ASSERT_EQ(static_output_shapes[0], StaticShape({3, 100}));
+    ASSERT_EQ(static_output_shapes[1], StaticShape({3}));
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_shape_inference.cpp
new file mode 100644
index 00000000000..140a6eb10cb
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/ctc_greedy_decoder_shape_inference.cpp
@@ -0,0 +1,24 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <ctc_greedy_decoder_shape_inference.hpp>
+#include <openvino/op/ops.hpp>
+#include <openvino/op/parameter.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, CtcGreedyDecoderTest) {
+    auto P = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1});
+    auto I = std::make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{-1, -1});
+    auto G = std::make_shared<op::v0::CTCGreedyDecoder>(P, I, false);
+    // Test StaticShape
+    std::vector<StaticShape> static_input_shapes = {StaticShape{100, 3, 1200}, StaticShape{100, 3}},
+                             static_output_shapes = {StaticShape{}};
+    shape_inference(G.get(), static_input_shapes, static_output_shapes);
+    ASSERT_EQ(static_output_shapes[0], StaticShape({3, 100, 1, 1}));
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/extract_image_patches_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/extract_image_patches_shape_inference.cpp
new file mode 100644
index 00000000000..2e9698d9ccf
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/extract_image_patches_shape_inference.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <extract_image_patches_shape_inference.hpp>
+#include <openvino/op/ops.hpp>
+#include <openvino/op/parameter.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, ExtractImagePatchesTest) {
+    auto data = std::make_shared<op::v0::Parameter>(element::i32, PartialShape{-1, -1, -1, -1});
+    auto sizes = Shape{3, 3};
+    auto strides = Strides{5, 5};
+    auto rates = Shape{1, 1};
+    auto padTypePadding = op::PadType::VALID;
+    auto extractImagePatches =
+        std::make_shared<op::v3::ExtractImagePatches>(data, sizes, strides, rates, padTypePadding);
+    // Test StaticShape
+    std::vector<StaticShape> static_input_shapes = {StaticShape{64, 3, 10, 10}}, static_output_shapes = {StaticShape{}};
+
+    shape_inference(extractImagePatches.get(), static_input_shapes, static_output_shapes);
+    ASSERT_EQ(static_output_shapes[0], (StaticShape{64, 27, 2, 2}));
+}
diff --git a/src/tests/unit/cpu/shape_inference_test/reverse_sequence_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/reverse_sequence_shape_inference.cpp
new file mode 100644
index 00000000000..73563eb47e9
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/reverse_sequence_shape_inference.cpp
@@ -0,0 +1,23 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <openvino/op/parameter.hpp>
+#include <reverse_sequence_shape_inference.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, ReverseSequenceTest) {
+    auto data = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1});
+    auto seq_lengths = std::make_shared<op::v0::Parameter>(element::i32, PartialShape{4});
+    auto reverse_seq = std::make_shared<op::v0::ReverseSequence>(data, seq_lengths);
+    // Test StaticShape
+    std::vector<StaticShape> static_input_shapes = {StaticShape{4, 3, 2}, StaticShape{4}},
+                             static_output_shapes = {StaticShape{}};
+    shape_inference(reverse_seq.get(), static_input_shapes, static_output_shapes);
+    ASSERT_EQ(static_output_shapes[0], (StaticShape{4, 3, 2}));
+}
\ No newline at end of file

From 7e85ada7e4585b498f51ee742e312e194972f058 Mon Sep 17 00:00:00 2001
From: Liubov Talamanova <piccione-mail@yandex.ru>
Date: Wed, 22 Dec 2021 19:45:08 +0300
Subject: [PATCH 097/160] Handle exception with version (#9376)

---
 tools/pot/openvino/tools/pot/version.py | 2 +-
 tools/pot/setup.py                      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/pot/openvino/tools/pot/version.py b/tools/pot/openvino/tools/pot/version.py
index 4ed39f922e4..1b22b50012a 100644
--- a/tools/pot/openvino/tools/pot/version.py
+++ b/tools/pot/openvino/tools/pot/version.py
@@ -14,7 +14,7 @@ def generate_pot_version():
         branch_name = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).strip().decode()
         commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip().decode()
         return "custom_{}_{}".format(branch_name, commit_hash)
-    except subprocess.CalledProcessError:
+    except (subprocess.CalledProcessError, FileNotFoundError):
         return "unknown version"
 
 
diff --git a/tools/pot/setup.py b/tools/pot/setup.py
index b3114abc090..2c9bc1c3bb8 100644
--- a/tools/pot/setup.py
+++ b/tools/pot/setup.py
@@ -19,7 +19,7 @@ def generate_pot_version():
         branch_name = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).strip().decode()
         commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip().decode()
         return "custom_{}_{}".format(branch_name, commit_hash)
-    except subprocess.CalledProcessError:
+    except (subprocess.CalledProcessError, FileNotFoundError):
         return "unknown version"
 
 

From 7e0bf0dad5923dd3c0be1366f85ce85378ea3386 Mon Sep 17 00:00:00 2001
From: Mikhail Ryzhov <mikhail.ryzhov@intel.com>
Date: Wed, 22 Dec 2021 20:01:06 +0300
Subject: [PATCH 098/160] [GNA] Precision convert support (#9282)

* Rebase master

* [gna] Fixed export/import precision

* Revert "[gna] Fixed export/import precision"

This reverts commit d381a2e216e3ee2d88b2532b122f6b0dd42d0960.

* Rebase master

* [gna] Fixed export/import precision

* Revert "[gna] Fixed export/import precision"

* Removed convert nodes

* Added convert transformations

* Output casting

* Fixed convert function

* Added functional tests

* Update inference-engine/tests/functional/plugin/gna/preprocess_tests/precision_convert.cpp

Co-authored-by: Nadezhda Ageeva <nkogteva@gmail.com>
---
 .../src/convert_function_to_cnn_network.cpp   |  12 +-
 .../intel_gna/backend/gna_limitations.cpp     |   1 +
 src/plugins/intel_gna/gna_plugin.cpp          | 160 ++++-----
 src/plugins/intel_gna/preprocessing.cpp       |  17 -
 src/plugins/intel_gna/preprocessing.hpp       |  22 +-
 .../transformations/remove_converts.cpp       |  84 +++++
 .../transformations/remove_converts.hpp       |  98 ++++++
 .../preprocess_tests/precision_convert.cpp    | 164 +++++++++
 .../transformations/gna_remove_convert.cpp    | 326 ++++++++++++++++++
 9 files changed, 772 insertions(+), 112 deletions(-)
 create mode 100644 src/plugins/intel_gna/transformations/remove_converts.cpp
 create mode 100644 src/plugins/intel_gna/transformations/remove_converts.hpp
 create mode 100644 src/tests/functional/plugin/gna/preprocess_tests/precision_convert.cpp
 create mode 100644 src/tests/unit/gna/ngraph/transformations/gna_remove_convert.cpp

diff --git a/src/common/legacy/src/convert_function_to_cnn_network.cpp b/src/common/legacy/src/convert_function_to_cnn_network.cpp
index 87328cea152..38aa15ff06b 100644
--- a/src/common/legacy/src/convert_function_to_cnn_network.cpp
+++ b/src/common/legacy/src/convert_function_to_cnn_network.cpp
@@ -2075,11 +2075,13 @@ void convertFunctionToICNNNetwork(const std::shared_ptr<const ::ngraph::Function
     InputsDataMap resultInputDataMap;
     cnnNetworkImpl->getInputsInfo(resultInputDataMap);
     IE_ASSERT(resultInputDataMap.size() == thisInputDataMap.size());
-    for (auto i : resultInputDataMap) {
-        auto &thisInputData = *thisInputDataMap[i.first];
-        i.second->setPrecision(thisInputData.getPrecision());
-        i.second->setLayout(thisInputData.getLayout());
-        i.second->getPreProcess() = thisInputData.getPreProcess();
+    auto params = graph->get_parameters();
+    for ( const auto &param : params ) {
+        const std::string input_name = param->get_friendly_name();
+        auto &thisInputData = *thisInputDataMap[input_name];
+        resultInputDataMap[input_name]->setPrecision(thisInputData.getPrecision());
+        resultInputDataMap[input_name]->setLayout(thisInputData.getLayout());
+        resultInputDataMap[input_name]->getPreProcess() = thisInputData.getPreProcess();
     }
 }
 
diff --git a/src/plugins/intel_gna/backend/gna_limitations.cpp b/src/plugins/intel_gna/backend/gna_limitations.cpp
index ec620763293..77b352c4856 100644
--- a/src/plugins/intel_gna/backend/gna_limitations.cpp
+++ b/src/plugins/intel_gna/backend/gna_limitations.cpp
@@ -344,6 +344,7 @@ bool ValidateConvConcatAxis(const InferenceEngine::ConcatLayer* concat_layer) {
 }
 
 bool AreLayersSupported(InferenceEngine::CNNNetwork& network, std::string& errMessage, bool userWarning) {
+    IE_SUPPRESS_DEPRECATED_START
     InferenceEngine::InputsDataMap inputs = network.getInputsInfo();
     std::unordered_set<InferenceEngine::CNNLayer *> allLayers;
     InferenceEngine::CNNLayerPtr startLayer;
diff --git a/src/plugins/intel_gna/gna_plugin.cpp b/src/plugins/intel_gna/gna_plugin.cpp
index d3bcb691fc6..20d9d39add6 100644
--- a/src/plugins/intel_gna/gna_plugin.cpp
+++ b/src/plugins/intel_gna/gna_plugin.cpp
@@ -70,10 +70,12 @@
 #include "transformations/convert_dwsc_to_scaleshifts.hpp"
 #include "transformations/op_conversions/lstm_cell_decomposition.hpp"
 #include "transformations/remove_single_input_concat.hpp"
+#include "transformations/remove_converts.hpp"
 #include "transformations/broadcast_const.hpp"
 #include "transformations/op_conversions/convert_mvn1_to_mvn6.hpp"
 #include "transformations/decompose_mvn.hpp"
 #include "transformations/substitute_softsign.hpp"
+#include "transformations/convert_precision.hpp"
 
 #include <ngraph/opsets/opset7.hpp>
 
@@ -270,20 +272,49 @@ void GNAPlugin::ExportScores(void *ptr_dst,
     }
 }
 
-void GNAPlugin::ImportFrames(
-                  void *ptr_dst,
-                  const void *ptr_src,
-                  Precision input_precision,
-                  float scaleFactor,
-                  intel_dnn_orientation_t orientation,
-                  uint32_t num_frames,
-                  uint32_t num_group,
-                  uint32_t num_vector_elements,
-                  uint32_t num_vector_stride) {
-    if (orientation == kDnnInterleavedOrientation) {
-        // TODO : fix that as well
-        if (input_precision == Precision::U8) {
-            auto src = reinterpret_cast<const uint8_t *>(ptr_src);
+void GNAPlugin::ImportFrames(void *ptr_dst,
+                            const void *ptr_src,
+                            Precision input_precision,
+                            float scaleFactor,
+                            intel_dnn_orientation_t orientation,
+                            uint32_t num_frames,
+                            uint32_t num_group,
+                            uint32_t num_vector_elements,
+                            uint32_t num_vector_stride) {
+    switch (input_precision) {
+    case Precision::U8:
+    case Precision::I8:
+    {
+        auto src = reinterpret_cast<const uint8_t *>(ptr_src);
+        if (!gnaFlags->input_low_precision) {
+            auto dst = reinterpret_cast<int16_t*>(ptr_dst);
+            copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
+        } else {
+            auto dst = reinterpret_cast<int8_t*>(ptr_dst);
+            copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
+        }
+        break;
+    }
+    case Precision::I16:
+    {
+        auto src = reinterpret_cast<const int16_t *>(ptr_src);
+        if (!gnaFlags->input_low_precision) {
+            auto dst = reinterpret_cast<int16_t*>(ptr_dst);
+            copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
+        } else {
+            auto dst = reinterpret_cast<int8_t*>(ptr_dst);
+            copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
+        }
+        break;
+    }
+    case Precision::FP32:
+    case Precision::I32:
+    {
+        auto src = reinterpret_cast<const float *>(ptr_src);
+        if (!gnadevice) {
+            auto dst = reinterpret_cast<float *>(ptr_dst);
+            copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
+        } else {
             if (!gnaFlags->input_low_precision) {
                 auto dst = reinterpret_cast<int16_t*>(ptr_dst);
                 copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
@@ -291,69 +322,11 @@ void GNAPlugin::ImportFrames(
                 auto dst = reinterpret_cast<int8_t*>(ptr_dst);
                 copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
             }
-        } else if (input_precision.size() == 2) {
-            auto src = reinterpret_cast<const int16_t *>(ptr_src);
-            if (!gnaFlags->input_low_precision) {
-                auto dst = reinterpret_cast<int16_t*>(ptr_dst);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            } else {
-                auto dst = reinterpret_cast<int8_t*>(ptr_dst);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            }
-        } else if (input_precision.size() == 4) {
-            if (!gnadevice) {
-                auto dst = reinterpret_cast<float *>(ptr_dst);
-                auto src = reinterpret_cast<const float *>(ptr_src);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            } else {
-                auto src = reinterpret_cast<const float *>(ptr_src);
-                if (!gnaFlags->input_low_precision) {
-                    auto dst = reinterpret_cast<int16_t*>(ptr_dst);
-                    copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-                } else {
-                    auto dst = reinterpret_cast<int8_t*>(ptr_dst);
-                    copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-                }
-            }
-        }
-    } else {
-        if (input_precision == Precision::U8) {
-            auto src = reinterpret_cast<const uint8_t *>(ptr_src);
-            if (!gnadevice) {
-                auto dst = reinterpret_cast<float *>(ptr_dst);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            } else if (!gnaFlags->input_low_precision) {
-                auto dst = reinterpret_cast<int16_t*>(ptr_dst);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            } else {
-                auto dst = reinterpret_cast<int8_t*>(ptr_dst);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            }
-        } else if (input_precision.size()== 2) {
-            auto src = reinterpret_cast<const int16_t *>(ptr_src);
-            if (!gnaFlags->input_low_precision) {
-                auto dst = reinterpret_cast<int16_t*>(ptr_dst);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            } else {
-                auto dst = reinterpret_cast<int8_t*>(ptr_dst);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            }
-        } else if (input_precision.size() == 4) {
-            if (!gnadevice) {
-                auto dst = reinterpret_cast<float *>(ptr_dst);
-                auto src = reinterpret_cast<const float *>(ptr_src);
-                copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-            } else {
-                auto src = reinterpret_cast<const float *>(ptr_src);
-                if (!gnaFlags->input_low_precision) {
-                    auto dst = reinterpret_cast<int16_t*>(ptr_dst);
-                    copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-                } else {
-                    auto dst = reinterpret_cast<int8_t*>(ptr_dst);
-                    copyInputData(dst, src, num_frames, num_group, num_vector_elements, num_vector_stride, orientation, scaleFactor);
-                }
-            }
         }
+        break;
+    }
+    default:
+        break;
     }
 }
 
@@ -690,15 +663,21 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
 
     bool isNgraphPassesUsed = false;
     bool fake_quantized = false;
+
     if (_network.getFunction()) {
         CNNNetwork clonedNetwork = InferenceEngine::cloneNetwork(_network);
         const auto& graph = clonedNetwork.getFunction();
         ngraph::pass::Manager manager;
         manager.register_pass<ngraph::pass::InitNodeInfo>();
         fake_quantized = ngraph::op::util::has_op_with_type<ngraph::opset7::FakeQuantize>(graph);
+        // In OV API 2.0(IRv10) default convertion to fp32 (inputs, outputs and weights) is disabled
+        // and we need to run the ConvertPrecision transformation to support old networks.
+        manager.register_pass<ngraph::pass::ConvertPrecision>(precisions_array{{ngraph::element::f16, ngraph::element::f32}});
         manager.register_pass<ngraph::pass::ConvertMVN1ToMVN6>();
         manager.register_pass<DecomposeMVN>();
         manager.register_pass<ngraph::pass::CommonOptimizations>();
+        manager.register_pass<RemoveInputConvert>();
+        manager.register_pass<RemoveOutputConvert>();
         manager.register_pass<ngraph::pass::LSTMCellDecomposition>();
         manager.register_pass<ConvertDWSCToScaleShifts>();
         manager.register_pass<ConvertPaddedToValidConv>();
@@ -754,7 +733,6 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         pass_config->disable<ngraph::pass::TransposeReduction>();
         manager.run_passes(graph);
         convertedNetwork = InferenceEngine::details::convertFunctionToICNNNetwork(graph, clonedNetwork);
-
         isNgraphPassesUsed = true;
     }
     IE_SUPPRESS_DEPRECATED_START
@@ -765,7 +743,8 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
     NetPass::ConvertPrecision(network, Precision::U64, Precision::I32);
     NetPass::ConvertPrecision(network, Precision::U32, Precision::I32);
 
-    //  Check the input network
+
+    //  Check the network
     std::string error;
     if (!GNAPluginNS::GNALimitations::AreLayersSupported(network, error, gnaFlags->log_level == PluginConfigParams::LOG_WARNING)) {
         THROW_GNA_EXCEPTION << error.c_str();
@@ -1293,7 +1272,7 @@ uint32_t GNAPlugin::QueueInference(const InferenceEngine::BlobMap &inputs, Infer
         ImportFrames(inputs_ptr_->at(input.first).ptrs[idx],
                      input.second->cbuffer().as<float *>(),
                      input.second->getTensorDesc().getPrecision(),
-                     gnaFlags->sw_fp32 ? 1.0f : inputs_ptr_->at(input.first).scale_factor,
+                     gnaFlags->sw_fp32 ? GNAPluginNS::kScaleFactorDefault : inputs_ptr_->at(input.first).scale_factor,
                      inputOrientation,
                      importedFrames,
                      targetGroups,
@@ -1455,11 +1434,24 @@ GnaWaitStatus GNAPlugin::WaitFor(uint32_t request_idx, int64_t millisTimeout) {
                 fprintf(f, "\n\n");
             }
 #endif
-            ConvertToFloat(outputBlob->buffer(),
-                outputBlob->buffer(),
-                elementsPerBatch,
-                batchSize,
-                outputDesc.scale_factor);
+            switch (outputBlob->getTensorDesc().getPrecision()) {
+            case InferenceEngine::Precision::FP32 :
+                UnscaleAndCast(outputBlob->buffer().as<float*>(),
+                              outputBlob->buffer().as<int32_t*>(),
+                              elementsPerBatch, batchSize, outputDesc.scale_factor);
+                break;
+
+            case InferenceEngine::Precision::I32 :
+                UnscaleAndCast(outputBlob->buffer().as<int32_t*>(),
+                              outputBlob->buffer().as<int32_t*>(),
+                              elementsPerBatch, batchSize, outputDesc.scale_factor);
+                break;
+
+            default:
+                THROW_GNA_EXCEPTION << "Unsupported target precision: " << outputBlob->getTensorDesc().getPrecision() << std::endl;
+                break;
+            }
+
 #ifdef PLOT
             if (f) {
                 if (isScalar) {
diff --git a/src/plugins/intel_gna/preprocessing.cpp b/src/plugins/intel_gna/preprocessing.cpp
index 3c316c419e9..8168708da29 100644
--- a/src/plugins/intel_gna/preprocessing.cpp
+++ b/src/plugins/intel_gna/preprocessing.cpp
@@ -38,20 +38,3 @@ void GNAPluginNS::ConvertToInt16(int16_t *ptr_dst,
         ptr_dst[i] = GNAPluginNS::ConvertFloatToInt16(ptr_src[i]*scale_factor);
     }
 }
-
-void GNAPluginNS::ConvertToFloat(float *ptr_dst,
-                                 int32_t *ptr_src,
-                                 const uint32_t num_rows,
-                                 const uint32_t num_columns,
-                                 const float scale_factor) {
-    if (!ptr_dst || !ptr_src) {
-        return;
-    }
-    for (uint32_t i = 0; i < num_rows; i++) {
-        int32_t *ptr_int_row = ptr_src + i * num_columns;
-        float *ptr_float_row = ptr_dst + i * num_columns;
-        for (uint32_t j = 0; j < num_columns; j++) {
-            ptr_float_row[j] = static_cast<float>(ptr_int_row[j]) / scale_factor;
-        }
-    }
-}
diff --git a/src/plugins/intel_gna/preprocessing.hpp b/src/plugins/intel_gna/preprocessing.hpp
index aac61f2887b..664e0198e12 100644
--- a/src/plugins/intel_gna/preprocessing.hpp
+++ b/src/plugins/intel_gna/preprocessing.hpp
@@ -14,12 +14,22 @@ void ConvertToInt16(int16_t *ptr_dst,
                     const uint32_t num_columns,
                     const float scale_factor);
 
-void ConvertToFloat(float *ptr_dst,
-                    int32_t *ptr_src,
-                    const uint32_t num_rows,
-                    const uint32_t num_columns,
-                    const float scale_factor);
-
 int16_t ConvertFloatToInt16(float src);
 int8_t ConvertFloatToInt8(float src);
+
+template<typename T1, typename T2>
+inline void UnscaleAndCast(T2 *ptr_dst, T1 *ptr_src, const uint32_t num_rows, const uint32_t num_columns,
+                                       const float scale_factor) {
+    if (!ptr_dst || !ptr_src) {
+        return;
+    }
+    for (uint32_t i = 0; i < num_rows; i++) {
+        T1 *ptr_int_row = ptr_src + i * num_columns;
+        T2 *ptr_float_row = ptr_dst + i * num_columns;
+        for (uint32_t j = 0; j < num_columns; j++) {
+            ptr_float_row[j] = static_cast<T2>(ptr_int_row[j]) / scale_factor;
+        }
+    }
+}
+
 }  // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/transformations/remove_converts.cpp b/src/plugins/intel_gna/transformations/remove_converts.cpp
new file mode 100644
index 00000000000..2e62935dba2
--- /dev/null
+++ b/src/plugins/intel_gna/transformations/remove_converts.cpp
@@ -0,0 +1,84 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/cc/ngraph/itt.hpp>
+
+#include "transformations/remove_converts.hpp"
+
+#include <memory>
+#include <vector>
+
+#include <ngraph/opsets/opset4.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/pass/manager.hpp>
+
+namespace GNAPluginNS {
+    NGRAPH_RTTI_DEFINITION(RemoveInputConvert, "RemoveInputConvert", 0);
+
+    RemoveInputConvert::RemoveInputConvert() {
+        MATCHER_SCOPE(RemoveInputConvert);
+
+        const auto input = ngraph::pattern::wrap_type<ngraph::opset8::Parameter>(ngraph::pattern::type_matches_any(kSupportedInputTypesFrom));
+        const auto convert = ngraph::pattern::wrap_type<ngraph::opset8::Convert>({input},
+                                                                                 ngraph::pattern::type_matches_any(kSupportedInputTypesTo));
+
+        ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
+            const auto& pattern_map = m.get_pattern_value_map();
+
+            auto convert_node = pattern_map.at(convert).get_node_shared_ptr();
+            auto input_node = pattern_map.at(input).get_node_shared_ptr();
+
+            // check the supported combinations
+            auto from_type = input_node->get_element_type();
+            auto to_type = convert_node->get_element_type();
+            if (std::count(kSupportedInputConverts.begin(), kSupportedInputConverts.end(), std::make_pair(from_type, to_type)) == 0)
+                return false;
+
+            // replace input precision with convert's one
+            if (auto param = ov::as_type_ptr<ngraph::opset8::Parameter>(input_node)) {
+                param->set_element_type(to_type);
+            }
+
+            ngraph::replace_output_update_name(convert_node->output(0), convert_node->input_value(0));
+            return true;
+        };
+
+        auto m = std::make_shared<ngraph::pattern::Matcher>(convert, matcher_name);
+        this->register_matcher(m, callback);
+    }
+
+    NGRAPH_RTTI_DEFINITION(RemoveOutputConvert, "RemoveOutputConvert", 0);
+    RemoveOutputConvert::RemoveOutputConvert() {
+        MATCHER_SCOPE(RemoveOutputConvert);
+
+        auto output = ngraph::pattern::any_input(ngraph::pattern::type_matches_any(kSupportedOutputTypesFrom));
+        const auto convert = ngraph::pattern::wrap_type<ngraph::opset8::Convert>({output},
+                                                                                 ngraph::pattern::type_matches_any(kSupportedOutputTypesTo));
+        const auto result = ngraph::pattern::wrap_type<ngraph::opset8::Result>({convert},
+                                                                               ngraph::pattern::type_matches_any(kSupportedOutputTypesTo));
+
+        ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
+            const auto& pattern_map = m.get_pattern_value_map();
+
+            auto output_node = pattern_map.at(output).get_node_shared_ptr();
+            auto convert_node = pattern_map.at(convert).get_node_shared_ptr();
+
+            // check the supported combinations
+            auto from_type = output_node->get_element_type();
+            auto to_type = convert_node->get_element_type();
+            if (std::count(kSupportedOutputConverts.begin(), kSupportedOutputConverts.end(), std::make_pair(from_type, to_type)) == 0) {
+                return false;
+            }
+
+            // the result presicion will be changed automaically
+            ngraph::replace_output_update_name(convert_node->output(0), convert_node->input_value(0));
+            return true;
+        };
+
+        auto m = std::make_shared<ngraph::pattern::Matcher>(result, matcher_name);
+        this->register_matcher(m, callback);
+    }
+
+} // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/transformations/remove_converts.hpp b/src/plugins/intel_gna/transformations/remove_converts.hpp
new file mode 100644
index 00000000000..7850e999139
--- /dev/null
+++ b/src/plugins/intel_gna/transformations/remove_converts.hpp
@@ -0,0 +1,98 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/pass/graph_rewrite.hpp>
+
+namespace GNAPluginNS {
+
+/**
+ * @brief remove convert layers after inputs and changing it's precision
+ * to support preprocessing conversions from user's precision to network presicion
+ *
+ * Searches for next pattern
+ *     Any input layer
+ *           |
+ *         Convert
+ *           |
+ *        Any layer
+ *
+ * And transforms to
+ *     Any input layer
+ *           |
+ *        Any layer
+ */
+const std::vector<ov::element::Type> kSupportedInputTypesFrom = {
+    ov::element::u8,
+    ov::element::i16,
+    ov::element::f32
+};
+
+const std::vector<ov::element::Type> kSupportedInputTypesTo = {
+    ov::element::u8,
+    ov::element::i8,
+    ov::element::i16,
+    ov::element::i32,
+    ov::element::f32
+};
+
+const std::vector<std::pair<ov::element::Type, ov::element::Type>> kSupportedInputConverts {
+    //     FROM      ->       TO
+    {ov::element::u8, ov::element::u8},
+    {ov::element::u8, ov::element::i8},
+    {ov::element::u8, ov::element::i16},
+    {ov::element::i16, ov::element::i8},
+    {ov::element::i16, ov::element::i16},
+    {ov::element::f32, ov::element::i8},
+    {ov::element::f32, ov::element::i16},
+    {ov::element::f32, ov::element::i32},
+    {ov::element::f32, ov::element::f32}
+};
+
+class RemoveInputConvert : public ngraph::pass::MatcherPass {
+public:
+  NGRAPH_RTTI_DECLARATION;
+  RemoveInputConvert();
+};
+
+/**
+ * @brief remove convert layers after before outputs changing it's precision
+ * to support postprocessing conversions from network to user's precision
+ *
+ * Searches for next pattern
+ *        Any layer
+ *           |
+ *         Convert
+ *           |
+ *     Any output layer
+ *
+ * And transforms to
+ *        Any layer
+ *           |
+ *    Any output layer
+ */
+
+const std::vector<ov::element::Type> kSupportedOutputTypesFrom = {
+    ov::element::f32
+};
+
+const std::vector<ov::element::Type> kSupportedOutputTypesTo = {
+    ov::element::i32,
+    ov::element::f32
+};
+
+const std::vector<std::pair<ov::element::Type, ov::element::Type>> kSupportedOutputConverts {
+    //     FROM      ->       TO
+    {ov::element::f32, ov::element::f32},
+    {ov::element::f32, ov::element::i32}
+};
+
+class RemoveOutputConvert : public ngraph::pass::MatcherPass {
+public:
+  NGRAPH_RTTI_DECLARATION;
+  RemoveOutputConvert();
+};
+
+} // namespace GNAPluginNS
diff --git a/src/tests/functional/plugin/gna/preprocess_tests/precision_convert.cpp b/src/tests/functional/plugin/gna/preprocess_tests/precision_convert.cpp
new file mode 100644
index 00000000000..426e6e9439e
--- /dev/null
+++ b/src/tests/functional/plugin/gna/preprocess_tests/precision_convert.cpp
@@ -0,0 +1,164 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "common_test_utils/common_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace ov::test;
+
+namespace LayerTestsDefinitions {
+
+typedef std::tuple<
+        ov::element::Type,      // Net precision
+        ov::element::Type,      // Input precision
+        ov::element::Type,      // Output precision
+        std::string,            // Device name
+        std::map<std::string, std::string>  // Configuration
+> preprocessTestParamsSet;
+
+class PreprocessGNATest : public testing::WithParamInterface<preprocessTestParamsSet>,
+                                virtual public SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<preprocessTestParamsSet>& obj) {
+        ov::element::Type net_type, in_type, out_type;
+        std::string target_device;
+        std::map<std::string, std::string> conf;
+
+        std::tie(net_type, in_type, out_type, target_device, conf) = obj.param;
+
+        std::ostringstream result;
+        result << "netPRC=" << net_type << "_";
+        result << "inPRC=" << in_type << "_";
+        result << "outPRC=" << out_type << "_";
+        result << "trgDev=" << target_device;
+        for (auto const &conf_i : conf) {
+            result << "_configItem=" << conf_i.first.c_str() << "_" << conf_i.second.c_str();
+        }
+        return result.str();
+    }
+protected:
+    void SetUp() override {
+        ov::element::Type net_type;
+        std::vector<InputShape> input_shapes = static_shapes_to_test_representation({{8, 2}, {8, 2}});
+        std::map<std::string, std::string> conf;
+        abs_threshold = std::numeric_limits<int32_t>::max();
+        rel_threshold = std::numeric_limits<int32_t>::max();
+
+        std::tie(net_type, inType, outType, targetDevice, conf) = this->GetParam();
+
+        configuration.insert(conf.begin(), conf.end());
+
+        init_input_shapes({input_shapes});
+
+        auto params = ngraph::builder::makeDynamicParams(net_type, inputDynamicShapes);
+        auto paramOuts = ngraph::helpers::convert2OutputVector(
+                ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+        auto concat = std::make_shared<ngraph::opset8::Concat>(paramOuts, 1);
+        ngraph::ResultVector results{std::make_shared<ngraph::opset8::Result>(concat)};
+        function = std::make_shared<ngraph::Function>(results, params, "concat");
+    }
+};
+
+class PreprocessGNAUnsupportedTest : public PreprocessGNATest {
+public:
+    PreprocessGNAUnsupportedTest(std::string error_str) : exp_error_str_(error_str) {}
+    void run() override {
+        try {
+            PreprocessGNATest::compile_model();
+            FAIL() << "GNA's unsupported layers were not detected during LoadNetwork()";
+        }
+        catch (std::runtime_error& e) {
+            const std::string errorMsg = e.what();
+            const auto expectedMsg = exp_error_str_;
+            ASSERT_STR_CONTAINS(errorMsg, expectedMsg);
+            EXPECT_TRUE(errorMsg.find(expectedMsg) != std::string::npos)
+            << "Wrong error message, actual error message: " << errorMsg
+            << ", expected: " << expectedMsg;
+        }
+    }
+
+private:
+    std::string exp_error_str_;
+};
+
+class PreprocessGNAUnsupportedInputsTest : public PreprocessGNAUnsupportedTest {
+public:
+    PreprocessGNAUnsupportedInputsTest() : PreprocessGNAUnsupportedTest("The plugin does not support input precision") {}
+};
+
+class PreprocessGNAUnsupportedOutputsTest : public PreprocessGNAUnsupportedTest {
+public:
+    PreprocessGNAUnsupportedOutputsTest() : PreprocessGNAUnsupportedTest("The plugin does not support layer") {}
+};
+
+TEST_P(PreprocessGNATest, CompareWithRefs) {
+    run();
+}
+
+TEST_P(PreprocessGNAUnsupportedInputsTest, CompareWithRefs) {
+    run();
+}
+
+TEST_P(PreprocessGNAUnsupportedOutputsTest, CompareWithRefs) {
+    run();
+}
+
+std::map<std::string, std::string> config = {
+    {
+        {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
+        {"GNA_SCALE_FACTOR_0", "1"},
+        {"GNA_SCALE_FACTOR_1", "1"}
+    }
+};
+
+ov::element::TypeVector inputTypesUnsupported = {
+    ov::element::i32
+};
+
+ov::element::TypeVector outputTypesUnsupported = {
+    ov::element::u8,
+    ov::element::i16,
+};
+
+ov::element::TypeVector outputTypesSupported = {
+    ov::element::i32,
+    ov::element::f32
+};
+
+ov::element::TypeVector netTypes = {
+    ov::element::f16,
+    ov::element::f32
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Preprocess, PreprocessGNATest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::Values(ov::element::f32),
+                                ::testing::ValuesIn(outputTypesSupported),
+                                ::testing::Values(CommonTestUtils::DEVICE_GNA),
+                                ::testing::Values(config)),
+                         PreprocessGNATest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Preprocess, PreprocessGNAUnsupportedInputsTest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::ValuesIn(inputTypesUnsupported),
+                                ::testing::Values(ov::element::f32),
+                                ::testing::Values(CommonTestUtils::DEVICE_GNA),
+                                ::testing::Values(config)),
+                         PreprocessGNATest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Preprocess, PreprocessGNAUnsupportedOutputsTest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::Values(ov::element::f32),
+                                ::testing::ValuesIn(outputTypesUnsupported),
+                                ::testing::Values(CommonTestUtils::DEVICE_GNA),
+                                ::testing::Values(config)),
+                         PreprocessGNATest::getTestCaseName);
+
+} // namespace LayerTestsDefinitions
\ No newline at end of file
diff --git a/src/tests/unit/gna/ngraph/transformations/gna_remove_convert.cpp b/src/tests/unit/gna/ngraph/transformations/gna_remove_convert.cpp
new file mode 100644
index 00000000000..08b793756d4
--- /dev/null
+++ b/src/tests/unit/gna/ngraph/transformations/gna_remove_convert.cpp
@@ -0,0 +1,326 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include "ngraph_functions/builders.hpp"
+#include <transformations/init_node_info.hpp>
+#include "transformations/remove_converts.hpp"
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+namespace testing {
+
+typedef std::tuple<
+        ov::element::Type,     // Net precision
+        ov::element::Type      // Convert precision
+> removeConvertTestParams;
+
+class RemoveInputConvertTest: public CommonTestUtils::TestsCommon,
+                         public ::testing::WithParamInterface<removeConvertTestParams> {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<removeConvertTestParams>& obj) {
+        ov::element::Type net_precision, target_precision;
+        std::tie(net_precision, target_precision) = obj.param;
+
+        std::ostringstream result;
+        result << "netPRC=" << net_precision << "_";
+        result << "trgPRC=" << target_precision << "_";
+
+        return result.str();
+    }
+    void SetUp() override;
+    virtual void Validate();
+    virtual void Run();
+public:
+    std::shared_ptr<ngraph::Function> func_, ref_func_no_convert_, ref_func_convert_;
+    ov::element::Type net_precision_, target_precision_;
+};
+
+void RemoveInputConvertTest::Run() {
+    SetUp();
+    Validate();
+}
+
+void RemoveInputConvertTest::SetUp() {
+    const ngraph::Shape input_shape{10};
+
+    std::tie(net_precision_, target_precision_) = this->GetParam();
+
+    // test function
+    {
+        auto params = std::make_shared<ngraph::opset8::Parameter>(target_precision_, input_shape);
+        auto conversion = ngraph::builder::makeConversion(params, net_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+        auto add_const = ngraph::opset8::Constant::create(net_precision_, input_shape, {10});
+        auto add = std::make_shared<ngraph::opset8::Add>(conversion, add_const);
+
+        auto result = std::make_shared<ngraph::opset8::Result>(add);
+        func_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result},
+                                                       ngraph::ParameterVector{params},
+                                                       "Conversion");
+    }
+
+    // ref function convert should be removed
+    {
+        auto params = std::make_shared<ngraph::opset8::Parameter>(net_precision_, input_shape);
+        auto add_const = ngraph::opset8::Constant::create(net_precision_, input_shape, {10});
+        auto add = std::make_shared<ngraph::opset8::Add>(params, add_const);
+
+        auto result = std::make_shared<ngraph::opset8::Result>(add);
+        ref_func_no_convert_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result},
+                                                           ngraph::ParameterVector{params},
+                                                           "Conversion");
+    }
+
+    // ref function convert should not be removed
+    ref_func_convert_ = ngraph::clone_function(*func_);
+}
+
+void RemoveInputConvertTest::Validate() {
+    ngraph::pass::Manager m;
+    m.register_pass<ngraph::pass::InitNodeInfo>();
+    m.register_pass<GNAPluginNS::RemoveInputConvert>();
+    m.run_passes(func_);
+    ASSERT_NO_THROW(check_rt_info(func_));
+
+    const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::ATTRIBUTES);
+    FunctionsComparator::Result result;
+    if (std::count(GNAPluginNS::kSupportedInputConverts.begin(),
+                   GNAPluginNS::kSupportedInputConverts.end(),
+                   std::make_pair(target_precision_, net_precision_)) == 0) {
+        result = func_comparator(func_, ref_func_convert_);
+    } else {
+        result = func_comparator(func_, ref_func_no_convert_);
+    }
+
+    ASSERT_TRUE(result.valid);
+}
+
+class RemoveOutputConvertTest: public RemoveInputConvertTest {
+public:
+    void SetUp() override {
+        const ngraph::Shape input_shape{10};
+
+        std::tie(net_precision_, target_precision_) = this->GetParam();
+
+        // test function
+        {
+            auto params = std::make_shared<ngraph::opset8::Parameter>(net_precision_, input_shape);
+            auto add_const = ngraph::opset8::Constant::create(net_precision_, input_shape, {10});
+            auto add = std::make_shared<ngraph::opset8::Add>(params, add_const);
+            auto conversion = ngraph::builder::makeConversion(add, target_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+            auto result = std::make_shared<ngraph::opset8::Result>(conversion);
+            func_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result}, ngraph::ParameterVector{params},
+                                                       "Conversion");
+        }
+
+        // ref function
+        {
+            auto params = std::make_shared<ngraph::opset8::Parameter>(net_precision_, input_shape);
+            auto add_const = ngraph::opset8::Constant::create(net_precision_, input_shape, {10});
+            auto add = std::make_shared<ngraph::opset8::Add>(params, add_const);
+
+            auto result = std::make_shared<ngraph::opset8::Result>(add);
+            ref_func_no_convert_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result}, ngraph::ParameterVector{params},
+                                                                      "Conversion");
+        }
+
+        // ref function convert should not be removed
+        ref_func_convert_ = ngraph::clone_function(*func_);
+    }
+    void Validate() override {
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<GNAPluginNS::RemoveOutputConvert>();
+        m.run_passes(func_);
+        ASSERT_NO_THROW(check_rt_info(func_));
+
+        const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::ATTRIBUTES);
+            FunctionsComparator::Result result;
+        if (std::count(GNAPluginNS::kSupportedOutputConverts.begin(),
+                       GNAPluginNS::kSupportedOutputConverts.end(),
+                       std::make_pair(net_precision_, target_precision_)) == 0) {
+            result = func_comparator(func_, ref_func_convert_);
+        } else {
+            result = func_comparator(func_, ref_func_no_convert_);
+        }
+
+        ASSERT_TRUE(result.valid);
+    }
+};
+
+class LeaveConvertTest: public RemoveInputConvertTest {
+public:
+    void SetUp() override {
+        const ngraph::Shape input_shape{10};
+
+        std::tie(net_precision_, target_precision_) = this->GetParam();
+
+        // test function
+        {
+            auto params = std::make_shared<ngraph::opset8::Parameter>(net_precision_, input_shape);
+            auto add_const = ngraph::opset8::Constant::create(net_precision_, input_shape, {10});
+            auto add1 = std::make_shared<ngraph::opset8::Add>(params, add_const);
+            auto conversion = ngraph::builder::makeConversion(add1, net_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+            auto add2 = std::make_shared<ngraph::opset8::Add>(conversion, add_const);
+            auto result = std::make_shared<ngraph::opset8::Result>(add2);
+            func_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result}, ngraph::ParameterVector{params}, "Conversion");
+        }
+
+        // ref function
+        ref_func_convert_ = ngraph::clone_function(*func_);
+    }
+    void Validate() override {
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<GNAPluginNS::RemoveInputConvert>();
+        m.register_pass<GNAPluginNS::RemoveOutputConvert>();
+        m.run_passes(func_);
+        ASSERT_NO_THROW(check_rt_info(func_));
+
+        const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::ATTRIBUTES);
+        const FunctionsComparator::Result result = func_comparator(func_, ref_func_convert_);
+        ASSERT_TRUE(result.valid);
+    }
+};
+
+class RemoveMultiInputsConvertTest: public RemoveInputConvertTest {
+public:
+    void SetUp() override {
+        std::tie(net_precision_, target_precision_) = this->GetParam();
+        const ngraph::Shape input_shape{1, 10};
+
+        // test function
+        {
+            auto input = ngraph::builder::makeParams(target_precision_, {input_shape, input_shape, input_shape});
+            auto convert1 = ngraph::builder::makeConversion(input[0], net_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+            auto convert2 = ngraph::builder::makeConversion(input[1], net_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+            auto convert3 = ngraph::builder::makeConversion(input[2], net_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+            auto mul1 = ngraph::builder::makeEltwise(convert1, convert2, ngraph::helpers::EltwiseTypes::ADD);
+            auto mul2 = ngraph::builder::makeEltwise(convert3, mul1, ngraph::helpers::EltwiseTypes::ADD);
+            auto result = std::make_shared<ngraph::opset8::Result>(mul2);
+            func_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result}, input, "multiple_input");
+        }
+
+        // ref function
+        {
+            auto input = ngraph::builder::makeParams(net_precision_, {input_shape, input_shape, input_shape});
+            auto mul1 = ngraph::builder::makeEltwise(input[0], input[1], ngraph::helpers::EltwiseTypes::ADD);
+            auto mul2 = ngraph::builder::makeEltwise(input[2], mul1, ngraph::helpers::EltwiseTypes::ADD);
+            auto result = std::make_shared<ngraph::opset8::Result>(mul2);
+            ref_func_no_convert_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result}, input, "multiple_input");
+        }
+
+        // ref function convert should not be removed
+        ref_func_convert_ = ngraph::clone_function(*func_);
+    }
+};
+
+class RemoveMultiOutputsConvertTest: public RemoveOutputConvertTest {
+public:
+    void SetUp() override {
+        std::tie(net_precision_, target_precision_) = this->GetParam();
+        const ngraph::Shape input_shape{1, 10};
+        // test function
+        {
+            auto input = ngraph::builder::makeParams(net_precision_, {input_shape, input_shape, input_shape, input_shape});
+            auto mul1 = ngraph::builder::makeEltwise(input[0], input[1], ngraph::helpers::EltwiseTypes::ADD);
+            auto mul2 = ngraph::builder::makeEltwise(input[2], input[3], ngraph::helpers::EltwiseTypes::ADD);
+            auto convert1 = ngraph::builder::makeConversion(mul1, target_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+            auto convert2 = ngraph::builder::makeConversion(mul2, target_precision_, ngraph::helpers::ConversionTypes::CONVERT);
+            auto result1 = std::make_shared<ngraph::opset8::Result>(convert1);
+            auto result2 = std::make_shared<ngraph::opset8::Result>(convert2);
+
+            func_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result1, result2}, input, "multiple_output");
+        }
+
+        // ref function
+        {
+            auto input = ngraph::builder::makeParams(net_precision_, {input_shape, input_shape, input_shape, input_shape});
+            auto mul1 = ngraph::builder::makeEltwise(input[0], input[1], ngraph::helpers::EltwiseTypes::ADD);
+            auto mul2 = ngraph::builder::makeEltwise(input[2], input[3], ngraph::helpers::EltwiseTypes::ADD);
+            auto result1 = std::make_shared<ngraph::opset8::Result>(mul1);
+            auto result2 = std::make_shared<ngraph::opset8::Result>(mul2);
+
+            ref_func_no_convert_ = std::make_shared<ngraph::Function>(ngraph::ResultVector{result1, result2}, input, "multiple_output");
+        }
+
+        // ref function convert should not be removed
+        ref_func_convert_ = ngraph::clone_function(*func_);
+    }
+};
+
+ov::element::TypeVector netTypes = {
+    ov::element::f16,
+    ov::element::f32,
+    ov::element::i8,
+    ov::element::u8,
+    ov::element::i16,
+    ov::element::i32,
+    ov::element::i64
+};
+
+ov::element::TypeVector targetTypes = {
+    ov::element::f16,
+    ov::element::f32,
+    ov::element::i8,
+    ov::element::u8,
+    ov::element::i16,
+    ov::element::i32,
+    ov::element::i64
+};
+
+TEST_P(RemoveInputConvertTest, CompareWithRefs) {
+    Run();
+}
+
+TEST_P(RemoveOutputConvertTest, CompareWithRefs) {
+    Run();
+}
+
+TEST_P(LeaveConvertTest, CompareWithRefs) {
+    Run();
+}
+
+TEST_P(RemoveMultiInputsConvertTest, CompareWithRefs) {
+    Run();
+}
+
+TEST_P(RemoveMultiOutputsConvertTest, CompareWithRefs) {
+    Run();
+}
+
+INSTANTIATE_TEST_SUITE_P(TransformationTests, RemoveInputConvertTest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::ValuesIn(targetTypes)),
+                         RemoveInputConvertTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(TransformationTests, RemoveOutputConvertTest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::ValuesIn(targetTypes)),
+                         RemoveOutputConvertTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(TransformationTests, LeaveConvertTest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::ValuesIn(targetTypes)),
+                         LeaveConvertTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(TransformationTests, RemoveMultiInputsConvertTest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::ValuesIn(targetTypes)),
+                         RemoveMultiInputsConvertTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(TransformationTests, RemoveMultiOutputsConvertTest,
+                         ::testing::Combine(
+                                ::testing::ValuesIn(netTypes),
+                                ::testing::ValuesIn(targetTypes)),
+                         RemoveMultiOutputsConvertTest::getTestCaseName);
+} // namespace testing

From 8f908db61e35f8c88ae573d1bf6724e563d31223 Mon Sep 17 00:00:00 2001
From: Mikhail Nosov <mikhail.nosov@intel.com>
Date: Thu, 23 Dec 2021 01:19:28 +0300
Subject: [PATCH 099/160] [OV20] Set tensors infer req (#9158)

* Fix incomprehensible error message during layout conversion when layout rank doesn't match with shape rank

* Stash

* stash

* Memcpy implementation
Added tests

* Revert "Fix incomprehensible error message during layout conversion when layout rank doesn't match with shape rank"

This reverts commit 37064741b28e0539838456009bdde82d24ad6698.

* Fix clang-format and remove redundant headers

* Covered "cached" case (+ tested on Myriad)

* Apply review comments
Introduced 'applyBatchedBlob' function which allows override 'memcpy' on inferefnce time

* clang-format fix

* Added dynamic shape case

* - Review comments
- Deep copy of parameters/results for caching from cnnNetwork. Deep copy logic is moved to Utils
- Caching Tests: return correct inputs/outputs map after ImportNetwork mock call

* Reworked according to discussion

Also introduced 'SetBlobsImpl' which throws 'Not implemented' exception by default.
Template plugin updates internal '_batched_inputs' map

* Updated according to moved tests

* don't support 'memcpy' for ROI tensors

* Fix caching tests

* Just to retrigger CI

* Correct offset padding (however there is no test update as current implementation will not hit here due to other checks)

* Fix clang-format

* Applied review comments

* Added check that 'get_tensor' throws if set_tensors/set_input_tensors is used

* Fix review comments - part 1

* Fix caching tests - mock implementation becomes more complicated
Cached mock model shall identify its inputs/outputs, otherwise core will assert on SetExeNetworkInfo stage

* More comments fix

* More comments fixes

* More cleanup

* And more style comment

* typo fix

* Try fix caching windows tests

* Blind attempt to fix Ubuntu20 CI
---
 .../src/template_infer_request.cpp            |   8 +
 .../src/template_infer_request.hpp            |   2 +
 ...nfer_async_request_thread_safe_default.hpp |  10 +
 .../interface/ie_iinfer_request_internal.hpp  |  61 +++++-
 .../interface/ie_iplugin_internal.hpp         |  12 +-
 .../openvino/runtime/infer_request.hpp        |  48 ++++-
 src/inference/src/cpp/ie_infer_request.cpp    |  46 +++++
 .../interface/ie_iinfer_request_internal.cpp  | 183 ++++++++++++++++++
 .../interface/ie_iplugin_internal.cpp         |  13 +-
 src/inference/src/ie_core.cpp                 |   6 +
 .../inference_engine/caching_test.cpp         |  93 +++++----
 11 files changed, 443 insertions(+), 39 deletions(-)

diff --git a/docs/template_plugin/src/template_infer_request.cpp b/docs/template_plugin/src/template_infer_request.cpp
index 256f4b503a9..d466014cb3e 100644
--- a/docs/template_plugin/src/template_infer_request.cpp
+++ b/docs/template_plugin/src/template_infer_request.cpp
@@ -269,6 +269,7 @@ static void blobCopy(const Blob::Ptr& src, const Blob::Ptr& dst) {
 void TemplateInferRequest::inferPreprocess() {
     OV_ITT_SCOPED_TASK(itt::domains::TemplatePlugin, _profilingTask[Preprocess]);
     auto start = Time::now();
+    convertBatchedInputBlobs();
     // NOTE: After IInferRequestInternal::execDataPreprocessing call
     //       input can points to other memory region than it was allocated in constructor.
     IInferRequestInternal::execDataPreprocessing(_deviceInputs);
@@ -480,6 +481,7 @@ void TemplateInferRequest::SetBlob(const std::string& name, const InferenceEngin
             _inputs[name] = userBlob;
             devBlob = userBlob;
         }
+        _batched_inputs.erase(name);
     } else {
         if (compoundBlobPassed) {
             IE_THROW(NotImplemented) << "cannot set compound blob: supported only for input pre-processing";
@@ -513,6 +515,12 @@ void TemplateInferRequest::SetBlob(const std::string& name, const InferenceEngin
 }
 // ! [infer_request:set_blob]
 
+// ! [infer_request:set_blobs_impl]
+void TemplateInferRequest::SetBlobsImpl(const std::string& name, const InferenceEngine::BatchedBlob::Ptr& batchedBlob) {
+    _batched_inputs[name] = batchedBlob;
+}
+// ! [infer_request:set_blobs_impl]
+
 // ! [infer_request:get_performance_counts]
 std::map<std::string, InferenceEngineProfileInfo> TemplateInferRequest::GetPerformanceCounts() const {
     std::map<std::string, InferenceEngineProfileInfo> perfMap;
diff --git a/docs/template_plugin/src/template_infer_request.hpp b/docs/template_plugin/src/template_infer_request.hpp
index 12fa18ceea6..694df35ac9f 100644
--- a/docs/template_plugin/src/template_infer_request.hpp
+++ b/docs/template_plugin/src/template_infer_request.hpp
@@ -46,6 +46,8 @@ public:
     InferenceEngine::Blob::Ptr GetBlob(const std::string& name) override;
     void SetBlob(const std::string& name, const InferenceEngine::Blob::Ptr& userBlob) override;
 
+    void SetBlobsImpl(const std::string& name, const InferenceEngine::BatchedBlob::Ptr& batchedBlob) override;
+
 private:
     void createInferRequest();
     void allocateDeviceBuffers();
diff --git a/src/inference/dev_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp b/src/inference/dev_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp
index f6e63fcbcd7..2006e20c835 100644
--- a/src/inference/dev_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp
+++ b/src/inference/dev_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp
@@ -244,6 +244,16 @@ public:
         _syncRequest->SetBlob(name, data, info);
     }
 
+    void SetBlobs(const std::string& name, const std::vector<Blob::Ptr>& blobs) override {
+        CheckState();
+        _syncRequest->SetBlobs(name, blobs);
+    }
+
+    BatchedBlob::Ptr GetBlobs(const std::string& name) override {
+        CheckState();
+        return _syncRequest->GetBlobs(name);
+    }
+
     Blob::Ptr GetBlob(const std::string& name) override {
         CheckState();
         return _syncRequest->GetBlob(name);
diff --git a/src/inference/dev_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp b/src/inference/dev_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp
index ea7e117677c..c771c2bbc60 100644
--- a/src/inference/dev_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp
+++ b/src/inference/dev_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp
@@ -9,8 +9,8 @@
 #include <string>
 
 #include "cpp/ie_infer_request.hpp"
-#include "ie_blob.h"
 #include "ie_common.h"
+#include "ie_compound_blob.h"
 #include "ie_input_info.hpp"
 #include "ie_preprocess_data.hpp"
 #include "openvino/core/node_output.hpp"
@@ -87,6 +87,31 @@ public:
      */
     virtual void SetBlob(const std::string& name, const Blob::Ptr& data);
 
+    /**
+     * @brief Set batch of input data to infer. Default implementation performs basic validation and checks that all
+     * tensors are not remote. Plugin-specific implementations may override this behavior to handle remote tensors case.
+     * If plugin expects only memory blobs (not remote blobs), consider to override only SetBlobsImpl and reuse basic
+     * existing implementation
+     * @param name - an operation name of input or output blob.
+     * @param blobs - input blobs. The type of Blob must correspond to the model's input
+     * precision and size.
+     */
+    virtual void SetBlobs(const std::string& name, const std::vector<Blob::Ptr>& blobs);
+
+    /**
+     * @brief Set batch of input data to infer. Default implementation throws "Not implemented" exception
+     * To support 'set_input_tensors'/'set_tensors' plugin-specific implementations shall:
+     *  - Inside SetBlobsImpl: update 'InferenceEngine::IInferRequestInternal::batched_inputs' map
+     *  - Inside 'SetBlob': erase appropriate 'InferenceEngine::IInferRequestInternal::_batched_inputs[name]' item
+     *  - Inside 'InferImpl': call 'convertBatchedInputBlobs' on the beginning to convert many user blobs into single
+     * one
+     *  - If needed, override 'convertBatchedInputBlob' to perform custom concatenation and data copy to input blob
+     * @param name - an operation name of input or output blob.
+     * @param batched_blob - input blobs combined in batched blob. Called only if number of blobs > 1
+     * precision and size.
+     */
+    virtual void SetBlobsImpl(const std::string& name, const BatchedBlob::Ptr& batched_blob);
+
     /**
      * @brief Get input/output data to infer
      * @note Memory allocation doesn't happen
@@ -96,6 +121,14 @@ public:
      */
     virtual Blob::Ptr GetBlob(const std::string& name);
 
+    /**
+     * @brief Get input/output data to infer
+     * @note Memory allocation doesn't happen
+     * @param name - a name of input or output blob.
+     * @return data - a reference to input batched blob.
+     */
+    virtual BatchedBlob::Ptr GetBlobs(const std::string& name);
+
     /**
      * @brief Sets pre-process for input data
      * @param name Name of input blob.
@@ -232,6 +265,13 @@ protected:
      */
     bool findInputAndOutputBlobByName(const std::string& name, InputInfo::Ptr& foundInput, DataPtr& foundOutput) const;
 
+    /**
+     * @brief Concatenates _batched_inputs into single blob before inference
+     * It is expected that _batched_inputs map contains only valid BatchedBlob blobs with 2 or more blobs inside
+     * @throws Exception if error occurs
+     */
+    void convertBatchedInputBlobs();
+
     /**
      * @brief Checks whether pre-processing step is required for a given input
      * @param info InputInfo corresponding to input blob
@@ -245,6 +285,24 @@ protected:
 
     void addInputPreProcessingFor(const std::string& name, const Blob::Ptr& from, const Blob::Ptr& to);
 
+    /**
+     * @brief Performs actual concatenation of blobs into single tensor
+     * Default implementation may allocate memory for new blob containing user's input data
+     * Plugin is allowed to override this behavior
+     * @throws Exception if error occurs
+     */
+    virtual void convertBatchedInputBlob(const std::string& name,
+                                         const InferenceEngine::BatchedBlob::Ptr& batched_blob);
+
+    /**
+     * @brief Performs basic validation of user's blobs set via SetBlobs
+     * @note Plugin-specific implementations may call this function to performs basic validation inside 'SetBlobs'
+     * @param name - input name.
+     * @param blobs - input blobs. The type of Blob must correspond to the network input
+     * precision and size.
+     */
+    virtual void checkBlobsForBatch(const std::string& name, const std::vector<Blob::Ptr>& blobs);
+
     InferenceEngine::InputsDataMap _networkInputs;    //!< Holds information about network inputs info
     InferenceEngine::OutputsDataMap _networkOutputs;  //!< Holds information about network outputs data
     InferenceEngine::BlobMap _inputs;                 //!< A map of user passed blobs for network inputs
@@ -253,6 +311,7 @@ protected:
     std::vector<std::shared_ptr<const ov::Node>> _parameters;  //!< A vector of function inputs
     std::vector<std::shared_ptr<const ov::Node>> _results;     //!< A vector of function outputs
     std::map<std::string, PreProcessDataPtr> _preProcData;     //!< A map of pre-process data per input
+    std::map<std::string, BatchedBlob::Ptr> _batched_inputs;   //!< A map of user passed blobs for network inputs
     int m_curBatch = -1;                                       //!< Current batch value used in dynamic batching
 
     /**
diff --git a/src/inference/dev_api/cpp_interfaces/interface/ie_iplugin_internal.hpp b/src/inference/dev_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
index ad6690115be..39232e0ef86 100644
--- a/src/inference/dev_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
+++ b/src/inference/dev_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
@@ -84,6 +84,16 @@ INFERENCE_ENGINE_API_CPP(InputsDataMap) copyInfo(const InputsDataMap& networkInp
  */
 INFERENCE_ENGINE_API_CPP(OutputsDataMap) copyInfo(const OutputsDataMap& networkOutputs);
 
+/**
+ * @brief Set input and output information to executable network. This method is used to
+ * set additional information to InferenceEngine::IExecutableNetworkInternal created by device plugin.
+ * @param exeNetwork Executable network object
+ * @param function Model with initial execution info
+ */
+INFERENCE_ENGINE_API_CPP(void)
+SetExeNetworkInfo(const std::shared_ptr<IExecutableNetworkInternal>& exeNetwork,
+                  const std::shared_ptr<const ov::Model>& function);
+
 /**
  * @interface IInferencePlugin
  * @brief An API of plugin to be implemented by a plugin
@@ -314,7 +324,7 @@ protected:
      * @param function Function with initial execution info
      */
     void SetExeNetworkInfo(const std::shared_ptr<IExecutableNetworkInternal>& exeNetwork,
-                           const std::shared_ptr<ov::Model>& function);
+                           const std::shared_ptr<const ov::Model>& function);
 
     std::string _pluginName;                     //!< A device name that plugins enables
     std::map<std::string, std::string> _config;  //!< A map config keys -> values
diff --git a/src/inference/include/openvino/runtime/infer_request.hpp b/src/inference/include/openvino/runtime/infer_request.hpp
index 702a6dead8e..c10245fc754 100644
--- a/src/inference/include/openvino/runtime/infer_request.hpp
+++ b/src/inference/include/openvino/runtime/infer_request.hpp
@@ -102,6 +102,30 @@ public:
      */
     void set_tensor(const ov::Output<ov::Node>& port, const Tensor& tensor);
 
+    /**
+     * @brief Sets batch of tensors for input data to infer by input name
+     * Model input shall have batch dimension and number of 'tensors' shall match with batch size
+     * Current version supports set tensors to model inputs only. In case if `name` is associated
+     * with output (or any other non-input node) - exception will be thrown
+     *
+     * @param name Name of input tensor.
+     * @param tensors Input tensors for batched infer request. The type of each tensor must match the model
+     * input precision and size (except batch dimension). Total size of tensors shall match with input's size
+     */
+    void set_tensors(const std::string& name, const std::vector<Tensor>& tensors);
+
+    /**
+     * @brief Sets batch of tensors for input data to infer by input name
+     * Model input shall have batch dimension and number of 'tensors' shall match with batch size
+     * Current version supports set tensors to model inputs only. In case if `name` is associated
+     * with output (or any other non-input node) - exception will be thrown
+     *
+     * @param port Port of input tensor.
+     * @param tensors Input tensors for batched infer request. The type of each tensor must match the model
+     * input precision and size (except batch dimension). Total size of tensors shall match with input's size
+     */
+    void set_tensors(const ov::Output<const ov::Node>& port, const std::vector<Tensor>& tensors);
+
     /**
      * @brief Sets input tensor to infer
      *
@@ -109,21 +133,41 @@ public:
      * @param tensor Reference to input tensor. The type of a tensor must match the model input precision and size.
      */
     void set_input_tensor(size_t idx, const Tensor& tensor);
+
     /**
      * @brief Sets input tensor to infer models with single input
      *
      * @param tensor Reference to input tensor. If model has several inputs, an exception is thrown.
      */
-
     void set_input_tensor(const Tensor& tensor);
+
+    /**
+     * @brief Sets batch of tensors for single input data
+     * Model input shall have batch dimension and number of 'tensors' shall match with batch size
+     *
+     * @param tensors Input tensors for batched infer request. The type of each tensor must match the model
+     * input precision and size (except batch dimension). Total size of tensors shall match with input's size
+     */
+    void set_input_tensors(const std::vector<Tensor>& tensors);
+
+    /**
+     * @brief Sets batch of tensors for input data to infer by input name
+     * Model input shall have batch dimension and number of 'tensors' shall match with batch size
+     *
+     * @param idx Name of input tensor.
+     * @param tensors Input tensors for batched infer request. The type of each tensor must match the model
+     * input precision and size (except batch dimension). Total size of tensors shall match with input's size
+     */
+    void set_input_tensors(size_t idx, const std::vector<Tensor>& tensors);
+
     /**
      * @brief Sets output tensor to infer
      *
      * @param idx Index of output tensor.
      * @param tensor Reference to output tensor. The type of a tensor must match the model output precision and size.
      */
-
     void set_output_tensor(size_t idx, const Tensor& tensor);
+
     /**
      * @brief Sets output tensor to infer models with single output
      *
diff --git a/src/inference/src/cpp/ie_infer_request.cpp b/src/inference/src/cpp/ie_infer_request.cpp
index 3377afb6435..ee1479be8cc 100644
--- a/src/inference/src/cpp/ie_infer_request.cpp
+++ b/src/inference/src/cpp/ie_infer_request.cpp
@@ -272,6 +272,25 @@ void InferRequest::set_tensor(const std::string& name, const Tensor& tensor) {
     });
 }
 
+void InferRequest::set_tensors(const std::string& name, const std::vector<Tensor>& tensors) {
+    OV_INFER_REQ_CALL_STATEMENT({
+        ov::Output<const ov::Node> port;
+        OPENVINO_ASSERT(::getPort(port, name, {_impl->GetInputs()}),
+                        "set_tensors error. Input port for tensor name ",
+                        name,
+                        " was not found.");
+        set_tensors(port, tensors);
+    })
+}
+
+void InferRequest::set_tensors(const ov::Output<const ov::Node>& port, const std::vector<Tensor>& tensors) {
+    auto impls = std::vector<InferenceEngine::Blob::Ptr>();
+    std::transform(tensors.begin(), tensors.end(), std::back_inserter(impls), [](const Tensor& item) {
+        return item._impl;
+    });
+    OV_INFER_REQ_CALL_STATEMENT({ _impl->SetBlobs(get_legacy_name_from_port(port), impls); })
+}
+
 void InferRequest::set_input_tensor(size_t idx, const Tensor& tensor) {
     OV_INFER_REQ_CALL_STATEMENT({
         const auto& inputs = _impl->GetInputs();
@@ -294,6 +313,28 @@ void InferRequest::set_input_tensor(const Tensor& tensor) {
     });
 }
 
+void InferRequest::set_input_tensors(size_t idx, const std::vector<Tensor>& tensors) {
+    OV_INFER_REQ_CALL_STATEMENT({
+        OPENVINO_ASSERT(idx < _impl->GetInputs().size(),
+                        "set_input_tensors error. Input port for index ",
+                        idx,
+                        " is out of bounds. Model has only ",
+                        _impl->GetInputs().size(),
+                        " inputs");
+        set_tensors(_impl->GetInputs().at(idx)->output(0), tensors);
+    })
+}
+
+void InferRequest::set_input_tensors(const std::vector<Tensor>& tensors) {
+    OV_INFER_REQ_CALL_STATEMENT({
+        OPENVINO_ASSERT(_impl->GetInputs().size() == 1,
+                        "set_input_tensors(tensors) must be used for single-input models only. Model has ",
+                        _impl->GetInputs().size(),
+                        " inputs");
+        set_tensors(_impl->GetInputs().at(0)->output(0), tensors);
+    })
+}
+
 void InferRequest::set_output_tensor(size_t idx, const Tensor& tensor) {
     OV_INFER_REQ_CALL_STATEMENT({
         const auto& outputs = _impl->GetOutputs();
@@ -319,6 +360,11 @@ void InferRequest::set_output_tensor(const Tensor& tensor) {
 Tensor InferRequest::get_tensor(const ov::Output<const ov::Node>& port) {
     OV_INFER_REQ_CALL_STATEMENT({
         const auto& name = get_legacy_name_from_port(port);
+        OPENVINO_ASSERT(!_impl->GetBlobs(name),
+                        "get_tensor shall not be used together with batched "
+                        "set_tensors/set_input_tensors for name '",
+                        name,
+                        "'");
         auto blob = _impl->GetBlob(name);
         return {blob, _so};
     });
diff --git a/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
index 62ba6521932..f0024ed5c2d 100644
--- a/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
+++ b/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
@@ -9,6 +9,7 @@
 #include <openvino/core/partial_shape.hpp>
 #include <string>
 
+#include "cpp_interfaces/interface/ie_iexecutable_network_internal.hpp"
 #include "cpp_interfaces/interface/ie_iplugin_internal.hpp"
 #include "cpp_interfaces/plugin_itt.hpp"
 #include "debug.h"
@@ -135,6 +136,7 @@ void IInferRequestInternal::SetBlob(const std::string& name, const Blob::Ptr& us
             _inputs[name] = userBlob;
             devBlob = userBlob;
         }
+        _batched_inputs.erase(name);
     } else {
         if (compoundBlobPassed) {
             IE_THROW(NotImplemented) << "cannot set compound blob: supported only for input pre-processing";
@@ -159,6 +161,180 @@ void IInferRequestInternal::SetBlob(const std::string& name, const Blob::Ptr& us
     }
 }
 
+void IInferRequestInternal::SetBlobs(const std::string& name, const std::vector<Blob::Ptr>& blobs) {
+    if (blobs.size() == 1) {
+        SetBlob(name, blobs[0]);
+        return;
+    }
+
+    bool all_memory = std::all_of(blobs.begin(), blobs.end(), [](const Blob::Ptr& item) {
+        return item && item->is<MemoryBlob>() && !item->is<RemoteBlob>();
+    });
+    OPENVINO_ASSERT(all_memory,
+                    "set_input_tensors/set_tensors error. Default implementation support only local memory tensors");
+
+    checkBlobsForBatch(name, blobs);
+
+    SetBlobsImpl(name, std::make_shared<BatchedBlob>(blobs));
+}
+
+void IInferRequestInternal::SetBlobsImpl(const std::string& name, const BatchedBlob::Ptr& batched_blob) {
+    IE_THROW(NotImplemented) << "set_input_tensors/set_tensors are not supported by this plugin";
+}
+
+void IInferRequestInternal::checkBlobsForBatch(const std::string& name, const std::vector<Blob::Ptr>& blobs) {
+    OPENVINO_ASSERT(!blobs.empty(),
+                    "set_input_tensors/set_tensors can't be called with empty blobs for input '",
+                    name,
+                    "'");
+    OPENVINO_ASSERT(blobs.size() != 1,
+                    "Internal error (plugin): checkBlobsForBatch is not allowed to have only one blob inside batch "
+                    "for input '",
+                    name,
+                    "'");
+
+    std::shared_ptr<const ov::op::v0::Parameter> param;
+    const auto& inputs = GetInputs();
+    for (const auto& input : inputs) {
+        if (auto p = std::dynamic_pointer_cast<const ov::op::v0::Parameter>(input)) {
+            if (name == p->get_friendly_name()) {
+                param = p;
+                break;
+            }
+        }
+    }
+    OPENVINO_ASSERT(param, "set_input_tensors/set_tensors error. Parameter '", name, "' is not found");
+    OPENVINO_ASSERT(ov::layout::has_batch(param->get_layout()),
+                    "set_input_tensors/set_tensors can be used only for inputs with N(batch) dimension"
+                    " 'layout' defined. Current layout for '",
+                    name,
+                    "' is ",
+                    param->get_layout().to_string());
+    auto batch_idx = ov::layout::batch_idx(param->get_layout());
+    if (batch_idx < 0) {
+        batch_idx += static_cast<int64_t>(blobs[0]->getTensorDesc().getDims().size());
+    }
+    OPENVINO_ASSERT(batch_idx == 0,
+                    "set_input_tensors/set_tensors is not currently supported for batch dimension index ",
+                    batch_idx,
+                    " != 0");
+    std::for_each(blobs.begin(), blobs.end(), [&batch_idx](const Blob::Ptr& item) {
+        OPENVINO_ASSERT(item->getTensorDesc().getDims()[batch_idx] == 1,
+                        "set_input_tensors/set_tensors. Tensors shall represent one item in a batch, ",
+                        item->getTensorDesc().getDims()[batch_idx],
+                        " provided");
+    });
+    auto blobs_size = static_cast<int>(blobs.size());
+    if (param->get_partial_shape().rank().is_static()) {
+        OPENVINO_ASSERT(batch_idx >= 0 && batch_idx < param->get_partial_shape().rank().get_length(),
+                        "set_input_tensors/set_tensors error. Layout ",
+                        param->get_layout().to_string(),
+                        " is incorrect for operation with name '",
+                        name,
+                        "' with shape ",
+                        param->get_partial_shape());
+        auto batch = param->get_partial_shape()[batch_idx];
+
+        OPENVINO_ASSERT(batch.is_dynamic() || batch.get_length() == blobs_size,
+                        "set_input_tensors/set_tensors error. Input shape ",
+                        param->get_partial_shape(),
+                        "batch ",
+                        batch,
+                        "doesn't match with total blobs count: ",
+                        blobs_size);
+    }
+
+    // In future consider checking if blobs point to contiguous range of memory and use single 'SetBlob' instead
+    auto tmp_desc = blobs[0]->getTensorDesc();
+    tmp_desc.getDims()[batch_idx] = blobs_size;
+    auto blockingDims = tmp_desc.getBlockingDesc().getBlockDims();
+    blockingDims[batch_idx] = blobs_size;
+    auto blockingDesc = BlockingDesc(blockingDims, tmp_desc.getBlockingDesc().getOrder());
+    auto batched_desc = InferenceEngine::TensorDesc(tmp_desc.getPrecision(), tmp_desc.getDims(), blockingDesc);
+    auto desc_to_string = [](const TensorDesc& desc) {
+        std::stringstream s;
+        s << "{ " << desc.getLayout() << " " << desc.getPrecision().name();
+        s << "dim=(";
+        for (const auto& d : desc.getDims()) {
+            s << " " << d;
+        }
+        s << " ) }";
+        return s.str();
+    };
+    for (const auto& item : blobs) {
+        auto item_desc = item->getTensorDesc();
+        item_desc.getDims()[batch_idx] = batched_desc.getDims()[batch_idx];
+        OPENVINO_ASSERT(item_desc.getDims() == batched_desc.getDims() &&
+                            item_desc.getLayout() == batched_desc.getLayout() &&
+                            item_desc.getPrecision() == batched_desc.getPrecision() &&
+                            item_desc.getBlockingDesc().getOrder() == batched_desc.getBlockingDesc().getOrder(),
+                        "set_input_tensors/set_tensors error. Blob ",
+                        desc_to_string(item_desc),
+                        " is not compatible with batched blob ",
+                        desc_to_string(batched_desc));
+    }
+}
+
+void IInferRequestInternal::convertBatchedInputBlob(const std::string& name, const BatchedBlob::Ptr& batched_blob) {
+    auto tmp_desc = batched_blob->getBlob(0)->getTensorDesc();
+    tmp_desc.getDims()[0] = batched_blob->size();
+    auto blockingDims = tmp_desc.getBlockingDesc().getBlockDims();
+    blockingDims[0] = batched_blob->size();
+    auto blockingDesc = BlockingDesc(blockingDims, tmp_desc.getBlockingDesc().getOrder());
+    auto batched_desc = InferenceEngine::TensorDesc(tmp_desc.getPrecision(), tmp_desc.getDims(), blockingDesc);
+    std::shared_ptr<RemoteContext> remote_context;
+    MemoryBlob::Ptr mem_blob;
+    try {
+        auto net = getPointerToExecutableNetworkInternal();
+        if (net) {
+            remote_context = net->GetContext();
+        }
+    } catch (const NotImplemented&) {
+    }
+    if (remote_context) {
+        mem_blob = remote_context->CreateHostBlob(batched_desc);
+    } else {
+        mem_blob = std::dynamic_pointer_cast<MemoryBlob>(make_blob_with_precision(batched_desc));
+    }
+    OPENVINO_ASSERT(mem_blob, "Internal error - can't create host memory blob");
+    mem_blob->allocate();
+    auto ptr = mem_blob->wmap();
+
+    // Perform memory copy
+    for (size_t i = 0; i < batched_blob->size(); i++) {
+        const auto& blob = as<MemoryBlob>(batched_blob->getBlob(i));
+        OPENVINO_ASSERT(mem_blob, "Internal error - can't cast blob ", i, " to MemoryBlob");
+        const auto& blob_desc = blob->getTensorDesc().getBlockingDesc();
+        bool offsets_0 = std::all_of(blob_desc.getOffsetPaddingToData().begin(),
+                                     blob_desc.getOffsetPaddingToData().end(),
+                                     [](size_t dim) {
+                                         return dim == 0;
+                                     });
+        OPENVINO_ASSERT(offsets_0,
+                        "set_tensors/set_input_tensors - default combining is not supported for "
+                        "ROI tensors. All tensors offsets shall be 0");
+        OPENVINO_ASSERT(mem_blob->getTensorDesc().getBlockingDesc().getOrder() == blob_desc.getOrder(),
+                        "set_tensors/set_input_tensors - default combining is not supported for "
+                        "ROI tensors. Axis order shall be default");
+        OPENVINO_ASSERT(mem_blob->getTensorDesc().getBlockingDesc().getStrides() == blob_desc.getStrides(),
+                        "set_tensors/set_input_tensors - default combining is not supported for "
+                        "ROI tensors. Input blobs shall have default strides set");
+        memcpy(ptr.as<uint8_t*>() + i * blob->byteSize(),
+               blob->rmap().as<uint8_t*>() +
+                   blob->getTensorDesc().getBlockingDesc().getOffsetPadding() * blob->element_size(),
+               blob->byteSize());
+    }
+    SetBlob(name, mem_blob);
+}
+
+void IInferRequestInternal::convertBatchedInputBlobs() {
+    auto batched_copy = _batched_inputs;
+    for (const auto& item : batched_copy) {
+        convertBatchedInputBlob(item.first, item.second);
+    }
+    _batched_inputs = batched_copy;
+}
+
 Blob::Ptr IInferRequestInternal::GetBlob(const std::string& name) {
     OV_ITT_SCOPED_TASK(itt::domains::Plugin, "GetBlob");
     Blob::Ptr data;
@@ -201,6 +377,13 @@ Blob::Ptr IInferRequestInternal::GetBlob(const std::string& name) {
     return data;
 }
 
+BatchedBlob::Ptr IInferRequestInternal::GetBlobs(const std::string& name) {
+    if (_batched_inputs.count(name)) {
+        return _batched_inputs.at(name);
+    }
+    return nullptr;
+}
+
 void IInferRequestInternal::SetBlob(const std::string& name, const Blob::Ptr& data, const PreProcessInfo& info) {
     InputInfo::Ptr foundInput;
     DataPtr foundOutput;
diff --git a/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
index 945f06ef6e8..dd0a395f9ea 100644
--- a/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
+++ b/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
@@ -285,7 +285,13 @@ void IInferencePlugin::SetExeNetworkInfo(const std::shared_ptr<IExecutableNetwor
 }
 
 void IInferencePlugin::SetExeNetworkInfo(const std::shared_ptr<IExecutableNetworkInternal>& exeNetwork,
-                                         const std::shared_ptr<ov::Model>& function) {
+                                         const std::shared_ptr<const ov::Model>& function) {
+    InferenceEngine::SetExeNetworkInfo(exeNetwork, function);
+    exeNetwork->SetPointerToPlugin(shared_from_this());
+}
+
+void SetExeNetworkInfo(const std::shared_ptr<IExecutableNetworkInternal>& exeNetwork,
+                       const std::shared_ptr<const ov::Model>& function) {
     OPENVINO_ASSERT(exeNetwork != nullptr);
     OPENVINO_ASSERT(function != nullptr);
 
@@ -317,6 +323,7 @@ void IInferencePlugin::SetExeNetworkInfo(const std::shared_ptr<IExecutableNetwor
         // after transformation pipeline is run
         new_param->set_element_type(
             InferenceEngine::details::convertPrecision(inputsInfo.at(new_param->get_friendly_name())->getPrecision()));
+        new_param->set_layout(param->get_layout());
         new_param->validate_and_infer_types();
         const_params.emplace_back(new_param);
     }
@@ -333,12 +340,14 @@ void IInferencePlugin::SetExeNetworkInfo(const std::shared_ptr<IExecutableNetwor
         if (add_operation_names) {
             new_result->output(0).get_tensor().add_names({fake_param->get_friendly_name()});
         }
+        auto r = std::dynamic_pointer_cast<ov::op::v0::Result>(new_result);
+        OPENVINO_ASSERT(r, "Internal error. SetNetworkInfo failure casting output copy to Result");
+        r->set_layout(result->get_layout());
         const_results.emplace_back(new_result);
     }
 
     exeNetwork->setInputs(const_params);
     exeNetwork->setOutputs(const_results);
-    exeNetwork->SetPointerToPlugin(shared_from_this());
 }
 
 }  //  namespace InferenceEngine
diff --git a/src/inference/src/ie_core.cpp b/src/inference/src/ie_core.cpp
index ed56b123dac..13987458312 100644
--- a/src/inference/src/ie_core.cpp
+++ b/src/inference/src/ie_core.cpp
@@ -498,6 +498,9 @@ public:
             res = LoadNetworkFromCache(cacheManager, hash, plugin, parsed._config, context, loadedFromCache);
             if (!loadedFromCache) {
                 res = compile_model_impl(network, plugin, parsed._config, context, hash);
+            } else {
+                // Temporary workaround until all plugins support caching of original model inputs
+                InferenceEngine::SetExeNetworkInfo(res._ptr, network.getFunction());
             }
         } else {
             res = compile_model_impl(network, plugin, parsed._config, context, {});
@@ -525,6 +528,9 @@ public:
             res = LoadNetworkFromCache(cacheManager, hash, plugin, parsed._config, nullptr, loadedFromCache);
             if (!loadedFromCache) {
                 res = compile_model_impl(network, plugin, parsed._config, nullptr, hash, {}, forceDisableCache);
+            } else {
+                // Temporary workaround until all plugins support caching of original model inputs
+                InferenceEngine::SetExeNetworkInfo(res._ptr, network.getFunction());
             }
         } else {
             res = compile_model_impl(network, plugin, parsed._config, nullptr, {}, {}, forceDisableCache);
diff --git a/src/tests/functional/inference_engine/caching_test.cpp b/src/tests/functional/inference_engine/caching_test.cpp
index b6dba72f66c..1821d614de7 100644
--- a/src/tests/functional/inference_engine/caching_test.cpp
+++ b/src/tests/functional/inference_engine/caching_test.cpp
@@ -119,6 +119,7 @@ public:
 
 class MockExecutableNetwork : public IExecutableNetworkInternal {
     std::mutex m_pluginMutex;
+    std::shared_ptr<const ov::Model> m_model = nullptr;
 
 public:
     MockExecutableNetwork() {}
@@ -138,6 +139,9 @@ public:
     //     IExecutableNetworkInternal::Export(networkModel);
     // }
 
+    void set_model(const std::shared_ptr<const ov::Model>& model) { m_model = model; }
+    const std::shared_ptr<const ov::Model>& get_model() const { return m_model; }
+
     void SetPointerToPlugin(const IInferencePlugin::Ptr& plugin) override {
         std::lock_guard<std::mutex> guard(m_pluginMutex);
         IExecutableNetworkInternal::SetPointerToPlugin(plugin);
@@ -188,6 +192,8 @@ public:
     bool                        m_remoteContext = false;
     using CNNCallback = std::function<void(CNNNetwork&)>;
     CNNCallback                 m_cnnCallback = nullptr;
+    std::map<std::string, InputsDataMap> m_inputs_map;
+    std::map<std::string, OutputsDataMap> m_outputs_map;
 
     static std::string get_mock_engine_name() {
         std::string mockEngineName("mock_engine");
@@ -221,8 +227,9 @@ public:
         m_dirCreator = std::unique_ptr<MkDirGuard>(new MkDirGuard(m_cacheDir));
     }
 
-    static std::shared_ptr<MockExecutableNetwork> createMockIExecutableNet(const InputsDataMap& inputs_map = {},
-                                                                           const OutputsDataMap& outputs_map = {}) {
+    static std::shared_ptr<MockExecutableNetwork> createMockIExecutableNet(const std::string& name,
+                                                                           const InputsDataMap& inputs_map,
+                                                                           const OutputsDataMap& outputs_map) {
         auto mock = std::make_shared<MockExecutableNetwork>();
         ConstInputsDataMap inputMap;
         for (const auto &input_item : inputs_map) {
@@ -261,6 +268,12 @@ public:
                 }));
         EXPECT_CALL(*mock, setNetworkInputs(_)).Times(AnyNumber());
         EXPECT_CALL(*mock, setNetworkOutputs(_)).Times(AnyNumber());
+        mock->setNetworkInputs(copyInfo(inputs_map));
+        mock->setNetworkOutputs(copyInfo(outputs_map));
+        ON_CALL(*mock, Export(_)).WillByDefault(Invoke([name] (std::ostream& s) {
+            s << name;
+            s << ' ';
+        }));
         return mock;
     }
 
@@ -276,6 +289,8 @@ public:
     }
 
     void TearDown() override {
+        m_inputs_map = {};
+        m_outputs_map = {};
         for (const auto& net : networks) {
             EXPECT_TRUE(Mock::VerifyAndClearExpectations(net.get()));
         }
@@ -367,21 +382,35 @@ private:
         ON_CALL(plugin, ImportNetwork(_, _, _)).
                 WillByDefault(Invoke([&](std::istream &istr, const RemoteContext::Ptr&,
                                          const std::map<std::string, std::string> &) {
+            std::string name;
+            istr >> name;
+            char space;
+            istr.read(&space, 1);
             std::lock_guard<std::mutex> lock(mock_creation_mutex);
-            return createMockIExecutableNet();
+            return createMockIExecutableNet({}, m_inputs_map[name], m_outputs_map[name]);
         }));
 
         ON_CALL(plugin, ImportNetwork(_, _)).
                 WillByDefault(Invoke([&](std::istream &istr, const std::map<std::string, std::string> &) {
+            std::string name;
+            istr >> name;
+            char space;
+            istr.read(&space, 1);
             std::lock_guard<std::mutex> lock(mock_creation_mutex);
-            return createMockIExecutableNet();
+            return createMockIExecutableNet({}, m_inputs_map[name], m_outputs_map[name]);
         }));
 
         ON_CALL(plugin, LoadExeNetworkImpl(_, _, _)).
                 WillByDefault(Invoke([&](const CNNNetwork & cnn, const RemoteContext::Ptr&,
                                          const std::map<std::string, std::string> &) {
             std::lock_guard<std::mutex> lock(mock_creation_mutex);
-            auto exe_net = createMockIExecutableNet(cnn.getInputsInfo(), cnn.getOutputsInfo());
+            std::string name = cnn.getFunction()->get_friendly_name();
+            m_inputs_map[name] = cnn.getInputsInfo();
+            m_outputs_map[name] = cnn.getOutputsInfo();
+            auto exe_net = createMockIExecutableNet(cnn.getFunction()->get_friendly_name(),
+                                                    m_inputs_map[name],
+                                                    m_outputs_map[name]);
+            exe_net->set_model(cnn.getFunction());
             for (const auto& cb : m_post_mock_net_callbacks) {
                 cb(*exe_net);
             }
@@ -392,8 +421,14 @@ private:
         ON_CALL(plugin, LoadExeNetworkImpl(_, _)).
                 WillByDefault(Invoke([&](const CNNNetwork & cnn,
                                          const std::map<std::string, std::string> &) {
+            std::string name = cnn.getFunction()->get_friendly_name();
             std::lock_guard<std::mutex> lock(mock_creation_mutex);
-            auto exe_net = createMockIExecutableNet(cnn.getInputsInfo(), cnn.getOutputsInfo());
+            m_inputs_map[name] = cnn.getInputsInfo();
+            m_outputs_map[name] = cnn.getOutputsInfo();
+            auto exe_net = createMockIExecutableNet(cnn.getFunction()->get_friendly_name(),
+                                                    m_inputs_map[name],
+                                                    m_outputs_map[name]);
+            exe_net->set_model(cnn.getFunction());
             for (const auto& cb : m_post_mock_net_callbacks) {
                 cb(*exe_net);
             }
@@ -473,10 +508,10 @@ TEST_P(CachingTest, TestLoadCustomImportExport) {
         char a[sizeof(customData)];
         s.read(a, sizeof(customData));
         EXPECT_EQ(memcmp(a, customData, sizeof(customData)), 0);
-        auto mock = std::make_shared<MockExecutableNetwork>();
-        EXPECT_CALL(*mock, GetInputsInfo()).Times(AnyNumber()).WillRepeatedly(Return(ConstInputsDataMap{}));
-        EXPECT_CALL(*mock, GetOutputsInfo()).Times(AnyNumber()).WillRepeatedly(Return(ConstOutputsDataMap{}));
-        return mock;
+        std::string name;
+        s >> name;
+        std::lock_guard<std::mutex> lock(mock_creation_mutex);
+        return createMockIExecutableNet({}, m_inputs_map[name], m_outputs_map[name]);
     }));
 
     ON_CALL(*mockPlugin, ImportNetwork(_, _)).
@@ -484,15 +519,16 @@ TEST_P(CachingTest, TestLoadCustomImportExport) {
         char a[sizeof(customData)];
         s.read(a, sizeof(customData));
         EXPECT_EQ(memcmp(a, customData, sizeof(customData)), 0);
-        auto mock = std::make_shared<MockExecutableNetwork>();
-        EXPECT_CALL(*mock, GetInputsInfo()).Times(AnyNumber()).WillRepeatedly(Return(ConstInputsDataMap{}));
-        EXPECT_CALL(*mock, GetOutputsInfo()).Times(AnyNumber()).WillRepeatedly(Return(ConstOutputsDataMap{}));
-        return mock;
+        std::string name;
+        s >> name;
+        std::lock_guard<std::mutex> lock(mock_creation_mutex);
+        return createMockIExecutableNet({}, m_inputs_map[name], m_outputs_map[name]);
     }));
 
     m_post_mock_net_callbacks.emplace_back([&](MockExecutableNetwork& net) {
         ON_CALL(net, Export(_)).WillByDefault(Invoke([&] (std::ostream& s) {
             s.write(customData, sizeof(customData));
+            s << net.get_model()->get_friendly_name();
         }));
     });
 
@@ -1253,6 +1289,7 @@ TEST_P(CachingTest, LoadHetero_NoCacheMetric) {
         testLoad([&](Core &ie) {
             ie.SetConfig({{CONFIG_KEY(CACHE_DIR), m_cacheDir}});
             m_testFunction(ie);
+            networks.clear();
         });
     }
 }
@@ -1291,6 +1328,7 @@ TEST_P(CachingTest, LoadHetero_OneDevice) {
         testLoad([&](Core &ie) {
             ie.SetConfig({{CONFIG_KEY(CACHE_DIR), m_cacheDir}});
             m_testFunction(ie);
+            networks.clear();
         });
     }
 }
@@ -1331,29 +1369,13 @@ TEST_P(CachingTest, LoadHetero_TargetFallbackFromCore) {
             ie.SetConfig({{CONFIG_KEY(CACHE_DIR), m_cacheDir}});
             ie.SetConfig({{"TARGET_FALLBACK", "mock"}}, CommonTestUtils::DEVICE_HETERO);
             m_testFunction(ie);
+            networks.clear();
         });
     }
 }
 
 TEST_P(CachingTest, LoadHetero_MultiArchs) {
     EXPECT_CALL(*mockPlugin, GetMetric(_, _)).Times(AnyNumber());
-    const char customData[] = {1, 2, 3, 4, 5};
-    ON_CALL(*mockPlugin, ImportNetwork(_, _)).
-            WillByDefault(Invoke([&](std::istream &s, const std::map<std::string, std::string> &) {
-        char a[sizeof(customData)];
-        s.read(a, sizeof(customData));
-        EXPECT_EQ(memcmp(a, customData, sizeof(customData)), 0);
-        auto mock = std::make_shared<MockExecutableNetwork>();
-        EXPECT_CALL(*mock, GetInputsInfo()).Times(AnyNumber()).WillRepeatedly(Return(ConstInputsDataMap{}));
-        EXPECT_CALL(*mock, GetOutputsInfo()).Times(AnyNumber()).WillRepeatedly(Return(ConstOutputsDataMap{}));
-        return mock;
-    }));
-
-    m_post_mock_net_callbacks.emplace_back([&](MockExecutableNetwork& net) {
-        ON_CALL(net, Export(_)).WillByDefault(Invoke([&] (std::ostream& s) {
-            s.write(customData, sizeof(customData));
-        }));
-    });
 
     EXPECT_CALL(*mockPlugin, QueryNetwork(_, _)).Times(AnyNumber()).WillRepeatedly(
             Invoke([&](const CNNNetwork &network, const std::map<std::string, std::string> &config) {
@@ -1429,6 +1451,7 @@ TEST_P(CachingTest, LoadHetero_MultiArchs) {
         testLoad([&](Core &ie) {
             ie.SetConfig({{CONFIG_KEY(CACHE_DIR), m_cacheDir}});
             m_testFunction(ie);
+            networks.clear();
         });
     }
 }
@@ -1490,6 +1513,7 @@ TEST_P(CachingTest, LoadHetero_MultiArchs_TargetFallback_FromCore) {
             ie.SetConfig({{"TARGET_FALLBACK", "mock.51"}}, CommonTestUtils::DEVICE_HETERO);
             ie.SetConfig({{CONFIG_KEY(CACHE_DIR), m_cacheDir}});
             m_testFunction(ie);
+            networks.clear();
         });
     }
 }
@@ -1602,9 +1626,11 @@ TEST_P(CachingTest, LoadMulti_Archs) {
 
         EXPECT_CALL(*mockPlugin, ImportNetwork(_, _, _)).Times(0);
         EXPECT_CALL(*mockPlugin, ImportNetwork(_, _)).Times(TEST_DEVICE_MAX_COUNT / 2)
-                .WillRepeatedly(Invoke([&](std::istream &, const std::map<std::string, std::string> &) {
+                .WillRepeatedly(Invoke([&](std::istream &s, const std::map<std::string, std::string> &) {
+            std::string name;
+            s >> name;
             std::lock_guard<std::mutex> lock(mock_creation_mutex);
-            return createMockIExecutableNet();
+            return createMockIExecutableNet({}, m_inputs_map[name], m_outputs_map[name]);
         }));
         m_post_mock_net_callbacks.emplace_back([&](MockExecutableNetwork& net) {
             EXPECT_CALL(net, Export(_)).Times(1); // each net will be exported once
@@ -1666,6 +1692,7 @@ TEST_P(CachingTest, LoadMulti_NoCachingOnDevice) {
             // Verify that inputs and outputs are set for Multi Executable Network
             ASSERT_EQ(exeNet.GetInputsInfo().size(), inputMap.size());
             ASSERT_EQ(exeNet.GetOutputsInfo().size(), outputMap.size());
+            networks.clear();
         });
     }
 }

From 3d244a41ab4dfc0ae178ea489b62b40217ea3659 Mon Sep 17 00:00:00 2001
From: Luwei Zhou <luwei.zhou@intel.com>
Date: Thu, 23 Dec 2021 11:02:15 +0800
Subject: [PATCH 100/160] [shape_infer]Implement shape inference of Roll,
 ROIAlign,Proposal (#8610)

* Implement the proposal and experimental_detecron_generate_proposals

* Implement the proposal shape infer

* Add ROI_Align OP shape infer implement.

* Fix building issue

* Fix bug.

* Update test cases.

* Add test cases for the OPs

* Apply the CI coding style check.

* Move the shape_infer API to the new folder.

* Update some fix.

* Applied review comments

* Move the shape infer tests into new folder.

* Apply review comments.

* Fix missing header when mering with master
---
 .../utils/shape_inference/shape_inference.cpp |  16 ++-
 src/core/include/openvino/op/proposal.hpp     |   1 +
 ...ron_generate_proposals_shape_inference.hpp |  86 ++++++++++++++
 .../include/proposal_shape_inference.hpp      | 109 ++++++++++++++++++
 .../include/roi_align_shape_inference.hpp     |  77 +++++++++++++
 .../include/roll_shape_inference.hpp          |  73 ++++++++++++
 ...erimental_detectron_generate_proposals.cpp |  74 ++----------
 src/core/src/op/proposal.cpp                  |  94 ++++-----------
 src/core/src/op/roi_align.cpp                 |  64 ++--------
 src/core/src/op/roll.cpp                      |  54 ++-------
 ...perimental_detectron_generate_proposal.cpp |  39 +++++++
 .../cpu/shape_inference_test/proposal.cpp     |  53 +++++++++
 .../roi_align_shape_inference.cpp             |  26 +++++
 .../roll_shape_inference.cpp                  |  50 ++++++++
 14 files changed, 582 insertions(+), 234 deletions(-)
 create mode 100644 src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/proposal_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/roi_align_shape_inference.hpp
 create mode 100644 src/core/shape_inference/include/roll_shape_inference.hpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/experimental_detectron_generate_proposal.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/proposal.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/roi_align_shape_inference.cpp
 create mode 100644 src/tests/unit/cpu/shape_inference_test/roll_shape_inference.cpp

diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
index a597e406977..3e1a3b083d3 100644
--- a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
+++ b/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
@@ -52,6 +52,10 @@
 #include "variadic_split_shape_inference.hpp"
 #include "einsum_shape_inference.hpp"
 #include "strided_slice_shape_inference.hpp"
+#include "experimental_detectron_generate_proposals_shape_inference.hpp"
+#include "roi_align_shape_inference.hpp"
+#include "roll_shape_inference.hpp"
+#include "proposal_shape_inference.hpp"
 #include "static_shape.hpp"
 #include "tile_shape_inference.hpp"
 #include "utils.hpp"
@@ -204,6 +208,16 @@ void shape_inference(ov::Node* op,
         shape_infer(node, input_shapes, output_shapes);
     } else if (auto node = ov::as_type<ov::opset1::ReverseSequence>(op)) {
         shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset7::Roll>(op)) {
+      shape_infer(node, input_shapes, output_shapes, constant_data);
+    } else if (auto node = ov::as_type<ov::opset6::ExperimentalDetectronGenerateProposalsSingleImage>(op)) {
+      shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset4::Proposal>(op)) {
+      shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset1::Proposal>(op)) {
+      shape_infer(node, input_shapes, output_shapes);
+    } else if (auto node = ov::as_type<ov::opset3::ROIAlign>(op)) {
+      shape_infer(node, input_shapes, output_shapes);
     } else {
         ngraph::OutputVector new_inputs;
         for (size_t i = 0; i < op->get_input_size(); ++i) {
@@ -226,4 +240,4 @@ void shape_inference(ov::Node* op,
             output_shapes[i] = ov::StaticShape(partial_shape.to_shape());
         }
     }
-}
\ No newline at end of file
+}
diff --git a/src/core/include/openvino/op/proposal.hpp b/src/core/include/openvino/op/proposal.hpp
index add6145e9cb..3d20716456d 100644
--- a/src/core/include/openvino/op/proposal.hpp
+++ b/src/core/include/openvino/op/proposal.hpp
@@ -66,6 +66,7 @@ public:
 
 protected:
     Attributes m_attrs;
+    void validate_element_types();
 };
 }  // namespace v0
 
diff --git a/src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp b/src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp
new file mode 100644
index 00000000000..0ea1facdac9
--- /dev/null
+++ b/src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp
@@ -0,0 +1,86 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/op/experimental_detectron_generate_proposals.hpp>
+
+namespace ov {
+namespace op {
+namespace v6 {
+
+template <class T>
+void shape_infer(const ExperimentalDetectronGenerateProposalsSingleImage* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 4 && output_shapes.size() == 2);
+    auto post_nms_count = static_cast<size_t>(op->get_attrs().post_nms_count);
+
+    const auto& im_info_shape = input_shapes[0];
+    const auto& anchors_shape = input_shapes[1];
+    const auto& deltas_shape = input_shapes[2];
+    const auto& scores_shape = input_shapes[3];
+    const auto im_info_shape_rank = im_info_shape.rank();
+    NODE_VALIDATION_CHECK(op,
+                          im_info_shape_rank.compatible(1),
+                          "The 'input_im_info' input is expected to be a 1D. Got: ",
+                          im_info_shape);
+
+    if (im_info_shape_rank.is_static()) {
+        NODE_VALIDATION_CHECK(op,
+                              im_info_shape[0].compatible(3),
+                              "The 'input_im_info' shape is expected to be a compatible with [3]. Got: ",
+                              im_info_shape);
+    }
+
+    const auto anchors_shape_rank = anchors_shape.rank();
+    NODE_VALIDATION_CHECK(op,
+                          anchors_shape_rank.compatible(2),
+                          "The 'input_anchors' input is expected to be a 2D. Got: ",
+                          anchors_shape);
+    if (anchors_shape_rank.is_static()) {
+        NODE_VALIDATION_CHECK(op,
+                              anchors_shape[1].compatible(4),
+                              "The second dimension of 'input_anchors' should be compatible with 4. Got: ",
+                              anchors_shape[1]);
+    }
+    const auto deltas_shape_rank = deltas_shape.rank();
+    const auto scores_shape_rank = scores_shape.rank();
+
+    NODE_VALIDATION_CHECK(op,
+                          deltas_shape_rank.compatible(3),
+                          "The 'input_deltas' input is expected to be a 3D. Got: ",
+                          deltas_shape);
+    NODE_VALIDATION_CHECK(op,
+                          scores_shape_rank.compatible(3),
+                          "The 'input_scores' input is expected to be a 3D. Got: ",
+                          scores_shape);
+    if (deltas_shape_rank.is_static() && scores_shape_rank.is_static()) {
+        NODE_VALIDATION_CHECK(op,
+                              deltas_shape[1].compatible(scores_shape[1]),
+                              "Heights for inputs 'input_deltas' and 'input_scores' should be "
+                              "equal. Got: ",
+                              deltas_shape[1],
+                              scores_shape[1]);
+
+        NODE_VALIDATION_CHECK(op,
+                              deltas_shape[2].compatible(scores_shape[2]),
+                              "Width for inputs 'input_deltas' and 'input_scores' should be "
+                              "equal. Got: ",
+                              deltas_shape[2],
+                              scores_shape[2]);
+    }
+    auto& rois_shape = output_shapes[0];
+    auto& rois_scores_shape = output_shapes[1];
+
+    rois_shape.resize(2);
+    rois_scores_shape.resize(1);
+    rois_shape[0] = post_nms_count;
+    rois_shape[1] = 4;
+    rois_scores_shape[0] = post_nms_count;
+}
+
+}  // namespace v6
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/proposal_shape_inference.hpp b/src/core/shape_inference/include/proposal_shape_inference.hpp
new file mode 100644
index 00000000000..25375e62e8c
--- /dev/null
+++ b/src/core/shape_inference/include/proposal_shape_inference.hpp
@@ -0,0 +1,109 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/op/proposal.hpp>
+
+namespace ov {
+namespace op {
+namespace v0 {
+template <class OpType, class ShapeType>
+void infer_prop_shape(const OpType* op,
+                      const std::vector<ShapeType>& input_shapes,
+                      std::vector<ShapeType>& output_shapes) {
+    using DimType = typename std::iterator_traits<typename ShapeType::iterator>::value_type;
+    const auto& class_probs_ps = input_shapes[0];
+    const auto& bbox_deltas_ps = input_shapes[1];
+    const auto& image_shape_ps = input_shapes[2];
+
+    NODE_VALIDATION_CHECK(op,
+                          class_probs_ps.rank().compatible(4),
+                          "Proposal layer shape class_probs should be rank 4 compatible (",
+                          class_probs_ps,
+                          ").");
+
+    NODE_VALIDATION_CHECK(op,
+                          bbox_deltas_ps.rank().compatible(4),
+                          "Proposal layer shape bbox_deltas should be rank 4 compatible (",
+                          bbox_deltas_ps,
+                          ").");
+
+    NODE_VALIDATION_CHECK(op,
+                          image_shape_ps.rank().compatible(1),
+                          "Proposal layer shape image_shape should be rank 1 compatible (",
+                          image_shape_ps,
+                          ").");
+    if (bbox_deltas_ps.rank().is_static() && class_probs_ps.rank().is_static()) {
+        // check anchor count and batch number consistency
+        NODE_VALIDATION_CHECK(op,
+                              bbox_deltas_ps[1].compatible(class_probs_ps[1] * 2),
+                              "Anchor number inconsistent between class_probs (",
+                              class_probs_ps[1] * 2,
+                              "), and bbox_deltas (",
+                              bbox_deltas_ps[1],
+                              ").");
+
+        NODE_VALIDATION_CHECK(op,
+                              class_probs_ps[0].compatible(bbox_deltas_ps[0]),
+                              "Batch size inconsistent between class_probs (",
+                              class_probs_ps[0],
+                              ") and bbox deltas (",
+                              bbox_deltas_ps[0],
+                              ").");
+    }
+
+    if (image_shape_ps.is_static()) {
+        const auto image_shape_elem = image_shape_ps[0].get_length();
+        NODE_VALIDATION_CHECK(op,
+                              image_shape_elem >= 3 && image_shape_elem <= 4,
+                              "Image_shape 1D tensor must have => 3 and <= 4 elements (image_shape_shape[0]",
+                              image_shape_ps[0],
+                              ").");
+    }
+
+    auto out_dim = DimType{};
+
+    if (class_probs_ps.rank().is_static() && bbox_deltas_ps.rank().is_static()) {
+        DimType::merge(out_dim, class_probs_ps[0], bbox_deltas_ps[0]);
+    } else if (class_probs_ps.rank().is_static()) {
+        out_dim = class_probs_ps[0];
+    } else if (bbox_deltas_ps.rank().is_static()) {
+        out_dim = bbox_deltas_ps[0];
+    } else {
+        out_dim = Dimension::dynamic();
+    }
+
+    auto& proposed_boxes_shape = output_shapes[0];
+    proposed_boxes_shape.resize(2);
+    proposed_boxes_shape[0] = out_dim * op->get_attrs().post_nms_topn;
+    proposed_boxes_shape[1] = 5;
+}
+template <class T>
+void shape_infer(const ov::op::v0::Proposal* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 3 && output_shapes.size() == 1);
+    ov::op::v0::infer_prop_shape(op, input_shapes, output_shapes);
+}
+
+}  // namespace v0
+}  // namespace op
+}  // namespace ov
+
+namespace ov {
+namespace op {
+namespace v4 {
+
+template <class T>
+void shape_infer(const ov::op::v4::Proposal* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 3 && output_shapes.size() == 2);
+
+    ov::op::v0::infer_prop_shape(op, input_shapes, output_shapes);
+    const auto& proposals_ps = output_shapes[0];
+    auto& out_ps = output_shapes[1];
+    out_ps = T{proposals_ps[0]};
+}
+
+}  // namespace v4
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/shape_inference/include/roi_align_shape_inference.hpp b/src/core/shape_inference/include/roi_align_shape_inference.hpp
new file mode 100644
index 00000000000..820ece21bd7
--- /dev/null
+++ b/src/core/shape_inference/include/roi_align_shape_inference.hpp
@@ -0,0 +1,77 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/op/roi_align.hpp>
+
+#include "utils.hpp"
+
+namespace ov {
+namespace op {
+namespace v3 {
+
+template <class T>
+void shape_infer(const ov::op::v3::ROIAlign* op, const std::vector<T>& input_shapes, std::vector<T>& output_shapes) {
+    using DimType = typename std::iterator_traits<typename T::iterator>::value_type;
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 3 && output_shapes.size() == 1);
+
+    const auto& input_ps = input_shapes[0];
+    const auto& rois_ps = input_shapes[1];
+    const auto& batch_indices_ps = input_shapes[2];
+
+    const auto rois_ps_rank = rois_ps.rank();
+    const auto input_ps_rank = input_ps.rank();
+    const auto batch_indices_ps_rank = batch_indices_ps.rank();
+
+    NODE_VALIDATION_CHECK(op, input_ps_rank.compatible(4), "Expected a 4D tensor for the input data. Got: ", input_ps);
+
+    NODE_VALIDATION_CHECK(op, rois_ps_rank.compatible(2), "Expected a 2D tensor for the ROIs input. Got: ", rois_ps);
+
+    NODE_VALIDATION_CHECK(op,
+                          batch_indices_ps_rank.compatible(1),
+                          "Expected a 1D tensor for the batch indices input. Got: ",
+                          batch_indices_ps);
+
+    if (rois_ps_rank.is_static()) {
+        const auto& rois_second_dim = rois_ps[1];
+        NODE_VALIDATION_CHECK(op,
+                              rois_second_dim.compatible(4),
+                              "The second dimension of ROIs input should contain box coordinates. ",
+                              "op dimension is expected to be equal to 4. Got: ",
+                              rois_second_dim);
+
+        if (batch_indices_ps_rank.is_static()) {
+            NODE_VALIDATION_CHECK(op,
+                                  rois_ps[0].compatible(batch_indices_ps[0]),
+                                  "The first dimension of ROIs input must be equal to the first dimension ",
+                                  "of the batch indices input. Got: ",
+                                  rois_ps[0],
+                                  " and: ",
+                                  batch_indices_ps[0]);
+        }
+    }
+
+    auto& output_shape = output_shapes[0];
+    output_shape.resize(4);
+    output_shape[1] = input_ps_rank.is_static() ? input_ps[1] : -1;
+    output_shape[2] = op->get_pooled_h();
+    output_shape[3] = op->get_pooled_w();
+
+    // if either of those 2 dimensions is static its value will be used
+    // for the first dimension of the output shape - 'NUM_ROIS'
+    if (rois_ps_rank.is_static() && batch_indices_ps_rank.is_static()) {
+        DimType::merge(output_shape[0], batch_indices_ps[0], rois_ps[0]);
+    } else if (rois_ps_rank.is_static()) {
+        output_shape[0] = rois_ps[0];
+    } else if (batch_indices_ps_rank.is_static()) {
+        output_shape[0] = batch_indices_ps[0];
+    } else {
+        output_shape[0] = Dimension::dynamic();
+    }
+}
+
+}  // namespace v3
+}  // namespace op
+}  // namespace ov
\ No newline at end of file
diff --git a/src/core/shape_inference/include/roll_shape_inference.hpp b/src/core/shape_inference/include/roll_shape_inference.hpp
new file mode 100644
index 00000000000..f421a36ca47
--- /dev/null
+++ b/src/core/shape_inference/include/roll_shape_inference.hpp
@@ -0,0 +1,73 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/op/roll.hpp>
+
+#include "utils.hpp"
+
+namespace ov {
+namespace op {
+namespace v7 {
+
+template <class T>
+void shape_infer(const ov::op::v7::Roll* op,
+                 const std::vector<T>& input_shapes,
+                 std::vector<T>& output_shapes,
+                 const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}) {
+    NODE_VALIDATION_CHECK(op, input_shapes.size() == 3 && output_shapes.size() == 1);
+
+    const auto& data_pshape = input_shapes[0];
+    const auto& shift_pshape = input_shapes[1];
+    const auto& axes_pshape = input_shapes[2];
+
+    if (shift_pshape.rank().is_static()) {
+        const auto& shift_rank = shift_pshape.size();
+        NODE_VALIDATION_CHECK(op, shift_rank <= 1, "Shift must be a scalar or 1D tensor.");
+        // If shift is a scalar, than axes can be arbitrary 1d tensor and we don't need
+        // to check shift shape consistency with axes, otherwise the check is needed.
+        if (shift_rank == 1) {
+            NODE_VALIDATION_CHECK(op,
+                                  shift_pshape.compatible(axes_pshape),
+                                  "If shift is a 1D vector, axes must be a 1D tensor of the same size.");
+        }
+    }
+
+    if (axes_pshape.rank().is_static()) {
+        const auto& axes_rank = axes_pshape.size();
+        NODE_VALIDATION_CHECK(op, axes_rank <= 1, "Axes must be a scalar or 1D tensor.");
+    }
+
+    std::vector<int64_t> axes{};
+
+    if (get_data_as_int64<T>(2, op, axes, constant_data)) {
+        if (data_pshape.rank().is_static()) {
+            const auto& data_rank = data_pshape.size();
+            for (int64_t& axis : axes) {
+                NODE_VALIDATION_CHECK(op,
+                                      axis < static_cast<int64_t>(data_rank),
+                                      "Axes must be less than data tensor rank. Got "
+                                      "data tensor rank: ",
+                                      data_rank,
+                                      ", axis: ",
+                                      axis);
+                if (axis < 0) {
+                    axis += static_cast<int64_t>(data_rank);
+                }
+                NODE_VALIDATION_CHECK(op,
+                                      axis >= 0,
+                                      "Axes must be positive or equal to zero. Got "
+                                      "axis: ",
+                                      axis);
+            }
+        }
+    }
+
+    output_shapes[0] = input_shapes[0];
+}
+
+}  // namespace v7
+}  // namespace op
+}  // namespace ov
\ No newline at end of file
diff --git a/src/core/src/op/experimental_detectron_generate_proposals.cpp b/src/core/src/op/experimental_detectron_generate_proposals.cpp
index 9efad71f2a8..6b4d35f9c37 100644
--- a/src/core/src/op/experimental_detectron_generate_proposals.cpp
+++ b/src/core/src/op/experimental_detectron_generate_proposals.cpp
@@ -4,6 +4,8 @@
 
 #include "ngraph/op/experimental_detectron_generate_proposals.hpp"
 
+#include <experimental_detectron_generate_proposals_shape_inference.hpp>
+
 #include "itt.hpp"
 #include "ngraph/attribute_visitor.hpp"
 #include "ngraph/op/util/op_types.hpp"
@@ -47,69 +49,15 @@ bool op::v6::ExperimentalDetectronGenerateProposalsSingleImage::visit_attributes
 
 void op::v6::ExperimentalDetectronGenerateProposalsSingleImage::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v6_ExperimentalDetectronGenerateProposalsSingleImage_validate_and_infer_types);
-    auto post_nms_count = static_cast<size_t>(m_attrs.post_nms_count);
-    auto input_et = get_input_element_type(0);
 
-    set_output_size(2);
-    set_output_type(0, input_et, ov::Shape{post_nms_count, 4});
-    set_output_type(1, input_et, ov::Shape{post_nms_count});
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}, ov::PartialShape{}};
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0),
+                                                  get_input_partial_shape(1),
+                                                  get_input_partial_shape(2),
+                                                  get_input_partial_shape(3)};
+    shape_infer(this, input_shapes, output_shapes);
 
-    auto im_info_shape = get_input_partial_shape(0);
-    auto anchors_shape = get_input_partial_shape(1);
-    auto deltas_shape = get_input_partial_shape(2);
-    auto scores_shape = get_input_partial_shape(3);
-
-    if (im_info_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              im_info_shape.rank().get_length() == 1,
-                              "The 'input_im_info' input is expected to be a 1D. Got: ",
-                              im_info_shape);
-
-        NODE_VALIDATION_CHECK(this,
-                              im_info_shape[0].is_dynamic() || im_info_shape[0] == 3,
-                              "The 'input_im_info' shape is expected to be a [3]. Got: ",
-                              im_info_shape);
-    }
-
-    if (anchors_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              anchors_shape.rank().get_length() == 2,
-                              "The 'input_anchors' input is expected to be a 2D. Got: ",
-                              anchors_shape);
-
-        NODE_VALIDATION_CHECK(this,
-                              anchors_shape[1].is_dynamic() || anchors_shape[1] == 4,
-                              "The second dimension of 'input_anchors' should be 4. Got: ",
-                              anchors_shape[1]);
-    }
-
-    if (deltas_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              deltas_shape.rank().get_length() == 3,
-                              "The 'input_deltas' input is expected to be a 3D. Got: ",
-                              deltas_shape);
-    }
-    if (scores_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              scores_shape.rank().get_length() == 3,
-                              "The 'input_scores' input is expected to be a 3D. Got: ",
-                              scores_shape);
-    }
-    if (deltas_shape.rank().is_static() && scores_shape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(
-            this,
-            deltas_shape[1].is_dynamic() || scores_shape[1].is_dynamic() || deltas_shape[1] == scores_shape[1],
-            "Heights for inputs 'input_deltas' and 'input_scores' should be "
-            "equal. Got: ",
-            deltas_shape[1],
-            scores_shape[1]);
-
-        NODE_VALIDATION_CHECK(
-            this,
-            deltas_shape[2].is_dynamic() || scores_shape[2].is_dynamic() || deltas_shape[2] == scores_shape[2],
-            "Width for inputs 'input_deltas' and 'input_scores' should be "
-            "equal. Got: ",
-            deltas_shape[2],
-            scores_shape[2]);
-    }
+    const auto& input_et = get_input_element_type(0);
+    set_output_type(0, input_et, output_shapes[0]);
+    set_output_type(1, input_et, output_shapes[1]);
 }
diff --git a/src/core/src/op/proposal.cpp b/src/core/src/op/proposal.cpp
index 91db02c34f8..c0eea79ae52 100644
--- a/src/core/src/op/proposal.cpp
+++ b/src/core/src/op/proposal.cpp
@@ -4,6 +4,8 @@
 
 #include "ngraph/op/proposal.hpp"
 
+#include <proposal_shape_inference.hpp>
+
 #include "itt.hpp"
 #include "ngraph/op/constant.hpp"
 
@@ -21,12 +23,7 @@ op::v0::Proposal::Proposal(const Output<Node>& class_probs,
     constructor_validate_and_infer_types();
 }
 
-void op::v0::Proposal::validate_and_infer_types() {
-    NGRAPH_OP_SCOPE(v0_Proposal_validate_and_infer_types);
-    const auto& class_probs_ps = get_input_partial_shape(0);
-    const auto& bbox_deltas_ps = get_input_partial_shape(1);
-    const auto& image_shape_ps = get_input_partial_shape(2);
-    Dimension out_dim = Dimension::dynamic();
+void op::v0::Proposal::validate_element_types() {
     NODE_VALIDATION_CHECK(this,
                           get_input_element_type(0).is_real(),
                           "Proposal layer input class_probs should have floating point type (",
@@ -44,63 +41,17 @@ void op::v0::Proposal::validate_and_infer_types() {
                           "Proposal layer input image_shape should have floating point type (",
                           get_input_element_type(2),
                           ").");
+}
 
-    NODE_VALIDATION_CHECK(this,
-                          class_probs_ps.rank().compatible(4),
-                          "Proposal layer shape class_probs should be rank 4 compatible (",
-                          class_probs_ps,
-                          ").");
-
-    NODE_VALIDATION_CHECK(this,
-                          bbox_deltas_ps.rank().compatible(4),
-                          "Proposal layer shape bbox_deltas should be rank 4 compatible (",
-                          bbox_deltas_ps,
-                          ").");
-
-    NODE_VALIDATION_CHECK(this,
-                          image_shape_ps.rank().compatible(1),
-                          "Proposal layer shape image_shape should be rank 1 compatible (",
-                          image_shape_ps,
-                          ").");
-
-    if (bbox_deltas_ps.is_static() && class_probs_ps.is_static()) {
-        // class probs and bbox deltas shapes are static, check anchor count and batch number
-        // consistency
-        NODE_VALIDATION_CHECK(this,
-                              class_probs_ps[1].get_length() * 2 == bbox_deltas_ps[1].get_length(),
-                              "Anchor number inconsistent between class_probs (",
-                              class_probs_ps[1].get_length() / 2,
-                              "), and bbox_deltas (",
-                              bbox_deltas_ps[1].get_length() / 4,
-                              ").");
-
-        NODE_VALIDATION_CHECK(this,
-                              class_probs_ps[0] == bbox_deltas_ps[0],
-                              "Batch size inconsistent between class_probs (",
-                              class_probs_ps[0],
-                              ") and bbox deltas (",
-                              bbox_deltas_ps[0],
-                              ").");
-    }
-
-    if (image_shape_ps.is_static()) {
-        NODE_VALIDATION_CHECK(this,
-                              image_shape_ps[0].get_length() >= 3 && image_shape_ps[0].get_length() <= 4,
-                              "Image_shape 1D tensor must have => 3 and <= 4 elements (image_shape_shape[0]",
-                              image_shape_ps[0],
-                              ").");
-    }
-
-    if (class_probs_ps.rank().is_static() && bbox_deltas_ps.rank().is_static()) {
-        out_dim = (class_probs_ps[0] & bbox_deltas_ps[0]);
-    } else if (class_probs_ps.rank().is_static()) {
-        out_dim = class_probs_ps[0];
-    } else if (bbox_deltas_ps.rank().is_static()) {
-        out_dim = bbox_deltas_ps[0];
-    }
-
-    // intersect the batch size
-    set_output_type(0, get_input_element_type(0), ov::PartialShape{out_dim * m_attrs.post_nms_topn, 5});
+void op::v0::Proposal::validate_and_infer_types() {
+    NGRAPH_OP_SCOPE(v0_Proposal_validate_and_infer_types);
+    validate_element_types();
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0),
+                                                  get_input_partial_shape(1),
+                                                  get_input_partial_shape(2)};
+    shape_infer(this, input_shapes, output_shapes);
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
 
 shared_ptr<Node> op::v0::Proposal::clone_with_new_inputs(const OutputVector& new_args) const {
@@ -140,14 +91,17 @@ op::v4::Proposal::Proposal(const Output<Node>& class_probs,
 
 void op::v4::Proposal::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v4_Proposal_validate_and_infer_types);
-    v0::Proposal::validate_and_infer_types();
-    // Output shape was inferred in v0's validate_and_infer_types
-    const auto proposals_ps = get_output_partial_shape(0);
-    auto out_ps = ov::PartialShape{Dimension::dynamic()};
-    if (proposals_ps.rank().is_static() && proposals_ps.rank().compatible(2)) {
-        out_ps = ov::PartialShape{proposals_ps[0]};
-    }
-    set_output_type(1, get_input_element_type(0), out_ps);
+    v0::Proposal::validate_element_types();
+
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}, ov::PartialShape{}};
+    std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0),
+                                                  get_input_partial_shape(1),
+                                                  get_input_partial_shape(2)};
+    shape_infer(this, input_shapes, output_shapes);
+
+    const auto& input0_type = get_input_element_type(0);
+    set_output_type(0, input0_type, output_shapes[0]);
+    set_output_type(1, input0_type, output_shapes[1]);
 }
 
 std::shared_ptr<Node> op::v4::Proposal::clone_with_new_inputs(const OutputVector& new_args) const {
diff --git a/src/core/src/op/roi_align.cpp b/src/core/src/op/roi_align.cpp
index 9660e18e4e2..5e655a94ce1 100644
--- a/src/core/src/op/roi_align.cpp
+++ b/src/core/src/op/roi_align.cpp
@@ -4,6 +4,8 @@
 
 #include "ngraph/op/roi_align.hpp"
 
+#include <roi_align_shape_inference.hpp>
+
 #include "itt.hpp"
 #include "ngraph/runtime/host_tensor.hpp"
 #include "ngraph/runtime/reference/roi_align.hpp"
@@ -69,69 +71,25 @@ void op::v3::ROIAlign::validate_and_infer_types() {
                           "The data type for batch indices is expected to be an integer. Got: ",
                           get_input_element_type(2));
 
-    const auto& input_ps = get_input_partial_shape(0);
-    NODE_VALIDATION_CHECK(this,
-                          input_ps.rank().compatible(4),
-                          "Expected a 4D tensor for the input data. Got: ",
-                          input_ps);
-
-    const auto& rois_ps = get_input_partial_shape(1);
-    NODE_VALIDATION_CHECK(this,
-                          rois_ps.rank().compatible(2),
-                          "Expected a 2D tensor for the ROIs input. Got: ",
-                          rois_ps);
-
-    const auto& batch_indices_ps = get_input_partial_shape(2);
-    NODE_VALIDATION_CHECK(this,
-                          batch_indices_ps.rank().compatible(1),
-                          "Expected a 1D tensor for the batch indices input. Got: ",
-                          batch_indices_ps);
-
-    if (rois_ps.rank().is_static()) {
-        const auto rois_second_dim = rois_ps[1];
-        NODE_VALIDATION_CHECK(this,
-                              rois_second_dim.compatible(4),
-                              "The second dimension of ROIs input should contain box coordinates. ",
-                              "This dimension is expected to be equal to 4. Got: ",
-                              rois_second_dim);
-
-        if (batch_indices_ps.rank().is_static()) {
-            NODE_VALIDATION_CHECK(this,
-                                  rois_ps[0].compatible(batch_indices_ps[0]),
-                                  "The first dimension of ROIs input must be equal to the first dimension ",
-                                  "of the batch indices input. Got: ",
-                                  rois_ps[0],
-                                  " and: ",
-                                  batch_indices_ps[0]);
-        }
-    }
-
-    // the output shape should have the following format [NUM_ROIS, C, pooled_h, pooled_w]
-    auto output_shape = ov::PartialShape{{Dimension::dynamic(),
-                                          input_ps[1],
-                                          Dimension{static_cast<int64_t>(m_pooled_h)},
-                                          Dimension{static_cast<int64_t>(m_pooled_w)}}};
-
-    // if either of those 2 dimensions is static its value will be used
-    // for the first dimension of the output shape - 'NUM_ROIS'
-    if (rois_ps.rank().is_static() && rois_ps[0].is_static()) {
-        output_shape[0] = rois_ps[0];
-    } else if (batch_indices_ps.rank().is_static() && batch_indices_ps[0].is_static()) {
-        output_shape[0] = batch_indices_ps[0];
-    }
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+    const std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0),
+                                                        get_input_partial_shape(1),
+                                                        get_input_partial_shape(2)};
 
+    shape_infer(this, input_shapes, output_shapes);
     set_output_size(1);
-    set_output_type(0, get_input_element_type(0), output_shape);
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
+
+    const auto& input_ps = get_input_partial_shape(0);
 
     // if the channels dimension is not known
     // the first input should be used during the function specialization
     if (input_ps.rank().is_static() && input_ps[1].is_dynamic()) {
         set_input_is_relevant_to_shape(0);
     }
-
     // if the 'NUM_ROIS' value is not known
     // the last 2 inputs should be used during the function specialization
-    if (output_shape[0].is_dynamic()) {
+    if ((output_shapes[0])[0].is_dynamic()) {
         set_input_is_relevant_to_shape(1);
         set_input_is_relevant_to_shape(2);
     }
diff --git a/src/core/src/op/roll.cpp b/src/core/src/op/roll.cpp
index 0d5308255c6..43e3edb8a1f 100644
--- a/src/core/src/op/roll.cpp
+++ b/src/core/src/op/roll.cpp
@@ -5,6 +5,7 @@
 #include "ngraph/op/roll.hpp"
 
 #include <ngraph/validation_util.hpp>
+#include <roll_shape_inference.hpp>
 
 #include "itt.hpp"
 
@@ -31,54 +32,13 @@ void op::v7::Roll::validate_and_infer_types() {
                           axes_et.is_dynamic() || axes_et == element::i32 || axes_et == element::i64,
                           "Axes must have int32 or int64 element type.");
 
-    const auto& data_pshape = get_input_partial_shape(0);
-    const auto& shift_pshape = get_input_partial_shape(1);
-    const auto& axes_pshape = get_input_partial_shape(2);
+    std::vector<ov::PartialShape> output_shapes = {ov::PartialShape{}};
+    const std::vector<ov::PartialShape> input_shapes = {get_input_partial_shape(0),
+                                                        get_input_partial_shape(1),
+                                                        get_input_partial_shape(2)};
+    shape_infer(this, input_shapes, output_shapes);
 
-    if (shift_pshape.is_static()) {
-        const auto& shift_rank = shift_pshape.rank().get_length();
-        NODE_VALIDATION_CHECK(this, shift_rank <= 1, "Shift must be a scalar or 1D tensor.");
-    }
-
-    if (axes_pshape.is_static()) {
-        const auto& axes_rank = axes_pshape.rank().get_length();
-        NODE_VALIDATION_CHECK(this, axes_rank <= 1, "Axes must be a scalar or 1D tensor.");
-    }
-
-    // If shift is a scalar, than axes can be arbitrary 1d tensor and we don't need
-    // to check shift shape consistency with axes, otherwise the check is needed.
-    if (!(shift_pshape.is_static() && ngraph::is_scalar(shift_pshape.to_shape()))) {
-        NODE_VALIDATION_CHECK(this,
-                              shift_pshape.compatible(axes_pshape),
-                              "If shift is a 1D vector, axes must be a 1D tensor of the same size.");
-    }
-
-    if (const auto& const_axes = get_constant_from_source(input_value(2))) {
-        auto axes = const_axes->cast_vector<int64_t>();
-
-        if (data_pshape.is_static()) {
-            const auto& data_rank = data_pshape.rank().get_length();
-            for (int64_t& axis : axes) {
-                NODE_VALIDATION_CHECK(this,
-                                      axis < data_rank,
-                                      "Axes must be less than data tensor rank. Got "
-                                      "data tensor rank: ",
-                                      data_rank,
-                                      ", axis: ",
-                                      axis);
-                if (axis < 0) {
-                    axis += data_rank;
-                }
-                NODE_VALIDATION_CHECK(this,
-                                      axis >= 0,
-                                      "Axes must be positive or equal to zero. Got "
-                                      "axis: ",
-                                      axis);
-            }
-        }
-    }
-
-    set_output_type(0, get_input_element_type(0), get_input_partial_shape(0));
+    set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
 
 bool op::v7::Roll::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/tests/unit/cpu/shape_inference_test/experimental_detectron_generate_proposal.cpp b/src/tests/unit/cpu/shape_inference_test/experimental_detectron_generate_proposal.cpp
new file mode 100644
index 00000000000..ecf912a5a72
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/experimental_detectron_generate_proposal.cpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <openvino/op/experimental_detectron_generate_proposals.hpp>
+#include <openvino/op/parameter.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+using ExperimentalProposals = op::v6::ExperimentalDetectronGenerateProposalsSingleImage;
+
+TEST(StaticShapeInferenceTest, ExperimentalProposalsTest) {
+    ExperimentalProposals::Attributes attrs;
+    attrs.min_size = 0.0f;
+    attrs.nms_threshold = 0.699999988079071f;
+    attrs.post_nms_count = 1000;
+    attrs.pre_nms_count = 1000;
+    size_t post_nms_count = 1000;
+
+    auto im_info = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1});
+    auto anchors = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1});
+    auto deltas = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1});
+    auto scores = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1});
+
+    auto proposals = std::make_shared<ExperimentalProposals>(im_info, anchors, deltas, scores, attrs);
+
+    const std::vector<ov::StaticShape> input_shapes = {ov::StaticShape{3},
+                                                       ov::StaticShape{201600, 4},
+                                                       ov::StaticShape{12, 200, 336},
+                                                       ov::StaticShape{3, 200, 336}};
+    std::vector<ov::StaticShape> output_shapes = {ov::StaticShape{}, ov::StaticShape{}};
+    shape_inference(proposals.get(), input_shapes, output_shapes);
+
+    ASSERT_EQ(output_shapes[0], (StaticShape{post_nms_count, 4}));
+    ASSERT_EQ(output_shapes[1], (StaticShape{post_nms_count}));
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/proposal.cpp b/src/tests/unit/cpu/shape_inference_test/proposal.cpp
new file mode 100644
index 00000000000..af5889e117f
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/proposal.cpp
@@ -0,0 +1,53 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <openvino/op/parameter.hpp>
+#include <openvino/op/proposal.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, ProposalV0Test) {
+    op::v0::Proposal::Attributes attrs;
+    attrs.base_size = 1;
+    attrs.pre_nms_topn = 20;
+    attrs.post_nms_topn = 200;
+    const size_t batch_size = 7;
+
+    auto class_probs = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto class_bbox_deltas = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto image_shape = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1});
+    auto op = std::make_shared<op::v0::Proposal>(class_probs, class_bbox_deltas, image_shape, attrs);
+    const std::vector<ov::StaticShape> input_shapes = {ov::StaticShape{batch_size, 12, 34, 62},
+                                                       ov::StaticShape{batch_size, 24, 34, 62},
+                                                       ov::StaticShape{3}};
+    std::vector<ov::StaticShape> output_shapes = {ov::StaticShape{}};
+    shape_inference(op.get(), input_shapes, output_shapes);
+
+    ASSERT_EQ(output_shapes[0], (StaticShape{batch_size * attrs.post_nms_topn, 5}));
+}
+
+TEST(StaticShapeInferenceTest, ProposalV4Test) {
+    op::v0::Proposal::Attributes attrs;
+    attrs.base_size = 1;
+    attrs.pre_nms_topn = 20;
+    attrs.post_nms_topn = 200;
+    const size_t batch_size = 7;
+
+    auto class_probs = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto class_bbox_deltas = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    auto image_shape = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1});
+    auto op = std::make_shared<op::v4::Proposal>(class_probs, class_bbox_deltas, image_shape, attrs);
+    const std::vector<ov::StaticShape> input_shapes = {ov::StaticShape{batch_size, 12, 34, 62},
+                                                       ov::StaticShape{batch_size, 24, 34, 62},
+                                                       ov::StaticShape{3}};
+    std::vector<ov::StaticShape> output_shapes = {ov::StaticShape{}, ov::StaticShape{}};
+    shape_inference(op.get(), input_shapes, output_shapes);
+
+    ASSERT_EQ(output_shapes[0], (StaticShape{batch_size * attrs.post_nms_topn, 5}));
+    ASSERT_EQ(output_shapes[1], (StaticShape{batch_size * attrs.post_nms_topn}));
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/roi_align_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/roi_align_shape_inference.cpp
new file mode 100644
index 00000000000..d21c099baf9
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/roi_align_shape_inference.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <openvino/op/parameter.hpp>
+#include <openvino/op/roi_align.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+using namespace ov;
+
+TEST(StaticShapeInferenceTest, ROIAlignTest) {
+    const auto data = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
+    const auto rois = std::make_shared<op::v0::Parameter>(element::f32, PartialShape{-1, -1});
+    const auto batch_indices = std::make_shared<op::v0::Parameter>(element::i32, PartialShape{-1});
+    const auto op = std::make_shared<op::v3::ROIAlign>(data, rois, batch_indices, 2, 2, 1, 1.0f, "avg");
+    const std::vector<ov::StaticShape> input_shapes = {ov::StaticShape{2, 3, 5, 5},
+                                                       ov::StaticShape{7, 4},
+                                                       ov::StaticShape{7}};
+    std::vector<ov::StaticShape> output_shapes = {ov::StaticShape{}};
+    shape_inference(op.get(), input_shapes, output_shapes);
+
+    ASSERT_EQ(output_shapes[0], (StaticShape{7, 3, 2, 2}));
+}
\ No newline at end of file
diff --git a/src/tests/unit/cpu/shape_inference_test/roll_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/roll_shape_inference.cpp
new file mode 100644
index 00000000000..89aa0701860
--- /dev/null
+++ b/src/tests/unit/cpu/shape_inference_test/roll_shape_inference.cpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <openvino/op/constant.hpp>
+#include <openvino/op/parameter.hpp>
+#include <openvino/op/roll.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+
+TEST(StaticShapeInferenceTest, RollTest) {
+    auto arg =
+        std::make_shared<ov::op::v0::Parameter>(ov::element::f32,
+                                                ov::PartialShape{ov::Dimension::dynamic(), ov::Dimension::dynamic()});
+    auto shift = std::make_shared<ov::op::v0::Parameter>(ov::element::i64, ov::PartialShape{ov::Dimension::dynamic()});
+    auto axes = std::make_shared<ov::op::v0::Parameter>(ov::element::i32, ov::PartialShape{ov::Dimension::dynamic()});
+
+    auto roll = std::make_shared<ov::op::v7::Roll>(arg, shift, axes);
+
+    int32_t axes_val[] = {0, 1, -1};
+    auto axes_tensor = std::make_shared<ngraph::runtime::HostTensor>(ov::element::i32, ov::Shape{3}, axes_val);
+
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
+    constant_data[2] = axes_tensor;
+
+    const std::vector<ov::StaticShape> input_shapes = {ov::StaticShape{3, 3, 3},
+                                                       ov::StaticShape{3},
+                                                       ov::StaticShape{3}};
+    std::vector<ov::StaticShape> output_shapes = {ov::StaticShape{}};
+    shape_inference(roll.get(), input_shapes, output_shapes, constant_data);
+    ASSERT_EQ(output_shapes[0], input_shapes[0]);
+}
+
+TEST(StaticShapeInferenceTest, RollTestWithConstAxis) {
+    auto arg =
+        std::make_shared<ov::op::v0::Parameter>(ov::element::f32,
+                                                ov::PartialShape{ov::Dimension::dynamic(), ov::Dimension::dynamic()});
+    auto shift = std::make_shared<ov::op::v0::Parameter>(ov::element::i64, ov::PartialShape{ov::Dimension::dynamic()});
+    auto axes = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{3}, std::vector<int32_t>{0, 1, -1});
+    auto roll = std::make_shared<ov::op::v7::Roll>(arg, shift, axes);
+
+    const std::vector<ov::StaticShape> input_shapes = {ov::StaticShape{3, 3, 3},
+                                                       ov::StaticShape{3},
+                                                       ov::StaticShape{3}};
+    std::vector<ov::StaticShape> output_shapes = {ov::StaticShape{}};
+    shape_inference(roll.get(), input_shapes, output_shapes);
+    ASSERT_EQ(output_shapes[0], input_shapes[0]);
+}

From 16b39d15d0237ae022cad9251c65374c691cd82f Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 23 Dec 2021 07:19:34 +0300
Subject: [PATCH 101/160] Added BWD macro for all new ops (#9356)

---
 src/core/include/openvino/op/detection_output.hpp | 1 +
 src/core/include/openvino/op/matrix_nms.hpp       | 1 +
 src/core/include/openvino/op/max_pool.hpp         | 1 +
 src/core/include/openvino/op/multiclass_nms.hpp   | 1 +
 src/core/include/openvino/op/softmax.hpp          | 1 +
 src/core/src/op/detection_output.cpp              | 1 +
 src/core/src/op/matrix_nms.cpp                    | 1 +
 src/core/src/op/max_pool.cpp                      | 1 +
 src/core/src/op/multiclass_nms.cpp                | 2 ++
 src/core/src/op/softmax.cpp                       | 1 +
 10 files changed, 11 insertions(+)

diff --git a/src/core/include/openvino/op/detection_output.hpp b/src/core/include/openvino/op/detection_output.hpp
index 4bc2e542b27..b4365f3de07 100644
--- a/src/core/include/openvino/op/detection_output.hpp
+++ b/src/core/include/openvino/op/detection_output.hpp
@@ -70,6 +70,7 @@ public:
     using Attributes = op::util::DetectionOutputBase::AttributesBase;
 
     OPENVINO_OP("DetectionOutput", "opset8", op::util::DetectionOutputBase);
+    BWDCMP_RTTI_DECLARATION;
 
     DetectionOutput() = default;
     /// \brief Constructs a DetectionOutput operation
diff --git a/src/core/include/openvino/op/matrix_nms.hpp b/src/core/include/openvino/op/matrix_nms.hpp
index 907ecc6ec79..f12c64855d2 100644
--- a/src/core/include/openvino/op/matrix_nms.hpp
+++ b/src/core/include/openvino/op/matrix_nms.hpp
@@ -14,6 +14,7 @@ namespace v8 {
 class OPENVINO_API MatrixNms : public util::NmsBase {
 public:
     OPENVINO_OP("MatrixNms", "opset8", op::util::NmsBase);
+    BWDCMP_RTTI_DECLARATION;
 
     enum class DecayFunction { GAUSSIAN, LINEAR };
 
diff --git a/src/core/include/openvino/op/max_pool.hpp b/src/core/include/openvino/op/max_pool.hpp
index 33f4939b629..4934d7e33be 100644
--- a/src/core/include/openvino/op/max_pool.hpp
+++ b/src/core/include/openvino/op/max_pool.hpp
@@ -63,6 +63,7 @@ namespace v8 {
 class OPENVINO_API MaxPool : public op::util::MaxPoolBase {
 public:
     OPENVINO_OP("MaxPool", "opset8", op::util::MaxPoolBase);
+    BWDCMP_RTTI_DECLARATION;
 
     /// \brief Constructs an empty MaxPool operation.
     MaxPool() = default;
diff --git a/src/core/include/openvino/op/multiclass_nms.hpp b/src/core/include/openvino/op/multiclass_nms.hpp
index 5a3d108f6f0..e52191a4bbe 100644
--- a/src/core/include/openvino/op/multiclass_nms.hpp
+++ b/src/core/include/openvino/op/multiclass_nms.hpp
@@ -14,6 +14,7 @@ namespace v8 {
 class OPENVINO_API MulticlassNms : public util::NmsBase {
 public:
     OPENVINO_OP("MulticlassNms", "opset8", op::util::NmsBase);
+    BWDCMP_RTTI_DECLARATION;
 
     /// \brief Structure that specifies attributes of the operation
     struct Attributes {
diff --git a/src/core/include/openvino/op/softmax.hpp b/src/core/include/openvino/op/softmax.hpp
index 7decaa14a46..825e5bf531c 100644
--- a/src/core/include/openvino/op/softmax.hpp
+++ b/src/core/include/openvino/op/softmax.hpp
@@ -51,6 +51,7 @@ namespace v8 {
 class OPENVINO_API Softmax : public Op {
 public:
     OPENVINO_OP("Softmax", "opset8");
+    BWDCMP_RTTI_DECLARATION;
 
     Softmax() = default;
     /// \brief Constructs a softmax operation.
diff --git a/src/core/src/op/detection_output.cpp b/src/core/src/op/detection_output.cpp
index 27936f16bf6..cac74673f34 100644
--- a/src/core/src/op/detection_output.cpp
+++ b/src/core/src/op/detection_output.cpp
@@ -64,6 +64,7 @@ bool ov::op::v0::DetectionOutput::visit_attributes(AttributeVisitor& visitor) {
 }
 
 // ------------------------------ V8 ------------------------------
+BWDCMP_RTTI_DEFINITION(ov::op::v8::DetectionOutput);
 ov::op::v8::DetectionOutput::DetectionOutput(const Output<Node>& box_logits,
                                              const Output<Node>& class_preds,
                                              const Output<Node>& proposals,
diff --git a/src/core/src/op/matrix_nms.cpp b/src/core/src/op/matrix_nms.cpp
index ee5f0f959c3..026bb7cd47f 100644
--- a/src/core/src/op/matrix_nms.cpp
+++ b/src/core/src/op/matrix_nms.cpp
@@ -17,6 +17,7 @@
 #include "ngraph/util.hpp"
 
 using namespace ngraph;
+BWDCMP_RTTI_DEFINITION(ov::op::v8::MatrixNms);
 
 op::v8::MatrixNms::MatrixNms() : NmsBase(m_attrs.output_type, m_attrs.nms_top_k, m_attrs.keep_top_k) {}
 
diff --git a/src/core/src/op/max_pool.cpp b/src/core/src/op/max_pool.cpp
index ed9775c1d0c..4b5aada2cc8 100644
--- a/src/core/src/op/max_pool.cpp
+++ b/src/core/src/op/max_pool.cpp
@@ -16,6 +16,7 @@ using namespace std;
 using namespace ngraph;
 
 BWDCMP_RTTI_DEFINITION(op::v1::MaxPool);
+BWDCMP_RTTI_DEFINITION(op::v8::MaxPool);
 
 op::v1::MaxPool::MaxPool(const Output<Node>& arg,
                          const Strides& strides,
diff --git a/src/core/src/op/multiclass_nms.cpp b/src/core/src/op/multiclass_nms.cpp
index 39cdfc3691c..73a0fe286a4 100644
--- a/src/core/src/op/multiclass_nms.cpp
+++ b/src/core/src/op/multiclass_nms.cpp
@@ -18,6 +18,8 @@
 
 using namespace ngraph;
 
+BWDCMP_RTTI_DEFINITION(ov::op::v8::MulticlassNms);
+
 op::v8::MulticlassNms::MulticlassNms() : NmsBase(m_attrs.output_type, m_attrs.nms_top_k, m_attrs.keep_top_k) {}
 
 op::v8::MulticlassNms::MulticlassNms(const Output<Node>& boxes, const Output<Node>& scores, const Attributes& attrs)
diff --git a/src/core/src/op/softmax.cpp b/src/core/src/op/softmax.cpp
index 22ea4133a1a..c11642641a4 100644
--- a/src/core/src/op/softmax.cpp
+++ b/src/core/src/op/softmax.cpp
@@ -101,6 +101,7 @@ bool op::v1::Softmax::has_evaluate() const {
 }
 
 // *** SOFTMAX OP SET V8 ***
+BWDCMP_RTTI_DEFINITION(op::v8::Softmax);
 
 op::v8::Softmax::Softmax(const Output<Node>& arg, const int64_t axis) : Op({arg}), m_axis(axis) {
     constructor_validate_and_infer_types();

From 42350a705eb2dced88fbdb828decf11d5c295b90 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 23 Dec 2021 08:16:23 +0300
Subject: [PATCH 102/160] Remove legacy targets (#9333)

* Remove some legacy targets

* Replace some targets

* Removed inference_engine_plugin_api dependency

* Minor comment for developer config

* Fixed include paths

* Small fixes for static build

* Try to fix build pyopenvino

* Fixed comments

* Try to fix build

* Include OpenVINODeveloperPackage inside InferenceEngineDeveloperPackageConfig

* Try to fix GAPI tests
---
 cmake/developer_package/add_ie_target.cmake   |  8 +++++++
 .../frontends/frontends.cmake                 |  2 +-
 cmake/developer_package/plugins/plugins.cmake | 10 ++++----
 cmake/extra_modules.cmake                     | 24 +++++++++++++------
 ...renceEngineDeveloperPackageConfig.cmake.in |  5 ++++
 cmake/templates/OpenVINOConfig.cmake.in       |  6 -----
 .../OpenVINODeveloperPackageConfig.cmake.in   |  6 +++--
 docs/snippets/CMakeLists.txt                  |  3 +--
 docs/template_plugin/CMakeLists.txt           |  6 ++---
 docs/template_plugin/src/CMakeLists.txt       |  7 ++----
 .../tests/functional/CMakeLists.txt           |  4 ++--
 .../src/mkldnn_plugin/CMakeLists.txt          |  7 +-----
 .../src/vpu/common/CMakeLists.txt             |  6 ++---
 .../src/vpu/graph_transformer/CMakeLists.txt  |  4 ++--
 .../thirdparty/clDNN/src/CMakeLists.txt       |  4 ++--
 src/bindings/c/src/CMakeLists.txt             |  2 --
 .../python/src/pyopenvino/CMakeLists.txt      |  6 +++--
 src/cmake/ov_runtime.cmake                    | 20 +++++++++++-----
 src/common/legacy/CMakeLists.txt              |  9 +++----
 .../CMakeLists.txt                            | 11 ---------
 .../offline_transformations/CMakeLists.txt    |  4 ++--
 src/common/preprocessing/CMakeLists.txt       |  8 +++----
 src/common/snippets/CMakeLists.txt            |  4 ++--
 src/common/transformations/CMakeLists.txt     |  8 -------
 src/core/CMakeLists.txt                       | 13 ----------
 src/core/tests/CMakeLists.txt                 |  2 +-
 src/core/tests/engines_util/CMakeLists.txt    |  4 +---
 src/core/tests/frontend/shared/CMakeLists.txt |  4 ++--
 src/core/tests/util/CMakeLists.txt            |  2 +-
 src/frontends/common/CMakeLists.txt           | 11 ---------
 src/frontends/ir/CMakeLists.txt               |  6 ++---
 src/frontends/onnx/frontend/CMakeLists.txt    |  2 +-
 src/frontends/onnx/onnx_common/CMakeLists.txt |  2 +-
 src/frontends/paddle/CMakeLists.txt           |  2 +-
 src/inference/CMakeLists.txt                  | 14 -----------
 src/plugins/auto/CMakeLists.txt               |  2 --
 src/plugins/hetero/CMakeLists.txt             |  3 +--
 src/plugins/intel_gna/CMakeLists.txt          |  2 +-
 src/plugins/intel_gpu/CMakeLists.txt          |  8 ++-----
 .../subgraphs_dumper/tests/CMakeLists.txt     |  3 +--
 .../functional/plugin/cpu/CMakeLists.txt      |  2 +-
 .../functional/plugin/shared/CMakeLists.txt   |  2 +-
 .../common_test_utils/CMakeLists.txt          | 10 ++++----
 .../functional_test_utils/CMakeLists.txt      |  7 +++---
 .../mocks/mock_engine/CMakeLists.txt          |  2 +-
 .../lpt_ngraph_functions/CMakeLists.txt       |  2 +-
 .../ngraph_functions/CMakeLists.txt           |  2 +-
 src/tests/unit/auto/CMakeLists.txt            |  6 ++---
 src/tests/unit/vpu/CMakeLists.txt             |  4 ++--
 .../functional/shared_tests/CMakeLists.txt    |  4 ++--
 .../readers/ir_reader_v7/CMakeLists.txt       |  2 +-
 tests/fuzz/src/CMakeLists.txt                 |  2 +-
 .../mock_mo_python_api/CMakeLists.txt         |  2 +-
 .../ov_mock_mo_frontend/CMakeLists.txt        |  3 +--
 54 files changed, 123 insertions(+), 181 deletions(-)

diff --git a/cmake/developer_package/add_ie_target.cmake b/cmake/developer_package/add_ie_target.cmake
index 0aff25cddc2..a940847423d 100644
--- a/cmake/developer_package/add_ie_target.cmake
+++ b/cmake/developer_package/add_ie_target.cmake
@@ -133,6 +133,10 @@ function(addIeTarget)
     endif()
 endfunction()
 
+function(ov_add_target)
+    addIeTarget(${ARGV})
+endfunction()
+
 #[[
 Wrapper function over addIeTarget, that also adds a test with the same name.
 You could use
@@ -163,3 +167,7 @@ function(addIeTargetTest)
             COMPONENT tests
             EXCLUDE_FROM_ALL)
 endfunction()
+
+function(ov_add_test_target)
+    addIeTargetTest(${ARGV})
+endfunction()
diff --git a/cmake/developer_package/frontends/frontends.cmake b/cmake/developer_package/frontends/frontends.cmake
index 43842a66114..e585b29d044 100644
--- a/cmake/developer_package/frontends/frontends.cmake
+++ b/cmake/developer_package/frontends/frontends.cmake
@@ -177,7 +177,7 @@ macro(ov_add_frontend)
 
     ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
-    target_link_libraries(${TARGET_NAME} PRIVATE frontend_common::static ${OV_FRONTEND_LINK_LIBRARIES})
+    target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime ${OV_FRONTEND_LINK_LIBRARIES})
 
     # WA for TF frontends which always requires protobuf (not protobuf-lite)
     # if TF FE is built in static mode, use protobuf for all other FEs
diff --git a/cmake/developer_package/plugins/plugins.cmake b/cmake/developer_package/plugins/plugins.cmake
index 6068d4e5af0..8095dba3f0d 100644
--- a/cmake/developer_package/plugins/plugins.cmake
+++ b/cmake/developer_package/plugins/plugins.cmake
@@ -80,11 +80,7 @@ function(ie_add_plugin)
         ie_add_vs_version_file(NAME ${IE_PLUGIN_NAME}
             FILEDESCRIPTION "Inference Engine ${IE_PLUGIN_DEVICE_NAME} device plugin library")
 
-        if(TARGET IE::inference_engine_plugin_api)
-            target_link_libraries(${IE_PLUGIN_NAME} PRIVATE IE::inference_engine IE::inference_engine_plugin_api)
-        else()
-            target_link_libraries(${IE_PLUGIN_NAME} PRIVATE inference_engine inference_engine_plugin_api)
-        endif()
+        target_link_libraries(${IE_PLUGIN_NAME} PRIVATE openvino::runtime openvino::runtime::dev)
 
         if(WIN32)
             set_target_properties(${IE_PLUGIN_NAME} PROPERTIES COMPILE_PDB_NAME ${IE_PLUGIN_NAME})
@@ -170,6 +166,10 @@ function(ie_add_plugin)
     set(${IE_PLUGIN_DEVICE_NAME}_AS_EXTENSION "${IE_PLUGIN_AS_EXTENSION}" CACHE INTERNAL "" FORCE)
 endfunction()
 
+function(ov_add_plugin)
+    ie_add_plugin(${ARGN})
+endfunction()
+
 #
 # ie_register_plugins_dynamic(MAIN_TARGET <main target name>
 #                             POSSIBLE_PLUGINS <list of plugins which can be build by this repo>)
diff --git a/cmake/extra_modules.cmake b/cmake/extra_modules.cmake
index b9dc0d91337..8262742d955 100644
--- a/cmake/extra_modules.cmake
+++ b/cmake/extra_modules.cmake
@@ -63,22 +63,32 @@ function(register_extra_modules)
     openvino_developer_export_targets(COMPONENT core TARGETS ngraph)
 
     set(InferenceEngineDeveloperPackage_DIR "${CMAKE_CURRENT_BINARY_DIR}/runtime")
+    set(OpenVINODeveloperPackage_DIR "${CMAKE_BINARY_DIR}/runtime")
 
-    function(generate_fake_dev_package)
-        set(iedevconfig_file "${InferenceEngineDeveloperPackage_DIR}/InferenceEngineDeveloperPackageConfig.cmake")
-        file(REMOVE "${iedevconfig_file}")
+    function(generate_fake_dev_package NS)
+        if (NS STREQUAL "openvino")
+            set(devconfig_file "${OpenVINODeveloperPackage_DIR}/OpenVINODeveloperPackageConfig.cmake")
+        else()
+            set(devconfig_file "${InferenceEngineDeveloperPackage_DIR}/InferenceEngineDeveloperPackageConfig.cmake")
+        endif()
+        file(REMOVE "${devconfig_file}")
 
-        file(WRITE "${iedevconfig_file}" "\# !! AUTOGENERATED: DON'T EDIT !!\n\n")
-        file(APPEND "${iedevconfig_file}" "ie_deprecated_no_errors()\n")
+        file(WRITE "${devconfig_file}" "\# !! AUTOGENERATED: DON'T EDIT !!\n\n")
+        file(APPEND "${devconfig_file}" "ie_deprecated_no_errors()\n")
 
         foreach(target IN LISTS ${openvino_export_components})
             if(target)
-                file(APPEND "${iedevconfig_file}" "add_library(IE::${target} ALIAS ${target})\n")
+                file(APPEND "${devconfig_file}" "add_library(${NS}::${target} ALIAS ${target})\n")
             endif()
         endforeach()
+        if ("${NS}" STREQUAL "openvino")
+            file(APPEND "${devconfig_file}" "add_library(${NS}::runtime ALIAS ov_runtime)\n")
+            file(APPEND "${devconfig_file}" "add_library(${NS}::runtime::dev ALIAS ov_runtime_dev)\n")
+        endif()
     endfunction()
 
-    generate_fake_dev_package()
+    generate_fake_dev_package("openvino")
+    generate_fake_dev_package("IE")
 
     # automatically import plugins from the 'runtime/plugins' folder
     file(GLOB local_extra_modules "runtime/plugins/*")
diff --git a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
index ef7d2beec02..5bc30250661 100644
--- a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
+++ b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
@@ -49,6 +49,11 @@ find_dependency(ngraph
                 NO_CMAKE_FIND_ROOT_PATH
                 NO_DEFAULT_PATH)
 
+find_dependency(OpenVINODeveloperPackage
+                PATHS "${CMAKE_CURRENT_LIST_DIR}"
+                NO_CMAKE_FIND_ROOT_PATH
+                NO_DEFAULT_PATH)
+
 if(TARGET openvino::runtime AND NOT TARGET IE::runtime)
     add_library(IE::runtime INTERFACE IMPORTED)
     set_target_properties(IE::runtime PROPERTIES
diff --git a/cmake/templates/OpenVINOConfig.cmake.in b/cmake/templates/OpenVINOConfig.cmake.in
index 5ab40ad9153..cade1754859 100644
--- a/cmake/templates/OpenVINOConfig.cmake.in
+++ b/cmake/templates/OpenVINOConfig.cmake.in
@@ -32,14 +32,8 @@
 #   `openvino::runtime::c`
 #   The OpenVINO C Inference Runtime
 #
-#   `openvino::core`
-#   The OpenVINO C++ Core Runtime
-#
 #  Frontend specific targets:
 #
-#   `openvino::frontend::common`
-#   OpenVINO frontend common
-#
 #   `openvino::frontend::onnx`
 #   ONNX FrontEnd target (optional)
 #
diff --git a/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
index da3a7b0427b..b280c11fb62 100644
--- a/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
+++ b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
@@ -6,13 +6,15 @@
 
 include(CMakeFindDependencyMacro)
 
+set_and_check(OpenVINO_SOURCE_DIR "@OpenVINO_SOURCE_DIR@")
+
 # Variables to export in plugin's projects
 
 set(ie_options "@IE_OPTIONS@;CMAKE_BUILD_TYPE;CMAKE_SKIP_RPATH")
 list(APPEND ie_options CMAKE_CXX_COMPILER_LAUNCHER CMAKE_C_COMPILER_LAUNCHER)
 file(TO_CMAKE_PATH "${CMAKE_CURRENT_LIST_DIR}" cache_path)
 
-message(STATUS "The following CMake options are exported from Inference Engine Developer package")
+message(STATUS "The following CMake options are exported from OpenVINO Developer package")
 message("")
 foreach(option IN LISTS ie_options)
     if(NOT DEFINED "${option}")
@@ -30,7 +32,7 @@ set_and_check(gflags_DIR "@gflags_BINARY_DIR@")
 #
 
 find_dependency(IEDevScripts
-                PATHS "@OpenVINO_SOURCE_DIR@/cmake/developer_package"
+                PATHS "${OpenVINO_SOURCE_DIR}/cmake/developer_package"
                 NO_CMAKE_FIND_ROOT_PATH
                 NO_DEFAULT_PATH)
 
diff --git a/docs/snippets/CMakeLists.txt b/docs/snippets/CMakeLists.txt
index e240d0b3cfe..a073b5caa24 100644
--- a/docs/snippets/CMakeLists.txt
+++ b/docs/snippets/CMakeLists.txt
@@ -56,5 +56,4 @@ if(NOT MSVC)
     endif()
 endif()
 
-target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_plugin_api
-    ngraph inference_engine_transformations)
+target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime openvino::runtime::dev)
diff --git a/docs/template_plugin/CMakeLists.txt b/docs/template_plugin/CMakeLists.txt
index 77b90650db5..3080c74b619 100644
--- a/docs/template_plugin/CMakeLists.txt
+++ b/docs/template_plugin/CMakeLists.txt
@@ -5,11 +5,11 @@
 # [cmake:main]
 cmake_minimum_required(VERSION 3.13)
 
-project(InferenceEngineTemplatePlugin)
+project(OpenVINOTemplatePlugin)
 
-set(IE_MAIN_TEMPLATE_PLUGIN_SOURCE_DIR ${InferenceEngineTemplatePlugin_SOURCE_DIR})
+set(IE_MAIN_TEMPLATE_PLUGIN_SOURCE_DIR ${OpenVINOTemplatePlugin_SOURCE_DIR})
 
-find_package(InferenceEngineDeveloperPackage REQUIRED)
+find_package(OpenVINODeveloperPackage REQUIRED)
 
 if(CMAKE_COMPILER_IS_GNUCXX)
     ie_add_compiler_flags(-Wall)
diff --git a/docs/template_plugin/src/CMakeLists.txt b/docs/template_plugin/src/CMakeLists.txt
index 18b20731e18..f413a8f3260 100644
--- a/docs/template_plugin/src/CMakeLists.txt
+++ b/docs/template_plugin/src/CMakeLists.txt
@@ -9,7 +9,7 @@ file(GLOB_RECURSE SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 file(GLOB_RECURSE HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
 
 # adds a shared library with plugin
-ie_add_plugin(NAME ${TARGET_NAME}
+ov_add_plugin(NAME ${TARGET_NAME}
               DEVICE_NAME "TEMPLATE"
               SOURCES ${SOURCES} ${HEADERS}
               SKIP_INSTALL # ATTENTION: uncomment to install component
@@ -25,11 +25,8 @@ target_include_directories(${TARGET_NAME} PRIVATE
 
 # link common Inference Engine libraries
 target_link_libraries(${TARGET_NAME} PRIVATE
-    IE::inference_engine
-    IE::inference_engine_transformations
     interpreter_backend
-    ngraph::reference
-    ${NGRAPH_LIBRARIES})
+    openvino::ngraph_reference)
 
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
diff --git a/docs/template_plugin/tests/functional/CMakeLists.txt b/docs/template_plugin/tests/functional/CMakeLists.txt
index 0a0309992b6..9015a50f922 100644
--- a/docs/template_plugin/tests/functional/CMakeLists.txt
+++ b/docs/template_plugin/tests/functional/CMakeLists.txt
@@ -5,13 +5,13 @@
 # [cmake:functional_tests]
 set(TARGET_NAME templateFuncTests)
 
-addIeTargetTest(
+ov_add_test_target(
         NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
         DEPENDENCIES
             templatePlugin
         LINK_LIBRARIES
-            IE::funcSharedTests
+            openvino::funcSharedTests
         INCLUDES
             "${IE_MAIN_TEMPLATE_PLUGIN_SOURCE_DIR}/include"
             "${CMAKE_CURRENT_SOURCE_DIR}/op_reference"
diff --git a/inference-engine/src/mkldnn_plugin/CMakeLists.txt b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
index e6397b6a77c..f2a9f6c5339 100644
--- a/inference-engine/src/mkldnn_plugin/CMakeLists.txt
+++ b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
@@ -41,9 +41,6 @@ if(SELECTIVE_BUILD STREQUAL "ON")
 endif()
 
 target_link_libraries(${TARGET_NAME} PRIVATE mkldnn
-                                             inference_engine
-                                             inference_engine_transformations
-                                             inference_engine_lp_transformations
                                              ov_shape_inference
                                              inference_engine_snippets)
 
@@ -71,10 +68,8 @@ add_library(${TARGET_NAME}_obj OBJECT ${SOURCES} ${HEADERS})
 link_system_libraries(${TARGET_NAME}_obj PUBLIC mkldnn)
 
 target_include_directories(${TARGET_NAME}_obj PRIVATE $<TARGET_PROPERTY:inference_engine_preproc_s,INTERFACE_INCLUDE_DIRECTORIES>
-                                                      $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>
-                                                      $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
+                                                      $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:openvino::itt,INTERFACE_INCLUDE_DIRECTORIES>
-                                                      $<TARGET_PROPERTY:inference_engine_lp_transformations,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:ov_shape_inference,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:inference_engine_snippets,INTERFACE_INCLUDE_DIRECTORIES>
                                               PUBLIC  ${CMAKE_CURRENT_SOURCE_DIR}
diff --git a/inference-engine/src/vpu/common/CMakeLists.txt b/inference-engine/src/vpu/common/CMakeLists.txt
index d691b3137c4..24ac28f87bf 100644
--- a/inference-engine/src/vpu/common/CMakeLists.txt
+++ b/inference-engine/src/vpu/common/CMakeLists.txt
@@ -36,8 +36,8 @@ function(add_common_target TARGET_NAME STATIC_IE)
             $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
 
     target_include_directories(${TARGET_NAME} SYSTEM PUBLIC
-            $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>
-            $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
+            $<TARGET_PROPERTY:openvino::runtime,INTERFACE_INCLUDE_DIRECTORIES>
+            $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>
             $<TARGET_PROPERTY:inference_engine_legacy,INTERFACE_INCLUDE_DIRECTORIES>)
 
     if(WIN32)
@@ -52,7 +52,7 @@ function(add_common_target TARGET_NAME STATIC_IE)
 
     openvino_developer_export_targets(COMPONENT vpu TARGETS ${TARGET_NAME})
 
-    target_link_libraries(${TARGET_NAME} PUBLIC ngraph inference_engine_transformations
+    target_link_libraries(${TARGET_NAME} PUBLIC openvino::runtime openvino::runtime::dev
                                          PRIVATE openvino::itt)
 
     if(NOT STATIC_IE)
diff --git a/inference-engine/src/vpu/graph_transformer/CMakeLists.txt b/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
index 13130799383..40a3751c2ef 100644
--- a/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
+++ b/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
@@ -53,8 +53,8 @@ function(add_graph_transformer_target TARGET_NAME STATIC_IE)
 
     target_link_libraries(${TARGET_NAME}
         PUBLIC
-            ngraph
-            inference_engine_plugin_api
+            openvino::runtime
+            openvino::runtime::dev
         PRIVATE
             openvino::itt
     )
diff --git a/inference-engine/thirdparty/clDNN/src/CMakeLists.txt b/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
index f4d66f8872e..f56f727d435 100644
--- a/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
@@ -37,8 +37,8 @@ target_link_libraries(${TARGET_NAME} PUBLIC OpenCL)
 target_link_libraries(${TARGET_NAME} PRIVATE ov_intel_gpu_kernels
                                              ov_intel_gpu_runtime
                                              openvino::itt
-                                             inference_engine_plugin_api
-                                             inference_engine)
+                                             openvino::runtime::dev
+                                             openvino::runtime)
 
 if(ENABLE_ONEDNN_FOR_GPU)
     target_link_libraries(${TARGET_NAME} PUBLIC onednn_gpu_tgt)
diff --git a/src/bindings/c/src/CMakeLists.txt b/src/bindings/c/src/CMakeLists.txt
index 97c9930c14c..ba507379bec 100644
--- a/src/bindings/c/src/CMakeLists.txt
+++ b/src/bindings/c/src/CMakeLists.txt
@@ -34,8 +34,6 @@ set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME runtime::c)
 export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
        APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
-
 # install
 
 ie_cpack_add_component(core_c DEPENDS core)
diff --git a/src/bindings/python/src/pyopenvino/CMakeLists.txt b/src/bindings/python/src/pyopenvino/CMakeLists.txt
index 1a5d31bfcfa..adaf9b9f760 100644
--- a/src/bindings/python/src/pyopenvino/CMakeLists.txt
+++ b/src/bindings/python/src/pyopenvino/CMakeLists.txt
@@ -4,7 +4,7 @@
 project (pyopenvino)
 
 if(NOT DEFINED OpenVINO_SOURCE_DIR)
-    find_package(InferenceEngineDeveloperPackage REQUIRED)
+    find_package(OpenVINODeveloperPackage REQUIRED)
 endif()
 
 # PYTHON_VERSION_MAJOR and PYTHON_VERSION_MINOR are defined inside pybind11
@@ -56,8 +56,10 @@ pybind11_add_module(${PROJECT_NAME} MODULE ${SOURCES})
 
 if(TARGET offline_transformations)
     set(OFFLINE_TRANSFORMATIONS_LIB offline_transformations)
-else()
+elseif(TARGET IE::offline_transformations)
     set(OFFLINE_TRANSFORMATIONS_LIB IE::offline_transformations)
+else()
+    set(OFFLINE_TRANSFORMATIONS_LIB openvino::offline_transformations)
 endif()
 
 target_include_directories(${PROJECT_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/..")
diff --git a/src/cmake/ov_runtime.cmake b/src/cmake/ov_runtime.cmake
index 650b878a403..3ff4a28d36d 100644
--- a/src/cmake/ov_runtime.cmake
+++ b/src/cmake/ov_runtime.cmake
@@ -16,9 +16,10 @@ set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME runtime)
 ie_add_vs_version_file(NAME ${TARGET_NAME} FILEDESCRIPTION "OpenVINO runtime library")
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
-target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:$<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>>
-                                                 $<BUILD_INTERFACE:$<TARGET_PROPERTY:frontend_common,INTERFACE_INCLUDE_DIRECTORIES>>
-                                                 $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>>
+target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/core/include>
+                                                 $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/frontends/common/include>
+                                                 $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/inference/include>
+                                                 $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/inference/include/ie>
 )
 
 target_link_libraries(${TARGET_NAME} PRIVATE ngraph_reference
@@ -63,9 +64,16 @@ install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
 
 # --------------- OpenVINO runtime library dev ------------------------------
 add_library(${TARGET_NAME}_dev INTERFACE)
-target_link_libraries(${TARGET_NAME}_dev INTERFACE inference_engine_plugin_api)
-target_include_directories(${TARGET_NAME}_dev INTERFACE $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>>
-                                                        $<BUILD_INTERFACE:$<TARGET_PROPERTY:inference_engine_lp_transformations,INTERFACE_INCLUDE_DIRECTORIES>>)
+target_include_directories(${TARGET_NAME}_dev INTERFACE $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/common/transformations/include>
+                                                        $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/inference/dev_api>
+                                                        $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/common/low_precision_transformations/include>
+                                                        $<TARGET_PROPERTY:inference_engine_preproc,INTERFACE_INCLUDE_DIRECTORIES>
+                                                        )
+
+target_compile_definitions(${TARGET_NAME}_dev INTERFACE
+    $<TARGET_PROPERTY:inference_engine_preproc,INTERFACE_COMPILE_DEFINITIONS>)
+
+target_link_libraries(${TARGET_NAME}_dev INTERFACE ${TARGET_NAME} pugixml::static openvino::itt openvino::util)
 add_library(openvino::runtime::dev ALIAS ${TARGET_NAME}_dev)
 set_ie_threading_interface_for(${TARGET_NAME}_dev)
 set_target_properties(${TARGET_NAME}_dev PROPERTIES EXPORT_NAME runtime::dev)
diff --git a/src/common/legacy/CMakeLists.txt b/src/common/legacy/CMakeLists.txt
index 05587686b60..76823f4afaf 100644
--- a/src/common/legacy/CMakeLists.txt
+++ b/src/common/legacy/CMakeLists.txt
@@ -37,9 +37,7 @@ target_include_directories(${TARGET_NAME}_obj PRIVATE
     ${PUBLIC_HEADERS_DIR}
     ${CMAKE_CURRENT_SOURCE_DIR}/src
     $<TARGET_PROPERTY:inference_engine_obj,SOURCE_DIR>/src # For CNNNetworkNGraphImpl
-    $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>
-    $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
-    $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>
+    $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>
     $<TARGET_PROPERTY:pugixml::static,INTERFACE_INCLUDE_DIRECTORIES>)
 
 target_compile_definitions(${TARGET_NAME}_obj PRIVATE $<TARGET_PROPERTY:ngraph,INTERFACE_COMPILE_DEFINITIONS>)
@@ -54,9 +52,8 @@ add_library(${TARGET_NAME} STATIC EXCLUDE_FROM_ALL
     ${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp
     $<TARGET_OBJECTS:${TARGET_NAME}_obj>)
 
-target_link_libraries(${TARGET_NAME} PUBLIC inference_engine
-                                     PRIVATE pugixml::static openvino::itt
-                                             ngraph inference_engine_transformations)
+target_link_libraries(${TARGET_NAME} PUBLIC openvino::runtime
+                                     PRIVATE pugixml::static openvino::itt openvino::runtime::dev)
 
 target_include_directories(${TARGET_NAME} INTERFACE
     $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>)
diff --git a/src/common/low_precision_transformations/CMakeLists.txt b/src/common/low_precision_transformations/CMakeLists.txt
index d0bdc4a11a2..cf19e3f36a1 100644
--- a/src/common/low_precision_transformations/CMakeLists.txt
+++ b/src/common/low_precision_transformations/CMakeLists.txt
@@ -52,14 +52,3 @@ target_include_directories(${TARGET_NAME} INTERFACE
 # developer package
 
 openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME})
-
-# install
-
-# TODO: remove this
-if(BUILD_SHARED_LIBS)
-    install(TARGETS ${TARGET_NAME}
-            RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-            LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
-else()
-    ov_install_static_lib(${TARGET_NAME} core)
-endif()
diff --git a/src/common/offline_transformations/CMakeLists.txt b/src/common/offline_transformations/CMakeLists.txt
index 359cda2fa77..25fefe22c26 100644
--- a/src/common/offline_transformations/CMakeLists.txt
+++ b/src/common/offline_transformations/CMakeLists.txt
@@ -19,8 +19,8 @@ source_group("include" FILES ${PUBLIC_HEADERS})
 
 add_library(${TARGET_NAME} STATIC EXCLUDE_FROM_ALL ${LIBRARY_SRC} ${PUBLIC_HEADERS})
 
-target_link_libraries(${TARGET_NAME} PUBLIC ngraph inference_engine_transformations ngraph::reference
-                                     PRIVATE openvino::itt pugixml::static openvino::frontend::common
+target_link_libraries(${TARGET_NAME} PUBLIC openvino::runtime::dev
+                                     PRIVATE openvino::itt pugixml::static openvino::runtime ngraph::reference
                                              nlohmann_json_schema_validator nlohmann_json)
 
 target_include_directories(${TARGET_NAME} PUBLIC ${PUBLIC_HEADERS_DIR}
diff --git a/src/common/preprocessing/CMakeLists.txt b/src/common/preprocessing/CMakeLists.txt
index a15fa60e039..c0baef8cc06 100644
--- a/src/common/preprocessing/CMakeLists.txt
+++ b/src/common/preprocessing/CMakeLists.txt
@@ -99,7 +99,7 @@ ie_faster_build(${TARGET_NAME}_obj
 )
 
 target_compile_definitions(${TARGET_NAME}_obj PRIVATE IMPLEMENT_INFERENCE_ENGINE_PLUGIN
-                                                      $<TARGET_PROPERTY:inference_engine,INTERFACE_COMPILE_DEFINITIONS>
+                                                      $<TARGET_PROPERTY:openvino::runtime,INTERFACE_COMPILE_DEFINITIONS>
                                                       $<TARGET_PROPERTY:fluid,INTERFACE_COMPILE_DEFINITIONS>
                                                       $<$<BOOL:${ENABLE_GAPI_PREPROCESSING}>:ENABLE_GAPI_PREPROCESSING>)
 
@@ -139,7 +139,7 @@ if(ENABLE_GAPI_PREPROCESSING)
     target_link_libraries(${TARGET_NAME} PRIVATE fluid openvino::itt openvino::util)
 
     if(BUILD_SHARED_LIBS)
-        target_link_libraries(${TARGET_NAME} PRIVATE inference_engine)
+        target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime)
     else()
         # for static linkage the dependencies are in opposite order
         target_link_libraries(ov_runtime PRIVATE ${TARGET_NAME})
@@ -163,7 +163,7 @@ endif()
 
 target_include_directories(${TARGET_NAME} INTERFACE
     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>
-    $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
+    $<TARGET_PROPERTY:ov_runtime,INTERFACE_INCLUDE_DIRECTORIES>)
 
 # Static library used for unit tests which are always built
 
@@ -178,7 +178,7 @@ if(WIN32)
     set_target_properties(${TARGET_NAME}_s PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}_s)
 endif()
 
-target_link_libraries(${TARGET_NAME}_s PRIVATE fluid openvino::itt inference_engine)
+target_link_libraries(${TARGET_NAME}_s PRIVATE fluid openvino::itt openvino::runtime)
 
 target_compile_definitions(${TARGET_NAME}_s INTERFACE USE_STATIC_IE)
 
diff --git a/src/common/snippets/CMakeLists.txt b/src/common/snippets/CMakeLists.txt
index de6b5dd3992..7eb2c5712d3 100644
--- a/src/common/snippets/CMakeLists.txt
+++ b/src/common/snippets/CMakeLists.txt
@@ -25,8 +25,8 @@ ie_faster_build(${TARGET_NAME}
     UNITY
 )
 
-target_link_libraries(${TARGET_NAME} PUBLIC ngraph inference_engine_transformations
-                                     PRIVATE ngraph_reference)
+target_link_libraries(${TARGET_NAME} PUBLIC openvino::runtime
+                                     PRIVATE ngraph_reference openvino::runtime::dev)
 
 target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
                                           PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
diff --git a/src/common/transformations/CMakeLists.txt b/src/common/transformations/CMakeLists.txt
index 97deaaa294f..af300ca433e 100644
--- a/src/common/transformations/CMakeLists.txt
+++ b/src/common/transformations/CMakeLists.txt
@@ -53,11 +53,3 @@ target_link_libraries(${TARGET_NAME} INTERFACE openvino::runtime)
 # developer package
 
 openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME})
-
-# install
-
-# TODO remove this
-install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
diff --git a/src/core/CMakeLists.txt b/src/core/CMakeLists.txt
index 1ebda071d61..6562d8127e2 100644
--- a/src/core/CMakeLists.txt
+++ b/src/core/CMakeLists.txt
@@ -114,23 +114,10 @@ add_library(openvino::core ALIAS ngraph)
 
 target_include_directories(ngraph INTERFACE $<BUILD_INTERFACE:${OV_CORE_INCLUDE_PATH}>)
 
-#-----------------------------------------------------------------------------------------------
-# Export for build tree
-#-----------------------------------------------------------------------------------------------
-
-export(TARGETS ngraph NAMESPACE openvino::
-       APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
-
 #-----------------------------------------------------------------------------------------------
 # Installation logic...
 #-----------------------------------------------------------------------------------------------
 
-install(TARGETS ngraph EXPORT OpenVINOTargets
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core
-        INCLUDES DESTINATION runtime/include)
-
 install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/include/
         DESTINATION "runtime/include"
         COMPONENT core_dev
diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt
index d47c2b65e0e..1e3dbf4a88b 100644
--- a/src/core/tests/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -409,7 +409,7 @@ set(SRC
 
 # For type relaxed types
 set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/threading.cpp
-        PROPERTIES INCLUDE_DIRECTORIES $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>)
+        PROPERTIES INCLUDE_DIRECTORIES $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>)
 
 # Add include path to so_extension.hpp
 set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/extension.cpp
diff --git a/src/core/tests/engines_util/CMakeLists.txt b/src/core/tests/engines_util/CMakeLists.txt
index 0d585cb16aa..9165e936cfb 100644
--- a/src/core/tests/engines_util/CMakeLists.txt
+++ b/src/core/tests/engines_util/CMakeLists.txt
@@ -8,9 +8,7 @@ add_library(engines_test_util STATIC EXCLUDE_FROM_ALL ${ENGINES_UTIL_SRC})
 
 ie_faster_build(engines_test_util UNITY)
 
-target_include_directories(engines_test_util PRIVATE $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
-target_include_directories(engines_test_util PRIVATE $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>)
-target_link_libraries(engines_test_util PUBLIC ngraph inference_engine inference_engine_transformations gtest gmock ngraph_test_util)
+target_link_libraries(engines_test_util PUBLIC openvino::runtime openvino::runtime::dev gtest gmock ngraph_test_util)
 target_include_directories(engines_test_util PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 
 add_clang_format_target(engines_test_util_clang FOR_SOURCES ${ENGINES_UTIL_SRC})
diff --git a/src/core/tests/frontend/shared/CMakeLists.txt b/src/core/tests/frontend/shared/CMakeLists.txt
index 71cf6f5c92a..b2fa3cb7ba1 100644
--- a/src/core/tests/frontend/shared/CMakeLists.txt
+++ b/src/core/tests/frontend/shared/CMakeLists.txt
@@ -14,8 +14,8 @@ add_subdirectory(test_builtin_extensions_2)
 
 target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
 target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/../..)
-target_link_libraries(${TARGET_NAME} PUBLIC frontend_common engines_test_util
-        offline_transformations ngraph cnpy commonTestUtils ngraph_test_util openvino::util
+target_link_libraries(${TARGET_NAME} PUBLIC engines_test_util openvino::runtime
+        offline_transformations cnpy commonTestUtils ngraph_test_util openvino::util
         nlohmann_json_schema_validator test_builtin_extensions_1 test_builtin_extensions_2)
 
 target_compile_definitions(${TARGET_NAME}
diff --git a/src/core/tests/util/CMakeLists.txt b/src/core/tests/util/CMakeLists.txt
index b0f7cad2323..59bdad22fa1 100644
--- a/src/core/tests/util/CMakeLists.txt
+++ b/src/core/tests/util/CMakeLists.txt
@@ -8,7 +8,7 @@ add_library(ngraph_test_util STATIC EXCLUDE_FROM_ALL ${UTIL_SRC})
 
 ie_faster_build(ngraph_test_util UNITY)
 
-target_link_libraries(ngraph_test_util PUBLIC ngraph gtest gmock)
+target_link_libraries(ngraph_test_util PUBLIC openvino::runtime gtest gmock)
 target_include_directories(ngraph_test_util PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 
 file(GLOB_RECURSE all_util_src "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp" "${CMAKE_CURRENT_SOURCE_DIR}/*.hpp")
diff --git a/src/frontends/common/CMakeLists.txt b/src/frontends/common/CMakeLists.txt
index 0402c98e6bf..96c4775cb3f 100644
--- a/src/frontends/common/CMakeLists.txt
+++ b/src/frontends/common/CMakeLists.txt
@@ -69,17 +69,6 @@ add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME})
 openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME})
 
 # Installation rules for shared version only
-
-set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME frontend::common)
-
-install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
-
 install(DIRECTORY ${FRONTEND_INCLUDE_DIR}/openvino
         DESTINATION ${FRONTEND_INSTALL_INCLUDE}
         COMPONENT core_dev)
-
-export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
-       APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
diff --git a/src/frontends/ir/CMakeLists.txt b/src/frontends/ir/CMakeLists.txt
index 120bae4b6d5..0a3d2617c39 100644
--- a/src/frontends/ir/CMakeLists.txt
+++ b/src/frontends/ir/CMakeLists.txt
@@ -4,9 +4,9 @@
 
 ov_add_frontend(NAME ir
                 FILEDESCRIPTION "FrontEnd to load OpenVINO IR file format"
-                LINK_LIBRARIES inference_engine_transformations pugixml::static
-                               # TODO: remove dependencies below in CVS-69781
-                               inference_engine inference_engine_plugin_api)
+                LINK_LIBRARIES pugixml::static
+                               # TODO: remove dependency below in CVS-69781
+                               openvino::runtime::dev)
 
 # Add include path to so_extension.hpp
 set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/src/frontend.cpp
diff --git a/src/frontends/onnx/frontend/CMakeLists.txt b/src/frontends/onnx/frontend/CMakeLists.txt
index aaf2b473682..f75a2917d81 100644
--- a/src/frontends/onnx/frontend/CMakeLists.txt
+++ b/src/frontends/onnx/frontend/CMakeLists.txt
@@ -7,7 +7,7 @@ ov_add_frontend(NAME onnx
                 PROTOBUF_LITE
                 SKIP_NCC_STYLE
                 FILEDESCRIPTION "FrontEnd to load and convert ONNX file format"
-                LINK_LIBRARIES ngraph::builder openvino::util onnx_common inference_engine_transformations)
+                LINK_LIBRARIES ngraph::builder openvino::util onnx_common openvino::runtime::dev)
 
 set(ONNX_OPSET_VERSION 15 CACHE INTERNAL "Supported version of ONNX operator set")
 target_compile_definitions(${TARGET_NAME} PRIVATE ONNX_OPSET_VERSION=${ONNX_OPSET_VERSION})
diff --git a/src/frontends/onnx/onnx_common/CMakeLists.txt b/src/frontends/onnx/onnx_common/CMakeLists.txt
index 7921a6d58fb..026882c8c90 100644
--- a/src/frontends/onnx/onnx_common/CMakeLists.txt
+++ b/src/frontends/onnx/onnx_common/CMakeLists.txt
@@ -25,7 +25,7 @@ set(ONNX_COMMON_SRC_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src)
 target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${ONNX_COMMON_INCLUDE_DIR}>
                                                  $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>)
 
-target_link_libraries(${TARGET_NAME} PRIVATE ngraph)
+target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime)
 
 if(ONNX_USE_LITE_PROTO)
     link_system_libraries(${TARGET_NAME} PUBLIC onnx_proto onnx ${Protobuf_LITE_LIBRARIES})
diff --git a/src/frontends/paddle/CMakeLists.txt b/src/frontends/paddle/CMakeLists.txt
index c42ead4cf8f..1d043d7e812 100644
--- a/src/frontends/paddle/CMakeLists.txt
+++ b/src/frontends/paddle/CMakeLists.txt
@@ -6,4 +6,4 @@ ov_add_frontend(NAME paddle
                 LINKABLE_FRONTEND
                 PROTOBUF_LITE
                 FILEDESCRIPTION "FrontEnd to load and convert PaddlePaddle file format"
-                LINK_LIBRARIES inference_engine_transformations)
+                LINK_LIBRARIES openvino::runtime::dev)
diff --git a/src/inference/CMakeLists.txt b/src/inference/CMakeLists.txt
index 85b58bcba12..5aaeb7cb4ed 100644
--- a/src/inference/CMakeLists.txt
+++ b/src/inference/CMakeLists.txt
@@ -205,11 +205,6 @@ set_target_properties(${TARGET_NAME}_s PROPERTIES
 set_target_properties(${TARGET_NAME}_obj
                       PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
-# Export for build tree
-
-export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
-       APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
-
 # Export for developer package
 
 openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME}_plugin_api)
@@ -217,15 +212,6 @@ openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME}_p
 install(DIRECTORY "${PUBLIC_HEADERS_DIR}/" DESTINATION runtime/include
         COMPONENT core_dev)
 
-# set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME runtime)
-install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT core
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core
-        INCLUDES DESTINATION runtime/include
-                             # TODO: remove later once samples are updated
-                             runtime/include/ie)
-
 # Install static libraries for case BUILD_SHARED_LIBS=OFF
 
 ov_install_static_lib(${TARGET_NAME}_plugin_api core)
diff --git a/src/plugins/auto/CMakeLists.txt b/src/plugins/auto/CMakeLists.txt
index b144bfb4866..3b0094c7917 100644
--- a/src/plugins/auto/CMakeLists.txt
+++ b/src/plugins/auto/CMakeLists.txt
@@ -29,8 +29,6 @@ elseif(ENABLE_MULTI)
                   VERSION_DEFINES_FOR plugin.cpp)
 endif()
 
-target_link_libraries(${TARGET_NAME} PRIVATE ngraph inference_engine_transformations)
-
 set_ie_threading_interface_for(${TARGET_NAME})
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
diff --git a/src/plugins/hetero/CMakeLists.txt b/src/plugins/hetero/CMakeLists.txt
index 61c9eab2a94..5b3af67fdd3 100644
--- a/src/plugins/hetero/CMakeLists.txt
+++ b/src/plugins/hetero/CMakeLists.txt
@@ -17,8 +17,7 @@ ie_faster_build(${TARGET_NAME}
     UNITY
 )
 
-target_link_libraries(${TARGET_NAME} PRIVATE pugixml::static
-    ngraph inference_engine_transformations)
+target_link_libraries(${TARGET_NAME} PRIVATE pugixml::static)
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
diff --git a/src/plugins/intel_gna/CMakeLists.txt b/src/plugins/intel_gna/CMakeLists.txt
index e531dbf98be..417063188d3 100644
--- a/src/plugins/intel_gna/CMakeLists.txt
+++ b/src/plugins/intel_gna/CMakeLists.txt
@@ -38,7 +38,7 @@ ie_add_plugin(NAME ${TARGET_NAME}
 # Enable support of CC for the plugin
 ie_mark_target_as_cc(${TARGET_NAME})
 
-target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_legacy inference_engine_transformations
+target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_legacy
         Threads::Threads libGNA)
 target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
 
diff --git a/src/plugins/intel_gpu/CMakeLists.txt b/src/plugins/intel_gpu/CMakeLists.txt
index e109575acfb..aa65f408984 100644
--- a/src/plugins/intel_gpu/CMakeLists.txt
+++ b/src/plugins/intel_gpu/CMakeLists.txt
@@ -29,14 +29,10 @@ target_compile_options(${TARGET_NAME} PRIVATE
     $<$<CONFIG:Release>:$<IF:$<CXX_COMPILER_ID:MSVC>,/Os,-Os>>)
 
 target_link_libraries(${TARGET_NAME} PRIVATE ov_intel_gpu_graph
-                                             pugixml::static
-                                             inference_engine_transformations
-                                             inference_engine_lp_transformations
-                                             ngraph)
+                                             pugixml::static)
 
 target_include_directories(${TARGET_NAME} PRIVATE
-        ${CMAKE_CURRENT_SOURCE_DIR}/include/
-        $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>)
+        ${CMAKE_CURRENT_SOURCE_DIR}/include/)
 
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
diff --git a/src/tests/functional/plugin/conformance/subgraphs_dumper/tests/CMakeLists.txt b/src/tests/functional/plugin/conformance/subgraphs_dumper/tests/CMakeLists.txt
index b56b65c9b1f..a6811a77774 100644
--- a/src/tests/functional/plugin/conformance/subgraphs_dumper/tests/CMakeLists.txt
+++ b/src/tests/functional/plugin/conformance/subgraphs_dumper/tests/CMakeLists.txt
@@ -10,11 +10,10 @@ addIeTargetTest(
         ADDITIONAL_SOURCE_DIRS ${OpenVINO_SOURCE_DIR}/src/tests/functional/plugin/conformance/subgraphs_dumper/src/matchers
         INCLUDES
             ${OpenVINO_SOURCE_DIR}/src/tests/functional/plugin/conformance/subgraphs_dumper/include
-            $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>
         LINK_LIBRARIES
             PRIVATE
                 funcTestUtils
-                ngraph
+                openvino::runtime
                 pugixml::static
         ADD_CPPLINT
 )
diff --git a/src/tests/functional/plugin/cpu/CMakeLists.txt b/src/tests/functional/plugin/cpu/CMakeLists.txt
index e60fc572283..64fb4357db2 100644
--- a/src/tests/functional/plugin/cpu/CMakeLists.txt
+++ b/src/tests/functional/plugin/cpu/CMakeLists.txt
@@ -6,7 +6,7 @@ set(TARGET_NAME cpuFuncTests)
 
 add_library(cpuSpecificRtInfo STATIC ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp
                                      ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp)
-target_link_libraries(cpuSpecificRtInfo PRIVATE ngraph)
+target_link_libraries(cpuSpecificRtInfo PRIVATE openvino::runtime)
 
 set(INCLUDES ${CMAKE_CURRENT_SOURCE_DIR} $<TARGET_PROPERTY:MKLDNNPlugin,SOURCE_DIR>)
 set(DEPENDENCIES MKLDNNPlugin)
diff --git a/src/tests/functional/plugin/shared/CMakeLists.txt b/src/tests/functional/plugin/shared/CMakeLists.txt
index bca4f9d39e6..9c4834253ad 100644
--- a/src/tests/functional/plugin/shared/CMakeLists.txt
+++ b/src/tests/functional/plugin/shared/CMakeLists.txt
@@ -52,7 +52,7 @@ addIeTarget(
                 sharedTestClasses
             PRIVATE
                 openvino::util
-                inference_engine_transformations
+                openvino::runtime::dev
         DEPENDENCIES
             ${DEPENDENCIES}
 )
diff --git a/src/tests/ie_test_utils/common_test_utils/CMakeLists.txt b/src/tests/ie_test_utils/common_test_utils/CMakeLists.txt
index 6c9f0148e00..93f1557317a 100644
--- a/src/tests/ie_test_utils/common_test_utils/CMakeLists.txt
+++ b/src/tests/ie_test_utils/common_test_utils/CMakeLists.txt
@@ -15,12 +15,12 @@ function(add_common_utils ADD_TARGET_NAME)
                 tests
             LINK_LIBRARIES
                 PUBLIC
-                    ngraph
                     gtest
                     gtest_main
-                    inference_engine_transformations
                     ngraph_test_util
                     ngraphFunctions
+                    openvino::runtime
+                    openvino::runtime::dev
                 PRIVATE
                     openvino::util
     )
@@ -55,12 +55,10 @@ function(add_common_utils ADD_TARGET_NAME)
 
     target_include_directories(${ADD_TARGET_NAME}
         PUBLIC
-            $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>
+            $<TARGET_PROPERTY:openvino::runtime,INTERFACE_INCLUDE_DIRECTORIES>
         PRIVATE
             $<TARGET_PROPERTY:inference_engine_legacy,INTERFACE_INCLUDE_DIRECTORIES>
-            $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
-            $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>
-            )
+            $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>)
     target_include_directories(${ADD_TARGET_NAME} SYSTEM PUBLIC ${IE_TESTS_ROOT}/ie_test_utils)
 
     target_compile_definitions(${ADD_TARGET_NAME} PUBLIC ${ARGN})
diff --git a/src/tests/ie_test_utils/functional_test_utils/CMakeLists.txt b/src/tests/ie_test_utils/functional_test_utils/CMakeLists.txt
index ac31cc4b89c..93292edd945 100644
--- a/src/tests/ie_test_utils/functional_test_utils/CMakeLists.txt
+++ b/src/tests/ie_test_utils/functional_test_utils/CMakeLists.txt
@@ -19,15 +19,14 @@ addIeTarget(
     LINK_LIBRARIES
         PUBLIC
             commonTestUtils
-            inference_engine_transformations
-            inference_engine_lp_transformations
-            inference_engine
+            openvino::runtime
+            openvino::runtime::dev
         PRIVATE
             ngraphFunctions
             pugixml::static
     INCLUDES
         PUBLIC
-            $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
+            $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>
 )
 
 ie_faster_build(${TARGET_NAME}
diff --git a/src/tests/ie_test_utils/unit_test_utils/mocks/mock_engine/CMakeLists.txt b/src/tests/ie_test_utils/unit_test_utils/mocks/mock_engine/CMakeLists.txt
index d6343d8bfb7..bd07fbddcdb 100644
--- a/src/tests/ie_test_utils/unit_test_utils/mocks/mock_engine/CMakeLists.txt
+++ b/src/tests/ie_test_utils/unit_test_utils/mocks/mock_engine/CMakeLists.txt
@@ -32,7 +32,7 @@ target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 if(TARGET IE::inference_engine_plugin_api)
     target_link_libraries(${TARGET_NAME} PRIVATE IE::inference_engine_plugin_api)
 else()
-    target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_plugin_api)
+    target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime::dev)
 endif()
 
 target_link_libraries(${TARGET_NAME} PRIVATE inference_engine)
diff --git a/src/tests/ngraph_helpers/lpt_ngraph_functions/CMakeLists.txt b/src/tests/ngraph_helpers/lpt_ngraph_functions/CMakeLists.txt
index 546b42c963b..f01fce74c02 100644
--- a/src/tests/ngraph_helpers/lpt_ngraph_functions/CMakeLists.txt
+++ b/src/tests/ngraph_helpers/lpt_ngraph_functions/CMakeLists.txt
@@ -18,7 +18,7 @@ addIeTarget(
         LINK_LIBRARIES
             PRIVATE
                 ngraphFunctions
-                inference_engine_lp_transformations
+                openvino::runtime::dev
                 inference_engine_legacy
         ADD_CPPLINT
         DEPENDENCIES
diff --git a/src/tests/ngraph_helpers/ngraph_functions/CMakeLists.txt b/src/tests/ngraph_helpers/ngraph_functions/CMakeLists.txt
index 583dbca2679..a6fcf150d63 100644
--- a/src/tests/ngraph_helpers/ngraph_functions/CMakeLists.txt
+++ b/src/tests/ngraph_helpers/ngraph_functions/CMakeLists.txt
@@ -17,7 +17,7 @@ addIeTarget(
             ${CMAKE_CURRENT_SOURCE_DIR}/src
         LINK_LIBRARIES
             PUBLIC
-                ngraph
+                openvino::runtime
                 ngraph_reference
                 interpreter_backend
         ADD_CPPLINT
diff --git a/src/tests/unit/auto/CMakeLists.txt b/src/tests/unit/auto/CMakeLists.txt
index 9fb4cbf66bb..c7519d01628 100644
--- a/src/tests/unit/auto/CMakeLists.txt
+++ b/src/tests/unit/auto/CMakeLists.txt
@@ -15,11 +15,9 @@ addIeTargetTest(
         INCLUDES
             ${OpenVINO_SOURCE_DIR}/src/plugins/auto ${CMAKE_CURRENT_SOURCE_DIR}
         LINK_LIBRARIES
-            inference_engine_lp_transformations
             ngraphFunctions
-            inference_engine
-            ngraph
-            inference_engine_transformations
+            openvino::runtime
+            openvino::runtime::dev
             unitTestUtils
         ADD_CPPLINT
         DEPENDENCIES
diff --git a/src/tests/unit/vpu/CMakeLists.txt b/src/tests/unit/vpu/CMakeLists.txt
index cd505a6b7e0..e081b732032 100644
--- a/src/tests/unit/vpu/CMakeLists.txt
+++ b/src/tests/unit/vpu/CMakeLists.txt
@@ -20,8 +20,8 @@ addIeTargetTest(
         LINK_LIBRARIES
             vpu_graph_transformer_test_static
             mvnc
-            ngraph
-            inference_engine_lp_transformations # for ngraphFunctions
+            openvino::runtime
+            openvino::runtime::dev
         ADD_CPPLINT
         LABELS
             VPU
diff --git a/src/tests_deprecated/functional/shared_tests/CMakeLists.txt b/src/tests_deprecated/functional/shared_tests/CMakeLists.txt
index bf932bb0c81..6bb4ba313a3 100644
--- a/src/tests_deprecated/functional/shared_tests/CMakeLists.txt
+++ b/src/tests_deprecated/functional/shared_tests/CMakeLists.txt
@@ -5,8 +5,8 @@
 set(TARGET_NAME IESharedTests)
 
 list(APPEND SHARED_LIBRARIES
-        ngraph
         commonTestUtils
+        openvino::runtime
         ngraphFunctions
         ieTestHelpers
         )
@@ -46,7 +46,7 @@ target_include_directories(${TARGET_NAME} PUBLIC
         ${CMAKE_CURRENT_SOURCE_DIR}/lstm
         ${CMAKE_CURRENT_SOURCE_DIR}/common_single_layer_tests
         ${CMAKE_CURRENT_SOURCE_DIR}/single_layer_tests
-        $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>
 )
 
 if(OpenCV_FOUND)
diff --git a/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt b/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
index 6b9d7543a06..51a2ca8322c 100644
--- a/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
+++ b/src/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
@@ -34,7 +34,7 @@ target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_ENGINE_PLU
 
 target_include_directories(${TARGET_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/")
 
-target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_plugin_api
+target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime::dev
                                              inference_engine_legacy pugixml::static openvino::itt)
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
diff --git a/tests/fuzz/src/CMakeLists.txt b/tests/fuzz/src/CMakeLists.txt
index 72c22a8fd7a..224f84f26e4 100644
--- a/tests/fuzz/src/CMakeLists.txt
+++ b/tests/fuzz/src/CMakeLists.txt
@@ -17,7 +17,7 @@ foreach(test_source ${tests})
     add_fuzzer(${test_name} ${test_source})
 
     target_link_libraries(${test_name} PRIVATE
-        openvino::runtime openvino::frontend::common cnpy zlib)
+        openvino::runtime cnpy zlib)
 
     add_dependencies(fuzz ${test_name})
 
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
index 83ad038bf43..2bf85808537 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
+++ b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
@@ -34,7 +34,7 @@ add_subdirectory(${OpenVINO_SOURCE_DIR}/src/bindings/python/thirdparty/pybind11
 
 pybind11_add_module(${PYBIND_FE_NAME} MODULE ${PYBIND_FE_SRC})
 
-target_link_libraries(${PYBIND_FE_NAME} PRIVATE ngraph frontend_common::static)
+target_link_libraries(${PYBIND_FE_NAME} PRIVATE openvino::runtime)
 target_link_libraries(${PYBIND_FE_NAME} PRIVATE ${TARGET_FE_NAME})
 
 add_dependencies(${PYBIND_FE_NAME} ${TARGET_FE_NAME})
diff --git a/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
index aae056a2fd7..7667bfc89bc 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
+++ b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
@@ -15,8 +15,7 @@ add_library(${TARGET_FE_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS})
 
 target_include_directories(${TARGET_FE_NAME} PRIVATE ".")
 
-target_link_libraries(${TARGET_FE_NAME} PRIVATE frontend_common::static)
-target_link_libraries(${TARGET_FE_NAME} PUBLIC ngraph PRIVATE ngraph::builder)
+target_link_libraries(${TARGET_FE_NAME} PUBLIC openvino::runtime PRIVATE ngraph::builder)
 
 add_clang_format_target(${TARGET_FE_NAME}_clang FOR_TARGETS ${TARGET_FE_NAME})
 

From 20ee7fd2420e2c2fdb9e9f5db31b57f42ad05e48 Mon Sep 17 00:00:00 2001
From: Vladimir Gavrilov <vladimir.gavrilov@intel.com>
Date: Thu, 23 Dec 2021 09:44:47 +0300
Subject: [PATCH 103/160] Fix MO and nGraph to support the model
 context_rcnn_resnet101_snapshot_serengeti (#9255)

* Fixes in the infer function of MO operation Select.

* Fixes in the nGraph transformation SharedShapeOf.

* Deleted commented code.

* Added more tests for the infer function of the MO operation Select.

* Started to write tests for the transformation SharedShapeOf.

* Added more tests.

* Now the transformation can correctly process a mix of opset1::ShapeOf and opset8::ShapeOf.

* Small change.

* Used opset1 and opset3 instead of opset1 and opset8.

* Used get_output_element_type(0) instead of checking the version of ShapeOf.
---
 .../simplify_shape_of_sub_graph.cpp           |  22 ++-
 .../transformations/shared_shapeof_test.cpp   | 178 ++++++++++++++++++
 tools/mo/openvino/tools/mo/ops/select.py      |   7 +-
 tools/mo/unit_tests/mo/ops/select_test.py     |  28 +++
 4 files changed, 229 insertions(+), 6 deletions(-)
 create mode 100644 src/tests/functional/inference_engine/transformations/shared_shapeof_test.cpp

diff --git a/src/common/transformations/src/transformations/common_optimizations/simplify_shape_of_sub_graph.cpp b/src/common/transformations/src/transformations/common_optimizations/simplify_shape_of_sub_graph.cpp
index 8b9a1c3de9f..14dede1e6f8 100644
--- a/src/common/transformations/src/transformations/common_optimizations/simplify_shape_of_sub_graph.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/simplify_shape_of_sub_graph.cpp
@@ -20,6 +20,9 @@
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::SharedShapeOf, "SharedShapeOf", 0);
 
+static constexpr size_t index_for_int32 = 0;
+static constexpr size_t index_for_int64 = 1;
+
 bool ngraph::pass::SharedShapeOf::run_on_model(const std::shared_ptr<ngraph::Function>& f) {
     RUN_ON_FUNCTION_SCOPE(SharedShapeOf);
     bool graph_rewritten = false;
@@ -38,10 +41,21 @@ bool ngraph::pass::SharedShapeOf::run_on_model(const std::shared_ptr<ngraph::Fun
     for (const auto& pair : source_to_shape_of) {
         if (pair.second.size() < 2)
             continue;
-        const auto& root_ss = pair.second[0];
-        for (const auto& child_ss : pair.second)
-            if (root_ss->get_instance_id() != child_ss->get_instance_id() && root_ss->get_output_element_type(0) == root_ss->get_output_element_type(0))
-                graph_rewritten |= replace_output_update_name(child_ss->output(0), root_ss->output(0));
+
+        NodeVector nodes_for_different_types[2];
+        for (const auto& child : pair.second) {
+            const auto& type_of_output = child->get_output_element_type(0);
+            size_t index = (type_of_output == element::i32) ? index_for_int32 : index_for_int64;
+            nodes_for_different_types[index].push_back(child);
+        }
+        for (const auto& v : nodes_for_different_types) {
+            if (v.empty())
+                continue;
+            const auto& root_ss = v[0];
+            for (const auto& child_ss : v)
+                if (root_ss->get_instance_id() != child_ss->get_instance_id())
+                    graph_rewritten |= replace_output_update_name(child_ss->output(0), root_ss->output(0));
+        }
     }
     return graph_rewritten;
 }
diff --git a/src/tests/functional/inference_engine/transformations/shared_shapeof_test.cpp b/src/tests/functional/inference_engine/transformations/shared_shapeof_test.cpp
new file mode 100644
index 00000000000..a6585b6cb92
--- /dev/null
+++ b/src/tests/functional/inference_engine/transformations/shared_shapeof_test.cpp
@@ -0,0 +1,178 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <ngraph/pass/visualize_tree.hpp>
+#include <transformations/common_optimizations/simplify_shape_of_sub_graph.hpp>
+#include <transformations/init_node_info.hpp>
+#include <transformations/utils/utils.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST_F(TransformationTestsF, SharedShapeOfTest) {
+    ngraph::Shape input_shape { 120, 4 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto shapeof1_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof2_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+        auto shapeof3_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof4_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof5_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+        auto shapeof6_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof7_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+
+        auto shapeof1_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof3_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof3_i32, ngraph::element::i64);
+        auto shapeof4_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof4_i32, ngraph::element::i64);
+        auto shapeof6_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof6_i32, ngraph::element::i64);
+        auto shapeof7_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof7_i32, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1_i32_convert, shapeof2_i64, shapeof3_i32_convert, shapeof4_i32_convert,
+                                               shapeof5_i64, shapeof6_i32_convert, shapeof7_i32_convert};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::SharedShapeOf>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto shapeof1_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof2_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+
+        auto shapeof1_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof3_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof4_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof6_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof7_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1_i32_convert, shapeof2_i64, shapeof3_i32_convert, shapeof4_i32_convert,
+                                               shapeof2_i64, shapeof6_i32_convert, shapeof7_i32_convert};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, SharedShapeOfTestI64Only) {
+    ngraph::Shape input_shape { 120, 4 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto shapeof1_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+        auto shapeof2_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+        auto shapeof3_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1_i64, shapeof2_i64, shapeof3_i64};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::SharedShapeOf>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto shapeof1_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1_i64, shapeof1_i64, shapeof1_i64};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, SharedShapeOfTestI32Only) {
+    ngraph::Shape input_shape { 120, 4 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto shapeof1_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof2_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof3_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof4_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof5_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+
+        auto shapeof1_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof2_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof2_i32, ngraph::element::i64);
+        auto shapeof3_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof3_i32, ngraph::element::i64);
+        auto shapeof4_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof4_i32, ngraph::element::i64);
+        auto shapeof5_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof5_i32, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1_i32_convert, shapeof2_i32_convert, shapeof3_i32_convert,
+                                               shapeof4_i32_convert, shapeof5_i32_convert};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::SharedShapeOf>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto shapeof1_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+
+        auto shapeof1_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof2_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof3_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof4_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+        auto shapeof5_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof1_i32, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1_i32_convert, shapeof2_i32_convert, shapeof3_i32_convert,
+                                               shapeof4_i32_convert, shapeof5_i32_convert};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, SharedShapeOfTestMixed) {
+    ngraph::Shape input_shape { 120, 4 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto shapeof1 = std::make_shared<ngraph::opset1::ShapeOf>(input);
+        auto shapeof2_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+        auto shapeof3_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof4 = std::make_shared<ngraph::opset1::ShapeOf>(input);
+        auto shapeof5_i64 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i64);
+        auto shapeof6_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+        auto shapeof7_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+
+        auto shapeof3_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof3_i32, ngraph::element::i64);
+        auto shapeof6_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof6_i32, ngraph::element::i64);
+        auto shapeof7_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof7_i32, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1, shapeof2_i64, shapeof3_i32_convert, shapeof4,
+                                               shapeof5_i64, shapeof6_i32_convert, shapeof7_i32_convert};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::SharedShapeOf>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto shapeof1 = std::make_shared<ngraph::opset1::ShapeOf>(input);
+        auto shapeof2_i32 = std::make_shared<ngraph::opset8::ShapeOf>(input, ngraph::element::i32);
+
+        auto shapeof3_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof2_i32, ngraph::element::i64);
+        auto shapeof6_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof2_i32, ngraph::element::i64);
+        auto shapeof7_i32_convert = std::make_shared<ngraph::opset8::Convert>(shapeof2_i32, ngraph::element::i64);
+
+        ngraph::OutputVector inputs_of_concat {shapeof1, shapeof1, shapeof3_i32_convert, shapeof1,
+                                               shapeof1, shapeof6_i32_convert, shapeof7_i32_convert};
+
+        auto concat = std::make_shared<ngraph::opset8::Concat>(inputs_of_concat, 0);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ concat }, ngraph::ParameterVector{ input });
+    }
+}
diff --git a/tools/mo/openvino/tools/mo/ops/select.py b/tools/mo/openvino/tools/mo/ops/select.py
index 3d3520638a1..fa27e1f1c7e 100644
--- a/tools/mo/openvino/tools/mo/ops/select.py
+++ b/tools/mo/openvino/tools/mo/ops/select.py
@@ -50,6 +50,8 @@ class Select(Op):
             output_shape = bi_directional_shape_broadcasting(a_shape, b_shape)
             assert output_shape is not None, msg
 
+            output_is_scalar = len(output_shape) == 0
+
             # if Select was created from TF Where operations then 1D condition must have the same size
             # as 0-index dimension of output_shape. This condition is different from being numpy compatible
             # but by adding ones to the end we can achieve numpy compatibility, as in transformation SelectBroadcast.py
@@ -61,9 +63,10 @@ class Select(Op):
                             node_name, condition_shape, a_shape, b_shape)
 
                 # check equality only if both values non-dynamic
-                if is_fully_defined(condition_shape[0]) and is_fully_defined(output_shape[0]):
+                if is_fully_defined(condition_shape[0]) and not output_is_scalar and is_fully_defined(output_shape[0]):
                     assert condition_shape[0] == output_shape[0], msg_tf
-                condition_shape = np.concatenate((condition_shape, np.ones(len(output_shape) - 1)))
+                ones_shape = len(output_shape) if output_is_scalar else len(output_shape) - 1
+                condition_shape = np.concatenate((condition_shape, np.ones(ones_shape)))
 
             output_shape = bi_directional_shape_broadcasting(output_shape, condition_shape)
             assert output_shape is not None, msg
diff --git a/tools/mo/unit_tests/mo/ops/select_test.py b/tools/mo/unit_tests/mo/ops/select_test.py
index 0b6e5671405..f5e49420dd4 100644
--- a/tools/mo/unit_tests/mo/ops/select_test.py
+++ b/tools/mo/unit_tests/mo/ops/select_test.py
@@ -100,6 +100,34 @@ class TestSelect(unittest.TestCase):
                                                       out_value=np.ones([15, 3, 5], dtype=np.float))
         self.assertTrue(flag, msg)
 
+    def test_select_infer_condition_true_then_and_else_are_scalars(self):
+        flag, msg = self.build_select_graph_and_infer(condition_value=np.array([True], dtype=bool),
+                                                      then_value=np.array(3, dtype=np.float),
+                                                      else_value=np.array(1, dtype=np.float),
+                                                      out_value=np.array([3], dtype=np.float))
+        self.assertTrue(flag, msg)
+
+    def test_select_infer_condition_true_then_and_else_are_scalars_2(self):
+        flag, msg = self.build_select_graph_and_infer(condition_value=np.array(True, dtype=bool),
+                                                      then_value=np.array(3, dtype=np.float),
+                                                      else_value=np.array(1, dtype=np.float),
+                                                      out_value=np.array(3, dtype=np.float))
+        self.assertTrue(flag, msg)
+
+    def test_select_infer_condition_false_then_and_else_are_scalars(self):
+        flag, msg = self.build_select_graph_and_infer(condition_value=np.array([False], dtype=bool),
+                                                      then_value=np.array(3, dtype=np.float),
+                                                      else_value=np.array(1, dtype=np.float),
+                                                      out_value=np.array([1], dtype=np.float))
+        self.assertTrue(flag, msg)
+
+    def test_select_infer_condition_false_then_and_else_are_scalars_2(self):
+        flag, msg = self.build_select_graph_and_infer(condition_value=np.array(False, dtype=bool),
+                                                      then_value=np.array(3, dtype=np.float),
+                                                      else_value=np.array(1, dtype=np.float),
+                                                      out_value=np.array(1, dtype=np.float))
+        self.assertTrue(flag, msg)
+
     def test_select_infer_condition_false_2(self):
         flag, msg = self.build_select_graph_and_infer(condition_value=np.array([False], dtype=bool),
                                                       then_value=np.ones([15, 3, 5], dtype=np.float),

From 507a49826975e2e15faf87100e9f44c1891bbdc9 Mon Sep 17 00:00:00 2001
From: Sergey Shlyapnikov <sergey.shlyapnikov@intel.com>
Date: Thu, 23 Dec 2021 09:58:35 +0300
Subject: [PATCH 104/160] [GPU] Add OneDNN post ops description in graph dump
 mode (#9371)

---
 .../clDNN/src/include/to_string_utils.h       | 26 +++++++++++++++++++
 .../thirdparty/clDNN/src/program_node.cpp     | 17 +++++++++++-
 2 files changed, 42 insertions(+), 1 deletion(-)

diff --git a/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h b/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h
index 801895c275c..8cd459d2eb8 100644
--- a/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h
+++ b/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h
@@ -9,6 +9,10 @@
 #include "intel_gpu/runtime/device.hpp"
 #include "intel_gpu/primitives/primitive.hpp"
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#include "program_node.h"
+#endif
+
 
 #include <string>
 #include <sstream>
@@ -270,4 +274,26 @@ inline std::string allocation_type_to_str(allocation_type type) {
     }
 }
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+inline std::string onednn_post_op_type_to_str(onednn_post_op_type type) {
+    switch (type) {
+    case onednn_post_op_type::eltwise_act: return "eltwise_act";
+    case onednn_post_op_type::eltwise_clip: return "eltwise_clip";
+    case onednn_post_op_type::eltwise_linear: return "eltwise_linear";
+    case onednn_post_op_type::eltwise_round: return "eltwise_round";
+    case onednn_post_op_type::binary_mul: return "binary_mul";
+    case onednn_post_op_type::binary_add: return "binary_add";
+    case onednn_post_op_type::binary_max: return "binary_max";
+    case onednn_post_op_type::binary_min: return "binary_min";
+    case onednn_post_op_type::binary_relu: return "binary_relu";
+    case onednn_post_op_type::scale: return "scale";
+    case onednn_post_op_type::sum: return "sum";
+    case onednn_post_op_type::optimized: return "optimized";
+    case onednn_post_op_type::optimized_eltwise: return "optimized_eltwise";
+    case onednn_post_op_type::optimized_sum: return "optimized_sum";
+    default: return "unknown";
+    }
+}
+#endif
+
 }  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/program_node.cpp b/inference-engine/thirdparty/clDNN/src/program_node.cpp
index acc2b143bfd..089f9f2619d 100644
--- a/inference-engine/thirdparty/clDNN/src/program_node.cpp
+++ b/inference-engine/thirdparty/clDNN/src/program_node.cpp
@@ -101,7 +101,6 @@ std::unique_ptr<json_composite> program_node::desc_to_json() const {
     node_info->add("in data flow", bool_to_str(data_flow));
     node_info->add("output", bool_to_str(output));
 
-
     json_composite fused_nodes_info;
     size_t index = 0;
     for (auto& fused_desc : get_fused_primitives()) {
@@ -122,6 +121,22 @@ std::unique_ptr<json_composite> program_node::desc_to_json() const {
     }
     node_info->add("fused primitives", fused_nodes_info);
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    auto& onednn_post_ops = get_fused_primitives_onednn();
+    if (onednn_post_ops.size()) {
+        size_t post_op_index = 0;
+        json_composite post_ops_info;
+        for (auto& fused_prim_desc : onednn_post_ops) {
+            json_composite post_op_info;
+            post_op_info.add("post op", onednn_post_op_type_to_str(fused_prim_desc.op_type));
+            post_op_info.add("memory dependency", fused_prim_desc.mem_dep);
+            post_op_info.add("memory offset", fused_prim_desc.mem_offset);
+            post_ops_info.add("post ops idx " + std::to_string(post_op_index++), post_op_info);
+        }
+        node_info->add("onednn post ops", post_ops_info);
+    }
+#endif
+
     std::vector<std::string> deps_ptrs;
     {
         bool empty = true;

From 3b03728807ce1978d5d3fdcd54f1dbd378d2b8da Mon Sep 17 00:00:00 2001
From: Taylor Yeonbok Lee <taylor.lee@intel.com>
Date: Thu, 23 Dec 2021 16:20:04 +0900
Subject: [PATCH 105/160] [GPU] Fix get_estimated_device_mem_usage to handle
 mutable_data (#9297)

---
 inference-engine/thirdparty/clDNN/src/program.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/inference-engine/thirdparty/clDNN/src/program.cpp b/inference-engine/thirdparty/clDNN/src/program.cpp
index d4cb50f41c5..c43586d96db 100644
--- a/inference-engine/thirdparty/clDNN/src/program.cpp
+++ b/inference-engine/thirdparty/clDNN/src/program.cpp
@@ -1460,6 +1460,8 @@ std::pair<int64_t, int64_t> program::get_estimated_device_mem_usage() {
             const_sum += out_size;
         } else if (node->have_user_with_type<concatenation>() && node->get_users().size() == 1 && node->get_users().front()->can_be_optimized()) {
             continue;
+        } else if (node->is_type<mutable_data>() && node->get_dependencies().empty()) {
+            continue;
         } else {
             allocated_mem_ptrs.insert(primitive_inst::allocate_output(get_engine(), pool, *node, false));
         }

From 16490959e617c2b6d406c52dc51e20a3a6654620 Mon Sep 17 00:00:00 2001
From: Mingyu Kim <mingyu.kim@intel.com>
Date: Thu, 23 Dec 2021 16:58:33 +0900
Subject: [PATCH 106/160] [GPU] Use double blocked format if batch >= 16
 (#9357)

---
 inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
index 1646ca99a89..eb6553e1e29 100644
--- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
+++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
@@ -920,7 +920,7 @@ layout layout_optimizer::get_expected_layout(layout const& current_layout,
 
         if (i8_u8_input) {
             if ((non_grouped || valid_grouped || valid_int8_dw) && onednn_valid_post_ops && is_2d) {
-                if (input_layout.size.batch[0] % 16 == 0) {
+                if (input_layout.size.batch[0] >= 16) {
                     expected_format = cldnn::format::bs_fs_yx_bsv32_fsv32;
                 } else {
                     if (data_type_traits::is_floating_point(output_layout.data_type) &&

From 1fbfd426f02b0d2374bda1566f038e5dcde4a3a2 Mon Sep 17 00:00:00 2001
From: Szymon Durawa <szymon.durawa@intel.com>
Date: Thu, 23 Dec 2021 09:04:37 +0100
Subject: [PATCH 107/160] Add FE add_output with tests. (#7644)

---
 .../src/pyopenvino/frontend/inputmodel.cpp    |  22 ++
 .../test_frontend_onnx_editor.py              | 213 +++++++++++++++++
 ...e_source_and_merge_all_new_inputs.prototxt | 186 +++++++++++++++
 ..._sources_and_merge_all_new_inputs.prototxt | 191 +++++++++++++++
 ...her_edge_and_merge_all_new_inputs.prototxt | 217 ++++++++++++++++++
 ...one_edge_and_merge_all_new_inputs.prototxt | 208 +++++++++++++++++
 ..._sources_and_merge_all_new_inputs.prototxt | 208 +++++++++++++++++
 ...e_source_and_merge_all_new_inputs.prototxt | 208 +++++++++++++++++
 src/core/tests/onnx/onnx_editor.cpp           | 132 +++++++++++
 .../src/detail/subgraph_extraction.cpp        |  56 ++++-
 .../src/detail/subgraph_extraction.hpp        |   4 +-
 src/frontends/onnx/frontend/src/editor.cpp    |  33 ++-
 src/frontends/onnx/frontend/src/editor.hpp    |  19 +-
 .../onnx/frontend/src/input_model.cpp         | 104 ++++++++-
 .../onnx/frontend/src/input_model.hpp         |  14 ++
 15 files changed, 1803 insertions(+), 12 deletions(-)
 create mode 100644 src/core/tests/models/onnx/model_editor/reference/subgraph__cut_all_edges_from_one_source_and_merge_all_new_inputs.prototxt
 create mode 100644 src/core/tests/models/onnx/model_editor/reference/subgraph__cut_custom_edges_from_different_sources_and_merge_all_new_inputs.prototxt
 create mode 100644 src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_another_edge_and_merge_all_new_inputs.prototxt
 create mode 100644 src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_edge_and_merge_all_new_inputs.prototxt
 create mode 100644 src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_different_sources_and_merge_all_new_inputs.prototxt
 create mode 100644 src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_one_source_and_merge_all_new_inputs.prototxt

diff --git a/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp b/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
index 951bdf7c5c4..b9e64af0f67 100644
--- a/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/inputmodel.cpp
@@ -8,6 +8,7 @@
 
 #include "openvino/frontend/exception.hpp"
 #include "openvino/frontend/manager.hpp"
+#include "pyopenvino/core/common.hpp"
 
 namespace py = pybind11;
 
@@ -361,4 +362,25 @@ void regclass_frontend_InputModel(py::module m) {
                 type : ngraph.Type
                     New element type.
             )");
+
+    im.def(
+        "set_tensor_value",
+        [](ov::frontend::InputModel& self, const ov::frontend::Place::Ptr& place, py::array& value) {
+            // Convert to contiguous array if not already C-style.
+            auto tensor = Common::tensor_from_numpy(value, false);
+            self.set_tensor_value(place, (const void*)tensor.data());
+        },
+        py::arg("place"),
+        py::arg("value"),
+        R"(
+            Sets new element type for a place.
+
+            Parameters
+            ----------
+            place : Place
+                Model place.
+
+            value : ndarray
+                New value to assign.
+        )");
 }
diff --git a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
index 90430c357b0..fe02533d05e 100644
--- a/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
+++ b/src/bindings/python/tests/test_frontend/test_frontend_onnx_editor.py
@@ -5,6 +5,7 @@ import os
 import onnx
 import pytest
 from onnx.helper import make_graph, make_model, make_tensor_value_info
+import numpy as np
 from openvino.runtime import Dimension, PartialShape
 from openvino.frontend import FrontEndManager
 
@@ -208,6 +209,57 @@ def create_test_onnx_models():
     models["test_override_all_inputs.onnx"] = make_model(graph, producer_name="ONNX Importer",
                                                          opset_imports=[onnx.helper.make_opsetid("", 13)])
 
+    # Expected for cut_and_add_new_input_edge
+    input_tensors = [
+        make_tensor_value_info("in1", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("in2", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("in3", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("new_input", onnx.TensorProto.FLOAT, (2, 2)),
+    ]
+    output_tensors = [
+        make_tensor_value_info("out1", onnx.TensorProto.FLOAT, (1, 2)),
+        make_tensor_value_info("out2", onnx.TensorProto.FLOAT, (1, 2)),
+        make_tensor_value_info("out3", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("out4", onnx.TensorProto.FLOAT, (2, 2)),
+    ]
+    new_mul = onnx.helper.make_node("Mul", inputs=["new_input", "add_out"], outputs=["out4"])
+    graph = make_graph([add, split, relu, new_mul], "test_graph", input_tensors, output_tensors)
+    models["cut_and_add_new_input_edge.onnx"] = make_model(graph, producer_name="ONNX Importer",
+                                                           opset_imports=[onnx.helper.make_opsetid("", 13)])
+
+    # Expected for cut_and_add_new_input_place
+    input_tensors = [
+        make_tensor_value_info("in3", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("new_input", onnx.TensorProto.FLOAT, (2, 2)),
+    ]
+    output_tensors = [
+        make_tensor_value_info("out1", onnx.TensorProto.FLOAT, (1, 2)),
+        make_tensor_value_info("out2", onnx.TensorProto.FLOAT, (1, 2)),
+        make_tensor_value_info("out3", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("out4", onnx.TensorProto.FLOAT, (2, 2)),
+    ]
+    new_mul = onnx.helper.make_node("Mul", inputs=["new_input", "new_input"], outputs=["out4"])
+    new_split = onnx.helper.make_node("Split", inputs=["new_input"],
+                                      outputs=["out1", "out2"], name="split1", axis=0)
+    graph = make_graph([new_split, relu, new_mul], "test_graph", input_tensors, output_tensors)
+    models["cut_and_add_new_input_place.onnx"] = make_model(graph, producer_name="ONNX Importer",
+                                                            opset_imports=[onnx.helper.make_opsetid("", 13)])
+
+    # Expected for remove_output
+    input_tensors = [
+        make_tensor_value_info("in1", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("in2", onnx.TensorProto.FLOAT, (2, 2)),
+        make_tensor_value_info("in3", onnx.TensorProto.FLOAT, (2, 2)),
+    ]
+    output_tensors = [
+        make_tensor_value_info("out1", onnx.TensorProto.FLOAT, (1, 2)),
+        make_tensor_value_info("out2", onnx.TensorProto.FLOAT, (1, 2)),
+        make_tensor_value_info("out3", onnx.TensorProto.FLOAT, (2, 2)),
+    ]
+    graph = make_graph([add, relu, split], "test_graph", input_tensors, output_tensors)
+    models["remove_output.onnx"] = make_model(graph, producer_name="ONNX Importer",
+                                              opset_imports=[onnx.helper.make_opsetid("", 13)])
+
     # test partial shape
     input_tensors = [
         make_tensor_value_info("in1", onnx.TensorProto.FLOAT, (8, 16)),
@@ -837,6 +889,59 @@ def test_get_input_port():
     assert not split_op.get_input_port(input_name="not_existed")
 
 
+def test_add_output_place_is_not_output():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    place = model.get_place_by_tensor_name(tensor_name="add_out")
+    model.add_output(place)
+
+    out_names = [place.get_names()[0] for place in model.get_outputs()]
+    assert out_names == ["out1", "out2", "out3", "out4", "add_out"]
+
+
+def test_add_output_place_is_output():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    orig_func = fe.convert(model)
+
+    place = model.get_place_by_tensor_name(tensor_name="out1")
+    model.add_output(place)
+
+    result_func = fe.convert(model)
+
+    res = compare_functions(orig_func, result_func)
+    assert res
+
+
+def test_add_output_place_is_input():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    place = model.get_place_by_tensor_name(tensor_name="in1")
+    model.add_output(place)
+    result_func = fe.convert(model)
+
+    orig_model = fe.load("input_model.onnx")
+    orig_func = fe.convert(orig_model)
+
+    res = compare_functions(orig_func, result_func)
+    assert res
+
+
 def test_get_consuming_ports():
     skip_if_onnx_frontend_is_disabled()
     fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
@@ -1033,6 +1138,44 @@ def test_get_producing_port():
     assert split_op_in_port_prod_port.is_equal(add_op_out_port)
 
 
+def test_remove_output():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    place = model.get_place_by_tensor_name(tensor_name="out4")
+    model.remove_output(place)
+
+    expected_model = fe.load("remove_output.onnx")
+    expected_func = fe.convert(expected_model)
+    model_func = fe.convert(model)
+
+    res = compare_functions(model_func, expected_func)
+    assert res
+
+
+def test_remove_output_when_place_is_input():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    place = model.get_place_by_tensor_name(tensor_name="in1")
+    model.remove_output(place)
+
+    expected_model = fe.load("input_model.onnx")
+    expected_func = fe.convert(expected_model)
+    model_func = fe.convert(model)
+
+    res = compare_functions(model_func, expected_func)
+    assert res
+
+
 def test_get_place_by_operation_name_and_input_port():
     skip_if_onnx_frontend_is_disabled()
     fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
@@ -1063,6 +1206,76 @@ def test_get_place_by_operation_name_and_output_port():
     assert place1.is_equal(place2)
 
 
+def test_cut_and_add_new_input_place():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    place = model.get_place_by_tensor_name(tensor_name="add_out")
+
+    model.cut_and_add_new_input(place, "new_input")
+
+    expected_model = fe.load("cut_and_add_new_input_place.onnx")
+    expected_func = fe.convert(expected_model)
+    model_func = fe.convert(model)
+
+    res = compare_functions(model_func, expected_func)
+    assert res
+
+
+def test_cut_and_add_new_input_edge():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    out4 = model.get_place_by_tensor_name(tensor_name="out4")
+    mul_op = out4.get_producing_operation()
+    edge_mul0 = mul_op.get_input_port(input_port_index=0)
+
+    model.cut_and_add_new_input(edge_mul0, "new_input")
+
+    expected_model = fe.load("cut_and_add_new_input_edge.onnx")
+    expected_func = fe.convert(expected_model)
+    model_func = fe.convert(model)
+
+    res = compare_functions(model_func, expected_func)
+    assert res
+
+
+def test_set_tensor_value():
+    skip_if_onnx_frontend_is_disabled()
+    fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
+    assert fe
+
+    model = fe.load("input_model.onnx")
+    assert model
+
+    new_values = np.array([[1, 2], [3, 4]], dtype=np.float32)
+
+    place1 = model.get_place_by_tensor_name(tensor_name="in1")
+    model.set_tensor_value(place1, new_values)
+
+    model_func = fe.convert(model)
+
+    iter = None
+    current_ops = model_func.get_ordered_ops()
+
+    for i in range(len(current_ops)):
+        if (current_ops[i].get_friendly_name() == "in1"):
+            iter = i
+
+    assert current_ops[iter] is not None
+
+    retrieved_data = current_ops[iter].get_data()
+    assert np.allclose(new_values, retrieved_data)
+
+
 def test_not_supported_methods():
     skip_if_onnx_frontend_is_disabled()
     fe = fem.load_by_framework(framework=ONNX_FRONTEND_NAME)
diff --git a/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_all_edges_from_one_source_and_merge_all_new_inputs.prototxt b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_all_edges_from_one_source_and_merge_all_new_inputs.prototxt
new file mode 100644
index 00000000000..e17c6ebb21c
--- /dev/null
+++ b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_all_edges_from_one_source_and_merge_all_new_inputs.prototxt
@@ -0,0 +1,186 @@
+ir_version: 7
+producer_name: "test_data_generator"
+graph {
+  node {
+    input: "relu1"
+    input: "in2"
+    output: "add1"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "in3"
+    input: "in4"
+    output: "conv1"
+    op_type: "Conv"
+  }
+  node {
+    input: "relu1"
+    input: "add1"
+    output: "add2"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "add1"
+    input: "conv1"
+    output: "mul2"
+    name: ""
+    op_type: "Mul"
+  }
+  node {
+    input: "add2"
+    output: "split1"
+    output: "split2"
+    name: "split_name"
+    op_type: "Split"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "relu1"
+    input: "split1"
+    output: "mul1"
+    op_type: "Mul"
+  }
+  name: "subgraph_extraction_testing"
+  initializer {
+    dims: 1
+    dims: 1
+    dims: 1
+    dims: 1
+    data_type: 1
+    float_data: 1
+    name: "in4"
+  }
+  input {
+    name: "in2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+        }
+      }
+    }
+  }
+  input {
+    name: "in3"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in4"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "relu1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "split2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_custom_edges_from_different_sources_and_merge_all_new_inputs.prototxt b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_custom_edges_from_different_sources_and_merge_all_new_inputs.prototxt
new file mode 100644
index 00000000000..e3dae07e497
--- /dev/null
+++ b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_custom_edges_from_different_sources_and_merge_all_new_inputs.prototxt
@@ -0,0 +1,191 @@
+ir_version: 7
+producer_name: "test_data_generator"
+graph {
+  node {
+    input: "in1"
+    output: "relu1"
+    name: "relu1_name"
+    op_type: "Relu"
+  }
+  node {
+    input: "in3"
+    input: "in4"
+    output: "conv1"
+    op_type: "Conv"
+  }
+  node {
+    input: "add1"
+    input: "add1"
+    output: "add2"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "add1"
+    input: "conv1"
+    output: "mul2"
+    name: ""
+    op_type: "Mul"
+  }
+  node {
+    input: "add2"
+    output: "split1"
+    output: "split2"
+    name: "split_name"
+    op_type: "Split"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "relu1"
+    input: "split1"
+    output: "mul1"
+    op_type: "Mul"
+  }
+  name: "subgraph_extraction_testing"
+  initializer {
+    dims: 1
+    dims: 1
+    dims: 1
+    dims: 1
+    data_type: 1
+    float_data: 1
+    name: "in4"
+  }
+  input {
+    name: "in1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in3"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in4"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "add1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "split2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_another_edge_and_merge_all_new_inputs.prototxt b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_another_edge_and_merge_all_new_inputs.prototxt
new file mode 100644
index 00000000000..7525788f9f7
--- /dev/null
+++ b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_another_edge_and_merge_all_new_inputs.prototxt
@@ -0,0 +1,217 @@
+ir_version: 7
+producer_name: "test_data_generator"
+graph {
+  node {
+    input: "in1"
+    output: "relu1"
+    name: "relu1_name"
+    op_type: "Relu"
+  }
+  node {
+    input: "add1/placeholder_port_0"
+    input: "in2"
+    output: "add1"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "in3"
+    input: "in4"
+    output: "conv1"
+    op_type: "Conv"
+  }
+  node {
+    input: "add1"
+    input: "conv1"
+    output: "mul2"
+    name: ""
+    op_type: "Mul"
+  }
+  node {
+    input: "add2"
+    output: "split1"
+    output: "split2"
+    name: "split_name"
+    op_type: "Split"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "relu1"
+    input: "split1"
+    output: "mul1"
+    op_type: "Mul"
+  }
+  name: "subgraph_extraction_testing"
+  initializer {
+    dims: 1
+    dims: 1
+    dims: 1
+    dims: 1
+    data_type: 1
+    float_data: 1
+    name: "in4"
+  }
+  input {
+    name: "in1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+        }
+      }
+    }
+  }
+  input {
+    name: "in3"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in4"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "add1/placeholder_port_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "add2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "split2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_edge_and_merge_all_new_inputs.prototxt b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_edge_and_merge_all_new_inputs.prototxt
new file mode 100644
index 00000000000..89bc4b02ac5
--- /dev/null
+++ b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_one_edge_and_merge_all_new_inputs.prototxt
@@ -0,0 +1,208 @@
+ir_version: 7
+producer_name: "test_data_generator"
+graph {
+  node {
+    input: "in1"
+    output: "relu1"
+    name: "relu1_name"
+    op_type: "Relu"
+  }
+  node {
+    input: "add1/placeholder_port_0"
+    input: "in2"
+    output: "add1"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "in3"
+    input: "in4"
+    output: "conv1"
+    op_type: "Conv"
+  }
+  node {
+    input: "relu1"
+    input: "add1"
+    output: "add2"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "add1"
+    input: "conv1"
+    output: "mul2"
+    name: ""
+    op_type: "Mul"
+  }
+  node {
+    input: "add2"
+    output: "split1"
+    output: "split2"
+    name: "split_name"
+    op_type: "Split"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "relu1"
+    input: "split1"
+    output: "mul1"
+    op_type: "Mul"
+  }
+  name: "subgraph_extraction_testing"
+  initializer {
+    dims: 1
+    dims: 1
+    dims: 1
+    dims: 1
+    data_type: 1
+    float_data: 1
+    name: "in4"
+  }
+  input {
+    name: "in1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+        }
+      }
+    }
+  }
+  input {
+    name: "in3"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in4"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "add1/placeholder_port_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "split2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_different_sources_and_merge_all_new_inputs.prototxt b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_different_sources_and_merge_all_new_inputs.prototxt
new file mode 100644
index 00000000000..df1845bae03
--- /dev/null
+++ b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_different_sources_and_merge_all_new_inputs.prototxt
@@ -0,0 +1,208 @@
+ir_version: 7
+producer_name: "test_data_generator"
+graph {
+  node {
+    input: "in1"
+    output: "relu1"
+    name: "relu1_name"
+    op_type: "Relu"
+  }
+  node {
+    input: "relu1"
+    input: "in2"
+    output: "add1"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "in3"
+    input: "in4"
+    output: "conv1"
+    op_type: "Conv"
+  }
+  node {
+    input: "relu1"
+    input: "add2/placeholder_port_1"
+    output: "add2"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "add1"
+    input: "conv1"
+    output: "mul2"
+    name: ""
+    op_type: "Mul"
+  }
+  node {
+    input: "add2"
+    output: "split1"
+    output: "split2"
+    name: "split_name"
+    op_type: "Split"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "add2/placeholder_port_1"
+    input: "split1"
+    output: "mul1"
+    op_type: "Mul"
+  }
+  name: "subgraph_extraction_testing"
+  initializer {
+    dims: 1
+    dims: 1
+    dims: 1
+    dims: 1
+    data_type: 1
+    float_data: 1
+    name: "in4"
+  }
+  input {
+    name: "in1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+        }
+      }
+    }
+  }
+  input {
+    name: "in3"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in4"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "add2/placeholder_port_1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "split2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_one_source_and_merge_all_new_inputs.prototxt b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_one_source_and_merge_all_new_inputs.prototxt
new file mode 100644
index 00000000000..435aff8fbb7
--- /dev/null
+++ b/src/core/tests/models/onnx/model_editor/reference/subgraph__cut_two_edges_from_one_source_and_merge_all_new_inputs.prototxt
@@ -0,0 +1,208 @@
+ir_version: 7
+producer_name: "test_data_generator"
+graph {
+  node {
+    input: "in1"
+    output: "relu1"
+    name: "relu1_name"
+    op_type: "Relu"
+  }
+  node {
+    input: "relu1"
+    input: "in2"
+    output: "add1"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "in3"
+    input: "in4"
+    output: "conv1"
+    op_type: "Conv"
+  }
+  node {
+    input: "add2/placeholder_port_0"
+    input: "add1"
+    output: "add2"
+    name: "add_ambiguous_name"
+    op_type: "Add"
+  }
+  node {
+    input: "add1"
+    input: "conv1"
+    output: "mul2"
+    name: ""
+    op_type: "Mul"
+  }
+  node {
+    input: "add2"
+    output: "split1"
+    output: "split2"
+    name: "split_name"
+    op_type: "Split"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "add2/placeholder_port_0"
+    input: "split1"
+    output: "mul1"
+    op_type: "Mul"
+  }
+  name: "subgraph_extraction_testing"
+  initializer {
+    dims: 1
+    dims: 1
+    dims: 1
+    dims: 1
+    data_type: 1
+    float_data: 1
+    name: "in4"
+  }
+  input {
+    name: "in1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+        }
+      }
+    }
+  }
+  input {
+    name: "in3"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "in4"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "add2/placeholder_port_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "split2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mul2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/src/core/tests/onnx/onnx_editor.cpp b/src/core/tests/onnx/onnx_editor.cpp
index 2931a66c840..138c154e038 100644
--- a/src/core/tests/onnx/onnx_editor.cpp
+++ b/src/core/tests/onnx/onnx_editor.cpp
@@ -1567,3 +1567,135 @@ NGRAPH_TEST(onnx_editor, get_output_ports) {
             std::string::npos);
     }
 }
+
+NGRAPH_TEST(onnx_editor, add_output) {
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
+
+    editor.add_output({OutputEdge{0, 0}});
+
+    const auto edge1 = editor.find_output_edge(EditorNode{"add_node1"}, EditorOutput{"X"});
+    EXPECT_TRUE(editor.is_output(edge1));
+}
+
+NGRAPH_TEST(onnx_editor, get_tensor_element_type) {
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+
+    EXPECT_EQ(editor.get_input_type("in1"), (element::f32));
+    EXPECT_EQ(editor.get_input_type("in2"), (element::f32));
+    editor.set_input_types({{"in3", (element::f16)}});
+    EXPECT_EQ(editor.get_input_type("in3"), (element::f16));
+}
+
+NGRAPH_TEST(onnx_editor, subgraph__cut_one_edge_and_merge_all_new_inputs) {
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+
+    // InputEdge{1, "relu1"}
+    const auto input_edge_1 = editor.find_input_edge(EditorNode(EditorOutput("add1")), EditorInput(0));
+
+    editor.extract_subgraph({{input_edge_1}}, {}, true);
+
+    const auto ref_model = ngraph::file_util::path_join(
+        SERIALIZED_ZOO,
+        "onnx/model_editor/reference/subgraph__cut_one_edge_and_merge_all_new_inputs.onnx");
+
+    auto result = compare_onnx_models(editor.model_string(), ref_model);
+
+    // InputEdge{5, "add2"}
+    const auto input_edge_2 = editor.find_input_edge(EditorNode(EditorOutput("split1")), EditorInput(0));
+
+    editor.extract_subgraph({{input_edge_2}}, {}, true);
+
+    const auto ref_model1 = ngraph::file_util::path_join(
+        SERIALIZED_ZOO,
+        "onnx/model_editor/reference/subgraph__cut_one_another_edge_and_merge_all_new_inputs.onnx");
+
+    result = compare_onnx_models(editor.model_string(), ref_model1);
+
+    EXPECT_TRUE(result.is_ok) << result.error_message;
+}
+
+NGRAPH_TEST(onnx_editor, subgraph__cut_two_edges_from_one_source_and_merge_all_new_inputs) {
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+
+    // InputEdge{3, "relu1"}
+    const auto input_edge_1 = editor.find_input_edge(EditorNode(EditorOutput("add2")), EditorInput(0));
+    // InputEdge{6, "relu1"}
+    const auto input_edge_2 = editor.find_input_edge(EditorNode(EditorOutput("mul1")), EditorInput(0));
+
+    editor.extract_subgraph({{input_edge_1, input_edge_2}}, {}, true);
+
+    const auto ref_model = ngraph::file_util::path_join(
+        SERIALIZED_ZOO,
+        "onnx/model_editor/reference/subgraph__cut_two_edges_from_one_source_and_merge_all_new_inputs.onnx");
+
+    const auto result = compare_onnx_models(editor.model_string(), ref_model);
+
+    EXPECT_TRUE(result.is_ok) << result.error_message;
+}
+
+NGRAPH_TEST(onnx_editor, subgraph__cut_two_edges_from_different_sources_and_merge_all_new_inputs) {
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+
+    // InputEdge{3, "add1"}
+    const auto input_edge_1 = editor.find_input_edge(EditorNode(EditorOutput("add2")), EditorInput(1));
+    // InputEdge{6, "relu1"}
+    const auto input_edge_2 = editor.find_input_edge(EditorNode(EditorOutput("mul1")), EditorInput(0));
+
+    editor.extract_subgraph({{input_edge_1, input_edge_2}}, {}, true);
+
+    const auto ref_model = ngraph::file_util::path_join(
+        SERIALIZED_ZOO,
+        "onnx/model_editor/reference/subgraph__cut_two_edges_from_different_sources_and_merge_all_new_inputs.onnx");
+
+    const auto result = compare_onnx_models(editor.model_string(), ref_model);
+
+    EXPECT_TRUE(result.is_ok) << result.error_message;
+}
+
+NGRAPH_TEST(onnx_editor, subgraph__cut_all_edges_from_one_source_and_merge_all_new_inputs) {
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+
+    // InputEdge{3, "relu1"}
+    const auto input_edge_1 = editor.find_input_edge(EditorNode(EditorOutput("add2")), EditorInput(0));
+    // InputEdge{6, "relu1"}
+    const auto input_edge_2 = editor.find_input_edge(EditorNode(EditorOutput("mul1")), EditorInput(0));
+    // InputEdge{1, "relu1"}
+    const auto input_edge_3 = editor.find_input_edge(EditorNode(EditorOutput("add1")), EditorInput(0));
+
+    editor.extract_subgraph({{input_edge_1, input_edge_2, input_edge_3}}, {}, true);
+
+    const auto ref_model = ngraph::file_util::path_join(
+        SERIALIZED_ZOO,
+        "onnx/model_editor/reference/subgraph__cut_all_edges_from_one_source_and_merge_all_new_inputs.onnx");
+
+    const auto result = compare_onnx_models(editor.model_string(), ref_model);
+
+    EXPECT_TRUE(result.is_ok) << result.error_message;
+}
+
+NGRAPH_TEST(onnx_editor, subgraph__cut_custom_edges_from_different_sources_and_merge_all_new_inputs) {
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+
+    // InputEdge{3, "relu1"}
+    const auto input_edge_1 = editor.find_input_edge(EditorNode(EditorOutput("add2")), EditorInput(0));
+    // InputEdge{4, "add1"}
+    const auto input_edge_2 = editor.find_input_edge(EditorNode(EditorOutput("mul2")), EditorInput(0));
+    // InputEdge{3, "add1"}
+    const auto input_edge_3 = editor.find_input_edge(EditorNode(EditorOutput("add2")), EditorInput(1));
+
+    editor.extract_subgraph({{input_edge_2, input_edge_1, input_edge_3}}, {}, true);
+
+    const auto ref_model = ngraph::file_util::path_join(
+        SERIALIZED_ZOO,
+        "onnx/model_editor/reference/subgraph__cut_custom_edges_from_different_sources_and_merge_all_new_inputs.onnx");
+
+    const auto result = compare_onnx_models(editor.model_string(), ref_model);
+
+    EXPECT_TRUE(result.is_ok) << result.error_message;
+}
diff --git a/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp
index 980669c6d54..122c979841a 100644
--- a/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp
+++ b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp
@@ -283,17 +283,59 @@ SubgraphExtractor::SubgraphExtractor(ONNX_NAMESPACE::GraphProto& graph)
     }
 }
 
-void SubgraphExtractor::add_new_inputs(const std::vector<InputEdge>& new_inputs) {
-    for (const auto& input_edge : new_inputs) {
-        const auto tensor_name = get_input_tensor_name(m_onnx_graph, input_edge);
+void SubgraphExtractor::add_new_inputs(const std::vector<InputEdge>& new_inputs, const bool merge_inputs) {
+    if (merge_inputs && new_inputs.size() > 1) {
+        std::map<std::string, int> new_inputs_consumers;
+        int index = 0;
+        int input_consumers = new_inputs.size();
+
+        // count all input edges
+        for (const auto& input_edge : new_inputs) {
+            const auto name = get_input_tensor_name(m_onnx_graph, input_edge);
+            new_inputs_consumers[name] += 1;
+        }
+
+        // check if cutting will be performed for a Node (in that case set input_consumers to 0,
+        // it will reuse Node name as new input name)
+        for (const auto& input : new_inputs_consumers) {
+            if (input.second == m_tensor_consumers[input.first] && input.second > 1) {
+                input_consumers = 0;
+
+                // get index of the current edge from new_inputs in order to pass it to append_new_graph_input()
+                auto it = std::find_if(new_inputs.begin(), new_inputs.begin(), [&](const InputEdge& input_edge) {
+                    return get_input_tensor_name(m_onnx_graph, input_edge) == input.first;
+                });
+                index = std::distance(new_inputs.begin(), it);
+            }
+        }
+
+        const auto new_input = append_new_graph_input(m_onnx_graph, new_inputs[index], input_consumers);
+
+        // set input for all other incoming input edges
+        for (auto it = new_inputs.begin() + 1; it != new_inputs.end(); ++it) {
+            auto& target_node = *(m_onnx_graph.mutable_node(it->m_node_idx));
+            auto target_input = target_node.mutable_input(it->m_port_idx);
+            *target_input = new_input.second;
+        }
 
-        // in case an edge is connected to a single node, a new graph input should be added
-        // and connected to that node; the new edge is an edge between the node and new input
-        const auto new_input = append_new_graph_input(m_onnx_graph, input_edge, m_tensor_consumers[tensor_name]);
         if (new_input.first) {
             // the original edge should be replaced with a new one in the helper multimap
             // this information will later be used during the subgraph extraction stage
-            replace_input_edge(input_edge, new_input.second);
+            replace_input_edge(new_inputs[0], new_input.second);
+        }
+    } else {
+        for (const auto& input_edge : new_inputs) {
+            const auto tensor_name = get_input_tensor_name(m_onnx_graph, input_edge);
+
+            // in case an edge is connected to a single node, a new graph input should be added
+            // and connected to that node; the new edge is an edge between the node and new input
+            const auto new_input = append_new_graph_input(m_onnx_graph, input_edge, m_tensor_consumers[tensor_name]);
+
+            if (new_input.first) {
+                // the original edge should be replaced with a new one in the helper multimap
+                // this information will later be used during the subgraph extraction stage
+                replace_input_edge(input_edge, new_input.second);
+            }
         }
     }
 }
diff --git a/src/frontends/onnx/frontend/src/detail/subgraph_extraction.hpp b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.hpp
index e273bfcfd4a..585781f647e 100644
--- a/src/frontends/onnx/frontend/src/detail/subgraph_extraction.hpp
+++ b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.hpp
@@ -26,7 +26,9 @@ struct SubgraphExtractor {
 
     /// \brief Adds new inputs to the graph and connects them to the nodes indicated by
     ///        the provided input edges.
-    void add_new_inputs(const std::vector<InputEdge>& new_inputs);
+    /// \param merge_inputs Flag indicating wheteher new inputs are generated for each input edge (false)
+    ///                     or there is only one input created for provided edges (true)
+    void add_new_inputs(const std::vector<InputEdge>& new_inputs, const bool merge_inputs = false);
 
     /// \brief Adds new outputs to the graph with the same name as the nodes pointed to
     ///        by the input edges "new_outputs".
diff --git a/src/frontends/onnx/frontend/src/editor.cpp b/src/frontends/onnx/frontend/src/editor.cpp
index 911ae6ed4a3..f09fba33646 100644
--- a/src/frontends/onnx/frontend/src/editor.cpp
+++ b/src/frontends/onnx/frontend/src/editor.cpp
@@ -15,6 +15,7 @@
 #include "ngraph/log.hpp"
 #include "onnx_common/parser.hpp"
 #include "onnx_common/utils.hpp"
+#include "utils/common.hpp"
 #include "utils/onnx_internal.hpp"
 
 using namespace ov;
@@ -279,6 +280,25 @@ void onnx_editor::ONNXModelEditor::set_input_types(const std::map<std::string, e
     }
 }
 
+element::Type_t onnx_editor::ONNXModelEditor::get_input_type(const std::string& tensor_name) const {
+    auto* onnx_graph = m_pimpl->m_model_proto->mutable_graph();
+    auto* onnx_input = find_graph_input(*onnx_graph, tensor_name);
+
+    if (onnx_input != nullptr) {
+        const auto& type_proto = onnx_input->type();
+        if (!type_proto.has_tensor_type()) {
+            throw ov::Exception("The input is malformed - it doesn't contain the 'tensor_type' field. Cannot "
+                                "change the data type. Input name: " +
+                                onnx_input->name());
+        }
+        auto& tensor_type = type_proto.tensor_type();
+        auto type = tensor_type.elem_type();
+        return ngraph::onnx_import::common::get_ngraph_element_type(type);
+    } else {
+        throw ov::Exception("The tensor: " + tensor_name + " was not found in the input graph.");
+    }
+}
+
 void onnx_editor::ONNXModelEditor::set_input_shapes(const std::map<std::string, ngraph::PartialShape>& input_shapes) {
     auto* onnx_graph = m_pimpl->m_model_proto->mutable_graph();
 
@@ -337,7 +357,8 @@ PartialShape onnx_editor::ONNXModelEditor::get_tensor_shape(const std::string& t
 }
 
 void onnx_editor::ONNXModelEditor::extract_subgraph(const std::vector<InputEdge>& inputs,
-                                                    const std::vector<OutputEdge>& outputs) {
+                                                    const std::vector<OutputEdge>& outputs,
+                                                    const bool merge_inputs) {
     if (inputs.empty() && outputs.empty()) {
         return;
     }
@@ -346,7 +367,7 @@ void onnx_editor::ONNXModelEditor::extract_subgraph(const std::vector<InputEdge>
     onnx_shapes.infer_shapes();
 
     SubgraphExtractor editor{*(m_pimpl->m_model_proto->mutable_graph())};
-    editor.add_new_inputs(inputs);
+    editor.add_new_inputs(inputs, merge_inputs);
     editor.add_new_outputs(outputs);
     editor.extract_subgraph(outputs);
 
@@ -606,3 +627,11 @@ std::vector<std::string> onnx_editor::ONNXModelEditor::get_output_ports(const Ed
 std::shared_ptr<Model> onnx_editor::ONNXModelEditor::decode() {
     return ngraph::onnx_import::detail::decode_to_framework_nodes(m_pimpl->m_model_proto, m_model_path, m_telemetry);
 }
+
+void onnx_editor::ONNXModelEditor::add_output(const OutputEdge& output_edge) const {
+    auto onnx_graph = m_pimpl->m_model_proto->mutable_graph();
+    std::vector<onnx_editor::OutputEdge> onnx_output;
+    onnx_output.push_back(output_edge);
+    SubgraphExtractor editor{*onnx_graph};
+    editor.add_new_outputs(onnx_output);
+}
diff --git a/src/frontends/onnx/frontend/src/editor.hpp b/src/frontends/onnx/frontend/src/editor.hpp
index 1f4a37027ff..393ab4418ee 100644
--- a/src/frontends/onnx/frontend/src/editor.hpp
+++ b/src/frontends/onnx/frontend/src/editor.hpp
@@ -80,7 +80,12 @@ public:
     ///
     /// \param inputs A collection of input edges which become new inputs to the graph
     /// \param outputs A collection of output edges which become new outputs of the graph
-    void extract_subgraph(const std::vector<InputEdge>& inputs, const std::vector<OutputEdge>& outputs);
+    /// \param merge_inputs Flag indicates whether newly created inputs after cutting shall be independent or merged,
+    ///                     false - each cutted edge will be connected with one new input (default),
+    ///                     true - all input edges will be connected to one new input
+    void extract_subgraph(const std::vector<InputEdge>& inputs,
+                          const std::vector<OutputEdge>& outputs,
+                          const bool merge_inputs = false);
 
     /// \brief Modifies the in-memory representation of the model by setting custom input
     ///        values for inputs specified in the provided map.
@@ -276,6 +281,18 @@ public:
     ///
     std::shared_ptr<Model> decode();
 
+    /// \brief     Adds output to provided OutputEdge.
+    ///
+    /// \param output_edge An output_edge type where graph output shall be added.
+    ///
+    void add_output(const OutputEdge& output_edge) const;
+
+    /// \brief     Provides element type for given input tensor name.
+    ///
+    /// \param output_edge Name of tensor for which element type will be returned.
+    ///
+    element::Type_t get_input_type(const std::string& tensor_name) const;
+
 private:
     void update_mapper_if_needed() const;
 
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index 8ff8c67b4e8..c1072b48fff 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -232,6 +232,102 @@ void InputModel::override_all_inputs(const std::vector<ov::frontend::Place::Ptr>
 
 void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
                                   const std::vector<ov::frontend::Place::Ptr>& outputs) {
+    std::vector<onnx_editor::InputEdge> onnx_inputs = convert_place_to_input_edge(inputs);
+    std::vector<onnx_editor::OutputEdge> onnx_outputs = convert_place_to_output_edge(outputs);
+
+    m_editor->extract_subgraph(onnx_inputs, onnx_outputs);
+}
+
+ov::frontend::Place::Ptr InputModel::add_output(const ov::frontend::Place::Ptr& place) {
+    std::string name = place->get_names().at(0);
+
+    const auto& outputs = m_editor->model_outputs();
+    const auto& inputs = m_editor->model_inputs();
+
+    auto find_output = std::find(std::begin(outputs), std::end(outputs), name);
+    auto find_input = std::find(std::begin(inputs), std::end(inputs), name);
+
+    if (find_input != inputs.end()) {
+        return nullptr;
+    }
+
+    const auto output_port = place->get_producing_port();
+
+    if (find_output != outputs.end()) {
+        return place;
+    } else if (const auto tensor = std::dynamic_pointer_cast<PlaceTensor>(place)) {
+        auto tensor_name = tensor->get_names()[0];
+        auto output_edge = m_editor->find_output_edge(tensor_name);
+        m_editor->add_output(output_edge);
+    } else if (const auto onnx_output_edge = std::dynamic_pointer_cast<PlaceOutputEdge>(output_port)) {
+        NGRAPH_CHECK(onnx_output_edge, "Non-onnx output place was passed.");
+        m_editor->add_output(onnx_output_edge->get_output_edge());
+    } else {
+        return nullptr;
+    }
+
+    return std::make_shared<PlaceTensor>(name, m_editor);
+}
+
+void InputModel::remove_output(const ov::frontend::Place::Ptr& place) {
+    std::string name = place->get_names().at(0);
+    std::vector<ov::frontend::Place::Ptr> outputs = get_outputs();
+    const auto& output_names = m_editor->model_outputs();
+
+    auto find_output = std::find(output_names.begin(), output_names.end(), name);
+
+    if (find_output != output_names.end()) {
+        outputs.erase(std::remove_if(outputs.begin(),
+                                     outputs.end(),
+                                     [&place](ov::frontend::Place::Ptr const& output) {
+                                         return output->is_equal(place);
+                                     }),
+                      outputs.end());
+
+        extract_subgraph({}, {outputs});
+    }
+}
+
+void InputModel::cut_and_add_new_input(const ov::frontend::Place::Ptr& place, const std::string& new_name_optional) {
+    std::vector<ov::frontend::Place::Ptr> inputs = get_inputs();
+    std::vector<ov::frontend::Place::Ptr> outputs = get_outputs();
+
+    if (place->is_input())
+        return;
+
+    const auto edge_place = convert_place_to_input_edge({place});
+    const auto edge_outputs = convert_place_to_output_edge(outputs);
+
+    if (!edge_place.empty() && !edge_outputs.empty()) {
+        m_editor->extract_subgraph(edge_place, edge_outputs, true);
+
+        // change name for newly created input, it is the last entry in get_inputs()
+        if (!new_name_optional.empty()) {
+            auto new_inputs = get_inputs();
+            m_editor->set_tensor_name(new_inputs.back()->get_names().at(0), new_name_optional);
+        }
+    }
+}
+
+void InputModel::set_tensor_value(const ov::frontend::Place::Ptr& place, const void* value) {
+    std::map<std::string, std::shared_ptr<ngraph::op::Constant>> map;
+
+    if (const auto var_place = std::dynamic_pointer_cast<PlaceTensor>(place)) {
+        auto name = place->get_names().at(0);
+        auto p_shape = m_editor->get_tensor_shape(name);
+        auto el_type = m_editor->get_input_type(name);
+
+        std::shared_ptr<ngraph::op::Constant> constant =
+            ngraph::op::Constant::create(el_type, p_shape.to_shape(), value);
+
+        constant->set_friendly_name(name);
+        map.emplace(name, constant);
+        m_editor->set_input_values(map);
+    }
+}
+
+std::vector<onnx_editor::InputEdge> InputModel::convert_place_to_input_edge(
+    const std::vector<ov::frontend::Place::Ptr>& inputs) {
     std::vector<onnx_editor::InputEdge> onnx_inputs;
     onnx_inputs.reserve(inputs.size());
     for (const auto& input : inputs) {
@@ -260,6 +356,11 @@ void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& i
         }
     }
 
+    return onnx_inputs;
+}
+
+std::vector<onnx_editor::OutputEdge> InputModel::convert_place_to_output_edge(
+    const std::vector<ov::frontend::Place::Ptr>& outputs) {
     std::vector<onnx_editor::OutputEdge> onnx_outputs;
     onnx_outputs.reserve(outputs.size());
     for (const auto& output : outputs) {
@@ -283,7 +384,8 @@ void InputModel::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& i
                            });
         }
     }
-    m_editor->extract_subgraph(onnx_inputs, onnx_outputs);
+
+    return onnx_outputs;
 }
 
 void InputModel::add_tensor_names(std::shared_ptr<Model>& model) {
diff --git a/src/frontends/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
index d8f87c5d2ae..64d94b16129 100644
--- a/src/frontends/onnx/frontend/src/input_model.hpp
+++ b/src/frontends/onnx/frontend/src/input_model.hpp
@@ -54,16 +54,30 @@ public:
     void set_partial_shape(const ov::frontend::Place::Ptr& place, const ngraph::PartialShape& shape) override;
     ngraph::PartialShape get_partial_shape(const ov::frontend::Place::Ptr& place) const override;
     void set_element_type(const ov::frontend::Place::Ptr& place, const ngraph::element::Type& type) override;
+    ov::frontend::Place::Ptr add_output(const ov::frontend::Place::Ptr& place) override;
+    void remove_output(const ov::frontend::Place::Ptr& place) override;
 
     std::shared_ptr<Model> decode();
     std::shared_ptr<Model> convert();
 
+    void cut_and_add_new_input(const ov::frontend::Place::Ptr& place,
+                               const std::string& new_name_optional = "") override;
+
     // Editor features
     void override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) override;
     void override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) override;
     void extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
                           const std::vector<ov::frontend::Place::Ptr>& outputs) override;
 
+    // Editor tensor features
+    void set_tensor_value(const ov::frontend::Place::Ptr& place, const void* value) override;
+
+    // internal usage
+    std::vector<onnx_editor::InputEdge> convert_place_to_input_edge(
+        const std::vector<ov::frontend::Place::Ptr>& inputs);
+    std::vector<onnx_editor::OutputEdge> convert_place_to_output_edge(
+        const std::vector<ov::frontend::Place::Ptr>& outputs);
+
 private:
     std::shared_ptr<ov::onnx_editor::ONNXModelEditor> m_editor;
 

From 4eea535e78aa88d8c9d864ef51d4a7629dce8b7d Mon Sep 17 00:00:00 2001
From: Ilya Lavrenov <ilya.lavrenov@intel.com>
Date: Thu, 23 Dec 2021 11:37:06 +0300
Subject: [PATCH 108/160] Includes in frontends (#9378)

* Used full paths in public includes

* Fixed install of ONNX include
---
 cmake/developer_package/frontends/frontends.cmake             | 4 ++--
 src/frontends/common/include/openvino/frontend/exception.hpp  | 2 +-
 src/frontends/common/include/openvino/frontend/frontend.hpp   | 4 ++--
 .../common/include/openvino/frontend/input_model.hpp          | 4 ++--
 src/frontends/common/include/openvino/frontend/place.hpp      | 2 +-
 tools/pot/.gitignore                                          | 3 +++
 6 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/cmake/developer_package/frontends/frontends.cmake b/cmake/developer_package/frontends/frontends.cmake
index e585b29d044..34b3963b088 100644
--- a/cmake/developer_package/frontends/frontends.cmake
+++ b/cmake/developer_package/frontends/frontends.cmake
@@ -226,8 +226,8 @@ macro(ov_add_frontend)
 
         if(OV_FRONTEND_LINKABLE_FRONTEND)
             # install -dev part
-            install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/
-                    DESTINATION ${FRONTEND_INSTALL_INCLUDE}
+            install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/openvino
+                    DESTINATION ${FRONTEND_INSTALL_INCLUDE}/
                     COMPONENT core_dev
                     FILES_MATCHING PATTERN "*.hpp")
 
diff --git a/src/frontends/common/include/openvino/frontend/exception.hpp b/src/frontends/common/include/openvino/frontend/exception.hpp
index 71d14238d81..1887e750d5f 100644
--- a/src/frontends/common/include/openvino/frontend/exception.hpp
+++ b/src/frontends/common/include/openvino/frontend/exception.hpp
@@ -8,7 +8,7 @@
 #include <string>
 
 #include "openvino/core/except.hpp"
-#include "visibility.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/common/include/openvino/frontend/frontend.hpp b/src/frontends/common/include/openvino/frontend/frontend.hpp
index 15793500fc0..4005f711ca8 100644
--- a/src/frontends/common/include/openvino/frontend/frontend.hpp
+++ b/src/frontends/common/include/openvino/frontend/frontend.hpp
@@ -8,12 +8,12 @@
 #include <string>
 #include <vector>
 
-#include "input_model.hpp"
 #include "openvino/core/any.hpp"
 #include "openvino/core/extension.hpp"
 #include "openvino/core/model.hpp"
 #include "openvino/core/op_extension.hpp"
-#include "visibility.hpp"
+#include "openvino/frontend/input_model.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/common/include/openvino/frontend/input_model.hpp b/src/frontends/common/include/openvino/frontend/input_model.hpp
index ae104d30ea5..b81d5cfa32f 100644
--- a/src/frontends/common/include/openvino/frontend/input_model.hpp
+++ b/src/frontends/common/include/openvino/frontend/input_model.hpp
@@ -10,8 +10,8 @@
 
 #include "openvino/core/partial_shape.hpp"
 #include "openvino/core/type/element_type.hpp"
-#include "place.hpp"
-#include "visibility.hpp"
+#include "openvino/frontend/place.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/common/include/openvino/frontend/place.hpp b/src/frontends/common/include/openvino/frontend/place.hpp
index 8b9eb039b23..8cf8a28a6aa 100644
--- a/src/frontends/common/include/openvino/frontend/place.hpp
+++ b/src/frontends/common/include/openvino/frontend/place.hpp
@@ -8,7 +8,7 @@
 #include <string>
 #include <vector>
 
-#include "visibility.hpp"
+#include "openvino/frontend/visibility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/tools/pot/.gitignore b/tools/pot/.gitignore
index 9997a045fe4..624ac2933bd 100644
--- a/tools/pot/.gitignore
+++ b/tools/pot/.gitignore
@@ -105,3 +105,6 @@ ENV/
 
 # snapshots
 *.tar
+
+# version file
+version.txt

From bbceae3bc3d978a48bece2b2ade07db1b9c658fa Mon Sep 17 00:00:00 2001
From: Paul Youngsoo Ahn <paul.y.ahn@intel.com>
Date: Thu, 23 Dec 2021 17:48:34 +0900
Subject: [PATCH 109/160] [GPU] Add INT32/UINT32 to available input data types
 when load type is aligned in GetJitLoad (#9300) (#9300)

- Modify fusibility checking to allow sub/div eltwise fusing for other primitives
- Modify dump checking code to use node name in exec graph
---
 .../intel_gpu/runtime/debug_configuration.hpp |  4 ++-
 .../kernel_selector/core/common/jitter.cpp    |  2 +-
 .../clDNN/runtime/debug_configuration.cpp     | 26 ++++++++++++++++---
 .../prepare_primitive_fusing.cpp              |  5 ++--
 .../thirdparty/clDNN/src/network.cpp          | 18 ++++++-------
 5 files changed, 36 insertions(+), 19 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp
index 600a322491b..e64af92508f 100644
--- a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/debug_configuration.hpp
@@ -5,6 +5,7 @@
 #pragma once
 #include <string>
 #include <mutex>
+#include <vector>
 
 #ifdef GPU_DEBUG_CONFIG
 #define GPU_DEBUG_IF(cond) if (cond)
@@ -32,12 +33,13 @@ public:
     std::string dump_graphs;        // Dump optimized graph
     std::string dump_sources;       // Dump opencl sources
     std::string dump_layers_path;   // Enable dumping intermediate buffers and set the dest path
-    std::string dump_layers;        // Dump intermediate buffers of specified layers only, separated by space
+    std::vector<std::string> dump_layers;        // Dump intermediate buffers of specified layers only
     std::string dry_run_path;       // Dry run and serialize execution graph into the specified path
     int dump_layers_dst_only;       // Dump only output of layers
     int dump_layers_limit_batch;    // Limit the size of batch to dump
     int base_batch_for_memory_estimation; // Base batch size to be used in memory estimation
     static const debug_configuration *get_instance();
+    bool is_dumped_layer(const std::string& layerName) const;
 };
 
 }  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp
index f2de7e3e3ad..fda1ab60d40 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp
@@ -1848,7 +1848,7 @@ std::string FusedOpsCodeGenerator::GetJitLoad(const FusedOpsConfiguration& conf,
             std::string vs = vec_size > 1 ? toCodeString(vec_size)  : "";
             std::string block_read;
 
-            if (input_dt == Datatype::F32) {
+            if (input_dt == Datatype::F32 || input_dt == Datatype::INT32 || input_dt == Datatype::UINT32) {
                 block_read = CastToType(" intel_sub_group_block_read" + vs + "("
                                         + "(const __global uint*)(" + GetInputPtrName(input_id) + " + " + index_func_call_vec + "))",
                                         input_dt, vec_size);
diff --git a/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp b/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
index 39053475511..f8594735194 100644
--- a/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/debug_configuration.cpp
@@ -137,7 +137,6 @@ debug_configuration::debug_configuration()
         , dump_graphs(std::string())
         , dump_sources(std::string())
         , dump_layers_path(std::string())
-        , dump_layers(std::string())
         , dump_layers_dst_only(0)
         , dry_run_path(std::string())
         , disable_onednn(0)
@@ -151,20 +150,27 @@ debug_configuration::debug_configuration()
     get_gpu_debug_env_var("DumpGraphs", dump_graphs);
     get_gpu_debug_env_var("DumpSources", dump_sources);
     get_gpu_debug_env_var("DumpLayersPath", dump_layers_path);
-    get_gpu_debug_env_var("DumpLayers", dump_layers);
     get_gpu_debug_env_var("DumpLayersDstOnly", dump_layers_dst_only);
     get_gpu_debug_env_var("DumpLayersLimitBatch", dump_layers_limit_batch);
     get_gpu_debug_env_var("DisableOnednn", disable_onednn);
     get_gpu_debug_env_var("DryRunPath", dry_run_path);
     get_gpu_debug_env_var("BaseBatchForMemEstimation", base_batch_for_memory_estimation);
+    std::string dump_layers_str;
+    get_gpu_debug_env_var("DumpLayers", dump_layers_str);
 
     if (help > 0) {
         print_help_messages();
         exit(0);
     }
 
-    if (dump_layers.length() > 0)
-        dump_layers = " " + dump_layers + " "; // Insert delimiter for easier parsing when used
+    if (dump_layers_str.length() > 0) {
+        dump_layers_str = " " + dump_layers_str + " "; // Insert delimiter for easier parsing when used
+        std::stringstream ss(dump_layers_str);
+        std::string layer;
+        while (ss >> layer) {
+            dump_layers.push_back(layer);
+        }
+    }
 #endif
 }
 
@@ -180,4 +186,16 @@ const debug_configuration *debug_configuration::get_instance() {
     return nullptr;
 #endif
 }
+
+bool debug_configuration::is_dumped_layer(const std::string& layerName) const {
+#ifdef GPU_DEBUG_CONFIG
+    if (dump_layers.empty()) return true;
+    auto iter = std::find_if(dump_layers.begin(), dump_layers.end(), [&](const std::string& dl){
+        return (layerName.find(dl) != std::string::npos);
+    });
+    return (iter != dump_layers.end());
+#else
+    return false;
+#endif
+}
 } // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp
index e517c4cf1ef..6df79c97974 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp
@@ -940,8 +940,7 @@ void prepare_primitive_fusing::fuse_simple_primitives(program &p) {
 
             for (size_t i = 0; i < parents.size(); i++) {
                 can_fuse_parents[i] = (parents[i]->is_type<convolution>() && conv_supports_fusings(parents[i]->as<convolution>())) ||
-                                      ((prim->mode == eltwise_mode::sum || prim->mode == eltwise_mode::prod) &&
-                                      ((parents[i]->is_type<binary_convolution>() && bin_conv_supports_eltw_fusings(parents[i]->as<binary_convolution>())) ||
+                                      (parents[i]->is_type<binary_convolution>() && bin_conv_supports_eltw_fusings(parents[i]->as<binary_convolution>())) ||
                                       (parents[i]->is_type<mvn>() && mvn_supports_fusings(parents[i]->as<mvn>())) ||
                                       (parents[i]->is_type<deconvolution>()) ||
                                       (parents[i]->is_type<permute>()) ||
@@ -959,7 +958,7 @@ void prepare_primitive_fusing::fuse_simple_primitives(program &p) {
                                       (parents[i]->is_type<scatter_elements_update>()) ||
                                       (parents[i]->is_type<pooling>() && pooling_supports_fusings(parents[i]->as<pooling>())) ||
                                       (parents[i]->is_type<depth_to_space>() && dts_supports_fusings(parents[i]->as<depth_to_space>())) ||
-                                      (parents[i]->is_type<reduce>() && reduce_supports_fusings(parents[i]->as<reduce>()))));
+                                      (parents[i]->is_type<reduce>() && reduce_supports_fusings(parents[i]->as<reduce>()));
             }
 
             // Disable fusion to a node on constant path when second input is in data flow
diff --git a/inference-engine/thirdparty/clDNN/src/network.cpp b/inference-engine/thirdparty/clDNN/src/network.cpp
index 29210058d6b..22438b529a6 100644
--- a/inference-engine/thirdparty/clDNN/src/network.cpp
+++ b/inference-engine/thirdparty/clDNN/src/network.cpp
@@ -178,13 +178,14 @@ void dump<uint32_t>(memory::ptr mem, stream& stream, std::ofstream& file_stream)
 }
 
 static void log_memory_to_file(memory::ptr mem, stream& stream, std::string layerName) {
+    std::cout << "Dump " << layerName << std::endl;
     GPU_DEBUG_GET_INSTANCE(debug_config);
     std::string filename = layerName;
     std::replace(filename.begin(), filename.end(), '\\', '_');
     std::replace(filename.begin(), filename.end(), '/', '_');
     std::replace(filename.begin(), filename.end(), ' ', '_');
     std::replace(filename.begin(), filename.end(), ':', '_');
-        filename = debug_config->dump_layers_path + filename + ".txt";
+    filename = debug_config->dump_layers_path + filename + ".txt";
 
     std::ofstream file_stream(filename);
     auto mem_dt = mem->get_layout().data_type;
@@ -208,6 +209,7 @@ static void log_memory_to_file(memory::ptr mem, stream& stream, std::string laye
     (void)layerName;
 }
 #endif
+
 /*
 Network will always have net_id = 0 when it will be cldnn internal micronetwork (created i.e by propagate_constants
 opt pass).
@@ -627,6 +629,7 @@ std::map<primitive_id, network_output> network::execute(const std::vector<event:
     return result;
 }
 
+
 void network::execute_impl(const std::vector<event::ptr>& events) {
     OV_ITT_SCOPED_TASK(itt::domains::CLDNN, "NetworkImpl::Execute");
     // Wait for previous execution completion
@@ -647,19 +650,16 @@ void network::execute_impl(const std::vector<event::ptr>& events) {
     auto surf_lock = surfaces_lock::create(get_engine().type(), in_out_mem, get_stream());
 
     set_arguments();
-
     for (auto& inst : _exec_order) {
         GPU_DEBUG_IF(debug_config->dump_layers_path.length() > 0) {
             auto& node = _program->get_node(inst->id());
-            std::string layer_name = node.id();
+            const std::string layer_name = node.id();
             GPU_DEBUG_IF(debug_config->verbose >= 2) {
                 std::cerr << get_primitive_info(inst->id()) << std::endl;
             }
 
             GPU_DEBUG_IF(debug_config->dump_layers_dst_only == 0 &&
-                            (debug_config->dump_layers.length() == 0 ||
-                            (debug_config->dump_layers.length() != 0 && debug_config->dump_layers.find(" " + layer_name + " ") != std::string::npos))) {
-                std::cout << "Dump " << layer_name << " layer src" << std::endl;
+                            debug_config->is_dumped_layer(layer_name)) {
                 for (size_t i = 0; i < get_primitive(inst->id())->dependencies().size(); i++) {
                     log_memory_to_file(get_primitive(inst->id())->dep_memory_ptr(i), get_stream(),
                                     layer_name + "_src_" + std::to_string(i));
@@ -682,10 +682,8 @@ void network::execute_impl(const std::vector<event::ptr>& events) {
         GPU_DEBUG_IF(debug_config->dump_layers_path.length() > 0) {
             get_stream().finish();
             auto& node = _program->get_node(inst->id());
-            std::string layer_name = node.id();
-            GPU_DEBUG_IF(debug_config->dump_layers.length() == 0 ||
-                        (debug_config->dump_layers.length() != 0 && debug_config->dump_layers.find(" " + layer_name + " ") != std::string::npos)) {
-                std::cout << "Dump " << layer_name << " layer dst" << std::endl;
+            const std::string layer_name = node.id();
+            GPU_DEBUG_IF(debug_config->is_dumped_layer(layer_name)) {
                 log_memory_to_file(get_primitive(inst->id())->output_memory_ptr(), get_stream(), layer_name + "_dst_0");
             }
         }

From 60a11a63481e32c5f676324dbd4d07ecedf70a43 Mon Sep 17 00:00:00 2001
From: Vladislav Volkov <vladislav.volkov@intel.com>
Date: Thu, 23 Dec 2021 11:49:25 +0300
Subject: [PATCH 110/160] [CPU] Renamed CPU plugin to ov_intel_cpu_plugin
 (#9342)

---
 .ci/azure/linux_lohika.yml                    |  2 +-
 .ci/azure/linux_onnxruntime.yml               |  2 +-
 .ci/openvino-onnx/Dockerfile                  |  2 +-
 .gitmodules                                   |  4 +-
 CODEOWNERS                                    |  4 +-
 cmake/coverage.cmake                          |  8 +--
 cmake/features.cmake                          |  6 +-
 docs/IE_DG/Cross_Check_Tool.md                |  4 +-
 docs/IE_DG/inference_engine_intro.md          |  2 +-
 .../executable_network/get_metric.cpp         |  4 +-
 .../src/openvino/inference_engine/ie_api.pyx  |  2 +-
 .../ie_bridges/python/tests/test_IECore.py    |  6 +-
 inference-engine/src/CMakeLists.txt           |  4 --
 inference-engine/thirdparty/CMakeLists.txt    | 40 --------------
 .../ngraph_function_creation_sample/README.md |  2 +-
 scripts/CMakeLists.txt                        |  1 +
 src/bindings/c/tests/CMakeLists.txt           |  4 +-
 src/bindings/c/tests/ie_c_api_test.cpp        |  2 +-
 .../tests/test_inference_engine/test_core.py  |  2 +-
 src/cmake/ov_runtime.cmake                    |  2 +-
 src/core/tests/CMakeLists.txt                 |  4 +-
 src/core/tests/frontend/paddle/CMakeLists.txt |  4 +-
 src/core/tests/runtime/ie/unit_test.manifest  |  2 +-
 src/plugins/CMakeLists.txt                    |  4 ++
 .../plugins/intel_cpu}/CMakeLists.txt         | 20 ++++---
 .../plugins/intel_cpu/src}/config.cpp         |  0
 .../plugins/intel_cpu/src}/config.h           |  0
 .../plugins/intel_cpu/src}/cpu_shape.cpp      |  0
 .../plugins/intel_cpu/src}/cpu_shape.h        |  0
 .../plugins/intel_cpu/src}/cpu_types.cpp      |  0
 .../plugins/intel_cpu/src}/cpu_types.h        |  0
 .../plugins/intel_cpu/src}/docs/README.md     |  0
 .../intel_cpu/src}/docs/blob_dumping.md       |  0
 .../src}/docs/graph_serialization.md          |  0
 .../plugins/intel_cpu/src}/docs/verbose.md    |  0
 .../intel_cpu/src}/emitters/cpu_generator.cpp |  0
 .../intel_cpu/src}/emitters/cpu_generator.hpp |  0
 .../src}/emitters/jit_bf16_emitters.hpp       |  0
 .../src}/emitters/jit_eltwise_emitters.cpp    |  0
 .../src}/emitters/jit_eltwise_emitters.hpp    |  0
 .../intel_cpu/src}/emitters/jit_emitter.cpp   |  0
 .../intel_cpu/src}/emitters/jit_emitter.hpp   |  0
 .../src}/emitters/jit_load_store_emitters.cpp |  0
 .../src}/emitters/jit_load_store_emitters.hpp |  0
 .../src}/emitters/jit_mkldnn_emitters.cpp     |  0
 .../src}/emitters/jit_mkldnn_emitters.hpp     |  0
 .../src}/emitters/jit_mkldnn_ext_emitters.hpp |  0
 .../src}/emitters/jit_snippets_emitters.hpp   |  0
 .../src}/memory_desc/blocked_memory_desc.cpp  |  0
 .../src}/memory_desc/blocked_memory_desc.h    |  0
 .../memory_desc/cpu_blocked_memory_desc.cpp   |  0
 .../memory_desc/cpu_blocked_memory_desc.h     |  0
 .../src}/memory_desc/cpu_memory_desc.h        |  0
 .../memory_desc/cpu_memory_desc_utils.cpp     |  0
 .../src}/memory_desc/cpu_memory_desc_utils.h  |  0
 .../memory_desc/dnnl_blocked_memory_desc.cpp  |  0
 .../memory_desc/dnnl_blocked_memory_desc.h    |  0
 .../src}/memory_desc/dnnl_memory_desc.cpp     |  0
 .../src}/memory_desc/dnnl_memory_desc.h       |  0
 .../intel_cpu/src}/mkldnn/ie_mkldnn.cpp       |  0
 .../plugins/intel_cpu/src}/mkldnn/ie_mkldnn.h |  0
 .../intel_cpu/src}/mkldnn/iml_type_mapper.cpp |  0
 .../intel_cpu/src}/mkldnn/iml_type_mapper.h   |  0
 .../src}/mkldnn_async_infer_request.cpp       |  0
 .../src}/mkldnn_async_infer_request.h         |  0
 .../intel_cpu/src}/mkldnn_descriptor.cpp      |  0
 .../intel_cpu/src}/mkldnn_descriptor.h        |  0
 .../plugins/intel_cpu/src}/mkldnn_edge.cpp    |  0
 .../plugins/intel_cpu/src}/mkldnn_edge.h      |  0
 .../intel_cpu/src}/mkldnn_exec_network.cpp    |  0
 .../intel_cpu/src}/mkldnn_exec_network.h      |  0
 .../intel_cpu/src}/mkldnn_extension.cpp       |  0
 .../plugins/intel_cpu/src}/mkldnn_extension.h |  0
 .../intel_cpu/src}/mkldnn_extension_mngr.cpp  |  0
 .../intel_cpu/src}/mkldnn_extension_mngr.h    |  0
 .../intel_cpu/src}/mkldnn_extension_utils.cpp |  0
 .../intel_cpu/src}/mkldnn_extension_utils.h   |  0
 .../plugins/intel_cpu/src}/mkldnn_graph.cpp   |  0
 .../plugins/intel_cpu/src}/mkldnn_graph.h     |  0
 .../intel_cpu/src}/mkldnn_graph_dumper.cpp    |  0
 .../intel_cpu/src}/mkldnn_graph_dumper.h      |  0
 .../intel_cpu/src}/mkldnn_graph_optimizer.cpp |  0
 .../intel_cpu/src}/mkldnn_graph_optimizer.h   |  0
 .../intel_cpu/src}/mkldnn_infer_request.cpp   |  0
 .../intel_cpu/src}/mkldnn_infer_request.h     |  0
 .../plugins/intel_cpu/src}/mkldnn_itt.h       |  0
 .../plugins/intel_cpu/src}/mkldnn_memory.cpp  |  0
 .../plugins/intel_cpu/src}/mkldnn_memory.h    |  0
 .../intel_cpu/src}/mkldnn_memory_state.cpp    |  0
 .../intel_cpu/src}/mkldnn_memory_state.h      |  0
 .../plugins/intel_cpu/src}/mkldnn_node.cpp    |  2 +-
 .../plugins/intel_cpu/src}/mkldnn_node.h      |  0
 .../intel_cpu/src}/mkldnn_nodes_factory.cpp   |  0
 .../plugins/intel_cpu/src}/mkldnn_plugin.cpp  |  2 +-
 .../plugins/intel_cpu/src}/mkldnn_plugin.h    |  0
 .../intel_cpu/src}/mkldnn_primitive.cpp       |  0
 .../plugins/intel_cpu/src}/mkldnn_primitive.h |  0
 .../intel_cpu/src}/mkldnn_selective_build.h   |  0
 .../intel_cpu/src}/mkldnn_serialize.cpp       |  0
 .../plugins/intel_cpu/src}/mkldnn_serialize.h |  0
 .../intel_cpu/src}/mkldnn_weights_cache.cpp   |  0
 .../intel_cpu/src}/mkldnn_weights_cache.hpp   |  0
 .../align_matmul_input_ranks.cpp              |  0
 .../align_matmul_input_ranks.hpp              |  0
 .../convert_broadcast_to_tiles.cpp            |  0
 .../convert_broadcast_to_tiles.hpp            |  0
 .../convert_matmul_to_fc.cpp                  |  0
 .../convert_matmul_to_fc.hpp                  |  0
 .../convert_tile_to_seq_tiles.cpp             |  0
 .../convert_tile_to_seq_tiles.hpp             |  0
 .../convert_to_cpu_specific_opset.hpp         |  0
 .../convert_to_leaky_relu.cpp                 |  0
 .../convert_to_leaky_relu.hpp                 |  0
 .../convert_to_power_static.cpp               |  0
 .../convert_to_power_static.hpp               |  0
 .../convert_to_swish_cpu.cpp                  |  0
 .../convert_to_swish_cpu.hpp                  |  0
 .../ngraph_transformations/fc_bias_fusion.cpp |  0
 .../ngraph_transformations/fc_bias_fusion.hpp |  0
 .../move_eltwise_up_data_movement.cpp         |  0
 .../move_eltwise_up_data_movement.hpp         |  0
 .../op/fully_connected.cpp                    |  0
 .../op/fully_connected.hpp                    |  0
 .../ngraph_transformations/op/leaky_relu.cpp  |  0
 .../ngraph_transformations/op/leaky_relu.hpp  |  0
 .../op/power_static.cpp                       |  0
 .../op/power_static.hpp                       |  0
 .../ngraph_transformations/op/swish_cpu.cpp   |  0
 .../ngraph_transformations/op/swish_cpu.hpp   |  0
 .../reshape_fc_fusion.cpp                     |  0
 .../reshape_fc_fusion.hpp                     |  0
 .../ngraph_transformations/reshape_prelu.cpp  |  0
 .../ngraph_transformations/reshape_prelu.hpp  |  0
 .../rnn_sequences_optimization.cpp            |  0
 .../rnn_sequences_optimization.hpp            |  0
 .../snippets_mark_skipped.cpp                 |  0
 .../snippets_mark_skipped.hpp                 |  0
 .../nodes/common/blocked_desc_creator.cpp     |  0
 .../src}/nodes/common/blocked_desc_creator.h  |  0
 .../src}/nodes/common/cpu_convert.cpp         |  0
 .../intel_cpu/src}/nodes/common/cpu_convert.h |  0
 .../intel_cpu/src}/nodes/common/cpu_memcpy.h  |  0
 .../intel_cpu/src}/nodes/common/defs.h        |  0
 .../src}/nodes/common/dnnl_executor.cpp       |  0
 .../src}/nodes/common/dnnl_executor.h         |  0
 .../intel_cpu/src}/nodes/common/fp16_utils.h  |  0
 .../src}/nodes/common/permute_kernel.cpp      |  0
 .../src}/nodes/common/permute_kernel.h        |  0
 .../intel_cpu/src}/nodes/common/softmax.cpp   |  0
 .../intel_cpu/src}/nodes/common/softmax.h     |  0
 .../nodes/common/tile_broadcast_utils.cpp     |  0
 .../src}/nodes/common/tile_broadcast_utils.h  |  0
 .../intel_cpu/src}/nodes/common/uni_simd.h    |  0
 .../plugins/intel_cpu/src}/nodes/list.hpp     |  0
 .../src}/nodes/mkldnn_adaptive_pooling.cpp    |  0
 .../src}/nodes/mkldnn_adaptive_pooling.h      |  0
 .../src}/nodes/mkldnn_batch_to_space_node.cpp |  0
 .../src}/nodes/mkldnn_batch_to_space_node.h   |  0
 .../src}/nodes/mkldnn_bin_conv_node.cpp       |  0
 .../src}/nodes/mkldnn_bin_conv_node.h         |  0
 .../src}/nodes/mkldnn_broadcast_node.cpp      |  0
 .../src}/nodes/mkldnn_broadcast_node.h        |  0
 .../src}/nodes/mkldnn_bucketize_node.cpp      |  0
 .../src}/nodes/mkldnn_bucketize_node.h        |  0
 .../src}/nodes/mkldnn_concat_node.cpp         |  0
 .../intel_cpu/src}/nodes/mkldnn_concat_node.h |  0
 .../intel_cpu/src}/nodes/mkldnn_conv_node.cpp |  0
 .../intel_cpu/src}/nodes/mkldnn_conv_node.h   |  0
 .../src}/nodes/mkldnn_convert_node.cpp        |  0
 .../src}/nodes/mkldnn_convert_node.h          |  0
 .../nodes/mkldnn_ctc_greedy_decoder_node.cpp  |  0
 .../nodes/mkldnn_ctc_greedy_decoder_node.h    |  0
 ...mkldnn_ctc_greedy_decoder_seq_len_node.cpp |  0
 .../mkldnn_ctc_greedy_decoder_seq_len_node.h  |  0
 .../src}/nodes/mkldnn_ctc_loss_node.cpp       |  0
 .../src}/nodes/mkldnn_ctc_loss_node.h         |  0
 .../src}/nodes/mkldnn_cum_sum_node.cpp        |  0
 .../src}/nodes/mkldnn_cum_sum_node.h          |  0
 .../src}/nodes/mkldnn_deconv_node.cpp         |  0
 .../intel_cpu/src}/nodes/mkldnn_deconv_node.h |  0
 .../src}/nodes/mkldnn_def_conv_node.cpp       |  0
 .../src}/nodes/mkldnn_def_conv_node.h         |  0
 .../src}/nodes/mkldnn_depth_to_space_node.cpp |  0
 .../src}/nodes/mkldnn_depth_to_space_node.h   |  0
 .../nodes/mkldnn_detection_output_node.cpp    |  0
 .../src}/nodes/mkldnn_detection_output_node.h |  0
 .../intel_cpu/src}/nodes/mkldnn_dft_node.cpp  |  0
 .../intel_cpu/src}/nodes/mkldnn_dft_node.h    |  0
 .../src}/nodes/mkldnn_eltwise_node.cpp        |  0
 .../src}/nodes/mkldnn_eltwise_node.h          |  0
 .../mkldnn_embedding_bag_offset_sum_node.cpp  |  0
 .../mkldnn_embedding_bag_offset_sum_node.h    |  0
 .../mkldnn_embedding_bag_packed_sum_node.cpp  |  0
 .../mkldnn_embedding_bag_packed_sum_node.h    |  0
 .../nodes/mkldnn_embedding_bag_sum_node.cpp   |  0
 .../nodes/mkldnn_embedding_bag_sum_node.h     |  0
 .../mkldnn_embedding_segments_sum_node.cpp    |  0
 .../mkldnn_embedding_segments_sum_node.h      |  0
 ...mental_detectron_detection_output_node.cpp |  0
 ...rimental_detectron_detection_output_node.h |  0
 ...n_generate_proposals_single_image_node.cpp |  0
 ...ron_generate_proposals_single_image_node.h |  0
 ...ntal_detectron_priorgridgenerator_node.cpp |  0
 ...mental_detectron_priorgridgenerator_node.h |  0
 ...tal_detectron_roifeatureextractor_node.cpp |  0
 ...ental_detectron_roifeatureextractor_node.h |  0
 ...n_experimental_detectron_topkrois_node.cpp |  0
 ...dnn_experimental_detectron_topkrois_node.h |  0
 .../mkldnn_extract_image_patches_node.cpp     |  0
 .../nodes/mkldnn_extract_image_patches_node.h |  0
 .../src}/nodes/mkldnn_fake_quantize_node.cpp  |  0
 .../src}/nodes/mkldnn_fake_quantize_node.h    |  0
 .../src}/nodes/mkldnn_fullyconnected_node.cpp |  0
 .../src}/nodes/mkldnn_fullyconnected_node.h   |  0
 .../nodes/mkldnn_gather_elements_node.cpp     |  0
 .../src}/nodes/mkldnn_gather_elements_node.h  |  0
 .../src}/nodes/mkldnn_gather_nd_node.cpp      |  0
 .../src}/nodes/mkldnn_gather_nd_node.h        |  0
 .../src}/nodes/mkldnn_gather_node.cpp         |  0
 .../intel_cpu/src}/nodes/mkldnn_gather_node.h |  0
 .../src}/nodes/mkldnn_gather_tree_node.cpp    |  0
 .../src}/nodes/mkldnn_gather_tree_node.h      |  0
 .../src}/nodes/mkldnn_generic_node.cpp        |  0
 .../src}/nodes/mkldnn_generic_node.h          |  0
 .../intel_cpu/src}/nodes/mkldnn_grn_node.cpp  |  0
 .../intel_cpu/src}/nodes/mkldnn_grn_node.h    |  0
 .../intel_cpu/src}/nodes/mkldnn_if_node.cpp   |  0
 .../intel_cpu/src}/nodes/mkldnn_if_node.h     |  0
 .../src}/nodes/mkldnn_input_node.cpp          |  0
 .../intel_cpu/src}/nodes/mkldnn_input_node.h  |  0
 .../src}/nodes/mkldnn_interpolate_node.cpp    |  0
 .../src}/nodes/mkldnn_interpolate_node.h      |  0
 .../src}/nodes/mkldnn_log_softmax_node.cpp    |  0
 .../src}/nodes/mkldnn_log_softmax_node.h      |  0
 .../intel_cpu/src}/nodes/mkldnn_lrn_node.cpp  |  0
 .../intel_cpu/src}/nodes/mkldnn_lrn_node.h    |  0
 .../intel_cpu/src}/nodes/mkldnn_math_node.cpp |  0
 .../intel_cpu/src}/nodes/mkldnn_math_node.h   |  0
 .../src}/nodes/mkldnn_matmul_node.cpp         |  0
 .../intel_cpu/src}/nodes/mkldnn_matmul_node.h |  0
 .../src}/nodes/mkldnn_matrix_nms_node.cpp     |  0
 .../src}/nodes/mkldnn_matrix_nms_node.h       |  0
 .../src}/nodes/mkldnn_memory_node.cpp         |  0
 .../src}/nodes/mkldnn_memory_node.hpp         |  0
 .../src}/nodes/mkldnn_multiclass_nms.cpp      |  0
 .../src}/nodes/mkldnn_multiclass_nms.hpp      |  0
 .../intel_cpu/src}/nodes/mkldnn_mvn_node.cpp  |  0
 .../intel_cpu/src}/nodes/mkldnn_mvn_node.h    |  0
 .../nodes/mkldnn_non_max_suppression_node.cpp |  0
 .../nodes/mkldnn_non_max_suppression_node.h   |  0
 .../intel_cpu/src}/nodes/mkldnn_non_zero.cpp  |  0
 .../intel_cpu/src}/nodes/mkldnn_non_zero.h    |  0
 .../src}/nodes/mkldnn_normalize_node.cpp      |  0
 .../src}/nodes/mkldnn_normalize_node.h        |  0
 .../src}/nodes/mkldnn_one_hot_node.cpp        |  0
 .../src}/nodes/mkldnn_one_hot_node.h          |  0
 .../intel_cpu/src}/nodes/mkldnn_pad_node.cpp  |  0
 .../intel_cpu/src}/nodes/mkldnn_pad_node.h    |  0
 .../src}/nodes/mkldnn_pooling_node.cpp        |  0
 .../src}/nodes/mkldnn_pooling_node.h          |  0
 .../src}/nodes/mkldnn_proposal_node.cpp       |  0
 .../src}/nodes/mkldnn_proposal_node.h         |  0
 .../src}/nodes/mkldnn_psroi_pooling_node.cpp  |  0
 .../src}/nodes/mkldnn_psroi_pooling_node.h    |  0
 .../src}/nodes/mkldnn_range_node.cpp          |  0
 .../intel_cpu/src}/nodes/mkldnn_range_node.h  |  0
 .../src}/nodes/mkldnn_reduce_node.cpp         |  0
 .../intel_cpu/src}/nodes/mkldnn_reduce_node.h |  0
 .../src}/nodes/mkldnn_reference_node.cpp      |  0
 .../src}/nodes/mkldnn_reference_node.h        |  0
 .../src}/nodes/mkldnn_region_yolo_node.cpp    |  0
 .../src}/nodes/mkldnn_region_yolo_node.h      |  0
 .../src}/nodes/mkldnn_reorder_node.cpp        |  0
 .../src}/nodes/mkldnn_reorder_node.h          |  0
 .../src}/nodes/mkldnn_reorg_yolo_node.cpp     |  0
 .../src}/nodes/mkldnn_reorg_yolo_node.h       |  0
 .../src}/nodes/mkldnn_reshape_node.cpp        |  0
 .../src}/nodes/mkldnn_reshape_node.h          |  0
 .../nodes/mkldnn_reverse_sequence_node.cpp    |  0
 .../src}/nodes/mkldnn_reverse_sequence_node.h |  0
 .../intel_cpu/src}/nodes/mkldnn_rnn.cpp       |  0
 .../plugins/intel_cpu/src}/nodes/mkldnn_rnn.h |  0
 .../src}/nodes/mkldnn_roi_align_node.cpp      |  0
 .../src}/nodes/mkldnn_roi_align_node.h        |  0
 .../src}/nodes/mkldnn_roi_pooling_node.cpp    |  0
 .../src}/nodes/mkldnn_roi_pooling_node.h      |  0
 .../intel_cpu/src}/nodes/mkldnn_roll_node.cpp |  0
 .../intel_cpu/src}/nodes/mkldnn_roll_node.h   |  0
 .../src}/nodes/mkldnn_scatter_update_node.cpp |  0
 .../src}/nodes/mkldnn_scatter_update_node.h   |  0
 .../src}/nodes/mkldnn_select_node.cpp         |  0
 .../intel_cpu/src}/nodes/mkldnn_select_node.h |  0
 .../intel_cpu/src}/nodes/mkldnn_shapeof.cpp   |  0
 .../intel_cpu/src}/nodes/mkldnn_shapeof.h     |  0
 .../nodes/mkldnn_shuffle_channels_node.cpp    |  0
 .../src}/nodes/mkldnn_shuffle_channels_node.h |  0
 .../src}/nodes/mkldnn_softmax_node.cpp        |  0
 .../src}/nodes/mkldnn_softmax_node.h          |  0
 .../src}/nodes/mkldnn_space_to_batch_node.cpp |  0
 .../src}/nodes/mkldnn_space_to_batch_node.h   |  0
 .../src}/nodes/mkldnn_space_to_depth_node.cpp |  0
 .../src}/nodes/mkldnn_space_to_depth_node.h   |  0
 .../src}/nodes/mkldnn_split_node.cpp          |  0
 .../intel_cpu/src}/nodes/mkldnn_split_node.h  |  0
 .../src}/nodes/mkldnn_strided_slice_node.cpp  |  0
 .../src}/nodes/mkldnn_strided_slice_node.h    |  0
 .../src}/nodes/mkldnn_tensoriterator_node.cpp |  0
 .../src}/nodes/mkldnn_tensoriterator_node.h   |  0
 .../intel_cpu/src}/nodes/mkldnn_tile_node.cpp |  0
 .../intel_cpu/src}/nodes/mkldnn_tile_node.h   |  0
 .../intel_cpu/src}/nodes/mkldnn_topk_node.cpp |  0
 .../intel_cpu/src}/nodes/mkldnn_topk_node.h   |  0
 .../src}/nodes/mkldnn_transpose_node.cpp      |  0
 .../src}/nodes/mkldnn_transpose_node.h        |  0
 .../intel_cpu/src}/nodes/proposal_imp.cpp     |  0
 .../intel_cpu/src}/nodes/proposal_imp.hpp     |  0
 .../plugins/intel_cpu/src}/nodes/subgraph.cpp |  0
 .../plugins/intel_cpu/src}/nodes/subgraph.h   |  0
 .../intel_cpu/src}/normalize_preprocess.cpp   |  0
 .../intel_cpu/src}/normalize_preprocess.h     |  0
 .../plugins/intel_cpu/src}/perf_count.h       |  0
 .../plugins/intel_cpu/src}/utils/bfloat16.hpp |  0
 .../intel_cpu/src}/utils/blob_dump.cpp        |  0
 .../plugins/intel_cpu/src}/utils/blob_dump.h  |  0
 .../intel_cpu/src}/utils/cpu_utils.hpp        |  0
 .../intel_cpu/src}/utils/debug_capabilities.h |  0
 .../intel_cpu/src}/utils/general_utils.h      |  0
 .../intel_cpu/src}/utils/ngraph_utils.hpp     |  0
 .../intel_cpu/src}/utils/node_dumper.cpp      |  0
 .../intel_cpu/src}/utils/node_dumper.h        |  0
 .../rt_info/memory_formats_attribute.cpp      |  0
 .../rt_info/memory_formats_attribute.hpp      |  0
 .../utils/shape_inference/shape_inference.cpp |  0
 .../utils/shape_inference/shape_inference.hpp |  0
 .../shape_inference/static_dimension.cpp      |  0
 .../shape_inference/static_dimension.hpp      |  0
 .../utils/shape_inference/static_shape.cpp    |  0
 .../utils/shape_inference/static_shape.hpp    |  0
 .../plugins/intel_cpu/src}/utils/verbose.cpp  |  0
 .../plugins/intel_cpu/src}/utils/verbose.h    |  0
 .../intel_cpu/thirdparty/CMakeLists.txt       | 55 +++++++++++++++++++
 .../plugins/intel_cpu}/thirdparty/mkl-dnn     |  0
 src/tests/functional/plugin/CMakeLists.txt    |  2 +-
 .../plugin/conformance/test_runner/README.md  |  2 +-
 .../include/api_conformance_helpers.hpp       |  2 +-
 .../include/gflag_config.hpp                  |  3 +-
 .../functional/plugin/cpu/CMakeLists.txt      |  8 +--
 .../behavior/ov_plugin/core_integration.cpp   |  2 +-
 .../behavior/plugin/core_integration.cpp      |  2 +-
 .../hetero/synthetic.cpp                      |  4 +-
 .../multi/gpu_remote_blob_tests.cpp           |  4 +-
 .../executable_network/core_integration.cpp   |  2 +-
 .../ov_executable_network/get_metric.cpp      |  2 +-
 .../behavior/ov_plugin/core_integration.cpp   |  2 +-
 .../myriad_remote_blobs_tests.cpp             |  2 +-
 src/tests/unit/CMakeLists.txt                 |  2 +-
 src/tests/unit/cpu/CMakeLists.txt             | 12 ++--
 .../functional/gna/CMakeLists.txt             |  4 +-
 .../layers/myriad_layers_proposal_test.cpp    |  2 +-
 tests/conditional_compilation/test_cc.py      |  2 +-
 tests/utils/path_utils.py                     |  6 +-
 tools/deployment_manager/configs/darwin.json  |  2 +-
 tools/deployment_manager/configs/linux.json   |  2 +-
 tools/deployment_manager/configs/windows.json |  2 +-
 364 files changed, 143 insertions(+), 124 deletions(-)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu}/CMakeLists.txt (86%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/config.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/config.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/cpu_shape.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/cpu_shape.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/cpu_types.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/cpu_types.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/docs/README.md (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/docs/blob_dumping.md (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/docs/graph_serialization.md (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/docs/verbose.md (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/cpu_generator.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/cpu_generator.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_bf16_emitters.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_eltwise_emitters.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_eltwise_emitters.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_emitter.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_emitter.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_load_store_emitters.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_load_store_emitters.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_mkldnn_emitters.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_mkldnn_emitters.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_mkldnn_ext_emitters.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/emitters/jit_snippets_emitters.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/blocked_memory_desc.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/blocked_memory_desc.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/cpu_blocked_memory_desc.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/cpu_blocked_memory_desc.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/cpu_memory_desc.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/cpu_memory_desc_utils.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/cpu_memory_desc_utils.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/dnnl_blocked_memory_desc.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/dnnl_blocked_memory_desc.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/dnnl_memory_desc.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/memory_desc/dnnl_memory_desc.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn/ie_mkldnn.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn/ie_mkldnn.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn/iml_type_mapper.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn/iml_type_mapper.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_async_infer_request.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_async_infer_request.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_descriptor.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_descriptor.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_edge.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_edge.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_exec_network.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_exec_network.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_extension.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_extension.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_extension_mngr.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_extension_mngr.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_extension_utils.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_extension_utils.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_graph.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_graph.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_graph_dumper.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_graph_dumper.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_graph_optimizer.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_graph_optimizer.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_infer_request.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_infer_request.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_itt.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_memory.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_memory.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_memory_state.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_memory_state.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_node.cpp (99%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_nodes_factory.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_plugin.cpp (99%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_plugin.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_primitive.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_primitive.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_selective_build.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_serialize.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_serialize.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_weights_cache.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/mkldnn_weights_cache.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/align_matmul_input_ranks.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/align_matmul_input_ranks.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_broadcast_to_tiles.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_broadcast_to_tiles.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_matmul_to_fc.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_matmul_to_fc.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_tile_to_seq_tiles.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_tile_to_seq_tiles.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_to_cpu_specific_opset.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_to_leaky_relu.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_to_leaky_relu.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_to_power_static.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_to_power_static.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_to_swish_cpu.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/convert_to_swish_cpu.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/fc_bias_fusion.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/fc_bias_fusion.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/move_eltwise_up_data_movement.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/move_eltwise_up_data_movement.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/fully_connected.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/fully_connected.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/leaky_relu.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/leaky_relu.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/power_static.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/power_static.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/swish_cpu.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/op/swish_cpu.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/reshape_fc_fusion.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/reshape_fc_fusion.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/reshape_prelu.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/reshape_prelu.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/rnn_sequences_optimization.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/rnn_sequences_optimization.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/snippets_mark_skipped.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/ngraph_transformations/snippets_mark_skipped.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/blocked_desc_creator.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/blocked_desc_creator.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/cpu_convert.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/cpu_convert.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/cpu_memcpy.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/defs.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/dnnl_executor.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/dnnl_executor.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/fp16_utils.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/permute_kernel.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/permute_kernel.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/softmax.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/softmax.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/tile_broadcast_utils.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/tile_broadcast_utils.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/common/uni_simd.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/list.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_adaptive_pooling.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_adaptive_pooling.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_batch_to_space_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_batch_to_space_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_bin_conv_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_bin_conv_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_broadcast_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_broadcast_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_bucketize_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_bucketize_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_concat_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_concat_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_conv_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_conv_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_convert_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_convert_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_ctc_greedy_decoder_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_ctc_greedy_decoder_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_ctc_loss_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_ctc_loss_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_cum_sum_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_cum_sum_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_deconv_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_deconv_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_def_conv_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_def_conv_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_depth_to_space_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_depth_to_space_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_detection_output_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_detection_output_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_dft_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_dft_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_eltwise_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_eltwise_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_bag_offset_sum_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_bag_offset_sum_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_bag_packed_sum_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_bag_packed_sum_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_bag_sum_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_bag_sum_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_segments_sum_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_embedding_segments_sum_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_detection_output_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_detection_output_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_topkrois_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_experimental_detectron_topkrois_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_extract_image_patches_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_extract_image_patches_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_fake_quantize_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_fake_quantize_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_fullyconnected_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_fullyconnected_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_elements_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_elements_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_nd_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_nd_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_tree_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_gather_tree_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_generic_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_generic_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_grn_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_grn_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_if_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_if_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_input_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_input_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_interpolate_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_interpolate_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_log_softmax_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_log_softmax_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_lrn_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_lrn_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_math_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_math_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_matmul_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_matmul_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_matrix_nms_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_matrix_nms_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_memory_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_memory_node.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_multiclass_nms.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_multiclass_nms.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_mvn_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_mvn_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_non_max_suppression_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_non_max_suppression_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_non_zero.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_non_zero.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_normalize_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_normalize_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_one_hot_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_one_hot_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_pad_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_pad_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_pooling_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_pooling_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_proposal_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_proposal_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_psroi_pooling_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_psroi_pooling_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_range_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_range_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reduce_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reduce_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reference_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reference_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_region_yolo_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_region_yolo_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reorder_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reorder_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reorg_yolo_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reorg_yolo_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reshape_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reshape_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reverse_sequence_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_reverse_sequence_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_rnn.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_rnn.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_roi_align_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_roi_align_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_roi_pooling_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_roi_pooling_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_roll_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_roll_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_scatter_update_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_scatter_update_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_select_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_select_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_shapeof.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_shapeof.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_shuffle_channels_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_shuffle_channels_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_softmax_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_softmax_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_space_to_batch_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_space_to_batch_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_space_to_depth_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_space_to_depth_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_split_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_split_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_strided_slice_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_strided_slice_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_tensoriterator_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_tensoriterator_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_tile_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_tile_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_topk_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_topk_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_transpose_node.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/mkldnn_transpose_node.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/proposal_imp.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/proposal_imp.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/subgraph.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/nodes/subgraph.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/normalize_preprocess.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/normalize_preprocess.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/perf_count.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/bfloat16.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/blob_dump.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/blob_dump.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/cpu_utils.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/debug_capabilities.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/general_utils.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/ngraph_utils.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/node_dumper.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/node_dumper.h (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/rt_info/memory_formats_attribute.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/rt_info/memory_formats_attribute.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/shape_inference/shape_inference.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/shape_inference/shape_inference.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/shape_inference/static_dimension.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/shape_inference/static_dimension.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/shape_inference/static_shape.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/shape_inference/static_shape.hpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/verbose.cpp (100%)
 rename {inference-engine/src/mkldnn_plugin => src/plugins/intel_cpu/src}/utils/verbose.h (100%)
 create mode 100644 src/plugins/intel_cpu/thirdparty/CMakeLists.txt
 rename {inference-engine => src/plugins/intel_cpu}/thirdparty/mkl-dnn (100%)

diff --git a/.ci/azure/linux_lohika.yml b/.ci/azure/linux_lohika.yml
index 198e24433c9..6c65d958b8b 100644
--- a/.ci/azure/linux_lohika.yml
+++ b/.ci/azure/linux_lohika.yml
@@ -70,7 +70,7 @@ jobs:
       env -C ~/work
       ./buildreleasenolto.sh
       libinference_engine_preproc.so
-      MKLDNNPlugin
+      ov_intel_cpu_plugin
       ov_intel_gpu_plugin
       clDNN_unit_tests64
       gpuFuncTests
diff --git a/.ci/azure/linux_onnxruntime.yml b/.ci/azure/linux_onnxruntime.yml
index 6a3a81cfe27..7257b4f37d1 100644
--- a/.ci/azure/linux_onnxruntime.yml
+++ b/.ci/azure/linux_onnxruntime.yml
@@ -98,7 +98,7 @@ jobs:
         -DENABLE_OPENCV=OFF
         -DENABLE_CPPLINT=OFF
         -DENABLE_TESTS=OFF
-        -DENABLE_MKL_DNN=ON
+        -DENABLE_INTEL_CPU=ON
         -DENABLE_INTEL_GPU=OFF
         -DENABLE_PROFILING_ITT=OFF
         -DENABLE_SAMPLES=OFF
diff --git a/.ci/openvino-onnx/Dockerfile b/.ci/openvino-onnx/Dockerfile
index dc35c16a643..a2fd0dcbcf4 100644
--- a/.ci/openvino-onnx/Dockerfile
+++ b/.ci/openvino-onnx/Dockerfile
@@ -60,7 +60,7 @@ RUN cmake .. \
     -DENABLE_OPENCV=OFF \
     -DENABLE_CPPLINT=OFF \
     -DENABLE_TESTS=OFF \
-    -DENABLE_MKL_DNN=ON \
+    -DENABLE_INTEL_CPU=ON \
     -DENABLE_INTEL_GPU=OFF \
     -DENABLE_PROFILING_ITT=OFF \
     -DENABLE_SAMPLES=OFF \
diff --git a/.gitmodules b/.gitmodules
index f13e3c9db7e..4c0f0a395b7 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,5 +1,5 @@
-[submodule "inference-engine/thirdparty/mkl-dnn"]
-	path = inference-engine/thirdparty/mkl-dnn
+[submodule "src/plugins/intel_cpu/thirdparty/mkl-dnn"]
+	path = src/plugins/intel_cpu/thirdparty/mkl-dnn
 	url = https://github.com/openvinotoolkit/oneDNN.git
 	ignore = dirty
 [submodule "thirdparty/xbyak"]
diff --git a/CODEOWNERS b/CODEOWNERS
index 76c0ccf5c8a..da0b7e9b25f 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -38,9 +38,9 @@ Jenkinsfile  @openvinotoolkit/openvino-admins
 /src/tests_deprecated/readers/  @openvinotoolkit/openvino-ngraph-maintainers
 
 # IE CPU:
-/inference-engine/src/mkldnn_plugin/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
+/src/plugins/intel_cpu/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
 /src/common/low_precision_transformations/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
-/inference-engine/thirdparty/mkl-dnn/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
+/src/plugins/intel_cpu/thirdparty/mkl-dnn/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
 
 # IE GPU:
 /src/inference/include/ie/gpu/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
diff --git a/cmake/coverage.cmake b/cmake/coverage.cmake
index c374d022a20..fea3d596d13 100644
--- a/cmake/coverage.cmake
+++ b/cmake/coverage.cmake
@@ -58,10 +58,10 @@ ie_coverage_extract(INPUT "openvino" OUTPUT "template_plugin"
 ie_coverage_genhtml(INFO_FILE "template_plugin"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
-if(ENABLE_MKL_DNN)
-    ie_coverage_extract(INPUT "openvino" OUTPUT "mkldnn_plugin"
-                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/inference-engine/src/mkldnn_plugin/*")
-    ie_coverage_genhtml(INFO_FILE "mkldnn_plugin"
+if(ENABLE_INTEL_CPU)
+    ie_coverage_extract(INPUT "openvino" OUTPUT "intel_cpu_plugin"
+                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/plugins/intel_cpu/*")
+    ie_coverage_genhtml(INFO_FILE "intel_cpu_plugin"
                         PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 endif()
 
diff --git a/cmake/features.cmake b/cmake/features.cmake
index 8ceefe75d85..cca9a235183 100644
--- a/cmake/features.cmake
+++ b/cmake/features.cmake
@@ -6,7 +6,7 @@
 # Common cmake options
 #
 
-ie_dependent_option (ENABLE_MKL_DNN "MKL-DNN plugin for inference engine" ON "X86_64" OFF)
+ie_dependent_option (ENABLE_INTEL_CPU "CPU plugin for inference engine" ON "X86_64" OFF)
 
 ie_option (ENABLE_TESTS "unit, behavior and functional tests" OFF)
 
@@ -207,8 +207,8 @@ if (ENABLE_INTEL_GPU)
     add_definitions(-DENABLE_INTEL_GPU=1)
 endif()
 
-if (ENABLE_MKL_DNN)
-    add_definitions(-DENABLE_MKL_DNN=1)
+if (ENABLE_INTEL_CPU)
+    add_definitions(-DENABLE_INTEL_CPU=1)
 endif()
 
 if (ENABLE_INTEL_GNA)
diff --git a/docs/IE_DG/Cross_Check_Tool.md b/docs/IE_DG/Cross_Check_Tool.md
index d53d3dddfe4..0d3cd62d42b 100644
--- a/docs/IE_DG/Cross_Check_Tool.md
+++ b/docs/IE_DG/Cross_Check_Tool.md
@@ -72,7 +72,7 @@ InferenceEngine:
 
   API version ............ 1.0
   Build .................. lnx_20180510
-  Description ....... MKLDNNPlugin
+  Description ....... ov_intel_cpu_plugin
 
   API version ............ 0.1
   Build .................. ci-main-03659
@@ -135,7 +135,7 @@ InferenceEngine:
 
   API version ............ 1.0
   Build .................. ###
-  Description ....... MKLDNNPlugin
+  Description ....... ov_intel_cpu_plugin
 
 [ INFO ] Inputs detected: <list_of_input_layers>
 [ INFO ] Statistics will be dumped for 1 layers: <output_layer_name(s)>
diff --git a/docs/IE_DG/inference_engine_intro.md b/docs/IE_DG/inference_engine_intro.md
index 9692fabe72d..53b17b3ee13 100644
--- a/docs/IE_DG/inference_engine_intro.md
+++ b/docs/IE_DG/inference_engine_intro.md
@@ -70,7 +70,7 @@ The table below shows the plugin libraries and additional dependencies for Linux
 
 | Plugin | Library name for Linux      | Dependency libraries for Linux                              | Library name for Windows | Dependency libraries for Windows                                                                       | Library name for macOS       | Dependency libraries for macOS              |
 |--------|-----------------------------|-------------------------------------------------------------|--------------------------|--------------------------------------------------------------------------------------------------------|------------------------------|---------------------------------------------|
-| CPU    | `libMKLDNNPlugin.so`        | | `MKLDNNPlugin.dll`       | | `libMKLDNNPlugin.so`      | |
+| CPU    | `libov_intel_cpu_plugin.so`        | | `ov_intel_cpu_plugin.dll`       | | `libov_intel_cpu_plugin.so`      | |
 | GPU    | `libov_intel_gpu_plugin.so`         | `libOpenCL.so` | `ov_intel_gpu_plugin.dll`        | `OpenCL.dll` |  Is not supported            |  -                                          |
 | MYRIAD | `libmyriadPlugin.so`        | `libusb.so`,                                                | `myriadPlugin.dll`       | `usb.dll`                                                                                              | `libmyriadPlugin.so`      | `libusb.dylib`                              |
 | HDDL   | `libHDDLPlugin.so`          | `libbsl.so`, `libhddlapi.so`, `libmvnc-hddl.so`             | `HDDLPlugin.dll`         | `bsl.dll`, `hddlapi.dll`, `json-c.dll`, `libcrypto-1_1-x64.dll`, `libssl-1_1-x64.dll`, `mvnc-hddl.dll` |  Is not supported            |  -                                          |
diff --git a/docs/template_plugin/tests/functional/shared_tests_instances/behavior/executable_network/get_metric.cpp b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/executable_network/get_metric.cpp
index c36cd07ca93..817c6162310 100644
--- a/docs/template_plugin/tests/functional/shared_tests_instances/behavior/executable_network/get_metric.cpp
+++ b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/executable_network/get_metric.cpp
@@ -50,7 +50,7 @@ INSTANTIATE_TEST_SUITE_P(
 // Hetero Executable Network GetMetric
 //
 
-#ifdef ENABLE_MKL_DNN
+#ifdef ENABLE_INTEL_CPU
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassHeteroExecutableNetworlGetMetricTest, IEClassHeteroExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS,
@@ -68,5 +68,5 @@ INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassHeteroExecutableNetworlGetMetricTest, IEClassHeteroExecutableNetworkGetMetricTest_TARGET_FALLBACK,
         ::testing::Values(CommonTestUtils::DEVICE_TEMPLATE));
 
-#endif  // ENABLE_MKL_DNN
+#endif  // ENABLE_INTEL_CPU
 } // namespace
diff --git a/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx b/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
index a945f2caaf8..9be0999d34f 100644
--- a/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
+++ b/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
@@ -550,7 +550,7 @@ cdef class IECore:
         .. code-block:: python
 
             ie = IECore()
-            ie.register_plugin(plugin="MKLDNNPlugin", device_name="MY_NEW_PLUGIN")
+            ie.register_plugin(plugin="ov_intel_cpu_plugin", device_name="MY_NEW_PLUGIN")
         """
         self.impl.registerPlugin(plugin_name.encode(), device_name.encode())
 
diff --git a/inference-engine/ie_bridges/python/tests/test_IECore.py b/inference-engine/ie_bridges/python/tests/test_IECore.py
index f7b67e20feb..89a047d2e24 100644
--- a/inference-engine/ie_bridges/python/tests/test_IECore.py
+++ b/inference-engine/ie_bridges/python/tests/test_IECore.py
@@ -80,8 +80,8 @@ def test_query_network(device):
 def test_register_plugin():
     ie = IECore()
     if ie.get_metric("CPU", "FULL_DEVICE_NAME") == "arm_compute::NEON":
-        pytest.skip("Can't run on ARM plugin due-to MKLDNNPlugin specific test")
-    ie.register_plugin("MKLDNNPlugin", "BLA")
+        pytest.skip("Can't run on ARM plugin due-to ov_intel_cpu_plugin specific test")
+    ie.register_plugin("ov_intel_cpu_plugin", "BLA")
     net = ie.read_network(model=test_net_xml, weights=test_net_bin)
     exec_net = ie.load_network(net, "BLA")
     assert isinstance(exec_net, ExecutableNetwork), "Cannot load the network to the registered plugin with name 'BLA'"
@@ -92,7 +92,7 @@ def test_register_plugin():
 def test_register_plugins():
     ie = IECore()
     if ie.get_metric("CPU", "FULL_DEVICE_NAME") == "arm_compute::NEON":
-        pytest.skip("Can't run on ARM plugin due-to MKLDNNPlugin specific test")
+        pytest.skip("Can't run on ARM plugin due-to ov_intel_cpu_plugin specific test")
     if platform == "linux" or platform == "linux2":
         ie.register_plugins(plugins_xml)
     elif platform == "darwin":
diff --git a/inference-engine/src/CMakeLists.txt b/inference-engine/src/CMakeLists.txt
index ed4833563b7..0cdedf18f88 100644
--- a/inference-engine/src/CMakeLists.txt
+++ b/inference-engine/src/CMakeLists.txt
@@ -7,10 +7,6 @@ if(CMAKE_COMPILER_IS_GNUCXX)
     ie_add_compiler_flags(-Wmissing-declarations)
 endif()
 
-if(ENABLE_MKL_DNN)
-    add_subdirectory(mkldnn_plugin)
-endif()
-
 if(ENABLE_VPU)
     add_subdirectory(vpu)
 endif()
diff --git a/inference-engine/thirdparty/CMakeLists.txt b/inference-engine/thirdparty/CMakeLists.txt
index 4bf4d28b472..3a5e3e41a81 100644
--- a/inference-engine/thirdparty/CMakeLists.txt
+++ b/inference-engine/thirdparty/CMakeLists.txt
@@ -20,43 +20,3 @@ endif()
 if (ENABLE_INTEL_GPU)
     add_subdirectory(clDNN)
 endif()
-
-function(ie_add_mkldnn)
-    set(DNNL_ENABLE_JIT_PROFILING ${BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
-    if(BUILD_SHARED_LIBS AND ENABLE_PROFILING_ITT)
-        set(DNNL_ENABLE_ITT_TASKS ON CACHE BOOL "" FORCE)
-    else()
-        set(DNNL_ENABLE_ITT_TASKS OFF CACHE BOOL "" FORCE)
-    endif()
-    set(DNNL_ENABLE_CONCURRENT_EXEC ON CACHE BOOL "" FORCE)
-    set(DNNL_ENABLE_PRIMITIVE_CACHE OFF CACHE BOOL "" FORCE) ## TODO: try it later
-    set(DNNL_ENABLE_MAX_CPU_ISA ON CACHE BOOL "" FORCE)
-    set(DNNL_LIBRARY_TYPE STATIC CACHE BOOL "" FORCE)
-    set(DNNL_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
-    set(DNNL_BUILD_TESTS OFF CACHE BOOL "" FORCE)
-    set(DNNL_CPU_RUNTIME "${THREADING}" CACHE STRING "" FORCE)
-    set(DNNL_GPU_RUNTIME "NONE" CACHE STRING "" FORCE)
-    set(DNNL_BLAS_VENDOR "NONE" CACHE STRING "" FORCE)
-    set(SDL_cmake_included ON)  ## to skip internal SDL flags. SDL flags are already set on IE level
-    if (ANDROID OR ((CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") AND NOT (THREADING STREQUAL "OMP")))
-        set(OpenMP_cmake_included ON) ## to skip "omp simd" inside a code. Lead to some crashes inside NDK LLVM..
-    endif()
-
-    if(SUGGEST_OVERRIDE_SUPPORTED)
-        # xbyak compilation fails
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
-    endif()
-    if(CMAKE_COMPILER_IS_GNUCXX)
-        ie_add_compiler_flags(-Wno-undef)
-    elseif(UNIX AND CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -diag-disable=10121")
-    endif()
-
-    add_subdirectory(mkl-dnn EXCLUDE_FROM_ALL)
-    add_library(mkldnn ALIAS dnnl)
-    ov_install_static_lib(dnnl cpu)
-endfunction()
-
-if(ENABLE_MKL_DNN)
-    ie_add_mkldnn()
-endif()
diff --git a/samples/cpp/ngraph_function_creation_sample/README.md b/samples/cpp/ngraph_function_creation_sample/README.md
index 4f33bf87457..c5e8ef64851 100644
--- a/samples/cpp/ngraph_function_creation_sample/README.md
+++ b/samples/cpp/ngraph_function_creation_sample/README.md
@@ -95,7 +95,7 @@ ngraph_function_creation_sample.exe -m lenet.bin -i 7-ubyte
 [ INFO ] Loading Inference Engine
 [ INFO ] Device info:
         CPU
-        MKLDNNPlugin version ......... <version>
+        ov_intel_cpu_plugin version ......... <version>
         Build ........... <build>
 
 [ INFO ] Preparing input blobs
diff --git a/scripts/CMakeLists.txt b/scripts/CMakeLists.txt
index 1afd8a06c20..ddb5d4950e7 100644
--- a/scripts/CMakeLists.txt
+++ b/scripts/CMakeLists.txt
@@ -10,6 +10,7 @@ ie_shellcheck_process(DIRECTORY "${OpenVINO_SOURCE_DIR}"
                       SKIP "${OpenVINO_SOURCE_DIR}/bin"
                            "${OpenVINO_SOURCE_DIR}/build"
                            "${OpenVINO_SOURCE_DIR}/thirdparty"
+                           "${OpenVINO_SOURCE_DIR}/src/plugins/intel_cpu/thirdparty"
                            "${OpenVINO_SOURCE_DIR}/src/bindings/python/thirdparty/pybind11"
                            "${IE_MAIN_SOURCE_DIR}/thirdparty"
                            "${OpenVINO_SOURCE_DIR}/tools/pot/thirdparty"
diff --git a/src/bindings/c/tests/CMakeLists.txt b/src/bindings/c/tests/CMakeLists.txt
index 60f0c139ac6..9135c944b7a 100644
--- a/src/bindings/c/tests/CMakeLists.txt
+++ b/src/bindings/c/tests/CMakeLists.txt
@@ -26,8 +26,8 @@ if(ENABLE_AUTO OR ENABLE_MULTI)
     add_dependencies(${TARGET_NAME} ov_auto_plugin)
 endif()
 
-if(ENABLE_MKL_DNN)
-    add_dependencies(${TARGET_NAME} MKLDNNPlugin)
+if(ENABLE_INTEL_CPU)
+    add_dependencies(${TARGET_NAME} ov_intel_cpu_plugin)
 endif()
 
 if(ENABLE_INTEL_GPU)
diff --git a/src/bindings/c/tests/ie_c_api_test.cpp b/src/bindings/c/tests/ie_c_api_test.cpp
index 0e0e463ef95..0559cab7998 100644
--- a/src/bindings/c/tests/ie_c_api_test.cpp
+++ b/src/bindings/c/tests/ie_c_api_test.cpp
@@ -157,7 +157,7 @@ TEST(ie_core_register_plugin, registerPlugin) {
     IE_EXPECT_OK(ie_core_read_network(core, xml, bin, &network));
     EXPECT_NE(nullptr, network);
 
-    const char *plugin_name = "MKLDNNPlugin";
+    const char *plugin_name = "ov_intel_cpu_plugin";
     const char *device_name = "BLA";
     IE_EXPECT_OK(ie_core_register_plugin(core, plugin_name, device_name));
 
diff --git a/src/bindings/python/tests/test_inference_engine/test_core.py b/src/bindings/python/tests/test_inference_engine/test_core.py
index 3806cd2348b..12436b06738 100644
--- a/src/bindings/python/tests/test_inference_engine/test_core.py
+++ b/src/bindings/python/tests/test_inference_engine/test_core.py
@@ -208,7 +208,7 @@ def test_query_model(device):
 @pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU", reason="Device independent test")
 def test_register_plugin():
     ie = Core()
-    ie.register_plugin("MKLDNNPlugin", "BLA")
+    ie.register_plugin("ov_intel_cpu_plugin", "BLA")
     func = ie.read_model(model=test_net_xml, weights=test_net_bin)
     exec_net = ie.compile_model(func, "BLA")
     assert isinstance(exec_net, CompiledModel), \
diff --git a/src/cmake/ov_runtime.cmake b/src/cmake/ov_runtime.cmake
index 3ff4a28d36d..d9194b520de 100644
--- a/src/cmake/ov_runtime.cmake
+++ b/src/cmake/ov_runtime.cmake
@@ -48,7 +48,7 @@ ie_mark_target_as_cc(${TARGET_NAME})
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
 ie_register_plugins(MAIN_TARGET ${TARGET_NAME}
-                    POSSIBLE_PLUGINS ov_auto_plugin ov_hetero_plugin ov_intel_gpu_plugin ov_intel_gna_plugin MKLDNNPlugin myriadPlugin)
+                    POSSIBLE_PLUGINS ov_auto_plugin ov_hetero_plugin ov_intel_gpu_plugin ov_intel_gna_plugin ov_intel_cpu_plugin myriadPlugin)
 
 # Export for build tree
 
diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt
index 1e3dbf4a88b..ad6dd6bdc22 100644
--- a/src/core/tests/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -422,13 +422,13 @@ endif()
 
 list(APPEND UNIT_TESTS_DEPENDENCIES template_ov_extension)
 
-if (ENABLE_MKL_DNN AND ENABLE_OV_CORE_BACKEND_UNIT_TESTS)
+if (ENABLE_INTEL_CPU AND ENABLE_OV_CORE_BACKEND_UNIT_TESTS)
     message(STATUS "OV_CORE_TESTS: IE:CPU enabled")
     set(ACTIVE_BACKEND_LIST ${ACTIVE_BACKEND_LIST} "IE:CPU")
     if (ENABLE_STRICT_DEPENDENCIES)
         # For convinience add a runtime dependency to build along with this target.
         # Warning: Parallel build with -GNinja may not be efficient.
-        list(APPEND UNIT_TESTS_DEPENDENCIES MKLDNNPlugin)
+        list(APPEND UNIT_TESTS_DEPENDENCIES ov_intel_cpu_plugin)
     endif()
 endif()
 
diff --git a/src/core/tests/frontend/paddle/CMakeLists.txt b/src/core/tests/frontend/paddle/CMakeLists.txt
index 998be1684ef..1110935523e 100644
--- a/src/core/tests/frontend/paddle/CMakeLists.txt
+++ b/src/core/tests/frontend/paddle/CMakeLists.txt
@@ -58,6 +58,6 @@ add_dependencies(${TARGET_NAME} paddle_test_models)
 add_dependencies(${TARGET_NAME} ov_paddle_frontend)
 
 # Fuzzy tests for PaddlePaddle use IE_CPU engine
-if (ENABLE_MKL_DNN)
-    add_dependencies(${TARGET_NAME} MKLDNNPlugin)
+if (ENABLE_INTEL_CPU)
+    add_dependencies(${TARGET_NAME} ov_intel_cpu_plugin)
 endif()
diff --git a/src/core/tests/runtime/ie/unit_test.manifest b/src/core/tests/runtime/ie/unit_test.manifest
index 6042755ad67..070b0cff870 100644
--- a/src/core/tests/runtime/ie/unit_test.manifest
+++ b/src/core/tests/runtime/ie/unit_test.manifest
@@ -226,7 +226,7 @@ onnx_size_op_single
 onnx_size_op_graph_end
 onnx_size_op_graph_middle
 
-# /openvino/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp:747
+# /openvino/src/plugins/intel_cpu/mkldnn_graph.cpp:747
 # Output blob byte size is not equal network output byte size (64!=216)." thrown in the test body.
 onnx_model_quant_conv_linear_3d
 
diff --git a/src/plugins/CMakeLists.txt b/src/plugins/CMakeLists.txt
index ff57fd290ac..54f90dca336 100644
--- a/src/plugins/CMakeLists.txt
+++ b/src/plugins/CMakeLists.txt
@@ -6,6 +6,10 @@ if(ENABLE_HETERO)
     add_subdirectory(hetero)
 endif()
 
+if(ENABLE_INTEL_CPU)
+    add_subdirectory(intel_cpu)
+endif()
+
 if(ENABLE_INTEL_GPU)
     add_subdirectory(intel_gpu)
 endif()
diff --git a/inference-engine/src/mkldnn_plugin/CMakeLists.txt b/src/plugins/intel_cpu/CMakeLists.txt
similarity index 86%
rename from inference-engine/src/mkldnn_plugin/CMakeLists.txt
rename to src/plugins/intel_cpu/CMakeLists.txt
index f2a9f6c5339..7b501f22b02 100644
--- a/inference-engine/src/mkldnn_plugin/CMakeLists.txt
+++ b/src/plugins/intel_cpu/CMakeLists.txt
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME "MKLDNNPlugin")
+set(TARGET_NAME "ov_intel_cpu_plugin")
 
 if(CMAKE_COMPILER_IS_GNUCC)
     ie_add_compiler_flags(-Wno-all)
@@ -16,11 +16,13 @@ if (ENABLE_CPU_DEBUG_CAPS)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DCPU_DEBUG_CAPS")
 endif()
 
-file(GLOB_RECURSE SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-file(GLOB_RECURSE HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.h
-                          ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
+file(GLOB_RECURSE SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+file(GLOB_RECURSE HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.h
+                          ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
 
-addVersionDefines(mkldnn_plugin.cpp CI_BUILD_NUMBER)
+addVersionDefines(${CMAKE_CURRENT_SOURCE_DIR}/src/mkldnn_plugin.cpp CI_BUILD_NUMBER)
+
+add_subdirectory(thirdparty)
 
 # create plugin
 
@@ -45,7 +47,7 @@ target_link_libraries(${TARGET_NAME} PRIVATE mkldnn
                                              inference_engine_snippets)
 
 target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_EXTENSION_API)
-target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
+target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
 
 target_include_directories(${TARGET_NAME} SYSTEM PRIVATE
     $<TARGET_PROPERTY:mkldnn,INCLUDE_DIRECTORIES>)
@@ -54,8 +56,8 @@ target_include_directories(${TARGET_NAME} SYSTEM PRIVATE
 # TODO: The same for proposal, proposalONNX, topk
 cross_compiled_file(${TARGET_NAME}
         ARCH AVX2 ANY
-                    nodes/proposal_imp.cpp
-        API         nodes/proposal_imp.hpp
+                    src/nodes/proposal_imp.cpp
+        API         src/nodes/proposal_imp.hpp
         NAME        proposal_exec
         NAMESPACE   InferenceEngine::Extensions::Cpu::XARCH
 )
@@ -72,7 +74,7 @@ target_include_directories(${TARGET_NAME}_obj PRIVATE $<TARGET_PROPERTY:inferenc
                                                       $<TARGET_PROPERTY:openvino::itt,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:ov_shape_inference,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:inference_engine_snippets,INTERFACE_INCLUDE_DIRECTORIES>
-                                              PUBLIC  ${CMAKE_CURRENT_SOURCE_DIR}
+                                              PUBLIC  ${CMAKE_CURRENT_SOURCE_DIR}/src
                                                       $<TARGET_PROPERTY:openvino::conditional_compilation,INTERFACE_INCLUDE_DIRECTORIES>)
 
 target_include_directories(${TARGET_NAME}_obj SYSTEM PUBLIC $<TARGET_PROPERTY:mkldnn,INCLUDE_DIRECTORIES>)
diff --git a/inference-engine/src/mkldnn_plugin/config.cpp b/src/plugins/intel_cpu/src/config.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/config.cpp
rename to src/plugins/intel_cpu/src/config.cpp
diff --git a/inference-engine/src/mkldnn_plugin/config.h b/src/plugins/intel_cpu/src/config.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/config.h
rename to src/plugins/intel_cpu/src/config.h
diff --git a/inference-engine/src/mkldnn_plugin/cpu_shape.cpp b/src/plugins/intel_cpu/src/cpu_shape.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/cpu_shape.cpp
rename to src/plugins/intel_cpu/src/cpu_shape.cpp
diff --git a/inference-engine/src/mkldnn_plugin/cpu_shape.h b/src/plugins/intel_cpu/src/cpu_shape.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/cpu_shape.h
rename to src/plugins/intel_cpu/src/cpu_shape.h
diff --git a/inference-engine/src/mkldnn_plugin/cpu_types.cpp b/src/plugins/intel_cpu/src/cpu_types.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/cpu_types.cpp
rename to src/plugins/intel_cpu/src/cpu_types.cpp
diff --git a/inference-engine/src/mkldnn_plugin/cpu_types.h b/src/plugins/intel_cpu/src/cpu_types.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/cpu_types.h
rename to src/plugins/intel_cpu/src/cpu_types.h
diff --git a/inference-engine/src/mkldnn_plugin/docs/README.md b/src/plugins/intel_cpu/src/docs/README.md
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/docs/README.md
rename to src/plugins/intel_cpu/src/docs/README.md
diff --git a/inference-engine/src/mkldnn_plugin/docs/blob_dumping.md b/src/plugins/intel_cpu/src/docs/blob_dumping.md
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/docs/blob_dumping.md
rename to src/plugins/intel_cpu/src/docs/blob_dumping.md
diff --git a/inference-engine/src/mkldnn_plugin/docs/graph_serialization.md b/src/plugins/intel_cpu/src/docs/graph_serialization.md
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/docs/graph_serialization.md
rename to src/plugins/intel_cpu/src/docs/graph_serialization.md
diff --git a/inference-engine/src/mkldnn_plugin/docs/verbose.md b/src/plugins/intel_cpu/src/docs/verbose.md
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/docs/verbose.md
rename to src/plugins/intel_cpu/src/docs/verbose.md
diff --git a/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.cpp b/src/plugins/intel_cpu/src/emitters/cpu_generator.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/cpu_generator.cpp
rename to src/plugins/intel_cpu/src/emitters/cpu_generator.cpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/cpu_generator.hpp b/src/plugins/intel_cpu/src/emitters/cpu_generator.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/cpu_generator.hpp
rename to src/plugins/intel_cpu/src/emitters/cpu_generator.hpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_bf16_emitters.hpp b/src/plugins/intel_cpu/src/emitters/jit_bf16_emitters.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_bf16_emitters.hpp
rename to src/plugins/intel_cpu/src/emitters/jit_bf16_emitters.hpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.cpp b/src/plugins/intel_cpu/src/emitters/jit_eltwise_emitters.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.cpp
rename to src/plugins/intel_cpu/src/emitters/jit_eltwise_emitters.cpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.hpp b/src/plugins/intel_cpu/src/emitters/jit_eltwise_emitters.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_eltwise_emitters.hpp
rename to src/plugins/intel_cpu/src/emitters/jit_eltwise_emitters.hpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.cpp b/src/plugins/intel_cpu/src/emitters/jit_emitter.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_emitter.cpp
rename to src/plugins/intel_cpu/src/emitters/jit_emitter.cpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_emitter.hpp b/src/plugins/intel_cpu/src/emitters/jit_emitter.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_emitter.hpp
rename to src/plugins/intel_cpu/src/emitters/jit_emitter.hpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_load_store_emitters.cpp b/src/plugins/intel_cpu/src/emitters/jit_load_store_emitters.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_load_store_emitters.cpp
rename to src/plugins/intel_cpu/src/emitters/jit_load_store_emitters.cpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_load_store_emitters.hpp b/src/plugins/intel_cpu/src/emitters/jit_load_store_emitters.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_load_store_emitters.hpp
rename to src/plugins/intel_cpu/src/emitters/jit_load_store_emitters.hpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_emitters.cpp b/src/plugins/intel_cpu/src/emitters/jit_mkldnn_emitters.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_emitters.cpp
rename to src/plugins/intel_cpu/src/emitters/jit_mkldnn_emitters.cpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_emitters.hpp b/src/plugins/intel_cpu/src/emitters/jit_mkldnn_emitters.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_emitters.hpp
rename to src/plugins/intel_cpu/src/emitters/jit_mkldnn_emitters.hpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_ext_emitters.hpp b/src/plugins/intel_cpu/src/emitters/jit_mkldnn_ext_emitters.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_mkldnn_ext_emitters.hpp
rename to src/plugins/intel_cpu/src/emitters/jit_mkldnn_ext_emitters.hpp
diff --git a/inference-engine/src/mkldnn_plugin/emitters/jit_snippets_emitters.hpp b/src/plugins/intel_cpu/src/emitters/jit_snippets_emitters.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/emitters/jit_snippets_emitters.hpp
rename to src/plugins/intel_cpu/src/emitters/jit_snippets_emitters.hpp
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.cpp b/src/plugins/intel_cpu/src/memory_desc/blocked_memory_desc.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.cpp
rename to src/plugins/intel_cpu/src/memory_desc/blocked_memory_desc.cpp
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.h b/src/plugins/intel_cpu/src/memory_desc/blocked_memory_desc.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.h
rename to src/plugins/intel_cpu/src/memory_desc/blocked_memory_desc.h
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.cpp b/src/plugins/intel_cpu/src/memory_desc/cpu_blocked_memory_desc.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.cpp
rename to src/plugins/intel_cpu/src/memory_desc/cpu_blocked_memory_desc.cpp
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.h b/src/plugins/intel_cpu/src/memory_desc/cpu_blocked_memory_desc.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/cpu_blocked_memory_desc.h
rename to src/plugins/intel_cpu/src/memory_desc/cpu_blocked_memory_desc.h
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc.h b/src/plugins/intel_cpu/src/memory_desc/cpu_memory_desc.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc.h
rename to src/plugins/intel_cpu/src/memory_desc/cpu_memory_desc.h
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp b/src/plugins/intel_cpu/src/memory_desc/cpu_memory_desc_utils.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp
rename to src/plugins/intel_cpu/src/memory_desc/cpu_memory_desc_utils.cpp
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.h b/src/plugins/intel_cpu/src/memory_desc/cpu_memory_desc_utils.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.h
rename to src/plugins/intel_cpu/src/memory_desc/cpu_memory_desc_utils.h
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp b/src/plugins/intel_cpu/src/memory_desc/dnnl_blocked_memory_desc.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp
rename to src/plugins/intel_cpu/src/memory_desc/dnnl_blocked_memory_desc.cpp
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.h b/src/plugins/intel_cpu/src/memory_desc/dnnl_blocked_memory_desc.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.h
rename to src/plugins/intel_cpu/src/memory_desc/dnnl_blocked_memory_desc.h
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.cpp b/src/plugins/intel_cpu/src/memory_desc/dnnl_memory_desc.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.cpp
rename to src/plugins/intel_cpu/src/memory_desc/dnnl_memory_desc.cpp
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.h b/src/plugins/intel_cpu/src/memory_desc/dnnl_memory_desc.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/memory_desc/dnnl_memory_desc.h
rename to src/plugins/intel_cpu/src/memory_desc/dnnl_memory_desc.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn/ie_mkldnn.cpp b/src/plugins/intel_cpu/src/mkldnn/ie_mkldnn.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn/ie_mkldnn.cpp
rename to src/plugins/intel_cpu/src/mkldnn/ie_mkldnn.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn/ie_mkldnn.h b/src/plugins/intel_cpu/src/mkldnn/ie_mkldnn.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn/ie_mkldnn.h
rename to src/plugins/intel_cpu/src/mkldnn/ie_mkldnn.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.cpp b/src/plugins/intel_cpu/src/mkldnn/iml_type_mapper.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.cpp
rename to src/plugins/intel_cpu/src/mkldnn/iml_type_mapper.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.h b/src/plugins/intel_cpu/src/mkldnn/iml_type_mapper.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.h
rename to src/plugins/intel_cpu/src/mkldnn/iml_type_mapper.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_async_infer_request.cpp b/src/plugins/intel_cpu/src/mkldnn_async_infer_request.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_async_infer_request.cpp
rename to src/plugins/intel_cpu/src/mkldnn_async_infer_request.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_async_infer_request.h b/src/plugins/intel_cpu/src/mkldnn_async_infer_request.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_async_infer_request.h
rename to src/plugins/intel_cpu/src/mkldnn_async_infer_request.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_descriptor.cpp b/src/plugins/intel_cpu/src/mkldnn_descriptor.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_descriptor.cpp
rename to src/plugins/intel_cpu/src/mkldnn_descriptor.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_descriptor.h b/src/plugins/intel_cpu/src/mkldnn_descriptor.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_descriptor.h
rename to src/plugins/intel_cpu/src/mkldnn_descriptor.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_edge.cpp b/src/plugins/intel_cpu/src/mkldnn_edge.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_edge.cpp
rename to src/plugins/intel_cpu/src/mkldnn_edge.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_edge.h b/src/plugins/intel_cpu/src/mkldnn_edge.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_edge.h
rename to src/plugins/intel_cpu/src/mkldnn_edge.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp b/src/plugins/intel_cpu/src/mkldnn_exec_network.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp
rename to src/plugins/intel_cpu/src/mkldnn_exec_network.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.h b/src/plugins/intel_cpu/src/mkldnn_exec_network.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_exec_network.h
rename to src/plugins/intel_cpu/src/mkldnn_exec_network.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension.cpp b/src/plugins/intel_cpu/src/mkldnn_extension.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_extension.cpp
rename to src/plugins/intel_cpu/src/mkldnn_extension.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension.h b/src/plugins/intel_cpu/src/mkldnn_extension.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_extension.h
rename to src/plugins/intel_cpu/src/mkldnn_extension.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension_mngr.cpp b/src/plugins/intel_cpu/src/mkldnn_extension_mngr.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_extension_mngr.cpp
rename to src/plugins/intel_cpu/src/mkldnn_extension_mngr.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension_mngr.h b/src/plugins/intel_cpu/src/mkldnn_extension_mngr.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_extension_mngr.h
rename to src/plugins/intel_cpu/src/mkldnn_extension_mngr.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.cpp b/src/plugins/intel_cpu/src/mkldnn_extension_utils.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.cpp
rename to src/plugins/intel_cpu/src/mkldnn_extension_utils.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.h b/src/plugins/intel_cpu/src/mkldnn_extension_utils.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_extension_utils.h
rename to src/plugins/intel_cpu/src/mkldnn_extension_utils.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp b/src/plugins/intel_cpu/src/mkldnn_graph.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
rename to src/plugins/intel_cpu/src/mkldnn_graph.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph.h b/src/plugins/intel_cpu/src/mkldnn_graph.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_graph.h
rename to src/plugins/intel_cpu/src/mkldnn_graph.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_dumper.cpp b/src/plugins/intel_cpu/src/mkldnn_graph_dumper.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_graph_dumper.cpp
rename to src/plugins/intel_cpu/src/mkldnn_graph_dumper.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_dumper.h b/src/plugins/intel_cpu/src/mkldnn_graph_dumper.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_graph_dumper.h
rename to src/plugins/intel_cpu/src/mkldnn_graph_dumper.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp b/src/plugins/intel_cpu/src/mkldnn_graph_optimizer.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
rename to src/plugins/intel_cpu/src/mkldnn_graph_optimizer.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h b/src/plugins/intel_cpu/src/mkldnn_graph_optimizer.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h
rename to src/plugins/intel_cpu/src/mkldnn_graph_optimizer.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp b/src/plugins/intel_cpu/src/mkldnn_infer_request.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
rename to src/plugins/intel_cpu/src/mkldnn_infer_request.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.h b/src/plugins/intel_cpu/src/mkldnn_infer_request.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_infer_request.h
rename to src/plugins/intel_cpu/src/mkldnn_infer_request.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_itt.h b/src/plugins/intel_cpu/src/mkldnn_itt.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_itt.h
rename to src/plugins/intel_cpu/src/mkldnn_itt.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_memory.cpp b/src/plugins/intel_cpu/src/mkldnn_memory.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_memory.cpp
rename to src/plugins/intel_cpu/src/mkldnn_memory.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_memory.h b/src/plugins/intel_cpu/src/mkldnn_memory.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_memory.h
rename to src/plugins/intel_cpu/src/mkldnn_memory.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_memory_state.cpp b/src/plugins/intel_cpu/src/mkldnn_memory_state.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_memory_state.cpp
rename to src/plugins/intel_cpu/src/mkldnn_memory_state.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_memory_state.h b/src/plugins/intel_cpu/src/mkldnn_memory_state.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_memory_state.h
rename to src/plugins/intel_cpu/src/mkldnn_memory_state.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp b/src/plugins/intel_cpu/src/mkldnn_node.cpp
similarity index 99%
rename from inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
rename to src/plugins/intel_cpu/src/mkldnn_node.cpp
index 14fd8b886be..f9c2f5a6867 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
+++ b/src/plugins/intel_cpu/src/mkldnn_node.cpp
@@ -1154,7 +1154,7 @@ MKLDNNNode* MKLDNNNode::NodesFactory::create(const std::shared_ptr<ngraph::Node>
     // getExceptionDescWithoutStatus removes redundant information from the exception message. For instance, the NotImplemented
     // exception is generated in the form: full_path_to_src_file:line_number [ NOT_IMPLEMENTED ] reason.
     // An example for gather node:
-    // /path-to-openVino-root/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.cpp:42 [ NOT_IMPLEMENTED ] Only opset7 Gather operation is supported
+    // /path-to-openVino-root/src/plugins/intel_cpu/nodes/mkldnn_gather_node.cpp:42 [ NOT_IMPLEMENTED ] Only opset7 Gather operation is supported
     // The most important part of the message is the reason, so the lambda trims everything up to "]"
     // Note that the op type and its friendly name will also be provided if we fail to create the node.
     auto getExceptionDescWithoutStatus = [](const InferenceEngine::Exception& ex) {
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.h b/src/plugins/intel_cpu/src/mkldnn_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_node.h
rename to src/plugins/intel_cpu/src/mkldnn_node.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp b/src/plugins/intel_cpu/src/mkldnn_nodes_factory.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
rename to src/plugins/intel_cpu/src/mkldnn_nodes_factory.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
similarity index 99%
rename from inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
rename to src/plugins/intel_cpu/src/mkldnn_plugin.cpp
index 193df08ec59..8a4cc5c6d89 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
@@ -876,5 +876,5 @@ InferenceEngine::IExecutableNetworkInternal::Ptr Engine::ImportNetwork(std::istr
     return execNetwork;
 }
 
-static const Version version = {{2, 1}, CI_BUILD_NUMBER, "MKLDNNPlugin"};
+static const Version version = {{2, 1}, CI_BUILD_NUMBER, "ov_intel_cpu_plugin"};
 IE_DEFINE_PLUGIN_CREATE_FUNCTION(Engine, version)
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.h b/src/plugins/intel_cpu/src/mkldnn_plugin.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_plugin.h
rename to src/plugins/intel_cpu/src/mkldnn_plugin.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_primitive.cpp b/src/plugins/intel_cpu/src/mkldnn_primitive.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_primitive.cpp
rename to src/plugins/intel_cpu/src/mkldnn_primitive.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_primitive.h b/src/plugins/intel_cpu/src/mkldnn_primitive.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_primitive.h
rename to src/plugins/intel_cpu/src/mkldnn_primitive.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_selective_build.h b/src/plugins/intel_cpu/src/mkldnn_selective_build.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_selective_build.h
rename to src/plugins/intel_cpu/src/mkldnn_selective_build.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_serialize.cpp b/src/plugins/intel_cpu/src/mkldnn_serialize.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_serialize.cpp
rename to src/plugins/intel_cpu/src/mkldnn_serialize.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_serialize.h b/src/plugins/intel_cpu/src/mkldnn_serialize.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_serialize.h
rename to src/plugins/intel_cpu/src/mkldnn_serialize.h
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_weights_cache.cpp b/src/plugins/intel_cpu/src/mkldnn_weights_cache.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_weights_cache.cpp
rename to src/plugins/intel_cpu/src/mkldnn_weights_cache.cpp
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_weights_cache.hpp b/src/plugins/intel_cpu/src/mkldnn_weights_cache.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/mkldnn_weights_cache.hpp
rename to src/plugins/intel_cpu/src/mkldnn_weights_cache.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/align_matmul_input_ranks.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/align_matmul_input_ranks.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/align_matmul_input_ranks.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/align_matmul_input_ranks.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/align_matmul_input_ranks.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/align_matmul_input_ranks.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/align_matmul_input_ranks.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/align_matmul_input_ranks.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_broadcast_to_tiles.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_broadcast_to_tiles.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_broadcast_to_tiles.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_broadcast_to_tiles.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_broadcast_to_tiles.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_broadcast_to_tiles.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_broadcast_to_tiles.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_broadcast_to_tiles.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_matmul_to_fc.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_matmul_to_fc.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_matmul_to_fc.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_matmul_to_fc.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_matmul_to_fc.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_matmul_to_fc.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_matmul_to_fc.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_matmul_to_fc.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_tile_to_seq_tiles.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_tile_to_seq_tiles.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_tile_to_seq_tiles.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_tile_to_seq_tiles.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_tile_to_seq_tiles.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_tile_to_seq_tiles.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_tile_to_seq_tiles.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_tile_to_seq_tiles.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_to_cpu_specific_opset.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_to_cpu_specific_opset.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_leaky_relu.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_to_leaky_relu.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_leaky_relu.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_to_leaky_relu.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_leaky_relu.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_to_leaky_relu.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_leaky_relu.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_to_leaky_relu.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_power_static.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_to_power_static.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_power_static.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_to_power_static.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_power_static.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_to_power_static.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_power_static.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_to_power_static.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_swish_cpu.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_to_swish_cpu.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_swish_cpu.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_to_swish_cpu.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_swish_cpu.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/convert_to_swish_cpu.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_swish_cpu.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/convert_to_swish_cpu.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/fc_bias_fusion.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/fc_bias_fusion.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/fc_bias_fusion.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/fc_bias_fusion.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/fc_bias_fusion.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/fc_bias_fusion.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/fc_bias_fusion.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/fc_bias_fusion.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/move_eltwise_up_data_movement.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/move_eltwise_up_data_movement.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/move_eltwise_up_data_movement.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/move_eltwise_up_data_movement.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/move_eltwise_up_data_movement.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/move_eltwise_up_data_movement.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/move_eltwise_up_data_movement.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/move_eltwise_up_data_movement.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/fully_connected.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/fully_connected.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/fully_connected.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/fully_connected.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/leaky_relu.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/leaky_relu.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/leaky_relu.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/leaky_relu.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/leaky_relu.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/leaky_relu.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/leaky_relu.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/leaky_relu.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/power_static.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/power_static.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/power_static.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/power_static.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/power_static.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/power_static.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/power_static.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/power_static.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/swish_cpu.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/swish_cpu.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/swish_cpu.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/swish_cpu.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/swish_cpu.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/op/swish_cpu.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/op/swish_cpu.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/op/swish_cpu.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_fc_fusion.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/reshape_fc_fusion.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_fc_fusion.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/reshape_fc_fusion.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_fc_fusion.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/reshape_fc_fusion.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_fc_fusion.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/reshape_fc_fusion.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_prelu.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/reshape_prelu.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_prelu.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/reshape_prelu.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_prelu.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/reshape_prelu.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/reshape_prelu.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/reshape_prelu.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/rnn_sequences_optimization.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/rnn_sequences_optimization.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/rnn_sequences_optimization.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/rnn_sequences_optimization.hpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.cpp b/src/plugins/intel_cpu/src/ngraph_transformations/snippets_mark_skipped.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.cpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/snippets_mark_skipped.cpp
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.hpp b/src/plugins/intel_cpu/src/ngraph_transformations/snippets_mark_skipped.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/ngraph_transformations/snippets_mark_skipped.hpp
rename to src/plugins/intel_cpu/src/ngraph_transformations/snippets_mark_skipped.hpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/blocked_desc_creator.cpp b/src/plugins/intel_cpu/src/nodes/common/blocked_desc_creator.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/blocked_desc_creator.cpp
rename to src/plugins/intel_cpu/src/nodes/common/blocked_desc_creator.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/blocked_desc_creator.h b/src/plugins/intel_cpu/src/nodes/common/blocked_desc_creator.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/blocked_desc_creator.h
rename to src/plugins/intel_cpu/src/nodes/common/blocked_desc_creator.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp b/src/plugins/intel_cpu/src/nodes/common/cpu_convert.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp
rename to src/plugins/intel_cpu/src/nodes/common/cpu_convert.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.h b/src/plugins/intel_cpu/src/nodes/common/cpu_convert.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.h
rename to src/plugins/intel_cpu/src/nodes/common/cpu_convert.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_memcpy.h b/src/plugins/intel_cpu/src/nodes/common/cpu_memcpy.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/cpu_memcpy.h
rename to src/plugins/intel_cpu/src/nodes/common/cpu_memcpy.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/defs.h b/src/plugins/intel_cpu/src/nodes/common/defs.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/defs.h
rename to src/plugins/intel_cpu/src/nodes/common/defs.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.cpp b/src/plugins/intel_cpu/src/nodes/common/dnnl_executor.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.cpp
rename to src/plugins/intel_cpu/src/nodes/common/dnnl_executor.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.h b/src/plugins/intel_cpu/src/nodes/common/dnnl_executor.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/dnnl_executor.h
rename to src/plugins/intel_cpu/src/nodes/common/dnnl_executor.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/fp16_utils.h b/src/plugins/intel_cpu/src/nodes/common/fp16_utils.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/fp16_utils.h
rename to src/plugins/intel_cpu/src/nodes/common/fp16_utils.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/permute_kernel.cpp b/src/plugins/intel_cpu/src/nodes/common/permute_kernel.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/permute_kernel.cpp
rename to src/plugins/intel_cpu/src/nodes/common/permute_kernel.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/permute_kernel.h b/src/plugins/intel_cpu/src/nodes/common/permute_kernel.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/permute_kernel.h
rename to src/plugins/intel_cpu/src/nodes/common/permute_kernel.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/softmax.cpp b/src/plugins/intel_cpu/src/nodes/common/softmax.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/softmax.cpp
rename to src/plugins/intel_cpu/src/nodes/common/softmax.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/softmax.h b/src/plugins/intel_cpu/src/nodes/common/softmax.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/softmax.h
rename to src/plugins/intel_cpu/src/nodes/common/softmax.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.cpp b/src/plugins/intel_cpu/src/nodes/common/tile_broadcast_utils.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.cpp
rename to src/plugins/intel_cpu/src/nodes/common/tile_broadcast_utils.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.h b/src/plugins/intel_cpu/src/nodes/common/tile_broadcast_utils.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.h
rename to src/plugins/intel_cpu/src/nodes/common/tile_broadcast_utils.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/uni_simd.h b/src/plugins/intel_cpu/src/nodes/common/uni_simd.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/common/uni_simd.h
rename to src/plugins/intel_cpu/src/nodes/common/uni_simd.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/list.hpp b/src/plugins/intel_cpu/src/nodes/list.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/list.hpp
rename to src/plugins/intel_cpu/src/nodes/list.hpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_adaptive_pooling.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_adaptive_pooling.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h b/src/plugins/intel_cpu/src/nodes/mkldnn_adaptive_pooling.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_adaptive_pooling.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_batch_to_space_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_batch_to_space_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_batch_to_space_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_batch_to_space_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_bin_conv_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_bin_conv_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_bin_conv_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_bin_conv_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_broadcast_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_broadcast_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_broadcast_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_broadcast_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_bucketize_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_bucketize_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_bucketize_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_bucketize_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_bucketize_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_concat_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_concat_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_concat_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_concat_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_conv_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_conv_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_conv_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_conv_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_convert_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_convert_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_convert_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_convert_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_ctc_loss_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_ctc_loss_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_ctc_loss_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_ctc_loss_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_cum_sum_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_cum_sum_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_cum_sum_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_cum_sum_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_deconv_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_deconv_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_deconv_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_deconv_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_def_conv_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_def_conv_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_def_conv_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_def_conv_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_def_conv_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_def_conv_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_def_conv_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_def_conv_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_depth_to_space_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_depth_to_space_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_depth_to_space_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_depth_to_space_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_detection_output_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_detection_output_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_detection_output_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_detection_output_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_dft_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_dft_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_dft_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_dft_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_dft_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_dft_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_dft_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_dft_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_eltwise_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_eltwise_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_eltwise_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_eltwise_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_offset_sum_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_offset_sum_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_packed_sum_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_packed_sum_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_sum_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_sum_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_sum_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_bag_sum_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_segments_sum_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_segments_sum_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_embedding_segments_sum_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_embedding_segments_sum_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_topkrois_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_extract_image_patches_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_extract_image_patches_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_extract_image_patches_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_extract_image_patches_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_fake_quantize_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_fake_quantize_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_fake_quantize_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_fake_quantize_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_fake_quantize_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_fullyconnected_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_fullyconnected_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_fullyconnected_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_fullyconnected_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_elements_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_elements_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_elements_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_elements_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_elements_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_nd_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_nd_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_nd_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_nd_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_tree_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_tree_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_tree_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_tree_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_tree_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_gather_tree_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_tree_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_gather_tree_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_generic_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_generic_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_generic_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_generic_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_generic_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_generic_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_generic_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_generic_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_grn_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_grn_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_grn_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_grn_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_grn_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_grn_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_grn_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_grn_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_if_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_if_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_if_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_if_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_if_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_if_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_input_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_input_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_input_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_input_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_input_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_interpolate_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_interpolate_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_interpolate_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_interpolate_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_log_softmax_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_log_softmax_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_log_softmax_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_log_softmax_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_lrn_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_lrn_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_lrn_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_lrn_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_math_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_math_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_math_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_math_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_matmul_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_matmul_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_matmul_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_matmul_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_matrix_nms_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_matrix_nms_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_matrix_nms_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_matrix_nms_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_memory_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_memory_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_memory_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_memory_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_memory_node.hpp b/src/plugins/intel_cpu/src/nodes/mkldnn_memory_node.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_memory_node.hpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_memory_node.hpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_multiclass_nms.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_multiclass_nms.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp b/src/plugins/intel_cpu/src/nodes/mkldnn_multiclass_nms.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_multiclass_nms.hpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_mvn_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_mvn_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_mvn_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_mvn_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_non_max_suppression_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_non_max_suppression_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_non_max_suppression_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_non_max_suppression_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_non_zero.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_non_zero.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h b/src/plugins/intel_cpu/src/nodes/mkldnn_non_zero.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_non_zero.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_normalize_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_normalize_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_normalize_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_normalize_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_one_hot_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_one_hot_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_one_hot_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_one_hot_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_one_hot_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_pooling_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_pooling_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_pooling_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_pooling_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_proposal_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_proposal_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_proposal_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_proposal_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_psroi_pooling_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_psroi_pooling_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_psroi_pooling_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_psroi_pooling_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_psroi_pooling_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_psroi_pooling_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_psroi_pooling_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_psroi_pooling_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_range_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_range_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_range_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_range_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_range_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_reduce_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reduce_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_reduce_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reduce_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_reference_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reference_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_reference_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reference_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_region_yolo_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_region_yolo_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_region_yolo_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_region_yolo_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_region_yolo_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_region_yolo_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_region_yolo_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_region_yolo_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_reorder_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reorder_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_reorder_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorder_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reorder_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_reorg_yolo_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reorg_yolo_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_reorg_yolo_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reorg_yolo_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_reshape_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reshape_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_reshape_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reshape_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reverse_sequence_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_reverse_sequence_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reverse_sequence_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reverse_sequence_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reverse_sequence_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_reverse_sequence_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_reverse_sequence_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_reverse_sequence_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_rnn.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_rnn.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.h b/src/plugins/intel_cpu/src/nodes/mkldnn_rnn.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_rnn.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_roi_align_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_roi_align_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_roi_align_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_roi_align_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_roi_pooling_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_roi_pooling_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_roi_pooling_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_pooling_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_roi_pooling_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roll_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_roll_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_roll_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_roll_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roll_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_roll_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_roll_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_roll_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_scatter_update_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_scatter_update_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_scatter_update_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_scatter_update_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_select_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_select_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_select_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_select_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_select_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_shapeof.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_shapeof.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.h b/src/plugins/intel_cpu/src/nodes/mkldnn_shapeof.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_shapeof.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_shapeof.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_shuffle_channels_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_shuffle_channels_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_shuffle_channels_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_shuffle_channels_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_shuffle_channels_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_softmax_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_softmax_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_softmax_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_softmax_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_softmax_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_space_to_batch_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_space_to_batch_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_space_to_batch_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_batch_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_space_to_batch_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_space_to_depth_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_space_to_depth_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_space_to_depth_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_space_to_depth_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_space_to_depth_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_split_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_split_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_split_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_split_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_strided_slice_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_strided_slice_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_strided_slice_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_strided_slice_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_strided_slice_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tensoriterator_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_tensoriterator_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tensoriterator_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_tensoriterator_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_tile_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_tile_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_tile_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_tile_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_transpose_node.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.cpp
rename to src/plugins/intel_cpu/src/nodes/mkldnn_transpose_node.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_transpose_node.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/mkldnn_transpose_node.h
rename to src/plugins/intel_cpu/src/nodes/mkldnn_transpose_node.h
diff --git a/inference-engine/src/mkldnn_plugin/nodes/proposal_imp.cpp b/src/plugins/intel_cpu/src/nodes/proposal_imp.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/proposal_imp.cpp
rename to src/plugins/intel_cpu/src/nodes/proposal_imp.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/proposal_imp.hpp b/src/plugins/intel_cpu/src/nodes/proposal_imp.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/proposal_imp.hpp
rename to src/plugins/intel_cpu/src/nodes/proposal_imp.hpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/subgraph.cpp b/src/plugins/intel_cpu/src/nodes/subgraph.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/subgraph.cpp
rename to src/plugins/intel_cpu/src/nodes/subgraph.cpp
diff --git a/inference-engine/src/mkldnn_plugin/nodes/subgraph.h b/src/plugins/intel_cpu/src/nodes/subgraph.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/nodes/subgraph.h
rename to src/plugins/intel_cpu/src/nodes/subgraph.h
diff --git a/inference-engine/src/mkldnn_plugin/normalize_preprocess.cpp b/src/plugins/intel_cpu/src/normalize_preprocess.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/normalize_preprocess.cpp
rename to src/plugins/intel_cpu/src/normalize_preprocess.cpp
diff --git a/inference-engine/src/mkldnn_plugin/normalize_preprocess.h b/src/plugins/intel_cpu/src/normalize_preprocess.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/normalize_preprocess.h
rename to src/plugins/intel_cpu/src/normalize_preprocess.h
diff --git a/inference-engine/src/mkldnn_plugin/perf_count.h b/src/plugins/intel_cpu/src/perf_count.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/perf_count.h
rename to src/plugins/intel_cpu/src/perf_count.h
diff --git a/inference-engine/src/mkldnn_plugin/utils/bfloat16.hpp b/src/plugins/intel_cpu/src/utils/bfloat16.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/bfloat16.hpp
rename to src/plugins/intel_cpu/src/utils/bfloat16.hpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/blob_dump.cpp b/src/plugins/intel_cpu/src/utils/blob_dump.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/blob_dump.cpp
rename to src/plugins/intel_cpu/src/utils/blob_dump.cpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/blob_dump.h b/src/plugins/intel_cpu/src/utils/blob_dump.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/blob_dump.h
rename to src/plugins/intel_cpu/src/utils/blob_dump.h
diff --git a/inference-engine/src/mkldnn_plugin/utils/cpu_utils.hpp b/src/plugins/intel_cpu/src/utils/cpu_utils.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/cpu_utils.hpp
rename to src/plugins/intel_cpu/src/utils/cpu_utils.hpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/debug_capabilities.h b/src/plugins/intel_cpu/src/utils/debug_capabilities.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/debug_capabilities.h
rename to src/plugins/intel_cpu/src/utils/debug_capabilities.h
diff --git a/inference-engine/src/mkldnn_plugin/utils/general_utils.h b/src/plugins/intel_cpu/src/utils/general_utils.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/general_utils.h
rename to src/plugins/intel_cpu/src/utils/general_utils.h
diff --git a/inference-engine/src/mkldnn_plugin/utils/ngraph_utils.hpp b/src/plugins/intel_cpu/src/utils/ngraph_utils.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/ngraph_utils.hpp
rename to src/plugins/intel_cpu/src/utils/ngraph_utils.hpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/node_dumper.cpp b/src/plugins/intel_cpu/src/utils/node_dumper.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/node_dumper.cpp
rename to src/plugins/intel_cpu/src/utils/node_dumper.cpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/node_dumper.h b/src/plugins/intel_cpu/src/utils/node_dumper.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/node_dumper.h
rename to src/plugins/intel_cpu/src/utils/node_dumper.h
diff --git a/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp b/src/plugins/intel_cpu/src/utils/rt_info/memory_formats_attribute.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp
rename to src/plugins/intel_cpu/src/utils/rt_info/memory_formats_attribute.cpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp b/src/plugins/intel_cpu/src/utils/rt_info/memory_formats_attribute.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp
rename to src/plugins/intel_cpu/src/utils/rt_info/memory_formats_attribute.hpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp b/src/plugins/intel_cpu/src/utils/shape_inference/shape_inference.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.cpp
rename to src/plugins/intel_cpu/src/utils/shape_inference/shape_inference.cpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.hpp b/src/plugins/intel_cpu/src/utils/shape_inference/shape_inference.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/shape_inference/shape_inference.hpp
rename to src/plugins/intel_cpu/src/utils/shape_inference/shape_inference.hpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.cpp b/src/plugins/intel_cpu/src/utils/shape_inference/static_dimension.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.cpp
rename to src/plugins/intel_cpu/src/utils/shape_inference/static_dimension.cpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.hpp b/src/plugins/intel_cpu/src/utils/shape_inference/static_dimension.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.hpp
rename to src/plugins/intel_cpu/src/utils/shape_inference/static_dimension.hpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.cpp b/src/plugins/intel_cpu/src/utils/shape_inference/static_shape.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.cpp
rename to src/plugins/intel_cpu/src/utils/shape_inference/static_shape.cpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.hpp b/src/plugins/intel_cpu/src/utils/shape_inference/static_shape.hpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.hpp
rename to src/plugins/intel_cpu/src/utils/shape_inference/static_shape.hpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/verbose.cpp b/src/plugins/intel_cpu/src/utils/verbose.cpp
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/verbose.cpp
rename to src/plugins/intel_cpu/src/utils/verbose.cpp
diff --git a/inference-engine/src/mkldnn_plugin/utils/verbose.h b/src/plugins/intel_cpu/src/utils/verbose.h
similarity index 100%
rename from inference-engine/src/mkldnn_plugin/utils/verbose.h
rename to src/plugins/intel_cpu/src/utils/verbose.h
diff --git a/src/plugins/intel_cpu/thirdparty/CMakeLists.txt b/src/plugins/intel_cpu/thirdparty/CMakeLists.txt
new file mode 100644
index 00000000000..33d3383ec8f
--- /dev/null
+++ b/src/plugins/intel_cpu/thirdparty/CMakeLists.txt
@@ -0,0 +1,55 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+if((CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") AND (MSVC_VERSION VERSION_GREATER_EQUAL "1910"))
+    # 1910 version of Visual Studio 2017
+    # This flagis needed for enabling SIMD vectorization with command '#pragma omp simd'.
+    # Compilation with '/openmp:experimental' key allow us to enable vectorizatikon capability in MSVC.
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /openmp:experimental")
+endif()
+
+if(ENABLE_LTO)
+    set(CMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE ON)
+endif()
+
+function(ie_add_mkldnn)
+    set(DNNL_ENABLE_JIT_PROFILING ${BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
+    if(BUILD_SHARED_LIBS AND ENABLE_PROFILING_ITT)
+        set(DNNL_ENABLE_ITT_TASKS ON CACHE BOOL "" FORCE)
+    else()
+        set(DNNL_ENABLE_ITT_TASKS OFF CACHE BOOL "" FORCE)
+    endif()
+    set(DNNL_ENABLE_CONCURRENT_EXEC ON CACHE BOOL "" FORCE)
+    set(DNNL_ENABLE_PRIMITIVE_CACHE OFF CACHE BOOL "" FORCE) ## TODO: try it later
+    set(DNNL_ENABLE_MAX_CPU_ISA ON CACHE BOOL "" FORCE)
+    set(DNNL_LIBRARY_TYPE STATIC CACHE BOOL "" FORCE)
+    set(DNNL_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
+    set(DNNL_BUILD_TESTS OFF CACHE BOOL "" FORCE)
+    set(DNNL_CPU_RUNTIME "${THREADING}" CACHE STRING "" FORCE)
+    set(DNNL_GPU_RUNTIME "NONE" CACHE STRING "" FORCE)
+    set(DNNL_BLAS_VENDOR "NONE" CACHE STRING "" FORCE)
+    set(SDL_cmake_included ON)  ## to skip internal SDL flags. SDL flags are already set on IE level
+    if (ANDROID OR ((CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") AND NOT (THREADING STREQUAL "OMP")))
+        set(OpenMP_cmake_included ON) ## to skip "omp simd" inside a code. Lead to some crashes inside NDK LLVM..
+    endif()
+
+    if(SUGGEST_OVERRIDE_SUPPORTED)
+        # xbyak compilation fails
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
+    endif()
+    if(CMAKE_COMPILER_IS_GNUCXX)
+        ie_add_compiler_flags(-Wno-undef)
+        ie_add_compiler_flags(-Wno-missing-declarations)
+    elseif(UNIX AND CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -diag-disable=10121")
+    endif()
+
+    add_subdirectory(mkl-dnn EXCLUDE_FROM_ALL)
+    add_library(mkldnn ALIAS dnnl)
+    ov_install_static_lib(dnnl cpu)
+endfunction()
+
+if(ENABLE_INTEL_CPU)
+    ie_add_mkldnn()
+endif()
diff --git a/inference-engine/thirdparty/mkl-dnn b/src/plugins/intel_cpu/thirdparty/mkl-dnn
similarity index 100%
rename from inference-engine/thirdparty/mkl-dnn
rename to src/plugins/intel_cpu/thirdparty/mkl-dnn
diff --git a/src/tests/functional/plugin/CMakeLists.txt b/src/tests/functional/plugin/CMakeLists.txt
index 174dc9816be..5e60c1a9ace 100644
--- a/src/tests/functional/plugin/CMakeLists.txt
+++ b/src/tests/functional/plugin/CMakeLists.txt
@@ -4,7 +4,7 @@
 
 add_subdirectory(shared)
 
-if (ENABLE_MKL_DNN)
+if (ENABLE_INTEL_CPU)
     add_subdirectory(cpu)
 endif()
 
diff --git a/src/tests/functional/plugin/conformance/test_runner/README.md b/src/tests/functional/plugin/conformance/test_runner/README.md
index 350419118c4..0d6ba495df6 100644
--- a/src/tests/functional/plugin/conformance/test_runner/README.md
+++ b/src/tests/functional/plugin/conformance/test_runner/README.md
@@ -20,7 +20,7 @@ The target is able to take the following command-line arguments:
 * `-h` prints target command-line options with description.
 * `--device` specifies target device.
 * `--input_folders` specifies folders with IRs to run. The separator is `,`.
-* `--plugin_lib_name` is name of plugin library. The example is MKLDNNPlugin. Use only with unregistered in IE Core devices.
+* `--plugin_lib_name` is name of plugin library. The example is ov_intel_cpu_plugin. Use only with unregistered in IE Core devices.
 * `--disable_test_config` allows to ignore all skipped tests with the exception of `DISABLED_` prefix using.
 * `--skip_config_path` allows to specify paths to files contain regular expressions list to skip tests.
 * `--extend_report` allows not to re-write device results to the report (add results of this run to the existing). Mutually exclusive with --report_unique_name.
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
index 6a6499e460e..e3ec583bb0b 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
@@ -20,7 +20,7 @@ inline const std::string getPluginLibNameByDevice(const std::string& deviceName)
             { "HDDL", "HDDLPlugin" },
             { "VPUX", "VPUXPlugin" },
             { "AUTO", "ov_auto_plugin" },
-            { "CPU", "MKLDNNPlugin" },
+            { "CPU", "ov_intel_cpu_plugin" },
             { "GNA", "ov_intel_gna_plugin" },
             { "GPU", "ov_intel_gpu_plugin" },
             { "HETERO", "ov_hetero_plugin" },
diff --git a/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp b/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp
index 73f20c17627..98a47ba8f46 100644
--- a/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp
+++ b/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp
@@ -17,7 +17,8 @@ static const char target_device_message[] = "Required. Specify the target device
                                             "Use \"-d HETERO:<comma-separated_devices_list>\" format to specify HETERO plugin. "
                                             "The application looks for a suitable plugin for the specified device.";
 static const char input_folders_message[] = "Required. Paths to the input folders with IRs. Delimiter is `,` symbol.";
-static const char target_plugin_message[] = "Optional. Name of plugin library. The example is MKLDNNPlugin. Use only with unregistered in IE Core devices";
+static const char target_plugin_message[] =
+    "Optional. Name of plugin library. The example is ov_intel_cpu_plugin. Use only with unregistered in IE Core devices";
 static const char output_folder_message[] = "Optional. Paths to the output folder to save report.  Default value is \".\"";
 static const char report_unique_name_message[] = "Optional. Allow to save report with unique name (report_pid_timestamp.xml). "
                                                  "Mutually exclusive with --extend_report. Default value is false";
diff --git a/src/tests/functional/plugin/cpu/CMakeLists.txt b/src/tests/functional/plugin/cpu/CMakeLists.txt
index 64fb4357db2..1faac645fa0 100644
--- a/src/tests/functional/plugin/cpu/CMakeLists.txt
+++ b/src/tests/functional/plugin/cpu/CMakeLists.txt
@@ -4,12 +4,12 @@
 
 set(TARGET_NAME cpuFuncTests)
 
-add_library(cpuSpecificRtInfo STATIC ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp
-                                     ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp)
+add_library(cpuSpecificRtInfo STATIC $<TARGET_PROPERTY:ov_intel_cpu_plugin,SOURCE_DIR>/src/utils/rt_info/memory_formats_attribute.hpp
+                                     $<TARGET_PROPERTY:ov_intel_cpu_plugin,SOURCE_DIR>/src/utils/rt_info/memory_formats_attribute.cpp)
 target_link_libraries(cpuSpecificRtInfo PRIVATE openvino::runtime)
 
-set(INCLUDES ${CMAKE_CURRENT_SOURCE_DIR} $<TARGET_PROPERTY:MKLDNNPlugin,SOURCE_DIR>)
-set(DEPENDENCIES MKLDNNPlugin)
+set(INCLUDES ${CMAKE_CURRENT_SOURCE_DIR} $<TARGET_PROPERTY:ov_intel_cpu_plugin,SOURCE_DIR>/src)
+set(DEPENDENCIES ov_intel_cpu_plugin)
 set(LINK_LIBRARIES funcSharedTests cpuSpecificRtInfo)
 if (ENABLE_OV_ONNX_FRONTEND)
     list(APPEND INCLUDES "${OpenVINO_SOURCE_DIR}/docs/onnx_custom_op")
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
index 60eee73f6fa..6738a1e833b 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
@@ -15,7 +15,7 @@ namespace {
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_OVClassCommon, OVClassBasicTestP,
-        ::testing::Values(std::make_pair("MKLDNNPlugin", "CPU")));
+        ::testing::Values(std::make_pair("ov_intel_cpu_plugin", "CPU")));
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_OVClassNetworkTestP, OVClassNetworkTestP,
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/core_integration.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/core_integration.cpp
index 88dd3e01a23..57983543e12 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/core_integration.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/core_integration.cpp
@@ -15,7 +15,7 @@ namespace {
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassCommon, IEClassBasicTestP,
-        ::testing::Values(std::make_pair("MKLDNNPlugin", "CPU")));
+        ::testing::Values(std::make_pair("ov_intel_cpu_plugin", "CPU")));
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassNetworkTestP, IEClassNetworkTestP,
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp
index 7fdb7497141..56dd75c6808 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp
@@ -16,13 +16,13 @@ using namespace HeteroTests;
 
 INSTANTIATE_TEST_SUITE_P(smoke_SingleMajorNode, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"CPU0", "MKLDNNPlugin"}, {"CPU1", "MKLDNNPlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"CPU0", "ov_intel_cpu_plugin"}, {"CPU1", "ov_intel_cpu_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::_singleMajorNodeFunctions)),
                         HeteroSyntheticTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(nightly_RandomMajorNodes, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"CPU0", "MKLDNNPlugin"}, {"CPU1", "MKLDNNPlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"CPU0", "ov_intel_cpu_plugin"}, {"CPU1", "ov_intel_cpu_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::_randomMajorNodeFunctions)),
                         HeteroSyntheticTest::getTestCaseName);
 
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/multi/gpu_remote_blob_tests.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/multi/gpu_remote_blob_tests.cpp
index f33a3844410..0628e9bd0f2 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/multi/gpu_remote_blob_tests.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/multi/gpu_remote_blob_tests.cpp
@@ -11,7 +11,7 @@
 const std::vector<DevicesNamesAndSupportPair> device_names_and_support_for_remote_blobs {
         {{GPU}, true}, // GPU via MULTI,
         {{"GPU.0"}, true}, // GPU.0 via MULTI,
-#ifdef ENABLE_MKL_DNN
+#ifdef ENABLE_INTEL_CPU
         {{GPU, CPU}, true}, // GPU+CPU
         {{CPU, GPU}, true}, // CPU+GPU
 #endif
@@ -48,7 +48,7 @@ TEST_P(MultiDevice_Test, cannotInferRemoteBlobIfNotInitializedForDevice) {
 }
 
 const std::vector<DevicesNames> device_names_and_support_for_remote_blobs2 {
-#ifdef ENABLE_MKL_DNN
+#ifdef ENABLE_INTEL_CPU
         {CPU},  // stand-alone CPU via MULTI (no GPU), no OCL context
 #endif
         {"GPU.1"},  // another GPU (the test will test its presence), different OCL contexts
diff --git a/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/executable_network/core_integration.cpp b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/executable_network/core_integration.cpp
index 29cecf57b32..524117e2342 100644
--- a/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/executable_network/core_integration.cpp
+++ b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/executable_network/core_integration.cpp
@@ -23,7 +23,7 @@ INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassImportExportTestP, IEClassImportExportTestP,
         ::testing::Values(std::string(CommonTestUtils::DEVICE_MYRIAD), "HETERO:" + std::string(CommonTestUtils::DEVICE_MYRIAD)));
 
-#if defined(ENABLE_MKL_DNN) && ENABLE_MKL_DNN
+#if defined(ENABLE_INTEL_CPU) && ENABLE_INTEL_CPU
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassImportExportTestP_HETERO_CPU, IEClassImportExportTestP,
diff --git a/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/get_metric.cpp b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/get_metric.cpp
index de31f68120d..436a1e1bf2e 100644
--- a/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/get_metric.cpp
+++ b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_executable_network/get_metric.cpp
@@ -24,7 +24,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_OVClassImportExportTestP,
                          ::testing::Values(std::string(CommonTestUtils::DEVICE_MYRIAD),
                                            "HETERO:" + std::string(CommonTestUtils::DEVICE_MYRIAD)));
 
-#if defined(ENABLE_MKL_DNN) && ENABLE_MKL_DNN
+#if defined(ENABLE_INTEL_CPU) && ENABLE_INTEL_CPU
 
 INSTANTIATE_TEST_SUITE_P(smoke_OVClassImportExportTestP_HETERO_CPU,
                          OVClassImportExportTestP,
diff --git a/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
index 8f6d3bab139..f6ed8f5c10a 100644
--- a/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
+++ b/src/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
@@ -53,7 +53,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_OVClassImportExportTestP,
                          ::testing::Values(std::string(CommonTestUtils::DEVICE_MYRIAD),
                                            "HETERO:" + std::string(CommonTestUtils::DEVICE_MYRIAD)));
 
-#if defined(ENABLE_MKL_DNN) && ENABLE_MKL_DNN
+#if defined(ENABLE_INTEL_CPU) && ENABLE_INTEL_CPU
 
 INSTANTIATE_TEST_SUITE_P(smoke_OVClassImportExportTestP_HETERO_CPU,
                          OVClassImportExportTestP,
diff --git a/src/tests/functional/plugin/myriad/shared_tests_instances/myriad_remote_blobs_tests.cpp b/src/tests/functional/plugin/myriad/shared_tests_instances/myriad_remote_blobs_tests.cpp
index 15324128621..a5148dadcad 100644
--- a/src/tests/functional/plugin/myriad/shared_tests_instances/myriad_remote_blobs_tests.cpp
+++ b/src/tests/functional/plugin/myriad/shared_tests_instances/myriad_remote_blobs_tests.cpp
@@ -9,7 +9,7 @@
 
 const std::vector<DevicesNamesAndSupportPair> device_names_and_support_for_remote_blobs {
         {{MYRIAD}, false}, // MYX via MULTI
-#ifdef ENABLE_MKL_DNN
+#ifdef ENABLE_INTEL_CPU
         {{CPU, MYRIAD}, false},  // CPU+MYX
 #endif
 };
diff --git a/src/tests/unit/CMakeLists.txt b/src/tests/unit/CMakeLists.txt
index e2204b7371d..088a0a7d945 100644
--- a/src/tests/unit/CMakeLists.txt
+++ b/src/tests/unit/CMakeLists.txt
@@ -12,7 +12,7 @@ endif()
 
 add_subdirectory(inference_engine)
 
-if (ENABLE_MKL_DNN)
+if (ENABLE_INTEL_CPU)
     add_subdirectory(cpu)
 endif ()
 
diff --git a/src/tests/unit/cpu/CMakeLists.txt b/src/tests/unit/cpu/CMakeLists.txt
index 7f66c147d95..be1a036431a 100644
--- a/src/tests/unit/cpu/CMakeLists.txt
+++ b/src/tests/unit/cpu/CMakeLists.txt
@@ -8,11 +8,11 @@ addIeTargetTest(
         NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
         INCLUDES
-            $<TARGET_PROPERTY:MKLDNNPlugin,SOURCE_DIR>
-            $<TARGET_PROPERTY:MKLDNNPlugin,SOURCE_DIR>/nodes
+            $<TARGET_PROPERTY:ov_intel_cpu_plugin,SOURCE_DIR>/src
+            $<TARGET_PROPERTY:ov_intel_cpu_plugin,SOURCE_DIR>/src/nodes
             $<TARGET_PROPERTY:openvino::conditional_compilation,INTERFACE_INCLUDE_DIRECTORIES>
         OBJECT_FILES
-            $<TARGET_OBJECTS:MKLDNNPlugin_obj>
+            $<TARGET_OBJECTS:ov_intel_cpu_plugin_obj>
         LINK_LIBRARIES
             gtest
             gtest_main
@@ -35,6 +35,6 @@ ie_faster_build(${TARGET_NAME}
 )
 
 target_include_directories(${TARGET_NAME} SYSTEM PRIVATE
-        ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/src/common
-        ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/src/cpu
-        ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/include)
+    $<TARGET_PROPERTY:mkldnn,SOURCE_DIR>/src/common
+    $<TARGET_PROPERTY:mkldnn,SOURCE_DIR>/src/cpu
+    $<TARGET_PROPERTY:mkldnn,SOURCE_DIR>/include)
diff --git a/src/tests_deprecated/functional/gna/CMakeLists.txt b/src/tests_deprecated/functional/gna/CMakeLists.txt
index e9987edc70e..6c45c35c2a8 100644
--- a/src/tests_deprecated/functional/gna/CMakeLists.txt
+++ b/src/tests_deprecated/functional/gna/CMakeLists.txt
@@ -19,8 +19,8 @@ if(ENABLE_HETERO)
     list(APPEND DEPENDENCIES ov_hetero_plugin)
 endif()
 
-if(ENABLE_MKL_DNN)
-    list(APPEND DEPENDENCIES MKLDNNPlugin)
+if(ENABLE_INTEL_CPU)
+    list(APPEND DEPENDENCIES ov_intel_cpu_plugin)
 endif()
 
 add_executable(${TARGET_NAME} ${TEST_SRC} ${TEST_INCLUDE})
diff --git a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp
index 5bc6ebd40ab..c0e154bb20f 100644
--- a/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp
+++ b/src/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp
@@ -304,7 +304,7 @@ TEST_F(myriadLayersTestsProposal_smoke, Caffe) {
 
 TEST_F(myriadLayersTestsProposal_smoke, CaffeNoClipBeforeNms) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED();
-    // Verify only 20 ranked proposal output with GT values - reference get from MKLDNN plugin
+    // Verify only 20 ranked proposal output with GT values - reference get from CPU plugin
     std::vector<float> gt_values = {
         0, 72.408f,   58.925f,  197.062f, 177.856f,
         0, 6.907f,    75.193f,  50.726f,  144.589f,
diff --git a/tests/conditional_compilation/test_cc.py b/tests/conditional_compilation/test_cc.py
index 48ebc7c9294..080ea355865 100644
--- a/tests/conditional_compilation/test_cc.py
+++ b/tests/conditional_compilation/test_cc.py
@@ -125,7 +125,7 @@ def test_verify(test_id, prepared_models, openvino_ref, artifacts, tolerance=1e-
 @pytest.mark.dependency(depends=["cc_collect", "minimized_pkg"])
 def test_libs_size(test_id, models, openvino_ref, artifacts):  # pylint: disable=unused-argument
     """Test if libraries haven't increased in size after conditional compilation."""
-    libraries = ["ov_runtime", "MKLDNNPlugin"]
+    libraries = ["ov_runtime", "ov_intel_cpu_plugin"]
     minimized_pkg = artifacts / test_id / "install_pkg"
     ref_libs_size = get_lib_sizes(openvino_ref, libraries)
     lib_sizes = get_lib_sizes(minimized_pkg, libraries)
diff --git a/tests/utils/path_utils.py b/tests/utils/path_utils.py
index bd678681de5..97b87c1bf32 100644
--- a/tests/utils/path_utils.py
+++ b/tests/utils/path_utils.py
@@ -36,9 +36,9 @@ def get_lib_path(lib_name):
     """Function for getting absolute path in OpenVINO directory to specific lib"""
     os_name = get_os_name()
     all_libs = {
-        'MKLDNNPlugin': {
-            'Windows': Path('runtime/bin/intel64/Release/MKLDNNPlugin.dll'),
-            'Linux': Path('runtime/lib/intel64/libMKLDNNPlugin.so')},
+        'ov_intel_cpu_plugin': {
+            'Windows': Path('runtime/bin/intel64/Release/ov_intel_cpu_plugin.dll'),
+            'Linux': Path('runtime/lib/intel64/libov_intel_cpu_plugin.so')},
         'ov_runtime': {
             'Windows': Path('runtime/bin/intel64/Release/ov_runtime.dll'),
             'Linux': Path('runtime/lib/intel64/libov_runtime.so')}
diff --git a/tools/deployment_manager/configs/darwin.json b/tools/deployment_manager/configs/darwin.json
index 575fa4d8bc5..b52ac8b9f10 100644
--- a/tools/deployment_manager/configs/darwin.json
+++ b/tools/deployment_manager/configs/darwin.json
@@ -33,7 +33,7 @@
       "group": ["ie"],
       "dependencies" : ["ie_core"],
       "files": [
-        "runtime/lib/intel64/libMKLDNNPlugin.so"
+        "runtime/lib/intel64/libov_intel_cpu_plugin.so"
       ]
     },
     "vpu": {
diff --git a/tools/deployment_manager/configs/linux.json b/tools/deployment_manager/configs/linux.json
index 05e8ee9c983..74801042f81 100644
--- a/tools/deployment_manager/configs/linux.json
+++ b/tools/deployment_manager/configs/linux.json
@@ -39,7 +39,7 @@
       "group": ["ie"],
       "dependencies" : ["ie_core"],
       "files": [
-        "runtime/lib/intel64/libMKLDNNPlugin.so"
+        "runtime/lib/intel64/libov_intel_cpu_plugin.so"
       ]
     },
     "gpu": {
diff --git a/tools/deployment_manager/configs/windows.json b/tools/deployment_manager/configs/windows.json
index 729f2496ea8..34af1c23979 100644
--- a/tools/deployment_manager/configs/windows.json
+++ b/tools/deployment_manager/configs/windows.json
@@ -33,7 +33,7 @@
       "group": ["ie"],
       "dependencies" : ["ie_core"],
       "files": [
-        "runtime/bin/intel64/Release/MKLDNNPlugin.dll"
+        "runtime/bin/intel64/Release/ov_intel_cpu_plugin.dll"
       ]
     },
     "gpu": {

From 882f30cd61b11d7d757e3b9aded2254cc65eed9f Mon Sep 17 00:00:00 2001
From: Anton Romanov <anton.romanov@intel.com>
Date: Thu, 23 Dec 2021 11:53:17 +0300
Subject: [PATCH 111/160] Added install wheel on azure for samples tests
 (#9179)

* Added install wheel on azure for samples tests

* minor change
---
 .ci/azure/linux.yml                           |  5 ++-
 .ci/azure/windows.yml                         |  7 +++-
 .../common/samples_common_test_clas.py        | 30 ++++++--------
 .../smoke_tests/requirements.txt              |  1 +
 tools/CMakeLists.txt                          | 39 -------------------
 5 files changed, 22 insertions(+), 60 deletions(-)

diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
index 383a22feb07..16b39649234 100644
--- a/.ci/azure/linux.yml
+++ b/.ci/azure/linux.yml
@@ -203,6 +203,9 @@ jobs:
   - script: ls -alR $(INSTALL_DIR)
     displayName: 'List install files'
 
+  - script: python3 -m pip install $(INSTALL_DIR)/tools/*.whl --find-links=$(INSTALL_DIR)/tools
+    displayName: 'Install wheels'
+
   - script: |
       set -e
       mkdir $(INSTALL_DIR)/opencv/
@@ -324,11 +327,11 @@ jobs:
     continueOnError: false
 
   - script: |
+      export PATH=$HOME/.local/bin:$PATH
       export IE_APP_PATH=$(INSTALL_DIR)/samples_bin
       export IE_APP_PYTHON_PATH=$(INSTALL_DIR)/samples/python/
       export SHARE=$(INSTALL_DIR)/tests/smoke_tests/samples_smoke_tests_data/
       export WORKSPACE=$(INSTALL_DIR)
-      export IE_APP_PYTHON_TOOL_PATH=$(INSTALL_DIR)/tools/
       . $(SETUPVARS) && python3 -m pytest $(INSTALL_DIR)/tests/smoke_tests/  --env_conf $(INSTALL_DIR)/tests/smoke_tests/env_config.yml -s --junitxml=$(BUILD_DIR)/TEST-SamplesSmokeTests.xml
     workingDirectory: $(INSTALL_DIR)/samples_bin
     displayName: 'Samples Smoke Tests'
diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index 7363d058c0f..98d94cfe67e 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -109,6 +109,7 @@ jobs:
       python -m pip install --upgrade pip
       rem For running Python API tests
       python -m pip install -r $(REPO_DIR)\inference-engine\ie_bridges\python\src\requirements-dev.txt
+      python -m pip install -r $(REPO_DIR)\inference-engine\ie_bridges\python\wheel\requirements-dev.txt
       rem For running Paddle frontend unit tests
       python -m pip install -r $(REPO_DIR)\src\core\tests\frontend\paddle\requirements_dev.txt
       rem For running ONNX frontend unit tests
@@ -132,7 +133,7 @@ jobs:
 
   - script: |
       set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -G "Ninja Multi-Config" -DENABLE_ONEDNN_FOR_GPU=OFF -DENABLE_INTEL_GNA=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_INTEL_GPU=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_GAPI_PREPROCESSING=$(CMAKE_BUILD_SHARED_LIBS) -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DPYTHON_INCLUDE_DIR="C:\hostedtoolcache\windows\Python\3.7.6\x64\include" -DPYTHON_LIBRARY="C:\hostedtoolcache\windows\Python\3.7.6\x64\libs\python37.lib" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
+      call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -G "Ninja Multi-Config" -DENABLE_ONEDNN_FOR_GPU=OFF -DENABLE_WHEEL=ON -DENABLE_INTEL_GNA=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_INTEL_GPU=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_GAPI_PREPROCESSING=$(CMAKE_BUILD_SHARED_LIBS) -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DPYTHON_INCLUDE_DIR="C:\hostedtoolcache\windows\Python\3.7.6\x64\include" -DPYTHON_LIBRARY="C:\hostedtoolcache\windows\Python\3.7.6\x64\libs\python37.lib" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
     workingDirectory: $(BUILD_DIR)
     displayName: 'CMake'
 
@@ -155,6 +156,9 @@ jobs:
   - script: dir $(INSTALL_DIR) /s
     displayName: 'List install files'
 
+  - script: for %%f  in ($(INSTALL_DIR)\tools\*.whl) do (python -m pip install  %%f --find-links=$(INSTALL_DIR)\tools\)
+    displayName: 'Install Wheels'
+
   - script: call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)"  $(REPO_DIR)\tests\samples_tests
     workingDirectory: $(BUILD_SAMPLES_TESTS_DIR)
     displayName: 'CMake'
@@ -191,7 +195,6 @@ jobs:
       set IE_APP_PYTHON_PATH=$(INSTALL_DIR)\samples\python\
       set SHARE=$(INSTALL_DIR)\tests\smoke_tests\samples_smoke_tests_data\
       set WORKSPACE=$(INSTALL_DIR)
-      set IE_APP_PYTHON_TOOL_PATH=$(INSTALL_DIR)\tools\
       python -m pytest $(INSTALL_DIR)\tests\smoke_tests\  --env_conf $(INSTALL_DIR)\tests\smoke_tests\env_config.yml -s --junitxml=TEST-SamplesSmokeTests.xml
     workingDirectory: $(INSTALL_DIR)
     displayName: 'Samples Smoke Tests'
diff --git a/tests/samples_tests/smoke_tests/common/samples_common_test_clas.py b/tests/samples_tests/smoke_tests/common/samples_common_test_clas.py
index 0e4b72fbb48..0d21008f885 100644
--- a/tests/samples_tests/smoke_tests/common/samples_common_test_clas.py
+++ b/tests/samples_tests/smoke_tests/common/samples_common_test_clas.py
@@ -26,6 +26,7 @@ import zipfile
 
 import logging as log
 from common.common_utils import shell
+from distutils import spawn
 
 log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
 
@@ -142,7 +143,7 @@ def getting_samples_data_zip(url, samples_path, size_of_chunk=128):
         if os.path.exists(samples_path):
             print("\nRemoving samples_smoke_tests_data.zip...")
             os.remove(samples_path)	
-		
+
     except Exception:
         print(f"Exception during downloading samples_smoke_tests_data.zip")
 
@@ -150,12 +151,8 @@ class SamplesCommonTestClass():
 
     @classmethod
     def made_executable_path(cls, path1, path2, sample_type='C++'):
-        # This exception is made for benchmark_app, because it locates in another place.
-        if 'benchmark_app' in path2 and 'python' in sample_type.lower():
-            executable_path = os.path.join(path1, 'benchmark_tool', path2)
-        else:
-            executable_path = os.path.join(path1, path2, path2) if 'python' in sample_type.lower() \
-                else os.path.join(path1, path2)
+        executable_path = os.path.join(path1, path2, path2) if 'python' in sample_type.lower() \
+            else os.path.join(path1, path2)
         is_windows = sys.platform.startswith('win')
         if 'python' in sample_type.lower():
             executable_path += '.py'
@@ -168,6 +165,9 @@ class SamplesCommonTestClass():
         if is_windows and not 'python' in sample_type.lower():
             executable_path += '.exe'
 
+        # This exeption is made for benchmark_app, because it locates in another place.
+        if 'benchmark_app' in path2 and 'python' in sample_type.lower():
+            executable_path = spawn.find_executable(str('benchmark_app'))
         # if not hasattr(cls, 'executable_path'):
         cls.executable_path = executable_path
 
@@ -360,16 +360,10 @@ class SamplesCommonTestClass():
         param_cp = dict(param)
         sample_type = param_cp.get('sample_type', "C++")
         if 'python' in sample_type.lower():
-            if 'benchmark_app' in self.sample_name:
-                assert os.environ.get('IE_APP_PYTHON_TOOL_PATH') is not None, \
-                    "IE_APP_PYTHON_TOOL_PATH environment variable is not specified!"
-                self.made_executable_path(os.environ.get('IE_APP_PYTHON_TOOL_PATH'), self.sample_name,
-                                          sample_type=sample_type)
-            else:
-                assert os.environ.get('IE_APP_PYTHON_PATH') is not None, \
-                    "IE_APP_PYTHON_PATH environment variable is not specified!"
-                self.made_executable_path(os.environ.get('IE_APP_PYTHON_PATH'), self.sample_name,
-                                          sample_type=sample_type)
+            assert os.environ.get('IE_APP_PYTHON_PATH') is not None, \
+                "IE_APP_PYTHON_PATH environment variable is not specified!"
+            self.made_executable_path(os.environ.get('IE_APP_PYTHON_PATH'), self.sample_name,
+                                      sample_type=sample_type)
         else:
             self.made_executable_path(os.environ.get('IE_APP_PATH'), self.sample_name, sample_type=sample_type)
 
@@ -462,4 +456,4 @@ class SamplesCommonTestClass():
         if not is_save and os.path.exists(self.output_dir):
             shutil.rmtree(self.output_dir)
         filenames = glob.glob('out*.bmp')
-        [os.remove(fn) for fn in filenames]
+        [os.remove(fn) for fn in filenames]
\ No newline at end of file
diff --git a/tests/samples_tests/smoke_tests/requirements.txt b/tests/samples_tests/smoke_tests/requirements.txt
index 04617a4facd..acbf75765d5 100644
--- a/tests/samples_tests/smoke_tests/requirements.txt
+++ b/tests/samples_tests/smoke_tests/requirements.txt
@@ -1,3 +1,4 @@
+requests
 pyyaml
 wheel
 test-generator
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 5d5862d4cd3..b4ae8fbc3f2 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -42,45 +42,6 @@ foreach(EXTRAS ${EXTRAS_LIST})
         EXCLUDE_FROM_ALL)
 endforeach()
 
-# copy benchmark_app scripts
-
-if(ENABLE_PYTHON)
-    find_package(PythonInterp 3 REQUIRED)
-    set(PYTHON_VERSION python${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR})
-
-    if(WIN32)
-        set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/$<CONFIG>/python_api/${PYTHON_VERSION}/openvino)
-    else()
-        set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/python_api/${PYTHON_VERSION}/openvino)
-    endif()
-
-    # creates a copy inside bin directory for developers to have ability running python benchmark_app
-    add_custom_target(python_tools_benchmark ALL
-        COMMAND ${CMAKE_COMMAND} -E make_directory ${PYTHON_BRIDGE_OUTPUT_DIRECTORY}/tools
-        COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/benchmark_tool/openvino/tools/benchmark
-                                                   ${PYTHON_BRIDGE_OUTPUT_DIRECTORY}/tools/benchmark
-    )
-
-    # install BA tool for tests
-    install(FILES benchmark_tool/README.md
-                  benchmark_tool/requirements.txt
-            DESTINATION tools/benchmark_tool
-            COMPONENT tests
-            EXCLUDE_FROM_ALL)
-
-    install(PROGRAMS benchmark_tool/benchmark_app.py
-            DESTINATION tools/benchmark_tool
-            COMPONENT tests
-            EXCLUDE_FROM_ALL)
-
-    # install openvino/tools/benchmark as a python package
-    install(DIRECTORY benchmark_tool/openvino/tools/benchmark
-            DESTINATION python/${PYTHON_VERSION}/openvino/tools
-            USE_SOURCE_PERMISSIONS
-            COMPONENT tests
-            EXCLUDE_FROM_ALL)
-endif()
-
 add_subdirectory(mo)
 
 file(WRITE ${CMAKE_CURRENT_SOURCE_DIR}/pot/version.txt ${CI_BUILD_NUMBER})

From c4ce6c5430de47f71f4e5c03a1be8113e1f2b628 Mon Sep 17 00:00:00 2001
From: Elizaveta Lobanova <elizaveta.lobanova@intel.com>
Date: Thu, 23 Dec 2021 12:06:23 +0300
Subject: [PATCH 112/160] [IE SAMPLE] Fixed inputs and outputs element type
 initialization (#9375)

---
 samples/cpp/speech_sample/main.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/samples/cpp/speech_sample/main.cpp b/samples/cpp/speech_sample/main.cpp
index b035c724d56..ce4b69861a9 100644
--- a/samples/cpp/speech_sample/main.cpp
+++ b/samples/cpp/speech_sample/main.cpp
@@ -89,6 +89,8 @@ int main(int argc, char* argv[]) {
         ov::preprocess::PrePostProcessor proc(model);
         for (int i = 0; i < model->inputs().size(); i++) {
             proc.input(i).tensor().set_element_type(ov::element::f32).set_layout(tensor_layout);
+        }
+        for (int i = 0; i < model->outputs().size(); i++) {
             proc.output(i).tensor().set_element_type(ov::element::f32);
         }
         model = proc.build();

From 9b04cc0a10dcb895ce23a98963b22df4af4dbcdf Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 23 Dec 2021 12:40:11 +0300
Subject: [PATCH 113/160] Extend reshape() method for index and for one input
 (#9369)

* Added reshape by index interface

* Added reshape for function with one input
---
 .../src/pyopenvino/frontend/extensions.cpp    |   2 +-
 .../src/pyopenvino/frontend/frontend.cpp      |   2 +-
 .../python/src/pyopenvino/frontend/place.cpp  |   2 +-
 .../graph/{function.cpp => model.cpp}         |  36 ++-
 .../graph/{function.hpp => model.hpp}         |   0
 .../python/src/pyopenvino/pyopenvino.cpp      |   2 +-
 src/core/include/openvino/core/model.hpp      |   2 +
 src/core/src/model.cpp                        |  20 ++
 src/core/tests/CMakeLists.txt                 |   2 +-
 src/core/tests/{function.cpp => model.cpp}    | 248 ++++++++++++------
 10 files changed, 230 insertions(+), 86 deletions(-)
 rename src/bindings/python/src/pyopenvino/graph/{function.cpp => model.cpp} (96%)
 rename src/bindings/python/src/pyopenvino/graph/{function.hpp => model.hpp} (100%)
 rename src/core/tests/{function.cpp => model.cpp} (88%)

diff --git a/src/bindings/python/src/pyopenvino/frontend/extensions.cpp b/src/bindings/python/src/pyopenvino/frontend/extensions.cpp
index 9bcf4090e8c..45285d86079 100644
--- a/src/bindings/python/src/pyopenvino/frontend/extensions.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/extensions.cpp
@@ -12,7 +12,7 @@
 #include "openvino/frontend/exception.hpp"
 #include "openvino/frontend/extension/decoder_transformation.hpp"
 #include "openvino/frontend/extension/telemetry.hpp"
-#include "pyopenvino/graph/function.hpp"
+#include "pyopenvino/graph/model.hpp"
 
 namespace py = pybind11;
 
diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
index a510fa45998..4bbd6e50570 100644
--- a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
@@ -10,7 +10,7 @@
 #include "openvino/frontend/exception.hpp"
 #include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/manager.hpp"
-#include "pyopenvino/graph/function.hpp"
+#include "pyopenvino/graph/model.hpp"
 
 namespace py = pybind11;
 
diff --git a/src/bindings/python/src/pyopenvino/frontend/place.cpp b/src/bindings/python/src/pyopenvino/frontend/place.cpp
index b5937deba81..8fe49354058 100644
--- a/src/bindings/python/src/pyopenvino/frontend/place.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/place.cpp
@@ -8,7 +8,7 @@
 
 #include "openvino/frontend/exception.hpp"
 #include "openvino/frontend/manager.hpp"
-#include "pyopenvino/graph/function.hpp"
+#include "pyopenvino/graph/model.hpp"
 
 namespace py = pybind11;
 
diff --git a/src/bindings/python/src/pyopenvino/graph/function.cpp b/src/bindings/python/src/pyopenvino/graph/model.cpp
similarity index 96%
rename from src/bindings/python/src/pyopenvino/graph/function.cpp
rename to src/bindings/python/src/pyopenvino/graph/model.cpp
index bb6314ed5b8..57e3a692c53 100644
--- a/src/bindings/python/src/pyopenvino/graph/function.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/model.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "pyopenvino/graph/function.hpp"
+#include "pyopenvino/graph/model.hpp"
 
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
@@ -308,6 +308,40 @@ void regclass_graph_Model(py::module m) {
 
     function.def("validate_nodes_and_infer_types", &ov::Model::validate_nodes_and_infer_types);
 
+    function.def(
+        "reshape",
+        [](ov::Model& self, const ov::PartialShape& partial_shape) {
+            self.reshape(partial_shape);
+        },
+        py::arg("partial_shapes"),
+        R"(
+                Parameters
+                ----------
+                partial_shapes : PartialShape
+                    Index of Output.
+
+                Returns
+                ----------
+                reshape : void
+             )");
+
+    function.def(
+        "reshape",
+        [](ov::Model& self, const std::map<size_t, ov::PartialShape>& partial_shapes) {
+            self.reshape(partial_shapes);
+        },
+        py::arg("partial_shapes"),
+        R"(
+                Parameters
+                ----------
+                partial_shapes : Dict[int, PartialShape]
+                    Index of Output.
+
+                Returns
+                ----------
+                reshape : void
+             )");
+
     function.def(
         "reshape",
         [](ov::Model& self, const std::map<std::string, ov::PartialShape>& partial_shapes) {
diff --git a/src/bindings/python/src/pyopenvino/graph/function.hpp b/src/bindings/python/src/pyopenvino/graph/model.hpp
similarity index 100%
rename from src/bindings/python/src/pyopenvino/graph/function.hpp
rename to src/bindings/python/src/pyopenvino/graph/model.hpp
diff --git a/src/bindings/python/src/pyopenvino/pyopenvino.cpp b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
index e028e3ad389..572d5f27e29 100644
--- a/src/bindings/python/src/pyopenvino/pyopenvino.cpp
+++ b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
@@ -12,7 +12,7 @@
 #include "pyopenvino/graph/axis_vector.hpp"
 #include "pyopenvino/graph/coordinate.hpp"
 #include "pyopenvino/graph/coordinate_diff.hpp"
-#include "pyopenvino/graph/function.hpp"
+#include "pyopenvino/graph/model.hpp"
 #include "pyopenvino/graph/node.hpp"
 #include "pyopenvino/graph/node_factory.hpp"
 #include "pyopenvino/graph/node_input.hpp"
diff --git a/src/core/include/openvino/core/model.hpp b/src/core/include/openvino/core/model.hpp
index 2e158729e46..1dcaabd5507 100644
--- a/src/core/include/openvino/core/model.hpp
+++ b/src/core/include/openvino/core/model.hpp
@@ -116,6 +116,8 @@ public:
     ov::Output<ov::Node> add_output(const std::string& op_name, size_t output_idx);
     ov::Output<ov::Node> add_output(const ov::Output<ov::Node>& port);
 
+    void reshape(const ov::PartialShape& partial_shape);
+    void reshape(const std::map<size_t, ov::PartialShape>& partial_shapes);
     void reshape(const std::map<std::string, ov::PartialShape>& partial_shapes);
     void reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>& partial_shapes);
 
diff --git a/src/core/src/model.cpp b/src/core/src/model.cpp
index d619856bfcb..647fd3fa280 100644
--- a/src/core/src/model.cpp
+++ b/src/core/src/model.cpp
@@ -778,6 +778,26 @@ ov::Output<ov::Node> ov::Model::input(const std::string& tensor_name) {
     throw ov::Exception("Input for tensor name " + tensor_name + " was not found.");
 }
 
+void ov::Model::reshape(const ov::PartialShape& partial_shape) {
+    if (m_parameters.size() != 1) {
+        throw ov::Exception("reshape(const ov::PartialShape&) must be called on a Model with exactly one parameter.");
+    }
+    std::map<size_t, ov::PartialShape> shapes;
+    shapes[0] = partial_shape;
+    reshape(shapes);
+}
+
+void ov::Model::reshape(const std::map<size_t, ov::PartialShape>& partial_shapes) {
+    std::map<ov::Output<ov::Node>, ov::PartialShape> const_pshape;
+    std::unordered_map<ov::Node*, std::string> port_tensor_map;
+    for (const auto& it : partial_shapes) {
+        const auto port = input(it.first);
+        port_tensor_map[port.get_node()] = it.first;
+        const_pshape[port] = it.second;
+    }
+    reshape(const_pshape);
+}
+
 void ov::Model::reshape(const std::map<std::string, ov::PartialShape>& partial_shapes) {
     std::map<ov::Output<ov::Node>, ov::PartialShape> const_pshape;
     std::unordered_map<ov::Node*, std::string> port_tensor_map;
diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt
index ad6dd6bdc22..7cfe5caa6ee 100644
--- a/src/core/tests/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -53,7 +53,7 @@ set(SRC
     file_util.cpp
     float16.cpp
     framework_node.cpp
-    function.cpp
+    model.cpp
     graph_rewrite.cpp
     input_output_assign.cpp
     int4.cpp
diff --git a/src/core/tests/function.cpp b/src/core/tests/model.cpp
similarity index 88%
rename from src/core/tests/function.cpp
rename to src/core/tests/model.cpp
index a4c07935bfa..bcfb41f36d1 100644
--- a/src/core/tests/function.cpp
+++ b/src/core/tests/model.cpp
@@ -2,16 +2,17 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/core/model.hpp"
+
 #include <gtest/gtest.h>
 
 #include <shared_node_info.hpp>
 #include <test_common.hpp>
 
-#include "openvino/core/model.hpp"
 #include "openvino/core/partial_shape.hpp"
 #include "openvino/opsets/opset8.hpp"
 
-TEST(function, get_input_by_tensor_name) {
+TEST(model, get_input_by_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -28,7 +29,7 @@ TEST(function, get_input_by_tensor_name) {
     EXPECT_EQ(input.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_output_by_tensor_name) {
+TEST(model, get_output_by_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -50,7 +51,7 @@ TEST(function, get_output_by_tensor_name) {
     EXPECT_EQ(output.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_input_by_tensor_index_without_name) {
+TEST(model, get_input_by_tensor_index_without_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
 
@@ -66,7 +67,7 @@ TEST(function, get_input_by_tensor_index_without_name) {
     EXPECT_EQ(input.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_output_by_tensor_index_without_name) {
+TEST(model, get_output_by_tensor_index_without_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
 
@@ -85,7 +86,7 @@ TEST(function, get_output_by_tensor_index_without_name) {
     EXPECT_EQ(output.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_incorrect_output_by_tensor_name) {
+TEST(model, get_incorrect_output_by_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -99,7 +100,7 @@ TEST(function, get_incorrect_output_by_tensor_name) {
     EXPECT_THROW(f->output("input"), ov::Exception);
 }
 
-TEST(function, get_incorrect_input_by_tensor_name) {
+TEST(model, get_incorrect_input_by_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -113,7 +114,7 @@ TEST(function, get_incorrect_input_by_tensor_name) {
     EXPECT_THROW(f->input("relu_t"), ov::Exception);
 }
 
-TEST(function, get_input_by_index) {
+TEST(model, get_input_by_index) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -130,7 +131,7 @@ TEST(function, get_input_by_index) {
     EXPECT_EQ(input.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_output_by_index) {
+TEST(model, get_output_by_index) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -148,7 +149,7 @@ TEST(function, get_output_by_index) {
     EXPECT_EQ(output.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_input_without_index) {
+TEST(model, get_input_without_index) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -165,7 +166,7 @@ TEST(function, get_input_without_index) {
     EXPECT_EQ(input.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_output_without_index) {
+TEST(model, get_output_without_index) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -183,7 +184,7 @@ TEST(function, get_output_without_index) {
     EXPECT_EQ(output.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_incorrect_output_by_index) {
+TEST(model, get_incorrect_output_by_index) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -197,7 +198,7 @@ TEST(function, get_incorrect_output_by_index) {
     EXPECT_THROW(f->output(2), std::exception);
 }
 
-TEST(function, get_incorrect_input_by_index) {
+TEST(model, get_incorrect_input_by_index) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -211,7 +212,7 @@ TEST(function, get_incorrect_input_by_index) {
     EXPECT_THROW(f->input(2), std::exception);
 }
 
-TEST(function, incorrect_multiple_inputs_outputs_function) {
+TEST(model, incorrect_multiple_inputs_outputs_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1, 2, 3, 3});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input1"});
@@ -237,7 +238,7 @@ TEST(function, incorrect_multiple_inputs_outputs_function) {
     EXPECT_THROW(f->output(), ov::Exception);
 }
 
-TEST(function, multiple_inputs_outputs_function) {
+TEST(model, multiple_inputs_outputs_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1, 3, 3, 3});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input1"});
@@ -286,7 +287,7 @@ TEST(function, multiple_inputs_outputs_function) {
     EXPECT_EQ(f->outputs().size(), 2);
 }
 
-TEST(function, get_input_by_tensor_name_from_const) {
+TEST(model, get_input_by_tensor_name_from_const) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -303,7 +304,7 @@ TEST(function, get_input_by_tensor_name_from_const) {
     EXPECT_EQ(input.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_output_by_tensor_name_from_const_function) {
+TEST(model, get_output_by_tensor_name_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -325,7 +326,7 @@ TEST(function, get_output_by_tensor_name_from_const_function) {
     EXPECT_EQ(output.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_incorrect_output_by_tensor_name_from_const_function) {
+TEST(model, get_incorrect_output_by_tensor_name_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -339,7 +340,7 @@ TEST(function, get_incorrect_output_by_tensor_name_from_const_function) {
     EXPECT_THROW(f->output("input"), ov::Exception);
 }
 
-TEST(function, get_incorrect_input_by_tensor_name_from_const_function) {
+TEST(model, get_incorrect_input_by_tensor_name_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -353,7 +354,7 @@ TEST(function, get_incorrect_input_by_tensor_name_from_const_function) {
     EXPECT_THROW(f->input("relu_t"), ov::Exception);
 }
 
-TEST(function, get_input_by_index_from_const_function) {
+TEST(model, get_input_by_index_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -370,7 +371,7 @@ TEST(function, get_input_by_index_from_const_function) {
     EXPECT_EQ(input.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_output_by_index_from_const_function) {
+TEST(model, get_output_by_index_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -388,7 +389,7 @@ TEST(function, get_output_by_index_from_const_function) {
     EXPECT_EQ(output.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_input_without_index_from_const_function) {
+TEST(model, get_input_without_index_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -405,7 +406,7 @@ TEST(function, get_input_without_index_from_const_function) {
     EXPECT_EQ(input.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_output_without_index_from_const_function) {
+TEST(model, get_output_without_index_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -423,7 +424,7 @@ TEST(function, get_output_without_index_from_const_function) {
     EXPECT_EQ(output.get_partial_shape(), ov::PartialShape{1});
 }
 
-TEST(function, get_incorrect_output_by_index_from_const_function) {
+TEST(model, get_incorrect_output_by_index_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -437,7 +438,7 @@ TEST(function, get_incorrect_output_by_index_from_const_function) {
     EXPECT_THROW(f->output(2), std::exception);
 }
 
-TEST(function, get_incorrect_input_by_index_from_const_function) {
+TEST(model, get_incorrect_input_by_index_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -451,7 +452,7 @@ TEST(function, get_incorrect_input_by_index_from_const_function) {
     EXPECT_THROW(f->input(2), std::exception);
 }
 
-TEST(function, incorrect_multiple_inputs_outputs_function_from_const_function) {
+TEST(model, incorrect_multiple_inputs_outputs_model_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1, 2, 3, 3});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input1"});
@@ -477,7 +478,7 @@ TEST(function, incorrect_multiple_inputs_outputs_function_from_const_function) {
     EXPECT_THROW(f->output(), ov::Exception);
 }
 
-TEST(function, multiple_inputs_outputs_function_from_const_function) {
+TEST(model, multiple_inputs_outputs_model_from_const_model) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1, 3, 3, 3});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input1"});
@@ -526,7 +527,7 @@ TEST(function, multiple_inputs_outputs_function_from_const_function) {
     EXPECT_EQ(f->outputs().size(), 2);
 }
 
-TEST(function_reshape, ReshapedDynamicShapeLayout) {
+TEST(model_reshape, ReshapedDynamicShapeLayout) {
     std::shared_ptr<ov::Model> ngraph;
     {
         ov::PartialShape shape({-1, 3, 22, 22});
@@ -549,7 +550,7 @@ TEST(function_reshape, ReshapedDynamicShapeLayout) {
     EXPECT_FALSE(ngraph->get_parameters().front()->get_partial_shape().is_dynamic());
 }
 
-TEST(function_reshape, ReshapeBatchReLU) {
+TEST(model_reshape, ReshapeBatchReLU) {
     std::shared_ptr<ov::Model> ngraph;
     {
         ov::PartialShape shape({1, 3, 22, 22});
@@ -578,7 +579,7 @@ TEST(function_reshape, ReshapeBatchReLU) {
     EXPECT_EQ(ngraph->get_results()[0]->get_shape(), ov::Shape({2, 3, 22, 22}));
 }
 
-TEST(function_reshape, ReshapeSpatialReLU) {
+TEST(model_reshape, ReshapeSpatialReLU) {
     std::shared_ptr<ov::Model> ngraph;
     {
         ov::PartialShape shape({1, 3, 22, 22});
@@ -607,7 +608,7 @@ TEST(function_reshape, ReshapeSpatialReLU) {
     EXPECT_EQ(ngraph->get_results()[0]->get_shape(), ov::Shape({1, 3, 25, 25}));
 }
 
-TEST(function_reshape, ReshapeSpatialReLUWithoutReplaceParameter) {
+TEST(model_reshape, ReshapeSpatialReLUWithoutReplaceParameter) {
     std::shared_ptr<ov::Model> ngraph;
     {
         ov::PartialShape shape({1, 3, 22, 22});
@@ -634,7 +635,7 @@ TEST(function_reshape, ReshapeSpatialReLUWithoutReplaceParameter) {
     EXPECT_EQ(ngraph->output().get_partial_shape(), ov::Shape({1, 3, 25, 25}));
 }
 
-TEST(function_reshape, ReshapeSpatialReLUStaticToDynamic) {
+TEST(model_reshape, ReshapeSpatialReLUStaticToDynamic) {
     const ov::PartialShape refShape{1, 3, ov::Dimension::dynamic(), 25};
     std::shared_ptr<ov::Model> ngraph;
     {
@@ -666,7 +667,7 @@ TEST(function_reshape, ReshapeSpatialReLUStaticToDynamic) {
     EXPECT_EQ(ngraph->output(0).get_partial_shape(), refShape);
 }
 
-TEST(function_reshape, ReshapeSpatialReLUStaticToFullyDynamic) {
+TEST(model_reshape, ReshapeSpatialReLUStaticToFullyDynamic) {
     const ov::PartialShape refShape = ov::PartialShape::dynamic();
     std::shared_ptr<ov::Model> ngraph;
     {
@@ -698,7 +699,7 @@ TEST(function_reshape, ReshapeSpatialReLUStaticToFullyDynamic) {
     EXPECT_EQ(ngraph->output().get_partial_shape(), refShape);
 }
 
-TEST(function_reshape, ReshapeSpatialReLUDynamicToDynamic) {
+TEST(model_reshape, ReshapeSpatialReLUDynamicToDynamic) {
     const ov::PartialShape refShape{1, 3, ov::Dimension::dynamic(), 25};
     std::shared_ptr<ov::Model> ngraph;
     {
@@ -730,7 +731,7 @@ TEST(function_reshape, ReshapeSpatialReLUDynamicToDynamic) {
     EXPECT_EQ(ngraph->output().get_partial_shape(), refShape);
 }
 
-TEST(function_reshape, TestInvalidReshape) {
+TEST(model_reshape, TestInvalidReshape) {
     std::shared_ptr<ov::Model> f;
     {
         auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::Shape{1, 1000, 4});
@@ -748,7 +749,7 @@ TEST(function_reshape, TestInvalidReshape) {
     EXPECT_NO_THROW(f->reshape({{"tensor", ov::Shape({1, 1000, 4})}}));
 }
 
-TEST(function_reshape, TestReshapeWithInvalidTensorName) {
+TEST(model_reshape, TestReshapeWithInvalidTensorName) {
     std::shared_ptr<ov::Model> f;
     {
         auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::Shape{1, 1000, 4});
@@ -766,7 +767,7 @@ TEST(function_reshape, TestReshapeWithInvalidTensorName) {
     EXPECT_ANY_THROW(f->reshape({{"param", ov::Shape({4, 4, 4})}}));
 }
 
-TEST(function_reshape, TestReshapeWithInvalidShapesForTheSameTensor) {
+TEST(model_reshape, TestReshapeWithInvalidShapesForTheSameTensor) {
     std::shared_ptr<ov::Model> f;
     {
         auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::Shape{1, 1000, 4});
@@ -781,7 +782,38 @@ TEST(function_reshape, TestReshapeWithInvalidShapesForTheSameTensor) {
     EXPECT_ANY_THROW(f->reshape({{"tensor1", ov::Shape({2, 500, 4})}, {"tensor2", ov::Shape({4, 250, 4})}}));
 }
 
-TEST(function_reshape, ReshapeBatchReLUByPort) {
+TEST(model_reshape, ReshapeBatchReLUByIndex) {
+    std::shared_ptr<ov::Model> ngraph;
+    ov::Output<ov::Node> port;
+    {
+        ov::PartialShape shape({1, 3, 22, 22});
+        ov::element::Type type(ov::element::Type_t::f32);
+        auto param = std::make_shared<ov::op::v0::Parameter>(type, shape);
+        param->get_output_tensor(0).set_names({"tensor", "tensor2"});
+        port = param->output(0);
+        auto relu = std::make_shared<ov::op::v0::Relu>(param);
+        auto result = std::make_shared<ov::op::v0::Result>(relu);
+
+        ov::ParameterVector params = {param};
+        ov::ResultVector results = {result};
+
+        ngraph = std::make_shared<ov::Model>(results, params);
+    }
+
+    EXPECT_EQ(ngraph->get_parameters()[0]->get_shape(), ov::Shape({1, 3, 22, 22}));
+    EXPECT_EQ(ngraph->get_results()[0]->get_shape(), ov::Shape({1, 3, 22, 22}));
+
+    {
+        std::map<size_t, ov::PartialShape> new_shape;
+        new_shape[0] = ov::PartialShape{2, 3, 22, 22};
+        EXPECT_NO_THROW(ngraph->reshape(new_shape));
+    }
+
+    EXPECT_EQ(ngraph->get_parameters()[0]->get_shape(), ov::Shape({2, 3, 22, 22}));
+    EXPECT_EQ(ngraph->get_results()[0]->get_shape(), ov::Shape({2, 3, 22, 22}));
+}
+
+TEST(model_reshape, ReshapeBatchReLUByPort) {
     std::shared_ptr<ov::Model> ngraph;
     ov::Output<ov::Node> port;
     {
@@ -812,7 +844,63 @@ TEST(function_reshape, ReshapeBatchReLUByPort) {
     EXPECT_EQ(ngraph->get_results()[0]->get_shape(), ov::Shape({2, 3, 22, 22}));
 }
 
-TEST(function, add_output_tensor_name) {
+TEST(model_reshape, ReshapeBatchReLUWithOneInput) {
+    std::shared_ptr<ov::Model> ngraph;
+    ov::Output<ov::Node> port;
+    {
+        ov::PartialShape shape({1, 3, 22, 22});
+        ov::element::Type type(ov::element::Type_t::f32);
+        auto param = std::make_shared<ov::op::v0::Parameter>(type, shape);
+        param->get_output_tensor(0).set_names({"tensor", "tensor2"});
+        port = param->output(0);
+        auto relu = std::make_shared<ov::op::v0::Relu>(param);
+        auto result = std::make_shared<ov::op::v0::Result>(relu);
+
+        ov::ParameterVector params = {param};
+        ov::ResultVector results = {result};
+
+        ngraph = std::make_shared<ov::Model>(results, params);
+    }
+
+    EXPECT_EQ(ngraph->get_parameters()[0]->get_shape(), ov::Shape({1, 3, 22, 22}));
+    EXPECT_EQ(ngraph->get_results()[0]->get_shape(), ov::Shape({1, 3, 22, 22}));
+
+    {
+        ov::PartialShape new_shape;
+        new_shape = ov::PartialShape{2, 3, 22, 22};
+        EXPECT_NO_THROW(ngraph->reshape(new_shape));
+    }
+
+    EXPECT_EQ(ngraph->get_parameters()[0]->get_shape(), ov::Shape({2, 3, 22, 22}));
+    EXPECT_EQ(ngraph->get_results()[0]->get_shape(), ov::Shape({2, 3, 22, 22}));
+}
+
+TEST(model_reshape, IncoreectReshapeBatchWithMultipleInputs) {
+    auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1, 3, 3, 3});
+    arg0->set_friendly_name("data");
+    arg0->get_output_tensor(0).set_names({"input1"});
+
+    auto arg1 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1, 2, 3, 3});
+    arg1->set_friendly_name("data1");
+    arg1->get_output_tensor(0).set_names({"input2", "data1"});
+
+    auto concat = std::make_shared<ov::opset8::Concat>(ov::NodeVector{arg0, arg1}, 1);
+    concat->set_friendly_name("concat");
+    concat->get_output_tensor(0).set_names({"concat_t"});
+    auto result1 = std::make_shared<ov::opset8::Result>(concat);
+
+    auto shape_of = std::make_shared<ov::opset8::ShapeOf>(concat);
+    shape_of->set_friendly_name("shape_of");
+    shape_of->get_output_tensor(0).set_names({"shape_of_t", "identity"});
+    auto result2 = std::make_shared<ov::opset8::Result>(shape_of);
+    auto f = std::make_shared<ov::Model>(ov::ResultVector{result1, result2}, ov::ParameterVector{arg0, arg1});
+
+    f->validate_nodes_and_infer_types();
+    ov::PartialShape shape({1, 3, 22, 22});
+    EXPECT_THROW(f->reshape(shape), ov::Exception);
+}
+
+TEST(model, add_output_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -839,7 +927,7 @@ TEST(function, add_output_tensor_name) {
     EXPECT_EQ(out1.get_node(), f->get_results()[1].get());
 }
 
-TEST(function, add_output_op_name) {
+TEST(model, add_output_op_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -865,7 +953,7 @@ TEST(function, add_output_op_name) {
     EXPECT_EQ(f->get_results()[1]->input_value(0).get_node(), relu1.get());
 }
 
-TEST(function, add_output_port) {
+TEST(model, add_output_port) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -889,7 +977,7 @@ TEST(function, add_output_port) {
     EXPECT_EQ(f->get_results()[1]->input_value(0).get_node(), relu1.get());
 }
 
-TEST(function, add_output_incorrect_tensor_name) {
+TEST(model, add_output_incorrect_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -910,7 +998,7 @@ TEST(function, add_output_incorrect_tensor_name) {
     EXPECT_EQ(f->get_results().size(), 1);
 }
 
-TEST(function, add_output_op_incorrect_name) {
+TEST(model, add_output_op_incorrect_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -931,7 +1019,7 @@ TEST(function, add_output_op_incorrect_name) {
     EXPECT_EQ(f->get_results().size(), 1);
 }
 
-TEST(function, add_output_op_name_incorrect_idx) {
+TEST(model, add_output_op_name_incorrect_idx) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -952,7 +1040,7 @@ TEST(function, add_output_op_name_incorrect_idx) {
     EXPECT_EQ(f->get_results().size(), 1);
 }
 
-TEST(function, add_output_port_to_result) {
+TEST(model, add_output_port_to_result) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input"});
@@ -988,7 +1076,7 @@ bool all_ops_have_same_info(const std::shared_ptr<ov::Model>& f) {
 }
 }  // namespace
 
-TEST(function, topological_sort_caching_basic) {
+TEST(model, topological_sort_caching_basic) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -996,11 +1084,11 @@ TEST(function, topological_sort_caching_basic) {
     auto f = std::make_shared<ov::Model>(ov::ResultVector{result}, ov::ParameterVector{arg0});
 
     auto shared_info = ov::ModelAccessor(f).get_shared_info();
-    // Check that after function creation which call get_ordered_ops
+    // Check that after model creation which call get_ordered_ops
     // cache is set to true value
     ASSERT_TRUE(shared_info->get_use_topological_cache());
 
-    // Check that nodes contains the same shared info after function creation
+    // Check that nodes contains the same shared info after model creation
     ASSERT_EQ(ov::NodeAccessor(arg0).get_shared_info().size(), 1);
     ASSERT_TRUE(ov::NodeAccessor(arg0).get_shared_info().count(shared_info));
 
@@ -1016,7 +1104,7 @@ TEST(function, topological_sort_caching_basic) {
     ASSERT_EQ(f->get_ordered_ops().size(), 4);
 }
 
-TEST(function, topological_sort_caching_replace_node) {
+TEST(model, topological_sort_caching_replace_node) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1029,11 +1117,11 @@ TEST(function, topological_sort_caching_replace_node) {
     auto new_relu = std::make_shared<ov::opset8::Relu>(relu1);
     ov::replace_node(relu2, new_relu);
 
-    // Function has changed so cache must be updated
+    // model has changed so cache must be updated
     ASSERT_FALSE(shared_info->get_use_topological_cache());
 
     // Before get_ordered_ops, new_node shouldn't have shared_info, but after
-    // it will be set to the function shared_info and cache will be used.
+    // it will be set to the model shared_info and cache will be used.
     ASSERT_FALSE(ov::NodeAccessor(new_relu).get_shared_info().count(shared_info));
     ASSERT_EQ(f->get_ordered_ops().size(), 4);
     ASSERT_TRUE(ov::NodeAccessor(new_relu).get_shared_info().count(shared_info));
@@ -1041,7 +1129,7 @@ TEST(function, topological_sort_caching_replace_node) {
     ASSERT_TRUE(all_ops_have_same_info(f));
 }
 
-TEST(function, topological_sort_caching_replace_source_output) {
+TEST(model, topological_sort_caching_replace_source_output) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1053,7 +1141,7 @@ TEST(function, topological_sort_caching_replace_source_output) {
 
     relu2->input(0).replace_source_output(relu1);
 
-    // Function has changed so cache must be updated
+    // model has changed so cache must be updated
     ASSERT_FALSE(shared_info->get_use_topological_cache());
 
     ASSERT_EQ(f->get_ordered_ops().size(), 4);
@@ -1061,7 +1149,7 @@ TEST(function, topological_sort_caching_replace_source_output) {
     ASSERT_TRUE(all_ops_have_same_info(f));
 }
 
-TEST(function, topological_sort_caching_dangling_node) {
+TEST(model, topological_sort_caching_dangling_node) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1073,13 +1161,13 @@ TEST(function, topological_sort_caching_dangling_node) {
 
     auto new_relu = std::make_shared<ov::opset8::Relu>(relu1);
 
-    // Function has not changed so cache mustn't be updated
+    // model has not changed so cache mustn't be updated
     ASSERT_TRUE(shared_info->get_use_topological_cache());
-    // Dangling node is not in Function
+    // Dangling node is not in model
     ASSERT_EQ(f->get_ordered_ops().size(), 4);
 }
 
-TEST(function, topological_sort_caching_replace_output) {
+TEST(model, topological_sort_caching_replace_output) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1092,14 +1180,14 @@ TEST(function, topological_sort_caching_replace_output) {
     auto new_relu = std::make_shared<ov::opset8::Relu>(relu1);
     relu2->output(0).replace(new_relu);
 
-    // Function has changed so cache must be updated
+    // model has changed so cache must be updated
     ASSERT_FALSE(shared_info->get_use_topological_cache());
     ASSERT_EQ(f->get_ordered_ops().size(), 4);
     ASSERT_TRUE(shared_info->get_use_topological_cache());
     ASSERT_TRUE(all_ops_have_same_info(f));
 }
 
-TEST(function, topological_sort_caching_set_argument) {
+TEST(model, topological_sort_caching_set_argument) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1111,14 +1199,14 @@ TEST(function, topological_sort_caching_set_argument) {
 
     relu2->set_argument(0, arg0);
 
-    // Function has changed so cache must be updated
+    // model has changed so cache must be updated
     ASSERT_FALSE(shared_info->get_use_topological_cache());
     ASSERT_EQ(f->get_ordered_ops().size(), 3);
     ASSERT_TRUE(shared_info->get_use_topological_cache());
     ASSERT_TRUE(all_ops_have_same_info(f));
 }
 
-TEST(function, topological_sort_caching_set_arguments) {
+TEST(model, topological_sort_caching_set_arguments) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1130,14 +1218,14 @@ TEST(function, topological_sort_caching_set_arguments) {
 
     relu2->set_arguments({arg0->output(0)});
 
-    // Function has changed so cache must be updated
+    // model has changed so cache must be updated
     ASSERT_FALSE(shared_info->get_use_topological_cache());
     ASSERT_EQ(f->get_ordered_ops().size(), 3);
     ASSERT_TRUE(shared_info->get_use_topological_cache());
     ASSERT_TRUE(all_ops_have_same_info(f));
 }
 
-TEST(function, topological_sort_caching_add_cf) {
+TEST(model, topological_sort_caching_add_cf) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1149,14 +1237,14 @@ TEST(function, topological_sort_caching_add_cf) {
 
     relu2->add_control_dependency(arg0);
 
-    // Function has changed so cache must be updated
+    // model has changed so cache must be updated
     ASSERT_FALSE(shared_info->get_use_topological_cache());
     ASSERT_EQ(f->get_ordered_ops().size(), 4);
     ASSERT_TRUE(shared_info->get_use_topological_cache());
     ASSERT_TRUE(all_ops_have_same_info(f));
 }
 
-TEST(function, topological_sort_caching_result_parameter_sink) {
+TEST(model, topological_sort_caching_result_parameter_sink) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu1 = std::make_shared<ov::opset8::Relu>(arg0);
     auto relu2 = std::make_shared<ov::opset8::Relu>(relu1);
@@ -1195,7 +1283,7 @@ TEST(function, topological_sort_caching_result_parameter_sink) {
     check_caching_status(4);
 }
 
-TEST(function, topological_sort_caching_multiple_components) {
+TEST(model, topological_sort_caching_multiple_components) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu0 = std::make_shared<ov::opset8::Relu>(arg0);
     auto result0 = std::make_shared<ov::opset8::Result>(relu0);
@@ -1211,7 +1299,7 @@ TEST(function, topological_sort_caching_multiple_components) {
     ASSERT_TRUE(all_ops_have_same_info(f));
 }
 
-TEST(function, topological_sort_caching_shared_nodes) {
+TEST(model, topological_sort_caching_shared_nodes) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     auto relu0 = std::make_shared<ov::opset8::Relu>(arg0);
     auto result0 = std::make_shared<ov::opset8::Result>(relu0);
@@ -1224,7 +1312,7 @@ TEST(function, topological_sort_caching_shared_nodes) {
 
     for (auto&& node : f1->get_ordered_ops()) {
         auto node_info = ov::NodeAccessor(node).get_shared_info();
-        // As two Functions owns the same node so node will have two shared_info objects
+        // As two models owns the same node so node will have two shared_info objects
         ASSERT_EQ(node_info.size(), 2);
         ASSERT_TRUE(node_info.count(f1_shared_info));
         ASSERT_TRUE(node_info.count(f2_shared_info));
@@ -1286,14 +1374,14 @@ static std::shared_ptr<ov::Model> create_add(ov::element::Type type,
 }
 }  // namespace bs_utils
 
-TEST(function, get_batch_size) {
+TEST(model, get_batch_size) {
     auto f = bs_utils::create_n_inputs(ov::element::f32, {{1, 512, 512, 3}, {1, 3, 224, 224}}, {"NHWC", "NCHW"});
 
     EXPECT_NO_THROW(ov::get_batch(f));
     EXPECT_EQ(ov::get_batch(f), 1);
 }
 
-TEST(function, get_batch_size_with_conflict) {
+TEST(model, get_batch_size_with_conflict) {
     auto f = bs_utils::create_n_inputs(ov::element::f32,
                                        {ov::PartialShape::dynamic(), {5, 6}, {1, 3, 224, 224}, {3, 1}},
                                        {"NCHW", "D...", "NCHW", "N???"});
@@ -1315,7 +1403,7 @@ TEST(function, get_batch_size_with_conflict) {
     }
 }
 
-TEST(function, get_batch_size_without_batches) {
+TEST(model, get_batch_size_without_batches) {
     auto f = bs_utils::create_n_inputs(ov::element::f32, {{1, 3, 224, 224}, {1, 3, 224, 224}}, {"?C...", ov::Layout()});
 
     // TODO: replace with EXPECT_THAT after upgrade gtest to v1.11
@@ -1331,21 +1419,21 @@ TEST(function, get_batch_size_without_batches) {
     }
 }
 
-TEST(function, get_batch_size_without_one_layout) {
+TEST(model, get_batch_size_without_one_layout) {
     auto f = bs_utils::create_n_inputs(ov::element::f32,
                                        {{ov::Dimension::dynamic(), 3, 224, 224}, {10, 20}},
                                        {"N...", "HW"});
     EXPECT_EQ(ov::get_batch(f), ov::Dimension::dynamic());
 }
 
-TEST(function, get_batch_size_ranges) {
+TEST(model, get_batch_size_ranges) {
     auto f = bs_utils::create_n_inputs(ov::element::f32,
                                        {{ov::Dimension(1, 10), 3, 224, 224}, {ov::Dimension(5, 15), 3, 224, 224}},
                                        {"NCHW", "NCHW"});
     EXPECT_EQ(ov::get_batch(f), ov::Dimension(5, 10));
 }
 
-TEST(function, set_batch_size) {
+TEST(model, set_batch_size) {
     auto f = bs_utils::create_n_inputs(ov::element::f32,
                                        {{1, 512, 512, 3}, {ov::Dimension::dynamic(), 3, 224, 224}, {1, 5}},
                                        {"NHWC", "NCHW", "??"});
@@ -1356,7 +1444,7 @@ TEST(function, set_batch_size) {
     EXPECT_EQ(f->input("tensor_input2").get_partial_shape(), (ov::PartialShape{1, 5}));
 }
 
-TEST(function, set_batch_size_ranges) {
+TEST(model, set_batch_size_ranges) {
     auto f = bs_utils::create_n_inputs(ov::element::f32,
                                        {{ov::Dimension(1, 10), 3, 224, 224}, {ov::Dimension(5, 15), 3, 224, 224}},
                                        {"NCHW", "NCHW"});
@@ -1365,7 +1453,7 @@ TEST(function, set_batch_size_ranges) {
     EXPECT_EQ(f->input("tensor_input1").get_partial_shape(), (ov::PartialShape{42, 3, 224, 224}));
 }
 
-TEST(function, set_batch_size_fully_dynamic) {
+TEST(model, set_batch_size_fully_dynamic) {
     auto f =
         bs_utils::create_n_inputs(ov::element::f32, {ov::PartialShape::dynamic(), {1, 3, 224, 224}}, {"NCHW", "NCHW"});
     EXPECT_NO_THROW(ov::set_batch(f, 42));
@@ -1373,14 +1461,14 @@ TEST(function, set_batch_size_fully_dynamic) {
     EXPECT_EQ(f->input("tensor_input1").get_partial_shape(), (ov::PartialShape{42, 3, 224, 224}));
 }
 
-TEST(function, set_batch_size_dynamic_layout) {
+TEST(model, set_batch_size_dynamic_layout) {
     auto f = bs_utils::create_n_inputs(ov::element::f32, {{3, 224, 224, 1}, {1, 3, 224, 224}}, {"...N", "NCHW"});
     EXPECT_NO_THROW(ov::set_batch(f, 42));
     EXPECT_EQ(f->input("tensor_input0").get_partial_shape(), (ov::PartialShape{3, 224, 224, 42}));
     EXPECT_EQ(f->input("tensor_input1").get_partial_shape(), (ov::PartialShape{42, 3, 224, 224}));
 }
 
-TEST(function, set_batch_size_with_conflict) {
+TEST(model, set_batch_size_with_conflict) {
     auto f = bs_utils::create_n_inputs(ov::element::f32,
                                        {ov::PartialShape::dynamic(), {5, 6}, {1, 3, 224, 224}, {3, 1}},
                                        {"NCHW", "D...", "NCHW", "N???"});
@@ -1402,7 +1490,7 @@ TEST(function, set_batch_size_with_conflict) {
     }
 }
 
-TEST(function, set_batch_size_without_batches) {
+TEST(model, set_batch_size_without_batches) {
     auto f = bs_utils::create_n_inputs(ov::element::f32, {{1, 3, 224, 224}, {1, 3, 224, 224}}, {"?C...", ov::Layout()});
 
     // TODO: replace with EXPECT_THAT after upgrade gtest to v1.11
@@ -1418,7 +1506,7 @@ TEST(function, set_batch_size_without_batches) {
     }
 }
 
-TEST(function, set_batch_size_validation_throw) {
+TEST(model, set_batch_size_validation_throw) {
     auto f = bs_utils::create_add(ov::element::f32, {1, 3, 224, 224}, "NCHW", ov::Layout());
 
     // TODO: replace with EXPECT_THAT after upgrade gtest to v1.11

From b241d5227eca7e3760bd30010a634cf946fb4c1f Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 23 Dec 2021 12:45:02 +0300
Subject: [PATCH 114/160] Moved compile_tool to new API (#8501)

* Moved compile_tool to new API

* Fixed comments and added new tests

* Fixed comments

* Fixed build

* Fixed comments

* Fixed unit tests

* Fixed compilation

* Fixed legacy message

* Fixed readme

* Fixed comments

* FIxed build

* Fixed build

* Fixed tests

* Applied comments

Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
---
 .../utils/include/samples/args_helper.hpp     |  11 ++
 samples/cpp/common/utils/src/args_helper.cpp  | 183 ++++++++++++++++--
 src/core/tests/preprocess.cpp                 |  43 ++++
 tools/compile_tool/README.md                  |  30 +--
 tools/compile_tool/main.cpp                   | 105 +++++++---
 5 files changed, 321 insertions(+), 51 deletions(-)

diff --git a/samples/cpp/common/utils/include/samples/args_helper.hpp b/samples/cpp/common/utils/include/samples/args_helper.hpp
index 2a162a9d69a..eea28cbe394 100644
--- a/samples/cpp/common/utils/include/samples/args_helper.hpp
+++ b/samples/cpp/common/utils/include/samples/args_helper.hpp
@@ -43,3 +43,14 @@ void processLayout(InferenceEngine::CNNNetwork& network,
 
 void printInputAndOutputsInfo(const InferenceEngine::CNNNetwork& network);
 void printInputAndOutputsInfo(const ov::Model& network);
+
+void configurePrePostProcessing(std::shared_ptr<ov::Model>& function,
+                                const std::string& ip,
+                                const std::string& op,
+                                const std::string& iop,
+                                const std::string& il,
+                                const std::string& ol,
+                                const std::string& iol,
+                                const std::string& iml,
+                                const std::string& oml,
+                                const std::string& ioml);
diff --git a/samples/cpp/common/utils/src/args_helper.cpp b/samples/cpp/common/utils/src/args_helper.cpp
index 74072891d94..50cd936662d 100644
--- a/samples/cpp/common/utils/src/args_helper.cpp
+++ b/samples/cpp/common/utils/src/args_helper.cpp
@@ -157,18 +157,18 @@ InferenceEngine::Precision getPrecision(std::string value, const supported_preci
 
 InferenceEngine::Precision getPrecision(const std::string& value) {
     static const supported_precisions_t supported_precisions = {
-        {"FP32", InferenceEngine::Precision::FP32},
-        {"FP16", InferenceEngine::Precision::FP16},
-        {"BF16", InferenceEngine::Precision::BF16},
-        {"U64", InferenceEngine::Precision::U64},
-        {"I64", InferenceEngine::Precision::I64},
-        {"U32", InferenceEngine::Precision::U32},
-        {"I32", InferenceEngine::Precision::I32},
-        {"U16", InferenceEngine::Precision::U16},
-        {"I16", InferenceEngine::Precision::I16},
-        {"U8", InferenceEngine::Precision::U8},
-        {"I8", InferenceEngine::Precision::I8},
-        {"BOOL", InferenceEngine::Precision::BOOL},
+        {"FP32", InferenceEngine::Precision::FP32}, {"f32", InferenceEngine::Precision::FP32},
+        {"FP16", InferenceEngine::Precision::FP16}, {"f16", InferenceEngine::Precision::FP16},
+        {"BF16", InferenceEngine::Precision::BF16}, {"bf16", InferenceEngine::Precision::BF16},
+        {"U64", InferenceEngine::Precision::U64},   {"u64", InferenceEngine::Precision::U64},
+        {"I64", InferenceEngine::Precision::I64},   {"i64", InferenceEngine::Precision::I64},
+        {"U32", InferenceEngine::Precision::U32},   {"u32", InferenceEngine::Precision::U32},
+        {"I32", InferenceEngine::Precision::I32},   {"i32", InferenceEngine::Precision::I32},
+        {"U16", InferenceEngine::Precision::U16},   {"u16", InferenceEngine::Precision::U16},
+        {"I16", InferenceEngine::Precision::I16},   {"i16", InferenceEngine::Precision::I16},
+        {"U8", InferenceEngine::Precision::U8},     {"u8", InferenceEngine::Precision::U8},
+        {"I8", InferenceEngine::Precision::I8},     {"i8", InferenceEngine::Precision::I8},
+        {"BOOL", InferenceEngine::Precision::BOOL}, {"boolean", InferenceEngine::Precision::BOOL},
     };
 
     return getPrecision(value, supported_precisions);
@@ -197,6 +197,32 @@ void setPrecisions(const InferenceEngine::CNNNetwork& network, const std::string
     }
 }
 
+using supported_type_t = std::unordered_map<std::string, ov::element::Type>;
+ov::element::Type getType(std::string value, const supported_type_t& supported_precisions) {
+    std::transform(value.begin(), value.end(), value.begin(), ::toupper);
+
+    const auto precision = supported_precisions.find(value);
+    if (precision == supported_precisions.end()) {
+        throw std::logic_error("\"" + value + "\"" + " is not a valid precision");
+    }
+
+    return precision->second;
+}
+ov::element::Type getType(const std::string& value) {
+    static const supported_type_t supported_types = {
+        {"FP32", ov::element::f32}, {"f32", ov::element::f32},      {"FP16", ov::element::f16},
+        {"f16", ov::element::f16},  {"BF16", ov::element::bf16},    {"bf16", ov::element::bf16},
+        {"U64", ov::element::u64},  {"u64", ov::element::u64},      {"I64", ov::element::i64},
+        {"i64", ov::element::i64},  {"U32", ov::element::u32},      {"u32", ov::element::u32},
+        {"I32", ov::element::i32},  {"i32", ov::element::i32},      {"U16", ov::element::u16},
+        {"u16", ov::element::u16},  {"I16", ov::element::i16},      {"i16", ov::element::i16},
+        {"U8", ov::element::u8},    {"u8", ov::element::u8},        {"I8", ov::element::i8},
+        {"i8", ov::element::i8},    {"BOOL", ov::element::boolean}, {"boolean", ov::element::boolean},
+    };
+
+    return getType(value, supported_types);
+}
+
 }  // namespace
 
 void processPrecision(InferenceEngine::CNNNetwork& network,
@@ -299,7 +325,6 @@ void setLayouts(const InferenceEngine::CNNNetwork& network, const std::string io
         }
     }
 }
-
 }  // namespace
 
 void processLayout(InferenceEngine::CNNNetwork& network,
@@ -373,3 +398,135 @@ void printInputAndOutputsInfo(const ov::Model& network) {
         slog::info << "        output shape: " << shape << slog::endl;
     }
 }
+
+void configurePrePostProcessing(std::shared_ptr<ov::Model>& model,
+                                const std::string& ip,
+                                const std::string& op,
+                                const std::string& iop,
+                                const std::string& il,
+                                const std::string& ol,
+                                const std::string& iol,
+                                const std::string& iml,
+                                const std::string& oml,
+                                const std::string& ioml) {
+    auto preprocessor = ov::preprocess::PrePostProcessor(model);
+    const auto inputs = model->inputs();
+    const auto outputs = model->outputs();
+    if (!ip.empty()) {
+        auto type = getType(ip);
+        for (size_t i = 0; i < inputs.size(); i++) {
+            preprocessor.input(i).tensor().set_element_type(type);
+        }
+    }
+
+    if (!op.empty()) {
+        auto type = getType(op);
+        for (size_t i = 0; i < outputs.size(); i++) {
+            preprocessor.output(i).tensor().set_element_type(type);
+        }
+    }
+
+    if (!iop.empty()) {
+        const auto user_precisions_map = parseArgMap(iop);
+        for (auto&& item : user_precisions_map) {
+            const auto& tensor_name = item.first;
+            const auto type = getType(item.second);
+
+            bool tensorFound = false;
+            for (size_t i = 0; i < inputs.size(); i++) {
+                if (inputs[i].get_names().count(tensor_name)) {
+                    preprocessor.input(i).tensor().set_element_type(type);
+                    tensorFound = true;
+                    break;
+                }
+            }
+            if (!tensorFound) {
+                for (size_t i = 0; i < outputs.size(); i++) {
+                    if (outputs[i].get_names().count(tensor_name)) {
+                        preprocessor.output(i).tensor().set_element_type(type);
+                        tensorFound = true;
+                        break;
+                    }
+                }
+            }
+            OPENVINO_ASSERT(!tensorFound, "Model doesn't have input/output with tensor name: ", tensor_name);
+        }
+    }
+    if (!il.empty()) {
+        for (size_t i = 0; i < inputs.size(); i++) {
+            preprocessor.input(i).tensor().set_layout(ov::Layout(il));
+        }
+    }
+
+    if (!ol.empty()) {
+        for (size_t i = 0; i < outputs.size(); i++) {
+            preprocessor.output(i).tensor().set_layout(ov::Layout(ol));
+        }
+    }
+
+    if (!iol.empty()) {
+        const auto user_precisions_map = parseArgMap(iol);
+        for (auto&& item : user_precisions_map) {
+            const auto& tensor_name = item.first;
+
+            bool tensorFound = false;
+            for (size_t i = 0; i < inputs.size(); i++) {
+                if (inputs[i].get_names().count(tensor_name)) {
+                    preprocessor.input(i).tensor().set_layout(ov::Layout(item.second));
+                    tensorFound = true;
+                    break;
+                }
+            }
+            if (!tensorFound) {
+                for (size_t i = 0; i < outputs.size(); i++) {
+                    if (outputs[i].get_names().count(tensor_name)) {
+                        preprocessor.output(i).tensor().set_layout(ov::Layout(item.second));
+                        tensorFound = true;
+                        break;
+                    }
+                }
+            }
+            OPENVINO_ASSERT(!tensorFound, "Model doesn't have input/output with tensor name: ", tensor_name);
+        }
+    }
+
+    if (!iml.empty()) {
+        for (size_t i = 0; i < inputs.size(); i++) {
+            preprocessor.input(i).model().set_layout(ov::Layout(iml));
+        }
+    }
+
+    if (!oml.empty()) {
+        for (size_t i = 0; i < outputs.size(); i++) {
+            preprocessor.output(i).model().set_layout(ov::Layout(oml));
+        }
+    }
+
+    if (!ioml.empty()) {
+        const auto user_precisions_map = parseArgMap(ioml);
+        for (auto&& item : user_precisions_map) {
+            const auto& tensor_name = item.first;
+
+            bool tensorFound = false;
+            for (size_t i = 0; i < inputs.size(); i++) {
+                if (inputs[i].get_names().count(tensor_name)) {
+                    preprocessor.input(i).model().set_layout(ov::Layout(item.second));
+                    tensorFound = true;
+                    break;
+                }
+            }
+            if (!tensorFound) {
+                for (size_t i = 0; i < outputs.size(); i++) {
+                    if (outputs[i].get_names().count(tensor_name)) {
+                        preprocessor.output(i).model().set_layout(ov::Layout(item.second));
+                        tensorFound = true;
+                        break;
+                    }
+                }
+            }
+            OPENVINO_ASSERT(!tensorFound, "Model doesn't have input/output with tensor name: ", tensor_name);
+        }
+    }
+
+    model = preprocessor.build();
+}
diff --git a/src/core/tests/preprocess.cpp b/src/core/tests/preprocess.cpp
index 5a9fa25d9bb..d1f8c3a5356 100644
--- a/src/core/tests/preprocess.cpp
+++ b/src/core/tests/preprocess.cpp
@@ -80,6 +80,8 @@ TEST(pre_post_process, convert_element_type_and_scale) {
 
 TEST(pre_post_process, convert_element_type_implicit) {
     auto f = create_simple_function(element::i32, Shape{1, 3, 224, 224});
+    EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::i32);
+    EXPECT_EQ(f->get_results().front()->get_element_type(), element::i32);
     auto p = PrePostProcessor(f);
     p.input().tensor().set_element_type(element::f32);
     f = p.build();
@@ -87,6 +89,28 @@ TEST(pre_post_process, convert_element_type_implicit) {
     EXPECT_EQ(f->get_results().front()->get_element_type(), element::i32);
 }
 
+TEST(pre_post_process, convert_element_type_implicit_several_time) {
+    auto f = create_simple_function(element::i32, Shape{1, 3, 224, 224});
+    EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::i32);
+    EXPECT_EQ(f->get_results().front()->get_element_type(), element::i32);
+    PrePostProcessor preprocessor(f);
+    preprocessor.input().tensor().set_layout(ov::Layout("NHWC"));
+    preprocessor.input().model().set_layout(ov::Layout("NCHW"));
+    preprocessor.input().tensor().set_element_type(element::f16);
+    preprocessor.input().tensor().set_element_type(element::i32);
+    preprocessor.input().tensor().set_element_type(element::u32);
+    preprocessor.input().tensor().set_element_type(element::f32);
+    preprocessor.output().tensor().set_element_type(element::f16);
+    preprocessor.output().tensor().set_element_type(element::i32);
+    preprocessor.output().tensor().set_element_type(element::u32);
+    preprocessor.output().tensor().set_element_type(element::f32);
+    preprocessor.output().tensor().set_element_type(element::u64);
+    f = preprocessor.build();
+    EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::f32);
+    EXPECT_EQ(f->get_parameters().front()->get_layout().to_string(), "[N,H,W,C]");
+    EXPECT_EQ(f->get_results().front()->get_element_type(), element::u64);
+}
+
 TEST(pre_post_process, convert_element_type_same) {
     auto f = create_simple_function(element::i32, Shape{1, 3, 224, 224});
     auto old_size = f->get_ops().size();
@@ -448,6 +472,25 @@ TEST(pre_post_process, convert_color_duplicate_internal_subnames_mean) {
     EXPECT_NO_THROW(f = p.build());
 }
 
+TEST(pre_post_process, convert_layout_implicit_several_time) {
+    auto f = create_simple_function(element::i32, Shape{1, 3, 224, 224});
+    EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::i32);
+    EXPECT_EQ(f->get_results().front()->get_element_type(), element::i32);
+    PrePostProcessor preprocessor(f);
+    preprocessor.input().tensor().set_layout("NWHC");
+    preprocessor.input().tensor().set_layout("CHWN");
+    preprocessor.input().tensor().set_layout("NHCW");
+    preprocessor.input().tensor().set_layout("NCHW");
+    preprocessor.input().tensor().set_layout("NHWC");
+    preprocessor.output().tensor().set_layout("NWHC");
+    preprocessor.output().tensor().set_layout("CHWN");
+    preprocessor.output().tensor().set_layout("NHCW");
+    preprocessor.output().tensor().set_layout("NCHW");
+    f = preprocessor.build();
+    EXPECT_EQ(f->get_parameters().front()->get_layout().to_string(), "[N,H,W,C]");
+    EXPECT_EQ(f->get_results().front()->get_layout().to_string(), "[N,C,H,W]");
+}
+
 TEST(pre_post_process, unsupported_model_color_format) {
     auto f = create_simple_function(element::f32, PartialShape{1, 4, 4, 3});
     EXPECT_THROW(auto p = PrePostProcessor(f); p.input().tensor().set_color_format(ColorFormat::NV12_SINGLE_PLANE);
diff --git a/tools/compile_tool/README.md b/tools/compile_tool/README.md
index 279425f7f77..23142f22368 100644
--- a/tools/compile_tool/README.md
+++ b/tools/compile_tool/README.md
@@ -21,10 +21,8 @@ Running the application with the `-h` option yields the following usage message:
 
 ```sh
 ./compile_tool -h
-Inference Engine:
-        API version ............ 2.1
-        Build .................. custom_vv/compile-tool_8b57af00330063c7f302aaac4d41805de21fc54a
-        Description ....... API
+OpenVINO Runtime version ......... 2022.1.0
+Build ........... custom_changed_compile_tool_183a1adfcd7a001974fe1c5cfa21ec859b70ca2c
 
 compile_tool [OPTIONS]
 
@@ -44,24 +42,31 @@ compile_tool [OPTIONS]
                                              Notice that quotes are required.
                                              Overwrites precision from ip and op options for specified layers.
     -il                          <value>     Optional. Specifies layout for all input layers of the network.
-    -ol                          <value>     Optional. Specifies layout for all input layers of the network.
+    -ol                          <value>     Optional. Specifies layout for all output layers of the network.
     -iol                        "<value>"    Optional. Specifies layout for input and output layers by name.
                                              Example: -iol "input:NCHW, output:NHWC".
                                              Notice that quotes are required.
                                              Overwrites layout from il and ol options for specified layers.
+    -iml                         <value>     Optional. Specifies model layout for all input layers of the network.
+    -oml                         <value>     Optional. Specifies model layout for all output layers of the network.
+    -ioml                       "<value>"    Optional. Specifies model layout for input and output tensors by name.
+                                             Example: -ionl "input:NCHW, output:NHWC".
+                                             Notice that quotes are required.
+                                             Overwrites layout from il and ol options for specified layers.
+    -ov_api_1_0                              Optional. Compile model to legacy format for usage in Inference Engine API,
+                                             by default compiles to OV 2.0 API
 
  MYRIAD-specific options:
-      -VPU_NUMBER_OF_SHAVES      <value>     Optional. Specifies number of shaves.
+    -VPU_NUMBER_OF_SHAVES        <value>     Optional. Specifies number of shaves.
                                              Should be set with "VPU_NUMBER_OF_CMX_SLICES".
                                              Overwrites value from config.
 
-      -VPU_NUMBER_OF_CMX_SLICES  <value>     Optional. Specifies number of CMX slices.
+    -VPU_NUMBER_OF_CMX_SLICES    <value>     Optional. Specifies number of CMX slices.
                                              Should be set with "VPU_NUMBER_OF_SHAVES".
                                              Overwrites value from config.
-      -VPU_TILING_CMX_LIMIT_KB   <value>     Optional. Specifies CMX limit for data tiling.
+    -VPU_TILING_CMX_LIMIT_KB     <value>     Optional. Specifies CMX limit for data tiling.
                                              Value should be equal or greater than -1.
                                              Overwrites value from config.
-
 ```
 
 Running the application with the empty list of options yields an error message.
@@ -75,11 +80,10 @@ For example, to compile a blob for inference on an Intel® Neural Compute Stick
 ### Import a Compiled Blob File to Your Application
 
 To import a blob with the network from a generated file into your application, use the
-`InferenceEngine::Core::ImportNetwork` method:
+`ov::runtime::Core::import_model` method:
 
 ```cpp
-InferenceEngine::Core ie;
+ov::runtime::Core ie;
 std::ifstream file{"model_name.blob"};
-InferenceEngine::ExecutableNetwork = ie.ImportNetwork(file, "MYRIAD", {});
+ov::runtime::CompiledModel compiled_model = ie.import_model(file, "MYRIAD", {});
 ```
-
diff --git a/tools/compile_tool/main.cpp b/tools/compile_tool/main.cpp
index 81a7de0de1d..e57167ebf12 100644
--- a/tools/compile_tool/main.cpp
+++ b/tools/compile_tool/main.cpp
@@ -15,6 +15,7 @@
 #include <gflags/gflags.h>
 
 #include "inference_engine.hpp"
+#include "openvino/openvino.hpp"
 #include <vpu/vpu_plugin_config.hpp>
 #include <vpu/private_plugin_config.hpp>
 #include <vpu/utils/string.hpp>
@@ -59,7 +60,7 @@ static constexpr char inputs_layout_message[] =
                                              "Optional. Specifies layout for all input layers of the network.";
 
 static constexpr char outputs_layout_message[] =
-                                             "Optional. Specifies layout for all input layers of the network.";
+                                             "Optional. Specifies layout for all output layers of the network.";
 
 static constexpr char iol_message[] =
                                              "Optional. Specifies layout for input and output layers by name.\n"
@@ -67,6 +68,22 @@ static constexpr char iol_message[] =
 "                                             Notice that quotes are required.\n"
 "                                             Overwrites layout from il and ol options for specified layers.";
 
+static constexpr char inputs_model_layout_message[] =
+                                             "Optional. Specifies model layout for all input layers of the network.";
+
+static constexpr char outputs_model_layout_message[] =
+                                             "Optional. Specifies model layout for all output layers of the network.";
+
+static constexpr char ioml_message[] =
+                                             "Optional. Specifies model layout for input and output tensors by name.\n"
+"                                             Example: -ionl \"input:NCHW, output:NHWC\".\n"
+"                                             Notice that quotes are required.\n"
+"                                             Overwrites layout from il and ol options for specified layers.";
+
+static constexpr char api1_message[] =
+                                             "Optional. Compile model to legacy format for usage in Inference Engine API,\n"
+"                                             by default compiles to OV 2.0 API";
+
 // MYRIAD-specific
 static constexpr char number_of_shaves_message[] =
                                              "Optional. Specifies number of shaves.\n"
@@ -95,6 +112,10 @@ DEFINE_string(iop, "", iop_message);
 DEFINE_string(il, "", inputs_layout_message);
 DEFINE_string(ol, "", outputs_layout_message);
 DEFINE_string(iol, "", iol_message);
+DEFINE_string(iml, "", inputs_model_layout_message);
+DEFINE_string(oml, "", outputs_model_layout_message);
+DEFINE_string(ioml, "", ioml_message);
+DEFINE_bool(ov_api_1_0, false, api1_message);
 DEFINE_string(VPU_NUMBER_OF_SHAVES, "", number_of_shaves_message);
 DEFINE_string(VPU_NUMBER_OF_CMX_SLICES, "", number_of_cmx_slices_message);
 DEFINE_string(VPU_TILING_CMX_LIMIT_KB, "", tiling_cmx_limit_message);
@@ -114,6 +135,10 @@ static void showUsage() {
     std::cout << "    -il                          <value>     "   << inputs_layout_message        << std::endl;
     std::cout << "    -ol                          <value>     "   << outputs_layout_message       << std::endl;
     std::cout << "    -iol                        \"<value>\"    "   << iol_message                << std::endl;
+    std::cout << "    -iml                         <value>     "   << inputs_model_layout_message  << std::endl;
+    std::cout << "    -oml                         <value>     "   << outputs_model_layout_message << std::endl;
+    std::cout << "    -ioml                       \"<value>\"    "   << ioml_message               << std::endl;
+    std::cout << "    -ov_api_1_0                              "   << api1_message                 << std::endl;
     std::cout                                                                                      << std::endl;
     std::cout << " MYRIAD-specific options:                    "                                   << std::endl;
     std::cout << "      -VPU_NUMBER_OF_SHAVES      <value>     "   << number_of_shaves_message     << std::endl;
@@ -263,41 +288,71 @@ int main(int argc, char* argv[]) {
     TimeDiff loadNetworkTimeElapsed {0};
 
     try {
-        std::cout << "Inference Engine: " << InferenceEngine::GetInferenceEngineVersion() << std::endl;
-        std::cout << std::endl;
+        const auto& version = ov::get_openvino_version();
+        std::cout << version.description << " version ......... ";
+        std::cout << OPENVINO_VERSION_MAJOR << "." << OPENVINO_VERSION_MINOR << "." << OPENVINO_VERSION_PATCH << std::endl;
+
+        std::cout << "Build ........... ";
+        std::cout << version.buildNumber << std::endl;
 
         if (!parseCommandLine(&argc, &argv)) {
             return EXIT_SUCCESS;
         }
+        if (FLAGS_ov_api_1_0) {
+            InferenceEngine::Core ie;
+            if (!FLAGS_log_level.empty()) {
+                ie.SetConfig({{CONFIG_KEY(LOG_LEVEL), FLAGS_log_level}}, FLAGS_d);
+            }
 
-        InferenceEngine::Core ie;
-        if (!FLAGS_log_level.empty()) {
-            ie.SetConfig({{CONFIG_KEY(LOG_LEVEL), FLAGS_log_level}}, FLAGS_d);
-        }
+            auto network = ie.ReadNetwork(FLAGS_m);
 
-        auto network = ie.ReadNetwork(FLAGS_m);
+            setDefaultIO(network);
+            processPrecision(network, FLAGS_ip, FLAGS_op, FLAGS_iop);
+            processLayout(network, FLAGS_il, FLAGS_ol, FLAGS_iol);
 
-        setDefaultIO(network);
-        processPrecision(network, FLAGS_ip, FLAGS_op, FLAGS_iop);
-        processLayout(network, FLAGS_il, FLAGS_ol, FLAGS_iol);
+            printInputAndOutputsInfo(network);
 
-        printInputAndOutputsInfo(network);
+            auto timeBeforeLoadNetwork = std::chrono::steady_clock::now();
+            auto executableNetwork = ie.LoadNetwork(network, FLAGS_d, configure());
+            loadNetworkTimeElapsed = std::chrono::duration_cast<TimeDiff>(std::chrono::steady_clock::now() - timeBeforeLoadNetwork);
 
-        auto timeBeforeLoadNetwork = std::chrono::steady_clock::now();
-        auto executableNetwork = ie.LoadNetwork(network, FLAGS_d, configure());
-        loadNetworkTimeElapsed = std::chrono::duration_cast<TimeDiff>(std::chrono::steady_clock::now() - timeBeforeLoadNetwork);
+            std::string outputName = FLAGS_o;
+            if (outputName.empty()) {
+                outputName = getFileNameFromPath(fileNameNoExt(FLAGS_m)) + ".blob";
+            }
 
-        std::string outputName = FLAGS_o;
-        if (outputName.empty()) {
-            outputName = getFileNameFromPath(fileNameNoExt(FLAGS_m)) + ".blob";
-        }
-
-        std::ofstream outputFile{outputName, std::ios::out | std::ios::binary};
-        if (!outputFile.is_open()) {
-            std::cout << "Output file " << outputName << " can't be opened for writing" << std::endl;
-            return EXIT_FAILURE;
+            std::ofstream outputFile{outputName, std::ios::out | std::ios::binary};
+            if (!outputFile.is_open()) {
+                std::cout << "Output file " << outputName << " can't be opened for writing" << std::endl;
+                return EXIT_FAILURE;
+            } else {
+                executableNetwork.Export(outputFile);
+            }
         } else {
-            executableNetwork.Export(outputFile);
+            ov::runtime::Core core;
+            if (!FLAGS_log_level.empty()) {
+                core.set_config({{CONFIG_KEY(LOG_LEVEL), FLAGS_log_level}}, FLAGS_d);
+            }
+
+            auto model = core.read_model(FLAGS_m);
+
+            configurePrePostProcessing(model, FLAGS_ip, FLAGS_op, FLAGS_iop, FLAGS_il, FLAGS_ol, FLAGS_iol, FLAGS_iml, FLAGS_oml, FLAGS_ioml);
+            printInputAndOutputsInfo(*model);
+            auto timeBeforeLoadNetwork = std::chrono::steady_clock::now();
+            auto compiledModel = core.compile_model(model, FLAGS_d, configure());
+            loadNetworkTimeElapsed = std::chrono::duration_cast<TimeDiff>(std::chrono::steady_clock::now() - timeBeforeLoadNetwork);
+            std::string outputName = FLAGS_o;
+            if (outputName.empty()) {
+                outputName = getFileNameFromPath(fileNameNoExt(FLAGS_m)) + ".blob";
+            }
+
+            std::ofstream outputFile{outputName, std::ios::out | std::ios::binary};
+            if (!outputFile.is_open()) {
+                std::cout << "Output file " << outputName << " can't be opened for writing" << std::endl;
+                return EXIT_FAILURE;
+            } else {
+                compiledModel.export_model(outputFile);
+            }
         }
     } catch (const std::exception& error) {
         std::cerr << error.what() << std::endl;

From f316801ccdaa66c06726df39cb92010e03d4dc35 Mon Sep 17 00:00:00 2001
From: Irina Efode <irina.efode@intel.com>
Date: Thu, 23 Dec 2021 12:51:01 +0300
Subject: [PATCH 115/160] [IE TESTS] Move remote tests to
 Behavior/ov_infer_request (#9233)

* [IE TESTS] Move remote tests to Behavior/ov_infer_request

* Move instance

* Apply comments
---
 .../behavior/{ov_remote.cpp => ov_plugin/remote.cpp}            | 2 +-
 .../include/behavior/{ov_remote.hpp => ov_plugin/remote.hpp}    | 0
 .../shared/src/behavior/{ov_remote.cpp => ov_plugin/remote.cpp} | 2 +-
 3 files changed, 2 insertions(+), 2 deletions(-)
 rename src/tests/functional/plugin/gpu/shared_tests_instances/behavior/{ov_remote.cpp => ov_plugin/remote.cpp} (97%)
 rename src/tests/functional/plugin/shared/include/behavior/{ov_remote.hpp => ov_plugin/remote.hpp} (100%)
 rename src/tests/functional/plugin/shared/src/behavior/{ov_remote.cpp => ov_plugin/remote.cpp} (99%)

diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_remote.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/remote.cpp
similarity index 97%
rename from src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_remote.cpp
rename to src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/remote.cpp
index 759476fb3ba..a429ca37c6d 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_remote.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/remote.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "behavior/ov_remote.hpp"
+#include "behavior/ov_plugin/remote.hpp"
 #include "common_test_utils/test_constants.hpp"
 
 using namespace ov::test;
diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_remote.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_plugin/remote.hpp
similarity index 100%
rename from src/tests/functional/plugin/shared/include/behavior/ov_remote.hpp
rename to src/tests/functional/plugin/shared/include/behavior/ov_plugin/remote.hpp
diff --git a/src/tests/functional/plugin/shared/src/behavior/ov_remote.cpp b/src/tests/functional/plugin/shared/src/behavior/ov_plugin/remote.cpp
similarity index 99%
rename from src/tests/functional/plugin/shared/src/behavior/ov_remote.cpp
rename to src/tests/functional/plugin/shared/src/behavior/ov_plugin/remote.cpp
index 5b1fd882658..f484ad3c17d 100644
--- a/src/tests/functional/plugin/shared/src/behavior/ov_remote.cpp
+++ b/src/tests/functional/plugin/shared/src/behavior/ov_plugin/remote.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "behavior/ov_remote.hpp"
+#include "behavior/ov_plugin/remote.hpp"
 #include "transformations/utils/utils.hpp"
 #include "functional_test_utils/skip_tests_config.hpp"
 #include "ngraph_functions/subgraph_builders.hpp"

From 7618fc8752a593350a2b5b1b344969f1e282abe8 Mon Sep 17 00:00:00 2001
From: Irina Efode <irina.efode@intel.com>
Date: Thu, 23 Dec 2021 13:00:41 +0300
Subject: [PATCH 116/160] [IE TESTS][CONFORMANCE] Update conformance ReadMe
 files (#9374)

* [IE TESTS][CONFORMANCE] Update conformance ReadMe files

* Fix links
---
 .../plugin/conformance/test_runner/README.md  | 37 ++++++++++++++++---
 .../include/gflag_config.hpp                  |  2 +-
 2 files changed, 33 insertions(+), 6 deletions(-)

diff --git a/src/tests/functional/plugin/conformance/test_runner/README.md b/src/tests/functional/plugin/conformance/test_runner/README.md
index 0d6ba495df6..93160a8f617 100644
--- a/src/tests/functional/plugin/conformance/test_runner/README.md
+++ b/src/tests/functional/plugin/conformance/test_runner/README.md
@@ -3,6 +3,10 @@
 ## Description
 Conformance suit is a set of tests with parameters independent from plug-in specific and limitations. It contains:
 * `ReadIR`. Allow to read IRs from folders recursive, infer it and compare results with reference.
+* `OpImplCheckTest`. Allow to check operation plugin implementation status (`Implemented`/`Not implemented`).
+
+  **NOTE**: This test suite is in active development. The implementation status based on all other test results.
+* 
 
 ## How to build
 Run the following command in build directory:
@@ -15,21 +19,36 @@ Run the following command in build directory:
    make conformanceTests
    ```
    
-## How to run
+## How to generate Conformance operation set
+Run the following commands:
+1. Clone [`Open Model Zoo repo`](https://github.com/openvinotoolkit/open_model_zoo)
+2. Download all possible models using [Downloader tool](https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/model_tools/downloader.py) from 
+   the repo.
+3. Convert downloaded models to IR files using [Converter tool](https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/model_tools/converter.py) from the repo.
+4. Run [Subgraph dumper](./../subgraphs_dumper/README.md) to collect unique operation set from the models.
+> **NOTE**:
+> 
+> The conformance suite run in internal CI checks is based on `2021.4` release. **Please, use `Model optimizer`, 'Subgraph dumper' and `Open Model Zoo` tools 
+> from `2021.4`.**
+
+## How to run operation conformance suite
 The target is able to take the following command-line arguments:
 * `-h` prints target command-line options with description.
 * `--device` specifies target device.
 * `--input_folders` specifies folders with IRs to run. The separator is `,`.
 * `--plugin_lib_name` is name of plugin library. The example is ov_intel_cpu_plugin. Use only with unregistered in IE Core devices.
 * `--disable_test_config` allows to ignore all skipped tests with the exception of `DISABLED_` prefix using.
-* `--skip_config_path` allows to specify paths to files contain regular expressions list to skip tests.
+* `--skip_config_path` allows to specify paths to files contain regular expressions list to skip tests. [Examples](./op_conformance_runner/skip_configs)
+* `--config_path` allows to specify path to file contains plugin config. [Example](./op_conformance_runner/config/config_example.txt)
 * `--extend_report` allows not to re-write device results to the report (add results of this run to the existing). Mutually exclusive with --report_unique_name.
 * `--report_unique_name` allows to save report with unique name (report_pid_timestamp.xml). Mutually exclusive with --extend_report.
 * `--save_report_timeout` allows to try to save report in cycle using timeout (in seconds).
 * `--output_folder` Paths to the output folder to save report.
 * All `gtest` command-line parameters
 
-The result of execution is `report.xml` file. It demonstrates tests statistic like pass rate, passed, crashed, skipped and failed tests per operation for 
+The result of execution is `report.xml` file. It demonstrates tests statistic like pass rate, passed, crashed, skipped failed tests and plugin implementation 
+per 
+operation for 
 devices.
 
 > **NOTE**:
@@ -37,6 +56,8 @@ devices.
 > Using of GTest parallel tool to run `conformanceTests` helps to report crashed tests and collect correct statistic 
 > after unexpected crashes. 
 > 
+> Use [`Gtest parallel`](https://github.com/google/gtest-parallel) from official repository with [this fix](https://github.com/google/gtest-parallel/pull/76).
+> 
 > The example of usage is:
 > ```
 > python3 gtest_parallel.py /path/to/openvino/bin/intel64/Debug/conformanceTests -d . 
@@ -53,9 +74,15 @@ devices.
 > python3 merge_xmls.py --input_folders=/path/to/temp_output_report_folder --output_folder=/path/to/output_report_folder --output_filename=report_aggregated
 > ```
 
-## How to build operation coverage report
+## How to create operation conformance report
 Run [the script](./../../../../ie_test_utils/functional_test_utils/layer_tests_summary/summarize.py) to generate `html` report.
 The example of using the script is:
 ```
 python3 summarize.py --xml /opt/repo/infrastructure-master/thirdparty/gtest-parallel/report.xml --out /opt/repo/infrastructure-master/thirdparty/gtest-parallel/
-```
\ No newline at end of file
+```
+> **NOTE**:
+>
+> Please, do not forget to copy [styles folder](./../../../../ie_test_utils/functional_test_utils/layer_tests_summary/template) to the output directory. It 
+> helps to provide report with the filters and other usable features.
+
+Report contains statistic based on conformance results and filter fields at the top of the page.
\ No newline at end of file
diff --git a/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp b/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp
index 98a47ba8f46..b8dd12c90b8 100644
--- a/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp
+++ b/src/tests/functional/plugin/conformance/test_runner/conformance_infra/include/gflag_config.hpp
@@ -26,7 +26,7 @@ static const char save_report_timeout_message[] = "Optional. Allow to try to sav
                                                   "Default value is 60 seconds";
 static const char skip_config_path_message[] = "Optional. Allows to specify paths to files contain regular expressions list to skip tests. "
                                                "Delimiter is `,` symbol. Default value is empty string.";
-static const char config_path_message[] = "Optional. Allows to specify path to files contain plugin config. "
+static const char config_path_message[] = "Optional. Allows to specify path to file contains plugin config. "
                                           "Default value is empty string.";
 
 

From 516272aeee5cc34598b68baa8ee3de7cfdc39f1a Mon Sep 17 00:00:00 2001
From: Vitaliy Urusovskij <vitaliy.urusovskij@intel.com>
Date: Thu, 23 Dec 2021 13:12:45 +0300
Subject: [PATCH 117/160] Fix stack-buffer-overflow in
 ScatterElementsUpdateTest (#9387)

---
 .../scatter_elements_update_shape_inference.cpp                 | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tests/unit/cpu/shape_inference_test/scatter_elements_update_shape_inference.cpp b/src/tests/unit/cpu/shape_inference_test/scatter_elements_update_shape_inference.cpp
index 491069475a2..935a1fac113 100644
--- a/src/tests/unit/cpu/shape_inference_test/scatter_elements_update_shape_inference.cpp
+++ b/src/tests/unit/cpu/shape_inference_test/scatter_elements_update_shape_inference.cpp
@@ -24,7 +24,7 @@ TEST(StaticShapeInferenceTest, ScatterElementsUpdateTest) {
     int32_t axis_shape_val[] = {2};
     std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> constant_data;
     constant_data[3] =
-        std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{2}, axis_shape_val);
+        std::make_shared<ngraph::runtime::HostTensor>(ngraph::element::Type_t::i32, Shape{1}, axis_shape_val);
     std::vector<StaticShape> input_shapes = {StaticShape{1000, 256, 7, 7},
                                              StaticShape{125, 20, 7, 6},
                                              StaticShape{125, 20, 7, 6},

From 41ace9d4e6c53188805b2f116a961ba5aacc191e Mon Sep 17 00:00:00 2001
From: Evgenya Stepyreva <evgenya.stepyreva@intel.com>
Date: Thu, 23 Dec 2021 13:41:27 +0300
Subject: [PATCH 118/160] Use opsets in sample of function creation (#7792)

---
 samples/cpp/ngraph_function_creation_sample/main.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/samples/cpp/ngraph_function_creation_sample/main.cpp b/samples/cpp/ngraph_function_creation_sample/main.cpp
index 95ad7693a37..8883a21b14a 100644
--- a/samples/cpp/ngraph_function_creation_sample/main.cpp
+++ b/samples/cpp/ngraph_function_creation_sample/main.cpp
@@ -12,6 +12,7 @@
 
 // clang-format off
 #include "openvino/openvino.hpp"
+#include "openvino/opsets/opset1.hpp"
 #include "openvino/opsets/opset8.hpp"
 #include "ngraph/util.hpp"
 
@@ -108,7 +109,7 @@ std::shared_ptr<ov::Model> createNgraphFunction(const std::string& path_to_weigh
     Shape padBeginShape{0, 0};
     Shape padEndShape{0, 0};
 
-    auto maxPoolingNodeFirst = std::make_shared<op::v1::MaxPool>(addNodeFirst->output(0),
+    auto maxPoolingNodeFirst = std::make_shared<opset1::MaxPool>(addNodeFirst->output(0),
                                                                  Strides{2, 2},
                                                                  padBeginShape,
                                                                  padEndShape,
@@ -138,7 +139,7 @@ std::shared_ptr<ov::Model> createNgraphFunction(const std::string& path_to_weigh
         std::make_shared<opset8::Add>(convolutionNodeSecond->output(0), addSecondConstantNode->output(0));
 
     // -------MAXPOOL 2--------
-    auto maxPoolingNodeSecond = std::make_shared<op::v1::MaxPool>(addNodeSecond->output(0),
+    auto maxPoolingNodeSecond = std::make_shared<opset1::MaxPool>(addNodeSecond->output(0),
                                                                   Strides{2, 2},
                                                                   padBeginShape,
                                                                   padEndShape,
@@ -152,7 +153,7 @@ std::shared_ptr<ov::Model> createNgraphFunction(const std::string& path_to_weigh
         std::make_shared<opset8::Constant>(element::Type_t::i64, reshapeFirstShape, data + reshapeOffset);
 
     auto reshapeFirstNode =
-        std::make_shared<op::v1::Reshape>(maxPoolingNodeSecond->output(0), reshapeFirstConstantNode->output(0), true);
+        std::make_shared<opset8::Reshape>(maxPoolingNodeSecond->output(0), reshapeFirstConstantNode->output(0), true);
 
     // -------MatMul 1---------
     auto matMulFirstShape = Shape{500, 800};
@@ -179,7 +180,7 @@ std::shared_ptr<ov::Model> createNgraphFunction(const std::string& path_to_weigh
         std::make_shared<opset8::Constant>(element::Type_t::i64, reshapeSecondShape, data + reshapeOffset);
 
     auto reshapeSecondNode =
-        std::make_shared<op::v1::Reshape>(reluNode->output(0), reshapeSecondConstantNode->output(0), true);
+        std::make_shared<opset8::Reshape>(reluNode->output(0), reshapeSecondConstantNode->output(0), true);
 
     // -------MatMul 2---------
     auto matMulSecondShape = Shape{10, 500};

From a94a6a774e7c0bf1216e3cfa62872c701068a130 Mon Sep 17 00:00:00 2001
From: Artyom Anokhov <artyom.anokhov@intel.com>
Date: Thu, 23 Dec 2021 13:48:17 +0300
Subject: [PATCH 119/160] DeploymentManager::configs: Removed OpenCV component
 with their python folder. (#9367)

DeploymentManager::main: Refactored code with python-black module. Added compressing for Win-archives. Added shortcuts for options.
---
 tools/deployment_manager/configs/darwin.json  |  12 --
 tools/deployment_manager/configs/linux.json   |  12 --
 tools/deployment_manager/configs/windows.json |  12 --
 tools/deployment_manager/deployman/main.py    | 143 ++++++++++++------
 4 files changed, 93 insertions(+), 86 deletions(-)

diff --git a/tools/deployment_manager/configs/darwin.json b/tools/deployment_manager/configs/darwin.json
index b52ac8b9f10..501f15599c0 100644
--- a/tools/deployment_manager/configs/darwin.json
+++ b/tools/deployment_manager/configs/darwin.json
@@ -46,22 +46,10 @@
         "runtime/lib/intel64/pcie-ma2x8x.mvcmd"
       ]
     },
-    "opencv": {
-      "ui_name": "OpenCV",
-      "group": ["opencv"],
-      "dependencies" : [],
-      "files": [
-        "extras/opencv/version.txt",
-        "extras/opencv/setupvars.sh",
-        "extras/opencv/lib",
-        "extras/opencv/bin"
-      ]
-    },
     "python_common": {
       "group": ["python"],
       "dependencies" : ["ie_core"],
       "files": [
-        "python/python3",
         "python/requirements.txt"
       ]
     },
diff --git a/tools/deployment_manager/configs/linux.json b/tools/deployment_manager/configs/linux.json
index 74801042f81..e5b4889e34a 100644
--- a/tools/deployment_manager/configs/linux.json
+++ b/tools/deployment_manager/configs/linux.json
@@ -85,22 +85,10 @@
         "runtime/3rdparty/hddl"
       ]
     },
-    "opencv": {
-      "ui_name": "OpenCV",
-      "group": ["opencv"],
-      "dependencies" : [],
-      "files": [
-        "extras/opencv/version.txt",
-        "extras/opencv/setupvars.sh",
-        "extras/opencv/lib",
-        "extras/opencv/bin"
-      ]
-    },
     "python_common": {
       "group": ["python"],
       "dependencies" : ["ie_core"],
       "files": [
-        "python/python3",
         "python/requirements.txt"
       ]
     },
diff --git a/tools/deployment_manager/configs/windows.json b/tools/deployment_manager/configs/windows.json
index 34af1c23979..1eb0498a652 100644
--- a/tools/deployment_manager/configs/windows.json
+++ b/tools/deployment_manager/configs/windows.json
@@ -74,22 +74,10 @@
         "runtime/3rdparty/hddl"
       ]
     },
-    "opencv": {
-      "ui_name": "OpenCV",
-      "group": ["opencv"],
-      "dependencies" : [],
-      "files": [
-        "extras/opencv/version.txt",
-        "extras/opencv/setupvars.bat",
-        "extras/opencv/lib",
-        "extras/opencv/bin"
-      ]
-    },
     "python_common": {
       "group": ["python"],
       "dependencies" : ["ie_core"],
       "files": [
-        "python/python3",
         "python/requirements.txt"
       ]
     },
diff --git a/tools/deployment_manager/deployman/main.py b/tools/deployment_manager/deployman/main.py
index b4a46d1ba81..4764e1eae21 100644
--- a/tools/deployment_manager/deployman/main.py
+++ b/tools/deployment_manager/deployman/main.py
@@ -14,7 +14,7 @@
  limitations under the License.
 """
 
-__version__ = '0.6'
+__version__ = "0.6"
 
 import os
 import argparse
@@ -26,7 +26,7 @@ from deployman.logger import init_logger
 from deployman.config import ConfigReader, ComponentFactory, Component
 from deployman.ui import UserInterface
 
-logger = init_logger('WARNING')
+logger = init_logger("WARNING")
 
 
 # main class
@@ -42,7 +42,7 @@ class DeploymentManager:
         dependencies_names = []
         logger.debug("Updating dependencies...")
         for target in self.selected_targets:
-            if hasattr(target, 'dependencies'):
+            if hasattr(target, "dependencies"):
                 dependencies_names.extend(target.dependencies)
         # remove duplications
         dependencies_names = list(dict.fromkeys(dependencies_names))
@@ -58,33 +58,42 @@ class DeploymentManager:
     def get_mandatory_component(self):
         for _target in self.components:
             _target: Component
-            if hasattr(_target, 'mandatory'):
+            if hasattr(_target, "mandatory"):
                 if not _target.is_exist():
                     FileNotFoundError("Mandatory component {} not available.".format(_target.name))
                 self.mandatory_components.append(_target)
 
     @staticmethod
     def packing_binaries(archive_name: str, target_dir: str, source_dir: str):
-        logger.info('Archiving deploy package')
-        if os.name == 'posix':
-            archive_path = DeploymentManager.packing_binaries_posix(archive_name, target_dir, source_dir)
+        logger.info("Archiving deploy package")
+        if os.name == "posix":
+            archive_path = DeploymentManager.packing_binaries_posix(
+                archive_name, target_dir, source_dir
+            )
         else:
-            archive_path = DeploymentManager.packing_binaries_windows(archive_name, target_dir, source_dir)
-        logger.setLevel('INFO')
-        logger.info("Deployment archive is ready. "
-                    "You can find it here:\n\t{}".format(os.path.join(target_dir, archive_path)))
+            archive_path = DeploymentManager.packing_binaries_windows(
+                archive_name, target_dir, source_dir
+            )
+        logger.setLevel("INFO")
+        logger.info(
+            "Deployment archive is ready. "
+            "You can find it here:\n\t{}".format(os.path.join(target_dir, archive_path))
+        )
 
     @staticmethod
     def packing_binaries_posix(archive_name: str, target_dir: str, source_dir: str) -> str:
-        extension = 'tar.gz'
-        archive_file_name = '{}.{}'.format(archive_name, extension)
+        extension = "tar.gz"
+        archive_file_name = "{}.{}".format(archive_name, extension)
         archive_path = os.path.join(target_dir, archive_file_name)
 
         import tarfile
+
         with tarfile.open(archive_path, "w:gz") as tar_pac:
             total_files_number = DeploymentManager.count_files_number(source_dir)
             count = 0
-            logger.info('Total number of files to add to the package: {}'.format(total_files_number))
+            logger.info(
+                "Total number of files to add to the package: {}".format(total_files_number)
+            )
             for root, dirs, files in os.walk(source_dir):
                 for file in files:
                     count += 1
@@ -92,23 +101,29 @@ class DeploymentManager:
                     if not os.path.isfile(full_path):
                         continue
                     relative_path = str(Path(full_path).relative_to(source_dir))
-                    logger.info('Add {} {}/{} file to the package'.format(relative_path,
-                                                                          count,
-                                                                          total_files_number))
+                    logger.info(
+                        "Add {} {}/{} file to the package".format(
+                            relative_path, count, total_files_number
+                        )
+                    )
                     tar_pac.add(full_path, arcname=relative_path)
         return archive_path
 
     @staticmethod
     def packing_binaries_windows(archive_name: str, target_dir: str, source_dir: str) -> str:
-        extension = 'zip'
-        archive_file_name = '{}.{}'.format(archive_name, extension)
+        extension = "zip"
+        archive_file_name = "{}.{}".format(archive_name, extension)
         archive_path = os.path.join(target_dir, archive_file_name)
 
-        from zipfile import ZipFile
+        from zipfile import ZipFile, ZIP_DEFLATED
+
+        compression = ZIP_DEFLATED
         with ZipFile(archive_path, "w") as zip_pac:
             total_files_number = DeploymentManager.count_files_number(source_dir)
             count = 0
-            logger.info('Total number of files to add to the package: {}'.format(total_files_number))
+            logger.info(
+                "Total number of files to add to the package: {}".format(total_files_number)
+            )
             for root, dirs, files in os.walk(source_dir):
                 for file in files:
                     count += 1
@@ -116,10 +131,14 @@ class DeploymentManager:
                     if not os.path.isfile(full_path):
                         continue
                     relative_path = str(Path(full_path).relative_to(source_dir))
-                    logger.info('Add {} {}/{} file to the package'.format(relative_path,
-                                                                          count,
-                                                                          total_files_number))
-                    zip_pac.write(os.path.join(root, file), arcname=relative_path)
+                    logger.info(
+                        "Add {} {}/{} file to the package".format(
+                            relative_path, count, total_files_number
+                        )
+                    )
+                    zip_pac.write(
+                        os.path.join(root, file), arcname=relative_path, compress_type=compression
+                    )
         return archive_path
 
     @staticmethod
@@ -135,7 +154,7 @@ class DeploymentManager:
         # get mandatory components
         self.get_mandatory_component()
 
-        logger.info('Collection information for components')
+        logger.info("Collection information for components")
         with tempfile.TemporaryDirectory() as tmpdirname:
             for target in self.selected_targets:
                 target: Component
@@ -150,14 +169,16 @@ class DeploymentManager:
                     target.copy_files(tmpdirname)
             if self.args.user_data and os.path.exists(self.args.user_data):
                 from shutil import copytree
-                logger.info('Storing user data for deploy package ')
-                copytree(self.args.user_data,
-                         os.path.join(
-                             tmpdirname,
-                             os.path.basename(self.args.user_data.rstrip(os.path.sep))),
-                         symlinks=True)
-            self.packing_binaries(self.args.archive_name,
-                                  self.args.output_dir, tmpdirname)
+
+                logger.info("Storing user data for deploy package ")
+                copytree(
+                    self.args.user_data,
+                    os.path.join(
+                        tmpdirname, os.path.basename(self.args.user_data.rstrip(os.path.sep))
+                    ),
+                    symlinks=True,
+                )
+            self.packing_binaries(self.args.archive_name, self.args.output_dir, tmpdirname)
 
 
 def main():
@@ -168,13 +189,13 @@ def main():
     components = []
 
     for component in cfg.components:
-        components.append(ComponentFactory.create_component(component,
-                                                            cfg.components[component],
-                                                            logger))
+        components.append(
+            ComponentFactory.create_component(component, cfg.components[component], logger)
+        )
 
     # list for only available components
     available_targets = []
-    help_msg = ''
+    help_msg = ""
 
     for component in components:
         if component.is_exist() and not component.invisible:
@@ -183,15 +204,33 @@ def main():
 
     parser = argparse.ArgumentParser(description="", formatter_class=argparse.RawTextHelpFormatter)
 
-    parser.add_argument("--targets", nargs="+", help="List of targets. "
-                                                     "Possible values: \n{}".format(help_msg))
-    parser.add_argument("--user_data", type=str, help="Path to user data that will be added to "
-                                                      "the deployment package", default=None)
-    parser.add_argument("--output_dir", type=str, help="Output directory for deployment archive",
-                        default=os.getenv("HOME", os.path.join(os.path.join(
-                            os.path.dirname(__file__), os.pardir))))
-    parser.add_argument("--archive_name", type=str, help="Name for deployment archive",
-                        default="openvino_deploy_package", )
+    parser.add_argument(
+        "--targets",
+        "-t",
+        nargs="+",
+        help="List of targets. " "Possible values: \n{}".format(help_msg),
+    )
+    parser.add_argument(
+        "--user_data",
+        "-u",
+        type=str,
+        help="Path to user data that will be added to " "the deployment package",
+        default=None,
+    )
+    parser.add_argument(
+        "--output_dir",
+        "-o",
+        type=str,
+        help="Output directory for deployment archive",
+        default=os.getenv("HOME", os.path.join(os.path.join(os.path.dirname(__file__), os.pardir))),
+    )
+    parser.add_argument(
+        "--archive_name",
+        "-name",
+        type=str,
+        help="Name for deployment archive",
+        default="openvino_deploy_package",
+    )
     parser.add_argument("--version", action="version", version="%(prog)s " + __version__)
 
     logger.info("Parsing command line arguments")
@@ -199,8 +238,10 @@ def main():
 
     selected_targets = []
     if not available_targets:
-        exit("No available targets to packaging detected.\n"
-             "Please check your OpenVINO installation.")
+        exit(
+            "No available targets to packaging detected.\n"
+            "Please check your OpenVINO installation."
+        )
 
     ui = UserInterface(__version__, args, available_targets, logger)
     if not args.targets:
@@ -211,7 +252,9 @@ def main():
         for target in args.targets:
             target_name = target.lower()
             if not any(target_name == _target.name.lower() for _target in available_targets):
-                raise ValueError("You input incorrect target. {} is not available.".format(target_name))
+                raise ValueError(
+                    "You input incorrect target. {} is not available.".format(target_name)
+                )
             for _target in available_targets:
                 if _target.name.lower() == target_name:
                     selected_targets.append(_target)

From d0cdf14c47efb33c807da9ec0bf5bd88edade77b Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 23 Dec 2021 14:02:44 +0300
Subject: [PATCH 120/160] Fixed address sanitizer (#9364)

---
 .../tests/functional/op_reference/base_reference_test.hpp    | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/template_plugin/tests/functional/op_reference/base_reference_test.hpp b/docs/template_plugin/tests/functional/op_reference/base_reference_test.hpp
index 8582a92901d..69a2f02a3d7 100644
--- a/docs/template_plugin/tests/functional/op_reference/base_reference_test.hpp
+++ b/docs/template_plugin/tests/functional/op_reference/base_reference_test.hpp
@@ -58,7 +58,10 @@ ov::runtime::Tensor CreateTensor(const ov::Shape& shape,
                                  const ov::element::Type& element_type,
                                  const std::vector<T>& values) {
     ov::runtime::Tensor tensor{element_type, shape};
-    std::memcpy(tensor.data(), values.data(), sizeof(T) * values.size());
+    size_t size = sizeof(T) * values.size();
+    if (tensor.get_byte_size() < size)
+        size = tensor.get_byte_size();
+    std::memcpy(tensor.data(), values.data(), size);
 
     return tensor;
 }

From da67ba135c2cb131839442132c42e44607349a74 Mon Sep 17 00:00:00 2001
From: Andrey Sapozhnikov <andrey.sapozhnikov@intel.com>
Date: Thu, 23 Dec 2021 14:32:58 +0300
Subject: [PATCH 121/160] [GNA] Remove GNA Library versioning (#9319)

---
 cmake/dependencies.cmake                      |  17 +-
 cmake/features.cmake                          |  21 -
 inference-engine/cmake/libGNAConfig.cmake     |  32 +-
 src/plugins/intel_gna/CMakeLists.txt          |  10 +-
 .../intel_gna/backend/am_intel_dnn.cpp        | 412 +-----------------
 .../intel_gna/backend/am_intel_dnn.hpp        |  20 +-
 src/plugins/intel_gna/backend/dnn.cpp         |   6 -
 src/plugins/intel_gna/backend/dnn.hpp         |   2 -
 src/plugins/intel_gna/backend/dnn_types.h     |   4 -
 src/plugins/intel_gna/backend/gna_types.h     |   5 -
 .../intel_gna/gna2_model_debug_log.cpp        |   3 +-
 .../intel_gna/gna2_model_debug_log.hpp        |   4 -
 .../intel_gna/gna2_model_export_helper.cpp    |   3 -
 .../intel_gna/gna2_model_export_helper.hpp    |   4 -
 src/plugins/intel_gna/gna2_model_helper.cpp   |   4 -
 src/plugins/intel_gna/gna2_model_helper.hpp   |   4 -
 src/plugins/intel_gna/gna_api_wrapper.hpp     |  52 ---
 src/plugins/intel_gna/gna_data_types.hpp      |   4 -
 src/plugins/intel_gna/gna_device.cpp          | 141 +-----
 src/plugins/intel_gna/gna_device.hpp          |  59 +--
 src/plugins/intel_gna/gna_graph_compiler.cpp  |   5 -
 src/plugins/intel_gna/gna_graph_compiler.hpp  |   3 +-
 .../intel_gna/gna_lib_ver_selector.hpp        |  16 -
 src/plugins/intel_gna/gna_model_serial.cpp    | 321 --------------
 src/plugins/intel_gna/gna_model_serial.hpp    |   6 -
 src/plugins/intel_gna/gna_plugin.cpp          | 103 +----
 src/plugins/intel_gna/gna_plugin.hpp          |  11 -
 src/plugins/intel_gna/gna_plugin_config.cpp   |  32 --
 src/plugins/intel_gna/gna_plugin_config.hpp   |  12 -
 .../intel_gna/gna_plugin_entry_points.cpp     |   2 -
 .../intel_gna/gna_plugin_query_api.cpp        |  11 -
 .../intel_gna/optimizer/gna_pass_manager.cpp  |   3 -
 src/plugins/intel_gna/runtime/cnn.cpp         |   4 -
 src/plugins/intel_gna/runtime/cnn.h           |   2 -
 .../runtime/gna_float_runtime_op.cpp          |   4 -
 src/tests/unit/gna/gna_api_stub.cpp           | 197 ---------
 src/tests/unit/gna/gna_mock_api.hpp           |  57 ---
 src/tests/unit/gna/gna_plugin_config_test.cpp |  22 +-
 src/tests/unit/gna/gna_wait_test.cpp          |   3 -
 .../functional/gna/CMakeLists.txt             |  10 -
 .../lstm/lstm_cell_test.cpp                   |   6 -
 src/tests_deprecated/unit/CMakeLists.txt      |   6 -
 .../unit/engines/gna/gna_aminteldnn_test.cpp  |  12 -
 .../unit/engines/gna/gna_api_stub.cpp         | 202 +--------
 .../unit/engines/gna/gna_cppwrapper_test.cpp  |  12 -
 .../unit/engines/gna/gna_graph_aot_test.cpp   |  19 -
 .../unit/engines/gna/gna_matcher.cpp          |  78 +---
 .../unit/engines/gna/gna_mock_api.hpp         |  67 +--
 .../engines/gna/i16_quantisation_test.cpp     |  19 -
 .../engines/gna/layers/gna_squeeze_test.cpp   |   7 -
 50 files changed, 50 insertions(+), 2009 deletions(-)

diff --git a/cmake/dependencies.cmake b/cmake/dependencies.cmake
index cd433dbf639..5087a87a9bb 100644
--- a/cmake/dependencies.cmake
+++ b/cmake/dependencies.cmake
@@ -276,20 +276,8 @@ if(ENABLE_INTEL_GNA)
             GNA_LIB_DIR
             libGNA_INCLUDE_DIRS
             libGNA_LIBRARIES_BASE_PATH)
-    if(GNA_LIBRARY_VERSION STREQUAL "GNA1")
-        RESOLVE_DEPENDENCY(GNA
-                ARCHIVE_UNIFIED "GNA/gna_20181120.zip"
-                TARGET_PATH "${TEMP}/gna"
-                SHA256 "b631d6cc5f6cca4a89a3f5dfa383066f3282fee25d633d9085c605bdd8090210")
-    else()
-        if(GNA_LIBRARY_VERSION STREQUAL "GNA1_1401")
-            set(GNA_VERSION "01.00.00.1401")
-            set(GNA_HASH "cc954e67525006bf8bd353a6682e38bf208f6d74e973e0fc292850e721f17452")
-        endif()
-        if(GNA_LIBRARY_VERSION STREQUAL "GNA2")
-            set(GNA_VERSION "03.00.00.1455")
-            set(GNA_HASH "8ac1af18eb32777b00193f4f8c252ee4f8bd64a9069138b4a5aaeebd82ead464")
-        endif()
+        set(GNA_VERSION "03.00.00.1455")
+        set(GNA_HASH "8ac1af18eb32777b00193f4f8c252ee4f8bd64a9069138b4a5aaeebd82ead464")
 
         set(FILES_TO_EXTRACT_LIST gna_${GNA_VERSION}/include)
         if(WIN32)
@@ -304,7 +292,6 @@ if(ENABLE_INTEL_GNA)
                 VERSION_REGEX ".*_([0-9]+.[0-9]+.[0-9]+.[0-9]+).*"
                 FILES_TO_EXTRACT FILES_TO_EXTRACT_LIST
                 SHA256 ${GNA_HASH})
-    endif()
     update_deps_cache(GNA "${GNA}" "Path to GNA root folder")
     debug_message(STATUS "gna=" ${GNA})
 endif()
diff --git a/cmake/features.cmake b/cmake/features.cmake
index cca9a235183..1a238a20e2a 100644
--- a/cmake/features.cmake
+++ b/cmake/features.cmake
@@ -87,22 +87,6 @@ ie_dependent_option (ENABLE_TBBBIND_2_5 "Enable TBBBind_2_5 static usage in Open
 ie_dependent_option (ENABLE_INTEL_GNA "GNA support for inference engine" ON
     "NOT APPLE;NOT ANDROID;X86_64;CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 5.4" OFF)
 
-if (ENABLE_INTEL_GNA)
-    if (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.4)
-        set (DEFAULT_GNA_LIB GNA1)
-    else()
-        set (DEFAULT_GNA_LIB GNA2)
-    endif()
-    set(GNA_LIBRARY_VERSION "${DEFAULT_GNA_LIB}" CACHE STRING "GNAVersion")
-    set_property(CACHE GNA_LIBRARY_VERSION PROPERTY STRINGS "GNA1" "GNA1_1401" "GNA2")
-    list (APPEND IE_OPTIONS GNA_LIBRARY_VERSION)
-    if (NOT GNA_LIBRARY_VERSION STREQUAL "GNA1" AND
-        NOT GNA_LIBRARY_VERSION STREQUAL "GNA1_1401" AND
-        NOT GNA_LIBRARY_VERSION STREQUAL "GNA2")
-        message(FATAL_ERROR "GNA_LIBRARY_VERSION should be set to GNA1, GNA1_1401 or GNA2. Default option is ${DEFAULT_GNA_LIB}")
-    endif()
-endif()
-
 if(ENABLE_TESTS OR BUILD_SHARED_LIBS)
     set(ENABLE_IR_V7_READER_DEFAULT ON)
 else()
@@ -213,11 +197,6 @@ endif()
 
 if (ENABLE_INTEL_GNA)
     add_definitions(-DENABLE_INTEL_GNA)
-
-    if (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.4)
-        message(WARNING "${GNA_LIBRARY_VERSION} is not supported on GCC version ${CMAKE_CXX_COMPILER_VERSION}. Fallback to GNA1")
-        set(GNA_LIBRARY_VERSION GNA1)
-    endif()
 endif()
 
 print_enabled_features()
diff --git a/inference-engine/cmake/libGNAConfig.cmake b/inference-engine/cmake/libGNAConfig.cmake
index cf70b22430a..e203249d0ee 100644
--- a/inference-engine/cmake/libGNAConfig.cmake
+++ b/inference-engine/cmake/libGNAConfig.cmake
@@ -19,22 +19,8 @@ set(GNA_LIBS_LIST
         "libGNA::API"
         "libGNA::KERNEL")
 
-if (GNA_LIBRARY_VERSION STREQUAL "GNA1")
-    # use old version of GNA Library from gna_20181120
-    if (WIN32)
-        set(GNA_LIB_DIR x64 CACHE STRING "" FORCE)
-    else ()
-        list(APPEND GNA_LIBS_LIST
-                "libGNA::OLD_API_LIB")
-        set(GNA_LIB_DIR lib CACHE STRING "" FORCE)
-        set(GNA_KERNEL_LIB_NAME gna_kernel CACHE STRING "" FORCE)
-    endif()
-    set(libGNA_INCLUDE_DIRS "${GNA}/${GNA_PLATFORM_DIR}/include" CACHE STRING "" FORCE)
-else()
-    # use current version of GNA library
-    set(GNA_LIB_DIR x64 CACHE STRING "" FORCE)
-    set(libGNA_INCLUDE_DIRS "${GNA}/include" CACHE STRING "" FORCE)
-endif()
+set(GNA_LIB_DIR x64 CACHE STRING "" FORCE)
+set(libGNA_INCLUDE_DIRS "${GNA}/include" CACHE STRING "" FORCE)
 set(libGNA_LIBRARIES_BASE_PATH ${GNA}/${GNA_PLATFORM_DIR}/${GNA_LIB_DIR} CACHE STRING "" FORCE)
 
 add_library(libGNA::KERNEL SHARED IMPORTED)
@@ -44,18 +30,6 @@ find_library(GNA_KERNEL_LIBRARY
         ${libGNA_LIBRARIES_BASE_PATH})
 set_target_properties(libGNA::KERNEL PROPERTIES IMPORTED_LOCATION ${GNA_KERNEL_LIBRARY})
 
-if ((GNA_LIBRARY_VERSION STREQUAL "GNA1") AND (NOT WIN32))
-    add_library(libGNA::OLD_API_LIB SHARED IMPORTED)
-    find_library(GNA_API_LIBRARY
-            gna_api
-            HINTS
-            ${libGNA_LIBRARIES_BASE_PATH})
-    set_target_properties(libGNA::OLD_API_LIB PROPERTIES IMPORTED_LOCATION ${GNA_API_LIBRARY})
-    target_link_libraries(libGNA::OLD_API_LIB INTERFACE libGNA::KERNEL)
-    set_target_properties(libGNA::OLD_API_LIB PROPERTIES IMPORTED_NO_SONAME TRUE)
-    set_target_properties(libGNA::KERNEL PROPERTIES IMPORTED_NO_SONAME TRUE)
-endif()
-
 add_library(libGNA::API INTERFACE IMPORTED)
 set_property(TARGET libGNA::API PROPERTY INTERFACE_INCLUDE_DIRECTORIES ${libGNA_INCLUDE_DIRS})
 
@@ -75,6 +49,6 @@ endforeach(_lib_name)
 if (WIN32)
     set_target_properties(libGNA::KERNEL PROPERTIES
         IMPORTED_IMPLIB ${GNA_KERNEL_LIBRARY})
-elseif(NOT GNA_LIBRARY_VERSION STREQUAL "GNA1")
+else()
     set_target_properties(libGNA PROPERTIES INTERFACE_LINK_OPTIONS "-Wl,-rpath-link,${libGNA_LIBRARIES_BASE_PATH}")
 endif ()
diff --git a/src/plugins/intel_gna/CMakeLists.txt b/src/plugins/intel_gna/CMakeLists.txt
index 417063188d3..73557e42120 100644
--- a/src/plugins/intel_gna/CMakeLists.txt
+++ b/src/plugins/intel_gna/CMakeLists.txt
@@ -21,12 +21,6 @@ find_package(libGNA REQUIRED
              PATHS "${IE_MAIN_SOURCE_DIR}/cmake"
              NO_DEFAULT_PATH)
 
-if(GNA_LIBRARY_VERSION STREQUAL "GNA2")
-    set(GNA_LIBRARY_VERSION_NUMBER 2)
-else()
-    set(GNA_LIBRARY_VERSION_NUMBER 1)
-endif()
-
 #
 # Shared plugin library
 #
@@ -45,8 +39,7 @@ target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
 target_compile_definitions(${TARGET_NAME}
     PRIVATE
         _NO_MKL_
-    PUBLIC
-        GNA_LIB_VER=${GNA_LIBRARY_VERSION_NUMBER})
+    )
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
@@ -61,7 +54,6 @@ target_compile_definitions(${TARGET_NAME}_test_static
             _NO_MKL_
             IMPLEMENT_INFERENCE_ENGINE_PLUGIN
         PUBLIC
-            GNA_LIB_VER=${GNA_LIBRARY_VERSION_NUMBER}
             INTEGER_LOW_P
             USE_STATIC_IE)
 
diff --git a/src/plugins/intel_gna/backend/am_intel_dnn.cpp b/src/plugins/intel_gna/backend/am_intel_dnn.cpp
index c77aa370280..d3d635a693b 100644
--- a/src/plugins/intel_gna/backend/am_intel_dnn.cpp
+++ b/src/plugins/intel_gna/backend/am_intel_dnn.cpp
@@ -28,15 +28,9 @@
 #include "gna_limitations.hpp"
 #include "layers/gna_convolution_layer.hpp"
 
-#if GNA_LIB_VER == 2
 #include <gna2-model-api.h>
 #include "gna2_model_helper.hpp"
 #include "gna2_model_debug_log.hpp"
-#else
-#include <gna-api-types-xnn.h>
-#include <map>
-
-#endif
 
 /**
  * whether to dump weights and biases
@@ -213,7 +207,6 @@ void GNAPluginNS::backend::AMIntelDNN::InitConvolutional1DComponentPrivate(intel
     }
 }
 
-#if GNA_LIB_VER == 2
 void GNAPluginNS::backend::AMIntelDNN::InitConvolutional2DComponentPrivate(intel_dnn_component_t& comp,
     OvGnaTensor inputTensor,
     OvGnaTensor outputTensor,
@@ -292,7 +285,6 @@ void GNAPluginNS::backend::AMIntelDNN::updateNumberOfOutputsIfPoolingEnabled(Gna
         }
     }
 }
-#endif
 
 void GNAPluginNS::backend::AMIntelDNN::InitMaxpoolComponentPrivate(intel_dnn_component_t &comp,
     std::array<uint32_t, 3> inCHW,
@@ -1105,7 +1097,6 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_
                 }
                     break;
                 case kDnnConvolutional2dOp: {
-#if GNA_LIB_VER == 2
                     const auto output_scale_factor = component[i].output_scale_factor;
                     const auto weight_scale_factor = component[i].op.conv2D.weight_scale_factor;
                     const auto convolution_stride_0 = component[i].op.conv2D.convStride[0];
@@ -1125,10 +1116,6 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_
                     out_file << "<zero_padding_0> " << std::dec << zero_padding_0 << "\n";
                     out_file << "<zero_padding_1> " << std::dec << zero_padding_1 << "\n";
                     out_file << "\n";
-#else
-                    fprintf(stderr, "Unsupported GNA Library version (!= 2) in WriteDnnText's kDnnConvolutional2dOp case!\n");
-                    throw - 1;
-#endif
                 }
                     break;
                 case kDnnRecurrentOp: {
@@ -1406,47 +1393,27 @@ uint32_t GNAPluginNS::backend::AMIntelDNN::CountLayers() {
     return n;
 }
 
-#if GNA_LIB_VER == 2
 void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(Gna2Model *gnaModel, const std::string& gnaCompileTarget) {
     Gna2Operation * gnaOperation;
     if (gnaModel == nullptr)
         THROW_GNA_EXCEPTION << "Invalid input parameter";
     if (gnaModel->Operations != nullptr)
         THROW_GNA_EXCEPTION << "InitGNAStruct can't work on preallocated layers array";
-#else
-void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet) {
-    intel_nnet_layer_t *pLayer;
-    if (ptr_nnet == nullptr)
-        THROW_GNA_EXCEPTION << "Invalid input parameter";
-    if (ptr_nnet->pLayers != nullptr)
-        THROW_GNA_EXCEPTION << "InitGNAStruct can't work on preallocated layers array";
-#endif
 
     if (component.empty())
         THROW_GNA_EXCEPTION << "empty model in GNAPluginNS::backend::AMIntelDNN::InitGNAStruct()";
 
-#if GNA_LIB_VER == 2
     gnaModel->NumberOfOperations = CountLayers();
     gnaModel->Operations = reinterpret_cast<Gna2Operation*>(gnaUserAllocator(gnaModel->NumberOfOperations * sizeof(Gna2Operation)));
     if (gnaModel->Operations == nullptr)
         THROW_GNA_EXCEPTION << "out of memory in GNAPluginNS::backend::AMIntelDNN::InitGNAStruct()";
     memset(gnaModel->Operations, 0, gnaModel->NumberOfOperations * sizeof(Gna2Operation));
     gnaOperation = gnaModel->Operations;
-#else
-    ptr_nnet->nLayers = CountLayers();
-    ptr_nnet->nGroup = num_group_in();
-    ptr_nnet->pLayers = reinterpret_cast<intel_nnet_layer_t *>(_mm_malloc(ptr_nnet->nLayers * sizeof(intel_nnet_layer_t), 64));
-    if (ptr_nnet->pLayers == nullptr)
-        THROW_GNA_EXCEPTION << "out of memory in GNAPluginNS::backend::AMIntelDNN::FillGNAStruct()";
-    memset(ptr_nnet->pLayers, 0, ptr_nnet->nLayers * sizeof(intel_nnet_layer_t));
-    pLayer = ptr_nnet->pLayers;
-#endif
     for (int i = 0; i < component.size(); i++) {
         // std::cout << "Component + " << i <<"=GNA_" << std::distance(ptr_nnet->pLayers, pLayer) << "\n";
         auto& comp = component[i];
         switch (comp.operation) {
             case kDnnAffineOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitFullyConnectedAffine(gnaOperation, gnaUserAllocator, gnaUserFree,
                     createGna2Tensor2D(comp.num_rows_in, comp.num_columns_in, comp.num_bytes_per_input, comp.ptr_inputs),
                     createGna2Tensor2D(comp.num_rows_out, comp.num_columns_out, comp.num_bytes_per_output, comp.ptr_outputs),
@@ -1454,37 +1421,8 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
                     createGna2BiasTensor1D(comp.num_rows_out, comp.op.affine.num_bytes_per_bias, comp.op.affine.ptr_biases),
                     nullptr);
                 AdvanceOperationIfAllApplied(component, i, gnaOperation);
-#else
-                pLayer->nInputRows = component[i].num_rows_in;
-                pLayer->nInputColumns = component[i].num_columns_in;
-                pLayer->nOutputRows = component[i].num_rows_out;
-                pLayer->nOutputColumns = component[i].num_columns_out;
-                pLayer->nBytesPerInput = component[i].num_bytes_per_input;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;  //  will be overwritten if PWL op is needed
-                pLayer->nBytesPerIntermediateOutput = sizeof(int32_t);
-                pLayer->pInputs = component[i].ptr_inputs;
-                pLayer->pOutputsIntermediate = component[i].ptr_outputs;
-                pLayer->pOutputs = component[i].ptr_outputs;  //  will be overwritten if PWL op is needed
-                pLayer->nLayerKind = INTEL_AFFINE;
-                {
-                    pLayer->pLayerStruct = _mm_malloc(sizeof(intel_affine_layer_t), 64);
-                    if (pLayer->pLayerStruct == nullptr) {
-                        THROW_GNA_EXCEPTION << "could not allocate memory for INTEL_AFFINE layer structure.";
-                    }
-                    auto pAffineLayer = reinterpret_cast<intel_affine_layer_t *>(pLayer->pLayerStruct);
-                    pAffineLayer->pwl.pSegments = nullptr;
-                    pAffineLayer->pwl.nSegments = 0;
-
-                    pAffineLayer->affine.nBytesPerBias = component[i].op.affine.num_bytes_per_bias;
-                    pAffineLayer->affine.nBytesPerWeight = component[i].op.affine.num_bytes_per_weight;
-                    pAffineLayer->affine.pBiases = component[i].op.affine.ptr_biases;
-                    pAffineLayer->affine.pWeights = component[i].op.affine.ptr_weights;
-                }
-                AdvanceOperationIfAllApplied(component, i, pLayer);
-#endif
                 break;
             case kDnnDiagonalOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitElementWiseAffine(gnaOperation, gnaUserAllocator, gnaUserFree,
                     createGna2Tensor2D(comp.num_rows_in, comp.num_columns_in, comp.num_bytes_per_input, comp.ptr_inputs),
                     createGna2Tensor2D(comp.num_rows_out, comp.num_columns_out, comp.num_bytes_per_output, comp.ptr_outputs),
@@ -1492,37 +1430,8 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
                     createGna2Tensor1D(comp.num_rows_out, comp.op.affine.num_bytes_per_bias, comp.op.affine.ptr_biases),
                     nullptr);
                 AdvanceOperationIfAllApplied(component, i, gnaOperation);
-#else
-                pLayer->nInputRows = component[i].num_rows_in;
-                pLayer->nInputColumns = component[i].num_columns_in;
-                pLayer->nOutputRows = component[i].num_rows_out;
-                pLayer->nOutputColumns = component[i].num_columns_out;
-                pLayer->nBytesPerInput = component[i].num_bytes_per_input;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;  //  will be overwritten if PWL op is needed
-                pLayer->nBytesPerIntermediateOutput = sizeof(int32_t);
-                pLayer->pInputs = component[i].ptr_inputs;
-                pLayer->pOutputsIntermediate = component[i].ptr_outputs;
-                pLayer->pOutputs = component[i].ptr_outputs;  //  will be overwritten if PWL op is needed
-                pLayer->nLayerKind = INTEL_AFFINE_DIAGONAL;
-                {
-                    pLayer->pLayerStruct = _mm_malloc(sizeof(intel_affine_layer_t), 64);
-                    if (pLayer->pLayerStruct == nullptr) {
-                        THROW_GNA_EXCEPTION << "could not allocate memory for INTEL_AFFINE_DIAGONAL layer structure.";
-                    }
-                    auto pDiagonalLayer = reinterpret_cast<intel_affine_layer_t *>(pLayer->pLayerStruct);
-                    pDiagonalLayer->pwl.pSegments = nullptr;
-                    pDiagonalLayer->pwl.nSegments = 0;
-
-                    pDiagonalLayer->affine.nBytesPerBias = component[i].op.affine.num_bytes_per_bias;
-                    pDiagonalLayer->affine.nBytesPerWeight = component[i].op.affine.num_bytes_per_weight;
-                    pDiagonalLayer->affine.pBiases = component[i].op.affine.ptr_biases;
-                    pDiagonalLayer->affine.pWeights = component[i].op.affine.ptr_weights;
-                }
-                AdvanceOperationIfAllApplied(component, i, pLayer);
-#endif
                 break;
             case kDnnRecurrentOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitRecurrent(
                         gnaOperation,
                         gnaUserAllocator,
@@ -1549,39 +1458,8 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
                         nullptr,
                         create_uint32_parameter(1));    // TODO: GNA2: Handle other delays
                 AdvanceOperationIfAllApplied(component, i, gnaOperation);
-#else
-                pLayer->nInputRows = component[i].num_rows_in;
-                pLayer->nInputColumns = component[i].num_columns_in;
-                pLayer->nOutputRows = component[i].num_rows_out;
-                pLayer->nOutputColumns = component[i].num_columns_out;
-                pLayer->nBytesPerInput = component[i].num_bytes_per_input;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;  //  will be overwritten if PWL op is needed
-                pLayer->nBytesPerIntermediateOutput = sizeof(int32_t);
-                pLayer->pInputs = component[i].ptr_inputs;
-                pLayer->pOutputsIntermediate = component[i].ptr_outputs;
-                pLayer->pOutputs = component[i].ptr_outputs;  //  will be overwritten if PWL op is needed
-                pLayer->nLayerKind = INTEL_RECURRENT;
-                {
-                    pLayer->pLayerStruct = _mm_malloc(sizeof(intel_recurrent_layer_t), 64);
-                    if (pLayer->pLayerStruct == nullptr) {
-                        THROW_GNA_EXCEPTION << "could not allocate memory for INTEL_RECURRENT layer structure.";
-                    }
-                    auto pRecurrentLayer = reinterpret_cast<intel_recurrent_layer_t *>(pLayer->pLayerStruct);
-
-                    pRecurrentLayer->pFeedbackBuffer = component[i].op.recurrent.ptr_feedbacks;
-                    pRecurrentLayer->pwl.pSegments = nullptr;
-                    pRecurrentLayer->pwl.nSegments = 0;
-
-                    pRecurrentLayer->affine.nBytesPerBias = component[i].op.recurrent.num_bytes_per_bias;
-                    pRecurrentLayer->affine.nBytesPerWeight = component[i].op.recurrent.num_bytes_per_weight;
-                    pRecurrentLayer->affine.pBiases = component[i].op.recurrent.ptr_biases;
-                    pRecurrentLayer->affine.pWeights = component[i].op.recurrent.ptr_weights;
-                }
-                AdvanceOperationIfAllApplied(component, i, pLayer);
-#endif
                 break;
             case kDnnConvolutional1dOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitConvolution(
                         gnaOperation,
                         gnaUserAllocator,
@@ -1613,45 +1491,8 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
                         nullptr);
 
                 AdvanceCnnOperationIfAllApplied(component, i, gnaOperation);
-#else
-                pLayer->nInputRows = component[i].num_rows_in;
-                pLayer->nInputColumns = component[i].num_columns_in;
-                pLayer->nOutputRows = component[i].num_rows_out;
-                pLayer->nOutputColumns = component[i].num_columns_out;
-                pLayer->nBytesPerInput = component[i].num_bytes_per_input;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;  //  will be overwritten
-                pLayer->nBytesPerIntermediateOutput = sizeof(int32_t);
-                pLayer->pInputs = component[i].ptr_inputs;
-                pLayer->pOutputsIntermediate = component[i].ptr_outputs;
-                pLayer->pOutputs = component[i].ptr_outputs;  //  will be overwritten
-                pLayer->nLayerKind = INTEL_CONVOLUTIONAL;
-                {
-                    pLayer->pLayerStruct = _mm_malloc(sizeof(intel_convolutional_layer_t), 64);
-                    if (pLayer->pLayerStruct == nullptr) {
-                        THROW_GNA_EXCEPTION << "could not allocate memory for INTEL_CONVOLUTIONAL layer structure.";
-                    }
-                    auto pConvolutionalLayer = reinterpret_cast<intel_convolutional_layer_t *>(pLayer->pLayerStruct);
-                    pConvolutionalLayer->nBytesBias = component[i].op.conv1D.num_bytes_per_bias;
-                    pConvolutionalLayer->nBytesFilterCoefficient = component[i].op.conv1D.num_bytes_per_weight;
-                    pConvolutionalLayer->nFilters = component[i].op.conv1D.num_filters;
-                    pConvolutionalLayer->nFilterRows = comp.op.conv1D.num_filter_coefficients / comp.op.conv1D.convStride;
-                    pConvolutionalLayer->nFilterCoefficients = component[i].op.conv1D.num_filter_coefficients;
-                    pConvolutionalLayer->nFeatureMaps = 1;
-                    pConvolutionalLayer->nFeatureMapColumns = component[i].op.conv1D.convStride;
-                    pConvolutionalLayer->nFeatureMapRows = pLayer->nInputColumns / pConvolutionalLayer->nFeatureMapColumns;
-                    pConvolutionalLayer->poolType = INTEL_NO_POOLING;  //  will be overwritten
-                    pConvolutionalLayer->nPoolSize = 0;  //  will be overwritten
-                    pConvolutionalLayer->nPoolStride = 0;  //  will be overwritten
-                    pConvolutionalLayer->pwl.nSegments = 0;  //  will be overwritten
-                    pConvolutionalLayer->pwl.pSegments = nullptr;  //  will be overwritten
-                    pConvolutionalLayer->pBiases = component[i].op.conv1D.ptr_biases;
-                    pConvolutionalLayer->pFilters = component[i].op.conv1D.ptr_filters;
-                }
-                AdvanceCnnOperationIfAllApplied(component, i, pLayer);
-#endif
                 break;
             case kDnnConvolutional2dOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitConvolution(
                     gnaOperation,
                     gnaUserAllocator,
@@ -1672,14 +1513,10 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
                         comp.op.conv2D.zeroPadding[0], comp.op.conv2D.zeroPadding[1]));
 
                 AdvanceCnnOperationIfAllApplied(component, i, gnaOperation);
-#else
-                THROW_GNA_EXCEPTION << "Type kDnnConvolutional2dOp is only supported for GNA library 2.X";
-#endif
                 break;
             case kDnnMaxPoolOp:
                 if (i == 0) {
                     THROW_GNA_EXCEPTION << "Pooling component with no preceeding component";
-#if  GNA_LIB_VER == 2
                 } else if (gnaOperation->Type == Gna2OperationTypeConvolution) {
                     auto pwlOperand = gnaOperation->Operands[PwlOpIdx];
                     if (pwlOperand != nullptr && pwlOperand->Shape.Dimensions[0] != 0 &&
@@ -1737,34 +1574,11 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
                         }
                         AdvanceOperationIfAllApplied(component, i, gnaOperation);
                     }
-#else
-                } else if (pLayer->nLayerKind == INTEL_CONVOLUTIONAL) {
-                    if (pLayer->pLayerStruct == nullptr) {
-                        THROW_GNA_EXCEPTION << "INTEL_CONVOLUTIONAL layer structure was not initialized.";
-                    }
-                    auto pConvolutionalLayer = reinterpret_cast<intel_convolutional_layer_t *>(pLayer->pLayerStruct);
-                    // it is possible to have activation preceding to maxpool
-                    if (pConvolutionalLayer->pwl.nSegments != 0) {
-                        THROW_GNA_EXCEPTION << "Encountered activation component before pooling component at." << i;
-                    } else {
-                        pConvolutionalLayer->poolType = INTEL_MAX_POOLING;
-                        pConvolutionalLayer->nPoolSize = component[i].op.maxpool.poolingWindowXY[0];
-                        pConvolutionalLayer->nPoolStride = component[i].op.maxpool.poolingStrideXY[0];
-
-                        // number of output columns correction - based on GNA-library expectations
-                        auto nFltSize = pConvolutionalLayer->nFilterCoefficients;
-                        auto fltStrideSz = pConvolutionalLayer->nFeatureMaps * pConvolutionalLayer->nFeatureMapColumns;  // always move 1 "row"
-                        auto outFromConv = outputFromConv(pLayer->nInputColumns, nFltSize, fltStrideSz);
-                        // FLAT input matrix, pooled outputs per filter
-                        pLayer->nOutputColumns = pConvolutionalLayer->nFilters * outputFromPoolingLegacy(outFromConv, pConvolutionalLayer->nPoolStride);
-                    }
-#endif
                 } else {
                     THROW_GNA_EXCEPTION << "Pooling component applied to non-convolutional layer";
                 }
                 break;
             case kDnnPiecewiselinearOp:
-#if  GNA_LIB_VER == 2
                 {
                     IE_ASSERT(gnaOperation->Operands != nullptr);
                     IE_ASSERT(OutOpIdx < gnaOperation->NumberOfOperands);
@@ -1811,108 +1625,25 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
                     }
                 }
                 AdvancePwlOperationIfAllApplied(component, i, gnaOperation);
-#else
-                pLayer->pOutputs = component[i].ptr_outputs;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;
-                if (pLayer->pLayerStruct == nullptr) {
-                    THROW_GNA_EXCEPTION << "["<< i <<"]"<< pLayer->nLayerKind << " layer structure was not initialized.";
-                }
-                if (i == 0) {
-                    THROW_GNA_EXCEPTION << "PWL component with no preceding component.";
-                } else if ((component[i - 1].operation == kDnnAffineOp)
-                           || (component[i - 1].operation == kDnnDiagonalOp)) {
-                    auto pAffineLayer = reinterpret_cast<intel_affine_layer_t *>(pLayer->pLayerStruct);
-                    pAffineLayer->pwl.nSegments = component[i].op.pwl.num_segments;
-                    pAffineLayer->pwl.pSegments = component[i].op.pwl.ptr_segments;
-                } else if (component[i - 1].operation == kDnnRecurrentOp) {
-                    auto pRecurrentLayer = reinterpret_cast<intel_recurrent_layer_t *>(pLayer->pLayerStruct);
-                    pRecurrentLayer->pwl.nSegments = component[i].op.pwl.num_segments;
-                    pRecurrentLayer->pwl.pSegments = component[i].op.pwl.ptr_segments;
-                } else if ((component[i - 1].operation == kDnnConvolutional1dOp)
-                           || ((component[i - 1].operation == kDnnMaxPoolOp)
-                               && (component[i - 2].operation == kDnnConvolutional1dOp))) {
-                    auto pConvolutionalLayer = reinterpret_cast<intel_convolutional_layer_t *>(pLayer->pLayerStruct);
-                    pConvolutionalLayer->pwl.nSegments = component[i].op.pwl.num_segments;
-                    pConvolutionalLayer->pwl.pSegments = component[i].op.pwl.ptr_segments;
-                }
-                pLayer++;
-#endif
                 break;
             case kDnnInterleaveOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitInterleave(gnaOperation, gnaUserAllocator, gnaUserFree,
                     createGna2Tensor2D(comp.num_rows_in, comp.num_columns_in, comp.num_bytes_per_input, comp.ptr_inputs),
                     createGna2Tensor2D(comp.num_rows_out, comp.num_columns_out, comp.num_bytes_per_output, comp.ptr_outputs));
                 gnaOperation++;
-#else
-                pLayer->nInputRows = component[i].num_rows_in;
-                pLayer->nInputColumns = component[i].num_columns_in;
-                pLayer->nOutputRows = component[i].num_rows_out;
-                pLayer->nOutputColumns = component[i].num_columns_out;
-                pLayer->nBytesPerInput = component[i].num_bytes_per_input;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;
-                pLayer->nBytesPerIntermediateOutput = sizeof(int32_t);
-                pLayer->pInputs = component[i].ptr_inputs;
-                pLayer->pOutputsIntermediate = nullptr;
-                pLayer->pOutputs = component[i].ptr_outputs;
-                pLayer->nLayerKind = INTEL_INTERLEAVE;
-                pLayer->pLayerStruct = nullptr;
-                pLayer++;
-#endif
                 break;
             case kDnnDeinterleaveOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitDeInterleave(gnaOperation, gnaUserAllocator, gnaUserFree,
                     createGna2Tensor2D(comp.num_rows_in, comp.num_columns_in, comp.num_bytes_per_input, comp.ptr_inputs),
                     createGna2Tensor2D(comp.num_rows_out, comp.num_columns_out, comp.num_bytes_per_output, comp.ptr_outputs));
                 gnaOperation++;
-#else
-                pLayer->nInputRows = component[i].num_rows_in;
-                pLayer->nInputColumns = component[i].num_columns_in;
-                pLayer->nOutputRows = component[i].num_rows_out;
-                pLayer->nOutputColumns = component[i].num_columns_out;
-                pLayer->nBytesPerInput = component[i].num_bytes_per_input;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;
-                pLayer->nBytesPerIntermediateOutput = sizeof(int32_t);
-                pLayer->pInputs = component[i].ptr_inputs;
-                pLayer->pOutputsIntermediate = nullptr;
-                pLayer->pOutputs = component[i].ptr_outputs;
-                pLayer->nLayerKind = INTEL_DEINTERLEAVE;
-                pLayer->pLayerStruct = nullptr;
-                pLayer++;
-#endif
                 break;
             case kDnnCopyOp:
-#if  GNA_LIB_VER == 2
                 HelperGna2OperationInitCopy(gnaOperation, gnaUserAllocator, gnaUserFree,
                     createGna2Tensor2D(comp.num_columns_in, comp.num_rows_in, comp.num_bytes_per_input, comp.ptr_inputs),
                     createGna2Tensor2D(comp.num_columns_out, comp.num_rows_out, comp.num_bytes_per_output, comp.ptr_outputs),
                     create_shape2D_parameter(comp.op.copy.num_copy_columns, comp.op.copy.num_copy_rows));
                 gnaOperation++;
-#else
-                pLayer->nInputRows = component[i].num_columns_in;
-                pLayer->nInputColumns = component[i].num_rows_in;
-                pLayer->nOutputRows = component[i].num_columns_out;
-                pLayer->nOutputColumns = component[i].num_rows_out;
-                pLayer->nBytesPerInput = component[i].num_bytes_per_input;
-                pLayer->nBytesPerOutput = component[i].num_bytes_per_output;
-                pLayer->nBytesPerIntermediateOutput = sizeof(int32_t);
-                pLayer->pInputs = component[i].ptr_inputs;
-                pLayer->pOutputsIntermediate = nullptr;
-                pLayer->pOutputs = component[i].ptr_outputs;
-                pLayer->nLayerKind = INTEL_COPY;
-                pLayer->pLayerStruct = nullptr;
-                {
-                    pLayer->pLayerStruct = _mm_malloc(sizeof(intel_copy_layer_t), 64);
-                    if (pLayer->pLayerStruct == nullptr) {
-                        THROW_GNA_EXCEPTION << pLayer->nLayerKind << " could not allocate memory for INTEL_COPY layer structure.";
-                    }
-                    auto *pCopyLayer = reinterpret_cast<intel_copy_layer_t *>(pLayer->pLayerStruct);
-                    pCopyLayer->nCopyRows = component[i].op.copy.num_copy_columns;
-                    pCopyLayer->nCopyCols = component[i].op.copy.num_copy_rows;
-                }
-                pLayer++;
-#endif
                 break;
             default: {
                 THROW_GNA_EXCEPTION << "GNA does yet not support " << intel_dnn_operation_name[component[i].operation];
@@ -1920,13 +1651,9 @@ void GNAPluginNS::backend::AMIntelDNN::InitGNAStruct(intel_nnet_type_t *ptr_nnet
         }
     }
     // enable debugging of partial array of components
-#if  GNA_LIB_VER == 2
     gnaModel->NumberOfOperations = std::distance(gnaModel->Operations, gnaOperation);
-#else
-    ptr_nnet->nLayers = std::distance(ptr_nnet->pLayers, pLayer);
-#endif
 }
-#if  GNA_LIB_VER == 2
+
 void GNAPluginNS::backend::AMIntelDNN::DestroyGNAStruct(Gna2Model *gnaModel) {
     if (gnaModel->Operations != nullptr) {
         for (int i = 0; i < gnaModel->NumberOfOperations; i++) {
@@ -1946,143 +1673,7 @@ void GNAPluginNS::backend::AMIntelDNN::DestroyGNAStruct(Gna2Model *gnaModel) {
     }
     gnaModel->NumberOfOperations = 0;
 }
-#else
-void GNAPluginNS::backend::AMIntelDNN::DestroyGNAStruct(intel_nnet_type_t *ptr_nnet) {
-    ptr_nnet->nGroup = 0;
-    if (ptr_nnet->pLayers != nullptr) {
-        for (int i = 0; i < ptr_nnet->nLayers; i++) {
-            switch (ptr_nnet->pLayers[i].nLayerKind) {
-                case INTEL_AFFINE:break;
-                case INTEL_AFFINE_DIAGONAL:break;
-                case INTEL_RECURRENT:break;
-                case INTEL_CONVOLUTIONAL:break;
-                case INTEL_INTERLEAVE:break;
-                case INTEL_DEINTERLEAVE:break;
-                case INTEL_COPY:break;
-                default:break;
-            }
-            if (ptr_nnet->pLayers[i].pLayerStruct != nullptr) {
-                _mm_free(ptr_nnet->pLayers[i].pLayerStruct);
-            }
-        }
-        if (ptr_nnet->pLayers != nullptr) {
-            _mm_free(ptr_nnet->pLayers);
-        }
-    }
-    ptr_nnet->nLayers = 0;
-}
-#endif
 
-
-#if GNA_LIB_VER == 1
-void GNAPluginNS::backend::AMIntelDNN::WriteInputAndOutputTextGNA(intel_nnet_type_t * nnet) {
-#ifdef LIGHT_DUMP
-    if (nnet) {
-        for (int i = 0; i < nnet->nLayers; i++) {
-            auto component = nnet->pLayers;
-            std::stringstream out_file_name;
-            auto getLayerType = [](decltype(INTEL_AFFINE) kind){
-                switch (kind){
-                    case INTEL_AFFINE : return "affine";
-                    case INTEL_AFFINE_DIAGONAL : return "diag";
-                    case INTEL_RECURRENT : return "recurrent";
-                    case INTEL_CONVOLUTIONAL : return "convolution";
-                    case INTEL_INTERLEAVE : return "interleave";
-                    case INTEL_DEINTERLEAVE : return "deinterleave";
-                    case INTEL_COPY : return "copy";
-                    default: return "unknown";
-                }
-            };
-            out_file_name << std::setfill('0') << std::setw(2) << i << "_"
-                          << getLayerType(component[i].nLayerKind)
-                          << "-" << nnet->pLayers[i].nInputRows
-                          << "-" << nnet->pLayers[i].nOutputRows;
-
-            auto dumpFilePrefixGNA = getDumpFilePrefixGNA();
-            auto inputfileName = dumpFilePrefixGNA + out_file_name.str() + "_input.txt";
-            auto outFileName = dumpFilePrefixGNA + out_file_name.str() + "_output.txt";
-            auto pwlFileName = dumpFilePrefixGNA + out_file_name.str() + "_pwl.txt";
-            auto refOutputFileName = getRefFolderName() + out_file_name.str() + "_output.txt";
-
-
-
-            std::ofstream out_file(outFileName.c_str(), std::ios::out);
-            std::ofstream pwl_file(pwlFileName.c_str(), std::ios::out);
-            std::ifstream ref_out_file(refOutputFileName.c_str(), std::ios::in);
-            std::ofstream in_file(inputfileName.c_str(), std::ios::out);
-            if (!out_file || !in_file) {
-                return;
-            }
-
-            float  summOfDiff = 0.f;
-            float  summOfSqDiff = 0.f;
-            float  maxD = 0.0f;
-            int    numItems = 0;
-
-            auto write_pwl = [&pwl_file](intel_pwl_func_t & pwl) {
-                for (int k =0; k < pwl.nSegments; k++) {
-                    pwl_file << pwl.pSegments[k].slope << ", " << pwl.pSegments[k].xBase << ", " << pwl.pSegments[k].yBase << "\n";
-                }
-            };
-            if (nnet->pLayers[i].nLayerKind == INTEL_AFFINE || nnet->pLayers[i].nLayerKind == INTEL_AFFINE_DIAGONAL) {
-                auto affine = reinterpret_cast<intel_affine_layer_t*>(nnet->pLayers[i].pLayerStruct);
-                write_pwl(affine->pwl);
-            }
-            if (nnet->pLayers[i].nLayerKind == INTEL_CONVOLUTIONAL) {
-                auto conv = reinterpret_cast<intel_convolutional_layer_t*>(nnet->pLayers[i].pLayerStruct);
-                write_pwl(conv->pwl);
-            }
-
-            for (int k = 0; k < component[i].nOutputRows; k++) {
-                for (int j = 0; j < component[i].nOutputColumns; j++) {
-                    float floatValue = 0.f;
-                    if (component[i].nBytesPerOutput == 4) {
-                        auto value = (reinterpret_cast<int32_t *>(component[i].pOutputs)[k * component[i].nOutputColumns + j]);
-                        floatValue = (static_cast<float>(value) / 1.0);
-                    } else {
-                        auto value = reinterpret_cast<int16_t *>(component[i].pOutputs)[k * component[i].nOutputColumns + j];
-                        floatValue = (static_cast<float>(value) / 1.0);
-                    }
-                    out_file << std::setw(8) << floatValue << "\n";
-                    if (ref_out_file) {
-                        float ref_value = 0.f;
-                        ref_out_file >> ref_value;
-                        float diff = (ref_value - floatValue);
-                        diff = diff  < 0 ? -diff : diff;
-                        summOfDiff += diff;
-                        summOfSqDiff += diff * diff;
-                        maxD = std::max(maxD, diff);
-                        numItems++;
-                    }
-                }
-            }
-            if (numItems) {
-                auto rmse = sqrt(summOfSqDiff / numItems);
-                auto avg = summOfDiff / numItems;
-                std :: cout << std::left << std::setw(55) << out_file_name.str()
-                            << " RMSE="<< std::fixed << std::setprecision(5) << std::right << std::setw(8) << rmse
-                            << " avg=" << std::fixed << std::setprecision(5) << std::right << std::setw(8) << avg
-                            << " maxD="<< std::fixed << std::setprecision(5) << std::right << std::setw(8) << maxD << std::endl;
-            }
-
-
-            for (int k = 0; k < component[i].nInputRows; k++) {
-                for (int j = 0; j < component[i].nInputColumns; j++) {
-                    if (component[i].nBytesPerInput == 4) {
-                        in_file << std::setw(8)
-                                << (reinterpret_cast<int32_t *>(component[i].pInputs)[k * component[i].nInputColumns + j]);
-                    } else {
-                        in_file << std::setw(8)
-                                << (reinterpret_cast<int16_t *>(component[i].pInputs)[k * component[i].nInputColumns + j]);
-                    }
-                    in_file << "\n";
-                }
-            }
-        }
-    }
-#endif
-}
-#else
 void GNAPluginNS::backend::AMIntelDNN::WriteInputAndOutputTextGNA(const Gna2Model & model) {
 #ifdef LIGHT_DUMP
     WriteInputAndOutputTextGNAImpl(
@@ -2091,7 +1682,6 @@ void GNAPluginNS::backend::AMIntelDNN::WriteInputAndOutputTextGNA(const Gna2Mode
         getRefFolderName());
 #endif
 }
-#endif
 
 void GNAPluginNS::backend::AMIntelDNN::WriteInputAndOutputText() {
 #ifdef LIGHT_DUMP
diff --git a/src/plugins/intel_gna/backend/am_intel_dnn.hpp b/src/plugins/intel_gna/backend/am_intel_dnn.hpp
index c87c8bf97b1..083717f6a40 100644
--- a/src/plugins/intel_gna/backend/am_intel_dnn.hpp
+++ b/src/plugins/intel_gna/backend/am_intel_dnn.hpp
@@ -14,10 +14,8 @@
 
 #include "gna_plugin_log.hpp"
 
-#if GNA_LIB_VER == 2
 #include <gna2-model-api.h>
 #include <gna/gna_config.hpp>
-#endif
 
 namespace GNAPluginNS {
 namespace backend {
@@ -125,7 +123,6 @@ public:
                                             true);
     }
 
-#if GNA_LIB_VER == 2
     template<class A, class B, class C, class D>
     static void InitConvolutional2DComponent(intel_dnn_component_t& comp,
         OvGnaTensor inputTensor,
@@ -161,7 +158,6 @@ public:
     // Recomputes number of outputs from CNN1D operations using legacy or new formula
     // If isOperationCnnLegacySpecific() is true the number of outputs will also be recomputed for legacy compatibility
     static void updateNumberOfOutputsIfPoolingEnabled(Gna2Model& gnaModel, bool useLegacyFormula);
-#endif
 
     template<class A, class B>
     static void InitMaxpoolComponent(intel_dnn_component_t &cmp,
@@ -300,17 +296,9 @@ public:
 
     void WriteDnnText(const char *filename, intel_dnn_number_type_t logging_precision);
 
-
-#if GNA_LIB_VER == 2
     void InitGNAStruct(Gna2Model *gnaModel, const std::string& gnaCompileTarget = InferenceEngine::GNAConfigParams::GNA_TARGET_2_0);
+
     void DestroyGNAStruct(Gna2Model *gnaModel);
-#else
-
-    void InitGNAStruct(intel_nnet_type_t *ptr_nnet);
-
-    void DestroyGNAStruct(intel_nnet_type_t *ptr_nnet);
-
-#endif
 
     uint32_t *ptr_active_outputs() { return (ptr_active_outputs_); }
 
@@ -345,11 +333,7 @@ public:
 
     void WriteInputAndOutputText();
 
-#if GNA_LIB_VER == 1
-    void WriteInputAndOutputTextGNA(intel_nnet_type_t *nnet);
-#else
     void WriteInputAndOutputTextGNA(const Gna2Model & model);
-#endif
 
     void BeginNewWrite(uint32_t index);
 
@@ -444,7 +428,6 @@ private:
                                                     void *&ptr_biases,
                                                     bool postInitMem);
 
-#if GNA_LIB_VER == 2
     static void InitConvolutional2DComponentPrivate(intel_dnn_component_t& comp,
         OvGnaTensor inputTensor,
         OvGnaTensor outputTensor,
@@ -458,7 +441,6 @@ private:
         void*& ptr_outputs,
         void*& ptr_filters,
         void*& ptr_biases);
-#endif
 
     static void InitAffineComponentPrivate(intel_dnn_component_t &comp,
                                            uint32_t num_rows_in,
diff --git a/src/plugins/intel_gna/backend/dnn.cpp b/src/plugins/intel_gna/backend/dnn.cpp
index fb7ecd13010..023b02fb0af 100644
--- a/src/plugins/intel_gna/backend/dnn.cpp
+++ b/src/plugins/intel_gna/backend/dnn.cpp
@@ -5,15 +5,9 @@
 #include <cstdio>
 #include <cmath>
 
-
-#if GNA_LIB_VER == 2
 #include <gna2-model-api.h>
 #include "gna2_model_helper.hpp"
 #include "gna2_model_debug_log.hpp"
-#else
-#include <gna-api-types-xnn.h>
-
-#endif
 
 #ifndef _NO_MKL_
 #include <mkl_dnn.h>
diff --git a/src/plugins/intel_gna/backend/dnn.hpp b/src/plugins/intel_gna/backend/dnn.hpp
index a005468c118..1765b507a11 100644
--- a/src/plugins/intel_gna/backend/dnn.hpp
+++ b/src/plugins/intel_gna/backend/dnn.hpp
@@ -18,9 +18,7 @@
 #include "am_intel_dnn.hpp"
 #include "dnn_types.h"
 
-#if GNA_LIB_VER == 2
 #include <gna2-model-api.h>
-#endif
 
 #define DNN_MAX_BATCH_SIZE 8
 #define DNN_MAX_INPUTS 3072
diff --git a/src/plugins/intel_gna/backend/dnn_types.h b/src/plugins/intel_gna/backend/dnn_types.h
index 499f8a7dd84..8dfc6758a40 100644
--- a/src/plugins/intel_gna/backend/dnn_types.h
+++ b/src/plugins/intel_gna/backend/dnn_types.h
@@ -215,7 +215,6 @@ OvGnaType OvGnaTypeIntFromBytes(T bytesPerElement) {
     return r->second;
 }
 
-#if GNA_LIB_VER == 2
 enum OvGnaMode {
     OvGnaModeDefault = 0,
     OvGnaModeDisabled = -1
@@ -252,12 +251,9 @@ inline std::string OvGnaModeToString(OvGnaMode mode) {
     }
     return r->second;
 }
-#endif
 
 struct intel_dnn_component_t {
-#if GNA_LIB_VER == 2
     std::vector < OvGnaTensor > tensors;
-#endif
     uint32_t num_rows_in;
     uint32_t num_columns_in;
     uint32_t num_rows_out;
diff --git a/src/plugins/intel_gna/backend/gna_types.h b/src/plugins/intel_gna/backend/gna_types.h
index 4756d4bff71..f74fb652645 100644
--- a/src/plugins/intel_gna/backend/gna_types.h
+++ b/src/plugins/intel_gna/backend/gna_types.h
@@ -4,9 +4,6 @@
 
 #pragma once
 
-#if GNA_LIB_VER == 1
-#include "gna_lib_ver_selector.hpp"
-#else
 #include <cstdint>
 
 /** PWL Segment - as read directly by the accelerator */
@@ -160,5 +157,3 @@ typedef enum  _acceleration {
 } gna_acceleration;
 
 static_assert(4 == sizeof(gna_acceleration), "Invalid size of gna_acceleration");
-
-#endif
diff --git a/src/plugins/intel_gna/gna2_model_debug_log.cpp b/src/plugins/intel_gna/gna2_model_debug_log.cpp
index 9ba52cd9471..262070f697e 100644
--- a/src/plugins/intel_gna/gna2_model_debug_log.cpp
+++ b/src/plugins/intel_gna/gna2_model_debug_log.cpp
@@ -6,7 +6,7 @@
 #include <algorithm>
 #include <vector>
 #include <iostream>
-#if GNA_LIB_VER == 2
+
 #include "gna2_model_debug_log.hpp"
 #include "gna2-model-api.h"
 
@@ -432,4 +432,3 @@ void DumpGna2Model(const Gna2Model& gnaModel, const std::string dumpFolderNameGN
         }
     }
 }
-#endif
diff --git a/src/plugins/intel_gna/gna2_model_debug_log.hpp b/src/plugins/intel_gna/gna2_model_debug_log.hpp
index 5b6378fa2cd..1c8029ae71a 100644
--- a/src/plugins/intel_gna/gna2_model_debug_log.hpp
+++ b/src/plugins/intel_gna/gna2_model_debug_log.hpp
@@ -4,13 +4,9 @@
 
 #pragma once
 
-#if GNA_LIB_VER == 2
-
 #include <string>
 
 #include "gna2-model-api.h"
 
 void WriteInputAndOutputTextGNAImpl(const Gna2Model & gnaModel, const std::string dumpFolderNameGNA, const std::string refFolderName);
 void DumpGna2Model(const Gna2Model& gnaModel, const std::string dumpFolderNameGNA, bool dumpData);
-
-#endif
diff --git a/src/plugins/intel_gna/gna2_model_export_helper.cpp b/src/plugins/intel_gna/gna2_model_export_helper.cpp
index 1b126bdbd89..dc922cb8e4a 100644
--- a/src/plugins/intel_gna/gna2_model_export_helper.cpp
+++ b/src/plugins/intel_gna/gna2_model_export_helper.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#if GNA_LIB_VER == 2
 #include "gna2_model_export_helper.hpp"
 #include "gna2_model_helper.hpp"
 #include "gna_device.hpp"
@@ -101,5 +100,3 @@ void ExportGnaDescriptorPartiallyFilled(uint32_t number_of_layers, std::ostream&
     outStream.width(scratchPadSize);
     outStream << constScratchFill;
 }
-
-#endif
diff --git a/src/plugins/intel_gna/gna2_model_export_helper.hpp b/src/plugins/intel_gna/gna2_model_export_helper.hpp
index 564f9eaf8fe..49b4a7c6cde 100644
--- a/src/plugins/intel_gna/gna2_model_export_helper.hpp
+++ b/src/plugins/intel_gna/gna2_model_export_helper.hpp
@@ -4,8 +4,6 @@
 
 #pragma once
 
-#if GNA_LIB_VER == 2
-
 #include "gna2-common-api.h"
 #include "gna2-model-suecreek-header.h"
 
@@ -23,5 +21,3 @@ void ExportLdForDeviceVersion(
     Gna2DeviceVersion deviceVersionToExport);
 
 void ExportGnaDescriptorPartiallyFilled(uint32_t numberOfLayers, std::ostream & outStream);
-
-#endif
diff --git a/src/plugins/intel_gna/gna2_model_helper.cpp b/src/plugins/intel_gna/gna2_model_helper.cpp
index a2c25b8c5ca..69d19e12080 100644
--- a/src/plugins/intel_gna/gna2_model_helper.cpp
+++ b/src/plugins/intel_gna/gna2_model_helper.cpp
@@ -2,8 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#if GNA_LIB_VER == 2
-
 #if defined __INTEL_COMPILER || defined _MSC_VER
 #include <malloc.h>
 #else
@@ -310,5 +308,3 @@ void HelperGna2OperationInitDeInterleave(Gna2Operation * operation,
     HelperGna2OperationSetOperand(operation, userAllocator, userFree, InOpIdx, inputs);
     HelperGna2OperationSetOperand(operation, userAllocator, userFree, OutOpIdx, outputs);
 }
-
-#endif
diff --git a/src/plugins/intel_gna/gna2_model_helper.hpp b/src/plugins/intel_gna/gna2_model_helper.hpp
index a9e773a99e5..253da0b1bd2 100644
--- a/src/plugins/intel_gna/gna2_model_helper.hpp
+++ b/src/plugins/intel_gna/gna2_model_helper.hpp
@@ -4,8 +4,6 @@
 
 #pragma once
 
-#if GNA_LIB_VER == 2
-
 #include <gna2-common-api.h>
 #include <gna2-model-api.h>
 #include "backend/dnn_types.h"
@@ -124,5 +122,3 @@ void HelperGna2OperationInitDeInterleave(
     Gna2Operation * operation,
     Gna2UserAllocator userAllocator, GnaUserFree userFree,
     Gna2Tensor * inputs, Gna2Tensor * outputs);
-
-#endif
diff --git a/src/plugins/intel_gna/gna_api_wrapper.hpp b/src/plugins/intel_gna/gna_api_wrapper.hpp
index 4569c3e8016..5a637529768 100644
--- a/src/plugins/intel_gna/gna_api_wrapper.hpp
+++ b/src/plugins/intel_gna/gna_api_wrapper.hpp
@@ -12,10 +12,8 @@
 #include "backend/gna_types.h"
 #include "gna_plugin_log.hpp"
 
-#if GNA_LIB_VER == 2
 #include <gna2-model-api.h>
 #include <gna2_model_helper.hpp>
-#endif
 
 namespace GNAPluginNS {
 
@@ -27,7 +25,6 @@ template <class T>
 class CPPWrapper {
 };
 
-#if GNA_LIB_VER == 2
 template <>
 class CPPWrapper<Gna2Model> {
  public:
@@ -79,54 +76,5 @@ class CPPWrapper<Gna2Model> {
         return *this;
     }
 };
-#else
-template <>
-class CPPWrapper<gna_nnet_type_t> {
-public:
-    intel_nnet_type_t obj;
-
-    CPPWrapper() {
-        obj.nLayers = 0;
-        obj.pLayers = nullptr;
-        obj.nGroup = 0;
-    }
-
-    /**
-     * creates nnet structure of n layers
-     * @param n - number  of layers
-     */
-    explicit CPPWrapper(size_t n) {
-        if (n == 0) {
-            THROW_GNA_EXCEPTION << "Can't allocate array of intel_nnet_layer_t objects of zero length";
-        }
-        obj.pLayers = reinterpret_cast<intel_nnet_layer_t *>(_mm_malloc(n * sizeof(intel_nnet_layer_t), 64));
-        if (obj.pLayers == nullptr) {
-            THROW_GNA_EXCEPTION << "out of memory in while allocating " << n << " GNA layers";
-        }
-        obj.nLayers = n;
-        for (int i = 0; i < obj.nLayers; i++) {
-            obj.pLayers[i].pLayerStruct = nullptr;
-        }
-        obj.nGroup = 0;
-    }
-    ~CPPWrapper() {
-        for (int i = 0; i < obj.nLayers; i++) {
-            if (obj.pLayers[i].pLayerStruct != nullptr) {
-                _mm_free(obj.pLayers[i].pLayerStruct);
-            }
-        }
-        _mm_free(obj.pLayers);
-    }
-    intel_nnet_type_t * operator ->() {
-        return &obj;
-    }
-    intel_nnet_type_t * operator *() {
-        return &obj;
-    }
-    operator intel_nnet_type_t &() {
-        return *this;
-    }
-};
-#endif
 
 }  // namespace GNAPluginNS
diff --git a/src/plugins/intel_gna/gna_data_types.hpp b/src/plugins/intel_gna/gna_data_types.hpp
index 955be2398b8..a5b05465053 100644
--- a/src/plugins/intel_gna/gna_data_types.hpp
+++ b/src/plugins/intel_gna/gna_data_types.hpp
@@ -32,11 +32,7 @@ static inline bool FoundPartToTranspose(const std::vector<TranspositionInfo> &tr
 }
 
 namespace GNAPluginNS {
-#if  GNA_LIB_VER == 2
     using dnn_ptr = std::shared_ptr<CPPWrapper<Gna2Model>>;
-#else
-    using dnn_ptr = std::shared_ptr<CPPWrapper<intel_nnet_type_t>>;
-#endif
     using allocator_type = GNAPluginNS::memory::PolymorphAllocator<uint8_t>;
     using gna_memory_type = GNAPluginNS::memory::GNAMemory<allocator_type>;
     using DnnComponentsForLayer = std::list<std::pair<std::string, intel_dnn_component_t>>;
diff --git a/src/plugins/intel_gna/gna_device.cpp b/src/plugins/intel_gna/gna_device.cpp
index 67a731849c3..381d2b87c8a 100644
--- a/src/plugins/intel_gna/gna_device.cpp
+++ b/src/plugins/intel_gna/gna_device.cpp
@@ -10,7 +10,6 @@
 #include <mutex>
 #include <vector>
 
-#if GNA_LIB_VER == 2
 #include "gna_api_wrapper.hpp"
 #include "gna2-capability-api.h"
 #include "gna2-device-api.h"
@@ -19,10 +18,6 @@
 #include "gna2-memory-api.h"
 #include "gna2_model_export_helper.hpp"
 #include "gna2_model_debug_log.hpp"
-#else
-#include "gna-api-status.h"
-#include "gna-api.h"
-#endif
 
 #include "backend/am_intel_dnn.hpp"
 #include "gna/gna_config.hpp"
@@ -36,12 +31,8 @@ std::mutex GNADeviceHelper::acrossPluginsSync{};
 uint8_t* GNADeviceHelper::alloc(uint32_t size_requested, uint32_t *size_granted) {
     std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
     void * memPtr = nullptr;
-#if GNA_LIB_VER == 1
-    memPtr = GNAAlloc(nGNAHandle, size_requested, size_granted);
-#else
     const auto status = Gna2MemoryAlloc(size_requested, size_granted, &memPtr);
     checkGna2Status(status, "Gna2MemoryAlloc");
-#endif
     if (memPtr == nullptr) {
         THROW_GNA_EXCEPTION << "GNAAlloc failed to allocate memory. Requested: " << size_requested << " Granted: " << *(size_granted);
     }
@@ -52,25 +43,17 @@ uint8_t* GNADeviceHelper::alloc(uint32_t size_requested, uint32_t *size_granted)
 
 void GNADeviceHelper::free(void * ptr) {
     std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
-#if GNA_LIB_VER == 1
-    GNAFree(nGNAHandle);
-#else
     const auto status = Gna2MemoryFree(ptr);
     checkGna2Status(status, "Gna2MemoryFree");
-#endif
 }
 
 std::string GNADeviceHelper::getGnaLibraryVersionPrivate() {
-#if GNA_LIB_VER == 1
-    return "1.X";
-#else
     char buffer[64] = {};
     const auto status = Gna2GetLibraryVersion(buffer, sizeof(buffer));
     if (status != Gna2StatusSuccess) {
         return "2.Gna2GetLibraryVersionReturned[" + std::to_string(status) + "]";
     }
     return buffer;
-#endif
 }
 
 std::string GNADeviceHelper::GetGnaLibraryVersion() {
@@ -78,21 +61,6 @@ std::string GNADeviceHelper::GetGnaLibraryVersion() {
     return gnaLibraryVersion;
 }
 
-#if GNA_LIB_VER == 1
-uint32_t GNADeviceHelper::propagate(const intel_nnet_type_t *pNeuralNetwork,
-                   const uint32_t *pActiveIndices,
-                   uint32_t nActiveIndices,
-                   intel_gna_proc_t nGNAProcType) {
-    std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
-    uint32_t reqId;
-
-    nGNAStatus = GNAPropagateForward(nGNAHandle, pNeuralNetwork,
-                                     pActiveIndices, nActiveIndices, &reqId, nGNAProcType);
-    checkStatus();
-    return reqId;
-}
-#else
-
 void GNADeviceHelper::setUpActiveList(const uint32_t requestConfigId, uint32_t layerIndex, uint32_t* ptr_active_indices, uint32_t num_active_indices) {
     std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
     const auto status = Gna2RequestConfigEnableActiveList(requestConfigId, layerIndex, num_active_indices, ptr_active_indices);
@@ -152,7 +120,7 @@ uint32_t GNADeviceHelper::createModel(Gna2Model& gnaModel) const {
 
     GNAPluginNS::backend::AMIntelDNN::updateNumberOfOutputsIfPoolingEnabled(gnaModel, legacyExecTarget);
 
-#if GNA_LIB_VER == 2 && defined MODEL_DUMP
+#ifdef MODEL_DUMP
     std::string path =
 #ifdef _WIN32
         ".\\";
@@ -411,11 +379,9 @@ const std::map <const std::pair<Gna2OperationType, int32_t>, const std::string>
             {{Gna2OperationTypeThreshold, 0}, "Input"},
             {{Gna2OperationTypeThreshold, 1}, "Output"}
 };
-#endif
 
 GnaWaitStatus GNADeviceHelper::wait(uint32_t reqId, int64_t millisTimeout) {
     std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
-#if GNA_LIB_VER == 2
     const auto status = Gna2RequestWait(reqId, millisTimeout);
     if (status == Gna2StatusWarningDeviceBusy) {
         return GNA_REQUEST_PENDING;
@@ -425,44 +391,16 @@ GnaWaitStatus GNADeviceHelper::wait(uint32_t reqId, int64_t millisTimeout) {
         return GNA_REQUEST_ABORTED;
     }
     checkGna2Status(status, "Gna2RequestWait");
-#else
-    if (isPerformanceMeasuring) {
-        nGNAStatus = GNAWaitPerfRes(nGNAHandle, millisTimeout, reqId, &nGNAPerfResults);
-    } else {
-        nGNAStatus = GNAWait(nGNAHandle, millisTimeout, reqId);
-    }
-    checkStatus();
-#endif
     updateGnaPerfCounters();
     return GNA_REQUEST_COMPLETED;
 }
 
-#if GNA_LIB_VER == 1
-GNADeviceHelper::DumpResult GNADeviceHelper::dumpXnn(const intel_nnet_type_t *pNeuralNetwork,
-                                    const uint32_t *pActiveIndices,
-                                    uint32_t nActiveIndices) {
-#else
 GNADeviceHelper::DumpResult GNADeviceHelper::dumpXnn(const uint32_t modelId) {
-#endif
     DumpResult r;
 
-#if GNA_LIB_VER == 1
-    if (!pNeuralNetwork) {
-        THROW_GNA_EXCEPTION << "GNADumpXnn got invalid NeuralNetwork parameter \n";
-    }
-    r.model.reset(GNADumpXnn(pNeuralNetwork,
-                             pActiveIndices,
-                             nActiveIndices,
-                             &r.header,
-                             &nGNAStatus,
-                             [](size_t count)-> void* {return new char[count]();}),
-                             [](void * ptr) {::operator delete[](ptr);});
-    checkStatus();
-#else
     r.model.reset(
         ExportSueLegacyUsingGnaApi2(modelId, nGnaDeviceIndex, &r.header),
         gnaUserFree);
-#endif
 
     if (r.model == nullptr) {
         THROW_GNA_EXCEPTION << "GNADumpXnn returned nullptr";
@@ -471,7 +409,27 @@ GNADeviceHelper::DumpResult GNADeviceHelper::dumpXnn(const uint32_t modelId) {
     return r;
 }
 
-#if GNA_LIB_VER == 2
+void GNADeviceHelper::dumpXnnForDeviceVersion(
+    const uint32_t modelId,
+    std::ostream & outStream,
+    const Gna2DeviceVersion targetDeviceVersion) {
+
+    Gna2ModelSueCreekHeader sueHeader;
+    auto ptr = ExportSueLegacyUsingGnaApi2(modelId, nGnaDeviceIndex, &sueHeader);
+    gnaUserFree(ptr);
+
+    ExportGnaDescriptorPartiallyFilled(sueHeader.NumberOfLayers, outStream);
+
+    ExportLdForDeviceVersion(modelId, outStream, targetDeviceVersion);
+    if (dumpXNNROPtr == nullptr) {
+        THROW_GNA_EXCEPTION << "Bad RO pointer (nullptr)";
+    }
+    outStream.write(static_cast<const char*>(dumpXNNROPtr), dumpXNNROSize);
+
+    // TODO: GNA2: remove
+    outStream.write("Gna2ModelSueCreekHeader", 24);
+    outStream.write(reinterpret_cast<const char*>(&sueHeader), sizeof(sueHeader));
+}
 
 void GNADeviceHelper::createVirtualDevice(Gna2DeviceVersion devVersion, std::string purpose) {
     const auto status = Gna2DeviceCreateForExport(devVersion, &nGnaDeviceIndex);
@@ -482,22 +440,9 @@ void GNADeviceHelper::updateGnaDeviceVersion() {
     const auto status = Gna2DeviceGetVersion(nGnaDeviceIndex, &detectedGnaDevVersion);
     checkGna2Status(status, "Gna2DeviceGetVersion");
 }
-#endif
-
-#if GNA_LIB_VER == 1
-void GNADeviceHelper::checkStatus() const {
-    if ((nGNAStatus != GNA_NOERROR) && (nGNAStatus != GNA_SSATURATE)) {
-        THROW_GNA_EXCEPTION << "Bad GNA status " << nGNAStatus << ", " << GNAStatusName[nGNAStatus];
-    }
-}
-#endif
 
 void GNADeviceHelper::open(uint8_t n_threads) {
     std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
-#if GNA_LIB_VER == 1
-    nGNAHandle = GNADeviceOpenSetThreads(&nGNAStatus, n_threads);
-    checkStatus();
-#else
     updateGnaDeviceVersion();
     const auto gnaExecTarget = parseTarget(executionTarget);
     if (useDeviceEmbeddedExport) {
@@ -516,16 +461,10 @@ void GNADeviceHelper::open(uint8_t n_threads) {
     // TODO: GNA2: uncomment when scratchpad repaired
     // status = Gna2DeviceSetNumberOfThreads(nGnaDeviceIndex, n_threads);
     // checkGna2Status(status);
-#endif
     deviceOpened = true;
 }
 
 void GNADeviceHelper::close() {
-#if GNA_LIB_VER == 1
-    std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
-    GNADeviceClose(nGNAHandle);
-    nGNAHandle = 0;
-#else
     auto requestsToClose = unwaitedRequestIds;
     for (auto requestId : requestsToClose) {
         try {
@@ -541,46 +480,22 @@ void GNADeviceHelper::close() {
     } catch (...) {
         gnawarn() << "GNA Device was not successfully closed with status " << status << std::endl;
     }
-#endif
     deviceOpened = false;
 }
 
 void GNADeviceHelper::setOMPThreads(uint8_t const n_threads) {
     std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
-#if GNA_LIB_VER == 1
-    gmmSetThreads(n_threads);
-#else
     const auto status = Gna2DeviceSetNumberOfThreads(nGnaDeviceIndex, n_threads);
     checkGna2Status(status, "Gna2DeviceSetNumberOfThreads");
-#endif
 }
 
 void GNADeviceHelper::updateGnaPerfCounters() {
     if (!isPerformanceMeasuring)
         return;
-#if GNA_LIB_VER == 2
     instrumentationTotal[0] = instrumentationResults[0];
     instrumentationTotal[1] = instrumentationResults[1];
     instrumentationResults[0] = 0;
     instrumentationResults[1] = 0;
-#else
-    nGNAPerfResultsTotal.hw.stall = nGNAPerfResults.hw.stall;
-    nGNAPerfResultsTotal.hw.total = nGNAPerfResults.hw.total;
-    nGNAPerfResultsTotal.lib.submit = nGNAPerfResults.lib.submit;
-    nGNAPerfResultsTotal.lib.preprocess = nGNAPerfResults.lib.preprocess;
-    nGNAPerfResultsTotal.lib.process = nGNAPerfResults.lib.process;
-    nGNAPerfResultsTotal.lib.scoring = nGNAPerfResults.lib.scoring;
-    nGNAPerfResultsTotal.lib.total = nGNAPerfResults.lib.total;
-    nGNAPerfResultsTotal.lib.ioctlSubmit = nGNAPerfResults.lib.ioctlSubmit;
-    nGNAPerfResultsTotal.lib.ioctlWaitOn = nGNAPerfResults.lib.ioctlWaitOn;
-
-    nGNAPerfResultsTotal.total.start = nGNAPerfResults.total.start;
-    nGNAPerfResultsTotal.total.stop = nGNAPerfResults.total.stop;
-
-    nGNAPerfResultsTotal.drv.startHW = nGNAPerfResults.drv.startHW;
-    nGNAPerfResultsTotal.drv.scoreHW = nGNAPerfResults.drv.scoreHW;
-    nGNAPerfResultsTotal.drv.intProc = nGNAPerfResults.drv.intProc;
-#endif
 }
 
 void GNADeviceHelper::getGnaPerfCounters(std::map<std::string, InferenceEngine::InferenceEngineProfileInfo>& retPerfCounters) {
@@ -590,27 +505,15 @@ void GNADeviceHelper::getGnaPerfCounters(std::map<std::string, InferenceEngine::
     info.execution_index = 0;
     info.realTime_uSec = 0;
     // Hardware
-#if GNA_LIB_VER == 1
-    info.realTime_uSec = nGNAPerfResultsTotal.hw.total;
-#else
     info.realTime_uSec = instrumentationTotal[0];
-#endif
     retPerfCounters["1.1 Total scoring time in HW"] = info;
-#if GNA_LIB_VER == 1
-    info.realTime_uSec = nGNAPerfResultsTotal.hw.stall;
-#else
     info.realTime_uSec = instrumentationTotal[1];
-#endif
     retPerfCounters["1.2 Stall scoring time in HW"] = info;
 }
 
 std::string GNADeviceHelper::getEffectiveGnaCompileTarget() const {
-#if GNA_LIB_VER == 1
-    return InferenceEngine::GNAConfigParams::GNA_TARGET_2_0;
-#else
     if (getTargetDevice(false) == Gna2DeviceVersion3_0) {
         return InferenceEngine::GNAConfigParams::GNA_TARGET_3_0;
     }
     return InferenceEngine::GNAConfigParams::GNA_TARGET_2_0;
-#endif
 }
diff --git a/src/plugins/intel_gna/gna_device.hpp b/src/plugins/intel_gna/gna_device.hpp
index 2955a084eba..7d82d68d355 100644
--- a/src/plugins/intel_gna/gna_device.hpp
+++ b/src/plugins/intel_gna/gna_device.hpp
@@ -15,7 +15,6 @@
 
 #include <ie_common.h>
 
-#if GNA_LIB_VER == 2
 #include "gna2-common-api.h"
 #include "gna2-inference-api.h"
 #include "gna2-instrumentation-api.h"
@@ -23,11 +22,6 @@
 #include "gna2-memory-api.h"
 #include "gna2-model-api.h"
 #include "gna2-model-suecreek-header.h"
-#else
-#include <gna-api.h>
-#include "gna-api-dumper.h"
-#include "gna-api-instrumentation.h"
-#endif
 
 enum GnaWaitStatus : int {
     GNA_REQUEST_COMPLETED = 0,  // and removed from GNA library queue
@@ -44,12 +38,6 @@ class GNADeviceHelper {
         static std::string gnaLibraryVersion{ ", GNA library version: " + GNADeviceHelper::GetGnaLibraryVersion() };
         return gnaLibraryVersion;
     }
-#if GNA_LIB_VER == 1
-    intel_gna_status_t nGNAStatus = GNA_NOERROR;
-    intel_gna_handle_t nGNAHandle = 0;
-    intel_gna_perf_t nGNAPerfResults;
-    intel_gna_perf_t nGNAPerfResultsTotal;
-#else
     uint32_t nGnaDeviceIndex = 0;
     bool swExactMode = false;
     Gna2DeviceVersion detectedGnaDevVersion = Gna2DeviceVersionSoftwareEmulation;
@@ -70,16 +58,10 @@ class GNADeviceHelper {
     uint32_t instrumentationConfigId = 0;
     std::set<uint32_t> unwaitedRequestIds;
 #define MAX_TIMEOUT 500000
-#endif
     bool isPerformanceMeasuring = false;
     bool deviceOpened = false;
+
 public:
-#if GNA_LIB_VER == 1
-    explicit GNADeviceHelper(uint8_t lib_async_n_threads = 1,
-                            bool use_openmp = false,
-                            bool isPerformanceMeasuring = false) :
-                                    isPerformanceMeasuring(isPerformanceMeasuring) {
-#else
     explicit GNADeviceHelper(std::string executionTargetIn = "",
          std::string compileTargetIn = "",
          bool swExactModeIn = false,
@@ -95,20 +77,17 @@ public:
          nGnaDeviceIndex{selectGnaDevice()},
          useDeviceEmbeddedExport(deviceEmbedded),
          exportGeneration(static_cast<Gna2DeviceVersion>(deviceVersionParsed)) {
-#endif
         open(lib_async_n_threads);
         initGnaPerfCounters();
 
         // check GNA Library version
         const auto gnaLibVersion = GetGnaLibraryVersion();
-#if GNA_LIB_VER == 2
         if (gnaLibVersion.rfind("2.1", 0) == 0) {
             isGnaLibVersion2_1 = true;
         }
         if (gnaLibVersion.rfind("3.0", 0) == 0) {
             isGnaLibVersion3_0 = true;
         }
-#endif
 
         if (use_openmp) {
             uint8_t num_cores = std::thread::hardware_concurrency();
@@ -126,12 +105,6 @@ public:
 
     uint8_t *alloc(uint32_t size_requested, uint32_t *size_granted);
 
-#if GNA_LIB_VER == 1
-    uint32_t propagate(const intel_nnet_type_t *pNeuralNetwork,
-                       const uint32_t *pActiveIndices,
-                       uint32_t nActiveIndices,
-                       intel_gna_proc_t nGNAProcType);
-#else
     void setUpActiveList(unsigned req_config_id, uint32_t layerIndex, uint32_t* ptr_active_indices, uint32_t num_active_indices);
     uint32_t propagate(const uint32_t requestConfigId, Gna2AccelerationMode gna2AccelerationMode);
     uint32_t createModel(Gna2Model& gnaModel) const;
@@ -151,32 +124,22 @@ public:
     bool enforceLegacyCnnNeeded() const;
     static void checkGna2Status(Gna2Status status, const std::string& from);
     static void checkGna2Status(Gna2Status status, const Gna2Model& gnaModel);
-#endif
     GnaWaitStatus wait(uint32_t id, int64_t millisTimeout = MAX_TIMEOUT);
 
     struct DumpResult {
-#if GNA_LIB_VER == 2
         Gna2ModelSueCreekHeader header;
-#else
-        intel_gna_model_header header;
-#endif
         std::shared_ptr<void> model;
     };
 
     const void * dumpXNNROPtr = nullptr;
     uint32_t dumpXNNROSize = 0;
 
-#if GNA_LIB_VER == 1
-    DumpResult dumpXnn(const intel_nnet_type_t *pNeuralNetwork,
-                 const uint32_t *pActiveIndices,
-                 uint32_t nActiveIndices);
-    intel_gna_status_t getGNAStatus() const noexcept {
-        return nGNAStatus;
-    }
-#else
-
     DumpResult dumpXnn(const uint32_t modelId);
-#endif
+
+    void dumpXnnForDeviceVersion(const uint32_t modelId,
+        std::ostream & outStream,
+        Gna2DeviceVersion targetDeviceVersion);
+
     void free(void * ptr);
 
     void updateGnaPerfCounters();
@@ -184,14 +147,12 @@ public:
                         InferenceEngine::InferenceEngineProfileInfo>& retPerfCounters);
     static std::string GetGnaLibraryVersion();
     std::string getEffectiveGnaCompileTarget() const;
+
  private:
     void open(uint8_t const n_threads);
 
     void close();
     static std::string getGnaLibraryVersionPrivate();
-#if GNA_LIB_VER == 1
-    void checkStatus() const;
-#else
     static const std::map <Gna2ItemType, const std::string> errorTypes;
     static const std::map <Gna2ErrorType, const std::string> errorReasons;
     static const std::map <Gna2OperationType, const std::string> operationTypes;
@@ -206,20 +167,14 @@ public:
 
     void createVirtualDevice(Gna2DeviceVersion devVersion, std::string purpose = "");
     void updateGnaDeviceVersion();
-#endif
     void setOMPThreads(uint8_t const n_threads);
 
     void initGnaPerfCounters() {
         std::unique_lock<std::mutex> lockGnaCalls{ acrossPluginsSync };
-#if GNA_LIB_VER == 1
-        nGNAPerfResults = {{0, 0, 0, 0, 0, 0, 0}, {0, 0}, {0, 0, 0}, {0, 0}};
-        nGNAPerfResultsTotal = {{0, 0, 0, 0, 0, 0, 0}, {0, 0}, {0, 0, 0}, {0, 0}};
-#else
         const auto status = Gna2InstrumentationConfigCreate(TotalGna2InstrumentationPoints,
             gna2InstrumentationPoints,
             instrumentationResults,
             &instrumentationConfigId);
         checkGna2Status(status, "Gna2InstrumentationConfigCreate");
-#endif
     }
 };  // NOLINT
diff --git a/src/plugins/intel_gna/gna_graph_compiler.cpp b/src/plugins/intel_gna/gna_graph_compiler.cpp
index 1b94d2ade84..fbc81329edf 100644
--- a/src/plugins/intel_gna/gna_graph_compiler.cpp
+++ b/src/plugins/intel_gna/gna_graph_compiler.cpp
@@ -306,10 +306,8 @@ void GNAGraphCompiler::ConvolutionPrimitive(InferenceEngine::CNNLayerPtr layer)
         // OpenVino Default layout is   NCHW
         // GNA Convolution input is     NHCW
         // When layer layout is in NHWC it means that is was created by PassManager
-#if GNA_LIB_VER == 2
         return finalizeConvolution2DPrimitive(layer, in_batch, in_channels, in_height, in_width,
                                               out_batch, out_channels, out_height, out_width);
-#endif
         THROW_GNA_LAYER_EXCEPTION(layer) << "Convolution 2D is not supported on GNA 1.0 library";
     }
     finalizeConvolution1DPrimitive(layer, in_batch, in_channels, in_width,
@@ -519,8 +517,6 @@ void GNAGraphCompiler::finalizeConvolution1DPrimitive(InferenceEngine::CNNLayerP
     }
 }
 
-#if GNA_LIB_VER == 2
-
 void GNAGraphCompiler::finalizeConvolution2DPrimitive(InferenceEngine::CNNLayerPtr layer,
     uint32_t in_batch, uint32_t in_channels, uint32_t in_height, uint32_t in_width,
     uint32_t out_batch, uint32_t out_channels, uint32_t out_height, uint32_t out_width) {
@@ -661,7 +657,6 @@ void GNAGraphCompiler::finalizeConvolution2DPrimitive(InferenceEngine::CNNLayerP
         gnamem->readonly().push_value(layer, ptr_biases, 0.0f, out_channels, 64);
     }
 }
-#endif
 
 void GNAGraphCompiler::PowerPrimitive(InferenceEngine::CNNLayerPtr layer) {
     auto& power = dynamic_cast<PowerLayer&>(*layer.get());
diff --git a/src/plugins/intel_gna/gna_graph_compiler.hpp b/src/plugins/intel_gna/gna_graph_compiler.hpp
index 131f16da674..5331b1b6a6d 100644
--- a/src/plugins/intel_gna/gna_graph_compiler.hpp
+++ b/src/plugins/intel_gna/gna_graph_compiler.hpp
@@ -129,11 +129,10 @@ public:
     void finalizeConvolution1DPrimitive(InferenceEngine::CNNLayerPtr,
         uint32_t in_batch, uint32_t in_channels, uint32_t in_width,
         uint32_t out_batch, uint32_t out_channels, uint32_t out_width);
-#if GNA_LIB_VER == 2
+
     void finalizeConvolution2DPrimitive(InferenceEngine::CNNLayerPtr,
         uint32_t in_batch, uint32_t in_channels, uint32_t in_height, uint32_t in_width,
         uint32_t out_batch, uint32_t out_channels, uint32_t out_height, uint32_t out_width);
-#endif
 
     void Reset();
 };
diff --git a/src/plugins/intel_gna/gna_lib_ver_selector.hpp b/src/plugins/intel_gna/gna_lib_ver_selector.hpp
index d9bab715e9a..dbc49d3d49c 100644
--- a/src/plugins/intel_gna/gna_lib_ver_selector.hpp
+++ b/src/plugins/intel_gna/gna_lib_ver_selector.hpp
@@ -4,8 +4,6 @@
 
 #pragma once
 
-#if GNA_LIB_VER == 2
-
 #include <cstdint>
 
 #define nLayerKind operation
@@ -29,17 +27,3 @@
  * Used for calculating memory sizes of GNA data arrays
  */
 #define ALIGN64(number) ALIGN(number, 64)
-
-#else
-
-#include <gna-api.h>
-#include <gna-api-types-xnn.h>
-
-#define gna_pwl_segment_t intel_pwl_segment_t
-#define gna_compound_bias_t intel_compound_bias_t
-#define gna_nnet_layer_t intel_nnet_layer_t
-#define gna_nnet_type_t intel_nnet_type_t
-#define gna_affine_func_t intel_affine_func_t
-#define gna_affine_layer_t intel_affine_layer_t
-#define gna_convolutional_layer_t intel_convolutional_layer_t
-#endif
diff --git a/src/plugins/intel_gna/gna_model_serial.cpp b/src/plugins/intel_gna/gna_model_serial.cpp
index 8c23810364b..3aac5a8c662 100644
--- a/src/plugins/intel_gna/gna_model_serial.cpp
+++ b/src/plugins/intel_gna/gna_model_serial.cpp
@@ -218,8 +218,6 @@ GNAPluginNS::HeaderLatest::RuntimeEndPoint GNAModelSerial::ReadEndPoint(std::ist
 #define offsetFromBase(field)\
 getOffsetFromBase(field, #field)
 
-#if GNA_LIB_VER == 2
-
 inline bool IsEmptyTensor(const Gna2Tensor& t) {
     return t.Type == Gna2DataTypeNone &&
         t.Data == nullptr &&
@@ -532,325 +530,6 @@ void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostrea
     // once structure has been written lets push gna graph
     os.write(reinterpret_cast<char*>(basePointer), gnaGraphSize);
 }
-#else
-
-void GNAModelSerial::Import(void *basePointer,
-        size_t gnaGraphSize,
-        std::istream & is,
-        std::shared_ptr<GNAPluginNS::InputDesc> inputsDesc,
-        std::vector<GNAPluginNS::OutputDesc> &desc,
-        InferenceEngine::InputsDataMap& inputsDataMap,
-        InferenceEngine::OutputsDataMap& outputsDataMap,
-        TranspositionInfoMap& inputsTranspositionInfo,
-        TranspositionInfoMap& outputsTranspositionInfo) {
-    is.exceptions(std::istream::failbit);
-
-    if (modelHeader.version.major == 2) {
-        if (modelHeader.version.minor >= 5) {
-            for (int inputIx = 0; inputIx < modelHeader.nTransposeInputs; ++inputIx) {
-                std::string inputName;
-                std::vector<TranspositionInfo> transpositionInfo;
-                ImportTranspositionInfo(is, inputName, transpositionInfo);
-                inputsTranspositionInfo[inputName] = transpositionInfo;
-            }
-            for (int outputIx = 0; outputIx < modelHeader.nTransposeOutputs; ++outputIx) {
-                std::string outputName;
-                std::vector<TranspositionInfo> transpositionInfo;
-                ImportTranspositionInfo(is, outputName, transpositionInfo);
-                outputsTranspositionInfo[outputName] = transpositionInfo;
-            }
-        }
-    }
-    ImportInputs(is, basePointer, inputsDesc, inputsDataMap);
-    ImportOutputs(is, basePointer, desc, outputsDataMap);
-
-    auto readPwl = [&is, basePointer](intel_pwl_func_t & value) {
-        readBits(value.nSegments, is);
-        if (value.nSegments != 0) {
-            readOffset(value.pSegments, basePointer, is);
-        } else {
-            value.pSegments = nullptr;
-        }
-    };
-
-    for (auto layer = ptr_nnet->pLayers; layer != ptr_nnet->pLayers + ptr_nnet->nLayers; ++layer) {
-        readBits(layer->nInputColumns, is);
-        readBits(layer->nInputRows, is);
-        readBits(layer->nOutputColumns, is);
-        readBits(layer->nOutputRows, is);
-        readBits(layer->nBytesPerInput, is);
-        readBits(layer->nBytesPerOutput, is);
-        readBits(layer->nBytesPerIntermediateOutput, is);
-        readNBits<32>(layer->nLayerKind, is);
-
-        // reading layers structs
-        switch (layer->nLayerKind) {
-        case INTEL_AFFINE_DIAGONAL:
-        case INTEL_AFFINE: {
-            layer->pLayerStruct = _mm_malloc(sizeof(intel_affine_layer_t), 64);
-            if (layer->pLayerStruct == nullptr) {
-                THROW_GNA_EXCEPTION << "could not allocate memory for intel_affine_layer_t structure.";
-            }
-
-            auto &affine = *reinterpret_cast<intel_affine_layer_t *>(layer->pLayerStruct);
-            readBits(affine.affine.nBytesPerWeight, is);
-            readBits(affine.affine.nBytesPerBias, is);
-            readOffset(affine.affine.pWeights, basePointer, is);
-            readOffset(affine.affine.pBiases, basePointer, is);
-            readPwl(affine.pwl);
-            break;
-        }
-        case INTEL_CONVOLUTIONAL: {
-            layer->pLayerStruct = _mm_malloc(sizeof(intel_convolutional_layer_t), 64);
-            if (layer->pLayerStruct == nullptr) {
-                THROW_GNA_EXCEPTION << "could not allocate memory for intel_convolutional_layer_t structure.";
-            }
-
-            auto &convolution = *reinterpret_cast<intel_convolutional_layer_t *>(layer->pLayerStruct);
-            readBits(convolution.nFilterCoefficients, is);
-            readBits(convolution.nBytesFilterCoefficient, is);
-            readBits(convolution.nBytesBias, is);
-            readBits(convolution.nFilters, is);
-            readBits(convolution.nFeatureMaps, is);
-            readBits(convolution.nFeatureMapRows, is);
-            readBits(convolution.nFeatureMapColumns, is);
-            readBits(convolution.nFilterRows, is);
-            readOffset(convolution.pFilters, basePointer, is);
-            readOffset(convolution.pBiases, basePointer, is);
-            readBits(convolution.nPoolSize, is);
-            readBits(convolution.nPoolStride, is);
-            readBits(convolution.poolType, is);
-            readPwl(convolution.pwl);
-            break;
-        }
-
-        case INTEL_COPY: {
-            layer->pLayerStruct = _mm_malloc(sizeof(intel_copy_layer_t), 64);
-            if (layer->pLayerStruct == nullptr) {
-                THROW_GNA_EXCEPTION << "could not allocate memory for intel_copy_layer_t structure.";
-            }
-
-            auto &copy = *reinterpret_cast<intel_copy_layer_t *>(layer->pLayerStruct);
-            readBits(copy.nCopyRows, is);
-            readBits(copy.nCopyCols, is);
-            break;
-        }
-
-        case INTEL_RECURRENT:
-            THROW_GNA_EXCEPTION << "Importing of recurrent layer not supported";
-        case INTEL_INTERLEAVE:
-            THROW_GNA_EXCEPTION << "Importing of interleave layer not supported";
-        case INTEL_DEINTERLEAVE:
-            THROW_GNA_EXCEPTION << "Importing of deinterleave layer not supported";
-        default:
-            THROW_GNA_EXCEPTION << "Importing of unknown GNA layer kind(" << layer->nLayerKind << ")  not supported";
-        }
-
-        // reading offsets of inputs/outputs
-        readOffset(layer->pInputs, basePointer, is);
-        if (layer->nLayerKind == INTEL_COPY) {
-            layer->pOutputsIntermediate = nullptr;
-        } else {
-            readOffset(layer->pOutputsIntermediate, basePointer, is);
-        }
-        readOffset(layer->pOutputs, basePointer, is);
-    }
-
-    // writing memory information
-    uint32_t nStates = 0;
-    readBits(nStates, is);
-    if (pstates != nullptr) {
-        pstates->resize(nStates);
-    }
-
-    for (int i = 0; i != nStates; i++) {
-        void *pSegment;
-        if ( modelHeader.version.major == 2 ) {
-            if ( modelHeader.version.minor < 6 ) {
-                readOffset(pSegment, basePointer, is);
-                uint32_t segmentSz = 0;
-                readBits(segmentSz, is);
-                if (pstates) {
-                    (*pstates)[i] = std::make_tuple( pSegment, segmentSz, "noname", 1.0f);
-                }
-            } else {
-                readOffset(pSegment, basePointer, is);
-                uint32_t segmentSz = 0;
-                readBits(segmentSz, is);
-                uint32_t nameSize = 0;
-                readNBits<32>(nameSize, is);
-                std::string inName(nameSize, '\0');
-                readNBytes(&inName[0], nameSize, is);
-                float scale_factor = 1.0f;
-                readBits(scale_factor, is);
-                if (pstates) {
-                    (*pstates)[i] = std::make_tuple( pSegment, segmentSz, inName.substr(0, nameSize - 1), scale_factor );
-                }
-            }
-        }
-    }
-
-
-    // once structure has been read lets read whole gna graph
-    is.read(reinterpret_cast<char*>(basePointer), gnaGraphSize);
-}
-
-/**
- *
- * @param ptr_nnet
- * @param gnaAllocSize - it can be calculated based on nnet, however it will overcomplicate export
- * about base adress it is relatively easy to calculate
- * @param os
- */
-
-void GNAModelSerial::Export(void * basePointer, size_t gnaGraphSize, std::ostream & os) const {
-    os.exceptions(std::ostream::failbit);
-
-    std::vector<intel_nnet_layer_t>
-        layers(ptr_nnet->pLayers, ptr_nnet->pLayers + ptr_nnet->nLayers);
-
-
-    // all offsets will be from this pointer
-    auto getOffsetFromBase = [basePointer, &gnaGraphSize](void * pointer, const char * name = nullptr) {
-        auto offset = static_cast<uint64_t >(std::distance(reinterpret_cast<uint8_t*>(basePointer), reinterpret_cast<uint8_t*>(pointer)));
-        if (offset > gnaGraphSize) {
-            THROW_GNA_EXCEPTION << "offset to " << (name == nullptr ? "" : name) << "(0x" << pointer
-                               << ") not in range segment returned from GNAAlloc(0x" << basePointer << "-0x"
-                               << reinterpret_cast<void*>(reinterpret_cast<uint8_t*>(basePointer) + gnaGraphSize) << ")";
-        }
-        return offset;
-    };
-
-    auto writePwl = [&os, getOffsetFromBase] (intel_pwl_func_t & value) {
-        writeBits(value.nSegments, os);
-        // export require certain offset, since offset from base to nullptr cannot be correct, we are not store it at all
-        if (value.nSegments != 0) {
-            writeBits(offsetFromBase(value.pSegments), os);
-        }
-    };
-
-    auto convert_to_serial = [getOffsetFromBase](const HeaderLatest::RuntimeEndPoint& ep){
-        HeaderLatest::RuntimeEndPoint out;
-        out.elements_count = ep.elements_count;
-        out.element_size = ep.element_size;
-        out.descriptor_offset = offsetFromBase(ep.descriptor_ptr);
-        out.scaleFactor = ep.scaleFactor;
-        out.orientation = ep.orientation;
-        return out;
-    };
-
-    /**
-     * writing header
-     */
-    HeaderLatest::ModelHeader header;
-    header.gnam[0] = 'G';
-    header.gnam[1] = 'N';
-    header.gnam[2] = 'A';
-    header.gnam[3] = 'M';
-    header.version.major = 1u;
-    header.version.minor = 1u;
-    header.gnaMemSize = gnaGraphSize;
-    header.layersCount = layers.size();
-    header.nGroup = ptr_nnet->nGroup;
-    header.nInputs = 1;
-    header.nOutputs = 1;
-    header.headerSize = sizeof(HeaderLatest::ModelHeader);
-    header.nTransposeInputs = transposeInputsInfo.size();
-    header.nTransposeOutputs = transposeOutputsInfo.size();
-
-    ExportTranspositionInfo(os, transposeInputsInfo);
-    ExportTranspositionInfo(os, transposeOutputsInfo);
-
-    writeBits(header, os);
-    writeBits(convert_to_serial(inputs[0]), os);
-    writeBits(convert_to_serial(outputs[0]), os);
-
-    for (auto & layer : layers) {
-        writeBits(layer.nInputColumns, os);
-        writeBits(layer.nInputRows, os);
-        writeBits(layer.nOutputColumns, os);
-        writeBits(layer.nOutputRows, os);
-        writeBits(layer.nBytesPerInput, os);
-        writeBits(layer.nBytesPerOutput, os);
-        writeBits(layer.nBytesPerIntermediateOutput, os);
-        writeBits(static_cast<uint32_t>(layer.nLayerKind), os);
-
-        // writing layers structs
-        switch (layer.nLayerKind) {
-            case INTEL_AFFINE_DIAGONAL:
-            case INTEL_AFFINE: {
-                auto &affine = *reinterpret_cast<intel_affine_layer_t *>(layer.pLayerStruct);
-                writeBits(affine.affine.nBytesPerWeight, os);
-                writeBits(affine.affine.nBytesPerBias, os);
-                writeBits(offsetFromBase(affine.affine.pWeights), os);
-                writeBits(offsetFromBase(affine.affine.pBiases), os);
-                writePwl(affine.pwl);
-                break;
-            }
-            case INTEL_CONVOLUTIONAL: {
-                auto &convolution = *reinterpret_cast<intel_convolutional_layer_t *>(layer.pLayerStruct);
-                writeBits(convolution.nFilterCoefficients, os);
-                writeBits(convolution.nBytesFilterCoefficient, os);
-                writeBits(convolution.nBytesBias, os);
-                writeBits(convolution.nFilters, os);
-                writeBits(convolution.nFeatureMaps, os);
-                writeBits(convolution.nFeatureMapRows, os);
-                writeBits(convolution.nFeatureMapColumns, os);
-                writeBits(convolution.nFilterRows, os);
-                writeBits(offsetFromBase(convolution.pFilters), os);
-                writeBits(offsetFromBase(convolution.pBiases), os);
-                writeBits(convolution.nPoolSize, os);
-                writeBits(convolution.nPoolStride, os);
-                writeBits(convolution.poolType, os);
-                writePwl(convolution.pwl);
-                break;
-            }
-
-            case INTEL_COPY: {
-                auto &copy = *reinterpret_cast<intel_copy_layer_t *>(layer.pLayerStruct);
-                writeBits(copy.nCopyRows, os);
-                writeBits(copy.nCopyCols, os);
-                break;
-            }
-
-            case INTEL_RECURRENT:
-                THROW_GNA_EXCEPTION << "Exporting of recurrent layer not supported";
-            case INTEL_INTERLEAVE:
-                THROW_GNA_EXCEPTION << "Exporting of interleave layer not supported";
-            case INTEL_DEINTERLEAVE:
-                THROW_GNA_EXCEPTION << "Exporting of deinterleave layer not supported";
-            default:
-                THROW_GNA_EXCEPTION << "Exporting of unknown GNA layer kind(" << layer.nLayerKind << ")  not supported";
-        }
-
-        // writing offsets from base.
-        writeBits(offsetFromBase(layer.pInputs), os);
-        if (layer.nLayerKind != INTEL_COPY) {
-            writeBits(offsetFromBase(layer.pOutputsIntermediate), os);
-        }
-        writeBits(offsetFromBase(layer.pOutputs), os);
-    }
-    // writing memory information
-    writeBits(static_cast<uint32_t>(states.size()), os);
-    for (auto && state : states) {
-        void* gna_ptr = nullptr;
-        uint32_t reserved_size = 0;
-        std::string name;
-        float scale_factor = 1.0f;
-        std::tie(gna_ptr, reserved_size, name, scale_factor) = state;
-        writeBits(offsetFromBase(gna_ptr), os);
-        writeBits(reserved_size, os);
-        const auto nameSize = strlen(name.c_str()) + 1;
-        writeBits(static_cast<uint32_t>(nameSize), os);
-        writeNBytes(name.c_str(), nameSize, os);
-        writeBits(scale_factor, os);
-    }
-
-    // once structure has been written lets push gna graph
-    os.write(reinterpret_cast<char*>(basePointer), gnaGraphSize);
-}
-
-#endif
 
 void GNAModelSerial::ImportInputs(std::istream &is, void* basePtr, GNAPluginNS::GnaInputs &inputs) {
     for (auto &input : inputs.Get()) {
diff --git a/src/plugins/intel_gna/gna_model_serial.hpp b/src/plugins/intel_gna/gna_model_serial.hpp
index 1173fa3368b..6604e55734c 100644
--- a/src/plugins/intel_gna/gna_model_serial.hpp
+++ b/src/plugins/intel_gna/gna_model_serial.hpp
@@ -12,9 +12,7 @@
 #include "descriptions/gna_desc.hpp"
 #include "gna_plugin_log.hpp"
 #include "serial/headers/latest/gna_model_header.hpp"
-#if GNA_LIB_VER == 2
 #include "gna2-model-api.h"
-#endif
 
 
 /**
@@ -25,12 +23,8 @@ public:
     using MemoryType = std::vector<std::tuple<void*, uint32_t, std::string, float>>;
 
 private:
-#if GNA_LIB_VER == 2
     Gna2Model * gna2model_;
     MemoryType states, *pstates_ = nullptr;
-#else
-    intel_nnet_type_t *ptr_nnet_;
-#endif
     GNAPluginNS::GnaInputs inputs_;
     GNAPluginNS::GnaOutputs outputs_;
     TranspositionInfoMap inputs_transpose_info_;
diff --git a/src/plugins/intel_gna/gna_plugin.cpp b/src/plugins/intel_gna/gna_plugin.cpp
index 20d9d39add6..32b397ef81e 100644
--- a/src/plugins/intel_gna/gna_plugin.cpp
+++ b/src/plugins/intel_gna/gna_plugin.cpp
@@ -79,7 +79,6 @@
 
 #include <ngraph/opsets/opset7.hpp>
 
-#if GNA_LIB_VER == 2
 #include <gna2-model-api.h>
 
 inline uint32_t ToByteSize(const Gna2DataType type) {
@@ -102,7 +101,7 @@ inline uint32_t ToByteSize(const Gna2DataType type) {
 }
 
 constexpr uint32_t GNAPluginNS::GNAPlugin::FAKE_REQUEST_CONFIG_ID;
-#endif
+
 using namespace InferenceEngine;
 using namespace std;
 using namespace GNAPluginNS;
@@ -354,11 +353,6 @@ void GNAPlugin::Init() {
 
 void GNAPlugin::InitGNADevice() {
     OV_ITT_SCOPED_TASK(itt::domains::GNA_LT, "InitGNADevice");
-#if GNA_LIB_VER == 1
-    gnadevice = std::make_shared<GNADeviceHelper>(gnaFlags->gna_lib_async_threads_num,
-                                                  gnaFlags->gna_openmp_multithreading,
-                                                  gnaFlags->performance_counting);
-#else
     gnadevice = std::make_shared<GNADeviceHelper>(config.gnaExecTarget,
                 config.gnaCompileTarget,
                 config.swExactMode,
@@ -367,7 +361,6 @@ void GNAPlugin::InitGNADevice() {
                 gnaFlags->performance_counting,
                 !config.dumpXNNPath.empty(),
                 GetDeviceVersionFromString(config.dumpXNNGeneration));
-#endif
     size_t page_size_bytes = 4096;
     gnamem = std::make_shared<gna_memory_type>(memory::make_polymorph<memory::GNAAllocator>(gnadevice), page_size_bytes);
     graphCompiler.setGNAMemoryPtr(gnamem);
@@ -805,9 +798,7 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         passes->registerPass<BreakFusingOfOutputLayersPass>();
         passes->registerPass<InsertDiagonalLayerPass>();
         passes->registerPass<HandleMultipleActivationsForTheLayerPass>();
-#if GNA_LIB_VER == 2
         passes->registerPass<ForbidActivationFusingPass>();
-#endif
         passes->registerPass<FuseMultipleIdentitiesPass>();
         passIdx = passes->run(passIdx);
     };
@@ -1003,31 +994,18 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         dnn->InitActiveList(NULL);
     }
 
-#if GNA_LIB_VER == 2
     gnaModels.push_back(std::make_tuple(make_shared<CPPWrapper<Gna2Model>>()));
-#else
-    nnets.emplace_back(make_shared<CPPWrapper<intel_nnet_type_t>>(), -1, InferenceEngine::BlobMap());
-#endif
 
     if (!gnaFlags->sw_fp32 && !graphCompiler.dnnComponents.components.empty()) {
         // number of layer gets calculated inside that InitGNAStruct function
-#if GNA_LIB_VER == 2
         dnn->InitGNAStruct(&std::get<0>(gnaModels.front())->obj, effectiveGnaCompileTarget);
-#else
-        dnn->InitGNAStruct(&std::get<0>(nnets.front())->obj);
-#endif
     }
 
     // creating same gna RW segment for parallel infer requests
     for (int i = 1; i != gnaFlags->gna_lib_async_threads_num; i++) {
-#if GNA_LIB_VER == 2
         gnaModels.push_back(std::make_tuple(make_shared<CPPWrapper<Gna2Model>>()));
         // this can be improved by just copy all structures, but we are too lazy
         dnn->InitGNAStruct(&std::get<0>(gnaModels.back())->obj, effectiveGnaCompileTarget);
-#else
-        nnets.emplace_back(make_shared<CPPWrapper<intel_nnet_type_t>>(), -1, InferenceEngine::BlobMap());
-        dnn->InitGNAStruct(&std::get<0>(nnets.back())->obj);
-#endif
         // relocate rw pointers to new offset
         auto basePtr = reinterpret_cast<uint8_t*>(pParallelExecutionData) + rwSegmentSize * (i - 1);
 
@@ -1049,18 +1027,10 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
             relocate(output.ptrs[i], output.ptrs[0]);
         }
 
-#if GNA_LIB_VER == 2
         for (int j = 0; j != std::get<0>(gnaModels.front())->obj.NumberOfOperations; j++) {
             auto & gnaOperation = std::get<0>(gnaModels[i])->obj.Operations[j];
             relocate(const_cast<Gna2Tensor*>(gnaOperation.Operands[0])->Data, gnaOperation.Operands[0]->Data);
             relocate(const_cast<Gna2Tensor*>(gnaOperation.Operands[1])->Data, gnaOperation.Operands[1]->Data);
-#else
-        for (int j = 0; j != std::get<0>(nnets.front())->obj.nLayers; j++) {
-            auto & layer = std::get<0>(nnets[i])->obj.pLayers[j];
-            relocate(layer.pInputs, layer.pInputs);
-            relocate(layer.pOutputs, layer.pOutputs);
-            relocate(layer.pOutputsIntermediate, layer.pOutputsIntermediate);
-#endif
         }
     }
 
@@ -1134,12 +1104,9 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
 #ifdef PLOT
     dnn->WriteGraphWizModel("gna-blob.dot");
 #endif
-#if GNA_LIB_VER == 2
     createRequestConfigsForGnaModels();
-#endif
 }
 
-#if GNA_LIB_VER == 2
 void GNAPlugin::createRequestConfigsForGnaModels() {
     if (!gnadevice || trivialTopology) {
         gnaRequestConfigToRequestIdMap.push_back(std::make_tuple(FAKE_REQUEST_CONFIG_ID, -1, InferenceEngine::BlobMap()));
@@ -1153,8 +1120,6 @@ void GNAPlugin::createRequestConfigsForGnaModels() {
     }
 }
 
-#endif
-
 int GNAPlugin::GetDeviceVersionFromString(const std::string deviceString) {
     if (deviceString.empty())
         return static_cast<int>(Gna2DeviceVersionEmbedded1_0);
@@ -1172,16 +1137,7 @@ void GNAPlugin::DumpXNNToFile() const {
         THROW_GNA_EXCEPTION << "Cannot generate XNNDump for float network";
     }
     std::ofstream dumpStream(config.dumpXNNPath, std::ios::out | std::ios::binary);
-#if GNA_LIB_VER == 1
-    if (versionInt != 0x10E)
-        THROW_GNA_EXCEPTION << "Wrong GNA version for embedded model dump: " << config.dumpXNNGeneration;
-    auto dump = gnadevice->dumpXnn(&std::get<0>(nnets.front())->obj, ptr_active_indices, num_active_indices);
-    dump.header.rw_region_size = gnamem->getRWBytes();
-    dump.header.input_scaling_factor = _inputs.begin()->second.scale_factor;
-    dump.header.output_scaling_factor =  outputs_.begin()->second.scale_factor;
-    dumpStream.write(reinterpret_cast<char*>(&dump.header), sizeof(intel_gna_model_header));
-    dumpStream.write(reinterpret_cast<char*>(dump.model.get()), dump.header.model_size);
-#else
+
     auto const modelId = gnadevice->createModel(std::get<0>(gnaModels.front())->obj);
     auto dump = gnadevice->dumpXnn(modelId);
     dump.header.RwRegionSize = gnamem->getRWBytes();
@@ -1190,13 +1146,10 @@ void GNAPlugin::DumpXNNToFile() const {
     dumpStream.write(reinterpret_cast<char*>(&dump.header), sizeof(Gna2ModelSueCreekHeader));
     dumpStream.write(reinterpret_cast<char*>(dump.model.get()), dump.header.ModelSize);
     gnadevice->releaseModel(modelId);
-#endif
 }
 
 uint32_t GNAPlugin::QueueInference(const InferenceEngine::BlobMap &inputs, InferenceEngine::BlobMap &result) {
-#if GNA_LIB_VER == 2
     auto& nnets = gnaRequestConfigToRequestIdMap;
-#endif
     auto freeNnet = std::find_if(std::begin(nnets), std::end(nnets), [](decltype(nnets.front()) & item) {
         return std::get<1>(item) == -1;
     });
@@ -1304,15 +1257,10 @@ uint32_t GNAPlugin::QueueInference(const InferenceEngine::BlobMap &inputs, Infer
             std::get<1>(*freeNnet) = 1;
         }
     } else {
-#if GNA_LIB_VER == 1
-        auto nnet = std::get<0>(*freeNnet).get();
-        std::get<1>(*freeNnet) = gnadevice->propagate(&nnet->obj, ptr_active_indices, num_active_indices, config.gna_proc_type);
-#else
         const auto reqConfigId = std::get<0>(*freeNnet);
         if (ptr_active_indices != nullptr && num_active_indices > 0 && activeLayerIndex != 0xffffffff)
             gnadevice->setUpActiveList(reqConfigId, activeLayerIndex, ptr_active_indices, num_active_indices);
         std::get<1>(*freeNnet) = gnadevice->propagate(reqConfigId, config.pluginGna2AccMode);
-#endif
     }
 
 #ifdef PLOT
@@ -1334,9 +1282,7 @@ bool GNAPlugin::Wait(uint32_t request_idx) {
 }
 
 GnaWaitStatus GNAPlugin::WaitFor(uint32_t request_idx, int64_t millisTimeout) {
-#if GNA_LIB_VER == 2
     auto& nnets = gnaRequestConfigToRequestIdMap;
-#endif
     // TODO: GNA2: check whether necessary
     if (nnets.size() <= request_idx) return GNA_REQUEST_COMPLETED;
     // already synced TODO: might be copy required ???
@@ -1359,11 +1305,7 @@ GnaWaitStatus GNAPlugin::WaitFor(uint32_t request_idx, int64_t millisTimeout) {
     if (dnn->num_components() != 0) {
         dnn->WriteInputAndOutputText();
     }
-#if GNA_LIB_VER == 1
-    dnn->WriteInputAndOutputTextGNA(&std::get<0>(nnets[request_idx])->obj);
-#else
     dnn->WriteInputAndOutputTextGNA(std::get<0>(gnaModels[request_idx])->obj);
-#endif
 #endif
     int output_idx = 0;
     for (auto && outputBlobIt : request) {
@@ -1564,18 +1506,9 @@ InferenceEngine::IExecutableNetworkInternal::Ptr GNAPlugin::ImportNetwork(std::i
     void *basePtr = nullptr;
     gnamem->reserve_ptr(nullptr, &basePtr, header.gnaMemSize);
     gnamem->commit();
-#if GNA_LIB_VER == 2
     gnaModels.push_back(std::make_tuple(make_shared<CPPWrapper<Gna2Model>>(header.layersCount)));
-#else
-    nnets.emplace_back(make_shared<CPPWrapper<intel_nnet_type_t>>(header.layersCount), -1, InferenceEngine::BlobMap());
-    std::get<0>(nnets.back())->obj.nGroup = header.nGroup;
-#endif
     GNAModelSerial::MemoryType  mt;
-#if GNA_LIB_VER == 2
     auto serial = GNAModelSerial(&std::get<0>(gnaModels.back())->obj, mt);
-#else
-    auto serial = GNAModelSerial(&std::get<0>(nnets.back())->obj, mt);
-#endif
 
     serial.setHeader(header);
     serial.Import(basePtr,
@@ -1602,23 +1535,11 @@ InferenceEngine::IExecutableNetworkInternal::Ptr GNAPlugin::ImportNetwork(std::i
         }
     }
 
-#if GNA_LIB_VER == 2
     auto getOrientation = [](Gna2Operation & gnaOperation) {
         return gnaOperation.Type == Gna2OperationTypeConvolution ?
             kDnnNonInterleavedOrientation : kDnnInterleavedOrientation;
     };
     (void)getOrientation;
-#else
-    auto getOrientation = [](intel_nnet_layer_t & layer) {
-        return layer.nLayerKind == INTEL_CONVOLUTIONAL ?
-           kDnnNonInterleavedOrientation : kDnnInterleavedOrientation;
-    };
-#endif
-
-#if GNA_LIB_VER == 1
-    (*inputs_ptr_)["input"].orientation = getOrientation(std::get<0>(nnets.back())->obj.pLayers[0]);
-    outputsDesc[0].orientation = getOrientation(std::get<0>(nnets.back())->obj.pLayers[std::get<0>(nnets.back())->obj.nLayers - 1]);
-#endif
 
     if (header.doRotateInput) {
         for (auto && input : inputs_data_map_) {
@@ -1643,12 +1564,8 @@ InferenceEngine::IExecutableNetworkInternal::Ptr GNAPlugin::ImportNetwork(std::i
 #ifdef PLOT
     dnn->WriteGraphWizModel("gna-blob-imported.dot");
 #endif
-#if GNA_LIB_VER == 2
     trivialTopology = (std::get<0>(gnaModels.back())->obj.NumberOfOperations == 0);
     createRequestConfigsForGnaModels();
-#else
-    trivialTopology = (std::get<0>(nnets.back())->obj.nLayers == 0);
-#endif
     return {};
 }
 
@@ -1662,25 +1579,11 @@ void GNAPlugin::Export(std::ostream &outStream) {
         THROW_GNA_EXCEPTION << " network not loaded";
     }
 
-#if GNA_LIB_VER == 1
-    if (_inputs.size() != 1) {
-        THROW_GNA_EXCEPTION << " exporting network with multiple inputs not supported";
-    }
-#endif
-
     // TODO: nnet group parameter looks only used in application - so can we move this line into load network.
     IE_ASSERT(!inputs_data_map_.empty());
     auto inputDims = inputs_data_map_.begin()->second->getTensorDesc().getDims();
-    if (inputDims.size() == 2) {
-#if GNA_LIB_VER == 1
-        std::get<0>(nnets.front())->obj.nGroup = inputDims[0];
-#endif
-    }
-#if GNA_LIB_VER == 2
+
     Gna2Model* modelToSerial = &std::get<0>(gnaModels.front())->obj;
-#else
-    intel_nnet_type_t* modelToSerial = &std::get<0>(nnets.front())->obj;
-#endif
     auto serial = GNAModelSerial(modelToSerial,
                                  *(inputs_ptr_),
                                  outputs_)
diff --git a/src/plugins/intel_gna/gna_plugin.hpp b/src/plugins/intel_gna/gna_plugin.hpp
index a757ecab201..f999b576327 100644
--- a/src/plugins/intel_gna/gna_plugin.hpp
+++ b/src/plugins/intel_gna/gna_plugin.hpp
@@ -23,10 +23,7 @@
 #include "gna_plugin_log.hpp"
 #include "gna_plugin_config.hpp"
 #include <legacy/ie_util_internal.hpp>
-
-#if GNA_LIB_VER == 2
 #include <gna2-model-api.h>
-#endif
 
 namespace GNAPluginNS {
 class GNAPlugin : public InferenceEngine::IInferencePlugin {
@@ -45,17 +42,11 @@ class GNAPlugin : public InferenceEngine::IInferencePlugin {
     /**
      * @brief - copy of nnet structure and indicator that related infer request not yet synced
      */
-#if GNA_LIB_VER == 1
-    std::vector<std::tuple<dnn_ptr, int32_t, InferenceEngine::BlobMap>> nnets;
-#else
     static constexpr uint32_t FAKE_REQUEST_CONFIG_ID = 0xffffffff;
     std::vector<std::tuple<dnn_ptr>> gnaModels;
     std::vector<std::tuple<uint32_t, int64_t, InferenceEngine::BlobMap>> gnaRequestConfigToRequestIdMap;
-#endif
 
-#if GNA_LIB_VER == 2
     uint32_t activeLayerIndex = 0xffffffff;
-#endif
     TranspositionInfoMap transpose_inputs_info;
     TranspositionInfoMap transpose_outputs_info;
     uint32_t *ptr_active_indices = nullptr;
@@ -64,9 +55,7 @@ class GNAPlugin : public InferenceEngine::IInferencePlugin {
     uint32_t dnn_dump_write_index = 0;
     intel_dnn_number_type_t output_type = kDnnInt;
 
-#if GNA_LIB_VER == 2
     void createRequestConfigsForGnaModels();
-#endif
 
     static int GetDeviceVersionFromString(const std::string deviceString);
 
diff --git a/src/plugins/intel_gna/gna_plugin_config.cpp b/src/plugins/intel_gna/gna_plugin_config.cpp
index 4f2fca2089a..1210eea099d 100644
--- a/src/plugins/intel_gna/gna_plugin_config.cpp
+++ b/src/plugins/intel_gna/gna_plugin_config.cpp
@@ -15,17 +15,6 @@ using namespace InferenceEngine::details;
 
 namespace GNAPluginNS {
 
-#if GNA_LIB_VER == 1
-static const caseless_unordered_map<std::string, uint32_t> supported_values = {
-        {GNAConfigParams::GNA_AUTO,     GNA_AUTO},
-        {GNAConfigParams::GNA_HW,       GNA_HARDWARE},
-        {GNAConfigParams::GNA_SW,       GNA_SOFTWARE},
-        {GNAConfigParams::GNA_SW_EXACT, GNA_SOFTWARE & GNA_HARDWARE}
-};
-static const  std::vector<std::string> supported_values_on_gna2 = {
-        GNAConfigParams::GNA_HW_WITH_SW_FBACK,
-};
-#else
 static const caseless_unordered_map <std::string, std::pair<Gna2AccelerationMode, bool>> supported_values = {
                 {GNAConfigParams::GNA_AUTO,             {Gna2AccelerationModeAuto,                         false}},
                 {GNAConfigParams::GNA_HW,               {Gna2AccelerationModeHardware,                     false}},
@@ -33,7 +22,6 @@ static const caseless_unordered_map <std::string, std::pair<Gna2AccelerationMode
                 {GNAConfigParams::GNA_SW,               {Gna2AccelerationModeSoftware,                     false}},
                 {GNAConfigParams::GNA_SW_EXACT,         {Gna2AccelerationModeSoftware,                     true}},
         };
-#endif
 
 static const std::set<std::string> supportedTargets = {
     GNAConfigParams::GNA_TARGET_2_0,
@@ -90,24 +78,11 @@ void Config::UpdateFromMap(const std::map<std::string, std::string>& config) {
                 if (value == GNA_CONFIG_VALUE(SW_FP32)) {
                     gnaFlags.sw_fp32 = true;
                 } else {
-#if GNA_LIB_VER == 1
-                    auto is_gna2_mode = std::find(
-                            supported_values_on_gna2.begin(),
-                            supported_values_on_gna2.end(),
-                            value);
-                    if (is_gna2_mode != supported_values_on_gna2.end()) {
-                        THROW_GNA_EXCEPTION << "This GNA device mode requires GNA2 library: " << value;
-                    }
-#endif
                     THROW_GNA_EXCEPTION << "GNA device mode unsupported: " << value;
                 }
             } else {
-#if GNA_LIB_VER == 1
-                gna_proc_type = static_cast<intel_gna_proc_t>(procType->second);
-#else
                 pluginGna2AccMode = procType->second.first;
                 swExactMode = procType->second.second;
-#endif
             }
         } else if (key == GNA_CONFIG_KEY(EXEC_TARGET) || key == GNA_CONFIG_KEY(COMPILE_TARGET)) {
             if (supportedTargets.count(value) == 0) {
@@ -255,18 +230,11 @@ void Config::AdjustKeyMapValues() {
         device_mode = GNA_CONFIG_VALUE(SW_FP32);
     } else {
         for (auto&& value : supported_values) {
-#if GNA_LIB_VER == 1
-            if (value.second == gna_proc_type) {
-                device_mode = value.first;
-                break;
-            }
-#else
             if (value.second.first == pluginGna2AccMode &&
                 value.second.second == swExactMode) {
                 device_mode = value.first;
                 break;
             }
-#endif
         }
     }
     IE_ASSERT(!device_mode.empty());
diff --git a/src/plugins/intel_gna/gna_plugin_config.hpp b/src/plugins/intel_gna/gna_plugin_config.hpp
index 309882dd126..ca2d36b41e6 100644
--- a/src/plugins/intel_gna/gna_plugin_config.hpp
+++ b/src/plugins/intel_gna/gna_plugin_config.hpp
@@ -4,12 +4,8 @@
 
 #pragma once
 
-#if GNA_LIB_VER == 1
-#include <gna-api.h>
-#else
 #include <gna2-inference-api.h>
 #include <gna2-common-api.h>
-#endif
 #include "ie_precision.hpp"
 #include "descriptions/gna_flags.hpp"
 #include <vector>
@@ -35,12 +31,8 @@ struct Config {
         gnaPrecision = r.gnaPrecision;
         dumpXNNPath = r.dumpXNNPath;
         dumpXNNGeneration = r.dumpXNNGeneration;
-#if GNA_LIB_VER == 1
-        gna_proc_type = r.gna_proc_type;
-#else
         pluginGna2AccMode = r.pluginGna2AccMode;
         swExactMode = r.swExactMode;
-#endif
         inputScaleFactors = r.inputScaleFactors;
         gnaFlags = r.gnaFlags;
         std::lock_guard<std::mutex> lock(r.mtx4keyConfigMap);
@@ -60,12 +52,8 @@ struct Config {
     std::string gnaExecTarget;
     std::string gnaCompileTarget;
 
-#if GNA_LIB_VER == 1
-    intel_gna_proc_t gna_proc_type = static_cast<intel_gna_proc_t>(GNA_SOFTWARE & GNA_HARDWARE);
-#else
     Gna2AccelerationMode pluginGna2AccMode = Gna2AccelerationModeSoftware;
     bool swExactMode = true;
-#endif
 
     std::vector<float> inputScaleFactors;
     GNAFlags gnaFlags;
diff --git a/src/plugins/intel_gna/gna_plugin_entry_points.cpp b/src/plugins/intel_gna/gna_plugin_entry_points.cpp
index ea42cdd82d2..04a242f148a 100644
--- a/src/plugins/intel_gna/gna_plugin_entry_points.cpp
+++ b/src/plugins/intel_gna/gna_plugin_entry_points.cpp
@@ -12,9 +12,7 @@ using namespace GNAPluginNS;
 static const Version gnaPluginDescription = {
         {2, 1},
         CI_BUILD_NUMBER
-#if GNA_LIB_VER == 2
         "_with_GNA_LIB_VER==2"
-#endif
         ,
         "ov_intel_gna_plugin"
 };
diff --git a/src/plugins/intel_gna/gna_plugin_query_api.cpp b/src/plugins/intel_gna/gna_plugin_query_api.cpp
index 79f0959ab8c..964c66cec07 100644
--- a/src/plugins/intel_gna/gna_plugin_query_api.cpp
+++ b/src/plugins/intel_gna/gna_plugin_query_api.cpp
@@ -75,20 +75,9 @@ Parameter GNAPlugin::GetAvailableDevices() const {
 
     try {
         GNADeviceHelper hwHelper;
-#if GNA_LIB_VER == 1
-        try {
-            intel_nnet_type_t neuralNetwork = { 0 };
-            hwHelper.propagate(&neuralNetwork, nullptr, 0, GNA_HARDWARE);
-        }catch (...) {
-            if (hwHelper.getGNAStatus() != GNA_DEVNOTFOUND) {
-                devices.push_back("GNA_HW");
-            }
-        }
-#else
         if (hwHelper.hasGnaHw()) {
             devices.push_back("GNA_HW");
         }
-#endif
     }catch(...) {}
 
     return devices;
diff --git a/src/plugins/intel_gna/optimizer/gna_pass_manager.cpp b/src/plugins/intel_gna/optimizer/gna_pass_manager.cpp
index e804289f5c9..e053e36144d 100644
--- a/src/plugins/intel_gna/optimizer/gna_pass_manager.cpp
+++ b/src/plugins/intel_gna/optimizer/gna_pass_manager.cpp
@@ -1655,9 +1655,6 @@ void BroadcastConstPass::run() {
 
 void BreakFusingOfOutputLayersPass::run() {
     OV_ITT_SCOPED_TASK(itt::domains::GNA_LT, "BreakFusingOfOutputLayersPass");
-#if GNA_LIB_VER == 1
-    return;
-#endif
     OutputsDataMap outputsMap = this->getPassManager()->getNetwork().getOutputsInfo();
     for (auto layer : *pLayers) {
         /* Inserion of the second activation after pooling will break Conv - Pooling - Activation component
diff --git a/src/plugins/intel_gna/runtime/cnn.cpp b/src/plugins/intel_gna/runtime/cnn.cpp
index 78e04c3e5eb..841a4429656 100644
--- a/src/plugins/intel_gna/runtime/cnn.cpp
+++ b/src/plugins/intel_gna/runtime/cnn.cpp
@@ -186,8 +186,6 @@ void CNNMaxPool2DFloat(intel_dnn_component_t* component) {
 
 } // namespace
 
-#if GNA_LIB_VER == 2
-
 namespace {
 
 bool matchesPaddedArea(unsigned filterIndex, unsigned outputIndex, unsigned inputSize, unsigned paddingSize, unsigned stride) {
@@ -282,8 +280,6 @@ void CNN2DFilter32(intel_dnn_component_t* component) {
     }
 }
 
-#endif
-
 namespace {
 template<class T>
 bool is2D(T&& vec) {
diff --git a/src/plugins/intel_gna/runtime/cnn.h b/src/plugins/intel_gna/runtime/cnn.h
index 0911702963a..67a28e65307 100644
--- a/src/plugins/intel_gna/runtime/cnn.h
+++ b/src/plugins/intel_gna/runtime/cnn.h
@@ -14,6 +14,4 @@
 void CNNFilter32(intel_dnn_component_t *component);
 void CNNMaxPool(intel_dnn_component_t *component, intel_dnn_number_type_t number_type, const bool sumPoolingOverRide = false);
 
-#if GNA_LIB_VER == 2
 void CNN2DFilter32(intel_dnn_component_t* component);
-#endif
diff --git a/src/plugins/intel_gna/runtime/gna_float_runtime_op.cpp b/src/plugins/intel_gna/runtime/gna_float_runtime_op.cpp
index 49c1727fd5d..84677ebc9b4 100644
--- a/src/plugins/intel_gna/runtime/gna_float_runtime_op.cpp
+++ b/src/plugins/intel_gna/runtime/gna_float_runtime_op.cpp
@@ -117,11 +117,7 @@ void FP::ApplyConvolutional1DTransform(intel_dnn_component_t *component) {
 }
 
 void FP::ApplyConvolutional2DTransform(intel_dnn_component_t* component) {
-#if GNA_LIB_VER == 2
     CNN2DFilter32(component);
-#else
-    THROW_GNA_EXCEPTION << "Wrong GNA Library: GNA_LIB_VER != 2";
-#endif
 }
 
 void FP::ApplyPiecewiseLinearTransform(intel_dnn_component_t *component,
diff --git a/src/tests/unit/gna/gna_api_stub.cpp b/src/tests/unit/gna/gna_api_stub.cpp
index 656a5790a36..b85eb90d985 100644
--- a/src/tests/unit/gna/gna_api_stub.cpp
+++ b/src/tests/unit/gna/gna_api_stub.cpp
@@ -4,15 +4,9 @@
 
 #define INTEL_GNA_DLLEXPORT 1
 
-#if GNA_LIB_VER == 1
-#include <gna-api.h>
-#include <gna-api-instrumentation.h>
-#include <gna-api-dumper.h>
-#else
 #include <gna2-instrumentation-api.h>
 #include <gna2-inference-api.h>
 #include <gna2-model-export-api.h>
-#endif
 
 #include "gna_mock_api.hpp"
 
@@ -29,7 +23,6 @@ GNACppApi::~GNACppApi() {
 #ifdef __cplusplus
 extern "C" {  // API uses C linkage so that it can be used by C and C++ applications
 #endif
-#if GNA_LIB_VER == 2
 
 GNA2_API enum Gna2Status Gna2MemoryAlloc(
     uint32_t sizeRequested,
@@ -199,196 +192,6 @@ GNA2_API enum Gna2Status Gna2GetLibraryVersion(
     return Gna2StatusNullArgumentNotAllowed;
 }
 
-#elif GNA_LIB_VER == 1
-
-/**
- * intel_gna_status_t members printable descriptions
- *   Size: NUMGNASTATUS + 1
- */
-DLLDECL const char *GNAStatusName[] = {"status"};
-
-/**
- * intel_gmm_mode_t members printable descriptions
- *   Size: NUMGMMMODES + 1
- */
-DLLDECL const char *GMMModeName[] = {"model"};
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNAScoreGaussians(
-        intel_gna_handle_t handle,
-        const intel_feature_type_t *pFeatureType,
-        const intel_feature_t *pFeatureData,
-        const intel_gmm_type_t *pModelType,
-        const intel_gmm_t *pModelData,
-        const uint32_t *pActiveGMMIndices,
-        uint32_t nActiveGMMIndices,
-        uint32_t uMaximumScore,
-        intel_gmm_mode_t nGMMMode,
-        uint32_t *pScores,
-        uint32_t *pReqId,
-        intel_gna_proc_t nAccelerationType
-) {
-    if (current != nullptr) {
-        return current->GNAScoreGaussians(
-                // handle,
-                // pFeatureType,
-                pFeatureData,
-                pModelType,
-                pModelData,
-                pActiveGMMIndices,
-                nActiveGMMIndices,
-                uMaximumScore,
-                nGMMMode,
-                pScores,
-                pReqId,
-                nAccelerationType);
-    }
-    return GNA_NOERROR;
-}
-
-DLLDECL intel_gna_status_t GNAPropagateForward(
-        intel_gna_handle_t handle,
-        const intel_nnet_type_t *pNeuralNetwork,
-        const uint32_t *pActiveIndices,
-        uint32_t nActiveIndices,
-        uint32_t *pReqId,
-        intel_gna_proc_t nAccelerationType
-) {
-    if (current != nullptr) {
-        return current->GNAPropagateForward(
-                handle,
-                pNeuralNetwork,
-                pActiveIndices,
-                nActiveIndices,
-                pReqId,
-                nAccelerationType);
-    }
-    return GNA_NOERROR;
-}
-
-// TODO: add output status
-/**
- * // TODO: fill
- */
-DLLDECL void *GNAAlloc(
-        intel_gna_handle_t nGNADevice,   // handle to GNA accelerator
-        uint32_t sizeRequested,
-        uint32_t *sizeGranted
-) {
-    if (current != nullptr) {
-        return current->GNAAlloc(nGNADevice, sizeRequested, sizeGranted);
-    }
-    if (sizeGranted != nullptr) {
-        *sizeGranted = sizeRequested;
-    }
-    return reinterpret_cast<void *>(1);
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNAFree(
-        intel_gna_handle_t nGNADevice   // handle to GNA accelerator
-) {
-    if (current != nullptr) {
-        return current->GNAFree(nGNADevice);
-    }
-    return GNA_NOERROR;
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_handle_t GNADeviceOpen(
-        intel_gna_status_t *status            // Status of the call
-) {
-    if (current != nullptr) {
-        return current->GNADeviceOpen(status);
-    }
-    return 0;
-}
-
-/**
-* // TODO: fill
-*/
-DLLDECL intel_gna_handle_t GNADeviceOpenSetThreads(
-        intel_gna_status_t *status,            // Status of the call
-        uint8_t n_threads                // Number of worker threads
-) {
-    if (current != nullptr) {
-        return current->GNADeviceOpenSetThreads(status, n_threads);
-    }
-    return GNA_NOERROR;
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNADeviceClose(
-        intel_gna_handle_t nGNADevice  // handle to GNA accelerator
-) {
-    if (current != nullptr) {
-        return current->GNADeviceClose(nGNADevice);
-    }
-    return GNA_NOERROR;
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNAWait(
-        intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-        uint32_t nTimeoutMilliseconds,
-        uint32_t reqId                  // IN score request ID
-) {
-    if (current != nullptr) {
-        return current->GNAWait(nGNADevice, nTimeoutMilliseconds, reqId);
-    }
-    return GNA_NOERROR;
-}
-
-DLLDECL intel_gna_status_t GNAWaitPerfRes(
-        intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-        uint32_t nTimeoutMilliseconds,
-        uint32_t reqId,                 // IN score request ID
-        intel_gna_perf_t *nGNAPerfResults
-) {
-    if (current != nullptr) {
-        return current->GNAWaitPerfRes(nGNADevice,
-                                       nTimeoutMilliseconds,
-                                       reqId,
-                                       nGNAPerfResults);
-    }
-    return GNA_NOERROR;
-}
-
-DLLDECL void *GNADumpXnn(
-        const intel_nnet_type_t *neuralNetwork,
-        const uint32_t *activeIndices,
-        uint32_t activeIndicesCount,
-        intel_gna_model_header *modelHeader,
-        intel_gna_status_t *status,
-        intel_gna_alloc_cb customAlloc) {
-    if (current != nullptr) {
-        return current->GNADumpXnn(neuralNetwork,
-                                   activeIndices,
-                                   activeIndicesCount,
-                                   modelHeader,
-                                   status,
-                                   customAlloc);
-    }
-    return nullptr;
-}
-
-DLLDECL void gmmSetThreads(
-        int num
-) {
-    current->gmmSetThreads((num != 0) ? num : 1);
-}
-#endif  // GNA_LIB_VER == 1
-
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/tests/unit/gna/gna_mock_api.hpp b/src/tests/unit/gna/gna_mock_api.hpp
index c8c204da106..b8bbf2370b8 100644
--- a/src/tests/unit/gna/gna_mock_api.hpp
+++ b/src/tests/unit/gna/gna_mock_api.hpp
@@ -19,63 +19,7 @@ class GNACppApi {
 public:
     GNA_STUBS_EXPORT GNACppApi();
     GNA_STUBS_EXPORT ~GNACppApi();
-#if GNA_LIB_VER == 1
-    MOCK_METHOD10(GNAScoreGaussians, intel_gna_status_t(
-        // intel_gna_handle_t          nGNADevice,            // handle to GNA accelerator
-        // const intel_feature_type_t* pFeatureType,
-        const intel_feature_t*      pFeatureData,
-        const intel_gmm_type_t*     pModelType,
-        const intel_gmm_t*          pModelData,
-        const uint32_t*             pActiveGMMIndices,
-        uint32_t                    nActiveGMMIndices,
-        uint32_t                    uMaximumScore,
-        intel_gmm_mode_t            nGMMMode,
-        uint32_t*                   pScores,
-        uint32_t*                   pReqId,
-        intel_gna_proc_t            nAccelerationType));
 
-
-    MOCK_METHOD6(GNAPropagateForward, intel_gna_status_t(
-        intel_gna_handle_t          nGNADevice,            // handle to GNA accelerator
-        const intel_nnet_type_t*    pNeuralNetwork,
-        const uint32_t*             pActiveIndices,
-        uint32_t                    nActiveIndices,
-        uint32_t*                   pReqId,
-        intel_gna_proc_t            nAccelerationType));
-
-    MOCK_METHOD3(GNAAlloc, void *(
-        intel_gna_handle_t nGNADevice,   // handle to GNA accelerator
-        uint32_t           sizeRequested,
-        uint32_t*          sizeGranted));
-
-    MOCK_METHOD1(GNAFree, intel_gna_status_t(intel_gna_handle_t nGNADevice));
-
-    MOCK_METHOD1(GNADeviceOpen, intel_gna_handle_t(intel_gna_status_t* status));
-
-    MOCK_METHOD2(GNADeviceOpenSetThreads, intel_gna_handle_t(intel_gna_status_t* status, uint8_t n_threads));
-    MOCK_METHOD1(GNADeviceClose, intel_gna_status_t(intel_gna_handle_t nGNADevice));
-
-    MOCK_METHOD3(GNAWait, intel_gna_status_t(
-                 intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-                 uint32_t           nTimeoutMilliseconds,
-                 uint32_t           reqId));               // IN score request ID
-
-    MOCK_METHOD4(GNAWaitPerfRes, intel_gna_status_t(
-                 intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-                 uint32_t           nTimeoutMilliseconds,
-                 uint32_t           reqId,                 // IN score request ID
-                 intel_gna_perf_t*  nGNAPerfResults));
-
-    MOCK_METHOD6(GNADumpXnn, void* (
-        const intel_nnet_type_t*    neuralNetwork,
-        const uint32_t*             activeIndices,
-        uint32_t                    activeIndicesCount,
-        intel_gna_model_header*     modelHeader,
-        intel_gna_status_t*         status,
-        intel_gna_alloc_cb          customAlloc));
-
-    MOCK_METHOD1(gmmSetThreads, intel_gna_handle_t(uint8_t num));
-#else
     MOCK_METHOD3(Gna2MemoryAlloc, Gna2Status(
         uint32_t sizeRequested,
         uint32_t * sizeGranted,
@@ -83,5 +27,4 @@ public:
     MOCK_METHOD2(Gna2RequestWait, Gna2Status(
         uint32_t requestId,
         uint32_t timeoutMilliseconds));
-#endif
 };
diff --git a/src/tests/unit/gna/gna_plugin_config_test.cpp b/src/tests/unit/gna/gna_plugin_config_test.cpp
index 6d0a7c23413..542652f5ebc 100644
--- a/src/tests/unit/gna/gna_plugin_config_test.cpp
+++ b/src/tests/unit/gna/gna_plugin_config_test.cpp
@@ -97,38 +97,24 @@ TEST_F(GNAPluginConfigTest, GnaConfigFirmwareModelImageTest) {
 
 TEST_F(GNAPluginConfigTest, GnaConfigDeviceModeTest) {
     SetAndCompare(GNA_CONFIG_KEY(DEVICE_MODE), GNAConfigParams::GNA_HW);
-#if GNA_LIB_VER == 1
-    EXPECT_EQ(config.gna_proc_type, static_cast<intel_gna_proc_t>(GNA_HARDWARE));
-#else
     EXPECT_EQ(config.pluginGna2AccMode, Gna2AccelerationModeHardware);
     EXPECT_EQ(config.swExactMode, false);
-#endif
-#if GNA_LIB_VER == 2
     SetAndCompare(GNA_CONFIG_KEY(DEVICE_MODE), GNAConfigParams::GNA_HW_WITH_SW_FBACK);
     EXPECT_EQ(config.pluginGna2AccMode, Gna2AccelerationModeHardwareWithSoftwareFallback);
     EXPECT_EQ(config.swExactMode, false);
-#endif
     SetAndCompare(GNA_CONFIG_KEY(DEVICE_MODE), GNAConfigParams::GNA_SW);
-#if GNA_LIB_VER == 1
-    EXPECT_EQ(config.gna_proc_type, static_cast<intel_gna_proc_t>(GNA_SOFTWARE));
-#else
+
     EXPECT_EQ(config.pluginGna2AccMode, Gna2AccelerationModeSoftware);
     EXPECT_EQ(config.swExactMode, false);
-#endif
     SetAndCompare(GNA_CONFIG_KEY(DEVICE_MODE), GNAConfigParams::GNA_SW_EXACT);
-#if GNA_LIB_VER == 1
-    EXPECT_EQ(config.gna_proc_type, static_cast<intel_gna_proc_t>(GNA_SOFTWARE & GNA_HARDWARE));
-#else
+
     EXPECT_EQ(config.pluginGna2AccMode, Gna2AccelerationModeSoftware);
     EXPECT_EQ(config.swExactMode, true);
-#endif
     SetAndCompare(GNA_CONFIG_KEY(DEVICE_MODE), GNAConfigParams::GNA_AUTO);
-#if GNA_LIB_VER == 1
-    EXPECT_EQ(config.gna_proc_type, static_cast<intel_gna_proc_t>(GNA_AUTO));
-#else
+
     EXPECT_EQ(config.pluginGna2AccMode, Gna2AccelerationModeAuto);
     EXPECT_EQ(config.swExactMode, false);
-#endif
+
     ExpectThrow(GNA_CONFIG_KEY(DEVICE_MODE), "");
     ExpectThrow(GNA_CONFIG_KEY(DEVICE_MODE), "abc");
 }
diff --git a/src/tests/unit/gna/gna_wait_test.cpp b/src/tests/unit/gna/gna_wait_test.cpp
index 6da18d29551..a2bc7a550b2 100644
--- a/src/tests/unit/gna/gna_wait_test.cpp
+++ b/src/tests/unit/gna/gna_wait_test.cpp
@@ -2,8 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#if GNA_LIB_VER == 2
-
 #include <memory>
 
 #include <gtest/gtest.h>
@@ -66,4 +64,3 @@ TEST_F(GNAWaitTest, ReturnsGna2StatusWarningDeviceBusy) {
     GNAInferRequestForGNAWaitTest inferRequest{ plugin };
     ASSERT_EQ(InferenceEngine::RESULT_NOT_READY, inferRequest.Wait(0));
 }
-#endif
diff --git a/src/tests_deprecated/functional/gna/CMakeLists.txt b/src/tests_deprecated/functional/gna/CMakeLists.txt
index 6c45c35c2a8..a1e8dbea7cb 100644
--- a/src/tests_deprecated/functional/gna/CMakeLists.txt
+++ b/src/tests_deprecated/functional/gna/CMakeLists.txt
@@ -25,16 +25,9 @@ endif()
 
 add_executable(${TARGET_NAME} ${TEST_SRC} ${TEST_INCLUDE})
 
-if(GNA_LIBRARY_VERSION STREQUAL "GNA2")
-    set(GNA_LIBRARY_VERSION_NUMBER 2)
-else()
-    set(GNA_LIBRARY_VERSION_NUMBER 1)
-endif()
-
 target_compile_definitions(${TARGET_NAME}
         PRIVATE
             USE_GNA=ON
-            GNA_LIB_VER=${GNA_LIBRARY_VERSION_NUMBER}
         PUBLIC ${ARGV}
             DATA_PATH=\"${DATA_PATH}\"
             MODELS_PATH=\"${MODELS_PATH}\")
@@ -59,6 +52,3 @@ install(TARGETS ${TARGET_NAME}
         COMPONENT tests
         EXCLUDE_FROM_ALL)
 
-if(GNA_LIBRARY_VERSION STREQUAL "GNA1")
-    target_compile_definitions(${TARGET_NAME} PRIVATE GNA1_LIB)
-endif()
diff --git a/src/tests_deprecated/functional/gna/shared_tests_instance/lstm/lstm_cell_test.cpp b/src/tests_deprecated/functional/gna/shared_tests_instance/lstm/lstm_cell_test.cpp
index 378b593c216..346015a82a1 100644
--- a/src/tests_deprecated/functional/gna/shared_tests_instance/lstm/lstm_cell_test.cpp
+++ b/src/tests_deprecated/functional/gna/shared_tests_instance/lstm/lstm_cell_test.cpp
@@ -5,12 +5,6 @@
 #include <gna/gna_config.hpp>
 #include "lstm_cell_test.hpp"
 
-#if defined GNA_LIB_VER && GNA_LIB_VER == 2
-# define DISABLE_TEST_ON_GNA2 GTEST_SKIP();
-#else
-# define DISABLE_TEST_ON_GNA2
-#endif
-
 TEST_P(LSTMCellTestBase, GNA_sw_fp32_single_lstm_test) {
     runSingleLSTMTest({{"GNA_DEVICE_MODE", "GNA_SW_FP32"}, {"GNA_COMPACT_MODE", "NO"}});
 }
diff --git a/src/tests_deprecated/unit/CMakeLists.txt b/src/tests_deprecated/unit/CMakeLists.txt
index 8b198a20d60..c610eb27705 100644
--- a/src/tests_deprecated/unit/CMakeLists.txt
+++ b/src/tests_deprecated/unit/CMakeLists.txt
@@ -43,14 +43,8 @@ if (ENABLE_INTEL_GNA AND BUILD_SHARED_LIBS)
         add_library(libGNAStubs SHARED "${gna_stub}")
 
         find_package(libGNA)
-        if(GNA_LIBRARY_VERSION STREQUAL "GNA2")
-            set(GNA_LIBRARY_VERSION_NUMBER 2)
-        else()
-            set(GNA_LIBRARY_VERSION_NUMBER 1)
-        endif()
 
         target_include_directories(libGNAStubs PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/engines/gna/")
-        target_compile_definitions(libGNAStubs PUBLIC GNA_LIB_VER=${GNA_LIBRARY_VERSION_NUMBER})
         target_link_libraries(libGNAStubs PUBLIC libGNA::API gmock gtest)
         if(UNIX)
             set_target_properties(libGNAStubs PROPERTIES CXX_VISIBILITY_PRESET default)
diff --git a/src/tests_deprecated/unit/engines/gna/gna_aminteldnn_test.cpp b/src/tests_deprecated/unit/engines/gna/gna_aminteldnn_test.cpp
index d87709033db..a5270718080 100644
--- a/src/tests_deprecated/unit/engines/gna/gna_aminteldnn_test.cpp
+++ b/src/tests_deprecated/unit/engines/gna/gna_aminteldnn_test.cpp
@@ -14,19 +14,11 @@ using namespace testing;
 class GNA_AmIntelDnn_test : public GNATest<> {
 protected:
     GNAPluginNS::backend::AMIntelDNN amIntelDnn;
-#if GNA_LIB_VER == 2
     Gna2Model desc = {};
-#else
-    intel_nnet_type_t  desc = {};
-#endif
 };
 
 TEST_F(GNA_AmIntelDnn_test, intel_nnet_type_tSecondInitNotAllowed) {
-#if GNA_LIB_VER == 2
     desc.Operations = nullptr;
-#else
-    desc.pLayers = nullptr;
-#endif
     amIntelDnn.component.resize(1);
     amIntelDnn.component[0].operation = kDnnAffineOp;
     // First init is ok
@@ -45,10 +37,6 @@ TEST_F(GNA_AmIntelDnn_test, intel_nnet_type_t_pLayersIsNotNullptr) {
 }
 
 TEST_F(GNA_AmIntelDnn_test, ComponentIsEmpty) {
-#if GNA_LIB_VER == 2
     desc.Operations = nullptr;
-#else
-    desc.pLayers = nullptr;
-#endif
     ASSERT_THROW(amIntelDnn.InitGNAStruct(&desc), InferenceEngine::Exception);
 }
diff --git a/src/tests_deprecated/unit/engines/gna/gna_api_stub.cpp b/src/tests_deprecated/unit/engines/gna/gna_api_stub.cpp
index 92e0dfc21f7..82f26286e1f 100644
--- a/src/tests_deprecated/unit/engines/gna/gna_api_stub.cpp
+++ b/src/tests_deprecated/unit/engines/gna/gna_api_stub.cpp
@@ -4,15 +4,11 @@
 
 #define INTEL_GNA_DLLEXPORT 1
 
-#if GNA_LIB_VER == 1
-#include <gna-api.h>
-#include <gna-api-instrumentation.h>
-#include <gna-api-dumper.h>
-#else
+
 #include <gna2-instrumentation-api.h>
 #include <gna2-inference-api.h>
 #include <gna2-model-export-api.h>
-#endif
+
 #include "gna_mock_api.hpp"
 
 static GNACppApi * current = nullptr;
@@ -28,7 +24,6 @@ GNACppApi :: ~GNACppApi() {
 #ifdef __cplusplus
 extern "C" {  // API uses C linkage so that it can be used by C and C++ applications
 #endif
-#if GNA_LIB_VER == 2
 
 GNA2_API enum Gna2Status Gna2MemoryAlloc(
     uint32_t sizeRequested,
@@ -263,199 +258,6 @@ GNA2_API enum Gna2Status Gna2GetLibraryVersion(
     return Gna2StatusNullArgumentNotAllowed;
 }
 
-#elif GNA_LIB_VER == 1
-
-/**
- * intel_gna_status_t members printable descriptions
- *   Size: NUMGNASTATUS + 1
- */
-DLLDECL const char *GNAStatusName[] = {"status"};
-
-/**
- * intel_gmm_mode_t members printable descriptions
- *   Size: NUMGMMMODES + 1
- */
-DLLDECL const char *GMMModeName[] = {"model"};
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNAScoreGaussians(
-    intel_gna_handle_t          handle,
-    const intel_feature_type_t* pFeatureType,
-    const intel_feature_t*      pFeatureData,
-    const intel_gmm_type_t*     pModelType,
-    const intel_gmm_t*          pModelData,
-    const uint32_t*             pActiveGMMIndices,
-    uint32_t                    nActiveGMMIndices,
-    uint32_t                    uMaximumScore,
-    intel_gmm_mode_t            nGMMMode,
-    uint32_t*                   pScores,
-    uint32_t*                   pReqId,
-    intel_gna_proc_t            nAccelerationType
-) {
-    if (current != nullptr) {
-        return current->GNAScoreGaussians(
-            //handle,
-            //pFeatureType,
-            pFeatureData,
-            pModelType,
-            pModelData,
-            pActiveGMMIndices,
-            nActiveGMMIndices,
-            uMaximumScore,
-            nGMMMode,
-            pScores,
-            pReqId,
-            nAccelerationType);
-    }
-    return GNA_NOERROR;
-}
-
-DLLDECL intel_gna_status_t GNAPropagateForward(
-    intel_gna_handle_t          handle,
-    const intel_nnet_type_t*    pNeuralNetwork,
-    const uint32_t*             pActiveIndices,
-    uint32_t                    nActiveIndices,
-    uint32_t*                   pReqId,
-    intel_gna_proc_t            nAccelerationType
-) {
-    if (current != nullptr) {
-        return current->GNAPropagateForward(
-            handle,
-            pNeuralNetwork,
-            pActiveIndices,
-            nActiveIndices,
-            pReqId,
-            nAccelerationType);
-    }
-    return GNA_NOERROR;
-}
-
-// TODO: add output status
-/**
- * // TODO: fill
- */
-DLLDECL void *GNAAlloc(
-    intel_gna_handle_t nGNADevice,   // handle to GNA accelerator
-    uint32_t           sizeRequested,
-    uint32_t*          sizeGranted
-) {
-    if (current != nullptr) {
-        return current->GNAAlloc(nGNADevice, sizeRequested, sizeGranted);
-    }
-    if (sizeGranted != nullptr) {
-        *sizeGranted = sizeRequested;
-    }
-    return (void*)1;
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNAFree(
-    intel_gna_handle_t nGNADevice   // handle to GNA accelerator
-) {
-    if (current != nullptr) {
-        return current->GNAFree(nGNADevice);
-    }
-    return GNA_NOERROR;
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_handle_t GNADeviceOpen(
-    intel_gna_status_t* status	        // Status of the call
-) {
-    if (current != nullptr) {
-        return current->GNADeviceOpen(status);
-    }
-    return 0;
-
-}
-
-/**
-* // TODO: fill
-*/
-DLLDECL intel_gna_handle_t GNADeviceOpenSetThreads(
-    intel_gna_status_t* status,	        // Status of the call
-    uint8_t n_threads				// Number of worker threads
-) {
-    if (current != nullptr) {
-        return current->GNADeviceOpenSetThreads(status, n_threads);
-    }
-    return GNA_NOERROR;
-
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNADeviceClose(
-    intel_gna_handle_t nGNADevice // handle to GNA accelerator
-) {
-    if (current != nullptr) {
-        return current->GNADeviceClose(nGNADevice);
-    }
-    return GNA_NOERROR;
-
-}
-
-/**
- * // TODO: fill
- */
-DLLDECL intel_gna_status_t GNAWait(
-    intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-    uint32_t           nTimeoutMilliseconds,
-    uint32_t           reqId                  // IN score request ID
-) {
-    if (current != nullptr) {
-        return current->GNAWait(nGNADevice, nTimeoutMilliseconds, reqId);
-    }
-    return GNA_NOERROR;
-}
-
-DLLDECL intel_gna_status_t GNAWaitPerfRes(
-    intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-    uint32_t           nTimeoutMilliseconds,
-    uint32_t           reqId,                 // IN score request ID
-    intel_gna_perf_t*  nGNAPerfResults
-) {
-    if (current != nullptr) {
-        return current->GNAWaitPerfRes(nGNADevice,
-                                       nTimeoutMilliseconds,
-                                       reqId,
-                                       nGNAPerfResults);
-    }
-    return GNA_NOERROR;
-}
-
-DLLDECL void* GNADumpXnn(
-    const intel_nnet_type_t*    neuralNetwork,
-    const uint32_t*             activeIndices,
-    uint32_t                    activeIndicesCount,
-    intel_gna_model_header*     modelHeader,
-    intel_gna_status_t*         status,
-    intel_gna_alloc_cb          customAlloc) {
-        if (current != nullptr) {
-            return current->GNADumpXnn(neuralNetwork,
-                                        activeIndices,
-                                        activeIndicesCount,
-                                        modelHeader,
-                                        status,
-                                        customAlloc);
-        }
-        return nullptr;
-}
-
-DLLDECL void gmmSetThreads(
-    int num
-) {
-    current->gmmSetThreads((num != 0) ? num : 1);
-}
-#endif // GNA_LIB_VER == 1
-
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/tests_deprecated/unit/engines/gna/gna_cppwrapper_test.cpp b/src/tests_deprecated/unit/engines/gna/gna_cppwrapper_test.cpp
index 96abce2c605..cfbcb8adfdd 100644
--- a/src/tests_deprecated/unit/engines/gna/gna_cppwrapper_test.cpp
+++ b/src/tests_deprecated/unit/engines/gna/gna_cppwrapper_test.cpp
@@ -12,26 +12,14 @@ using namespace InferenceEngine;
 class GNA_CPPWrapper_test : public ::testing::Test {};
 
 TEST_F(GNA_CPPWrapper_test, CPPWrapperConstructorCannotWorkWithInputEqualToZero) {
-#if GNA_LIB_VER == 2
     ASSERT_THROW(GNAPluginNS::CPPWrapper<Gna2Model>(0), InferenceEngine::Exception);
-#else
-    ASSERT_THROW(GNAPluginNS::CPPWrapper<intel_nnet_type_t>(0), InferenceEngine::Exception);
-#endif
 }
 
 TEST_F(GNA_CPPWrapper_test, CPPWrapperConstructorCanWorkWithInputNotEqualToZero) {
-#if GNA_LIB_VER == 2
     ASSERT_NO_THROW(GNAPluginNS::CPPWrapper<Gna2Model>(3));
-    #else
-    ASSERT_NO_THROW(GNAPluginNS::CPPWrapper<intel_nnet_type_t>(3));
-#endif
 }
 
 TEST_F(GNA_CPPWrapper_test, CPPWrapperConstructorCanWorkWithoutAnyInput) {
-#if GNA_LIB_VER == 2
     ASSERT_NO_THROW(GNAPluginNS::CPPWrapper<Gna2Model>());
-#else
-    ASSERT_NO_THROW(GNAPluginNS::CPPWrapper<intel_nnet_type_t>());
-#endif
 }
 
diff --git a/src/tests_deprecated/unit/engines/gna/gna_graph_aot_test.cpp b/src/tests_deprecated/unit/engines/gna/gna_graph_aot_test.cpp
index b23e6e68a77..e8b73ca2deb 100644
--- a/src/tests_deprecated/unit/engines/gna/gna_graph_aot_test.cpp
+++ b/src/tests_deprecated/unit/engines/gna/gna_graph_aot_test.cpp
@@ -53,10 +53,6 @@ TEST_F(GNAAOTTests, DISABLED_AffineWith2AffineOutputs_canbe_export_imported) {
 }
 
 TEST_F(GNAAOTTests, DISABLED_AffineWith2AffineOutputs_canbe_imported_verify_structure) {
-// Disabled because of random fails: Issue-23611
-#if GNA_LIB_VER == 1
-    GTEST_SKIP();
-#endif
     auto & nnet_type = storage<gna_nnet_type_t>();
 
     // saving pointer to nnet - todo probably deep copy required
@@ -77,11 +73,6 @@ TEST_F(GNAAOTTests, DISABLED_AffineWith2AffineOutputs_canbe_imported_verify_stru
 }
 
 TEST_F(GNAAOTTests, DISABLED_TwoInputsModel_canbe_export_imported) {
-    // Disabled because of random conflicts with other tests: Issue-54220
-#if GNA_LIB_VER == 1
-    GTEST_SKIP();
-#endif
-
     const std::string X = registerFileForRemove(generateFileName("unit_tests.bin"));
 
     // running export to a file
@@ -97,11 +88,6 @@ TEST_F(GNAAOTTests, DISABLED_TwoInputsModel_canbe_export_imported) {
 }
 
 TEST_F(GNAAOTTests, DISABLED_PermuteModel_canbe_export_imported) {
-    // Disabled because of random conflicts with other tests: Issue-54220
-#if GNA_LIB_VER == 1
-    GTEST_SKIP();
-#endif
-
     const std::string X = registerFileForRemove(generateFileName("unit_tests.bin"));
 
     // running export to a file
@@ -114,11 +100,6 @@ TEST_F(GNAAOTTests, DISABLED_PermuteModel_canbe_export_imported) {
 }
 
 TEST_F(GNAAOTTests, DISABLED_PoolingModel_canbe_export_imported) {
-    // Disabled because of random conflicts with other tests: Issue-54220
-#if GNA_LIB_VER == 1
-    GTEST_SKIP();
-#endif
-
     const std::string X = registerFileForRemove(generateFileName("unit_tests.bin"));
 
     // running export to a file
diff --git a/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp b/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp
index 7d6105ad47e..c1e020949ce 100644
--- a/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp
+++ b/src/tests_deprecated/unit/engines/gna/gna_matcher.cpp
@@ -65,7 +65,7 @@ public:
         return true;
     }
 };
-#if GNA_LIB_VER == 2
+
 void expect_enqueue_calls(GNACppApi &mockApi){
     EXPECT_CALL(mockApi, Gna2ModelCreate(_,_,_)).Times(AtLeast(1)).WillRepeatedly(Invoke([](
         uint32_t deviceIndex,
@@ -97,7 +97,6 @@ void expect_enqueue_calls(GNACppApi &mockApi){
         EXPECT_CALL(mockApi, Gna2RequestWait(_, _)).Times(AtLeast(1)).WillRepeatedly(Return(Gna2StatusSuccess));
     }
 }
-#endif
 
 void GNAPropagateMatcher :: match() {
     try {
@@ -280,30 +279,6 @@ void GNAPropagateMatcher :: match() {
 
         if (_env.config[GNA_CONFIG_KEY(DEVICE_MODE)].compare(GNA_CONFIG_VALUE(SW_FP32)) != 0 &&
             !_env.matchThrows) {
-#if GNA_LIB_VER == 1
-            EXPECT_CALL(mockApi, GNAAlloc(_,_,_)).WillOnce(Invoke([&data](
-                intel_gna_handle_t nGNADevice,   // handle to GNA accelerator
-                uint32_t           sizeRequested,
-                uint32_t*          sizeGranted
-            ) {
-                data.resize(sizeRequested);
-                *sizeGranted = sizeRequested;
-                return &data.front();
-            }));
-            EXPECT_CALL(mockApi, GNADeviceOpenSetThreads(_, _)).WillOnce(Return(1));
-
-            if(_env.is_profiling_enabled == false) {
-                EXPECT_CALL(mockApi, GNAWait(_, _, _)).WillOnce(Return(GNA_NOERROR));
-            } else {
-                EXPECT_CALL(mockApi, GNAWaitPerfRes(_, _, _, _)).WillOnce(Return(GNA_NOERROR));
-            }
-
-            if(_env.is_setup_of_omp_theads_expected == true) {
-                EXPECT_CALL(mockApi, gmmSetThreads(_)).Times(1);
-            } else {
-                EXPECT_CALL(mockApi, gmmSetThreads(_)).Times(0);
-            }
-#elif GNA_LIB_VER == 2
             EXPECT_CALL(mockApi, Gna2MemoryAlloc(_, _, _)).WillOnce(Invoke([&data](
                 uint32_t sizeRequested,
                 uint32_t *sizeGranted,
@@ -333,9 +308,6 @@ void GNAPropagateMatcher :: match() {
             if(_env.is_setup_of_omp_theads_expected == true) {
                 EXPECT_CALL(mockApi, Gna2DeviceSetNumberOfThreads(_,_)).WillOnce(Return(Gna2StatusSuccess));
             }
-#else
-#error "Unsupported GNA_LIB_VER"
-#endif
             std::unique_ptr<NNetComponentMatcher> combined(new NNetComponentMatcher());
 
             for (auto & matchWhat : _env.whatToMatch) {
@@ -344,12 +316,7 @@ void GNAPropagateMatcher :: match() {
                         combined->add(new NNetPrecisionMatcher(_env.nnet_precision, INTEL_AFFINE));
                         break;
                     case GnaPluginTestEnvironment::matchProcType :
-#if GNA_LIB_VER == 1
-                        EXPECT_CALL(mockApi, GNAPropagateForward(_, _, _, _, _, Eq(_env.proc_type)))
-                            .WillOnce(Return(GNA_NOERROR));
-#elif GNA_LIB_VER == 2
                         expect_enqueue_calls(mockApi);
-#endif
                         break;
                     case GnaPluginTestEnvironment::matchPwlInserted :
                         combined->add(new PWLMatcher(_env.matchInserted, matchWhat.matchQuantity, _env.pwlsToMatchWith));
@@ -372,12 +339,7 @@ void GNAPropagateMatcher :: match() {
                         combined->add(new DiagLayerMatcher(_env.matchInserted, matchWhat.matchQuantity));
                         break;
                     case GnaPluginTestEnvironment::saveArgs :
-#if GNA_LIB_VER == 1
-                        EXPECT_CALL(mockApi, GNAPropagateForward(_, _, _, _, _, _))
-                            .WillOnce(DoAll(SaveArgPointee<1>(savedNet), Return(GNA_NOERROR)));
-#elif GNA_LIB_VER == 2
                         expect_enqueue_calls(mockApi);
-#endif
                         break;
                     case GnaPluginTestEnvironment::matchInputData :
                         combined->add(new InputDataMatcher(_env.input_processed));
@@ -398,21 +360,12 @@ void GNAPropagateMatcher :: match() {
                         SaveWeights(combined, _env.transposedData, _env.transposedArgsForSaving);
                         break;
                     default:
-#if GNA_LIB_VER == 1
-                        EXPECT_CALL(mockApi, GNAPropagateForward(_, _, _, _, _, _))
-                            .WillOnce(Return(GNA_NOERROR));
-#elif GNA_LIB_VER == 2
                         expect_enqueue_calls(mockApi);
-#endif
                         break;
                 }
             }
             if (combined && !combined->empty()) {
-#if GNA_LIB_VER == 1
-                EXPECT_CALL(mockApi, GNAPropagateForward(_, ::testing::MakeMatcher(combined.release()), _, _, _,_)).WillOnce(Return(GNA_NOERROR));
-#elif GNA_LIB_VER == 2
                 expect_enqueue_calls(mockApi);
-#endif
             }
         }
 
@@ -524,10 +477,6 @@ void GNAPluginAOTMatcher :: match() {
 
     GNACppApi mockApi;
     std::vector<std::vector<uint8_t>> data;
-#if GNA_LIB_VER == 1
-    EXPECT_CALL(mockApi, GNAAlloc(_,_,_)).WillOnce(DoAll(SetArgPointee<2>(10000), Return(&data.front())));
-    EXPECT_CALL(mockApi, GNADeviceOpenSetThreads(_, _)).WillOnce(Return(1));
-#elif GNA_LIB_VER == 2
     EXPECT_CALL(mockApi, Gna2MemoryAlloc(_, _, _)).Times(AtLeast(1)).WillRepeatedly(Invoke([&data](
         uint32_t sizeRequested,
         uint32_t *sizeGranted,
@@ -567,9 +516,6 @@ void GNAPluginAOTMatcher :: match() {
         }));
 
     EXPECT_CALL(mockApi, Gna2InstrumentationConfigAssignToRequestConfig(_,_)).Times(AtLeast(1)).WillRepeatedly(Return(Gna2StatusSuccess));
-#else
-#error "Not supported GNA_LIB_VER"
-#endif
     plugin.LoadNetwork(network);
     plugin.Export(_env.exportedModelFileName);
 }
@@ -624,17 +570,6 @@ void GNADumpXNNMatcher::match() {
     GNACppApi mockApi;
     std::vector<uint8_t> data(10000);
 
-#if GNA_LIB_VER == 1
-    if (!_env.matchThrows) {
-        EXPECT_CALL(mockApi, GNAAlloc(_,_,_)).WillOnce(DoAll(SetArgPointee<2>(10000), Return(&data.front())));
-        EXPECT_CALL(mockApi, GNADeviceOpenSetThreads(_, _)).WillOnce(Return(1));
-        intel_gna_model_header header = {};
-        header.model_size = 1;
-        EXPECT_CALL(mockApi, GNADumpXnn(_, _, _, _, _,_)).WillOnce(DoAll(SetArgPointee<3>(header), Return((void*)::operator new[](1))));
-        EXPECT_CALL(mockApi, GNAFree(_)).WillOnce(Return(GNA_NOERROR));
-        EXPECT_CALL(mockApi, GNADeviceClose(_)).WillOnce(Return(GNA_NOERROR));
-    }
-#elif GNA_LIB_VER == 2
     if (!_env.matchThrows) {
         EXPECT_CALL(mockApi, Gna2MemoryAlloc(_, _, _)).
             WillOnce(DoAll(SetArgPointee<1>(10000), SetArgPointee<2>(&data.front()), Return(Gna2StatusSuccess)));
@@ -695,10 +630,6 @@ void GNADumpXNNMatcher::match() {
 
         ON_CALL(mockApi, Gna2InstrumentationConfigAssignToRequestConfig(_,_)).WillByDefault(Return(Gna2StatusSuccess));
     }
-#else
-#error "Not supported GNA_LIB_VER"
-#endif
-
 
     try {
         // matching gna DumpXNN forward call.
@@ -755,12 +686,6 @@ void GNAQueryStateMatcher :: match() {
         }
     };
 
-#if GNA_LIB_VER == 1
-    EXPECT_CALL(mockApi, GNAAlloc(_,_,_)).WillOnce(DoAll(SetArgPointee<2>(10000), Return(&data.front())));
-    EXPECT_CALL(mockApi, GNADeviceOpenSetThreads(_, _)).WillOnce(Return(1));
-    EXPECT_CALL(mockApi, GNAFree(_)).WillOnce(Return(GNA_NOERROR));
-    EXPECT_CALL(mockApi, GNADeviceClose(_)).WillOnce(Return(GNA_NOERROR));
-#else
     EXPECT_CALL(mockApi, Gna2MemoryAlloc(_, _, _)).
         WillOnce(DoAll(SetArgPointee<1>(10000), SetArgPointee<2>(&data.front()), Return(Gna2StatusSuccess)));
 
@@ -797,7 +722,6 @@ void GNAQueryStateMatcher :: match() {
         }));
 
     EXPECT_CALL(mockApi, Gna2InstrumentationConfigAssignToRequestConfig(_,_)).Times(AtLeast(1)).WillRepeatedly(Return(Gna2StatusSuccess));
-#endif
 }
 
 
diff --git a/src/tests_deprecated/unit/engines/gna/gna_mock_api.hpp b/src/tests_deprecated/unit/engines/gna/gna_mock_api.hpp
index 15825f7c22c..65821fdaca9 100644
--- a/src/tests_deprecated/unit/engines/gna/gna_mock_api.hpp
+++ b/src/tests_deprecated/unit/engines/gna/gna_mock_api.hpp
@@ -4,15 +4,10 @@
 
 #pragma once
 #include <gmock/gmock-generated-function-mockers.h>
-#if GNA_LIB_VER == 1
-#include <gna-api.h>
-#include <gna-api-instrumentation.h>
-#include <gna-api-dumper.h>
-#else
 #include <gna2-instrumentation-api.h>
 #include <gna2-inference-api.h>
 #include <gna2-model-export-api.h>
-#endif
+
 #if defined(_WIN32)
     #ifdef libGNAStubs_EXPORTS
         #define GNA_STUBS_EXPORT __declspec(dllexport)
@@ -28,65 +23,6 @@ class GNACppApi {
  public:
     GNA_STUBS_EXPORT GNACppApi();
     GNA_STUBS_EXPORT ~GNACppApi();
-#if GNA_LIB_VER == 1
-    MOCK_METHOD10(GNAScoreGaussians, intel_gna_status_t(
-        //intel_gna_handle_t          nGNADevice,            // handle to GNA accelerator
-        //const intel_feature_type_t* pFeatureType,
-        const intel_feature_t*      pFeatureData,
-        const intel_gmm_type_t*     pModelType,
-        const intel_gmm_t*          pModelData,
-        const uint32_t*             pActiveGMMIndices,
-        uint32_t                    nActiveGMMIndices,
-        uint32_t                    uMaximumScore,
-        intel_gmm_mode_t            nGMMMode,
-        uint32_t*                   pScores,
-        uint32_t*                   pReqId,
-        intel_gna_proc_t            nAccelerationType));
-
-
-    MOCK_METHOD6(GNAPropagateForward, intel_gna_status_t (
-        intel_gna_handle_t          nGNADevice,            // handle to GNA accelerator
-        const intel_nnet_type_t*    pNeuralNetwork,
-        const uint32_t*             pActiveIndices,
-        uint32_t                    nActiveIndices,
-        uint32_t*                   pReqId,
-        intel_gna_proc_t            nAccelerationType));
-
-    MOCK_METHOD3(GNAAlloc, void *(
-        intel_gna_handle_t nGNADevice,   // handle to GNA accelerator
-        uint32_t           sizeRequested,
-        uint32_t*          sizeGranted));
-
-    MOCK_METHOD1(GNAFree, intel_gna_status_t (intel_gna_handle_t nGNADevice));
-
-    MOCK_METHOD1(GNADeviceOpen, intel_gna_handle_t (intel_gna_status_t* status));
-
-    MOCK_METHOD2(GNADeviceOpenSetThreads, intel_gna_handle_t (intel_gna_status_t* status, uint8_t n_threads));
-    MOCK_METHOD1(GNADeviceClose, intel_gna_status_t (intel_gna_handle_t nGNADevice));
-
-    MOCK_METHOD3(GNAWait, intel_gna_status_t(
-                 intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-                 uint32_t           nTimeoutMilliseconds,
-                 uint32_t           reqId                  // IN score request ID);
-    ));
-
-    MOCK_METHOD4(GNAWaitPerfRes, intel_gna_status_t(
-                 intel_gna_handle_t nGNADevice,            // handle to GNA accelerator
-                 uint32_t           nTimeoutMilliseconds,
-                 uint32_t           reqId,                 // IN score request ID);
-                 intel_gna_perf_t*  nGNAPerfResults
-    ));
-
-    MOCK_METHOD6(GNADumpXnn, void* (
-        const intel_nnet_type_t*    neuralNetwork,
-        const uint32_t*             activeIndices,
-        uint32_t                    activeIndicesCount,
-        intel_gna_model_header*     modelHeader,
-        intel_gna_status_t*         status,
-        intel_gna_alloc_cb          customAlloc));
-
-    MOCK_METHOD1(gmmSetThreads, intel_gna_handle_t (uint8_t num));
-#else
     MOCK_METHOD3(Gna2MemoryAlloc, Gna2Status (
         uint32_t sizeRequested,
         uint32_t * sizeGranted,
@@ -189,5 +125,4 @@ class GNACppApi {
     MOCK_METHOD2(Gna2GetLibraryVersion, Gna2Status(
         char* versionBuffer,
         uint32_t versionBufferSize));
-#endif
 };
diff --git a/src/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp b/src/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp
index b8f32671fe9..127dc7fd684 100644
--- a/src/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp
+++ b/src/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp
@@ -11,12 +11,6 @@
 #include "gna_matcher.hpp"
 #include <ie_core.hpp>
 
-#if defined GNA_LIB_VER && GNA_LIB_VER == 2
-# define DISABLE_TEST_ON_GNA2 GTEST_SKIP();
-#else
-# define DISABLE_TEST_ON_GNA2
-#endif
-
 using namespace InferenceEngine;
 using namespace GNAPluginNS;
 using namespace GNATestIRs;
@@ -242,19 +236,6 @@ TEST_F(I16QuantisationTest, multiple_inputs_supported) {
         .withGNAConfig(configKey + std::to_string(1), 2.0f).gna().propagate_forward()
         .called_with().pwl_inserted_into_nnet().once();
 }
-TEST_F(I16QuantisationTest, multiple_inputs_can_handle_individual_scale_factors) {
-    DISABLE_TEST_ON_GNA2
-    std::vector<float> input_data  = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
-    std::vector<float> input2_data = {2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0};
-    std::vector<float> result      = {0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
-
-    std::string configKey = GNA_CONFIG_KEY(SCALE_FACTOR) + std::string("_");
-
-    assert_that().onInferModel(two_inputs_to_affine())
-        .inNotCompactMode().gna().propagate_forward()
-        .called_with().withGNAConfig(configKey + std::to_string(0), 2.0f).And()
-        .withGNAConfig(configKey + std::to_string(1), 2.0f).returns().result().filledWith(16384).that().equals_to(result);
-}
 
 TEST_F(I16QuantisationTest, DISABLED_multiple_inputs_into_concat_supported) {
     assert_that().onInferModel(two_inputs_to_concat())
diff --git a/src/tests_deprecated/unit/engines/gna/layers/gna_squeeze_test.cpp b/src/tests_deprecated/unit/engines/gna/layers/gna_squeeze_test.cpp
index ce015857859..be123da157f 100644
--- a/src/tests_deprecated/unit/engines/gna/layers/gna_squeeze_test.cpp
+++ b/src/tests_deprecated/unit/engines/gna/layers/gna_squeeze_test.cpp
@@ -65,13 +65,6 @@ protected:
     }
 
     void runtest() {
-#if GNA_LIB_VER == 1
-        if (strcmp(::testing::UnitTest::GetInstance()->current_test_info()->name(),
-            "SqueezeTest/I16_squeeze_[3 1 2 4 4 3]_[1]") == 0) {
-            GTEST_SKIP();
-        }
-#endif
-
         InferenceEngine::Precision precision;
         bool is_squeeze;
         SqueezeCaseParam param;

From 5e1d241c11f254567f1320f83d4728763b50fba2 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 23 Dec 2021 14:59:24 +0300
Subject: [PATCH 122/160] Renamed template plugin and tests (#9389)

* Renamed template plugin and plugin's tests

* Renamed template_extension
---
 .ci/azure/linux.yml                            |  2 +-
 .ci/azure/windows.yml                          |  2 +-
 .../developer_package/IEDevScriptsConfig.cmake |  4 ++++
 .../compile_flags/os_flags.cmake               |  4 ++++
 docs/CMakeLists.txt                            |  6 +++---
 docs/IE_PLUGIN_DG/Building.md                  | 12 ++++++++----
 docs/template_extension/new/CMakeLists.txt     |  2 +-
 docs/template_plugin/CMakeLists.txt            |  4 ++--
 docs/template_plugin/src/CMakeLists.txt        |  6 +++---
 docs/template_plugin/src/template_plugin.cpp   |  2 +-
 .../tests/functional/CMakeLists.txt            |  6 +++---
 .../behavior/plugin/core_integration.cpp       |  2 +-
 .../hetero/synthetic.cpp                       | 16 ++++++++--------
 .../ie_bridges/python/tests/test_Blob.py       |  2 +-
 .../ie_bridges/python/tests/test_CDataPtr.py   |  2 +-
 .../ie_bridges/python/tests/test_DataPtr.py    |  2 +-
 .../ie_bridges/python/tests/test_IENetwork.py  |  2 +-
 .../python/tests/test_InferRequest.py          | 18 +++++++++---------
 src/core/tests/CMakeLists.txt                  |  4 ++--
 src/core/tests/engines_util/test_case.hpp      |  2 +-
 src/core/tests/extension.cpp                   |  2 +-
 .../functional/inference_engine/CMakeLists.txt |  2 +-
 .../ir_serialization/custom_ops.cpp            |  2 +-
 .../inference_engine/ov_extension_test.cpp     |  2 +-
 .../include/api_conformance_helpers.hpp        |  2 +-
 .../include/base/behavior_test_utils.hpp       |  2 +-
 .../include/base/ov_behavior_test_utils.hpp    |  2 +-
 .../src/ov_plugin_cache.cpp                    |  2 +-
 .../functional_test_utils/src/plugin_cache.cpp |  2 +-
 29 files changed, 65 insertions(+), 53 deletions(-)

diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
index 16b39649234..adb49d1c85f 100644
--- a/.ci/azure/linux.yml
+++ b/.ci/azure/linux.yml
@@ -293,7 +293,7 @@ jobs:
     displayName: 'IE FuncTests'
     continueOnError: false
 
-  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/templateFuncTests --gtest_filter=*smoke* --gtest_output=xml:TEST-templateFuncTests.xml
+  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/ov_template_func_tests --gtest_filter=*smoke* --gtest_output=xml:TEST-templateFuncTests.xml
     displayName: 'TEMPLATE FuncTests'
     continueOnError: false
 
diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index 98d94cfe67e..c4028ffc5f6 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -253,7 +253,7 @@ jobs:
     displayName: 'IE FuncTests'
     continueOnError: false
 
-  - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\templateFuncTests --gtest_output=xml:TEST-templateFuncTests.xml
+  - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\ov_template_func_tests --gtest_output=xml:TEST-templateFuncTests.xml
     displayName: 'TEMPLATE FuncTests'
     continueOnError: false
 
diff --git a/cmake/developer_package/IEDevScriptsConfig.cmake b/cmake/developer_package/IEDevScriptsConfig.cmake
index d4475a3e80e..a6c0f7a4290 100644
--- a/cmake/developer_package/IEDevScriptsConfig.cmake
+++ b/cmake/developer_package/IEDevScriptsConfig.cmake
@@ -257,6 +257,10 @@ function(ie_mark_target_as_cc TARGET_NAME)
     set_source_files_properties(${sources} PROPERTIES OBJECT_DEPENDS ${GENERATED_HEADER})
 endfunction()
 
+function(ov_mark_target_as_cc)
+    ie_mark_target_as_cc(${ARGN})
+endfunction()
+
 # check python package
 
 function(ie_check_pip_package full_name message_type)
diff --git a/cmake/developer_package/compile_flags/os_flags.cmake b/cmake/developer_package/compile_flags/os_flags.cmake
index e3945d9ecc4..8a6e06b2d41 100644
--- a/cmake/developer_package/compile_flags/os_flags.cmake
+++ b/cmake/developer_package/compile_flags/os_flags.cmake
@@ -193,6 +193,10 @@ macro(ie_add_compiler_flags)
     endforeach()
 endmacro()
 
+function(ov_add_compiler_flags)
+    ie_add_compiler_flags(${ARGN})
+endfunction()
+
 #
 # Forced includes certain header file to all target source files
 #
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 41b1402eee4..9aadbad4ae2 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -23,8 +23,8 @@ if(NOT ENABLE_DOCKER)
     add_subdirectory(template_extension)
 
     set(all_docs_targets
-        ie_docs_snippets templateFuncTests
-        template_extension template_ov_extension templatePlugin)
+        ie_docs_snippets ov_template_func_tests
+        template_extension ov_template_extension ov_template_plugin)
     foreach(target_name IN LISTS all_docs_targets)
         if(TARGET ${target_name})
             set_target_properties(${target_name} PROPERTIES FOLDER docs)
@@ -36,7 +36,7 @@ if(NOT ENABLE_DOCKER)
 
     # install
 
-    foreach(target templatePlugin template_extension template_ov_extension)
+    foreach(target ov_template_plugin template_extension ov_template_extension)
         if(TARGET ${target})
             install(TARGETS ${target}
                     LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH}
diff --git a/docs/IE_PLUGIN_DG/Building.md b/docs/IE_PLUGIN_DG/Building.md
index 35df6b5b165..9e4c8a7ba09 100644
--- a/docs/IE_PLUGIN_DG/Building.md
+++ b/docs/IE_PLUGIN_DG/Building.md
@@ -59,13 +59,17 @@ To build a plugin and its tests, run the following CMake scripts:
 - Root `CMakeLists.txt`, which finds the Inference Engine Developer Package using the `find_package` CMake command and adds the `src` and `tests` subdirectories with plugin sources and their tests respectively:
 
 ```cmake
-cmake_minimum_required(VERSION 3.13.3)
+cmake_minimum_required(VERSION 3.13)
 
-project(InferenceEngineTemplatePlugin)
+project(OpenVINOTemplatePlugin)
 
-set(IE_MAIN_TEMPLATE_PLUGIN_SOURCE_DIR ${InferenceEngineTemplatePlugin_SOURCE_DIR})
+set(TEMPLATE_PLUGIN_SOURCE_DIR ${OpenVINOTemplatePlugin_SOURCE_DIR})
 
-find_package(InferenceEngineDeveloperPackage REQUIRED)
+find_package(OpenVINODeveloperPackage REQUIRED)
+
+if(CMAKE_COMPILER_IS_GNUCXX)
+    ov_add_compiler_flags(-Wall)
+endif()
 
 add_subdirectory(src)
 
diff --git a/docs/template_extension/new/CMakeLists.txt b/docs/template_extension/new/CMakeLists.txt
index 7229eba8def..1ebe3354951 100644
--- a/docs/template_extension/new/CMakeLists.txt
+++ b/docs/template_extension/new/CMakeLists.txt
@@ -5,7 +5,7 @@
 # [cmake:extension]
 set(CMAKE_CXX_STANDARD 11)
 
-set(TARGET_NAME "template_ov_extension")
+set(TARGET_NAME "ov_template_extension")
 
 find_package(OpenVINO)
 
diff --git a/docs/template_plugin/CMakeLists.txt b/docs/template_plugin/CMakeLists.txt
index 3080c74b619..ca5ab0638d0 100644
--- a/docs/template_plugin/CMakeLists.txt
+++ b/docs/template_plugin/CMakeLists.txt
@@ -7,12 +7,12 @@ cmake_minimum_required(VERSION 3.13)
 
 project(OpenVINOTemplatePlugin)
 
-set(IE_MAIN_TEMPLATE_PLUGIN_SOURCE_DIR ${OpenVINOTemplatePlugin_SOURCE_DIR})
+set(TEMPLATE_PLUGIN_SOURCE_DIR ${OpenVINOTemplatePlugin_SOURCE_DIR})
 
 find_package(OpenVINODeveloperPackage REQUIRED)
 
 if(CMAKE_COMPILER_IS_GNUCXX)
-    ie_add_compiler_flags(-Wall)
+    ov_add_compiler_flags(-Wall)
 endif()
 
 add_subdirectory(src)
diff --git a/docs/template_plugin/src/CMakeLists.txt b/docs/template_plugin/src/CMakeLists.txt
index f413a8f3260..690248fc0ea 100644
--- a/docs/template_plugin/src/CMakeLists.txt
+++ b/docs/template_plugin/src/CMakeLists.txt
@@ -3,7 +3,7 @@
 #
 
 # [cmake:plugin]
-set(TARGET_NAME "templatePlugin")
+set(TARGET_NAME "ov_template_plugin")
 
 file(GLOB_RECURSE SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 file(GLOB_RECURSE HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
@@ -17,11 +17,11 @@ ov_add_plugin(NAME ${TARGET_NAME}
               ADD_CLANG_FORMAT)
 
 # Enable support of CC for the plugin
-ie_mark_target_as_cc(${TARGET_NAME})
+ov_mark_target_as_cc(${TARGET_NAME})
 
 target_include_directories(${TARGET_NAME} PRIVATE
     "${CMAKE_CURRENT_SOURCE_DIR}"
-    "${IE_MAIN_TEMPLATE_PLUGIN_SOURCE_DIR}/include")
+    "${TEMPLATE_PLUGIN_SOURCE_DIR}/include")
 
 # link common Inference Engine libraries
 target_link_libraries(${TARGET_NAME} PRIVATE
diff --git a/docs/template_plugin/src/template_plugin.cpp b/docs/template_plugin/src/template_plugin.cpp
index 8bf14f7dc71..0972f7c71ab 100644
--- a/docs/template_plugin/src/template_plugin.cpp
+++ b/docs/template_plugin/src/template_plugin.cpp
@@ -298,6 +298,6 @@ InferenceEngine::Parameter Plugin::GetMetric(const std::string& name,
 // ! [plugin:get_metric]
 
 // ! [plugin:create_plugin_engine]
-static const InferenceEngine::Version version = {{2, 1}, CI_BUILD_NUMBER, "templatePlugin"};
+static const InferenceEngine::Version version = {{2, 1}, CI_BUILD_NUMBER, "ov_template_plugin"};
 IE_DEFINE_PLUGIN_CREATE_FUNCTION(Plugin, version)
 // ! [plugin:create_plugin_engine]
diff --git a/docs/template_plugin/tests/functional/CMakeLists.txt b/docs/template_plugin/tests/functional/CMakeLists.txt
index 9015a50f922..48d4c00293c 100644
--- a/docs/template_plugin/tests/functional/CMakeLists.txt
+++ b/docs/template_plugin/tests/functional/CMakeLists.txt
@@ -3,17 +3,17 @@
 #
 
 # [cmake:functional_tests]
-set(TARGET_NAME templateFuncTests)
+set(TARGET_NAME ov_template_func_tests)
 
 ov_add_test_target(
         NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
         DEPENDENCIES
-            templatePlugin
+            ov_template_plugin
         LINK_LIBRARIES
             openvino::funcSharedTests
         INCLUDES
-            "${IE_MAIN_TEMPLATE_PLUGIN_SOURCE_DIR}/include"
+            "${TEMPLATE_PLUGIN_SOURCE_DIR}/include"
             "${CMAKE_CURRENT_SOURCE_DIR}/op_reference"
         ADD_CPPLINT
         LABELS
diff --git a/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp
index 83701ec9030..e39cdc4f9fb 100644
--- a/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp
+++ b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp
@@ -18,7 +18,7 @@ namespace {
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassBasicTestP, IEClassBasicTestP,
-        ::testing::Values(std::make_pair("templatePlugin", CommonTestUtils::DEVICE_TEMPLATE)));
+        ::testing::Values(std::make_pair("ov_template_plugin", CommonTestUtils::DEVICE_TEMPLATE)));
 
 INSTANTIATE_TEST_SUITE_P(
         smoke_IEClassNetworkTestP, IEClassNetworkTestP,
diff --git a/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp b/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp
index be09f976671..5a0adbf4fc4 100644
--- a/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp
+++ b/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp
@@ -16,13 +16,13 @@ using namespace HeteroTests;
 
 INSTANTIATE_TEST_SUITE_P(smoke_SingleMajorNode, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::_singleMajorNodeFunctions)),
                         HeteroSyntheticTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(nightly_RandomMajorNodes, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::_randomMajorNodeFunctions)),
                         HeteroSyntheticTest::getTestCaseName);
 
@@ -32,42 +32,42 @@ static std::vector<std::function<std::shared_ptr<ngraph::Function>()>> dynamicBu
 
 INSTANTIATE_TEST_SUITE_P(smoke_NonZeroMajorNode_dynamic, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::withMajorNodesFunctions(
                                         dynamicBuilders.front(), {"nonZero_1"}))),
                         HeteroSyntheticTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_NonZeroMajorNode_dynamic_batch, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::withMajorNodesFunctions(
                                         dynamicBuilders.front(), {"nonZero_1"}, true))),
                         HeteroSyntheticTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_SingleMajorNode_dynamic, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::singleMajorNodeFunctions(
                                         dynamicBuilders))),
                         HeteroSyntheticTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(nightly_RandomMajorNodes_dynamic, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::randomMajorNodeFunctions(
                                         dynamicBuilders))),
                         HeteroSyntheticTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_SingleMajorNode_dynamic_batch, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::singleMajorNodeFunctions(
                                         dynamicBuilders, true))),
                         HeteroSyntheticTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(nightly_RandomMajorNodes_dynamic_batch, HeteroSyntheticTest,
                         ::testing::Combine(
-                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
+                                ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "ov_template_plugin"}, {"TEMPLATE1", "ov_template_plugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::randomMajorNodeFunctions(
                                         dynamicBuilders, true))),
                         HeteroSyntheticTest::getTestCaseName);
diff --git a/inference-engine/ie_bridges/python/tests/test_Blob.py b/inference-engine/ie_bridges/python/tests/test_Blob.py
index 75a557fac9a..f117a8654e9 100644
--- a/inference-engine/ie_bridges/python/tests/test_Blob.py
+++ b/inference-engine/ie_bridges/python/tests/test_Blob.py
@@ -151,7 +151,7 @@ def test_blob_set_shape_after_async_infer():
     net = ng.function_to_cnn(function)
     net.reshape({"data": [(1, 5), 4, 20, 20]})
     ie_core = IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     request = exec_net.requests[0]
     request.async_infer({"data": np.ones([4, 4, 20, 20])})
diff --git a/inference-engine/ie_bridges/python/tests/test_CDataPtr.py b/inference-engine/ie_bridges/python/tests/test_CDataPtr.py
index 5287807f6a5..4ff30412fb9 100644
--- a/inference-engine/ie_bridges/python/tests/test_CDataPtr.py
+++ b/inference-engine/ie_bridges/python/tests/test_CDataPtr.py
@@ -64,7 +64,7 @@ def test_is_dynamic():
     function = create_relu([-1, 3, 20, 20])
     net = ng.function_to_cnn(function)
     ie = IECore()
-    ie.register_plugin("templatePlugin", "TEMPLATE")
+    ie.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie.load_network(net, "TEMPLATE")
     assert exec_net.outputs["out"].is_dynamic
     p_shape = ng.partial_shape_from_data(exec_net.outputs["out"])
diff --git a/inference-engine/ie_bridges/python/tests/test_DataPtr.py b/inference-engine/ie_bridges/python/tests/test_DataPtr.py
index 91c77a6c72c..7c1a15989e0 100644
--- a/inference-engine/ie_bridges/python/tests/test_DataPtr.py
+++ b/inference-engine/ie_bridges/python/tests/test_DataPtr.py
@@ -60,7 +60,7 @@ def test_is_dynamic():
         net.input_info["data"].input_data.shape
     assert  "Cannot return dims for Data with dynamic shapes!" in str(e.value)
     ie = IECore()
-    ie.register_plugin("templatePlugin", "TEMPLATE")
+    ie.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie.load_network(net, "TEMPLATE")
     assert exec_net.input_info["data"].input_data.is_dynamic
     p_shape = ng.partial_shape_from_data(exec_net.input_info["data"].input_data)
diff --git a/inference-engine/ie_bridges/python/tests/test_IENetwork.py b/inference-engine/ie_bridges/python/tests/test_IENetwork.py
index ebadfffb3f4..8dd240ce373 100644
--- a/inference-engine/ie_bridges/python/tests/test_IENetwork.py
+++ b/inference-engine/ie_bridges/python/tests/test_IENetwork.py
@@ -282,7 +282,7 @@ def test_create_two_exec_net():
     function = create_relu([ng.Dimension(0,5), ng.Dimension(4), ng.Dimension(20), ng.Dimension(20)])
     net = ng.function_to_cnn(function)
     ie_core = IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net1 = ie_core.load_network(net, "TEMPLATE", num_requests=2)
     assert ng.function_from_cnn(net) != None
     exec_net2 = ie_core.load_network(net, "TEMPLATE", num_requests=2)
diff --git a/inference-engine/ie_bridges/python/tests/test_InferRequest.py b/inference-engine/ie_bridges/python/tests/test_InferRequest.py
index e42624d49cf..72ef604f878 100644
--- a/inference-engine/ie_bridges/python/tests/test_InferRequest.py
+++ b/inference-engine/ie_bridges/python/tests/test_InferRequest.py
@@ -592,7 +592,7 @@ def test_infer_dynamic_network_with_set_shape(shape, p_shape, ref_shape):
     net = ng.function_to_cnn(function)
     net.reshape({"data": p_shape})
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     exec_net.requests[0].input_blobs["data"].set_shape(ref_shape)
     assert exec_net.requests[0].input_blobs["data"].tensor_desc.dims == ref_shape
@@ -616,7 +616,7 @@ def test_infer_dynamic_network_without_set_shape(shape, p_shape, ref_shape):
     net = ng.function_to_cnn(function)
     net.reshape({"data": p_shape})
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     exec_net.infer({"data": np.ones(ref_shape)})
     assert exec_net.requests[0].input_blobs["data"].tensor_desc.dims == ref_shape
@@ -639,7 +639,7 @@ def test_infer_dynamic_network_with_set_blob(shape, p_shape, ref_shape):
     net = ng.function_to_cnn(function)
     net.reshape({"data": p_shape})
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     tensor_desc = exec_net.requests[0].input_blobs["data"].tensor_desc
     tensor_desc.dims = ref_shape
@@ -662,7 +662,7 @@ def test_infer_dynamic_network_twice():
     net = ng.function_to_cnn(function)
     net.reshape({"data": p_shape})
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     request = exec_net.requests[0]
     request.infer({"data": np.ones(ref_shape1)})
@@ -681,7 +681,7 @@ def test_infer_dynamic_network_with_set_blob_twice():
     net = ng.function_to_cnn(function)
     net.reshape({"data": p_shape})
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     request = exec_net.requests[0]
     td = request.input_blobs['data'].tensor_desc
@@ -710,7 +710,7 @@ def test_async_infer_dynamic_network_3_requests(shapes):
     net = ng.function_to_cnn(function)
     net.reshape({"data": [3, 4, (20, 50), (20, 50)]})
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE", num_requests=3)
     for i,request in enumerate(exec_net.requests):
         request.async_infer({"data": np.ones(shapes[i])})
@@ -725,7 +725,7 @@ def test_set_blob_with_incorrect_name():
     function = create_encoder([4, 4, 20, 20])
     net = ng.function_to_cnn(function)
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     tensor_desc = exec_net.requests[0].input_blobs["data"].tensor_desc
     tensor_desc.dims = [4, 4, 20, 20]
@@ -740,7 +740,7 @@ def test_set_blob_with_incorrect_size():
     function = create_encoder([4, 4, 20, 20])
     net = ng.function_to_cnn(function)
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     tensor_desc = exec_net.requests[0].input_blobs["data"].tensor_desc
     tensor_desc.dims = [tensor_desc.dims[0]*2, 4, 20, 20]
@@ -760,7 +760,7 @@ def test_set_blob_after_async_infer():
     net = ng.function_to_cnn(function)
     net.reshape({"data": [(0, 5), 4, 20, 20]})
     ie_core = ie.IECore()
-    ie_core.register_plugin("templatePlugin", "TEMPLATE")
+    ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
     exec_net = ie_core.load_network(net, "TEMPLATE")
     request = exec_net.requests[0]
     tensor_desc = request.input_blobs['data'].tensor_desc
diff --git a/src/core/tests/CMakeLists.txt b/src/core/tests/CMakeLists.txt
index 7cfe5caa6ee..98cc6dea4d1 100644
--- a/src/core/tests/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -420,7 +420,7 @@ if(SUGGEST_OVERRIDE_SUPPORTED)
                                 PROPERTIES COMPILE_OPTIONS -Wno-suggest-override)
 endif()
 
-list(APPEND UNIT_TESTS_DEPENDENCIES template_ov_extension)
+list(APPEND UNIT_TESTS_DEPENDENCIES ov_template_extension)
 
 if (ENABLE_INTEL_CPU AND ENABLE_OV_CORE_BACKEND_UNIT_TESTS)
     message(STATUS "OV_CORE_TESTS: IE:CPU enabled")
@@ -446,7 +446,7 @@ if (ENABLE_OV_CORE_BACKEND_UNIT_TESTS)
     message(STATUS "OV_CORE_TESTS: INTERPRETER enabled")
     set(ACTIVE_BACKEND_LIST ${ACTIVE_BACKEND_LIST} INTERPRETER)
     if (ENABLE_STRICT_DEPENDENCIES)
-        list(APPEND UNIT_TESTS_DEPENDENCIES templatePlugin)
+        list(APPEND UNIT_TESTS_DEPENDENCIES ov_template_plugin)
     endif()
 endif()
 
diff --git a/src/core/tests/engines_util/test_case.hpp b/src/core/tests/engines_util/test_case.hpp
index 50842660b2a..07a488137a2 100644
--- a/src/core/tests/engines_util/test_case.hpp
+++ b/src/core/tests/engines_util/test_case.hpp
@@ -34,7 +34,7 @@ public:
     TestCase(const std::shared_ptr<Function>& function, const std::string& dev = "TEMPLATE") : m_function{function} {
         try {
             // Register template plugin
-            m_core.register_plugin(std::string("templatePlugin") + IE_BUILD_POSTFIX, "TEMPLATE");
+            m_core.register_plugin(std::string("ov_template_plugin") + IE_BUILD_POSTFIX, "TEMPLATE");
         } catch (...) {
         }
         m_request = m_core.compile_model(function, dev).create_infer_request();
diff --git a/src/core/tests/extension.cpp b/src/core/tests/extension.cpp
index 4dccb828a84..8ff8da706c9 100644
--- a/src/core/tests/extension.cpp
+++ b/src/core/tests/extension.cpp
@@ -12,7 +12,7 @@
 #include "so_extension.hpp"
 
 inline std::string get_extension_path() {
-    return ov::util::make_plugin_library_name<char>({}, std::string("template_ov_extension") + IE_BUILD_POSTFIX);
+    return ov::util::make_plugin_library_name<char>({}, std::string("ov_template_extension") + IE_BUILD_POSTFIX);
 }
 
 TEST(extension, load_extension) {
diff --git a/src/tests/functional/inference_engine/CMakeLists.txt b/src/tests/functional/inference_engine/CMakeLists.txt
index 0e8eae35322..e108134920b 100644
--- a/src/tests/functional/inference_engine/CMakeLists.txt
+++ b/src/tests/functional/inference_engine/CMakeLists.txt
@@ -26,7 +26,7 @@ set(LINK_LIBRARIES
 set(DEPENDENCIES
     mock_engine
     template_extension
-    template_ov_extension
+    ov_template_extension
     lptNgraphFunctions
     sharedTestClasses
     test_model_zoo
diff --git a/src/tests/functional/inference_engine/ir_serialization/custom_ops.cpp b/src/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
index b2ec82f2d8d..7990392d6fc 100644
--- a/src/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
@@ -28,7 +28,7 @@ static std::string get_extension_path() {
 }
 
 static std::string get_ov_extension_path() {
-    return FileUtils::makePluginLibraryName<char>({}, std::string("template_ov_extension") + IE_BUILD_POSTFIX);
+    return FileUtils::makePluginLibraryName<char>({}, std::string("ov_template_extension") + IE_BUILD_POSTFIX);
 }
 
 class CustomOpsSerializationTest : public ::testing::Test {
diff --git a/src/tests/functional/inference_engine/ov_extension_test.cpp b/src/tests/functional/inference_engine/ov_extension_test.cpp
index a360c063b13..3a4844e6db9 100644
--- a/src/tests/functional/inference_engine/ov_extension_test.cpp
+++ b/src/tests/functional/inference_engine/ov_extension_test.cpp
@@ -174,7 +174,7 @@ public:
 namespace {
 
 std::string getOVExtensionPath() {
-    return FileUtils::makePluginLibraryName<char>({}, std::string("template_ov_extension") + IE_BUILD_POSTFIX);
+    return FileUtils::makePluginLibraryName<char>({}, std::string("ov_template_extension") + IE_BUILD_POSTFIX);
 }
 
 }  // namespace
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
index e3ec583bb0b..a39c1b451de 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
@@ -26,7 +26,7 @@ inline const std::string getPluginLibNameByDevice(const std::string& deviceName)
             { "HETERO", "ov_hetero_plugin" },
             { "MULTI", "ov_multi_plugin" },
             { "MYRIAD", "myriadPlugin" },
-            { "TEMPLATE", "templatePlugin" },
+            { "TEMPLATE", "ov_template_plugin" },
     };
     if (devices.find(deviceName) == devices.end()) {
         throw std::runtime_error("Incorrect device name");
diff --git a/src/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp b/src/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp
index 7c4e595b945..345fe60698b 100644
--- a/src/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp
+++ b/src/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp
@@ -105,7 +105,7 @@ inline InferenceEngine::Core createIECoreWithTemplate() {
     PluginCache::get().reset();
     InferenceEngine::Core ie;
 #ifndef OPENVINO_STATIC_LIBRARY
-    std::string pluginName = "templatePlugin";
+    std::string pluginName = "ov_template_plugin";
     pluginName += IE_BUILD_POSTFIX;
     ie.RegisterPlugin(pluginName, CommonTestUtils::DEVICE_TEMPLATE);
 #endif // !OPENVINO_STATIC_LIBRARY
diff --git a/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp b/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp
index 17b38057408..17149d0448f 100644
--- a/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp
+++ b/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp
@@ -70,7 +70,7 @@ inline ov::runtime::Core createCoreWithTemplate() {
     ov::test::utils::PluginCache::get().reset();
     ov::runtime::Core core;
 #ifndef OPENVINO_STATIC_LIBRARY
-    std::string pluginName = "templatePlugin";
+    std::string pluginName = "ov_template_plugin";
     pluginName += IE_BUILD_POSTFIX;
     core.register_plugin(pluginName, CommonTestUtils::DEVICE_TEMPLATE);
 #endif // !OPENVINO_STATIC_LIBRARY
diff --git a/src/tests/ie_test_utils/functional_test_utils/src/ov_plugin_cache.cpp b/src/tests/ie_test_utils/functional_test_utils/src/ov_plugin_cache.cpp
index 0867f057385..c28ac16be8b 100644
--- a/src/tests/ie_test_utils/functional_test_utils/src/ov_plugin_cache.cpp
+++ b/src/tests/ie_test_utils/functional_test_utils/src/ov_plugin_cache.cpp
@@ -54,7 +54,7 @@ std::shared_ptr<ov::runtime::Core> PluginCache::core(const std::string &deviceTo
 
     // register template plugin if it is needed
     try {
-        std::string pluginName = "templatePlugin";
+        std::string pluginName = "ov_template_plugin";
         pluginName += IE_BUILD_POSTFIX;
         ov_core->register_plugin(pluginName, "TEMPLATE");
     } catch (...) {
diff --git a/src/tests/ie_test_utils/functional_test_utils/src/plugin_cache.cpp b/src/tests/ie_test_utils/functional_test_utils/src/plugin_cache.cpp
index 291ace87e1c..605249aea0a 100644
--- a/src/tests/ie_test_utils/functional_test_utils/src/plugin_cache.cpp
+++ b/src/tests/ie_test_utils/functional_test_utils/src/plugin_cache.cpp
@@ -55,7 +55,7 @@ std::shared_ptr<InferenceEngine::Core> PluginCache::ie(const std::string &device
 
     // register template plugin if it is needed
     try {
-        std::string pluginName = "templatePlugin";
+        std::string pluginName = "ov_template_plugin";
         pluginName += IE_BUILD_POSTFIX;
         ie_core->RegisterPlugin(pluginName, "TEMPLATE");
     } catch (...) {}

From 5346a5226c3536baacc3010bf2a58d288bfd372e Mon Sep 17 00:00:00 2001
From: Andrei Molotkov <andrei.molotkov@intel.com>
Date: Thu, 23 Dec 2021 15:04:26 +0300
Subject: [PATCH 123/160] [GPU] Mark all nodes with dynamic shape as
 unsupported (#9372)

---
 src/plugins/intel_gpu/src/plugin/plugin.cpp | 24 ++++++++++++---------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/src/plugins/intel_gpu/src/plugin/plugin.cpp b/src/plugins/intel_gpu/src/plugin/plugin.cpp
index bb69e631749..879f15976fa 100644
--- a/src/plugins/intel_gpu/src/plugin/plugin.cpp
+++ b/src/plugins/intel_gpu/src/plugin/plugin.cpp
@@ -390,37 +390,41 @@ QueryNetworkResult Plugin::QueryNetwork(const CNNNetwork& network,
     std::vector<std::shared_ptr<ngraph::Node>> nextLayerDependent;
 
     auto layerIsSupported = [&](std::shared_ptr<ngraph::Node> node) {
+        if (node->is_dynamic()) {
+            return false;
+        }
         if (ngraph::is_type<const ngraph::op::v0::DetectionOutput>(node) ||
             ngraph::is_type<const ngraph::op::v0::PriorBox>(node) ||
             ngraph::is_type<const ngraph::op::v0::PriorBoxClustered>(node) ||
             ngraph::is_type<const ngraph::op::v0::Proposal>(node)) {
             return false;
-        } else if (ngraph::is_type<const ngraph::op::v1::Split>(node)) {
+        }
+        if (ngraph::is_type<const ngraph::op::v1::Split>(node)) {
             splitNames.emplace(node->get_friendly_name());
             splits.push_back(node);
             return false;
-        } else if (ngraph::is_type<const ngraph::op::v0::Concat>(node)) {
+        }
+        if (ngraph::is_type<const ngraph::op::v0::Concat>(node)) {
             concatNames.emplace(node->get_friendly_name());
             concats.push_back(node);
             return false;
-        } else if (ngraph::is_type<const ngraph::op::v1::Reshape>(node) ||
+        }
+        if (ngraph::is_type<const ngraph::op::v1::Reshape>(node) ||
                    ngraph::is_type<const ngraph::op::v0::Squeeze>(node) ||
                    ngraph::is_type<const ngraph::op::v0::Unsqueeze>(node) ||
                    ngraph::is_type<const ngraph::op::v1::Transpose>(node)) {
             depLayerNames.emplace(node->get_friendly_name());
             nextLayerDependent.push_back(node);
             return false;
-        } else if (ngraph::is_type<const ngraph::op::v0::Constant>(node)) {
+        }
+        if (ngraph::is_type<const ngraph::op::v0::Constant>(node)) {
             constantsNames.emplace(node->get_friendly_name());
             constants.push_back(node);
             return false;
-        } else if (prog.IsOpSupported(network, node) &&
-                   !ngraph::op::is_parameter(node) &&
-                   !ngraph::op::is_output(node)) {
-            return true;
-        } else {
-            return false;
         }
+        return prog.IsOpSupported(network, node) &&
+               !ngraph::op::is_parameter(node) &&
+               !ngraph::op::is_output(node);
     };
 
     // Get ops after transformations and check if it's supported

From 8315fe0e19860234ff1e707b41a03644a59b4016 Mon Sep 17 00:00:00 2001
From: serhii-pavlovskyi-altran
 <82883030+serhii-pavlovskyi-altran@users.noreply.github.com>
Date: Thu, 23 Dec 2021 14:49:43 +0200
Subject: [PATCH 124/160] [GPU] Range v4 partial implementation (#8907)

---
 .../single_layer_tests/range.cpp              |  35 +++++
 .../clDNN/api/intel_gpu/graph/program.hpp     |   2 +-
 .../clDNN/api/intel_gpu/primitives/range.hpp  |  19 +++
 .../kernel_selector/common/common_types.h     |   1 +
 .../actual_kernels/range/range_kernel_ref.cpp |  86 ++++++++++++
 .../actual_kernels/range/range_kernel_ref.h   |  34 +++++
 .../range/range_kernel_selector.cpp           |  28 ++++
 .../range/range_kernel_selector.h             |  13 ++
 .../core/cl_kernels/range_ref.cl              |  11 ++
 .../thirdparty/clDNN/src/impls/ocl/range.cpp  |  61 +++++++++
 .../clDNN/src/impls/ocl/register.cpp          |   1 +
 .../clDNN/src/impls/ocl/register.hpp          |   2 +
 .../clDNN/src/include/json_object.h           |   8 +-
 .../clDNN/src/include/primitive_inst.h        |  10 +-
 .../thirdparty/clDNN/src/include/range_inst.h |  32 +++++
 .../thirdparty/clDNN/src/program.cpp          |   2 +-
 .../thirdparty/clDNN/src/range.cpp            |  41 ++++++
 .../clDNN/tests/test_cases/range_gpu_test.cpp |  83 ++++++++++++
 src/common/legacy/include/legacy/ie_layers.h  | 122 +++++++++++++++++-
 .../src/convert_function_to_cnn_network.cpp   |   2 +-
 .../include/low_precision/network_helper.hpp  |   2 +-
 .../intel_gpu/plugin/primitives_list.hpp      |   1 +
 .../include/intel_gpu/plugin/program.hpp      |   2 +-
 .../intel_gpu/src/plugin/ops/range.cpp        |  35 +++++
 .../inference_engine/task_executor_tests.cpp  |   1 +
 .../simplify_second_input_of_reshape_test.cpp |   3 +-
 .../src/base/layer_test_utils.cpp             |   2 +-
 .../src/single_layer/range.cpp                |  21 +--
 28 files changed, 625 insertions(+), 35 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/range.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/range.hpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.h
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.h
 create mode 100644 inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/range_ref.cl
 create mode 100644 inference-engine/thirdparty/clDNN/src/impls/ocl/range.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/src/include/range_inst.h
 create mode 100644 inference-engine/thirdparty/clDNN/src/range.cpp
 create mode 100644 inference-engine/thirdparty/clDNN/tests/test_cases/range_gpu_test.cpp
 create mode 100644 src/plugins/intel_gpu/src/plugin/ops/range.cpp

diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/range.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/range.cpp
new file mode 100644
index 00000000000..5cf0e9a114b
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/range.cpp
@@ -0,0 +1,35 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <initializer_list>
+
+#include <single_layer_tests/range.hpp>
+#include <common_test_utils/test_constants.hpp>
+
+
+namespace {
+
+const std::initializer_list<float> start { 1.0, 1.2 };
+const std::initializer_list<float> stop { 5.0, 5.2 };
+const std::initializer_list<float> step { 1.0, 0.1 };
+
+const std::initializer_list<InferenceEngine::Precision> netPrecisions {
+    InferenceEngine::Precision::FP32,
+    InferenceEngine::Precision::FP16
+};
+
+using LayerTestsDefinitions::RangeLayerTest;
+INSTANTIATE_TEST_SUITE_P(smoke_Basic, RangeLayerTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(start),
+                                ::testing::ValuesIn(stop),
+                                ::testing::ValuesIn(step),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                ::testing::Values(InferenceEngine::Layout::ANY),
+                                ::testing::Values(InferenceEngine::Layout::ANY),
+                                ::testing::Values(CommonTestUtils::DEVICE_GPU)),
+                        RangeLayerTest::getTestCaseName);
+}  // namespace
diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/graph/program.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/graph/program.hpp
index f5ae92107f2..c427e5c9f2e 100644
--- a/inference-engine/thirdparty/clDNN/api/intel_gpu/graph/program.hpp
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/graph/program.hpp
@@ -231,7 +231,7 @@ public:
     static void init_primitives();
     void compile();
     void init_kernels();
-    kernel_id add_kernel(const std::shared_ptr<kernel_string> kernel_sring);
+    kernel_id add_kernel(const std::shared_ptr<kernel_string>& kernel_sring);
     kernel::ptr get_kernel(kernel_id id);
 
     void load_tuning_cache();
diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/range.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/range.hpp
new file mode 100644
index 00000000000..de01cdf2cba
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/range.hpp
@@ -0,0 +1,19 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "primitive.hpp"
+
+namespace cldnn {
+struct range: public primitive_base<range> {
+    CLDNN_DECLARE_PRIMITIVE(range)
+
+    range(const primitive_id &id, const std::vector<primitive_id> &input, const layout &output_layout,
+          const primitive_id &ext_prim_id = { }) :
+        primitive_base { id, input, ext_prim_id, output_layout.data_padding }, output_layout { output_layout } {
+    }
+    layout output_layout;
+};
+}  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
index 480ec8cc868..a2508c3354b 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h
@@ -20,6 +20,7 @@ enum class KernelType {
     LRN,
     NORMALIZE,
     POOLING,
+    RANGE,
     ROI_POOLING,
     ROI_ALIGN,
     FULLY_CONNECTED,
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.cpp
new file mode 100644
index 00000000000..093bf0d306a
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.cpp
@@ -0,0 +1,86 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "range_kernel_ref.h"
+
+#include <kernel_selector_utils.h>
+
+namespace kernel_selector {
+
+namespace {
+
+CommonDispatchData SetDefault(const range_params &params) {
+    CommonDispatchData dispatchData;
+
+    dispatchData.gws = { 1, 1, params.output.X().v }; // TODO: these could be split better
+    dispatchData.lws = GetOptimalLocalWorkGroupSizes(dispatchData.gws, params.engineInfo);
+
+    return dispatchData;
+}
+
+}  // namespace
+
+KernelsData RangeKernelRef::GetKernelsData(const Params &params, const optional_params &options) const {
+    KernelsData kernels_data;
+    if (!Validate(params, options))
+        return kernels_data;
+    kernels_data.push_back(KernelData::Default<range_params>(params));
+    KernelData &kernel_data = kernels_data.front();
+    auto &derived_params = dynamic_cast<range_params&>(*kernel_data.params.get());
+    auto dispatch_data = SetDefault(derived_params);
+    auto entry_point = GetEntryPoint(kernelName, derived_params.layerID, params, options);
+    auto jit_constants = MakeBaseParamsJitConstants(derived_params);
+    auto jit = CreateJit(kernelName, jit_constants, entry_point);
+    auto &clKernelData = kernel_data.kernels[0];
+    FillCLKernelData(clKernelData, dispatch_data, params.engineInfo, kernelName, jit, entry_point, DEFAULT,
+                     false, false, 3);
+    auto &arguments = clKernelData.params.arguments;
+    arguments.erase(arguments.begin()+1); // stop is not used by kernel
+    return kernels_data;
+}
+
+KernelsPriority RangeKernelRef::GetKernelsPriority(const Params& /*params*/, const optional_params& /*options*/) const {
+    return DONT_USE_IF_HAVE_SOMETHING_ELSE;
+}
+
+ParamsKey RangeKernelRef::GetSupportedKey() const {
+    ParamsKey k;
+    k.EnableInputDataType(Datatype::INT8);
+    k.EnableInputDataType(Datatype::UINT8);
+    k.EnableInputDataType(Datatype::INT16);
+    k.EnableInputDataType(Datatype::UINT16);
+    k.EnableInputDataType(Datatype::INT32);
+    k.EnableInputDataType(Datatype::UINT32);
+    k.EnableInputDataType(Datatype::INT64);
+    k.EnableInputDataType(Datatype::F16);
+    k.EnableInputDataType(Datatype::F32);
+    k.EnableOutputDataType(Datatype::INT8);
+    k.EnableOutputDataType(Datatype::UINT8);
+    k.EnableOutputDataType(Datatype::INT16);
+    k.EnableOutputDataType(Datatype::UINT16);
+    k.EnableOutputDataType(Datatype::INT32);
+    k.EnableOutputDataType(Datatype::UINT32);
+    k.EnableOutputDataType(Datatype::INT64);
+    k.EnableOutputDataType(Datatype::F16);
+    k.EnableOutputDataType(Datatype::F32);
+    k.EnableInputLayout(DataLayout::bfyx);
+    k.EnableOutputLayout(DataLayout::bfyx);
+    return k;
+}
+
+bool RangeKernelRef::Validate(const Params &p, const optional_params &o) const {
+    if (p.GetType() != KernelType::RANGE || o.GetType() != KernelType::RANGE)
+        return false;
+
+    auto &params = dynamic_cast<const range_params&>(p);
+    if (params.inputs.size() != 3)
+        return false;
+
+    for (auto &input : params.inputs)
+        if (input.LogicalSize() != 1)
+            return false;
+    return true;
+}
+
+}  // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.h
new file mode 100644
index 00000000000..dff2263e49f
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_ref.h
@@ -0,0 +1,34 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "kernel_base_opencl.h"
+
+namespace kernel_selector {
+
+struct range_params: public base_params {
+    range_params() :
+        base_params { KernelType::RANGE } {
+    }
+};
+
+struct range_optional_params: optional_params {
+    range_optional_params() :
+        optional_params { KernelType::RANGE } {
+    }
+};
+
+class RangeKernelRef: public KernelBaseOpenCL {
+    KernelsData GetKernelsData(const Params &params, const optional_params &options) const override;
+    KernelsPriority GetKernelsPriority(const Params &params, const optional_params &options) const override;
+    ParamsKey GetSupportedKey() const override;
+    bool Validate(const Params &p, const optional_params &o) const override;
+public:
+    RangeKernelRef() :
+        KernelBaseOpenCL { "range_ref" } {
+    }
+};
+
+}  // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.cpp
new file mode 100644
index 00000000000..9869574d8e6
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "range_kernel_selector.h"
+#include "range_kernel_ref.h"
+
+namespace kernel_selector {
+namespace {
+
+class range_kernel_selector: public kernel_selector_base {
+    KernelsData GetBestKernels(const Params &params, const optional_params &options) const override {
+        return GetNaiveBestKernel(params, options, KernelType::RANGE);
+    }
+public:
+    range_kernel_selector() {
+        Attach<RangeKernelRef>();
+    }
+};
+
+}  // namespace
+
+kernel_selector_base& range_instance() {
+    static range_kernel_selector instance;
+    return instance;
+}
+
+}  // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.h
new file mode 100644
index 00000000000..3452d3f8094
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/range/range_kernel_selector.h
@@ -0,0 +1,13 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "kernel_selector.h"
+
+namespace kernel_selector {
+
+kernel_selector_base& range_instance();
+
+}  // namespace kernel_selector
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/range_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/range_ref.cl
new file mode 100644
index 00000000000..dcf225b9962
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/range_ref.cl
@@ -0,0 +1,11 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+KERNEL (range_ref)(const __global INPUT0_TYPE *startP, const __global INPUT2_TYPE *stepP, __global OUTPUT_TYPE *output)
+{
+    const uint i = get_global_id(2);
+    const OUTPUT_TYPE start = TO_OUTPUT_TYPE(*startP);
+    const OUTPUT_TYPE step = TO_OUTPUT_TYPE(*stepP);
+    output[i] = start + TO_OUTPUT_TYPE(i) * step;
+}
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/range.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/range.cpp
new file mode 100644
index 00000000000..9c8845383e0
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/range.cpp
@@ -0,0 +1,61 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <range_inst.h>
+#include "primitive_base.hpp"
+#include <impls/implementation_map.hpp>
+#include <kernel_selector_helper.h>
+#include <range/range_kernel_selector.h>
+#include <range/range_kernel_ref.h>
+#include <intel_gpu/runtime/error_handler.hpp>
+
+namespace cldnn {
+namespace ocl {
+
+struct range_impl : typed_primitive_impl_ocl<range> {
+    using typed_primitive_impl_ocl::typed_primitive_impl_ocl;
+
+    std::unique_ptr<primitive_impl> clone() const override {
+        return make_unique<range_impl>(*this);
+    }
+
+    static primitive_impl* create(const range_node& arg) {
+        auto params = get_default_params<kernel_selector::range_params>(arg);
+        for (int i : {1, 2})
+            params.inputs.push_back(convert_data_tensor(arg.input(i).get_output_layout()));
+        auto optional_params =
+            get_default_optional_params<kernel_selector::range_optional_params>(arg.get_program());
+
+        auto& kernel_selector = kernel_selector::range_instance();
+        auto best_kernels = kernel_selector.GetBestKernels(params, optional_params);
+
+        CLDNN_ERROR_BOOL(arg.id(),
+                         "Best_kernel.empty()",
+                         best_kernels.empty(),
+                         "Cannot find a proper kernel with this arguments");
+
+        return new range_impl{arg, best_kernels.front()};
+    }
+};
+
+namespace detail {
+
+attach_range_impl::attach_range_impl() {
+    implementation_map<range>::add(
+        impl_types::ocl,
+        range_impl::create,
+        {
+            std::make_tuple(data_types::u8, format::bfyx),
+            std::make_tuple(data_types::i8, format::bfyx),
+            std::make_tuple(data_types::f16, format::bfyx),
+            std::make_tuple(data_types::f32, format::bfyx),
+            std::make_tuple(data_types::i32, format::bfyx),
+            std::make_tuple(data_types::i64, format::bfyx),
+        });
+}
+
+}  // namespace detail
+}  // namespace ocl
+}  // namespace cldnn
+
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
index 365dafc3ff5..af11dc0eb16 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.cpp
@@ -49,6 +49,7 @@ void register_implementations() {
     REGISTER_OCL(pyramid_roi_align);
     REGISTER_OCL(quantize);
     REGISTER_OCL(random_uniform);
+    REGISTER_OCL(range);
     REGISTER_OCL(reduce);
     REGISTER_OCL(region_yolo);
     REGISTER_OCL(reorder);
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
index 26e52c8e3ee..6cf4b02dc58 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/register.hpp
@@ -40,6 +40,7 @@
 #include "intel_gpu/primitives/pyramid_roi_align.hpp"
 #include "intel_gpu/primitives/quantize.hpp"
 #include "intel_gpu/primitives/random_uniform.hpp"
+#include "intel_gpu/primitives/range.hpp"
 #include "intel_gpu/primitives/reduce.hpp"
 #include "intel_gpu/primitives/region_yolo.hpp"
 #include "intel_gpu/primitives/reorder.hpp"
@@ -119,6 +120,7 @@ REGISTER_OCL(pooling);
 REGISTER_OCL(pyramid_roi_align);
 REGISTER_OCL(quantize);
 REGISTER_OCL(random_uniform);
+REGISTER_OCL(range);
 REGISTER_OCL(reduce);
 REGISTER_OCL(region_yolo);
 REGISTER_OCL(reorder);
diff --git a/inference-engine/thirdparty/clDNN/src/include/json_object.h b/inference-engine/thirdparty/clDNN/src/include/json_object.h
index 02870c2cf91..c4ec537364b 100644
--- a/inference-engine/thirdparty/clDNN/src/include/json_object.h
+++ b/inference-engine/thirdparty/clDNN/src/include/json_object.h
@@ -20,7 +20,7 @@ using json_map = std::unordered_map<json_key, json_base_ptr>;
 
 class json_base {
 public:
-    virtual void dump(std::ostream& out, int offset) = 0;
+    virtual void dump(std::ostream& out, int offset) const = 0;
     virtual ~json_base() = default;
 };
 
@@ -32,7 +32,7 @@ private:
 public:
     explicit json_leaf(const Type& val) : value(val) {}
     explicit json_leaf(Type&& val) : value(std::move(val)) {}
-    void dump(std::ostream& out, int) override { out << value << ",\n"; }
+    void dump(std::ostream& out, int) const override { out << value << ",\n"; }
 };
 
 template <class Type>
@@ -43,7 +43,7 @@ private:
 public:
     explicit json_basic_array(const std::vector<Type>& arr) : values(arr) {}
     explicit json_basic_array(std::vector<Type>&& arr) : values(std::move(arr)) {}
-    void dump(std::ostream& out, int) override {
+    void dump(std::ostream& out, int) const override {
         const char* delim = "";
         for (size_t i = 0; i < values.size(); i++) {
             out << delim << values[i];
@@ -58,7 +58,7 @@ private:
     json_map children;
 
 public:
-    void dump(std::ostream& out, int offset = 1) override {
+    void dump(std::ostream& out, int offset = 1) const override {
         std::string spaces_brackets(std::max(offset - 1, 0) * 4, ' ');
         std::string spaces_content(offset * 4, ' ');
         out << "\n" << spaces_brackets << "{\n";
diff --git a/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h b/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h
index 9ca751dc5a5..7c3f6546c66 100644
--- a/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h
+++ b/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h
@@ -33,15 +33,7 @@ class typed_primitive_inst;
     Base class for all implementations.
 */
 struct primitive_impl {
-    // NOTE: This constuctor in necessary since the spec says:
-    //   A defaulted default constructor for class X is defined as deleted if: [...] any non-variant non-static data
-    //   member of const-qualified type (or array thereof) with no brace-orequal-initializer does not have a
-    //   user-provided default constructor.
-    // and the classes with only declared brace-orequal-initializer on members are not considered to have user-provided
-    // default constructor:
-    //   A special member function is user-provided if it is user-declared and not explicitly defaulted or deleted
-    //   on its first declaration.
-    primitive_impl() : _weights_reorder_params() {}
+    primitive_impl() = default;
     explicit primitive_impl(const kernel_selector::weights_reorder_params& params, std::string kernel_name = "")
         : _weights_reorder_params(params), _kernel_name(kernel_name) {}
     virtual ~primitive_impl() = default;
diff --git a/inference-engine/thirdparty/clDNN/src/include/range_inst.h b/inference-engine/thirdparty/clDNN/src/include/range_inst.h
new file mode 100644
index 00000000000..74169832f60
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/include/range_inst.h
@@ -0,0 +1,32 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "intel_gpu/primitives/range.hpp"
+#include "primitive_inst.h"
+
+#include <string>
+
+namespace cldnn {
+template <>
+struct typed_program_node<range> : public typed_program_node_base<range> {
+    using typed_program_node_base::typed_program_node_base;
+
+    program_node& input(std::size_t i = 0) const { return get_dependency(i); }
+};
+using range_node = typed_program_node<range>;
+
+template <>
+class typed_primitive_inst<range> : public typed_primitive_inst_base<range> {
+public:
+    static layout calc_output_layout(range_node const& node);
+    static std::string to_string(range_node const& node);
+
+    typed_primitive_inst(network& network, range_node const& desc);
+};
+
+using range_inst = typed_primitive_inst<range>;
+
+}  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/program.cpp b/inference-engine/thirdparty/clDNN/src/program.cpp
index c43586d96db..364a3b36c5c 100644
--- a/inference-engine/thirdparty/clDNN/src/program.cpp
+++ b/inference-engine/thirdparty/clDNN/src/program.cpp
@@ -164,7 +164,7 @@ void program::load_tuning_cache() {
     }
 }
 
-kernel_id program::add_kernel(const std::shared_ptr<kernel_string> kernelSring) {
+kernel_id program::add_kernel(const std::shared_ptr<kernel_string>& kernelSring) {
     return _kernels_cache->set_kernel_source(kernelSring, false);
 }
 
diff --git a/inference-engine/thirdparty/clDNN/src/range.cpp b/inference-engine/thirdparty/clDNN/src/range.cpp
new file mode 100644
index 00000000000..299a1367bb8
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/range.cpp
@@ -0,0 +1,41 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "range_inst.h"
+#include "primitive_type_base.h"
+#include "json_object.h"
+#include <string>
+
+namespace cldnn {
+namespace {
+std::string lexical_cast(const json_base& j, int offset = 1) {
+    std::stringstream os;
+    j.dump(os, offset);
+    return os.str();
+}
+}  // namespace
+
+primitive_type_id range::type_id() {
+    static primitive_type_base<range> instance;
+    return &instance;
+}
+
+layout range_inst::calc_output_layout(range_node const& node) {
+    return node.get_primitive()->output_layout;
+}
+
+std::string range_inst::to_string(range_node const& node) {
+    auto desc = node.get_primitive();
+    auto node_info = node.desc_to_json();
+
+    json_composite op_info;
+    op_info.add("output_type", data_type_traits::name(desc->output_layout.data_type));
+
+    node_info->add("range info", std::move(op_info));
+    return lexical_cast(*node_info);
+}
+
+range_inst::typed_primitive_inst(network& network, range_node const& node) : typed_primitive_inst_base{network, node} {}
+
+}  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/range_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/range_gpu_test.cpp
new file mode 100644
index 00000000000..9863bffc856
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/range_gpu_test.cpp
@@ -0,0 +1,83 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <test_utils.h>
+
+#include <intel_gpu/primitives/input_layout.hpp>
+#include <intel_gpu/primitives/range.hpp>
+
+namespace cldnn {
+namespace {
+
+struct RangeArg {
+    primitive_id name;
+    memory::ptr p;
+    RangeArg(data_types dt, const char name[]) :
+        name { name }, p { tests::get_test_engine().allocate_memory( { dt, format::bfyx, tensor { spatial() } }) } {
+    }
+    void addTo(topology &t) const {
+        t.add(input_layout { name, p->get_layout() });
+    }
+    void setData(network &n) const {
+        n.set_input_data(name, p);
+    }
+};
+
+struct RangeArgs {
+    data_types dt;
+    RangeArg start { dt, "start" };
+    RangeArg stop { dt, "stop" };
+    RangeArg step { dt, "step" };
+    explicit RangeArgs(data_types dt) :
+        dt { dt } {
+    }
+    memory::ptr run(int outLen) const {
+        topology topology;
+        start.addTo(topology);
+        stop.addTo(topology);
+        step.addTo(topology);
+        topology.add(range { "range", { start.name, stop.name, step.name }, { dt, format::bfyx,
+            tensor { spatial(outLen) } } });
+
+        network network { tests::get_test_engine(), topology };
+
+        start.setData(network);
+        stop.setData(network);
+        step.setData(network);
+
+        auto outputs = network.execute();
+        return outputs.at("range").get_memory();
+    }
+};
+
+template<typename T> void doSmokeRange(T start, T stop, T step) {
+    RangeArgs args { type_to_data_type<T>::value };
+
+    tests::set_values(args.start.p, { start });
+    tests::set_values(args.stop.p, { stop });
+    tests::set_values(args.step.p, { step });
+
+    T outLen = (stop - start) / step + 1;
+
+    auto output = args.run(outLen);
+    mem_lock<T> output_ptr { output, tests::get_test_stream() };
+
+    for (std::size_t i = 0; i < outLen; ++i)
+        EXPECT_EQ(start + i * step, output_ptr[i]);
+}
+
+void doSmokeRangeAllTypes(int start, int stop, int step) {
+    doSmokeRange<std::int8_t>(start, stop, step);
+    doSmokeRange<std::uint8_t>(start, stop, step);
+    doSmokeRange<int>(start, stop, step);
+    doSmokeRange<float>(start, stop, step);
+    doSmokeRange<std::int64_t>(start, stop, step);
+}
+
+TEST(smoke, Range) {
+    doSmokeRangeAllTypes(1, 21, 2);
+    doSmokeRangeAllTypes(4, 0, -1);
+}
+}  // namespace
+}  // namespace cldnn
diff --git a/src/common/legacy/include/legacy/ie_layers.h b/src/common/legacy/include/legacy/ie_layers.h
index 0d45b41b28d..27fdbc12233 100644
--- a/src/common/legacy/include/legacy/ie_layers.h
+++ b/src/common/legacy/include/legacy/ie_layers.h
@@ -442,6 +442,11 @@ public:
     */
     explicit WeightableLayer(const LayerParams & prms);
 
+    /**
+     * @brief Constructs a WeightableLayer instance and initiates layer parameters with the given values
+     */
+    using CNNLayer::CNNLayer;
+
     IE_SUPPRESS_DEPRECATED_END
 
     /**
@@ -453,11 +458,6 @@ public:
      */
     Blob::Ptr _biases;
 
-    /**
-     * @brief Constructs a WeightableLayer instance and initiates layer parameters with the given values
-     */
-    using CNNLayer::CNNLayer;
-
     ~WeightableLayer() override;
 };
 
@@ -555,8 +555,10 @@ public:
  */
 class DeconvolutionLayer: public ConvolutionLayer {
 public:
+    IE_SUPPRESS_DEPRECATED_START
     using ConvolutionLayer::ConvolutionLayer;
     using ConvolutionLayer::operator=;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~DeconvolutionLayer() override;
 };
@@ -567,8 +569,10 @@ public:
  */
 class DeformableConvolutionLayer: public ConvolutionLayer {
 public:
+    IE_SUPPRESS_DEPRECATED_START
     using ConvolutionLayer::ConvolutionLayer;
     using ConvolutionLayer::operator=;
+    IE_SUPPRESS_DEPRECATED_END
 
     /**
      * @brief Number of deformable groups
@@ -779,7 +783,9 @@ public:
     /**
      * @brief Creates a new FullyConnectedLayer instance and initializes layer parameters with the given values.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using WeightableLayer::WeightableLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~FullyConnectedLayer() override;
 };
@@ -803,7 +809,9 @@ public:
      * If batch is used, then batch needs to be specified as an input dimension also
      * In current implementation 1 means channels, 0 - batch
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ConcatLayer() override;
 };
@@ -822,7 +830,9 @@ public:
     /**
      * @brief Creates a new SplitLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SplitLayer() override;
 };
@@ -857,7 +867,9 @@ public:
     /**
      * @brief Creates a new NormLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~NormLayer() override;
 };
@@ -875,7 +887,9 @@ public:
     /**
      * @brief Creates a new SoftMaxLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SoftMaxLayer() override;
 };
@@ -890,7 +904,9 @@ public:
      * @brief A default constructor. Creates a new GRNLayer instance and initializes layer parameters with the given
      * values.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     /**
      * @brief Bias for squares sum
@@ -910,7 +926,9 @@ public:
      * @brief A default constructor. Creates a new MVNLayer instance and initializes layer parameters with the given
      * values.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     /**
      * @brief Indicate that mean value is calculated across channels
@@ -939,7 +957,9 @@ public:
     /**
      * @brief Creates a new ReLULayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ReLULayer() override;
 };
@@ -964,7 +984,9 @@ public:
     /**
      * @brief Creates a new ClampLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ClampLayer() override;
 };
@@ -1037,7 +1059,9 @@ public:
     /**
      * @brief Creates a new EltwiseLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~EltwiseLayer() override;
 };
@@ -1064,7 +1088,9 @@ public:
     /**
      * @brief Creates a new CropLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~CropLayer() override;
 };
@@ -1091,7 +1117,9 @@ public:
     /**
      * @brief Creates a new ReshapeLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ReshapeLayer() override;
 };
@@ -1114,7 +1142,9 @@ public:
     /**
      * @brief Creates a new TileLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~TileLayer() override;
 };
@@ -1134,7 +1164,9 @@ public:
     /**
      * @brief Creates a new ScaleShiftLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using WeightableLayer::WeightableLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ScaleShiftLayer() override;
 };
@@ -1172,7 +1204,9 @@ public:
 
     Body body;  //!< A Tensor Iterator body
 
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~TensorIterator() override;
 };
@@ -1183,7 +1217,9 @@ public:
  */
 class RNNCellBase: public WeightableLayer {
 public:
+    IE_SUPPRESS_DEPRECATED_START
     using WeightableLayer::WeightableLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     /**
      * @brief Direct type of recurrent cell (including subtypes)
@@ -1276,8 +1312,10 @@ public:
  */
 class LSTMCell: public RNNCellBase {
 public:
+    IE_SUPPRESS_DEPRECATED_START
     using RNNCellBase::RNNCellBase;
     using RNNCellBase::operator=;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~LSTMCell() override;
 };
@@ -1318,8 +1356,10 @@ public:
  */
 class GRUCell: public RNNCellBase {
 public:
+    IE_SUPPRESS_DEPRECATED_START
     using RNNCellBase::RNNCellBase;
     using RNNCellBase::operator=;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~GRUCell() override;
 };
@@ -1355,8 +1395,10 @@ public:
  */
 class RNNCell: public RNNCellBase {
 public:
+    IE_SUPPRESS_DEPRECATED_START
     using RNNCellBase::RNNCellBase;
     using RNNCellBase::operator=;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~RNNCell() override;
 };
@@ -1392,7 +1434,9 @@ public:
  */
 class RNNSequenceLayer: public RNNCellBase {
 public:
+    IE_SUPPRESS_DEPRECATED_START
     using RNNCellBase::RNNCellBase;
+    IE_SUPPRESS_DEPRECATED_END
 
     /**
      * @brief An axis by which iteration is performed
@@ -1435,7 +1479,9 @@ public:
      *
      * @param prms Initial layer parameters
      */
+    IE_SUPPRESS_DEPRECATED_START
     using WeightableLayer::WeightableLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~PReLULayer() override;
 };
@@ -1464,7 +1510,9 @@ public:
     /**
      * @brief Creates a new PowerLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~PowerLayer() override;
 };
@@ -1483,7 +1531,9 @@ public:
     /**
      * @brief Creates a new BatchNormalizationLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using WeightableLayer::WeightableLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~BatchNormalizationLayer() override;
 };
@@ -1515,7 +1565,9 @@ public:
     /**
      * @brief Creates a new GemmLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~GemmLayer() override;
 };
@@ -1553,7 +1605,9 @@ public:
     /**
      * @brief Creates a new PadLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~PadLayer() override;
 };
@@ -1573,7 +1627,9 @@ public:
     /**
      * @brief Creates a new GatherLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~GatherLayer() override;
 };
@@ -1614,7 +1670,9 @@ public:
     /**
      * @brief Creates a new StridedSliceLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~StridedSliceLayer() override;
 };
@@ -1639,7 +1697,9 @@ public:
     /**
      * @brief Creates a new ShuffleChannelsLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ShuffleChannelsLayer() override;
 };
@@ -1659,7 +1719,9 @@ public:
     /**
      * @brief Creates a new DepthToSpaceLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~DepthToSpaceLayer() override;
 };
@@ -1679,7 +1741,9 @@ public:
     /**
      * @brief Creates a new SpaceToDepthLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SpaceToDepthLayer() override;
 };
@@ -1709,7 +1773,9 @@ public:
     /**
      * @brief Creates a new SpaceToBatchLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SpaceToBatchLayer() override;
 };
@@ -1742,7 +1808,9 @@ public:
     /**
      * @brief Creates a new BatchToSpaceLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~BatchToSpaceLayer() override;
 };
@@ -1758,7 +1826,9 @@ public:
     /**
      * @brief Creates a new SparseFillEmptyRowsLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SparseFillEmptyRowsLayer() override;
 };
@@ -1773,7 +1843,9 @@ public:
     /**
      * @brief Creates a new SparseSegmentReduceLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SparseSegmentReduceLayer() override;
 };
@@ -1788,7 +1860,9 @@ public:
     /**
     * @brief Creates a new ExperimentalSparseWeightedReduceLayer instance.
     */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ExperimentalSparseWeightedReduceLayer() override;
 };
@@ -1803,7 +1877,9 @@ public:
     /**
     * @brief Creates a new SparseToDenseLayer instance.
     */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SparseToDenseLayer() override;
 };
@@ -1823,7 +1899,9 @@ public:
     /**
     * @brief Creates a new BucketizeLayer instance.
     */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~BucketizeLayer() override;
 };
@@ -1849,7 +1927,9 @@ public:
     /**
      * @brief Creates a new ReverseSequence instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ReverseSequenceLayer() override;
 };
@@ -1884,7 +1964,9 @@ public:
     /**
      * @brief Creates a new OneHot instance
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~OneHotLayer() override;
 };
@@ -1900,7 +1982,9 @@ public:
     /**
      * @brief Creates a new RangeLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~RangeLayer() override;
 };
@@ -1916,7 +2000,9 @@ public:
     /**
      * @brief Creates a new Fill instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~FillLayer() override;
 };
@@ -1934,7 +2020,9 @@ public:
     /**
      * @brief Creates a new SelectLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~SelectLayer() override;
 };
@@ -1950,7 +2038,9 @@ public:
     /**
      * @brief Creates a new Broadcast instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~BroadcastLayer() override;
 };
@@ -1971,7 +2061,9 @@ public:
     /**
      * @brief Creates a new QuantizeLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~QuantizeLayer() override;
 };
@@ -1987,7 +2079,9 @@ public:
     /**
      * @brief Creates a new Math instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~MathLayer() override;
 };
@@ -2008,7 +2102,9 @@ public:
     /**
      * @brief Creates a new Reduce instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ReduceLayer() override;
 };
@@ -2037,7 +2133,9 @@ public:
     /**
      * @brief Creates a new TopKLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~TopKLayer() override;
 };
@@ -2066,7 +2164,9 @@ public:
     /**
      * @brief Creates a new UniqueLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~UniqueLayer() override;
 };
@@ -2093,7 +2193,9 @@ public:
     /**
      * @brief Creates a new NonMaxSuppressionLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~NonMaxSuppressionLayer() override;
 };
@@ -2107,7 +2209,9 @@ public:
     /**
      * @brief Creates a new ScatterUpdateLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ScatterUpdateLayer() override;
 };
@@ -2121,7 +2225,9 @@ public:
     /**
      * @brief Creates a new ScatterElementsUpdateLayer instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     ~ScatterElementsUpdateLayer() override;
 };
@@ -2156,7 +2262,9 @@ public:
     /**
      * @brief Creates a new ExperimentalDetectronPriorGridGenerator instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     virtual ~ExperimentalDetectronPriorGridGeneratorLayer();
 };
@@ -2173,7 +2281,9 @@ public:
     /**
      * @brief Creates a new ExperimentalDetectronTopKROIs instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     virtual ~ExperimentalDetectronTopKROIs();
 };
@@ -2203,7 +2313,9 @@ public:
     /**
      * @brief Creates a new ExperimentalDetectronGenerateProposalsSingleImage instance.
      */
+    IE_SUPPRESS_DEPRECATED_START
     using CNNLayer::CNNLayer;
+    IE_SUPPRESS_DEPRECATED_END
 
     virtual ~ExperimentalDetectronGenerateProposalsSingleImageLayer();
 };
diff --git a/src/common/legacy/src/convert_function_to_cnn_network.cpp b/src/common/legacy/src/convert_function_to_cnn_network.cpp
index 38aa15ff06b..bed35e85f3d 100644
--- a/src/common/legacy/src/convert_function_to_cnn_network.cpp
+++ b/src/common/legacy/src/convert_function_to_cnn_network.cpp
@@ -321,7 +321,7 @@ public:
     }
 
     void addSpecificCreator(const std::vector<std::string>& forTypes, const CreatorFor& creator) {
-        for (const auto type : forTypes) {
+        for (const auto& type : forTypes) {
             creators[type] = creator;
         }
     }
diff --git a/src/common/low_precision_transformations/include/low_precision/network_helper.hpp b/src/common/low_precision_transformations/include/low_precision/network_helper.hpp
index 30d9c407a4c..5f7d6a74113 100644
--- a/src/common/low_precision_transformations/include/low_precision/network_helper.hpp
+++ b/src/common/low_precision_transformations/include/low_precision/network_helper.hpp
@@ -300,7 +300,7 @@ public:
     static void reassign(
         const std::shared_ptr<typename SharedAttribute::SharedValueAttribute::SharedValue>& sharedValue,
         const std::vector<std::weak_ptr<typename SharedAttribute::SharedValueAttribute>>& attributes) {
-        for (const auto attributeWeakPtr : attributes) {
+        for (const auto& attributeWeakPtr : attributes) {
             auto attribute = attributeWeakPtr.lock();
             if (attribute == nullptr) {
                 continue;
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
index 0963273d9d1..68beb6214c9 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
@@ -178,6 +178,7 @@ REGISTER_FACTORY(v4, LSTMCell);
 REGISTER_FACTORY(v4, Mish);
 // REGISTER_FACTORY(v4, NonMaxSuppression); Supported via v4 -> v5 internal conversion
 REGISTER_FACTORY(v4, Proposal);
+REGISTER_FACTORY(v4, Range);
 REGISTER_FACTORY(v4, ReduceL1);
 REGISTER_FACTORY(v4, ReduceL2);
 REGISTER_FACTORY(v4, SoftPlus);
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/program.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/program.hpp
index e365826653e..f2cdcdf7c79 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/program.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/program.hpp
@@ -135,7 +135,7 @@ public:
     }
 
     template<typename PType>
-    void AddPrimitive(PType prim) {
+    void AddPrimitive(const PType& prim) {
         if (m_topology == nullptr) {
             IE_THROW() << "m_topology object was not created in ov::runtime::intel_gpu::Program";
         }
diff --git a/src/plugins/intel_gpu/src/plugin/ops/range.cpp b/src/plugins/intel_gpu/src/plugin/ops/range.cpp
new file mode 100644
index 00000000000..04ab9904228
--- /dev/null
+++ b/src/plugins/intel_gpu/src/plugin/ops/range.cpp
@@ -0,0 +1,35 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <intel_gpu/plugin/program.hpp>
+#include <intel_gpu/plugin/common_utils.hpp>
+
+#include <intel_gpu/primitives/range.hpp>
+#include <ngraph/op/range.hpp>
+
+namespace ov {
+namespace runtime {
+namespace intel_gpu {
+
+static void CreateRangeOp(Program &p, const std::shared_ptr<ngraph::op::v4::Range> &op) {
+    p.ValidateInputs(op, { 3 });
+    auto &outShape = op->get_output_shape(0);
+    {
+        auto r = outShape.size();
+        if (r != 1)
+            throw std::runtime_error { "range v4 output rank is " + std::to_string(r) };
+    }
+    cldnn::tensor outTensor { cldnn::spatial(outShape[0]) };
+    auto outDataType = DataTypeFromPrecision(op->get_output_element_type(0));
+    cldnn::layout outLayout { outDataType, cldnn::format::bfyx, outTensor };
+    cldnn::range prim { layer_type_name_ID(op), p.GetInputPrimitiveIDs(op), outLayout, op->get_friendly_name() };
+    p.AddPrimitive(prim);
+    p.AddPrimitiveToProfiler(op);
+}
+
+REGISTER_FACTORY_IMPL(v4, Range);
+
+}  // namespace intel_gpu
+}  // namespace runtime
+}  // namespace ov
diff --git a/src/tests/functional/inference_engine/task_executor_tests.cpp b/src/tests/functional/inference_engine/task_executor_tests.cpp
index 482dbd2dfa4..dd060f38d6a 100644
--- a/src/tests/functional/inference_engine/task_executor_tests.cpp
+++ b/src/tests/functional/inference_engine/task_executor_tests.cpp
@@ -10,6 +10,7 @@
 #include <threading/ie_cpu_streams_executor.hpp>
 #include <threading/ie_immediate_executor.hpp>
 #include <ie_system_conf.h>
+#include <thread>
 
 using namespace ::testing;
 using namespace std;
diff --git a/src/tests/functional/inference_engine/transformations/simplify_second_input_of_reshape_test.cpp b/src/tests/functional/inference_engine/transformations/simplify_second_input_of_reshape_test.cpp
index 452c9552eb5..99017008459 100644
--- a/src/tests/functional/inference_engine/transformations/simplify_second_input_of_reshape_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/simplify_second_input_of_reshape_test.cpp
@@ -18,12 +18,13 @@
 using namespace testing;
 using namespace ngraph;
 
+namespace {
 auto gather = [](const std::shared_ptr<Node> input, std::vector<int64_t> indices) -> Output<Node> {
     std::shared_ptr<Node> indices_node = opset7::Constant::create(element::i64, {indices.size()}, indices);
     std::shared_ptr<Node> axis_node = opset7::Constant::create(element::i64, {}, { 0 });
     return std::make_shared<opset7::Gather>(input, indices_node, axis_node);
 };
-
+}
 auto fake_quantize = [](const std::shared_ptr<Node> input) -> Output<Node> {
     auto il = opset7::Constant::create(element::f32, Shape{}, { 0.f });
     auto ih = opset7::Constant::create(element::f32, Shape{}, { 25.5f });
diff --git a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
index cb3d90f46d6..823e9f524d9 100644
--- a/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
+++ b/src/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
@@ -407,7 +407,7 @@ std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>> LayerTe
     auto refInputsTypes = std::vector<ngraph::element::Type>(inputs.size());
     for (std::size_t i = 0; i < inputs.size(); ++i) {
         const auto &input = inputs[i];
-        const auto &inputSize = input->byteSize();
+        const auto inputSize = input->byteSize();
 
         auto &referenceInput = referenceInputs[i];
         referenceInput.resize(inputSize);
diff --git a/src/tests/functional/shared_test_classes/src/single_layer/range.cpp b/src/tests/functional/shared_test_classes/src/single_layer/range.cpp
index a008c09991d..b6f57ae914c 100644
--- a/src/tests/functional/shared_test_classes/src/single_layer/range.cpp
+++ b/src/tests/functional/shared_test_classes/src/single_layer/range.cpp
@@ -25,7 +25,9 @@ std::string RangeLayerTest::getTestCaseName(const testing::TestParamInfo<RangePa
     result << "inL=" << inLayout << separator;
     result << "outL=" << outLayout << separator;
     result << "trgDev=" << targetDevice;
-    return result.str();
+    auto str = result.str();
+    replace(str.begin(), str.end(), '.', '_');
+    return str;
 }
 
 void RangeLayerTest::Infer() {
@@ -46,17 +48,16 @@ void RangeLayerTest::Infer() {
 
 void RangeLayerTest::SetUp() {
     InferenceEngine::Precision netPrecision;
-    std::tie(start, stop, step, netPrecision, inPrc, outPrc, inLayout, outLayout, targetDevice) = GetParam();
+    tie(start, stop, step, netPrecision, inPrc, outPrc, inLayout, outLayout, targetDevice) = GetParam();
     auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+    std::vector<std::pair<std::string, std::vector<size_t>>> inputs {{"start", {}}, {"stop", {}}, {"step", {}}};
+    auto params = ngraph::builder::makeParams(ngPrc, inputs);
+    auto range = std::make_shared<ngraph::opset4::Range>(params[0], params[1], params[2], ngPrc);
 
-    auto params = ngraph::builder::makeParams(ngPrc, {std::vector<size_t>(), std::vector<size_t>(), std::vector<size_t>()});
-    params[0]->set_friendly_name("start");
-    params[1]->set_friendly_name("stop");
-    params[2]->set_friendly_name("step");
-
-    auto range = std::make_shared<ngraph::opset3::Range>(params[0], params[1], params[2]);
-    const ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(range)};
-    function = std::make_shared<ngraph::Function>(results, params, "Range");
+    function = std::make_shared<ngraph::Function>(
+        std::make_shared<ngraph::opset1::Result>(range),
+        params,
+        "Range");
 }
 
 std::string RangeNumpyLayerTest::getTestCaseName(const testing::TestParamInfo<RangeParams>& obj) {

From 3ee5bcaf4dcb6a9aabd541eccd1dd6153dfdcd37 Mon Sep 17 00:00:00 2001
From: Katarzyna Mitrus <katarzyna.mitrus@intel.com>
Date: Thu, 23 Dec 2021 14:13:56 +0100
Subject: [PATCH 125/160] Return (-1) if max_int stop/start provided (#9386)

---
 src/core/src/op/slice.cpp          | 25 +++++++--
 src/core/tests/type_prop/slice.cpp | 82 +++++++++++++++++++++++++++---
 2 files changed, 96 insertions(+), 11 deletions(-)

diff --git a/src/core/src/op/slice.cpp b/src/core/src/op/slice.cpp
index e4a7f2c54e4..860e9db0ea4 100644
--- a/src/core/src/op/slice.cpp
+++ b/src/core/src/op/slice.cpp
@@ -60,6 +60,20 @@ int64_t get_sliced_dim_size(int64_t start, int64_t stop, int64_t step, int64_t d
     return sliced_dim_size;
 }
 
+bool is_max_int(element::Type_t ind_type, int64_t value) {
+    int64_t max_type_value = 0;
+    switch (ind_type) {
+    case element::i32:
+        max_type_value = std::numeric_limits<typename element_type_traits<element::i32>::value_type>::max();
+        break;
+    case element::i64:
+        max_type_value = std::numeric_limits<typename element_type_traits<element::i64>::value_type>::max();
+        break;
+    default:
+        return false;
+    }
+    return max_type_value == value;
+}
 }  // namespace
 
 bool op::v8::Slice::visit_attributes(AttributeVisitor& visitor) {
@@ -288,16 +302,17 @@ PartialShape op::v8::Slice::calculate_output_shape(const std::vector<int64_t>& s
 
         // Avoid negative index normalization without upper bounds
         if (!axis_dim.get_interval().has_upper_bound()) {
-            if ((step < 0 && start < 0 && stop > 0) || (step > 0 && stop < 0 && start > 0)) {
+            if (is_max_int(get_input_element_type(2), stop) || is_max_int(get_input_element_type(1), start)) {
+                output_shape[norm_axis] = Dimension(-1);
+                continue;
+            } else if ((step < 0 && start < 0 && stop > 0) || (step > 0 && stop < 0 && start > 0)) {
                 output_shape[norm_axis] = Dimension(-1);
                 continue;
             } else if (step < 0 && start > 0 && stop < 0) {
-                int64_t max_out_dim = start >= INT32_MAX ? INT64_MAX : start + 1;
-                output_shape[norm_axis] = Dimension(0, max_out_dim);
+                output_shape[norm_axis] = Dimension(0, start + 1);
                 continue;
             } else if (step > 0 && stop > 0 && start < 0) {
-                int64_t max_out_dim = stop >= INT32_MAX ? INT64_MAX : stop;
-                output_shape[norm_axis] = Dimension(0, max_out_dim);
+                output_shape[norm_axis] = Dimension(0, stop);
                 continue;
             }
         }
diff --git a/src/core/tests/type_prop/slice.cpp b/src/core/tests/type_prop/slice.cpp
index 40733aec8e2..462e2e839b4 100644
--- a/src/core/tests/type_prop/slice.cpp
+++ b/src/core/tests/type_prop/slice.cpp
@@ -386,7 +386,7 @@ TEST(type_prop, slice_v8_basic_const_inputs_MAX_MIN_INT_dynamic_dimensions) {
                                     Dimension(10, 20),
                                     Dimension(30, 40),
                                     Dimension(0, 50),
-                                    Dimension(0, INT32_MAX)};
+                                    Dimension(-1)};
 
     std::vector<int32_t> start_val{2, 2, INT32_MIN, INT32_MIN, INT32_MIN, INT32_MIN, INT32_MIN, 0, 0};
     std::vector<int32_t> stop_val{10, INT32_MAX, 5, 10, 15, 25, INT32_MAX, 50, INT32_MAX};
@@ -536,11 +536,7 @@ TEST(type_prop, slice_v8_basic_const_inputs_data_full_dynamic_dims_neg_step_neg_
 
 TEST(type_prop, slice_v8_basic_const_inputs_data_full_dynamic_dims_neg_step_mix_ind) {
     PartialShape data_shape{Dimension(-1), Dimension(-1), Dimension(-1), Dimension(-1), Dimension(-1)};
-    PartialShape expected_out_shape{Dimension(0, 6),
-                                    Dimension(0, 6),
-                                    Dimension(-1),
-                                    Dimension(0, INT32_MAX - 5),
-                                    Dimension(-1)};
+    PartialShape expected_out_shape{Dimension(0, 6), Dimension(0, 6), Dimension(-1), Dimension(0, -1), Dimension(-1)};
 
     std::vector<int32_t> start_val{5, 5, -10, INT32_MAX, INT32_MAX};
     std::vector<int32_t> stop_val{-10, INT32_MIN, 5, 5, INT32_MIN};
@@ -557,6 +553,80 @@ TEST(type_prop, slice_v8_basic_const_inputs_data_full_dynamic_dims_neg_step_mix_
     EXPECT_EQ(op->get_output_partial_shape(0), expected_out_shape);
 }
 
+TEST(type_prop, slice_v8_basic_const_inputs_dynamic_dims_maxint32) {
+    PartialShape data_shape{Dimension(0, 2000), Dimension(-1), 4};
+    PartialShape expected_out_shape{Dimension(0, 2000), Dimension(-1), Dimension(4)};
+
+    std::vector<int32_t> start_val{0, 0, 0};
+    std::vector<int32_t> stop_val{INT32_MAX, INT32_MAX, INT32_MAX};
+    std::vector<int32_t> step_val{1, 1, 1};
+
+    std::vector<int32_t> axes_val(start_val.size());
+    std::iota(axes_val.begin(), axes_val.end(), 0);
+
+    element::Type_t et = element::i32;
+    std::vector<std::vector<int32_t>> input_vals{start_val, stop_val, step_val, axes_val};
+    const auto op = make_slice_op_const_inputs(input_vals, data_shape, et);
+
+    EXPECT_EQ(op->get_element_type(), et);
+    EXPECT_EQ(op->get_output_partial_shape(0), expected_out_shape);
+}
+
+TEST(type_prop, slice_v8_basic_const_inputs_dynamic_dims_maxint64) {
+    PartialShape data_shape{Dimension(0, 2000), Dimension(-1), 4};
+    PartialShape expected_out_shape{Dimension(0, 2000), Dimension(-1), Dimension(4)};
+
+    std::vector<int64_t> start_val{0, 0, 0};
+    std::vector<int64_t> stop_val{INT64_MAX, INT64_MAX, INT64_MAX};
+    std::vector<int64_t> step_val{1, 1, 1};
+
+    std::vector<int64_t> axes_val(start_val.size());
+    std::iota(axes_val.begin(), axes_val.end(), 0);
+
+    element::Type_t et = element::i64;
+    std::vector<std::vector<int64_t>> input_vals{start_val, stop_val, step_val, axes_val};
+    const auto op = make_slice_op_const_inputs(input_vals, data_shape, et);
+
+    EXPECT_EQ(op->get_element_type(), et);
+    EXPECT_EQ(op->get_output_partial_shape(0), expected_out_shape);
+}
+
+TEST(type_prop, slice_v8_basic_const_inputs_dynamic_dims_maxint32_start1) {
+    PartialShape data_shape{Dimension(0, 2000), Dimension(-1), 4};
+    PartialShape expected_out_shape{Dimension(0, 2000), Dimension(-1), Dimension(4)};
+
+    std::vector<int32_t> start_val{1};
+    std::vector<int32_t> stop_val{INT32_MAX};
+    std::vector<int32_t> step_val{1};
+
+    std::vector<int32_t> axes_val{1};
+
+    element::Type_t et = element::i32;
+    std::vector<std::vector<int32_t>> input_vals{start_val, stop_val, step_val, axes_val};
+    const auto op = make_slice_op_const_inputs(input_vals, data_shape, et);
+
+    EXPECT_EQ(op->get_element_type(), et);
+    EXPECT_EQ(op->get_output_partial_shape(0), expected_out_shape);
+}
+
+TEST(type_prop, slice_v8_basic_const_inputs_dynamic_dims_maxint64_start1) {
+    PartialShape data_shape{Dimension(0, 2000), Dimension(-1), 4};
+    PartialShape expected_out_shape{Dimension(0, 2000), Dimension(-1), Dimension(4)};
+
+    std::vector<int64_t> start_val{1};
+    std::vector<int64_t> stop_val{INT64_MAX};
+    std::vector<int64_t> step_val{1};
+
+    std::vector<int64_t> axes_val{1};
+
+    element::Type_t et = element::i64;
+    std::vector<std::vector<int64_t>> input_vals{start_val, stop_val, step_val, axes_val};
+    const auto op = make_slice_op_const_inputs(input_vals, data_shape, et);
+
+    EXPECT_EQ(op->get_element_type(), et);
+    EXPECT_EQ(op->get_output_partial_shape(0), expected_out_shape);
+}
+
 TEST(type_prop, slice_v8_basic_const_inputs_MAX_MIN_INT_64_dynamic_dimensions_neg_step) {
     PartialShape data_shape{10,
                             Dimension(10, 20),

From 3a61afa2d329f14adfbbc52df5b1185e1d50b1d8 Mon Sep 17 00:00:00 2001
From: Evgenya Stepyreva <evgenya.stepyreva@intel.com>
Date: Thu, 23 Dec 2021 16:17:34 +0300
Subject: [PATCH 126/160] Shape as i32 value (#9343)

* Shape as value propagation in i32

* Comments adressed

* code style

* Modifies test to cover both Numpy and Bidirectional broadcasting

* MYR dynamic tests: made cases truly dynamic. Due to better shape inference it turned out that test cases were actually static.

* Deleting static shape test cases
---
 src/core/src/op/broadcast.cpp                 | 44 +++++++++++--------
 src/core/src/validation_util.cpp              | 15 +++++--
 src/core/tests/type_prop/broadcast.cpp        | 40 ++++++++++++++---
 .../subgraph_tests/nonzero_broadcast.cpp      |  4 --
 4 files changed, 69 insertions(+), 34 deletions(-)

diff --git a/src/core/src/op/broadcast.cpp b/src/core/src/op/broadcast.cpp
index 0a6fbd96a20..9a8d5a6fdff 100644
--- a/src/core/src/op/broadcast.cpp
+++ b/src/core/src/op/broadcast.cpp
@@ -68,17 +68,16 @@ std::pair<bool, AxisSet> op::v3::Broadcast::get_broadcast_axes() const {
 
 namespace {
 ov::PartialShape get_result_shape_bidirectional(const Node* this_ptr,
-                                                const ov::PartialShape& arg_shape,
-                                                ov::Shape& target_shape) {
-    if (arg_shape.rank().is_dynamic()) {
+                                                ov::PartialShape arg_shape,
+                                                ov::PartialShape target_shape) {
+    if (arg_shape.rank().is_dynamic() || target_shape.rank().is_dynamic()) {
         return ov::PartialShape::dynamic();
     }
-    auto arg_shape_vec = static_cast<std::vector<Dimension>>(arg_shape);
     ov::PartialShape result_shape;
     // Add left padding to shorter target or argument shape
-    const auto target_padded_rank = std::max(arg_shape_vec.size(), target_shape.size());
-    while (arg_shape_vec.size() < target_padded_rank) {
-        arg_shape_vec.insert(arg_shape_vec.begin(), 1);
+    const auto target_padded_rank = std::max(arg_shape.size(), target_shape.size());
+    while (arg_shape.size() < target_padded_rank) {
+        arg_shape.insert(arg_shape.begin(), 1);
     }
     while (target_shape.size() < target_padded_rank) {
         target_shape.insert(target_shape.begin(), 1);
@@ -86,23 +85,30 @@ ov::PartialShape get_result_shape_bidirectional(const Node* this_ptr,
 
     result_shape = target_shape;
     for (size_t i = 0; i < target_shape.size(); ++i) {
-        if (arg_shape_vec[i].is_dynamic()) {
-            if (target_shape[i] == 1) {
-                result_shape[i] = Dimension::dynamic();
+        const auto& arg_dim = arg_shape[i];
+        const auto& target_dim = target_shape[i];
+
+        if (arg_dim.is_dynamic() || target_dim.is_dynamic()) {
+            if (target_dim == 1 || (arg_dim.is_static() && arg_dim != 1)) {
+                result_shape[i] = arg_dim;
+            } else if (arg_dim == 1 || (target_dim.is_static() && target_dim != 1)) {
+                result_shape[i] = target_dim;
             } else {
-                result_shape[i] = target_shape[i];
+                result_shape[i] = Dimension(std::min(arg_dim.get_min_length(), target_dim.get_min_length()),
+                                            std::max(arg_dim.get_max_length(), target_dim.get_max_length()));
             }
             continue;
         }
-        const size_t arg_shape_dim = arg_shape_vec[i].get_length();
+
+        const auto& arg_shape_dim = arg_shape[i].get_length();
+        const auto& target_shape_dim = target_shape[i].get_length();
         NODE_VALIDATION_CHECK(this_ptr,
-                              arg_shape_dim == 1 || target_shape[i] == 1 || arg_shape_dim == target_shape[i],
+                              arg_shape_dim == 1 || target_shape[i] == 1 || arg_shape_dim == target_shape_dim,
                               "Broadcast incorrect target shape. Expecting either 1 or ",
                               arg_shape_dim,
                               ". Got ",
                               target_shape[i]);
-
-        result_shape[i] = std::max(arg_shape_dim, target_shape[i]);
+        result_shape[i] = std::max(arg_shape_dim, target_shape_dim);
     }
     return result_shape;
 }
@@ -112,7 +118,8 @@ bool op::v3::Broadcast::broadcast_evaluate(const HostTensorVector& outputs, cons
     if (get_broadcast_spec().m_type == op::BroadcastType::BIDIRECTIONAL) {
         auto arg_shape = inputs[0]->get_shape();
         ov::Shape target_shape = op::util::BroadcastBase::get_target_shape(inputs[1]);
-        ov::PartialShape result_shape = get_result_shape_bidirectional(this, ov::PartialShape{arg_shape}, target_shape);
+        ov::PartialShape result_shape =
+            get_result_shape_bidirectional(this, ov::PartialShape{arg_shape}, ov::PartialShape{target_shape});
         auto pair_broadcast_axes = get_broadcast_axes_bidirectional(arg_shape, result_shape.to_shape());
         return op::util::BroadcastBase::evaluate_broadcast(inputs[0],
                                                            outputs[0],
@@ -141,9 +148,8 @@ void op::v3::Broadcast::validate_and_infer_types() {
         if (get_input_partial_shape(0).rank().is_static() && get_input_partial_shape(1).is_static()) {
             auto arg_shape = get_input_partial_shape(0);
 
-            const auto shape_constant = get_constant_from_source(input_value(1));
-            if (shape_constant) {
-                auto target_shape = shape_constant->get_shape_val();
+            PartialShape target_shape;
+            if (evaluate_as_partial_shape(input_value(1), target_shape)) {
                 result_shape = get_result_shape_bidirectional(this, arg_shape, target_shape);
             }
         }
diff --git a/src/core/src/validation_util.cpp b/src/core/src/validation_util.cpp
index cb62ec2fc44..300d9f6842b 100644
--- a/src/core/src/validation_util.cpp
+++ b/src/core/src/validation_util.cpp
@@ -1287,13 +1287,20 @@ bool ov::evaluate_as_partial_shape(const Output<Node>& output, PartialShape& psh
     std::tie(lb, ub) = evaluate_both_bounds(output);
     bool shape_defined = false;
     if (lb && ub) {
-        const auto lower_bound = std::make_shared<op::v0::Constant>(lb)->cast_vector<int64_t>();
-        const auto upper_bound = std::make_shared<op::v0::Constant>(ub)->cast_vector<int64_t>();
+        auto lower_bound = std::make_shared<op::v0::Constant>(lb)->cast_vector<int64_t>();
+        auto upper_bound = std::make_shared<op::v0::Constant>(ub)->cast_vector<int64_t>();
         NGRAPH_CHECK(lower_bound.size() == upper_bound.size());
         vector<Dimension> resulting_pshape(lower_bound.size());
         for (size_t i = 0; i < lower_bound.size(); ++i) {
-            NGRAPH_CHECK(lower_bound[i] >= 0 && upper_bound[i] >= 0);
-            resulting_pshape[i] = {lower_bound[i], upper_bound[i]};
+            auto low = lower_bound[i], up = upper_bound[i];
+            NGRAPH_CHECK(low >= 0 && up >= 0);
+            if (output.get_element_type() == element::i32 && low != up) {
+                if (up == std::numeric_limits<std::int32_t>::max())
+                    up = std::numeric_limits<std::int64_t>::max();
+                if (low == std::numeric_limits<std::int32_t>::max())
+                    low = std::numeric_limits<std::int64_t>::max();
+            }
+            resulting_pshape[i] = {low, up};
         }
         pshape = PartialShape(resulting_pshape);
         shape_defined = true;
diff --git a/src/core/tests/type_prop/broadcast.cpp b/src/core/tests/type_prop/broadcast.cpp
index 094377bb5e2..51f2d855ef1 100644
--- a/src/core/tests/type_prop/broadcast.cpp
+++ b/src/core/tests/type_prop/broadcast.cpp
@@ -51,9 +51,9 @@ TYPED_TEST_P(BroadcastTests, broadcast_target_shape_as_concat_with_constants) {
     auto axes_mapping = op::Constant::create<int64_t>(element::i64, Shape{1}, {1});
     auto bc = make_shared<TypeParam>(param, target_shape, axes_mapping, "NONE");
     ASSERT_TRUE(bc->get_output_partial_shape(0).rank().is_static());
-    ASSERT_TRUE(bc->get_output_partial_shape(0).rank().same_scheme(Rank{4}));
+    ASSERT_EQ(bc->get_output_partial_shape(0).rank(), (Rank{4}));
     ASSERT_TRUE(bc->get_output_partial_shape(0).is_static());
-    ASSERT_TRUE(bc->get_output_partial_shape(0).same_scheme(PartialShape{1, 16, 50, 50}));
+    ASSERT_EQ(bc->get_output_partial_shape(0), (PartialShape{1, 16, 50, 50}));
 }
 
 TYPED_TEST_P(BroadcastTests, broadcast_target_shape_as_concat_with_node) {
@@ -71,7 +71,7 @@ TYPED_TEST_P(BroadcastTests, broadcast_target_shape_as_concat_with_node) {
     auto axes_mapping = op::Constant::create<int64_t>(element::i64, Shape{1}, {1});
     auto bc = make_shared<TypeParam>(param, target_shape, axes_mapping, "NONE");
     ASSERT_TRUE(bc->get_output_partial_shape(0).rank().is_static());
-    ASSERT_TRUE(bc->get_output_partial_shape(0).rank().same_scheme(Rank{4}));
+    ASSERT_EQ(bc->get_output_partial_shape(0).rank(), (Rank{4}));
     ASSERT_TRUE(bc->get_output_partial_shape(0).is_dynamic());
     ASSERT_EQ(bc->get_output_partial_shape(0), PartialShape({Dimension::dynamic(), 16, 50, 50}));
 }
@@ -738,7 +738,7 @@ TEST(type_prop, broadcast_v3_output_rank_not_deduced) {
 
     const auto broadcast_v3 = make_shared<op::v3::Broadcast>(arg, shape, broadcast_spec);
 
-    ASSERT_TRUE(broadcast_v3->get_output_partial_shape(0).same_scheme(PartialShape::dynamic()));
+    ASSERT_EQ(broadcast_v3->get_output_partial_shape(0), (PartialShape::dynamic()));
 }
 
 TEST(type_prop, broadcast_v3_output_rank_deduced_from_arg) {
@@ -747,7 +747,7 @@ TEST(type_prop, broadcast_v3_output_rank_deduced_from_arg) {
     const auto broadcast_spec = op::BroadcastType::BIDIRECTIONAL;
 
     const auto broadcast_v3 = make_shared<op::v3::Broadcast>(arg, shape, broadcast_spec);
-    ASSERT_TRUE(broadcast_v3->get_output_partial_shape(0).same_scheme(PartialShape{Dimension::dynamic(), 8, 6, 4}));
+    ASSERT_EQ(broadcast_v3->get_output_partial_shape(0), (PartialShape{Dimension::dynamic(), 8, 6, 4}));
 }
 
 TEST(type_prop, broadcast_v3_output_rank_deduced_from_new_shape_input) {
@@ -758,8 +758,8 @@ TEST(type_prop, broadcast_v3_output_rank_deduced_from_new_shape_input) {
     const auto broadcast_v3 = make_shared<op::v3::Broadcast>(arg, shape, broadcast_spec);
     ASSERT_TRUE(broadcast_v3->get_output_partial_shape(0).rank().is_static());
     ASSERT_EQ(broadcast_v3->get_output_partial_shape(0).rank().get_length(), 5);
-    ASSERT_TRUE(broadcast_v3->get_output_partial_shape(0).same_scheme(
-        PartialShape{8, 6, Dimension::dynamic(), 5, Dimension::dynamic()}));
+    ASSERT_EQ(broadcast_v3->get_output_partial_shape(0),
+              (PartialShape{8, 6, Dimension::dynamic(), 5, Dimension::dynamic()}));
 }
 
 TEST(type_prop, broadcast_v3_bidirectional_dynamic_input) {
@@ -859,3 +859,29 @@ TEST(type_prop, broadcast_v3_bidirectional_partially_dynamic_input) {
     ASSERT_EQ(bc->get_output_partial_shape(0).rank().get_length(), 4);
     ASSERT_EQ(bc->get_output_partial_shape(0), (PartialShape{1, Dimension::dynamic(), 50, 50}));
 }
+
+TEST(type_prop, broadcast_i32_shape_value) {
+    const auto arg = make_shared<op::Parameter>(element::f32, PartialShape({5, -1}));
+    const auto shape = make_shared<op::v3::ShapeOf>(arg, element::i64);
+    const auto broadcast_spec = op::BroadcastType::BIDIRECTIONAL;
+
+    const auto broadcast_v3 = make_shared<op::v3::Broadcast>(arg, shape, broadcast_spec);
+
+    ASSERT_EQ(broadcast_v3->get_output_partial_shape(0), PartialShape({5, -1}));
+
+    // shape type resetting
+    shape->set_output_type(element::i32);
+    arg->revalidate_and_infer_types();
+    shape->revalidate_and_infer_types();
+    broadcast_v3->revalidate_and_infer_types();
+
+    ASSERT_EQ(broadcast_v3->get_output_partial_shape(0), PartialShape({5, -1}));
+
+    // broadcast type resetting
+    broadcast_v3->set_broadcast_spec(op::BroadcastType::NUMPY);
+    arg->revalidate_and_infer_types();
+    shape->revalidate_and_infer_types();
+    broadcast_v3->revalidate_and_infer_types();
+
+    ASSERT_EQ(broadcast_v3->get_output_partial_shape(0), PartialShape({5, -1}));
+}
\ No newline at end of file
diff --git a/src/tests/functional/plugin/myriad/subgraph_tests/nonzero_broadcast.cpp b/src/tests/functional/plugin/myriad/subgraph_tests/nonzero_broadcast.cpp
index 70d4a2fcd57..dab8937352d 100644
--- a/src/tests/functional/plugin/myriad/subgraph_tests/nonzero_broadcast.cpp
+++ b/src/tests/functional/plugin/myriad/subgraph_tests/nonzero_broadcast.cpp
@@ -125,11 +125,7 @@ TEST_P(NonZero_Broadcast, CompareWithReference) {
 
 std::vector<BroadcastInputParams> broadcastTestParams = {
         { DataShapeWithUpperBound{ {1, 1, 4}, {} }, DataShapeWithUpperBound{ {200, 2, 4}, {300, 2, 4} }, {} },
-        { DataShapeWithUpperBound{ {15, 14}, {} }, DataShapeWithUpperBound{ {2, 16, 1, 14}, {2, 16, 15, 14} }, {} },
         { DataShapeWithUpperBound{ {15, 1}, {} }, DataShapeWithUpperBound{ {1, 16, 15, 14}, {2, 16, 15, 14} }, {} },
-        { DataShapeWithUpperBound{ {2, 16, 15, 14}, {} }, DataShapeWithUpperBound{ {1, 15, 14}, {16, 15, 14} }, {} },
-        { DataShapeWithUpperBound{ {2, 16, 15, 14}, {} }, DataShapeWithUpperBound{ {16,  1,  1}, {16,  1,  14}}, {} },
-        { DataShapeWithUpperBound{ {16, 15, 1}, {} }, DataShapeWithUpperBound{ {2, 1, 15, 14}, {2, 16, 15, 14} }, {} },
         { DataShapeWithUpperBound{ {142, 1, 1, 64}, {300, 1, 1, 64} }, DataShapeWithUpperBound { {142, 3, 64, 64}, {300, 3, 64, 64} }, {} },
         { DataShapeWithUpperBound{ {1}, {} }, DataShapeWithUpperBound{ {1, 800}, {1, 1000} }, {0} },
         { DataShapeWithUpperBound{ {4}, {} }, DataShapeWithUpperBound{ {100, 4}, {1000, 4} }, {1} },

From 8740438a3bb3bd9f1a0b32617428fdcfbd6b4167 Mon Sep 17 00:00:00 2001
From: Gleb Kazantaev <gleb.kazantaev@intel.com>
Date: Thu, 23 Dec 2021 16:39:41 +0300
Subject: [PATCH 127/160] Fix SmartReshpae TransposeMatMul pass (#9397)

---
 src/core/src/pass/smart_reshape/matmul_sr.cpp | 16 ++++++------
 .../cnn_network/matmul_sr_tests.cpp           | 26 ++++++++++++++-----
 2 files changed, 27 insertions(+), 15 deletions(-)

diff --git a/src/core/src/pass/smart_reshape/matmul_sr.cpp b/src/core/src/pass/smart_reshape/matmul_sr.cpp
index fea411877f5..be2299950cb 100644
--- a/src/core/src/pass/smart_reshape/matmul_sr.cpp
+++ b/src/core/src/pass/smart_reshape/matmul_sr.cpp
@@ -136,19 +136,19 @@ ngraph::pass::TransposeMatMul::TransposeMatMul() {
         };
 
         NodeVector fused_nodes;
-        auto input_A = matmul->get_input_node_shared_ptr(0);
+        auto input_A = matmul->input_value(0);
         bool transpose_A = matmul->get_transpose_a();
-        if (transpose_is_fusable(input_A)) {
-            fused_nodes.push_back(input_A);
-            input_A = input_A->get_input_node_shared_ptr(0);
+        if (transpose_is_fusable(input_A.get_node_shared_ptr())) {
+            fused_nodes.push_back(input_A.get_node_shared_ptr());
+            input_A = input_A.get_node()->input_value(0);
             transpose_A = !transpose_A;
         }
 
-        auto input_B = matmul->get_input_node_shared_ptr(1);
+        auto input_B = matmul->input_value(1);
         auto transpose_B = matmul->get_transpose_b();
-        if (transpose_is_fusable(input_B)) {
-            fused_nodes.push_back(input_B);
-            input_B = input_B->get_input_node_shared_ptr(0);
+        if (transpose_is_fusable(input_B.get_node_shared_ptr())) {
+            fused_nodes.push_back(input_B.get_node_shared_ptr());
+            input_B = input_B.get_node()->input_value(0);
             transpose_B = !transpose_B;
         }
 
diff --git a/src/tests/functional/inference_engine/cnn_network/matmul_sr_tests.cpp b/src/tests/functional/inference_engine/cnn_network/matmul_sr_tests.cpp
index 33940bfab6d..4e41ff75bc7 100644
--- a/src/tests/functional/inference_engine/cnn_network/matmul_sr_tests.cpp
+++ b/src/tests/functional/inference_engine/cnn_network/matmul_sr_tests.cpp
@@ -272,11 +272,17 @@ TEST(SmartReshapeTransposeMatMulTests, TransposeBothMatMulFuse) {
 TEST(SmartReshapeTransposeMatMulTests, TransposeBothMatMulWithAttrFuse) {
     std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
     {
-        auto data_A = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 3, 2});
-        auto data_B = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 3, 5});
+        auto data_A = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 2});
+        auto split_A = std::make_shared<ngraph::opset4::VariadicSplit>(data_A,
+                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0}),
+                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 1}));
+        auto data_B = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 5});
+        auto split_B = std::make_shared<ngraph::opset4::VariadicSplit>(data_B,
+                                                                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0}),
+                                                                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 1}));
         auto order = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {0, 2, 1});
-        auto transpose_A = std::make_shared<ngraph::opset4::Transpose>(data_A, order);
-        auto transpose_B = std::make_shared<ngraph::opset4::Transpose>(data_B, order);
+        auto transpose_A = std::make_shared<ngraph::opset4::Transpose>(split_A->output(0), order);
+        auto transpose_B = std::make_shared<ngraph::opset4::Transpose>(split_B->output(1), order);
         auto matmul = std::make_shared<ngraph::opset4::MatMul>(transpose_A, transpose_B, false, true);
         f = std::make_shared<ngraph::Function>(ngraph::NodeVector{matmul}, ngraph::ParameterVector{data_A, data_B});
 
@@ -287,9 +293,15 @@ TEST(SmartReshapeTransposeMatMulTests, TransposeBothMatMulWithAttrFuse) {
         ASSERT_NO_THROW(check_rt_info(f));
     }
     {
-        auto data_A = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 3, 2});
-        auto data_B = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 3, 5});
-        auto matmul = std::make_shared<ngraph::opset4::MatMul>(data_A, data_B, true, false);
+        auto data_A = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 2});
+        auto split_A = std::make_shared<ngraph::opset4::VariadicSplit>(data_A,
+                                                                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0}),
+                                                                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 1}));
+        auto data_B = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 5});
+        auto split_B = std::make_shared<ngraph::opset4::VariadicSplit>(data_B,
+                                                                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0}),
+                                                                       ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 1}));
+        auto matmul = std::make_shared<ngraph::opset4::MatMul>(split_A->output(0), split_B->output(1), true, false);
         f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{matmul}, ngraph::ParameterVector{data_A, data_B});
     }
 

From c9704e7ed4ca07dd1eb9297b9b2f60b7cd38d693 Mon Sep 17 00:00:00 2001
From: Gleb Kazantaev <gleb.kazantaev@intel.com>
Date: Thu, 23 Dec 2021 17:28:52 +0300
Subject: [PATCH 128/160] Eltwise->Transpose sinking for preprocessing ops
 (#9383)

---
 .../transpose_sinking.hpp                     |  12 ++
 .../transformations/rt_info/attributes.hpp    |   2 +-
 .../rt_info/preprocessing_attribute.hpp       |  28 +++++
 .../rt_info/reverse_input_channels.hpp        |  27 -----
 .../common_optimizations/ric_fusion.cpp       |   2 +-
 .../transpose_sinking.cpp                     |  49 ++++++++
 .../transformations/rt_info/attributes.cpp    |   2 +-
 .../pass/rt_info/preprocessing_attribute.cpp  |  15 +++
 .../pass/rt_info/reverse_input_channels.cpp   |  15 ---
 .../src/preprocess/preprocess_steps_impl.cpp  |   6 +-
 ...sts.cpp => preprocessing_fusion_tests.cpp} | 109 ++++++++++++++++++
 .../transpose_sinking_test.cpp                |  31 +----
 12 files changed, 221 insertions(+), 77 deletions(-)
 create mode 100644 src/common/transformations/include/transformations/rt_info/preprocessing_attribute.hpp
 delete mode 100644 src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp
 create mode 100644 src/core/src/pass/rt_info/preprocessing_attribute.cpp
 delete mode 100644 src/core/src/pass/rt_info/reverse_input_channels.cpp
 rename src/tests/functional/inference_engine/transformations/{ric_fusion_tests.cpp => preprocessing_fusion_tests.cpp} (86%)

diff --git a/src/common/transformations/include/transformations/common_optimizations/transpose_sinking.hpp b/src/common/transformations/include/transformations/common_optimizations/transpose_sinking.hpp
index 93d975db624..cbef136638e 100644
--- a/src/common/transformations/include/transformations/common_optimizations/transpose_sinking.hpp
+++ b/src/common/transformations/include/transformations/common_optimizations/transpose_sinking.hpp
@@ -18,6 +18,7 @@ namespace pass {
 
 class TRANSFORMATIONS_API TransposeSinking;
 class TRANSFORMATIONS_API TransposeConvert;
+class TRANSFORMATIONS_API TransposeEltwise;
 class TRANSFORMATIONS_API TransposeReduction;
 class TRANSFORMATIONS_API TransposeFQReduction;
 class TRANSFORMATIONS_API TransposeFuse;
@@ -55,6 +56,16 @@ public:
     TransposeConvert();
 };
 
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief TransposeEltwise transformation sinks Transpose through Eltwise
+ */
+class ngraph::pass::TransposeEltwise : public ngraph::pass::MatcherPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    TransposeEltwise();
+};
+
 /**
  * @ingroup ie_transformation_common_api
  * @brief TransposeFuse transformation eliminates 2 consequtive Transposes if they result in no changes to input or fuses them
@@ -77,6 +88,7 @@ public:
         add_matcher<ngraph::pass::TransposeFQReduction>();
         add_matcher<ngraph::pass::TransposeReduction>();
         add_matcher<ngraph::pass::TransposeConvert>();
+        add_matcher<ngraph::pass::TransposeEltwise>();
         add_matcher<ngraph::pass::TransposeFuse>();
     }
 };
diff --git a/src/common/transformations/include/transformations/rt_info/attributes.hpp b/src/common/transformations/include/transformations/rt_info/attributes.hpp
index 29ec755d21e..243a43172b5 100644
--- a/src/common/transformations/include/transformations/rt_info/attributes.hpp
+++ b/src/common/transformations/include/transformations/rt_info/attributes.hpp
@@ -20,7 +20,7 @@
 #include <transformations/rt_info/old_api_map_order_attribute.hpp>
 #include <transformations/rt_info/primitives_priority_attribute.hpp>
 #include <transformations/rt_info/strides_property.hpp>
-#include <transformations/rt_info/reverse_input_channels.hpp>
+#include <transformations/rt_info/preprocessing_attribute.hpp>
 #include <transformations/rt_info/decompression.hpp>
 #include <transformations_visibility.hpp>
 #include <utility>
diff --git a/src/common/transformations/include/transformations/rt_info/preprocessing_attribute.hpp b/src/common/transformations/include/transformations/rt_info/preprocessing_attribute.hpp
new file mode 100644
index 00000000000..73c9a4a8402
--- /dev/null
+++ b/src/common/transformations/include/transformations/rt_info/preprocessing_attribute.hpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/node.hpp>
+#include <ngraph/variant.hpp>
+#include <openvino/core/rtti.hpp>
+#include <openvino/core/runtime_attribute.hpp>
+
+namespace ov {
+NGRAPH_API bool is_preprocesing_node(const std::shared_ptr<ngraph::Node>& node);
+
+NGRAPH_API void set_is_preprocessing_node(std::shared_ptr<ngraph::Node> node);
+
+/*
+ * PreprocessingAttribute attribute indicates that operation can be fused
+ * by different fusion transformation for cases when some information is unknown
+ * but we can rely on information that operation is a pre-processing operation.
+ */
+class NGRAPH_API PreprocessingAttribute : public ov::RuntimeAttribute {
+public:
+    OPENVINO_RTTI("preprocessing", "0");
+    PreprocessingAttribute() = default;
+    bool visit_attributes(AttributeVisitor& visitor) override { return true; };
+};
+} // namespace ov
diff --git a/src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp b/src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp
deleted file mode 100644
index 2fd8f9426a9..00000000000
--- a/src/common/transformations/include/transformations/rt_info/reverse_input_channels.hpp
+++ /dev/null
@@ -1,27 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <ngraph/node.hpp>
-#include <ngraph/variant.hpp>
-#include <openvino/core/rtti.hpp>
-#include <openvino/core/runtime_attribute.hpp>
-
-namespace ov {
-NGRAPH_API bool is_reverse_input_channels(const std::shared_ptr<ngraph::Node>& node);
-
-NGRAPH_API void set_is_reverse_input_channels(std::shared_ptr<ngraph::Node> node);
-
-/*
- * ReverseInputChannels attribute indicates that operation can be fused
- * by ReverseInputChannels fusion transformation for cases when shape is dynamic.
- */
-class NGRAPH_API ReverseInputChannels : public ov::RuntimeAttribute {
-public:
-    OPENVINO_RTTI("reverse_input_channels", "0");
-    ReverseInputChannels() = default;
-    bool visit_attributes(AttributeVisitor& visitor) override { return true; };
-};
-} // namespace ov
diff --git a/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp
index 85b071ef735..741e9af9786 100644
--- a/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp
@@ -234,7 +234,7 @@ public:
 
             const auto axis_value = axis->cast_vector<int64_t>().at(0);
 
-            if (ov::is_reverse_input_channels(output.get_node_shared_ptr())) {
+            if (ov::is_preprocesing_node(output.get_node_shared_ptr())) {
                 ric_attr::init(output, {}, axis_value);
                 return true;
             }
diff --git a/src/common/transformations/src/transformations/common_optimizations/transpose_sinking.cpp b/src/common/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
index 4f3aab6a252..72b0db608a2 100644
--- a/src/common/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
@@ -17,6 +17,7 @@
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::TransposeSinking, "TransposeSinking", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::TransposeConvert, "TransposeConvert", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::TransposeEltwise, "TransposeEltwise", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::TransposeReduction, "TransposeReduction", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::TransposeFQReduction, "TransposeFQReduction", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::TransposeFuse, "TransposeFuse", 0);
@@ -61,6 +62,54 @@ std::shared_ptr<ngraph::opset6::Constant> get_reversed_order_constant(const std:
 
 } // namespace
 
+ngraph::pass::TransposeEltwise::TransposeEltwise() {
+    MATCHER_SCOPE(TransposeEltwise);
+
+    auto eltwise_data_input_p = pattern::any_input();
+    auto eltwise_const_input_p = pattern::wrap_type<opset6::Constant>();
+    auto eltwise_p = pattern::wrap_type<op::util::BinaryElementwiseArithmetic>({eltwise_data_input_p, eltwise_const_input_p},
+        [](const Output<Node> & output) {
+            return ov::is_preprocesing_node(output.get_node_shared_ptr());
+    });
+    auto transpose_p = pattern::wrap_type<opset6::Transpose>({eltwise_p,
+                                                              pattern::wrap_type<opset6::Constant>()},
+                                                              pattern::consumers_count(1));
+
+    auto callback = [=](ngraph::pattern::Matcher &m) {
+        const auto &pattern_to_output = m.get_pattern_value_map();
+        auto eltwise = pattern_to_output.at(eltwise_p).get_node_shared_ptr();
+        auto eltwise_const_input = pattern_to_output.at(eltwise_const_input_p);
+        auto eltwise_data_input = pattern_to_output.at(eltwise_data_input_p);
+        auto transpose = pattern_to_output.at(transpose_p).get_node_shared_ptr();
+
+        const auto & order_size = transpose->get_input_shape(1).at(0);
+        const auto & shape = eltwise_const_input.get_shape();
+        if (shape.size() != order_size && ov::shape_size(shape) != 1) {
+            // TODO: temporary restrictions
+            return false;
+        }
+
+        if (ov::shape_size(shape) != 1) {
+            eltwise_const_input = std::make_shared<opset6::Transpose>(eltwise_const_input, transpose->input_value(1));
+            if (auto const_node = ov::get_constant_from_source(eltwise_const_input)) {
+                eltwise_const_input = const_node;
+            }
+        }
+
+        auto new_transpose = transpose->clone_with_new_inputs({eltwise_data_input, transpose->input_value(1)});
+        auto new_eltwise = eltwise->clone_with_new_inputs({new_transpose, eltwise_const_input});
+        register_new_node(new_transpose);
+
+        new_transpose->set_friendly_name(eltwise->get_friendly_name());
+        copy_runtime_info({eltwise, transpose}, {new_transpose, new_eltwise});
+        replace_node(transpose, new_eltwise);
+        return true;
+    };
+
+    auto m = std::make_shared<ngraph::pattern::Matcher>(transpose_p, matcher_name);
+    register_matcher(m, callback);
+}
+
 ngraph::pass::TransposeConvert::TransposeConvert() {
     MATCHER_SCOPE(TransposeConvert);
 
diff --git a/src/common/transformations/src/transformations/rt_info/attributes.cpp b/src/common/transformations/src/transformations/rt_info/attributes.cpp
index 17f2a512c13..7aadb5908df 100644
--- a/src/common/transformations/src/transformations/rt_info/attributes.cpp
+++ b/src/common/transformations/src/transformations/rt_info/attributes.cpp
@@ -16,7 +16,7 @@ ov::pass::Attributes::Attributes() {
     register_factory<Decompression>();
     register_factory<ov::preprocess::TensorInfoMemoryType>();
     register_factory<StridesPropagation>();
-    register_factory<ReverseInputChannels>();
+    register_factory<PreprocessingAttribute>();
 }
 
 ov::Any ov::pass::Attributes::create_by_type_info(const ov::DiscreteTypeInfo& type_info) {
diff --git a/src/core/src/pass/rt_info/preprocessing_attribute.cpp b/src/core/src/pass/rt_info/preprocessing_attribute.cpp
new file mode 100644
index 00000000000..4e1b18a53c8
--- /dev/null
+++ b/src/core/src/pass/rt_info/preprocessing_attribute.cpp
@@ -0,0 +1,15 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/rt_info/preprocessing_attribute.hpp"
+
+#include "ngraph/node.hpp"
+
+bool ov::is_preprocesing_node(const std::shared_ptr<ngraph::Node>& node) {
+    return node->get_rt_info().count(PreprocessingAttribute::get_type_info_static());
+}
+
+void ov::set_is_preprocessing_node(std::shared_ptr<ngraph::Node> node) {
+    node->get_rt_info().emplace(PreprocessingAttribute::get_type_info_static(), PreprocessingAttribute{});
+}
\ No newline at end of file
diff --git a/src/core/src/pass/rt_info/reverse_input_channels.cpp b/src/core/src/pass/rt_info/reverse_input_channels.cpp
deleted file mode 100644
index 770f591cf0c..00000000000
--- a/src/core/src/pass/rt_info/reverse_input_channels.cpp
+++ /dev/null
@@ -1,15 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "transformations/rt_info/reverse_input_channels.hpp"
-
-#include "ngraph/node.hpp"
-
-bool ov::is_reverse_input_channels(const std::shared_ptr<ngraph::Node>& node) {
-    return node->get_rt_info().count(ReverseInputChannels::get_type_info_static());
-}
-
-void ov::set_is_reverse_input_channels(std::shared_ptr<ngraph::Node> node) {
-    node->get_rt_info().emplace(ReverseInputChannels::get_type_info_static(), ReverseInputChannels{});
-}
\ No newline at end of file
diff --git a/src/core/src/preprocess/preprocess_steps_impl.cpp b/src/core/src/preprocess/preprocess_steps_impl.cpp
index 2672d60d482..39a6ca1ee03 100644
--- a/src/core/src/preprocess/preprocess_steps_impl.cpp
+++ b/src/core/src/preprocess/preprocess_steps_impl.cpp
@@ -11,7 +11,7 @@
 #include "openvino/op/nv12_to_bgr.hpp"
 #include "openvino/op/nv12_to_rgb.hpp"
 #include "openvino/opsets/opset8.hpp"
-#include "transformations/rt_info/reverse_input_channels.hpp"
+#include "transformations/rt_info/preprocessing_attribute.hpp"
 
 namespace ov {
 namespace preprocess {
@@ -56,6 +56,7 @@ void PreStepsList::add_scale_impl(const std::vector<float>& values) {
         auto constant = op::v0::Constant::create(element_type, shape, values);
 
         auto new_op = std::make_shared<op::v1::Divide>(nodes[0], constant);
+        set_is_preprocessing_node(new_op);
         return std::make_tuple(std::vector<Output<Node>>{new_op}, false);
     });
 }
@@ -83,6 +84,7 @@ void PreStepsList::add_mean_impl(const std::vector<float>& values) {
         auto constant = op::v0::Constant::create(element_type, shape, values);
 
         auto new_op = std::make_shared<op::v1::Subtract>(nodes[0], constant);
+        set_is_preprocessing_node(new_op);
         return std::make_tuple(std::vector<Output<Node>>{new_op}, false);
     });
 }
@@ -366,7 +368,7 @@ void PreStepsList::add_reverse_channels() {
         auto resp = reverse_channels(nodes, function, context);
         auto outputs = std::get<0>(resp);
         OPENVINO_ASSERT(outputs.size() == 1, "Internal error: reverse_channels returned unexpected number of outputs");
-        set_is_reverse_input_channels(outputs.at(0).get_node_shared_ptr());
+        set_is_preprocessing_node(outputs.at(0).get_node_shared_ptr());
         return resp;
     });
 }
diff --git a/src/tests/functional/inference_engine/transformations/ric_fusion_tests.cpp b/src/tests/functional/inference_engine/transformations/preprocessing_fusion_tests.cpp
similarity index 86%
rename from src/tests/functional/inference_engine/transformations/ric_fusion_tests.cpp
rename to src/tests/functional/inference_engine/transformations/preprocessing_fusion_tests.cpp
index 6fe7848e9ff..bd58f819150 100644
--- a/src/tests/functional/inference_engine/transformations/ric_fusion_tests.cpp
+++ b/src/tests/functional/inference_engine/transformations/preprocessing_fusion_tests.cpp
@@ -10,6 +10,8 @@
 #include <ngraph/function.hpp>
 #include <ngraph/opsets/opset8.hpp>
 #include <transformations/common_optimizations/ric_fusion.hpp>
+#include <transformations/common_optimizations/transpose_sinking.hpp>
+#include <transformations/common_optimizations/moc_transformations.hpp>
 #include <transformations/init_node_info.hpp>
 #include <ngraph_functions/utils/ngraph_helpers.hpp>
 #include <openvino/core/preprocess/pre_post_process.hpp>
@@ -95,6 +97,26 @@ void apply_reverse_input_channels(std::shared_ptr<Function> f, std::vector<std::
     }
     p.build();
 }
+
+void apply_mean_value(std::shared_ptr<Function> f, std::vector<std::pair<int64_t, std::string>> data, std::vector<float> values) {
+    using namespace ov::preprocess;
+    PrePostProcessor p(f);
+    for (auto item : data) {
+        p.input(item.first).tensor().set_layout(ov::Layout(item.second));
+        p.input(item.first).preprocess().mean(values);
+    }
+    p.build();
+}
+
+void apply_scale_value(std::shared_ptr<Function> f, std::vector<std::pair<int64_t, std::string>> data, std::vector<float> values) {
+    using namespace ov::preprocess;
+    PrePostProcessor p(f);
+    for (auto item : data) {
+        p.input(item.first).tensor().set_layout(ov::Layout(item.second));
+        p.input(item.first).preprocess().scale(values);
+    }
+    p.build();
+}
 } // namespace
 
 TEST_F(TransformationTestsF, RICFusionSimple) {
@@ -672,3 +694,90 @@ TEST_F(TransformationTestsF, RICFusionSplitConcatDetectionNegative3) {
         manager.register_pass<pass::ReverseInputChannelsFusion>();
     }
 }
+
+TEST_F(TransformationTestsF, FuseConvertLayout) {
+    {
+        auto input = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 3, 64 });
+        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 2, 0 });
+        auto transpose = std::make_shared<ngraph::opset6::Transpose>(input, order);
+        auto relu = std::make_shared<ngraph::opset6::Relu>(transpose);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ relu }, ngraph::ParameterVector{ input });
+
+        using namespace ov::preprocess;
+        PrePostProcessor p(function);
+        p.input(0).tensor().set_element_type(element::f16);
+        p.input(0).preprocess().convert_layout({2, 0, 1});
+        p.build();
+
+        manager.register_pass<ngraph::pass::TransposeSinking>();
+    }
+
+    {
+        auto input = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f16, ngraph::Shape{ 3, 64, 1 });
+        auto convert = std::make_shared<ngraph::opset6::Convert>(input, element::f32);
+        auto relu = std::make_shared<ngraph::opset6::Relu>(convert);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ relu }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, FuseScaleValue) {
+    {
+        auto input = create_param({ 1, 64, 64, 3 });
+        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 });
+        auto transpose = std::make_shared<ngraph::opset6::Transpose>(input, order);
+        auto conv = create_conv(transpose, {6, 3, 3, 3});
+
+        function = std::make_shared<Function>(NodeVector{ conv }, ParameterVector{ input });
+
+        using namespace ov::preprocess;
+        PrePostProcessor p(function);
+        p.input(0).tensor().set_layout("NHWC");
+        p.input(0).preprocess().scale(1.3);
+        p.build();
+
+        manager.register_pass<pass::MOCTransformations>(false);
+    }
+
+    {
+        auto input = create_param({ 1, 64, 64, 3 });
+        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 });
+        auto transpose = std::make_shared<ngraph::opset6::Transpose>(input, order);
+        auto conv = create_conv(transpose, {6, 3, 3, 3});
+        function_ref = std::make_shared<Function>(NodeVector{ conv }, ParameterVector{ input });
+    }
+
+    disable_rt_info_check();
+    enable_accuracy_check();
+}
+
+TEST_F(TransformationTestsF, FuseScaleValues) {
+    {
+        auto input = create_param({ 1, 64, 64, 3 });
+        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 });
+        auto transpose = std::make_shared<ngraph::opset6::Transpose>(input, order);
+        auto conv = create_conv(transpose, {6, 3, 3, 3});
+
+        function = std::make_shared<Function>(NodeVector{ conv }, ParameterVector{ input });
+
+        using namespace ov::preprocess;
+        PrePostProcessor p(function);
+        p.input(0).tensor().set_layout("NHWC");
+        p.input(0).preprocess().scale({1.3, 0.2, 4.1});
+        p.build();
+
+        manager.register_pass<pass::MOCTransformations>(false);
+    }
+
+    {
+        auto input = create_param({ 1, 64, 64, 3 });
+        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 });
+        auto transpose = std::make_shared<ngraph::opset6::Transpose>(input, order);
+        auto conv = create_conv(transpose, {6, 3, 3, 3});
+        function_ref = std::make_shared<Function>(NodeVector{ conv }, ParameterVector{ input });
+    }
+
+    disable_rt_info_check();
+    enable_accuracy_check();
+}
diff --git a/src/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp b/src/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp
index 6a31bcdc482..71e4f3e92b0 100644
--- a/src/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp
+++ b/src/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp
@@ -312,33 +312,4 @@ TEST_F(TransformationTestsF, TransposeConvertNegativeConsumers) {
         function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ convert, transpose }, ngraph::ParameterVector{ input });
         manager.register_pass<ngraph::pass::TransposeConvert>();
     }
-}
-
-TEST_F(TransformationTestsF, TransposePreProcessing) {
-    {
-        auto input = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 3, 64 });
-        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 2, 1, 0 });
-        auto transpose = std::make_shared<ngraph::opset6::Transpose>(input, order);
-        auto relu = std::make_shared<ngraph::opset6::Relu>(transpose);
-
-        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ relu }, ngraph::ParameterVector{ input });
-
-        using namespace ov::preprocess;
-        PrePostProcessor p(function);
-        p.input(0).tensor().set_element_type(element::f16);
-        p.input(0).preprocess().convert_layout({2, 0, 1});
-        p.build();
-
-        manager.register_pass<ngraph::pass::TransposeSinking>();
-    }
-
-    {
-        auto input = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f16, ngraph::Shape{ 3, 64, 1 });
-        auto convert = std::make_shared<ngraph::opset6::Convert>(input, element::f32);
-        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 1, 0, 2 });
-        auto transpose = std::make_shared<ngraph::opset6::Transpose>(convert, order);
-        auto relu = std::make_shared<ngraph::opset6::Relu>(transpose);
-
-        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ relu }, ngraph::ParameterVector{ input });
-    }
-}
+}
\ No newline at end of file

From 557d20e2e2479d6223e445de67001b04d71bc9e0 Mon Sep 17 00:00:00 2001
From: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>
Date: Thu, 23 Dec 2021 18:12:23 +0300
Subject: [PATCH 129/160] Update install guides for wheels (#9390)

* Update install guides for wheels

* remove extra comma

* License update
---
 docs/install_guides/pypi-openvino-dev.md | 24 +++++++++++-------------
 docs/install_guides/pypi-openvino-rt.md  | 15 +++++++--------
 2 files changed, 18 insertions(+), 21 deletions(-)

diff --git a/docs/install_guides/pypi-openvino-dev.md b/docs/install_guides/pypi-openvino-dev.md
index e0827d0c53d..cc594364998 100644
--- a/docs/install_guides/pypi-openvino-dev.md
+++ b/docs/install_guides/pypi-openvino-dev.md
@@ -1,7 +1,7 @@
 # Intel® Distribution of OpenVINO™ Toolkit Developer Package 
 Copyright © 2018-2021 Intel Corporation
 > **LEGAL NOTICE**: Your use of this software and any required dependent software (the
-“Software Package”) is subject to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf) for the Software Package, which may also include notices, disclaimers, or
+“Software Package”) is subject to the terms and conditions of the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html) for the Software Package, which may also include notices, disclaimers, or
 license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
 
 >Intel is committed to the respect of human rights and avoiding complicity in human rights abuses, a policy reflected in the [Intel Global Human Rights Principles](https://www.intel.com/content/www/us/en/policy/policy-human-rights.html). Accordingly, by accessing the Intel material on this platform you agree that you will not use the material in a product or application that causes or contributes to a violation of an internationally recognized human right.
@@ -15,11 +15,11 @@ OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applicatio
 
 | Component        | Console Script                                                                   | Description                                                                                                                                                                                                                                                                                                   |  
 |------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Model Optimizer](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model Optimizer** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*.                                               |
-| [Benchmark Tool](https://docs.openvinotoolkit.org/latest/openvino_inference_engine_tools_benchmark_tool_README.html)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes.                                              |
-| [Accuracy Checker](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker_annotation_converters.html) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker**  is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker.                                             |
-| [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models. Optimizations are also available through the [API](https://docs.openvinotoolkit.org/latest/pot_compression_api_README.html).                                            |
-| [Model Downloader and other Open Model Zoo tools](https://docs.openvinotoolkit.org/latest/omz_tools_downloader.html)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](https://docs.openvinotoolkit.org/latest/omz_models_group_public.html) and [Intel](https://docs.openvinotoolkit.org/latest/omz_models_group_intel.html)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the Inference Engine Intermediate Representation (IR) using Model Optimizer. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format.
+| [Model Optimizer](https://docs.openvino.ai/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model Optimizer** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*.                                               |
+| [Benchmark Tool](https://docs.openvino.ai/latest/openvino_inference_engine_tools_benchmark_tool_README.html)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes.                                              |
+| [Accuracy Checker](https://docs.openvino.ai/latest/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/latest/omz_tools_accuracy_checker_annotation_converters.html) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker**  is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker.                                             |
+| [Post-Training Optimization Tool](https://docs.openvino.ai/latest/pot_README.html)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models. Optimizations are also available through the [API](https://docs.openvino.ai/latest/pot_compression_api_README.html).                                            |
+| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/latest/omz_tools_downloader.html)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](https://docs.openvino.ai/latest/omz_models_group_public.html) and [Intel](https://docs.openvino.ai/latest/omz_models_group_intel.html)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the Inference Engine Intermediate Representation (IR) using Model Optimizer. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format.
 
 > **NOTE**: The developer package also installs the OpenVINO™ runtime package as a dependency.
 
@@ -37,11 +37,10 @@ The table below lists the supported operating systems and Python* versions requi
 | Supported Operating System                                   | [Python* Version (64-bit)](https://www.python.org/) |
 | :------------------------------------------------------------| :---------------------------------------------------|
 |   Ubuntu* 18.04 long-term support (LTS), 64-bit              | 3.6, 3.7, 3.8                                       |
-|   Ubuntu* 20.04 long-term support (LTS), 64-bit              | 3.6, 3.7, 3.8                                       |
+|   Ubuntu* 20.04 long-term support (LTS), 64-bit              | 3.6, 3.7, 3.8, 3.9                                  |
 |   Red Hat* Enterprise Linux* 8, 64-bit                       | 3.6, 3.8                                            |
-|   CentOS* 7, 64-bit                                          | 3.6, 3.7, 3.8                                       |
-|   macOS* 10.15.x                                             | 3.6, 3.7, 3.8                                       |
-|   Windows 10*, 64-bit                                        | 3.6, 3.7, 3.8                                       |
+|   macOS* 10.15.x                                             | 3.6, 3.7, 3.8, 3.9                                  |
+|   Windows 10*, 64-bit                                        | 3.6, 3.7, 3.8, 3.9                                  |
 
 > **NOTE**: This package can be installed on other versions of macOS, Linux and Windows, but only the specific versions above are fully validated.
 
@@ -87,7 +86,6 @@ To install and configure the components of the development package for working w
 | DL Framework                                                                     | Extra                           |
 | :------------------------------------------------------------------------------- | :-------------------------------|
 |   [Caffe*](https://caffe.berkeleyvision.org/)                                    | caffe                           |
-|   [Caffe2*](https://github.com/pytorch/pytorch)                                  | caffe2                          |
 |   [Kaldi*](https://github.com/kaldi-asr/kaldi)                                   | kaldi                           |
 |   [MXNet*](https://mxnet.apache.org/)                                            | mxnet                           |
 |   [ONNX*](https://github.com/microsoft/onnxruntime/)                             | onnx                            |
@@ -110,7 +108,7 @@ For example, to install and configure the components for working with TensorFlow
 
 - To verify that Inference Engine from the **runtime package** is available, run the command below:
    ```sh
-   python -c "from openvino.inference_engine import IECore"
+   python -c "from openvino.runtime import Core"
    ```
    If installation was successful, you will not see any error messages (no console output).
 
@@ -130,5 +128,5 @@ sudo apt-get install libpython3.7
 ## Additional Resources
 
 - [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit)
-- [OpenVINO™ toolkit online documentation](https://docs.openvinotoolkit.org)
+- [OpenVINO™ toolkit online documentation](https://docs.openvino.ai)
 - [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
diff --git a/docs/install_guides/pypi-openvino-rt.md b/docs/install_guides/pypi-openvino-rt.md
index 96b3c5cdc14..ad8009437cd 100644
--- a/docs/install_guides/pypi-openvino-rt.md
+++ b/docs/install_guides/pypi-openvino-rt.md
@@ -1,7 +1,7 @@
 # Intel® Distribution of OpenVINO™ Toolkit Runtime Package
 Copyright © 2018-2021 Intel Corporation
 > **LEGAL NOTICE**: Your use of this software and any required dependent software (the
-“Software Package”) is subject to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf) for the Software Package, which may also include notices, disclaimers, or
+“Software Package”) is subject to the terms and conditions of the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html) for the Software Package, which may also include notices, disclaimers, or
 license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
 
 >Intel is committed to the respect of human rights and avoiding complicity in human rights abuses, a policy reflected in the [Intel Global Human Rights Principles](https://www.intel.com/content/www/us/en/policy/policy-human-rights.html). Accordingly, by accessing the Intel material on this platform you agree that you will not use the material in a product or application that causes or contributes to a violation of an internationally recognized human right.
@@ -19,7 +19,7 @@ The **runtime package** includes the following components installed by default:
 
 | Component                                                                                           | Description                                                                                                                                                                                                                                                                                                   |  
 |-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Inference Engine](https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_inference_engine_intro.html)               | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.                                                                                                                                                                |
+| [Inference Engine](https://docs.openvino.ai/latest/openvino_docs_IE_DG_inference_engine_intro.html)               | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.                                                                                                                                                                |
 
 ## System Requirements
 The complete list of supported hardware is available in the [Release Notes](https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html#inpage-nav-8).
@@ -29,11 +29,10 @@ The table below lists supported operating systems and Python* versions required
 | Supported Operating System                                   | [Python* Version (64-bit)](https://www.python.org/) |
 | :------------------------------------------------------------| :---------------------------------------------------|
 |   Ubuntu* 18.04 long-term support (LTS), 64-bit              | 3.6, 3.7, 3.8                                       |
-|   Ubuntu* 20.04 long-term support (LTS), 64-bit              | 3.6, 3.7, 3.8                                       |
+|   Ubuntu* 20.04 long-term support (LTS), 64-bit              | 3.6, 3.7, 3.8, 3.9                                  |
 |   Red Hat* Enterprise Linux* 8, 64-bit                       | 3.6, 3.8                                            |
-|   CentOS* 7, 64-bit                                          | 3.6, 3.7, 3.8                                       |
-|   macOS* 10.15.x versions                                    | 3.6, 3.7, 3.8                                       |
-|   Windows 10*, 64-bit                                        | 3.6, 3.7, 3.8                                       |
+|   macOS* 10.15.x versions                                    | 3.6, 3.7, 3.8, 3.9                                  |
+|   Windows 10*, 64-bit                                        | 3.6, 3.7, 3.8, 3.9                                  |
 
 > **NOTE**: This package can be installed on other versions of Linux and Windows OSes, but only the specific versions above are fully validated.
 
@@ -83,7 +82,7 @@ Run the command below: <br>
 
 Run the command below:
 ```sh
-python -c "from openvino.inference_engine import IECore"
+python -c "from openvino.runtime import Core"
 ```
    
 If installation was successful, you will not see any error messages (no console output).
@@ -104,6 +103,6 @@ sudo apt-get install libpython3.7
 ## Additional Resources
 
 - [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit)
-- [OpenVINO™ toolkit online documentation](https://docs.openvinotoolkit.org)
+- [OpenVINO™ toolkit online documentation](https://docs.openvino.ai)
 - [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
 

From f91999b1bc1b8e60d6b27f7cf89c9e678ff83816 Mon Sep 17 00:00:00 2001
From: Andrey Somsikov <andrey.somsikov@intel.com>
Date: Thu, 23 Dec 2021 18:14:27 +0300
Subject: [PATCH 130/160] Update setupvars.bat with CMAKE_BUILD_TYPE (#9327)

* Update setupvars.bat with CMAKE_BUILD_TYPE

 setupvars.bat paths set depends on CMAKE_BUILD_TYPE.
 RelWithDebInfo didn't work correctly.

* Check binaries path before patching setupvars

* Fix loosing semicolon in setupvars,bat
---
 scripts/CMakeLists.txt | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/scripts/CMakeLists.txt b/scripts/CMakeLists.txt
index ddb5d4950e7..28a0ac4dbde 100644
--- a/scripts/CMakeLists.txt
+++ b/scripts/CMakeLists.txt
@@ -31,14 +31,21 @@ ie_shellcheck_process(DIRECTORY "${OpenVINO_SOURCE_DIR}"
 ie_cpack_add_component(setupvars REQUIRED)
 
 if(UNIX)
-    install(PROGRAMS setupvars/setupvars.sh
-            DESTINATION .
-            COMPONENT setupvars)
+    set(_setupvars_file setupvars/setupvars.sh)
 elseif(WIN32)
-    install(PROGRAMS setupvars/setupvars.bat
-            DESTINATION .
-            COMPONENT setupvars)
+    set(_setupvars_file setupvars/setupvars.bat)
+    if (USE_BUILD_TYPE_SUBFOLDER AND CMAKE_BUILD_TYPE AND NOT CMAKE_BUILD_TYPE EQUAL "Debug")
+        # Patch primary configuration in setupvars.bat which is "Release" by default.
+        # Note setupvars secondary configuration is always "Debug".
+        file(READ "${_setupvars_file}" _setupvars_content)
+        string(REPLACE "Release" ${CMAKE_BUILD_TYPE} _setupvars_content "${_setupvars_content}")
+        set(_setupvars_file "${CMAKE_BINARY_DIR}/${_setupvars_file}")
+        file(WRITE "${_setupvars_file}" "${_setupvars_content}")
+    endif()
 endif()
+install(PROGRAMS "${_setupvars_file}"
+        DESTINATION .
+        COMPONENT setupvars)
 
 # install install_dependencies
 

From 9e91cf5c0879f4b6e5d2fd95be36c2d6f20a9188 Mon Sep 17 00:00:00 2001
From: Nikolay Tyukaev <nikolay.tyukaev@intel.com>
Date: Thu, 23 Dec 2021 18:15:09 +0300
Subject: [PATCH 131/160] fix doc tests (#9400)

---
 docs/scripts/tests/conftest.py | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/docs/scripts/tests/conftest.py b/docs/scripts/tests/conftest.py
index 9d3cb29d229..afb1da4a6a2 100644
--- a/docs/scripts/tests/conftest.py
+++ b/docs/scripts/tests/conftest.py
@@ -14,11 +14,10 @@ with errors can be skipped (--doxygen-skip) or excluded temporary
 Usage:
 pytest --doxygen doxygen.log --html doc-generation.html test_doc-generation.py
 """
-from inspect import getsourcefile
-from contextlib import contextmanager
-import os
+
+import re
+import copy
 import pytest
-from distutils.util import strtobool
 from utils.log import parse
 
 
@@ -105,7 +104,16 @@ def pytest_generate_tests(metafunc):
         exclude_links.remove('ovms')
 
     filtered_keys = filter(lambda line: not any([line.startswith(repo) for repo in exclude_links]), all_files)
-    files = {key: all_files[key] for key in filtered_keys}
+    files_with_errors = {key: all_files[key] for key in filtered_keys}
+    ref_pattern = "unable to resolve reference to '{}"
+    for file, errors in copy.deepcopy(files_with_errors).items():
+        for error in errors:
+            for ex_link in exclude_links:
+                if re.match(re.compile(ref_pattern.format(ex_link)), error):
+                    files_with_errors[file].remove(error)
+            if not len(errors):
+                files_with_errors.pop(file)
+
     # read mute lists
     marks = dict()
     marks.update(
@@ -120,6 +128,6 @@ def pytest_generate_tests(metafunc):
         metafunc.parametrize(
             'doxygen_errors', [
                 pytest.param(errors, marks=marks[file])
-                if file in marks else errors for file, errors in files.items()
+                if file in marks else errors for file, errors in files_with_errors.items()
             ],
-            ids=list(files.keys()))
+            ids=list(files_with_errors.keys()))

From 65c0f2daa729a11d611f8bd477e75378bde8d648 Mon Sep 17 00:00:00 2001
From: Evgenya Stepyreva <evgenya.stepyreva@intel.com>
Date: Thu, 23 Dec 2021 19:06:44 +0300
Subject: [PATCH 132/160] Update Convert_Mask_RCNN.md (#9406)

---
 .../convert_model/onnx_specific/Convert_Mask_RCNN.md          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md b/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md
index 92ac1a9a22f..fb613bacf4a 100644
--- a/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md
+++ b/docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md
@@ -1,8 +1,8 @@
 # Convert ONNX* Mask R-CNN Model to the Intermediate Representation {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Mask_RCNN}
 
-These instructions are applicable only to the [Mask R-CNN model](https://onnxzoo.blob.core.windows.net/models/opset_10/mask_rcnn/mask_rcnn_R_50_FPN_1x.onnx) converted to the ONNX* file format from the [facebookresearch/maskrcnn-benchmark model](https://github.com/facebookresearch/maskrcnn-benchmark).
+These instructions are applicable only to the Mask R-CNN model converted to the ONNX* file format from the [facebookresearch/maskrcnn-benchmark model](https://github.com/facebookresearch/maskrcnn-benchmark).
 
-**Step 1**. Download the [pre-trained model file](https://onnxzoo.blob.core.windows.net/models/opset_10/mask_rcnn/mask_rcnn_R_50_FPN_1x.onnx).
+**Step 1**. Download the pre-trained model file from [onnx/models](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/mask-rcnn) (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
 
 **Step 2**. To generate the Intermediate Representation (IR) of the model, change your current working directory to the Model Optimizer installation directory and run the Model Optimizer with the following parameters:
 ```sh

From 7bfbb46d73ddfd85b74066538244e6b68bcd06a2 Mon Sep 17 00:00:00 2001
From: Mikhail Nosov <mikhail.nosov@intel.com>
Date: Fri, 24 Dec 2021 02:56:45 +0300
Subject: [PATCH 133/160] [FE API]: Shared object (SO) holder to frontend's
 library for FrontEnd/InputModel/ov::Model  (#9308)

* Squashed commit of previous work

* Fix mock tests

* clang

* Fix rebase errors

* remove unnecessary changes

* One more finding

* Copy ov::Model runtime info as well

* Fix review comments

* Commit missing file

* Copy m_shared_object when cloning model

* removed copy_shared_objects and use clone_model(model, NodeMap) as a friend for ov::Model

* Added OPENVINO_API to forward declaration

* add OPENVINO_API to friend function declaration
---
 .../ov_mock_py_frontend/mock_py_frontend.cpp  |   4 +
 .../ov_mock_py_frontend/mock_py_frontend.hpp  |  33 ++--
 .../pyngraph_mock_frontend_api.cpp            |  39 +----
 .../test_frontend/test_frontendmanager.py     | 143 ++++++++++--------
 src/core/include/openvino/core/model.hpp      |  14 ++
 src/core/src/graph_util.cpp                   |   1 +
 src/core/tests/frontend/frontend_manager.cpp  |  54 +++++++
 src/core/tests/frontend/mock_frontend.cpp     |  21 ++-
 .../include/openvino/frontend/exception.hpp   |  10 ++
 .../include/openvino/frontend/frontend.hpp    |  30 +++-
 .../include/openvino/frontend/input_model.hpp |  16 +-
 src/frontends/common/src/frontend.cpp         |  67 ++++++--
 src/frontends/common/src/input_model.cpp      |  90 +++++++----
 src/frontends/common/src/manager.cpp          |  22 ++-
 .../ov_mock_mo_frontend/mock_mo_frontend.hpp  |  17 ++-
 15 files changed, 395 insertions(+), 166 deletions(-)

diff --git a/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.cpp b/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.cpp
index fd48b138bff..189c844d526 100644
--- a/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.cpp
+++ b/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.cpp
@@ -7,6 +7,10 @@
 #include "openvino/frontend/manager.hpp"
 #include "openvino/frontend/visibility.hpp"
 
+FeStat FrontEndMockPy::m_stat = {};
+ModelStat InputModelMockPy::m_stat = {};
+PlaceStat PlaceMockPy::m_stat = {};
+
 using namespace ngraph;
 using namespace ov::frontend;
 
diff --git a/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.hpp b/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.hpp
index a9598d9da9a..e3fbd5b7eba 100644
--- a/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.hpp
+++ b/src/bindings/python/tests/mock/ov_mock_py_frontend/mock_py_frontend.hpp
@@ -96,7 +96,7 @@ struct MOCK_API PlaceStat {
 };
 
 class MOCK_API PlaceMockPy : public Place {
-    mutable PlaceStat m_stat;
+    static PlaceStat m_stat;
 
 public:
     std::vector<std::string> get_names() const override {
@@ -296,9 +296,13 @@ public:
     }
 
     //---------------Stat--------------------
-    PlaceStat get_stat() const {
+    static PlaceStat get_stat() {
         return m_stat;
     }
+
+    static void clear_stat() {
+        m_stat = {};
+    }
 };
 
 ////////////////////////////////
@@ -330,7 +334,7 @@ struct MOCK_API ModelStat {
 
     // Arguments tracking
     std::string m_lastArgString;
-    int m_lastArgInt;
+    int m_lastArgInt = -1;
     Place::Ptr m_lastArgPlace = nullptr;
     std::vector<Place::Ptr> m_lastArgInputPlaces;
     std::vector<Place::Ptr> m_lastArgOutputPlaces;
@@ -430,7 +434,7 @@ struct MOCK_API ModelStat {
 };
 
 class MOCK_API InputModelMockPy : public InputModel {
-    mutable ModelStat m_stat;
+    static ModelStat m_stat;
 
 public:
     std::vector<Place::Ptr> get_inputs() const override {
@@ -565,9 +569,13 @@ public:
     }
 
     //---------------Stat--------------------
-    ModelStat get_stat() const {
+    static ModelStat get_stat() {
         return m_stat;
     }
+
+    static void clear_stat() {
+        m_stat = {};
+    }
 };
 
 /////////////////////////////////////////////////////////
@@ -603,26 +611,27 @@ struct MOCK_API FeStat {
     int get_name() const {
         return m_get_name;
     }
+
     int supported() const {
         return m_supported;
     }
 };
 
 class MOCK_API FrontEndMockPy : public FrontEnd {
-    mutable FeStat m_stat;
+    static FeStat m_stat;
 
 public:
-    FrontEndMockPy() {}
+    FrontEndMockPy() = default;
 
     InputModel::Ptr load_impl(const std::vector<ov::Any>& params) const override {
-        if (params.size() > 0 && params[0].is<std::string>())
+        if (!params.empty() && params[0].is<std::string>())
             m_stat.m_load_paths.push_back(params[0].as<std::string>());
         return std::make_shared<InputModelMockPy>();
     }
 
     bool supported_impl(const std::vector<ov::Any>& params) const override {
         m_stat.m_supported++;
-        if (params.size() > 0 && params[0].is<std::string>()) {
+        if (!params.empty() && params[0].is<std::string>()) {
             auto path = params[0].as<std::string>();
             if (path.find(".test_mock_py_mdl") != std::string::npos) {
                 return true;
@@ -659,7 +668,11 @@ public:
         return "mock_py";
     }
 
-    FeStat get_stat() const {
+    static FeStat get_stat() {
         return m_stat;
     }
+
+    static void clear_stat() {
+        m_stat = {};
+    }
 };
diff --git a/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp b/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
index 3d71be89673..6021a9d5093 100644
--- a/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
+++ b/src/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
@@ -12,17 +12,8 @@ using namespace ngraph;
 using namespace ov::frontend;
 
 static void register_mock_frontend_stat(py::module m) {
-    m.def(
-        "get_fe_stat",
-        [](const std::shared_ptr<FrontEnd>& fe) {
-            std::shared_ptr<FrontEndMockPy> ptr = std::dynamic_pointer_cast<FrontEndMockPy>(fe);
-            if (ptr) {
-                auto stat = ptr->get_stat();
-                return stat;
-            }
-            return FeStat();
-        },
-        py::arg("frontend"));
+    m.def("get_fe_stat", &FrontEndMockPy::get_stat);
+    m.def("clear_fe_stat", &FrontEndMockPy::clear_stat);
 
     py::class_<FeStat> feStat(m, "FeStat", py::dynamic_attr());
     feStat.def_property_readonly("load_paths", &FeStat::load_paths);
@@ -36,17 +27,8 @@ static void register_mock_frontend_stat(py::module m) {
 }
 
 static void register_mock_model_stat(py::module m) {
-    m.def(
-        "get_mdl_stat",
-        [](const std::shared_ptr<InputModel>& mdl) {
-            std::shared_ptr<InputModelMockPy> ptr = std::dynamic_pointer_cast<InputModelMockPy>(mdl);
-            if (ptr) {
-                auto stat = ptr->get_stat();
-                return stat;
-            }
-            return ModelStat();
-        },
-        py::arg("model"));
+    m.def("get_mdl_stat", &InputModelMockPy::get_stat);
+    m.def("clear_mdl_stat", &InputModelMockPy::clear_stat);
 
     py::class_<ModelStat> mdlStat(m, "ModelStat", py::dynamic_attr());
     mdlStat.def_property_readonly("get_inputs", &ModelStat::get_inputs);
@@ -86,17 +68,8 @@ static void register_mock_model_stat(py::module m) {
 }
 
 static void register_mock_place_stat(py::module m) {
-    m.def(
-        "get_place_stat",
-        [](const Place::Ptr& fe) {
-            std::shared_ptr<PlaceMockPy> ptr = std::dynamic_pointer_cast<PlaceMockPy>(fe);
-            if (ptr) {
-                auto stat = ptr->get_stat();
-                return stat;
-            }
-            return PlaceStat();
-        },
-        py::arg("place"));
+    m.def("get_place_stat", &PlaceMockPy::get_stat);
+    m.def("clear_place_stat", &PlaceMockPy::clear_stat);
 
     py::class_<PlaceStat> placeStat(m, "PlaceStat", py::dynamic_attr());
 
diff --git a/src/bindings/python/tests/test_frontend/test_frontendmanager.py b/src/bindings/python/tests/test_frontend/test_frontendmanager.py
index 6a5dee350d0..7beb4531411 100644
--- a/src/bindings/python/tests/test_frontend/test_frontendmanager.py
+++ b/src/bindings/python/tests/test_frontend/test_frontendmanager.py
@@ -13,7 +13,8 @@ import pytest
 
 mock_available = True
 try:
-    from pybind_mock_frontend import get_fe_stat, get_mdl_stat, get_place_stat
+    from pybind_mock_frontend import get_fe_stat, clear_fe_stat,\
+        get_mdl_stat, clear_mdl_stat, get_place_stat, clear_place_stat
 except Exception:
     print("No mock frontend available")
     mock_available = False
@@ -26,6 +27,12 @@ mock_needed = pytest.mark.skipif(not mock_available,
                                  reason="mock fe is not available")
 
 
+def clear_all_stat():
+    clear_fe_stat()
+    clear_mdl_stat()
+    clear_place_stat()
+
+
 # ---------- FrontEnd tests ---------------
 def test_pickle():
     pickle.dumps(fem)
@@ -44,75 +51,82 @@ def test_load_by_unknown_framework():
 
 @mock_needed
 def test_load():
+    clear_all_stat()
     fe = fem.load_by_framework(framework="mock_py")
     assert fe is not None
     model = fe.load("abc.bin")
     assert model is not None
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert "abc.bin" in stat.load_paths
 
 
 @mock_needed
 def test_load_by_model():
+    clear_all_stat()
     fe = fem.load_by_model(model_path="abc.test_mock_py_mdl")
     assert fe is not None
     assert fe.get_name() == "mock_py"
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert stat.get_name == 1
     assert stat.supported == 1
 
 
 @mock_needed
 def test_convert_model():
+    clear_all_stat()
     fe = fem.load_by_framework(framework="mock_py")
     assert fe is not None
     model = fe.load(path="")
     func = fe.convert(model=model)
     assert func is not None
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert stat.convert_model == 1
 
 
 @mock_needed
 def test_convert_partially():
+    clear_all_stat()
     fe = fem.load_by_framework(framework="mock_py")
     assert fe is not None
     model = fe.load(path="")
     func = fe.convert_partially(model=model)
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert stat.convert_partially == 1
     fe.convert(function=func)
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert stat.convert == 1
 
 
 @mock_needed
 def test_decode_and_normalize():
+    clear_all_stat()
     fe = fem.load_by_framework(framework="mock_py")
     assert fe is not None
     model = fe.load(path="")
     func = fe.decode(model=model)
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert stat.decode == 1
     fe.normalize(function=func)
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert stat.normalize == 1
     assert stat.decode == 1
 
 
 @mock_needed
 def test_get_name():
+    clear_all_stat()
     fe = fem.load_by_framework(framework="mock_py")
     assert fe is not None
     name = fe.get_name()
     assert name == "mock_py"
-    stat = get_fe_stat(fe)
+    stat = get_fe_stat()
     assert stat.get_name == 1
 
 
 # --------InputModel tests-----------------
 @mock_needed
 def init_model():
+    clear_all_stat()
     fe = fem.load_by_framework(framework="mock_py")
     model = fe.load(path="")
     return model
@@ -123,7 +137,7 @@ def test_model_get_inputs():
     model = init_model()
     for i in range(1, 10):
         model.get_inputs()
-        stat = get_mdl_stat(model)
+        stat = get_mdl_stat()
         assert stat.get_inputs == i
 
 
@@ -132,7 +146,7 @@ def test_model_get_outputs():
     model = init_model()
     for i in range(1, 10):
         model.get_outputs()
-        stat = get_mdl_stat(model)
+        stat = get_mdl_stat()
         assert stat.get_outputs == i
 
 
@@ -142,7 +156,7 @@ def test_model_get_place_by_tensor_name():
     for i in range(1, 10):
         name = str(i)
         model.get_place_by_tensor_name(tensor_name=name)
-        stat = get_mdl_stat(model)
+        stat = get_mdl_stat()
         assert stat.get_place_by_tensor_name == i
         assert stat.lastArgString == name
 
@@ -153,7 +167,7 @@ def test_model_get_place_by_operation_name():
     for i in range(1, 10):
         name = str(i)
         model.get_place_by_operation_name(operation_name=name)
-        stat = get_mdl_stat(model)
+        stat = get_mdl_stat()
         assert stat.get_place_by_operation_name == i
         assert stat.lastArgString == name
 
@@ -164,7 +178,7 @@ def test_model_get_place_by_operation_name_and_input_port():
     for i in range(1, 10):
         name = str(i)
         model.get_place_by_operation_name_and_input_port(operation_name=name, input_port_index=i * 2)
-        stat = get_mdl_stat(model)
+        stat = get_mdl_stat()
         assert stat.get_place_by_operation_and_input_port == i
         assert stat.lastArgString == name
         assert stat.lastArgInt == i * 2
@@ -176,7 +190,7 @@ def test_model_get_place_by_operation_name_and_output_port():
     for i in range(1, 10):
         name = str(i)
         model.get_place_by_operation_name_and_output_port(operation_name=name, output_port_index=i * 2)
-        stat = get_mdl_stat(model)
+        stat = get_mdl_stat()
         assert stat.get_place_by_operation_and_output_port == i
         assert stat.lastArgString == name
         assert stat.lastArgInt == i * 2
@@ -187,7 +201,7 @@ def test_model_set_name_for_tensor():
     model = init_model()
     place = model.get_place_by_tensor_name(tensor_name="")
     model.set_name_for_tensor(tensor=place, new_name="1234")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.set_name_for_tensor == 1
     assert stat.lastArgString == "1234"
     assert stat.lastArgPlace == place
@@ -198,7 +212,7 @@ def test_model_add_name_for_tensor():
     model = init_model()
     place = model.get_place_by_tensor_name(tensor_name="")
     model.add_name_for_tensor(tensor=place, new_name="1234")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.add_name_for_tensor == 1
     assert stat.lastArgString == "1234"
     assert stat.lastArgPlace == place
@@ -209,7 +223,7 @@ def test_model_set_name_for_operation():
     model = init_model()
     place = model.get_place_by_operation_name(operation_name="")
     model.set_name_for_operation(operation=place, new_name="1111")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.set_name_for_operation == 1
     assert stat.lastArgString == "1111"
     assert stat.lastArgPlace == place
@@ -219,7 +233,7 @@ def test_model_set_name_for_operation():
 def test_model_free_name_for_tensor():
     model = init_model()
     model.free_name_for_tensor(name="2222")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.free_name_for_tensor == 1
     assert stat.lastArgString == "2222"
 
@@ -228,7 +242,7 @@ def test_model_free_name_for_tensor():
 def test_model_free_name_for_operation():
     model = init_model()
     model.free_name_for_operation(name="3333")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.free_name_for_operation == 1
     assert stat.lastArgString == "3333"
 
@@ -238,7 +252,7 @@ def test_model_set_name_for_dimension():
     model = init_model()
     place = model.get_place_by_operation_name(operation_name="")
     model.set_name_for_dimension(place=place, dim_index=123, dim_name="4444")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.set_name_for_dimension == 1
     assert stat.lastArgString == "4444"
     assert stat.lastArgInt == 123
@@ -250,12 +264,12 @@ def test_model_cut_and_add_new_input():
     model = init_model()
     place = model.get_place_by_operation_name("")
     model.cut_and_add_new_input(place=place, new_name="5555")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.cut_and_add_new_input == 1
     assert stat.lastArgString == "5555"
     assert stat.lastArgPlace == place
     model.cut_and_add_new_input(place=place)
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.cut_and_add_new_input == 2
     assert stat.lastArgString == ""
     assert stat.lastArgPlace == place
@@ -266,12 +280,12 @@ def test_model_cut_and_add_new_output():
     model = init_model()
     place = model.get_place_by_operation_name("")
     model.cut_and_add_new_output(place=place, new_name="5555")
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.cut_and_add_new_output == 1
     assert stat.lastArgString == "5555"
     assert stat.lastArgPlace == place
     model.cut_and_add_new_output(place=place)
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.cut_and_add_new_output == 2
     assert stat.lastArgString == ""
     assert stat.lastArgPlace == place
@@ -283,7 +297,7 @@ def test_model_add_output():
     place = model.get_place_by_operation_name("")
     place2 = model.add_output(place=place)
     assert place2 is not None
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.add_output == 1
     assert stat.lastArgPlace == place
 
@@ -293,7 +307,7 @@ def test_model_remove_output():
     model = init_model()
     place = model.get_place_by_operation_name("")
     model.remove_output(place=place)
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.remove_output == 1
     assert stat.lastArgPlace == place
 
@@ -304,7 +318,7 @@ def test_model_set_partial_shape():
     place = model.get_place_by_tensor_name(tensor_name="")
     test_shape = PartialShape([1, 2, 3, 4])
     model.set_partial_shape(place=place, shape=test_shape)
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.set_partial_shape == 1
     assert stat.lastArgPlace == place
     assert stat.lastArgPartialShape == test_shape
@@ -316,7 +330,7 @@ def test_model_get_partial_shape():
     place = model.get_place_by_tensor_name(tensor_name="")
     shape = model.get_partial_shape(place=place)
     assert shape is not None
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.get_partial_shape == 1
     assert stat.lastArgPlace == place
 
@@ -327,7 +341,7 @@ def test_model_override_all_inputs():
     place1 = model.get_place_by_tensor_name(tensor_name="p1")
     place2 = model.get_place_by_tensor_name(tensor_name="p2")
     model.override_all_inputs(inputs=[place1, place2])
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.override_all_inputs == 1
     assert len(stat.lastArgInputPlaces) == 2
     assert stat.lastArgInputPlaces[0] == place1
@@ -340,7 +354,7 @@ def test_model_override_all_outputs():
     place1 = model.get_place_by_tensor_name(tensor_name="p1")
     place2 = model.get_place_by_tensor_name(tensor_name="p2")
     model.override_all_outputs(outputs=[place1, place2])
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.override_all_outputs == 1
     assert len(stat.lastArgOutputPlaces) == 2
     assert stat.lastArgOutputPlaces[0] == place1
@@ -355,7 +369,7 @@ def test_model_extract_subgraph():
     place3 = model.get_place_by_tensor_name(tensor_name="p3")
     place4 = model.get_place_by_tensor_name(tensor_name="p4")
     model.extract_subgraph(inputs=[place1, place2], outputs=[place3, place4])
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.extract_subgraph == 1
     assert len(stat.lastArgInputPlaces) == 2
     assert stat.lastArgInputPlaces[0] == place1
@@ -370,7 +384,7 @@ def test_model_set_element_type():
     model = init_model()
     place = model.get_place_by_tensor_name(tensor_name="")
     model.set_element_type(place=place, type=get_element_type(np.int32))
-    stat = get_mdl_stat(model)
+    stat = get_mdl_stat()
     assert stat.set_element_type == 1
     assert stat.lastArgPlace == place
     assert stat.lastArgElementType == get_element_type(np.int32)
@@ -379,6 +393,7 @@ def test_model_set_element_type():
 # ----------- Place test ------------
 @mock_needed
 def init_place():
+    clear_all_stat()
     fe = fem.load_by_framework(framework="mock_py")
     model = fe.load(path="")
     place = model.get_place_by_tensor_name(tensor_name="")
@@ -389,7 +404,7 @@ def init_place():
 def test_place_is_input():
     _, place = init_place()
     assert place.is_input() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.is_input == 1
 
 
@@ -397,7 +412,7 @@ def test_place_is_input():
 def test_place_is_output():
     _, place = init_place()
     assert place.is_output() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.is_output == 1
 
 
@@ -405,7 +420,7 @@ def test_place_is_output():
 def test_place_get_names():
     _, place = init_place()
     assert place.get_names() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_names == 1
 
 
@@ -414,7 +429,7 @@ def test_place_is_equal():
     model, place = init_place()
     place2 = model.get_place_by_tensor_name("2")
     assert place.is_equal(other=place2) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.is_equal == 1
     assert stat.lastArgPlace == place2
 
@@ -424,7 +439,7 @@ def test_place_is_equal_data():
     model, place = init_place()
     place2 = model.get_place_by_tensor_name("2")
     assert place.is_equal_data(other=place2) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.is_equal_data == 1
     assert stat.lastArgPlace == place2
 
@@ -433,20 +448,20 @@ def test_place_is_equal_data():
 def test_place_get_consuming_operations():
     _, place = init_place()
     assert place.get_consuming_operations(output_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_consuming_operations == 1
     assert stat.lastArgInt == 22
     assert place.get_consuming_operations() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_consuming_operations == 2
     assert stat.lastArgInt == -1
     assert place.get_consuming_operations(output_name="2") is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_consuming_operations == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
     assert place.get_consuming_operations(output_name="3", output_port_index=33) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_consuming_operations == 4
     assert stat.lastArgInt == 33
     assert stat.lastArgString == "3"
@@ -456,20 +471,20 @@ def test_place_get_consuming_operations():
 def test_place_get_target_tensor():
     _, place = init_place()
     assert place.get_target_tensor(output_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_target_tensor == 1
     assert stat.lastArgInt == 22
     assert place.get_target_tensor() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_target_tensor == 2
     assert stat.lastArgInt == -1
     assert place.get_target_tensor(output_name="2") is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_target_tensor == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
     assert place.get_target_tensor(output_name="3", output_port_index=33) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_target_tensor == 4
     assert stat.lastArgInt == 33
     assert stat.lastArgString == "3"
@@ -479,20 +494,20 @@ def test_place_get_target_tensor():
 def test_place_get_producing_operation():
     _, place = init_place()
     assert place.get_producing_operation(input_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_producing_operation == 1
     assert stat.lastArgInt == 22
     assert place.get_producing_operation() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_producing_operation == 2
     assert stat.lastArgInt == -1
     assert place.get_producing_operation(input_name="2") is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_producing_operation == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
     assert place.get_producing_operation(input_name="3", input_port_index=33) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_producing_operation == 4
     assert stat.lastArgInt == 33
     assert stat.lastArgString == "3"
@@ -502,7 +517,7 @@ def test_place_get_producing_operation():
 def test_place_get_producing_port():
     _, place = init_place()
     assert place.get_producing_port() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_producing_port == 1
 
 
@@ -510,11 +525,11 @@ def test_place_get_producing_port():
 def test_place_get_input_port():
     _, place = init_place()
     assert place.get_input_port() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_input_port == 1
     assert stat.lastArgInt == -1
     assert place.get_input_port(input_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_input_port == 2
     assert stat.lastArgInt == 22
 
@@ -523,12 +538,12 @@ def test_place_get_input_port():
 def test_place_get_input_port2():
     _, place = init_place()
     assert place.get_input_port(input_name="abc") is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_input_port == 1
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "abc"
     assert place.get_input_port(input_name="abcd", input_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_input_port == 2
     assert stat.lastArgInt == 22
     assert stat.lastArgString == "abcd"
@@ -538,11 +553,11 @@ def test_place_get_input_port2():
 def test_place_get_output_port():
     _, place = init_place()
     assert place.get_output_port() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_output_port == 1
     assert stat.lastArgInt == -1
     assert place.get_output_port(output_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_output_port == 2
     assert stat.lastArgInt == 22
 
@@ -551,12 +566,12 @@ def test_place_get_output_port():
 def test_place_get_output_port2():
     _, place = init_place()
     assert place.get_output_port(output_name="abc") is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_output_port == 1
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "abc"
     assert place.get_output_port(output_name="abcd", output_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_output_port == 2
     assert stat.lastArgInt == 22
     assert stat.lastArgString == "abcd"
@@ -566,7 +581,7 @@ def test_place_get_output_port2():
 def test_place_get_consuming_ports():
     _, place = init_place()
     assert place.get_consuming_ports() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_consuming_ports == 1
 
 
@@ -574,20 +589,20 @@ def test_place_get_consuming_ports():
 def test_place_get_source_tensor():
     _, place = init_place()
     assert place.get_source_tensor() is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_source_tensor == 1
     assert stat.lastArgInt == -1
     assert place.get_source_tensor(input_port_index=22) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_source_tensor == 2
     assert stat.lastArgInt == 22
     assert place.get_source_tensor(input_name="2") is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_source_tensor == 3
     assert stat.lastArgInt == -1
     assert stat.lastArgString == "2"
     assert place.get_source_tensor(input_name="3", input_port_index=33) is not None
-    stat = get_place_stat(place)
+    stat = get_place_stat()
     assert stat.get_source_tensor == 4
     assert stat.lastArgInt == 33
     assert stat.lastArgString == "3"
diff --git a/src/core/include/openvino/core/model.hpp b/src/core/include/openvino/core/model.hpp
index 1dcaabd5507..bc2c51e754a 100644
--- a/src/core/include/openvino/core/model.hpp
+++ b/src/core/include/openvino/core/model.hpp
@@ -24,9 +24,23 @@
 #include "openvino/runtime/tensor.hpp"
 
 namespace ov {
+class Model;
+
+OPENVINO_API
+std::shared_ptr<Model> clone_model(const Model& func, std::unordered_map<Node*, std::shared_ptr<Node>>& node_map);
+
+namespace frontend {
+class FrontEnd;
+}
+
 class ModelAccessor;
 /// A user-defined function.
 class OPENVINO_API Model : public std::enable_shared_from_this<Model> {
+    friend class frontend::FrontEnd;
+    friend OPENVINO_API std::shared_ptr<Model> clone_model(const Model& func,
+                                                           std::unordered_map<Node*, std::shared_ptr<Node>>& node_map);
+    std::shared_ptr<void> m_shared_object;  // Frontend plugin shared object handle.
+
 public:
     static const ::ov::DiscreteTypeInfo& get_type_info_static() {
         static const ::ov::DiscreteTypeInfo type_info{"Function", 0};
diff --git a/src/core/src/graph_util.cpp b/src/core/src/graph_util.cpp
index b2e5b494bb6..a5e05553238 100644
--- a/src/core/src/graph_util.cpp
+++ b/src/core/src/graph_util.cpp
@@ -356,6 +356,7 @@ std::shared_ptr<ov::Model> ov::clone_model(const ov::Model& func, ngraph::NodeMa
     auto result =
         std::make_shared<ov::Model>(cloned_results, cloned_sinks, cloned_params, cloned_vars, func.get_friendly_name());
     result->get_rt_info() = func.get_rt_info();
+    result->m_shared_object = func.m_shared_object;
     return result;
 }
 
diff --git a/src/core/tests/frontend/frontend_manager.cpp b/src/core/tests/frontend/frontend_manager.cpp
index bbfa0c8a3e0..aeaad79a2de 100644
--- a/src/core/tests/frontend/frontend_manager.cpp
+++ b/src/core/tests/frontend/frontend_manager.cpp
@@ -100,6 +100,60 @@ TEST(FrontEndManagerTest, testMockPluginFrontEnd) {
     NGRAPH_SUPPRESS_DEPRECATED_END
 }
 
+TEST(FrontEndManagerTest, testFEMDestroy_FrontEndHolder) {
+    FrontEnd::Ptr fe;
+    {
+        FrontEndManager fem;
+        auto frontends = fem.get_available_front_ends();
+        ASSERT_NE(std::find(frontends.begin(), frontends.end(), "mock1"), frontends.end());
+        ASSERT_NO_THROW(fe = fem.load_by_framework("mock1"));
+    }
+    ASSERT_EQ(fe->get_name(), "mock1");
+}
+
+TEST(FrontEndManagerTest, testFEMDestroy_InputModelHolder) {
+    InputModel::Ptr input_model;
+    {
+        std::shared_ptr<ov::Model> model;
+        FrontEndManager fem;
+        auto fe = fem.load_by_framework("mock1");
+        input_model = fe->load("test");
+        model = fe->convert(input_model);
+        ASSERT_EQ(model->get_friendly_name(), "mock1_model");
+    }
+    ASSERT_TRUE(input_model);
+}
+
+TEST(FrontEndManagerTest, testFEMDestroy_OVModelHolder) {
+    std::shared_ptr<ov::Model> model;
+    {
+        FrontEndManager fem;
+        auto fe = fem.load_by_framework("mock1");
+        auto input_model = fe->load("test");
+        model = fe->convert(input_model);
+        ASSERT_EQ(model->get_friendly_name(), "mock1_model");
+        ASSERT_TRUE(model->get_rt_info().count("mock_test"));
+        ASSERT_EQ(model->get_rt_info()["mock_test"].as<std::string>(), std::string(1024, 't'));
+    }
+    ASSERT_EQ(model->get_friendly_name(), "mock1_model");
+}
+
+TEST(FrontEndManagerTest, testFEMDestroy_OVModelHolder_Clone) {
+    std::shared_ptr<ov::Model> model_clone;
+    {
+        FrontEndManager fem;
+        auto fe = fem.load_by_framework("mock1");
+        auto input_model = fe->load("test");
+        auto model = fe->convert(input_model);
+        ASSERT_EQ(model->get_friendly_name(), "mock1_model");
+        ASSERT_TRUE(model->get_rt_info().count("mock_test"));
+        ASSERT_EQ(model->get_rt_info()["mock_test"].as<std::string>(), std::string(1024, 't'));
+        model_clone = ov::clone_model(*model);
+    }
+    ASSERT_EQ(model_clone->get_rt_info()["mock_test"].as<std::string>(), std::string(1024, 't'));
+    ASSERT_EQ(model_clone->get_friendly_name(), "mock1_model");
+}
+
 TEST(FrontEndManagerTest, testDefaultFrontEnd) {
     FrontEndManager fem;
     FrontEnd::Ptr fe;
diff --git a/src/core/tests/frontend/mock_frontend.cpp b/src/core/tests/frontend/mock_frontend.cpp
index eccd09e91c8..fb3d79485b0 100644
--- a/src/core/tests/frontend/mock_frontend.cpp
+++ b/src/core/tests/frontend/mock_frontend.cpp
@@ -5,6 +5,7 @@
 #include "ngraph/visibility.hpp"
 #include "openvino/frontend/manager.hpp"
 #include "openvino/frontend/visibility.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 // Defined if we are building the plugin DLL (instead of using it)
 #ifdef ov_mock1_frontend_EXPORTS
@@ -16,11 +17,29 @@
 using namespace ngraph;
 using namespace ov::frontend;
 
+class InputModelMock : public InputModel {};
+
 class FrontEndMock : public FrontEnd {
 public:
     std::string get_name() const override {
         return "mock1";
     }
+
+    InputModel::Ptr load_impl(const std::vector<ov::Any>& variants) const override {
+        return std::make_shared<InputModelMock>();
+    }
+
+    std::shared_ptr<ov::Model> convert(const InputModel::Ptr& model) const override {
+        auto shape = Shape{1, 2, 300, 300};
+        auto param = std::make_shared<ov::opset8::Parameter>(ov::element::f32, shape);
+        std::vector<float> data(ov::shape_size(shape), 1.f);
+        auto constant = ov::opset8::Constant::create(ov::element::f32, shape, data);
+        auto op = std::make_shared<ov::opset8::Add>(param, constant);
+        auto res = std::make_shared<ov::opset8::Result>(op);
+        auto ov_model = std::make_shared<ov::Model>(ResultVector({res}), ParameterVector({param}), "mock1_model");
+        ov_model->get_rt_info()["mock_test"] = std::string(1024, 't');
+        return ov_model;
+    }
 };
 
 extern "C" MOCK_API FrontEndVersion GetAPIVersion() {
@@ -28,7 +47,7 @@ extern "C" MOCK_API FrontEndVersion GetAPIVersion() {
 }
 
 extern "C" MOCK_API void* GetFrontEndData() {
-    FrontEndPluginInfo* res = new FrontEndPluginInfo();
+    auto* res = new FrontEndPluginInfo();
     res->m_name = "mock1";
     res->m_creator = []() {
         return std::make_shared<FrontEndMock>();
diff --git a/src/frontends/common/include/openvino/frontend/exception.hpp b/src/frontends/common/include/openvino/frontend/exception.hpp
index 1887e750d5f..8a7d8ba4e88 100644
--- a/src/frontends/common/include/openvino/frontend/exception.hpp
+++ b/src/frontends/common/include/openvino/frontend/exception.hpp
@@ -80,6 +80,16 @@ public:
                            false,                                 \
                            #NAME " is not implemented for this FrontEnd class")
 
+/// \brief Assert macro.
+/// \param COND Condition. If 'false', throws 'NotImplementedFailure'
+/// \param NAME Name of the function that is not implemented
+/// \throws ::ov::frontend::NotImplementedFailure
+#define FRONT_END_CHECK_IMPLEMENTED(COND, NAME)                   \
+    OPENVINO_ASSERT_HELPER(::ov::frontend::NotImplementedFailure, \
+                           "",                                    \
+                           (COND),                                \
+                           #NAME " is not implemented for this FrontEnd class")
+
 /// \brief Assert macro.
 /// \param MSG Error message
 /// \throws ::ov::frontend::GeneralFailure
diff --git a/src/frontends/common/include/openvino/frontend/frontend.hpp b/src/frontends/common/include/openvino/frontend/frontend.hpp
index 4005f711ca8..796a5a5fd9c 100644
--- a/src/frontends/common/include/openvino/frontend/frontend.hpp
+++ b/src/frontends/common/include/openvino/frontend/frontend.hpp
@@ -20,12 +20,26 @@ namespace frontend {
 /// \brief An interface for identifying a frontend for a particular framework.
 /// Provides an ability to load and convert of input model
 class FRONTEND_API FrontEnd {
-public:
-    typedef std::shared_ptr<FrontEnd> Ptr;
+    friend class FrontEndManager;
 
+    std::shared_ptr<void> m_shared_object = {};  // Library handle
+    std::shared_ptr<FrontEnd> m_actual = {};
+
+public:
+    using Ptr = std::shared_ptr<FrontEnd>;
+
+    /// \brief Default constructor
     FrontEnd();
 
-    virtual ~FrontEnd() = 0;
+    FrontEnd(const FrontEnd&) = delete;
+
+    FrontEnd(FrontEnd&&) = delete;
+
+    FrontEnd& operator=(const FrontEnd&) = delete;
+
+    FrontEnd& operator=(FrontEnd&&) = delete;
+
+    virtual ~FrontEnd();
 
     /// \brief Validates if FrontEnd can recognize model with parameters specified.
     /// Same parameters should be used to load model.
@@ -93,17 +107,21 @@ public:
     /// \brief Register base extension in the FrontEnd
     /// \param extension base extension
     virtual void add_extension(const std::shared_ptr<ov::Extension>& extension);
+
     /// \brief Register base extensions in the FrontEnd
     /// \param extensions vector of extensions
     void add_extension(const std::vector<std::shared_ptr<ov::Extension>>& extensions);
+
     /// \brief Registers extension
     /// \param library_path path to library with ov::Extension
     void add_extension(const std::string& library_path);
 
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
+
     /// \brief Registers extension
     /// \param library_path path to library with ov::Extension
     void add_extension(const std::wstring& library_path);
+
 #endif
 
     /// @brief Registers extension
@@ -113,6 +131,7 @@ public:
         std::shared_ptr<ov::Extension> ext = std::make_shared<T>(extension);
         add_extension(ext);
     }
+
     /// @brief Registers extensions
     /// @param extension Extension class which is inherited from ov::Extension class
     template <class T,
@@ -126,7 +145,12 @@ public:
 
 protected:
     virtual bool supported_impl(const std::vector<ov::Any>& variants) const;
+
     virtual InputModel::Ptr load_impl(const std::vector<ov::Any>& variants) const;
+
+private:
+    static std::shared_ptr<ov::Model> create_copy(const std::shared_ptr<ov::Model>& ov_model,
+                                                  const std::shared_ptr<void>& shared_object);
 };
 
 template <>
diff --git a/src/frontends/common/include/openvino/frontend/input_model.hpp b/src/frontends/common/include/openvino/frontend/input_model.hpp
index b81d5cfa32f..88e32bfdda1 100644
--- a/src/frontends/common/include/openvino/frontend/input_model.hpp
+++ b/src/frontends/common/include/openvino/frontend/input_model.hpp
@@ -36,10 +36,20 @@ namespace frontend {
 /// All editing requests affect the model representation that is held behind the scene
 /// successive method calls observe a new graph structure.
 class FRONTEND_API InputModel {
-public:
-    typedef std::shared_ptr<InputModel> Ptr;
+    std::shared_ptr<void> m_shared_object;
+    std::shared_ptr<InputModel> m_actual;
+    friend class FrontEnd;
 
-    virtual ~InputModel() = 0;
+public:
+    using Ptr = std::shared_ptr<InputModel>;
+
+    InputModel() = default;
+    InputModel(const InputModel&) = delete;
+    InputModel(InputModel&&) = delete;
+    InputModel& operator=(const InputModel&) = delete;
+    InputModel& operator=(InputModel&&) = delete;
+
+    virtual ~InputModel() = default;
 
     /////  Searching for places  /////
 
diff --git a/src/frontends/common/src/frontend.cpp b/src/frontends/common/src/frontend.cpp
index 7ba6de2e78e..605a8e170b3 100644
--- a/src/frontends/common/src/frontend.cpp
+++ b/src/frontends/common/src/frontend.cpp
@@ -16,57 +16,102 @@
 using namespace ov;
 using namespace ov::frontend;
 
+std::shared_ptr<ov::Model> FrontEnd::create_copy(const std::shared_ptr<ov::Model>& ov_model,
+                                                 const std::shared_ptr<void>& shared_object) {
+    // Recreate ov::Model using main runtime, not FrontEnd's one
+    auto copy = std::make_shared<Model>(ov_model->get_results(),
+                                        ov_model->get_sinks(),
+                                        ov_model->get_parameters(),
+                                        ov_model->get_variables(),
+                                        ov_model->get_friendly_name());
+    copy->m_shared_object = shared_object;
+    copy->get_rt_info() = ov_model->get_rt_info();
+    return copy;
+}
+
 FrontEnd::FrontEnd() = default;
 
 FrontEnd::~FrontEnd() = default;
 
 bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
+    if (m_actual) {
+        return m_actual->supported_impl(variants);
+    }
     return false;
 }
 
-InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& params) const {
-    FRONT_END_NOT_IMPLEMENTED(load_impl);
-}
-std::shared_ptr<Model> FrontEnd::convert(const InputModel::Ptr& model) const {
-    FRONT_END_NOT_IMPLEMENTED(convert);
+InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const {
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, load_impl);
+    auto model = std::make_shared<InputModel>();
+    model->m_shared_object = m_shared_object;
+    model->m_actual = m_actual->load_impl(variants);
+    return model;
 }
 
-void FrontEnd::convert(const std::shared_ptr<Model>&) const {
-    FRONT_END_NOT_IMPLEMENTED(convert);
+std::shared_ptr<ov::Model> FrontEnd::convert(const InputModel::Ptr& model) const {
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, convert);
+    return FrontEnd::create_copy(m_actual->convert(model->m_actual), m_shared_object);
+}
+
+void FrontEnd::convert(const std::shared_ptr<Model>& model) const {
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, convert);
+    m_actual->convert(model);
 }
 
 std::shared_ptr<Model> FrontEnd::convert_partially(const InputModel::Ptr& model) const {
-    FRONT_END_NOT_IMPLEMENTED(convert_partially);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, convert_partially);
+    return FrontEnd::create_copy(m_actual->convert_partially(model->m_actual), m_shared_object);
 }
 
 std::shared_ptr<Model> FrontEnd::decode(const InputModel::Ptr& model) const {
-    FRONT_END_NOT_IMPLEMENTED(decode);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, decode);
+    return FrontEnd::create_copy(m_actual->decode(model->m_actual), m_shared_object);
 }
 
 void FrontEnd::normalize(const std::shared_ptr<Model>& model) const {
-    FRONT_END_NOT_IMPLEMENTED(normalize);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, normalize);
+    m_actual->normalize(model);
 }
 
 void FrontEnd::add_extension(const std::shared_ptr<ov::Extension>& extension) {
+    if (m_actual) {
+        m_actual->add_extension(extension);
+        return;
+    }
     // Left unimplemented intentionally.
     // Each frontend can support own set of extensions, so this method should be implemented on the frontend side
 }
 
 void FrontEnd::add_extension(const std::vector<std::shared_ptr<ov::Extension>>& extensions) {
+    if (m_actual) {
+        m_actual->add_extension(extensions);
+        return;
+    }
     for (const auto& ext : extensions)
         add_extension(ext);
 }
 
 void FrontEnd::add_extension(const std::string& library_path) {
+    if (m_actual) {
+        m_actual->add_extension(library_path);
+        return;
+    }
     add_extension(ov::detail::load_extensions(library_path));
 }
 
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
 void FrontEnd::add_extension(const std::wstring& library_path) {
+    if (m_actual) {
+        m_actual->add_extension(library_path);
+        return;
+    }
     add_extension(ov::detail::load_extensions(library_path));
 }
 #endif
 
 std::string FrontEnd::get_name() const {
-    return std::string();
+    if (!m_actual) {
+        return {};
+    }
+    return m_actual->get_name();
 }
diff --git a/src/frontends/common/src/input_model.cpp b/src/frontends/common/src/input_model.cpp
index 385b50afb83..0385e8f4d18 100644
--- a/src/frontends/common/src/input_model.cpp
+++ b/src/frontends/common/src/input_model.cpp
@@ -12,103 +12,137 @@
 using namespace ov;
 using namespace ov::frontend;
 
-InputModel::~InputModel() = default;
-
 std::vector<Place::Ptr> InputModel::get_inputs() const {
-    return {};
+    if (!m_actual) {
+        return {};
+    }
+    return m_actual->get_inputs();
 }
 
 std::vector<Place::Ptr> InputModel::get_outputs() const {
-    return {};
+    if (!m_actual) {
+        return {};
+    }
+    return m_actual->get_outputs();
 }
 
 Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensor_name) const {
-    return nullptr;
+    if (!m_actual) {
+        return {};
+    }
+    return m_actual->get_place_by_tensor_name(tensor_name);
 }
 
 Place::Ptr InputModel::get_place_by_operation_name(const std::string& operation_name) const {
-    return nullptr;
+    if (!m_actual) {
+        return {};
+    }
+    return m_actual->get_place_by_operation_name(operation_name);
 }
 
 Place::Ptr InputModel::get_place_by_operation_name_and_input_port(const std::string& operation_name,
                                                                   int input_port_index) {
-    return nullptr;
+    if (!m_actual) {
+        return {};
+    }
+    return m_actual->get_place_by_operation_name_and_input_port(operation_name, input_port_index);
 }
 
 Place::Ptr InputModel::get_place_by_operation_name_and_output_port(const std::string& operation_name,
                                                                    int output_port_index) {
-    return nullptr;
+    if (!m_actual) {
+        return {};
+    }
+    return m_actual->get_place_by_operation_name_and_output_port(operation_name, output_port_index);
 }
 
 void InputModel::set_name_for_tensor(const Place::Ptr& tensor, const std::string& new_name) {
-    FRONT_END_NOT_IMPLEMENTED(set_name_for_tensor);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, set_name_for_tensor);
+    m_actual->set_name_for_tensor(tensor, new_name);
 }
 
 void InputModel::add_name_for_tensor(const Place::Ptr& tensor, const std::string& new_name) {
-    FRONT_END_NOT_IMPLEMENTED(add_name_for_tensor);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, add_name_for_tensor);
+    m_actual->add_name_for_tensor(tensor, new_name);
 }
 
 void InputModel::set_name_for_operation(const Place::Ptr& operation, const std::string& new_name) {
-    FRONT_END_NOT_IMPLEMENTED(set_name_for_operation);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, set_name_for_operation);
+    m_actual->set_name_for_operation(operation, new_name);
 }
 
 void InputModel::free_name_for_tensor(const std::string& name) {
-    FRONT_END_NOT_IMPLEMENTED(free_name_for_tensor);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, free_name_for_tensor);
+    m_actual->free_name_for_tensor(name);
 }
 
 void InputModel::free_name_for_operation(const std::string& name) {
-    FRONT_END_NOT_IMPLEMENTED(free_name_for_operation);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, free_name_for_operation);
+    m_actual->free_name_for_operation(name);
 }
 
 void InputModel::set_name_for_dimension(const Place::Ptr& place, size_t shape_dim_index, const std::string& dim_name) {
-    FRONT_END_NOT_IMPLEMENTED(set_name_for_dimension);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, set_name_for_dimension);
+    m_actual->set_name_for_dimension(place, shape_dim_index, dim_name);
 }
 
 void InputModel::cut_and_add_new_input(const Place::Ptr& place, const std::string& new_name_optional) {
-    FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_input);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, cut_and_add_new_input);
+    m_actual->cut_and_add_new_input(place, new_name_optional);
 }
 
 void InputModel::cut_and_add_new_output(const Place::Ptr& place, const std::string& new_name_optional) {
-    FRONT_END_NOT_IMPLEMENTED(cut_and_add_new_output);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, cut_and_add_new_output);
+    m_actual->cut_and_add_new_output(place, new_name_optional);
 }
 
 Place::Ptr InputModel::add_output(const Place::Ptr& place) {
-    FRONT_END_NOT_IMPLEMENTED(add_output);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, add_output);
+    return m_actual->add_output(place);
 }
 
 void InputModel::remove_output(const Place::Ptr& place) {
-    FRONT_END_NOT_IMPLEMENTED(remove_output);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, remove_output);
+    m_actual->remove_output(place);
 }
 
 void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
-    FRONT_END_NOT_IMPLEMENTED(override_all_outputs);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, override_all_outputs);
+    m_actual->override_all_outputs(outputs);
 }
 
 void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
-    FRONT_END_NOT_IMPLEMENTED(override_all_inputs);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, override_all_inputs);
+    m_actual->override_all_inputs(inputs);
 }
 
 void InputModel::extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) {
-    FRONT_END_NOT_IMPLEMENTED(extract_subgraph);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, extract_subgraph);
+    m_actual->extract_subgraph(inputs, outputs);
 }
 
 // Setting tensor properties
-void InputModel::set_partial_shape(const Place::Ptr& place, const PartialShape&) {
-    FRONT_END_NOT_IMPLEMENTED(set_partial_shape);
+void InputModel::set_partial_shape(const Place::Ptr& place, const PartialShape& shape) {
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, set_partial_shape);
+    m_actual->set_partial_shape(place, shape);
 }
 
 PartialShape InputModel::get_partial_shape(const Place::Ptr& place) const {
-    FRONT_END_NOT_IMPLEMENTED(get_partial_shape);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, get_partial_shape);
+    return m_actual->get_partial_shape(place);
 }
 
-void InputModel::set_element_type(const Place::Ptr& place, const element::Type&) {
-    FRONT_END_NOT_IMPLEMENTED(set_element_type);
+void InputModel::set_element_type(const Place::Ptr& place, const element::Type& type) {
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, set_element_type);
+    m_actual->set_element_type(place, type);
 }
 
 void InputModel::set_tensor_value(const Place::Ptr& place, const void* value) {
-    FRONT_END_NOT_IMPLEMENTED(set_tensor_value);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, set_tensor_value);
+    m_actual->set_tensor_value(place, value);
 }
 
 void InputModel::set_tensor_partial_value(const Place::Ptr& place, const void* min_value, const void* max_value) {
-    FRONT_END_NOT_IMPLEMENTED(set_tensor_partial_value);
+    FRONT_END_CHECK_IMPLEMENTED(m_actual, set_tensor_partial_value);
+    m_actual->set_tensor_partial_value(place, min_value, max_value);
 }
diff --git a/src/frontends/common/src/manager.cpp b/src/frontends/common/src/manager.cpp
index 8d5b2c1b1df..263c2ba7e16 100644
--- a/src/frontends/common/src/manager.cpp
+++ b/src/frontends/common/src/manager.cpp
@@ -44,7 +44,10 @@ public:
             });
             if (plugin_it != m_plugins.end()) {
                 if (plugin_it->load()) {
-                    return plugin_it->get_creator().m_creator();
+                    auto fe_obj = std::make_shared<FrontEnd>();
+                    fe_obj->m_shared_object = plugin_it->get_so_pointer();
+                    fe_obj->m_actual = plugin_it->get_creator().m_creator();
+                    return fe_obj;
                 }
             }
         }
@@ -52,7 +55,10 @@ public:
         for (auto& plugin : m_plugins) {
             plugin.load();
             if (plugin.get_creator().m_name == framework) {
-                return plugin.get_creator().m_creator();
+                auto fe_obj = std::make_shared<FrontEnd>();
+                fe_obj->m_shared_object = plugin.get_so_pointer();
+                fe_obj->m_actual = plugin.get_creator().m_creator();
+                return fe_obj;
             }
         }
         FRONT_END_INITIALIZATION_CHECK(false, "FrontEnd for Framework ", framework, " is not found");
@@ -86,10 +92,13 @@ public:
             auto fe = plugin.get_creator().m_creator();
             OPENVINO_ASSERT(fe, "Frontend error: frontend '", plugin.get_creator().m_name, "' created null FrontEnd");
             if (fe->supported(variants)) {
-                return fe;
+                auto fe_obj = std::make_shared<FrontEnd>();
+                fe_obj->m_shared_object = plugin.get_so_pointer();
+                fe_obj->m_actual = fe;
+                return fe_obj;
             }
         }
-        return {};
+        return nullptr;
     }
 
     void register_front_end(const std::string& name, FrontEndFactory creator) {
@@ -175,7 +184,10 @@ private:
             auto fe = plugin_info.get_creator().m_creator();
             if (fe && fe->supported(variants)) {
                 // Priority FE (e.g. IR) is found and is suitable
-                return fe;
+                auto fe_obj = std::make_shared<FrontEnd>();
+                fe_obj->m_shared_object = plugin_info.get_so_pointer();
+                fe_obj->m_actual = fe;
+                return fe_obj;
             }
         }
         return {};
diff --git a/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.hpp b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.hpp
index 4343606c80e..8c7aa8ef665 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.hpp
+++ b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/mock_mo_frontend.hpp
@@ -66,7 +66,7 @@ struct MOCK_API PlaceStat
 
     // Arguments tracking
     std::string m_lastArgString;
-    int m_lastArgInt;
+    int m_lastArgInt = -1;
     Place::Ptr m_lastArgPlace = nullptr;
 
     // Getters
@@ -95,8 +95,8 @@ class MOCK_API PlaceMockPy : public Place
     int m_portIndex = -1;
 
 public:
-    PlaceMockPy(const std::string& name = {}, bool is_op = false, int portIndex = -1)
-        : m_name(name)
+    explicit PlaceMockPy(std::string name = {}, bool is_op = false, int portIndex = -1)
+        : m_name(std::move(name))
         , m_is_op(is_op)
         , m_portIndex(portIndex)
     {
@@ -200,7 +200,7 @@ public:
     bool is_equal_data(const Ptr& another) const override
     {
         if (m_is_op)
-            throw "Not implemented";
+            throw std::runtime_error("Not implemented");
         m_stat.m_is_equal_data++;
         m_stat.m_lastArgPlace = another;
         std::shared_ptr<PlaceMockPy> mock = std::dynamic_pointer_cast<PlaceMockPy>(another);
@@ -243,7 +243,7 @@ struct MOCK_API ModelStat
 
     // Arguments tracking
     std::string m_lastArgString;
-    int m_lastArgInt;
+    int m_lastArgInt = -1;
     Place::Ptr m_lastArgPlace = nullptr;
     std::vector<Place::Ptr> m_lastArgInputPlaces;
     std::vector<Place::Ptr> m_lastArgOutputPlaces;
@@ -412,10 +412,11 @@ class MOCK_API FrontEndMockPy : public FrontEnd
     static FeStat m_stat;
 
 public:
-    FrontEndMockPy() {}
+    FrontEndMockPy() = default;
 
     std::shared_ptr<ov::Model> convert(const InputModel::Ptr& model) const override
     {
+        std::cout << "MVN: convert called\n";
         m_stat.m_convert_model++;
         return std::make_shared<ov::Model>(ov::NodeVector{}, ov::ParameterVector{});
     }
@@ -427,7 +428,7 @@ public:
 private:
     InputModel::Ptr load_impl(const std::vector<ov::Any>& params) const override
     {
-        if (params.size() > 0 && params[0].is<std::string>())
+        if (!params.empty() && params[0].is<std::string>())
         {
             auto path = params[0].as<std::string>();
             m_stat.m_load_paths.push_back(path);
@@ -438,7 +439,7 @@ private:
     bool supported_impl(const std::vector<ov::Any>& params) const override
     {
         m_stat.m_supported++;
-        if (params.size() > 0 && params[0].is<std::string>())
+        if (!params.empty() && params[0].is<std::string>())
         {
             auto path = params[0].as<std::string>();
             if (path.find(".test_mo_mock_mdl") != std::string::npos)

From a40b5bf15ee288222566b4550f6c0adcb8ab7223 Mon Sep 17 00:00:00 2001
From: Alexey Varyzgin <alexey.varyzgin@intel.com>
Date: Thu, 23 Dec 2021 23:13:30 -0800
Subject: [PATCH 134/160] [CPU][INT8][Intel OMZ / Public] Third dimension issue
 in FuseConvolutionAndZeroPoints (#9385)

---
 src/plugins/intel_cpu/src/mkldnn_graph_optimizer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/plugins/intel_cpu/src/mkldnn_graph_optimizer.cpp b/src/plugins/intel_cpu/src/mkldnn_graph_optimizer.cpp
index 3a6fa6c1df9..7dfc6e448a5 100644
--- a/src/plugins/intel_cpu/src/mkldnn_graph_optimizer.cpp
+++ b/src/plugins/intel_cpu/src/mkldnn_graph_optimizer.cpp
@@ -578,7 +578,7 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndZeroPoints(MKLDNNGraph &graph) {
         ptrdiff_t OC = weightsConstantDims[0 + groupOffset];
         ptrdiff_t IC = weightsConstantDims[1 + groupOffset];
         ptrdiff_t KD = weightsConstantDims.size() == (5 + groupOffset) ? weightsConstantDims[weightsConstantDims.size() - 3] : 1;
-        ptrdiff_t KH = node->getInputShapeAtPort(0).getRank() > (3 + groupOffset) ? weightsConstantDims[weightsConstantDims.size() - 2] : 1;
+        ptrdiff_t KH = weightsConstantDims.size() == (3 + groupOffset) ? 1 : weightsConstantDims[weightsConstantDims.size() - 2];
         ptrdiff_t KW = weightsConstantDims[weightsConstantDims.size() - 1];
 
         for (size_t g = 0; g < G; g++) {

From da209932725ae58ad1738759cdff27592d040422 Mon Sep 17 00:00:00 2001
From: Nikolay Tyukaev <nikolay.tyukaev@intel.com>
Date: Fri, 24 Dec 2021 11:15:17 +0300
Subject: [PATCH 135/160] add master version (#9408)

* add master version

* fix

* fixes
---
 docs/conf.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 2f364b00aa8..bbb5f78e134 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -81,10 +81,10 @@ html_theme_options = {
 }
 
 html_context = {
-    'current_version': '2021.4',
+    'current_version': 'nightly',
     'current_language': 'English',
     'languages': (('English', '/latest'), ('Chinese', '/cn/latest')),
-    'versions': (('Latest', '/latest'), ('2021.4', '/2021.4'),
+    'versions': (('Latest', '/latest'), ('nightly', '/nightly'), ('2021.4', '/2021.4'),
                 ('2021.3', '/2021.3'), ('2021.2', '/2021.2'), ('2021.1', '/2021.1'),
                  ('2020.4', '/2020.4'), ('2020.3', '/2020.3'), ('2020.2', '/2020.1'),
                   ('2020.1', '/2020.1'), ('2019_R3.1', '/2019_R3.1'), ('2019_R3', '/2019_R3'),
@@ -97,7 +97,7 @@ repositories = {
     'openvino': {
         'github_user': 'openvinotoolkit',
         'github_repo': 'openvino',
-        'github_version': 'releases/2021/4',
+        'github_version': 'master',
         'host_url': 'https://github.com'
     },
     'open_model_zoo': {

From 31b6b034bc8c8fd2583b2400ea3fddba3cf190e4 Mon Sep 17 00:00:00 2001
From: okhovan <93921274+okhovan@users.noreply.github.com>
Date: Fri, 24 Dec 2021 10:18:58 +0200
Subject: [PATCH 136/160] [GPU] MaxPool-8 (#9064)

---
 .../api/intel_gpu/primitives/pooling.hpp      | 61 +++++++++++++-
 .../pooling/pooling_kernel_base.cpp           | 27 ++++++-
 .../pooling/pooling_kernel_base.h             | 10 +++
 .../pooling/pooling_kernel_gpu_ref.cpp        |  2 +
 .../cl_kernels/pooling_gpu_b_fs_yx_fsv4.cl    |  4 +-
 .../pooling_gpu_b_fs_zyx_fsv16_imad.cl        |  4 +-
 .../pooling_gpu_bs_fs_yx_bsv16_fsv16.cl       |  4 +-
 .../pooling_gpu_byxf_padding_opt.cl           |  2 +-
 .../cl_kernels/pooling_gpu_fs_b_yx_fsv32.cl   |  4 +-
 .../core/cl_kernels/pooling_gpu_int8_ref.cl   |  6 +-
 .../core/cl_kernels/pooling_gpu_ref.cl        | 63 +++++++++++++--
 .../core/kernel_selector_params.h             |  4 +
 .../clDNN/src/impls/ocl/pooling.cpp           | 21 +++++
 .../intel_gpu/plugin/primitives_list.hpp      |  1 +
 .../intel_gpu/src/plugin/ops/pooling.cpp      | 77 +++++++++++++++++-
 .../single_layer_tests/pooling.cpp            | 16 ++++
 .../plugin/cpu/single_layer_tests/pooling.cpp | 19 ++++-
 .../single_layer_tests/pooling.cpp            | 79 ++++++++++++++++---
 .../single_layer/pooling.hpp                  |  2 +
 .../src/single_layer/pooling.cpp              | 22 +++++-
 .../include/ngraph_functions/builders.hpp     |  4 +-
 .../ngraph_functions/src/pooling.cpp          |  7 +-
 22 files changed, 391 insertions(+), 48 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/pooling.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/pooling.hpp
index 8e269775eac..b228f9bb601 100644
--- a/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/pooling.hpp
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/primitives/pooling.hpp
@@ -164,9 +164,54 @@ struct pooling : public primitive_base<pooling> {
           size(0, 0, 0, 0),
           with_output_size(false) {}
 
+    /// @brief Constructs pooling primitive that supports MaxPool features from opset8 (dilation and indices output).
+    /// @param id This primitive id.
+    /// @param input Input primitive id.
+    /// @param indices_output Indices output primitive id.
+    /// @param size Pooling kernel size.
+    /// @param stride Defines shift in input buffer between adjacent calculations of output values.
+    /// @param dilation Defines index of next pixel to select when pooling.
+    /// @param pad Defines logical pad value added to input tensor.
+    /// @param pad_end Defines a shift, relative to the end of padding shape.
+    /// @param axis First dimension of input that should be used to calculate the upper bound of index output.
+    /// @param index_element_type Data type of index output.
+    /// @param output_size User-defined output data size of the primitive (w/o padding).
+    pooling(const primitive_id& id,
+            const primitive_id& input,
+            const primitive_id& indices_output,
+            const tensor& size,
+            const tensor& stride,
+            const tensor& dilation,
+            const tensor& pad,
+            const tensor& pad_end,
+            int64_t axis,
+            data_types index_element_type,
+            tensor output_size,
+            const data_types output_data_type,
+            const primitive_id& ext_prim_id = "",
+            const padding& output_padding = padding())
+            : primitive_base(id, {input, indices_output}, ext_prim_id, output_padding, optional_data_type{output_data_type}),
+              argmax(""),
+              indices_output(indices_output),
+              mode(pooling_mode::max),
+              global_pooling(false),
+              pad(pad),
+              stride(stride),
+              dilation(dilation),
+              size(size),
+              with_output_size(true),
+              output_size(output_size),
+              pad_end(pad_end),
+              axis(axis),
+              index_element_type(index_element_type),
+              maxPoolOpset8Features(true)
+              {}
+
     /// @brief Primitive id which contains indices of each max pooling region.
     /// Indices must be in flattened bfyx format with no padding. Needs to be fp32 data type.
     primitive_id argmax;
+    /// @brief Primitive id which contains indices output.
+    primitive_id indices_output;
     /// @brief Pooling mode.
     pooling_mode mode;
     /// @brief Global pooling (kernel size is equal to the spatial dimension of input tensor)
@@ -175,6 +220,8 @@ struct pooling : public primitive_base<pooling> {
     tensor pad;
     /// @brief Defines shift in input buffer between adjacent calculations of output values.
     tensor stride;
+    /// @brief Defines index of next pixel to select when pooling
+    tensor dilation;
     /// @brief Pooling kernel size.
     tensor size;
     /// @brief Indicates that the primitive has user-defined output size (non-zero value).
@@ -183,12 +230,20 @@ struct pooling : public primitive_base<pooling> {
     tensor output_size;
     /// @brief Defines a shift, relative to the end of padding shape.
     tensor pad_end;
+    /// @brief first dimension of input that should be used to calculate the upper bound of index output
+    int64_t axis;
+    /// @brief type of index output
+    data_types index_element_type;
+    bool maxPoolOpset8Features{false};
 
 protected:
     std::vector<std::reference_wrapper<const primitive_id>> get_dependencies() const override {
-        if (argmax.empty())
-            return {};
-        return {argmax};
+        std::vector<std::reference_wrapper<const primitive_id>> ret;
+        if (!argmax.empty())
+            ret.push_back(argmax);
+        if (!indices_output.empty())
+            ret.push_back(indices_output);
+        return ret;
     }
 };
 /// @}
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp
index 5ea0c3753d2..4b22a88b557 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp
@@ -60,8 +60,25 @@ JitConstants PoolingKernelBase::GetJitConstants(const pooling_params& pp, Poolin
         MakeJitConstant(toString(pp.divMode) + "_KERNEL_DIVIDER", 1),
     });
 
+    if (pp.maxPoolOpset8Features) {
+        mem_consts.AddConstants({MakeJitConstant("DILATION", pp.poolDilation)});
+
+        if (pp.poolAxis != 0) {
+            size_t indices_upper_bound = 1;
+            const auto& dims = pp.inputs[0].GetDims();
+            for (auto d = dims.crbegin() + pp.poolAxis; d != dims.crend(); ++d) {
+                indices_upper_bound *= d->v;
+            }
+            if (indices_upper_bound != 0 && indices_upper_bound != 1) {
+                mem_consts.AddConstants({MakeJitConstant("INDICES_UPPER_BOUND", indices_upper_bound)});
+            }
+        }
+
+        mem_consts.Merge(MakeTypeJitConstants(pp.poolIndexElementType, "SELECTED_INDICES"));
+    }
+
     if (dispatchData.needsBoundary) {
-        mem_consts.AddConstant(MakeJitConstant("CHECK_BOUNDRY", 1));
+        mem_consts.AddConstant(MakeJitConstant("CHECK_BOUNDARY", 1));
     }
 
     if (EnableRound(pp)) {
@@ -78,6 +95,8 @@ bool PoolingKernelBase::NeedsBoundaryCheck(const pooling_params& pp) const {
 
     if (pp.poolPad.x != 0 || pp.poolPad.y != 0 || pp.poolPad.z != 0) {
         return true;
+    } else if (pp.poolDilation.x > 1 || pp.poolDilation.y > 1 || pp.poolDilation.z > 1) {
+        return true;
     } else if ((((input.X().v - pp.poolSize.x) / pp.poolStride.x) + 1) < output.X().v ||
                (((input.Y().v - pp.poolSize.y) / pp.poolStride.y) + 1) < output.Y().v ||
                (((input.Z().v - pp.poolSize.z) / pp.poolStride.z) + 1) < output.Z().v) {
@@ -181,9 +200,13 @@ KernelsData PoolingKernelBase::GetCommonKernelsData(const Params& params,
     auto& kernel = kd.kernels[0];
     FillCLKernelData(kernel, dispatchData, params.engineInfo, kernelName, jit, entry_point, DEFAULT, false, false, 1,
                      GetFusedPrimitiveInputsCount(params));
+    uint32_t param_idx = 1;
     if (orgParams.poolType == PoolType::MAX_WITH_ARGMAX)
-        kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 1});
+        kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, param_idx++});
 
+    if (orgParams.maxPoolOpset8Features) {
+        kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, param_idx++});
+    }
 
     return {kd};
 }
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.h
index b8ccab77783..ba0e244a963 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.h
@@ -4,6 +4,7 @@
 
 #pragma once
 
+#include "common_types.h"
 #include "kernel_base_opencl.h"
 #include "kernel_selector_params.h"
 
@@ -21,6 +22,10 @@ struct pooling_params : public base_params {
     uSize poolSize;
     uSize poolStride;
     uSize poolPad;
+    bool maxPoolOpset8Features = false;
+    uSize poolDilation{1, 1, 1};
+    Datatype poolIndexElementType = Datatype::INT64;
+    int64_t poolAxis = 0;
 
     ParamsKey GetParamsKey() const override {
         ParamsKey k = base_params::GetParamsKey();
@@ -29,6 +34,11 @@ struct pooling_params : public base_params {
         k.EnablePoolRemainder(remainderAction);
         k.EnablePoolKernelDividerMode(divMode);
 
+        if (maxPoolOpset8Features) {
+            k.EnablePoolDilation();
+            k.EnablePoolIndicesOutput();
+        }
+
         return k;
     }
 };
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_ref.cpp
index a0b2a923311..97f7ea84505 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_ref.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_ref.cpp
@@ -27,6 +27,8 @@ ParamsKey PoolingKernelGPURef::GetSupportedKey() const {
     k.EnablePoolKernelDividerMode(KernelDividerMode::DYNAMIC);
     k.EnablePoolKernelDividerMode(KernelDividerMode::DYNAMIC_WITH_PADDING);
     k.EnableDifferentTypes();
+    k.EnablePoolDilation();
+    k.EnablePoolIndicesOutput();
     return k;
 }
 
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_yx_fsv4.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_yx_fsv4.cl
index 4f0030da876..2c0df8e5048 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_yx_fsv4.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_yx_fsv4.cl
@@ -51,7 +51,7 @@ KERNEL(pooling_gpu_b_fs_yx_fsv4)(
 
     ACCUMULATOR_TYPE result[4] = { INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL };
 
-#ifdef CHECK_BOUNDRY
+#ifdef CHECK_BOUNDARY
     if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X ||
         offset_y + POOL_SIZE_Y < 0 || offset_y >= INPUT0_SIZE_Y)
     {
@@ -96,7 +96,7 @@ KERNEL(pooling_gpu_b_fs_yx_fsv4)(
     const int wend = min(offset_x + POOL_SIZE_X, INPUT0_SIZE_X + PADDING_SIZE_X);
     const uint num_elements = (hend - offset_y) * (wend - offset_x);
 #endif
-#else // !CHECK_BOUNDRY
+#else // !CHECK_BOUNDARY
     uint input_idx = GET_DATA_B_FS_YX_FSV4_INDEX(INPUT0, b, f, offset_y, offset_x);
 
     for(uint j = 0; j < POOL_SIZE_Y; j++)
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl
index ca8342d1225..a28b1206dd0 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl
@@ -255,7 +255,7 @@ KERNEL(pooling_gpu_b_fs_zyx_fsv16)(
     ACCUMULATOR_TYPE result[FEATURE_SLICE_SIZE] = { INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL,
                                                     INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL, INIT_VAL };
 
-#ifdef CHECK_BOUNDRY
+#ifdef CHECK_BOUNDARY
     if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X ||
         offset_y + POOL_SIZE_Y < 0 || offset_y >= INPUT0_SIZE_Y ||
         offset_z + POOL_SIZE_Z < 0 || offset_z >= INPUT0_SIZE_Z)
@@ -341,7 +341,7 @@ KERNEL(pooling_gpu_b_fs_zyx_fsv16)(
     const int wend = min(offset_x + POOL_SIZE_X, INPUT0_SIZE_X + PADDING_SIZE_X);
     const uint num_elements = (dend - offset_z) * (hend - offset_y) * (wend - offset_x);
 #endif
-#else // !CHECK_BOUNDRY
+#else // !CHECK_BOUNDARY
 #if INPUT0_DIMS == 4
     uint input_idx = INPUT0_GET_INDEX(b, f, offset_y, offset_x);
 #else
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_bs_fs_yx_bsv16_fsv16.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_bs_fs_yx_bsv16_fsv16.cl
index d036c960698..06908a1a5d5 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_bs_fs_yx_bsv16_fsv16.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_bs_fs_yx_bsv16_fsv16.cl
@@ -48,7 +48,7 @@ KERNEL(pooling_gpu_bs_fs_yx_bsv16_fsv16)(const __global INPUT0_TYPE* input,
     const uint input_fs_pitch = input_y_pitch * (INPUT0_PAD_BEFORE_SIZE_Y + INPUT0_SIZE_Y + INPUT0_PAD_AFTER_SIZE_Y);
     int16 result = INIT_VAL;
 
-#ifdef CHECK_BOUNDRY
+#ifdef CHECK_BOUNDARY
     uint batch_and_feature_offset = GET_DATA_BS_FS_YX_BSV16_FSV16_INDEX(INPUT0, b, f, 0, 0);
     if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X || offset_y + POOL_SIZE_Y < 0 ||
         offset_y >= INPUT0_SIZE_Y) {
@@ -88,7 +88,7 @@ KERNEL(pooling_gpu_bs_fs_yx_bsv16_fsv16)(const __global INPUT0_TYPE* input,
     const int wend = min(offset_x + POOL_SIZE_X, INPUT0_SIZE_X + PADDING_SIZE_X);
     const uint num_elements = (hend - offset_y) * (wend - offset_x);
 #endif
-#else  // !CHECK_BOUNDRY
+#else  // !CHECK_BOUNDARY
     uint input_idx = GET_DATA_BS_FS_YX_BSV16_FSV16_INDEX(INPUT0, b, f, offset_y, offset_x);
     __attribute__((opencl_unroll_hint(POOL_SIZE_Y)))
     for (uint j = 0; j < POOL_SIZE_Y; j++) {
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_byxf_padding_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_byxf_padding_opt.cl
index 15c32b7d3f7..b1901958fd3 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_byxf_padding_opt.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_byxf_padding_opt.cl
@@ -53,7 +53,7 @@ KERNEL(pooling_gpu_byxf_opt)(
     const int offset_x = (int)x*STRIDE_SIZE_X - PADDING_SIZE_X;
     const int offset_y = (int)y*STRIDE_SIZE_Y - PADDING_SIZE_Y;
 
-#ifdef CHECK_BOUNDRY
+#ifdef CHECK_BOUNDARY
     if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X ||
         offset_y + POOL_SIZE_Y < 0 || offset_y >= INPUT0_SIZE_Y)
     {
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_fs_b_yx_fsv32.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_fs_b_yx_fsv32.cl
index d98c6a49bed..bf0ce4e8a88 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_fs_b_yx_fsv32.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_fs_b_yx_fsv32.cl
@@ -84,7 +84,7 @@ KERNEL(pooling_gpu_fs_b_yx_fsv32)(
     const size_t fs_offset = fs * fs_pitch; // locate beginning of feature tile
     const size_t b_offset = b * b_pitch;   // locate beginning of batch
 
-#ifdef CHECK_BOUNDRY
+#ifdef CHECK_BOUNDARY
     if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X ||
         offset_y + POOL_SIZE_Y < 0 || offset_y >= INPUT0_SIZE_Y)
     {
@@ -121,7 +121,7 @@ KERNEL(pooling_gpu_fs_b_yx_fsv32)(
     const int wend = min(offset_x + POOL_SIZE_X, INPUT0_SIZE_X + PADDING_SIZE_X);
     const uint num_elements = (hend - offset_y) * (wend - offset_x);
 #endif
-#else // !CHECK_BOUNDRY
+#else // !CHECK_BOUNDARY
     for(uint in_dy = 0; in_dy < POOL_SIZE_Y; in_dy++)
     {
         const size_t input_offset_y = (offset_y + in_dy) * y_pitch;
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_int8_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_int8_ref.cl
index 456d615b191..4cbf5a2f713 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_int8_ref.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_int8_ref.cl
@@ -92,7 +92,7 @@ KERNEL(pooling_gpu_int8_ref)(
 
     ACCUMULATOR_TYPE result = INIT_VAL;
 
-#ifdef CHECK_BOUNDRY
+#ifdef CHECK_BOUNDARY
     if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X ||
         offset_y + POOL_SIZE_Y < 0 || offset_y >= INPUT0_SIZE_Y ||
         offset_z + POOL_SIZE_Z < 0 || offset_z >= INPUT0_SIZE_Z)
@@ -155,7 +155,7 @@ KERNEL(pooling_gpu_int8_ref)(
 
 #endif  // DYNAMIC_WITH_PADDING_KERNEL_DIVIDER
 
-#else  // CHECK_BOUNDRY
+#else  // CHECK_BOUNDARY
 
 #if OUTPUT_DIMS == 5
     for(uint l = 0; l < POOL_SIZE_Z; l++)
@@ -179,7 +179,7 @@ KERNEL(pooling_gpu_int8_ref)(
     const uint num_elementes = POOL_SIZE_X*POOL_SIZE_Y*POOL_SIZE_Z;
 #endif
 
-#endif // CHECK_BOUNDRY
+#endif // CHECK_BOUNDARY
 
 #if defined AVG_POOLING
 #if ENABLE_ROUND
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_ref.cl
index 6d2d3a7c123..db18b69d9de 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_ref.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_ref.cl
@@ -28,6 +28,9 @@ KERNEL(pooling_gpu)(
 #if MAX_WITH_ARGMAX_POOLING
 , __global float* arg_max
 #endif
+#ifdef SELECTED_INDICES_TYPE
+, __global SELECTED_INDICES_TYPE* indices
+#endif
 #if HAS_FUSED_OPS_DECLS
     , FUSED_OPS_DECLS
 #endif
@@ -91,11 +94,15 @@ KERNEL(pooling_gpu)(
 
     ACCUMULATOR_TYPE result = INIT_VAL;
 
+#ifdef SELECTED_INDICES_TYPE
+    uint result_idx = 0;
+#endif
+
 #if MAX_WITH_ARGMAX_POOLING
     uint arg_max_idx = 0;
 #endif
 
-#ifdef CHECK_BOUNDRY
+#ifdef CHECK_BOUNDARY
     if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X ||
         offset_y + POOL_SIZE_Y < 0 || offset_y >= INPUT0_SIZE_Y ||
         offset_z + POOL_SIZE_Z < 0 || offset_z >= INPUT0_SIZE_Z)
@@ -107,6 +114,16 @@ KERNEL(pooling_gpu)(
     uint num_elementes = 0;
 #endif
 
+#ifndef DILATION_SIZE_X
+    #define DILATION_SIZE_X 1
+#endif
+#ifndef DILATION_SIZE_Y
+    #define DILATION_SIZE_Y 1
+#endif
+#ifndef DILATION_SIZE_Z
+    #define DILATION_SIZE_Z 1
+#endif
+
 #if OUTPUT_DIMS == 5
     const uint batch_and_feature_offset = INPUT0_GET_INDEX(b, f, 0, 0, 0);
 #else
@@ -116,20 +133,20 @@ KERNEL(pooling_gpu)(
 #if OUTPUT_DIMS == 5
     for(uint l = 0; l < POOL_SIZE_Z; l++)
     {
-        int input_offset_z = offset_z + l;
+        int input_offset_z = offset_z + (l * DILATION_SIZE_Z);
         bool zero_z = input_offset_z >= INPUT0_SIZE_Z || input_offset_z < 0;
         if (!zero_z)
         {
 #endif
             for(uint j = 0; j < POOL_SIZE_Y; j++)
             {
-                int input_offset_y = offset_y + j;
+                int input_offset_y = offset_y + (j * DILATION_SIZE_Y);
                 bool zero_y = input_offset_y >= INPUT0_SIZE_Y || input_offset_y < 0;
                 if(!zero_y)
                 {
                     for(uint i = 0; i < POOL_SIZE_X; i++)
                     {
-                        int input_offset_x = offset_x + i;
+                        int input_offset_x = offset_x + (i * DILATION_SIZE_X);
                         bool zero = input_offset_x >= INPUT0_SIZE_X || input_offset_x < 0;
                         if(!zero)
                         {
@@ -159,7 +176,17 @@ KERNEL(pooling_gpu)(
                                 arg_max_idx = input_idx_bfyx_no_padding;
                             }
 #endif
-                            result = FUNC_CALL(apply_pooling)(result, TO_ACCUMULATOR_TYPE(input[input_idx]));
+                            const ACCUMULATOR_TYPE casted_input = TO_ACCUMULATOR_TYPE(input[input_idx]);
+                            #ifdef SELECTED_INDICES_TYPE
+                                if (casted_input > result)
+                                {
+                                    result = casted_input;
+                                    result_idx = input_idx;
+                                }
+                            #else
+                                result = FUNC_CALL(apply_pooling)(result, casted_input);
+                            #endif
+
 
 #ifdef DYNAMIC_KERNEL_DIVIDER
                             num_elementes++;
@@ -185,7 +212,7 @@ KERNEL(pooling_gpu)(
 
 #endif  // DYNAMIC_WITH_PADDING_KERNEL_DIVIDER
 
-#else  // CHECK_BOUNDRY
+#else  // CHECK_BOUNDARY
 
 #if  OUTPUT_DIMS == 5  // 3D
     uint input_idx = INPUT0_GET_INDEX(b, f, offset_z, offset_y, offset_x);
@@ -227,7 +254,16 @@ KERNEL(pooling_gpu)(
                 uint input_idx = INPUT0_GET_INDEX(b, f, offset_y + j, offset_x + i);
                 result = FUNC_CALL(apply_pooling)(result, TO_ACCUMULATOR_TYPE(input[input_idx]));
     #else
-                result = FUNC_CALL(apply_pooling)(result, TO_ACCUMULATOR_TYPE(input[input_idx]));
+                #ifdef SELECTED_INDICES_TYPE
+                    const current_input_value = input[input_idx];
+                    if (current_input_value > result)
+                    {
+                        result = current_input_value;
+                        result_idx = input_idx;
+                    }
+                #else
+                    result = FUNC_CALL(apply_pooling)(result, TO_ACCUMULATOR_TYPE(input[input_idx]));
+                #endif
                 input_idx += INPUT0_X_PITCH;
     #endif
 #endif
@@ -253,7 +289,7 @@ KERNEL(pooling_gpu)(
     const uint num_elementes = POOL_SIZE_X*POOL_SIZE_Y*POOL_SIZE_Z;
 #endif
 
-#endif // CHECK_BOUNDRY
+#endif // CHECK_BOUNDARY
 
 #if defined AVG_POOLING
     #if defined(DYNAMIC_KERNEL_DIVIDER) || defined(DYNAMIC_WITH_PADDING_KERNEL_DIVIDER)
@@ -280,6 +316,13 @@ KERNEL(pooling_gpu)(
 #endif
     output[output_pos] = final_result;
 
+#ifdef SELECTED_INDICES_TYPE
+    #ifdef INDICES_UPPER_BOUND
+        result_idx %= INDICES_UPPER_BOUND;
+    #endif
+    indices[output_pos] = TO_SELECTED_INDICES_TYPE(result_idx);
+#endif
+
 #if MAX_WITH_ARGMAX_POOLING
     //INPUT1 macro stands for Argmax
     const uint arg_max_pos = GET_DATA_INDEX_5D(INPUT1, b, f, z, y, x);
@@ -288,3 +331,7 @@ KERNEL(pooling_gpu)(
 }
 
 #undef INIT_VAL
+
+#undef DILATION_SIZE_X
+#undef DILATION_SIZE_Y
+#undef DILATION_SIZE_Z
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h
index 06bc8435ca7..970d04e118e 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h
@@ -124,6 +124,8 @@ public:
                         uint32_t dynamicKenrelDivider : 1;
                         uint32_t dynamicKenrelDividerWithPadding : 1;
                         uint32_t position_sensitive : 1;
+                        uint32_t dilation : 1;
+                        uint32_t indices_output : 1;
                     } pooling;
                     struct conv_t {
                         uint32_t split : 1;
@@ -281,6 +283,8 @@ public:
     void EnablePoolKernelDividerMode(KernelDividerMode m);
     void EnablePoolType(PoolType t);
     void EnablePoolRemainder(PoolRemainder r);
+    void EnablePoolDilation() { key.restrict.val.dedicated.pooling.dilation = 1; }
+    void EnablePoolIndicesOutput() { key.restrict.val.dedicated.pooling.indices_output = 1; }
     void EnableQuantization(QuantizationType q);
     void EnablePositionSensitivePooling() { key.restrict.val.dedicated.pooling.position_sensitive = 1; }
     void EnableSplitSupport() { key.restrict.val.dedicated.conv.split = 1; }
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/pooling.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/pooling.cpp
index 1128fd1f2da..ae97081374f 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/pooling.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/pooling.cpp
@@ -89,6 +89,24 @@ public:
             get_default_optional_params<kernel_selector::pooling_optional_params>(arg.get_program());
 
         const auto primitive = arg.get_primitive();
+
+        pool_params.maxPoolOpset8Features = primitive->maxPoolOpset8Features;
+        if (pool_params.maxPoolOpset8Features) {
+            switch (primitive->index_element_type) {
+                case cldnn::data_types::i32: {
+                    pool_params.poolIndexElementType = kernel_selector::Datatype::INT32;
+                    break;
+                }
+                case cldnn::data_types::i64: {
+                    pool_params.poolIndexElementType = kernel_selector::Datatype::INT64;
+                    break;
+                }
+                default:
+                    throw std::runtime_error{"Not supported index element type"};
+            }
+            pool_params.poolAxis = primitive->axis;
+        }
+
         const auto& stride = primitive->stride;
         const auto& pad = primitive->pad;
         const auto& input_sizes = arg.input().get_output_layout().size;
@@ -134,6 +152,9 @@ public:
 
         pp.poolStride = {(uint32_t)stride.spatial[0], (uint32_t)stride.spatial[1], (uint32_t)stride.spatial[2]};
 
+        const auto& dilation = primitive->dilation;
+        pp.poolDilation = {(uint32_t)dilation.spatial[0], (uint32_t)dilation.spatial[1], (uint32_t)dilation.spatial[2]};
+
         auto& kernel_selector = kernel_selector::pooling_kernel_selector::Instance();
         auto best_kernels = kernel_selector.GetBestKernels(pool_params, pool_optional_params);
 
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
index 68beb6214c9..f80252a017a 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
@@ -221,6 +221,7 @@ REGISTER_FACTORY(v8, NV12toBGR);
 REGISTER_FACTORY(v8, I420toRGB);
 REGISTER_FACTORY(v8, I420toBGR);
 REGISTER_FACTORY(v8, RandomUniform)
+REGISTER_FACTORY(v8, MaxPool);
 
 // --------------------------- Supported internal ops --------------------------- //
 REGISTER_FACTORY(internal, NonMaxSuppressionIEInternal);
diff --git a/src/plugins/intel_gpu/src/plugin/ops/pooling.cpp b/src/plugins/intel_gpu/src/plugin/ops/pooling.cpp
index c2c6565cd9e..9c5b25216d4 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/pooling.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/pooling.cpp
@@ -8,6 +8,7 @@
 #include "ngraph/op/max_pool.hpp"
 #include "ngraph/op/avg_pool.hpp"
 
+#include "intel_gpu/primitives/mutable_data.hpp"
 #include "intel_gpu/primitives/pooling.hpp"
 
 namespace ov {
@@ -17,6 +18,7 @@ namespace intel_gpu {
 struct PoolingParameters {
     cldnn::tensor kernel;
     cldnn::tensor stride;
+    cldnn::tensor dilation;
     cldnn::tensor pad_begin;
     cldnn::tensor pad_end;
 };
@@ -24,10 +26,15 @@ struct PoolingParameters {
 static PoolingParameters GetPoolingParameters(const ngraph::Shape& kernel,
                                               const ngraph::Strides& strides,
                                               const ngraph::Shape& pads_begin,
-                                              const ngraph::Shape& pads_end) {
+                                              const ngraph::Shape& pads_end,
+                                              const ngraph::Strides& dilations = {}) {
     cldnn::tensor k, s, pb, pe;
-    if (pads_begin.size() != strides.size() || pads_end.size() != strides.size() || kernel.size() != strides.size())
-        IE_THROW() << "Strides, KernelSizes and Pads are supposed to have the same elements count";
+    cldnn::tensor d{cldnn::batch(1), cldnn::feature(1), cldnn::spatial(1, 1, 1)};
+    const auto is_dilation_specified = !dilations.empty();
+
+    if (pads_begin.size() != strides.size() || pads_end.size() != strides.size() || kernel.size() != strides.size()
+        || (is_dilation_specified && dilations.size() != strides.size()))
+        IE_THROW() << "Strides, KernelSizes, Pads (and Dilations, if specified) are supposed to have the same elements count";
 
     std::vector<cldnn::tensor::value_type> pb_casted(pads_begin.begin(), pads_begin.end());
     std::vector<cldnn::tensor::value_type> pe_casted(pads_end.begin(), pads_end.end());
@@ -37,6 +44,9 @@ static PoolingParameters GetPoolingParameters(const ngraph::Shape& kernel,
             s = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(strides[2], strides[1], strides[0]));
             pb = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pb_casted[2], pb_casted[1], pb_casted[0]));
             pe = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pe_casted[2], pe_casted[1], pe_casted[0]));
+            if (is_dilation_specified) {
+                d = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(dilations[2], dilations[1], dilations[0]));
+            }
             break;
         }
         case 2: {
@@ -44,6 +54,9 @@ static PoolingParameters GetPoolingParameters(const ngraph::Shape& kernel,
             s = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(strides[1], strides[0], 1));
             pb = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pb_casted[1], pb_casted[0], 0));
             pe = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pe_casted[1], pe_casted[0], 0));
+            if (is_dilation_specified) {
+                d = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(dilations[1], dilations[0], 1));
+            }
             break;
         }
         case 1: {
@@ -51,12 +64,15 @@ static PoolingParameters GetPoolingParameters(const ngraph::Shape& kernel,
             s = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(strides[0], 1, 1));
             pb = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pb_casted[0], 0, 0));
             pe = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pe_casted[0], 0, 0));
+            if (is_dilation_specified) {
+                d = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(dilations[0], 1, 1));
+            }
             break;
         }
         default: IE_THROW() << "Unsupported pooling parameters size. Only 1d, 2d, and 3d cases are supported";
     }
 
-    return {k, s, pb, pe};
+    return {k, s, d, pb, pe};
 }
 
 static void CreateAvgPoolOp(Program& p, const std::shared_ptr<ngraph::op::v1::AvgPool>& op) {
@@ -99,7 +115,60 @@ static void CreateMaxPoolOp(Program& p, const std::shared_ptr<ngraph::op::v1::Ma
     p.AddPrimitiveToProfiler(op);
 }
 
+static void CreateMaxPoolOp(Program& p, const std::shared_ptr<ngraph::op::v8::MaxPool>& op) {
+    p.ValidateInputs(op, {1});
+    if (op->get_output_size() != 2) {
+        IE_THROW() << "MaxPool opset 8 requires 2 outputs";
+    }
+    auto inputPrimitives = p.GetInputPrimitiveIDs(op);
+    const auto layer_type_name = layer_type_name_ID(op);
+    const auto layerName = layer_type_name + ".0";
+
+    const auto mutable_precision = op->get_output_element_type(1);
+    const auto output_shape = op->get_output_shape(1);
+    cldnn::layout mutableLayout = cldnn::layout(DataTypeFromPrecision(mutable_precision),
+                                                DefaultFormatForDims(output_shape.size()),
+                                                tensor_from_dims(output_shape));
+    const auto shared_memory = p.GetEngine().allocate_memory(mutableLayout);
+    const cldnn::primitive_id maxpool_mutable_id_w = layer_type_name + "_md_write";
+    const auto op_friendly_name = op->get_friendly_name();
+    const auto indices_mutable_prim = cldnn::mutable_data(maxpool_mutable_id_w,
+                                                          shared_memory,
+                                                          op_friendly_name);
+    p.primitiveIDs[maxpool_mutable_id_w] = maxpool_mutable_id_w;
+    p.AddPrimitive(indices_mutable_prim);
+    inputPrimitives.push_back(maxpool_mutable_id_w);
+
+    const auto params = GetPoolingParameters(op->get_kernel(), op->get_strides(), op->get_pads_begin(), op->get_pads_end(), op->get_dilations());
+    auto poolPrim = cldnn::pooling(layerName,
+                                   inputPrimitives[0],
+                                   inputPrimitives.back(),
+                                   params.kernel,
+                                   params.stride,
+                                   params.dilation,
+                                   params.pad_begin,
+                                   params.pad_end,
+                                   op->get_axis(),
+                                   DataTypeFromPrecision(op->get_index_element_type()),
+                                   tensor_from_dims(op->get_output_shape(0)),
+                                   DataTypeFromPrecision(op->get_output_element_type(0)),
+                                   op_friendly_name);
+    p.AddPrimitive(poolPrim);
+
+    const cldnn::primitive_id maxpool_mutable_id_r = layer_type_name + ".1";
+    const auto indices_mutable_id_r = cldnn::mutable_data(maxpool_mutable_id_r,
+                                                          { layerName },
+                                                          shared_memory,
+                                                          op_friendly_name);
+    p.primitiveIDs[maxpool_mutable_id_r] = maxpool_mutable_id_r;
+    p.AddPrimitive(indices_mutable_id_r);
+
+    p.AddPrimitiveToProfiler(poolPrim, op);
+}
+
+
 REGISTER_FACTORY_IMPL(v1, MaxPool);
+REGISTER_FACTORY_IMPL(v8, MaxPool);
 REGISTER_FACTORY_IMPL(v1, AvgPool);
 
 }  // namespace intel_gpu
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp
index 7b7e79da274..1a053f53663 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp
@@ -363,6 +363,8 @@ const auto maxPoolv8_ExplicitPad_FloorRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(dilation),
         ::testing::ValuesIn(padBegins),
         ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::FLOOR),
         ::testing::Values(ngraph::op::PadType::EXPLICIT)
 );
@@ -386,6 +388,8 @@ const auto maxPoolv8_SameUpperPad_FloorRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(dilation),
         ::testing::ValuesIn(padBegins),
         ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::FLOOR),
         ::testing::Values(ngraph::op::PadType::SAME_UPPER)
 );
@@ -409,6 +413,8 @@ const auto maxPoolv8_SameLowerPad_FloorRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(dilation),
         ::testing::ValuesIn(padBegins),
         ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::FLOOR),
         ::testing::Values(ngraph::op::PadType::SAME_LOWER)
 );
@@ -432,6 +438,8 @@ const auto maxPoolv8_ExplicitPad_FloorRounding_5Dinput_Params = ::testing::Combi
         ::testing::Values(dilation3D[0]),
         ::testing::ValuesIn(padBegins3D),
         ::testing::ValuesIn(padEnds3D),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::FLOOR),
         ::testing::Values(ngraph::op::PadType::EXPLICIT)
 );
@@ -455,6 +463,8 @@ const auto maxPoolv8_SameUpperPad_FloorRounding_5Dinput_Params = ::testing::Comb
         ::testing::ValuesIn(dilation3D),
         ::testing::ValuesIn(padBegins3D),
         ::testing::ValuesIn(padEnds3D),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::FLOOR),
         ::testing::Values(ngraph::op::PadType::SAME_UPPER)
 );
@@ -478,6 +488,8 @@ const auto maxPoolv8_SameLowerPad_CeilRounding_5Dinput_Params = ::testing::Combi
         ::testing::ValuesIn(dilation3D),
         ::testing::ValuesIn(padBegins3D),
         ::testing::ValuesIn(padEnds3D),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::CEIL),
         ::testing::Values(ngraph::op::PadType::SAME_LOWER)
 );
@@ -501,6 +513,8 @@ const auto maxPoolv8_ExplicitPad_CeilRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(dilation),
         ::testing::ValuesIn(padBegins),
         ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::CEIL),
         ::testing::Values(ngraph::op::PadType::EXPLICIT)
 );
@@ -549,6 +563,8 @@ const auto maxPoolv8_ValidPad_Params = ::testing::Combine(
         ::testing::ValuesIn(dilation),
         ::testing::Values(std::vector<size_t>({0, 0})),
         ::testing::Values(std::vector<size_t>({0, 0})),
+        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(0),
         ::testing::Values(ngraph::op::RoundingType::FLOOR),  // placeholder value - Rounding Type not applicable for Valid pad type
         ::testing::Values(ngraph::op::PadType::VALID)
 );
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp
index 191d35e33ab..22af383877a 100644
--- a/src/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp
@@ -144,7 +144,9 @@ public:
         std::vector<size_t> padBegin, padEnd;
         ngraph::op::PadType padType;
         ngraph::op::RoundingType roundingType;
-        std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = basicParamsSet;
+        ngraph::element::Type indexElementType;
+        int64_t axis;
+        std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) = basicParamsSet;
 
         std::ostringstream results;
         results << "IS=(";
@@ -181,7 +183,9 @@ protected:
         std::vector<size_t> padBegin, padEnd;
         ngraph::op::PadType padType;
         ngraph::op::RoundingType roundingType;
-        std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = basicParamsSet;
+        ngraph::element::Type indexElementType;
+        int64_t axis;
+        std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) = basicParamsSet;
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
         if (selectedType.empty()) {
             selectedType = getPrimitiveType();
@@ -192,7 +196,8 @@ protected:
 
         auto params = ngraph::builder::makeDynamicParams(inPrc, inputDynamicShapes);
         std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makeMaxPoolingV8(params[0], stride, dilation, padBegin, padEnd,
-                                                                                  kernel, roundingType, padType);
+                                                                                  kernel, roundingType, padType,
+                                                                                  indexElementType, axis);
         pooling->get_rt_info() = getCPUInfo();
         ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(pooling->output(0))};
         function = std::make_shared<ngraph::Function>(results, params, "MaxPooling");
@@ -375,15 +380,19 @@ const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax4D = {
 
 const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D = {
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {1, 1}, {0, 0}, {0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
 };
 
 const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D_ref = {
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 2}, {1, 2}, {0, 0}, {0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 1}, {2, 2}, {0, 0}, {0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
 };
 
@@ -467,15 +476,19 @@ const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax5D = {
 
 const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D = {
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
 };
 
 const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D_ref = {
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
         LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 3, 4}, {2, 2, 2}, {2, 1, 1}, {1, 1, 1}, {1, 2, 2},
+                                                        ngraph::element::Type_t::i32, 0,
                                                         ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
 };
 
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/pooling.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/pooling.cpp
index bf4c365ae95..9baaf9c1765 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/pooling.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/pooling.cpp
@@ -9,6 +9,7 @@
 
 using namespace ngraph::helpers;
 using namespace LayerTestsDefinitions;
+using namespace ngraph::element;
 
 namespace {
 const std::vector<InferenceEngine::Precision> netPrecisions = {
@@ -20,14 +21,20 @@ const std::vector<std::vector<size_t >> kernels = {{3, 3},
                                                           {3, 5}};
 const std::vector<std::vector<size_t >> strides = {{1, 1},
                                                           {1, 2}};
+const std::vector<std::vector<size_t >> dilations = {{1, 1},
+                                                          {1, 2}};
 const std::vector<std::vector<size_t >> padBegins = {{0, 0},
                                                             {0, 2}};
 const std::vector<std::vector<size_t >> padEnds = {{0, 0},
                                                           {0, 2}};
 const std::vector<ngraph::op::RoundingType> roundingTypes = {ngraph::op::RoundingType::CEIL,
                                                              ngraph::op::RoundingType::FLOOR};
+const std::vector<ngraph::element::Type_t> indexElementTypes = {ngraph::element::Type_t::i32};
+const std::vector<int64_t> axes = {0, 2};
+const std::vector<size_t > inputShapeSmall = {1, 3, 30, 30};
+const std::vector<size_t > inputShapeLarge = {1, 3, 50, 50};
 
-////* ========== Max Polling ========== */
+////* ========== Max Pooling ========== */
 /* +========== Explicit Pad Floor Rounding ========== */
 const auto maxPool_ExplicitPad_FloorRounding_Params = ::testing::Combine(
         ::testing::Values(PoolingTypes::MAX),
@@ -40,7 +47,7 @@ const auto maxPool_ExplicitPad_FloorRounding_Params = ::testing::Combine(
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_FloorRpunding, PoolingLayerTest,
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_FloorRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_ExplicitPad_FloorRounding_Params,
                                 ::testing::ValuesIn(netPrecisions),
@@ -48,7 +55,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_FloorRpunding, PoolingLayerTe
                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 50, 50})),
+                                ::testing::Values(inputShapeLarge),
                                 ::testing::Values(CommonTestUtils::DEVICE_GPU)),
                         PoolingLayerTest::getTestCaseName);
 
@@ -65,7 +72,7 @@ const auto maxPool_ExplicitPad_CeilRounding_Params = ::testing::Combine(
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_CeilRpunding, PoolingLayerTest,
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_CeilRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_ExplicitPad_CeilRounding_Params,
                                 ::testing::ValuesIn(netPrecisions),
@@ -73,7 +80,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_CeilRpunding, PoolingLayerTes
                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 50, 50})),
+                                ::testing::Values(inputShapeLarge),
                                 ::testing::Values(CommonTestUtils::DEVICE_GPU)),
                         PoolingLayerTest::getTestCaseName);
 
@@ -100,7 +107,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_ExplicitPad_CeilRounding, PoolingLayerTes
                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
                                ::testing::Values(InferenceEngine::Layout::ANY),
                                ::testing::Values(InferenceEngine::Layout::ANY),
-                               ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                               ::testing::Values(inputShapeSmall),
                                ::testing::Values(CommonTestUtils::DEVICE_GPU)),
                        PoolingLayerTest::getTestCaseName);
 
@@ -125,11 +132,11 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_ExplicitPad_FloorRounding, PoolingLayerTe
                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                ::testing::Values(inputShapeSmall),
                                 ::testing::Values(CommonTestUtils::DEVICE_GPU)),
                         PoolingLayerTest::getTestCaseName);
 
-////* ========== Avg and Max Polling Cases ========== */
+////* ========== Avg and Max Pooling Cases ========== */
 /*    ========== Valid Pad Rounding Not Applicable ========== */
 const auto allPools_ValidPad_Params = ::testing::Combine(
         ::testing::Values(PoolingTypes::MAX, PoolingTypes::AVG),
@@ -151,7 +158,61 @@ INSTANTIATE_TEST_SUITE_P(smoke_MAX_and_AVGPool_ValidPad, PoolingLayerTest,
                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 50, 50})),
+                                ::testing::Values(inputShapeLarge),
                                 ::testing::Values(CommonTestUtils::DEVICE_GPU)),
                         PoolingLayerTest::getTestCaseName);
+
+
+
+////* ========== MaxPool v8 ========== */
+///* +========== Explicit Pad Floor Rounding ========== */
+const auto maxPool8_ExplicitPad_FloorRounding_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernels),
+        ::testing::ValuesIn(strides),
+        ::testing::ValuesIn(dilations),
+        ::testing::ValuesIn(padBegins),
+        ::testing::ValuesIn(padEnds),
+        ::testing::ValuesIn(indexElementTypes),
+        ::testing::ValuesIn(axes),
+        ::testing::Values(ngraph::op::RoundingType::FLOOR),
+        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool8_ExplicitPad_FloorRounding, MaxPoolingV8LayerTest,
+                        ::testing::Combine(
+                                maxPool8_ExplicitPad_FloorRounding_Params,
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                ::testing::Values(InferenceEngine::Layout::ANY),
+                                ::testing::Values(InferenceEngine::Layout::ANY),
+                                ::testing::Values(inputShapeSmall),
+                                ::testing::Values(CommonTestUtils::DEVICE_GPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
+/* ========== Explicit Pad Ceil Rounding ========== */
+const auto maxPool8_ExplicitPad_CeilRounding_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernels),
+        ::testing::Values(std::vector<size_t>({1, 1})),
+        ::testing::ValuesIn(dilations),
+        ::testing::ValuesIn(padBegins),
+        ::testing::ValuesIn(padEnds),
+        ::testing::ValuesIn(indexElementTypes),
+        ::testing::ValuesIn(axes),
+        ::testing::Values(ngraph::op::RoundingType::CEIL),
+        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool8_ExplicitPad_CeilRounding, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPool8_ExplicitPad_CeilRounding_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(inputShapeSmall),
+                                 ::testing::Values(CommonTestUtils::DEVICE_GPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
 }  // namespace
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/pooling.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/pooling.hpp
index 6730e6c2b56..7c9f68cdc72 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/pooling.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/pooling.hpp
@@ -54,6 +54,8 @@ typedef std::tuple<
         std::vector<size_t>,            // Dilation
         std::vector<size_t>,            // Pad begin
         std::vector<size_t>,            // Pad end
+        ngraph::element::Type_t,        // Index element type
+        int64_t,                        // Axis
         ngraph::op::RoundingType,       // Rounding type
         ngraph::op::PadType             // Pad type
 > maxPoolV8SpecificParams;
diff --git a/src/tests/functional/shared_test_classes/src/single_layer/pooling.cpp b/src/tests/functional/shared_test_classes/src/single_layer/pooling.cpp
index 3dc2b2c7a36..86934706694 100644
--- a/src/tests/functional/shared_test_classes/src/single_layer/pooling.cpp
+++ b/src/tests/functional/shared_test_classes/src/single_layer/pooling.cpp
@@ -106,7 +106,9 @@ std::string MaxPoolingV8LayerTest::getTestCaseName(const testing::TestParamInfo<
     std::vector<size_t> padBegin, padEnd;
     ngraph::op::PadType padType;
     ngraph::op::RoundingType roundingType;
-    std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = poolParams;
+    ngraph::element::Type indexElementType;
+    int64_t axis;
+    std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) = poolParams;
 
     std::ostringstream result;
     result << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_";
@@ -115,6 +117,8 @@ std::string MaxPoolingV8LayerTest::getTestCaseName(const testing::TestParamInfo<
     result << "D" << CommonTestUtils::vec2str(dilation) << "_";
     result << "PB" << CommonTestUtils::vec2str(padBegin) << "_";
     result << "PE" << CommonTestUtils::vec2str(padEnd) << "_";
+    result << "IET" << indexElementType << "_";
+    result << "A" << axis << "_";
     result << "Rounding=" << roundingType << "_";
     result << "AutoPad=" << padType << "_";
     result << "netPRC=" << netPrecision.name() << "_";
@@ -201,7 +205,9 @@ void MaxPoolingV8LayerTest::SetUp() {
     std::vector<size_t> padBegin, padEnd;
     ngraph::op::PadType padType;
     ngraph::op::RoundingType roundingType;
-    std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = poolParams;
+    ngraph::element::Type indexElementType;
+    int64_t axis;
+    std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) = poolParams;
 
     auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
     auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
@@ -209,9 +215,17 @@ void MaxPoolingV8LayerTest::SetUp() {
             ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
 
     std::shared_ptr<ngraph::Node> maxPool = ngraph::builder::makeMaxPoolingV8(paramOuts[0], stride, dilation, padBegin, padEnd,
-                                                                              kernel, roundingType, padType);
+                                                                              kernel, roundingType, padType,
+                                                                              indexElementType, axis);
 
-    ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(maxPool->output(0))};
+    const auto maxPoolV8_second_output_is_supported = targetDevice == CommonTestUtils::DEVICE_GPU;
+    ngraph::ResultVector results;
+    if (maxPoolV8_second_output_is_supported) {
+        results = {std::make_shared<ngraph::opset3::Result>(maxPool->output(0)),
+                   std::make_shared<ngraph::opset3::Result>(maxPool->output(1))};
+    } else {
+        results = { std::make_shared<ngraph::opset3::Result>(maxPool->output(0)) };
+    }
     function = std::make_shared<ngraph::Function>(results, params, "MaxPoolV8");
 }
 
diff --git a/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp b/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp
index b790b831c23..671bf77e88d 100644
--- a/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp
+++ b/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp
@@ -435,7 +435,9 @@ std::shared_ptr<Node> makeMaxPoolingV8(const ngraph::Output<Node> &in,
                                        const std::vector<size_t> &padsEnd,
                                        const std::vector<size_t> &kernel,
                                        const op::RoundingType &roundingType,
-                                       const op::PadType &padType);
+                                       const op::PadType &padType,
+                                       const ov::element::Type &indexElementType,
+                                       const int64_t axis);
 
 std::shared_ptr<Node> makeROIPooling(const Output<Node>& input,
                                      const Output<Node>& coords,
diff --git a/src/tests/ngraph_helpers/ngraph_functions/src/pooling.cpp b/src/tests/ngraph_helpers/ngraph_functions/src/pooling.cpp
index 43f26ba0272..f7202908a68 100644
--- a/src/tests/ngraph_helpers/ngraph_functions/src/pooling.cpp
+++ b/src/tests/ngraph_helpers/ngraph_functions/src/pooling.cpp
@@ -42,9 +42,12 @@ std::shared_ptr<Node> makeMaxPoolingV8(const ngraph::Output<Node> &in,
                                        const std::vector<size_t> &padsEnd,
                                        const std::vector<size_t> &kernel,
                                        const op::RoundingType &roundingType,
-                                       const op::PadType &padType) {
+                                       const op::PadType &padType,
+                                       const ov::element::Type &indexElementType,
+                                       const int64_t axis) {
     std::shared_ptr<ngraph::Node> pooling = std::make_shared<ngraph::opset8::MaxPool>(in, strides, dilation, padsBegin, padsEnd,
-                                                                                      kernel, roundingType, padType);
+                                                                                      kernel, roundingType, padType,
+                                                                                      indexElementType, axis);
     return pooling;
 }
 

From 6b8cfac82cfec15b4fa79f27559c75288781b607 Mon Sep 17 00:00:00 2001
From: Anton Romanov <anton.romanov@intel.com>
Date: Fri, 24 Dec 2021 11:37:05 +0300
Subject: [PATCH 137/160] Refactor install wheels on azure (#9394)

---
 .ci/azure/linux.yml   | 2 +-
 .ci/azure/windows.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
index adb49d1c85f..33e19d63cf6 100644
--- a/.ci/azure/linux.yml
+++ b/.ci/azure/linux.yml
@@ -203,7 +203,7 @@ jobs:
   - script: ls -alR $(INSTALL_DIR)
     displayName: 'List install files'
 
-  - script: python3 -m pip install $(INSTALL_DIR)/tools/*.whl --find-links=$(INSTALL_DIR)/tools
+  - script: python3 -m pip install openvino-dev --find-links=$(INSTALL_DIR)/tools
     displayName: 'Install wheels'
 
   - script: |
diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index c4028ffc5f6..537a919ed61 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -156,7 +156,7 @@ jobs:
   - script: dir $(INSTALL_DIR) /s
     displayName: 'List install files'
 
-  - script: for %%f  in ($(INSTALL_DIR)\tools\*.whl) do (python -m pip install  %%f --find-links=$(INSTALL_DIR)\tools\)
+  - script: python -m pip install openvino-dev --find-links=$(INSTALL_DIR)\tools
     displayName: 'Install Wheels'
 
   - script: call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)"  $(REPO_DIR)\tests\samples_tests

From bd2880812fcbe0255d0c90ea03731b3cac9b8ed2 Mon Sep 17 00:00:00 2001
From: Yegor Kruglov <yegor.kruglov@intel.com>
Date: Fri, 24 Dec 2021 11:38:21 +0300
Subject: [PATCH 138/160] FifoQueueDequeue replacer (#8891)

* added_replacer

* updated comments

* move cut to fifo_replacer

* extend shape serializer for parametr node

* warning message and docstrings

* docs update

* doc fix
---
 .../Supported_Frameworks_Layers.md            |   2 +
 tools/mo/automation/package_BOM.txt           |   1 +
 .../tools/mo/front/tf/QueueDequeue_ext.py     |  43 ++++++
 .../tools/mo/front/tf/fifo_replacer.py        |  58 +++++++-
 tools/mo/openvino/tools/mo/ops/parameter.py   |   2 +
 .../mo/front/tf/fifo_replacer_test.py         | 138 +++++++++++++++++-
 6 files changed, 242 insertions(+), 2 deletions(-)
 create mode 100644 tools/mo/openvino/tools/mo/front/tf/QueueDequeue_ext.py

diff --git a/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md b/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md
index 03efe148ad8..d1968f37a70 100644
--- a/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md
+++ b/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md
@@ -269,7 +269,9 @@ Some TensorFlow\* operations do not match to any Inference Engine layer, but are
 | Placeholder |  |
 | PlaceholderWithDefault |  |
 | Prod |  |
+| QueueDequeue | Supported only when it is part of a sub-graph of the special form |
 | QueueDequeueUpToV2 | Supported only when it is part of a sub-graph of the special form |
+| QueueDequeueV2 | Supported only when it is part of a sub-graph of the special form |
 | RandomUniform |  |
 | RandomUniformInt |  |
 | Range |  |
diff --git a/tools/mo/automation/package_BOM.txt b/tools/mo/automation/package_BOM.txt
index 65440c98af6..8acb802577a 100644
--- a/tools/mo/automation/package_BOM.txt
+++ b/tools/mo/automation/package_BOM.txt
@@ -604,6 +604,7 @@ openvino/tools/mo/front/tf/placeholder_ext.py
 openvino/tools/mo/front/tf/placeholder_with_default_ext.py
 openvino/tools/mo/front/tf/pooling_ext.py
 openvino/tools/mo/front/tf/prelu.py
+openvino/tools/mo/front/tf/QueueDequeue_ext.py
 openvino/tools/mo/front/tf/random_uniform_ext.py
 openvino/tools/mo/front/tf/random_uniform_int_ext.py
 openvino/tools/mo/front/tf/range_ext.py
diff --git a/tools/mo/openvino/tools/mo/front/tf/QueueDequeue_ext.py b/tools/mo/openvino/tools/mo/front/tf/QueueDequeue_ext.py
new file mode 100644
index 00000000000..c60726a2ae8
--- /dev/null
+++ b/tools/mo/openvino/tools/mo/front/tf/QueueDequeue_ext.py
@@ -0,0 +1,43 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+from openvino.tools.mo.front.extractor import FrontExtractorOp
+from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor, tf_tensor_shape
+from openvino.tools.mo.graph.graph import Node
+from openvino.tools.mo.ops.op import Op
+
+
+def get_attrs(node: Node):
+    shapes = node.pb.attr["_output_shapes"].list.shape
+    tf_types = node.pb.attr["component_types"].list.type
+    extracted_types = []
+    for t in tf_types:
+        extracted_types.append(tf_dtype_extractor(t))
+    result_shapes = []
+    for shape_pb in shapes:
+        result_shapes.append(tf_tensor_shape(shape_pb))
+    assert len(result_shapes) == len(extracted_types), "Output shapes do not match output" \
+                                                       "types in the node {}".format(node.soft_get('name', node.id))
+    attrs = {"shapes": result_shapes, "types": extracted_types, 'out_ports_count': len(result_shapes)}
+    return attrs
+
+
+class QueueDequeueV1Extractor(FrontExtractorOp):
+    op = "QueueDequeue"
+    enabled = True
+
+    @classmethod
+    def extract(cls, node):
+        attrs = get_attrs(node)
+        Op.update_node_stat(node, attrs)
+        return cls.enabled
+
+
+class QueueDequeueV2Extractor(FrontExtractorOp):
+    op = "QueueDequeueV2"
+    enabled = True
+
+    @classmethod
+    def extract(cls, node):
+        attrs = get_attrs(node)
+        Op.update_node_stat(node, attrs)
+        return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py b/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py
index bc2fc7cfec5..8b537db8e5e 100644
--- a/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py
+++ b/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py
@@ -2,12 +2,19 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import logging as log
+from collections import defaultdict
 
 import numpy as np
 
+from openvino.tools.mo.front.extractor import add_input_ops
+from openvino.tools.mo.front.output_cut import OutputCut
+from openvino.tools.mo.front.user_data_repack import UserDataRepack
+from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_precision, SUPPORTED_DATA_TYPES
+
 from openvino.tools.mo.ops.parameter import Parameter
-from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
+from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph, FrontReplacementPattern
 from openvino.tools.mo.graph.graph import Graph, Node
+from openvino.tools.mo.utils.error import Error
 
 
 class FIFOQueue(FrontReplacementSubgraph):
@@ -113,3 +120,52 @@ class QueueDequeueManyV2(FrontReplacementSubgraph):
         graph.remove_node(match['queue_deque'].id)
         graph.remove_node(match['fifo_queue'].id)
 
+
+class FIFOQueueDequeueCut(FrontReplacementPattern):
+    """
+    Cuts FIFOQueue -> QueueDequeue pattern in order to enable Out Of the Box (OOB) usage.
+    Pass runs only if user didn't specify any input names and shapes.
+    This transformation relies on output shapes and types extracted from QueueDequeue node.
+    In the meantime, the transformations FIFOQueue and QueueDequeueManyV2 expects output shapes and types extracted
+    from FIFOQueue node.
+    """
+    enabled = True
+    graph_condition = [lambda graph: graph.graph['cmd_params'].input is None]
+
+    def run_before(self):
+        return [OutputCut]
+
+    def run_after(self):
+        return [UserDataRepack]
+
+    def find_and_replace_pattern(self, graph: Graph):
+        fifo_qd_shapes = defaultdict(list)
+        for node in graph.get_op_nodes():
+            if node.op not in ["QueueDequeue", "QueueDequeueV2"]:
+                continue
+
+            new_inputs = ""
+            fifo_qd_name = node.soft_get('name', node.id)
+            for port_idx, port in node.out_ports().items():
+                if port.disconnected():
+                    continue
+                if not np_data_type_to_precision(node.types[port_idx]) in SUPPORTED_DATA_TYPES:
+                    raise Error("Data type {} is not supported for the"
+                                "node {}".format(node.types[port_idx], fifo_qd_name))
+
+                fifo_qd_shapes[fifo_qd_name].append(dict(
+                    shape=node.shapes[port_idx],
+                    out=port_idx,
+                    data_type=node.types[port_idx]
+                ))
+                new_inputs += "{}:{}, ".format(fifo_qd_name, port_idx)
+
+            log.error(
+                "Found TF {} operation in the model. "
+                "PLEASE NOTE, the model will contain new input(s) ".format(node.op)
+                + new_inputs +
+                "created due to automatically triggered pruning transformation for this operation.",
+                extra={'is_warning': True}
+            )
+
+        add_input_ops(graph, fifo_qd_shapes, True)
diff --git a/tools/mo/openvino/tools/mo/ops/parameter.py b/tools/mo/openvino/tools/mo/ops/parameter.py
index abaf36be870..9d8989044fe 100644
--- a/tools/mo/openvino/tools/mo/ops/parameter.py
+++ b/tools/mo/openvino/tools/mo/ops/parameter.py
@@ -50,6 +50,8 @@ class Parameter(Op):
         if not node.has_valid('user_shape'):
             return ','.join([str(i) for i in unmask_shape(node.shape)])
         shape = node.soft_get('user_shape')
+        if isinstance(shape, np.ma.masked_array):
+            shape = unmask_shape(shape)
         return ','.join(map(serialize_dimension, shape))
 
     def supported_attrs(self):
diff --git a/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py b/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
index c2af703dc39..9e601554bd7 100644
--- a/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
+++ b/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
@@ -5,7 +5,9 @@ import unittest
 
 import numpy as np
 
-from openvino.tools.mo.front.tf.fifo_replacer import FIFOQueue
+from openvino.tools.mo.front.common.partial_infer.utils import shape_array
+from openvino.tools.mo.front.tf.fifo_replacer import FIFOQueue, FIFOQueueDequeueCut
+from openvino.tools.mo.utils.ir_engine.compare_graphs import compare_graphs
 from unit_tests.utils.graph import build_graph_with_edge_attrs
 
 
@@ -64,3 +66,137 @@ class TestFIFOQueueReplacement(unittest.TestCase):
             self.fail("Can't get new placeholder. Broken edge. Additional information: {}".format(e))
         self.assertEqual(new_ph_dict['name'], 'batch_join/fifo_queue')
         self.assertTrue(np.array_equal(new_ph_dict['shape'], np.array([1, 2, 3])))
+
+
+class FIFOQueueDequeueCutTest(unittest.TestCase):
+    def test_one_output_v1(self):
+        graph = build_graph_with_edge_attrs(
+            {
+                'queue_dequeue': {'kind': 'op', 'op': 'QueueDequeue', 'shapes': shape_array([[2, 2]]),
+                                  'types': [np.int32]},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+            },
+            [
+                ('queue_dequeue', 'sub', {'out': 0, 'in': 0}),
+            ]
+        )
+
+        graph_ref = build_graph_with_edge_attrs(
+            {
+                'parameter_1': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([2, 2]), 'type': np.int32},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+            },
+            [
+                ('parameter_1', 'sub', {'out': 0, 'in': 0}),
+            ]
+        )
+
+        FIFOQueueDequeueCut().find_and_replace_pattern(graph)
+
+        flag, msg = compare_graphs(graph, graph_ref, last_node='sub')
+        self.assertTrue(flag, msg)
+
+    def test_one_output_v2(self):
+        graph = build_graph_with_edge_attrs(
+            {
+                'queue_dequeue': {'kind': 'op', 'op': 'QueueDequeueV2', 'shapes': shape_array([[2, 2]]),
+                                  'types': [np.int32]},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+            },
+            [
+                ('queue_dequeue', 'sub', {'out': 0, 'in': 0}),
+            ]
+        )
+
+        graph_ref = build_graph_with_edge_attrs(
+            {
+                'parameter_1': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([2, 2]), 'type': np.int32},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+            },
+            [
+                ('parameter_1', 'sub', {'out': 0, 'in': 0}),
+            ]
+        )
+
+        FIFOQueueDequeueCut().find_and_replace_pattern(graph)
+
+        flag, msg = compare_graphs(graph, graph_ref, last_node='sub')
+        self.assertTrue(flag, msg)
+
+    def test_two_outputs_v1(self):
+        graph = build_graph_with_edge_attrs(
+            {
+                'queue_dequeue': {'kind': 'op', 'op': 'QueueDequeue', 'shapes': [shape_array([2, 2]),
+                                                                                 shape_array([1, 1])],
+                                 'types': [np.int32, np.float32]},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+                'add': {'kind': 'op', 'op': 'Add'},
+                'concat': {'kind': 'op', 'op': 'Concat'}
+            },
+            [
+                ('queue_dequeue', 'sub', {'out': 0, 'in': 0}),
+                ('queue_dequeue', 'add', {'out': 1, 'in': 0}),
+                ('sub', 'concat', {'out': 0, 'in': 0}),
+                ('add', 'concat', {'out': 0, 'in': 1})
+            ]
+        )
+
+        graph_ref = build_graph_with_edge_attrs(
+            {
+                'parameter_1': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([2, 2]), 'data_type': np.int32},
+                'parameter_2': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([1, 1]), 'data_type': np.float32},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+                'add': {'kind': 'op', 'op': 'Add'},
+                'concat': {'kind': 'op', 'op': 'Concat'}
+            },
+            [
+                ('parameter_1', 'sub', {'out': 0, 'in': 0}),
+                ('parameter_2', 'add', {'out': 0, 'in': 0}),
+                ('sub', 'concat', {'out': 0, 'in': 0}),
+                ('add', 'concat', {'out': 0, 'in': 1})
+            ]
+        )
+
+        FIFOQueueDequeueCut().find_and_replace_pattern(graph)
+
+        flag, msg = compare_graphs(graph, graph_ref, last_node='concat', check_op_attrs=True)
+        self.assertTrue(flag, msg)
+
+    def test_two_outputs_v2(self):
+        graph = build_graph_with_edge_attrs(
+            {
+                'queue_dequeue': {'kind': 'op', 'op': 'QueueDequeueV2', 'shapes': [shape_array([2, 2]),
+                                                                                   shape_array([1, 1])],
+                                 'types': [np.int32, np.float32]},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+                'add': {'kind': 'op', 'op': 'Add'},
+                'concat': {'kind': 'op', 'op': 'Concat'}
+            },
+            [
+                ('queue_dequeue', 'sub', {'out': 0, 'in': 0}),
+                ('queue_dequeue', 'add', {'out': 1, 'in': 0}),
+                ('sub', 'concat', {'out': 0, 'in': 0}),
+                ('add', 'concat', {'out': 0, 'in': 1})
+            ]
+        )
+
+        graph_ref = build_graph_with_edge_attrs(
+            {
+                'parameter_1': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([2, 2]), 'data_type': np.int32},
+                'parameter_2': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([1, 1]), 'data_type': np.float32},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+                'add': {'kind': 'op', 'op': 'Add'},
+                'concat': {'kind': 'op', 'op': 'Concat'}
+            },
+            [
+                ('parameter_1', 'sub', {'out': 0, 'in': 0}),
+                ('parameter_2', 'add', {'out': 0, 'in': 0}),
+                ('sub', 'concat', {'out': 0, 'in': 0}),
+                ('add', 'concat', {'out': 0, 'in': 1})
+            ]
+        )
+
+        FIFOQueueDequeueCut().find_and_replace_pattern(graph)
+
+        flag, msg = compare_graphs(graph, graph_ref, last_node='concat', check_op_attrs=True)
+        self.assertTrue(flag, msg)
\ No newline at end of file

From 5da7a1119ca57c66b661145c55cac64b41cf2c7f Mon Sep 17 00:00:00 2001
From: Eugeny Volosenkov <eugeny.volosenkov@intel.com>
Date: Fri, 24 Dec 2021 11:43:07 +0300
Subject: [PATCH 139/160] Fix ChangeOutputTypeAttributes and CenterNet model
 conversion (#9230)

* fix fp16 issue

* fix comments

* add test for scalar case

* fix prev commit

* fix test

* revert to size
---
 .../mo/openvino/tools/mo/back/ChangeOutputTypeAttributes.py | 4 ++--
 .../unit_tests/mo/back/ChangeOutputTypeAttributes_test.py   | 6 ++++++
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/tools/mo/openvino/tools/mo/back/ChangeOutputTypeAttributes.py b/tools/mo/openvino/tools/mo/back/ChangeOutputTypeAttributes.py
index 73b02e70028..959953178bd 100644
--- a/tools/mo/openvino/tools/mo/back/ChangeOutputTypeAttributes.py
+++ b/tools/mo/openvino/tools/mo/back/ChangeOutputTypeAttributes.py
@@ -85,8 +85,8 @@ def assert_that_is_castable_to_fp16(node: Node):
     original_output = node.out_port(0).data.get_value()
     node.infer(node)
     casted_output = node.out_port(0).data.get_value()
-    original_output_len = len(original_output) if hasattr(original_output, '__len__') else None
-    casted_output_len = len(casted_output) if hasattr(casted_output, '__len__') else None
+    original_output_len = original_output.size if hasattr(original_output, 'size') else None
+    casted_output_len = casted_output.size if hasattr(casted_output, 'size') else None
 
     if original_output_len != casted_output_len:
         raise Error("Try to convert with --data_type=FP32 argument. "
diff --git a/tools/mo/unit_tests/mo/back/ChangeOutputTypeAttributes_test.py b/tools/mo/unit_tests/mo/back/ChangeOutputTypeAttributes_test.py
index 470ebfe440a..f385a62d601 100644
--- a/tools/mo/unit_tests/mo/back/ChangeOutputTypeAttributes_test.py
+++ b/tools/mo/unit_tests/mo/back/ChangeOutputTypeAttributes_test.py
@@ -64,6 +64,12 @@ class ChangeOutputTypeAttributesTests(unittest.TestCase):
         graph, graph_ref = build_cast_test_graphs(input_data, dst_type_str='FP16')
         self.assertRaises(Error, ChangeOutputTypeAttributes().find_and_replace_pattern, graph)
 
+    def test_cast_with_scalar(self):
+        input_data = np.array(4)
+        graph, graph_ref = build_cast_test_graphs(input_data, dst_type_str='FP16')
+        ChangeOutputTypeAttributes().find_and_replace_pattern(graph)
+        (flag, resp) = compare_graphs(graph, graph_ref, 'res', check_op_attrs=True)
+        self.assertTrue(flag, resp)
 
 def build_range_test_graphs(start=0, limit=10, delta=1, dst_type_str='FP16',
                             src_type_str='FP32', returns_shape_value=None):

From bc5da8d5223b91dc393ae5b9847376548e4a6f20 Mon Sep 17 00:00:00 2001
From: Liubov Talamanova <piccione-mail@yandex.ru>
Date: Fri, 24 Dec 2021 12:34:23 +0300
Subject: [PATCH 140/160] [POT] Handle exception (#9405)

---
 tools/pot/openvino/tools/pot/version.py | 2 +-
 tools/pot/setup.py                      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/pot/openvino/tools/pot/version.py b/tools/pot/openvino/tools/pot/version.py
index 1b22b50012a..f5274603cf1 100644
--- a/tools/pot/openvino/tools/pot/version.py
+++ b/tools/pot/openvino/tools/pot/version.py
@@ -14,7 +14,7 @@ def generate_pot_version():
         branch_name = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).strip().decode()
         commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip().decode()
         return "custom_{}_{}".format(branch_name, commit_hash)
-    except (subprocess.CalledProcessError, FileNotFoundError):
+    except Exception: # pylint:disable=W0703
         return "unknown version"
 
 
diff --git a/tools/pot/setup.py b/tools/pot/setup.py
index 2c9bc1c3bb8..b993033d8c3 100644
--- a/tools/pot/setup.py
+++ b/tools/pot/setup.py
@@ -19,7 +19,7 @@ def generate_pot_version():
         branch_name = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).strip().decode()
         commit_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip().decode()
         return "custom_{}_{}".format(branch_name, commit_hash)
-    except (subprocess.CalledProcessError, FileNotFoundError):
+    except Exception: # pylint:disable=W0703
         return "unknown version"
 
 

From 49b5e5728ba62a3e93ce63557accc85664abfc04 Mon Sep 17 00:00:00 2001
From: Maxim Shevtsov <maxim.y.shevtsov@intel.com>
Date: Fri, 24 Dec 2021 12:55:22 +0300
Subject: [PATCH 141/160] Auto Batching impl  (#7883)

* auto-batching POC squashed (all commits from auto-batch-2021.3 branch)

(cherry picked from commit d7742f2c747bc514a126cc9a4d5b99f0ff5cbbc7)

* applying/accomodating the API changes after rebase to the master

* replaying modified version of actual batch selection

* eearly experiments with model mem footprint

* changes from rebasing to the latest master

* experimenting with DG1 on the batch size selection, also collecting the mem footprint

* WIP:moving the auto-batching to the icore to let the MULT/AUTO support that, ALLOW_AUTO_BATCHING as a conventional config key. still fials hot device swap

* quick-n-dirty batch footpint vs device total mem

* code style

* testing which models perform badly due to kernels and NOT (batched) footprint

* stub  pipeline task to comunicate the readiness rather than promise/future

* quick-n-dirty timeout impl

* explicit _completionTasks,reverting BA to use the timeout

* inputs outputs copies, works with AUTO and demo now

* accomodate the config per device-id, after rebase to the latest master

* allowing the auto-batching only with tput hint to let more conventional tests pass

* fix the pre-mature timeout restaring via waiting for batch1 requests completion

* moved the bacthed request statring ( along with input copies) to the dedicated thread

* [IE CLDNN] Disable bs_fs_yx_bsv16_fsv16 format for int8 convolution

* code style

* increasing the timeout to test the ssd_* models perf (timeout?) issues

* reducing number of output stuff in BA to avoid bloating the logs in experiments

* more aggressive batching for experiments, not limited to 32 and also 4 as a min

* more accurate timeout debugging info

* getting the reqs limitation from the plugin SetConfig as well

* refactor the reshape logic a bit to accomodate CPU for bathcing, also added remeote context

* let the benchamrk_app to consume specific batch values for the auto-batching such as BATCH:GPU(4)

* auto-batching functional test (with results check vs ref) and GPU instance for that

* fixed arithemtic on blobs ptrs

* clang

* handling possible batched network failure

* BATCH as the constants device name in test

* ENABLE_BATCH

* func tests for CPU, also DetectionOutput hetero tests (CPU and GPU)

* DetectionOutput hetero test for the CPU

* reenabling the Auto-Batching in the AUTO

* auto-batching device enabled in the test

* fixed the DO test

* improve the loading loop logic

* brushed the config keys

* allow hetero code-path for explicit device name like BATCH:GPU(4), used in the hetero code-path tests

* fix the test after refactoring

* clang

* moving ThreadSafeQueue to the ie_parallel, as it is re-used in the AUTO/MULTI and BATCH now

* auto-batching hetero test (subgraph with DetectionOutput)

* fixed minor changes that were result of experiments with impl

* code-style

* brushing, disabling CPU's HETERO tests until planned activity for 22.2

* removing home-baked MAX_BATCH_SZIE and swicthing to the official impl by GPU team

* remote blobs tests for the auto-batching (old API)

* brushed names a bit

* CreateContext and LoadNEtwork with context for the Auto-Batching plus remote-blobs tests

* fixed the ieUnitTests with adding CreateContext stub to the MockICore

* clang

* improved remote-blobs tests

* revert the back BA from exeprimenents with AB + device_use_mem

* conformance tests for BATCH, alos batch size 1 is default for BATCH:DEVICE

* remote blobs 2.0 tests, issue with context having the orig device name

* debugging DG1 perf drop (presumably due to non-fitting the device-mem)

* disbaling WA with batch/=2 for excesive mem footptint, leaving only streams 2

* remote blobs 2.0 tests for different tensor sharing types

* converting assert to throw to accomodate legacy API where the lock() was possible to be called

* revert the timeout back to avoid mixing the studies, fixed the footprint calc

* reverting to estimating the max batch by extrapolating from bacth1 size

* more conservative footptint etimation (with bacth1), graceful bacth 1 handling without duplication

* even graceful batch 1 handling without duplication

* WA for MAX_BATCH_SIZE failure, removing batch4 as a min for the auto-batching

* AutoBatchPlugin -> ov_auto_batch_plugin

* WA for gcc 4.8

* clang

* fix misprint

* fixed errors resulted from recent OV's Variant to Any transition

* skip auto-batching for already-batched networks

* AUTO_BATCH_TIMEOUT and tests

* GPU-specific L3

* switched to pure config, also improved ALLOW_AUTO_BATCHING config key handling logic

* debugging device info

* enabling the config tests for the GPU and fixing the Auto-batching tests to pass

* making the default (when not recognized the driver) cache size more aggressive, to accomodate recent HW with old drivers

* skip auto-batching for RNNs and alikes (e.g. single CHW input)

* fixed fallback to the bacth1 and moved HETERO path under condition to avoid bloating

* brushing

* Auto plugin GetMetric support gpu auto-batch

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>

* add test case

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>

* add comments on test

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>

* brushing the vars names, alos adding the excpetion handling

* disabling the auto-batching for the networks with non-batched outputs and faster-rcnn and alikes (CVS-74085) to minimize the of #failures

* add try catch

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>

* brushing the code changed in the GPU plugin

* Auto-Batch requests tests

* brushed varibles a bit (ref)

* cleaned debug output from the ie_core

* cleaned cmake for the Auto-Batch

* removed batchN estimation from batch1

* cleaned from debug printf

* comments, cleanup

* WA the mock test errors introduced with merging the https://github.com/myshevts/openvino/pull/13

* Adding back  removed batchN estimation from batch1 to debug degradations on DG1 (resulted from too optimistic MAX_BATCH_SIZE?). This partially reverts commit e8f1738ac19d20dd56f36d4e824bf273fd6ea917.

* brushing ie_core.cpp

* fix 32bit compilation

* Code review: ENABLE_AUTO_BATCH

* consolidate the auot-batching logic in ie_core.cpp into single ApplyAutoBAtching

* renamed brushed the OPTIMAL_BATCH (now with_SIZE) and mimicks the MAX_BATCH_SZIE  wrt MODEL_PTR

* default value for the OPTIMAL_BATCH_SIZE

* clang

* accomodate new func tests location

* fix shuffle of headers after clang + copyrights

* fixed misprint made during code refactoring

* moving the common therad-safe containers (like ThreadSafeQueue) to the dedicated dev_api header

* switch from the device name to the OPTIMAL_BATCH_SIZE metric presence as a conditin to consider Auto-Batching

* switching from the unsafe size() and minimizing time under lock

* code style

* brushed the ApplyAutoBatching

* brushed the netric/config names and descriptions

* completed the core intergration tests for the auto-batching

* ExecGraphInfo and check for incorrect cfg

* removed explicit dependencies from cmake file of the plugin

* disabling Auto-Batching thru the tput hint (to preserve current product default), only excplicit like BATCH:GPU used in the tests

Co-authored-by: Roman Lyamin <roman.lyamin@intel.com>
Co-authored-by: Hu, Yuan2 <yuan2.hu@intel.com>
---
 cmake/features.cmake                          |   2 +
 docs/IE_DG/supported_plugins/GPU.md           |   3 +
 docs/snippets/GPU_Metric1.cpp                 |   8 +
 .../api/intel_gpu/runtime/device_info.hpp     |   5 +
 .../benchmark_app/remote_blobs_filling.cpp    |   1 +
 samples/cpp/benchmark_app/utils.cpp           |   6 +-
 src/bindings/c/tests/CMakeLists.txt           |   4 +
 src/inference/dev_api/ie_icore.hpp            |  28 +
 .../dev_api/performance_heuristics.hpp        |   8 +-
 .../threading/ie_thread_safe_containers.hpp   |  86 +++
 src/inference/include/ie/ie_plugin_config.hpp |  21 +
 src/inference/src/ie_core.cpp                 | 137 +++-
 src/plugins/CMakeLists.txt                    |   4 +
 src/plugins/auto/executable_network.cpp       |  25 +-
 src/plugins/auto/executable_network.hpp       |  80 +-
 src/plugins/auto_batch/CMakeLists.txt         |  20 +
 src/plugins/auto_batch/auto_batch.cpp         | 731 ++++++++++++++++++
 src/plugins/auto_batch/auto_batch.hpp         | 159 ++++
 src/plugins/intel_cpu/src/mkldnn_plugin.cpp   |   4 +-
 src/plugins/intel_gpu/src/plugin/plugin.cpp   |  74 +-
 .../inference_engine/CMakeLists.txt           |   4 +
 .../include/api_conformance_helpers.hpp       |  11 +-
 .../src/behavior/infer_request/callback.cpp   |   6 +
 .../src/behavior/infer_request/io_blob.cpp    |   6 +
 .../behavior/infer_request/multitheading.cpp  |   6 +
 .../infer_request/set_blob_by_type.cpp        |   6 +
 .../src/behavior/infer_request/wait.cpp       |   5 +
 .../auto_batching/auto_batching_tests.cpp     |  31 +
 .../cldnn_remote_blob_tests.cpp               |  44 +-
 .../gpu_remote_tensor_tests.cpp               |  78 +-
 .../auto_batching/auto_batching_tests.cpp     |  31 +
 .../executable_network/exec_net_base.cpp      |  11 +
 .../executable_network/get_metric.cpp         |  10 +-
 .../behavior/infer_request/callback.cpp       |  10 +
 .../behavior/infer_request/multithreading.cpp |  10 +
 .../behavior/infer_request/wait.cpp           |  19 +-
 .../behavior/ov_plugin/core_integration.cpp   |  10 +-
 .../behavior/plugin/configuration_tests.cpp   |  57 +-
 .../behavior/plugin/core_integration.cpp      |  31 +-
 .../functional/plugin/shared/CMakeLists.txt   |   5 +
 .../auto_batching/auto_batching_tests.hpp     | 161 ++++
 .../common_test_utils/test_constants.hpp      |   1 +
 .../cpp_interfaces/interface/mock_icore.hpp   |   3 +
 .../ngraph_functions/subgraph_builders.hpp    |  38 +
 .../unit/auto/exec_network_get_metrics.cpp    |  61 +-
 .../behavior/shared_tests/CMakeLists.txt      |   5 +
 .../functional/shared_tests/CMakeLists.txt    |   4 +
 47 files changed, 1882 insertions(+), 188 deletions(-)
 create mode 100644 src/inference/dev_api/threading/ie_thread_safe_containers.hpp
 create mode 100644 src/plugins/auto_batch/CMakeLists.txt
 create mode 100644 src/plugins/auto_batch/auto_batch.cpp
 create mode 100644 src/plugins/auto_batch/auto_batch.hpp
 create mode 100644 src/tests/functional/plugin/cpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp
 create mode 100644 src/tests/functional/plugin/gpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp
 create mode 100644 src/tests/functional/plugin/shared/include/auto_batching/auto_batching_tests.hpp

diff --git a/cmake/features.cmake b/cmake/features.cmake
index 1a238a20e2a..e1655250327 100644
--- a/cmake/features.cmake
+++ b/cmake/features.cmake
@@ -100,6 +100,8 @@ ie_option (ENABLE_GAPI_PREPROCESSING "Enables G-API preprocessing" ON)
 ie_option (ENABLE_MULTI "Enables MULTI Device Plugin" ON)
 ie_option (ENABLE_AUTO "Enables AUTO Device Plugin" ON)
 
+ie_option (ENABLE_AUTO_BATCH "Enables Auto-Batching Plugin" ON)
+
 ie_option (ENABLE_HETERO "Enables Hetero Device Plugin" ON)
 
 ie_option (ENABLE_TEMPLATE "Enable template plugin" ON)
diff --git a/docs/IE_DG/supported_plugins/GPU.md b/docs/IE_DG/supported_plugins/GPU.md
index ae294da6770..7dc8f7b0023 100644
--- a/docs/IE_DG/supported_plugins/GPU.md
+++ b/docs/IE_DG/supported_plugins/GPU.md
@@ -141,6 +141,9 @@ When specifying key values as raw strings (that is, when using Python API), omit
 
 @snippet snippets/GPU_Metric1.cpp part1
 
+* OPTIMAL_BATCH_SIZE : Returns _optimal_ batch size for a given network on the given GPU device. The returned value is aligned to power of 2. Also, MODEL_PTR is the required option for this metric since the optimal batch size highly depends on the model. If the MODEL_PTR is not given, the value of 1 is returned. The example code to set the required and optional configs for this metric is available in the following snippet:
+
+@snippet snippets/GPU_Metric1.cpp part2
 ## GPU Context and Video Memory Sharing RemoteBlob API
 
 See [RemoteBlob API of GPU Plugin](GPU_RemoteBlob_API.md)
diff --git a/docs/snippets/GPU_Metric1.cpp b/docs/snippets/GPU_Metric1.cpp
index 50ccb61a4cd..3d5ee4129db 100644
--- a/docs/snippets/GPU_Metric1.cpp
+++ b/docs/snippets/GPU_Metric1.cpp
@@ -14,4 +14,12 @@ options.insert(std::make_pair("AVAILABLE_DEVICE_MEM_SIZE", available_device_mem_
 
 auto max_batch_size = core.GetMetric("GPU", GPU_METRIC_KEY(MAX_BATCH_SIZE), options).as<uint32_t>();
 //! [part1]
+//! [part2]
+std::map<std::string, Parameter> opt = {{"MODEL_PTR", cnnNetwork.getFunction()}}; // Required. Same usage as for the MAX_BATCH_SIZE above. If not set, the OPTIONAL_BATCH_SIZE returns 1.
+// This is not entirely GPU-specific metric (so METRIC_KEY is used rather than GPU_METRIC_KEY below),
+// but the GPU is the only device that supports that at the moment.
+// For the GPU, the metric already accommodates limitation for the on-device memory that the MAX_BATCH_SIZE poses.
+// so OPTIMAL_BATCH_SIZE is always less than MAX_BATCH_SIZE. Unlike the latter it is also aligned to the power of 2.
+auto optimal_batch_size = core.GetMetric("GPU", METRIC_KEY(OPTIMAL_BATCH_SIZE), options).as<unsigned int>();
+//! [part2]
 }
diff --git a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/device_info.hpp b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/device_info.hpp
index f1398341304..4350046bcd7 100644
--- a/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/device_info.hpp
+++ b/inference-engine/thirdparty/clDNN/api/intel_gpu/runtime/device_info.hpp
@@ -6,6 +6,7 @@
 
 #include <string>
 #include <vector>
+#include <tuple>
 
 namespace cldnn {
 /// @addtogroup cpp_api C++ API
@@ -25,6 +26,10 @@ struct gfx_version {
     uint16_t major;
     uint8_t minor;
     uint8_t revision;
+    friend bool operator < (const gfx_version& l, const gfx_version& r)  {
+        return std::tie(l.major, l.minor, l.revision)
+               < std::tie(r.major, r.minor, r.revision); // same order
+    }
 };
 
 /// @brief Information about the device properties and capabilities.
diff --git a/samples/cpp/benchmark_app/remote_blobs_filling.cpp b/samples/cpp/benchmark_app/remote_blobs_filling.cpp
index cdb30ceb7e4..6a98825f87c 100644
--- a/samples/cpp/benchmark_app/remote_blobs_filling.cpp
+++ b/samples/cpp/benchmark_app/remote_blobs_filling.cpp
@@ -124,6 +124,7 @@ std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> getRemoteInputBlo
         }
 
         auto blob = InferenceEngine::gpu::make_shared_blob(desc, context, clBuffer.back());
+        blob->allocate();
         remoteBlobs[name].push_back(blob);
     };
 
diff --git a/samples/cpp/benchmark_app/utils.cpp b/samples/cpp/benchmark_app/utils.cpp
index 752539b5873..734c096abde 100644
--- a/samples/cpp/benchmark_app/utils.cpp
+++ b/samples/cpp/benchmark_app/utils.cpp
@@ -109,8 +109,10 @@ std::vector<float> splitFloat(const std::string& s, char delim) {
 
 std::vector<std::string> parseDevices(const std::string& device_string) {
     std::string comma_separated_devices = device_string;
-    if (comma_separated_devices.find(":") != std::string::npos) {
-        comma_separated_devices = comma_separated_devices.substr(comma_separated_devices.find(":") + 1);
+    auto colon = comma_separated_devices.find(":");
+    if (colon != std::string::npos) {
+        auto bracket = comma_separated_devices.find("(");  // e.g. in BATCH:GPU(4)
+        comma_separated_devices = comma_separated_devices.substr(colon + 1, bracket - colon - 1);
     }
     if ((comma_separated_devices == "MULTI") || (comma_separated_devices == "HETERO"))
         return std::vector<std::string>();
diff --git a/src/bindings/c/tests/CMakeLists.txt b/src/bindings/c/tests/CMakeLists.txt
index 9135c944b7a..8b0a128212b 100644
--- a/src/bindings/c/tests/CMakeLists.txt
+++ b/src/bindings/c/tests/CMakeLists.txt
@@ -26,6 +26,10 @@ if(ENABLE_AUTO OR ENABLE_MULTI)
     add_dependencies(${TARGET_NAME} ov_auto_plugin)
 endif()
 
+if(ENABLE_AUTO_BATCH)
+    add_dependencies(${TARGET_NAME} ov_auto_batch_plugin)
+endif()
+
 if(ENABLE_INTEL_CPU)
     add_dependencies(${TARGET_NAME} ov_intel_cpu_plugin)
 endif()
diff --git a/src/inference/dev_api/ie_icore.hpp b/src/inference/dev_api/ie_icore.hpp
index 4949ab51b68..0bc0bb528e1 100644
--- a/src/inference/dev_api/ie_icore.hpp
+++ b/src/inference/dev_api/ie_icore.hpp
@@ -16,6 +16,7 @@
 #include "cpp/ie_cnn_network.h"
 #include "cpp_interfaces/interface/ie_iexecutable_network_internal.hpp"
 #include "ie_parameter.hpp"
+#include "ie_remote_context.hpp"
 #include "threading/ie_itask_executor.hpp"
 
 namespace InferenceEngine {
@@ -60,6 +61,22 @@ public:
                                                     const std::string& deviceName,
                                                     const std::map<std::string, std::string>& config = {}) = 0;
 
+    /**
+     * @brief Creates an executable network from a network object.
+     *
+     * Users can create as many networks as they need and use
+     *        them simultaneously (up to the limitation of the hardware resources)
+     *
+     * @param network CNNNetwork object acquired from Core::ReadNetwork
+     * @param remoteCtx  "Remote" (non-CPU) accelerator device-specific execution context to use
+     * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load
+     * operation
+     * @return An executable network reference
+     */
+    virtual SoExecutableNetworkInternal LoadNetwork(const CNNNetwork& network,
+                                                    const RemoteContext::Ptr& remoteCtx,
+                                                    const std::map<std::string, std::string>& config = {}) = 0;
+
     /**
      * @brief Creates an executable network from a model file.
      *
@@ -142,6 +159,16 @@ public:
      */
     virtual bool DeviceSupportsImportExport(const std::string& deviceName) const = 0;
 
+    /**
+     * @brief Create a new shared context object on specified accelerator device
+     * using specified plugin-specific low level device API parameters (device handle, pointer, etc.)
+     * @param deviceName Name of a device to create new shared context on.
+     * @param params Map of device-specific shared context parameters.
+     * @return A shared pointer to a created remote context.
+     */
+    virtual InferenceEngine::RemoteContext::Ptr CreateContext(const std::string& deviceName,
+                                                              const InferenceEngine::ParamMap&) = 0;
+
     virtual bool isNewAPI() const = 0;
 
     /**
@@ -165,6 +192,7 @@ public:
 
     static std::vector<std::string> getHeteroDevices(std::string fallbackDevice);
     static std::vector<std::string> getMultiDevices(std::string devicesList);
+    static std::string getBatchDevice(std::string devicesList);
 };
 
 }  // namespace InferenceEngine
diff --git a/src/inference/dev_api/performance_heuristics.hpp b/src/inference/dev_api/performance_heuristics.hpp
index aeb4ebcfaf0..0f68401c11b 100644
--- a/src/inference/dev_api/performance_heuristics.hpp
+++ b/src/inference/dev_api/performance_heuristics.hpp
@@ -23,14 +23,12 @@ struct MemBandwidthPressure {
 
 static MemBandwidthPressure MemBandwidthPressureTolerance(
     const std::shared_ptr<ngraph::Function> nGraphFunc,
-    const float L2_cache_size,
-    const float L3_cache_size,
+    const float cache_size,
     const float memThresholdAssumeLimited = MemBandwidthPressure::LIMITED) {
     int total_convs = 0, mem_limited_convs = 0, compute_convs = 0, total_gemms = 0, mem_limited_gemms = 0,
         total_deconvs = 0, compute_deconvs = 0, mem_limited_deconvs = 0;
-    auto memLimitedFactor = [&](int size_data_moved, int datatype_size) -> float {
-        return (L2_cache_size * 1.0f /*util factor, tbd */
-                / (size_data_moved * datatype_size));
+    auto memLimitedFactor = [&](int size_data_moved, int datatype_size = 4) -> float {
+        return (cache_size / (size_data_moved * datatype_size));
     };
     auto isLowPrecision = [&](ngraph::element::Type type) -> bool {
         return (type == ngraph::element::i8) || (type == ngraph::element::u8);
diff --git a/src/inference/dev_api/threading/ie_thread_safe_containers.hpp b/src/inference/dev_api/threading/ie_thread_safe_containers.hpp
new file mode 100644
index 00000000000..3849339d2a2
--- /dev/null
+++ b/src/inference/dev_api/threading/ie_thread_safe_containers.hpp
@@ -0,0 +1,86 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#pragma once
+
+#include <cstddef>
+#include <mutex>
+#include <queue>
+#include <type_traits>
+
+#include "ie_parallel.hpp"
+#if ((IE_THREAD == IE_THREAD_TBB) || (IE_THREAD == IE_THREAD_TBB_AUTO))
+#    include <tbb/concurrent_queue.h>
+#endif
+
+namespace InferenceEngine {
+
+template <typename T>
+class ThreadSafeQueueWithSize {
+public:
+    void push(T value) {
+        std::lock_guard<std::mutex> lock(_mutex);
+        _queue.push(std::move(value));
+    }
+    bool try_pop(T& value) {
+        std::lock_guard<std::mutex> lock(_mutex);
+        if (!_queue.empty()) {
+            value = std::move(_queue.front());
+            _queue.pop();
+            return true;
+        } else {
+            return false;
+        }
+    }
+    size_t size() {
+        std::lock_guard<std::mutex> lock(_mutex);
+        return _queue.size();
+    }
+
+protected:
+    std::queue<T> _queue;
+    std::mutex _mutex;
+};
+#if ((IE_THREAD == IE_THREAD_TBB) || (IE_THREAD == IE_THREAD_TBB_AUTO))
+template <typename T>
+using ThreadSafeQueue = tbb::concurrent_queue<T>;
+template <typename T>
+using ThreadSafeBoundedQueue = tbb::concurrent_bounded_queue<T>;
+#else
+template <typename T>
+using ThreadSafeQueue = ThreadSafeQueueWithSize<T>;
+template <typename T>
+class ThreadSafeBoundedQueue {
+public:
+    ThreadSafeBoundedQueue() = default;
+    bool try_push(T value) {
+        std::lock_guard<std::mutex> lock(_mutex);
+        if (_capacity) {
+            _queue.push(std::move(value));
+        }
+        return _capacity;
+    }
+    bool try_pop(T& value) {
+        std::lock_guard<std::mutex> lock(_mutex);
+        if (_capacity && !_queue.empty()) {
+            value = std::move(_queue.front());
+            _queue.pop();
+            return true;
+        } else {
+            return false;
+        }
+    }
+    void set_capacity(std::size_t newCapacity) {
+        std::lock_guard<std::mutex> lock(_mutex);
+        _capacity = newCapacity;
+    }
+
+protected:
+    std::queue<T> _queue;
+    std::mutex _mutex;
+    bool _capacity = false;
+};
+#endif
+}  // namespace InferenceEngine
diff --git a/src/inference/include/ie/ie_plugin_config.hpp b/src/inference/include/ie/ie_plugin_config.hpp
index 09f62301f7e..b30c403f588 100644
--- a/src/inference/include/ie/ie_plugin_config.hpp
+++ b/src/inference/include/ie/ie_plugin_config.hpp
@@ -118,6 +118,18 @@ DECLARE_METRIC_VALUE(BATCHED_BLOB);
  * String value for metric name is "RANGE_FOR_STREAMS".
  */
 DECLARE_METRIC_KEY(RANGE_FOR_STREAMS, std::tuple<unsigned int, unsigned int>);
+/**
+ * @brief Metric to query information optimal batch size for the given device and the network
+ *
+ * Metric returns a value of unsigned int type,
+ * Returns optimal batch size for a given network on the given device. The returned value is aligned to power of 2.
+ * Also, MODEL_PTR is the required option for this metric since the optimal batch size depends on the model,
+ * so if the MODEL_PTR is not given, the result of the metric is always 1.
+ * For the GPU the metric is queried automatically whenever the OpenVINO performance hint for the throughput is used,
+ * so that the result (>1) governs the automatic batching (transparently to the application).
+ * The automatic batching can be disabled with ALLOW_AUTO_BATCHING set to NO
+ */
+DECLARE_METRIC_KEY(OPTIMAL_BATCH_SIZE, unsigned int);
 
 /**
  * @brief Metric to provide a hint for a range for number of async infer requests. If device supports streams,
@@ -250,6 +262,15 @@ DECLARE_CONFIG_KEY(PERFORMANCE_HINT_NUM_REQUESTS);
 DECLARE_CONFIG_VALUE(YES);
 DECLARE_CONFIG_VALUE(NO);
 
+/**
+ * @brief Auto-batching configuration, string for the device + batch size, e.g. "GPU(4)"
+ */
+DECLARE_CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG);
+/**
+ * @brief Auto-batching configuration: string with timeout (in ms), e.g. "100"
+ */
+DECLARE_CONFIG_KEY(AUTO_BATCH_TIMEOUT);
+
 /**
  * @brief Limit `#threads` that are used by Inference Engine for inference on the CPU.
  */
diff --git a/src/inference/src/ie_core.cpp b/src/inference/src/ie_core.cpp
index 13987458312..29c543f97c7 100644
--- a/src/inference/src/ie_core.cpp
+++ b/src/inference/src/ie_core.cpp
@@ -46,6 +46,7 @@
 #endif
 
 using namespace InferenceEngine::PluginConfigParams;
+using namespace InferenceEngine;
 using namespace std::placeholders;
 
 namespace ov {
@@ -94,6 +95,9 @@ Parsed<T> parseDeviceNameIntoConfig(const std::string& deviceName, const std::ma
             config_[ie::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES] =
                 deviceName.substr(std::string("AUTO:").size());
         }
+    } else if (deviceName_.find("BATCH:") == 0) {
+        deviceName_ = "BATCH";
+        config_[CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG)] = deviceName.substr(6);
     } else {
         ie::DeviceIDParser parser(deviceName_);
         deviceName_ = parser.getDeviceName();
@@ -480,14 +484,22 @@ public:
         return newAPI;
     }
 
-    ov::runtime::SoPtr<ie::IExecutableNetworkInternal> LoadNetwork(const ie::CNNNetwork& network,
-                                                                   const std::shared_ptr<ie::RemoteContext>& context,
-                                                                   const std::map<std::string, std::string>& config) {
+    ov::runtime::SoPtr<ie::IExecutableNetworkInternal> LoadNetwork(
+        const ie::CNNNetwork& network,
+        const std::shared_ptr<ie::RemoteContext>& context,
+        const std::map<std::string, std::string>& config) override {
         OV_ITT_SCOPE(FIRST_INFERENCE, ie::itt::domains::IE_LT, "Core::LoadNetwork::RemoteContext");
         if (context == nullptr) {
             IE_THROW() << "Remote context is null";
         }
+        // have to deduce the device name/config from the context first
         auto parsed = parseDeviceNameIntoConfig(context->getDeviceName(), config);
+        std::string& deviceName = parsed._deviceName;
+        std::map<std::string, std::string>& config_with_batch = parsed._config;
+        // if auto-batching is applicable, the below function will patch the device name and config accordingly:
+        ApplyAutoBatching(network, deviceName, config_with_batch);
+        parsed = parseDeviceNameIntoConfig(deviceName, config_with_batch);
+
         auto plugin = GetCPPPluginByName(parsed._deviceName);
         ov::runtime::SoPtr<ie::IExecutableNetworkInternal> res;
         auto cacheManager = coreConfig.getCacheConfig()._cacheManager;
@@ -508,12 +520,59 @@ public:
         return res;
     }
 
+    void ApplyAutoBatching(const ie::CNNNetwork& network,
+                           std::string& deviceName,
+                           std::map<std::string, std::string>& config_with_batch) {
+        if (deviceName.find("BATCH") != std::string::npos) {
+            // explicitly enabled Auto-Batching e.g. in the tests
+            auto pos = deviceName.find_first_of(":");
+            if (pos != std::string::npos) {
+                auto deviceNameWithBatchSize = deviceName.substr(pos + 1);
+                auto deviceNameWithoutBatch = DeviceIDParser::getBatchDevice(deviceNameWithBatchSize);
+                auto function = network.getFunction();
+                // have to execute the DetectionOutput separately (without batching)
+                // as this layer mix-in the values from the different inputs (batch id)
+                bool bDetectionOutput = false;
+                const std::string detectionOutputOpName = ngraph::op::DetectionOutput::get_type_info_static().name;
+                const std::string resultOpName = ngraph::op::Result::get_type_info_static().name;
+                for (auto&& node : function->get_ops()) {
+                    auto isDetectionOutputParent = [&detectionOutputOpName](decltype(node)& nd) {
+                        for (size_t n = 0; n < nd->get_input_size(); n++) {
+                            if (detectionOutputOpName == nd->get_input_node_ptr(n)->get_type_info().name)
+                                return true;
+                        }
+                        return false;
+                    };
+
+                    if ((detectionOutputOpName == node->get_type_info().name) ||
+                        ((resultOpName == node->get_type_info().name) && isDetectionOutputParent(node))) {
+                        node->get_rt_info()["affinity"] = deviceNameWithoutBatch;
+                        bDetectionOutput = true;
+                    } else {
+                        node->get_rt_info()["affinity"] = "BATCH";
+                    }
+                }
+                if (bDetectionOutput) {
+                    deviceName = "HETERO:BATCH," + deviceNameWithoutBatch;
+                    config_with_batch[CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG)] = deviceNameWithBatchSize;
+                } else {
+                    deviceName = "BATCH:" + deviceNameWithBatchSize;
+                }
+            }
+        }
+    }
+
     ie::SoExecutableNetworkInternal LoadNetwork(const ie::CNNNetwork& network,
-                                                const std::string& deviceName,
+                                                const std::string& deviceNameOrig,
                                                 const std::map<std::string, std::string>& config) override {
         OV_ITT_SCOPE(FIRST_INFERENCE, ie::itt::domains::IE_LT, "Core::LoadNetwork::CNN");
-        bool forceDisableCache = config.count(CONFIG_KEY_INTERNAL(FORCE_DISABLE_CACHE)) > 0;
-        auto parsed = parseDeviceNameIntoConfig(deviceName, config);
+        std::string deviceName = deviceNameOrig;
+        std::map<std::string, std::string> config_with_batch = config;
+        // if auto-batching is applicable, the below function will patch the device name and config accordingly:
+        ApplyAutoBatching(network, deviceName, config_with_batch);
+
+        bool forceDisableCache = config_with_batch.count(CONFIG_KEY_INTERNAL(FORCE_DISABLE_CACHE)) > 0;
+        auto parsed = parseDeviceNameIntoConfig(deviceName, config_with_batch);
         if (forceDisableCache) {
             // remove this config key from parsed as plugins can throw unsupported exception
             parsed._config.erase(CONFIG_KEY_INTERNAL(FORCE_DISABLE_CACHE));
@@ -732,6 +791,19 @@ public:
         return devices;
     }
 
+    /**
+     * @brief Create a new shared context object on specified accelerator device
+     * using specified plugin-specific low level device API parameters (device handle, pointer, etc.)
+     * @param deviceName Name of a device to create new shared context on.
+     * @param params Map of device-specific shared context parameters.
+     * @return A shared pointer to a created remote context.
+     */
+    InferenceEngine::RemoteContext::Ptr CreateContext(const std::string& deviceName,
+                                                      const InferenceEngine::ParamMap& params) override {
+        auto parsed = ov::runtime::parseDeviceNameIntoConfig(deviceName, params);
+        return GetCPPPluginByName(parsed._deviceName).create_context(parsed._config)._ptr;
+    }
+
     /**
      * @brief Returns reference to CPP plugin wrapper by a device name
      * @param deviceName A name of device
@@ -1030,6 +1102,12 @@ public:
                     deviceNames = ie::DeviceIDParser::getMultiDevices(deviceName.substr(pos + 1));
                 }
                 deviceNames.emplace_back("AUTO");
+            } else if (deviceName.find("BATCH") == 0) {
+                auto pos = deviceName.find_first_of(":");
+                if (pos != std::string::npos) {
+                    deviceNames = {ie::DeviceIDParser::getBatchDevice(deviceName.substr(pos + 1))};
+                }
+                deviceNames.push_back("BATCH");
             } else {
                 deviceNames.push_back(deviceName);
             }
@@ -1120,8 +1198,8 @@ std::vector<std::string> DeviceIDParser::getHeteroDevices(std::string fallbackDe
 }
 
 std::vector<std::string> DeviceIDParser::getMultiDevices(std::string devicesList) {
-    std::vector<std::string> deviceNames;
-    auto trim_request_info = [](std::string device_with_requests) {
+    std::set<std::string> deviceNames;
+    auto trim_request_info = [](const std::string& device_with_requests) {
         auto opening_bracket = device_with_requests.find_first_of('(');
         return device_with_requests.substr(0, opening_bracket);
     };
@@ -1132,14 +1210,36 @@ std::vector<std::string> DeviceIDParser::getMultiDevices(std::string devicesList
     // we skip the #requests info here
     while ((pos = devicesList.find(delimiter)) != std::string::npos) {
         auto d = devicesList.substr(0, pos);
-        deviceNames.push_back(trim_request_info(d));
+        if (d.find("BATCH") == 0) {
+            deviceNames.insert("BATCH");
+            auto p = d.find_first_of(":");
+            if (p != std::string::npos)
+                deviceNames.insert(DeviceIDParser::getBatchDevice(d.substr(p + 1)));
+        } else {
+            deviceNames.insert(trim_request_info(d));
+        }
         devicesList.erase(0, pos + 1);
     }
 
-    if (!devicesList.empty())
-        deviceNames.push_back(trim_request_info(devicesList));
+    if (!devicesList.empty()) {
+        if (devicesList.find("BATCH") == 0) {
+            deviceNames.insert("BATCH");
+            auto p = devicesList.find_first_of(":");
+            if (p != std::string::npos)
+                deviceNames.insert(DeviceIDParser::getBatchDevice(devicesList.substr(p + 1)));
+        } else {
+            deviceNames.insert(trim_request_info(devicesList));
+        }
+    }
+    return std::vector<std::string>(deviceNames.begin(), deviceNames.end());
+}
 
-    return deviceNames;
+std::string DeviceIDParser::getBatchDevice(std::string device) {
+    auto trim_request_info = [](const std::string& device_with_requests) {
+        auto opening_bracket = device_with_requests.find_first_of('(');
+        return device_with_requests.substr(0, opening_bracket);
+    };
+    return trim_request_info(device);
 }
 
 class Core::Impl : public ov::runtime::CoreImpl {
@@ -1207,18 +1307,7 @@ ExecutableNetwork Core::LoadNetwork(const std::string& modelPath, const std::map
 }
 
 RemoteContext::Ptr Core::CreateContext(const std::string& deviceName, const ParamMap& params) {
-    if (deviceName.find("HETERO") == 0) {
-        IE_THROW() << "HETERO device does not support remote context";
-    }
-    if (deviceName.find("MULTI") == 0) {
-        IE_THROW() << "MULTI device does not support remote context";
-    }
-    if (deviceName.find("AUTO") == 0) {
-        IE_THROW() << "AUTO device does not support remote context";
-    }
-
-    auto parsed = ov::runtime::parseDeviceNameIntoConfig(deviceName, params);
-    return _impl->GetCPPPluginByName(parsed._deviceName).create_context(parsed._config)._ptr;
+    return _impl->CreateContext(deviceName, params);
 }
 
 RemoteContext::Ptr Core::GetDefaultContext(const std::string& deviceName) {
diff --git a/src/plugins/CMakeLists.txt b/src/plugins/CMakeLists.txt
index 54f90dca336..1c041774a2f 100644
--- a/src/plugins/CMakeLists.txt
+++ b/src/plugins/CMakeLists.txt
@@ -21,3 +21,7 @@ endif()
 if(ENABLE_AUTO OR ENABLE_MULTI)
     add_subdirectory(auto)
 endif()
+
+if(ENABLE_AUTO_BATCH)
+    add_subdirectory(auto_batch)
+endif()
diff --git a/src/plugins/auto/executable_network.cpp b/src/plugins/auto/executable_network.cpp
index 0f63b63f114..e5814fe891b 100644
--- a/src/plugins/auto/executable_network.cpp
+++ b/src/plugins/auto/executable_network.cpp
@@ -156,7 +156,8 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
                                                            , _needPerfCounters(needPerfCounters)
                                                            , _multiPlugin(plugin)
                                                            , _context(context)
-                                                           , _workModeIsAUTO(true) {
+                                                           , _workModeIsAUTO(true)
+                                                           , _network(network) {
     if (_multiPlugin->GetCore() == nullptr) {
         IE_THROW() << "Please, work with " << _multiPlugin->GetName() << " device via InferencEngine::Core object";
     }
@@ -667,10 +668,30 @@ InferenceEngine::Parameter MultiDeviceExecutableNetwork::GetMetric(const std::st
                 real = _loadContext[ACTUALDEVICE].
                     executableNetwork->GetMetric(name).as<unsigned int>();
             } else {
+                IE_ASSERT(_loadContext[CPU].isAlready == true);
                 real = _loadContext[CPU].
                     executableNetwork->GetMetric(name).as<unsigned int>();
+                std::unique_lock<std::mutex> lock(_confMutex);
+                auto deviceInfo =  _loadContext[ACTUALDEVICE].deviceInfo;
+                lock.unlock();
+                if (deviceInfo.deviceName.find("GPU") != std::string::npos) {
+                    const auto& mode = deviceInfo.config.find(CONFIG_KEY(PERFORMANCE_HINT));
+                    if (mode != deviceInfo.config.end() && mode->second == CONFIG_VALUE(THROUGHPUT)) {
+                         std::map<std::string, InferenceEngine::Parameter> options;
+                         options["MODEL_PTR"] = _network.getFunction(); // CNNntework
+                         try {
+                             auto optimalBatchSize = _core->GetMetric(deviceInfo.deviceName,
+                                     METRIC_KEY(OPTIMAL_BATCH_SIZE), options).as<unsigned int>();
+                             auto rangeOfStreams = _core->GetMetric(deviceInfo.deviceName,
+                                     METRIC_KEY(RANGE_FOR_STREAMS), options).as<std::tuple<unsigned int, unsigned int>>();
+                             real = (std::max)(real, std::get<1>(rangeOfStreams) * optimalBatchSize);
+                         } catch (const InferenceEngine::Exception &iie) {
+                             LOG_WARNING("[AUTOPLUGIN]get optimal infer requset num for GPU auto-batch failed :%s", iie.what());
+                         }
+                    }
+                }
             }
-            unsigned int res = std::max(8u, real);
+            unsigned int res = (std::max)(8u, real);
             IE_SET_METRIC_RETURN(OPTIMAL_NUMBER_OF_INFER_REQUESTS, res);
         }
 
diff --git a/src/plugins/auto/executable_network.hpp b/src/plugins/auto/executable_network.hpp
index 45efc0450c1..2c963d912d8 100644
--- a/src/plugins/auto/executable_network.hpp
+++ b/src/plugins/auto/executable_network.hpp
@@ -7,22 +7,17 @@
 
 #include <atomic>
 #include <mutex>
-#include <queue>
 #include <unordered_map>
 #include <map>
 #include <vector>
 #include <string>
 
-#include <cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp>
-#include <ie_parallel.hpp>
-#include <threading/ie_itask_executor.hpp>
-#include <threading/ie_executor_manager.hpp>
+#include "cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp"
+#include "threading/ie_thread_safe_containers.hpp"
+#include "threading/ie_itask_executor.hpp"
+#include "threading/ie_executor_manager.hpp"
 #include "ie_icore.hpp"
 
-#if (IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO)
-# include <tbb/concurrent_queue.h>
-#endif
-
 #ifdef  MULTIUNITTEST
 #define MOCKTESTMACRO virtual
 #define MultiDevicePlugin MockMultiDevicePlugin
@@ -79,66 +74,6 @@ enum AutoLoadContextIndex {
 template<typename T>
 using DeviceMap = std::unordered_map<DeviceName, T>;
 
-#if ((IE_THREAD == IE_THREAD_TBB) || (IE_THREAD == IE_THREAD_TBB_AUTO))
-template <typename T>
-using ThreadSafeQueue = tbb::concurrent_queue<T>;
-template <typename T>
-using ThreadSafeBoundedQueue = tbb::concurrent_bounded_queue<T>;
-#else
-template <typename T>
-class ThreadSafeQueue {
-public:
-    void push(T value) {
-        std::lock_guard<std::mutex> lock(_mutex);
-        _queue.push(std::move(value));
-    }
-    bool try_pop(T& value) {
-        std::lock_guard<std::mutex> lock(_mutex);
-        if (!_queue.empty()) {
-            value = std::move(_queue.front());
-            _queue.pop();
-            return true;
-        } else {
-            return false;
-        }
-    }
-protected:
-    std::queue<T>   _queue;
-    std::mutex      _mutex;
-};
-template <typename T>
-class ThreadSafeBoundedQueue {
-public:
-    ThreadSafeBoundedQueue() = default;
-    bool try_push(T value) {
-        std::lock_guard<std::mutex> lock(_mutex);
-        if (_capacity) {
-            _queue.push(std::move(value));
-        }
-        return _capacity;
-    }
-    bool try_pop(T& value) {
-        std::lock_guard<std::mutex> lock(_mutex);
-        if (_capacity && !_queue.empty()) {
-            value = std::move(_queue.front());
-            _queue.pop();
-            return true;
-        } else {
-            return false;
-        }
-    }
-    void set_capacity(std::size_t newCapacity) {
-        std::lock_guard<std::mutex> lock(_mutex);
-        _capacity = newCapacity;
-    }
-
-protected:
-    std::queue<T>   _queue;
-    std::mutex      _mutex;
-    bool            _capacity = false;
-};
-#endif
-
 class MultiDeviceExecutableNetwork : public InferenceEngine::ExecutableNetworkThreadSafeDefault,
                                      public InferenceEngine::ITaskExecutor {
 public:
@@ -148,7 +83,7 @@ public:
         InferenceEngine::Task                     _task;
         std::exception_ptr                        _exceptionPtr = nullptr;
     };
-    using NotBusyWorkerRequests = ThreadSafeBoundedQueue<WorkerInferRequest*>;
+    using NotBusyWorkerRequests = InferenceEngine::ThreadSafeBoundedQueue<WorkerInferRequest*>;
 
     explicit MultiDeviceExecutableNetwork(const DeviceMap<InferenceEngine::SoExecutableNetworkInternal>&        networksPerDevice,
                                           const std::vector<DeviceInformation>&                                 networkDevices,
@@ -186,8 +121,8 @@ public:
     std::vector<DeviceInformation>                              _devicePriorities;
     const std::vector<DeviceInformation>                        _devicePrioritiesInitial;
     DeviceMap<InferenceEngine::SoExecutableNetworkInternal>     _networksPerDevice;
-    ThreadSafeQueue<InferenceEngine::Task>                      _inferPipelineTasks;
-    DeviceMap<std::unique_ptr<ThreadSafeQueue<InferenceEngine::Task>>> _inferPipelineTasksDeviceSpecific;
+    InferenceEngine::ThreadSafeQueue<InferenceEngine::Task>                      _inferPipelineTasks;
+    DeviceMap<std::unique_ptr<InferenceEngine::ThreadSafeQueue<InferenceEngine::Task>>> _inferPipelineTasksDeviceSpecific;
     DeviceMap<NotBusyWorkerRequests>                            _idleWorkerRequests;
     DeviceMap<std::vector<WorkerInferRequest>>                  _workerRequests;
     std::unordered_map<std::string, InferenceEngine::Parameter> _config;
@@ -217,6 +152,7 @@ private:
     std::promise<void>                                                  _firstLoadPromise;
     mutable AutoLoadContext                                             _loadContext[CONTEXTNUM];
     mutable std::mutex                                                  _confMutex;
+    const InferenceEngine::CNNNetwork                                   _network;
 };
 
 }  // namespace MultiDevicePlugin
diff --git a/src/plugins/auto_batch/CMakeLists.txt b/src/plugins/auto_batch/CMakeLists.txt
new file mode 100644
index 00000000000..0eb9dd31f06
--- /dev/null
+++ b/src/plugins/auto_batch/CMakeLists.txt
@@ -0,0 +1,20 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(TARGET_NAME "ov_auto_batch_plugin")
+
+file(GLOB SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+file(GLOB HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
+
+ie_add_plugin(NAME ${TARGET_NAME}
+              DEVICE_NAME "BATCH"
+              SOURCES ${SOURCES} ${HEADERS}
+              VERSION_DEFINES_FOR auto_batch.cpp ADD_CLANG_FORMAT)
+
+target_link_libraries(${TARGET_NAME} PRIVATE Threads::Threads)
+
+ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
+
+set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
diff --git a/src/plugins/auto_batch/auto_batch.cpp b/src/plugins/auto_batch/auto_batch.cpp
new file mode 100644
index 00000000000..104e856201f
--- /dev/null
+++ b/src/plugins/auto_batch/auto_batch.cpp
@@ -0,0 +1,731 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#include "auto_batch.hpp"
+
+#include <cpp_interfaces/interface/ie_internal_plugin_config.hpp>
+#include <ie_icore.hpp>
+#include <ie_ngraph_utils.hpp>
+#include <ie_performance_hints.hpp>
+#include <iostream>
+#include <map>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+namespace AutoBatchPlugin {
+using namespace InferenceEngine;
+
+std::vector<std::string> supported_configKeys = {CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG), CONFIG_KEY(AUTO_BATCH_TIMEOUT)};
+
+template <Precision::ePrecision precision>
+Blob::Ptr create_shared_blob_on_top_of_batched_blob(Blob::Ptr batched_blob, size_t batch_id, size_t batch_num) {
+    typedef typename PrecisionTrait<precision>::value_type TYPE;
+    typedef typename std::add_pointer<TYPE>::type TYPEPTR;
+    auto ptr = batched_blob->buffer().as<TYPEPTR>();
+    auto sizePerBatch = batched_blob->size() / batch_num;
+    auto layout = batched_blob->getTensorDesc().getLayout();
+    SizeVector dims = batched_blob->getTensorDesc().getDims();
+    // the below code is a placeholder for the WIP (22.1) functionality
+    // that will check the reshaping by the batch is robust (CVS-51744)
+    if (layout == InferenceEngine::Layout::NC || layout == InferenceEngine::Layout::NCDHW ||
+        layout == InferenceEngine::Layout::NCHW || layout == InferenceEngine::Layout::NHWC ||
+        layout == InferenceEngine::Layout::NDHWC) {
+        dims[0] = 1;
+        assert(batched_blob->getTensorDesc().getPrecision() == precision);
+        return make_shared_blob<TYPE>({precision, dims, batched_blob->getTensorDesc().getLayout()},
+                                      ptr + sizePerBatch * batch_id,
+                                      sizePerBatch);
+    } else {
+        // same blob for all requests (e.g. constants)
+        return make_shared_blob<TYPE>({precision, dims, batched_blob->getTensorDesc().getLayout()}, ptr);
+    }
+}
+
+// ------------------------------AutoBatchInferRequest----------------------------
+AutoBatchInferRequest::AutoBatchInferRequest(const InputsDataMap& networkInputs,
+                                             const OutputsDataMap& networkOutputs,
+                                             AutoBatchExecutableNetwork::WorkerInferRequest& workerRequestPtr,
+                                             int batch_id,
+                                             int num_batch,
+                                             bool needPerfCounters)
+    : IInferRequestInternal(networkInputs, networkOutputs),
+      _myBatchedRequestWrapper(workerRequestPtr),
+      _needPerfCounters(needPerfCounters),
+      _batchId(batch_id),
+      _batchSize(num_batch) {
+    // Allocate all input blobs
+    for (const auto& it : networkInputs) {
+        auto blob = _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first);
+        Blob::Ptr res;
+        switch (it.second->getTensorDesc().getPrecision()) {
+        case InferenceEngine::Precision::FP32:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::FP32>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        case InferenceEngine::Precision::I32:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::I32>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        case InferenceEngine::Precision::I8:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::I8>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        case InferenceEngine::Precision::U16:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::U16>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+
+        case InferenceEngine::Precision::I16:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::I16>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+
+            break;
+        case InferenceEngine::Precision::U8:
+        case InferenceEngine::Precision::BOOL:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::U8>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        default:
+            IE_THROW() << "Unsupported input precision " << it.second->getTensorDesc().getPrecision();
+        }
+        _inputs[it.first] = res;
+    }
+    // Allocate all output blobs
+    for (const auto& it : networkOutputs) {
+        auto blob = _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first);
+        Blob::Ptr res;
+        switch (it.second->getTensorDesc().getPrecision()) {
+        case InferenceEngine::Precision::FP32:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::FP32>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        case InferenceEngine::Precision::I32:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::I32>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        case InferenceEngine::Precision::I8:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::I8>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        case InferenceEngine::Precision::U16:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::U16>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+
+        case InferenceEngine::Precision::I16:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::I16>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+
+            break;
+        case InferenceEngine::Precision::U8:
+        case InferenceEngine::Precision::BOOL:
+            res = create_shared_blob_on_top_of_batched_blob<InferenceEngine::Precision::U8>(
+                _myBatchedRequestWrapper._inferRequestBatched->GetBlob(it.first),
+                batch_id,
+                num_batch);
+            break;
+        default:
+            IE_THROW(NotImplemented) << "Unsupported input precision " << it.second->getTensorDesc().getPrecision();
+        }
+        _outputs[it.first] = res;
+    }
+}
+
+void AutoBatchInferRequest::SetBlobsToAnotherRequest(SoIInferRequestInternal& req) {
+    for (const auto& it : _networkInputs) {
+        auto& name = it.first;
+        // this request is already in BUSY state, so using the internal functions safely
+        auto blob = GetBlob(name);
+        if (req->GetBlob(name) != blob)
+            req->SetBlob(name, blob);
+    }
+    for (const auto& it : _networkOutputs) {
+        auto& name = it.first;
+        // this request is already in BUSY state, so using the internal functions safely
+        auto blob = GetBlob(name);
+        if (req->GetBlob(name) != blob)
+            req->SetBlob(name, blob);
+    }
+}
+
+void AutoBatchInferRequest::CopyInputsIfNeeded() {
+    for (const auto& it : _networkInputs) {
+        auto& name = it.first;
+        // this request is already in BUSY state, so using the internal functions safely
+        CopyBlobIfNeeded(GetBlob(name), _myBatchedRequestWrapper._inferRequestBatched->GetBlob(name), true);
+    }
+}
+
+void AutoBatchInferRequest::CopyBlobIfNeeded(InferenceEngine::Blob::CPtr src,
+                                             InferenceEngine::Blob::Ptr dst,
+                                             bool bInput) {
+    auto bufferDst = dst->buffer();
+    auto ptrDst = bufferDst.as<char*>();
+    auto bufferSrc = src->cbuffer();
+    auto ptrSrc = bufferSrc.as<const char*>();
+    ptrdiff_t szDst = dst->byteSize();
+    ptrdiff_t szSrc = src->byteSize();
+    if (bInput) {
+        ptrdiff_t offset = szSrc != szDst ? _batchId * szDst / _batchSize : 0;
+        if ((ptrDst + offset) == ptrSrc)
+            return;
+        else
+            memcpy(ptrDst + offset, ptrSrc, szSrc);
+    } else {
+        ptrdiff_t offset = szSrc != szDst ? _batchId * szSrc / _batchSize : 0;
+        if ((ptrSrc + offset) == ptrDst)
+            return;
+        else
+            memcpy(ptrDst, ptrSrc + offset, szDst);
+    }
+}
+
+void AutoBatchInferRequest::CopyOutputsIfNeeded() {
+    for (const auto& it : _networkOutputs) {
+        auto& name = it.first;
+        // this request is already in BUSY state, so using the internal functions safely
+        CopyBlobIfNeeded(_myBatchedRequestWrapper._inferRequestBatched->GetBlob(name), GetBlob(name), false);
+    }
+}
+
+std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> AutoBatchInferRequest::GetPerformanceCounts() const {
+    return _perfMap;
+}
+
+AutoBatchAsyncInferRequest::AutoBatchAsyncInferRequest(
+    const AutoBatchInferRequest::Ptr& inferRequest,
+    const bool needPerfCounters,
+    InferenceEngine::SoIInferRequestInternal& inferRequestWithoutBatch,
+    const ITaskExecutor::Ptr& callbackExecutor)
+    : AsyncInferRequestThreadSafeDefault(inferRequest, nullptr, callbackExecutor),
+      _inferRequestWithoutBatch(inferRequestWithoutBatch),
+      _inferRequest{inferRequest} {
+    // this executor starts the inference while  the task (checking the result) is passed to the next stage
+    struct ThisRequestExecutor : public ITaskExecutor {
+        explicit ThisRequestExecutor(AutoBatchAsyncInferRequest* _this_) : _this{_this_} {}
+        void run(Task task) override {
+            auto& workerInferRequest = _this->_inferRequest->_myBatchedRequestWrapper;
+            std::pair<AutoBatchAsyncInferRequest*, InferenceEngine::Task> t;
+            t.first = _this;
+            t.second = std::move(task);
+            workerInferRequest._tasks.push(t);
+            // it is ok to call size() here as the queue only grows (and the bulk removal happens under the mutex)
+            const int sz = workerInferRequest._tasks.size();
+            if (sz == workerInferRequest._batchSize) {
+                workerInferRequest._cond.notify_one();
+            }
+        };
+        AutoBatchAsyncInferRequest* _this = nullptr;
+    };
+    _pipeline = {
+        {/*TaskExecutor*/ std::make_shared<ThisRequestExecutor>(this), /*task*/ [this, needPerfCounters] {
+             if (this->_inferRequest->_exceptionPtr)  // if the exception happened in the batch1 fallback
+                 std::rethrow_exception(this->_inferRequest->_exceptionPtr);
+             if (this->_inferRequest->_myBatchedRequestWrapper._exceptionPtr)  // when the batchN execution failed
+                 std::rethrow_exception(this->_inferRequest->_myBatchedRequestWrapper._exceptionPtr);
+             this->_inferRequest->CopyOutputsIfNeeded();
+         }}};
+}
+
+void AutoBatchAsyncInferRequest::Infer_ThreadUnsafe() {
+    InferUsingAsync();
+}
+
+AutoBatchAsyncInferRequest::~AutoBatchAsyncInferRequest() {
+    StopAndWait();
+}
+
+// ------------------------------AutoBatchExecutableNetwork----------------------------
+AutoBatchExecutableNetwork::AutoBatchExecutableNetwork(
+    const InferenceEngine::SoExecutableNetworkInternal& networkWithBatch,
+    const InferenceEngine::SoExecutableNetworkInternal& networkWithoutBatch,
+    const DeviceInformation& networkDevice,
+    const std::unordered_map<std::string, InferenceEngine::Parameter>& config,
+    const bool needPerfCounters)
+    : InferenceEngine::ExecutableNetworkThreadSafeDefault(nullptr,
+                                                          std::make_shared<InferenceEngine::ImmediateExecutor>()),
+      _network{networkWithBatch},
+      _networkWithoutBatch{networkWithoutBatch},
+      _config{config},
+      _needPerfCounters{needPerfCounters} {
+    // WA for gcc 4.8 ( fails compilation with member init-list)
+    _device = networkDevice;
+    auto time_out = config.find(CONFIG_KEY(AUTO_BATCH_TIMEOUT));
+    if (time_out != config.end())
+        _timeOut = ParseTimeoutValue(time_out->second.as<std::string>());
+}
+
+AutoBatchExecutableNetwork::~AutoBatchExecutableNetwork() {
+    _terminate = true;
+    for (auto w : _workerRequests) {
+        w->_thread.join();
+    }
+    _workerRequests.clear();
+}
+
+unsigned int AutoBatchExecutableNetwork::ParseTimeoutValue(const std::string& s) {
+    auto val = std::stoi(s);
+    if (val < 0)
+        IE_THROW(ParameterMismatch) << "Value for the " << CONFIG_KEY(AUTO_BATCH_TIMEOUT) << " should be unsigned int";
+    return val;
+}
+
+std::shared_ptr<InferenceEngine::RemoteContext> AutoBatchExecutableNetwork::GetContext() const {
+    return _network->GetContext();
+}
+
+InferenceEngine::IInferRequestInternal::Ptr AutoBatchExecutableNetwork::CreateInferRequestImpl(
+    InferenceEngine::InputsDataMap networkInputs,
+    InferenceEngine::OutputsDataMap networkOutputs) {
+    // todo : guard request creation from another thread/on-the-fly
+    auto num = _numRequestsCreated++;
+    auto batch_id = num % _device.batchForDevice;
+    if (!batch_id) {  // need new request
+        _workerRequests.push_back(std::make_shared<WorkerInferRequest>());
+        auto workerRequestPtr = _workerRequests.back();
+        workerRequestPtr->_inferRequestBatched = {_network->CreateInferRequest(), _network._so};
+        workerRequestPtr->_batchSize = _device.batchForDevice;
+        workerRequestPtr->_completionTasks.resize(workerRequestPtr->_batchSize);
+        workerRequestPtr->_inferRequestBatched->SetCallback(
+            [workerRequestPtr, this](std::exception_ptr exceptionPtr) mutable {
+                if (exceptionPtr)
+                    workerRequestPtr->_exceptionPtr = exceptionPtr;
+                IE_ASSERT(workerRequestPtr->_completionTasks.size() == (size_t)workerRequestPtr->_batchSize);
+                // notify the individual requests on the completion
+                for (int c = 0; c < workerRequestPtr->_batchSize; c++) {
+                    workerRequestPtr->_completionTasks[c]();
+                }
+                // reset the timeout
+                workerRequestPtr->_cond.notify_one();
+            });
+
+        workerRequestPtr->_thread = std::thread([workerRequestPtr, this] {
+            while (1) {
+                std::cv_status status;
+                {
+                    std::unique_lock<std::mutex> lock(workerRequestPtr->_mutex);
+                    status = workerRequestPtr->_cond.wait_for(lock, std::chrono::milliseconds(_timeOut));
+                }
+                if (_terminate) {
+                    break;
+                } else {
+                    // as we pop the tasks from the queue only here
+                    // it is ok to call size() (as the _tasks can only grow in parallel)
+                    const int sz = workerRequestPtr->_tasks.size();
+                    if (sz == workerRequestPtr->_batchSize) {
+                        std::pair<AutoBatchAsyncInferRequest*, InferenceEngine::Task> t;
+                        for (int n = 0; n < sz; n++) {
+                            IE_ASSERT(workerRequestPtr->_tasks.try_pop(t));
+                            workerRequestPtr->_completionTasks[n] = std::move(t.second);
+                            t.first->_inferRequest->CopyInputsIfNeeded();
+                        }
+                        workerRequestPtr->_inferRequestBatched->StartAsync();
+                    } else if ((status == std::cv_status::timeout) && sz) {
+                        // timeout to collect the batch is over, have to execute the requests in the batch1 mode
+                        std::pair<AutoBatchAsyncInferRequest*, InferenceEngine::Task> t;
+                        // popping all tasks collected by the moment of the time-out and execute each with batch1
+                        std::atomic<int> arrived = {0};
+                        std::promise<void> all_completed;
+                        auto all_completed_future = all_completed.get_future();
+                        for (int n = 0; n < sz; n++) {
+                            IE_ASSERT(workerRequestPtr->_tasks.try_pop(t));
+                            t.first->_inferRequestWithoutBatch->SetCallback(
+                                [t, sz, &arrived, &all_completed](std::exception_ptr p) {
+                                    if (p)
+                                        t.first->_inferRequest->_exceptionPtr = p;
+                                    t.second();
+                                    if (sz == ++arrived)
+                                        all_completed.set_value();
+                                });
+                            t.first->_inferRequest->SetBlobsToAnotherRequest(t.first->_inferRequestWithoutBatch);
+                            t.first->_inferRequestWithoutBatch->StartAsync();
+                        }
+                        all_completed_future.get();
+                        // now when all the tasks for this batch are completed, start waiting for the timeout again
+                    }
+                }
+            }
+        });
+    }
+    return std::make_shared<AutoBatchInferRequest>(networkInputs,
+                                                   networkOutputs,
+                                                   *_workerRequests.back(),
+                                                   batch_id,
+                                                   _device.batchForDevice,
+                                                   _needPerfCounters);
+}
+
+InferenceEngine::IInferRequestInternal::Ptr AutoBatchExecutableNetwork::CreateInferRequest() {
+    auto syncRequestImpl = CreateInferRequestImpl(_networkInputs, _networkOutputs);
+    syncRequestImpl->setPointerToExecutableNetworkInternal(shared_from_this());
+    InferenceEngine::SoIInferRequestInternal inferRequestWithoutBatch = {_networkWithoutBatch->CreateInferRequest(),
+                                                                         _networkWithoutBatch._so};
+    return std::make_shared<AutoBatchAsyncInferRequest>(
+        std::static_pointer_cast<AutoBatchInferRequest>(syncRequestImpl),
+        _needPerfCounters,
+        inferRequestWithoutBatch,
+        _callbackExecutor);
+}
+
+std::shared_ptr<ngraph::Function> AutoBatchExecutableNetwork::GetExecGraphInfo() {
+    return _network->GetExecGraphInfo() ? _network->GetExecGraphInfo() : _networkWithoutBatch->GetExecGraphInfo();
+}
+
+void AutoBatchExecutableNetwork::SetConfig(const std::map<std::string, InferenceEngine::Parameter>& config) {
+    auto timeout = config.find(CONFIG_KEY(AUTO_BATCH_TIMEOUT));
+    if (timeout == config.end() || config.size() > 1) {
+        IE_THROW() << "The only config that can be changed on the fly for the AutoBatching the is the "
+                   << CONFIG_KEY(AUTO_BATCH_TIMEOUT);
+    } else {
+        _timeOut = ParseTimeoutValue(timeout->second.as<std::string>());
+    }
+}
+
+InferenceEngine::Parameter AutoBatchExecutableNetwork::GetConfig(const std::string& name) const {
+    auto it = _config.find(name);
+    if (it != _config.end()) {
+        return it->second;
+    } else {
+        // find config key among networks config keys
+        auto param = _network->GetMetric(METRIC_KEY(SUPPORTED_CONFIG_KEYS));
+        for (auto&& configKey : param.as<std::vector<std::string>>()) {
+            if (configKey == name) {
+                return _network->GetConfig(configKey);
+            }
+        }
+        IE_THROW(NotFound) << name << " not found in the ExecutableNetwork config";
+    }
+}
+
+InferenceEngine::Parameter AutoBatchExecutableNetwork::GetMetric(const std::string& name) const {
+    if (name == METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)) {
+        auto reqs = 0;
+        try {
+            auto hint = _network->GetConfig(CONFIG_KEY(PERFORMANCE_HINT_NUM_REQUESTS)).as<std::string>();
+            reqs = InferenceEngine::PerfHintsConfig::CheckPerformanceHintRequestValue(hint);
+            if (!reqs)  // no limitations from user, let's deduce the full blown #requests
+                // (multiplied by the devices capabilities to run multiple <batched> requests for further perf)
+                reqs = _device.batchForDevice *
+                       _network->GetMetric(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)).as<unsigned int>();
+        } catch (const InferenceEngine::Exception& iie) {
+        }
+        reqs = std::max(reqs, _device.batchForDevice);  // round up to the possible  user's value
+        IE_SET_METRIC_RETURN(OPTIMAL_NUMBER_OF_INFER_REQUESTS, reqs);
+    } else if (name == METRIC_KEY(NETWORK_NAME)) {
+        IE_SET_METRIC_RETURN(NETWORK_NAME, _network->GetMetric(METRIC_KEY(NETWORK_NAME)).as<std::string>());
+    } else if (name == METRIC_KEY(SUPPORTED_METRICS)) {
+        IE_SET_METRIC_RETURN(SUPPORTED_METRICS,
+                             {METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS),
+                              METRIC_KEY(SUPPORTED_METRICS),
+                              METRIC_KEY(NETWORK_NAME),
+                              METRIC_KEY(SUPPORTED_CONFIG_KEYS)});
+    } else if (name == METRIC_KEY(SUPPORTED_CONFIG_KEYS)) {
+        IE_SET_METRIC_RETURN(SUPPORTED_CONFIG_KEYS,
+                             {CONFIG_KEY(AUTO_BATCH_TIMEOUT)});  // only timeout can be changed on the fly
+    } else {
+        IE_THROW() << "Unsupported Network metric: " << name;
+    }
+}
+
+// ------------------------------AutoBatchInferencePlugin----------------------------
+
+namespace {
+
+std::map<std::string, std::string> mergeConfigs(std::map<std::string, std::string> config,
+                                                const std::map<std::string, std::string>& local) {
+    for (auto&& kvp : local) {
+        config[kvp.first] = kvp.second;
+    }
+    return config;
+}
+
+}  // namespace
+
+std::map<std::string, std::string> AutoBatchInferencePlugin::GetSupportedConfig(
+    const std::map<std::string, std::string>& config,
+    const std::string& deviceName) const {
+    std::vector<std::string> supportedConfigKeys = GetCore()->GetMetric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS));
+    std::map<std::string, std::string> supportedConfig;
+    for (auto&& key : supportedConfigKeys) {
+        auto itKey = config.find(key);
+        if (config.end() != itKey) {
+            supportedConfig[key] = itKey->second;
+        }
+    }
+    return supportedConfig;
+}
+
+DeviceInformation AutoBatchInferencePlugin::ParseBatchDevice(const std::string& deviceWithBatch) {
+    auto&& d = deviceWithBatch;
+    auto openingBracket = d.find_first_of('(');
+    auto closingBracket = d.find_first_of(')', openingBracket);
+    auto deviceName = d.substr(0, openingBracket);
+
+    int batch = 1;
+    if (closingBracket != std::string::npos && openingBracket < closingBracket) {
+        batch = std::stol(d.substr(openingBracket + 1, closingBracket - 1));
+
+        if (batch <= 0) {
+            IE_THROW() << "Batch value for '" << deviceName << "' must be > 0, while " << batch << "is passed";
+        }
+    }
+    return {deviceName, {{}}, batch};
+}
+
+DeviceInformation AutoBatchInferencePlugin::ParseMetaDevice(const std::string& devicesBatchCfg,
+                                                            const std::map<std::string, std::string>& config) const {
+    auto getDeviceConfig = [&](const DeviceName& deviceWithID) {
+        DeviceIDParser deviceParser(deviceWithID);
+        std::string deviceName = deviceParser.getDeviceName();
+        std::map<std::string, std::string> tconfig = mergeConfigs(_config, config);
+
+        // set device ID if any
+        std::string deviceIDLocal = deviceParser.getDeviceID();
+        if (!deviceIDLocal.empty()) {
+            tconfig[PluginConfigParams::KEY_DEVICE_ID] = deviceIDLocal;
+        }
+
+        return GetSupportedConfig(tconfig, deviceName);
+    };
+
+    auto metaDevice = ParseBatchDevice(devicesBatchCfg);
+    metaDevice.config = getDeviceConfig(metaDevice.deviceName);
+
+    auto cfg = config;
+    // check that no irrelevant config-keys left
+    for (auto k : config) {
+        const auto& name = k.first;
+        auto found_in_supported_cfg = std::find(supported_configKeys.begin(), supported_configKeys.end(), k.first);
+        auto found_in_device_cfg = metaDevice.config.find(k.first);
+        if (found_in_device_cfg == metaDevice.config.end() && found_in_supported_cfg == supported_configKeys.end()) {
+            IE_THROW() << "Unsupported config key: " << name;
+        }
+    }
+    return metaDevice;
+}
+
+RemoteContext::Ptr AutoBatchInferencePlugin::CreateContext(const InferenceEngine::ParamMap& config) {
+    auto cfg = config;
+    auto it = cfg.find(CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG));
+    if (it == cfg.end())
+        IE_THROW() << "Value for KEY_AUTO_BATCH is not set";
+
+    auto val = it->second;
+    auto metaDevice = ParseMetaDevice(val, std::map<std::string, std::string>());
+    cfg.erase(it);
+    return GetCore()->CreateContext(metaDevice.deviceName, cfg);
+}
+
+Parameter AutoBatchInferencePlugin::GetConfig(const std::string& name,
+                                              const std::map<std::string, Parameter>& options) const {
+    if (supported_configKeys.end() != std::find(supported_configKeys.begin(), supported_configKeys.end(), name)) {
+        auto it = _config.find(name);
+        if (it == _config.end()) {
+            IE_THROW() << "Value for " << name << " is not set";
+        } else {
+            return {it->second};
+        }
+    } else {
+        IE_THROW() << "Unsupported config key: " << name;
+    }
+}
+
+void AutoBatchInferencePlugin::CheckConfig(const std::map<std::string, std::string>& config) {
+    for (auto&& kvp : config) {
+        const auto name = kvp.first;
+        const auto val = kvp.second;
+        if (supported_configKeys.end() == std::find(supported_configKeys.begin(), supported_configKeys.end(), name))
+            IE_THROW() << "Unsupported config key: " << name;
+        if (name == CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG)) {
+            ParseBatchDevice(val);
+        } else if (name == CONFIG_KEY(AUTO_BATCH_TIMEOUT)) {
+            try {
+                auto t = std::stoi(val);
+                if (t < 0)
+                    IE_THROW(ParameterMismatch);
+            } catch (const std::exception& e) {
+                IE_THROW(ParameterMismatch)
+                    << " Expecting unsigned int value for " << CONFIG_KEY(AUTO_BATCH_TIMEOUT) << " got " << val;
+            }
+        }
+    }
+}
+
+void AutoBatchInferencePlugin::SetConfig(const std::map<std::string, std::string>& config) {
+    CheckConfig(config);
+    for (auto&& kvp : config) {
+        _config[kvp.first] = kvp.second;
+    }
+}
+
+static const Version version = {{2, 1}, CI_BUILD_NUMBER, "AutoBatchPlugin"};
+IE_DEFINE_PLUGIN_CREATE_FUNCTION(AutoBatchInferencePlugin, version)
+
+AutoBatchInferencePlugin::AutoBatchInferencePlugin() {
+    _pluginName = "BATCH";
+}
+
+InferenceEngine::Parameter AutoBatchInferencePlugin::GetMetric(
+    const std::string& name,
+    const std::map<std::string, InferenceEngine::Parameter>& options) const {
+    if (name == METRIC_KEY(SUPPORTED_METRICS)) {
+        std::vector<std::string> metrics;
+        metrics.push_back(METRIC_KEY(SUPPORTED_METRICS));
+        metrics.push_back(METRIC_KEY(FULL_DEVICE_NAME));
+        metrics.push_back(METRIC_KEY(SUPPORTED_CONFIG_KEYS));
+        IE_SET_METRIC_RETURN(SUPPORTED_METRICS, metrics);
+    } else if (name == METRIC_KEY(FULL_DEVICE_NAME)) {
+        IE_SET_METRIC_RETURN(FULL_DEVICE_NAME, _pluginName);
+    } else if (name == METRIC_KEY(SUPPORTED_CONFIG_KEYS)) {
+        IE_SET_METRIC_RETURN(SUPPORTED_CONFIG_KEYS, supported_configKeys);
+    } else {
+        IE_THROW(NotFound) << "Unsupported metric key " << name;
+    }
+}
+
+IExecutableNetworkInternal::Ptr AutoBatchInferencePlugin::LoadExeNetworkImpl(
+    const InferenceEngine::CNNNetwork& network,
+    const std::map<std::string, std::string>& config) {
+    return LoadNetworkImpl(network, nullptr, config);
+}
+
+InferenceEngine::IExecutableNetworkInternal::Ptr AutoBatchInferencePlugin::LoadNetworkImpl(
+    const InferenceEngine::CNNNetwork& network,
+    const std::shared_ptr<InferenceEngine::RemoteContext> ctx,
+    const std::map<std::string, std::string>& config) {
+    if (GetCore() == nullptr) {
+        IE_THROW() << "Please, work with MULTI device via InferencEngine::Core object";
+    }
+
+    auto fullConfig = mergeConfigs(_config, config);
+    auto device_batch = fullConfig.find(CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG));
+    if (device_batch == fullConfig.end()) {
+        IE_THROW() << "KEY_AUTO_BATCH key is not set for BATCH device";
+    }
+
+    auto metaDevice = ParseMetaDevice(device_batch->second, fullConfig);
+    const auto& deviceName = metaDevice.deviceName;
+    const auto& deviceConfig = metaDevice.config;
+    const auto perfConfig = fullConfig.find(PluginConfigParams::KEY_PERF_COUNT);
+    const bool enablePerfCounters = (fullConfig.end() != perfConfig) && (perfConfig->second == PluginConfigParams::YES);
+
+    auto report_footprint = [](std::shared_ptr<ICore> pCore, std::string device) -> size_t {
+        size_t footprint = 0;
+        // TODO: use the per-network metric (22.2) rather than plugin-level
+        auto stats = pCore->GetMetric(device, GPU_METRIC_KEY(MEMORY_STATISTICS)).as<std::map<std::string, uint64_t>>();
+        for (auto s : stats)
+            if (s.first.find("_current") != std::string::npos)
+                footprint += s.second;
+        return footprint;
+    };
+
+    size_t batch1_footprint = 0;
+    if (deviceName.find("GPU") != std::string::npos)
+        batch1_footprint = report_footprint(GetCore(), deviceName);
+    auto executableNetworkWithoutBatch = ctx ? GetCore()->LoadNetwork(network, ctx, deviceConfig)
+                                             : GetCore()->LoadNetwork(network, deviceName, deviceConfig);
+    if (deviceName.find("GPU") != std::string::npos) {
+        batch1_footprint = report_footprint(GetCore(), deviceName) - batch1_footprint;
+        if (batch1_footprint) {
+            const uint64_t total_mem = GetCore()->GetMetric(deviceName, GPU_METRIC_KEY(DEVICE_TOTAL_MEM_SIZE));
+            const int estimated_batch = (total_mem - batch1_footprint) / batch1_footprint;
+            int closest = pow(2, floor(log(estimated_batch) / log(2)));
+            closest = std::max(1, closest);
+            metaDevice.batchForDevice = std::min(metaDevice.batchForDevice, closest);
+        }
+    }
+    // auto-batch settings
+    std::unordered_map<std::string, InferenceEngine::Parameter> networkConfig;
+    for (auto c : fullConfig) {
+        if (supported_configKeys.end() != std::find(supported_configKeys.begin(), supported_configKeys.end(), c.first))
+            networkConfig.insert(c);
+    }
+
+    InferenceEngine::SoExecutableNetworkInternal executableNetworkWithBatch;
+    if (metaDevice.batchForDevice > 1) {
+        try {
+            CNNNetwork clonedNetwork(InferenceEngine::details::cloneNetwork(network));
+            const InputsDataMap inputInfo = clonedNetwork.getInputsInfo();
+            ICNNNetwork::InputShapes shapes = clonedNetwork.getInputShapes();
+            for (const InputsDataMap::value_type& item : inputInfo) {
+                auto layout = item.second->getTensorDesc().getLayout();
+                // the below code is a placeholder for the WIP (22.1) functionality
+                // that will check the reshaping by the batch is robust (CVS-51744)
+                if (layout == InferenceEngine::Layout::NC || layout == InferenceEngine::Layout::NCDHW ||
+                    layout == InferenceEngine::Layout::NCHW || layout == InferenceEngine::Layout::NHWC ||
+                    layout == InferenceEngine::Layout::NDHWC) {
+                    assert(1 == shapes[item.first][0]);  // do not reshape/re-batch originally batched networks
+                    shapes[item.first][0] = metaDevice.batchForDevice;
+                }
+            }
+            clonedNetwork.reshape(shapes);
+            executableNetworkWithBatch =
+                ctx ? GetCore()->LoadNetwork(CNNNetwork{clonedNetwork}, ctx, deviceConfig)
+                    : GetCore()->LoadNetwork(CNNNetwork{clonedNetwork}, deviceName, deviceConfig);
+        } catch (...) {
+            executableNetworkWithBatch = {nullptr, nullptr};
+        }
+    }
+
+    if (!executableNetworkWithBatch) {
+        executableNetworkWithBatch = executableNetworkWithoutBatch;
+        metaDevice.batchForDevice = 1;
+    }
+
+    return std::make_shared<AutoBatchExecutableNetwork>(executableNetworkWithBatch,
+                                                        executableNetworkWithoutBatch,
+                                                        metaDevice,
+                                                        networkConfig,
+                                                        enablePerfCounters);
+}
+
+InferenceEngine::IExecutableNetworkInternal::Ptr AutoBatchInferencePlugin::LoadExeNetworkImpl(
+    const InferenceEngine::CNNNetwork& network,
+    const std::shared_ptr<InferenceEngine::RemoteContext>& context,
+    const std::map<std::string, std::string>& config) {
+    return LoadNetworkImpl(network, context, config);
+}
+
+InferenceEngine::QueryNetworkResult AutoBatchInferencePlugin::QueryNetwork(
+    const InferenceEngine::CNNNetwork& network,
+    const std::map<std::string, std::string>& config) const {
+    auto cfg = config;
+    for (auto c : cfg) {
+        if (c.first == CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG)) {
+            auto val = c.second;
+            cfg.erase(c.first);
+            auto metaDevice = ParseMetaDevice(val, cfg);
+            return GetCore()->QueryNetwork(network, metaDevice.deviceName, cfg);
+        }
+    }
+    IE_THROW() << "Value for KEY_AUTO_BATCH is not set";
+}
+}  // namespace AutoBatchPlugin
diff --git a/src/plugins/auto_batch/auto_batch.hpp b/src/plugins/auto_batch/auto_batch.hpp
new file mode 100644
index 00000000000..95660798417
--- /dev/null
+++ b/src/plugins/auto_batch/auto_batch.hpp
@@ -0,0 +1,159 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#pragma once
+
+#include <atomic>
+#include <map>
+#include <mutex>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp"
+#include "cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp"
+#include "cpp_interfaces/interface/ie_iplugin_internal.hpp"
+#include "ie_metric_helpers.hpp"
+#include "threading/ie_thread_safe_containers.hpp"
+
+namespace AutoBatchPlugin {
+
+using DeviceName = std::string;
+
+struct DeviceInformation {
+    DeviceName deviceName;
+    std::map<std::string, std::string> config;
+    int batchForDevice;
+};
+
+class AutoBatchAsyncInferRequest;
+class AutoBatchExecutableNetwork : public InferenceEngine::ExecutableNetworkThreadSafeDefault {
+public:
+    using Ptr = std::shared_ptr<AutoBatchExecutableNetwork>;
+    struct WorkerInferRequest {
+        using Ptr = std::shared_ptr<WorkerInferRequest>;
+        InferenceEngine::SoIInferRequestInternal _inferRequestBatched;
+        int _batchSize;
+        InferenceEngine::ThreadSafeQueueWithSize<std::pair<AutoBatchAsyncInferRequest*, InferenceEngine::Task>> _tasks;
+        std::vector<InferenceEngine::Task> _completionTasks;
+        std::thread _thread;
+        std::condition_variable _cond;
+        std::mutex _mutex;
+        std::exception_ptr _exceptionPtr;
+    };
+
+    explicit AutoBatchExecutableNetwork(
+        const InferenceEngine::SoExecutableNetworkInternal& networkForDevice,
+        const InferenceEngine::SoExecutableNetworkInternal& networkForDeviceWithoutBatch,
+        const DeviceInformation& networkDevices,
+        const std::unordered_map<std::string, InferenceEngine::Parameter>& config,
+        const bool needPerfCounters = false);
+
+    void SetConfig(const std::map<std::string, InferenceEngine::Parameter>& config) override;
+    InferenceEngine::Parameter GetConfig(const std::string& name) const override;
+    InferenceEngine::Parameter GetMetric(const std::string& name) const override;
+    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequest() override;
+    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequestImpl(
+        InferenceEngine::InputsDataMap networkInputs,
+        InferenceEngine::OutputsDataMap networkOutputs) override;
+    std::shared_ptr<InferenceEngine::RemoteContext> GetContext() const override;
+    std::shared_ptr<ngraph::Function> GetExecGraphInfo() override;
+    virtual ~AutoBatchExecutableNetwork();
+
+protected:
+    static unsigned int ParseTimeoutValue(const std::string&);
+    std::atomic_bool _terminate = {false};
+    DeviceInformation _device;
+    InferenceEngine::SoExecutableNetworkInternal _network;
+    InferenceEngine::SoExecutableNetworkInternal _networkWithoutBatch;
+    std::vector<WorkerInferRequest::Ptr> _workerRequests;
+    std::unordered_map<std::string, InferenceEngine::Parameter> _config;
+    bool _needPerfCounters = false;
+    std::atomic_size_t _numRequestsCreated = {0};
+    std::atomic_int _timeOut = {1000};  // in ms
+};
+
+class AutoBatchInferRequest : public InferenceEngine::IInferRequestInternal {
+public:
+    using Ptr = std::shared_ptr<AutoBatchInferRequest>;
+    explicit AutoBatchInferRequest(const InferenceEngine::InputsDataMap& networkInputs,
+                                   const InferenceEngine::OutputsDataMap& networkOutputs,
+                                   AutoBatchExecutableNetwork::WorkerInferRequest& workerRequestPtr,
+                                   int batch_id,
+                                   int num_batch,
+                                   bool _needPerfCounters = false);
+    std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> GetPerformanceCounts() const override;
+
+    // Batch-Device impl specific: sets the data (blobs from the device request to the batched device request)
+    void SetBlobsToAnotherRequest(InferenceEngine::SoIInferRequestInternal& req);
+    void CopyInputsIfNeeded();
+    void CopyOutputsIfNeeded();
+    AutoBatchExecutableNetwork::WorkerInferRequest& _myBatchedRequestWrapper;
+    std::exception_ptr _exceptionPtr;
+
+protected:
+    std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> _perfMap;
+    bool _needPerfCounters = false;
+    void CopyBlobIfNeeded(InferenceEngine::Blob::CPtr src, InferenceEngine::Blob::Ptr dst, bool bInput);
+    size_t _batchId;
+    size_t _batchSize;
+};
+
+class AutoBatchAsyncInferRequest : public InferenceEngine::AsyncInferRequestThreadSafeDefault {
+public:
+    using Ptr = std::shared_ptr<AutoBatchAsyncInferRequest>;
+
+    explicit AutoBatchAsyncInferRequest(const AutoBatchInferRequest::Ptr& inferRequest,
+                                        const bool needPerfCounters,
+                                        InferenceEngine::SoIInferRequestInternal& inferRequestWithoutBatch,
+                                        const InferenceEngine::ITaskExecutor::Ptr& callbackExecutor);
+    void Infer_ThreadUnsafe() override;
+    virtual ~AutoBatchAsyncInferRequest();
+
+    InferenceEngine::SoIInferRequestInternal _inferRequestWithoutBatch;
+    AutoBatchInferRequest::Ptr _inferRequest;
+};
+
+class AutoBatchInferencePlugin : public InferenceEngine::IInferencePlugin {
+public:
+    AutoBatchInferencePlugin();
+    virtual ~AutoBatchInferencePlugin() = default;
+    InferenceEngine::IExecutableNetworkInternal::Ptr LoadExeNetworkImpl(
+        const InferenceEngine::CNNNetwork& network,
+        const std::map<std::string, std::string>& config) override;
+    InferenceEngine::IExecutableNetworkInternal::Ptr LoadExeNetworkImpl(
+        const InferenceEngine::CNNNetwork& network,
+        const std::shared_ptr<InferenceEngine::RemoteContext>& context,
+        const std::map<std::string, std::string>& config) override;
+
+    void SetConfig(const std::map<std::string, std::string>& config) override;
+    void CheckConfig(const std::map<std::string, std::string>& config);
+
+    InferenceEngine::Parameter GetConfig(
+        const std::string& name,
+        const std::map<std::string, InferenceEngine::Parameter>& options) const override;
+    InferenceEngine::QueryNetworkResult QueryNetwork(const InferenceEngine::CNNNetwork& network,
+                                                     const std::map<std::string, std::string>& config) const override;
+    InferenceEngine::Parameter GetMetric(
+        const std::string& name,
+        const std::map<std::string, InferenceEngine::Parameter>& options) const override;
+    InferenceEngine::RemoteContext::Ptr CreateContext(const InferenceEngine::ParamMap&) override;
+
+protected:
+    DeviceInformation ParseMetaDevice(const std::string& devicesBatchCfg,
+                                      const std::map<std::string, std::string>& config) const;
+
+    std::map<std::string, std::string> GetSupportedConfig(const std::map<std::string, std::string>& config,
+                                                          const DeviceName& deviceName) const;
+    static DeviceInformation ParseBatchDevice(const std::string& deviceWithBatch);
+
+    InferenceEngine::IExecutableNetworkInternal::Ptr LoadNetworkImpl(
+        const InferenceEngine::CNNNetwork& network,
+        const std::shared_ptr<InferenceEngine::RemoteContext> context,
+        const std::map<std::string, std::string>& config);
+};
+
+}  // namespace AutoBatchPlugin
diff --git a/src/plugins/intel_cpu/src/mkldnn_plugin.cpp b/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
index 8a4cc5c6d89..70aa7e97cc0 100644
--- a/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
+++ b/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
@@ -609,11 +609,9 @@ Engine::LoadExeNetworkImpl(const InferenceEngine::CNNNetwork &network, const std
                 // the more "capable" the CPU in general, the more streams we may want to keep to keep it utilized
                 const float memThresholdAssumeLimitedForISA = ov::MemBandwidthPressure::LIMITED/isaSpecificThreshold;
                 const float L2_cache_size = mkldnn::utils::get_cache_size(2 /*level*/, true /*per core */);
-                const float L3_cache_size = mkldnn::utils::get_cache_size(3, false);
                 ov::MemBandwidthPressure networkToleranceForLowCache = ov::MemBandwidthPressureTolerance(
                         clonedNetwork.getFunction(),
-                        L2_cache_size, L3_cache_size,
-                        memThresholdAssumeLimitedForISA);
+                        L2_cache_size, memThresholdAssumeLimitedForISA);
                 // num of phys CPU cores (most aggressive value for #streams)
                 const auto num_cores = getNumberOfCPUCores();
                 // less aggressive
diff --git a/src/plugins/intel_gpu/src/plugin/plugin.cpp b/src/plugins/intel_gpu/src/plugin/plugin.cpp
index 879f15976fa..7450b67be0e 100644
--- a/src/plugins/intel_gpu/src/plugin/plugin.cpp
+++ b/src/plugins/intel_gpu/src/plugin/plugin.cpp
@@ -28,6 +28,7 @@
 
 #include "intel_gpu/runtime/device_query.hpp"
 #include "intel_gpu/runtime/debug_configuration.hpp"
+#include <performance_heuristics.hpp>
 #ifdef __linux__
 # include <dlfcn.h>
 #endif
@@ -681,6 +682,7 @@ Parameter Plugin::GetMetric(const std::string& name, const std::map<std::string,
         metrics.push_back(METRIC_KEY(RANGE_FOR_STREAMS));
         metrics.push_back(METRIC_KEY(DEVICE_TYPE));
         metrics.push_back(METRIC_KEY(DEVICE_GOPS));
+        metrics.push_back(METRIC_KEY(OPTIMAL_BATCH_SIZE));
         metrics.push_back(GPU_METRIC_KEY(MAX_BATCH_SIZE));
         metrics.push_back(GPU_METRIC_KEY(DEVICE_TOTAL_MEM_SIZE));
         metrics.push_back(GPU_METRIC_KEY(UARCH_VERSION));
@@ -716,6 +718,76 @@ Parameter Plugin::GetMetric(const std::string& name, const std::map<std::string,
               << static_cast<int>(device_info.gfx_ver.revision);
         }
         IE_SET_METRIC_RETURN(GPU_UARCH_VERSION, s.str());
+    } else if (name == METRIC_KEY(OPTIMAL_BATCH_SIZE)) {
+        auto next_pow_of_2 = [] (float x) {
+            return pow(2, ceil(log(x)/log(2)));
+        };
+        auto closest_pow_of_2 = [] (float x) {
+            return pow(2, floor(log(x)/log(2)));
+        };
+        auto model_param = options.find("MODEL_PTR");
+        if (model_param == options.end()) {
+            GPU_DEBUG_IF(debug_config->verbose >= 1) {
+                GPU_DEBUG_COUT << "[GPU_OPTIMAL_BATCH_SIZE] MODELS_PTR is not set: return 1" << std::endl;
+            }
+            IE_SET_METRIC_RETURN(OPTIMAL_BATCH_SIZE, static_cast<unsigned int>(1));
+        }
+        std::shared_ptr<ngraph::Function> model;
+        try {
+            model = model_param->second.as<std::shared_ptr<ngraph::Function>>();
+        } catch (...) {
+            IE_THROW() << "[GPU_OPTIMAL_BATCH_SIZE] MODEL_PTR should be std::shared_ptr<ngraph::Function> type";
+        }
+        GPU_DEBUG_IF(debug_config->verbose >= 1) {
+            GPU_DEBUG_COUT << "DEVICE_INFO:"
+                           << "gfx_version.major, " << device_info.gfx_ver.major
+                           << "gfx_version.minor " << std::to_string(device_info.gfx_ver.minor) << std::endl;
+        }
+        static std::map<cldnn::gfx_version, size_t> gen_kbytes_per_bank = {
+                {{12, 0, 0}, 480},  // TGL
+                {{12, 1, 0}, 2048}, // DG1
+                {{12, 5, 0}, 320},
+                {{12, 7, 0}, 512},
+        };
+        size_t L3_cache_size = device_info.gfx_ver.major && (device_info.gfx_ver.major <= 9)
+                ? 768 * 1024 // Gen9
+                : 2 * 768 * 1024;  //reasonable default when no arch has been detected (e.g. due to old driver ver)
+        cldnn::gfx_version gen = {device_info.gfx_ver.major, device_info.gfx_ver.minor, 0 /*ignore the revision*/};
+        auto val = gen_kbytes_per_bank.find(gen);
+        if (gen_kbytes_per_bank.end() != val) {
+            auto kbytes_per_bank = val->second;
+            auto num_banks_per_slice = device_info.num_sub_slices_per_slice > 4
+                                       ? next_pow_of_2(device_info.num_sub_slices_per_slice)
+                                       : 2 * device_info.num_sub_slices_per_slice;
+            L3_cache_size = kbytes_per_bank * 1024 * num_banks_per_slice * device_info.num_slices;
+            GPU_DEBUG_IF(debug_config->verbose >= 1) {
+                GPU_DEBUG_COUT << "DEVICE_INFO:"
+                               << "num_slices " << device_info.num_slices
+                               << ", num_sub_slices_per_slice " << device_info.num_sub_slices_per_slice
+                               << ", num_banks_per_slice " << num_banks_per_slice
+                               << ", gen_kbytes_per_bank : " << kbytes_per_bank
+                               << ", L3_cache_size is (MB): " << float(L3_cache_size) / 1024 / 1024 << std::endl;
+            }
+        }
+        Config config = _impl->m_configs.GetConfig(device_id);
+        auto networkCloned = CloneAndTransformNetwork(CNNNetwork(model), config);
+        ov::MemBandwidthPressure memPressure = ov::MemBandwidthPressureTolerance(networkCloned.getFunction(), L3_cache_size);
+        unsigned int batch = 1;
+        if (memPressure.max_mem_tolerance != ov::MemBandwidthPressure::UNKNOWN)
+            batch = std::max(1.0, 16 * closest_pow_of_2(memPressure.max_mem_tolerance));
+        std::map<std::string, InferenceEngine::Parameter> options_for_max_batch;
+        options_for_max_batch["MODEL_PTR"] = model;
+        options_for_max_batch["GPU_THROUGHPUT_STREAMS"] = CONFIG_VALUE(GPU_THROUGHPUT_AUTO);
+        auto max_batch_size = GetMetric(GPU_METRIC_KEY(MAX_BATCH_SIZE), options_for_max_batch).as<unsigned int>();
+        unsigned int closest = closest_pow_of_2(max_batch_size);
+        batch = std::min(closest, batch);
+        batch = std::min(256u, batch); //batch 256 is a max
+        GPU_DEBUG_IF(debug_config->verbose >= 1) {
+            GPU_DEBUG_COUT << memPressure.max_mem_tolerance << std::endl;
+            GPU_DEBUG_COUT << "MAX_BATCH: " << max_batch_size << std::endl;
+            GPU_DEBUG_COUT << "ACTUAL OPTIMAL BATCH: " << batch << std::endl;
+        }
+        IE_SET_METRIC_RETURN(OPTIMAL_BATCH_SIZE, batch);
     } else if (name == METRIC_KEY(FULL_DEVICE_NAME)) {
         auto deviceName = StringRightTrim(device_info.dev_name, "NEO", false);
         deviceName += std::string(" (") + (device_info.dev_type == cldnn::device_type::discrete_gpu ? "dGPU" : "iGPU") + ")";
@@ -885,7 +957,7 @@ Parameter Plugin::GetMetric(const std::string& name, const std::map<std::string,
             TransformationsPipeline transformations(config, device_info);
             transformations.apply(nGraphFunc);
             program = std::make_shared<Program>(cloned_network, engine, config, false, true);
-            std::pair<int64_t, int64_t> device_memory_usage =  program->GetCompiledProgram(0)->get_estimated_device_mem_usage();
+            std::pair<int64_t, int64_t> device_memory_usage = program->GetCompiledProgram(0)->get_estimated_device_mem_usage();
             int64_t mem_for_general = std::max(static_cast<int64_t>(1L),
                     static_cast<int64_t>(static_cast<int64_t>(available_device_mem) - device_memory_usage.first));
             int64_t mem_per_batch = std::max(static_cast<int64_t>(1L), (device_memory_usage.second / static_cast<int64_t>(base_batch_size)));
diff --git a/src/tests/functional/inference_engine/CMakeLists.txt b/src/tests/functional/inference_engine/CMakeLists.txt
index e108134920b..922faa37c82 100644
--- a/src/tests/functional/inference_engine/CMakeLists.txt
+++ b/src/tests/functional/inference_engine/CMakeLists.txt
@@ -48,6 +48,10 @@ if(ENABLE_AUTO OR ENABLE_MULTI)
     list(APPEND DEPENDENCIES ov_auto_plugin)
 endif()
 
+if(ENABLE_AUTO_BATCH)
+    list(APPEND DEPENDENCIES ov_auto_batch_plugin)
+endif()
+
 if (NOT ENABLE_OV_ONNX_FRONTEND)
     list(APPEND EXCLUDED_SOURCE_PATHS "${CMAKE_CURRENT_SOURCE_DIR}/onnx_reader")
 endif()
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
index a39c1b451de..0a120f0157b 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
@@ -24,6 +24,7 @@ inline const std::string getPluginLibNameByDevice(const std::string& deviceName)
             { "GNA", "ov_intel_gna_plugin" },
             { "GPU", "ov_intel_gpu_plugin" },
             { "HETERO", "ov_hetero_plugin" },
+            { "BATCH", "ov_auto_batch_plugin" },
             { "MULTI", "ov_multi_plugin" },
             { "MYRIAD", "myriadPlugin" },
             { "TEMPLATE", "ov_template_plugin" },
@@ -42,6 +43,11 @@ inline const std::pair<std::string, std::string> generateDefaultHeteroConfig() {
     return { "TARGET_FALLBACK" , ConformanceTests::targetDevice };
 }
 
+inline const std::pair<std::string, std::string> generateDefaultBatchConfig() {
+    // auto-batching with batch 1 (no real batching in fact, but full machinery is in action)
+    return { CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , std::string(ConformanceTests::targetDevice)};
+}
+
 inline const std::vector<std::map<std::string, std::string>> generateConfigs(const std::string& targetDevice,
                                                                              const std::vector<std::map<std::string, std::string>>& config = {}) {
     std::pair<std::string, std::string> defaultConfig;
@@ -49,6 +55,8 @@ inline const std::vector<std::map<std::string, std::string>> generateConfigs(con
         defaultConfig = generateDefaultMultiConfig();
     } else if (targetDevice ==  std::string(CommonTestUtils::DEVICE_HETERO)) {
         defaultConfig = generateDefaultHeteroConfig();
+    } else if (targetDevice ==  std::string(CommonTestUtils::DEVICE_BATCH)) {
+        defaultConfig = generateDefaultBatchConfig();
     } else {
         throw std::runtime_error("Incorrect target device: " + targetDevice);
     }
@@ -70,7 +78,8 @@ inline const std::string generateComplexDeviceName(const std::string& deviceName
 
 inline const std::vector<std::string> returnAllPossibleDeviceCombination() {
     std::vector<std::string> res{ConformanceTests::targetDevice};
-    std::vector<std::string> devices{CommonTestUtils::DEVICE_HETERO, CommonTestUtils::DEVICE_AUTO, CommonTestUtils::DEVICE_MULTI};
+    std::vector<std::string> devices{CommonTestUtils::DEVICE_HETERO, CommonTestUtils::DEVICE_AUTO,
+                                     CommonTestUtils::DEVICE_BATCH, CommonTestUtils::DEVICE_MULTI};
     for (const auto& device : devices) {
         res.emplace_back(generateComplexDeviceName(device));
     }
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/callback.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/callback.cpp
index 7137300df77..b089f77889e 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/callback.cpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/callback.cpp
@@ -33,4 +33,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, InferRequestCallbackTests,
                                  ::testing::Values(CommonTestUtils::DEVICE_HETERO),
                                  ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_HETERO))),
                          InferRequestCallbackTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Batch_BehaviorTests, InferRequestCallbackTests,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                 ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_BATCH))),
+                         InferRequestCallbackTests::getTestCaseName);
 }  // namespace
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/io_blob.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/io_blob.cpp
index 21d7dcc0c86..3aacb8e80b1 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/io_blob.cpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/io_blob.cpp
@@ -36,4 +36,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, InferRequestIOBBlobTest,
                                  ::testing::Values(CommonTestUtils::DEVICE_HETERO),
                                  ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_HETERO))),
                          InferRequestIOBBlobTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Batch_BehaviorTests, InferRequestIOBBlobTest,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                 ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_BATCH))),
+                         InferRequestIOBBlobTest::getTestCaseName);
 }  // namespace
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/multitheading.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/multitheading.cpp
index ea24706a8df..26c38ada713 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/multitheading.cpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/multitheading.cpp
@@ -38,4 +38,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, InferRequestMultithreadingT
                                  ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_HETERO))),
                          InferRequestMultithreadingTests::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Batch_BehaviorTests, InferRequestMultithreadingTests,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                 ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_BATCH))),
+                         InferRequestMultithreadingTests::getTestCaseName);
+
 }  // namespace
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/set_blob_by_type.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/set_blob_by_type.cpp
index 49a2b23bd73..af064831d76 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/set_blob_by_type.cpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/set_blob_by_type.cpp
@@ -46,4 +46,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_Behavior_Hetero, InferRequestSetBlobByType,
                                             ::testing::Values(CommonTestUtils::DEVICE_HETERO),
                                             ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_HETERO))),
                          InferRequestSetBlobByType::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Behavior_Batch, InferRequestSetBlobByType,
+                         ::testing::Combine(::testing::ValuesIn(setBlobTypes),
+                                            ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                            ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_BATCH))),
+                         InferRequestSetBlobByType::getTestCaseName);
 } // namespace
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/wait.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/wait.cpp
index e70458bdf4a..a600c31e746 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/wait.cpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/behavior/infer_request/wait.cpp
@@ -37,4 +37,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, InferRequestWaitTests,
                                  ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_HETERO))),
                          InferRequestWaitTests::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Batch_BehaviorTests, InferRequestWaitTests,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                 ::testing::ValuesIn(generateConfigs(CommonTestUtils::DEVICE_BATCH))),
+                         InferRequestWaitTests::getTestCaseName);
 }  // namespace
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp
new file mode 100644
index 00000000000..05b0da43c67
--- /dev/null
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp
@@ -0,0 +1,31 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <auto_batching/auto_batching_tests.hpp>
+
+const std::vector<bool>   get_vs_set{ true, false };
+const std::vector<size_t> num_streams{ 1, 2 };
+const std::vector<size_t> num_requests{ 1, 3, 8, 9, 16, 64 };
+const std::vector<size_t> num_batch{ 1, 4, 8, 16, 32, 64, 128, 256 };
+using namespace AutoBatchingTests;
+
+namespace {
+INSTANTIATE_TEST_SUITE_P(smoke_AutoBatching_CPU, AutoBatching_Test,
+        ::testing::Combine(
+                ::testing::Values(CommonTestUtils::DEVICE_CPU),
+                ::testing::ValuesIn(get_vs_set),
+                ::testing::ValuesIn(num_streams),
+                ::testing::ValuesIn(num_requests),
+                ::testing::ValuesIn(num_batch)),
+                         AutoBatching_Test::getTestCaseName);
+// TODO: for 22.2 (CVS-68949)
+//INSTANTIATE_TEST_SUITE_P(smoke_AutoBatching_CPU, AutoBatching_Test_DetectionOutput,
+//                         ::testing::Combine(
+//                                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
+//                                 ::testing::ValuesIn(get_vs_set),
+//                                 ::testing::ValuesIn(num_streams),
+//                                 ::testing::ValuesIn(num_requests),
+//                                 ::testing::ValuesIn(num_batch)),
+//                         AutoBatching_Test_DetectionOutput::getTestCaseName);
+
+}  // namespace
\ No newline at end of file
diff --git a/src/tests/functional/plugin/gpu/remote_blob_tests/cldnn_remote_blob_tests.cpp b/src/tests/functional/plugin/gpu/remote_blob_tests/cldnn_remote_blob_tests.cpp
index 95aecd6b357..986f3f1a809 100644
--- a/src/tests/functional/plugin/gpu/remote_blob_tests/cldnn_remote_blob_tests.cpp
+++ b/src/tests/functional/plugin/gpu/remote_blob_tests/cldnn_remote_blob_tests.cpp
@@ -21,16 +21,27 @@ using namespace ::testing;
 using namespace InferenceEngine;
 using namespace InferenceEngine::gpu;
 
-class RemoteBlob_Test : public CommonTestUtils::TestsCommon {
+class RemoteBlob_Test : public CommonTestUtils::TestsCommon, public testing::WithParamInterface<bool> {
 protected:
     std::shared_ptr<ngraph::Function> fn_ptr;
+    std::string deviceName;
 
+public:
     void SetUp() override {
         fn_ptr = ngraph::builder::subgraph::makeSplitMultiConvConcat();
+        deviceName = CommonTestUtils::DEVICE_GPU;
+        auto with_auto_batching = this->GetParam();
+        if (with_auto_batching) { // BATCH:GPU
+            deviceName = std::string(CommonTestUtils::DEVICE_BATCH) + ":" + deviceName;
+        }
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<bool>& obj) {
+        auto with_auto_batch = obj.param;
+        return std::string("RemoteBlob_Test") + (with_auto_batch ? "_WITH_AUTO_BATCHING": "");
     }
 };
 
-TEST_F(RemoteBlob_Test, smoke_canInputUserBlob) {
+TEST_P(RemoteBlob_Test, smoke_canInputUserBlob) {
 #if defined(ANDROID)
     GTEST_SKIP();
 #endif
@@ -41,7 +52,7 @@ TEST_F(RemoteBlob_Test, smoke_canInputUserBlob) {
 
     // TODO: Issue: investigate issue with IECore
     auto ie = InferenceEngine::Core();
-    auto exec_net = ie.LoadNetwork(net, CommonTestUtils::DEVICE_GPU);
+    auto exec_net = ie.LoadNetwork(net, deviceName);
 
     // regular inference
     auto inf_req_regular = exec_net.CreateInferRequest();
@@ -70,6 +81,7 @@ TEST_F(RemoteBlob_Test, smoke_canInputUserBlob) {
 
     Blob::Ptr shared_blob = make_shared_blob(net.getInputsInfo().begin()->second->getTensorDesc(), cldnn_context,
                                              shared_buffer);
+    shared_blob->allocate();
     inf_req_shared.SetBlob(net.getInputsInfo().begin()->first, shared_blob);
 
     inf_req_shared.Infer();
@@ -85,7 +97,7 @@ TEST_F(RemoteBlob_Test, smoke_canInputUserBlob) {
 }
 
 
-TEST_F(RemoteBlob_Test, smoke_canInputPluginRemoteBlob) {
+TEST_P(RemoteBlob_Test, smoke_canInputPluginRemoteBlob) {
 #if defined(ANDROID)
     GTEST_SKIP();
 #endif
@@ -96,7 +108,7 @@ TEST_F(RemoteBlob_Test, smoke_canInputPluginRemoteBlob) {
 
     // TODO: Issue: investigate issue with IECore
     auto ie = InferenceEngine::Core();
-    auto exec_net = ie.LoadNetwork(net, CommonTestUtils::DEVICE_GPU);
+    auto exec_net = ie.LoadNetwork(net, deviceName);
 
     // regular inference
     auto inf_req_regular = exec_net.CreateInferRequest();
@@ -139,7 +151,7 @@ TEST_F(RemoteBlob_Test, smoke_canInputPluginRemoteBlob) {
 }
 
 
-TEST_F(RemoteBlob_Test, smoke_canInferOnUserContext) {
+TEST_P(RemoteBlob_Test, smoke_canInferOnUserContext) {
     auto fn_ptr = ngraph::builder::subgraph::makeSplitMultiConvConcat();
     CNNNetwork net(fn_ptr);
 
@@ -149,7 +161,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserContext) {
     auto blob = FuncTestUtils::createAndFillBlob(net.getInputsInfo().begin()->second->getTensorDesc());
 
     auto ie = PluginCache::get().ie();
-    auto exec_net_regular = ie->LoadNetwork(net, CommonTestUtils::DEVICE_GPU);
+    auto exec_net_regular = ie->LoadNetwork(net, deviceName);
 
     // regular inference
     auto inf_req_regular = exec_net_regular.CreateInferRequest();
@@ -161,7 +173,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserContext) {
 
     // inference using remote blob
     auto ocl_instance = std::make_shared<OpenCL>();
-    auto remote_context = make_shared_context(*ie, CommonTestUtils::DEVICE_GPU, ocl_instance->_context.get());
+    auto remote_context = make_shared_context(*ie, deviceName, ocl_instance->_context.get());
     auto exec_net_shared = ie->LoadNetwork(net, remote_context);
     auto inf_req_shared = exec_net_shared.CreateInferRequest();
     inf_req_shared.SetBlob(net.getInputsInfo().begin()->first, fakeImageData);
@@ -178,7 +190,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserContext) {
     }
 }
 
-TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_out_of_order) {
+TEST_P(RemoteBlob_Test, smoke_canInferOnUserQueue_out_of_order) {
 #if defined _WIN32
     GTEST_SKIP();
 #endif
@@ -191,7 +203,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_out_of_order) {
     auto blob = FuncTestUtils::createAndFillBlob(net.getInputsInfo().begin()->second->getTensorDesc());
 
     auto ie = PluginCache::get().ie();
-    auto exec_net_regular = ie->LoadNetwork(net, CommonTestUtils::DEVICE_GPU);
+    auto exec_net_regular = ie->LoadNetwork(net, deviceName);
 
     // regular inference
     auto inf_req_regular = exec_net_regular.CreateInferRequest();
@@ -214,7 +226,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_out_of_order) {
 
     // In this scenario we create shared OCL queue and run simple pre-process action and post-process action (buffer copies in both cases)
     // without calling thread blocks
-    auto remote_context = make_shared_context(*ie, CommonTestUtils::DEVICE_GPU, ocl_instance->_queue.get());
+    auto remote_context = make_shared_context(*ie, deviceName, ocl_instance->_queue.get());
     auto exec_net_shared = ie->LoadNetwork(net, remote_context);
     auto inf_req_shared = exec_net_shared.CreateInferRequest();
 
@@ -270,7 +282,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_out_of_order) {
     }
 }
 
-TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_in_order) {
+TEST_P(RemoteBlob_Test, smoke_canInferOnUserQueue_in_order) {
 #if defined _WIN32
     GTEST_SKIP();
 #endif
@@ -283,7 +295,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_in_order) {
     auto blob = FuncTestUtils::createAndFillBlob(net.getInputsInfo().begin()->second->getTensorDesc());
 
     auto ie = PluginCache::get().ie();
-    auto exec_net_regular = ie->LoadNetwork(net, CommonTestUtils::DEVICE_GPU);
+    auto exec_net_regular = ie->LoadNetwork(net, deviceName);
 
     // regular inference
     auto inf_req_regular = exec_net_regular.CreateInferRequest();
@@ -307,7 +319,7 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_in_order) {
 
     // In this scenario we create shared OCL queue and run simple pre-process action and post-process action (buffer copies in both cases)
     // without calling thread blocks
-    auto remote_context = make_shared_context(*ie, CommonTestUtils::DEVICE_GPU, ocl_instance->_queue.get());
+    auto remote_context = make_shared_context(*ie, deviceName, ocl_instance->_queue.get());
     auto exec_net_shared = ie->LoadNetwork(net, remote_context);
     auto inf_req_shared = exec_net_shared.CreateInferRequest();
 
@@ -358,6 +370,10 @@ TEST_F(RemoteBlob_Test, smoke_canInferOnUserQueue_in_order) {
     }
 }
 
+std::vector<bool> with_auto_batching {true, false};
+INSTANTIATE_TEST_SUITE_P(smoke_RemoteBlob, RemoteBlob_Test, ::testing::ValuesIn(with_auto_batching),
+        RemoteBlob_Test::getTestCaseName);
+
 class BatchedBlob_Test : public CommonTestUtils::TestsCommon, public testing::WithParamInterface<size_t> {
     void SetUp() override {
         num_batch = this->GetParam();
diff --git a/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp b/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
index b4ae7c0ea9d..8c4e43984ef 100644
--- a/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
+++ b/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
@@ -30,6 +30,7 @@ protected:
     }
 };
 
+std::vector<bool> ov_with_auto_batching {true, false};
 enum class RemoteTensorSharingType {
     USER_CL_TENSOR = 0,
     PLUGIN_CL_TENSOR = 1,
@@ -54,17 +55,34 @@ std::ostream& operator<<(std::ostream& stream, RemoteTensorSharingType sharing_t
     return stream;
 }
 
-class OVRemoteTensorInputBlob_Test : public OVRemoteTensor_Test, public testing::WithParamInterface<RemoteTensorSharingType> {
+using RemoteTensorSharingTestOptionsParams = std::tuple<RemoteTensorSharingType, bool /*auto-batching*/>;
+
+class OVRemoteTensorInputBlob_Test : public OVRemoteTensor_Test,
+        public testing::WithParamInterface<RemoteTensorSharingTestOptionsParams> {
+protected:
+    std::shared_ptr<ngraph::Function> fn_ptr;
+    std::string deviceName;
+
 public:
     void SetUp() override {
         fn_ptr = ngraph::builder::subgraph::makeSplitMultiConvConcat();
+        deviceName = CommonTestUtils::DEVICE_GPU;
+        RemoteTensorSharingType sharing_type;
+        bool with_auto_batching;
+        std::tie(sharing_type, with_auto_batching) = this->GetParam();
+        if (with_auto_batching)  // BATCH:GPU
+            deviceName = std::string(CommonTestUtils::DEVICE_BATCH) + ":" + deviceName;
     }
-
-    static std::string getTestCaseName(testing::TestParamInfo<RemoteTensorSharingType> obj) {
-        RemoteTensorSharingType sharing_type = obj.param;
+    static std::string getTestCaseName(const testing::TestParamInfo<RemoteTensorSharingTestOptionsParams>& obj) {
+        RemoteTensorSharingType sharing_type;
+        bool with_auto_batching;
+        std::tie(sharing_type, with_auto_batching) = obj.param;
 
         std::ostringstream result;
+        result << "OVRemoteTensorInputBlob_Test_";
         result << sharing_type;
+        if (with_auto_batching)
+            result << "_WITH_AUTO_BATCHING";
         return result.str();
     }
 };
@@ -81,9 +99,17 @@ TEST_P(OVRemoteTensorInputBlob_Test, smoke_canInputRemoteTensor) {
     p.input().preprocess().convert_element_type(ov::element::f32);
 
     auto function = p.build();
-    auto exec_net = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    RemoteTensorSharingType sharing_type;
+    bool with_auto_batching;
+    std::tie(sharing_type, with_auto_batching) = GetParam();
 
-    RemoteTensorSharingType sharing_type = GetParam();
+    // auto-batching relies on availability of the lock() for the tensor (and the *USM_DEVICE is not lockable)
+    if (with_auto_batching
+            && (RemoteTensorSharingType::USER_USM_DEVICE_TENSOR == sharing_type
+                    || RemoteTensorSharingType::PLUGIN_USM_DEVICE_TENSOR == sharing_type))
+        GTEST_SKIP();
+
+    auto exec_net = ie.compile_model(function, deviceName);
 
     // regular inference
     auto inf_req_regular = exec_net.create_infer_request();
@@ -244,6 +270,7 @@ TEST_P(OVRemoteTensorInputBlob_Test, smoke_canInputRemoteTensor) {
 INSTANTIATE_TEST_SUITE_P(
     smoke_GPU,
     OVRemoteTensorInputBlob_Test,
+    ::testing::Combine(
         ::testing::ValuesIn(std::vector<RemoteTensorSharingType>{RemoteTensorSharingType::USER_CL_TENSOR,
                                                                  RemoteTensorSharingType::PLUGIN_CL_TENSOR,
                                                                  RemoteTensorSharingType::USER_USM_HOST_TENSOR,
@@ -251,9 +278,29 @@ INSTANTIATE_TEST_SUITE_P(
                                                                  RemoteTensorSharingType::PLUGIN_USM_HOST_TENSOR,
                                                                  RemoteTensorSharingType::PLUGIN_USM_DEVICE_TENSOR,
                                                                  RemoteTensorSharingType::PLUGIN_HOST_TENSOR}),
+        ::testing::ValuesIn(ov_with_auto_batching)),
         OVRemoteTensorInputBlob_Test::getTestCaseName);
 
-TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContext) {
+class OVRemoteTensor_TestsWithContext : public OVRemoteTensor_Test, public testing::WithParamInterface<bool> {
+protected:
+    std::shared_ptr<ngraph::Function> fn_ptr;
+    std::string deviceName;
+public:
+    void SetUp() override {
+        fn_ptr = ngraph::builder::subgraph::makeSplitMultiConvConcat();
+        deviceName = CommonTestUtils::DEVICE_GPU;
+        auto with_auto_batching = this->GetParam();
+        if (with_auto_batching) { // BATCH:GPU
+            deviceName = std::string(CommonTestUtils::DEVICE_BATCH) + ":" + deviceName;
+        }
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<bool>& obj) {
+        auto with_auto_batch = obj.param;
+        return std::string("RemoteTensor_Test") + (with_auto_batch ? "_WITH_AUTO_BATCHING": "");
+    }
+};
+
+TEST_P(OVRemoteTensor_TestsWithContext, smoke_canInferOnUserContext) {
     auto ie = ov::runtime::Core();
 
     using namespace ov::preprocess;
@@ -262,7 +309,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContext) {
     p.input().preprocess().convert_element_type(ov::element::f32);
     auto function = p.build();
 
-    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto exec_net_regular = ie.compile_model(function, deviceName);
     auto input = function->get_parameters().at(0);
     auto output = function->get_results().at(0);
 
@@ -296,7 +343,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContext) {
     }
 }
 
-TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContextWithMultipleDevices) {
+TEST_P(OVRemoteTensor_TestsWithContext, smoke_canInferOnUserContextWithMultipleDevices) {
     auto ie = ov::runtime::Core();
 
     using namespace ov::preprocess;
@@ -305,7 +352,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContextWithMultipleDevices) {
     p.input().preprocess().convert_element_type(ov::element::f32);
     auto function = p.build();
 
-    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto exec_net_regular = ie.compile_model(function, deviceName);
     auto input = function->get_parameters().at(0);
     auto output = function->get_results().at(0);
 
@@ -344,7 +391,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContextWithMultipleDevices) {
     }
 }
 
-TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_out_of_order) {
+TEST_P(OVRemoteTensor_TestsWithContext, smoke_canInferOnUserQueue_out_of_order) {
     auto ie = ov::runtime::Core();
 
     using namespace ov::preprocess;
@@ -353,7 +400,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_out_of_order) {
     p.input().preprocess().convert_element_type(ov::element::f32);
     auto function = p.build();
 
-    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto exec_net_regular = ie.compile_model(function, deviceName);
     auto input = function->get_parameters().at(0);
     auto output = function->get_results().at(0);
 
@@ -423,7 +470,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_out_of_order) {
     }
 }
 
-TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_in_order) {
+TEST_P(OVRemoteTensor_TestsWithContext, smoke_canInferOnUserQueue_in_order) {
     auto ie = ov::runtime::Core();
 
     using namespace ov::preprocess;
@@ -432,7 +479,7 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_in_order) {
     p.input().preprocess().convert_element_type(ov::element::f32);
     auto function = p.build();
 
-    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto exec_net_regular = ie.compile_model(function, deviceName);
     auto input = function->get_parameters().at(0);
     auto output = function->get_results().at(0);
 
@@ -498,6 +545,9 @@ TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_in_order) {
     }
 }
 
+INSTANTIATE_TEST_SUITE_P(smoke_RemoteTensor, OVRemoteTensor_TestsWithContext, ::testing::ValuesIn(ov_with_auto_batching),
+                         OVRemoteTensor_TestsWithContext::getTestCaseName);
+
 TEST_F(OVRemoteTensor_Test, NV12toBGR_image) {
 #if defined(ANDROID)
     GTEST_SKIP();
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp
new file mode 100644
index 00000000000..e8128730f42
--- /dev/null
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/auto_batching/auto_batching_tests.cpp
@@ -0,0 +1,31 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <auto_batching/auto_batching_tests.hpp>
+
+const std::vector<size_t> num_streams{ 2 };
+const std::vector<bool>   get_vs_set{ true, false };
+const std::vector<size_t> num_requests{ 1, 8, 16, 64 };
+const std::vector<size_t> num_batch{ 1, 8, 32, 256 };
+using namespace AutoBatchingTests;
+
+namespace AutoBatchingTests {
+
+INSTANTIATE_TEST_SUITE_P(smoke_AutoBatching_GPU, AutoBatching_Test,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_GPU),
+                                 ::testing::ValuesIn(get_vs_set),
+                                 ::testing::ValuesIn(num_streams),
+                                 ::testing::ValuesIn(num_requests),
+                                 ::testing::ValuesIn(num_batch)),
+                         AutoBatching_Test::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AutoBatching_GPU, AutoBatching_Test_DetectionOutput,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_GPU),
+                                 ::testing::ValuesIn(get_vs_set),
+                                 ::testing::ValuesIn(num_streams),
+                                 ::testing::ValuesIn(num_requests),
+                                 ::testing::ValuesIn(num_batch)),
+                         AutoBatching_Test_DetectionOutput::getTestCaseName);
+}  // namespace AutoBatchingTests
\ No newline at end of file
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/exec_net_base.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/exec_net_base.cpp
index 1082853b862..ec7181ec6a0 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/exec_net_base.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/exec_net_base.cpp
@@ -52,6 +52,10 @@ const std::vector<std::map<std::string, std::string>> autoConfig = {
         {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU}},
 };
 
+const std::vector<std::map<std::string, std::string>> autoBatchConfig = {
+        {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU}},
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, ExecNetSetPrecision,
                          ::testing::Combine(
                                  ::testing::ValuesIn(netPrecisions),
@@ -72,4 +76,11 @@ INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, ExecNetSetPrecision,
                                  ::testing::Values(CommonTestUtils::DEVICE_AUTO),
                                  ::testing::ValuesIn(autoConfig)),
                          ExecNetSetPrecision::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AutoBatch_BehaviorTests, ExecNetSetPrecision,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                 ::testing::ValuesIn(autoBatchConfig)),
+                         ExecNetSetPrecision::getTestCaseName);
 }  // namespace
\ No newline at end of file
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/get_metric.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/get_metric.cpp
index 3f8f7bd30df..3c0bfd785f4 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/get_metric.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/executable_network/get_metric.cpp
@@ -22,27 +22,27 @@ namespace {
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_OPTIMAL_NUMBER_OF_INFER_REQUESTS,
-        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU")
+        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU", "BATCH:GPU")
 );
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS,
-        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU")
+        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU", "BATCH:GPU")
 );
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_SUPPORTED_METRICS,
-        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU")
+        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU", "BATCH:GPU")
 );
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_NETWORK_NAME,
-        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU")
+        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU", "BATCH:GPU")
 );
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_ThrowsUnsupported,
-        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU")
+        ::testing::Values("GPU", "MULTI:GPU", "HETERO:GPU", "AUTO:GPU,CPU", "BATCH:GPU")
 );
 
 //
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/callback.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/callback.cpp
index dfaa591dd96..68025694559 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/callback.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/callback.cpp
@@ -19,6 +19,10 @@ const std::vector<std::map<std::string, std::string>> autoConfigs = {
         {InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU}}
 };
 
+const std::vector<std::map<std::string, std::string>> autoBatchConfigs = {
+        {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU}},
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, InferRequestCallbackTests,
         ::testing::Combine(
             ::testing::Values(CommonTestUtils::DEVICE_GPU),
@@ -36,4 +40,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, InferRequestCallbackTests,
             ::testing::Values(CommonTestUtils::DEVICE_AUTO),
             ::testing::ValuesIn(autoConfigs)),
         InferRequestCallbackTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AutoBatch_BehaviorTests, InferRequestCallbackTests,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                 ::testing::ValuesIn(autoBatchConfigs)),
+                         InferRequestCallbackTests::getTestCaseName);
 }  // namespace
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/multithreading.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/multithreading.cpp
index a23ea031001..429e1695ebe 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/multithreading.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/multithreading.cpp
@@ -18,6 +18,10 @@ const std::vector<std::map<std::string, std::string>> autoconfigs = {
         {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, std::string(CommonTestUtils::DEVICE_CPU) + "," + CommonTestUtils::DEVICE_GPU}}
 };
 
+const std::vector<std::map<std::string, std::string>> auto_batch_configs = {
+        {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU}},
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, InferRequestMultithreadingTests,
                         ::testing::Combine(
                                 ::testing::Values(CommonTestUtils::DEVICE_GPU),
@@ -36,4 +40,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, InferRequestMultithreadingTes
                                 ::testing::ValuesIn(autoconfigs)),
                         InferRequestMultithreadingTests::getTestCaseName);
 
+
+INSTANTIATE_TEST_SUITE_P(smoke_AutoBatch_BehaviorTests, InferRequestMultithreadingTests,
+                         ::testing::Combine(
+                                 ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                 ::testing::ValuesIn(auto_batch_configs)),
+                         InferRequestMultithreadingTests::getTestCaseName);
 }  // namespace
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/wait.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/wait.cpp
index 41da3069a87..77b717b6605 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/wait.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/infer_request/wait.cpp
@@ -19,6 +19,11 @@ namespace {
              CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU}}
     };
 
+
+    const std::vector<std::map<std::string, std::string>> autoBatchConfigs = {
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU}},
+    };
+
     INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, InferRequestWaitTests,
                             ::testing::Combine(
                                     ::testing::Values(CommonTestUtils::DEVICE_GPU),
@@ -32,9 +37,15 @@ namespace {
                             InferRequestWaitTests::getTestCaseName);
 
     INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, InferRequestWaitTests,
-                            ::testing::Combine(
-                                    ::testing::Values(CommonTestUtils::DEVICE_AUTO),
-                                    ::testing::ValuesIn(autoConfigs)),
-                            InferRequestWaitTests::getTestCaseName);
+                             ::testing::Combine(
+                                     ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                     ::testing::ValuesIn(autoConfigs)),
+                             InferRequestWaitTests::getTestCaseName);
+
+    INSTANTIATE_TEST_SUITE_P(smoke_AutoBatch_BehaviorTests, InferRequestWaitTests,
+                             ::testing::Combine(
+                                     ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                                     ::testing::ValuesIn(autoBatchConfigs)),
+                             InferRequestWaitTests::getTestCaseName);
 
 }  // namespace
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
index 55fe2c973a7..f03794f7ac3 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
@@ -30,11 +30,11 @@ INSTANTIATE_TEST_SUITE_P(nightly_OVClassNetworkTestP, OVClassNetworkTestP, ::tes
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
         OVClassGetMetricTest_SUPPORTED_CONFIG_KEYS,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO"));
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH"));
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
         OVClassGetMetricTest_SUPPORTED_METRICS,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO"));
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH"));
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
         OVClassGetMetricTest_AVAILABLE_DEVICES,
@@ -42,7 +42,7 @@ INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
         OVClassGetMetricTest_FULL_DEVICE_NAME,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO"));
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH"));
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
         OVClassGetMetricTest_OPTIMIZATION_CAPABILITIES,
@@ -62,11 +62,11 @@ INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
         OVClassGetMetricTest_ThrowUnsupported,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO"));
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH"));
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetConfigTest,
         OVClassGetConfigTest_ThrowUnsupported,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO"));
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH"));
 
 INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetAvailableDevices, OVClassGetAvailableDevices, ::testing::Values("GPU"));
 
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
index 826d3f1fc47..9e3a44d6fad 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
@@ -104,6 +104,29 @@ namespace {
                  CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
                 {InferenceEngine::MultiDeviceConfigParams::KEY_AUTO_NETWORK_PRIORITY, "should be int"}}
     };
+
+
+    const std::vector<std::map<std::string, std::string>> auto_batch_inconfigs = {
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG), CommonTestUtils::DEVICE_GPU},
+                    {CONFIG_KEY(AUTO_BATCH_TIMEOUT), "-1"}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG), CommonTestUtils::DEVICE_GPU},
+                    {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, "DOESN'T EXIST"}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU},
+                    {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY},
+                    {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "-1"}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU},
+                    {InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, "ON"}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU},
+                    {InferenceEngine::PluginConfigParams::KEY_CONFIG_FILE, "unknown_file"}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU},
+                    {InferenceEngine::PluginConfigParams::KEY_DUMP_KERNELS, "ON"}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU},
+                    {InferenceEngine::PluginConfigParams::KEY_TUNING_MODE, "TUNING_UNKNOWN_MODE"}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU},
+                    {InferenceEngine::PluginConfigParams::KEY_DEVICE_ID, "DEVICE_UNKNOWN"}},
+    };
+
+
     IE_SUPPRESS_DEPRECATED_END
 
     INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, IncorrectConfigTests,
@@ -125,6 +148,12 @@ namespace {
                             IncorrectConfigTests::getTestCaseName);
 
 
+    INSTANTIATE_TEST_SUITE_P(smoke_AutoBatch_BehaviorTests, IncorrectConfigTests,
+             ::testing::Combine(
+                     ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                     ::testing::ValuesIn(auto_batch_inconfigs)),
+             IncorrectConfigTests::getTestCaseName);
+
     const std::vector<std::map<std::string, std::string>> conf = {
             {}
     };
@@ -167,17 +196,6 @@ namespace {
     };
     IE_SUPPRESS_DEPRECATED_END
 
-    const std::vector<std::map<std::string, std::string>> multiconf = {
-            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU}},
-            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU},
-                {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::THROUGHPUT}},
-            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU},
-                {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY}},
-            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU},
-                {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY},
-                {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "1"}}
-    };
-
     const std::vector<std::map<std::string, std::string>> autoConfigs = {
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU},
@@ -232,6 +250,12 @@ namespace {
              {InferenceEngine::MultiDeviceConfigParams::KEY_AUTO_NETWORK_PRIORITY, "2"}}
     };
 
+    const std::vector<std::map<std::string, std::string>> auto_batch_configs = {
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU}},
+            {{CONFIG_KEY(AUTO_BATCH_DEVICE_CONFIG) , CommonTestUtils::DEVICE_GPU},
+             {CONFIG_KEY(AUTO_BATCH_TIMEOUT) , "1"}},
+    };
+
     INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, DefaultValuesConfigTests,
             ::testing::Combine(
                 ::testing::Values(CommonTestUtils::DEVICE_GPU),
@@ -255,4 +279,15 @@ namespace {
                     ::testing::Values(CommonTestUtils::DEVICE_AUTO),
                     ::testing::ValuesIn(autoinconfigs)),
             IncorrectConfigAPITests::getTestCaseName);
+    INSTANTIATE_TEST_SUITE_P(smoke_AutoBatch_BehaviorTests, IncorrectConfigAPITests,
+             ::testing::Combine(
+                     ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                     ::testing::ValuesIn(auto_batch_inconfigs)),
+             IncorrectConfigAPITests::getTestCaseName);
+
+    INSTANTIATE_TEST_SUITE_P(smoke_AutoBatch_BehaviorTests, CorrectConfigTests,
+             ::testing::Combine(
+                     ::testing::Values(CommonTestUtils::DEVICE_BATCH),
+                     ::testing::ValuesIn(auto_batch_configs)),
+             CorrectConfigTests::getTestCaseName);
 } // namespace
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/core_integration.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/core_integration.cpp
index b758bba7496..703532fdab6 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/core_integration.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/core_integration.cpp
@@ -35,12 +35,12 @@ INSTANTIATE_TEST_SUITE_P(
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassGetMetricTest, IEClassGetMetricTest_SUPPORTED_CONFIG_KEYS,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO")
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH")
 );
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassGetMetricTest, IEClassGetMetricTest_SUPPORTED_METRICS,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO")
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH")
 );
 
 INSTANTIATE_TEST_SUITE_P(
@@ -50,7 +50,7 @@ INSTANTIATE_TEST_SUITE_P(
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassGetMetricTest, IEClassGetMetricTest_FULL_DEVICE_NAME,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO")
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH")
 );
 
 INSTANTIATE_TEST_SUITE_P(
@@ -80,12 +80,12 @@ INSTANTIATE_TEST_SUITE_P(
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassGetMetricTest, IEClassGetMetricTest_ThrowUnsupported,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO")
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH")
 );
 
 INSTANTIATE_TEST_SUITE_P(
         nightly_IEClassGetConfigTest, IEClassGetConfigTest_ThrowUnsupported,
-        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO")
+        ::testing::Values("GPU", "MULTI", "HETERO", "AUTO", "BATCH")
 );
 
 INSTANTIATE_TEST_SUITE_P(
@@ -115,6 +115,26 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Values("GPU")
 );
 
+using IEClassGetMetricTest_GPU_OPTIMAL_BATCH_SIZE = BehaviorTestsUtils::IEClassBaseTestP;
+TEST_P(IEClassGetMetricTest_GPU_OPTIMAL_BATCH_SIZE, GetMetricAndPrintNoThrow) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    InferenceEngine::Core ie;
+    InferenceEngine::Parameter p;
+
+    std::map<std::string, InferenceEngine::Parameter> _options = {{"MODEL_PTR", simpleCnnNetwork.getFunction()}};
+    ASSERT_NO_THROW(p = ie.GetMetric(deviceName, METRIC_KEY(OPTIMAL_BATCH_SIZE), _options).as<unsigned int>());
+    unsigned int t = p;
+
+    std::cout << "GPU device optimal batch size: " << t << std::endl;
+
+    ASSERT_METRIC_SUPPORTED_IE(METRIC_KEY(OPTIMAL_BATCH_SIZE));
+}
+
+INSTANTIATE_TEST_SUITE_P(
+        nightly_IEClassExecutableNetworkGetMetricTest, IEClassGetMetricTest_GPU_OPTIMAL_BATCH_SIZE,
+        ::testing::Values("GPU")
+);
+
 using IEClassGetMetricTest_GPU_MAX_BATCH_SIZE_DEFAULT = BehaviorTestsUtils::IEClassBaseTestP;
 TEST_P(IEClassGetMetricTest_GPU_MAX_BATCH_SIZE_DEFAULT, GetMetricAndPrintNoThrow) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
@@ -135,6 +155,7 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Values("GPU")
 );
 
+
 using IEClassGetMetricTest_GPU_MAX_BATCH_SIZE_STREAM_DEVICE_MEM = BehaviorTestsUtils::IEClassBaseTestP;
 TEST_P(IEClassGetMetricTest_GPU_MAX_BATCH_SIZE_STREAM_DEVICE_MEM, GetMetricAndPrintNoThrow) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
diff --git a/src/tests/functional/plugin/shared/CMakeLists.txt b/src/tests/functional/plugin/shared/CMakeLists.txt
index 9c4834253ad..3c63543d663 100644
--- a/src/tests/functional/plugin/shared/CMakeLists.txt
+++ b/src/tests/functional/plugin/shared/CMakeLists.txt
@@ -16,6 +16,11 @@ if(ENABLE_AUTO OR ENABLE_MULTI)
     list(APPEND DEPENDENCIES ov_auto_plugin)
 endif()
 
+if(ENABLE_AUTO_BATCH)
+    list(APPEND DEPENDENCIES ov_auto_batch_plugin)
+endif()
+
+
 # remove once CVS-69781 is fixed
 if(ENABLE_OV_IR_FRONTEND)
     list(APPEND DEPENDENCIES ov_ir_frontend)
diff --git a/src/tests/functional/plugin/shared/include/auto_batching/auto_batching_tests.hpp b/src/tests/functional/plugin/shared/include/auto_batching/auto_batching_tests.hpp
new file mode 100644
index 00000000000..5c7c425713b
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/auto_batching/auto_batching_tests.hpp
@@ -0,0 +1,161 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <string>
+#include <utility>
+#include <vector>
+#include <memory>
+
+#include <gpu/gpu_config.hpp>
+#include <common_test_utils/test_common.hpp>
+#include <functional_test_utils/plugin_cache.hpp>
+
+#include "ngraph_functions/subgraph_builders.hpp"
+#include "functional_test_utils/blob_utils.hpp"
+
+using namespace ::testing;
+using namespace InferenceEngine;
+
+namespace AutoBatchingTests {
+using AutoBatchTwoNetsParams = std::tuple<
+        std::string,             // device name
+        bool,  // get or set blob
+        size_t,  // number of streams
+        size_t,  // number of requests
+        size_t>; // batch size>
+
+class AutoBatching_Test : public CommonTestUtils::TestsCommon,
+                          public testing::WithParamInterface<AutoBatchTwoNetsParams> {
+    void SetUp() override {
+        std::tie(device_name, use_get_blob, num_streams, num_requests, num_batch) = this->GetParam();
+        fn_ptrs = {ngraph::builder::subgraph::makeSingleConv(),
+                   ngraph::builder::subgraph::makeMultiSingleConv()};
+    };
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<AutoBatchTwoNetsParams> &obj) {
+        size_t streams, requests, batch;
+        bool use_get_blob;
+        std::string device_name;
+        std::tie(device_name, use_get_blob, streams, requests, batch) = obj.param;
+        return device_name + std::string(use_get_blob ? "_get_blob" : "_set_blob") + "_batch_size_" +
+               std::to_string(batch) +
+               "_num_streams_" + std::to_string(streams) + "_num_req_" + std::to_string(requests);
+    }
+
+protected:
+    std::string device_name;
+    bool use_get_blob;
+    size_t num_streams;
+    size_t num_requests;
+    size_t num_batch;
+    std::vector<std::shared_ptr<ngraph::Function>> fn_ptrs;
+
+    void TestAutoBatch() {
+        std::vector<InferenceEngine::CNNNetwork> nets;
+        for (auto &fn_ptr : fn_ptrs) {
+            nets.push_back(CNNNetwork(fn_ptr));
+        }
+
+        auto ie = InferenceEngine::Core();
+        std::vector<std::string> outputs;
+        std::vector<InferRequest> irs;
+        std::vector<std::vector<uint8_t>> ref;
+        std::vector<int> outElementsCount;
+
+        for (size_t i = 0; i < nets.size(); ++i) {
+            auto net = nets[i];
+            auto inputs = net.getInputsInfo();
+            for (auto n : inputs) {
+                n.second->setPrecision(Precision::FP32);
+            }
+            std::map<std::string, std::string> config;
+            if (device_name.find("GPU") != std::string::npos)
+                config[CONFIG_KEY(GPU_THROUGHPUT_STREAMS)] = std::to_string(num_streams);
+            if (device_name.find("CPU") != std::string::npos)
+                config[CONFIG_KEY(CPU_THROUGHPUT_STREAMS)] = std::to_string(num_streams);
+            // minimize timeout to reduce test time
+            config[CONFIG_KEY(AUTO_BATCH_TIMEOUT)] = std::to_string(1);
+            auto exec_net_ref = ie.LoadNetwork(net, std::string(CommonTestUtils::DEVICE_BATCH) + ":" +
+                                                    device_name + "(" + std::to_string(num_batch) + ")",
+                                               config);
+
+            for (size_t j = 0; j < num_requests; j++) {
+                outputs.push_back(net.getOutputsInfo().begin()->first); //single output
+                outElementsCount.push_back(
+                        std::accumulate(begin(fn_ptrs[i]->get_output_shape(0)), end(fn_ptrs[i]->get_output_shape(0)), 1,
+                                        std::multiplies<size_t>()));
+
+                auto inf_req = exec_net_ref.CreateInferRequest();
+                irs.push_back(inf_req);
+
+                std::vector<std::vector<uint8_t>> inData;
+                for (auto n : inputs) {
+                    auto blob = FuncTestUtils::createAndFillBlob(n.second->getTensorDesc());
+                    if (use_get_blob)
+                        memcpy(reinterpret_cast<void *>(inf_req.GetBlob(n.first)->buffer().as<uint8_t*>()),
+                               reinterpret_cast<const void *>(blob->cbuffer().as<uint8_t*>()), blob->byteSize());
+                    else
+                        inf_req.SetBlob(n.first, blob);
+
+                    const auto inBlob = inf_req.GetBlob(n.first);
+                    const auto blobSize = inBlob->byteSize();
+                    const auto inBlobBuf = inBlob->cbuffer().as<uint8_t *>();
+                    inData.push_back(std::vector<uint8_t>(inBlobBuf, inBlobBuf + blobSize));
+                }
+                auto refOutData = ngraph::helpers::interpreterFunction(fn_ptrs[i], {inData}).front().second;
+                ref.push_back(refOutData);
+            }
+        }
+
+        const int niter = 1;
+        for (int i = 0; i < niter; i++) {
+            for (auto ir : irs) {
+                ir.StartAsync();
+            }
+
+            for (auto ir : irs) {
+                ir.Wait(InferRequest::RESULT_READY);
+            }
+        }
+
+        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
+        for (size_t i = 0; i < irs.size(); ++i) {
+            const auto &refBuffer = ref[i].data();
+            ASSERT_EQ(outElementsCount[i], irs[i].GetBlob(outputs[i])->size());
+            FuncTestUtils::compareRawBuffers(irs[i].GetBlob(outputs[i])->buffer().as<float *>(),
+                                             reinterpret_cast<const float *>(refBuffer), outElementsCount[i],
+                                             outElementsCount[i],
+                                             thr);
+        }
+    }
+};
+
+class AutoBatching_Test_DetectionOutput : public AutoBatching_Test {
+public:
+    void SetUp() override {
+        std::tie(device_name, use_get_blob, num_streams, num_requests, num_batch) = this->GetParam();
+        fn_ptrs = {ngraph::builder::subgraph::makeEltwisePlusDetectionOutput(),
+                   ngraph::builder::subgraph::makeEltwisePlusDetectionOutput()};
+    };
+
+    static std::string getTestCaseName(const testing::TestParamInfo<AutoBatchTwoNetsParams> &obj) {
+        size_t streams, requests, batch;
+        bool use_get_blob;
+        std::string device_name;
+        std::tie(device_name, use_get_blob, streams, requests, batch) = obj.param;
+        return "DetectionOutput_HETERO_" + device_name + std::string(use_get_blob ? "_get_blob" : "_set_blob") +
+               "_batch_size_" + std::to_string(batch) +
+               "_num_streams_" + std::to_string(streams) + "_num_req_" + std::to_string(requests);
+    }
+};
+
+TEST_P(AutoBatching_Test, compareAutoBatchingToSingleBatch) {
+    TestAutoBatch();
+}
+
+TEST_P(AutoBatching_Test_DetectionOutput, compareAutoBatchingToSingleBatch) {
+    TestAutoBatch();
+}
+
+}  // namespace AutoBatchingTests
\ No newline at end of file
diff --git a/src/tests/ie_test_utils/common_test_utils/test_constants.hpp b/src/tests/ie_test_utils/common_test_utils/test_constants.hpp
index 7d8087fb925..158352a72b4 100644
--- a/src/tests/ie_test_utils/common_test_utils/test_constants.hpp
+++ b/src/tests/ie_test_utils/common_test_utils/test_constants.hpp
@@ -10,6 +10,7 @@ const char DEVICE_AUTO[] = "AUTO";
 const char DEVICE_CPU[] = "CPU";
 const char DEVICE_GNA[] = "GNA";
 const char DEVICE_GPU[] = "GPU";
+const char DEVICE_BATCH[] = "BATCH";
 const char DEVICE_HDDL[] = "HDDL";
 const char DEVICE_MYRIAD[] = "MYRIAD";
 const char DEVICE_KEEMBAY[] = "VPUX";
diff --git a/src/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_icore.hpp b/src/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_icore.hpp
index 2f7fb1730d1..dab3bdd16c7 100644
--- a/src/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_icore.hpp
+++ b/src/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_icore.hpp
@@ -26,6 +26,9 @@ public:
     MOCK_METHOD3(ImportNetwork, InferenceEngine::SoExecutableNetworkInternal(
         std::istream&, const std::shared_ptr<InferenceEngine::RemoteContext>&, const std::map<std::string, std::string>&));
 
+    MOCK_METHOD2(CreateContext, InferenceEngine::RemoteContext::Ptr(const std::string& deviceName,
+            const InferenceEngine::ParamMap& params));
+
     MOCK_CONST_METHOD3(QueryNetwork, InferenceEngine::QueryNetworkResult(
         const InferenceEngine::CNNNetwork&, const std::string&, const std::map<std::string, std::string>&));
 
diff --git a/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp b/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp
index a518b080af3..3609d54bab4 100644
--- a/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp
+++ b/src/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp
@@ -242,6 +242,44 @@ inline std::shared_ptr<ngraph::Function> makeSingleConv(std::vector<size_t> inpu
     return fn_ptr;
 }
 
+inline std::shared_ptr<ngraph::Function> makeEltwisePlusDetectionOutput(std::vector<std::vector<size_t>> inShapes =
+        {{1, 60}, {1, 165}, {1, 1, 75}},
+                                                                         ngraph::element::Type_t type = ngraph::element::Type_t::f32) {
+    // adding Eltwise so that we can tests Auto-Batching's HETERO code-path that splits the DetectionOutput and the rest of the network
+    auto params = ngraph::builder::makeParams(ngraph::element::f32, inShapes);
+    auto paramOuts = ngraph::helpers::convert2OutputVector(
+            ngraph::helpers::castOps2Nodes<ngraph::opset3::Parameter>(params));
+    ngraph::OutputVector outs;
+    for (size_t i = 0; i < inShapes.size(); i++) {
+        auto shape = inShapes[i];
+        auto p = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{shape});
+        auto add = ngraph::builder::makeEltwise(paramOuts[i], p, ngraph::helpers::EltwiseTypes::ADD);
+        params.push_back(p);
+        outs.push_back(add->output(0));
+    }
+    ngraph::op::DetectionOutput::Attributes attr;
+    attr.num_classes = 11;
+    attr.background_label_id = 0;
+    attr.top_k = 75;
+    attr.variance_encoded_in_target = true;
+    attr.keep_top_k = {50};
+    attr.code_type = std::string{"caffe.PriorBoxParameter.CORNER"};
+    attr.share_location = true;
+    attr.nms_threshold = 0.5f;
+    attr.confidence_threshold = 0.5f;
+    attr.clip_after_nms = false;
+    attr.clip_before_nms = false;
+    attr.decrease_label_id = false;
+    attr.normalized = false;
+    attr.input_height = 1;
+    attr.input_width = 1;
+    attr.objectness_score = 0.4f;
+
+    auto detOut = ngraph::builder::makeDetectionOutput(outs, attr);
+    ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(detOut)};
+    return std::make_shared<ngraph::Function>(results, params, "EltWiseWithDetectionOutput");
+}
+
 inline std::shared_ptr<ngraph::Function> makeMultiSingleConv(std::vector<size_t> inputShape = {1, 3, 24, 24},
     ngraph::element::Type type = ngraph::element::Type_t::f32) {
     auto param0 = std::make_shared<ngraph::opset1::Parameter>(type, ngraph::Shape(inputShape));
diff --git a/src/tests/unit/auto/exec_network_get_metrics.cpp b/src/tests/unit/auto/exec_network_get_metrics.cpp
index 9c01e0fe7ab..b1d8ead498d 100644
--- a/src/tests/unit/auto/exec_network_get_metrics.cpp
+++ b/src/tests/unit/auto/exec_network_get_metrics.cpp
@@ -38,6 +38,7 @@ using Config = std::map<std::string, std::string>;
 using namespace MockMultiDevice;
 
 using ConfigParams = std::tuple<
+        bool,                        // if THROUGHPUT
         unsigned int,                // cpu OPTIMAL_NUMBER_OF_INFER_REQUESTS
         int,                         // cpu infer requet num of customer want
         bool,                        // if cpu sleep, cpu device will load slow
@@ -77,12 +78,18 @@ public:
         unsigned int expectOptimalNum;
         bool cpuSleep;
         bool gpuSleep;
-        std::tie(cpuOptimalNum, cpuCustomerNum, cpuSleep,
+        bool isThroughput;
+        std::tie(isThroughput, cpuOptimalNum, cpuCustomerNum, cpuSleep,
                  gpuOptimalNum, gpuCustomerNum, gpuSleep, expectOptimalNum) = obj.param;
         std::ostringstream result;
         result << "cpuOptimalNum_" << cpuOptimalNum << "cpuCustomerNum_" << cpuCustomerNum;
         result << "gpuOptimalNum_" << gpuOptimalNum << "gpuCustomerNum_" << gpuCustomerNum;
         result << "expectOptimalNum_" << expectOptimalNum;
+        if (isThroughput) {
+            result << "_isThroughput" << "true";
+        } else {
+            result << "__isThroughput" << "false";
+        }
         if (cpuSleep) {
             result << "_cpuSleep_" << "true";
         } else {
@@ -147,7 +154,7 @@ public:
        IE_SET_METRIC(SUPPORTED_CONFIG_KEYS, supportConfigs, {});
        ON_CALL(*core, GetMetric(_, StrEq(METRIC_KEY(SUPPORTED_CONFIG_KEYS)), _))
            .WillByDefault(RETURN_MOCK_VALUE(supportConfigs));
-       EXPECT_CALL(*core, GetMetric(_, StrEq(METRIC_KEY(SUPPORTED_CONFIG_KEYS)), _)).Times(AnyNumber());
+       EXPECT_CALL(*core, GetMetric(_, _, _)).Times(AnyNumber());
 
        // test auto plugin
        config.insert({CONFIG_KEY_INTERNAL(MULTI_WORK_MODE_AS_AUTO), InferenceEngine::PluginConfigParams::YES});
@@ -168,11 +175,24 @@ TEST_P(ExecNetworkGetMetric, OPTIMAL_NUMBER_OF_INFER_REQUESTS) {
     unsigned int expectOptimalNum;
     bool cpuSleep;
     bool gpuSleep;
-    std::tie(cpuOptimalNum, cpuCustomerNum, cpuSleep,
+    bool isThroughput;
+    std::tie(isThroughput, cpuOptimalNum, cpuCustomerNum, cpuSleep,
              gpuOptimalNum, gpuCustomerNum, gpuSleep, expectOptimalNum) = this->GetParam();
-
-    metaDevices.push_back({CommonTestUtils::DEVICE_CPU, {}, cpuCustomerNum, ""});
-    metaDevices.push_back({CommonTestUtils::DEVICE_GPU, {}, gpuCustomerNum, ""});
+    if (isThroughput) {
+        metaDevices.push_back({CommonTestUtils::DEVICE_CPU, {{CONFIG_KEY(PERFORMANCE_HINT),
+                    InferenceEngine::PluginConfigParams::THROUGHPUT}}, cpuCustomerNum, ""});
+        metaDevices.push_back({CommonTestUtils::DEVICE_GPU, {{CONFIG_KEY(PERFORMANCE_HINT),
+                    InferenceEngine::PluginConfigParams::THROUGHPUT}}, gpuCustomerNum, ""});
+        IE_SET_METRIC(OPTIMAL_BATCH_SIZE, optimalBatchNum, 256);
+        IE_SET_METRIC(RANGE_FOR_STREAMS, rangeOfStreams, std::make_tuple<unsigned int, unsigned int>(1, 2));
+        ON_CALL(*core.get(), GetMetric(StrEq(CommonTestUtils::DEVICE_GPU), StrEq(METRIC_KEY(OPTIMAL_BATCH_SIZE)), _))
+            .WillByDefault(RETURN_MOCK_VALUE(optimalBatchNum));
+        ON_CALL(*core.get(), GetMetric(StrEq(CommonTestUtils::DEVICE_GPU), StrEq(METRIC_KEY(RANGE_FOR_STREAMS)), _))
+            .WillByDefault(RETURN_MOCK_VALUE(rangeOfStreams));
+    } else {
+        metaDevices.push_back({CommonTestUtils::DEVICE_CPU, {}, cpuCustomerNum, ""});
+        metaDevices.push_back({CommonTestUtils::DEVICE_GPU, {}, gpuCustomerNum, ""});
+    }
     ON_CALL(*plugin, SelectDevice(_, _, _)).WillByDefault(Return(metaDevices[1]));
     ON_CALL(*plugin, ParseMetaDevices(_, _)).WillByDefault(Return(metaDevices));
     EXPECT_CALL(*plugin, ParseMetaDevices(_, _)).Times(1);
@@ -241,27 +261,28 @@ TEST_P(ExecNetworkGetMetric, OPTIMAL_NUMBER_OF_INFER_REQUESTS) {
 }
 
 
-// ConfigParams {unsigned int, int, bool,
+// ConfigParams {bool, unsigned int, int, bool,
 //               unsigned int, int, bool, unsigned int}
 //
 // every element for ConfigParams
-// {cpuOptimalNum, customer hope for cpu infer requset num, if cpu sleep when load,
+// {is throughput mode, cpuOptimalNum, customer hope for cpu infer requset num, if cpu sleep when load,
 //  gpuOptimalNum, customer hope for gpu infer requset num, if gpu sleep when load,
 //  expectOptimalNum of Auto ExecNetwork}
 //
 const std::vector<ConfigParams> testConfigs = {
-                                               ConfigParams {1, -1, false, 2, -1, true, 8},
-                                               ConfigParams {1, -1, false, 10, -1, true, 8},
-                                               ConfigParams {12, -1, false, 2, -1, true, 12},
-                                               ConfigParams {12, -1, false, 10, -1, true, 12},
-                                               ConfigParams {1, -1, true, 2, -1, false, 8},
-                                               ConfigParams {1, -1, true, 10, -1, false, 10},
-                                               ConfigParams {6, -1, true, 2, -1, false, 8},
-                                               ConfigParams {6, -1, true, 10, -1, false, 10},
-                                               ConfigParams {6, 4, false, 2, 3, true, 8},
-                                               ConfigParams {6, 4, false, 10, 3, true, 8},
-                                               ConfigParams {1, 4, true, 2, 3, false, 8},
-                                               ConfigParams {1, 4, true, 10, 3, false, 10}
+                                               ConfigParams {false, 1, -1, false, 2, -1, true, 8},
+                                               ConfigParams {false, 1, -1, false, 10, -1, true, 8},
+                                               ConfigParams {false, 12, -1, false, 2, -1, true, 12},
+                                               ConfigParams {false, 12, -1, false, 10, -1, true, 12},
+                                               ConfigParams {false, 1, -1, true, 2, -1, false, 8},
+                                               ConfigParams {false, 1, -1, true, 10, -1, false, 10},
+                                               ConfigParams {false, 6, -1, true, 2, -1, false, 8},
+                                               ConfigParams {false, 6, -1, true, 10, -1, false, 10},
+                                               ConfigParams {false, 6, 4, false, 2, 3, true, 8},
+                                               ConfigParams {false, 6, 4, false, 10, 3, true, 8},
+                                               ConfigParams {false, 1, 4, true, 2, 3, false, 8},
+                                               ConfigParams {false, 1, 4, true, 10, 3, false, 10},
+                                               ConfigParams {true, 1, 4, false, 10, 3, true, 512}
                                               };
 
 INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, ExecNetworkGetMetric,
diff --git a/src/tests_deprecated/behavior/shared_tests/CMakeLists.txt b/src/tests_deprecated/behavior/shared_tests/CMakeLists.txt
index 82fcfd2183b..761ee14f6fd 100644
--- a/src/tests_deprecated/behavior/shared_tests/CMakeLists.txt
+++ b/src/tests_deprecated/behavior/shared_tests/CMakeLists.txt
@@ -14,6 +14,11 @@ if(ENABLE_AUTO OR ENABLE_MULTI)
     add_dependencies(${TARGET_NAME} ov_auto_plugin)
 endif()
 
+if(ENABLE_AUTO_BATCH)
+    add_dependencies(${TARGET_NAME} ov_auto_batch_plugin)
+endif()
+
+
 target_include_directories(${TARGET_NAME} PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/plugin_tests")
 
 target_link_libraries(${TARGET_NAME} PUBLIC
diff --git a/src/tests_deprecated/functional/shared_tests/CMakeLists.txt b/src/tests_deprecated/functional/shared_tests/CMakeLists.txt
index 6bb4ba313a3..fcbfbcdcc59 100644
--- a/src/tests_deprecated/functional/shared_tests/CMakeLists.txt
+++ b/src/tests_deprecated/functional/shared_tests/CMakeLists.txt
@@ -25,6 +25,10 @@ if(ENABLE_AUTO OR ENABLE_MULTI)
     add_dependencies(${TARGET_NAME} ov_auto_plugin)
 endif()
 
+if(ENABLE_AUTO_BATCH)
+    add_dependencies(${TARGET_NAME} ov_auto_batch_plugin)
+endif()
+
 set_ie_threading_interface_for(${TARGET_NAME})
 
 ie_faster_build(${TARGET_NAME}

From 9cc4504b786926ca42d5c05b32e76639dc8d840c Mon Sep 17 00:00:00 2001
From: Mikhail Nosov <mikhail.nosov@intel.com>
Date: Fri, 24 Dec 2021 13:01:51 +0300
Subject: [PATCH 142/160] Removed OV_FRONTEND_PATH from 'setupvars' scripts
 (#9396)

* Removed OV_FRONTEND_PATH from 'setupvars' scripts

* Update linux.yml

* Change mock frontend's install dir for static builds

* revert linux.yml
---
 scripts/setupvars/setupvars.bat                     |  1 -
 scripts/setupvars/setupvars.sh                      |  1 -
 src/core/tests/frontend/CMakeLists.txt              |  2 +-
 .../ov_mock_mo_frontend/CMakeLists.txt              | 13 ++++++++++---
 4 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/scripts/setupvars/setupvars.bat b/scripts/setupvars/setupvars.bat
index ec466e0f533..c5235b9158a 100644
--- a/scripts/setupvars/setupvars.bat
+++ b/scripts/setupvars/setupvars.bat
@@ -45,7 +45,6 @@ set "ngraph_DIR=%INTEL_OPENVINO_DIR%\runtime\cmake"
 set "OpenVINO_DIR=%INTEL_OPENVINO_DIR%\runtime\cmake"
 set "HDDL_INSTALL_DIR=%INTEL_OPENVINO_DIR%\runtime\3rdparty\hddl"
 set "OPENVINO_LIB_PATHS=%INTEL_OPENVINO_DIR%\runtime\bin\intel64\Release;%INTEL_OPENVINO_DIR%\runtime\bin\intel64\Debug;%HDDL_INSTALL_DIR%\bin;%OPENVINO_LIB_PATHS%"
-set "OV_FRONTEND_PATH=%INTEL_OPENVINO_DIR%\runtime\bin\intel64\Release;%INTEL_OPENVINO_DIR%\runtime\bin\intel64\Debug;%OV_FRONTEND_PATH%"
 
 :: TBB
 if exist %INTEL_OPENVINO_DIR%\runtime\3rdparty\tbb (
diff --git a/scripts/setupvars/setupvars.sh b/scripts/setupvars/setupvars.sh
index d59b8ebf8d7..99b651f545d 100755
--- a/scripts/setupvars/setupvars.sh
+++ b/scripts/setupvars/setupvars.sh
@@ -31,7 +31,6 @@ if [ -e "$INSTALLDIR/runtime" ]; then
 
     system_type=$(ls "$INSTALLDIR/runtime/lib/")
     IE_PLUGINS_PATH=$INSTALLDIR/runtime/lib/$system_type
-    export OV_FRONTEND_PATH=${IE_PLUGINS_PATH}${OV_FRONTEND_PATH:+:$OV_FRONTEND_PATH}
 
     export HDDL_INSTALL_DIR=$INSTALLDIR/runtime/3rdparty/hddl
     if [[ "$OSTYPE" == "darwin"* ]]; then
diff --git a/src/core/tests/frontend/CMakeLists.txt b/src/core/tests/frontend/CMakeLists.txt
index 971b6533119..cacff7f5e0e 100644
--- a/src/core/tests/frontend/CMakeLists.txt
+++ b/src/core/tests/frontend/CMakeLists.txt
@@ -28,7 +28,7 @@ add_dependencies(ov_core_unit_tests ${MOCK1_FE_NAME})
 
 add_clang_format_target(${MOCK1_FE_NAME}_clang FOR_TARGETS ${MOCK1_FE_NAME})
 
-if(BUILD_SHARED_LIBS OR NOT WIN32)
+if(BUILD_SHARED_LIBS)
     install(TARGETS ${MOCK1_FE_NAME}
             RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests OPTIONAL EXCLUDE_FROM_ALL
             LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests OPTIONAL EXCLUDE_FROM_ALL)
diff --git a/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
index 7667bfc89bc..9b409e4ff36 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
+++ b/tools/mo/unit_tests/mock_mo_frontend/ov_mock_mo_frontend/CMakeLists.txt
@@ -19,6 +19,13 @@ target_link_libraries(${TARGET_FE_NAME} PUBLIC openvino::runtime PRIVATE ngraph:
 
 add_clang_format_target(${TARGET_FE_NAME}_clang FOR_TARGETS ${TARGET_FE_NAME})
 
-install(TARGETS ${TARGET_FE_NAME}
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+if(BUILD_SHARED_LIBS)
+    install(TARGETS ${TARGET_FE_NAME}
+            RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+else()
+    set(PYTHON_VERSION python${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR})
+    install(TARGETS ${TARGET_FE_NAME}
+            RUNTIME DESTINATION python/${PYTHON_VERSION}/openvino COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION python/${PYTHON_VERSION}/openvino COMPONENT tests EXCLUDE_FROM_ALL)
+endif()

From 43c45d3065f98dac29fb36885ff9a5bc2691143d Mon Sep 17 00:00:00 2001
From: Mikhail Ryzhov <mikhail.ryzhov@intel.com>
Date: Fri, 24 Dec 2021 13:02:43 +0300
Subject: [PATCH 143/160] Moved gna library cmake to plugin dir (#9393)

---
 src/plugins/intel_gna/CMakeLists.txt                            | 2 +-
 .../plugins/intel_gna}/cmake/libGNAConfig.cmake                 | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename {inference-engine => src/plugins/intel_gna}/cmake/libGNAConfig.cmake (100%)

diff --git a/src/plugins/intel_gna/CMakeLists.txt b/src/plugins/intel_gna/CMakeLists.txt
index 73557e42120..7e61f337d9d 100644
--- a/src/plugins/intel_gna/CMakeLists.txt
+++ b/src/plugins/intel_gna/CMakeLists.txt
@@ -18,7 +18,7 @@ file(GLOB_RECURSE HEADERS
 addVersionDefines(gna_plugin_entry_points.cpp CI_BUILD_NUMBER)
 
 find_package(libGNA REQUIRED
-             PATHS "${IE_MAIN_SOURCE_DIR}/cmake"
+             PATHS "${CMAKE_CURRENT_SOURCE_DIR}/cmake"
              NO_DEFAULT_PATH)
 
 #
diff --git a/inference-engine/cmake/libGNAConfig.cmake b/src/plugins/intel_gna/cmake/libGNAConfig.cmake
similarity index 100%
rename from inference-engine/cmake/libGNAConfig.cmake
rename to src/plugins/intel_gna/cmake/libGNAConfig.cmake

From 3f35e2a32179ad055904700c3bfbbdd41c4f5fa6 Mon Sep 17 00:00:00 2001
From: Maxim Vafin <maxim.vafin@intel.com>
Date: Fri, 24 Dec 2021 14:00:37 +0300
Subject: [PATCH 144/160] Enable new FP16 and support mixed precision by MO
 (#8514)

* Enable new FP16 format and support mixed precision

* Apply review comments

* Fix issue with fp64 in FakeQuantWithMinMaxVars.py

* Enabme decompression converts fusing for CPU plugin

* Apply review feedback

* Fix code style

* Fix issue with np.full and apply review feedback

* Apply review feedback

* Fix HardSigmoid onnx extractor

* Replace np.arrays that were skipped with mo_array

* Fix compress_quantized_weights_test.py

* Fix import issues

* Apply review feedback and fix type of fusing linops in MO

* Apply review feedback

* Fix types for Mean/Scales and MXNET zeros

* Add RandomUniform_8 to ConvertPrecision

* Fix merge issue

* Fix consts names collision in GPU plugin
---
 .../common_optimizations.cpp                  |   4 +-
 .../compress_float_constants.cpp              |   1 +
 src/core/src/pass/convert_precision.cpp       |  13 +-
 src/inference/src/cnn_network_ngraph_impl.cpp |   2 +
 src/plugins/intel_cpu/src/mkldnn_plugin.cpp   |   7 +
 src/plugins/intel_gna/gna_plugin.cpp          |   7 +
 tools/mo/automation/package_BOM.txt           |   1 +
 .../tools/mo/back/ConvolutionNormalizer.py    |  17 +-
 .../tools/mo/back/CropToStridedSlice.py       |   5 +-
 tools/mo/openvino/tools/mo/back/CutMemory.py  |   7 +-
 .../tools/mo/back/InterpolateReshape.py       |   5 +-
 .../tools/mo/back/LeakyReLUMutation.py        |   5 +-
 .../tools/mo/back/NormalizeToNormalizeL2.py   |   5 +-
 .../tools/mo/back/PackBinaryWeights.py        |   5 +-
 .../tools/mo/back/ProposalMutation.py         |   7 +-
 .../tools/mo/back/ReverseInputChannels.py     |  11 +-
 .../mo/back/compress_quantized_weights.py     |  42 ++++-
 .../tools/mo/back/priorbox_mutation.py        |  27 ++--
 .../mo/front/AttributedClampNormalizer.py     |   5 +-
 .../tools/mo/front/FillToBroadcast.py         |   5 +-
 .../tools/mo/front/InterpolateNormalizer.py   |  71 +++++----
 .../mo/front/InterpolateV1ToInterpolate.py    |   5 +-
 tools/mo/openvino/tools/mo/front/Log1p.py     |   3 +-
 .../tools/mo/front/MatMul_normalizer.py       |   7 +-
 .../tools/mo/front/PowerToEltwises.py         |   9 +-
 .../mo/front/binary_quantize_normalization.py |   5 +-
 .../tools/mo/front/broadcast_with_range.py    |   5 +-
 .../tools/mo/front/caffe/batchnorm_ext.py     |   5 +-
 .../tools/mo/front/caffe/binarization.py      |   5 +-
 tools/mo/openvino/tools/mo/front/caffe/bn.py  |   9 +-
 .../openvino/tools/mo/front/caffe/conv_ext.py |  19 ++-
 .../tools/mo/front/caffe/elementwise_ext.py   |   5 +-
 .../mo/front/caffe/eltwise_add_normalize.py   |   5 +-
 .../tools/mo/front/caffe/extractor.py         |   1 -
 .../tools/mo/front/caffe/extractors/utils.py  |   7 +-
 .../openvino/tools/mo/front/caffe/loader.py   |  13 +-
 .../tools/mo/front/caffe/permute_ext.py       |   3 +-
 .../tools/mo/front/caffe/pooling_ext.py       |  11 +-
 .../tools/mo/front/caffe/priorbox_ext.py      |  15 +-
 .../tools/mo/front/caffe/proposal_ext.py      |   7 +-
 .../tools/mo/front/caffe/regionyolo_ext.py    |   7 +-
 .../tools/mo/front/caffe/scale_ext.py         |  10 +-
 .../tools/mo/front/common/extractors/utils.py |   8 +-
 .../openvino/tools/mo/front/common/layout.py  |   7 +-
 .../common/partial_infer/caffe_fallback.py    |   3 +-
 .../mo/front/common/partial_infer/crop.py     |   7 +-
 .../common/partial_infer/multi_box_prior.py   |   7 +-
 .../mo/front/common/partial_infer/utils.py    |  25 ++-
 .../mo/front/freeze_placeholder_value.py      |   7 +-
 .../tools/mo/front/interpolate_reshape.py     |   3 +-
 .../front/kaldi/extractors/add_shift_ext.py   |   2 +-
 .../convolutional_1d_component_ext.py         |  15 +-
 .../extractors/convolutional_component_ext.py |  13 +-
 .../mo/front/kaldi/extractors/copy_ext.py     |   3 +-
 .../front/kaldi/extractors/max_pooling_ext.py |   9 +-
 .../kaldi/extractors/tdnncomponent_ext.py     |   3 +-
 .../tools/mo/front/kaldi/loader/loader.py     |  11 +-
 .../front/kaldi/replace_lstm_node_pattern.py  |   5 +-
 .../openvino/tools/mo/front/mxnet/conv_ext.py |  37 ++---
 .../mo/front/mxnet/custom_rpn_proposal.py     |   7 +-
 .../mo/front/mxnet/deformable_conv_ext.py     |  15 +-
 .../tools/mo/front/mxnet/elementwise_ext.py   |  19 +--
 .../mo/front/mxnet/extractors/slice_axis.py   |   4 +-
 .../mo/front/mxnet/gluoncv_ssd_anchors.py     |   6 +-
 .../openvino/tools/mo/front/mxnet/loader.py   |   7 +-
 .../mxnet/modulated_deformable_conv_ext.py    |  15 +-
 .../openvino/tools/mo/front/mxnet/pad_ext.py  |   5 +-
 .../tools/mo/front/mxnet/pooling_ext.py       |  13 +-
 .../tools/mo/front/mxnet/proposal_ext.py      |   7 +-
 .../tools/mo/front/mxnet/slice_ext.py         |   9 +-
 .../tools/mo/front/mxnet/slice_replacers.py   |  10 +-
 .../openvino/tools/mo/front/mxnet/softmax.py  |   5 +-
 .../mo/front/mxnet/ssd_anchor_reshape.py      |   5 +-
 .../tools/mo/front/mxnet/transpose_ext.py     |   3 +-
 .../tools/mo/front/mxnet/zeros_ext.py         |   7 +-
 .../tools/mo/front/onnx/LoopNormalize.py      |   4 +-
 .../tools/mo/front/onnx/affine_ext.py         |   5 +-
 .../mo/front/onnx/constant_of_shape_ext.py    |   3 +-
 .../openvino/tools/mo/front/onnx/conv_ext.py  |  22 +--
 .../mo/front/onnx/deformable_conv_ext.py      |  23 +--
 .../front/onnx/detection_output_onnx_ext.py   |   6 +-
 .../tools/mo/front/onnx/elementwise_ext.py    |   5 +-
 .../tools/mo/front/onnx/gather_ext.py         |   5 +-
 .../openvino/tools/mo/front/onnx/gemm_ext.py  |   5 +-
 .../tools/mo/front/onnx/group_norm_ext.py     |   9 +-
 .../openvino/tools/mo/front/onnx/gru_ext.py   |   7 +-
 .../tools/mo/front/onnx/hard_sigmoid_ext.py   |   5 +-
 .../tools/mo/front/onnx/image_scaler_ext.py   |   5 +-
 .../openvino/tools/mo/front/onnx/lstm_ext.py  |   9 +-
 .../mo/front/onnx/mask_rcnn_conversion.py     |   4 +-
 .../onnx/mean_variance_normalization_ext.py   |   4 +-
 .../tools/mo/front/onnx/pooling_ext.py        |  18 +--
 .../mo/front/onnx/priorbox_clustered_ext.py   |   9 +-
 .../tools/mo/front/onnx/priorbox_ext.py       |  11 +-
 .../tools/mo/front/onnx/reshape_ext.py        |   6 +-
 .../openvino/tools/mo/front/onnx/rnn_ext.py   |   7 +-
 .../mo/front/onnx/roifeatureextractor_ext.py  |   6 +-
 .../tools/mo/front/onnx/squeeze_ext.py        |   5 +-
 .../tools/mo/front/onnx/unsqueeze_ext.py      |   5 +-
 .../tools/mo/front/onnx/upsample_ext.py       |   5 +-
 .../mo/openvino/tools/mo/front/reciprocal.py  |   5 +-
 .../tools/mo/front/softsign_replacer.py       |   2 +-
 .../tools/mo/front/split_normalizer.py        |   3 +-
 tools/mo/openvino/tools/mo/front/sub.py       |   3 +-
 .../tools/mo/front/tf/AutomlEfficientDet.py   |   6 +-
 .../mo/front/tf/FakeQuantWithMinMaxVars.py    |  23 ++-
 .../front/tf/GNMT_DynamicSequenceLengths.py   |   5 +-
 .../tools/mo/front/tf/MapFNTransformation.py  |   4 +-
 .../tools/mo/front/tf/ObjectDetectionAPI.py   |  10 +-
 .../RetinaNetFilteredDetectionsReplacement.py |  34 ++--
 .../tools/mo/front/tf/WhileNormalize.py       |   3 +-
 .../tools/mo/front/tf/bucketize_ext.py        |   3 +-
 .../mo/openvino/tools/mo/front/tf/conv_ext.py |   4 +-
 .../tools/mo/front/tf/extractors/fused_bn.py  |   5 +-
 .../mo/front/tf/extractors/strided_slice.py   |   7 +-
 .../tools/mo/front/tf/extractors/utils.py     |  15 +-
 .../tools/mo/front/tf/fifo_queue_v2_ext.py    |   7 +-
 .../openvino/tools/mo/front/tf/graph_utils.py |  13 +-
 .../front/tf/nearest_neighbor_upsampling.py   |   8 +-
 .../tools/mo/front/tf/partial_infer/tf.py     |   7 +-
 tools/mo/openvino/tools/mo/main.py            |   8 +-
 .../tools/mo/middle/AddMeanScaleValues.py     |   5 +
 .../tools/mo/middle/ApplyPermutations.py      |   5 +-
 .../tools/mo/middle/BinarizeWeightsM1P1.py    |   3 +-
 .../mo/middle/BlockLSTMtoLSTMSequence.py      |   5 +-
 .../tools/mo/middle/ConvToBinaryConv.py       |   7 +-
 .../mo/middle/ConvertGroupedStridedSlice.py   |   7 +-
 .../tools/mo/middle/CustomSubgraphCall.py     |   7 +-
 .../openvino/tools/mo/middle/DecomposeBias.py |   5 +-
 .../tools/mo/middle/DilatedConvolution.py     |   3 +-
 .../tools/mo/middle/EltwiseChecker.py         |   2 +-
 .../mo/middle/FusedBatchNormNonConstant.py    |   5 +-
 .../mo/openvino/tools/mo/middle/GroupNorm.py  |   3 +-
 .../InterpolateSequenceToInterpolate.py       |   8 +-
 .../mo/middle/MXNetRNNSequenceNormalize.py    |  14 +-
 .../tools/mo/middle/MXNetSplitMultiLayers.py  |   5 +-
 .../mo/middle/MakeKaldiConstReshapable.py     |   5 +-
 .../tools/mo/middle/MulFakeQuantizeFuse.py    |   3 +-
 .../mo/middle/ONNXRNNSequenceNormalize.py     |   7 +-
 .../mo/middle/RNNSequenceNormalizeToIE.py     |   8 +-
 .../mo/middle/RemoveDuplicationMemory.py      |  17 +-
 .../middle/ReplaceMemoryOffsetWithSplice.py   |  11 +-
 .../mo/middle/ReplaceSpliceNodePattern.py     |   6 +-
 .../mo/openvino/tools/mo/middle/ScaleInput.py |   5 +-
 .../mo/middle/SharedWeightsDuplication.py     |   5 +-
 .../tools/mo/middle/SliceConverter.py         |   2 +-
 .../mo/middle/SplitConcatPairToInterpolate.py |   9 +-
 .../tools/mo/middle/StridedSliceNormalizer.py |   3 +-
 .../tools/mo/middle/StridedSliceReplacer.py   |   4 +-
 .../mo/middle/TF_lstm_cell_to_generic.py      |   5 +-
 .../mo/middle/TensorIteratorCondition.py      |   7 +-
 .../tools/mo/middle/TensorIteratorInput.py    |   5 +-
 .../UnsqueezeTileReshapeBlockToInterpolate.py |   6 +-
 .../openvino/tools/mo/middle/passes/conv.py   |   9 +-
 .../mo/middle/passes/fusing/decomposition.py  |   9 +-
 .../middle/passes/fusing/fuse_grouped_conv.py |  13 +-
 .../middle/passes/fusing/fuse_linear_ops.py   |  13 +-
 .../middle/passes/fusing/fuse_linear_seq.py   |  20 ++-
 .../passes/fusing/resnet_optimization.py      |  29 ++--
 .../mo/middle/quantize_linear_resolver.py     |   3 +-
 tools/mo/openvino/tools/mo/ops/Exit.py        |   7 +-
 tools/mo/openvino/tools/mo/ops/GRU.py         |   5 +-
 tools/mo/openvino/tools/mo/ops/MatMul.py      |   9 +-
 .../mo/openvino/tools/mo/ops/NextIteration.py |   7 +-
 .../mo/openvino/tools/mo/ops/ONNXResize11.py  |   4 +-
 tools/mo/openvino/tools/mo/ops/RNN.py         |   3 +-
 tools/mo/openvino/tools/mo/ops/TensorArray.py |   7 +-
 .../tools/mo/ops/TensorArrayScatter.py        |   5 +-
 .../openvino/tools/mo/ops/TensorArraySize.py  |   5 +-
 .../openvino/tools/mo/ops/activation_ops.py   |   1 -
 tools/mo/openvino/tools/mo/ops/bucketize.py   |   3 +-
 tools/mo/openvino/tools/mo/ops/const.py       |   7 +-
 tools/mo/openvino/tools/mo/ops/convolution.py |  18 +++
 tools/mo/openvino/tools/mo/ops/einsum.py      |   2 +-
 tools/mo/openvino/tools/mo/ops/elementwise.py |  33 +---
 .../mo/openvino/tools/mo/ops/fakequantize.py  |   9 +-
 tools/mo/openvino/tools/mo/ops/gather.py      |   3 +-
 tools/mo/openvino/tools/mo/ops/interpolate.py |   6 +-
 .../mo/openvino/tools/mo/ops/lstm_sequence.py |   3 +-
 tools/mo/openvino/tools/mo/ops/non_zero.py    |   3 +-
 tools/mo/openvino/tools/mo/ops/op.py          |   5 +-
 tools/mo/openvino/tools/mo/ops/pooling.py     |   7 +-
 tools/mo/openvino/tools/mo/ops/priorbox.py    |  15 +-
 tools/mo/openvino/tools/mo/ops/range.py       |   7 +-
 tools/mo/openvino/tools/mo/ops/scatternd.py   |   2 +-
 tools/mo/openvino/tools/mo/ops/select.py      |   4 +-
 tools/mo/openvino/tools/mo/ops/shape.py       |   3 +-
 tools/mo/openvino/tools/mo/ops/size.py        |   3 +-
 .../tools/mo/ops/sparse_fill_empty_rows.py    |   3 +-
 .../tools/mo/ops/sparse_segment_mean.py       |   2 +-
 .../tools/mo/ops/sparse_segment_sqrtn.py      |   2 +-
 .../tools/mo/ops/sparse_segment_sum.py        |   2 +-
 tools/mo/openvino/tools/mo/ops/unique.py      |   5 +-
 tools/mo/openvino/tools/mo/pipeline/common.py |   6 +-
 .../openvino/tools/mo/utils/broadcasting.py   |   3 +-
 .../mo/openvino/tools/mo/utils/cli_parser.py  |   3 +-
 .../tools/mo/utils/ir_reader/restore_graph.py |   4 +-
 .../mo/openvino/tools/mo/utils/type_utils.py  |  35 +++++
 .../back/compress_quantized_weights_test.py   | 145 +++++++++++++-----
 .../front/common/partial_infer/utils_test.py  |  16 +-
 200 files changed, 999 insertions(+), 779 deletions(-)
 create mode 100644 tools/mo/openvino/tools/mo/utils/type_utils.py

diff --git a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
index 9399107f65f..a8213292b08 100644
--- a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
@@ -112,7 +112,9 @@ bool ngraph::pass::CommonOptimizations::run_on_model(const std::shared_ptr<ngrap
     // before CommonOptimization pipeline execution
     manager.register_pass<ngraph::pass::MOCTransformations>(true, false);
 
-    manager.register_pass<ov::pass::ConvertCompressedOnlyToLegacy, false>();
+    // Enabling conversion of FP16 IR to legacy representation, each plugin have to disable it
+    // after support for FP16 IR is implemented
+    manager.register_pass<ov::pass::ConvertCompressedOnlyToLegacy>();
 
     // TODO: move to KMB
     manager.register_pass<ngraph::pass::WeightsDequantizeToFakeQuantize>();
diff --git a/src/common/transformations/src/transformations/common_optimizations/compress_float_constants.cpp b/src/common/transformations/src/transformations/common_optimizations/compress_float_constants.cpp
index b723201f0ce..86c3026c516 100644
--- a/src/common/transformations/src/transformations/common_optimizations/compress_float_constants.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/compress_float_constants.cpp
@@ -73,6 +73,7 @@ ov::pass::CompressFloatConstantsImpl::CompressFloatConstantsImpl() {
         }
         auto convert = std::make_shared<ov::opset8::Convert>(new_const, const_node->get_element_type());
 
+        new_const->set_friendly_name(const_node->get_friendly_name() + "_compressed");
         convert->set_friendly_name(const_node->get_friendly_name());
         ngraph::copy_runtime_info(const_node, convert);
         ov::mark_as_decompression(convert);
diff --git a/src/core/src/pass/convert_precision.cpp b/src/core/src/pass/convert_precision.cpp
index 7cb3a8adf13..24cf3e7a7b8 100644
--- a/src/core/src/pass/convert_precision.cpp
+++ b/src/core/src/pass/convert_precision.cpp
@@ -24,6 +24,7 @@ bool fuse_type_to_constant(const std::shared_ptr<ngraph::Node>& node,
                            const std::vector<ngraph::Input<ngraph::Node>>& consumers);
 bool fuse_type_to_shapeof(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_shapeof_v0(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
+bool fuse_type_to_random_uniform_v8(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_range_v4(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_parameter(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_convert(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
@@ -314,20 +315,20 @@ bool fuse_type_to_shapeof(const std::shared_ptr<ngraph::Node>& node, element::Ty
     return false;
 }
 
-bool fuse_type_to_range_v4(const std::shared_ptr<ngraph::Node>& node, element::Type to, size_t idx) {
-    if (auto range = ov::as_type_ptr<opset4::Range>(node)) {
+bool fuse_type_to_random_uniform_v8(const std::shared_ptr<ngraph::Node>& node, element::Type to, size_t idx) {
+    if (auto random_uniform = ov::as_type_ptr<opset8::RandomUniform>(node)) {
         if (to.is_integral_number() || to.is_real()) {
-            range->set_output_type(to);
+            random_uniform->set_out_type(to);
             return true;
         }
     }
     return false;
 }
 
-bool fuse_type_to_random_uniform_v8(const std::shared_ptr<ngraph::Node>& node, element::Type to, size_t idx) {
-    if (auto random_uniform = ov::as_type_ptr<opset8::RandomUniform>(node)) {
+bool fuse_type_to_range_v4(const std::shared_ptr<ngraph::Node>& node, element::Type to, size_t idx) {
+    if (auto range = ov::as_type_ptr<opset4::Range>(node)) {
         if (to.is_integral_number() || to.is_real()) {
-            random_uniform->set_out_type(to);
+            range->set_output_type(to);
             return true;
         }
     }
diff --git a/src/inference/src/cnn_network_ngraph_impl.cpp b/src/inference/src/cnn_network_ngraph_impl.cpp
index 4a704d2f600..5d576fae943 100644
--- a/src/inference/src/cnn_network_ngraph_impl.cpp
+++ b/src/inference/src/cnn_network_ngraph_impl.cpp
@@ -34,6 +34,7 @@
 #include <legacy/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.hpp>
 #include <transformations/common_optimizations/remove_concat_zero_dim_input.hpp>
 #include <transformations/common_optimizations/remove_multi_subgraph_op_dangling_params.hpp>
+#include <transformations/disable_decompression_convert_constant_folding.hpp>
 #include <transformations/low_precision/disable_convert_constant_folding_on_const_path.hpp>
 #include <transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp>
 #include <transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp>
@@ -441,6 +442,7 @@ void CNNNetworkNGraphImpl::reshape(const std::map<std::string, ngraph::PartialSh
                 manager.register_pass<::ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE>();
                 manager.register_pass<::ngraph::pass::ConvertMatrixNmsToMatrixNmsIE>();
                 manager.register_pass<::ngraph::pass::DisableConvertConstantFoldingOnConstPath>();
+                manager.register_pass<::ov::pass::DisableDecompressionConvertConstantFolding>();
                 manager.register_pass<::ngraph::pass::ConstantFolding>();
                 // OneHotToLegacy changes output precision
                 manager.register_pass<::ngraph::pass::ConvertOneHotToOneHotIEMatcher>()->detect_output_type(
diff --git a/src/plugins/intel_cpu/src/mkldnn_plugin.cpp b/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
index 70aa7e97cc0..6b3640290d8 100644
--- a/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
+++ b/src/plugins/intel_cpu/src/mkldnn_plugin.cpp
@@ -35,6 +35,7 @@
 #include <transformations/common_optimizations/nop_elimination.hpp>
 #include <transformations/common_optimizations/wrap_interpolate_into_transposes.hpp>
 #include <transformations/common_optimizations/transpose_sinking.hpp>
+#include "transformations/common_optimizations/convert_compression_only_to_legacy.hpp"
 #include <transformations/op_conversions/convert_broadcast_to_tiles.hpp>
 #include <transformations/op_conversions/convert_depth_to_space.hpp>
 #include <transformations/op_conversions/convert_shuffle_channels3.hpp>
@@ -77,6 +78,7 @@
 #include <transformations/op_conversions/convert_reduce_to_pooling.hpp>
 #include <transformations/convert_precision.hpp>
 #include <transformations/init_node_info.hpp>
+#include <transformations/disable_decompression_convert_constant_folding.hpp>
 #include <transformations/rt_info/fused_names_attribute.hpp>
 #include <transformations/op_conversions/fq_decomposition.hpp>
 #include <transformations/utils/utils.hpp>
@@ -356,6 +358,11 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
             });
 
     // List of enabled/disabled transformations
+
+    // Allow FP16 Converts to be folded and FP16 constants to be upgraded to FP32 data type
+    pass_config->disable<ov::pass::DisableDecompressionConvertConstantFolding>();
+    pass_config->disable<ov::pass::ConvertCompressedOnlyToLegacy>();
+
     pass_config->disable<ngraph::pass::ConvertGELU>();
     pass_config->disable<ngraph::pass::ConvertShuffleChannels3>();
     pass_config->disable<ngraph::pass::Gelu7Downgrade>();
diff --git a/src/plugins/intel_gna/gna_plugin.cpp b/src/plugins/intel_gna/gna_plugin.cpp
index 32b397ef81e..c396aee3bbe 100644
--- a/src/plugins/intel_gna/gna_plugin.cpp
+++ b/src/plugins/intel_gna/gna_plugin.cpp
@@ -55,6 +55,8 @@
 #include <transformations/common_optimizations/relu_fake_quantize_fusion.hpp>
 #include <transformations/common_optimizations/add_fake_quantize_fusion.hpp>
 #include <transformations/common_optimizations/transpose_sinking.hpp>
+#include "transformations/common_optimizations/convert_compression_only_to_legacy.hpp"
+#include "transformations/disable_decompression_convert_constant_folding.hpp"
 #include <transformations/utils/utils.hpp>
 
 #include "transformations/remove_extra_reshapes.hpp"
@@ -716,6 +718,11 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         // UnrollTI should be the last transformation in the transformation pipeline
         manager.register_pass<ngraph::pass::UnrollTensorIterator>();
         const auto& pass_config = manager.get_pass_config();
+
+        // Allowing FP16 Converts to be folded and FP16 constants to upgrade to FP32 data type
+        pass_config->disable<ov::pass::ConvertCompressedOnlyToLegacy>();
+        pass_config->disable<ov::pass::DisableDecompressionConvertConstantFolding>();
+
         pass_config->disable<ngraph::pass::FakeQuantizeMulFusion>();
         pass_config->disable<ngraph::pass::FakeQuantizeReshapeFusion>();
         pass_config->disable<ngraph::pass::PullTransposeThroughFQUp>();
diff --git a/tools/mo/automation/package_BOM.txt b/tools/mo/automation/package_BOM.txt
index 8acb802577a..607a19cc74b 100644
--- a/tools/mo/automation/package_BOM.txt
+++ b/tools/mo/automation/package_BOM.txt
@@ -1084,6 +1084,7 @@ openvino/tools/mo/utils/telemetry_params.py
 openvino/tools/mo/utils/telemetry_stub.py
 openvino/tools/mo/utils/telemetry_utils.py
 openvino/tools/mo/utils/tensorboard_util.py
+openvino/tools/mo/utils/type_utils.py
 openvino/tools/mo/utils/unsupported_ops.py
 openvino/tools/mo/utils/utils.py
 openvino/tools/mo/utils/version.py
diff --git a/tools/mo/openvino/tools/mo/back/ConvolutionNormalizer.py b/tools/mo/openvino/tools/mo/back/ConvolutionNormalizer.py
index 10c57170361..fd640faeb32 100644
--- a/tools/mo/openvino/tools/mo/back/ConvolutionNormalizer.py
+++ b/tools/mo/openvino/tools/mo/back/ConvolutionNormalizer.py
@@ -6,6 +6,7 @@ import numpy as np
 from openvino.tools.mo.back.ReshapeMutation import ReshapeMutation
 from openvino.tools.mo.back.ReverseInputChannels import ApplyReverseChannels
 from openvino.tools.mo.back.replacement import BackReplacementPattern
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array, is_fully_defined, int64_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input, create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node
@@ -207,15 +208,15 @@ class DeconvolutionNormalizer(BackReplacementPattern):
             shape_src = node.in_port(2).get_source()
             node.in_port(2).disconnect()
 
-            ss_0 = create_op_with_const_inputs(graph, StridedSlice, {1: np.array([2], dtype=np.int32),
-                                                                     2: np.array([in_rank], dtype=np.int32),
-                                                                     3: np.array([1], dtype=np.int32)},
+            ss_0 = create_op_with_const_inputs(graph, StridedSlice, {1: mo_array([2], dtype=np.int32),
+                                                                     2: mo_array([in_rank], dtype=np.int32),
+                                                                     3: mo_array([1], dtype=np.int32)},
                                                {'name': node_name + '/ss_0_port',
-                                                'begin_mask': np.array([1], dtype=np.int32),
-                                                'end_mask': np.array([0], dtype=np.int32),
-                                                'new_axis_mask': np.array([0], dtype=np.int32),
-                                                'shrink_axis_mask': np.array([0], dtype=np.int32),
-                                                'ellipsis_mask': np.array([0], dtype=np.int32)})
+                                                'begin_mask': mo_array([1], dtype=np.int32),
+                                                'end_mask': mo_array([0], dtype=np.int32),
+                                                'new_axis_mask': mo_array([0], dtype=np.int32),
+                                                'shrink_axis_mask': mo_array([0], dtype=np.int32),
+                                                'ellipsis_mask': mo_array([0], dtype=np.int32)})
 
             shape_src.connect(ss_0.in_port(0))
             ss_0.out_port(0).connect(node.in_port(2))
diff --git a/tools/mo/openvino/tools/mo/back/CropToStridedSlice.py b/tools/mo/openvino/tools/mo/back/CropToStridedSlice.py
index 94a8f49ef1b..0ee96b965e1 100644
--- a/tools/mo/openvino/tools/mo/back/CropToStridedSlice.py
+++ b/tools/mo/openvino/tools/mo/back/CropToStridedSlice.py
@@ -7,6 +7,7 @@ from openvino.tools.mo.back.ForceStrictPrecision import ForceStrictPrecision
 from openvino.tools.mo.ops.elementwise import Add
 from openvino.tools.mo.back.replacement import BackReplacementPattern
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.ops.const import Const
 from openvino.tools.mo.ops.shape import Shape
@@ -38,7 +39,7 @@ class CropToStridedSlice(BackReplacementPattern):
 
     @staticmethod
     def list_to_ndarray(val):
-        return np.array(val) if np.array(val).ndim != 0 else np.array([val])
+        return mo_array(val) if mo_array(val).ndim != 0 else mo_array([val])
 
     def replace_pattern(self, graph: Graph, match: [str, Node]):
         node = match['crop']
@@ -75,7 +76,7 @@ class CropToStridedSlice(BackReplacementPattern):
 
             begin = Const(graph, {'value': self.mask_normalizer(shape_rank, node_axis, node_offset),
                                   'name': ss.name + '/begin'}).create_node()
-            end_values = np.array([node_offset[i] + node_dim[i] for i in range(len(node_dim))])
+            end_values = mo_array([node_offset[i] + node_dim[i] for i in range(len(node_dim))])
             end = Const(graph, {'value': self.mask_normalizer(shape_rank, node_axis, end_values),
                                 'name': ss.name + '/end'}).create_node()
         elif node.has_valid('crop_begin') and node.has_valid('crop_end'):
diff --git a/tools/mo/openvino/tools/mo/back/CutMemory.py b/tools/mo/openvino/tools/mo/back/CutMemory.py
index 0e269c7179d..d85659b1c11 100644
--- a/tools/mo/openvino/tools/mo/back/CutMemory.py
+++ b/tools/mo/openvino/tools/mo/back/CutMemory.py
@@ -1,10 +1,9 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.parameter import Parameter
 from openvino.tools.mo.back.replacement import BackReplacementPattern
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.crop import Crop
 from openvino.tools.mo.utils.logger import log
@@ -68,7 +67,7 @@ class CutMemoryOutput(BackReplacementPattern):
         in_node_port = node.in_port(0).get_source()
         node.in_port(0).disconnect()
         node.out_port(0).disconnect()
-        crop = Crop(graph, {'name': 'Result_for_'+node_id, 'dim': np.array([1]), 'offset': np.array([0]),
-                            'axis': np.array([0])}).create_node()
+        crop = Crop(graph, {'name': 'Result_for_'+node_id, 'dim': mo_array([1]), 'offset': mo_array([0]),
+                            'axis': mo_array([0])}).create_node()
         in_node_port.connect(crop.in_port(0))
         crop.out_port(0).connect(out_node_port)
diff --git a/tools/mo/openvino/tools/mo/back/InterpolateReshape.py b/tools/mo/openvino/tools/mo/back/InterpolateReshape.py
index a2ff84dc2f9..7b81b5ed579 100644
--- a/tools/mo/openvino/tools/mo/back/InterpolateReshape.py
+++ b/tools/mo/openvino/tools/mo/back/InterpolateReshape.py
@@ -9,6 +9,7 @@ from openvino.tools.mo.ops.interpolate import Interpolate
 from openvino.tools.mo.back.replacement import BackReplacementPattern
 from openvino.tools.mo.front.caffe.extractors.utils import get_canonical_axis_index
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs, create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.shape import Shape
@@ -71,7 +72,7 @@ class InterpolateConcat(BackReplacementPattern):
         shape = Shape(graph, {'name': src.node.soft_get('name', src.node.id) + '/Shape'}).create_node()
         shape.in_port(0).connect(src)
         gather = create_op_with_const_inputs(graph, Gather,
-                                             {1: np.array(interp_axes, dtype=np.int32), 2: int64_array(0)},
+                                             {1: mo_array(interp_axes, dtype=np.int32), 2: int64_array(0)},
                                              {'name': shape.name + '/Gathered'}, shape)
         interpolate.in_port(1).get_connection().set_source(gather.out_port(0))
 
@@ -129,7 +130,7 @@ class InterpolateReshapeWA(BackReplacementPattern):
         name = interpolate.soft_get('name', interpolate.id)
         shape = Shape(graph, {'name': name + '/ShapeOf'}).create_node()
         shape.in_port(0).connect(interpolate.in_port(0).get_source())
-        gather = create_op_with_const_inputs(graph, Gather, {1: np.array(axes, dtype=np.int32), 2: int64_array(0)},
+        gather = create_op_with_const_inputs(graph, Gather, {1: mo_array(axes, dtype=np.int32), 2: int64_array(0)},
                                              {'name': shape.name + '/Gathered'}, shape)
         multipliers = output_shape[axes] / input_shape[axes]
         mul = create_op_node_with_second_input(graph, Mul, multipliers, {'name': gather.name + '/Multiplied'}, gather)
diff --git a/tools/mo/openvino/tools/mo/back/LeakyReLUMutation.py b/tools/mo/openvino/tools/mo/back/LeakyReLUMutation.py
index 3eb5c87a0b8..3babbe202c5 100644
--- a/tools/mo/openvino/tools/mo/back/LeakyReLUMutation.py
+++ b/tools/mo/openvino/tools/mo/back/LeakyReLUMutation.py
@@ -1,11 +1,10 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.back.ForceStrictPrecision import ForceStrictPrecision
 from openvino.tools.mo.ops.prelu import PReLU
 from openvino.tools.mo.back.replacement import BackReplacementPattern
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph, rename_node
 from openvino.tools.mo.ops.const import Const
 
@@ -36,7 +35,7 @@ class LeakyReLUMutation(BackReplacementPattern):
         prelu = PReLU(graph, dict(name=relu_name)).create_node()
         rename_node(prelu, relu_name)
 
-        const = Const(graph, dict(name=relu_name + "/weights", value=np.array([relu.negative_slope]))).create_node()
+        const = Const(graph, dict(name=relu_name + "/weights", value=mo_array([relu.negative_slope]))).create_node()
 
         relu.in_port(0).get_connection().set_destination(prelu.in_port(0))
         const.out_port(0).connect(prelu.in_port(1))
diff --git a/tools/mo/openvino/tools/mo/back/NormalizeToNormalizeL2.py b/tools/mo/openvino/tools/mo/back/NormalizeToNormalizeL2.py
index 17edb8fe513..6bf90bbb7c6 100644
--- a/tools/mo/openvino/tools/mo/back/NormalizeToNormalizeL2.py
+++ b/tools/mo/openvino/tools/mo/back/NormalizeToNormalizeL2.py
@@ -8,6 +8,7 @@ from openvino.tools.mo.ops.elementwise import Mul
 from openvino.tools.mo.ops.normalize_l2 import NormalizeL2Op
 from openvino.tools.mo.back.replacement import BackReplacementPattern
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, rename_node
 
@@ -48,11 +49,11 @@ class NormalizeToNormalizeL2(BackReplacementPattern):
         # in the code below we intentionally use get_source() to get the out port. Because updating the out port will
         # update the Const node 'value' and 'shape' attributes
         if node.channel_shared or all(weights == weights[0]):
-            node.in_port(1).get_source().data.set_value(np.array([weights[0]]))
+            node.in_port(1).get_source().data.set_value(mo_array([weights[0]]))
         else:
             new_shape = np.ones((len(node.in_port(0).data.get_shape())), dtype=np.int64)
             new_shape[1] = -1
-            node.in_port(1).get_source().data.set_value(np.array(weights).reshape(new_shape))
+            node.in_port(1).get_source().data.set_value(mo_array(weights).reshape(new_shape))
 
         mul = Mul(graph, {'name': output_name}).create_node()
         rename_node(mul, output_name)
diff --git a/tools/mo/openvino/tools/mo/back/PackBinaryWeights.py b/tools/mo/openvino/tools/mo/back/PackBinaryWeights.py
index 03a497720ca..ba77c4a8fd6 100644
--- a/tools/mo/openvino/tools/mo/back/PackBinaryWeights.py
+++ b/tools/mo/openvino/tools/mo/back/PackBinaryWeights.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.back.replacement import BackReplacementPattern
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph
 
 
@@ -28,11 +29,11 @@ class PackBinaryWeights(BackReplacementPattern):
         weights_rounded = np.round(weights)
         assert np.all(np.isclose(weights, weights_rounded))
         assert len(conv.in_node(1).out_nodes()) == 1
-        weights_rounded = np.array(weights_rounded, dtype=np.int32) + 1  # -1 --> 0
+        weights_rounded = mo_array(weights_rounded, dtype=np.int32) + 1  # -1 --> 0
         # Reversing element in chunks by 8 elements to pack bits correctly
         # First need to pad data with necessary number of element to make the length dividable by 8
         pad = (-len(weights_rounded)) % 8
-        weights_rounded = np.array(np.concatenate((weights_rounded, np.zeros([pad]))), dtype=np.int32)
+        weights_rounded = mo_array(np.concatenate((weights_rounded, np.zeros([pad]))), dtype=np.int32)
         assert len(weights_rounded) % 8 == 0
         weights_rounded = weights_rounded.reshape([len(weights_rounded) // 8, 8])
         weights_rounded = np.flip(weights_rounded, axis=1)
diff --git a/tools/mo/openvino/tools/mo/back/ProposalMutation.py b/tools/mo/openvino/tools/mo/back/ProposalMutation.py
index ebae6ed1b75..03f202e64ba 100644
--- a/tools/mo/openvino/tools/mo/back/ProposalMutation.py
+++ b/tools/mo/openvino/tools/mo/back/ProposalMutation.py
@@ -9,6 +9,7 @@ from openvino.tools.mo.back.ReshapeMutation import ReshapeMutation
 from openvino.tools.mo.back.StridedSliceMasksNormalizer import StridedSliceMasksNormalizer
 from openvino.tools.mo.back.replacement import BackReplacementPattern
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs, create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.reshape import Reshape
@@ -42,9 +43,9 @@ class ProposalMutation(BackReplacementPattern):
                       'Elements with indices 4 and 5 will be ignored.'.format(node.soft_get('name', node.id)),
                       extra={'is_warning': True})
 
-            cropped_im_info = create_op_with_const_inputs(graph, StridedSlice, {1: np.array([0, 0], dtype=np.int32),
-                                                                                2: np.array([1, 3], dtype=np.int32),
-                                                                                3: np.array([1, 1], dtype=np.int32)},
+            cropped_im_info = create_op_with_const_inputs(graph, StridedSlice, {1: mo_array([0, 0], dtype=np.int32),
+                                                                                2: mo_array([1, 3], dtype=np.int32),
+                                                                                3: mo_array([1, 1], dtype=np.int32)},
                                                           {'name': 'cropped_im_info',
                                                            'begin_mask': int64_array([1, 1]),
                                                            'end_mask': int64_array([1, 1]),
diff --git a/tools/mo/openvino/tools/mo/back/ReverseInputChannels.py b/tools/mo/openvino/tools/mo/back/ReverseInputChannels.py
index 53e6460fcac..fe12c7dad42 100644
--- a/tools/mo/openvino/tools/mo/back/ReverseInputChannels.py
+++ b/tools/mo/openvino/tools/mo/back/ReverseInputChannels.py
@@ -9,6 +9,7 @@ from openvino.tools.mo.ops.gather import Gather
 from openvino.tools.mo.ops.split import Split
 from openvino.tools.mo.back.replacement import BackReplacementPattern
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.graph.graph import Node
@@ -221,7 +222,7 @@ class ReverseChannelsPropagationDown(BackReplacementPattern):
 
         # insert ReverseChannels on weights port of Convolution
         ric_to_move_to_weights = reverse_channels if group == 1 else reverse_channels.copy_node()
-        ric_to_move_to_weights['axis'] = np.array(channel_idx)
+        ric_to_move_to_weights['axis'] = mo_array(channel_idx)
         src = node.in_port(1).get_connection().get_source()
         node.in_port(1).get_connection().set_source(ric_to_move_to_weights.out_port(0))
         src.disconnect()
@@ -235,8 +236,8 @@ class ReverseChannelsPropagationDown(BackReplacementPattern):
             multiplier = int(bottom_channels / group)
             new_order = np.take(np.arange(bottom_channels).reshape((group, multiplier)),
                                 indices=reverse_channels.order, axis=0).flatten()
-            reverse_channels['axis'] = np.array(reverse_channels.axis.copy())
-            reverse_channels['order'] = np.array(new_order)
+            reverse_channels['axis'] = mo_array(reverse_channels.axis.copy())
+            reverse_channels['order'] = mo_array(new_order)
 
             node.out_port(0).get_connection().set_source(reverse_channels.out_port(0))
             node.out_port(0).disconnect()
@@ -285,7 +286,7 @@ class ReverseChannelsPropagationDown(BackReplacementPattern):
 
         # reversing eltwise inputs where applicable
         for port, axis in port_axis:
-            ric_copy = reverse_channels.copy_node({'axis': np.array(axis), 'order': np.array(reverse_channels.order)})
+            ric_copy = reverse_channels.copy_node({'axis': mo_array(axis), 'order': mo_array(reverse_channels.order)})
 
             src = port.get_connection().get_source()
             port.get_connection().set_source(ric_copy.out_port(0))
@@ -452,7 +453,7 @@ class ReverseChannelsPropagationUp(BackReplacementPattern):
 
         copies = []
         for port, axis in port_axis:
-            reverse_channels_copy = reverse_channels.copy_node({'axis': np.array(axis)})
+            reverse_channels_copy = reverse_channels.copy_node({'axis': mo_array(axis)})
 
             src = port.get_connection().get_source()
             if src.node.soft_get('type') == 'Parameter':
diff --git a/tools/mo/openvino/tools/mo/back/compress_quantized_weights.py b/tools/mo/openvino/tools/mo/back/compress_quantized_weights.py
index 88bb4f5ce7e..5225f250c41 100644
--- a/tools/mo/openvino/tools/mo/back/compress_quantized_weights.py
+++ b/tools/mo/openvino/tools/mo/back/compress_quantized_weights.py
@@ -6,11 +6,13 @@ from typing import Dict
 import numpy as np
 
 from openvino.tools.mo.ops.Cast import Cast
-from openvino.tools.mo.ops.elementwise import Sub, Div, Mul, Negative, Equal
+from openvino.tools.mo.ops.elementwise import Sub, Div, Mul, Equal
 from openvino.tools.mo.ops.select import Select
 from openvino.tools.mo.back.replacement import BackReplacementPattern
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.passes.convert_data_type import data_type_str_to_np, np_data_type_to_destination_type, packed_I4
+from openvino.tools.mo.middle.pattern_match import apply_pattern
 from openvino.tools.mo.ops.const import Const
 
 
@@ -90,7 +92,7 @@ class CompressQuantizeWeights(BackReplacementPattern):
         16: (packed_I4, "signed"),
     }
 
-    def pattern(self):
+    def pattern1(self):
         return dict(
             nodes=[
                 ('const', dict(type='Const')),
@@ -103,6 +105,27 @@ class CompressQuantizeWeights(BackReplacementPattern):
             ]
         )
 
+    def pattern2(self):
+        return dict(
+            nodes=[
+                ('const', dict(type='Const')),
+                ('const_d', dict()),
+                ('convert', dict(type='Convert')),
+                ('convert_d', dict()),
+                ('fake_quantize', dict(type='FakeQuantize', levels=lambda x: x is not None and 2 < x <= 256)),
+            ],
+            edges=[
+                ('const', 'const_d'),
+                ('const_d', 'convert'),
+                ('convert', 'convert_d'),
+                ('convert_d', 'fake_quantize', {'in': 0}),
+            ]
+        )
+
+    def find_and_replace_pattern(self, graph: Graph):
+        apply_pattern(graph, **self.pattern1(), action=self.replace_pattern)  # pylint: disable=no-member
+        apply_pattern(graph, **self.pattern2(), action=self.replace_pattern)  # pylint: disable=no-member
+
     @staticmethod
     def quantize_data(fake_quantize: Node, dst_type: type, quantized_type: type, mode: str):
         graph = fake_quantize.graph
@@ -120,8 +143,8 @@ class CompressQuantizeWeights(BackReplacementPattern):
         assert mode in ["signed", "unsigned"]
         i_min_value = -(levels // 2) if mode == "signed" else 0
 
-        i_min = np.array([i_min_value], dtype=dst_type)
-        i_max = np.array(levels + i_min - 1, dtype=dst_type)
+        i_min = mo_array([i_min_value], dtype=dst_type)
+        i_max = mo_array(levels + i_min - 1, dtype=dst_type)
 
         assert i_max - i_min == levels - 1
         out_low = Const(graph, dict(name=name + '/Copy/out_low', value=i_min)).create_node()
@@ -183,7 +206,7 @@ class CompressQuantizeWeights(BackReplacementPattern):
         shift.in_port(0).connect(in_low)
         shift.in_port(1).connect(descaled_output_low.out_port(0))
 
-        zero = Const(graph, {'name': name + '/zero', 'value': np.array(0, dtype=dst_type)}).create_node()
+        zero = Const(graph, {'name': name + '/zero', 'value': mo_array(0, dtype=dst_type)}).create_node()
         scale_eq_zero = Equal(graph, {'name': name + '/scale_eq_zero'}).create_node()
         scale_eq_zero.in_port(0).connect(scale.out_port(0))
         scale_eq_zero.in_port(1).connect(zero.out_port(0))
@@ -209,9 +232,12 @@ class CompressQuantizeWeights(BackReplacementPattern):
     def replace_pattern(self, graph: Graph, match: Dict[str, Node]):
         fake_quantize = match['fake_quantize']
 
-        dst_type = match['const'].value.dtype
-        if np.issubdtype(dst_type, np.floating):
-            dst_type = data_type_str_to_np(graph.graph['cmd_params'].data_type)
+        if 'convert' in match:
+            dst_type = match['convert'].dst_type
+            match['convert']['stop_value_propagation'] = False
+            Cast.infer(match['convert'])
+        else:
+            dst_type = match['const'].value.dtype
 
         quantized_type, mode = None, None
         for quantization_levels in sorted(self.QUANTIZATION_MAP):
diff --git a/tools/mo/openvino/tools/mo/back/priorbox_mutation.py b/tools/mo/openvino/tools/mo/back/priorbox_mutation.py
index e9b82a0e48b..e937219e99c 100644
--- a/tools/mo/openvino/tools/mo/back/priorbox_mutation.py
+++ b/tools/mo/openvino/tools/mo/back/priorbox_mutation.py
@@ -6,6 +6,7 @@ import numpy as np
 from openvino.tools.mo.back.ForceStrictPrecision import ForceStrictPrecision
 from openvino.tools.mo.back.replacement import BackReplacementPattern
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph, rename_nodes
 from openvino.tools.mo.ops.const import Const
@@ -37,17 +38,17 @@ class PriorboxMutation(BackReplacementPattern):
 
         assert len(node.in_ports()) == 2
 
-        begin = Const(graph, {'value': np.array([2], dtype=np.int32), 'name': name + '/ss_begin'}).create_node()
-        end = Const(graph, {'value': np.array([4], dtype=np.int32), 'name': name + '/ss_end'}).create_node()
-        stride = Const(graph, {'value': np.array([1], dtype=np.int32), 'name': name + '/ss_stride'}).create_node()
+        begin = Const(graph, {'value': mo_array([2], dtype=np.int32), 'name': name + '/ss_begin'}).create_node()
+        end = Const(graph, {'value': mo_array([4], dtype=np.int32), 'name': name + '/ss_end'}).create_node()
+        stride = Const(graph, {'value': mo_array([1], dtype=np.int32), 'name': name + '/ss_stride'}).create_node()
 
         shape_0 = Shape(graph, {'name': name + '/0_port'}).create_node()
         ss_0 = StridedSlice(graph, {'name': name + '/ss_0_port',
-                                    'begin_mask': np.array([1], dtype=np.int32),
-                                    'end_mask': np.array([0], dtype=np.int32),
-                                    'new_axis_mask': np.array([0], dtype=np.int32),
-                                    'shrink_axis_mask': np.array([0], dtype=np.int32),
-                                    'ellipsis_mask': np.array([0], dtype=np.int32)}).create_node()
+                                    'begin_mask': mo_array([1], dtype=np.int32),
+                                    'end_mask': mo_array([0], dtype=np.int32),
+                                    'new_axis_mask': mo_array([0], dtype=np.int32),
+                                    'shrink_axis_mask': mo_array([0], dtype=np.int32),
+                                    'ellipsis_mask': mo_array([0], dtype=np.int32)}).create_node()
 
         shape_0.out_port(0).connect(ss_0.in_port(0))
         begin.out_port(0).connect(ss_0.in_port(1))
@@ -61,11 +62,11 @@ class PriorboxMutation(BackReplacementPattern):
 
         shape_1 = Shape(graph, {'name': name + '/1_port'}).create_node()
         ss_1 = StridedSlice(graph, {'name': name + '/ss_1_port',
-                                    'begin_mask': np.array([1], dtype=np.int32),
-                                    'end_mask': np.array([0], dtype=np.int32),
-                                    'new_axis_mask': np.array([0], dtype=np.int32),
-                                    'shrink_axis_mask': np.array([0], dtype=np.int32),
-                                    'ellipsis_mask': np.array([0], dtype=np.int32)}).create_node()
+                                    'begin_mask': mo_array([1], dtype=np.int32),
+                                    'end_mask': mo_array([0], dtype=np.int32),
+                                    'new_axis_mask': mo_array([0], dtype=np.int32),
+                                    'shrink_axis_mask': mo_array([0], dtype=np.int32),
+                                    'ellipsis_mask': mo_array([0], dtype=np.int32)}).create_node()
 
         shape_1.out_port(0).connect(ss_1.in_port(0))
         begin.out_port(0).connect(ss_1.in_port(1))
diff --git a/tools/mo/openvino/tools/mo/front/AttributedClampNormalizer.py b/tools/mo/openvino/tools/mo/front/AttributedClampNormalizer.py
index 87ed8cfe493..42c6b87e85c 100644
--- a/tools/mo/openvino/tools/mo/front/AttributedClampNormalizer.py
+++ b/tools/mo/openvino/tools/mo/front/AttributedClampNormalizer.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, rename_node
@@ -24,8 +25,8 @@ class AttributedClampNormalizer(FrontReplacementPattern):
             min_value = attr_clamp.soft_get('min', np.finfo(np.float32).min)
             max_value = attr_clamp.soft_get('max', np.finfo(np.float32).max)
             new_clamp = create_op_with_const_inputs(graph, Clamp,
-                                                    {1: np.array(min_value, dtype=np.float32),
-                                                     2: np.array(max_value, dtype=np.float32)},
+                                                    {1: float32_array(min_value),
+                                                     2: float32_array(max_value)},
                                                     {'name': original_name})
             rename_node(new_clamp, original_name)
 
diff --git a/tools/mo/openvino/tools/mo/front/FillToBroadcast.py b/tools/mo/openvino/tools/mo/front/FillToBroadcast.py
index 08415fef7b5..5fb11b436cf 100644
--- a/tools/mo/openvino/tools/mo/front/FillToBroadcast.py
+++ b/tools/mo/openvino/tools/mo/front/FillToBroadcast.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.broadcast import Broadcast
@@ -30,7 +29,7 @@ class FillToBroadcast(FrontReplacementPattern):
             assert fill_node.has_valid('fill_value')
             assert fill_node.has_and_set('input_as_shape')
 
-            const = Const(graph, {'value': np.array(fill_node.fill_value), 'name': name + '/value'}).create_node()
+            const = Const(graph, {'value': mo_array(fill_node.fill_value), 'name': name + '/value'}).create_node()
             broadcast_node = Broadcast(graph, {'name': name + '/Broadcast'}).create_node()
             fill_node.in_port(0).get_connection().set_destination(broadcast_node.in_port(1))
             const.out_port(0).connect(broadcast_node.in_port(0))
diff --git a/tools/mo/openvino/tools/mo/front/InterpolateNormalizer.py b/tools/mo/openvino/tools/mo/front/InterpolateNormalizer.py
index 93938004326..8c6f2b72855 100644
--- a/tools/mo/openvino/tools/mo/front/InterpolateNormalizer.py
+++ b/tools/mo/openvino/tools/mo/front/InterpolateNormalizer.py
@@ -4,9 +4,8 @@
 import inspect
 import logging as log
 
-import numpy as np
-
 from openvino.tools.mo.ops.elementwise import Mul, Add
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.const import Const
@@ -25,17 +24,17 @@ class InterpolateNormalizer(FrontReplacementOp):
         if 1 not in node.in_ports() or node.in_port(1).disconnected():
 
             if node.has_valid('factor') and not node.has_valid('width') and not node.has_valid('height'):
-                factor = Const(graph, {'value': np.array(node.factor)}).create_node()
+                factor = Const(graph, {'value': mo_array(node.factor)}).create_node()
 
                 shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
 
-                begin = Const(graph, {'value': np.array([2])}).create_node()
-                end = Const(graph, {'value': np.array([4])}).create_node()
-                stride = Const(graph, {'value': np.array([1])}).create_node()
-                ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
-                                          'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
-                                          'shrink_axis_mask': np.array([0]),
-                                          'ellipsis_mask': np.array([0])}).create_node()
+                begin = Const(graph, {'value': mo_array([2])}).create_node()
+                end = Const(graph, {'value': mo_array([4])}).create_node()
+                stride = Const(graph, {'value': mo_array([1])}).create_node()
+                ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': mo_array([1]),
+                                          'end_mask': mo_array([0]), 'new_axis_mask': mo_array([0]),
+                                          'shrink_axis_mask': mo_array([0]),
+                                          'ellipsis_mask': mo_array([0])}).create_node()
 
                 mul = Mul(graph, {'name': node.name + '/factor_mul_'}).create_node()
 
@@ -55,13 +54,13 @@ class InterpolateNormalizer(FrontReplacementOp):
             else:
                 shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
 
-                begin = Const(graph, {'value': np.array([2])}).create_node()
-                end = Const(graph, {'value': np.array([4])}).create_node()
-                stride = Const(graph, {'value': np.array([1])}).create_node()
-                ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
-                                          'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
-                                          'shrink_axis_mask': np.array([0]),
-                                          'ellipsis_mask': np.array([0])}).create_node()
+                begin = Const(graph, {'value': mo_array([2])}).create_node()
+                end = Const(graph, {'value': mo_array([4])}).create_node()
+                stride = Const(graph, {'value': mo_array([1])}).create_node()
+                ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': mo_array([1]),
+                                          'end_mask': mo_array([0]), 'new_axis_mask': mo_array([0]),
+                                          'shrink_axis_mask': mo_array([0]),
+                                          'ellipsis_mask': mo_array([0])}).create_node()
 
                 source = node.in_port(0).get_connection().get_source()
                 source.connect(shape.in_port(0))
@@ -71,7 +70,7 @@ class InterpolateNormalizer(FrontReplacementOp):
                 stride.out_port(0).connect(ss.in_port(3))
 
                 pads_value = node.pads_begin + node.pads_end
-                pads_const = Const(graph, {'value': np.array(pads_value)}).create_node()
+                pads_const = Const(graph, {'value': mo_array(pads_value)}).create_node()
                 add = Add(graph, {'name': node.name + '/pad_add'}).create_node()
                 ss.out_port(0).connect(add.in_port(0))
                 add.in_port(1).connect(pads_const.out_port(0))
@@ -83,18 +82,18 @@ class InterpolateNormalizer(FrontReplacementOp):
                         return None
 
                     const = Const(graph, {'name': node.name + '/pre_shrink_sub_const',
-                                          'value': np.array(-1)}).create_node()
+                                          'value': mo_array(-1)}).create_node()
                     sub = Add(graph, {'name': node.name + '/pre_shrink_sub'}).create_node()
                     add.out_port(0).connect(sub.in_port(0))
                     sub.in_port(1).connect(const.out_port(0))
 
-                    const = Const(graph, {'value': np.array(1 / shrink_factor),
+                    const = Const(graph, {'value': mo_array(1 / shrink_factor),
                                           'name': node.name + 'shrink_factor_div_const'}).create_node()
                     div = Mul(graph, {'name': node.name + 'shrink_factor_div'}).create_node()
                     sub.out_port(0).connect(div.in_port(0))
                     div.in_port(1).connect(const.out_port(0))
 
-                    const = Const(graph, {'name': node.name + '/shrink_factor_add_one_const', 'value': np.array(1)
+                    const = Const(graph, {'name': node.name + '/shrink_factor_add_one_const', 'value': mo_array(1)
                                           }).create_node()
                     add = Add(graph, {'name': node.name + '/shrink_factor_add_one'}).create_node()
                     div.out_port(0).connect(add.in_port(0))
@@ -118,13 +117,13 @@ class InterpolateNormalizer(FrontReplacementOp):
                     # Commented out section represents reshape that used in deeplab-caffe
                     # Uncomment the following lines, if your model was trained with deeplab-caffe
                     # or have the same reshape method
-                    # const = Const(graph, {'value': np.array(-1),
+                    # const = Const(graph, {'value': mo_array(-1),
                     #                       'name': node.name + 'zoom_factor_deeplab-caffe_sub_const'}).create_node()
                     # sub = Add(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_sub'}).create_node()
                     # add.out_port(0).connect(sub.in_port(0))
                     # const.out_port(0).connect(sub.in_port(1))
                     #
-                    # const = Const(graph, {'value': np.array(zoom_factor - 1),
+                    # const = Const(graph, {'value': mo_array(zoom_factor - 1),
                     #                       'name': node.name + 'zoom_factor_deeplab-caffe_mul_const'}).create_node()
                     # mul = Mul(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_mul'}).create_node()
                     # sub.out_port(0).connect(mul.in_port(0))
@@ -139,7 +138,7 @@ class InterpolateNormalizer(FrontReplacementOp):
                     # sum.out_port(0).connect(node.in_port(1))
 
                     # Comment out the following lines if you use the reshape method from previous section
-                    const = Const(graph, {'value': np.array(zoom_factor),
+                    const = Const(graph, {'value': mo_array(zoom_factor),
                                           'name': node.name + '/zoom_factor_mul_const'}).create_node()
                     mul = Mul(graph, {'name': node.name + '/zoom_factor_mul'}).create_node()
 
@@ -151,7 +150,7 @@ class InterpolateNormalizer(FrontReplacementOp):
                     mul.out_port(0).connect(node.in_port(1))
 
                 elif node.soft_get('width') != 0 and node.soft_get('height') != 0:
-                    const = Const(graph, {'value': np.array([node.height, node.width])}).create_node()
+                    const = Const(graph, {'value': mo_array([node.height, node.width])}).create_node()
                     node.add_input_port(1, skip_if_exist=True)
                     assert node.in_port(1).disconnected()
                     const.out_port(0).connect(node.in_port(1))
@@ -166,23 +165,23 @@ class InterpolateNormalizer(FrontReplacementOp):
                         log.error('Zoom factor should be positive in node {}'.format(node.id))
                         return None
 
-                    const = Const(graph, {'value': np.array(-1)}).create_node()
+                    const = Const(graph, {'value': mo_array(-1)}).create_node()
                     sub = Add(graph, {'name': node.name + '/shrink_zoom_factor_sub'}).create_node()
                     add.out_port(0).connect(sub.in_port(0))
                     const.out_port(0).connect(sub.in_port(1))
 
-                    const = Const(graph, {'value': np.array(1 / (shrink_factor + 1))}).create_node()
+                    const = Const(graph, {'value': mo_array(1 / (shrink_factor + 1))}).create_node()
                     div = Mul(graph, {'name': node.name + '/shrink_factor_div'}).create_node()
                     sub.out_port(0).connect(div.in_port(0))
                     const.out_port(0).connect(div.in_port(1))
 
-                    const = Const(graph, {'value': np.array(-1),
+                    const = Const(graph, {'value': mo_array(-1),
                                           'name': node.name + 'shrink_zoom_factor_sum_const'}).create_node()
                     sum = Add(graph, {'name': node.name + '/shrink_zoom_factor_sum'}).create_node()
                     div.out_port(0).connect(sum.in_port(0))
                     const.out_port(0).connect(sum.in_port(1))
 
-                    const = Const(graph, {'value': np.array(zoom_factor - 1)}).create_node()
+                    const = Const(graph, {'value': mo_array(zoom_factor - 1)}).create_node()
                     mul = Mul(graph, {'name': node.name + '/zoom_factor_mul'}).create_node()
                     sum.out_port(0).connect(mul.in_port(0))
                     const.out_port(0).connect(mul.in_port(1))
@@ -198,13 +197,13 @@ class InterpolateNormalizer(FrontReplacementOp):
             if node.soft_get('fw') == 'caffe':
                 shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
 
-                begin = Const(graph, {'value': np.array([2])}).create_node()
-                end = Const(graph, {'value': np.array([4])}).create_node()
-                stride = Const(graph, {'value': np.array([1])}).create_node()
-                ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
-                                          'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
-                                          'shrink_axis_mask': np.array([0]),
-                                          'ellipsis_mask': np.array([0])}).create_node()
+                begin = Const(graph, {'value': mo_array([2])}).create_node()
+                end = Const(graph, {'value': mo_array([4])}).create_node()
+                stride = Const(graph, {'value': mo_array([1])}).create_node()
+                ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': mo_array([1]),
+                                          'end_mask': mo_array([0]), 'new_axis_mask': mo_array([0]),
+                                          'shrink_axis_mask': mo_array([0]),
+                                          'ellipsis_mask': mo_array([0])}).create_node()
 
                 source = node.in_port(1).get_connection().get_source()
                 node.in_port(1).disconnect()
diff --git a/tools/mo/openvino/tools/mo/front/InterpolateV1ToInterpolate.py b/tools/mo/openvino/tools/mo/front/InterpolateV1ToInterpolate.py
index 15ae6e6a0b1..7295be7a811 100644
--- a/tools/mo/openvino/tools/mo/front/InterpolateV1ToInterpolate.py
+++ b/tools/mo/openvino/tools/mo/front/InterpolateV1ToInterpolate.py
@@ -1,10 +1,9 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.interpolate import Interpolate
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, rename_nodes
@@ -30,7 +29,7 @@ class InterpolateV1ToInterpolate(FrontReplacementPattern):
             interpolate1_name = node.soft_get('name', node.id)
             interpolate4 = create_op_with_const_inputs(graph, Interpolate,
                                                        {
-                                                           2: np.array([1.0, 1.0]),
+                                                           2: mo_array([1.0, 1.0]),
                                                            3: int64_array(node.axes)
                                                        },
                                                        {
diff --git a/tools/mo/openvino/tools/mo/front/Log1p.py b/tools/mo/openvino/tools/mo/front/Log1p.py
index c125001dc8a..cef7d4f0f49 100644
--- a/tools/mo/openvino/tools/mo/front/Log1p.py
+++ b/tools/mo/openvino/tools/mo/front/Log1p.py
@@ -5,6 +5,7 @@ import numpy as np
 
 from openvino.tools.mo.ops.activation_ops import Log
 from openvino.tools.mo.ops.elementwise import Add
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.graph.graph import Graph, Node, rename_nodes
 from openvino.tools.mo.ops.const import Const
@@ -23,7 +24,7 @@ class Log1p(FrontReplacementOp):
         const_dtype = np.float32
         if node.has_valid('data_type'):
             const_dtype = node.data_type
-        const = Const(graph, {'value': np.array([1], dtype=const_dtype)}).create_node()
+        const = Const(graph, {'value': mo_array([1], dtype=const_dtype)}).create_node()
         add = Add(graph, {'name': node.name + '/Add_'}).create_node()
         log = Log(graph, {'name': node.name + '/Log_'}).create_node()
 
diff --git a/tools/mo/openvino/tools/mo/front/MatMul_normalizer.py b/tools/mo/openvino/tools/mo/front/MatMul_normalizer.py
index bd42c7eafca..328028049a5 100644
--- a/tools/mo/openvino/tools/mo/front/MatMul_normalizer.py
+++ b/tools/mo/openvino/tools/mo/front/MatMul_normalizer.py
@@ -3,12 +3,11 @@
 
 import math
 
-import numpy as np
-
 from openvino.tools.mo.ops.MatMul import MatMul
 from openvino.tools.mo.ops.elementwise import Add, Mul
 from openvino.tools.mo.ops.transpose import Transpose
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.front.subgraph_matcher import SubgraphMatch
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
@@ -78,7 +77,7 @@ class GemmDecomposer(FrontReplacementSubgraph):
             name = node.soft_get('name', node.id)
             node_output_port = node.out_port(0)
             if node.has_valid('alpha') and not math.isclose(node.alpha, 1):
-                mul_alpha = create_op_with_const_inputs(graph, Mul, {1: np.array(node.alpha)},
+                mul_alpha = create_op_with_const_inputs(graph, Mul, {1: mo_array(node.alpha)},
                                                        {'name': name + '/Alpha', 'can_be_scaleshift': False})
                 node_output_port.get_connection().insert_node(mul_alpha)
                 node_output_port = mul_alpha.out_port(0)
@@ -93,7 +92,7 @@ class GemmDecomposer(FrontReplacementSubgraph):
                 node.in_port(2).get_connection().set_destination(bias_node.in_port(1))
                 node_output_port.connect(bias_node.in_port(0))
                 if node.has_valid('beta') and not math.isclose(node.beta, 1):
-                    bias_node.insert_op_on_input_port(in_port_idx=1, new_op_class=Mul, value=np.array(node.beta),
+                    bias_node.insert_op_on_input_port(in_port_idx=1, new_op_class=Mul, value=mo_array(node.beta),
                                                       new_op_attrs={'name': name + '/Beta',
                                                                     'can_be_scaleshift': False})
                     del node['beta']
diff --git a/tools/mo/openvino/tools/mo/front/PowerToEltwises.py b/tools/mo/openvino/tools/mo/front/PowerToEltwises.py
index 0b29183ae0c..298e8e9b03b 100644
--- a/tools/mo/openvino/tools/mo/front/PowerToEltwises.py
+++ b/tools/mo/openvino/tools/mo/front/PowerToEltwises.py
@@ -1,9 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.elementwise import Mul, Add, Pow
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.const import Const
@@ -19,21 +18,21 @@ class PowerToEltwises(FrontReplacementOp):
         out_port = op.in_port(0).get_source()
 
         if op.soft_get('scale', 1) != 1:
-            const = Const(graph, {'value': np.array(op.scale)}).create_node()
+            const = Const(graph, {'value': mo_array(op.scale)}).create_node()
             mul = Mul(graph, {'name': op.name + '/mul_'}).create_node()
             const.out_port(0).connect(mul.in_port(1))
             out_port.connect(mul.in_port(0))
             out_port = mul.out_port(0)
 
         if op.soft_get('shift', 0) != 0:
-            const = Const(graph, {'value': np.array(op.shift)}).create_node()
+            const = Const(graph, {'value': mo_array(op.shift)}).create_node()
             add = Add(graph, {'name': op.name + '/add_'}).create_node()
             const.out_port(0).connect(add.in_port(1))
             out_port.connect(add.in_port(0))
             out_port = add.out_port(0)
 
         if op.soft_get('power', 1) != 1:
-            const = Const(graph, {'value': np.array(op.power)}).create_node()
+            const = Const(graph, {'value': mo_array(op.power)}).create_node()
             pow = Pow(graph, {'name': op.name + '/pow_'}).create_node()
             const.out_port(0).connect(pow.in_port(1))
             out_port.connect(pow.in_port(0))
diff --git a/tools/mo/openvino/tools/mo/front/binary_quantize_normalization.py b/tools/mo/openvino/tools/mo/front/binary_quantize_normalization.py
index 08d9e0a0da2..f82db77defc 100644
--- a/tools/mo/openvino/tools/mo/front/binary_quantize_normalization.py
+++ b/tools/mo/openvino/tools/mo/front/binary_quantize_normalization.py
@@ -1,9 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.elementwise import Add, Mul
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.const import Const
@@ -33,7 +32,7 @@ class BinaryFakeQuantizeNormalization(FrontReplacementPattern):
         quantize = match['quantize']
 
         sum_node = Add(graph, dict()).create_node()
-        const = Const(graph, {'value': np.array(0.5)}).create_node()
+        const = Const(graph, {'value': mo_array(0.5)}).create_node()
         mul_node = Mul(graph, dict()).create_node()
 
         mul_node.in_port(0).connect(sum_node.out_port(0))
diff --git a/tools/mo/openvino/tools/mo/front/broadcast_with_range.py b/tools/mo/openvino/tools/mo/front/broadcast_with_range.py
index 6c367fd3cca..422c7a3e6d3 100644
--- a/tools/mo/openvino/tools/mo/front/broadcast_with_range.py
+++ b/tools/mo/openvino/tools/mo/front/broadcast_with_range.py
@@ -8,6 +8,7 @@ from openvino.tools.mo.ops.gather import Gather
 from openvino.tools.mo.ops.range import Range
 from openvino.tools.mo.ops.select import Select
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs, create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph, rename_nodes, Node
@@ -72,8 +73,8 @@ class ExpandRangeConstant(FrontReplacementSubgraph):
         const.out_port(0).connect(shapeof_node.in_port(0))
 
         range_node = create_op_with_const_inputs(graph, Range,
-                                                 {0: np.array(0, dtype=value.dtype),
-                                                  2: np.array(1, dtype=value.dtype)},
+                                                 {0: mo_array(0, dtype=value.dtype),
+                                                  2: mo_array(1, dtype=value.dtype)},
                                                  {'name': const_name + '/Range', 'dtype': value.dtype})
         select_node.out_port(0).connect(range_node.in_port(1))
 
diff --git a/tools/mo/openvino/tools/mo/front/caffe/batchnorm_ext.py b/tools/mo/openvino/tools/mo/front/caffe/batchnorm_ext.py
index 288949144aa..7dbfef451cc 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/batchnorm_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/batchnorm_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.BatchNormInference import BatchNormInference
 from openvino.tools.mo.front.caffe.extractors.utils import embed_input
 from openvino.tools.mo.front.extractor import FrontExtractorOp
@@ -22,8 +23,8 @@ class BatchNormalizationExtractor(FrontExtractorOp):
         if pb_model:
             blobs = pb_model.blobs
             assert len(blobs) >= 2, 'BatchNorm accepts not less then two input blobs'
-            mean = np.array(blobs[0].data)
-            variance = np.array(blobs[1].data)
+            mean = mo_array(blobs[0].data)
+            variance = mo_array(blobs[1].data)
 
             if len(blobs) == 3:
                 scale = blobs[2].data[0]
diff --git a/tools/mo/openvino/tools/mo/front/caffe/binarization.py b/tools/mo/openvino/tools/mo/front/caffe/binarization.py
index 78821c0abf9..9202e5dab33 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/binarization.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/binarization.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.ops.fakequantize import FakeQuantize
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.graph.graph import Node, Graph
@@ -19,7 +18,7 @@ class BinarizationToQuantize(FrontReplacementOp):
     def replace_op(self, graph: Graph, node: Node):
         in_node_0 = node.in_node(0)
 
-        broadcast = lambda x: np.array([x], dtype=np.float32)
+        broadcast = lambda x: float32_array([x])
         threshold = Const(graph, {'name': node.id + "/Input_1", "value": broadcast(0)}).create_node()
         in_1 = threshold
         in_2 = threshold
diff --git a/tools/mo/openvino/tools/mo/front/caffe/bn.py b/tools/mo/openvino/tools/mo/front/caffe/bn.py
index 80611fa5a6d..c57ddc268bd 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/bn.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/bn.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.caffe.extractors.utils import input_as_const
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.scale_shift import ScaleShiftOp
@@ -28,10 +29,10 @@ class BNToScaleShift(FrontReplacementOp):
         if len(blobs) != 4:
             raise Error("Incorrect number of blobs in BN layer {}".format(node.id))
 
-        mean = np.array(blobs[0].data)
-        var = np.array(blobs[1].data)
-        betta = np.array(blobs[2].data)
-        gamma = np.array(blobs[3].data)
+        mean = mo_array(blobs[0].data)
+        var = mo_array(blobs[1].data)
+        betta = mo_array(blobs[2].data)
+        gamma = mo_array(blobs[3].data)
 
         gamma = gamma + np.repeat(param.eps, gamma.shape)
 
diff --git a/tools/mo/openvino/tools/mo/front/caffe/conv_ext.py b/tools/mo/openvino/tools/mo/front/caffe/conv_ext.py
index 7cd70fbe831..553dc8a863f 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/conv_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/conv_ext.py
@@ -1,10 +1,9 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.front.caffe.extractors.utils import get_spatial_attr, get_list_from_container, weights_biases
 from openvino.tools.mo.front.common.extractors.utils import layout_attrs
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.ops.convolution import Convolution
 from openvino.tools.mo.utils.error import Error
@@ -97,18 +96,18 @@ def conv_create_attrs(params):
     return {
         'bias_addable': True,
         'bias_term': params['bias_term'],
-        'pad': np.array([[0, 0], [0, 0], [params['padding'][1], params['padding'][1]],
-                         [params['padding'][0], params['padding'][0]]], dtype=np.int64),
-        'pad_spatial_shape': np.array([[params['padding'][1], params['padding'][1]],
-                                       [params['padding'][0], params['padding'][0]]], dtype=np.int64),
-        'dilation': np.array([1, 1, params['dilate'][1], params['dilate'][0]], dtype=np.int64),
+        'pad': int64_array([[0, 0], [0, 0], [params['padding'][1], params['padding'][1]],
+                         [params['padding'][0], params['padding'][0]]]),
+        'pad_spatial_shape': int64_array([[params['padding'][1], params['padding'][1]],
+                                       [params['padding'][0], params['padding'][0]]]),
+        'dilation': int64_array([1, 1, params['dilate'][1], params['dilate'][0]]),
         'output_spatial_shape': None,
         'output_shape': None,
-        'stride': np.array([1, 1, params['stride'][1], params['stride'][0]], dtype=np.int64),
+        'stride': int64_array([1, 1, params['stride'][1], params['stride'][0]]),
         'group': params['group'],
         'output': params['output'],
-        'kernel_spatial': np.array([params['kernel'][1], params['kernel'][0]], dtype=np.int64),
-        'kernel_spatial_idx': np.array([2, 3], dtype=np.int64),
+        'kernel_spatial': int64_array([params['kernel'][1], params['kernel'][0]]),
+        'kernel_spatial_idx': int64_array([2, 3]),
         'reshape_kernel': True,
 
         'input_feature_channel': 1,
diff --git a/tools/mo/openvino/tools/mo/front/caffe/elementwise_ext.py b/tools/mo/openvino/tools/mo/front/caffe/elementwise_ext.py
index 529fcf2821a..fc079a8be06 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/elementwise_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/elementwise_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.elementwise import Add, Mul, Maximum
 from openvino.tools.mo.front.caffe.collect_attributes import merge_attrs
 from openvino.tools.mo.front.caffe.extractors.utils import embed_input
@@ -53,7 +52,7 @@ class EltwiseExtractor(FrontExtractorOp):
 
         lin_op_class = eltwise_caffe_map[operation]
 
-        mapping_rule = merge_attrs(param, {'coeff': np.array(param.coeff)})
+        mapping_rule = merge_attrs(param, {'coeff': mo_array(param.coeff)})
         mapping_rule.update(layout_attrs())
 
         assert len(param.coeff) <= input_len
diff --git a/tools/mo/openvino/tools/mo/front/caffe/eltwise_add_normalize.py b/tools/mo/openvino/tools/mo/front/caffe/eltwise_add_normalize.py
index ba04f019d17..36c1e4102d9 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/eltwise_add_normalize.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/eltwise_add_normalize.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.eltwise_n import EltwiseNReplacement
 from openvino.tools.mo.ops.elementwise import Mul
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
@@ -25,7 +24,7 @@ class EltwiseAddNormalize(FrontReplacementPattern):
     def __insert_mul_node_with_coeff(node: Node, port: int, coeff: float):
         if coeff != 1:
             mul_node = Mul(node.graph, {'name': node.id + '/coeff_mul'}).create_node()
-            const_node = Const(node.graph, {'name': node.id + '/coeff', 'value': np.array([coeff])}).create_node()
+            const_node = Const(node.graph, {'name': node.id + '/coeff', 'value': mo_array([coeff])}).create_node()
             node.in_port(port).get_connection().insert_node(mul_node)
             const_node.out_port(0).connect(mul_node.in_port(1))
 
diff --git a/tools/mo/openvino/tools/mo/front/caffe/extractor.py b/tools/mo/openvino/tools/mo/front/caffe/extractor.py
index 7afc53f3b21..5bb71a31dd5 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/extractor.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/extractor.py
@@ -2,7 +2,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from openvino.tools.mo.front.caffe.extractors.native_caffe import native_caffe_node_extractor
-from openvino.tools.mo.front.common.partial_infer.elemental import copy_shape_infer
 from openvino.tools.mo.front.common.register_custom_ops import extension_op_extractor
 from openvino.tools.mo.front.extractor import CaffePythonFrontExtractorOp
 from openvino.tools.mo.graph.graph import Node
diff --git a/tools/mo/openvino/tools/mo/front/caffe/extractors/utils.py b/tools/mo/openvino/tools/mo/front/caffe/extractors/utils.py
index 925dc6cc473..71727137a6b 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/extractors/utils.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/extractors/utils.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Node
 from openvino.tools.mo.ops.const import Const
 
@@ -16,7 +17,7 @@ def dim_to_shape(dim):
     Returns:
         shape of the layer as np.array
     """
-    return np.array(dim, dtype=np.int64)
+    return int64_array(dim)
 
 
 def embed_input(attrs: dict, port: int, name: str, value: np.array, bin_name: str = None):
@@ -39,7 +40,9 @@ def embed_input(attrs: dict, port: int, name: str, value: np.array, bin_name: st
 
     # memory safe value conversion to numpy;
     # previously we used `np.array(value)` and it was greedy for memory on caffe models especially
-    val = np.ndarray(shape=(len(value),))
+    # previously we always created float64 np.ndarray, now we force float32, we can't get data type from "value" for
+    # Caffe, because it comes as float64 from protobuf
+    val = np.ndarray(shape=(len(value),), dtype=np.float32)
     for i, item in enumerate(value):
         val[i] = item
     attrs[name] = val
diff --git a/tools/mo/openvino/tools/mo/front/caffe/loader.py b/tools/mo/openvino/tools/mo/front/caffe/loader.py
index d34c15fc9eb..f5ce3b25a1e 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/loader.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/loader.py
@@ -3,14 +3,15 @@
 
 import importlib
 import logging as log
+import mmap
 import os
 import sys
 
-import mmap
 import numpy as np
 from google.protobuf import text_format
 from google.protobuf.internal import api_implementation
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
 from openvino.tools.mo.front.extractor import add_outputs_identity
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.utils.error import Error, FrameworkError
@@ -37,7 +38,7 @@ def parse_mean(file_path: str, in_shape: np.ndarray, mean_file_offsets: [tuple,
 
     try:
         blob.ParseFromString(data)
-        data = np.array(blob.data)  # pylint: disable=no-member
+        data = mo_array(blob.data)  # pylint: disable=no-member
 
         if blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'):
             data = data.reshape(blob.channels, blob.height, blob.width)  # pylint: disable=no-member
@@ -203,7 +204,7 @@ def caffe_pb_to_nx(graph, proto, model):
         # input_dim: 3
         # input_dim: 500
         # input_dim: 500
-        input_dims = [np.array(list(proto.input_dim), dtype=np.int64)]
+        input_dims = [int64_array(list(proto.input_dim))]
         input_names = [proto.input[0]]
 
     elif len(list(proto.input)) == 1 and len(list(proto.input_shape)):
@@ -216,7 +217,7 @@ def caffe_pb_to_nx(graph, proto, model):
         #     dim: 227
         #     dim: 227
         # }
-        input_dims = [np.array(proto.input_shape[0].dim, dtype=np.int64)]
+        input_dims = [int64_array(proto.input_shape[0].dim)]
         input_names = [proto.input[0]]
 
     elif len(proto.input_shape) > 0:
@@ -236,7 +237,7 @@ def caffe_pb_to_nx(graph, proto, model):
         #     dim: 3
         # }
         for i in range(len(proto.input_shape)):
-            input_dims.append(np.array(proto.input_shape[i].dim, dtype=np.int64))
+            input_dims.append(int64_array(proto.input_shape[i].dim))
             input_names.append(proto.input[i])
 
     for i in range(len(input_names)):
@@ -283,7 +284,7 @@ def caffe_pb_to_nx(graph, proto, model):
                 }
                 """
                 dims = map(int, list(filter(None, str(list(input_param.shape)[0]).split('dim:'))))
-                input_dims.append(np.array(list(dims), dtype=np.int64))
+                input_dims.append(int64_array(list(dims)))
                 input_names.append(layer.name)
 
         node_id = graph.unique_id(layer.name)
diff --git a/tools/mo/openvino/tools/mo/front/caffe/permute_ext.py b/tools/mo/openvino/tools/mo/front/caffe/permute_ext.py
index 984a81d8341..1547c9894a9 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/permute_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/permute_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.transpose import Transpose
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 
@@ -14,5 +15,5 @@ class PermuteFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         order = node.pb.permute_param.order
-        Transpose.update_node_stat(node, {'order': np.array(order, dtype=np.int32)})
+        Transpose.update_node_stat(node, {'order': mo_array(order, dtype=np.int32)})
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/caffe/pooling_ext.py b/tools/mo/openvino/tools/mo/front/caffe/pooling_ext.py
index 96d0e0be9f0..ef71a14b453 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/pooling_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/pooling_ext.py
@@ -1,10 +1,9 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.front.caffe.extractors.utils import get_spatial_attr
 from openvino.tools.mo.front.common.extractors.utils import layout_attrs
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.ops.pooling import Pooling
 
@@ -50,10 +49,10 @@ class PoolingFrontExtractor(FrontExtractorOp):
             rt = 'floor'
 
         attrs = {
-            'window': np.array([1, 1, kernel[1], kernel[0]], dtype=np.int64),
-            'stride': np.array([1, 1, stride[1], stride[0]], dtype=np.int64),
-            'pad': np.array([[0, 0], [0, 0], [padding[1], padding[1]], [padding[0], padding[0]]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[padding[1], padding[1]], [padding[0], padding[0]]], dtype=np.int64),
+            'window': int64_array([1, 1, kernel[1], kernel[0]]),
+            'stride': int64_array([1, 1, stride[1], stride[0]]),
+            'pad': int64_array([[0, 0], [0, 0], [padding[1], padding[1]], [padding[0], padding[0]]]),
+            'pad_spatial_shape': int64_array([[padding[1], padding[1]], [padding[0], padding[0]]]),
             'pool_method': method,
             'exclude_pad': exclude_pad,
             'global_pool': global_pooling,
diff --git a/tools/mo/openvino/tools/mo/front/caffe/priorbox_ext.py b/tools/mo/openvino/tools/mo/front/caffe/priorbox_ext.py
index e42333e270e..623520a8911 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/priorbox_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/priorbox_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.priorbox import PriorBoxOp
 from openvino.tools.mo.front.caffe.collect_attributes import merge_attrs
 from openvino.tools.mo.front.common.extractors.utils import layout_attrs
@@ -23,9 +22,9 @@ class PriorBoxFrontExtractor(FrontExtractorOp):
             variance = [0.1]
 
         update_attrs = {
-            'aspect_ratio': np.array(param.aspect_ratio),
-            'min_size': np.array(param.min_size),
-            'max_size': np.array(param.max_size),
+            'aspect_ratio': mo_array(param.aspect_ratio),
+            'min_size': mo_array(param.min_size),
+            'max_size': mo_array(param.max_size),
             'flip': int(param.flip),
             'clip': int(param.clip),
             'variance': list(variance),
@@ -42,11 +41,11 @@ class PriorBoxFrontExtractor(FrontExtractorOp):
         # so check if it is set or set to default
         fields = [field[0].name for field in param.ListFields()]
         if 'density' in fields:
-            update_attrs['density'] = np.array(param.density)
+            update_attrs['density'] = mo_array(param.density)
         if 'fixed_size' in fields:
-            update_attrs['fixed_size'] = np.array(param.fixed_size)
+            update_attrs['fixed_size'] = mo_array(param.fixed_size)
         if 'fixed_ratio' in fields:
-            update_attrs['fixed_ratio'] = np.array(param.fixed_ratio)
+            update_attrs['fixed_ratio'] = mo_array(param.fixed_ratio)
 
         mapping_rule = merge_attrs(param, update_attrs)
 
diff --git a/tools/mo/openvino/tools/mo/front/caffe/proposal_ext.py b/tools/mo/openvino/tools/mo/front/caffe/proposal_ext.py
index 7fe7e9db0ad..50fd56d086f 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/proposal_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/proposal_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.proposal import ProposalOp
 from openvino.tools.mo.front.caffe.collect_attributes import merge_attrs
 from openvino.tools.mo.front.extractor import FrontExtractorOp
@@ -20,8 +19,8 @@ class ProposalFrontExtractor(FrontExtractorOp):
             'feat_stride': param.feat_stride,
             'base_size': param.base_size,
             'min_size': param.min_size,
-            'ratio': np.array(param.ratio),
-            'scale': np.array(param.scale),
+            'ratio': mo_array(param.ratio),
+            'scale': mo_array(param.scale),
             'pre_nms_topn': param.pre_nms_topn,
             'post_nms_topn': param.post_nms_topn,
             'nms_thresh': param.nms_thresh
diff --git a/tools/mo/openvino/tools/mo/front/caffe/regionyolo_ext.py b/tools/mo/openvino/tools/mo/front/caffe/regionyolo_ext.py
index 565492b4f9f..22ba3f91f22 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/regionyolo_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/regionyolo_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.regionyolo import RegionYoloOp
 from openvino.tools.mo.front.caffe.collect_attributes import merge_attrs
 from openvino.tools.mo.front.common.extractors.utils import layout_attrs
@@ -28,8 +27,8 @@ class RegionYoloFrontExtractor(FrontExtractorOp):
             'classes': classes,
             'num': num,
             'do_softmax': int(param.do_softmax),
-            'anchors': np.array(param.anchors),
-            'mask': np.array(param.mask)
+            'anchors': mo_array(param.anchors),
+            'mask': mo_array(param.mask)
         }
 
         flatten_attrs = {
diff --git a/tools/mo/openvino/tools/mo/front/caffe/scale_ext.py b/tools/mo/openvino/tools/mo/front/caffe/scale_ext.py
index f6a4cbbc43c..1c95290b1f1 100644
--- a/tools/mo/openvino/tools/mo/front/caffe/scale_ext.py
+++ b/tools/mo/openvino/tools/mo/front/caffe/scale_ext.py
@@ -1,10 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.front.caffe.extractors.utils import embed_input, weights_biases
-from openvino.tools.mo.front.common.partial_infer.elemental import copy_shape_infer
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.ops.scale_shift import ScaleShiftOp
 from openvino.tools.mo.utils.utils import NamedAttrsClass
@@ -25,8 +23,8 @@ class ScaleFrontExtractor(FrontExtractorOp):
         
         if model is None and len(pb.bottom) == 1:
             # default weights and biases for scale layer if the caffemodel file doesn't contain them
-            model = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([1])}),
-                                                 NamedAttrsClass({'data': np.array([0])})])})
+            model = NamedAttrsClass({'blobs': mo_array([NamedAttrsClass({'data': mo_array([1])}),
+                                                 NamedAttrsClass({'data': mo_array([0])})])})
         # scale with 1 input and 1 or 2 blobs
         if model and len(model.blobs) != 0 and len(pb.bottom) == 1:
             attrs.update(weights_biases(param.bias_term, model))
@@ -34,7 +32,7 @@ class ScaleFrontExtractor(FrontExtractorOp):
         elif len(pb.bottom) == 2 and param.bias_term:
             if model is None or len(model.blobs) == 0:
                 # default bias for scale layer with 2 inputs if the caffemodel file doesn't contain them
-                model = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([0])})])})
+                model = NamedAttrsClass({'blobs': mo_array([NamedAttrsClass({'data': mo_array([0])})])})
 
             embed_input(attrs, 1, 'biases', model.blobs[0].data)
         ScaleShiftOp.update_node_stat(node, attrs)
diff --git a/tools/mo/openvino/tools/mo/front/common/extractors/utils.py b/tools/mo/openvino/tools/mo/front/common/extractors/utils.py
index b03dab621e1..3dfd42342f3 100644
--- a/tools/mo/openvino/tools/mo/front/common/extractors/utils.py
+++ b/tools/mo/openvino/tools/mo/front/common/extractors/utils.py
@@ -1,13 +1,13 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 
 
 def layout_attrs():
     return {
-        'spatial_dims': np.array([2, 3], dtype=np.int64),
-        'channel_dims': np.array([1], dtype=np.int64),
-        'batch_dims': np.array([0], dtype=np.int64),
+        'spatial_dims': int64_array([2, 3]),
+        'channel_dims': int64_array([1]),
+        'batch_dims': int64_array([0]),
         'layout': 'NCHW'
     }
diff --git a/tools/mo/openvino/tools/mo/front/common/layout.py b/tools/mo/openvino/tools/mo/front/common/layout.py
index f2cf32c5457..a6a1e37c1d8 100644
--- a/tools/mo/openvino/tools/mo/front/common/layout.py
+++ b/tools/mo/openvino/tools/mo/front/common/layout.py
@@ -4,10 +4,11 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import dynamic_dimension_value
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
 from openvino.tools.mo.utils.error import Error
 
-nchw_to_nhwc_permute = np.array([0, 2, 3, 1], dtype=np.int64)
-nhwc_to_nchw_permute = np.array([0, 3, 1, 2], dtype=np.int64)
+nchw_to_nhwc_permute = int64_array([0, 2, 3, 1])
+nhwc_to_nchw_permute = int64_array([0, 3, 1, 2])
 supported_layouts = ('NCHW', 'NHWC')
 # the attribute 'layout' in the graph.graph can have two values only: "NCHW" or "NHWC". If the tensor has 5 dimensions
 # then it is necessary to transform "NCHW" to "NCDHW" and "NHWC" to "NDHWC" respectively. The dictionary below id used
@@ -22,7 +23,7 @@ def convert_shape(shape: np.array, permute: np.array):
     result = [0, 0, 0, 0]
     for ind, perm_ind in enumerate(permute):
         result[ind] = shape[perm_ind]
-    return np.array(result)
+    return mo_array(result)
 
 
 def get_depth_dim(layout: str, shape_len: int):
diff --git a/tools/mo/openvino/tools/mo/front/common/partial_infer/caffe_fallback.py b/tools/mo/openvino/tools/mo/front/common/partial_infer/caffe_fallback.py
index 7cb2acf0db1..c559925f9f4 100644
--- a/tools/mo/openvino/tools/mo/front/common/partial_infer/caffe_fallback.py
+++ b/tools/mo/openvino/tools/mo/front/common/partial_infer/caffe_fallback.py
@@ -6,6 +6,7 @@ import os
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.utils.error import Error
 from openvino.tools.mo.utils.find_inputs import find_inputs
@@ -104,5 +105,5 @@ def caffe_native_node_infer(node: Node):
         )
 
     for iout in range(len(node.out_nodes())):
-        output_shape = np.array(net.blobs[node.top].data.shape, dtype=np.int64)
+        output_shape = int64_array(net.blobs[node.top].data.shape)
         node.out_node(iout).shape = output_shape
diff --git a/tools/mo/openvino/tools/mo/front/common/partial_infer/crop.py b/tools/mo/openvino/tools/mo/front/common/partial_infer/crop.py
index 2675c34e3ef..cb554ab92fc 100644
--- a/tools/mo/openvino/tools/mo/front/common/partial_infer/crop.py
+++ b/tools/mo/openvino/tools/mo/front/common/partial_infer/crop.py
@@ -3,9 +3,8 @@
 
 import logging as log
 
-import numpy as np
-
 from openvino.tools.mo.front.caffe.extractors.utils import get_canonical_axis_index
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 
 
 def crop_infer(node):
@@ -31,11 +30,11 @@ def crop_infer(node):
     if any(s is None for s in shapes):
         return
 
-    input_shape = np.array(shapes[0])
+    input_shape = mo_array(shapes[0])
     start_axis = get_canonical_axis_index(input_shape, node.axis)
     node.axis = start_axis
 
-    reference_shape = np.array(shapes[1])
+    reference_shape = mo_array(shapes[1])
     input_dim = input_shape.size
 
     # set new shape to current shape
diff --git a/tools/mo/openvino/tools/mo/front/common/partial_infer/multi_box_prior.py b/tools/mo/openvino/tools/mo/front/common/partial_infer/multi_box_prior.py
index 8008d2b0c98..5d05bc8414d 100644
--- a/tools/mo/openvino/tools/mo/front/common/partial_infer/multi_box_prior.py
+++ b/tools/mo/openvino/tools/mo/front/common/partial_infer/multi_box_prior.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Node
 
 
@@ -13,6 +12,6 @@ def multi_box_prior_infer_mxnet(node: Node):
     num_ratios = len(node.aspect_ratio)
     num_priors = len(node.min_size) + num_ratios - 1
     if v10:
-        node.out_node(0).shape = np.array([2, data_H * data_W * num_priors * 4], dtype=np.int64)
+        node.out_node(0).shape = int64_array([2, data_H * data_W * num_priors * 4])
     else:
-        node.out_node(0).shape = np.array([1, 2, data_H * data_W * num_priors * 4], dtype=np.int64)
+        node.out_node(0).shape = int64_array([1, 2, data_H * data_W * num_priors * 4])
diff --git a/tools/mo/openvino/tools/mo/front/common/partial_infer/utils.py b/tools/mo/openvino/tools/mo/front/common/partial_infer/utils.py
index 207c1b07006..7d648dcef30 100644
--- a/tools/mo/openvino/tools/mo/front/common/partial_infer/utils.py
+++ b/tools/mo/openvino/tools/mo/front/common/partial_infer/utils.py
@@ -179,14 +179,25 @@ def int64_array(value: Union[Iterable[Union[float, int]], float, int]) -> np.nda
     return np.array(value, dtype=np.int64)
 
 
-def float_array(value: Union[Iterable[Union[float, int]], float, int]) -> np.ndarray:
-    return np.array(value, dtype=np.float64)
-
-
 def float32_array(value: Union[Iterable[Union[float, int]], float, int]) -> np.ndarray:
     return np.array(value, dtype=np.float32)
 
 
+def float_array(value: Union[Iterable[Union[float, int]], float, int]) -> np.ndarray:
+    return float32_array(value)
+
+
+def mo_array(value: Union[Iterable[Union[float, int]], float, int], dtype=None) -> np.ndarray:
+    """
+    This function acts in a same way as np.array except for the case when dtype is not provided
+    and np.array return fp64 array this function returns fp32 array
+    """
+    x = np.array(value, dtype=dtype)
+    if not isinstance(value, np.ndarray) and x.dtype == np.float64 and dtype != np.float64:
+        x = x.astype(np.float32)
+    return x
+
+
 def mark_input_bins(node, names=('weights', 'biases'), start_port: int = 1):
     """
     Preparing necessary attributes for edges at input ports starting from start_port.
@@ -200,9 +211,9 @@ def mark_input_bins(node, names=('weights', 'biases'), start_port: int = 1):
 
 def assign_dims_to_weights(node, spatial, input_channel, output_channel=None, dims_number=None):
     if spatial is not None:
-        node['spatial_dims'] = np.array(spatial, dtype=np.int64)
-    node['input_channel_dim'] = np.array(input_channel, dtype=np.int64)
-    node['output_channel_dim'] = np.array(output_channel, dtype=np.int64)
+        node['spatial_dims'] = int64_array(spatial)
+    node['input_channel_dim'] = int64_array(input_channel)
+    node['output_channel_dim'] = int64_array(output_channel)
     if 'dim_attrs' in node and 'input_channel_dim' not in node['dim_attrs']:
         node['dim_attrs'].append('input_channel_dim')
     node['dims_number'] = dims_number
diff --git a/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py b/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py
index 9d03bb7c7e0..9f9beb545d8 100644
--- a/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py
+++ b/tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py
@@ -5,6 +5,7 @@ import logging as log
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.passes.convert_data_type import SUPPORTED_DATA_TYPES
@@ -46,14 +47,14 @@ class FreezePlaceholderValue(FrontReplacementSubgraph):
             string_value = graph.graph['freeze_placeholder'][name]
             try:
                 if data_type != np.bool:
-                    value = np.array(string_value, dtype=data_type)
+                    value = mo_array(string_value, dtype=data_type)
                 elif data_type == np.bool and graph.graph['fw'] == 'tf':
                     from openvino.tools.mo.front.tf.common import tf_data_type_cast
                     if isinstance(string_value, list):
                         casted_list = list()
-                        for v in np.array(string_value):
+                        for v in mo_array(string_value):
                             casted_list.append(tf_data_type_cast[ph.data_type](v))
-                        value = np.array(string_value, dtype=data_type)
+                        value = mo_array(string_value, dtype=data_type)
                     else:
                         value = tf_data_type_cast[ph.data_type](string_value)
                 else:
diff --git a/tools/mo/openvino/tools/mo/front/interpolate_reshape.py b/tools/mo/openvino/tools/mo/front/interpolate_reshape.py
index 6401bc1fd4a..e18e4dafafe 100644
--- a/tools/mo/openvino/tools/mo/front/interpolate_reshape.py
+++ b/tools/mo/openvino/tools/mo/front/interpolate_reshape.py
@@ -6,6 +6,7 @@ import numpy as np
 from openvino.tools.mo.ops.gather import Gather
 from openvino.tools.mo.ops.interpolate import Interpolate
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node
@@ -150,7 +151,7 @@ class InterpolateWithConcat(FrontReplacementPattern):
         shape = Shape(graph, {'name': src.node.soft_get('name', src.node.id) + '/Shape'}).create_node()
         shape.in_port(0).connect(src)
         gather = create_op_with_const_inputs(graph, Gather,
-                                             {1: np.array(interp_axes, dtype=np.int32), 2: int64_array(0)},
+                                             {1: mo_array(interp_axes, dtype=np.int32), 2: int64_array(0)},
                                              {'name': shape.name + '/Gathered'}, input_node=shape)
         interpolate.in_port(1).get_connection().set_source(gather.out_port(0))
 
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/extractors/add_shift_ext.py b/tools/mo/openvino/tools/mo/front/kaldi/extractors/add_shift_ext.py
index 380d2bc97de..8ef94a608cd 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/extractors/add_shift_ext.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/extractors/add_shift_ext.py
@@ -20,7 +20,7 @@ class AddShiftFrontExtractor(FrontExtractorOp):
         biases = read_binary_vector(pb)
         bias_term = True
         mapping_rule = {'bias_term': bias_term}
-        embed_input(mapping_rule, 1, 'weights', np.ones(biases.shape))
+        embed_input(mapping_rule, 1, 'weights', np.ones(biases.shape, dtype=np.float32))
         embed_input(mapping_rule, 2, 'biases', biases)
         ScaleShiftOp.update_node_stat(node, mapping_rule)
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_1d_component_ext.py b/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_1d_component_ext.py
index 31361882682..da12cc55cf5 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_1d_component_ext.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_1d_component_ext.py
@@ -1,10 +1,9 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.front.caffe.extractors.utils import embed_input
 from openvino.tools.mo.front.common.extractors.utils import layout_attrs
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.kaldi.loader.utils import read_token_value, collect_until_whitespace, find_next_tag
 from openvino.tools.mo.front.kaldi.utils import read_learning_info, read_binary_matrix, read_binary_vector
@@ -59,12 +58,12 @@ class ConvolutionalComponentFrontExtractor(FrontExtractorOp):
             'output': output,
             'patch_stride': patch_stride,
             'bias_term': None,
-            'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64),
-            'dilation': np.array([1, 1, 1, 1], dtype=np.int64),
-            'kernel': np.array([1, 1, 1, kernel], dtype=np.int64),
-            'stride': np.array([1, 1, 1, stride], dtype=np.int64),
-            'kernel_spatial': np.array([1, kernel], dtype=np.int64),
+            'pad': int64_array([[0, 0], [0, 0], [0, 0], [0, 0]]),
+            'pad_spatial_shape': int64_array([[0, 0], [0, 0]]),
+            'dilation': int64_array([1, 1, 1, 1]),
+            'kernel': int64_array([1, 1, 1, kernel]),
+            'stride': int64_array([1, 1, 1, stride]),
+            'kernel_spatial': int64_array([1, kernel]),
             'input_feature_channel': 1,
             'output_feature_channel': 0,
             'kernel_spatial_idx': [2, 3],
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_component_ext.py b/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_component_ext.py
index 95476551d35..d839389fd2a 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_component_ext.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/extractors/convolutional_component_ext.py
@@ -5,6 +5,7 @@ import numpy as np
 
 from openvino.tools.mo.front.caffe.extractors.utils import embed_input
 from openvino.tools.mo.front.common.extractors.utils import layout_attrs
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.kaldi.loader.utils import read_token_value, collect_until_whitespace
 from openvino.tools.mo.front.kaldi.utils import read_learning_info, read_binary_matrix, read_binary_vector
@@ -53,12 +54,12 @@ class ConvolutionalComponentFrontExtractor(FrontExtractorOp):
             'output': output,
             'patch_stride': patch_stride,
             'bias_term': None,
-            'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64),
-            'dilation': np.array([1, 1, 1, 1], dtype=np.int64),
-            'kernel': np.array([1, 1, 1, kernel], dtype=np.int64),
-            'stride': np.array([1, 1, 1, stride], dtype=np.int64),
-            'kernel_spatial': np.array([1, kernel], dtype=np.int64),
+            'pad': int64_array([[0, 0], [0, 0], [0, 0], [0, 0]]),
+            'pad_spatial_shape': int64_array([[0, 0], [0, 0]]),
+            'dilation': int64_array([1, 1, 1, 1]),
+            'kernel': int64_array([1, 1, 1, kernel]),
+            'stride': int64_array([1, 1, 1, stride]),
+            'kernel_spatial': int64_array([1, kernel]),
             'input_feature_channel': 1,
             'output_feature_channel': 0,
             'kernel_spatial_idx': [2, 3],
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/extractors/copy_ext.py b/tools/mo/openvino/tools/mo/front/kaldi/extractors/copy_ext.py
index 0a30d4657fd..653f58ae669 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/extractors/copy_ext.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/extractors/copy_ext.py
@@ -6,6 +6,7 @@ import numpy as np
 from openvino.tools.mo.ops.gather import Gather
 from openvino.tools.mo.ops.transpose import Transpose
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.front.kaldi.loader.utils import read_binary_integer32_token, read_blob
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
@@ -25,7 +26,7 @@ class CopyFrontExtractor(FrontReplacementOp):
         node_name = node.soft_get('name', node.id)
         const_attrs = {
                        'name': node_name + '/indexes',
-                       'value': np.array(weights),
+                       'value': mo_array(weights),
                        'shape': [weights_size],
                        'data_type': np.int32
                       }
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/extractors/max_pooling_ext.py b/tools/mo/openvino/tools/mo/front/kaldi/extractors/max_pooling_ext.py
index 8b0e31af237..16bc5957831 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/extractors/max_pooling_ext.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/extractors/max_pooling_ext.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.extractors.utils import layout_attrs
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.kaldi.loader.utils import read_token_value, collect_until_token, \
     read_binary_integer32_token, find_next_tag, read_placeholder
@@ -35,12 +36,12 @@ class MaxPoolingComponentFrontExtractor(FrontExtractorOp):
             raise Error('Can not extract parameters for {}'.format(node))
 
         mapping_rule = {
-            'window': np.array([1, 1, 1, kernel], dtype=np.int64),
-            'stride': np.array([1, 1, 1, stride], dtype=np.int64),
+            'window': int64_array([1, 1, 1, kernel]),
+            'stride': int64_array([1, 1, 1, stride]),
             'pool_stride': pool_stride,
             'pool_step': pool_step,
-            'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64),
+            'pad': int64_array([[0, 0], [0, 0], [0, 0], [0, 0]]),
+            'pad_spatial_shape': int64_array([[0, 0], [0, 0]]),
             'pool_method': 'max',
         }
         mapping_rule.update(layout_attrs())
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/extractors/tdnncomponent_ext.py b/tools/mo/openvino/tools/mo/front/kaldi/extractors/tdnncomponent_ext.py
index aa805a8b849..8e676d4202b 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/extractors/tdnncomponent_ext.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/extractors/tdnncomponent_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.kaldi.loader.utils import read_binary_bool_token, read_binary_integer32_token, collect_until_token, \
     read_binary_float_token
@@ -48,7 +49,7 @@ class TdnnComponentFrontExtractor(FrontExtractorOp):
         collect_until_token(pb, b'<RankInOut>')
         rank_in_out = read_binary_integer32_token(pb), read_binary_integer32_token(pb)
 
-        biases = np.array(bias_params) if len(bias_params) != 0 else None
+        biases = mo_array(bias_params) if len(bias_params) != 0 else None
         attrs = {
             'weights': np.reshape(weights, weights_shape),
             'biases': biases,
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/loader/loader.py b/tools/mo/openvino/tools/mo/front/kaldi/loader/loader.py
index e0fccc6fcd4..7ada65f5a9c 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/loader/loader.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/loader/loader.py
@@ -10,6 +10,7 @@ import numpy as np
 from openvino.tools.mo.ops.elementwise import Mul
 from openvino.tools.mo.ops.split import AttributedVariadicSplit
 from openvino.tools.mo.front.common.partial_infer.utils import float_array, int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import add_outputs_identity
 from openvino.tools.mo.front.kaldi.loader.utils import find_next_tag, read_placeholder, find_next_component, get_name_from_path, \
     find_end_of_component, end_of_nnet_tag, read_binary_integer32_token, get_parameters, read_token_value, \
@@ -157,7 +158,7 @@ def load_kalid_nnet1_model(graph, file_descr, name):
 
         prev_node = Node(graph, prev_layer_id)
         if prev_node.op == 'Parameter':
-            prev_node['shape'] = np.array([1, layer_i], dtype=np.int64)
+            prev_node['shape'] = int64_array([1, layer_i])
 
         prev_node.add_output_port(0)
         Node(graph, layer_id).add_input_port(0)
@@ -190,7 +191,7 @@ def load_kalid_nnet2_model(graph, file_descr, nnet_name):
         if prev_node.op == 'Parameter':
             parameters = Node(graph, layer_id).parameters
             input_dim = read_token_value(parameters, b'<InputDim>')
-            prev_node['shape'] = np.array([1, input_dim], dtype=np.int64)
+            prev_node['shape'] = int64_array([1, input_dim])
         prev_node.add_output_port(0)
         Node(graph, layer_id).add_input_port(0)
         graph.create_edge(prev_node, Node(graph, layer_id), 0, 0, create_edge_attrs(prev_layer_id, layer_id, prev_layer_id))
@@ -323,7 +324,7 @@ def read_node(file_descr, graph, component_layer_map, layer_node_map):
     if tokens[0] == b'input-node':
         in_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
         in_name = str(in_name).strip('b').replace('\'', "")
-        in_shape = np.array([1, s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0]], dtype=np.int)
+        in_shape = mo_array([1, s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0]], dtype=np.int)
 
         if in_name not in layer_node_map:
             graph.add_node(in_name, name=in_name, kind='op', op='Parameter', parameters=None, shape=in_shape)
@@ -399,12 +400,12 @@ def read_node(file_descr, graph, component_layer_map, layer_node_map):
         if layer_name in layer_node_map:
             node_name = layer_node_map[layer_name]
             node = Node(graph, node_name)
-            node['parameters'] = {'offset': np.array([offset]), 'dim': np.array([dim]), 'axis': np.array([1])}
+            node['parameters'] = {'offset': mo_array([offset]), 'dim': mo_array([dim]), 'axis': mo_array([1])}
             node['op'] = 'Crop'
         else:
             node_name = graph.unique_id(prefix=layer_name)
             graph.add_node(node_name,
-                           parameters={'offset': np.array([offset]), 'dim': np.array([dim]), 'axis': np.array([1])},
+                           parameters={'offset': mo_array([offset]), 'dim': mo_array([dim]), 'axis': mo_array([1])},
                            op='Crop',
                            kind='op')
             layer_node_map[layer_name] = node_name
diff --git a/tools/mo/openvino/tools/mo/front/kaldi/replace_lstm_node_pattern.py b/tools/mo/openvino/tools/mo/front/kaldi/replace_lstm_node_pattern.py
index 74867acb947..5d457c3b68e 100644
--- a/tools/mo/openvino/tools/mo/front/kaldi/replace_lstm_node_pattern.py
+++ b/tools/mo/openvino/tools/mo/front/kaldi/replace_lstm_node_pattern.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.middle.MakeKaldiConstReshapable import create_const_with_batch_from_input
 from openvino.tools.mo.ops.MatMul import FullyConnected
 from openvino.tools.mo.ops.activation_ops import Tanh, Sigmoid
@@ -188,8 +189,8 @@ class ReplaceLSTMNodePattern(FrontReplacementOp):
         join_forget_remember_sum.in_port(1).connect(join_remember_candidates_mul.out_port(0))
 
         # (7)Eltwise(sum) -> Clamp
-        join_forget_clamp = create_op_with_const_inputs(graph, Clamp, {1: np.array(-node.clip_value, dtype=np.float32),
-                                                                       2: np.array(node.clip_value, dtype=np.float32)},
+        join_forget_clamp = create_op_with_const_inputs(graph, Clamp, {1: float32_array(-node.clip_value),
+                                                                       2: float32_array(node.clip_value)},
                                                         {'name': 'join_forget_clamp'},
                                                         join_forget_remember_sum)
         #
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/conv_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/conv_ext.py
index 1cb43dfc1bf..3381ba88bc0 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/conv_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/conv_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
 from openvino.tools.mo.ops.convolution import Convolution
@@ -24,21 +25,21 @@ class ConvFrontExtractor(FrontExtractorOp):
         output = attr.int("num_filter", None)
         bias_term = not attr.bool("no_bias", False)
 
-        final_dilations = np.array([1, 1, *[d for d in dilate]], dtype=np.int64) if dilate is not None else None
+        final_dilations = int64_array([1, 1, *[d for d in dilate]]) if dilate is not None else None
 
         node_attrs = {
             'op': __class__.op,
             'bias_addable': True,
             'bias_term': bias_term,
-            'pad': np.array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[pad, pad] for pad in padding], dtype=np.int64),
+            'pad': int64_array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]]),
+            'pad_spatial_shape': int64_array([[pad, pad] for pad in padding]),
             'dilation': final_dilations,
             'output_spatial_shape': None,
             'output_shape': None,
-            'stride': np.array([1, 1, *[s for s in stride]], dtype=np.int64),
+            'stride': int64_array([1, 1, *[s for s in stride]]),
             'group': group,
             'output': output,
-            'kernel_spatial': np.array([k for k in kernel], dtype=np.int64),
+            'kernel_spatial': int64_array([k for k in kernel]),
 
             'input_feature_channel': 1,
             'output_feature_channel': 0,
@@ -46,8 +47,8 @@ class ConvFrontExtractor(FrontExtractorOp):
             'reshape_kernel': True,
 
             'spatial_dims': None,
-            'channel_dims': np.array([1], dtype=np.int64),
-            'batch_dims': np.array([0], dtype=np.int64),
+            'channel_dims': int64_array([1]),
+            'batch_dims': int64_array([0]),
             'layout': 'NCHW',
         }
 
@@ -65,9 +66,9 @@ class DeconvFrontExtractor(FrontExtractorOp):
         padding = np.add.reduce(node.pad, axis=1)
         padding[node.spatial_dims] = node.stride[node.spatial_dims] * (input_shape[node.spatial_dims] - 1) + 1 + \
                                      (kernel_shape[node.spatial_dims] - 1) * node.dilation[node.spatial_dims]
-        padding[node.spatial_dims] = padding[node.spatial_dims] - node.output_spatial_shape;
+        padding[node.spatial_dims] = padding[node.spatial_dims] - node.output_spatial_shape
         padding[node.spatial_dims] = (padding[node.spatial_dims] + 1) / 2
-        return np.array([[0, 0], [0, 0], *[[pad, pad] for pad in padding[2:]]], dtype=np.int64)
+        return int64_array([[0, 0], [0, 0], *[[pad, pad] for pad in padding[2:]]])
 
     @classmethod
     def extract(cls, node):
@@ -82,39 +83,39 @@ class DeconvFrontExtractor(FrontExtractorOp):
         bias_term = not attr.bool("no_bias", True)
         target_shape = attr.tuple("target_shape", int, None)
         if target_shape:
-            target_shape = np.array(target_shape, dtype=np.int64)
+            target_shape = int64_array(target_shape)
 
-        final_dilations = np.array([1, 1, *[d for d in dilate]], dtype=np.int64) if dilate is not None else None
+        final_dilations = int64_array([1, 1, *[d for d in dilate]]) if dilate is not None else None
         node_attrs = {
             'op': __class__.op,
             'type': 'Deconvolution',
             'bias_addable': True,
             'bias_term': bias_term,
-            'pad': np.array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[pad, pad] for pad in padding], dtype=np.int64),
+            'pad': int64_array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]]),
+            'pad_spatial_shape': int64_array([[pad, pad] for pad in padding]),
             'dilation': final_dilations,
             'output_spatial_shape': target_shape,
             'original_output_spatial_shape': target_shape,
             'output_shape': None,
-            'stride': np.array([1, 1, *[s for s in stride]], dtype=np.int64),
+            'stride': int64_array([1, 1, *[s for s in stride]]),
             'group': group,
             'output': output,
-            'kernel_spatial': np.array([k for k in kernel], dtype=np.int64),
+            'kernel_spatial': int64_array([k for k in kernel]),
             'input_feature_channel': 1,
             'output_feature_channel': 0,
             'kernel_spatial_idx': None,
             'reshape_kernel': True,
 
             'spatial_dims': None,
-            'channel_dims': np.array([1], dtype=np.int64),
-            'batch_dims': np.array([0], dtype=np.int64),
+            'channel_dims': int64_array([1]),
+            'batch_dims': int64_array([0]),
             'layout': 'NCHW',
             'get_pad': DeconvFrontExtractor.get_pad,
         }
 
         output_padding = attr.tuple("adj", int, None)
         if target_shape is None and output_padding:
-            node_attrs["output_padding"] = np.array([0, 0, *[s for s in output_padding]], dtype=np.int64)
+            node_attrs["output_padding"] = int64_array([0, 0, *[s for s in output_padding]])
 
         # update the attributes of the node
         Convolution.update_node_stat(node, node_attrs)
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/custom_rpn_proposal.py b/tools/mo/openvino/tools/mo/front/mxnet/custom_rpn_proposal.py
index 368c98c0489..b15e7f0ba94 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/custom_rpn_proposal.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/custom_rpn_proposal.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.proposal import ProposalOp
 from openvino.tools.mo.front.extractor import MXNetCustomFrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
@@ -26,8 +25,8 @@ class RPNProposalMXNetFrontExtractor(MXNetCustomFrontExtractorOp):
             'feat_stride': feat_stride,
             'base_size': 0,
             'min_size': min_size,
-            'ratio': np.array(ratio),
-            'scale': np.array(scale),
+            'ratio': mo_array(ratio),
+            'scale': mo_array(scale),
             'pre_nms_topn': pre_nms_topn,
             'post_nms_topn': post_nms_topn,
             'nms_thresh': nms_thresh,
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/deformable_conv_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/deformable_conv_ext.py
index 922471a4b1b..3c431c2ca1c 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/deformable_conv_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/deformable_conv_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
 from openvino.tools.mo.ops.deformable_convolution import DeformableConvolution
@@ -25,22 +26,22 @@ class DeformableConvolutionExtractor(FrontExtractorOp):
         output = attr.int("num_filter", None)
         bias_term = attr.str("no_bias", 'False') == 'False'
 
-        final_dilations = np.array([1, 1, *[d for d in dilate]], dtype=np.int64) if dilate is not None else None
+        final_dilations = int64_array([1, 1, *[d for d in dilate]]) if dilate is not None else None
 
         node_attrs = {
             'op': __class__.op,
             'bias_addable': True,
             'bias_term': bias_term,
-            'pad': np.array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[pad, pad] for pad in padding], dtype=np.int64),
+            'pad': int64_array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]]),
+            'pad_spatial_shape': int64_array([[pad, pad] for pad in padding]),
             'dilation': final_dilations,
             'output_spatial_shape': None,
             'output_shape': None,
-            'stride': np.array([1, 1, *[s for s in stride]], dtype=np.int64),
+            'stride': int64_array([1, 1, *[s for s in stride]]),
             'group': num_group,
             'deformable_group': num_deformable_group,
             'output': output,
-            'kernel_spatial': np.array([k for k in kernel], dtype=np.int64),
+            'kernel_spatial': int64_array([k for k in kernel]),
 
             'input_feature_channel': 1,
             'output_feature_channel': 0,
@@ -49,8 +50,8 @@ class DeformableConvolutionExtractor(FrontExtractorOp):
             'weights_index': 2,
 
             'spatial_dims': None,
-            'channel_dims': np.array([1], dtype=np.int64),
-            'batch_dims': np.array([0], dtype=np.int64),
+            'channel_dims': int64_array([1]),
+            'batch_dims': int64_array([0]),
             'layout': 'NCHW',
         }
 
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/elementwise_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/elementwise_ext.py
index ad1afa47790..50c441317e3 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/elementwise_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/elementwise_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.elementwise import Mul, Sub, Add, Maximum, Minimum, Div, Greater, GreaterEqual, Equal, Less, \
     LessEqual, Pow, NotEqual, LogicalAnd, LogicalOr, Round
 from openvino.tools.mo.front.extractor import FrontExtractorOp
@@ -259,7 +260,7 @@ class PlusScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 0.0)], dtype=np.float32)
+        node['scalar'] = mo_array([attrs.float('scalar', 0.0)], dtype=np.float32)
         return cls.enabled
 
 
@@ -270,7 +271,7 @@ class MinusScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 0.0)])
+        node['scalar'] = mo_array([attrs.float('scalar', 0.0)])
         return cls.enabled
 
 
@@ -281,7 +282,7 @@ class MulScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 1.0)], dtype=np.float32)
+        node['scalar'] = mo_array([attrs.float('scalar', 1.0)], dtype=np.float32)
         return cls.enabled
 
 
@@ -303,7 +304,7 @@ class GreaterScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 1.0)])
+        node['scalar'] = mo_array([attrs.float('scalar', 1.0)])
         return cls.enabled
 
 
@@ -314,7 +315,7 @@ class GreaterEqualScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 1.0)])
+        node['scalar'] = mo_array([attrs.float('scalar', 1.0)])
         return cls.enabled
 
 
@@ -325,7 +326,7 @@ class EqualScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 1.0)])
+        node['scalar'] = mo_array([attrs.float('scalar', 1.0)])
         return cls.enabled
 
 
@@ -336,7 +337,7 @@ class NotEqualScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 1.0)])
+        node['scalar'] = mo_array([attrs.float('scalar', 1.0)])
         return cls.enabled
 
 
@@ -347,7 +348,7 @@ class LesserScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 1.0)])
+        node['scalar'] = mo_array([attrs.float('scalar', 1.0)])
         return cls.enabled
 
 
@@ -358,7 +359,7 @@ class LesserEqualScalarFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        node['scalar'] = np.array([attrs.float('scalar', 1.0)])
+        node['scalar'] = mo_array([attrs.float('scalar', 1.0)])
         return cls.enabled
 
 
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/extractors/slice_axis.py b/tools/mo/openvino/tools/mo/front/mxnet/extractors/slice_axis.py
index 0b1c0aef88b..1ae45c2bdaa 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/extractors/slice_axis.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/extractors/slice_axis.py
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from openvino.tools.mo.front.caffe.extractors.utils import get_canonical_axis_index
-import numpy as np
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.utils.error import Error
 
 
@@ -26,7 +26,7 @@ def mxnet_slice_axis_infer(node):
     node.axis = get_canonical_axis_index(in_shape, node.axis)
     slice_axis = node.axis
 
-    new_shape = np.array(in_shape, dtype=np.int64)
+    new_shape = int64_array(in_shape)
     new_shape[slice_axis] = new_shape[slice_axis] / len(node.out_nodes())
 
     axis_size = in_shape[slice_axis]
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/gluoncv_ssd_anchors.py b/tools/mo/openvino/tools/mo/front/mxnet/gluoncv_ssd_anchors.py
index 29d5d8047f6..d610c77b853 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/gluoncv_ssd_anchors.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/gluoncv_ssd_anchors.py
@@ -1,15 +1,13 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from typing import Dict
 
 from openvino.tools.mo.front.mxnet.mx_reshape_to_reshape import MXReshapeToReshape
 from openvino.tools.mo.front.mxnet.ssd_detection_output_replacer import SsdPatternDetectionOutputReplacer
 from openvino.tools.mo.ops.elementwise import Div, Add, Sub
 from openvino.tools.mo.ops.split import Split
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph, Node
@@ -33,7 +31,7 @@ def calculate_prior_box_value(value: Node, value_to_div: Port, value_to_add: Por
     graph = value.graph
     dtype = data_type_str_to_np(graph.graph['cmd_params'].data_type)
     _min = Sub(graph, dict(name=value.name + '/Sub')).create_node()
-    div = create_op_node_with_second_input(graph, Div, np.array([2], dtype=dtype), op_attrs=dict(name=value.name + '/Div'))
+    div = create_op_node_with_second_input(graph, Div, mo_array([2], dtype=dtype), op_attrs=dict(name=value.name + '/Div'))
     div.in_port(0).connect(value_to_div)
     _min.in_port(0).connect(value_to_add)
     _min.in_port(1).connect(div.out_port(0))
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/loader.py b/tools/mo/openvino/tools/mo/front/mxnet/loader.py
index a81dbe7c6e9..ee31b6d5723 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/loader.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/loader.py
@@ -8,6 +8,7 @@ import os
 import mxnet as mx
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import add_outputs_identity
 from openvino.tools.mo.front.mxnet.extractor import common_mxnet_fields
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_node_edges, load_params, init_rnn_states, create_mxnet_edge
@@ -100,11 +101,11 @@ def symbol2nx(graph, model_nodes, model_params, input_names: str = ''):
     fw_name_map = {}
     for i, node in enumerate(model_nodes):
         if node['name'] in model_params._arg_params and node['name'] not in input_names:
-            node['value'] = np.array(model_params._arg_params[node['name']].asnumpy(), dtype=np.float32)
+            node['value'] = mo_array(model_params._arg_params[node['name']].asnumpy(), dtype=np.float32)
         elif node['name'] in model_params._aux_params and node['name'] not in input_names:
-            node['value'] = np.array(model_params._aux_params[node['name']].asnumpy(), dtype=np.float32)
+            node['value'] = mo_array(model_params._aux_params[node['name']].asnumpy(), dtype=np.float32)
         elif node['name'] in names_rnn_states:
-            node['value'] = np.zeros(rnn_states[node['name']])
+            node['value'] = np.zeros(rnn_states[node['name']], dtype=np.float32)
         node_name = graph.unique_id(node['name'])
         graph.add_node(node_name, **symbol_attrs(node))
         if hasattr(graph, 'op_names_statistic') and 'op' in node:
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/modulated_deformable_conv_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/modulated_deformable_conv_ext.py
index 84200aee6da..a76967e850d 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/modulated_deformable_conv_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/modulated_deformable_conv_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
 from openvino.tools.mo.ops.deformable_convolution import DeformableConvolution
@@ -25,22 +26,22 @@ class ModulatedDeformableConvolutionExtractor(FrontExtractorOp):
         output = attr.int("num_filter", None)
         bias_term = attr.str("no_bias", 'False') == 'False'
 
-        final_dilations = np.array([1, 1, *[d for d in dilate]], dtype=np.int64) if dilate is not None else None
+        final_dilations = int64_array([1, 1, *[d for d in dilate]]) if dilate is not None else None
 
         node_attrs = {
             'op': __class__.op,
             'bias_addable': True,
             'bias_term': bias_term,
-            'pad': np.array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[pad, pad] for pad in padding], dtype=np.int64),
+            'pad': int64_array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]]),
+            'pad_spatial_shape': int64_array([[pad, pad] for pad in padding]),
             'dilation': final_dilations,
             'output_spatial_shape': None,
             'output_shape': None,
-            'stride': np.array([1, 1, *[s for s in stride]], dtype=np.int64),
+            'stride': int64_array([1, 1, *[s for s in stride]]),
             'group': num_group,
             'deformable_group': num_deformable_group,
             'output': output,
-            'kernel_spatial': np.array([k for k in kernel], dtype=np.int64),
+            'kernel_spatial': int64_array([k for k in kernel]),
             'bilinear_interpolation_pad': True,
 
             'input_feature_channel': 1,
@@ -51,8 +52,8 @@ class ModulatedDeformableConvolutionExtractor(FrontExtractorOp):
             'in_ports_count': 4,
 
             'spatial_dims': None,
-            'channel_dims': np.array([1], dtype=np.int64),
-            'batch_dims': np.array([0], dtype=np.int64),
+            'channel_dims': int64_array([1]),
+            'batch_dims': int64_array([0]),
             'layout': 'NCHW',
         }
 
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/pad_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/pad_ext.py
index 9ddcedb08b2..d82786448a0 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/pad_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/pad_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
 from openvino.tools.mo.ops.pad import AttributedPad
@@ -15,7 +14,7 @@ class PadFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
-        pads = np.array(list(attrs.tuple('pad_width', int, None)))
+        pads = mo_array(list(attrs.tuple('pad_width', int, None)))
         pads = pads.reshape([-1, 2])
         value = attrs.float('constant_value', 0.0)
 
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/pooling_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/pooling_ext.py
index 2439f7ec1ec..e43c6bec762 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/pooling_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/pooling_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
 from openvino.tools.mo.ops.pooling import Pooling
@@ -23,16 +24,16 @@ class PoolingFrontExtractor(FrontExtractorOp):
         rt = 'floor'
 
         data = {
-            'window': np.array([1, 1, *[k for k in kernel]], dtype=np.int64),
-            'stride': np.array([1, 1, *[s for s in stride]], dtype=np.int64),
-            'pad': np.array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]], dtype=np.int64),
-            'pad_spatial_shape': np.array([[pad, pad] for pad in padding], dtype=np.int64),
+            'window': int64_array([1, 1, *[k for k in kernel]]),
+            'stride': int64_array([1, 1, *[s for s in stride]]),
+            'pad': int64_array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]]),
+            'pad_spatial_shape': int64_array([[pad, pad] for pad in padding]),
             'pool_method': method,
             'exclude_pad': False,
             'output_spatial_shape': None,
             'spatial_dims': None,
-            'channel_dims': np.array([1], dtype=np.int64),
-            'batch_dims': np.array([0], dtype=np.int64),
+            'channel_dims': int64_array([1]),
+            'batch_dims': int64_array([0]),
             'layout': 'NCHW',
             'rounding_type': rt,
         }
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/proposal_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/proposal_ext.py
index d99068ac907..8137dbce699 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/proposal_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/proposal_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.proposal import ProposalOp
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
@@ -25,9 +24,9 @@ class ProposalFrontExtractor(FrontExtractorOp):
 
         update_attrs = {
             'feat_stride': feat_stride,
-            'ratio': np.array(ratio),
+            'ratio': mo_array(ratio),
             'min_size': min_size,
-            'scale': np.array(scale),
+            'scale': mo_array(scale),
             'pre_nms_topn': pre_nms_topn,
             'post_nms_topn': post_nms_topn,
             'nms_thresh': nms_thresh,
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/slice_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/slice_ext.py
index 51627a47e5b..476966de114 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/slice_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/slice_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
 from openvino.tools.mo.ops.slice import MXSlice
@@ -16,9 +15,9 @@ class SliceFrontExtractor(FrontExtractorOp):
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
         node_attrs = {
-            'crop_begin': np.array(attrs.tuple("begin", int, ())),
-            'crop_end': np.array(attrs.tuple("end", int, ())),
-            'step': np.array(attrs.tuple("step", int, ())),
+            'crop_begin': mo_array(attrs.tuple("begin", int, ())),
+            'crop_end': mo_array(attrs.tuple("end", int, ())),
+            'step': mo_array(attrs.tuple("step", int, ())),
         }
 
         MXSlice.update_node_stat(node, node_attrs)
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/slice_replacers.py b/tools/mo/openvino/tools/mo/front/mxnet/slice_replacers.py
index a7a6a9109a0..d3b6283ff16 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/slice_replacers.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/slice_replacers.py
@@ -17,11 +17,11 @@ class MXSliceToStridedSliceReplacer(FrontReplacementOp):
         node = match['op']
 
         strided_slice_node = StridedSlice(graph, dict(name=node.id + '/strided_slice_',
-                                                      shrink_axis_mask=np.array(np.zeros(len(node.crop_begin), dtype=np.int64)),
-                                                      new_axis_mask=np.array(np.zeros(len(node.crop_begin), dtype=np.int64)),
-                                                      ellipsis_mask=np.array(np.zeros(len(node.crop_begin), dtype=np.int64)),
-                                                      begin_mask=np.array(np.ones(len(node.crop_begin), dtype=np.int64)),
-                                                      end_mask=np.array(np.ones(len(node.crop_end), dtype=np.int64)))).create_node()
+                                                      shrink_axis_mask=np.zeros(len(node.crop_begin), dtype=np.int64),
+                                                      new_axis_mask=np.zeros(len(node.crop_begin), dtype=np.int64),
+                                                      ellipsis_mask=np.zeros(len(node.crop_begin), dtype=np.int64),
+                                                      begin_mask=np.ones(len(node.crop_begin), dtype=np.int64),
+                                                      end_mask=np.ones(len(node.crop_end), dtype=np.int64))).create_node()
         node.in_port(0).get_connection().set_destination(strided_slice_node.in_port(0))
         node.out_port(0).get_connection().set_source(strided_slice_node.out_port(0))
 
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/softmax.py b/tools/mo/openvino/tools/mo/front/mxnet/softmax.py
index 635302168d1..bf94b5b76ce 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/softmax.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/softmax.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.elementwise import Mul
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.graph.graph import Graph
@@ -26,7 +25,7 @@ class SoftmaxFrontReplacementSubgraph(FrontReplacementSubgraph):
             in_node = node.in_node()
             out_nodes = [node for node in node.out_nodes().values()]
             graph.remove_edge(node.in_node().id, node.id)
-            temperature = np.array([1.0 / node.temperature])
+            temperature = mo_array([1.0 / node.temperature])
             scalar_value_op = Const(graph, dict(value=temperature, shape=temperature.shape,
                                                 symbol_dict={'name': node.id + '/const'}))
             mul_op = Mul(graph, dict(name=node.id + '/mul_', symbol_dict={'name': node.id + '/mul_'}))
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/ssd_anchor_reshape.py b/tools/mo/openvino/tools/mo/front/mxnet/ssd_anchor_reshape.py
index d8a72b67421..d0674327b95 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/ssd_anchor_reshape.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/ssd_anchor_reshape.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.mxnet.eltwise_scalar_replacers import MulScalarFrontReplacer
 from openvino.tools.mo.front.mxnet.ssd_detection_output_replacer import SsdPatternDetectionOutputReplacer
 from openvino.tools.mo.front.split_normalizer import AttributedSplitToSplit
@@ -76,7 +75,7 @@ class SsdPatternAnchorReshape(FrontReplacementSubgraph):
                 if v in variants_dict.keys():
                     variants_dict[v] = Node(graph, k).in_nodes()[1].value[0]
 
-        variants = np.array([variants_dict['mul_scalar1x'], variants_dict['mul_scalar1y'],
+        variants = mo_array([variants_dict['mul_scalar1x'], variants_dict['mul_scalar1y'],
                              variants_dict['mul_scalar2x'], variants_dict['mul_scalar2y']] * int(const.value.size / 4)).reshape(const.value.shape)
         priorbox_variants = Const(graph, dict(value=variants, name=const.id + '/priorbox_variants')).create_node()
         variants_slice_like = SliceLike(graph, dict(axes=slice_like.axes,
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/transpose_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/transpose_ext.py
index 49a995d066e..52b725d2004 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/transpose_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/transpose_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.transpose import Transpose
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
@@ -16,5 +17,5 @@ class TransposeFrontExtractor(FrontExtractorOp):
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
         order = list(attrs.tuple("axes", int, None))
-        Transpose.update_node_stat(node, {'order': np.array(order, dtype=np.int32)})
+        Transpose.update_node_stat(node, {'order': mo_array(order, dtype=np.int32)})
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/mxnet/zeros_ext.py b/tools/mo/openvino/tools/mo/front/mxnet/zeros_ext.py
index d4f6462a075..c76168422ed 100644
--- a/tools/mo/openvino/tools/mo/front/mxnet/zeros_ext.py
+++ b/tools/mo/openvino/tools/mo/front/mxnet/zeros_ext.py
@@ -16,6 +16,11 @@ class ZerosFrontExtractor(FrontExtractorOp):
     def extract(cls, node):
         attrs = get_mxnet_layer_attrs(node.symbol_dict)
         shape = list(attrs.tuple('shape', int, None))
+        dtype = attrs.tuple('dtype', str, None)
+        if dtype and len(dtype) == 1:
+            dtype = dtype[0]
+        else:
+            dtype = np.float32
         zero_shapes = []
         for i, s in enumerate(shape):
             if s == 0:
@@ -24,7 +29,7 @@ class ZerosFrontExtractor(FrontExtractorOp):
 
         update_attrs = {
             'shape': np.ndarray(shape),
-            'value': np.zeros(shape),
+            'value': np.zeros(shape, dtype=dtype),
             'zero_shapes': zero_shapes
         }
 
diff --git a/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py b/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py
index 1d05c1c529a..7cbb3deba53 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py
@@ -6,7 +6,7 @@ import numpy as np
 from openvino.tools.mo.front.pass_separator import FrontStart
 from openvino.tools.mo.front.restore_ports import RestorePorts
 from openvino.tools.mo.ops.loop import Loop
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node
@@ -39,7 +39,7 @@ class ONNXLoopNormalize(FrontReplacementSubgraph):
         # connect "execution condition" input if it is not connected with default value True
         if not loop_node.is_in_port_connected(1):
             loop_node.add_input_port(1, skip_if_exist=True)
-            Const(loop_node.graph, {'name': loop_name + '/execution_cond', 'value': np.array(True, dtype=np.bool)}).\
+            Const(loop_node.graph, {'name': loop_name + '/execution_cond', 'value': mo_array(True, dtype=np.bool)}).\
                 create_node().out_port(0).connect(loop_node.in_port(1))
 
         # scan output need Unsqueeze over axis 0
diff --git a/tools/mo/openvino/tools/mo/front/onnx/affine_ext.py b/tools/mo/openvino/tools/mo/front/onnx/affine_ext.py
index 1add8443af8..ac7c19da424 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/affine_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/affine_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 
@@ -14,7 +13,7 @@ class AffineFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        dst_type = lambda x: np.array(x)
+        dst_type = lambda x: mo_array(x)
 
         scale = onnx_attr(node, 'alpha', 'f', default=None, dst_type=dst_type)
         bias = onnx_attr(node, 'beta', 'f', default=None, dst_type=dst_type)
diff --git a/tools/mo/openvino/tools/mo/front/onnx/constant_of_shape_ext.py b/tools/mo/openvino/tools/mo/front/onnx/constant_of_shape_ext.py
index f2828ab72bb..0a1789f3d47 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/constant_of_shape_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/constant_of_shape_ext.py
@@ -4,6 +4,7 @@
 import numpy as np
 from onnx import numpy_helper
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 from openvino.tools.mo.ops.constant_of_shape import ConstantOfShape
@@ -15,7 +16,7 @@ class ConstantOfShapeExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        fill_value = onnx_attr(node, 'value', 't', default=np.array([0.0]), dst_type=lambda x: numpy_helper.to_array(x))
+        fill_value = onnx_attr(node, 'value', 't', default=mo_array([0.0]), dst_type=lambda x: numpy_helper.to_array(x))
 
         ConstantOfShape.update_node_stat(node, {'fill_value': fill_value})
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/onnx/conv_ext.py b/tools/mo/openvino/tools/mo/front/onnx/conv_ext.py
index 3e46eb51e87..c9f161b46e7 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/conv_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/conv_ext.py
@@ -18,27 +18,27 @@ class ConvFrontExtractor(FrontExtractorOp):
     def extract(cls, node):
         # Extract pads attribute
         # In case if pads is not specified it will be set in default (1) in infer function
-        pads = onnx_attr(node, 'pads', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
+        pads = onnx_attr(node, 'pads', 'ints', default=None, dst_type=lambda x: int64_array(x))
         assert pads is None or len(pads) % 2 == 0
         final_pad = None
         if pads is not None:
             pads = pads.reshape([2, -1])
             pads = np.transpose(pads)
-            final_pad = np.array([[0, 0], [0, 0], *pads], dtype=np.int64)
+            final_pad = int64_array([[0, 0], [0, 0], *pads])
 
         # Extract dilations attribute
         # In case if dilations is not specified it will be set in default (1) in infer function
-        dilations = onnx_attr(node, 'dilations', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
-        final_dilations = np.array([1, 1, *dilations], dtype=np.int64) if dilations is not None else None
+        dilations = onnx_attr(node, 'dilations', 'ints', default=None, dst_type=lambda x: int64_array(x))
+        final_dilations = int64_array([1, 1, *dilations]) if dilations is not None else None
 
         # Extract dilations attribute
         # In case if dilations is not specified it will be set in default (1) in infer function
-        strides = onnx_attr(node, 'strides', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
-        final_strides = np.array([1, 1, *strides], dtype=np.int64) if strides is not None else None
+        strides = onnx_attr(node, 'strides', 'ints', default=None, dst_type=lambda x: int64_array(x))
+        final_strides = int64_array([1, 1, *strides]) if strides is not None else None
 
         kernel_shape = onnx_attr(node, 'kernel_shape', 'ints', default=None)
         auto_pad = onnx_attr(node, 'auto_pad', 's', default=None, dst_type=get_onnx_autopad)
-        group = onnx_attr(node, 'group', 'i', default=1, dst_type=lambda x: np.array(x, dtype=np.int64))
+        group = onnx_attr(node, 'group', 'i', default=1, dst_type=lambda x: int64_array(x))
 
         attrs = {
             'op': __class__.op,
@@ -46,22 +46,22 @@ class ConvFrontExtractor(FrontExtractorOp):
             'bias_addable': True,
             'bias_term': None,
             'pad': final_pad,
-            'pad_spatial_shape': np.array(pads, dtype=np.int64) if pads is not None else None,
+            'pad_spatial_shape': int64_array(pads) if pads is not None else None,
             'dilation': final_dilations,
             'output_spatial_shape': None,
             'output_shape': None,
             'stride': final_strides,
             'group': group,
             'output': None,
-            'kernel_spatial': np.array(kernel_shape, dtype=np.int64) if kernel_shape is not None else None,
+            'kernel_spatial': int64_array(kernel_shape) if kernel_shape is not None else None,
 
             'input_feature_channel': 1,
             'output_feature_channel': 0,
             'kernel_spatial_idx': None,  # Will be calculated in infer function (np.array([2, 3]))
 
             'spatial_dims': None,  # Will be calculated in infer function
-            'channel_dims': np.array([1], dtype=np.int64),
-            'batch_dims': np.array([0], dtype=np.int64),
+            'channel_dims': int64_array([1]),
+            'batch_dims': int64_array([0]),
             'layout': 'NCHW'
         }
 
diff --git a/tools/mo/openvino/tools/mo/front/onnx/deformable_conv_ext.py b/tools/mo/openvino/tools/mo/front/onnx/deformable_conv_ext.py
index 95f46017ac9..a55371cf761 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/deformable_conv_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/deformable_conv_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr, get_onnx_autopad
 from openvino.tools.mo.ops.deformable_convolution import DeformableConvolution
@@ -16,27 +17,27 @@ class DeformableConvExtractor(FrontExtractorOp):
     def extract(cls, node):
         # Extract pads attribute
         # In case if pads is not specified it will be set in default (1) in infer function
-        pads = onnx_attr(node, 'pads', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
+        pads = onnx_attr(node, 'pads', 'ints', default=None, dst_type=lambda x: int64_array(x))
         assert pads is None or len(pads) % 2 == 0
         final_pad = None
         if pads is not None:
             pads = pads.reshape([2, -1])
             pads = np.transpose(pads)
-            final_pad = np.array([[0, 0], [0, 0], *pads], dtype=np.int64)
+            final_pad = int64_array([[0, 0], [0, 0], *pads])
 
         # Extract dilations attribute
         # In case if dilations is not specified it will be set in default (1) in infer function
-        dilations = onnx_attr(node, 'dilations', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
-        final_dilations = np.array([1, 1, *dilations], dtype=np.int64) if dilations is not None else None
+        dilations = onnx_attr(node, 'dilations', 'ints', default=None, dst_type=lambda x: int64_array(x))
+        final_dilations = int64_array([1, 1, *dilations]) if dilations is not None else None
 
         # Extract dilations attribute
         # In case if dilations is not specified it will be set in default (1) in infer function
-        strides = onnx_attr(node, 'strides', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
-        final_strides = np.array([1, 1, *strides], dtype=np.int64) if strides is not None else None
+        strides = onnx_attr(node, 'strides', 'ints', default=None, dst_type=lambda x: int64_array(x))
+        final_strides = int64_array([1, 1, *strides]) if strides is not None else None
 
         kernel_shape = onnx_attr(node, 'kernel_shape', 'ints', default=None)
         auto_pad = onnx_attr(node, 'auto_pad', 's', default=None, dst_type=get_onnx_autopad)
-        group = onnx_attr(node, 'group', 'i', default=1, dst_type=lambda x: np.array(x, dtype=np.int64))
+        group = onnx_attr(node, 'group', 'i', default=1, dst_type=lambda x: int64_array(x))
         deformable_groups = onnx_attr(node, 'deformable_groups', 'i', default=1)
 
         attrs = {
@@ -45,7 +46,7 @@ class DeformableConvExtractor(FrontExtractorOp):
             'bias_addable': False,
             'bias_term': False,
             'pad': final_pad,
-            'pad_spatial_shape': np.array(pads, dtype=np.int64) if pads is not None else None,
+            'pad_spatial_shape': int64_array(pads) if pads is not None else None,
             'dilation': final_dilations,
             'output_spatial_shape': None,
             'output_shape': None,
@@ -54,15 +55,15 @@ class DeformableConvExtractor(FrontExtractorOp):
             'deformable_group': deformable_groups,
             'output': None,
             'weights_index': 2,
-            'kernel_spatial': np.array(kernel_shape, dtype=np.int64) if kernel_shape is not None else None,
+            'kernel_spatial': int64_array(kernel_shape) if kernel_shape is not None else None,
 
             'input_feature_channel': 1,
             'output_feature_channel': 0,
             'kernel_spatial_idx': None,  # Will be calculated in infer function (np.array([2, 3]))
 
             'spatial_dims': None,  # Will be calculated in infer function
-            'channel_dims': np.array([1], dtype=np.int64),
-            'batch_dims': np.array([0], dtype=np.int64),
+            'channel_dims': int64_array([1]),
+            'batch_dims': int64_array([0]),
             'layout': 'NCHW'
         }
 
diff --git a/tools/mo/openvino/tools/mo/front/onnx/detection_output_onnx_ext.py b/tools/mo/openvino/tools/mo/front/onnx/detection_output_onnx_ext.py
index 5686efb9e54..f2b9c6759db 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/detection_output_onnx_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/detection_output_onnx_ext.py
@@ -3,8 +3,7 @@
 
 from math import log
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 from openvino.tools.mo.ops.detection_output_onnx import ExperimentalDetectronDetectionOutput
@@ -23,8 +22,7 @@ class ExperimentalDetectronDetectionOutputFrontExtractor(FrontExtractorOp):
                      post_nms_count=onnx_attr(node, 'post_nms_count', 'i', 2000),
                      score_threshold=onnx_attr(node, 'score_threshold', 'f', 0.05),
                      max_delta_log_wh=onnx_attr(node, 'max_delta_log_wh', 'f', log(1000. / 16.)),
-                     deltas_weights=np.array(onnx_attr(node, 'deltas_weights', 'floats', [10., 10., 5., 5.]),
-                                             dtype=np.float32)
+                     deltas_weights=float32_array(onnx_attr(node, 'deltas_weights', 'floats', [10., 10., 5., 5.]))
                      )
         ExperimentalDetectronDetectionOutput.update_node_stat(node, attrs)
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/onnx/elementwise_ext.py b/tools/mo/openvino/tools/mo/front/onnx/elementwise_ext.py
index 02d44284b23..be779020b58 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/elementwise_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/elementwise_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.elementwise import Add, Sub, Mul, Div, Pow, Less, Equal, Greater, LogicalAnd, LogicalOr, LogicalXor, \
     Round, GreaterEqual, LessEqual
 from openvino.tools.mo.front.extractor import FrontExtractorOp
@@ -103,7 +102,7 @@ class ScaleFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node: Node):
-        scale = onnx_attr(node, 'scale', 'f', default=np.array(1.0), dst_type=lambda x: np.array(x))
+        scale = onnx_attr(node, 'scale', 'f', default=mo_array(1.0), dst_type=lambda x: mo_array(x))
         AttributedPower.update_node_stat(node, {'scale': scale})
         return cls.enabled
 
diff --git a/tools/mo/openvino/tools/mo/front/onnx/gather_ext.py b/tools/mo/openvino/tools/mo/front/onnx/gather_ext.py
index 289609a2501..7188d6bb24d 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/gather_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/gather_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.ops.gather import AttributedGather
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -15,7 +14,7 @@ class GatherFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = {
-            'axis': np.array(onnx_attr(node, 'axis', 'i', default=0), dtype=np.int64)
+            'axis': int64_array(onnx_attr(node, 'axis', 'i', default=0))
         }
 
         AttributedGather.update_node_stat(node, attrs)
diff --git a/tools/mo/openvino/tools/mo/front/onnx/gemm_ext.py b/tools/mo/openvino/tools/mo/front/onnx/gemm_ext.py
index 868dc468edb..40f90a88f68 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/gemm_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/gemm_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.ops.MatMul import GemmONNX
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -21,7 +20,7 @@ class GemmFrontExtractor(FrontExtractorOp):
             'transpose_b': onnx_attr(node, 'transB', 'i', 0),
             'broadcast_c': onnx_attr(node, 'broadcast', 'i', 1),
             # TODO: there is no axis in onnx operators.md
-            'axis': np.array(onnx_attr(node, 'axis', 'i', default=0), dtype=np.int64)
+            'axis': int64_array(onnx_attr(node, 'axis', 'i', default=0))
         }
         GemmONNX.update_node_stat(node, attrs)
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py b/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py
index 75cf60991d9..bd25a14a0d3 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 from openvino.tools.mo.ops.group_norm import GroupNorm
@@ -15,8 +16,8 @@ class ExperimentalDetectronGroupNorm(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = {
-            'eps': np.array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
-            'num_groups': np.array(onnx_attr(node, 'num_groups', 'i', default=1), dtype=np.int64),
+            'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
+            'num_groups': int64_array(onnx_attr(node, 'num_groups', 'i', default=1)),
         }
         GroupNorm.update_node_stat(node, attrs)
         return cls.enabled
@@ -29,8 +30,8 @@ class GroupNormExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         attrs = {
-            'eps': np.array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
-            'num_groups': np.array(onnx_attr(node, 'num_groups', 'i', default=1), dtype=np.int64),
+            'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
+            'num_groups': int64_array(onnx_attr(node, 'num_groups', 'i', default=1)),
         }
         GroupNorm.update_node_stat(node, attrs)
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/onnx/gru_ext.py b/tools/mo/openvino/tools/mo/front/onnx/gru_ext.py
index e50f86c27c5..091d78ba49f 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/gru_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/gru_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array, int64_array
 from openvino.tools.mo.ops.GRU import GRU
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -15,9 +16,9 @@ class GRUFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         activation_alpha = onnx_attr(node, 'activation_alpha', 'floats',
-                                     default=None, dst_type=lambda x: np.array(x, dtype=np.float32))
+                                     default=None, dst_type=lambda x: float32_array(x))
         activation_beta = onnx_attr(node, 'activation_beta', 'floats',
-                                    default=None, dst_type=lambda x: np.array(x, dtype=np.float32))
+                                    default=None, dst_type=lambda x: float32_array(x))
         activations = onnx_attr(node, 'activations', 'strings', default=None,
                                 dst_type=lambda x: list(map(lambda s: s.decode(encoding="utf-8").lower(), list(x))))
         clip = onnx_attr(node, 'clip', 'f', default=None)
@@ -39,7 +40,7 @@ class GRUFrontExtractor(FrontExtractorOp):
             'activations': activations,
             'clip': clip,
             'direction': onnx_attr(node, 'direction', 's', b'forward').decode().lower(),
-            'hidden_size': np.array(onnx_attr(node, 'hidden_size', 'i'), dtype=np.int64),
+            'hidden_size': int64_array(onnx_attr(node, 'hidden_size', 'i')),
             'linear_before_reset': linear_before_reset,
         }
 
diff --git a/tools/mo/openvino/tools/mo/front/onnx/hard_sigmoid_ext.py b/tools/mo/openvino/tools/mo/front/onnx/hard_sigmoid_ext.py
index 335a63721f3..e958bb752e9 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/hard_sigmoid_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/hard_sigmoid_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.hard_sigmoid import HardSigmoid
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -18,7 +17,7 @@ class HardSigmoidFrontExtractor(FrontReplacementOp):
         alpha = onnx_attr(node, 'alpha', 'f', default=0.2)
         beta = onnx_attr(node, 'beta', 'f', default=0.5)
 
-        hard_sigmoid = create_op_with_const_inputs(graph, HardSigmoid, {1: np.array(alpha), 2: np.array(beta)},
+        hard_sigmoid = create_op_with_const_inputs(graph, HardSigmoid, {1: mo_array(alpha), 2: mo_array(beta)},
                                                    {'name': node.name + '/HardSigmoid_'})
 
         node.in_port(0).get_connection().set_destination(hard_sigmoid.in_port(0))
diff --git a/tools/mo/openvino/tools/mo/front/onnx/image_scaler_ext.py b/tools/mo/openvino/tools/mo/front/onnx/image_scaler_ext.py
index 81c3709553c..4663b5cbb81 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/image_scaler_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/image_scaler_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 
@@ -13,9 +14,9 @@ class ImageScalerFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        dst_type = lambda x: np.array(x)
+        dst_type = lambda x: mo_array(x)
 
-        scale = onnx_attr(node, 'scale', 'f', default=np.array(1.0), dst_type=dst_type)
+        scale = onnx_attr(node, 'scale', 'f', default=mo_array(1.0), dst_type=dst_type)
         bias = onnx_attr(node, 'bias', 'floats', default=None, dst_type=dst_type)
 
         # Expand dims for bias in case if it is not scalar
diff --git a/tools/mo/openvino/tools/mo/front/onnx/lstm_ext.py b/tools/mo/openvino/tools/mo/front/onnx/lstm_ext.py
index f772984bf99..ded402793b0 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/lstm_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/lstm_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array, int64_array
 from openvino.tools.mo.ops.LSTM import LSTM
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -15,9 +14,9 @@ class LSTMFrontExtractor(FrontExtractorOp):
     @classmethod
     def extract(cls, node):
         activation_alpha = onnx_attr(node, 'activation_alpha', 'floats',
-                                     default=None, dst_type=lambda x: np.array(x, dtype=np.float32))
+                                     default=None, dst_type=lambda x: float32_array(x))
         activation_beta = onnx_attr(node, 'activation_beta', 'floats',
-                                     default=None, dst_type=lambda x: np.array(x, dtype=np.float32))
+                                     default=None, dst_type=lambda x: float32_array(x))
         activations = onnx_attr(node, 'activations', 'strings', default=None,
                                 dst_type=lambda x: list(map(lambda s: s.decode(encoding="utf-8").lower(), list(x))))
         clip = onnx_attr(node, 'clip', 'f', default=None)
@@ -39,7 +38,7 @@ class LSTMFrontExtractor(FrontExtractorOp):
             'activations': activations,
             'clip': clip,
             'direction': onnx_attr(node, 'direction', 's', b'forward').decode().lower(),
-            'hidden_size': np.array(onnx_attr(node, 'hidden_size', 'i'), dtype=np.int64),
+            'hidden_size': int64_array(onnx_attr(node, 'hidden_size', 'i')),
             'input_forget': input_forget,
         }
 
diff --git a/tools/mo/openvino/tools/mo/front/onnx/mask_rcnn_conversion.py b/tools/mo/openvino/tools/mo/front/onnx/mask_rcnn_conversion.py
index 6332c36a093..5274f4cd829 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/mask_rcnn_conversion.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/mask_rcnn_conversion.py
@@ -8,7 +8,7 @@ from openvino.tools.mo.ops.Cast import Cast
 from openvino.tools.mo.ops.detection_output_onnx import ExperimentalDetectronDetectionOutput
 from openvino.tools.mo.ops.parameter import Parameter
 from openvino.tools.mo.ops.roifeatureextractor_onnx import ExperimentalDetectronROIFeatureExtractor
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.front.tf.replacement import FrontReplacementFromConfigFileGeneral
 from openvino.tools.mo.graph.graph import Graph
@@ -53,7 +53,7 @@ def insert_do(graph: Graph, replacement_descriptions: dict):
 
     do_node = ExperimentalDetectronDetectionOutput(graph, {'name': 'DetectionOutput',
                                                            'class_agnostic_box_regression': 0,
-                                                           'deltas_weights': np.array([10.0, 10.0, 5.0, 5.0]),
+                                                           'deltas_weights': mo_array([10.0, 10.0, 5.0, 5.0]),
                                                            'max_delta_log_wh':
                                                                replacement_descriptions['max_delta_log_wh'],
                                                            'nms_threshold': replacement_descriptions['nms_threshold'],
diff --git a/tools/mo/openvino/tools/mo/front/onnx/mean_variance_normalization_ext.py b/tools/mo/openvino/tools/mo/front/onnx/mean_variance_normalization_ext.py
index 15978cd17d2..86081d580f0 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/mean_variance_normalization_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/mean_variance_normalization_ext.py
@@ -1,8 +1,6 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.mvn import MVNOnnx
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
@@ -17,7 +15,7 @@ class MeanVarianceNormalizationExtractor(FrontExtractorOp):
     def extract(cls, node):
         axes = onnx_attr(node, 'axes', 'ints',
                          default=int64_array([0, 2, 3]),
-                         dst_type=lambda x: np.array(x, dtype=np.int64))
+                         dst_type=lambda x: int64_array(x))
 
         attrs = {
             'eps': 1e-9,
diff --git a/tools/mo/openvino/tools/mo/front/onnx/pooling_ext.py b/tools/mo/openvino/tools/mo/front/onnx/pooling_ext.py
index 59ae1816016..fd441ea9232 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/pooling_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/pooling_ext.py
@@ -67,10 +67,10 @@ class GlobalMaxPoolFrontExtractor(FrontExtractorOp):
 
 
 def common_onnx_pool_extractor(node):
-    kernel_shape = onnx_attr(node, 'kernel_shape', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
-    final_kernel_shape = np.array([1, 1, *[x for x in kernel_shape]], dtype=np.int64) if kernel_shape is not None else None
+    kernel_shape = onnx_attr(node, 'kernel_shape', 'ints', default=None, dst_type=lambda x: int64_array(x))
+    final_kernel_shape = int64_array([1, 1, *[x for x in kernel_shape]]) if kernel_shape is not None else None
 
-    pads = onnx_attr(node, 'pads', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
+    pads = onnx_attr(node, 'pads', 'ints', default=None, dst_type=lambda x: int64_array(x))
 
     if kernel_shape is not None and pads is not None and kernel_shape.size * 2 != pads.size:
         log.warning('Node {} has pad = {} which is ill-formed -- it should have even amount of elements.'.format(
@@ -86,12 +86,12 @@ def common_onnx_pool_extractor(node):
         assert len(pads) % 2 == 0
         pads = pads.reshape([2, -1])
         pads = np.transpose(pads)
-        final_pads = np.array([[0, 0], [0, 0], *[p for p in pads]], dtype=np.int64)
+        final_pads = int64_array([[0, 0], [0, 0], *[p for p in pads]])
 
     # Extract strides attribute
     # In case if strides is not specified it will be set in default (1) in infer function
-    strides = onnx_attr(node, 'strides', 'ints', default=None, dst_type=lambda x: np.array(x, dtype=np.int64))
-    final_strides = np.array([1, 1, *[x for x in strides]], dtype=np.int64) if strides is not None else None
+    strides = onnx_attr(node, 'strides', 'ints', default=None, dst_type=lambda x: int64_array(x))
+    final_strides = int64_array([1, 1, *[x for x in strides]]) if strides is not None else None
 
     dilation = onnx_attr(node, 'dilations', 'ints', default=None, dst_type=lambda x: int64_array(x))
     final_dilation = int64_array([1, 1, *[x for x in dilation]]) if dilation is not None else None
@@ -121,7 +121,7 @@ def common_onnx_pool_extractor(node):
         'window': final_kernel_shape,
         'stride': final_strides,
         'pad': final_pads,
-        'pad_spatial_shape': np.array(pads, dtype=np.int64) if pads is not None else None,
+        'pad_spatial_shape': int64_array(pads) if pads is not None else None,
         'pool_method': method,
         'exclude_pad': True if exclude_pad else False,
         'global_pool': global_pooling,
@@ -130,8 +130,8 @@ def common_onnx_pool_extractor(node):
         'dilation': final_dilation,
 
         'spatial_dims': None,
-        'channel_dims': np.array([1], dtype=np.int64),
-        'batch_dims': np.array([0], dtype=np.int64),
+        'channel_dims': int64_array([1]),
+        'batch_dims': int64_array([0]),
         'layout': 'NCHW',
 
         'pooling_convention': pooling_convention
diff --git a/tools/mo/openvino/tools/mo/front/onnx/priorbox_clustered_ext.py b/tools/mo/openvino/tools/mo/front/onnx/priorbox_clustered_ext.py
index cf231554e98..8afb175cf8a 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/priorbox_clustered_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/priorbox_clustered_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.ops.priorbox_clustered import PriorBoxClusteredOp
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -14,13 +13,13 @@ class PriorBoxClusteredFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        variance = onnx_attr(node, 'variance', 'floats', default=[], dst_type=lambda x: np.array(x, dtype=np.float32))
+        variance = onnx_attr(node, 'variance', 'floats', default=[], dst_type=lambda x: float32_array(x))
         if len(variance) == 0:
             variance = [0.1]
 
         update_attrs = {
-            'width': onnx_attr(node, 'width', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)),
-            'height': onnx_attr(node, 'height', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)),
+            'width': onnx_attr(node, 'width', 'floats', dst_type=lambda x: float32_array(x)),
+            'height': onnx_attr(node, 'height', 'floats', dst_type=lambda x: float32_array(x)),
             'flip': onnx_attr(node, 'flip', 'i', default=0),
             'clip': onnx_attr(node, 'clip', 'i', default=0),
             'variance': list(variance),
diff --git a/tools/mo/openvino/tools/mo/front/onnx/priorbox_ext.py b/tools/mo/openvino/tools/mo/front/onnx/priorbox_ext.py
index 9440f7b7dca..a8f747b9a24 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/priorbox_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/priorbox_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.ops.priorbox import PriorBoxOp
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -14,14 +13,14 @@ class PriorBoxFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        variance = onnx_attr(node, 'variance', 'floats', default=[], dst_type=lambda x: np.array(x, dtype=np.float32))
+        variance = onnx_attr(node, 'variance', 'floats', default=[], dst_type=lambda x: float32_array(x))
         if len(variance) == 0:
             variance = [0.1]
 
         update_attrs = {
-            'aspect_ratio': onnx_attr(node, 'aspect_ratio', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)),
-            'min_size': onnx_attr(node, 'min_size', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)),
-            'max_size': onnx_attr(node, 'max_size', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)),
+            'aspect_ratio': onnx_attr(node, 'aspect_ratio', 'floats', dst_type=lambda x: float32_array(x)),
+            'min_size': onnx_attr(node, 'min_size', 'floats', dst_type=lambda x: float32_array(x)),
+            'max_size': onnx_attr(node, 'max_size', 'floats', dst_type=lambda x: float32_array(x)),
             'flip': onnx_attr(node, 'flip', 'i', default=0),
             'clip': onnx_attr(node, 'clip', 'i', default=0),
             'variance': list(variance),
diff --git a/tools/mo/openvino/tools/mo/front/onnx/reshape_ext.py b/tools/mo/openvino/tools/mo/front/onnx/reshape_ext.py
index 8dee8bc9f4d..803cb5b78fe 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/reshape_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/reshape_ext.py
@@ -1,8 +1,6 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 from openvino.tools.mo.ops.reshape import Reshape
@@ -15,7 +13,7 @@ class ReshapeFrontExtractor(FrontExtractorOp):
     def extract(cls, node):
         dim = onnx_attr(node, 'shape', 'ints', None)
         if dim is not None:
-            dim = np.array(dim, dtype=np.int64)
+            dim = int64_array(dim)
             Reshape.update_node_stat(node, {'dim': dim})
         else:
             Reshape.update_node_stat(node)
diff --git a/tools/mo/openvino/tools/mo/front/onnx/rnn_ext.py b/tools/mo/openvino/tools/mo/front/onnx/rnn_ext.py
index 159f159ebaf..e242ae36f32 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/rnn_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/rnn_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array, int64_array
 from openvino.tools.mo.ops.RNN import RNN
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -17,9 +18,9 @@ class RNNFrontExtractor(FrontExtractorOp):
         direction = onnx_attr(node, 'direction', 's', b'forward').decode().lower()
 
         activation_alpha = onnx_attr(node, 'activation_alpha', 'floats',
-                                     default=None, dst_type=lambda x: np.array(x, dtype=np.float32))
+                                     default=None, dst_type=lambda x: float32_array(x))
         activation_beta = onnx_attr(node, 'activation_beta', 'floats',
-                                    default=None, dst_type=lambda x: np.array(x, dtype=np.float32))
+                                    default=None, dst_type=lambda x: float32_array(x))
         activations = onnx_attr(node, 'activations', 'strings',
                                 default=['tanh', 'tanh'] if direction == 'bidirectional' else ['tanh'],
                                 dst_type=lambda x: list(map(lambda s: s.decode(encoding="utf-8").lower(), list(x))))
@@ -45,7 +46,7 @@ class RNNFrontExtractor(FrontExtractorOp):
             'activations': activations,
             'clip': clip,
             'direction': direction,
-            'hidden_size': np.array(onnx_attr(node, 'hidden_size', 'i'), dtype=np.int64),
+            'hidden_size': int64_array(onnx_attr(node, 'hidden_size', 'i')),
         }
 
         RNN.update_node_stat(node, attrs)
diff --git a/tools/mo/openvino/tools/mo/front/onnx/roifeatureextractor_ext.py b/tools/mo/openvino/tools/mo/front/onnx/roifeatureextractor_ext.py
index b06e54087ab..cf45faa7473 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/roifeatureextractor_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/roifeatureextractor_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.ops.roifeatureextractor_onnx import ExperimentalDetectronROIFeatureExtractor
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
@@ -20,8 +19,7 @@ class ExperimentalDetectronROIFeatureExtractorFrontExtractor(FrontExtractorOp):
                      num_classes=onnx_attr(node, 'num_classes', 'i', 81),
                      post_nms_count=onnx_attr(node, 'post_nms_count', 'i', 2000),
                      score_threshold=onnx_attr(node, 'score_threshold', 'f', 0.05),
-                     pyramid_scales=np.array(onnx_attr(node, 'pyramid_scales', 'ints', [4, 8, 16, 32, 64]),
-                                             dtype=np.int64),
+                     pyramid_scales=int64_array(onnx_attr(node, 'pyramid_scales', 'ints', [4, 8, 16, 32, 64])),
                      )
 
         ExperimentalDetectronROIFeatureExtractor.update_node_stat(node, attrs)
diff --git a/tools/mo/openvino/tools/mo/front/onnx/squeeze_ext.py b/tools/mo/openvino/tools/mo/front/onnx/squeeze_ext.py
index 6349e933938..c9c856a8056 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/squeeze_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/squeeze_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 from openvino.tools.mo.ops.squeeze import Squeeze
@@ -14,7 +13,7 @@ class SqueezeFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        axis = np.array(onnx_attr(node, 'axes', 'ints', default=[]), dtype=np.int64)
+        axis = int64_array(onnx_attr(node, 'axes', 'ints', default=[]))
 
         attrs = {
             'squeeze_dims': axis if len(axis) != 0 else None
diff --git a/tools/mo/openvino/tools/mo/front/onnx/unsqueeze_ext.py b/tools/mo/openvino/tools/mo/front/onnx/unsqueeze_ext.py
index e169e4a30d0..84bac1f028c 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/unsqueeze_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/unsqueeze_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
 from openvino.tools.mo.ops.expand_dims import ExpandDims
@@ -17,7 +16,7 @@ class UnsqueezeFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        axis = np.array(onnx_attr(node, 'axes', 'ints', default=[]), dtype=np.int64)
+        axis = int64_array(onnx_attr(node, 'axes', 'ints', default=[]))
 
         ExpandDims.update_node_stat(node, {'expand_axis': axis})
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/onnx/upsample_ext.py b/tools/mo/openvino/tools/mo/front/onnx/upsample_ext.py
index 71e5e1375f4..a239295f465 100644
--- a/tools/mo/openvino/tools/mo/front/onnx/upsample_ext.py
+++ b/tools/mo/openvino/tools/mo/front/onnx/upsample_ext.py
@@ -3,8 +3,7 @@
 
 import math
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.ops.ONNXResize10 import ONNXResize10
 from openvino.tools.mo.ops.upsample import UpsampleOp
 from openvino.tools.mo.front.extractor import FrontExtractorOp
@@ -24,7 +23,7 @@ class UpsampleFrontExtractor(FrontExtractorOp):
             ONNXResize10.update_node_stat(node, {'mode': mode})
         else:
             mode = onnx_attr(node, 'mode', 's', default='nearest', dst_type=lambda x: x.decode())
-            scales = onnx_attr(node, 'scales', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32))
+            scales = onnx_attr(node, 'scales', 'floats', dst_type=lambda x: float32_array(x))
             width_scale = onnx_attr(node, 'width_scale', 'f')
             height_scale = onnx_attr(node, 'height_scale', 'f')
 
diff --git a/tools/mo/openvino/tools/mo/front/reciprocal.py b/tools/mo/openvino/tools/mo/front/reciprocal.py
index 608fddd5073..8ea80e7a29d 100644
--- a/tools/mo/openvino/tools/mo/front/reciprocal.py
+++ b/tools/mo/openvino/tools/mo/front/reciprocal.py
@@ -1,9 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.elementwise import Pow
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.const import Const
@@ -14,7 +13,7 @@ class ReciprocalReplacer(FrontReplacementOp):
     enabled = True
 
     def replace_op(self, graph: Graph, node: Node):
-        const = Const(graph, dict(value=np.array(-1.), name=node.name + '/reciprocal_pow_const_')).create_node()
+        const = Const(graph, dict(value=mo_array(-1.), name=node.name + '/reciprocal_pow_const_')).create_node()
         reciprocal = Pow(graph, {'name': node.name + '/reciprocal_pow_'}).create_node()
         node.in_port(0).get_connection().set_destination(reciprocal.in_port(0))
         const.out_port(0).connect(reciprocal.in_port(1))
diff --git a/tools/mo/openvino/tools/mo/front/softsign_replacer.py b/tools/mo/openvino/tools/mo/front/softsign_replacer.py
index 0519df67e0d..2a8410b703e 100644
--- a/tools/mo/openvino/tools/mo/front/softsign_replacer.py
+++ b/tools/mo/openvino/tools/mo/front/softsign_replacer.py
@@ -21,7 +21,7 @@ class SoftSign(FrontReplacementOp):
         abs_node = Abs(graph, {'name': "abs_" + node.id}).create_node()
         abs_node.in_port(0).connect(node.in_port(0).get_source())
 
-        add_node = create_op_node_with_second_input(graph, Add, np.ones([1]), {"name": node.id + "_plus_1"})
+        add_node = create_op_node_with_second_input(graph, Add, np.ones([1], dtype=np.float32), {"name": node.id + "_plus_1"})
         add_node.in_port(0).connect(abs_node.out_port(0))
         div_node = Div(graph, {"name": "div_" + node.id}).create_node()
         div_node.in_port(0).connect(node.in_port(0).get_source())
diff --git a/tools/mo/openvino/tools/mo/front/split_normalizer.py b/tools/mo/openvino/tools/mo/front/split_normalizer.py
index ec0bab4f845..a35bcc98837 100644
--- a/tools/mo/openvino/tools/mo/front/split_normalizer.py
+++ b/tools/mo/openvino/tools/mo/front/split_normalizer.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.ops.split import Split, VariadicSplit
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
@@ -30,7 +31,7 @@ class SqueezeAxis(FrontReplacementOp):
             name = node.soft_get('name', node.id)
             for out_port in node.out_ports().values():
                 if node.has_valid('axis'):
-                    squeeze_node = create_op_with_const_inputs(graph, Squeeze, {1: np.array(node.axis)},
+                    squeeze_node = create_op_with_const_inputs(graph, Squeeze, {1: mo_array(node.axis)},
                                                                {'name': name + '/Squeeze_'})
                     out_port.get_connection().insert_node(squeeze_node)
                 elif node.is_in_port_connected(1):
diff --git a/tools/mo/openvino/tools/mo/front/sub.py b/tools/mo/openvino/tools/mo/front/sub.py
index db7648c27f9..e555792fb65 100644
--- a/tools/mo/openvino/tools/mo/front/sub.py
+++ b/tools/mo/openvino/tools/mo/front/sub.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.ops.elementwise import Mul, Add
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node, rename_node
@@ -36,7 +37,7 @@ class Sub(FrontReplacementPattern):
 
         # restore mathematical equivalence to Sub operation: Sub(A, B) = Add(A, Mul(B, -1))
         const_dtype = sub.soft_get('data_type', np.float32)
-        negate = create_op_with_const_inputs(graph, Mul, {1: np.array(-1, dtype=const_dtype)}, {'name': name + '/neg_'})
+        negate = create_op_with_const_inputs(graph, Mul, {1: mo_array(-1, dtype=const_dtype)}, {'name': name + '/neg_'})
         add.in_port(1).get_connection().insert_node(negate)
 
     def find_and_replace_pattern(self, graph: Graph):
diff --git a/tools/mo/openvino/tools/mo/front/tf/AutomlEfficientDet.py b/tools/mo/openvino/tools/mo/front/tf/AutomlEfficientDet.py
index 3b934be7f3f..8b08fc9c080 100644
--- a/tools/mo/openvino/tools/mo/front/tf/AutomlEfficientDet.py
+++ b/tools/mo/openvino/tools/mo/front/tf/AutomlEfficientDet.py
@@ -10,7 +10,7 @@ from openvino.tools.mo.front.tf.pad_tf_to_pad import PadTFToPad
 from openvino.tools.mo.ops.DetectionOutput import DetectionOutput
 from openvino.tools.mo.ops.activation_ops import Sigmoid
 from openvino.tools.mo.ops.priorbox_clustered import PriorBoxClusteredOp
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array
 from openvino.tools.mo.front.tf.replacement import FrontReplacementFromConfigFileGeneral
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.passes.convert_data_type import data_type_str_to_np
@@ -89,8 +89,8 @@ class EfficientDet(FrontReplacementFromConfigFileGeneral):
             if inp_name not in graph:
                 break
             widths, heights = priors_generator.get(i)
-            prior_box_op = PriorBoxClusteredOp(graph, {'width': np.array(widths),
-                                                       'height': np.array(heights),
+            prior_box_op = PriorBoxClusteredOp(graph, {'width': mo_array(widths),
+                                                       'height': mo_array(heights),
                                                        'clip': 0, 'flip': 0,
                                                        'variance': replacement_descriptions['variance'],
                                                        'offset': 0.5})
diff --git a/tools/mo/openvino/tools/mo/front/tf/FakeQuantWithMinMaxVars.py b/tools/mo/openvino/tools/mo/front/tf/FakeQuantWithMinMaxVars.py
index dfc9084e0de..62821d8195f 100644
--- a/tools/mo/openvino/tools/mo/front/tf/FakeQuantWithMinMaxVars.py
+++ b/tools/mo/openvino/tools/mo/front/tf/FakeQuantWithMinMaxVars.py
@@ -3,14 +3,16 @@
 
 from typing import Dict
 
-from openvino.tools.mo.ops.elementwise import Sub, Div, Less, Round, Mul, Add, Greater
-from openvino.tools.mo.ops.fakequantize import FakeQuantize
-from openvino.tools.mo.ops.select import Select
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+import numpy as np
+
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementOp
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.ops.const import Const
+from openvino.tools.mo.ops.elementwise import Sub, Div, Less, Round, Mul, Add, Greater
+from openvino.tools.mo.ops.fakequantize import FakeQuantize
+from openvino.tools.mo.ops.select import Select
 
 
 class FakeQuantWithMinMaxVarsToQuantize(FrontReplacementOp):
@@ -32,6 +34,13 @@ class FakeQuantWithMinMaxVarsToQuantize(FrontReplacementOp):
         min_port_tuple = (node.in_port(1).get_source().node, node.in_port(1).get_source().idx)
         max_port_tuple = (node.in_port(2).get_source().node, node.in_port(2).get_source().idx)
 
+        if min_port_tuple[0].has_and_set('value') and max_port_tuple[0].has_and_set('value'):
+            assert min_port_tuple[0]['value'].dtype == max_port_tuple[0]['value'].dtype, \
+                'Type mismatch in port 1 and 2 of {}'.format(self.op)
+            dtype = max_port_tuple[0]['value'].dtype
+        else:
+            dtype = np.float32
+
         node.in_port(1).disconnect()
         node.in_port(2).disconnect()
 
@@ -41,7 +50,8 @@ class FakeQuantWithMinMaxVarsToQuantize(FrontReplacementOp):
         maximum = Select(graph, {'name': name + '/maximum'}).create_node([min_less_max, max_port_tuple, min_port_tuple])
 
         # to create zero of limits data type, we multiply it by integer zero
-        zero = create_op_node_with_second_input(graph, Mul, int64_array(0), {'name': name + '/zero'}, input_node=minimum)
+        zero = create_op_node_with_second_input(graph, Mul, mo_array(0, dtype=dtype), {'name': name + '/zero'},
+                                                input_node=minimum)
 
         # if 0 < min < max: min_adj = 0 and max_adj = max - min
         min_greater_zero = Greater(graph, {'name': name + '/if_minimum_greater_zero'}).create_node([minimum, zero])
@@ -58,7 +68,8 @@ class FakeQuantWithMinMaxVarsToQuantize(FrontReplacementOp):
         # scale = (max - min) / (2 ^ num_bits - 1),
         float_range = Sub(graph, {'name': name + '/float_range'}).create_node([maximum, minimum])
         quant_min_value, quant_max_value = int(node.narrow_range), 2 ** node.num_bits - 1
-        int_range = Const(graph, dict(name=name + '/int_range', value=quant_max_value - quant_min_value)).create_node()
+        int_range_value = mo_array(quant_max_value - quant_min_value, dtype=dtype)
+        int_range = Const(graph, dict(name=name + '/int_range', value=int_range_value)).create_node()
         scale = Div(graph, {'name': name + '/scale'}).create_node([float_range, int_range])
         # min_adj = scale * round(min / scale)
         descaled_min = Div(graph, {'name': name + '/descaled_min'}).create_node([minimum, scale])
diff --git a/tools/mo/openvino/tools/mo/front/tf/GNMT_DynamicSequenceLengths.py b/tools/mo/openvino/tools/mo/front/tf/GNMT_DynamicSequenceLengths.py
index 3c4ff14f823..17aea1f2382 100644
--- a/tools/mo/openvino/tools/mo/front/tf/GNMT_DynamicSequenceLengths.py
+++ b/tools/mo/openvino/tools/mo/front/tf/GNMT_DynamicSequenceLengths.py
@@ -3,8 +3,7 @@
 
 import logging as log
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.middle.TensorIteratorCondition import looking_for_op_in_list
 from openvino.tools.mo.ops.elementwise import Mul
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
@@ -115,7 +114,7 @@ class GNMT_sequence_lengths(FrontReplacementPattern):
         tensor_seq_len = looking_for_op_in_list([minimum.in_port(port).get_source().node for port in minimum.in_ports()], 'StridedSlice')
 
         # Create node for multiplying seq_len by 2
-        const = Const(graph, {'name': 'FakeSeqLenMultiplyer', 'value': np.array(2)}).create_node()
+        const = Const(graph, {'name': 'FakeSeqLenMultiplyer', 'value': mo_array(2)}).create_node()
         mul_op = Mul(graph, {'name': 'FakeSeqLen'}).create_node()
 
         const.out_port(0).get_connection().set_destination(mul_op.in_port(1))
diff --git a/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py b/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py
index 6ac18e8efe8..1a971ae5bc9 100644
--- a/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py
+++ b/tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py
@@ -7,7 +7,7 @@ import numpy as np
 
 from openvino.tools.mo.front.tf.WhileNormalize import WhileNormalize
 from openvino.tools.mo.ops.loop import Loop
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.front.tf.custom_subgraph_call import skip_nodes_by_condition
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
@@ -205,7 +205,7 @@ class MapFNOutputConcatenation(FrontReplacementSubgraph):
             if 'purpose' in record and record['purpose'] == 'execution_condition':
                 exec_cond_layer_id = record['internal_layer_id']
                 exec_cond_node = Loop.get_body_node_by_internal_id(loop_node, exec_cond_layer_id)
-                const_true = Const(body_graph, {'value': np.array(True, dtype=np.bool)}).create_node()
+                const_true = Const(body_graph, {'value': mo_array(True, dtype=np.bool)}).create_node()
                 exec_cond_node.in_port(0).get_connection().set_source(const_true.out_port(0))
 
     def find_and_replace_pattern(self, graph: Graph):
diff --git a/tools/mo/openvino/tools/mo/front/tf/ObjectDetectionAPI.py b/tools/mo/openvino/tools/mo/front/tf/ObjectDetectionAPI.py
index 86e034a1406..6f4fd5f6ef9 100644
--- a/tools/mo/openvino/tools/mo/front/tf/ObjectDetectionAPI.py
+++ b/tools/mo/openvino/tools/mo/front/tf/ObjectDetectionAPI.py
@@ -46,7 +46,7 @@ from openvino.tools.mo.ops.psroipooling import PSROIPoolingOp
 from openvino.tools.mo.ops.split import Split
 from openvino.tools.mo.ops.transpose import Transpose
 from openvino.tools.mo.front.common.layout import get_batch_dim, get_height_dim, get_width_dim
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array, dynamic_dimension
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, dynamic_dimension, mo_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementPattern
 from openvino.tools.mo.front.extractor import output_user_data_repack, add_output_ops
 from openvino.tools.mo.front.subgraph_matcher import SubgraphMatch
@@ -137,7 +137,7 @@ def _variance_from_pipeline_config(pipeline_config: PipelineConfig):
     :param pipeline_config: pipeline_config object to get variances from.
     :return: the numpy array with variances.
     """
-    return 1.0 / np.array([pipeline_config.get_param('frcnn_variance_x'),
+    return 1.0 / mo_array([pipeline_config.get_param('frcnn_variance_x'),
                            pipeline_config.get_param('frcnn_variance_y'),
                            pipeline_config.get_param('frcnn_variance_width'),
                            pipeline_config.get_param('frcnn_variance_height')])
@@ -246,7 +246,7 @@ def _create_prior_boxes_node(graph: Graph, pipeline_config: PipelineConfig):
         heights = [h * image_height * base_anchor_size[0] for h in heights]
 
         variance = _variance_from_pipeline_config(pipeline_config)
-        prior_box_op = PriorBoxClusteredOp(graph, {'width': np.array(widths), 'height': np.array(heights),
+        prior_box_op = PriorBoxClusteredOp(graph, {'width': mo_array(widths), 'height': mo_array(heights),
                                                    'clip': 0, 'flip': 0, 'variance': variance, 'offset': 0.5,
                                                    })
         # connect the PriorBoxClustered node with the "Cast" node of the Placeholder node because the pass that removes
@@ -291,7 +291,7 @@ def _create_multiscale_prior_boxes_node(graph: Graph, pipeline_config: PipelineC
         heights = [base_anchor_size * scale / sqrt(ar) for ar in aspect_ratios for scale in scales]
 
         variance = _variance_from_pipeline_config(pipeline_config)
-        prior_box_op = PriorBoxClusteredOp(graph, {'width': np.array(widths), 'height': np.array(heights),
+        prior_box_op = PriorBoxClusteredOp(graph, {'width': mo_array(widths), 'height': mo_array(heights),
                                                    'clip': 0, 'flip': 0, 'variance': variance,
                                                    'offset': 0.5,
                                                    })
@@ -1837,5 +1837,5 @@ class ObjectDetectionAPIConstValueOverride(FrontReplacementFromConfigFileGeneral
             if not node.has_valid('value'):
                 log.debug('Node with id {} does not have value'.format(node_id))
                 continue
-            node.value = np.array(pipeline_config.get_param(pipeline_config_name))
+            node.value = mo_array(pipeline_config.get_param(pipeline_config_name))
             node.value = node.value.reshape(node.shape)
diff --git a/tools/mo/openvino/tools/mo/front/tf/RetinaNetFilteredDetectionsReplacement.py b/tools/mo/openvino/tools/mo/front/tf/RetinaNetFilteredDetectionsReplacement.py
index 263169e88a6..63c07ae4274 100644
--- a/tools/mo/openvino/tools/mo/front/tf/RetinaNetFilteredDetectionsReplacement.py
+++ b/tools/mo/openvino/tools/mo/front/tf/RetinaNetFilteredDetectionsReplacement.py
@@ -8,7 +8,7 @@ from openvino.tools.mo.ops.DetectionOutput import DetectionOutput
 from openvino.tools.mo.ops.elementwise import Mul, Sub, Pow
 from openvino.tools.mo.ops.gather import Gather
 from openvino.tools.mo.ops.split import VariadicSplit
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array, float32_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, float32_array, mo_array
 from openvino.tools.mo.front.subgraph_matcher import SubgraphMatch
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input, create_op_with_const_inputs
 from openvino.tools.mo.front.tf.replacement import FrontReplacementFromConfigFileSubGraph
@@ -57,10 +57,10 @@ class RetinaNetFilteredDetectionsReplacement(FrontReplacementFromConfigFileSubGr
         begin = Const(graph, {'value': int64_array([-2])}).create_node()
         end = Const(graph, {'value': int64_array([-1])}).create_node()
         stride = Const(graph, {'value': int64_array([1])}).create_node()
-        shape_part_for_tiling = StridedSlice(graph, {'name': name + '/get_-2_dim', 'begin_mask': np.array([1]),
-                                                     'end_mask': np.array([1]), 'new_axis_mask': np.array([0]),
-                                                     'shrink_axis_mask': np.array([0]),
-                                                     'ellipsis_mask': np.array([0])}).create_node()
+        shape_part_for_tiling = StridedSlice(graph, {'name': name + '/get_-2_dim', 'begin_mask': int64_array([1]),
+                                                     'end_mask': int64_array([1]), 'new_axis_mask': int64_array([0]),
+                                                     'shrink_axis_mask': int64_array([0]),
+                                                     'ellipsis_mask': int64_array([0])}).create_node()
 
         sp_shape.out_port(0).connect(shape_part_for_tiling.in_port(0))
         begin.out_port(0).connect(shape_part_for_tiling.in_port(1))
@@ -83,7 +83,7 @@ class RetinaNetFilteredDetectionsReplacement(FrontReplacementFromConfigFileSubGr
         sp_reshape.in_port(1).connect(reshape_dim.out_port(0))
 
         concat = Concat(graph,
-                        {'name': name + '/priors_concat', 'axis': np.array(0), 'in_ports_count': 2}).create_node()
+                        {'name': name + '/priors_concat', 'axis': int64_array(0), 'in_ports_count': 2}).create_node()
         sp_reshape.out_port(0).connect(concat.in_port(0))
         tile.out_port(0).connect(concat.in_port(1))
 
@@ -116,9 +116,9 @@ class RetinaNetFilteredDetectionsReplacement(FrontReplacementFromConfigFileSubGr
         begin = Const(graph, {'value': int64_array([1])}).create_node()
         end = Const(graph, {'value': int64_array([3])}).create_node()
         stride = Const(graph, {'value': int64_array([1])}).create_node()
-        spatial = StridedSlice(graph, {'name': name + '/get_h_w', 'begin_mask': np.array([1]),
-                                       'end_mask': np.array([1]), 'new_axis_mask': np.array([0]),
-                                       'shrink_axis_mask': np.array([0]), 'ellipsis_mask': np.array([0])}).create_node()
+        spatial = StridedSlice(graph, {'name': name + '/get_h_w', 'begin_mask': int64_array([1]),
+                                       'end_mask': int64_array([1]), 'new_axis_mask': int64_array([0]),
+                                       'shrink_axis_mask': int64_array([0]), 'ellipsis_mask': int64_array([0])}).create_node()
 
         spatial.in_port(0).connect(shape.out_port(0))
         spatial.in_port(1).connect(begin.out_port(0))
@@ -159,16 +159,16 @@ class RetinaNetFilteredDetectionsReplacement(FrontReplacementFromConfigFileSubGr
         initial_priors_node = match.single_input_node(2)[0]
         priors_name = initial_priors_node.soft_get('name', initial_priors_node.id)
         # model calculates identical prior boxes for each batch, so we take first slice of them
-        begin = Const(graph, {'value': np.array([0, 0, 0], dtype=np.int32)}).create_node()
-        end = Const(graph, {'value': np.array([1, 0, 0], dtype=np.int32)}).create_node()
-        stride = Const(graph, {'value': np.array([1, 1, 1], dtype=np.int32)}).create_node()
+        begin = Const(graph, {'value': mo_array([0, 0, 0], dtype=np.int32)}).create_node()
+        end = Const(graph, {'value': mo_array([1, 0, 0], dtype=np.int32)}).create_node()
+        stride = Const(graph, {'value': mo_array([1, 1, 1], dtype=np.int32)}).create_node()
 
         priors_node = StridedSlice(graph, {'name': priors_name + '/0_batch_slice',
-                                           'begin_mask': np.array([1, 1, 1], dtype=np.int32),
-                                           'end_mask': np.array([1, 0, 0], dtype=np.int32),
-                                           'new_axis_mask': np.array([0], dtype=np.int32),
-                                           'shrink_axis_mask': np.array([0], dtype=np.int32),
-                                           'ellipsis_mask': np.array([0], dtype=np.int32)}).create_node()
+                                           'begin_mask': int64_array([1, 1, 1]),
+                                           'end_mask': int64_array([1, 0, 0]),
+                                           'new_axis_mask': int64_array([0]),
+                                           'shrink_axis_mask': int64_array([0]),
+                                           'ellipsis_mask': int64_array([0])}).create_node()
 
         initial_priors_node.out_port(0).connect(priors_node.in_port(0))
         begin.out_port(0).connect(priors_node.in_port(1))
diff --git a/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py b/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py
index fdcf24d5a58..12e2cc50f4e 100644
--- a/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py
+++ b/tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.ops.loop import Loop
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.graph.graph import Graph, Node
@@ -33,7 +34,7 @@ class WhileNormalize(FrontReplacementSubgraph):
 
         # connect execution condition port
         exec_cond_node = Const(graph, {'name': loop_name + '/ExecutionConditionValue',
-                                       'value': np.array(True, dtype=np.bool)}).create_node()
+                                       'value': mo_array(True, dtype=np.bool)}).create_node()
         loop_node.in_port(1).get_connection().set_source(exec_cond_node.out_port(0))
 
         loop_node.body.clean_up()
diff --git a/tools/mo/openvino/tools/mo/front/tf/bucketize_ext.py b/tools/mo/openvino/tools/mo/front/tf/bucketize_ext.py
index 77b94dbddeb..85a2eec504e 100644
--- a/tools/mo/openvino/tools/mo/front/tf/bucketize_ext.py
+++ b/tools/mo/openvino/tools/mo/front/tf/bucketize_ext.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.ops.bucketize import Bucketize
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 
@@ -13,6 +14,6 @@ class BucketizeFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
-        boundaries = np.array(node.pb.attr['boundaries'].list.f, dtype=np.float)
+        boundaries = float32_array(node.pb.attr['boundaries'].list.f)
         Bucketize.update_node_stat(node, {'boundaries': boundaries, 'with_right_bound': False, 'output_type': np.int32})
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/tf/conv_ext.py b/tools/mo/openvino/tools/mo/front/tf/conv_ext.py
index dec3dd73387..45b0ea72a0e 100644
--- a/tools/mo/openvino/tools/mo/front/tf/conv_ext.py
+++ b/tools/mo/openvino/tools/mo/front/tf/conv_ext.py
@@ -1,8 +1,6 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.front.common.partial_infer.utils import convert_tf_padding_to_str, int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.tf.extractors.utils import tf_data_format_channel, tf_data_format_batch, \
@@ -39,7 +37,7 @@ class DepthwiseConv2dNativeFrontExtractor(FrontExtractorOp):
     def extract(cls, node):
         attrs = tf_create_attrs(node, 2, 2)
         attrs.update({'op': __class__.op,
-                      'kernel_spatial_idx': np.array([0, 1], dtype=np.int64),
+                      'kernel_spatial_idx': int64_array([0, 1]),
                       'get_group': lambda node: node.kernel_shape[node.output_feature_channel],
                       'get_output_feature_dim': lambda node: node.kernel_shape[-1] * node.kernel_shape[-2],
                       'get_weights_permute': PermuteAttrs.Permutation(perm=int64_array([2, 3, 0, 1]),
diff --git a/tools/mo/openvino/tools/mo/front/tf/extractors/fused_bn.py b/tools/mo/openvino/tools/mo/front/tf/extractors/fused_bn.py
index b3b1ef42a03..9db9f9e54d4 100644
--- a/tools/mo/openvino/tools/mo/front/tf/extractors/fused_bn.py
+++ b/tools/mo/openvino/tools/mo/front/tf/extractors/fused_bn.py
@@ -3,14 +3,13 @@
 
 import logging as log
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor
 
 
 def tf_fused_bn_infer(node):
-    output_shape = np.array(node.in_node(0).shape)
+    output_shape = mo_array(node.in_node(0).shape)
     for port, out_node in node.out_nodes().items():
         out_node.shape = shape_array(output_shape)
 
diff --git a/tools/mo/openvino/tools/mo/front/tf/extractors/strided_slice.py b/tools/mo/openvino/tools/mo/front/tf/extractors/strided_slice.py
index 013db1f7429..1646b17d4ec 100644
--- a/tools/mo/openvino/tools/mo/front/tf/extractors/strided_slice.py
+++ b/tools/mo/openvino/tools/mo/front/tf/extractors/strided_slice.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.ops.strided_slice import StridedSlice
 
@@ -11,7 +12,7 @@ def int_to_array_bit_mask(im):
     list_repr = list(np.binary_repr(im))
     list_repr.reverse()
     list_repr = [int(li) for li in list_repr]
-    return np.array(list_repr, dtype=np.int32)
+    return mo_array(list_repr, dtype=np.int32)
 
 
 class StridedSliceFrontExtractor(FrontExtractorOp):
@@ -22,9 +23,9 @@ class StridedSliceFrontExtractor(FrontExtractorOp):
     def extract(cls, node):
         pb = node.pb
         bm = int_to_array_bit_mask(pb.attr["begin_mask"].i)
-        bm = np.array([1 - b for b in bm], dtype=np.int32)
+        bm = mo_array([1 - b for b in bm], dtype=np.int32)
         em = int_to_array_bit_mask(pb.attr["end_mask"].i)
-        em = np.array([1 - b for b in em], dtype=np.int32)
+        em = mo_array([1 - b for b in em], dtype=np.int32)
         attrs = {
             'begin_mask': bm,
             'end_mask': em,
diff --git a/tools/mo/openvino/tools/mo/front/tf/extractors/utils.py b/tools/mo/openvino/tools/mo/front/tf/extractors/utils.py
index 31193ce57b0..5b80f0263e7 100644
--- a/tools/mo/openvino/tools/mo/front/tf/extractors/utils.py
+++ b/tools/mo/openvino/tools/mo/front/tf/extractors/utils.py
@@ -5,6 +5,7 @@ import logging as log
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array, dynamic_dimension_value
 from openvino.tools.mo.front.tf.common import tf_data_type_decode
 from openvino.tools.mo.utils.error import Error
@@ -16,7 +17,7 @@ def tf_tensor_shape(pb):
 
 
 def tf_int_list(pb):
-    return np.array(pb.i, dtype=np.int64)
+    return int64_array(pb.i)
 
 
 def tf_dtype_extractor(pb_dtype, default=None):
@@ -59,17 +60,17 @@ def tf_tensor_content(tf_dtype, shape, pb_tensor):
     decode_err_msg = 'Failed to parse a tensor with Unicode characters. Note that Inference Engine does not support ' \
                      'string literals, so the string constant should be eliminated from the graph.'
     if pb_tensor.tensor_content:
-        value = np.array(np.frombuffer(pb_tensor.tensor_content, type_helper[0]))
+        value = mo_array(np.frombuffer(pb_tensor.tensor_content, type_helper[0]))
     else:
         # load typed value
         if type_helper[0] != np.str:
-            value = np.array(type_helper[1](pb_tensor), dtype=type_helper[0])
+            value = mo_array(type_helper[1](pb_tensor), dtype=type_helper[0])
         else:
             try:
-                value = np.array(type_helper[1](pb_tensor), dtype=type_helper[0])
+                value = mo_array(type_helper[1](pb_tensor), dtype=type_helper[0])
             except UnicodeDecodeError:
                 log.error(decode_err_msg, extra={'is_warning': True})
-                value = np.array(type_helper[1](pb_tensor))
+                value = mo_array(type_helper[1](pb_tensor))
 
     # Ignore an empty value, if len(shape) > 1
     # For example, value = [] and shape = [1, 1, 0]
@@ -85,10 +86,10 @@ def tf_tensor_content(tf_dtype, shape, pb_tensor):
         if value_length == 1:
             # return scalar if shape is [] otherwise broadcast according to shape
             try:
-                return np.array(value[0], dtype=type_helper[0])
+                return mo_array(value[0], dtype=type_helper[0])
             except UnicodeDecodeError:
                 log.error(decode_err_msg, extra={'is_warning': True})
-                return np.array(value[0])
+                return mo_array(value[0])
         else:
             # no shape, return value as is
             return value
diff --git a/tools/mo/openvino/tools/mo/front/tf/fifo_queue_v2_ext.py b/tools/mo/openvino/tools/mo/front/tf/fifo_queue_v2_ext.py
index 58fb1eac92f..1082b9b2bcf 100644
--- a/tools/mo/openvino/tools/mo/front/tf/fifo_queue_v2_ext.py
+++ b/tools/mo/openvino/tools/mo/front/tf/fifo_queue_v2_ext.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor
 from openvino.tools.mo.ops.op import Op
@@ -23,8 +22,8 @@ class FIFOQueueV2Extractor(FrontExtractorOp):
         for shape_pb in shapes:
             shape = shape_pb.dim
             if len(shape) == 3:
-                result_shapes.append(np.array([1, shape[0].size, shape[1].size, shape[2].size], dtype=np.int64))
+                result_shapes.append(int64_array([1, shape[0].size, shape[1].size, shape[2].size]))
             else:
-                result_shapes.append(np.array([dim.size for dim in shape], dtype=np.int64))
+                result_shapes.append(int64_array([dim.size for dim in shape]))
         Op.update_node_stat(node, {'shapes': result_shapes, 'types': extracted_types})
         return cls.enabled
diff --git a/tools/mo/openvino/tools/mo/front/tf/graph_utils.py b/tools/mo/openvino/tools/mo/front/tf/graph_utils.py
index a573fb01214..cdfbdeb83e0 100644
--- a/tools/mo/openvino/tools/mo/front/tf/graph_utils.py
+++ b/tools/mo/openvino/tools/mo/front/tf/graph_utils.py
@@ -10,6 +10,7 @@ from openvino.tools.mo.middle.InsertLayoutPropagationTransposes import mark_inpu
 from openvino.tools.mo.ops.activation_ops import Sigmoid
 from openvino.tools.mo.ops.elementwise import Add, Less, Mul
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.concat import Concat
 from openvino.tools.mo.ops.const import Const
@@ -74,14 +75,14 @@ def add_convolution_to_swap_xy_coordinates(graph: Graph, input_node: Node, coord
 
     if coordinates_size == 5:
         # zero indexed element is not box coordinate ("batch id" in case of Proposal)
-        conv_filter_data = np.array(np.array([[[[1, 0, 0, 0, 0],
+        conv_filter_data = mo_array(mo_array([[[[1, 0, 0, 0, 0],
                                                 [0, 0, 1, 0, 0],
                                                 [0, 1, 0, 0, 0],
                                                 [0, 0, 0, 0, 1],
                                                 [0, 0, 0, 1, 0]]]],
                                              dtype=np.float32))
     else:
-        conv_filter_data = np.array(np.array([[[[0, 1, 0, 0],
+        conv_filter_data = mo_array(mo_array([[[[0, 1, 0, 0],
                                                 [1, 0, 0, 0],
                                                 [0, 0, 0, 1],
                                                 [0, 0, 1, 0]]]],
@@ -94,8 +95,8 @@ def add_convolution_to_swap_xy_coordinates(graph: Graph, input_node: Node, coord
 
     conv_op = Convolution(graph, {
         'bias_addable': True,
-        'channel_dims': np.array([3]),
-        'batch_dims': np.array([0]),
+        'channel_dims': mo_array([3]),
+        'batch_dims': mo_array([0]),
         'input_feature_channel': 0,
         'output_feature_channel': 1,
         'group': 1,
@@ -116,7 +117,7 @@ def add_fake_background_loc(graph: Graph, input_node: Node):
     :param input_node: node producing the boxes coordinates.
     :return convolution node that adds slice of data for the "background" class.
     """
-    crop_op = Crop(graph, dict(axis=np.array([1]), offset=np.array([0]), dim=np.array([1]), nchw_layout=True))
+    crop_op = Crop(graph, dict(axis=mo_array([1]), offset=mo_array([0]), dim=mo_array([1]), nchw_layout=True))
     crop_node = crop_op.create_node([input_node], dict(name='crop_locs'))
 
     concat_op = Concat(graph, dict(axis=1, in_ports_count=2, nchw_layout=True))
@@ -164,7 +165,7 @@ def add_constant_to_negative_values(node: Node, port_idx: int, added_value: np.a
     graph = node.graph
 
     less_node = create_op_with_const_inputs(graph, Less,
-                                            {1: np.array(0, dtype=added_value.dtype)},
+                                            {1: mo_array(0, dtype=added_value.dtype)},
                                             {'name': negative_values_node_name + '/Less'})
     mul_node = create_op_with_const_inputs(graph, Mul, {1: added_value}, {'name': negative_values_node_name + '/Mul'})
 
diff --git a/tools/mo/openvino/tools/mo/front/tf/nearest_neighbor_upsampling.py b/tools/mo/openvino/tools/mo/front/tf/nearest_neighbor_upsampling.py
index df6b9c12581..b489da81e6a 100644
--- a/tools/mo/openvino/tools/mo/front/tf/nearest_neighbor_upsampling.py
+++ b/tools/mo/openvino/tools/mo/front/tf/nearest_neighbor_upsampling.py
@@ -3,11 +3,9 @@
 
 import logging as log
 
-import numpy as np
-
 from openvino.tools.mo.front.Pack import Pack
 from openvino.tools.mo.ops.interpolate import Interpolate
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array, float32_array
 from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.ops.const import Const
@@ -70,9 +68,9 @@ class NearestNeighborUpsampling(FrontReplacementSubgraph):
                               'name': resample_node.name + '/axes',
                               'value': int64_array([2, 3]) if graph.graph['layout'] == 'NCHW' else int64_array([1, 2])
                           }).create_node()
-        sizes_node = Const(graph, {'value': np.array([input_height * height_scale, input_width * width_scale]),
+        sizes_node = Const(graph, {'value': mo_array([input_height * height_scale, input_width * width_scale]),
                                    'name': resample_node.name + '/target_shape'}).create_node()
-        scales_node = Const(graph, {'value': np.array([height_scale, width_scale], dtype=np.float32),
+        scales_node = Const(graph, {'value': float32_array([height_scale, width_scale]),
                                     'name': resample_node.name + '/scales'}).create_node()
 
         match['reshape_2'].replace_node(resample_node)
diff --git a/tools/mo/openvino/tools/mo/front/tf/partial_infer/tf.py b/tools/mo/openvino/tools/mo/front/tf/partial_infer/tf.py
index b4d420af973..0fb5c3c04a6 100644
--- a/tools/mo/openvino/tools/mo/front/tf/partial_infer/tf.py
+++ b/tools/mo/openvino/tools/mo/front/tf/partial_infer/tf.py
@@ -18,6 +18,7 @@ from openvino.tools.mo.front.extractor import node_defs_to_str
 from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor, tf_tensor_shape, get_tf_node_port
 from openvino.tools.mo.graph.graph import Node
 from openvino.tools.mo.utils.graph import node_incoming_neighbourhood, node_outcoming_neighbourhood
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 
 
 def tf_native_tf_node_infer(node: Node):
@@ -54,9 +55,9 @@ def tf_native_tf_node_infer(node: Node):
     # the shape and value has been inferred and saved to the tmp_node's out nodes attribute. Let's copy it back!
     for tmp_out_port, tmp_out_node in tmp_node.out_nodes().items():
         if tmp_out_node.value is not None:
-            node.out_node(tmp_out_port).value = np.array(tmp_out_node.value)
+            node.out_node(tmp_out_port).value = mo_array(tmp_out_node.value)
         if tmp_out_node.shape is not None:
-            node.out_node(tmp_out_port).shape = np.array(tmp_out_node.shape)
+            node.out_node(tmp_out_port).shape = mo_array(tmp_out_node.shape)
         if tmp_out_node.data_type is not None:
             node.out_node(tmp_out_port).data_type = tmp_out_node.data_type
     # lets cleanup the temporary graph
@@ -128,7 +129,7 @@ def tf_subgraph_infer(node: Node):
     all_constants, output_tensors = get_subgraph_output_tensors(node)
     for out_port, tensor_value in output_tensors.items():
         out_node = node.out_node(out_port)
-        out_node.shape = np.array([dim for dim in tensor_value.shape])
+        out_node.shape = mo_array([dim for dim in tensor_value.shape])
         out_node.data_type = tensor_value.dtype
         log.debug("Inferred shape of the output tensor with index '{}' of the node '{}': '{}'".format(str(out_port),
                                                                                                       node.name,
diff --git a/tools/mo/openvino/tools/mo/main.py b/tools/mo/openvino/tools/mo/main.py
index 132c04d6c88..339077b2251 100644
--- a/tools/mo/openvino/tools/mo/main.py
+++ b/tools/mo/openvino/tools/mo/main.py
@@ -11,8 +11,6 @@ import traceback
 from collections import OrderedDict
 from copy import deepcopy
 
-import numpy as np
-
 try:
     import openvino_telemetry as tm
 except ImportError:
@@ -42,6 +40,7 @@ from openvino.tools.mo.utils.telemetry_utils import send_params_info, send_frame
 from openvino.tools.mo.utils.version import get_simplified_mo_version, get_simplified_ie_version
 from openvino.tools.mo.utils.versions_checker import check_requirements  # pylint: disable=no-name-in-module
 from openvino.tools.mo.utils.telemetry_utils import get_tid
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 
 # pylint: disable=no-name-in-module,import-error
 from openvino.frontend import FrontEndManager, TelemetryExtension
@@ -204,8 +203,7 @@ def arguments_post_parsing(argv: argparse.Namespace):
     except Exception as e:
         raise_ie_not_found()
 
-    # temporary disable new FP16 generation
-    if False and 'data_type' in argv and argv.data_type in ['FP16', 'half']:
+    if 'data_type' in argv and argv.data_type in ['FP16', 'half']:
         argv.data_type = 'FP32'
         argv.compress_fp16 = True
     else:
@@ -233,7 +231,7 @@ def arguments_post_parsing(argv: argparse.Namespace):
                     refer_to_faq_msg(17))
     elif is_caffe and argv.mean_file and argv.mean_file_offsets:
         values = get_tuple_values(argv.mean_file_offsets, t=int, num_exp_values=2)
-        mean_file_offsets = np.array([int(x) for x in values[0].split(',')])
+        mean_file_offsets = mo_array([int(x) for x in values[0].split(',')])
         if not all([offset >= 0 for offset in mean_file_offsets]):
             raise Error("Negative value specified for --mean_file_offsets option. "
                         "Please specify positive integer values in format '(x,y)'. " +
diff --git a/tools/mo/openvino/tools/mo/middle/AddMeanScaleValues.py b/tools/mo/openvino/tools/mo/middle/AddMeanScaleValues.py
index b964a8f88bf..6e95697839d 100644
--- a/tools/mo/openvino/tools/mo/middle/AddMeanScaleValues.py
+++ b/tools/mo/openvino/tools/mo/middle/AddMeanScaleValues.py
@@ -52,6 +52,11 @@ class AddMeanScaleValues(MiddleReplacementPattern):
         name = input_node.soft_get('name', input_node.id) + '/' + preprocessing_name
         preprocessing = create_op_with_const_inputs(graph, op=op, port_value_dict={1: value}, op_attrs={'name': name})
 
+        if input_node.op == 'Parameter' and input_node.has_and_set('data_type'):
+            dtype = input_node.data_type
+            if np.issubdtype(dtype, np.floating):
+                value = value.astype(dtype)
+
         if input_node.is_out_port_connected(0) and len(input_node.out_port(0).get_destinations()) == 1:
             # There are models with pattern Parameter(uint8) -> Convert(float).
             # Adding mean/scale leads to the following:
diff --git a/tools/mo/openvino/tools/mo/middle/ApplyPermutations.py b/tools/mo/openvino/tools/mo/middle/ApplyPermutations.py
index 79b3bd39536..db3fcb0b72c 100644
--- a/tools/mo/openvino/tools/mo/middle/ApplyPermutations.py
+++ b/tools/mo/openvino/tools/mo/middle/ApplyPermutations.py
@@ -3,12 +3,11 @@
 
 import logging as log
 
-import numpy as np
-
 from openvino.tools.mo.middle.InsertLayoutPropagationTransposes import is_input_data_in_correct_layout, \
     is_output_data_in_correct_layout
 from openvino.tools.mo.middle.LayoutChangeForConstantShapePaths import LayoutChangeForConstantShapePaths
 from openvino.tools.mo.middle.PreserveRuntimeInfo import PreserveRuntimeInfo
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.graph.perm_inputs import get_node_with_permutation
@@ -59,7 +58,7 @@ class ApplyPermutation(MiddleReplacementPattern):
                 assert len(node.value.shape) == len(node.permutation.perm), \
                     'Node {} has shape {} and permutation {} that does not match. Their lengths should be equal' \
                     ''.format(node.name, node.value.shape, node.permutation.perm)
-                node.value = np.array(node.value.transpose(node.permutation.perm))
+                node.value = mo_array(node.value.transpose(node.permutation.perm))
 
     @staticmethod
     def permute_op_nodes_attrs(graph: Graph):
diff --git a/tools/mo/openvino/tools/mo/middle/BinarizeWeightsM1P1.py b/tools/mo/openvino/tools/mo/middle/BinarizeWeightsM1P1.py
index 01c89407760..bae2ca8dd4c 100644
--- a/tools/mo/openvino/tools/mo/middle/BinarizeWeightsM1P1.py
+++ b/tools/mo/openvino/tools/mo/middle/BinarizeWeightsM1P1.py
@@ -9,6 +9,7 @@ from openvino.tools.mo.middle.CheckForCycle import CheckForCycle
 from openvino.tools.mo.middle.DeleteNotExecutable import DeleteNotExecutable
 from openvino.tools.mo.ops.elementwise import Mul, Pow
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.const import Const
@@ -116,7 +117,7 @@ class BinarizeWeightsM1P1(MiddleReplacementPattern):
             log.debug('BinarizeWeightsM1P1: len(match[\'quantized\'].out_nodes()) > 1')
             return
         power_of_exponent = Const(graph, {'name': quantize_name + '/DivNormalize/Power',
-                                          'value': np.array(-1.0)}).create_node_with_data()
+                                          'value': mo_array(-1.0)}).create_node_with_data()
         div_op = Pow(graph, {'name': quantize_name + '/DivNormalize'})
         div_output = div_op.create_node_with_data([mult_term, power_of_exponent])
 
diff --git a/tools/mo/openvino/tools/mo/middle/BlockLSTMtoLSTMSequence.py b/tools/mo/openvino/tools/mo/middle/BlockLSTMtoLSTMSequence.py
index c91c1bf3eb0..dc9a60c53f2 100644
--- a/tools/mo/openvino/tools/mo/middle/BlockLSTMtoLSTMSequence.py
+++ b/tools/mo/openvino/tools/mo/middle/BlockLSTMtoLSTMSequence.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.ops.LSTM import LSTM
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.utils.error import Error
@@ -182,9 +183,9 @@ class BlockLSTMtoLSTMSequence(MiddleReplacementPattern):
         weights = weights.transpose()
 
         weights_node.value = weights
-        weights_node.shape = np.array(weights.shape, dtype=np.int64)
+        weights_node.shape = int64_array(weights.shape)
         biases_node.value = biases
-        biases_node.shape = np.array(biases.shape, dtype=np.int64)
+        biases_node.shape = int64_array(biases.shape)
 
         attrs = dict(graph.get_edge_data(match['gather_1'].id, match['gather_1_data'].id)[0])
         attrs.update({'out': 2})
diff --git a/tools/mo/openvino/tools/mo/middle/ConvToBinaryConv.py b/tools/mo/openvino/tools/mo/middle/ConvToBinaryConv.py
index 94525c98619..af5744078cf 100644
--- a/tools/mo/openvino/tools/mo/middle/ConvToBinaryConv.py
+++ b/tools/mo/openvino/tools/mo/middle/ConvToBinaryConv.py
@@ -6,6 +6,7 @@ import logging as log
 import numpy as np
 
 from openvino.tools.mo.ops.elementwise import Mul, Add
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -80,7 +81,7 @@ class ConvToBinaryConv(MiddleReplacementPattern):
 
             operator_name = operator.soft_get('name', operator.id)
             add = create_op_node_with_second_input(graph, Add, weights_reduced, {'name': operator_name + '/Add_'})
-            mul = create_op_node_with_second_input(graph, Mul, np.array(0.5), {'name': operator_name + '/Mul_'})
+            mul = create_op_node_with_second_input(graph, Mul, mo_array(0.5), {'name': operator_name + '/Mul_'})
 
             add.out_port(0).connect(mul.in_port(0))
 
@@ -108,5 +109,5 @@ class ConvToBinaryConv(MiddleReplacementPattern):
         output_high = quantize.in_node(4)
 
         # Make sure that low/high values are exactly 0/1
-        output_low.value = np.zeros(output_low.shape)
-        output_high.value = np.ones(output_high.shape)
+        output_low.value = np.zeros(output_low.shape, dtype=np.float32)
+        output_high.value = np.ones(output_high.shape, dtype=np.float32)
diff --git a/tools/mo/openvino/tools/mo/middle/ConvertGroupedStridedSlice.py b/tools/mo/openvino/tools/mo/middle/ConvertGroupedStridedSlice.py
index b3643d513b9..39e44e03475 100644
--- a/tools/mo/openvino/tools/mo/middle/ConvertGroupedStridedSlice.py
+++ b/tools/mo/openvino/tools/mo/middle/ConvertGroupedStridedSlice.py
@@ -9,6 +9,7 @@ import numpy as np
 from openvino.tools.mo.middle.SliceConverter import ConvertSlice
 from openvino.tools.mo.ops.split import VariadicSplit
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph, Node, add_opoutput
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.const import Const
@@ -161,7 +162,7 @@ class ConvertGroupedStridedSlice(MiddleReplacementPattern):
             for l, r, out in sorted_split_dims:
                 # Save missing tensor part
                 if l > prev_r:
-                    shape = np.array(input_shape)
+                    shape = mo_array(input_shape)
                     size_splits.append(l - prev_r)
                     shape[split_channel_dim] = l - prev_r
                     data_node = Op._create_data_node(graph, 'fake_data_'+out_nodes[0].name, {'shape': shape})
@@ -225,7 +226,7 @@ class ConvertGroupedStridedSlice(MiddleReplacementPattern):
             return
 
         shape_out = ss_node.out_node().shape
-        dim = np.array(range(len(ss_node['shrink_axis_mask'])))[np.array(ss_node['shrink_axis_mask'], dtype=bool)]
+        dim = mo_array(range(len(ss_node['shrink_axis_mask'])))[mo_array(ss_node['shrink_axis_mask'], dtype=bool)]
         ss_shape = []
         i = 0
         k = 0
@@ -268,7 +269,7 @@ class ConvertGroupedStridedSlice(MiddleReplacementPattern):
             return
 
         shape_out = ss_node.out_node().shape
-        dim = np.array(range(len(ss_node['new_axis_mask'])))[np.array(ss_node['new_axis_mask'], dtype=bool)]
+        dim = mo_array(range(len(ss_node['new_axis_mask'])))[mo_array(ss_node['new_axis_mask'], dtype=bool)]
         ss_shape = []
         for i in range(0, len(ss_node['new_axis_mask'])):
             if not ss_node['new_axis_mask'][i]:
diff --git a/tools/mo/openvino/tools/mo/middle/CustomSubgraphCall.py b/tools/mo/openvino/tools/mo/middle/CustomSubgraphCall.py
index 68c2656bb57..38d9b332aea 100644
--- a/tools/mo/openvino/tools/mo/middle/CustomSubgraphCall.py
+++ b/tools/mo/openvino/tools/mo/middle/CustomSubgraphCall.py
@@ -7,6 +7,7 @@ import logging as log
 import numpy as np
 
 from openvino.tools.mo.front.common.layout import nhwc_to_nchw_permute
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array, shape_insert
 from openvino.tools.mo.front.extractor import update_ie_fields
 from openvino.tools.mo.graph.graph import Graph
@@ -206,15 +207,15 @@ class CustomSubgraphCall(MiddleReplacementPattern):
         # reshape shape data node
         reshape_shape_data_node_name = graph.unique_id("Reshape_shape_")
         graph.add_node(reshape_shape_data_node_name, kind='data', name=reshape_shape_data_node_name,
-                       value=np.array(data_node['shape']), shape=[1])
+                       value=mo_array(data_node['shape']), shape=[1])
 
         # reshaped data node
         reshaped_value = None
         if data_node['value'] is not None:
-            reshaped_value = np.array(data_node['value'])
+            reshaped_value = mo_array(data_node['value'])
         reshaped_data_node_name = graph.unique_id("reshaped_data_")
         graph.add_node(reshaped_data_node_name, kind='data', name=reshaped_data_node_name,
-                       shape=np.array(data_node['shape']), value=reshaped_value, nchw_layout=True)
+                       shape=mo_array(data_node['shape']), value=reshaped_value, nchw_layout=True)
 
         if is_out_node:
             add_opoutput(graph, reshaped_data_node_name, 0, False)
diff --git a/tools/mo/openvino/tools/mo/middle/DecomposeBias.py b/tools/mo/openvino/tools/mo/middle/DecomposeBias.py
index e22294a0931..cb5e4167640 100644
--- a/tools/mo/openvino/tools/mo/middle/DecomposeBias.py
+++ b/tools/mo/openvino/tools/mo/middle/DecomposeBias.py
@@ -1,9 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.elementwise import Add
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph, rename_nodes
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -44,7 +43,7 @@ class DecomposeBias(MiddleReplacementPattern):
                     dims_to_add = len(input_shape) - 2 if graph.graph['layout'] == 'NCHW' else 0
                     if dims_to_add > 0:
                         reshape = create_op_node_with_second_input(
-                            graph, Reshape, np.array([input_shape[1]] + [1] * dims_to_add, dtype=np.int64),
+                            graph, Reshape, int64_array([input_shape[1]] + [1] * dims_to_add),
                             {'name': node.id + '/Dims'})
                         add.in_port(1).get_connection().set_destination(reshape.in_port(0))
                         reshape.out_port(0).connect(add.in_port(1))
diff --git a/tools/mo/openvino/tools/mo/middle/DilatedConvolution.py b/tools/mo/openvino/tools/mo/middle/DilatedConvolution.py
index f182bbc3789..26a51de6cbe 100644
--- a/tools/mo/openvino/tools/mo/middle/DilatedConvolution.py
+++ b/tools/mo/openvino/tools/mo/middle/DilatedConvolution.py
@@ -5,6 +5,7 @@ import logging as log
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_insert
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -181,7 +182,7 @@ class DilatedConvolution1DConverter(MiddleReplacementPattern):
             log.debug('The block size must contain 1 element')
             return
 
-        unsqueeze_dims = np.array(unsqueeze.in_port(1).data.get_value())
+        unsqueeze_dims = mo_array(unsqueeze.in_port(1).data.get_value())
         if unsqueeze_dims.size != 1 or unsqueeze_dims.item() != 1:
             log.debug('The Unsqueeze dimension is not equal to 1')
             return
diff --git a/tools/mo/openvino/tools/mo/middle/EltwiseChecker.py b/tools/mo/openvino/tools/mo/middle/EltwiseChecker.py
index cab2c238e27..4797f8b864b 100644
--- a/tools/mo/openvino/tools/mo/middle/EltwiseChecker.py
+++ b/tools/mo/openvino/tools/mo/middle/EltwiseChecker.py
@@ -63,7 +63,7 @@ class EltwiseChecker(MiddleReplacementPattern):
         feature_dim = min(1, tensor_shape.size - 1) if node.graph.graph['layout'] == 'NCHW' else -1
         if feature_channel is not None:
             feature_dim = feature_channel
-        ones = np.ones(len(tensor_shape))
+        ones = np.ones(len(tensor_shape), dtype=np.float32)
         possible_shape = ones.copy()
         np.put(possible_shape, feature_dim, tensor_shape.item(feature_dim))
 
diff --git a/tools/mo/openvino/tools/mo/middle/FusedBatchNormNonConstant.py b/tools/mo/openvino/tools/mo/middle/FusedBatchNormNonConstant.py
index 26badb4f59a..a7ae32d5901 100644
--- a/tools/mo/openvino/tools/mo/middle/FusedBatchNormNonConstant.py
+++ b/tools/mo/openvino/tools/mo/middle/FusedBatchNormNonConstant.py
@@ -1,9 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.elementwise import Mul, Add, Pow
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.const import Const
@@ -51,7 +50,7 @@ class FusedBatchNormNonConstant(MiddleReplacementPattern):
         mean_add = Add(graph, dict(name=node.name + '/mean_add_'))
         variance_mul = Mul(graph, dict(name=node.name + '/variance_mul_'))
 
-        neg_const = Const(graph, dict(value=np.array(-1), name=node.name + '/mean_negate_'))
+        neg_const = Const(graph, dict(value=mo_array(-1), name=node.name + '/mean_negate_'))
         mean_negate = Mul(graph, dict(name=node.name + '/mean_negate_'))
         mean_arg = mean_add.create_node_with_data([
             node.in_node(0),
diff --git a/tools/mo/openvino/tools/mo/middle/GroupNorm.py b/tools/mo/openvino/tools/mo/middle/GroupNorm.py
index 36c23b79710..7a4879212f7 100644
--- a/tools/mo/openvino/tools/mo/middle/GroupNorm.py
+++ b/tools/mo/openvino/tools/mo/middle/GroupNorm.py
@@ -10,6 +10,7 @@ from openvino.tools.mo.ops.elementwise import Mul, Add
 from openvino.tools.mo.ops.mvn import MVN
 from openvino.tools.mo.ops.range import Range
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.passes.convert_data_type import data_type_str_to_np
@@ -66,7 +67,7 @@ class GroupNormToMVN(MiddleReplacementPattern):
                                         'name': group_norm_node.name + '/GroupSize'}).create_node()
 
         # calculate "features // group_size" value
-        reciprocal_group_size_node = Const(graph, {'value': np.array([1.0 / group_norm_node.num_groups]),
+        reciprocal_group_size_node = Const(graph, {'value': mo_array([1.0 / group_norm_node.num_groups]),
                                                    'name': group_norm_node.name + '/ReciprocalGroupSize'}).create_node()
 
         c_div_g_node = Mul(graph, {}).create_node()
diff --git a/tools/mo/openvino/tools/mo/middle/InterpolateSequenceToInterpolate.py b/tools/mo/openvino/tools/mo/middle/InterpolateSequenceToInterpolate.py
index be2e6332a5b..c2098059715 100644
--- a/tools/mo/openvino/tools/mo/middle/InterpolateSequenceToInterpolate.py
+++ b/tools/mo/openvino/tools/mo/middle/InterpolateSequenceToInterpolate.py
@@ -2,11 +2,13 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import logging as log
-import numpy as np
 from typing import List
 
+import numpy as np
+
 from openvino.tools.mo.ops.interpolate import Interpolate
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node, rename_nodes
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -201,7 +203,7 @@ def replace_sequence(seq: List[Node], graph: Graph):
         axis_to_size = sorted(list(dict(dims_and_scales_).items()), key=lambda x: x[0])
         axes_of_node = int64_array([z[0] for z in axis_to_size])
         sizes = shape_array([z[1] for z in axis_to_size])
-        scales = np.ones(len(axis_to_size))
+        scales = np.ones(len(axis_to_size), dtype=np.float32)
     else:
         for interp in seq:
             dims_and_scales_.extend(zip(Interpolate.get_axes(interp),
@@ -211,7 +213,7 @@ def replace_sequence(seq: List[Node], graph: Graph):
         axis_to_size = sorted(dims_and_scales_, key=lambda x: x[0])
         axes_of_node = int64_array([z[0] for z in axis_to_size])
         sizes = shape_array([z[1] for z in axis_to_size])
-        scales = np.array([z[2] for z in axis_to_size])
+        scales = mo_array([z[2] for z in axis_to_size])
 
     fst_interp_node = seq[0]
     last_interp_node = seq[-1]
diff --git a/tools/mo/openvino/tools/mo/middle/MXNetRNNSequenceNormalize.py b/tools/mo/openvino/tools/mo/middle/MXNetRNNSequenceNormalize.py
index 9d46e881f3a..4b2499d329d 100644
--- a/tools/mo/openvino/tools/mo/middle/MXNetRNNSequenceNormalize.py
+++ b/tools/mo/openvino/tools/mo/middle/MXNetRNNSequenceNormalize.py
@@ -4,7 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.ops.transpose import Transpose
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_insert
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_insert, mo_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.const import Const
@@ -85,13 +85,13 @@ class MXNetRNNSequenceNormalize(MiddleReplacementPattern):
         direction = 2 if rnn_layer.has_num_directions else 1
         bsize = (2 * rnn_layer.hidden_size * direction * 1) * rnn_layer.multiplier
 
-        W = np.array(params[0:len(params) - bsize])
-        B = np.array(params[len(params) - bsize:])
+        W = mo_array(params[0:len(params) - bsize])
+        B = mo_array(params[len(params) - bsize:])
 
         W = W.reshape((direction, -1))
         B = B.reshape((direction, -1))
 
-        W, R = np.array(W[:, 0:rnn_layer.hidden_size * rnn_layer.multiplier * input_size]), np.array(W[:, rnn_layer.hidden_size * rnn_layer.multiplier* input_size:])
+        W, R = mo_array(W[:, 0:rnn_layer.hidden_size * rnn_layer.multiplier * input_size]), mo_array(W[:, rnn_layer.hidden_size * rnn_layer.multiplier* input_size:])
 
         W, R = [x.reshape([
             direction,  # 0: num of directions
@@ -125,7 +125,7 @@ class MXNetRNNSequenceNormalize(MiddleReplacementPattern):
             Op.create_and_connect_input_data_node(
                 graph,
                 rnn_layer,
-                {'value': blob, 'shape': np.array(blob.shape, dtype=np.int64)},
+                {'value': blob, 'shape': int64_array(blob.shape)},
                 {'in': port, 'permutation': None}
             )
 
@@ -148,7 +148,7 @@ class MXNetRNNSequenceNormalize(MiddleReplacementPattern):
             Op.create_and_connect_input_data_node(
                 graph,
                 rnn_cell,
-                {'value': h_init, 'shape': np.array(h_init.shape, dtype=np.int64)},
+                {'value': h_init, 'shape': int64_array(h_init.shape)},
                 {'in': h_init_port, 'permutation': None}
             )
         else:
@@ -162,7 +162,7 @@ class MXNetRNNSequenceNormalize(MiddleReplacementPattern):
                 Op.create_and_connect_input_data_node(
                     graph,
                     rnn_cell,
-                    {'value': c_init, 'shape': np.array(c_init.shape, dtype=np.int64)},
+                    {'value': c_init, 'shape': int64_array(c_init.shape)},
                     {'in': c_init_port, 'permutation': None}
                 )
             else:
diff --git a/tools/mo/openvino/tools/mo/middle/MXNetSplitMultiLayers.py b/tools/mo/openvino/tools/mo/middle/MXNetSplitMultiLayers.py
index 29976709157..48775ab669b 100644
--- a/tools/mo/openvino/tools/mo/middle/MXNetSplitMultiLayers.py
+++ b/tools/mo/openvino/tools/mo/middle/MXNetSplitMultiLayers.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_insert, int64_array
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -139,7 +140,7 @@ class MXNetSplitLayersToRNNSequence(MiddleReplacementPattern):
             output_hidden = Op._create_data_node(
                 rnn_layer.graph,
                 name=rnn_layer.out_node(1).name + '/LayerSplit/' + str(l),
-                attrs={'shape': np.array(state_size)}
+                attrs={'shape': mo_array(state_size)}
             )
 
             current_data_nodes = [output_data, output_hidden]
@@ -148,7 +149,7 @@ class MXNetSplitLayersToRNNSequence(MiddleReplacementPattern):
                 output_cell = Op._create_data_node(
                     rnn_layer.graph,
                     name=rnn_layer.out_node(2).name + '/LayerSplit/' + str(l),
-                    attrs={'shape': np.array(state_size)}
+                    attrs={'shape': mo_array(state_size)}
                 )
                 current_data_nodes.append(output_cell)
 
diff --git a/tools/mo/openvino/tools/mo/middle/MakeKaldiConstReshapable.py b/tools/mo/openvino/tools/mo/middle/MakeKaldiConstReshapable.py
index 4ec96ce1803..d35d2d0985d 100644
--- a/tools/mo/openvino/tools/mo/middle/MakeKaldiConstReshapable.py
+++ b/tools/mo/openvino/tools/mo/middle/MakeKaldiConstReshapable.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input, create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Port
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -63,13 +64,13 @@ def create_const_with_batch_from_input(producer_port: Port, second_dim, value=0,
     init_value_prev_lstm_output = None
     for dest in mem_shape.out_port(0).get_destinations():
         if dest.node.soft_get('op') == "Broadcast" and \
-                dest.node.in_port(1).get_source().node.soft_get('value', []) == np.array([value], dtype=precision):
+                dest.node.in_port(1).get_source().node.soft_get('value', []) == mo_array([value], dtype=precision):
             init_value_prev_lstm_output = dest.node
             break
 
     if init_value_prev_lstm_output is None:
         init_value_prev_lstm_output = create_op_with_const_inputs(graph, Broadcast,
-                                                                  {0: np.array([value], dtype=precision)},
+                                                                  {0: mo_array([value], dtype=precision)},
                                                                   {'name': mem_shape.name + '/Broadcast'})
         init_value_prev_lstm_output.in_port(1).connect(mem_shape.out_port(0))
 
diff --git a/tools/mo/openvino/tools/mo/middle/MulFakeQuantizeFuse.py b/tools/mo/openvino/tools/mo/middle/MulFakeQuantizeFuse.py
index e969986ed91..f801b5a0da1 100644
--- a/tools/mo/openvino/tools/mo/middle/MulFakeQuantizeFuse.py
+++ b/tools/mo/openvino/tools/mo/middle/MulFakeQuantizeFuse.py
@@ -7,6 +7,7 @@ from typing import Dict, List
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.passes.fusing.helpers import get_tensor_in_port, get_value_in_port
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -29,7 +30,7 @@ def resolve_shared_inputs(node: Node, port_ids_to_duplicate: List[int]):
         if value is None:
             log.debug('Can not duplicate due no data for in_port {} of node {}'.format(port_id, node.name))
         for node, idxs in dst_port_map.items():
-            const = Const(graph, {'value': np.array(value),
+            const = Const(graph, {'value': mo_array(value),
                                   'name': node.soft_get('name', node.id) + '/duplicated_'}).create_node()
             for idx in idxs:
                 node.in_port(idx).disconnect()
diff --git a/tools/mo/openvino/tools/mo/middle/ONNXRNNSequenceNormalize.py b/tools/mo/openvino/tools/mo/middle/ONNXRNNSequenceNormalize.py
index 5188bf8805c..f89d4a986c8 100644
--- a/tools/mo/openvino/tools/mo/middle/ONNXRNNSequenceNormalize.py
+++ b/tools/mo/openvino/tools/mo/middle/ONNXRNNSequenceNormalize.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import compatible_dims
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.op import Op
@@ -117,7 +118,7 @@ class ONNXRNNSequenceNormalize(MiddleReplacementPattern):
             Op.create_and_connect_input_data_node(
                 graph,
                 rnn_layer,
-                {'value': blob, 'shape': np.array(blob.shape, dtype=np.int64)},
+                {'value': blob, 'shape': int64_array(blob.shape)},
                 {'in': port, 'permutation': None}
             )
 
@@ -139,7 +140,7 @@ class ONNXRNNSequenceNormalize(MiddleReplacementPattern):
             Op.create_and_connect_input_data_node(
                 graph,
                 rnn_layer,
-                {'value': h_init, 'shape': np.array(h_init.shape, dtype=np.int64)},
+                {'value': h_init, 'shape': int64_array(h_init.shape)},
                 {'in': h_init_port, 'permutation': None}
             )
 
@@ -150,7 +151,7 @@ class ONNXRNNSequenceNormalize(MiddleReplacementPattern):
                 Op.create_and_connect_input_data_node(
                     graph,
                     rnn_layer,
-                    {'value': c_init, 'shape': np.array(c_init.shape, dtype=np.int64)},
+                    {'value': c_init, 'shape': int64_array(c_init.shape)},
                     {'in': c_init_port, 'permutation': None}
                 )
 
diff --git a/tools/mo/openvino/tools/mo/middle/RNNSequenceNormalizeToIE.py b/tools/mo/openvino/tools/mo/middle/RNNSequenceNormalizeToIE.py
index 0d5cf22dcc1..064c4941765 100644
--- a/tools/mo/openvino/tools/mo/middle/RNNSequenceNormalizeToIE.py
+++ b/tools/mo/openvino/tools/mo/middle/RNNSequenceNormalizeToIE.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_delete
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_delete, mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -81,10 +81,10 @@ class RNNSequenceNormalize(MiddleReplacementPattern):
 
         # Sum component of B that correspond to W and R
         if lstm.op == 'GRU' and lstm.linear_before_reset:
-            B_shape = np.array(B.shape)
+            B_shape = mo_array(B.shape)
             B_shape[3] = 4
             B_shape[2] = 1
-            B_tmp = np.zeros(shape=B_shape)
+            B_tmp = np.zeros(shape=B_shape, dtype=np.float32)
             B_tmp[:, :, :, 0, :] = B[:, :, 0, 0, :] + B[:, :, 1, 0, :]
             B_tmp[:, :, :, 1, :] = B[:, :, 0, 1, :] + B[:, :, 1, 1, :]
             B_tmp[:, :, :, 2, :] = B[:, :, 0, 2, :][:, :, np.newaxis, :]
@@ -123,7 +123,7 @@ class RNNSequenceNormalize(MiddleReplacementPattern):
             Op.create_and_connect_input_data_node(
                 graph,
                 lstm,
-                {'value': blob, 'shape': np.array(blob.shape, dtype=np.int64)},
+                {'value': blob, 'shape': int64_array(blob.shape)},
                 {'in': port, 'bin': name, 'permutation': None}
             )
 
diff --git a/tools/mo/openvino/tools/mo/middle/RemoveDuplicationMemory.py b/tools/mo/openvino/tools/mo/middle/RemoveDuplicationMemory.py
index 74b62c88afa..991f8a98a33 100644
--- a/tools/mo/openvino/tools/mo/middle/RemoveDuplicationMemory.py
+++ b/tools/mo/openvino/tools/mo/middle/RemoveDuplicationMemory.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.crop import Crop
@@ -48,8 +47,8 @@ class RemoveMemoryDuplicationPattern(MiddleReplacementPattern):
                         child_of_child.disconnect()
                         crop_node = Crop(graph, {'name': graph.unique_id(prefix='Splice_crop_'),
                                                  'offset': (left_cont_out - left_cont) * mem_shape[-1],
-                                                 'dim': np.array([len(child['context']) * mem_shape[-1]]),
-                                                 'axis': np.array([-1])}).create_node()
+                                                 'dim': mo_array([len(child['context']) * mem_shape[-1]]),
+                                                 'axis': mo_array([-1])}).create_node()
                         child.out_port(0).connect(crop_node.in_port(0))
                         crop_node.out_port(0).connect(child_of_child)
                         crop_node.out_port(0).data.set_shape(child.out_port(0).data.get_shape())
@@ -116,8 +115,8 @@ class MergeNeighborSplicePattern(MiddleReplacementPattern):
                         # insert Crop if we have not one
                         crop_node = Crop(graph, {'name': graph.unique_id(prefix='Splice_crop_'),
                                                  'offset': (len(new_context) - len(rem_node.context)) * mem_shape[-1],
-                                                 'dim': np.array([len(rem_node['context']) * mem_shape[-1]]),
-                                                 'axis': np.array([-1])}).create_node()
+                                                 'dim': mo_array([len(rem_node['context']) * mem_shape[-1]]),
+                                                 'axis': mo_array([-1])}).create_node()
                         new_node.out_port(0).connect(crop_node.in_port(0))
                         crop_node.out_port(0).connect(out_port_rem)
                         crop_node.out_port(0).data.set_shape(out_transfer_shape)
@@ -129,9 +128,9 @@ class MergeNeighborSplicePattern(MiddleReplacementPattern):
                     if out_transfer['op'] != 'Crop':
                         # insert Crop if we have not one
                         crop_node = Crop(graph, {'name': graph.unique_id(prefix='Splice_crop_'),
-                                                 'offset': np.array([0]),
-                                                 'dim': np.array([len(new_node['context']) * mem_shape[-1]]),
-                                                 'axis': np.array([-1])}).create_node()
+                                                 'offset': mo_array([0]),
+                                                 'dim': mo_array([len(new_node['context']) * mem_shape[-1]]),
+                                                 'axis': mo_array([-1])}).create_node()
                         new_node.out_port(0).connect(crop_node.in_port(0))
                         out_port_rem.disconnect()
                         crop_node.out_port(0).connect(out_port_rem)
diff --git a/tools/mo/openvino/tools/mo/middle/ReplaceMemoryOffsetWithSplice.py b/tools/mo/openvino/tools/mo/middle/ReplaceMemoryOffsetWithSplice.py
index d0016fb2e93..aa522b90481 100644
--- a/tools/mo/openvino/tools/mo/middle/ReplaceMemoryOffsetWithSplice.py
+++ b/tools/mo/openvino/tools/mo/middle/ReplaceMemoryOffsetWithSplice.py
@@ -5,6 +5,7 @@ import numpy as np
 
 from openvino.tools.mo.ops.splice import Splice
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.assign import Assign
@@ -138,14 +139,14 @@ class ReplaceMemoryOffsetWithMemoryNodePattern(MiddleReplacementPattern):
         node_t = abs(node.t)
 
         init_value_memory_out = Const(graph, {'name': 'init_value_' + pair_name,
-                                              'value': np.zeros(int64_array([in_shape[0], in_shape[1]*node_t])),
+                                              'value': np.zeros(int64_array([in_shape[0], in_shape[1]*node_t]), dtype=np.float32),
                                               'shape': int64_array([in_shape[0], in_shape[1]*node_t])}).create_node()
         memory_out = ReadValue(graph, {'name': pair_name, 'variable_id': node_name+pair_name}).create_node()
         init_value_memory_out.out_port(0).connect(memory_out.in_port(0))
 
         if node_t > 1:
-            crop_concat = Crop(graph, {'name': 'Memory_crop', 'dim': np.array([in_shape[1]*(node_t-1)]),
-                                       'offset': np.array([in_shape[1]]), 'axis': np.array([1])}).create_node()
+            crop_concat = Crop(graph, {'name': 'Memory_crop', 'dim': mo_array([in_shape[1]*(node_t-1)]),
+                                       'offset': mo_array([in_shape[1]]), 'axis': mo_array([1])}).create_node()
             memory_out.out_port(0).connect(crop_concat.in_port(0))
             concat = Concat(graph, {'name': 'Memory_concat'}).create_node()
             concat.add_sequence_of_ports('in', range(2))
@@ -157,8 +158,8 @@ class ReplaceMemoryOffsetWithMemoryNodePattern(MiddleReplacementPattern):
             out = Result(graph, {'name': 'Memory_output'}).create_node()
             memory_in.out_port(0).connect(out.in_port(0))
 
-            crop_out = Crop(graph, {'name': 'Memory_crop_out', 'dim': np.array([in_shape[1]]),
-                                    'offset': np.array([0]), 'axis': np.array([1])}).create_node()
+            crop_out = Crop(graph, {'name': 'Memory_crop_out', 'dim': mo_array([in_shape[1]]),
+                                    'offset': mo_array([0]), 'axis': mo_array([1])}).create_node()
             memory_out.out_port(0).connect(crop_out.in_port(0))
             out_port.get_connection().set_source(crop_out.out_port(0))
         else:
diff --git a/tools/mo/openvino/tools/mo/middle/ReplaceSpliceNodePattern.py b/tools/mo/openvino/tools/mo/middle/ReplaceSpliceNodePattern.py
index ff4a0bcc119..47a5d0afbb1 100644
--- a/tools/mo/openvino/tools/mo/middle/ReplaceSpliceNodePattern.py
+++ b/tools/mo/openvino/tools/mo/middle/ReplaceSpliceNodePattern.py
@@ -98,7 +98,7 @@ class ReplaceSpliceNodePattern(MiddleReplacementPattern):
             memory_pair_id = unique_id('memory_for_const_dim')
             init_value_input_memory_const_dim = Const(graph, {'name': 'init_value_const_dim_in_memory',
                                                               'value': np.zeros(int64_array([in_shape[0],
-                                                                                             memory_size_constdim])),
+                                                                                             memory_size_constdim]), dtype=np.float32),
                                                               'shape': int64_array([in_shape[0],
                                                                                     memory_size_constdim])}).create_node()
             input_memory_const_dim = ReadValue(graph, {'name': 'const_dim_in_memory',
@@ -136,14 +136,14 @@ class ReplaceSpliceNodePattern(MiddleReplacementPattern):
             concat_const.in_port(0).connect(concat_node.out_port(0))
 
             init_value_input_memory = Const(graph, {'name': 'init_value_' + node.name,
-                                                    'value': np.zeros(int64_array([in_shape[0], memory_size])),
+                                                    'value': np.zeros(int64_array([in_shape[0], memory_size]), dtype=np.float32),
                                                     'shape': int64_array([in_shape[0], memory_size])}).create_node()
             init_value_input_memory.out_port(0).connect(input_memory.in_port(0))
             node.in_port(0).get_connection().set_destination(split.in_port(0))
             node.out_port(0).get_connection().set_source(concat_const.out_port(0))
         else:
             init_value_input_memory = Const(graph, {'name': 'init_value_' + node.name,
-                                                    'value': np.zeros(int64_array([in_shape[0], memory_size])),
+                                                    'value': np.zeros(int64_array([in_shape[0], memory_size]), dtype=np.float32),
                                                     'shape': int64_array([in_shape[0], memory_size])}).create_node()
             init_value_input_memory.out_port(0).connect(input_memory.in_port(0))
             node.in_port(0).get_connection().set_destination(concat_node.in_port(1))
diff --git a/tools/mo/openvino/tools/mo/middle/ScaleInput.py b/tools/mo/openvino/tools/mo/middle/ScaleInput.py
index 26f174b75c2..da38a61bcd0 100644
--- a/tools/mo/openvino/tools/mo/middle/ScaleInput.py
+++ b/tools/mo/openvino/tools/mo/middle/ScaleInput.py
@@ -1,9 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.middle.AddMeanScaleValues import AddMeanScaleValues
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 
@@ -35,4 +34,4 @@ class ScaleInput(MiddleReplacementPattern):
             return
         assert (len(match['placeholder'].out_nodes()))
 
-        AddMeanScaleValues.apply_scale(graph, match['placeholder'], {'scale': np.array([scale])})
+        AddMeanScaleValues.apply_scale(graph, match['placeholder'], {'scale': mo_array([scale])})
diff --git a/tools/mo/openvino/tools/mo/middle/SharedWeightsDuplication.py b/tools/mo/openvino/tools/mo/middle/SharedWeightsDuplication.py
index 4c2f0a6abb7..c8415e3e2b2 100644
--- a/tools/mo/openvino/tools/mo/middle/SharedWeightsDuplication.py
+++ b/tools/mo/openvino/tools/mo/middle/SharedWeightsDuplication.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 from openvino.tools.mo.ops.op import Op
@@ -35,7 +34,7 @@ class SharedWeightsDuplication(MiddleReplacementPattern):
                     out_node = Node(graph, v)
                     e_attrs = d
                     graph.remove_edge(node.id, out_node.id)
-                    data = Op.create_input_data_node(graph, "Copy_{}".format(node.id), np.array(node.value),
+                    data = Op.create_input_data_node(graph, "Copy_{}".format(node.id), mo_array(node.value),
                                                      graph.node[node.id])
 
                     graph.add_edges_from([(data.id, out_node.id, e_attrs)])
diff --git a/tools/mo/openvino/tools/mo/middle/SliceConverter.py b/tools/mo/openvino/tools/mo/middle/SliceConverter.py
index c400196ed57..20f3b528f6a 100644
--- a/tools/mo/openvino/tools/mo/middle/SliceConverter.py
+++ b/tools/mo/openvino/tools/mo/middle/SliceConverter.py
@@ -91,7 +91,7 @@ class ConvertSlice(MiddleReplacementPattern):
                 steps = node.in_port(4).data.get_value()
                 assert steps is not None, 'The input with steps is not constant for node {}'.format(node_name)
             else:
-                steps = np.ones([axes.size])
+                steps = np.ones([axes.size], dtype=np.int64)
 
             ss_begin_mask = np.zeros(len(input_shape), dtype=np.int64)
             ss_end_mask = np.zeros(len(input_shape), dtype=np.int64)
diff --git a/tools/mo/openvino/tools/mo/middle/SplitConcatPairToInterpolate.py b/tools/mo/openvino/tools/mo/middle/SplitConcatPairToInterpolate.py
index 29748655a52..97603b5bf13 100644
--- a/tools/mo/openvino/tools/mo/middle/SplitConcatPairToInterpolate.py
+++ b/tools/mo/openvino/tools/mo/middle/SplitConcatPairToInterpolate.py
@@ -2,14 +2,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import logging as log
-import numpy as np
 from typing import Optional
 
-from openvino.tools.mo.ops.activation_ops import Floor
+import numpy as np
+
 from openvino.tools.mo.ops.Cast import Cast
+from openvino.tools.mo.ops.activation_ops import Floor
 from openvino.tools.mo.ops.elementwise import Mul
 from openvino.tools.mo.ops.interpolate import Interpolate
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, float32_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -87,7 +88,7 @@ def get_split_scale(split: Node) -> int:
 
 def replace_interpolate_pattern(graph: Graph, match: dict):
     split = match['split']
-    scale = np.array([get_split_scale(split)], dtype=np.float32)
+    scale = float32_array([get_split_scale(split)])
     axis = int(split.in_port(1).get_connection().get_source().node.value)
     split_node_name = split.name
     axis_node = Const(graph, {'name': split_node_name + '/axis', 'value': int64_array([axis])}).create_node()
diff --git a/tools/mo/openvino/tools/mo/middle/StridedSliceNormalizer.py b/tools/mo/openvino/tools/mo/middle/StridedSliceNormalizer.py
index 1c1f7108cb5..b757f239ac6 100644
--- a/tools/mo/openvino/tools/mo/middle/StridedSliceNormalizer.py
+++ b/tools/mo/openvino/tools/mo/middle/StridedSliceNormalizer.py
@@ -6,6 +6,7 @@ import numpy as np
 from openvino.tools.mo.ops.split import VariadicSplit
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array, dynamic_dimension, dynamic_dimension_value, \
     is_dynamic_slice
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.graph.perm_inputs import PermuteInputs
@@ -247,4 +248,4 @@ class StridedSliceNormalizer(MiddleReplacementPattern):
                         res_slices[-1] is not None and not is_dynamic_slice(res_slices[-1]):
                     res_slices[-1] = slice(*res_slices[-1].indices(data_shape[in_idx]))  # convert negative begins/ends
                 in_idx += 1
-        node.slices = np.array(res_slices)
+        node.slices = mo_array(res_slices)
diff --git a/tools/mo/openvino/tools/mo/middle/StridedSliceReplacer.py b/tools/mo/openvino/tools/mo/middle/StridedSliceReplacer.py
index 58fd745b74d..c56460aaccc 100644
--- a/tools/mo/openvino/tools/mo/middle/StridedSliceReplacer.py
+++ b/tools/mo/openvino/tools/mo/middle/StridedSliceReplacer.py
@@ -49,8 +49,8 @@ class ReplaceStridedSliceWithSqueezeUnsqueeze(MiddleReplacementPattern):
             if np.prod(input_shape) != np.prod(output_shape):
                 continue
 
-            shrink_axis_mask = node.soft_get('shrink_axis_mask', np.zeros(len(input_shape)))
-            new_axis_mask = node.soft_get('new_axis_mask', np.zeros(len(input_shape)))
+            shrink_axis_mask = node.soft_get('shrink_axis_mask', np.zeros(len(input_shape), dtype=np.bool))
+            new_axis_mask = node.soft_get('new_axis_mask', np.zeros(len(input_shape), dtype=np.bool))
 
             is_shrink_axis_mask = any(x == 1 for x in shrink_axis_mask)
             is_new_axis_mask = any(x == 1 for x in new_axis_mask)
diff --git a/tools/mo/openvino/tools/mo/middle/TF_lstm_cell_to_generic.py b/tools/mo/openvino/tools/mo/middle/TF_lstm_cell_to_generic.py
index 80ee06b40e4..f34b736b404 100644
--- a/tools/mo/openvino/tools/mo/middle/TF_lstm_cell_to_generic.py
+++ b/tools/mo/openvino/tools/mo/middle/TF_lstm_cell_to_generic.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 
@@ -82,9 +83,9 @@ class TensorFlowLSTMtoGeneric(MiddleReplacementPattern):
         weights = weights.transpose()
 
         weights_node.value = weights
-        weights_node.shape = np.array(weights.shape, dtype=np.int64)
+        weights_node.shape = int64_array(weights.shape)
         biases_node.value = biases
-        biases_node.shape = np.array(biases.shape, dtype=np.int64)
+        biases_node.shape = int64_array(biases.shape)
 
         # Cut all extra inputs off
         for i in range(len(node.inputs), len(node.inputs) + len(node.extra_inputs)):
diff --git a/tools/mo/openvino/tools/mo/middle/TensorIteratorCondition.py b/tools/mo/openvino/tools/mo/middle/TensorIteratorCondition.py
index a335243e08e..560b5f7a0a2 100644
--- a/tools/mo/openvino/tools/mo/middle/TensorIteratorCondition.py
+++ b/tools/mo/openvino/tools/mo/middle/TensorIteratorCondition.py
@@ -8,6 +8,7 @@ import numpy as np
 from openvino.tools.mo.middle.TensorIterator_utils import delete_selects_from
 from openvino.tools.mo.ops.TensorIterator_ops import TensorIteratorCondition, TensorIteratorBackEdge
 from openvino.tools.mo.ops.identity import Identity
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
 from openvino.tools.mo.graph.graph import Graph, rename_nodes
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 
@@ -18,7 +19,7 @@ def make_nodes_1D(nodes: list):
     """
     for node in nodes:
         assert node.shape is None or len(node.shape) == 0
-        node.shape = np.array([1], dtype=np.int64)
+        node.shape = int64_array([1])
         if node.value is not None:
             node.value = np.reshape(node.value, node.shape)
 
@@ -184,8 +185,8 @@ Shape -> StridedSlice -> Enter -|    LogicalAnd --> LoopCond (data)
     @staticmethod
     def looking_for_iteration_counter(graph: Graph, match: dict):
         types = ['TensorIteratorInput', 'TensorIteratorOutput']
-        candidates = np.array([match['Identity_1_data'], match['Identity_2_data']])
-        results = np.array([False for i in range(len(candidates))])
+        candidates = mo_array([match['Identity_1_data'], match['Identity_2_data']])
+        results = mo_array([False for i in range(len(candidates))])
         for i, candidat in enumerate(candidates):
             for node in candidat.out_nodes():
                 if node['op'] in types:
diff --git a/tools/mo/openvino/tools/mo/middle/TensorIteratorInput.py b/tools/mo/openvino/tools/mo/middle/TensorIteratorInput.py
index f5ac4ba520f..6547afdf49f 100644
--- a/tools/mo/openvino/tools/mo/middle/TensorIteratorInput.py
+++ b/tools/mo/openvino/tools/mo/middle/TensorIteratorInput.py
@@ -3,10 +3,9 @@
 
 import logging as log
 
-import numpy as np
-
 from openvino.tools.mo.middle.AddIsCyclicAttribute import AddIsCyclicAttribute
 from openvino.tools.mo.ops.TensorIterator_ops import TensorIteratorInput
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
 
@@ -239,7 +238,7 @@ class BackEdgeSimpleInputMatcher(MiddleReplacementPattern):
                 graph.remove_edge(init_input.in_node(0).id, init_input.id)
 
             input_data_node = input_node.create_node_with_data(inputs=[init_input])
-            input_data_node.shape = np.array(init_input.shape, dtype=np.int64)
+            input_data_node.shape = int64_array(init_input.shape)
             graph.remove_edges_from([(init_input.id, match['BackEdge'].id)])
             graph.add_edges_from([(input_data_node.id, match['BackEdge'].id, {'in': 0, 'out': 0})])
 
diff --git a/tools/mo/openvino/tools/mo/middle/UnsqueezeTileReshapeBlockToInterpolate.py b/tools/mo/openvino/tools/mo/middle/UnsqueezeTileReshapeBlockToInterpolate.py
index 3877e65f870..871d26117f3 100644
--- a/tools/mo/openvino/tools/mo/middle/UnsqueezeTileReshapeBlockToInterpolate.py
+++ b/tools/mo/openvino/tools/mo/middle/UnsqueezeTileReshapeBlockToInterpolate.py
@@ -26,7 +26,7 @@ class UnsqueezeTileReshapeBlockToInterpolate(MiddleReplacementPattern):
     one after another, performing unsqueeze-ing over different dimensions, effectively performing interpolation over
     several dimensions.
 
-    These sequences are merged in the 'optimizer/openvino/tools/mo/middle/InterpolateSequenceToInterpolate.py' transformation
+    These sequences are merged in the 'optimizer/extensions/middle/InterpolateSequenceToInterpolate.py' transformation
     into a single Interpolate operation.
 
     The transformation is applicable only when all following conditions are fulfilled:
@@ -43,7 +43,7 @@ class UnsqueezeTileReshapeBlockToInterpolate(MiddleReplacementPattern):
     Example.
 
     Let data = np.arange(0, 1 * 2 * 3 * 4).reshape((1, 2, 3, 4)).astype(np.float32), that is
-        data = np.array([[[[ 0,  1,  2,  3],
+        data = mo_array([[[[ 0,  1,  2,  3],
                            [ 4,  5,  6,  7],
                            [ 8,  9, 10, 11]],
                           [[12, 13, 14, 15],
@@ -119,7 +119,7 @@ class UnsqueezeTileReshapeBlockToInterpolate(MiddleReplacementPattern):
         if input_rank_of_unsqueeze + 1 != len(second_input_of_tile.value):
             return False
 
-        expected_tile_constant = np.ones(input_rank_of_unsqueeze + 1)
+        expected_tile_constant = np.ones(input_rank_of_unsqueeze + 1, dtype=np.float32)
         expected_tile_constant[d_idx] = float(second_input_of_tile.value[d_idx])
 
         if not np.array_equal(expected_tile_constant, float32_array(second_input_of_tile.value)):
diff --git a/tools/mo/openvino/tools/mo/middle/passes/conv.py b/tools/mo/openvino/tools/mo/middle/passes/conv.py
index d8e25dd5c8b..8eda8602300 100644
--- a/tools/mo/openvino/tools/mo/middle/passes/conv.py
+++ b/tools/mo/openvino/tools/mo/middle/passes/conv.py
@@ -6,6 +6,7 @@ import logging as log
 import numpy as np
 
 from openvino.tools.mo.front.common.layout import get_batch_dim, get_features_dim
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.front.extractor import add_attrs_props
 from openvino.tools.mo.front.extractor import update_ie_fields
 from openvino.tools.mo.graph.graph import Node, Graph
@@ -98,15 +99,15 @@ def muladd_to_scaleshift_action(graph: Graph, match: dict):
 
     # Transform values
     weights.value = np.squeeze(weights.value)
-    weights.shape = np.array(weights.value.shape, dtype=np.int64)
+    weights.shape = int64_array(weights.value.shape)
 
     bias.value = np.squeeze(bias.value)
-    bias.shape = np.array(bias.value.shape, dtype=np.int64)
+    bias.shape = int64_array(bias.value.shape)
 
     # Broadcast weights if they are scalar
     if weights.value.ndim == 0 and bias.value.ndim == 1:
-        weights.value = np.full(bias.shape, weights.value.item())
-        weights.shape = np.array(weights.value.shape, dtype=np.int64)
+        weights.value = np.full(bias.shape, weights.value.item(), dtype=weights.value.dtype)
+        weights.shape = int64_array(weights.value.shape)
 
     if bias.shape != weights.shape:
         log.warning('Mul->Add to ScaleShift conversion stopped {} != {}'.format(weights.shape, bias.shape))
diff --git a/tools/mo/openvino/tools/mo/middle/passes/fusing/decomposition.py b/tools/mo/openvino/tools/mo/middle/passes/fusing/decomposition.py
index 17812b70534..ff2c9a50876 100644
--- a/tools/mo/openvino/tools/mo/middle/passes/fusing/decomposition.py
+++ b/tools/mo/openvino/tools/mo/middle/passes/fusing/decomposition.py
@@ -6,6 +6,7 @@ import logging as log
 import numpy as np
 
 from openvino.tools.mo.ops.elementwise import Mul, Add
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.graph.port import Port
@@ -14,7 +15,7 @@ from openvino.tools.mo.ops.reshape import Reshape
 
 
 def expand_node_shape(port: Port, broadcast_dims_cnt):
-    value = np.array(port.data.get_value())
+    value = mo_array(port.data.get_value())
     for idx in range(broadcast_dims_cnt):
         value = np.expand_dims(value, axis=-1)
     port.data.set_value(value)
@@ -82,8 +83,8 @@ def _fused_batch_norm_decomposition(graph: Graph, tinput: Port, toutput: Port, g
     mul1_node = Mul(graph, dict(name=batch_norm_name + "/mean", can_be_fused=can_be_fused)).create_node()
     add1_node = Add(graph, dict(name=batch_norm_name + "/variance", can_be_fused=can_be_fused)).create_node()
 
-    const_mul1_node = Const(graph, dict(name="data_mul_", value=np.array(mean))).create_node()
-    const_add1_node = Const(graph, dict(name="data_add_", value=np.array(variance))).create_node()
+    const_mul1_node = Const(graph, dict(name="data_mul_", value=mo_array(mean))).create_node()
+    const_add1_node = Const(graph, dict(name="data_add_", value=mo_array(variance))).create_node()
 
     # Broadcast const from scalar
     # We can broadcast only when const.value is scalar
@@ -145,7 +146,7 @@ def convert_scale_shift_to_mul_add(graph: Graph):
         # In case if we have constant weights/biases we have to broadcast them according to graph layout
         # otherwise we insert Reshape with broadcast dim attribute.
         def broadcast_value(port):
-            value = np.array(port.data.get_value())
+            value = mo_array(port.data.get_value())
             for idx in range(broadcast_dims_cnt):
                 value = np.expand_dims(value, axis=-1)
             port.data.set_value(value)
diff --git a/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_grouped_conv.py b/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_grouped_conv.py
index 7daacc7634b..fd473fc8067 100644
--- a/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_grouped_conv.py
+++ b/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_grouped_conv.py
@@ -5,6 +5,7 @@ import logging as log
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.fusing.helpers import get_next_operation
 
@@ -84,8 +85,8 @@ def concat_convolutions(graph: Graph, start_node: Node, last_node: Node):
     weights_node = gconv.in_node(1)
     bias_node = gconv.in_node(2) if has_biases else None
 
-    weights_value = np.array(weights_node.value)
-    bias_value = np.array(bias_node.value) if has_biases else None
+    weights_value = mo_array(weights_node.value)
+    bias_value = mo_array(bias_node.value) if has_biases else None
 
     # gconv.get_weights_permute.perm contains permutation indices
     # where feature dimension is set to zero position, so 0 value
@@ -97,12 +98,12 @@ def concat_convolutions(graph: Graph, start_node: Node, last_node: Node):
         if has_biases:
             bias_value = np.concatenate((bias_value, conv.in_node(2).value), axis=-1)  # Not validated
 
-    weights_node.value = np.array(weights_value)
-    weights_node.shape = np.array(weights_value.shape)
+    weights_node.value = mo_array(weights_value)
+    weights_node.shape = mo_array(weights_value.shape)
 
     if has_biases:
-        bias_node.value = np.array(bias_value)
-        bias_node.shape = np.array(bias_value.shape)
+        bias_node.value = mo_array(bias_value)
+        bias_node.shape = mo_array(bias_value.shape)
 
     log.debug('Start node : {} Last node : {}  Nodes inside : {}'.format(start_node.id, last_node.id,
                                                                          len(start_node.out_nodes())))
diff --git a/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_ops.py b/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_ops.py
index d9fb8f11629..2675e4686a0 100644
--- a/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_ops.py
+++ b/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_ops.py
@@ -5,6 +5,7 @@ import logging as log
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.fusing.helpers import backward_bfs, forward_bfs, get_value_in_port, \
     get_tensor_in_port
@@ -62,27 +63,27 @@ def _fuse_mul(graph: Graph, node: Node, fuse_nodes: list, backward: bool = True)
 
     for fuse_node in fuse_nodes:
         weights_port = fuse_node.in_port(1)
-        value = np.array(const_port.data.get_value())
+        value = mo_array(const_port.data.get_value())
 
         value = np.squeeze(value)
 
         # TODO : ch_dim should be equal to node.in_node(1).value.shape
         # We will multiply weights according output/input channel dimension
         ch_dim = weights_port.data.get_attr('output_channel_dim' if backward else 'input_channel_dim')
-        shape = np.array([weights_port.data.get_shape()[ch_dim]])
+        shape = mo_array([weights_port.data.get_shape()[ch_dim]])
 
         # Scalar broadcast
         if value.size == 1:
-            value = np.full(shape, value.item())
+            value = np.full(shape, value.item(), dtype=value.dtype)
 
         # Common broadcast for forward fusion
         if not backward:
             cnt = shape[-1] / value.shape[0]
             if fuse_node.layout == 'NCHW':
-                tmp = []
+                tmp = mo_array([], dtype=value.dtype)
                 for val in value:
                     tmp = np.concatenate((tmp, np.repeat(val, cnt)))
-                value = np.array(tmp)
+                value = mo_array(tmp)
             else:
                 value = np.tile(value, int(cnt))
 
@@ -91,7 +92,7 @@ def _fuse_mul(graph: Graph, node: Node, fuse_nodes: list, backward: bool = True)
         for x in range(wdims_number - ch_dim - 1):
             shape = np.append(shape, 1)
 
-        mul_val = np.array(value)
+        mul_val = mo_array(value)
         # If the value fails to reshape to the provided shape, skip fusing.
         # This can happen in case of group != 1 of the convolution.
         try:
diff --git a/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_seq.py b/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_seq.py
index b599d592f8e..f86f22d6f63 100644
--- a/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_seq.py
+++ b/tools/mo/openvino/tools/mo/middle/passes/fusing/fuse_linear_seq.py
@@ -7,6 +7,7 @@ import numpy as np
 
 from openvino.tools.mo.ops.elementwise import Mul, Add
 from openvino.tools.mo.front.common.partial_infer.utils import compatible_shapes
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.fusing.helpers import get_value_in_port, get_tensor_in_port
 from openvino.tools.mo.ops.const import Const
@@ -38,8 +39,13 @@ def _fuse_linear_sequence(graph: Graph, start_node: Node):
 
     init_dims_cnt = len(input_shape) - 2 if graph.graph['layout'] == 'NCHW' else 1
 
-    mul = np.ones([1 for x in range(init_dims_cnt)])
-    add = np.zeros([1 for x in range(init_dims_cnt)])
+    first_value = get_value_in_port(fnodes[0]).data.get_value()
+    if not isinstance(first_value, np.ndarray):
+        first_value = mo_array(first_value)
+    first_value_type = first_value.dtype
+
+    mul = np.ones([1 for x in range(init_dims_cnt)], dtype=first_value_type)
+    add = np.zeros([1 for x in range(init_dims_cnt)], dtype=first_value_type)
 
     first_mul_name = None
     first_add_name = None
@@ -58,7 +64,7 @@ def _fuse_linear_sequence(graph: Graph, start_node: Node):
 
     # If mul is scalar we broadcast it to biases shape
     if mul.shape != add.shape and len(mul.shape) == 1 and mul.shape[0] == 1:
-        mul = np.array([mul[0] for x in range(add.shape[0])])
+        mul = mo_array([mul[0] for x in range(add.shape[0])])
 
     assert (compatible_shapes(get_tensor_in_port(fnodes[0]).data.get_shape(), fnodes[-1].out_port(0).data.get_shape()))
 
@@ -78,8 +84,8 @@ def _fuse_linear_sequence(graph: Graph, start_node: Node):
     if any([x != 0 for x in np.nditer(add)]) and any([x != 1 for x in np.nditer(mul)]):
         #  Const\    Const\
         #  ----->Mul------>Add-->
-        mul_const = Const(graph, dict(name="data_mul_", value=np.array(mul))).create_node()
-        add_const = Const(graph, dict(name="data_add_", value=np.array(add))).create_node()
+        mul_const = Const(graph, dict(name="data_mul_", value=mo_array(mul))).create_node()
+        add_const = Const(graph, dict(name="data_add_", value=mo_array(add))).create_node()
 
         mul_node = mul_op.create_node()
         add_node = add_op.create_node()
@@ -93,7 +99,7 @@ def _fuse_linear_sequence(graph: Graph, start_node: Node):
     elif any([x != 1 for x in np.nditer(mul)]):
         #  Const\
         #  ----->Mul-->
-        mul_const = Const(graph, dict(name="data_mul_", value=np.array(mul))).create_node()
+        mul_const = Const(graph, dict(name="data_mul_", value=mo_array(mul))).create_node()
         mul_node = mul_op.create_node()
 
         in_port.get_connection().set_destination(mul_node.in_port(0))
@@ -102,7 +108,7 @@ def _fuse_linear_sequence(graph: Graph, start_node: Node):
     elif any([x != 0 for x in np.nditer(add)]):
         #  Const\
         #  ----->Add-->
-        add_const = Const(graph, dict(name="data_add_", value=np.array(add))).create_node()
+        add_const = Const(graph, dict(name="data_add_", value=mo_array(add))).create_node()
         add_node = add_op.create_node()
 
         in_port.get_connection().set_destination(add_node.in_port(0))
diff --git a/tools/mo/openvino/tools/mo/middle/passes/fusing/resnet_optimization.py b/tools/mo/openvino/tools/mo/middle/passes/fusing/resnet_optimization.py
index 74b324306e2..c8440c77856 100644
--- a/tools/mo/openvino/tools/mo/middle/passes/fusing/resnet_optimization.py
+++ b/tools/mo/openvino/tools/mo/middle/passes/fusing/resnet_optimization.py
@@ -5,6 +5,7 @@ import logging as log
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.fusing.helpers import get_next_operation
 from openvino.tools.mo.ops.pooling import Pooling
@@ -27,10 +28,10 @@ def _insert_pooling(graph: Graph, first_node: Node, second_node: Node, spatial_d
     eattrs = graph.get_edge_data(first_node.id, second_node.id)[0]
     graph.remove_edge(first_node.id, second_node.id)
 
-    pooling = Pooling(graph, dict(name='Pooling_', spatial_dims=spatial_dims, window=np.array([1, 1, 1, 1]),
+    pooling = Pooling(graph, dict(name='Pooling_', spatial_dims=spatial_dims, window=mo_array([1, 1, 1, 1]),
                                   output_spatial_shape=None,
-                                  stride=np.array(stride_prop), pad_spatial_shape=np.array([[0, 0], [0, 0]]),
-                                  pad=np.array([[0, 0], [0, 0], [0, 0], [0, 0]]), pool_method='max',
+                                  stride=mo_array(stride_prop), pad_spatial_shape=mo_array([[0, 0], [0, 0]]),
+                                  pad=mo_array([[0, 0], [0, 0], [0, 0], [0, 0]]), pool_method='max',
                                   is_partial_inferred=False))
     pooling_data = pooling.create_node_with_data([first_node])
 
@@ -46,7 +47,7 @@ def _check_next_ops(next_ops: list):
     stride_props = []
     for op in next_ops:
         if op.has_valid('stride_prop'):
-            stride_props.append(np.array(op.stride_prop))
+            stride_props.append(mo_array(op.stride_prop))
         else:
             continue
 
@@ -67,19 +68,19 @@ def _simple_stride_prop(graph: Graph, node: Node, spatial_dims, supported=True):
     if not supported or not all_ops_are_valid:
         # We have to insert pooling layers
         for op in next_ops:
-            if op.has_valid('stride_prop') and not np.array_equal(op.stride_prop[spatial_dims], np.array([1, 1])) and \
+            if op.has_valid('stride_prop') and not np.array_equal(op.stride_prop[spatial_dims], mo_array([1, 1])) and \
                     (op.has_valid('has_stride') == False or op.soft_get('has_stride') == False):
                 _insert_pooling(graph, node.out_node(), op, spatial_dims)
         # If Convolution is valid then set `stride_prop` to Convolution stride
-        node['stride_prop'] = np.array([1, 1, 1, 1])
+        node['stride_prop'] = mo_array([1, 1, 1, 1])
         return
 
     for op in next_ops:
         if op.soft_get('has_stride') == True:
-            op.stride = np.array([1, 1, 1, 1])
+            op.stride = mo_array([1, 1, 1, 1])
             log.debug("STRIDE PROP: {} {} strides was moved upper via {}".format(op.type, op.name, node.name))
 
-    node['stride_prop'] = np.array(stride_props[0]) if len(stride_props) > 0 else np.array([1, 1, 1, 1])
+    node['stride_prop'] = mo_array(stride_props[0]) if len(stride_props) > 0 else mo_array([1, 1, 1, 1])
     node['is_partial_inferred'] = False
     _clean_fw_tensor_attrs(node.out_node())
 
@@ -93,13 +94,13 @@ def _conv_stride_prop(graph: Graph, node: Node, spatial_dims, supported=True):
     stride_props, all_ops_are_valid = _check_next_ops(next_ops)
 
     def _check_convolution(node: Node):
-        return node.has_valid('kernel_spatial') and np.array_equal(node.kernel_spatial, np.array([1, 1]))
+        return node.has_valid('kernel_spatial') and np.array_equal(node.kernel_spatial, mo_array([1, 1]))
 
     # Check that all ops are valid and have same values
     if not all_ops_are_valid:
         # We have to insert pooling layers
         for op in next_ops:
-            if op.has_valid('stride_prop') and not np.array_equal(op.stride_prop[spatial_dims], np.array([1, 1])):
+            if op.has_valid('stride_prop') and not np.array_equal(op.stride_prop[spatial_dims], mo_array([1, 1])):
                 # Insert pooling
                 _insert_pooling(graph, node.out_node(), op, spatial_dims)
     elif len(stride_props) > 0:
@@ -107,13 +108,13 @@ def _conv_stride_prop(graph: Graph, node: Node, spatial_dims, supported=True):
         log.debug('STRIDE PROP: {} got new strides {}'.format(node.name, node.stride))
         for op in next_ops:
             if op.soft_get('has_stride') == True:
-                op.stride = np.array([1, 1, 1, 1])
+                op.stride = mo_array([1, 1, 1, 1])
         node['is_partial_inferred'] = False
         node['output_spatial_shape'] = False
         _clean_fw_tensor_attrs(node.out_node())
 
     # If Convolution is valid then set `stride_prop` to Convolution stride
-    node['stride_prop'] = np.array(node.stride) if _check_convolution(node) else np.array([1, 1, 1, 1])
+    node['stride_prop'] = mo_array(node.stride) if _check_convolution(node) else mo_array([1, 1, 1, 1])
 
 
 supported_ops = {
@@ -149,9 +150,9 @@ def stride_optimization(graph: Graph):
     """
     layout = graph.graph['layout']
     if layout == 'NCHW':
-        spatial_dims = np.array([2, 3])
+        spatial_dims = mo_array([2, 3])
     elif layout == 'NHWC':
-        spatial_dims = np.array([1, 2])
+        spatial_dims = mo_array([1, 2])
     else:
         log.warning('STRIDE PROP: layout {} is not supported'.format(layout))
         return
diff --git a/tools/mo/openvino/tools/mo/middle/quantize_linear_resolver.py b/tools/mo/openvino/tools/mo/middle/quantize_linear_resolver.py
index 3e05dd812e3..7b680a0c18a 100644
--- a/tools/mo/openvino/tools/mo/middle/quantize_linear_resolver.py
+++ b/tools/mo/openvino/tools/mo/middle/quantize_linear_resolver.py
@@ -7,6 +7,7 @@ from openvino.tools.mo.ops.Cast import Cast
 from openvino.tools.mo.ops.elementwise import Mul
 from openvino.tools.mo.ops.fakequantize import FakeQuantize
 from openvino.tools.mo.front.common.partial_infer.utils import float_array, int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
 from openvino.tools.mo.graph.graph import Graph, rename_nodes
 from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
@@ -47,7 +48,7 @@ class QuantizeLinearResolver(MiddleReplacementPattern):
                 zerop = quantize_node.in_port(2).get_source().node
             else:
                 zerop = Const(graph,
-                              {'value': np.array(0, dtype=np.uint8), 'name': node_name + '/ZeroPoint'}).create_node()
+                              {'value': mo_array(0, dtype=np.uint8), 'name': node_name + '/ZeroPoint'}).create_node()
 
             assert zerop.soft_get('type') == 'Const', 'only constant for zero_point is supported for QuantizeLinear'
             zero_point_type = zerop.value.dtype
diff --git a/tools/mo/openvino/tools/mo/ops/Exit.py b/tools/mo/openvino/tools/mo/ops/Exit.py
index 49d497dbd15..a6d3d8a1b3a 100644
--- a/tools/mo/openvino/tools/mo/ops/Exit.py
+++ b/tools/mo/openvino/tools/mo/ops/Exit.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -24,5 +23,5 @@ class Exit(Op):
         output_shape = node.in_node(0).shape
         output_value = node.in_node(0).value
         for _, out_node in node.graph.out_edges(node.id):
-            node.graph.node[out_node]['shape'] = np.array(output_shape)
-            node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
+            node.graph.node[out_node]['shape'] = mo_array(output_shape)
+            node.graph.node[out_node]['value'] = None if output_value is None else mo_array(output_value)
diff --git a/tools/mo/openvino/tools/mo/ops/GRU.py b/tools/mo/openvino/tools/mo/ops/GRU.py
index c403c195815..89af00ada0c 100644
--- a/tools/mo/openvino/tools/mo/ops/GRU.py
+++ b/tools/mo/openvino/tools/mo/ops/GRU.py
@@ -1,9 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from openvino.tools.mo.ops.RNN import rnn_infer
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -21,7 +20,7 @@ class GRU(Op):
             'infer': __class__.infer,
             'multiplier': 3,
             'multilayers': False,
-            'gate_order': np.array([0, 1, 2]),  # TODO: change it later
+            'gate_order': mo_array([0, 1, 2]),  # TODO: change it later
             'normalized': False,
 
             'activation_alpha': None,
diff --git a/tools/mo/openvino/tools/mo/ops/MatMul.py b/tools/mo/openvino/tools/mo/ops/MatMul.py
index fc377972aab..d1a42ca5bdf 100644
--- a/tools/mo/openvino/tools/mo/ops/MatMul.py
+++ b/tools/mo/openvino/tools/mo/ops/MatMul.py
@@ -5,11 +5,12 @@ import logging as log
 
 import numpy as np
 
-from openvino.tools.mo.front.common.partial_infer.utils import assign_dims_to_weights, int64_array, compatible_dims, compatible_shapes, \
+from openvino.tools.mo.front.common.partial_infer.utils import assign_dims_to_weights, compatible_dims, compatible_shapes, \
     shape_array, is_fully_defined, shape_delete, shape_insert
 from openvino.tools.mo.front.extractor import bool_to_str
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
+from openvino.tools.mo.utils.type_utils import override_data_type_of_constant
 
 
 class MatMul(Op):
@@ -26,6 +27,7 @@ class MatMul(Op):
             'transpose_a': False,
             'transpose_b': False,
             'infer': self.infer,
+            'type_infer': self.type_infer,
             'in_ports_count': 2,
             'out_ports_count': 1,
         }
@@ -147,6 +149,11 @@ class MatMul(Op):
         assign_dims_to_weights(node.in_node(1), None, in_ch, out_ch, node.in_port(1).data.get_shape().size)
         MatMul.value_propagation(node)
 
+    @staticmethod
+    def type_infer(node):
+        override_data_type_of_constant(node)
+        node.out_port(0).set_data_type(node.in_port(0).get_data_type())
+
 
 def transpose(value):
     num_of_dims = value.ndim
diff --git a/tools/mo/openvino/tools/mo/ops/NextIteration.py b/tools/mo/openvino/tools/mo/ops/NextIteration.py
index eb55ac18494..f81e29ccffc 100644
--- a/tools/mo/openvino/tools/mo/ops/NextIteration.py
+++ b/tools/mo/openvino/tools/mo/ops/NextIteration.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -24,5 +23,5 @@ class NextIteration(Op):
         output_shape = node.in_node(0).shape
         output_value = node.in_node(0).value
         for _, out_node in node.graph.out_edges(node.id):
-            node.graph.node[out_node]['shape'] = np.array(output_shape)
-            node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
+            node.graph.node[out_node]['shape'] = mo_array(output_shape)
+            node.graph.node[out_node]['value'] = None if output_value is None else mo_array(output_value)
diff --git a/tools/mo/openvino/tools/mo/ops/ONNXResize11.py b/tools/mo/openvino/tools/mo/ops/ONNXResize11.py
index f9fa92caabc..5b494a75e41 100644
--- a/tools/mo/openvino/tools/mo/ops/ONNXResize11.py
+++ b/tools/mo/openvino/tools/mo/ops/ONNXResize11.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -48,7 +48,7 @@ class ONNXResize11Op(Op):
             input2_value = node.in_port(2).data.get_value()
             assert input2_value is not None, \
                 "Node {} with op {} has no value in input port 2".format(node.soft_get('name', node.id), node.op)
-            scale = np.array(input2_value)
+            scale = mo_array(input2_value)
             output_shape = np.floor(input_shape * scale + 1.0e-6).astype(np.int64)
         else:
             # i.e. input 'sizes' is given
diff --git a/tools/mo/openvino/tools/mo/ops/RNN.py b/tools/mo/openvino/tools/mo/ops/RNN.py
index 46d875d1b3e..34cb2b8f10e 100644
--- a/tools/mo/openvino/tools/mo/ops/RNN.py
+++ b/tools/mo/openvino/tools/mo/ops/RNN.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import mark_input_bins, shape_array, shape_insert
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph, add_opoutput
 from openvino.tools.mo.ops.op import Op
 
@@ -20,7 +21,7 @@ class RNN(Op):
             'direction': 'forward',
             'infer': self.infer,
             'multiplier': 1,
-            'gate_order': np.array([0]),  # Only one gate in this cell
+            'gate_order': mo_array([0]),  # Only one gate in this cell
             'normalized': False,
 
             'activation_alpha': None,
diff --git a/tools/mo/openvino/tools/mo/ops/TensorArray.py b/tools/mo/openvino/tools/mo/ops/TensorArray.py
index e586b7278cf..75f6e37917c 100644
--- a/tools/mo/openvino/tools/mo/ops/TensorArray.py
+++ b/tools/mo/openvino/tools/mo/ops/TensorArray.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
@@ -33,7 +32,7 @@ class TensorArray(Op):
 
             out_node = node.out_node(0).id
             output_value = node.out_node(0).id
-            node.graph.node[out_node]['value'] = np.array(output_value)
+            node.graph.node[out_node]['value'] = mo_array(output_value)
 
             output_shape = node.graph.node[out_node]['value'].shape
             node.graph.node[out_node]['shape'] = shape_array(output_shape)
@@ -45,5 +44,5 @@ class TensorArray(Op):
             output_value = None
 
             out_node = node.out_node(1).id
-            node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
+            node.graph.node[out_node]['value'] = None if output_value is None else mo_array(output_value)
             node.graph.node[out_node]['shape'] = shape_array(output_shape)
diff --git a/tools/mo/openvino/tools/mo/ops/TensorArrayScatter.py b/tools/mo/openvino/tools/mo/ops/TensorArrayScatter.py
index a99a28f75af..7e2cac27d0b 100644
--- a/tools/mo/openvino/tools/mo/ops/TensorArrayScatter.py
+++ b/tools/mo/openvino/tools/mo/ops/TensorArrayScatter.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
@@ -39,4 +38,4 @@ class TensorArrayScatter(Op):
         output_value = flow_in.value
         for _, out_node in node.graph.out_edges(node.id):
             node.graph.node[out_node]['shape'] = shape_array(flow_in.shape)
-            node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
+            node.graph.node[out_node]['value'] = None if output_value is None else mo_array(output_value)
diff --git a/tools/mo/openvino/tools/mo/ops/TensorArraySize.py b/tools/mo/openvino/tools/mo/ops/TensorArraySize.py
index 6e51f92adb7..d9b9fc133b7 100644
--- a/tools/mo/openvino/tools/mo/ops/TensorArraySize.py
+++ b/tools/mo/openvino/tools/mo/ops/TensorArraySize.py
@@ -1,8 +1,7 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
@@ -28,7 +27,7 @@ class TensorArraySize(Op):
         ta_node = Node(node.graph, str(handle.value))
         assert ta_node.has_valid('size')
 
-        output_value = np.array(ta_node['size'])
+        output_value = mo_array(ta_node['size'])
 
         for _, out_node in node.graph.out_edges(node.id):
             node.graph.node[out_node]['shape'] = shape_array(output_value.shape)
diff --git a/tools/mo/openvino/tools/mo/ops/activation_ops.py b/tools/mo/openvino/tools/mo/ops/activation_ops.py
index 82711280c66..1a449d78795 100644
--- a/tools/mo/openvino/tools/mo/ops/activation_ops.py
+++ b/tools/mo/openvino/tools/mo/ops/activation_ops.py
@@ -202,7 +202,6 @@ class LeakyReLU(Op):
 
     @staticmethod
     def leaky_relu(values: np.ndarray, negative_slope: float):
-        values = values.astype(float)
         for index, x in np.ndenumerate(values):
             if x < 0:
                 values[index] = negative_slope * x
diff --git a/tools/mo/openvino/tools/mo/ops/bucketize.py b/tools/mo/openvino/tools/mo/ops/bucketize.py
index 5eab8a031e1..5e7bf887741 100644
--- a/tools/mo/openvino/tools/mo/ops/bucketize.py
+++ b/tools/mo/openvino/tools/mo/ops/bucketize.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import bool_to_str
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_destination_type
@@ -69,4 +70,4 @@ class Bucketize(Op):
 
         # compute if all input is constant
         if input_value is not None and buckets_value is not None:
-            node.out_port(0).data.set_value(np.array(np.digitize(input_value, buckets_value, right=node.with_right_bound), dtype=node.output_type))
+            node.out_port(0).data.set_value(mo_array(np.digitize(input_value, buckets_value, right=node.with_right_bound), dtype=node.output_type))
diff --git a/tools/mo/openvino/tools/mo/ops/const.py b/tools/mo/openvino/tools/mo/ops/const.py
index daf2ed9b99d..ac43c90d201 100644
--- a/tools/mo/openvino/tools/mo/ops/const.py
+++ b/tools/mo/openvino/tools/mo/ops/const.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
 from openvino.tools.mo.middle.passes.convert_data_type import data_type_str_to_np, np_data_type_to_destination_type, \
     precision_to_destination_type
 from openvino.tools.mo.ops.op import Op
@@ -27,11 +28,11 @@ class Const(Op):
             'type_infer': self.type_infer,
         }, attrs)
         if not isinstance(self.attrs['value'], np.ndarray):
-            self.attrs['value'] = np.array(self.attrs['value'])
+            self.attrs['value'] = mo_array(self.attrs['value'])
 
-        self.attrs['shape'] = np.array(self.attrs['value'].shape, dtype=np.int64)
+        self.attrs['shape'] = int64_array(self.attrs['value'].shape)
         if 'force_shape' in self.attrs and self.attrs['force_shape'] is not None:
-            self.attrs['shape'] = np.array(self.attrs['force_shape'], dtype=np.int64)
+            self.attrs['shape'] = int64_array(self.attrs['force_shape'])
 
         self.attrs['data_type'] = self.attrs['value'].dtype
         if 'force_type' in self.attrs and self.attrs['force_type'] is not None:
diff --git a/tools/mo/openvino/tools/mo/ops/convolution.py b/tools/mo/openvino/tools/mo/ops/convolution.py
index 7f8f7757c9e..97f6ea41c57 100644
--- a/tools/mo/openvino/tools/mo/ops/convolution.py
+++ b/tools/mo/openvino/tools/mo/ops/convolution.py
@@ -7,10 +7,12 @@ import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mark_input_bins, assign_dims_to_weights, \
     tf_window_op_pad_infer, dynamic_dimension_value, shape_array, is_fully_defined, undefined_shape_of_rank
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.onnx.extractors.utils import get_backend_pad
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.graph.perm_inputs import PermuteInputs
 from openvino.tools.mo.ops.op import Op, PermuteAttrs
+from openvino.tools.mo.pipeline.common import convert_const_node_value_type
 from openvino.tools.mo.utils.error import Error
 
 
@@ -24,6 +26,7 @@ class Convolution(Op):
             'version': 'opset1',
             'infer': self.infer,
             'reverse_infer': self.reverse_infer,
+            'type_infer': self.type_infer,
             'multiplication_transparent': True,
             'multiplication_transparent_ports': [(0, 0), (1, 0)],
             'in_ports_count': 3,
@@ -287,3 +290,18 @@ class Convolution(Op):
                     break
             if shape is not None:
                 node.in_port(0).data.set_shape(shape)
+
+    @staticmethod
+    def type_infer(node):
+        in_type_0 = node.in_port(0).get_data_type()
+        in_type_1 = node.in_port(1).get_data_type()
+        in_node_1 = node.in_port(1).get_source().node
+        # in case of input values data type mismatch we try to change the type of the constant to match the type of
+        # input at index 0.
+        if in_type_1 in [np.float16, np.float32, np.float64] and in_type_0 != in_type_1 and in_node_1.op == 'Const':
+            in_node_1 = node.in_port(1).get_source().node
+            log.error("Changing Const node '{}' data type from {} to {} for Convolution operation".format(
+                in_node_1.soft_get('name', in_node_1.id), in_type_1, in_type_0),
+                extra={'is_warning': True})
+            convert_const_node_value_type(in_node_1, in_type_0)
+        node.out_port(0).set_data_type(node.in_port(0).get_data_type())
diff --git a/tools/mo/openvino/tools/mo/ops/einsum.py b/tools/mo/openvino/tools/mo/ops/einsum.py
index ee0f7be8713..58e15cf98f1 100644
--- a/tools/mo/openvino/tools/mo/ops/einsum.py
+++ b/tools/mo/openvino/tools/mo/ops/einsum.py
@@ -5,7 +5,7 @@ import re
 
 import numpy as np
 
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_array
+from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 from openvino.tools.mo.utils.broadcasting import bi_directional_shape_broadcasting
diff --git a/tools/mo/openvino/tools/mo/ops/elementwise.py b/tools/mo/openvino/tools/mo/ops/elementwise.py
index ff12ed2465b..c887c51cac8 100644
--- a/tools/mo/openvino/tools/mo/ops/elementwise.py
+++ b/tools/mo/openvino/tools/mo/ops/elementwise.py
@@ -1,41 +1,14 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import logging as log
-
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.eltwise import eltwise_infer, bias_add_infer
+from openvino.tools.mo.front.common.partial_infer.utils import float32_array
 from openvino.tools.mo.graph.graph import Graph, Node
 from openvino.tools.mo.middle.passes.infer import copy_type_infer
 from openvino.tools.mo.ops.op import Op
-from openvino.tools.mo.pipeline.common import convert_const_node_value_type
-from openvino.tools.mo.utils.error import Error
-
-
-def override_data_type_of_constant(node: Node):
-    in_type_0 = node.in_port(0).get_data_type()
-    in_type_1 = node.in_port(1).get_data_type()
-    if in_type_0 != in_type_1:
-        # in case of input values data type mismatch we try to change the type of the constant to match the type of
-        # another input. The input values data type mismatch occur when the MO performs replacement of some
-        # operations like SquaredDifference of inputs with floating point data type to Power layer with the integer
-        # power value 2, or when replacing Neg operation with Mul with -1 as second input.
-        in_node_0 = node.in_port(0).get_source().node
-        in_node_1 = node.in_port(1).get_source().node
-
-        if in_node_0.op != 'Const' and in_node_1.op != 'Const':
-            raise Error("Elementwise operation '{}' has inputs of different data types: '{}' and '{}' "
-                        "that cannot be aligned".format(node.soft_get('name'), in_type_0, in_type_1))
-
-        if in_node_0.op == 'Const':
-            node_to_convert, src_type, dst_type = in_node_0, in_type_0, in_type_1
-        else:
-            node_to_convert, src_type, dst_type = in_node_1, in_type_1, in_type_0
-        log.error("Changing Const node '{}' data type from {} to {} for Elementwise operation".format(
-            node_to_convert.soft_get('name', node_to_convert.id), src_type, dst_type),
-            extra={'is_warning': True})
-        convert_const_node_value_type(node_to_convert, dst_type)
+from openvino.tools.mo.utils.type_utils import override_data_type_of_constant
 
 
 class Elementwise(Op):
@@ -133,7 +106,7 @@ class Pow(Elementwise):
     @staticmethod
     def operation(a, b):
         if np.any(b < 0) and np.issubdtype(a.dtype, np.signedinteger):
-            return np.array(a.astype(np.float32) ** b, dtype=np.float32)
+            return float32_array(a.astype(np.float32) ** b)
         return a ** b
 
 
diff --git a/tools/mo/openvino/tools/mo/ops/fakequantize.py b/tools/mo/openvino/tools/mo/ops/fakequantize.py
index 81d01dd7ec8..aba80e285c7 100644
--- a/tools/mo/openvino/tools/mo/ops/fakequantize.py
+++ b/tools/mo/openvino/tools/mo/ops/fakequantize.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, float32_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 from openvino.tools.mo.utils.error import Error
@@ -10,12 +11,12 @@ from openvino.tools.mo.utils.error import Error
 
 def broadcastable(broadcast_from, broadcast_to):
     """Check if shape broadcast_from can be broadcasted to broadcast_to"""
-    broadcast_to = np.array(broadcast_to, dtype=np.int64)
-    broadcast_from = np.array(broadcast_from, dtype=np.int64)
+    broadcast_to = int64_array(broadcast_to)
+    broadcast_from = int64_array(broadcast_from)
     if broadcast_from.size > broadcast_to.size:
         return False
     broadcast_from = np.concatenate(
-        (np.array([1] * (broadcast_to.size - broadcast_from.size), dtype=np.int64), broadcast_from))
+        (int64_array([1] * (broadcast_to.size - broadcast_from.size)), broadcast_from))
     return np.all(np.logical_or(broadcast_from == 1, broadcast_from == broadcast_to))
 
 
@@ -63,7 +64,7 @@ class FakeQuantize(Op):
 
         if all([node.in_node(i).has_valid('value') for i in range(5)]):
             x, input_low, input_high, output_low, output_high = \
-                [np.array(np.broadcast_to(node.value, x.value.shape), dtype=np.float32) for node in inputs]
+                [float32_array(np.broadcast_to(node.value, x.value.shape)) for node in inputs]
 
             assert node.has_valid('levels')
             assert isinstance(node.levels, int)
diff --git a/tools/mo/openvino/tools/mo/ops/gather.py b/tools/mo/openvino/tools/mo/ops/gather.py
index 701a586d0fc..c4c551fb9f3 100644
--- a/tools/mo/openvino/tools/mo/ops/gather.py
+++ b/tools/mo/openvino/tools/mo/ops/gather.py
@@ -5,6 +5,7 @@ import numpy as np
 
 from openvino.tools.mo.front.caffe.extractors.utils import get_canonical_axis_index
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array, is_fully_defined
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op, PermuteAttrs
 from openvino.tools.mo.utils.error import Error
@@ -150,7 +151,7 @@ class AttributedGather(Op):
         data_value = node.in_port(0).data.get_value()
         indices_value = node.in_port(1).data.get_value()
         if data_value is not None and indices_value is not None:
-            node.out_port(0).data.set_value(np.array(np.take(data_value, indices_value, axis), dtype=data_value.dtype))
+            node.out_port(0).data.set_value(mo_array(np.take(data_value, indices_value, axis), dtype=data_value.dtype))
             return
 
         shape = np.concatenate((data_shape[:axis], indices_shape))
diff --git a/tools/mo/openvino/tools/mo/ops/interpolate.py b/tools/mo/openvino/tools/mo/ops/interpolate.py
index 807f41cd293..69b5c00ae88 100644
--- a/tools/mo/openvino/tools/mo/ops/interpolate.py
+++ b/tools/mo/openvino/tools/mo/ops/interpolate.py
@@ -86,15 +86,15 @@ def correct_pad(pad, rank):
     if pad_len < rank:
         return np.pad(pad, (0, rank - pad_len), 'constant').astype(np.int64)
     elif pad_len > rank:
-        return np.array(pad[: rank]).astype(np.int64)
+        return int64_array(pad[: rank])
     else:
-        return np.array(pad, dtype=np.int64)
+        return int64_array(pad)
 
 
 def correct_scales_using_dst_shape(node, dst_shape, src_shape, axes):
     scales_value = node.in_port(2).data.get_value()
     if scales_value is None or len(scales_value) != len(dst_shape):
-        corrected_scales = np.zeros(len(dst_shape))
+        corrected_scales = np.zeros(len(dst_shape), dtype=np.float32)
         for i, axis in enumerate(list(axes)):
             corrected_scales[i] = dst_shape[i] / src_shape[axis]
         node.in_port(2).data.set_value(corrected_scales)
diff --git a/tools/mo/openvino/tools/mo/ops/lstm_sequence.py b/tools/mo/openvino/tools/mo/ops/lstm_sequence.py
index 37955ce972b..4de101d701e 100644
--- a/tools/mo/openvino/tools/mo/ops/lstm_sequence.py
+++ b/tools/mo/openvino/tools/mo/ops/lstm_sequence.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import mark_input_bins, shape_array, shape_insert
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Node, add_opoutput, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -66,7 +67,7 @@ class LSTMSequence(Op):
         return Op._create_data_node(
             node.graph,
             name=node.name + '/SplittedBiLSTM/{}/'.format(direction),
-            attrs={'value': node.value[index], 'shape': np.array(node.value[index].shape, dtype=np.int64)}
+            attrs={'value': node.value[index], 'shape': int64_array(node.value[index].shape)}
         )
 
     @staticmethod
diff --git a/tools/mo/openvino/tools/mo/ops/non_zero.py b/tools/mo/openvino/tools/mo/ops/non_zero.py
index bdb860d8060..95ad04be9c2 100644
--- a/tools/mo/openvino/tools/mo/ops/non_zero.py
+++ b/tools/mo/openvino/tools/mo/ops/non_zero.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import is_fully_defined, dynamic_dimension_value
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_destination_type
 from openvino.tools.mo.ops.op import Op
@@ -45,7 +46,7 @@ class NonZero(Op):
 
         input_value = node.in_port(0).data.get_value()
         if is_fully_defined(input_value):
-            node.out_port(0).data.set_value(np.array(np.nonzero(input_value), dtype=node.output_type))
+            node.out_port(0).data.set_value(mo_array(np.nonzero(input_value), dtype=node.output_type))
         else:
             if is_fully_defined(input_shape):
                 # output shape of NonZero is still static (upper bound)
diff --git a/tools/mo/openvino/tools/mo/ops/op.py b/tools/mo/openvino/tools/mo/ops/op.py
index db2f7195a8d..75c5c7f5648 100644
--- a/tools/mo/openvino/tools/mo/ops/op.py
+++ b/tools/mo/openvino/tools/mo/ops/op.py
@@ -9,6 +9,7 @@ import networkx as nx
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import int64_array, strict_compare_tensors
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import add_attrs_props, update_ie_fields
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.utils import class_registration
@@ -252,7 +253,7 @@ class Op(object):
         if attrs is None:
             attrs = {}
         data_node = graph.unique_id(name)
-        default_attrs = dict(kind='data', name=data_node, value=np.array(value), shape=np.array(value.shape),
+        default_attrs = dict(kind='data', name=data_node, value=mo_array(value), shape=mo_array(value.shape),
                              data_type=None, infer=None)
         default_attrs.update(attrs)
         graph.add_node(data_node, **add_attrs_props(default_attrs))
@@ -331,7 +332,7 @@ class Op(object):
             return
         for idx in range(dims_to_add):
             node.value = np.expand_dims(node.value, axis=-1)
-        node.shape = np.array(node.value.shape)
+        node.shape = mo_array(node.value.shape)
 
     @staticmethod
     def normalize_outputs(node: Node):
diff --git a/tools/mo/openvino/tools/mo/ops/pooling.py b/tools/mo/openvino/tools/mo/ops/pooling.py
index 2ff0e86f644..0e7ccd50e61 100644
--- a/tools/mo/openvino/tools/mo/ops/pooling.py
+++ b/tools/mo/openvino/tools/mo/ops/pooling.py
@@ -3,14 +3,15 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import tf_window_op_pad_infer, int64_array, shape_array, \
     dynamic_dimension_value, dynamic_dimension, undefined_shape_of_rank
+from openvino.tools.mo.front.extractor import bool_to_str
 from openvino.tools.mo.front.onnx.extractors.utils import get_backend_pad
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_destination_type
 from openvino.tools.mo.ops.op import Op, PermuteAttrs
 from openvino.tools.mo.utils.error import Error
-from openvino.tools.mo.front.extractor import bool_to_str
 
 
 poolings_map = {
@@ -132,7 +133,7 @@ class Pooling(Op):
             node.window[node.spatial_dims] = input_spatial_shape
 
         if not node.has_valid('dilation'):
-            node['dilation'] = np.ones(len(input_shape))
+            node['dilation'] = np.ones(len(input_shape), dtype=np.float32)
 
         if not node.has_valid('axis'):
             node['axis'] = 0
@@ -173,7 +174,7 @@ class Pooling(Op):
                 if padded_spatial_shape[idx] is not dynamic_dimension and stride_spatial[idx] is not dynamic_dimension:
                     output_spatial_shape[idx] = int(rounding(padded_spatial_shape[idx] / stride_spatial[idx])) + 1
 
-            original_pads = np.array([i[1] for i in node.pad_spatial_shape])
+            original_pads = mo_array([i[1] for i in node.pad_spatial_shape])
 
             for i in range(len(input_spatial_shape)):
                 if original_pads[i] and (output_spatial_shape[i] - 1) * stride_spatial[i] >= \
diff --git a/tools/mo/openvino/tools/mo/ops/priorbox.py b/tools/mo/openvino/tools/mo/ops/priorbox.py
index 04ba8385c0a..e399c7bffca 100644
--- a/tools/mo/openvino/tools/mo/ops/priorbox.py
+++ b/tools/mo/openvino/tools/mo/ops/priorbox.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.layout import get_width_dim, get_height_dim
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import attr_getter, bool_to_str
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
@@ -20,12 +21,12 @@ class PriorBoxOp(Op):
             'flip': True,
             'clip': True,
             'scale_all_sizes': True,
-            'max_size': np.array([]),
-            'min_size': np.array([]),
-            'aspect_ratio': np.array([]),
-            'density': np.array([]),
-            'fixed_size': np.array([]),
-            'fixed_ratio': np.array([]),
+            'max_size': mo_array([]),
+            'min_size': mo_array([]),
+            'aspect_ratio': mo_array([]),
+            'density': mo_array([]),
+            'fixed_size': mo_array([]),
+            'fixed_ratio': mo_array([]),
             'in_ports_count': 2,
             'out_ports_count': 1,
             'type_infer': self.type_infer,
@@ -88,7 +89,7 @@ class PriorBoxOp(Op):
             for s in node.aspect_ratio:
                 ar_seen.append(1.0 / s)
 
-        ar_seen = np.unique(np.array(ar_seen).round(decimals=6))
+        ar_seen = np.unique(mo_array(ar_seen).round(decimals=6))
 
         num_ratios = 0
         if len(node.min_size) > 0:
diff --git a/tools/mo/openvino/tools/mo/ops/range.py b/tools/mo/openvino/tools/mo/ops/range.py
index 0ef68769e36..fa76b76a0eb 100644
--- a/tools/mo/openvino/tools/mo/ops/range.py
+++ b/tools/mo/openvino/tools/mo/ops/range.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array, dynamic_dimension_value, is_fully_defined
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_destination_type
@@ -22,11 +23,11 @@ class Range(Op):
     >>> tf.range(1, 5, 2)
     <tf.Tensor 'range_2:0' shape = (2,) dtype = int32>
 
-    >>> np.array([0.5], dtype=np.float32)
+    >>> mo_array([0.5], dtype=np.float32)
     array([0.5], dtype=float32)
-    >>> np.arange(np.array([1], dtype=np.int32), np.array([5], dtype=np.int32), np.array([2], dtype=np.int32)).dtype
+    >>> np.arange(mo_array([1], dtype=np.int32), mo_array([5], dtype=np.int32), mo_array([2], dtype=np.int32)).dtype
     dtype('int64')
-    >>> np.arange(np.array([1], dtype=np.int32), np.array([5], dtype=np.int32), np.array([0.5], dtype=np.float32)).dtype
+    >>> np.arange(mo_array([1], dtype=np.int32), mo_array([5], dtype=np.int32), mo_array([0.5], dtype=np.float32)).dtype
     dtype('float64')
     """
     op = 'Range'
diff --git a/tools/mo/openvino/tools/mo/ops/scatternd.py b/tools/mo/openvino/tools/mo/ops/scatternd.py
index 883d81ef568..19d0542ada1 100644
--- a/tools/mo/openvino/tools/mo/ops/scatternd.py
+++ b/tools/mo/openvino/tools/mo/ops/scatternd.py
@@ -55,7 +55,7 @@ class ScatterNDBase(Op):
         expected_updates_shape = np.ma.concatenate((indices_shape[:-1], input_shape[indices_shape[-1]:]), axis=0)
         assert compatible_shapes(updates_shape, expected_updates_shape) or \
                (strict_compare_tensors(expected_updates_shape, []) and
-                strict_compare_tensors(updates_shape, np.ones(len(updates_shape)))), \
+                strict_compare_tensors(updates_shape, np.ones(len(updates_shape), dtype=np.int64))), \
             'The updates shape must be equal to indices_shape[:-1] + input_shape[indices_shape[-1]:] for the node ' \
             '"{}"'.format(node_name)
 
diff --git a/tools/mo/openvino/tools/mo/ops/select.py b/tools/mo/openvino/tools/mo/ops/select.py
index fa27e1f1c7e..001a58fa63b 100644
--- a/tools/mo/openvino/tools/mo/ops/select.py
+++ b/tools/mo/openvino/tools/mo/ops/select.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from openvino.tools.mo.front.common.partial_infer.utils import compatible_shapes, dynamic_dimension, shape_array, is_fully_defined, compatible_dims
+from openvino.tools.mo.front.common.partial_infer.utils import compatible_shapes, dynamic_dimension, shape_array, is_fully_defined
 from openvino.tools.mo.graph.graph import Node, Graph, Error
 from openvino.tools.mo.ops.op import Op
 from openvino.tools.mo.utils.broadcasting import bi_directional_shape_broadcasting, bi_directional_broadcasting
@@ -66,7 +66,7 @@ class Select(Op):
                 if is_fully_defined(condition_shape[0]) and not output_is_scalar and is_fully_defined(output_shape[0]):
                     assert condition_shape[0] == output_shape[0], msg_tf
                 ones_shape = len(output_shape) if output_is_scalar else len(output_shape) - 1
-                condition_shape = np.concatenate((condition_shape, np.ones(ones_shape)))
+                condition_shape = np.concatenate((condition_shape, np.ones(ones_shape, dtype=np.int64)))
 
             output_shape = bi_directional_shape_broadcasting(output_shape, condition_shape)
             assert output_shape is not None, msg
diff --git a/tools/mo/openvino/tools/mo/ops/shape.py b/tools/mo/openvino/tools/mo/ops/shape.py
index 58235445076..32710c68a2f 100644
--- a/tools/mo/openvino/tools/mo/ops/shape.py
+++ b/tools/mo/openvino/tools/mo/ops/shape.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.graph.graph import Graph
 from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_destination_type
@@ -59,7 +60,7 @@ class Shape(Op):
         if node.has_and_set('stop_value_propagation'):
             node.out_port(0).data.set_shape(input_shape.shape)
         else:
-            node.out_port(0).data.set_value(shape_array(np.array(input_shape, dtype=node.output_type)))
+            node.out_port(0).data.set_value(shape_array(mo_array(input_shape, dtype=node.output_type)))
 
     @staticmethod
     def type_infer(node):
diff --git a/tools/mo/openvino/tools/mo/ops/size.py b/tools/mo/openvino/tools/mo/ops/size.py
index 69dea5f6bdf..695f02ad025 100644
--- a/tools/mo/openvino/tools/mo/ops/size.py
+++ b/tools/mo/openvino/tools/mo/ops/size.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import is_fully_defined, shape_array, dynamic_dimension_value
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -41,6 +42,6 @@ class Size(Op):
             'Size `output_type` attribute must be int32 or int64, `{}` found'.format(np.dtype(node.output_type).name)
 
         if is_fully_defined(input_shape):
-            node.out_port(0).data.set_value(np.array(np.prod(input_shape), dtype=node.output_type))
+            node.out_port(0).data.set_value(mo_array(np.prod(input_shape), dtype=node.output_type))
         else:
             node.out_port(0).data.set_value(shape_array(dynamic_dimension_value))
diff --git a/tools/mo/openvino/tools/mo/ops/sparse_fill_empty_rows.py b/tools/mo/openvino/tools/mo/ops/sparse_fill_empty_rows.py
index 3099fb5952a..177a2d234d5 100644
--- a/tools/mo/openvino/tools/mo/ops/sparse_fill_empty_rows.py
+++ b/tools/mo/openvino/tools/mo/ops/sparse_fill_empty_rows.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import dynamic_dimension_value, is_fully_defined
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -50,7 +51,7 @@ class SparseFillEmptyRows(Op):
         assert shape.value is not None and shape.value.size == 2, \
             "SparseFillEmptyRows is supported only with constant shape value"
 
-        shape_value = np.array(shape.value, dtype=np.int64)
+        shape_value = int64_array(shape.value)
 
         # check that default value is scalar
         default_value = node.in_node(3)
diff --git a/tools/mo/openvino/tools/mo/ops/sparse_segment_mean.py b/tools/mo/openvino/tools/mo/ops/sparse_segment_mean.py
index be51dfb46f5..a58eb26d8c5 100644
--- a/tools/mo/openvino/tools/mo/ops/sparse_segment_mean.py
+++ b/tools/mo/openvino/tools/mo/ops/sparse_segment_mean.py
@@ -82,7 +82,7 @@ class SparseSegmentMean(Op):
 
         # infer
         num_adds = np.zeros(num_segments, dtype=np.int)
-        output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float)
+        output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float32)
         output_shape = output_value.shape
         for i in range(len(segment_ids_value)):
             segment_id = int(segment_ids_value[i])
diff --git a/tools/mo/openvino/tools/mo/ops/sparse_segment_sqrtn.py b/tools/mo/openvino/tools/mo/ops/sparse_segment_sqrtn.py
index fb326af48ef..e5688324c44 100644
--- a/tools/mo/openvino/tools/mo/ops/sparse_segment_sqrtn.py
+++ b/tools/mo/openvino/tools/mo/ops/sparse_segment_sqrtn.py
@@ -82,7 +82,7 @@ class SparseSegmentSqrtN(Op):
 
         # infer
         num_adds = np.zeros(num_segments, dtype=np.int)
-        output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float)
+        output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float32)
         output_shape = output_value.shape
         for i in range(len(segment_ids_value)):
             segment_id = int(segment_ids_value[i])
diff --git a/tools/mo/openvino/tools/mo/ops/sparse_segment_sum.py b/tools/mo/openvino/tools/mo/ops/sparse_segment_sum.py
index 06e2e322cb7..15dd5dd013f 100644
--- a/tools/mo/openvino/tools/mo/ops/sparse_segment_sum.py
+++ b/tools/mo/openvino/tools/mo/ops/sparse_segment_sum.py
@@ -81,7 +81,7 @@ class SparseSegmentSum(Op):
             "Some value in indices tensor is out of range"
 
         # infer
-        output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float)
+        output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float32)
         output_shape = output_value.shape
         for i in range(len(segment_ids_value)):
             segment_id = int(segment_ids_value[i])
diff --git a/tools/mo/openvino/tools/mo/ops/unique.py b/tools/mo/openvino/tools/mo/ops/unique.py
index 4e35b23dae4..311c7500ba6 100644
--- a/tools/mo/openvino/tools/mo/ops/unique.py
+++ b/tools/mo/openvino/tools/mo/ops/unique.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
 from openvino.tools.mo.graph.graph import Node, Graph
 from openvino.tools.mo.ops.op import Op
 
@@ -151,6 +152,6 @@ class Unique(Op):
         # write result to output nodes
         j = 0
         for out_node_ind in node.out_nodes():
-            node.out_node(out_node_ind).value = np.array(unique_output[j], dtype=np.float)
-            node.out_node(out_node_ind).shape = np.array(node.out_node(out_node_ind).value.shape, dtype=np.int64)
+            node.out_node(out_node_ind).value = mo_array(unique_output[j], dtype=np.float)
+            node.out_node(out_node_ind).shape = int64_array(node.out_node(out_node_ind).value.shape)
             j += 1
diff --git a/tools/mo/openvino/tools/mo/pipeline/common.py b/tools/mo/openvino/tools/mo/pipeline/common.py
index 3e2cd5b2b31..84e3bc50b2e 100644
--- a/tools/mo/openvino/tools/mo/pipeline/common.py
+++ b/tools/mo/openvino/tools/mo/pipeline/common.py
@@ -173,16 +173,14 @@ def convert_inputs_of_specific_ops(graph: Graph):
 
 def prepare_emit_ir(graph: Graph, data_type: str, output_dir: str, output_model_name: str,
                     mean_data: [list, None] = None, input_names: list = None, meta_info: dict = None,
-                    use_temporary_path=False, used_by_ir_reader=False):
+                    use_temporary_path=False, convert_types=False):
     if input_names is None:
         input_names = []
     if meta_info is None:
         meta_info = {}
     graph.strict_mode = False
 
-    # temporary disable new FP16 generation
-    # if not used_by_ir_reader:
-    if True:
+    if convert_types:
         # convert Parameter data types
         convert_data_type.convert_parameters_data_type(graph, data_type)
         # convert blobs (usually weights and biases)
diff --git a/tools/mo/openvino/tools/mo/utils/broadcasting.py b/tools/mo/openvino/tools/mo/utils/broadcasting.py
index 4d1be1aac38..32630ff0478 100644
--- a/tools/mo/openvino/tools/mo/utils/broadcasting.py
+++ b/tools/mo/openvino/tools/mo/utils/broadcasting.py
@@ -7,6 +7,7 @@ import numpy as np
 
 from openvino.tools.mo.front.common.partial_infer.utils import dynamic_dimension, shape_array, shape_insert, is_fully_defined, \
     dynamic_dimension_value
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 
 
 def make_equal_rank(shape_1: np.ndarray, shape_2: np.ndarray):
@@ -93,7 +94,7 @@ def explicit_shape_broadcasting(input_shape: np.array, target_shape: np.array, a
     """
     assert np.all(np.diff(axes_mapping) >= 0), "axes_mapping is not sorted"
     assert len(axes_mapping) == len(input_shape), "size of axes_mapping does not match to rank of input"
-    axes_mapping = np.array(list(map(lambda axis: axis + len(target_shape) if axis < 0 else axis, axes_mapping)))
+    axes_mapping = mo_array(list(map(lambda axis: axis + len(target_shape) if axis < 0 else axis, axes_mapping)))
 
     res = target_shape.copy()
     for i, axis in enumerate(axes_mapping):
diff --git a/tools/mo/openvino/tools/mo/utils/cli_parser.py b/tools/mo/openvino/tools/mo/utils/cli_parser.py
index 3d0056aaa70..604e2fc3f12 100644
--- a/tools/mo/openvino/tools/mo/utils/cli_parser.py
+++ b/tools/mo/openvino/tools/mo/utils/cli_parser.py
@@ -13,6 +13,7 @@ from typing import List, Union
 
 import numpy as np
 
+from openvino.tools.mo.front.common.partial_infer.utils import mo_array
 from openvino.tools.mo.front.extractor import split_node_in_port
 from openvino.tools.mo.middle.passes.convert_data_type import destination_type_to_np_data_type
 from openvino.tools.mo.utils import import_extensions
@@ -1195,7 +1196,7 @@ def parse_tuple_pairs(argv_values: str):
         dictionary with input name and tuple of values or list of values if mean/scale value is specified with input,
         e.g.:
         "data(10,20,30),info(11,22,33)" -> { 'data': [10,20,30], 'info': [11,22,33] }
-        "(10,20,30),(11,22,33)" -> [np.array(10,20,30), np.array(11,22,33)]
+        "(10,20,30),(11,22,33)" -> [mo_array(10,20,30), mo_array(11,22,33)]
     """
     res = {}
     if not argv_values:
diff --git a/tools/mo/openvino/tools/mo/utils/ir_reader/restore_graph.py b/tools/mo/openvino/tools/mo/utils/ir_reader/restore_graph.py
index 0e5aa67f8a2..a322294ba5c 100644
--- a/tools/mo/openvino/tools/mo/utils/ir_reader/restore_graph.py
+++ b/tools/mo/openvino/tools/mo/utils/ir_reader/restore_graph.py
@@ -4,8 +4,6 @@
 import logging as log
 from copy import copy
 
-import numpy as np
-
 from openvino.tools.mo.back.ConvolutionNormalizer import ConvolutionNormalizer, ConvolutionWithGroupsResolver
 from openvino.tools.mo.back.MarkNodesWithShapeValues import MarkNodesWithShapeValues
 from openvino.tools.mo.back.PackBinaryWeights import PackBinaryWeights
@@ -88,4 +86,4 @@ def save_restored_graph(graph: Graph, path: str, meta_data, name=None):
     for_graph_and_each_sub_graph_recursively(graph, RemoveConstOps().find_and_replace_pattern)
     for_graph_and_each_sub_graph_recursively(graph, CreateConstNodesReplacement().find_and_replace_pattern)
 
-    prepare_emit_ir(graph, data_type, path, name, meta_info=meta_data, used_by_ir_reader=True)
+    prepare_emit_ir(graph, data_type, path, name, meta_info=meta_data)
diff --git a/tools/mo/openvino/tools/mo/utils/type_utils.py b/tools/mo/openvino/tools/mo/utils/type_utils.py
new file mode 100644
index 00000000000..4ec7cacb84d
--- /dev/null
+++ b/tools/mo/openvino/tools/mo/utils/type_utils.py
@@ -0,0 +1,35 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import logging as log
+
+import numpy as np
+
+from openvino.tools.mo.graph.graph import Node
+from openvino.tools.mo.pipeline.common import convert_const_node_value_type
+from openvino.tools.mo.utils.error import Error
+
+
+def override_data_type_of_constant(node: Node, lhs_idx: int = 0, rhs_idx: int = 1):
+    in_type_0 = node.in_port(lhs_idx).get_data_type()
+    in_type_1 = node.in_port(rhs_idx).get_data_type()
+    if in_type_0 != in_type_1:
+        # in case of input values data type mismatch we try to change the type of the constant to match the type of
+        # another input.
+        in_node_0 = node.in_port(0).get_source().node
+        in_node_1 = node.in_port(1).get_source().node
+
+        if in_node_0.op == 'Const':
+            node_to_convert, src_type, dst_type = in_node_0, in_type_0, in_type_1
+        elif in_node_1.op == 'Const':
+            node_to_convert, src_type, dst_type = in_node_1, in_type_1, in_type_0
+        else:
+            raise Error("{} operation '{}' has inputs of different data types: '{}' and '{}' "
+                        "that cannot be aligned".format(node.soft_get('op'),
+                                                        node.soft_get('name'),
+                                                        in_type_0,
+                                                        in_type_1))
+        log.error("Changing Const node '{}' data type from {} to {} for {} operation".format(
+            node_to_convert.soft_get('name', node_to_convert.id), src_type, dst_type, node.soft_get('op')),
+            extra={'is_warning': True})
+        convert_const_node_value_type(node_to_convert, dst_type)
diff --git a/tools/mo/unit_tests/mo/back/compress_quantized_weights_test.py b/tools/mo/unit_tests/mo/back/compress_quantized_weights_test.py
index 04047f13271..c72b46a735e 100644
--- a/tools/mo/unit_tests/mo/back/compress_quantized_weights_test.py
+++ b/tools/mo/unit_tests/mo/back/compress_quantized_weights_test.py
@@ -27,6 +27,9 @@ def nodes_dict(original, transformed=None, levels=255, data=None, il=[-127], ih=
         **valued_const_with_data('weights', data),
         **valued_const_with_data('int_weights', int_data),
 
+        **regular_op_with_shaped_data(
+            'weights_cast', shape, {'type': 'Convert', 'op': 'Cast', 'infer': Cast.infer, 'dst_type': np.float32}),
+
         **regular_op_with_shaped_data(
             'cast', shape, {'type': 'Convert', 'op': 'Cast', 'infer': Cast.infer, 'dst_type': transformed}),
 
@@ -130,25 +133,9 @@ class CompressionQuantizeDequantizeSeparateTest(unittest.TestCase):
         (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
         self.assertTrue(flag, resp)
 
-
-@generator
-class CompressionDataTypeTest(unittest.TestCase):
-    @generate(*[
-        ('FP32', np.int64),
-        ('FP16', np.int64),
-        ('FP32', np.int32),
-        ('FP16', np.int32),
-        ('FP32', np.float64, np.float32),
-        ('FP16', np.float64, np.float16),
-        ('FP32', np.float32, np.float32),
-        ('FP16', np.float32, np.float16),
-        ('FP32', np.float16, np.float32),
-        ('FP16', np.float16, np.float16),
-    ])
-    def test_data_type(self, model_dtype, original, transformed=None):
-        if transformed is None:
-            transformed = original
-        nodes = nodes_dict(original, transformed)
+    def test_quantize_new_fp16(self):
+        original_type = np.float16
+        nodes = nodes_dict(original_type)
 
         graph = build_graph(nodes, [
             *connect('weights:0', '0:FQ'),
@@ -157,7 +144,52 @@ class CompressionDataTypeTest(unittest.TestCase):
             *connect('ol:0', '3:FQ'),
             *connect('oh:0', '4:FQ'),
             *connect('FQ:0', 'output'),
-        ], nodes_with_edges_only=True, cli=Namespace(data_type=model_dtype, static_shape=True))
+        ], nodes_with_edges_only=True)
+
+        error_message = 'Unexpected number of FakeQuantize nodes {} CompressQuantizeWeights.quantize_data call `{}`'
+        fq_nodes = graph.get_op_nodes(type='FakeQuantize')
+        self.assertEqual(len(fq_nodes), 1, error_message.format('before', len(fq_nodes)))
+        fake_quantize = fq_nodes[0]
+
+        CompressQuantizeWeights.quantize_data(fake_quantize, original_type, np.int8, "signed")
+        graph.clean_up()
+
+        fq_nodes = graph.get_op_nodes(type='FakeQuantize')
+        self.assertEqual(len(fq_nodes), 1, error_message.format('after', len(fq_nodes)))
+        self.assertEqual(fq_nodes[0].in_port(0).get_source().node.soft_get('type'), 'Const')
+        self.assertEqual(fq_nodes[0].in_port(0).get_source().node.data_type, np.int8)
+
+        graph_ref = build_graph(nodes, [
+            *connect('int_weights:0', '0:FQ'),
+            *connect('il:0', '1:FQ'),
+            *connect('ih:0', '2:FQ'),
+            *connect('ol:0', '3:FQ'),
+            *connect('oh:0', '4:FQ'),
+            *connect('FQ:0', 'output'),
+        ], nodes_with_edges_only=True)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
+        self.assertTrue(flag, resp)
+
+
+@generator
+class CompressionDataTypeTest(unittest.TestCase):
+    @generate(*[np.int64,
+                np.int32,
+                np.float64,
+                np.float32,
+                np.float16])
+    def test_data_type(self, original):
+        nodes = nodes_dict(original)
+
+        graph = build_graph(nodes, [
+            *connect('weights:0', '0:FQ'),
+            *connect('il:0', '1:FQ'),
+            *connect('ih:0', '2:FQ'),
+            *connect('ol:0', '3:FQ'),
+            *connect('oh:0', '4:FQ'),
+            *connect('FQ:0', 'output'),
+        ], nodes_with_edges_only=True, cli=Namespace(static_shape=True))
 
         CompressQuantizeWeights().find_and_replace_pattern(graph)
         graph.clean_up()
@@ -173,28 +205,70 @@ class CompressionDataTypeTest(unittest.TestCase):
         (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
         self.assertTrue(flag, resp)
 
+    def test_data_type_new_fp16(self):
+        nodes = nodes_dict(np.float16)
+
+        graph = build_graph(nodes, [
+            *connect('weights:0', '0:weights_cast'),
+            *connect('weights_cast:0', '0:FQ'),
+            *connect('il:0', '1:FQ'),
+            *connect('ih:0', '2:FQ'),
+            *connect('ol:0', '3:FQ'),
+            *connect('oh:0', '4:FQ'),
+            *connect('FQ:0', 'output'),
+        ], nodes_with_edges_only=True, cli=Namespace(data_type='FP16', static_shape=True))
+
+        CompressQuantizeWeights().find_and_replace_pattern(graph)
+        graph.clean_up()
+
+        graph_ref = build_graph(nodes, [
+            *connect('int_weights:0', '0:weights_cast'),
+            *connect('weights_cast:0', '0:sub'),
+            *connect('zp:0', '1:sub'),
+            *connect('sub:0', '0:mul'),
+            *connect('scale:0', '1:mul'),
+            *connect('mul:0', 'output'),
+        ], nodes_with_edges_only=True)
+        (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
+        self.assertTrue(flag, resp)
+
 
 @generator
 class AccuracyCheckFP32Test(unittest.TestCase):
     eps = np.finfo(np.float32).eps
 
     @generate(*[
-        ([-2.586, -1.338, 2.773, 4.414], [-2.586], [4.414], [-2.586], [4.414], 256),
-        ([-1.5, -0.32, 0.167, 2.8], [-1.5], [2.8], [-1.5], [2.8], 256),
-        ([1, 1 + eps, 1 + 2 * eps, 1 + 3 * eps], [1], [1 + 3 * eps], [1], [1 + 3 * eps], 256),
-        ([1.0, 2.0, 3.0, 4.0], [1], [4], [1], [4], 256),
+        ([-2.586, -1.338, 2.773, 4.414], [-2.586], [4.414], [-2.586], [4.414], 256, False),
+        ([-1.5, -0.32, 0.167, 2.8], [-1.5], [2.8], [-1.5], [2.8], 256, False),
+        ([1, 1 + eps, 1 + 2 * eps, 1 + 3 * eps], [1], [1 + 3 * eps], [1], [1 + 3 * eps], 256, False),
+        ([1.0, 2.0, 3.0, 4.0], [1], [4], [1], [4], 256, False),
+        ([-2.586, -1.338, 2.773, 4.414], [-2.586], [4.414], [-2.586], [4.414], 256, True),
+        ([-1.5, -0.32, 0.167, 2.8], [-1.5], [2.8], [-1.5], [2.8], 256, True),
+        ([1, 1 + eps, 1 + 2 * eps, 1 + 3 * eps], [1], [1 + 3 * eps], [1], [1 + 3 * eps], 256, True),
+        ([1.0, 2.0, 3.0, 4.0], [1], [4], [1], [4], 256, True),
     ])
-    def test_accuracy(self, data, in_low, in_high, out_low, out_high, levels):
-        nodes = nodes_dict(np.float32, None, levels, data, in_low, in_high, out_low, out_high)
-
-        graph = build_graph(nodes, [
-            *connect('weights:0', '0:FQ'),
-            *connect('il:0', '1:FQ'),
-            *connect('ih:0', '2:FQ'),
-            *connect('ol:0', '3:FQ'),
-            *connect('oh:0', '4:FQ'),
-            *connect('FQ:0', 'output'),
-        ], nodes_with_edges_only=True)
+    def test_accuracy(self, data, in_low, in_high, out_low, out_high, levels, add_cast):
+        if not add_cast:
+            nodes = nodes_dict(np.float32, None, levels, data, in_low, in_high, out_low, out_high)
+            graph = build_graph(nodes, [
+                *connect('weights:0', '0:FQ'),
+                *connect('il:0', '1:FQ'),
+                *connect('ih:0', '2:FQ'),
+                *connect('ol:0', '3:FQ'),
+                *connect('oh:0', '4:FQ'),
+                *connect('FQ:0', 'output'),
+            ], nodes_with_edges_only=True)
+        else:
+            nodes = nodes_dict(np.float16, None, levels, data, in_low, in_high, out_low, out_high)
+            graph = build_graph(nodes, [
+                *connect('weights:0', '0:weights_cast'),
+                *connect('weights_cast:0', '0:FQ'),
+                *connect('il:0', '1:FQ'),
+                *connect('ih:0', '2:FQ'),
+                *connect('ol:0', '3:FQ'),
+                *connect('oh:0', '4:FQ'),
+                *connect('FQ:0', 'output'),
+            ], nodes_with_edges_only=True)
         graph_ref = graph.copy()
 
         CompressQuantizeWeights().find_and_replace_pattern(graph)
@@ -249,6 +323,7 @@ class NegativeCompressionTestLevels(unittest.TestCase):
         (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
         self.assertTrue(flag, resp)
 
+
 @generator
 class ZeroPointOptimizerTestClass(unittest.TestCase):
     @generate(*[
diff --git a/tools/mo/unit_tests/mo/front/common/partial_infer/utils_test.py b/tools/mo/unit_tests/mo/front/common/partial_infer/utils_test.py
index 4b4bf03ef1a..ff6536af8b0 100644
--- a/tools/mo/unit_tests/mo/front/common/partial_infer/utils_test.py
+++ b/tools/mo/unit_tests/mo/front/common/partial_infer/utils_test.py
@@ -6,7 +6,7 @@ import unittest
 import numpy as np
 from generator import generator, generate
 
-from openvino.tools.mo.front.common.partial_infer.utils import int64_array, is_fully_defined, dynamic_dimension_value, \
+from openvino.tools.mo.front.common.partial_infer.utils import int64_array, mo_array, is_fully_defined, dynamic_dimension_value, \
     dynamic_dimension, shape_array, compatible_shapes, shape_delete, shape_insert, strict_compare_tensors
 from openvino.tools.mo.utils.error import Error
 
@@ -129,3 +129,17 @@ class ShapeInsertTest(unittest.TestCase):
         with self.assertRaisesRegex(Error, '.*Incorrect parameter type.*'):
             shape_insert(gen_masked_array([1, 2, 3], []), 2, {})
 
+
+@generator
+class mo_array_test(unittest.TestCase):
+    @generate(*[(mo_array([2, 3, 5, 7]), np.array([2, 3, 5, 7])),
+                (mo_array([2., 3., 5., 7.], dtype=np.float64), np.array([2., 3., 5., 7.])),
+                (mo_array([2., 3., 5., 7.]), np.array([2., 3., 5., 7.], dtype=np.float32)),
+                ])
+    def test_mo_array_positive(self, data, result):
+        self.assertEqual(data.dtype, result.dtype)
+
+    @generate(*[(mo_array([2., 3., 5., 7.]), np.array([2., 3., 5., 7.])),
+                ])
+    def test_mo_array_negative(self, data, result):
+        self.assertNotEqual(data.dtype, result.dtype)

From d1fd0d259efd584ae629be42af27ad5b21a6a38b Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Fri, 24 Dec 2021 14:24:09 +0300
Subject: [PATCH 145/160] Introduce get|set_layout helpers (#9401)

* Introduce get|set_layout helpers

* Added python tests and fixed comments

* Added non constant methods

* Update src/bindings/python/tests/test_ngraph/test_basic.py

Co-authored-by: Alexey Lebedev <alexey.lebedev@intel.com>

* Fixed tests

* FIxed code style

* Fixed func tests

Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
Co-authored-by: Alexey Lebedev <alexey.lebedev@intel.com>
---
 .../src/pyopenvino/graph/layout_helpers.cpp   |  7 ++++++
 .../python/tests/test_ngraph/test_basic.py    | 17 ++++++++++++++
 src/core/include/openvino/core/layout.hpp     | 16 +++++++++++++
 src/core/src/layout.cpp                       | 23 +++++++++++++++++++
 src/core/src/op/parameter.cpp                 | 12 ++--------
 src/core/src/op/result.cpp                    | 12 ++--------
 src/core/tests/preprocess.cpp                 | 15 ++++++++++++
 src/core/tests/type_prop/result.cpp           |  4 ++--
 .../rt_info_deserialization.cpp               |  8 +++----
 9 files changed, 88 insertions(+), 26 deletions(-)

diff --git a/src/bindings/python/src/pyopenvino/graph/layout_helpers.cpp b/src/bindings/python/src/pyopenvino/graph/layout_helpers.cpp
index f3c32cdcdc8..a20917b2aab 100644
--- a/src/bindings/python/src/pyopenvino/graph/layout_helpers.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/layout_helpers.cpp
@@ -23,4 +23,11 @@ void regmodule_graph_layout_helpers(py::module m) {
     mod.def("height_idx", &ov::layout::height_idx, py::arg("layout"));
     mod.def("has_width", &ov::layout::has_width, py::arg("layout"));
     mod.def("width_idx", &ov::layout::width_idx, py::arg("layout"));
+    mod.def("get_layout",
+            static_cast<ov::Layout (*)(const ov::Output<ov::Node>&)>(&ov::layout::get_layout),
+            py::arg("port"));
+    mod.def("get_layout",
+            static_cast<ov::Layout (*)(const ov::Output<const ov::Node>&)>(&ov::layout::get_layout),
+            py::arg("port"));
+    mod.def("set_layout", &ov::layout::set_layout, py::arg("port"), py::arg("layout"));
 }
diff --git a/src/bindings/python/tests/test_ngraph/test_basic.py b/src/bindings/python/tests/test_ngraph/test_basic.py
index cd887ed4a19..7e989ad1a5d 100644
--- a/src/bindings/python/tests/test_ngraph/test_basic.py
+++ b/src/bindings/python/tests/test_ngraph/test_basic.py
@@ -624,6 +624,23 @@ def test_coordinate_diff_iteration_methods():
     assert np.equal(coordinateDiff, data2).all()
 
 
+def test_get_and_set_layout():
+    shape = [2, 2]
+    parameter_a = ops.parameter(shape, dtype=np.float32, name="A")
+    parameter_b = ops.parameter(shape, dtype=np.float32, name="B")
+
+    model = Model(parameter_a + parameter_b, [parameter_a, parameter_b])
+
+    assert layout_helpers.get_layout(model.input(0)) == ov.Layout()
+    assert layout_helpers.get_layout(model.input(1)) == ov.Layout()
+
+    layout_helpers.set_layout(model.input(0), ov.Layout("CH"))
+    layout_helpers.set_layout(model.input(1), ov.Layout("HW"))
+
+    assert layout_helpers.get_layout(model.input(0)) == ov.Layout("CH")
+    assert layout_helpers.get_layout(model.input(1)) == ov.Layout("HW")
+
+
 def test_layout():
     layout = ov.Layout("NCWH")
     layout2 = ov.Layout("NCWH")
diff --git a/src/core/include/openvino/core/layout.hpp b/src/core/include/openvino/core/layout.hpp
index 1bfc56a6669..c472fb3ea08 100644
--- a/src/core/include/openvino/core/layout.hpp
+++ b/src/core/include/openvino/core/layout.hpp
@@ -9,6 +9,7 @@
 
 #include "openvino/core/attribute_adapter.hpp"
 #include "openvino/core/core_visibility.hpp"
+#include "openvino/core/node_output.hpp"
 #include "openvino/core/partial_shape.hpp"
 #include "openvino/core/rank.hpp"
 #include "openvino/core/runtime_attribute.hpp"
@@ -129,6 +130,21 @@ OPENVINO_API bool has_width(const Layout& layout);
 ///
 OPENVINO_API std::int64_t width_idx(const Layout& layout);
 
+/// \brief Sets Layout of port
+///
+/// \throws ov::Exception if port is not connected with Result or Parameter
+OPENVINO_API void set_layout(ov::Output<ov::Node> output, const ov::Layout& layout);
+
+/// \brief Gets Layout of port
+///
+/// \return layout from port and empty layout in other case
+OPENVINO_API ov::Layout get_layout(const ov::Output<ov::Node>& output);
+
+/// \brief Gets Layout of port
+///
+/// \return layout from port and empty layout in other case
+OPENVINO_API ov::Layout get_layout(const ov::Output<const ov::Node>& output);
+
 }  // namespace layout
 
 template <>
diff --git a/src/core/src/layout.cpp b/src/core/src/layout.cpp
index 244f8f2d7ef..109f1294c28 100644
--- a/src/core/src/layout.cpp
+++ b/src/core/src/layout.cpp
@@ -447,6 +447,29 @@ std::int64_t width_idx(const Layout& layout) {
     return layout.get_index_by_name(WIDTH);
 }
 
+ov::Layout get_layout(const ov::Output<const ov::Node>& output) {
+    auto it = output.get_rt_info().find(ov::LayoutAttribute::get_type_info_static());
+    if (it == output.get_rt_info().end()) {
+        return {};
+    }
+    return it->second.as<ov::LayoutAttribute>().value;
+}
+
+ov::Layout get_layout(const ov::Output<ov::Node>& output) {
+    return get_layout(ov::Output<const ov::Node>(output.get_node(), output.get_index()));
+}
+
+void set_layout(ov::Output<ov::Node> output, const ov::Layout& layout) {
+    OPENVINO_ASSERT(
+        dynamic_cast<ov::op::v0::Parameter*>(output.get_node()) || dynamic_cast<ov::op::v0::Result*>(output.get_node()),
+        "Layout can be set only for Parameter and Result operations.");
+    if (layout.empty()) {
+        output.get_rt_info().erase(ov::LayoutAttribute::get_type_info_static());
+    } else {
+        output.get_rt_info()[ov::LayoutAttribute::get_type_info_static()] = ov::LayoutAttribute(layout);
+    }
+}
+
 }  // namespace layout
 
 const std::string& AttributeAdapter<ov::Layout>::get() {
diff --git a/src/core/src/op/parameter.cpp b/src/core/src/op/parameter.cpp
index 7f00f2ffe7f..1af41689669 100644
--- a/src/core/src/op/parameter.cpp
+++ b/src/core/src/op/parameter.cpp
@@ -49,19 +49,11 @@ void op::Parameter::set_is_relevant_to_shapes(bool is_relevant) {
 }
 
 ov::Layout op::Parameter::get_layout() const {
-    auto it = output(0).get_rt_info().find(ov::LayoutAttribute::get_type_info_static());
-    if (it == output(0).get_rt_info().end()) {
-        return {};
-    }
-    return it->second.as<ov::LayoutAttribute>().value;
+    return ov::layout::get_layout(output(0));
 }
 
 void op::Parameter::set_layout(const ov::Layout& layout) {
-    if (layout.empty()) {
-        output(0).get_rt_info().erase(ov::LayoutAttribute::get_type_info_static());
-    } else {
-        output(0).get_rt_info()[ov::LayoutAttribute::get_type_info_static()] = ov::LayoutAttribute(layout);
-    }
+    ov::layout::set_layout(output(0), layout);
 }
 
 BWDCMP_RTTI_DEFINITION(ov::AttributeAdapter<ParameterVector>);
diff --git a/src/core/src/op/result.cpp b/src/core/src/op/result.cpp
index 91b0d1d99ce..93401645ceb 100644
--- a/src/core/src/op/result.cpp
+++ b/src/core/src/op/result.cpp
@@ -68,19 +68,11 @@ bool op::Result::constant_fold(OutputVector& output_values, const OutputVector&
 }
 
 ov::Layout op::Result::get_layout() const {
-    auto it = input(0).get_rt_info().find(ov::LayoutAttribute::get_type_info_static());
-    if (it == input(0).get_rt_info().end()) {
-        return {};
-    }
-    return it->second.as<ov::LayoutAttribute>().value;
+    return ov::layout::get_layout(output(0));
 }
 
 void op::Result::set_layout(const ov::Layout& layout) {
-    if (layout.empty()) {
-        input(0).get_rt_info().erase(ov::LayoutAttribute::get_type_info_static());
-    } else {
-        input(0).get_rt_info()[ov::LayoutAttribute::get_type_info_static()] = ov::LayoutAttribute{layout};
-    }
+    ov::layout::set_layout(output(0), layout);
 }
 
 BWDCMP_RTTI_DEFINITION(ov::AttributeAdapter<ResultVector>);
diff --git a/src/core/tests/preprocess.cpp b/src/core/tests/preprocess.cpp
index d1f8c3a5356..eb1bb522bc7 100644
--- a/src/core/tests/preprocess.cpp
+++ b/src/core/tests/preprocess.cpp
@@ -194,6 +194,7 @@ TEST(pre_post_process, tensor_element_type_and_scale) {
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::f32);
     EXPECT_EQ(f->get_output_element_type(0), element::i8);
     EXPECT_EQ(f->get_parameters().front()->get_layout(), Layout());
+    EXPECT_EQ(ov::layout::get_layout(f->input(0)), Layout());
 }
 
 TEST(pre_post_process, convert_color_nv12_rgb_single) {
@@ -208,6 +209,7 @@ TEST(pre_post_process, convert_color_nv12_rgb_single) {
     EXPECT_EQ(f->get_parameters().size(), 1);
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::u8);
     EXPECT_EQ(f->get_parameters().front()->get_layout(), "NHWC");
+    EXPECT_EQ(ov::layout::get_layout(f->input(0)), "NHWC");
     EXPECT_EQ(f->get_parameters().front()->get_partial_shape(), (PartialShape{Dimension::dynamic(), 3, 2, 1}));
     EXPECT_EQ(f->get_parameters().front()->get_friendly_name(), name);
     EXPECT_EQ(f->get_parameters().front()->get_output_tensor(0).get_names(), tensor_names);
@@ -568,6 +570,18 @@ TEST(pre_post_process, test_2_inputs_basic) {
     EXPECT_EQ(f->get_output_element_type(1), element::f32);
 }
 
+TEST(pre_post_process, set_model_input_layout_helper) {
+    auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 2, 1});
+    ov::layout::set_layout(f->input(0), "NCHW");
+    EXPECT_EQ(ov::layout::get_layout(f->input(0)), "NCHW");
+}
+
+TEST(pre_post_process, set_model_output_layout_helper) {
+    auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 2, 1});
+    ov::layout::set_layout(f->output(0), "NCHW");
+    EXPECT_EQ(ov::layout::get_layout(f->output(0)), "NCHW");
+}
+
 TEST(pre_post_process, reuse_model_layout_no_tensor_info) {
     auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 2, 1});
     f->get_parameters().front()->set_layout("NC??");
@@ -1192,6 +1206,7 @@ TEST(pre_post_process, postprocess_set_layout_model) {
     p.output().model().set_layout("NCHW");
     p.build();
     EXPECT_EQ(f->get_results()[0]->get_layout(), "NCHW");
+    EXPECT_EQ(ov::layout::get_layout(f->output(0)), "NCHW");
 }
 
 TEST(pre_post_process, postprocess_convert_layout_implicit) {
diff --git a/src/core/tests/type_prop/result.cpp b/src/core/tests/type_prop/result.cpp
index 6966c9f1997..cc5139509e3 100644
--- a/src/core/tests/type_prop/result.cpp
+++ b/src/core/tests/type_prop/result.cpp
@@ -36,7 +36,7 @@ TEST(type_prop, result_layout) {
     EXPECT_EQ(result->get_layout(), "NHWC");
     result->set_layout(ov::Layout());
     EXPECT_TRUE(result->get_layout().empty());
-    EXPECT_EQ(result->input(0).get_rt_info().count(ov::LayoutAttribute::get_type_info_static()), 0);
+    EXPECT_EQ(result->output(0).get_rt_info().count(ov::LayoutAttribute::get_type_info_static()), 0);
 }
 
 TEST(type_prop, result_layout_empty) {
@@ -48,6 +48,6 @@ TEST(type_prop, result_layout_empty) {
 TEST(type_prop, result_layout_invalid) {
     auto a = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
     auto result = make_shared<opset1::Result>(a);
-    result->input(0).get_rt_info()[ov::LayoutAttribute::get_type_info_static()] = "NCHW";  // incorrect way
+    result->output(0).get_rt_info()[ov::LayoutAttribute::get_type_info_static()] = "NCHW";  // incorrect way
     ASSERT_THROW(result->get_layout(), ov::Exception);
 }
diff --git a/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp b/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
index ff203e3a1fa..c6727ad2311 100644
--- a/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
+++ b/src/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
@@ -394,6 +394,9 @@ TEST_F(RTInfoDeserialization, NodeV11) {
             </input>
             <output>
                 <port id="2" precision="FP32" names="output_tensor">
+                    <rt_info>
+                        <attribute name="layout" version="0" layout="[N,H,W,C]"/>
+                    </rt_info>
                     <dim>1</dim>
                     <dim>22</dim>
                     <dim>22</dim>
@@ -407,9 +410,6 @@ TEST_F(RTInfoDeserialization, NodeV11) {
             </rt_info>
             <input>
                 <port id="0" precision="FP32">
-                    <rt_info>
-                        <attribute name="layout" version="0" layout="[N,H,W,C]"/>
-                    </rt_info>
                     <dim>1</dim>
                     <dim>22</dim>
                     <dim>22</dim>
@@ -819,6 +819,7 @@ TEST_F(RTInfoDeserialization, InputAndOutputV11) {
                 <port id="2" precision="FP32">
                     <rt_info>
                         <attribute name="fused_names" version="0" value="test4,test5"/>
+                        <attribute name="layout" version="0" layout="[?,C,H,W]" />
                     </rt_info>
                     <dim>1</dim>
                     <dim>3</dim>
@@ -832,7 +833,6 @@ TEST_F(RTInfoDeserialization, InputAndOutputV11) {
                 <port id="0" precision="FP32">
                     <rt_info>
                         <attribute name="fused_names" version="0" value="test5,test6"/>
-                        <attribute name="layout" version="0" layout="[?,C,H,W]" />
                     </rt_info>
                     <dim>1</dim>
                     <dim>3</dim>

From 91945ba122c4e809805b16b13316f5fa5d0773c6 Mon Sep 17 00:00:00 2001
From: Alexandra Sidorova <alexandra.sidorova@intel.com>
Date: Fri, 24 Dec 2021 15:08:42 +0300
Subject: [PATCH 146/160] [CPU] Added dynamism support for TensorIterator
 (#8879)

---
 src/bindings/python/tests/__init__.py         |   1 -
 .../python/tests/test_onnx/test_backend.py    |   7 -
 .../python/tests_compatibility/__init__.py    |   1 -
 .../test_onnx/test_backend.py                 |   7 -
 src/plugins/intel_cpu/src/cpu_types.cpp       |   2 +
 src/plugins/intel_cpu/src/cpu_types.h         |   6 +-
 .../src/nodes/mkldnn_tensoriterator_node.cpp  | 646 +++++++++++++-----
 .../src/nodes/mkldnn_tensoriterator_node.h    |  86 ++-
 .../plugin/cpu/single_layer_tests/loop.cpp    | 471 +++++++++++++
 .../single_layer_tests/tensor_iterator.cpp    | 157 +++++
 10 files changed, 1180 insertions(+), 204 deletions(-)
 create mode 100644 src/tests/functional/plugin/cpu/single_layer_tests/loop.cpp
 create mode 100644 src/tests/functional/plugin/cpu/single_layer_tests/tensor_iterator.cpp

diff --git a/src/bindings/python/tests/__init__.py b/src/bindings/python/tests/__init__.py
index 0513d7dfb95..ebb26410104 100644
--- a/src/bindings/python/tests/__init__.py
+++ b/src/bindings/python/tests/__init__.py
@@ -77,7 +77,6 @@ xfail_issue_48052 = xfail_test(reason="Dropout op is not supported in traning mo
 xfail_issue_45180 = xfail_test(reason="RuntimeError: Unsupported dynamic op: ReduceSum")
 xfail_issue_44851 = xfail_test(reason="Expected: Unsupported dynamic op: Broadcast")
 xfail_issue_44858 = xfail_test(reason="Expected: Unsupported dynamic op: Unsqueeze")
-xfail_issue_44956 = xfail_test(reason="Expected: Unsupported dynamic op: Loop")
 xfail_issue_44957 = xfail_test(reason="Expected: Unsupported dynamic op: NonZero")
 xfail_issue_44958 = xfail_test(reason="Expected: Unsupported dynamic op: Interpolate")
 xfail_issue_44965 = xfail_test(reason="Expected: RuntimeError: value info has no element")
diff --git a/src/bindings/python/tests/test_onnx/test_backend.py b/src/bindings/python/tests/test_onnx/test_backend.py
index 9ac78a48b92..cb49356b0aa 100644
--- a/src/bindings/python/tests/test_onnx/test_backend.py
+++ b/src/bindings/python/tests/test_onnx/test_backend.py
@@ -27,7 +27,6 @@ from tests import (
     xfail_issue_39658,
     xfail_issue_39662,
     xfail_issue_44858,
-    xfail_issue_44956,
     xfail_issue_44965,
     xfail_issue_44968,
     xfail_issue_45180,
@@ -265,12 +264,6 @@ tests_expected_to_fail = [
         "OnnxBackendNodeModelTest.test_unsqueeze_two_axes_cpu",
         "OnnxBackendNodeModelTest.test_unsqueeze_unsorted_axes_cpu",
     ),
-    (
-        xfail_issue_44956,
-        "OnnxBackendNodeModelTest.test_loop11_cpu",
-        "OnnxBackendNodeModelTest.test_range_int32_type_negative_delta_expanded_cpu",
-        "OnnxBackendNodeModelTest.test_range_float_type_positive_delta_expanded_cpu",
-    ),
     (
         xfail_issue_44965,
         "OnnxBackendNodeModelTest.test_loop13_seq_cpu",
diff --git a/src/bindings/python/tests_compatibility/__init__.py b/src/bindings/python/tests_compatibility/__init__.py
index 8b1a7baf257..1cb86e1a3b7 100644
--- a/src/bindings/python/tests_compatibility/__init__.py
+++ b/src/bindings/python/tests_compatibility/__init__.py
@@ -89,7 +89,6 @@ xfail_issue_48052 = xfail_test(reason="Dropout op is not supported in traning mo
 xfail_issue_45180 = xfail_test(reason="RuntimeError: Unsupported dynamic op: ReduceSum")
 xfail_issue_44851 = xfail_test(reason="Expected: Unsupported dynamic op: Broadcast")
 xfail_issue_44858 = xfail_test(reason="Expected: Unsupported dynamic op: Unsqueeze")
-xfail_issue_44956 = xfail_test(reason="Expected: Unsupported dynamic op: Loop")
 xfail_issue_44957 = xfail_test(reason="Expected: Unsupported dynamic op: NonZero")
 xfail_issue_44958 = xfail_test(reason="Expected: Unsupported dynamic op: Interpolate")
 xfail_issue_44965 = xfail_test(reason="Expected: RuntimeError: value info has no element")
diff --git a/src/bindings/python/tests_compatibility/test_onnx/test_backend.py b/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
index 2ba57b3a320..9a224d9c26e 100644
--- a/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
+++ b/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
@@ -27,7 +27,6 @@ from tests_compatibility import (
     xfail_issue_39658,
     xfail_issue_39662,
     xfail_issue_44858,
-    xfail_issue_44956,
     xfail_issue_44965,
     xfail_issue_44968,
     xfail_issue_45180,
@@ -265,12 +264,6 @@ tests_expected_to_fail = [
         "OnnxBackendNodeModelTest.test_unsqueeze_two_axes_cpu",
         "OnnxBackendNodeModelTest.test_unsqueeze_unsorted_axes_cpu",
     ),
-    (
-        xfail_issue_44956,
-        "OnnxBackendNodeModelTest.test_loop11_cpu",
-        "OnnxBackendNodeModelTest.test_range_int32_type_negative_delta_expanded_cpu",
-        "OnnxBackendNodeModelTest.test_range_float_type_positive_delta_expanded_cpu",
-    ),
     (
         xfail_issue_44965,
         "OnnxBackendNodeModelTest.test_loop13_seq_cpu",
diff --git a/src/plugins/intel_cpu/src/cpu_types.cpp b/src/plugins/intel_cpu/src/cpu_types.cpp
index 9facd0d07e8..1c87b16d7b0 100644
--- a/src/plugins/intel_cpu/src/cpu_types.cpp
+++ b/src/plugins/intel_cpu/src/cpu_types.cpp
@@ -468,6 +468,8 @@ std::string algToString(const Algorithm alg) {
     CASE(MathSoftPlus);
     CASE(MathSoftsign);
     CASE(MathTan);
+    CASE(TensorIteratorCommon);
+    CASE(TensorIteratorLoop);
 #undef CASE
     return "Undefined";
 }
diff --git a/src/plugins/intel_cpu/src/cpu_types.h b/src/plugins/intel_cpu/src/cpu_types.h
index 9299febc116..7a7144c348a 100644
--- a/src/plugins/intel_cpu/src/cpu_types.h
+++ b/src/plugins/intel_cpu/src/cpu_types.h
@@ -219,7 +219,11 @@ enum Algorithm {
     MathSinh,
     MathSoftPlus,
     MathSoftsign,
-    MathTan
+    MathTan,
+
+    // TensorIterator
+    TensorIteratorCommon,
+    TensorIteratorLoop
 };
 
 extern const InferenceEngine::details::caseless_unordered_map<std::string, Type> type_to_name_tbl;
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.cpp
index 164ad24727e..eedf53c0b19 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.cpp
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.cpp
@@ -6,26 +6,29 @@
 
 #include <string>
 #include <vector>
-#include <map>
 #include <mkldnn_extension_utils.h>
 #include <ie_ngraph_utils.hpp>
 #include <utils/general_utils.h>
 #include "common/blocked_desc_creator.h"
 #include "utils/ngraph_utils.hpp"
 #include "transformations/utils/utils.hpp"
+#include "common/cpu_memcpy.h"
 
 using namespace mkldnn;
 using namespace MKLDNNPlugin;
+using namespace InferenceEngine;
 using namespace InferenceEngine::details;
 
 namespace MKLDNNPlugin {
 
-static NodeConfig make_plain_config(const std::shared_ptr<ngraph::Node>& op) {
+#define THROW_ERROR IE_THROW() << getTypeStr() << " layer with name '" << getName() << "' "
+
+static NodeConfig make_plain_config(const std::shared_ptr<ov::Node>& op) {
     NodeConfig config;
 
     for (size_t i = 0; i < op->get_input_size(); i++) {
         const auto &origShape = op->get_input_partial_shape(i);
-        const auto& shape = Shape(origShape.rank().get_length() == 0 ? ngraph::PartialShape{1} : origShape);
+        const auto& shape = Shape(origShape.rank().get_length() == 0 ? ov::PartialShape{1} : origShape);
         const auto prec = InferenceEngine::details::convertPrecision(op->get_input_element_type(i));
 
         PortConfig data_conf {};
@@ -36,7 +39,7 @@ static NodeConfig make_plain_config(const std::shared_ptr<ngraph::Node>& op) {
 
     for (size_t i = 0; i < op->get_output_size(); i++) {
         const auto &origShape = op->get_output_partial_shape(i);
-        const auto& shape = Shape(origShape.rank().get_length() == 0 ? ngraph::PartialShape{1} : origShape);
+        const auto& shape = Shape(origShape.rank().get_length() == 0 ? ov::PartialShape{1} : origShape);
         const auto prec = InferenceEngine::details::convertPrecision(op->get_output_element_type(i));
 
         PortConfig data_conf {};
@@ -49,6 +52,31 @@ static NodeConfig make_plain_config(const std::shared_ptr<ngraph::Node>& op) {
     return config;
 }
 
+static void redefineToMemories(const std::vector<MKLDNNMemoryPtr>& to_mems, const std::shared_ptr<MemoryDesc> new_desc) {
+    const auto &currDesc = to_mems.front()->getDesc();
+    if (currDesc.getShape().isDynamic() || currDesc.getShape().getStaticDims() != new_desc->getShape().getStaticDims()) {
+        // WA [DS] : need to rewrite it. Updated copypaste is from MKLDNNNode::redefineOutputMemory
+        // this path is necessary if there are several edges per one port
+        // in this case edge memory share same physical memory
+        // so we need to find which edge allocate memory, reallocate memory and share this memory between other edges
+        size_t sharedEdgeNum = 0;
+        for (size_t j = 0; j < to_mems.size(); j++) {
+            if (!to_mems[j]->isUsedExternalStorage()) {
+                sharedEdgeNum = j;
+                break;
+            }
+        }
+
+        to_mems[sharedEdgeNum]->redefineDesc(new_desc);
+        void *data = to_mems[sharedEdgeNum]->GetData();
+        for (size_t j = 0; j < to_mems.size(); j++) {
+            if (j == sharedEdgeNum)
+                continue;
+            to_mems[j]->redefineDesc(new_desc, data);
+        }
+    }
+}
+
 class PortIteratorHelper : public PortMapHelper {
 public:
     PortIteratorHelper(const MKLDNNMemoryPtr &from, const MKLDNNMemoryPtr &to, bool sliced_src,
@@ -101,7 +129,7 @@ public:
 
         auto &chunk_mem = sliced_src ? mem_holder_src : mem_holder_dst;
         chunk_mem.set_data_handle(static_cast<uint8_t *>(full_mem.get_data_handle()) +
-                chunk_offset_in_byte + chunk_stride_in_byte * iter);
+                                          chunk_offset_in_byte + chunk_stride_in_byte * iter);
 
         reorder.execute(strm, mem_holder_src, mem_holder_dst);
     }
@@ -136,7 +164,7 @@ public:
     IterCountPortHelper(const MKLDNNMemoryPtr &to, const mkldnn::engine& eng) {
         // Only scalar I32 tensor is supported
         IE_ASSERT(to->GetDataType() == memory::data_type::s32);
-        IE_ASSERT(to->GetShape() == Shape(InferenceEngine::SizeVector{1}));
+        IE_ASSERT(to->GetShape() == Shape(VectorDims{1}));
         mem_holder_dst = to->GetPrimitive();
     }
 
@@ -171,8 +199,7 @@ class asIntCheck : public PortChecker {
 public:
     asIntCheck(const MKLDNNMemoryPtr &mem) {
         IE_ASSERT(mem->GetDataType() == memory::data_type::s32);
-        const auto a = Shape(InferenceEngine::SizeVector{1});
-        IE_ASSERT(mem->GetShape() == a);
+        IE_ASSERT(mem->GetShape() == Shape(InferenceEngine::SizeVector{1}));
         mem_holder = mem->GetPrimitive();
     }
 
@@ -196,96 +223,115 @@ private:
     int value;
 };
 
-}  // namespace MKLDNNPlugin
-
-static int getNumIteration(const std::shared_ptr<const ngraph::Node>& op, const std::vector<PortMap>& inputPortMap, const std::vector<PortMap>& outputPortMap) {
-    const auto isIterable = [](const PortMap& rule) { return rule.axis != -1; };
-
-    const auto getNumIterations = [](const PortMap& rule, const std::vector<size_t>& dimensions) -> int {
-        const auto axis = rule.axis;
-        if (axis < 0 || static_cast<std::size_t>(axis) >= dimensions.size()) {
-            IE_THROW() << R"(: Invalid "axis" value in an iteration component: )"
-                               << rule.axis  << ", dimensions number = " << dimensions.size() << " (out of range)";
-        }
-        const auto space = dimensions[axis];
-        const int start = static_cast<int>((rule.start < 0 ? (space + 1) : 0) + rule.start);
-        const int end   = static_cast<int>((rule.end   < 0 ? (space + 1) : 0) + rule.end);
-
-        const auto stride = rule.stride;
-        if (stride == 0) {
-            IE_THROW() << R"(: Invalid "stride" value in an iteration component: )" << rule.stride << " (infinite loop)";
-        }
-        const auto step = std::abs(stride);
-
-        const auto src = stride < 0 ? end : start;
-        const auto dst = stride < 0 ? start : end;
-        const auto length = dst - src;
-        if (src < 0 || src >= dst || dst > static_cast<int64_t>(space) || length < step) {
-            IE_THROW() << R"(: Invalid "start"/"stride"/"end" values in an iteration component)"
-                               << ": \"start\" = " << rule.start << ", \"stride\" = " << rule.stride  << ", \"end\" = " << rule.end;
-        }
-
-        if (length % step != 0) {
-            IE_THROW() << ": Each iteration must be the same size: length (" << length << ") is not divisible by step (" << step << ")";
-        }
-
-        return static_cast<int>(length / step);
-    };
-
-
-    int numIterations = 1;
-    bool isDefault = true;
-    for (const auto& rule : inputPortMap) {
-        if (!isIterable(rule)) {
-            continue;
-        }
-
-        if (rule.from < 0 || rule.from >= static_cast<int64_t>(op->get_input_size())) {
-            IE_THROW() << R"(: Invalid "from" value: "from" = )" << rule.from
-                               << " inputs number = " << op->get_input_size() << " (out of range)";
-        }
-
-        const auto currentNumIterations = getNumIterations(rule, op->get_input_shape(rule.from));
-        if (isDefault) {
-            isDefault = false;
-            numIterations = currentNumIterations;
-        } else if (numIterations != currentNumIterations) {
-            IE_THROW() << ": There are at least two different iterations numbers: " << numIterations << " and " << currentNumIterations;
-        }
-    }
-
-    for (const auto& rule : outputPortMap) {
-        if (!isIterable(rule)) {
-            continue;
-        }
-
-        if (rule.from < 0 || rule.from >= static_cast<int64_t>(op->get_output_size())) {
-            IE_THROW() << R"(: Invalid "from" value: "from" = )" << rule.from
-                               << " inputs number = " << op->get_output_size() << " (out of range)";
-        }
-
-        const auto currentNumIterations = getNumIterations(rule, op->get_output_shape(rule.from));
-        if (isDefault) {
-            isDefault = false;
-            numIterations = currentNumIterations;
-        } else if (numIterations != currentNumIterations) {
-            IE_THROW() << ": There are at least two different iterations numbers: " << numIterations << " and " << currentNumIterations;
-        }
-    }
-
-    return numIterations;
+DynamicBuffer::DynamicBuffer(const MKLDNNMemoryPtr &from, const std::vector<MKLDNNMemoryPtr> &to,
+                             const PortMap &map_rule) : from(from), to(to), map_rule(map_rule) {
+    elem_size = MKLDNNExtensionUtils::sizeOfDataType(from->GetDataType());
 }
 
-bool MKLDNNTensorIteratorNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
-    try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
+void DynamicBuffer::execute(const mkldnn::engine& eng, const int iter) {
+    if (iter == 0) {
+        init(eng);
+        return;
+    }
 
+    auto new_buffer = create_buffer(eng);
+    move_buffer(new_buffer);
+    move_data();
+}
+
+void DynamicBuffer::init(const mkldnn::engine& eng) {
+    chunk_offset_in_byte = 0;
+    buffer_offset_in_byte = 0;
+
+    const auto axis = map_rule.axis;
+    const auto stride = map_rule.stride;
+    const auto abs_stride = std::abs(stride);
+
+    auto src_mem = from->GetPrimitive();
+    auto src_desc = src_mem.get_desc();
+    auto dims = src_desc.dims();
+
+    if (dims[axis] != abs_stride)
+        IE_THROW() << "TensorIterator (Loop) has incorrect output shape[axis] after iteration for concatenation. " << abs_stride <<
+                   " is expected, but actual: " << dims[axis];
+
+    count = std::accumulate(dims.begin(), dims.begin() + map_rule.axis, 1, std::multiplies<size_t>());
+    len = std::accumulate(dims.begin() + map_rule.axis + 1, dims.end(), elem_size, std::multiplies<size_t>());
+    mem_holder_buffer.reset(new memory(src_desc, eng));
+    copy(reinterpret_cast<const uint8_t*>(from->GetPtr()), get_ptr(*mem_holder_buffer.get()), 0, 0, 1, from->GetSize());
+}
+
+std::shared_ptr<mkldnn::memory> DynamicBuffer::create_buffer(const mkldnn::engine& eng) {
+    const auto axis = map_rule.axis;
+    const auto stride = map_rule.stride;
+    const auto abs_stride = std::abs(stride);
+
+    const auto old_desc = mem_holder_buffer->get_desc();
+    auto dims = old_desc.dims();
+
+    if (from->getStaticDims()[axis] != abs_stride)
+        IE_THROW() << "TensorIterator (Loop) has incorrect output shape[axis] after iteration for concatenation. " << abs_stride <<
+        " is expected, but actual: " << from->getStaticDims()[axis];
+
+    dims[axis] += abs_stride;
+    mkldnn::memory::desc new_buffer_desc(dims, old_desc.data_type(), MKLDNNExtensionUtils::GetPlainFormatByRank(dims.size()));
+
+    if (stride > 0.0f) {
+        chunk_offset_in_byte += new_buffer_desc.data.format_desc.blocking.strides[axis] * elem_size * abs_stride;
+    } else {
+        buffer_offset_in_byte = from->GetPrimitive().get_desc().data.format_desc.blocking.strides[axis] * elem_size * abs_stride;
+    }
+
+    return std::make_shared<mkldnn::memory>(new_buffer_desc, eng);
+}
+
+void DynamicBuffer::move_buffer(std::shared_ptr<mkldnn::memory> new_buffer) {
+    const auto axis = map_rule.axis;
+    const auto src_stride = mem_holder_buffer->get_desc().dims()[axis] * len;
+    const auto dst_stride = new_buffer->get_desc().dims()[axis] * len;
+
+    copy(get_ptr(*mem_holder_buffer.get()), get_ptr(*new_buffer.get()) + buffer_offset_in_byte,
+         src_stride, dst_stride, count, src_stride);
+    mem_holder_buffer = new_buffer;
+}
+
+void DynamicBuffer::move_data() {
+    const auto axis = map_rule.axis;
+    const auto src_stride = abs(map_rule.stride) * len;
+    const auto dst_stride = mem_holder_buffer->get_desc().dims()[axis] * len;
+
+    copy(reinterpret_cast<const uint8_t*>(from->GetPtr()), get_ptr(*mem_holder_buffer.get()) + chunk_offset_in_byte,
+         src_stride, dst_stride, count, src_stride);
+}
+
+void DynamicBuffer::transfer(const MKLDNNNode* node) {
+    const auto desc = node->getBaseMemDescAtOutputPort(map_rule.from)->cloneWithNewDims(
+            MKLDNNExtensionUtils::convertToVectorDims(mem_holder_buffer->get_desc().dims()));
+    redefineToMemories(to, desc);
+
+    copy(get_ptr(*mem_holder_buffer.get()), reinterpret_cast<uint8_t*>(to.front()->GetPtr()), 0, 0, 1, to.front()->GetSize());
+}
+
+void DynamicBuffer::copy(const uint8_t* src, uint8_t* dst, const size_t src_stride, const size_t dst_stride, const size_t count, const size_t len) {
+    parallel_for(count, [&](const size_t i) {
+        cpu_memcpy(&dst[i * dst_stride], &src[i * src_stride], len);
+    });
+}
+
+uint8_t* DynamicBuffer::get_ptr(mkldnn::memory& prim) {
+    auto ptr = static_cast<uint8_t*>(prim.get_data_handle());
+    auto md = prim.get_desc().data;
+    mkldnn::impl::memory_desc_wrapper wrapper(md);
+    return ptr + wrapper.offset0() * wrapper.data_type_size();
+}
+
+}  // namespace MKLDNNPlugin
+
+bool MKLDNNTensorIteratorNode::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
+    try {
         if (!one_of(op->get_type_info(),
-                ngraph::op::v0::TensorIterator::get_type_info_static(),
-                ngraph::op::v5::Loop::get_type_info_static())) {
+                    ov::op::v0::TensorIterator::get_type_info_static(),
+                    ov::op::v5::Loop::get_type_info_static())) {
             errorMessage = "Only opset1 TensorIterator or opset5 Loop operations are supported.";
             return false;
         }
@@ -295,7 +341,7 @@ bool MKLDNNTensorIteratorNode::isSupportedOperation(const std::shared_ptr<const
     return true;
 }
 
-MKLDNNTensorIteratorNode::MKLDNNTensorIteratorNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache) :
+MKLDNNTensorIteratorNode::MKLDNNTensorIteratorNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache) :
         MKLDNNNode(op, eng, cache), ngraphOp(op) {
     std::string errorMessage;
     if (!isSupportedOperation(op, errorMessage)) {
@@ -304,19 +350,18 @@ MKLDNNTensorIteratorNode::MKLDNNTensorIteratorNode(const std::shared_ptr<ngraph:
 }
 
 void MKLDNNTensorIteratorNode::getSupportedDescriptors() {
-    auto tiOp = std::dynamic_pointer_cast<ngraph::op::util::SubGraphOp>(ngraphOp);
-    if (tiOp == nullptr) {
-        IE_THROW() << "Can't cast TensorIterator node with name: " << getName() << " to ngraph::op::util::SubGraphOp";
+    auto tiOp = ov::as_type_ptr<const ov::op::util::SubGraphOp>(ngraphOp);
+    if (!tiOp) {
+        THROW_ERROR << "cannot be cast to ov::op::util::SubGraphOp";
     }
-    const std::shared_ptr<const ngraph::Function> body = tiOp->get_function();
+    const std::shared_ptr<const ov::Model> body = tiOp->get_function();
     sub_graph.CreateGraph(body, ext_mng, weightCache);
 
     const auto &inMap = sub_graph.GetInputNodesMap();
     for (const auto &param : tiOp->get_function()->get_parameters()) {
         auto inNode = inMap.find(param->get_friendly_name());
         if (inNode != inMap.end()) {
-            auto inMem = inNode->second->getChildEdgeAt(0)->getMemoryPtr();
-            input_mem.push_back(inMem);
+            input_mems.push_back(getToMemories(inNode->second.get(), 0));
         }
     }
 
@@ -337,20 +382,20 @@ void MKLDNNTensorIteratorNode::getSupportedDescriptors() {
 
         std::string type_name = desc->get_type_info().name;
         if (type_name == "ConcatOutputDescription") {
-            auto output_desc = ::ngraph::as_type_ptr<ngraph::op::util::SubGraphOp::ConcatOutputDescription>(desc);
+            auto output_desc = ov::as_type_ptr<const ov::op::util::SubGraphOp::ConcatOutputDescription>(desc);
             IE_ASSERT(output_desc != nullptr);
 
             outputPortMap.emplace_back(PortMap {
-                static_cast<int>(output_desc->m_output_index), static_cast<int>(body_output_idx),
-                static_cast<int>(output_desc->m_axis), static_cast<int>(output_desc->m_stride),
-                static_cast<int>(output_desc->m_start), static_cast<int>(output_desc->m_end),
-                static_cast<int>(output_desc->m_part_size)});
+                    static_cast<int>(output_desc->m_output_index), static_cast<int>(body_output_idx),
+                    static_cast<int>(output_desc->m_axis), static_cast<int>(output_desc->m_stride),
+                    static_cast<int>(output_desc->m_start), static_cast<int>(output_desc->m_end),
+                    static_cast<int>(output_desc->m_part_size)});
         } else if (type_name == "BodyOutputDescription") {
-            auto output_desc = ::ngraph::as_type_ptr<ngraph::op::util::SubGraphOp::BodyOutputDescription>(desc);
+            auto output_desc = ov::as_type_ptr<const ov::op::util::SubGraphOp::BodyOutputDescription>(desc);
             IE_ASSERT(output_desc != nullptr);
 
             outputPortMap.emplace_back(PortMap {
-                static_cast<int>(output_desc->m_output_index), static_cast<int>(body_output_idx), -1, 1, 0, -1, 1});
+                    static_cast<int>(output_desc->m_output_index), static_cast<int>(body_output_idx), -1, 1, 0, -1, 1});
         } else {
             IE_THROW() << "Incorrect type of the output description.";
         }
@@ -360,31 +405,30 @@ void MKLDNNTensorIteratorNode::getSupportedDescriptors() {
     for (const auto& desc : tiOp->get_input_descriptions()) {
         auto body_input_index = desc->m_body_parameter_index;
 
-        if (const auto slice_desc = std::dynamic_pointer_cast<ngraph::op::util::SubGraphOp::SliceInputDescription>(desc)) {
+        if (auto slice_desc = ov::as_type_ptr<const ov::op::util::SubGraphOp::SliceInputDescription>(desc)) {
             inputPortMap.emplace_back(PortMap {
-                static_cast<int>(slice_desc->m_input_index), static_cast<int>(body_input_index),
-                static_cast<int>(slice_desc->m_axis), static_cast<int>(slice_desc->m_stride),
-                static_cast<int>(slice_desc->m_start), static_cast<int>(slice_desc->m_end),
-                static_cast<int>(slice_desc->m_part_size)});
-        } else if (const auto merge_desc = std::dynamic_pointer_cast<ngraph::op::util::SubGraphOp::MergedInputDescription>(desc)) {
+                    static_cast<int>(slice_desc->m_input_index), static_cast<int>(body_input_index),
+                    static_cast<int>(slice_desc->m_axis), static_cast<int>(slice_desc->m_stride),
+                    static_cast<int>(slice_desc->m_start), static_cast<int>(slice_desc->m_end),
+                    static_cast<int>(slice_desc->m_part_size)});
+        } else if (auto merge_desc = ov::as_type_ptr<const ov::op::util::SubGraphOp::MergedInputDescription>(desc)) {
             inputPortMap.emplace_back(PortMap {
-                static_cast<int>(merge_desc->m_input_index), static_cast<int>(body_input_index), -1, 1, 0, -1, 1});
+                    static_cast<int>(merge_desc->m_input_index), static_cast<int>(body_input_index), -1, 1, 0, -1, 1});
 
             auto body_output_idx = merge_desc->m_body_value_index;
 
             backEdges.emplace_back(PortMap {
-                static_cast<int>(body_output_idx), static_cast<int>(body_input_index), -1, 1, 0, -1, 1});
-        } else if (const auto inv_desc = std::dynamic_pointer_cast<ngraph::op::util::SubGraphOp::InvariantInputDescription>(desc)) {
+                    static_cast<int>(body_output_idx), static_cast<int>(body_input_index), -1, 1, 0, -1, 1});
+        } else if (auto inv_desc = ov::as_type_ptr<const ov::op::util::SubGraphOp::InvariantInputDescription>(desc)) {
             inputPortMap.emplace_back(PortMap {
                     static_cast<int>(inv_desc->m_input_index), static_cast<int>(body_input_index), -1, 1, 0, -1, 1});
         } else {
-            IE_THROW() << "Incorrect type of the input description.";
+            THROW_ERROR << "has incorrect type of the input description.";
         }
     }
 
-    n_iter = getNumIteration(ngraphOp, inputPortMap, outputPortMap);
-
-    if (const auto loopOp = std::dynamic_pointer_cast<const ngraph::op::v5::Loop>(ngraphOp)) {
+    if (auto loopOp = ov::as_type_ptr<const ov::op::v5::Loop>(ngraphOp)) {
+        algorithm = TensorIteratorLoop;
         auto spec_port = loopOp->get_special_body_ports();
         if (spec_port.current_iteration_input_idx != -1) {
             loopBodyCurrentIterationIdx.push_back(spec_port.current_iteration_input_idx);
@@ -394,74 +438,60 @@ void MKLDNNTensorIteratorNode::getSupportedDescriptors() {
         }
         loopTripCountIdx = 0;
         loopExecutionConditionIdx = 1;
+    } else if (auto ti = ov::as_type_ptr<const ov::op::v0::TensorIterator>(ngraphOp)) {
+        algorithm = TensorIteratorCommon;
+    } else {
+        THROW_ERROR << "isn't supported!";
     }
-
-    config = make_plain_config(ngraphOp);
 }
 
 void MKLDNNTensorIteratorNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
+    supportedPrimitiveDescriptors.emplace_back(make_plain_config(ngraphOp), impl_desc_type::unknown);
 }
 
-
 void MKLDNNTensorIteratorNode::createPrimitive() {
-    const auto &eng = getEngine();
-
-    for (auto map_rule : inputPortMap) {
-        auto &from_mem = getParentEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
-        auto &to_mem = input_mem[map_rule.to];
-
-        if (map_rule.axis == -1)
-            first_mappers.emplace_back(new BackEdgePortHelper(from_mem, to_mem, eng));
-        else
-            before_mappers.emplace_back(new PortIteratorHelper(from_mem, to_mem, true, map_rule, eng));
-    }
-
-    for (auto map_rule : outputPortMap) {
-        auto &to_mem = getChildEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
-        auto &from_mem = output_mem[map_rule.to];
-
-        if (map_rule.axis == -1)
-            last_mappers.emplace_back(new BackEdgePortHelper(from_mem, to_mem, eng));
-        else
-            after_mappers.emplace_back(new PortIteratorHelper(from_mem, to_mem, false, map_rule, eng));
-    }
-
-    for (auto map_rule : backEdges) {
-        auto from_mem = output_mem[map_rule.from];
-        auto to_mem = input_mem[map_rule.to];
-
-        before_mappers.emplace_back(new BackEdgePortHelper(from_mem, to_mem, eng));
-    }
-
-    // special purpose ports
-    for (auto idx : loopBodyCurrentIterationIdx) {
-        auto to_mem = input_mem[idx];
-        before_mappers.emplace_back(new IterCountPortHelper(to_mem, eng));
-    }
-
-    if (loopBodyConditionOutputIdx == -1) {
+    if (loopBodyConditionOutputIdx == -1)
         continue_cond_check.reset(new staticValueCheck(true)); // always true
-    } else {
-        auto mem = output_mem[loopBodyConditionOutputIdx];
-        continue_cond_check.reset(new asBoolCheck(mem));
-    }
-
-    if (loopTripCountIdx == -1) {
-        trip_count_check.reset(new staticValueCheck(n_iter)); // use statically calculated num of iteration
-    } else {
-        auto mem = getParentEdgesAtPort(loopTripCountIdx)[0]->getMemoryPtr();
-        trip_count_check.reset(new asIntCheck(mem));
-    }
-
-    if (loopExecutionConditionIdx == -1) {
+    if (loopExecutionConditionIdx == -1)
         initial_cond_check.reset(new staticValueCheck(true));
-    } else {
-        auto mem = getParentEdgesAtPort(loopExecutionConditionIdx)[0]->getMemoryPtr();
-        initial_cond_check.reset(new asBoolCheck(mem));
+
+    if (isDynamicNode())
+        prepareDynamicBuffers();
+
+    MKLDNNNode::createPrimitive();
+}
+
+bool MKLDNNTensorIteratorNode::needPrepareParams() const {
+    if (getAlgorithm() == TensorIteratorLoop) {
+        const auto tripCountPtr = reinterpret_cast<const uint32_t*>(getParentEdgesAtPort(loopTripCountIdx).front()->getMemoryPtr()->GetPtr());
+        const auto condPtr = reinterpret_cast<const uint8_t*>(getParentEdgesAtPort(loopExecutionConditionIdx).front()->getMemoryPtr()->GetPtr());
+        if (tripCountPtr[0] != lastUsedTripCount || condPtr[0] != lastUsedCond)
+            return true;
+    }
+
+    return MKLDNNNode::needPrepareParams();
+}
+
+void MKLDNNTensorIteratorNode::prepareParams() {
+    reshapeSubgraphInput();
+
+    first_mappers.clear();
+    before_mappers.clear();
+    back_mappers.clear();
+
+    prepareInputPorts();
+    prepareInitialCond();
+    prepareContinueCond();
+    prepareTripCount();
+    // special purpose ports
+    prepareLoopBodyCurrentIteration();
+
+    if (!isDynamicNode()) {
+        prepareOutputPorts();
+        prepareBackEdges();
     }
 }
 
@@ -485,7 +515,7 @@ void MKLDNNTensorIteratorNode::execute(mkldnn::stream strm) {
         continue_cond = continue_cond_check->getStatus();
 
         // copy data from subgraph iteration to outputs
-        // or to next iteration inputs
+        // or to the next iteration inputs
         for (auto &mapper : after_mappers)
             mapper->execute(strm, i);
     }
@@ -494,6 +524,266 @@ void MKLDNNTensorIteratorNode::execute(mkldnn::stream strm) {
         mapper->execute(strm);
 }
 
+void MKLDNNTensorIteratorNode::executeDynamicImpl(mkldnn::stream strm) {
+    const auto &eng = getEngine();
+    sub_graph.ResetInferCount();
+
+    bool continue_cond = initial_cond_check->getStatus();
+    int max_num_iter = trip_count_check->getStatus();
+
+    for (auto &mapper : first_mappers)
+        mapper->execute(strm);
+
+    if (!continue_cond || max_num_iter == 0)
+        THROW_ERROR << "has incorrect iteration count for dynamic execution";
+
+    // use  "i != max_num_iter" only to allow "-1" works like infinite loop
+    for (int i = 0; i != max_num_iter && continue_cond; i++) {
+        // copy data to subgraph iteration
+        for (auto &mapper : before_mappers)
+            mapper->execute(strm, i);
+        for (auto &mapper : back_mappers)
+            mapper->execute(strm, i);
+
+        sub_graph.Infer();
+
+        continue_cond = continue_cond_check->getStatus();
+
+        for (auto& buffer : buffers)
+            buffer->execute(eng, i);
+
+        // on the last iteration we shouldn't reshape body inputs and init back edges
+        if ((i + 1 != max_num_iter) && continue_cond)
+            prepareDynamicBackEdges();
+    }
+
+    reshapeAndFillOutput(strm);
+}
+
+/* *==============* Prepare reorders, edges between body and TI *==============* */
+
+void MKLDNNTensorIteratorNode::prepareInputPorts() {
+    const auto &eng = getEngine();
+    for (auto map_rule : inputPortMap) {
+        auto &from_mem = getParentEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
+        auto &to_mem = input_mems[map_rule.to].front();  // first memory is enough to get common memory ptr
+
+        if (map_rule.axis == -1)
+            first_mappers.emplace_back(std::make_shared<BackEdgePortHelper>(from_mem, to_mem, eng));
+        else
+            before_mappers.emplace_back(
+                    std::make_shared<PortIteratorHelper>(from_mem, to_mem, true, map_rule, eng));
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareOutputPorts() {
+    const auto &eng = getEngine();
+    for (auto map_rule : outputPortMap) {
+        auto &to_mem = getChildEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
+        auto &from_mem = output_mem[map_rule.to];
+
+        if (map_rule.axis == -1)
+            last_mappers.emplace_back(std::make_shared<BackEdgePortHelper>(from_mem, to_mem, eng));
+        else
+            after_mappers.emplace_back(std::make_shared<PortIteratorHelper>(from_mem, to_mem, false, map_rule, eng));
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareBackEdges() {
+    const auto &eng = getEngine();
+    for (auto map_rule : backEdges) {
+        auto from_mem = output_mem[map_rule.from];
+        auto to_mem = input_mems[map_rule.to].front();
+
+        before_mappers.emplace_back(std::make_shared<BackEdgePortHelper>(from_mem, to_mem, eng));
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareDynamicBackEdges() {
+    const auto &eng = getEngine();
+    back_mappers.clear();
+    for (auto map_rule : backEdges) {
+        auto from_mem = output_mem[map_rule.from];
+        auto to_mems = input_mems[map_rule.to];
+
+        const auto& desc = from_mem->getDesc();
+        redefineToMemories(to_mems, desc.clone());
+
+        // first memory is enough to get common memory ptr
+        back_mappers.emplace_back(std::make_shared<BackEdgePortHelper>(from_mem, to_mems.front(), eng));
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareDynamicBuffers() {
+    for (auto map_rule : outputPortMap) {
+        if (map_rule.axis != -1) {
+            auto to_mems = getToMemories(this, map_rule.from);
+            auto &from_mem = output_mem[map_rule.to];
+            buffers.emplace_back(std::make_shared<DynamicBuffer>(from_mem, to_mems, map_rule));
+        }
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareLoopBodyCurrentIteration() {
+    const auto &eng = getEngine();
+    for (auto idx : loopBodyCurrentIterationIdx) {
+        auto to_mem = input_mems[idx].front();  // first memory is enough to get common memory ptr
+        before_mappers.emplace_back(std::make_shared<IterCountPortHelper>(to_mem, eng));
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareContinueCond() {
+    if (loopBodyConditionOutputIdx != -1 || !continue_cond_check) {
+        auto mem = output_mem[loopBodyConditionOutputIdx];
+        continue_cond_check.reset(new asBoolCheck(mem));
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareInitialCond() {
+    if (loopExecutionConditionIdx != -1 || !initial_cond_check) {
+        auto mem = getParentEdgesAtPort(loopExecutionConditionIdx)[0]->getMemoryPtr();
+        initial_cond_check.reset(new asBoolCheck(mem));
+        lastUsedCond = initial_cond_check->getStatus();
+    }
+}
+
+void MKLDNNTensorIteratorNode::prepareTripCount() {
+    if (loopTripCountIdx == -1) {
+        trip_count_check.reset(new staticValueCheck(getNumIteration(inputPortMap, outputPortMap)));
+    } else {
+        auto mem = getParentEdgesAtPort(loopTripCountIdx)[0]->getMemoryPtr();
+        trip_count_check.reset(new asIntCheck(mem));
+    }
+    lastUsedTripCount = trip_count_check->getStatus();
+}
+
+/* *==============* *==============* *==============* *==============* *==============* */
+
+void MKLDNNTensorIteratorNode::reshapeSubgraphInput() {
+    for (auto map_rule : inputPortMap) {
+        auto &from_mem = getParentEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
+        auto &to_mems = input_mems[map_rule.to];
+        auto new_dims = from_mem->getStaticDims();
+        if (map_rule.axis != -1)
+            new_dims[map_rule.axis] = abs(map_rule.stride);
+
+        const auto desc = std::make_shared<CpuBlockedMemoryDesc>(to_mems.front()->getDesc().getPrecision(), Shape(new_dims));
+        redefineToMemories(to_mems, desc);
+    }
+}
+
+void MKLDNNTensorIteratorNode::reshapeAndFillOutput(mkldnn::stream strm) {
+    auto eng = strm.get_engine();
+    for (auto map_rule : outputPortMap) {
+        if (map_rule.axis == -1) {
+            auto to_mems = getToMemories(this, map_rule.from);
+            auto &from_mem = output_mem[map_rule.to];
+
+            const auto desc = getBaseMemDescAtOutputPort(map_rule.from)->cloneWithNewDims(from_mem->getStaticDims());
+            redefineToMemories(to_mems, desc);
+
+            PortMapHelper *mapper = new BackEdgePortHelper(from_mem, to_mems.front(), eng);
+            mapper->execute(strm);
+        }
+    }
+
+    for (auto buffer : buffers) {
+        buffer->transfer(this);
+    }
+}
+
+int MKLDNNTensorIteratorNode::getNumIteration(const std::vector<PortMap>& inputPortMap, const std::vector<PortMap>& outputPortMap) const {
+    const auto isIterable = [](const PortMap& rule) {
+        return rule.axis != -1;
+    };
+
+    const auto getNumIterations = [this](const PortMap& rule, const std::vector<size_t>& dimensions) -> int {
+        const auto axis = rule.axis;
+        if (axis < 0 || static_cast<std::size_t>(axis) >= dimensions.size()) {
+            THROW_ERROR << ": Invalid \"axis\" value in an iteration component: "
+                        << rule.axis  << ", dimensions number = " << dimensions.size() << " (out of range)";
+        }
+        const auto space = dimensions[axis];
+        const int start = static_cast<int>((rule.start < 0 ? (space + 1) : 0) + rule.start);
+        const int end   = static_cast<int>((rule.end   < 0 ? (space + 1) : 0) + rule.end);
+
+        const auto stride = rule.stride;
+        if (stride == 0) {
+            THROW_ERROR << ": Invalid \"stride\" value in an iteration component: " << rule.stride << " (infinite loop)";
+        }
+        const auto step = std::abs(stride);
+
+        const auto src = stride < 0 ? end : start;
+        const auto dst = stride < 0 ? start : end;
+        const auto length = dst - src;
+        if (src < 0 || src >= dst || dst > static_cast<int64_t>(space) || length < step) {
+            THROW_ERROR << ": Invalid \"start\",\"stride\",\"end\" values in an iteration component"
+                        << ": \"start\" = " << rule.start << ", \"stride\" = " << rule.stride  << ", \"end\" = " << rule.end;
+        }
+
+        if (length % step != 0) {
+            THROW_ERROR << ": Each iteration must be the same size: length (" << length << ") is not divisible by step (" << step << ")";
+        }
+
+        return static_cast<int>(length / step);
+    };
+
+
+    int numIterations = 1;
+    bool isDefault = true;
+    for (const auto& rule : inputPortMap) {
+        const auto& dims = getParentEdgesAtPort(rule.from)[0]->getMemoryPtr()->getStaticDims();
+        if (!isIterable(rule)) {
+            continue;
+        }
+
+        if (rule.from < 0 || rule.from >= static_cast<int64_t>(inputShapes.size())) {
+            THROW_ERROR << ": Invalid \"from\" value: \"from\" = " << rule.from
+                        << " inputs number = " << inputShapes.size() << " (out of range)";
+        }
+
+        const auto currentNumIterations = getNumIterations(rule, dims);
+        if (isDefault) {
+            isDefault = false;
+            numIterations = currentNumIterations;
+        } else if (numIterations != currentNumIterations) {
+            THROW_ERROR << ": There are at least two different iterations numbers: " << numIterations << " and " << currentNumIterations;
+        }
+    }
+
+    for (const auto& rule : outputPortMap) {
+        const auto& dims = getBaseMemDescAtOutputPort(rule.from)->getShape().getDims();
+        if (!isIterable(rule)) {
+            continue;
+        }
+
+        if (dims[rule.axis] == Shape::UNDEFINED_DIM)
+            continue;
+
+        if (rule.from < 0 || rule.from >= static_cast<int64_t>(outputShapes.size())) {
+            THROW_ERROR << ": Invalid \"from\" value: \"from\" = " << rule.from
+                        << " inputs number = " << outputShapes.size() << " (out of range)";
+        }
+
+        const auto currentNumIterations = getNumIterations(rule, dims);
+        if (isDefault) {
+            isDefault = false;
+            numIterations = currentNumIterations;
+        } else if (numIterations != currentNumIterations) {
+            THROW_ERROR << ": There are at least two different iterations numbers: " << numIterations << " and " << currentNumIterations;
+        }
+    }
+
+    return numIterations;
+}
+
+std::vector<MKLDNNMemoryPtr> MKLDNNTensorIteratorNode::getToMemories(const MKLDNNNode* node, const size_t port) const {
+    std::vector<MKLDNNMemoryPtr> memories;
+    for (auto edge : node->getChildEdgesAtPort(port))
+        memories.push_back(edge->getMemoryPtr());
+    return memories;
+}
+
 bool MKLDNNTensorIteratorNode::created() const {
     return getType() == TensorIterator;
 }
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.h
index 3ba49ae9ad9..43f67815d95 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.h
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_tensoriterator_node.h
@@ -9,6 +9,7 @@
 #include <string>
 #include <memory>
 #include <vector>
+#include <common/memory_desc_wrapper.hpp>
 
 namespace MKLDNNPlugin {
 
@@ -55,36 +56,102 @@ protected:
 };
 
 
+/**
+ * Class for storing intermediate output buffer state for dynamism when we don't know
+ * final output shape but we should concatenate output after each iteration
+ */
+class DynamicBuffer {
+public:
+    DynamicBuffer(const MKLDNNMemoryPtr &from, const std::vector<MKLDNNMemoryPtr> &to, const PortMap &map_rule);
+    ~DynamicBuffer() = default;
+
+    void execute(const mkldnn::engine& eng, const int iter);
+    void transfer(const MKLDNNNode* node);
+
+private:
+    void init(const mkldnn::engine& eng);
+
+    /* methods for resize and refill buffer */
+    std::shared_ptr<mkldnn::memory> create_buffer(const mkldnn::engine& eng);
+    void move_buffer(std::shared_ptr<mkldnn::memory> new_buffer);
+    void move_data();
+
+    static void copy(const uint8_t* src, uint8_t* dst, const size_t src_stride, const size_t dst_stride, const size_t count, const size_t len);
+    static uint8_t* get_ptr(mkldnn::memory& prim);
+
+    size_t len = 1lu;
+    size_t count = 1lu;
+    size_t elem_size = 0lu;
+    ptrdiff_t chunk_offset_in_byte = 0;
+    ptrdiff_t buffer_offset_in_byte = 0;
+
+    MKLDNNMemoryPtr from;
+    std::vector<MKLDNNMemoryPtr> to;
+    PortMap map_rule;
+
+    std::shared_ptr<mkldnn::memory> mem_holder_buffer;
+};
+
 class MKLDNNTensorIteratorNode : public MKLDNNNode {
 public:
-    MKLDNNTensorIteratorNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
+    MKLDNNTensorIteratorNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
 
-    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+    static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
     void initSupportedPrimitiveDescriptors() override;
     void getSupportedDescriptors() override;
     void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
+    bool isExecutable() const override { return true; }
 
     void setExtManager(const MKLDNNExtensionManager::Ptr& extMgr) { ext_mng = extMgr; }
 
+protected:
+    //  needShapeInfer() should return false
+    //  because we cannot resolve the output dimensions before the inference is completed
+    bool needShapeInfer() const override { return false; };
+
+    bool needPrepareParams() const override;
+    void prepareParams() override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
+
 private:
-    int n_iter = 0;
+    void prepareInputPorts();
+    void prepareOutputPorts();
+    void prepareBackEdges();
+    void prepareDynamicBackEdges();
+    void prepareDynamicBuffers();
+    void prepareLoopBodyCurrentIteration();
+    void prepareContinueCond();
+    void prepareInitialCond();
+    void prepareTripCount();
+
+    /* Dynamic support */
+    void reshapeSubgraphInput();
+    void reshapeAndFillOutput(mkldnn::stream strm);
+    int getNumIteration(const std::vector<PortMap>& inputPortMap, const std::vector<PortMap>& outputPortMap) const;
+
+    // this method get all memory ptrs of childs of one port to redefine descs for them
+    std::vector<MKLDNNMemoryPtr> getToMemories(const MKLDNNNode* node, const size_t port) const;
 
     MKLDNNExtensionManager::Ptr ext_mng;
     MKLDNNGraph sub_graph;
-    std::vector<MKLDNNMemoryPtr> input_mem, output_mem;
+    std::vector<std::vector<MKLDNNMemoryPtr>> input_mems;
+    std::vector<MKLDNNMemoryPtr> output_mem;
 
     std::vector<std::shared_ptr<PortMapHelper>>
         first_mappers,   /// < Applied once before loop
         last_mappers,    /// < Applied once after loop
         before_mappers,  /// < Applied before each iteration
-        after_mappers;   /// < Applied after each iteration
+        after_mappers,   /// < Applied after each iteration
+        back_mappers;    /// < Applied before each iteration for dynamic shapes
 
     std::shared_ptr<PortChecker>
         trip_count_check,      /// < Perform check of trip count value. value >= -1
-        initial_cond_check,   /// < Perform check of initial continue condition value. value [0, 1]
-        continue_cond_check;  /// < Perform check of continue condition value of body. value [0, 1]
+        initial_cond_check,    /// < Perform check of initial continue condition value. value [0, 1]
+        continue_cond_check;   /// < Perform check of continue condition value of body. value [0, 1]
+
+    std::vector<std::shared_ptr<DynamicBuffer>> buffers;
 
     std::vector<PortMap> inputPortMap;  //!< Input ports map
     std::vector<PortMap> outputPortMap;  //!< Output ports map
@@ -95,9 +162,10 @@ private:
     int loopTripCountIdx = -1;
     int loopExecutionConditionIdx = -1;
 
-    NodeConfig config;
+    int lastUsedTripCount = -1;
+    bool lastUsedCond = false;
 
-    const std::shared_ptr<ngraph::Node> ngraphOp;
+    const std::shared_ptr<ov::Node> ngraphOp;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/loop.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/loop.cpp
new file mode 100644
index 00000000000..c0970902fa9
--- /dev/null
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/loop.cpp
@@ -0,0 +1,471 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <shared_test_classes/single_layer/loop.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace ov;
+using namespace test;
+using namespace ngraph::helpers;
+
+namespace CPULayerTestsDefinitions {
+
+enum LOOP_IN_TYPE {
+    INVARIANT,
+    MERGED
+};
+
+using LoopParams = typename std::tuple<
+        InputLayerType,                                                    // TripCount is a constant?
+        int64_t,                                                           // TripCount, -1 means infinity
+        std::vector<InputShape>,                                           // InputShapes
+        std::vector<LOOP_IN_TYPE>,                                         // Type
+        ElementType>;                                                      // Input element type
+
+
+class LoopLayerCPUTest : public testing::WithParamInterface<LoopParams>,
+                         virtual public SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<LoopParams> obj) {
+        InputLayerType trip_count_type;
+        int64_t trip_count;
+        std::vector<InputShape> shapes;
+        std::vector<LOOP_IN_TYPE> types;
+        ElementType netType;
+        std::tie(trip_count_type, trip_count, shapes, types, netType) = obj.param;
+
+        std::ostringstream result;
+        for (size_t i = 0; i < shapes.size(); i++) {
+            result << "Input" << i << "_";
+            result << "IS=" << CommonTestUtils::partialShape2str({shapes[i].first}) << "_";
+            result << "TS=";
+            for (const auto& item : shapes[i].second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "types=";
+        for (auto type : types)
+            result << type << "_";
+        result << "trip_count_type=" << trip_count_type << "_";
+        result << "trip_count=" << trip_count << "_";
+        result << "netType=" << netType;
+        return result.str();
+}
+
+protected:
+    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+
+        // trip count
+        int i = 0;
+        if (funcInputs[i].get_node_shared_ptr()->get_friendly_name() == "trip_count") {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                                 funcInput.get_shape(), 10, 1);
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+            i++;
+        }
+
+        // parameters for body
+        for (; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                                 targetInputStaticShapes[i], 15, 0, 32768);
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+
+    void SetUp() override {
+        InputLayerType trip_count_type;
+        int64_t trip_count;
+        std::vector<InputShape> shapes;
+        std::vector<LOOP_IN_TYPE> types;
+        ElementType netType;
+        std::tie(trip_count_type, trip_count, shapes, types, netType) = this->GetParam();
+
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes(shapes);
+
+        auto params = ngraph::builder::makeDynamicParams(netType, inputDynamicShapes);
+
+        // Set up the cell body, a function from (Xi, Yi) -> (Zo)
+        // Body parameters
+        const std::vector<ngraph::PartialShape> body_params_shapes(shapes.size(), ngraph::PartialShape::dynamic());
+        ngraph::ParameterVector body_params;
+        for (const auto &pshape : body_params_shapes) {
+            body_params.emplace_back(std::make_shared<ngraph::opset1::Parameter>(netType, pshape));
+        }
+
+        auto body_condition_const = std::make_shared<ngraph::opset5::Constant>(ngraph::element::boolean, ngraph::Shape{1}, true);
+        auto exec_condition = std::make_shared<ngraph::opset5::Constant>(ngraph::element::boolean, ngraph::Shape{1}, true);
+        std::shared_ptr<ngraph::Node> trip_count_input;
+        int shift = 0;
+        if (trip_count_type == InputLayerType::PARAMETER) {
+            for (auto& target : targetStaticShapes)
+                target.insert(target.begin(), ngraph::Shape{});
+            trip_count_input = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::i64, ngraph::Shape{1});
+            trip_count_input->set_friendly_name("trip_count");
+            params.insert(params.begin(), ov::as_type_ptr<ngraph::opset5::Parameter>(trip_count_input));
+            shift++;
+        } else {
+            trip_count_input = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, trip_count);
+        }
+
+        // Body
+        std::shared_ptr<ngraph::Node> Zo = body_params[0];
+        for (int i = 1; i < body_params.size(); ++i) {
+            Zo = std::make_shared<ngraph::op::v1::Add>(body_params[i], Zo);
+        }
+
+        auto body = std::make_shared<ov::Model>(ngraph::OutputVector{body_condition_const, Zo},
+                                                       body_params);
+
+        auto loop = std::make_shared<ngraph::opset5::Loop>(trip_count_input, exec_condition);
+        loop->set_function(body);
+        loop->set_special_body_ports(ngraph::opset5::Loop::SpecialBodyPorts{-1, 0});
+
+        for (int i = 0; i < body_params.size(); ++i) {
+            if (types[i] == LOOP_IN_TYPE::INVARIANT) {
+                loop->set_invariant_input(body_params[i], params[shift + i]);
+            } else if (types[i] == LOOP_IN_TYPE::MERGED) {
+                // todo: support several merged inputs
+                // now supported only one in this sample
+                loop->set_merged_input(body_params[i], params[shift + i], Zo);
+            }
+        }
+
+        // Output 0 is last Zo
+        auto out0 = loop->get_iter_value(body_condition_const, -1);
+        auto out1 = loop->get_iter_value(Zo, -1);
+        // Output 1 is concat of Zos
+        // start=0, stride=1, part_size=1, end=-1, axis=1
+        auto out2 = loop->get_concatenated_slices(Zo, 0, 1, 1, -1, 1);
+
+        auto result0 = std::make_shared<ngraph::opset5::Result>(out0);
+        auto result1 = std::make_shared<ngraph::opset5::Result>(out1);
+        auto result2 = std::make_shared<ngraph::opset5::Result>(out2);
+        function = std::make_shared<ov::Model>(ngraph::ResultVector{result0, result1, result2}, params, "loop");
+    }
+};
+
+class LoopWhileLayerCPUTest : public LoopLayerCPUTest {
+protected:
+    // body:
+    // while (i < 10)
+    //  x += 2
+    //  i += 2
+
+    void SetUp() override {
+        InputLayerType trip_count_type;
+        int64_t trip_count;
+        std::vector<InputShape> shapes;
+        std::vector<LOOP_IN_TYPE> types;
+        std::tie(trip_count_type, trip_count, shapes, types, inType) = this->GetParam();
+
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes(shapes);
+        for (auto& target : targetStaticShapes)
+            target.insert(target.begin(), ngraph::Shape{});
+
+        auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+
+        // Body parameters
+        const std::vector<ngraph::PartialShape> body_params_shapes(shapes.size(), ngraph::PartialShape::dynamic());
+        ngraph::ParameterVector body_params = { std::make_shared<ngraph::opset1::Parameter>(ngraph::element::i64, ngraph::Shape{}) };
+        for (const auto &pshape : body_params_shapes) {
+            body_params.emplace_back(std::make_shared<ngraph::opset1::Parameter>(inType, pshape));
+        }
+
+        auto exec_condition = std::make_shared<ngraph::opset5::Constant>(ngraph::element::boolean, ngraph::Shape{}, true);
+        auto trip_count_input = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::i64, ngraph::Shape{});
+        trip_count_input->set_friendly_name("trip_count");
+        params.insert(params.begin(), trip_count_input);
+
+        // Body
+        auto const_body_cond = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{}, 10);
+        auto const_body_step = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{}, 2);
+        auto less = std::make_shared<ngraph::opset5::Less>(body_params[0], const_body_cond);
+        auto exec_idx = std::make_shared<ngraph::opset5::Add>(body_params[0], const_body_step);
+
+        auto node_const = std::make_shared<ngraph::opset5::Constant>(inType, ngraph::Shape{}, 2);
+        auto node = std::make_shared<ngraph::opset5::Add>(body_params[1], node_const);
+
+        // reference ngraph function is resized by input static shapes in tests but
+        // loop with pad in body has different input shape in each infer request so tests don't support it.
+        // Alternative - eltwise instead of pad
+        // const std::vector<int64_t> begin(inputDynamicShapes[0].rank().get_length(), 1);
+        // const std::vector<int64_t> end(inputDynamicShapes[0].rank().get_length(), 0);
+        // auto node = ngraph::builder::makePad(body_params[1], begin, end, .0f, PadMode::CONSTANT);
+
+        auto body = std::make_shared<ov::Model>(ngraph::OutputVector{less, exec_idx, node}, body_params);
+
+        auto loop = std::make_shared<ngraph::opset5::Loop>(params[0], exec_condition);
+        loop->set_function(body);
+        loop->set_special_body_ports(ngraph::opset5::Loop::SpecialBodyPorts{-1, 0});
+
+        loop->set_merged_input(body_params[0], params[0], exec_idx);
+        loop->set_merged_input(body_params[1], params[1], node);
+
+        auto out0 = loop->get_iter_value(exec_idx, -1);
+        auto out1 = loop->get_iter_value(node, -1);
+
+        auto result0 = std::make_shared<ngraph::opset5::Result>(out0);
+        auto result1 = std::make_shared<ngraph::opset5::Result>(out1);
+        function = std::make_shared<ov::Model>(ngraph::ResultVector{ result0, result1 }, params, "loop");
+    }
+};
+
+class LoopForDiffShapesLayerCPUTest : public LoopLayerCPUTest {
+    // parameter                   back edge
+    //    |                 |-------------------|
+    //    |-----------------|                   |
+    //  StridedSlice       Add ----- Constant   |
+    //    |                 |                   |
+    //    |                 |-------------------|
+    // ConcatOutput       Output
+
+protected:
+    void SetUp() override {
+        InputLayerType trip_count_type;
+        int64_t trip_count;
+        std::vector<InputShape> shapes;
+        std::vector<LOOP_IN_TYPE> types;
+        std::tie(trip_count_type, trip_count, shapes, types, inType) = this->GetParam();
+
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes(shapes);
+
+        auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+
+        // Set up the cell body, a function from (Xi, Yi) -> (Zo)
+        // Body parameters
+        const std::vector<ngraph::PartialShape> body_params_shapes(shapes.size(), ngraph::PartialShape::dynamic());
+        ngraph::ParameterVector body_params;
+        for (const auto &pshape : body_params_shapes) {
+            body_params.emplace_back(std::make_shared<ngraph::opset1::Parameter>(inType, pshape));
+        }
+
+        auto body_condition_const = std::make_shared<ngraph::opset5::Constant>(ngraph::element::boolean, ngraph::Shape{1}, true);
+        auto exec_condition = std::make_shared<ngraph::opset5::Constant>(ngraph::element::boolean, ngraph::Shape{1}, true);
+        std::shared_ptr<ngraph::Node> trip_count_input;
+        int shift = 0;
+        if (trip_count_type == InputLayerType::PARAMETER) {
+            for (auto& target : targetStaticShapes)
+                target.insert(target.begin(), ngraph::Shape{});
+            trip_count_input = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::i64, ngraph::Shape{1});
+            trip_count_input->set_friendly_name("trip_count");
+            params.insert(params.begin(), ov::as_type_ptr<ngraph::opset5::Parameter>(trip_count_input));
+            shift++;
+        } else {
+            trip_count_input = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, trip_count);
+        }
+
+        // Body
+        const auto axis = 1;
+        auto s = ngraph::builder::makeSlice(body_params[0], {0}, {1}, {1}, {axis}, inType);
+        auto constant = ngraph::builder::makeConstant(inType, std::vector<size_t>{1}, std::vector<float>{0.5});
+        auto eltwise = std::make_shared<ov::op::v1::Add>(body_params[0], constant);
+
+        auto body = std::make_shared<ov::Model>(ngraph::OutputVector{body_condition_const, s, eltwise}, body_params);
+
+        auto loop = std::make_shared<ngraph::opset5::Loop>(trip_count_input, exec_condition);
+        loop->set_function(body);
+        loop->set_special_body_ports(ngraph::opset5::Loop::SpecialBodyPorts{-1, 0});
+
+        loop->set_merged_input(body_params[0], params[shift], eltwise);
+
+        // Output 0 is last Zo
+        auto out0 = loop->get_iter_value(body_condition_const, -1);
+        auto out1 = loop->get_iter_value(eltwise, -1);
+        // Output 1 is concat of Zos
+        // start=0, stride=1, part_size=1, end=-1, axis=1
+        auto out2 = loop->get_concatenated_slices(s, 0, 1, 1, -1, 1);
+
+        auto result0 = std::make_shared<ngraph::opset5::Result>(out0);
+        auto result1 = std::make_shared<ngraph::opset5::Result>(out1);
+        auto result2 = std::make_shared<ngraph::opset5::Result>(out2);
+        function = std::make_shared<ov::Model>(ngraph::ResultVector{result0, result1, result2}, params, "loop");
+    }
+};
+
+TEST_P(LoopLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+}
+
+TEST_P(LoopWhileLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+}
+
+TEST_P(LoopForDiffShapesLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+}
+
+namespace {
+
+const std::vector<ElementType> inputPrecisions = {
+        ElementType::f32,
+        ElementType::bf16,
+        ElementType::i8
+};
+
+std::vector<InputLayerType> trip_count_type { InputLayerType::CONSTANT, InputLayerType::PARAMETER };
+std::vector<int64_t> trip_count { 1, 5 }; // works only if trip_count_type is constant
+
+// dim[axis] = 1 because loop supports concatenation only with stride = part_size = 1
+// first loop suit test is with output concatenation
+std::vector<std::vector<InputShape>> inputs = {
+    {  //first test suit
+        {   //dynamic shape for first input
+            {-1, 1, -1},
+            { // target static shapes
+                {10, 1, 10},
+                {1, 1, 1},
+                {1, 1, 1},
+                {5, 1, 3}
+            }
+        },
+        {   //dynamic shape for second input
+            {-1, -1, -1},
+            { // target static shapes
+                {1, 1, 1},
+                {5, 1, 2},
+                {5, 1, 2},
+                {5, 1, 3}
+            }
+        },
+        {   //dynamic shape for third input
+            {-1, 1, -1},
+            { // target static shapes
+                {10, 1, 10},
+                {5, 1, 2},
+                {5, 1, 2},
+                {5, 1, 3}
+            }
+        }
+    },
+
+    {  //second test suit
+        {   //dynamic shape for first input
+            {{1, 10}, 1, {1, 10}},
+            { // target static shapes
+                {8, 1, 8},
+                {1, 1, 1},
+                {1, 1, 1},
+                {1, 1, 1}
+            }
+        },
+        {   //dynamic shape for second input
+            {{1, 8}, 1, {1, 8}},
+            { // target static shapes
+                {8, 1, 8},
+                {1, 1, 1},
+                {1, 1, 1},
+                {5, 1, 3}
+            }
+        },
+        {   //dynamic shape for third input
+            {{1, 10}, -1, {1, 10}},
+            { // target static shapes
+                {8, 1, 8},
+                {1, 1, 1},
+                {1, 1, 1},
+                {5, 1, 3}
+            }
+        }
+    },
+};
+std::vector<LOOP_IN_TYPE> types = {
+        LOOP_IN_TYPE::INVARIANT, LOOP_IN_TYPE::INVARIANT, LOOP_IN_TYPE::MERGED
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LoopForCommon, LoopLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(trip_count_type),
+                                 ::testing::ValuesIn(trip_count),
+                                 ::testing::ValuesIn(inputs),
+                                 ::testing::Values(types),
+                                 ::testing::ValuesIn(inputPrecisions)),
+                         LoopLayerCPUTest::getTestCaseName);
+
+std::vector<std::vector<InputShape>> inputs_2 = {
+    {  //first test suit
+        {   //dynamic shape
+            {-1, -1},
+            { // target static shapes
+                {10, 10},
+                {1, 1},
+                {1, 1},
+                {5, 3}
+            }
+        },
+    },
+
+    {  //second test suit
+        {   //dynamic shape
+            {{1, 10}, {1, 10}},
+            { // target static shapes
+                {5, 2},
+                {2, 5},
+                {5, 5},
+                {5, 5}
+            }
+        },
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LoopWhileCommon, LoopWhileLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Values(trip_count_type[0]),
+                                 ::testing::Values(-1),
+                                 ::testing::ValuesIn(inputs_2),
+                                 ::testing::Values(std::vector<LOOP_IN_TYPE>{}),
+                                 ::testing::ValuesIn(inputPrecisions)),
+                         LoopWhileLayerCPUTest::getTestCaseName);
+
+std::vector<std::vector<InputShape>> inputs_3 = {
+        {  // first test suit
+            {
+                {-1, -1, -1},
+                { // target static shapes
+                     {10, 1, 10},
+                     {1, 10, 1},
+                     {1, 10, 1},
+                     {2, 2, 2},
+                }
+            },
+        },
+        {  // second test suit
+            {
+                {{0, 10}, {0, 10}, {0, 10}},
+                { // target static shapes
+                     {10, 5, 10},
+                     {1, 10, 1},
+                     {1, 10, 1},
+                     {2, 1, 2},
+                }
+            },
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LoopForDiffShapesConcat, LoopForDiffShapesLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(trip_count_type),
+                                 ::testing::ValuesIn(trip_count),
+                                 ::testing::ValuesIn(inputs_3),
+                                 ::testing::Values(std::vector<LOOP_IN_TYPE>{}),
+                                 ::testing::ValuesIn(inputPrecisions)),
+                         LoopLayerCPUTest::getTestCaseName);
+
+}  // namespace
+} // namespace CPULayerTestsDefinitions
diff --git a/src/tests/functional/plugin/cpu/single_layer_tests/tensor_iterator.cpp b/src/tests/functional/plugin/cpu/single_layer_tests/tensor_iterator.cpp
new file mode 100644
index 00000000000..b90ee0c82ce
--- /dev/null
+++ b/src/tests/functional/plugin/cpu/single_layer_tests/tensor_iterator.cpp
@@ -0,0 +1,157 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <shared_test_classes/single_layer/tensor_iterator.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace ov;
+using namespace test;
+
+namespace CPULayerTestsDefinitions {
+
+using TensorIteratorParams = typename std::tuple<
+        std::vector<InputShape>,                    // Input shapes
+        ngraph::op::RecurrentSequenceDirection,     // Direction
+        ElementType>;                               // element type
+
+
+class TensorIteratorCPUTest : public testing::WithParamInterface<TensorIteratorParams>,
+                              virtual public SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<TensorIteratorParams> obj) {
+        std::vector<InputShape> shapes;
+        ngraph::op::RecurrentSequenceDirection direction;
+        ElementType inType;
+        std::tie(shapes, direction, inType) = obj.param;
+
+        std::ostringstream result;
+        for (size_t i = 0; i < shapes.size(); i++) {
+            result << "Input" << i << "_";
+            result << "IS=" << CommonTestUtils::partialShape2str({shapes[i].first}) << "_";
+            result << "TS=";
+            for (const auto& item : shapes[i].second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "direction=" << direction << "_";
+        result << "netPRC=" << inType << "_";
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        std::vector<InputShape> shapes;
+        ngraph::op::RecurrentSequenceDirection direction;
+        ElementType inType;
+        std::tie(shapes, direction, inType) = this->GetParam();
+
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes({shapes});
+
+        const size_t sequence_axis = 1;
+        auto tensor_iterator = std::make_shared<ngraph::opset5::TensorIterator>();
+        auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+
+        ngraph::ParameterVector body_params;
+        for (size_t i = 0; i < shapes.size(); i++) {
+            ngraph::PartialShape shape = shapes[i].first;
+            shape[sequence_axis] = 1;
+            auto paramNode = std::make_shared<ngraph::opset1::Parameter>(inType, shape);
+            body_params.push_back(paramNode);
+        }
+        auto tanh = ngraph::builder::makeActivation(body_params[0], inType, ngraph::helpers::Tanh);
+        auto relu = ngraph::builder::makeActivation(body_params[1], inType, ngraph::helpers::Relu);
+        auto add = std::make_shared<ngraph::opset1::Add>(tanh, relu);
+
+        auto body = std::make_shared<ov::Model>(ngraph::OutputVector{add}, body_params, "body");
+        tensor_iterator->set_function(body);
+
+        if (direction == ngraph::op::RecurrentSequenceDirection::FORWARD) {
+            tensor_iterator->set_sliced_input(body_params[0], params[0], 0, 1, 1, -1, sequence_axis);
+            tensor_iterator->set_sliced_input(body_params[1], params[1], 0, 1, 1, -1, sequence_axis);
+            tensor_iterator->get_concatenated_slices(add, 0, 1, 1, -1, sequence_axis);
+        } else if (direction == ngraph::op::RecurrentSequenceDirection::REVERSE) {
+            tensor_iterator->set_sliced_input(body_params[0], params[0], -1, -1, 1, 0, sequence_axis);
+            tensor_iterator->set_sliced_input(body_params[1], params[1], -1, -1, 1, 0, sequence_axis);
+            tensor_iterator->get_concatenated_slices(add, -1, -1, 1, 0, sequence_axis);
+        } else {
+            NGRAPH_CHECK(false, "Bidirectional case is not supported.");
+        }
+
+        function = std::make_shared<ov::Model>(ngraph::OutputVector{tensor_iterator->output(0)}, params);
+    }
+};
+
+TEST_P(TensorIteratorCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+}
+
+namespace {
+
+const std::vector<ElementType> inputPrecisions = {
+        ElementType::f32,
+        ElementType::bf16,
+        ElementType::i8
+};
+
+std::vector<ngraph::op::RecurrentSequenceDirection> direction = {ngraph::op::RecurrentSequenceDirection::FORWARD,
+                                                                 ngraph::op::RecurrentSequenceDirection::REVERSE};
+std::vector<std::vector<InputShape>> inputs = {
+    {  //first test suit
+        {   //dynamic shape for first input
+            {-1, -1, -1},
+            {  // target static shapes
+                {10, 12, 10},
+                {10, 8, 10},
+                {1, 8, 2},
+                {5, 3, 3}
+            }
+        },
+        {   //dynamic shape for second input
+            {-1, -1, -1},
+            {  // target static shapes
+                {1, 12, 1},
+                {1, 8, 1},
+                {5, 8, 2},
+                {5, 3, 3}
+            }
+        },
+    },
+
+    {  //second test suit
+        {   //dynamic shape for first input
+            {{1, 12}, 5, {1, 12}},
+            {  // target static shapes
+                {1, 5, 1},
+                {5, 5, 5},
+                {1, 5, 1},
+                {5, 5, 5}
+             }
+        },
+        {   //dynamic shape for second input
+            {{1, 12}, 5, {1, 12}},
+            {  // target static shapes
+                {1, 5, 1},
+                {1, 5, 1},
+                {5, 5, 1},
+                {5, 5, 5}
+            }
+        },
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_TensorIteratorSimple, TensorIteratorCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(inputs),
+                                 ::testing::ValuesIn(direction),
+                                 ::testing::ValuesIn(inputPrecisions)),
+                         TensorIteratorCPUTest::getTestCaseName);
+
+}  // namespace
+} // namespace CPULayerTestsDefinitions

From 3ca80d12b25d4147a4d8aead69e89be44a88a1ba Mon Sep 17 00:00:00 2001
From: Alexey Lebedev <alexey.lebedev@intel.com>
Date: Fri, 24 Dec 2021 15:55:58 +0300
Subject: [PATCH 147/160] [tools] use ports instead parameters and results in
 benchmark tool (#9422)

* use ports instead parameters and results

* Fix element_type if preprocessing is skipped

* rename function to model

* rename exe_network to compiled_model
---
 .../openvino/tools/benchmark/benchmark.py     |  6 +-
 .../openvino/tools/benchmark/main.py          | 30 +++----
 .../tools/benchmark/utils/inputs_filling.py   |  2 +-
 .../openvino/tools/benchmark/utils/utils.py   | 78 +++++++++----------
 4 files changed, 57 insertions(+), 59 deletions(-)

diff --git a/tools/benchmark_tool/openvino/tools/benchmark/benchmark.py b/tools/benchmark_tool/openvino/tools/benchmark/benchmark.py
index 2b7544966bf..f0a2f034a7d 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/benchmark.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/benchmark.py
@@ -61,11 +61,11 @@ class Benchmark:
         weights_filename = os.path.abspath(head + BIN_EXTENSION) if ext == XML_EXTENSION else ""
         return self.core.read_model(model_filename, weights_filename)
 
-    def create_infer_requests(self, exe_network):
+    def create_infer_requests(self, compiled_model):
         if self.api_type == 'sync':
-            requests = [exe_network.create_infer_request()]
+            requests = [compiled_model.create_infer_request()]
         else:
-            requests = AsyncInferQueue(exe_network, self.nireq)
+            requests = AsyncInferQueue(compiled_model, self.nireq)
             self.nireq = len(requests)
         return requests
 
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/main.py b/tools/benchmark_tool/openvino/tools/benchmark/main.py
index 4dc77afa650..88100871012 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/main.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/main.py
@@ -218,7 +218,7 @@ def run(args):
             next_step()
 
             start_time = datetime.utcnow()
-            exe_network = benchmark.core.compile_model(args.path_to_model)
+            compiled_model = benchmark.core.compile_model(args.path_to_model, benchmark.device)
             duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
             logger.info(f"Compile model took {duration_ms} ms")
             if statistics:
@@ -226,15 +226,15 @@ def run(args):
                                           [
                                               ('load network time (ms)', duration_ms)
                                           ])
-            app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, exe_network.get_runtime_model().get_parameters())
+            app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, compiled_model.inputs)
             batch_size = get_network_batch_size(app_inputs_info)
         elif not is_network_compiled:
             # --------------------- 4. Read the Intermediate Representation of the network -----------------------------
             next_step()
 
             start_time = datetime.utcnow()
-            function = benchmark.read_model(args.path_to_model)
-            topology_name = function.get_name()
+            model = benchmark.read_model(args.path_to_model)
+            topology_name = model.get_name()
             duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
             logger.info(f"Read model took {duration_ms} ms")
             if statistics:
@@ -246,13 +246,13 @@ def run(args):
             # --------------------- 5. Resizing network to match image sizes and given batch ---------------------------
             next_step()
 
-            app_inputs_info, reshape = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, function.get_parameters())
+            app_inputs_info, reshape = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, model.inputs)
             if reshape:
                 start_time = datetime.utcnow()
                 shapes = { info.name : info.partial_shape for info in app_inputs_info }
                 logger.info(
                     'Reshaping model: {}'.format(', '.join("'{}': {}".format(k, str(v)) for k, v in shapes.items())))
-                function.reshape(shapes)
+                model.reshape(shapes)
                 duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
                 logger.info(f"Reshape model took {duration_ms} ms")
                 if statistics:
@@ -268,14 +268,14 @@ def run(args):
             # --------------------- 6. Configuring inputs and outputs of the model --------------------------------------------------
             next_step()
 
-            pre_post_processing(function, app_inputs_info, args.input_precision, args.output_precision, args.input_output_precision)
-            print_inputs_and_outputs_info(function)
+            pre_post_processing(model, app_inputs_info, args.input_precision, args.output_precision, args.input_output_precision)
+            print_inputs_and_outputs_info(model)
 
             # --------------------- 7. Loading the model to the device -------------------------------------------------
             next_step()
 
             start_time = datetime.utcnow()
-            exe_network = benchmark.core.compile_model(function, benchmark.device)
+            compiled_model = benchmark.core.compile_model(model, benchmark.device)
             duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
             logger.info(f"Compile model took {duration_ms} ms")
             if statistics:
@@ -295,7 +295,7 @@ def run(args):
             next_step()
 
             start_time = datetime.utcnow()
-            exe_network = benchmark.core.import_model(args.path_to_model)
+            compiled_model = benchmark.core.import_model(args.path_to_model)
             duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
             logger.info(f"Import model took {duration_ms} ms")
             if statistics:
@@ -303,7 +303,7 @@ def run(args):
                                           [
                                               ('import network time (ms)', duration_ms)
                                           ])
-            app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, exe_network.get_runtime_model().get_parameters())
+            app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, compiled_model.inputs)
             batch_size = get_network_batch_size(app_inputs_info)
 
         # --------------------- 8. Querying optimal runtime parameters --------------------------------------------------
@@ -314,7 +314,7 @@ def run(args):
                 keys = benchmark.core.get_metric(device, 'SUPPORTED_CONFIG_KEYS')
                 logger.info(f'DEVICE: {device}')
                 for k in keys:
-                    logger.info(f'  {k}  , {exe_network.get_config(k)}')
+                    logger.info(f'  {k}  , {compiled_model.get_config(k)}')
 
         # Update number of streams
         for device in device_number_streams.keys():
@@ -326,7 +326,7 @@ def run(args):
 
         # Create infer requests
         start_time = datetime.utcnow()
-        requests = benchmark.create_infer_requests(exe_network)
+        requests = benchmark.create_infer_requests(compiled_model)
         duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
         logger.info(f"Create {benchmark.nireq} infer requests took {duration_ms} ms")
         if statistics:
@@ -435,7 +435,7 @@ def run(args):
             logger.info(f"Inference Engine configuration settings were dumped to {args.dump_config}")
 
         if args.exec_graph_path:
-            dump_exec_graph(exe_network, args.exec_graph_path)
+            dump_exec_graph(compiled_model, args.exec_graph_path)
 
         if perf_counts:
             perfs_count_list = []
@@ -523,7 +523,7 @@ def run(args):
 
         print(f'Throughput: {fps:.2f} FPS')
 
-        del exe_network
+        del compiled_model
 
         next_step.step_id = 0
     except Exception as e:
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
index 6838f583af2..a1f95c07334 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
@@ -204,7 +204,7 @@ def get_dtype(precision):
       'f32' : (np.float32, np.finfo(np.float32).min, np.finfo(np.float32).max),
       'i32'  : (np.int32, np.iinfo(np.int32).min, np.iinfo(np.int32).max),
       'i64'  : (np.int64, np.iinfo(np.int64).min, np.iinfo(np.int64).max),
-      'fp16' : (np.float16, np.finfo(np.float16).min, np.finfo(np.float16).max),
+      'f16' : (np.float16, np.finfo(np.float16).min, np.finfo(np.float16).max),
       'i16'  : (np.int16, np.iinfo(np.int16).min, np.iinfo(np.int16).max),
       'u16'  : (np.uint16, np.iinfo(np.uint16).min, np.iinfo(np.uint16).max),
       'i8'   : (np.int8, np.iinfo(np.int8).min, np.iinfo(np.int8).max),
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py b/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py
index 39772c0e66b..c4765b74976 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py
@@ -71,52 +71,49 @@ def get_element_type(precision):
     raise Exception("Can't find openvino element type for precision: " + precision)
 
 
-def pre_post_processing(function: Model, app_inputs_info, input_precision: str, output_precision: str, input_output_precision: str):
-    pre_post_processor = PrePostProcessor(function)
+def pre_post_processing(model: Model, app_inputs_info, input_precision: str, output_precision: str, input_output_precision: str):
+    pre_post_processor = PrePostProcessor(model)
     if input_precision:
         element_type = get_element_type(input_precision)
-        for i in range(len(function.inputs)):
+        for i in range(len(model.inputs)):
             pre_post_processor.input(i).tensor().set_element_type(element_type)
             app_inputs_info[i].element_type = element_type
     if output_precision:
         element_type = get_element_type(output_precision)
-        for i in range(len(function.outputs)):
+        for i in range(len(model.outputs)):
             pre_post_processor.output(i).tensor().set_element_type(element_type)
     user_precision_map = {}
     if input_output_precision:
         user_precision_map = _parse_arg_map(input_output_precision)
-        input_names = get_input_output_names(function.get_parameters())
-        output_names = get_input_output_names(function.get_results())
+        input_names = get_input_output_names(model.inputs)
+        output_names = get_input_output_names(model.outputs)
         for node_name, precision in user_precision_map.items():
             user_precision_map[node_name] = get_element_type(precision)
         for name, element_type in user_precision_map.items():
             if name in input_names:
                 port = input_names.index(name)
                 app_inputs_info[port].element_type = element_type
-                pre_post_processor.input(port).tensor().set_element_type(element_type)
+                pre_post_processor.input(name).tensor().set_element_type(element_type)
             elif name in output_names:
-                port = output_names.index(name)
-                pre_post_processor.output(port).tensor().set_element_type(element_type)
+                pre_post_processor.output(name).tensor().set_element_type(element_type)
             else:
                 raise Exception(f"Node '{name}' does not exist in network")
 
     # update app_inputs_info
     if not input_precision:
-        inputs = function.inputs
+        inputs = model.inputs
         for i in range(len(inputs)):
             if app_inputs_info[i].name in user_precision_map.keys():
                 app_inputs_info[i].element_type = user_precision_map[app_inputs_info[i].name]
             elif app_inputs_info[i].is_image:
                 app_inputs_info[i].element_type = Type.u8
                 pre_post_processor.input(i).tensor().set_element_type(Type.u8)
-            else:
-                app_inputs_info[i].element_type = inputs[i].get_element_type()
 
     # set layout for model input
-    for port, info in enumerate(app_inputs_info):
-        pre_post_processor.input(port).model().set_layout(info.layout)
+    for info in app_inputs_info:
+        pre_post_processor.input(info.name).model().set_layout(info.layout)
 
-    function = pre_post_processor.build()
+    model = pre_post_processor.build()
 
 
 def _parse_arg_map(arg_map: str):
@@ -148,20 +145,19 @@ def get_precision(element_type: Type):
     raise Exception("Can't find  precision for openvino element type: " + str(element_type))
 
 
-def print_inputs_and_outputs_info(function: Model):
-    parameters = function.get_parameters()
-    input_names = get_input_output_names(parameters)
-    for i in range(len(parameters)):
-        logger.info(f"Network input '{input_names[i]}' precision {get_precision(parameters[i].get_element_type())}, "
-                                                    f"dimensions ({str(parameters[i].get_layout())}): "
-                                                    f"{' '.join(str(x) for x in parameters[i].get_partial_shape())}")
-    results = function.get_results()
-    output_names = get_input_output_names(results)
-    results = function.get_results()
-    for i in range(len(results)):
-        logger.info(f"Network output '{output_names[i]}' precision {get_precision(results[i].get_element_type())}, "
-                                        f"dimensions ({str(results[i].get_layout())}): "
-                                        f"{' '.join(str(x) for x in  results[i].get_output_partial_shape(0))}")
+def print_inputs_and_outputs_info(model: Model):
+    inputs = model.inputs
+    input_names = get_input_output_names(inputs)
+    for i in range(len(inputs)):
+        logger.info(f"Model input '{input_names[i]}' precision {get_precision(inputs[i].element_type)}, "
+                                                    f"dimensions ({str(inputs[i].node.layout)}): "
+                                                    f"{' '.join(str(x) for x in inputs[i].partial_shape)}")
+    outputs = model.outputs
+    output_names = get_input_output_names(outputs)
+    for i in range(len(outputs)):
+        logger.info(f"Model output '{output_names[i]}' precision {get_precision(outputs[i].element_type)}, "
+                                        f"dimensions ({str(outputs[i].node.layout)}): "
+                                        f"{' '.join(str(x) for x in  outputs[i].partial_shape)}")
 
 
 def get_number_iterations(number_iterations: int, nireq: int, num_shapes: int, api_type: str):
@@ -311,10 +307,10 @@ def process_help_inference_string(benchmark_app, device_number_streams):
     return output_string
 
 
-def dump_exec_graph(exe_network, model_path, weight_path = None):
+def dump_exec_graph(compiled_model, model_path, weight_path = None):
     if not weight_path:
         weight_path = model_path[:model_path.find(".xml")] + ".bin"
-    serialize(exe_network.get_runtime_model(), model_path, weight_path)
+    serialize(compiled_model.get_runtime_model(), model_path, weight_path)
 
 
 
@@ -361,8 +357,8 @@ def get_command_line_arguments(argv):
     return parameters
 
 
-def get_input_output_names(nodes):
-    return [node.friendly_name for node in nodes]
+def get_input_output_names(ports):
+    return [port.any_name for port in ports]
 
 
 def get_data_shapes_map(data_shape_string, input_names):
@@ -544,31 +540,33 @@ def parse_batch_size(batch_size_str):
         return Dimension(0)
 
 
-def get_inputs_info(shape_string, data_shape_string, layout_string, batch_size, scale_string, mean_string, parameters):
-    input_names = get_input_output_names(parameters)
+def get_inputs_info(shape_string, data_shape_string, layout_string, batch_size, scale_string, mean_string, inputs):
+    input_names = get_input_output_names(inputs)
     shape_map = parse_input_parameters(shape_string, input_names)
     data_shape_map = get_data_shapes_map(data_shape_string, input_names)
     layout_map = parse_input_parameters(layout_string, input_names)
     batch_size = parse_batch_size(batch_size)
     reshape = False
     input_info = []
-    for i in range(len(parameters)):
+    for i in range(len(inputs)):
         info = AppInputInfo()
         # Input name
         info.name = input_names[i]
+        # Input precision
+        info.element_type = inputs[i].element_type
         # Shape
-        info.original_shape = parameters[i].get_partial_shape()
+        info.original_shape = inputs[i].partial_shape
         if info.name in shape_map.keys():
             info.partial_shape = parse_partial_shape(shape_map[info.name])
             reshape = True
         else:
-            info.partial_shape = parameters[i].get_partial_shape()
+            info.partial_shape = inputs[i].partial_shape
 
         # Layout
         if info.name in layout_map.keys():
             info.layout = Layout(layout_map[info.name])
-        elif parameters[i].get_layout() != Layout():
-            info.layout = parameters[i].get_layout()
+        elif inputs[i].node.layout != Layout():
+            info.layout = inputs[i].node.layout
         else:
             image_colors_dim = Dimension(3)
             shape = info.partial_shape

From de136a651536611b587a7b59a742a1ddeb3aa287 Mon Sep 17 00:00:00 2001
From: Alexey Lebedev <alexey.lebedev@intel.com>
Date: Fri, 24 Dec 2021 16:42:48 +0300
Subject: [PATCH 148/160] [PYTHON API] fix model inputs and outputs property
 (#9407)

* fix inputs.property and add tests for reshape

* add is_instance in test

* fix code style
---
 .../python/src/pyopenvino/graph/model.cpp     |   5 -
 .../test_inference_engine/test_function.py    | 125 +++++++++---------
 2 files changed, 66 insertions(+), 64 deletions(-)

diff --git a/src/bindings/python/src/pyopenvino/graph/model.cpp b/src/bindings/python/src/pyopenvino/graph/model.cpp
index 57e3a692c53..c156ac53de6 100644
--- a/src/bindings/python/src/pyopenvino/graph/model.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/model.cpp
@@ -771,12 +771,7 @@ void regclass_graph_Model(py::module m) {
     });
 
     function.def_property_readonly("inputs", (std::vector<ov::Output<ov::Node>>(ov::Model::*)()) & ov::Model::inputs);
-    function.def_property_readonly("inputs",
-                                   (std::vector<ov::Output<const ov::Node>>(ov::Model::*)() const) & ov::Model::inputs);
     function.def_property_readonly("outputs", (std::vector<ov::Output<ov::Node>>(ov::Model::*)()) & ov::Model::outputs);
-    function.def_property_readonly(
-        "outputs",
-        (std::vector<ov::Output<const ov::Node>>(ov::Model::*)() const) & ov::Model::outputs);
     function.def_property_readonly("name", &ov::Model::get_name);
     function.def_property("friendly_name", &ov::Model::get_friendly_name, &ov::Model::set_friendly_name);
 }
diff --git a/src/bindings/python/tests/test_inference_engine/test_function.py b/src/bindings/python/tests/test_inference_engine/test_function.py
index d97e10cc079..c0dbfadf529 100644
--- a/src/bindings/python/tests/test_inference_engine/test_function.py
+++ b/src/bindings/python/tests/test_inference_engine/test_function.py
@@ -5,9 +5,15 @@ import numpy as np
 import pytest
 
 import openvino.runtime.opset8 as ops
+from openvino.runtime import Model, Tensor, Output, Dimension,\
+    Layout, Type, PartialShape, Shape, set_batch, get_batch
 
-from openvino.runtime import Model, Tensor, set_batch, get_batch, Dimension, Layout
-from openvino.runtime import Type, PartialShape, Shape
+
+def create_test_model():
+    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
+    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
+    add = ops.add(param1, param2)
+    return Model(add, [param1, param2], "TestFunction")
 
 
 def test_test_descriptor_tensor():
@@ -176,17 +182,13 @@ def test_add_outputs_incorrect_outputs_list():
 
 
 def test_validate_nodes_and_infer_types():
-    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
-    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
-    add = ops.add(param1, param2)
-    func = Model(add, [param1, param2], "TestFunction")
-
+    model = create_test_model()
     invalid_shape = Shape([3, 7])
     param3 = ops.parameter(invalid_shape, dtype=np.float32, name="data3")
-    func.replace_parameter(0, param3)
+    model.replace_parameter(0, param3)
 
     with pytest.raises(RuntimeError) as e:
-        func.validate_nodes_and_infer_types()
+        model.validate_nodes_and_infer_types()
     assert "Argument shapes are inconsistent" in str(e.value)
 
 
@@ -246,27 +248,19 @@ def test_replace_parameter():
 
 
 def test_evaluate():
-    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
-    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
-    add = ops.add(param1, param2)
-    func = Model(add, [param1, param2], "TestFunction")
-
+    model = create_test_model()
     input1 = np.array([2, 1], dtype=np.float32).reshape(2, 1)
     input2 = np.array([3, 7], dtype=np.float32).reshape(2, 1)
     out_tensor = Tensor("float32", Shape([2, 1]))
 
-    assert func.evaluate([out_tensor], [Tensor(input1), Tensor(input2)])
+    assert model.evaluate([out_tensor], [Tensor(input1), Tensor(input2)])
     assert np.allclose(out_tensor.data, np.array([5, 8]).reshape(2, 1))
 
 
 def test_evaluate_invalid_input_shape():
-    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
-    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
-    add = ops.add(param1, param2)
-    func = Model(add, [param1, param2], "TestFunction")
-
+    model = create_test_model()
     with pytest.raises(RuntimeError) as e:
-        assert func.evaluate(
+        assert model.evaluate(
             [Tensor("float32", Shape([2, 1]))],
             [Tensor("float32", Shape([3, 1])), Tensor("float32", Shape([3, 1]))],
         )
@@ -274,13 +268,10 @@ def test_evaluate_invalid_input_shape():
 
 
 def test_get_batch():
-    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
-    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
-    add = ops.add(param1, param2)
-    func = Model(add, [param1, param2], "TestFunction")
-    param = func.get_parameters()[0]
+    model = create_test_model()
+    param = model.get_parameters()[0]
     param.set_layout(Layout("NC"))
-    assert get_batch(func) == 2
+    assert get_batch(model) == 2
 
 
 def test_get_batch_CHWN():
@@ -296,49 +287,65 @@ def test_get_batch_CHWN():
 
 
 def test_set_batch_dimension():
-    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
-    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
-    add = ops.add(param1, param2)
-    func = Model(add, [param1, param2], "TestFunction")
-    func_param1 = func.get_parameters()[0]
-    func_param2 = func.get_parameters()[1]
+    model = create_test_model()
+    model_param1 = model.get_parameters()[0]
+    model_param2 = model.get_parameters()[1]
     # batch == 2
-    func_param1.set_layout(Layout("NC"))
-    assert get_batch(func) == 2
+    model_param1.set_layout(Layout("NC"))
+    assert get_batch(model) == 2
     # set batch to 1
-    set_batch(func, Dimension(1))
-    assert get_batch(func) == 1
+    set_batch(model, Dimension(1))
+    assert get_batch(model) == 1
     # check if shape of param 1 has changed
-    assert str(func_param1.get_output_shape(0) == {1, 1})
+    assert model_param1.get_output_shape(0) == PartialShape([1, 1])
     # check if shape of param 2 has not changed
-    assert str(func_param2.get_output_shape(0) == {2, 1})
+    assert model_param2.get_output_shape(0) == PartialShape([2, 1])
 
 
 def test_set_batch_int():
-    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
-    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
-    add = ops.add(param1, param2)
-    func = Model(add, [param1, param2], "TestFunction")
-    func_param1 = func.get_parameters()[0]
-    func_param2 = func.get_parameters()[1]
+    model = create_test_model()
+    model_param1 = model.get_parameters()[0]
+    model_param2 = model.get_parameters()[1]
     # batch == 2
-    func_param1.set_layout(Layout("NC"))
-    assert get_batch(func) == 2
+    model_param1.set_layout(Layout("NC"))
+    assert get_batch(model) == 2
     # set batch to 1
-    set_batch(func, 1)
-    assert get_batch(func) == 1
+    set_batch(model, 1)
+    assert get_batch(model) == 1
     # check if shape of param 1 has changed
-    assert str(func_param1.get_output_shape(0) == {1, 1})
+    assert model_param1.get_output_shape(0) == PartialShape([1, 1])
     # check if shape of param 2 has not changed
-    assert str(func_param2.get_output_shape(0) == {2, 1})
+    assert model_param2.get_output_shape(0) == PartialShape([2, 1])
 
 
 def test_set_batch_default_batch_size():
-    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
-    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
-    add = ops.add(param1, param2)
-    func = Model(add, [param1, param2], "TestFunction")
-    func_param1 = func.get_parameters()[0]
-    func_param1.set_layout(Layout("NC"))
-    set_batch(func)
-    assert func.is_dynamic()
+    model = create_test_model()
+    model_param1 = model.get_parameters()[0]
+    model_param1.set_layout(Layout("NC"))
+    set_batch(model)
+    assert model.is_dynamic()
+
+
+def test_reshape_with_ports():
+    model = create_test_model()
+    new_shape = PartialShape([1, 4])
+    for input in model.inputs:
+        assert isinstance(input, Output)
+        model.reshape({input: new_shape})
+        assert input.partial_shape == new_shape
+
+
+def test_reshape_with_indexes():
+    model = create_test_model()
+    new_shape = PartialShape([1, 4])
+    for index, input in enumerate(model.inputs):
+        model.reshape({index: new_shape})
+        assert input.partial_shape == new_shape
+
+
+def test_reshape_with_names():
+    model = create_test_model()
+    new_shape = PartialShape([1, 4])
+    for input in model.inputs:
+        model.reshape({input.any_name: new_shape})
+        assert input.partial_shape == new_shape

From fa2647f965932fc306953554ae8f47ac9f09a771 Mon Sep 17 00:00:00 2001
From: Alexandra Sidorova <alexandra.sidorova@intel.com>
Date: Fri, 24 Dec 2021 19:43:30 +0300
Subject: [PATCH 149/160] [CPU] Added dynamism support for If (#8967)

---
 .../intel_cpu/src/nodes/mkldnn_if_node.cpp    | 166 +++++++------
 .../intel_cpu/src/nodes/mkldnn_if_node.h      |  34 ++-
 .../intel_cpu/src/nodes/mkldnn_pad_node.cpp   |   2 +
 .../skip_tests_config.cpp                     |   2 +
 .../subgraph_tests/simple_if.cpp              | 174 +++++++++++---
 .../subgraph/simple_if.hpp                    |  24 +-
 .../src/subgraph/simple_if.cpp                | 225 +++++++++++++-----
 7 files changed, 451 insertions(+), 176 deletions(-)

diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.cpp
index 30a4bc9cc89..7267992fc26 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.cpp
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.cpp
@@ -4,34 +4,61 @@
 
 #include "mkldnn_if_node.h"
 
-#include <mkldnn_extension_utils.h>
-#include <ie_ngraph_utils.hpp>
-#include <transformations/utils/utils.hpp>
+#include "mkldnn_extension_utils.h"
+#include "ie_ngraph_utils.hpp"
+#include "transformations/utils/utils.hpp"
+#include "common/cpu_memcpy.h"
 
-#include <map>
 #include <string>
 #include <vector>
 
 using namespace MKLDNNPlugin;
 
-MKLDNNIfNode::PortMapHelper::PortMapHelper(const MKLDNNMemoryPtr &from, const MKLDNNMemoryPtr &to, const mkldnn::engine& eng) {
-    mem_holder_src = from->GetPrimitive();
-    mem_holder_dst = to->GetPrimitive();
-    reorder = {mem_holder_src, mem_holder_dst};
+MKLDNNIfNode::PortMapHelper::PortMapHelper(const MKLDNNMemoryPtr &from, const std::deque<MKLDNNMemoryPtr>& to,
+                                           const mkldnn::engine& eng) : srcMemPtr(from), dstMemPtrs(to) {
+    if (srcMemPtr->getDesc().isDefined())
+        size = srcMemPtr->GetSize();
 }
 
 void MKLDNNIfNode::PortMapHelper::execute(mkldnn::stream& strm) {
-    reorder.execute(strm, mem_holder_src, mem_holder_dst);
+    // if output shapes are changed,
+    // after subgraph inference we should redefine out memory of 'If'
+    redefineTo();
+
+    cpu_memcpy(dstMemPtrs.front()->GetPtr(), srcMemPtr->GetPtr(), size);
+}
+
+void MKLDNNIfNode::PortMapHelper::redefineTo() {
+    const auto &currDesc = dstMemPtrs.front()->getDesc();
+    if (currDesc.getShape().isDynamic() || currDesc.getShape().getStaticDims() != srcMemPtr->getStaticDims()) {
+        // WA [DS] : need to rewrite it. Copypaste from MKLDNNNode::redefineOutputMemory
+        // this path is necessary if there are several edges per one port
+        // in this case edge memory share same physical memory
+        // so we need to find which edge allocate memory, reallocate memory and share this memory between other edges
+        size_t sharedEdgeNum = 0;
+        for (size_t j = 0; j < dstMemPtrs.size(); j++) {
+            if (!dstMemPtrs[j]->isUsedExternalStorage()) {
+                sharedEdgeNum = j;
+                break;
+            }
+        }
+
+        const auto &memDesc = srcMemPtr->getDesc();
+        dstMemPtrs[sharedEdgeNum]->redefineDesc(memDesc);
+        void *data = dstMemPtrs[sharedEdgeNum]->GetData();
+        for (size_t j = 0; j < dstMemPtrs.size(); j++) {
+            if (j == sharedEdgeNum)
+                continue;
+            dstMemPtrs[j]->redefineDesc(memDesc, data);
+        }
+
+        size = srcMemPtr->GetSize();
+    }
 }
 
 bool MKLDNNIfNode::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "If node doesn't support op with dynamic shapes";
-            return false;
-        }
-        if (!one_of(op->get_type_info(),
-                ov::op::v8::If::get_type_info_static())) {
+        if (!one_of(op->get_type_info(), ov::op::v8::If::get_type_info_static())) {
             errorMessage = "Not supported If operation version " + std::to_string(op->get_type_info().version) +
                     " with name '" + op->get_friendly_name() + "'. Node If supports only opset8 version.";
             return false;
@@ -62,8 +89,7 @@ void MKLDNNIfNode::getSupportedDescriptors() {
     for (const auto &param : ifOp->get_then_body()->get_parameters()) {
         auto inNode = inMapThen.find(param->get_friendly_name());
         if (inNode != inMapThen.end()) {
-            auto inMem = inNode->second->getChildEdgeAt(0)->getMemoryPtr();
-            inputMemThen.push_back(inMem);
+            inputMemThen.push_back(getToMemories(inNode->second.get(), 0));
         } else {
             IE_THROW() << "Then body of node If with name " << getName() << " does not have input with name: "
                     << param->get_friendly_name();
@@ -74,8 +100,7 @@ void MKLDNNIfNode::getSupportedDescriptors() {
     for (const auto &param : ifOp->get_else_body()->get_parameters()) {
         auto inNode = inMapElse.find(param->get_friendly_name());
         if (inNode != inMapElse.end()) {
-            auto inMem = inNode->second->getChildEdgeAt(0)->getMemoryPtr();
-            inputMemElse.push_back(inMem);
+            inputMemElse.push_back(getToMemories(inNode->second.get(), 0));
         } else {
             IE_THROW() << "Else body of node If with name " << getName() << " does not have input with name: "
                     << param->get_friendly_name();
@@ -143,20 +168,16 @@ void MKLDNNIfNode::initSupportedPrimitiveDescriptors() {
     config.outConfs.reserve(getChildEdges().size());
 
     for (size_t i = 0; i < inputShapes.size(); i++) {
-        auto dims = inputShapes[i].getDims();
-
         PortConfig dataConf {};
         auto descCreator = BlockedDescCreator::getCommonCreators().at(LayoutType::ncsp);
-        dataConf.desc = descCreator->createSharedDesc(getOriginalInputPrecisionAtPort(i), Shape(dims));
+        dataConf.desc = descCreator->createSharedDesc(getOriginalInputPrecisionAtPort(i), getInputShapeAtPort(i));
         config.inConfs.emplace_back(dataConf);
     }
 
     for (size_t i = 0; i < outputShapes.size(); i++) {
-        auto dims = outputShapes[i].getDims();
-
         PortConfig dataConf {};
         auto descCreator = BlockedDescCreator::getCommonCreators().at(LayoutType::ncsp);
-        dataConf.desc = descCreator->createSharedDesc(getOriginalOutputPrecisionAtPort(i), Shape(dims));
+        dataConf.desc = descCreator->createSharedDesc(getOriginalOutputPrecisionAtPort(i), getOutputShapeAtPort(i));
         config.outConfs.push_back(dataConf);
     }
 
@@ -165,59 +186,68 @@ void MKLDNNIfNode::initSupportedPrimitiveDescriptors() {
     supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
 }
 
-
 void MKLDNNIfNode::createPrimitive() {
     const auto& eng = getEngine();
+    prepareBeforeMappers(true, eng);
+    prepareBeforeMappers(false, eng);
+    prepareAfterMappers(true, eng);
+    prepareAfterMappers(false, eng);
 
-    for (auto& map_rule : thenInputPortMap) {
-        auto &fromMem = getParentEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
-        auto &toMem = inputMemThen[map_rule.to];
-
-        beforeThenMappers.emplace_back(std::make_shared<PortMapHelper>(fromMem, toMem, eng));
-    }
-
-    for (auto& map_rule : elseInputPortMap) {
-        auto &fromMem = getParentEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
-        auto &toMem = inputMemElse[map_rule.to];
-
-        beforeElseMappers.emplace_back(std::make_shared<PortMapHelper>(fromMem, toMem, eng));
-    }
-
-    for (auto& map_rule : thenOutputPortMap) {
-        auto &toMem = getChildEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
-        auto &fromMem = outputMemThen[map_rule.to];
-
-        afterThenMappers.emplace_back(std::make_shared<PortMapHelper>(fromMem, toMem, eng));
-    }
-
-    for (auto& map_rule : elseOutputPortMap) {
-        auto &toMem = getChildEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
-        auto &fromMem = outputMemElse[map_rule.to];
-
-        afterElseMappers.emplace_back(std::make_shared<PortMapHelper>(fromMem, toMem, eng));
+    if (inputShapesDefined()) {
+        updateLastInputDims();
     }
 }
 
-void MKLDNNIfNode::execute(mkldnn::stream strm) {
-    const bool condition = *(reinterpret_cast<const bool*>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr()));
+void MKLDNNIfNode::prepareBeforeMappers(const bool isThen, const dnnl::engine& eng) {
+    auto &inputPortMap = isThen ? thenInputPortMap : elseInputPortMap;
+    auto &inputMems = isThen ? inputMemThen : inputMemElse;
+    auto &beforeMappers = isThen ? beforeThenMappers : beforeElseMappers;
+    for (auto& map_rule : inputPortMap) {
+        auto &fromMem = getParentEdgesAtPort(map_rule.from)[0]->getMemoryPtr();
+        auto &toMems = inputMems[map_rule.to];
 
-    if (condition) {
-        for (auto &mapper : beforeThenMappers)
-            mapper->execute(strm);
-        subGraphThen.ResetInferCount();
-        subGraphThen.Infer();
-        for (auto &mapper : afterThenMappers)
-            mapper->execute(strm);
-    } else {
-        for (auto &mapper : beforeElseMappers)
-            mapper->execute(strm);
-        subGraphElse.ResetInferCount();
-        subGraphElse.Infer();
-        for (auto &mapper : afterElseMappers)
-            mapper->execute(strm);
+        beforeMappers.emplace_back(std::make_shared<PortMapHelper>(fromMem, toMems, eng));
     }
 }
 
+void MKLDNNIfNode::prepareAfterMappers(const bool isThen, const dnnl::engine& eng) {
+    auto &outputPortMap = isThen ? thenOutputPortMap : elseOutputPortMap;
+    auto &outputMems = isThen ? outputMemThen : outputMemElse;
+    auto &afterMappers = isThen ? afterThenMappers : afterElseMappers;
+    for (auto& map_rule : outputPortMap) {
+        auto toMems = getToMemories(this, map_rule.from);
+        auto &fromMem = outputMems[map_rule.to];
+
+        afterMappers.emplace_back(std::make_shared<PortMapHelper>(fromMem, toMems, eng));
+    }
+}
+
+std::deque<MKLDNNMemoryPtr> MKLDNNIfNode::getToMemories(const MKLDNNNode* node, const size_t port) const {
+    std::deque<MKLDNNMemoryPtr> memories;
+    for (auto edge : node->getChildEdgesAtPort(port))
+        memories.push_back(edge->getMemoryPtr());
+    return memories;
+}
+
+void MKLDNNIfNode::execute(mkldnn::stream strm) {
+    const bool condition = static_cast<const bool>((reinterpret_cast<const uint8_t*>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr()))[0]);
+
+    auto& beforeMappers = condition ? beforeThenMappers : beforeElseMappers;
+    auto& afterMappers = condition ? afterThenMappers : afterElseMappers;
+    auto& subGraph = condition ? subGraphThen : subGraphElse;
+
+    for (auto &mapper : beforeMappers)
+        mapper->execute(strm);
+    subGraph.ResetInferCount();
+    subGraph.Infer();
+    for (auto &mapper : afterMappers)
+        mapper->execute(strm);
+}
+
+void MKLDNNIfNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 bool MKLDNNIfNode::created() const {
     return getType() == If;
 }
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.h
index c384945b442..674eaa1d025 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.h
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_if_node.h
@@ -23,9 +23,21 @@ public:
     void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
+    bool isExecutable() const override { return true; }
 
     void inline setExtManager(const MKLDNNExtensionManager::Ptr& extMgr) { ext_mng = extMgr; }
 
+protected:
+    void executeDynamicImpl(mkldnn::stream strm) override;
+    bool needPrepareParams() const override { return false; };
+    bool needShapeInfer() const override { return false; }
+
+private:
+    void prepareBeforeMappers(const bool isThen, const dnnl::engine& eng);
+    void prepareAfterMappers(const bool isThen, const dnnl::engine& eng);
+
+    std::deque<MKLDNNMemoryPtr> getToMemories(const MKLDNNNode* node, const size_t port) const;
+
     struct PortMap {
         int from; /**< Index of external/internal out data */
         int to;   /**< Index of external/internal in data */
@@ -33,20 +45,24 @@ public:
 
     class PortMapHelper {
     public:
-        PortMapHelper(const MKLDNNMemoryPtr& from, const MKLDNNMemoryPtr& to, const mkldnn::engine& eng);
-        virtual ~PortMapHelper() = default;
-        virtual void execute(mkldnn::stream& strm);
-    protected:
-        mkldnn::reorder reorder;
-        mkldnn::memory mem_holder_src;
-        mkldnn::memory mem_holder_dst;
+        PortMapHelper(const MKLDNNMemoryPtr& from, const std::deque<MKLDNNMemoryPtr>& to, const mkldnn::engine& eng);
+        ~PortMapHelper() = default;
+        void execute(mkldnn::stream& strm);
+
+    private:
+        void redefineTo();
+
+        MKLDNNMemoryPtr srcMemPtr;
+        std::deque<MKLDNNMemoryPtr> dstMemPtrs;
+
+        ptrdiff_t size;
     };
 
-private:
     MKLDNNExtensionManager::Ptr ext_mng;
     MKLDNNGraph subGraphThen;
     MKLDNNGraph subGraphElse;
-    std::deque<MKLDNNMemoryPtr> inputMemThen, inputMemElse, outputMemThen, outputMemElse;
+    std::vector<std::deque<MKLDNNMemoryPtr>> inputMemThen, inputMemElse;
+    std::deque<MKLDNNMemoryPtr> outputMemThen, outputMemElse;
 
     std::vector<std::shared_ptr<PortMapHelper>>
         beforeThenMappers,
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.cpp
index 40d80ca409b..7d4178e2866 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.cpp
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_pad_node.cpp
@@ -373,6 +373,8 @@ void MKLDNNPadNode::PadExecutor::padConstantCommon(MKLDNNMemoryPtr& srcMemPtr, M
         parallel_for(workAmount, [&](size_t i) {
             dstData[i] = value;
         });
+
+        return;
     }
 
     const T* srcData = reinterpret_cast<const T*>(srcMemPtr->GetPtr());
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index 1ff8c21a217..0c87ea285af 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -168,6 +168,8 @@ std::vector<std::string> disabledTestPatterns() {
         R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*UNSPECIFIED.*)",
         // Issue:
         R"(.*smoke_VariadicSplit4D_CPU_zero_dims.*)",
+        // Waiting for common fix of zero dims
+        R"(smoke_If.*TS=\(.*\.0.*\).*)",
     };
 
 #define FIX_62820 0
diff --git a/src/tests/functional/plugin/cpu/shared_tests_instances/subgraph_tests/simple_if.cpp b/src/tests/functional/plugin/cpu/shared_tests_instances/subgraph_tests/simple_if.cpp
index 064d8ca9974..5678295d0ca 100644
--- a/src/tests/functional/plugin/cpu/shared_tests_instances/subgraph_tests/simple_if.cpp
+++ b/src/tests/functional/plugin/cpu/shared_tests_instances/subgraph_tests/simple_if.cpp
@@ -7,51 +7,149 @@
 using namespace SubgraphTestsDefinitions;
 
 namespace {
-    std::vector<std::vector<std::vector<size_t>>> inputShapes = {
-            {{5, 7}, {5, 7}},
-            {{30, 20, 10}, {30, 20, 10}}
-    };
+std::vector<std::vector<ov::test::InputShape>> inputShapes = {
+        {
+            {{}, {{5, 7}}},
+            {{}, {{5, 7}}},
+        },
+        {
+            {{}, {{30, 20, 10}}},
+            {{}, {{30, 20, 10}}}
+        },
+        {
+            {
+                {-1, -1, -1},
+                {{10, 20, 5}, {10, 0, 5}, {1, 5, 5}}
+            },
+            {
+                {-1, -1, -1},
+                {{10, 20, 5}, {10, 0, 5}, {1, 1, 5}}
+            }
+        },
+        {
+            {
+                {-1, 5, -1},
+                {{10, 5, 10}, {2, 5, 5}, {1, 5, 5}}
+            },
+            {
+                {-1, 5, -1},
+                {{1, 5, 1}, {2, 5, 5}, {5, 5, 5}}
+            }
+        },
+        {
+            {
+                {{1, 10}, {1, 10}, {1, 10}},
+                {{2, 5, 10}, {2, 5, 1}, {1, 5, 5}}
+            },
+            {
+                {{1, 10}, {1, 10}, {1, 10}},
+                {{2, 5, 10}, {2, 1, 5}, {5, 5, 5}}
+            }
+        },
+};
 
-    std::vector<InferenceEngine::Precision> netPrecisions = {InferenceEngine::Precision::FP32,
-                                                             InferenceEngine::Precision::I8,
-    };
+const std::vector<ov::test::ElementType> inTypes = {
+        ov::test::ElementType::f32,
+        ov::test::ElementType::bf16,
+        ov::test::ElementType::i8
+};
 
-    std::vector<bool> conditions = {true, false};
+std::vector<bool> conditions = {true, false};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIfTest,
-                            ::testing::Combine(
-                                    ::testing::ValuesIn(inputShapes),
-                                    ::testing::ValuesIn(netPrecisions),
-                                    ::testing::ValuesIn(conditions),
-                                    ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                            SimpleIfTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIfTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes),
+                                ::testing::ValuesIn(inTypes),
+                                ::testing::ValuesIn(conditions),
+                                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        SimpleIfTest::getTestCaseName);
 
-    TEST_P(SimpleIfTest, CompareWithRefs) {
-        Run();
-    };
+TEST_P(SimpleIfTest, CompareWithRefs) {
+    run();
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIf2OutTest,
-                            ::testing::Combine(
-                                    ::testing::ValuesIn(inputShapes),
-                                    ::testing::ValuesIn(netPrecisions),
-                                    ::testing::ValuesIn(conditions),
-                                    ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                            SimpleIf2OutTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIf2OutTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes),
+                                ::testing::ValuesIn(inTypes),
+                                ::testing::ValuesIn(conditions),
+                                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        SimpleIf2OutTest::getTestCaseName);
 
-    TEST_P(SimpleIf2OutTest, CompareWithRefs) {
-        Run();
-    };
+TEST_P(SimpleIf2OutTest, CompareWithRefs) {
+    run();
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIfNotConstConditionTest,
-                            ::testing::Combine(
-                                    ::testing::ValuesIn(inputShapes),
-                                    ::testing::ValuesIn(netPrecisions),
-                                    ::testing::ValuesIn(conditions),
-                                    ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                            SimpleIfNotConstConditionTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIfNotConstConditionTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes),
+                                ::testing::ValuesIn(inTypes),
+                                ::testing::ValuesIn(conditions),
+                                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        SimpleIfNotConstConditionTest::getTestCaseName);
 
-    TEST_P(SimpleIfNotConstConditionTest, CompareWithRefs) {
-        Run();
-    };
+TEST_P(SimpleIfNotConstConditionTest, CompareWithRefs) {
+    run();
+};
+
+std::vector<std::vector<ov::test::InputShape>> inputShapes_2 = {
+        {
+            {
+                {-1, -1, -1},
+                {{10, 20, 5}, {10, 20, 5}, {1, 5, 5}}
+            },
+        },
+        {
+            {
+                {-1, 5, -1},
+                {{10, 5, 10}, {2, 5, 5}, {1, 5, 5}}
+            },
+        },
+        {
+            {
+                {{0, 10}, {0, 10}, {0, 10}},
+                {{2, 5, 10}, {2, 5, 1}, {2, 5, 0}, {1, 5, 5}}
+            },
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIfNotConstConditionAndInternalDynamismTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(inputShapes_2),
+                                 ::testing::ValuesIn(inTypes),
+                                 ::testing::ValuesIn(conditions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         SimpleIfNotConstConditionTest::getTestCaseName);
+
+TEST_P(SimpleIfNotConstConditionAndInternalDynamismTest, CompareWithRefs) {
+    run();
+};
+
+std::vector<std::vector<ov::test::InputShape>> inputShapes_3 = {
+        {
+            {
+                {-1, 2, -1},
+                {{1, 2, 0}, {2, 2, 5}}
+            },
+        },
+        {
+             {
+                {{0, 10}, {0, 10}, {0, 10}},
+                {{2, 5, 10}, {2, 0, 0}}
+             },
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_If, SimpleIfNotConstConditionAndDimsIncreaseTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(inputShapes_3),
+                                 ::testing::ValuesIn(inTypes),
+                                 ::testing::ValuesIn(conditions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         SimpleIfNotConstConditionTest::getTestCaseName);
+
+TEST_P(SimpleIfNotConstConditionAndDimsIncreaseTest, CompareWithRefs) {
+    run();
+};
 
 }  // namespace
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/simple_if.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/simple_if.hpp
index 2d59649f509..61aab99df09 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/simple_if.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/simple_if.hpp
@@ -7,24 +7,29 @@
 #include <tuple>
 #include <string>
 #include <vector>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "shared_test_classes/base/layer_test_utils.hpp"
 
 namespace SubgraphTestsDefinitions {
 
 using SimpleIfParamsTuple = typename std::tuple<
-        std::vector<std::vector<size_t>>,    // Input shapes
-        InferenceEngine::Precision,          // Network precision
+        std::vector<ov::test::InputShape>,   // Input shapes
+        ov::test::ElementType,               // Network precision
         bool,                                // If condition
         std::string                          // Device name
 >;
 
 class SimpleIfTest:
         public testing::WithParamInterface<SimpleIfParamsTuple>,
-        virtual public LayerTestsUtils::LayerTestsCommon {
+        virtual public ov::test::SubgraphBaseTest {
 public:
     static std::string getTestCaseName(const testing::TestParamInfo<SimpleIfParamsTuple> &obj);
 protected:
     void SetUp() override;
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override;
+
+    size_t inferNum = 0;
 };
 
 class SimpleIf2OutTest : public SimpleIfTest {
@@ -34,7 +39,7 @@ protected:
 
 class SimpleIfNotConstConditionTest : public SimpleIfTest {
 public:
-    InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo& info) const override;
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override;
 
 protected:
     void SetUp() override;
@@ -42,4 +47,15 @@ protected:
     bool condition;
 };
 
+class SimpleIfNotConstConditionAndInternalDynamismTest : public SimpleIfNotConstConditionTest {
+protected:
+    void SetUp() override;
+};
+
+class SimpleIfNotConstConditionAndDimsIncreaseTest : public SimpleIfNotConstConditionTest {
+protected:
+    void SetUp() override;
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override;
+};
+
 }  // namespace SubgraphTestsDefinitions
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/simple_if.cpp b/src/tests/functional/shared_test_classes/src/subgraph/simple_if.cpp
index c2620694d75..4cc469d17b2 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/simple_if.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/simple_if.cpp
@@ -4,36 +4,56 @@
 
 #include "shared_test_classes/subgraph/simple_if.hpp"
 #include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
 
 namespace SubgraphTestsDefinitions {
 std::string SimpleIfTest::getTestCaseName(const testing::TestParamInfo<SimpleIfParamsTuple> &obj) {
-    std::vector<std::vector<size_t>> inputShapes;
-    InferenceEngine::Precision netPrecision;
+    std::vector<ov::test::InputShape> shapes;
+    ov::test::ElementType inType;
     bool condition;
     std::string targetName;
-    std::tie(inputShapes, netPrecision, condition, targetName) = obj.param;
-    std::ostringstream results;
+    std::tie(shapes, inType, condition, targetName) = obj.param;
 
-    results << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_";
-    results << "netPRC=" << netPrecision.name() << "_";
+    std::ostringstream results;
+    for (size_t i = 0; i < shapes.size(); i++) {
+        results << "Input" << i << "_";
+        results << "IS=" << CommonTestUtils::partialShape2str({shapes[i].first}) << "_";
+        results << "TS=";
+        for (const auto &item : shapes[i].second) {
+            results << CommonTestUtils::vec2str(item) << "_";
+        }
+    }
+    results << "inType=" << inType << "_";
     results << "Cond=" << condition << "_";
     results << "targetDevice=" << targetName << "_";
     return results.str();
 }
 
-void SimpleIfTest::SetUp() {
-    std::vector<std::vector<size_t>> inputShapes;
-    auto netPrecision = InferenceEngine::Precision::UNSPECIFIED;
-    bool condition;
-    std::tie(inputShapes, netPrecision, condition, targetDevice) = this->GetParam();
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-    auto params = ngraph::builder::makeParams(ngPrc, {inputShapes[0], inputShapes[1]});
-    auto paramOuts = ngraph::helpers::convert2OutputVector(
-            ngraph::helpers::castOps2Nodes<ov::op::v0::Parameter>(params));
+void SimpleIfTest::compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) {
+    // in bodies there aren't nodes that work with dimension 0. So we shouldn't call SubgraphBaseTest::compare
+    bool hasZero = false;
+    for (auto shape : targetStaticShapes[inferNum]) {
+        hasZero = hasZero || std::any_of(shape.begin(), shape.end(), [](size_t dim) { return dim == 0; });
+    }
+    if (!hasZero) {
+        SubgraphBaseTest::compare(expected, actual);
+    }
 
-    auto p1 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[0]));
-    auto p2 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[1]));
-    auto p3 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[0]));
+    inferNum++;
+}
+
+void SimpleIfTest::SetUp() {
+    std::vector<ov::test::InputShape> shapes;
+    ov::test::ElementType inType;
+    bool condition;
+    std::tie(shapes, inType, condition, targetDevice) = this->GetParam();
+
+    init_input_shapes(shapes);
+    auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+
+    auto p1 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+    auto p2 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[1]);
+    auto p3 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
 
     auto thenOp = std::make_shared<ov::op::v1::Add>(p1, p2);
     auto res1 = std::make_shared<ov::op::v0::Result>(thenOp);
@@ -46,8 +66,8 @@ void SimpleIfTest::SetUp() {
     auto ifOp = std::make_shared<ov::op::v8::If>(condOp);
     ifOp->set_then_body(thenBody);
     ifOp->set_else_body(elseBody);
-    ifOp->set_input(paramOuts[0], p1, p3);
-    ifOp->set_input(paramOuts[1], p2, nullptr);
+    ifOp->set_input(params[0], p1, p3);
+    ifOp->set_input(params[1], p2, nullptr);
     auto res = ifOp->set_output(res1, res2);
 
     ov::ResultVector results{std::make_shared<ov::op::v0::Result>(res)};
@@ -55,19 +75,18 @@ void SimpleIfTest::SetUp() {
 }
 
 void SimpleIf2OutTest::SetUp() {
-    std::vector<std::vector<size_t>> inputShapes;
-    auto netPrecision = InferenceEngine::Precision::UNSPECIFIED;
+    std::vector<ov::test::InputShape> shapes;
+    ov::test::ElementType inType;
     bool condition;
-    std::tie(inputShapes, netPrecision, condition, targetDevice) = this->GetParam();
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-    auto params = ngraph::builder::makeParams(ngPrc, {inputShapes[0], inputShapes[1]});
-    auto paramOuts = ngraph::helpers::convert2OutputVector(
-            ngraph::helpers::castOps2Nodes<ov::op::v0::Parameter>(params));
+    std::tie(shapes, inType, condition, targetDevice) = this->GetParam();
 
-    auto p1 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[0]));
-    auto p2 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[1]));
-    auto p3 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[0]));
-    auto p4 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[1]));
+    init_input_shapes(shapes);
+    auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+
+    auto p1 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+    auto p2 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[1]);
+    auto p3 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+    auto p4 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[1]);
 
     auto thenOp = std::make_shared<ov::op::v1::Add>(p1, p2);
     auto res1 = std::make_shared<ov::op::v0::Result>(thenOp);
@@ -82,8 +101,8 @@ void SimpleIf2OutTest::SetUp() {
     auto ifOp = std::make_shared<ov::op::v8::If>(condOp);
     ifOp->set_then_body(thenBody);
     ifOp->set_else_body(elseBody);
-    ifOp->set_input(paramOuts[0], p1, p3);
-    ifOp->set_input(paramOuts[1], p2, p4);
+    ifOp->set_input(params[0], p1, p3);
+    ifOp->set_input(params[1], p2, p4);
     auto ifRes1 = ifOp->set_output(res1, res3);
     auto ifRes2 = ifOp->set_output(res2, res4);
 
@@ -92,22 +111,20 @@ void SimpleIf2OutTest::SetUp() {
 }
 
 void SimpleIfNotConstConditionTest::SetUp() {
-    std::vector<std::vector<size_t>> inputShapes;
-    auto netPrecision = InferenceEngine::Precision::UNSPECIFIED;
-    std::tie(inputShapes, netPrecision, condition, targetDevice) = this->GetParam();
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-    ov::ParameterVector params {
-        ngraph::builder::makeParams(ngPrc, {inputShapes[0]})[0],
-        ngraph::builder::makeParams(ngPrc, {inputShapes[1]})[0],
-        ngraph::builder::makeParams(ov::element::boolean, { {"condition", {1}} })[0]
-    };
-    auto paramOuts = ngraph::helpers::convert2OutputVector(
-            ngraph::helpers::castOps2Nodes<ov::op::v0::Parameter>(params));
+    std::vector<ov::test::InputShape> shapes;
+    ov::test::ElementType inType;
+    std::tie(shapes, inType, condition, targetDevice) = this->GetParam();
 
-    auto p1 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[0]));
-    auto p2 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[1]));
-    auto p3 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[0]));
-    auto p4 = std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::PartialShape(inputShapes[1]));
+    init_input_shapes(shapes);
+    for (auto &target : targetStaticShapes)
+        target.emplace_back(ov::Shape{});
+    auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+    params.emplace_back(std::make_shared<ov::op::v0::Parameter>(ov::element::Type_t::boolean, ov::Shape{}));
+
+    auto p1 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+    auto p2 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[1]);
+    auto p3 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+    auto p4 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[1]);
 
     auto thenOp = std::make_shared<ov::op::v1::Add>(p1, p2);
     auto res1 = std::make_shared<ov::op::v0::Result>(thenOp);
@@ -118,11 +135,11 @@ void SimpleIfNotConstConditionTest::SetUp() {
     auto thenBody = std::make_shared<ov::Model>(ov::OutputVector{res1, res2}, ov::ParameterVector{p1, p2});
     auto elseBody = std::make_shared<ov::Model>(ov::OutputVector{res3, res4}, ov::ParameterVector{p3, p4});
 
-    auto ifOp = std::make_shared<ov::op::v8::If>(paramOuts[2]);
+    auto ifOp = std::make_shared<ov::op::v8::If>(params[2]);
     ifOp->set_then_body(thenBody);
     ifOp->set_else_body(elseBody);
-    ifOp->set_input(paramOuts[0], p1, p3);
-    ifOp->set_input(paramOuts[1], p2, p4);
+    ifOp->set_input(params[0], p1, p3);
+    ifOp->set_input(params[1], p2, p4);
     auto ifRes1 = ifOp->set_output(res1, res3);
     auto ifRes2 = ifOp->set_output(res2, res4);
 
@@ -130,13 +147,107 @@ void SimpleIfNotConstConditionTest::SetUp() {
     function = std::make_shared<ov::Model>(results, params, "SimpleIfNotConstConditionTest");
 }
 
-InferenceEngine::Blob::Ptr SimpleIfNotConstConditionTest::GenerateInput(const InferenceEngine::InputInfo& info) const {
-    if (info.name() == "condition") {
-        bool conditionArr[1] = { condition };
-        return FuncTestUtils::createAndFillBlobWithFloatArray(info.getTensorDesc(), conditionArr, 1);
-    } else {
-        return FuncTestUtils::createAndFillBlob(info.getTensorDesc());
+void SimpleIfNotConstConditionTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
+    inputs.clear();
+    const auto& funcInputs = function->inputs();
+    for (size_t i = 0; i < funcInputs.size(); ++i) {
+        const auto& funcInput = funcInputs[i];
+        ov::runtime::Tensor tensor;
+
+        if (i + 1 == funcInputs.size()) {
+            tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+            auto *dataPtr = tensor.data<bool>();
+            dataPtr[0] = condition;
+        } else {
+            tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], 10, -5);
+        }
+
+        inputs.insert({funcInput.get_node_shared_ptr(), tensor});
     }
 }
 
+void SimpleIfNotConstConditionAndInternalDynamismTest::SetUp() {
+    std::vector<ov::test::InputShape> shapes;
+    ov::test::ElementType inType;
+    std::tie(shapes, inType, condition, targetDevice) = this->GetParam();
+
+    init_input_shapes(shapes);
+    for (auto &target : targetStaticShapes)
+        target.emplace_back(ov::Shape{});
+    auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+    params.emplace_back(std::make_shared<ov::op::v0::Parameter>(ov::element::Type_t::boolean, ov::Shape{}));
+
+    auto p1 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+    auto p2 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+
+    // then body
+    auto thenOp_0 = std::make_shared<ov::op::v3::NonZero>(p1, ov::element::i32);
+    auto thenOp_1 = std::make_shared<ov::op::v0::Convert>(thenOp_0, inType);
+    auto thenRes_0 = std::make_shared<ov::op::v0::Result>(p1);
+    auto thenRes_1 = std::make_shared<ov::op::v0::Result>(thenOp_1);
+    auto thenBody = std::make_shared<ov::Model>(ov::OutputVector{thenRes_0, thenRes_1}, ov::ParameterVector{p1});
+
+    // else body
+    auto add_const = std::make_shared<ov::op::v0::Constant>(inType, ov::Shape{}, std::vector<float>{ 2 });
+    auto elseOp_0 = std::make_shared<ov::op::v1::Add>(p2, add_const);
+    auto elseOp_1 = std::make_shared<ov::op::v3::NonZero>(elseOp_0, ov::element::i32);
+    auto elseOp_2 = std::make_shared<ov::op::v0::Convert>(elseOp_1, inType);
+    auto elseRes_0 = std::make_shared<ov::op::v0::Result>(elseOp_0);
+    auto elseRes_1 = std::make_shared<ov::op::v0::Result>(elseOp_2);
+    auto elseBody = std::make_shared<ov::Model>(ov::OutputVector{elseRes_0, elseRes_1}, ov::ParameterVector{p2});
+
+    auto ifOp = std::make_shared<ov::op::v8::If>(params[1]);
+    ifOp->set_then_body(thenBody);
+    ifOp->set_else_body(elseBody);
+    ifOp->set_input(params[0], p1, p2);
+    auto ifRes_0 = ifOp->set_output(thenRes_0, elseRes_0);
+    auto ifRes_1 = ifOp->set_output(thenRes_1, elseRes_1);
+
+    ov::ResultVector results{std::make_shared<ov::op::v0::Result>(ifRes_0), std::make_shared<ov::op::v0::Result>(ifRes_1)};
+    function = std::make_shared<ov::Model>(results, params, "SimpleIfNotConstConditionAndInternalDynamismTest");
+}
+
+void SimpleIfNotConstConditionAndDimsIncreaseTest::SetUp() {
+    std::vector<ov::test::InputShape> shapes;
+    ov::test::ElementType inType;
+    std::tie(shapes, inType, condition, targetDevice) = this->GetParam();
+
+    init_input_shapes(shapes);
+    for (auto &target : targetStaticShapes)
+        target.emplace_back(ov::Shape{});
+    auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+    params.emplace_back(std::make_shared<ov::op::v0::Parameter>(ov::element::Type_t::boolean, ov::Shape{}));
+
+    auto p1 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+    auto p2 = std::make_shared<ov::op::v0::Parameter>(inType, inputDynamicShapes[0]);
+
+    // then body
+    const std::vector<int64_t> pads(p1->get_partial_shape().rank().get_length(), 2);
+    auto thenOp = ngraph::builder::makePad(p1, pads, pads, 0, ngraph::helpers::PadMode::CONSTANT);
+    auto thenRes = std::make_shared<ov::op::v0::Result>(thenOp);
+    auto thenBody = std::make_shared<ov::Model>(ov::OutputVector{thenRes}, ov::ParameterVector{p1});
+
+    // else body
+    auto elseRes = std::make_shared<ov::op::v0::Result>(p2);
+    auto elseBody = std::make_shared<ov::Model>(ov::OutputVector{elseRes}, ov::ParameterVector{p2});
+
+    auto ifOp = std::make_shared<ov::op::v8::If>(params[1]);
+    ifOp->set_then_body(thenBody);
+    ifOp->set_else_body(elseBody);
+    ifOp->set_input(params[0], p1, p2);
+    auto ifRes = ifOp->set_output(thenRes, elseRes);
+
+    function = std::make_shared<ov::Model>(ov::ResultVector{std::make_shared<ov::op::v0::Result>(ifOp)},
+                                           params, "SimpleIfNotConstConditionAndDimsIncreaseTest");
+}
+
+void SimpleIfNotConstConditionAndDimsIncreaseTest::compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) {
+    const auto shape = targetStaticShapes[inferNum++].front();
+    if (!condition && std::any_of(shape.begin(), shape.end(), [](size_t dim) { return dim == 0; })) {
+        return;
+    }
+
+    SubgraphBaseTest::compare(expected, actual);
+}
+
 } // namespace SubgraphTestsDefinitions

From 6dcbb137486eede34aa93383f26f94c1e1c851a5 Mon Sep 17 00:00:00 2001
From: Anton Pankratov <anton.pankratov@intel.com>
Date: Fri, 24 Dec 2021 21:14:41 +0300
Subject: [PATCH 150/160] Removed implicit cast from runtime objects (#9419)

---
 src/core/include/openvino/runtime/tensor.hpp     | 11 -----------
 .../openvino/runtime/intel_gpu/ocl/dx.hpp        | 10 +++++-----
 .../openvino/runtime/intel_gpu/ocl/ocl.hpp       | 16 ++++++++--------
 .../openvino/runtime/intel_gpu/ocl/va.hpp        |  6 +++---
 .../include/openvino/runtime/remote_context.hpp  | 11 -----------
 .../inference_engine/ov_remote_tensor.cpp        |  2 --
 6 files changed, 16 insertions(+), 40 deletions(-)

diff --git a/src/core/include/openvino/runtime/tensor.hpp b/src/core/include/openvino/runtime/tensor.hpp
index 8dd2eb44a3b..c47aafe684e 100644
--- a/src/core/include/openvino/runtime/tensor.hpp
+++ b/src/core/include/openvino/runtime/tensor.hpp
@@ -207,17 +207,6 @@ public:
         T::type_check(*this);
         return *static_cast<const T*>(this);
     }
-
-    /**
-     * @brief Casts this Tensor object to the type T.
-     *
-     * @tparam T Type to cast to. Must represent a class derived from the Tensor
-     * @return T object
-     */
-    template <typename T, typename = typename std::enable_if<std::is_base_of<Tensor, T>::value>::type>
-    operator T() const {
-        return as<T>();
-    }
 };
 
 using TensorVector = std::vector<Tensor>;
diff --git a/src/inference/include/openvino/runtime/intel_gpu/ocl/dx.hpp b/src/inference/include/openvino/runtime/intel_gpu/ocl/dx.hpp
index b920edd2988..61a0ff77012 100644
--- a/src/inference/include/openvino/runtime/intel_gpu/ocl/dx.hpp
+++ b/src/inference/include/openvino/runtime/intel_gpu/ocl/dx.hpp
@@ -70,7 +70,7 @@ public:
      * @brief Checks that type defined runtime paramters are presented in remote object
      * @param remote_tensor remote tensor to check
      */
-    static void type_check(const RemoteTensor& remote_tensor) {
+    static void type_check(const Tensor& remote_tensor) {
         RemoteTensor::type_check(remote_tensor,
                                  {{GPU_PARAM_KEY(DEV_OBJECT_HANDLE), {}},
                                   {GPU_PARAM_KEY(VA_PLANE), {}},
@@ -138,7 +138,7 @@ public:
             {GPU_PARAM_KEY(VA_DEVICE), static_cast<gpu_handle_param>(device)},
             {GPU_PARAM_KEY(TILE_ID), target_tile_id}
         };
-        *this = core.create_context(device_name, context_params);
+        *this = core.create_context(device_name, context_params).as<D3DContext>();
     }
 
     /**
@@ -157,7 +157,7 @@ public:
         tensor_params[GPU_PARAM_KEY(MEM_HANDLE)] = static_cast<gpu_handle_param>(nv12_surf);
         tensor_params[GPU_PARAM_KEY(VA_PLANE)] = uint32_t(1);
         auto uv_tensor = create_tensor(element::u8, {1, 2, height / 2, width / 2}, tensor_params);
-        return std::make_pair(y_tensor, uv_tensor);
+        return std::make_pair(y_tensor.as<D3DSurface2DTensor>(), uv_tensor.as<D3DSurface2DTensor>());
     }
 
     /**
@@ -170,7 +170,7 @@ public:
     D3DBufferTensor create_tensor(const element::Type type, const Shape& shape, ID3D11Buffer* buffer) {
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(DX_BUFFER)},
                            {GPU_PARAM_KEY(DEV_OBJECT_HANDLE), static_cast<gpu_handle_param>(buffer)}};
-        create_tensor(type, shape, params);
+        create_tensor(type, shape, params).as<D3DBufferTensor>();
     }
 
     /**
@@ -189,7 +189,7 @@ public:
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(VA_SURFACE)},
                            {GPU_PARAM_KEY(DEV_OBJECT_HANDLE), static_cast<gpu_handle_param>(surface)},
                            {GPU_PARAM_KEY(VA_PLANE), plane}};
-        return create_tensor(type, shape, params);
+        return create_tensor(type, shape, params).as<D3DSurface2DTensor>();
     }
 };
 }  // namespace ocl
diff --git a/src/inference/include/openvino/runtime/intel_gpu/ocl/ocl.hpp b/src/inference/include/openvino/runtime/intel_gpu/ocl/ocl.hpp
index 42565232897..c311092cbd7 100644
--- a/src/inference/include/openvino/runtime/intel_gpu/ocl/ocl.hpp
+++ b/src/inference/include/openvino/runtime/intel_gpu/ocl/ocl.hpp
@@ -183,7 +183,7 @@ public:
         ParamMap context_params = {{GPU_PARAM_KEY(CONTEXT_TYPE), GPU_PARAM_VALUE(OCL)},
                                    {GPU_PARAM_KEY(OCL_CONTEXT), static_cast<gpu_handle_param>(ctx)},
                                    {GPU_PARAM_KEY(OCL_CONTEXT_DEVICE_ID), ctx_device_id}};
-        *this = core.create_context(device_name, context_params);
+        *this = core.create_context(device_name, context_params).as<ClContext>();
     }
 
     /**
@@ -200,7 +200,7 @@ public:
         ParamMap context_params = {{GPU_PARAM_KEY(CONTEXT_TYPE), GPU_PARAM_VALUE(OCL)},
                                    {GPU_PARAM_KEY(OCL_CONTEXT), static_cast<gpu_handle_param>(ctx)},
                                    {GPU_PARAM_KEY(OCL_QUEUE), static_cast<gpu_handle_param>(queue)}};
-        *this = core.create_context(device_name, context_params);
+        *this = core.create_context(device_name, context_params).as<ClContext>();
     }
 
     /**
@@ -243,7 +243,7 @@ public:
         auto y_tensor = create_tensor(element::u8, {1, 1, height, width}, tensor_params);
         tensor_params[GPU_PARAM_KEY(MEM_HANDLE)] = static_cast<gpu_handle_param>(nv12_image_plane_uv.get());
         auto uv_tensor = create_tensor(element::u8, {1, 2, height / 2, width / 2}, tensor_params);
-        return std::make_pair(y_tensor, uv_tensor);
+        return std::make_pair(y_tensor.as<ClImage2DTensor>(), uv_tensor.as<ClImage2DTensor>());
     }
 
     /**
@@ -256,7 +256,7 @@ public:
     ClBufferTensor create_tensor(const element::Type type, const Shape& shape, const cl_mem buffer) {
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(OCL_BUFFER)},
                            {GPU_PARAM_KEY(MEM_HANDLE), static_cast<gpu_handle_param>(buffer)}};
-        return create_tensor(type, shape, params);
+        return create_tensor(type, shape, params).as<ClBufferTensor>();
     }
 
     /**
@@ -280,7 +280,7 @@ public:
     ClImage2DTensor create_tensor(const element::Type type, const Shape& shape, const cl::Image2D& image) {
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(OCL_IMAGE2D)},
                            {GPU_PARAM_KEY(MEM_HANDLE), static_cast<gpu_handle_param>(image.get())}};
-        return create_tensor(type, shape, params);
+        return create_tensor(type, shape, params).as<ClImage2DTensor>();
     }
 
     /**
@@ -293,7 +293,7 @@ public:
     USMTensor create_tensor(const element::Type type, const Shape& shape, void* usm_ptr) {
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(USM_USER_BUFFER)},
                            {GPU_PARAM_KEY(MEM_HANDLE), static_cast<gpu_handle_param>(usm_ptr)}};
-        return create_tensor(type, shape, params);
+        return create_tensor(type, shape, params).as<USMTensor>();
     }
 
     /**
@@ -304,7 +304,7 @@ public:
      */
     USMTensor create_usm_host_tensor(const element::Type type, const Shape& shape) {
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(USM_HOST_BUFFER)}};
-        return create_tensor(type, shape, params);
+        return create_tensor(type, shape, params).as<USMTensor>();
     }
 
     /**
@@ -315,7 +315,7 @@ public:
      */
     USMTensor create_usm_device_tensor(const element::Type type, const Shape& shape) {
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(USM_DEVICE_BUFFER)}};
-        return create_tensor(type, shape, params);
+        return create_tensor(type, shape, params).as<USMTensor>();
     }
 };
 
diff --git a/src/inference/include/openvino/runtime/intel_gpu/ocl/va.hpp b/src/inference/include/openvino/runtime/intel_gpu/ocl/va.hpp
index ed846ce0bde..335038e55be 100644
--- a/src/inference/include/openvino/runtime/intel_gpu/ocl/va.hpp
+++ b/src/inference/include/openvino/runtime/intel_gpu/ocl/va.hpp
@@ -105,7 +105,7 @@ public:
         ParamMap context_params = {{GPU_PARAM_KEY(CONTEXT_TYPE), GPU_PARAM_VALUE(VA_SHARED)},
                                    {GPU_PARAM_KEY(VA_DEVICE), static_cast<gpu_handle_param>(device)},
                                    {GPU_PARAM_KEY(TILE_ID), target_tile_id}};
-        *this = core.create_context(device_name, context_params);
+        *this = core.create_context(device_name, context_params).as<VAContext>();
     }
 
     /**
@@ -125,7 +125,7 @@ public:
         auto y_tensor = create_tensor(element::u8, {1, 1, height, width}, tensor_params);
         tensor_params[GPU_PARAM_KEY(VA_PLANE)] = uint32_t(1);
         auto uv_tensor = create_tensor(element::u8, {1, 2, height / 2, width / 2}, tensor_params);
-        return std::make_pair(y_tensor, uv_tensor);
+        return std::make_pair(y_tensor.as<VASurfaceTensor>(), uv_tensor.as<VASurfaceTensor>());
     }
 
     /**
@@ -143,7 +143,7 @@ public:
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(VA_SURFACE)},
                            {GPU_PARAM_KEY(DEV_OBJECT_HANDLE), surface},
                            {GPU_PARAM_KEY(VA_PLANE), plane}};
-        return create_tensor(type, shape, params);
+        return create_tensor(type, shape, params).as<VASurfaceTensor>();
     }
 };
 }  // namespace ocl
diff --git a/src/inference/include/openvino/runtime/remote_context.hpp b/src/inference/include/openvino/runtime/remote_context.hpp
index 9b2a0e9c9d8..aa4942f6cd7 100644
--- a/src/inference/include/openvino/runtime/remote_context.hpp
+++ b/src/inference/include/openvino/runtime/remote_context.hpp
@@ -118,17 +118,6 @@ public:
         return *static_cast<const T*>(this);
     }
 
-    /**
-     * @brief Casts this RemoteContext object to the type T.
-     *
-     * @tparam T Type to cast to. Must represent a class derived from the RemoteContext
-     * @return T object
-     */
-    template <typename T>
-    operator T() const {
-        return as<T>();
-    }
-
     /**
      * @brief Returns name of the device on which underlying object is allocated.
      * Abstract method.
diff --git a/src/tests/functional/inference_engine/ov_remote_tensor.cpp b/src/tests/functional/inference_engine/ov_remote_tensor.cpp
index 68370f5aca9..9f7ca747cbe 100644
--- a/src/tests/functional/inference_engine/ov_remote_tensor.cpp
+++ b/src/tests/functional/inference_engine/ov_remote_tensor.cpp
@@ -23,7 +23,6 @@ TEST(RemoteTensorOVTests, throwsOnGetDeviceName) {
 TEST(RemoteTensorOVTests, remoteTensorFromEmptyTensorThrow) {
     ov::runtime::Tensor empty_tensor;
     ov::runtime::RemoteTensor remote_tensor;
-    ASSERT_THROW(remote_tensor = empty_tensor, ov::Exception);
     ASSERT_FALSE(empty_tensor.is<ov::runtime::RemoteTensor>());
     ASSERT_THROW(empty_tensor.as<ov::runtime::RemoteTensor>(), ov::Exception);
 }
@@ -31,7 +30,6 @@ TEST(RemoteTensorOVTests, remoteTensorFromEmptyTensorThrow) {
 TEST(RemoteTensorOVTests, remoteTensorConvertToRemoteThrow) {
     ov::runtime::Tensor tensor{ov::element::f32, {1, 2, 3, 4}};
     ov::runtime::RemoteTensor remote_tensor;
-    ASSERT_THROW(remote_tensor = tensor, ov::Exception);
     ASSERT_FALSE(tensor.is<ov::runtime::RemoteTensor>());
     ASSERT_THROW(tensor.as<ov::runtime::RemoteTensor>(), ov::Exception);
 }

From acfae31759304c6ade9f2f64fad1aeba4161d7f3 Mon Sep 17 00:00:00 2001
From: Roman Kazantsev <roman.kazantsev@intel.com>
Date: Sat, 25 Dec 2021 10:32:13 +0300
Subject: [PATCH 151/160] Extend FIFOQueueDequeue replacer to support OOB case
 (#9428)

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
---
 .../tools/mo/front/tf/fifo_replacer.py        | 13 +++--
 .../mo/front/tf/fifo_replacer_test.py         | 58 ++++++++++++-------
 2 files changed, 45 insertions(+), 26 deletions(-)

diff --git a/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py b/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py
index 8b537db8e5e..f0d951afede 100644
--- a/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py
+++ b/tools/mo/openvino/tools/mo/front/tf/fifo_replacer.py
@@ -6,14 +6,13 @@ from collections import defaultdict
 
 import numpy as np
 
+from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph, FrontReplacementPattern
 from openvino.tools.mo.front.extractor import add_input_ops
 from openvino.tools.mo.front.output_cut import OutputCut
 from openvino.tools.mo.front.user_data_repack import UserDataRepack
-from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_precision, SUPPORTED_DATA_TYPES
-
-from openvino.tools.mo.ops.parameter import Parameter
-from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph, FrontReplacementPattern
 from openvino.tools.mo.graph.graph import Graph, Node
+from openvino.tools.mo.middle.passes.convert_data_type import np_data_type_to_precision, SUPPORTED_DATA_TYPES
+from openvino.tools.mo.ops.parameter import Parameter
 from openvino.tools.mo.utils.error import Error
 
 
@@ -61,8 +60,10 @@ class FIFOQueue(FrontReplacementSubgraph):
         true_placeholder_shape = match['placeholder'].shape
         placeholder_shape = match['fifo_queue'].shapes[0]
         placeholder_data_type = match['fifo_queue'].types[0]
-        assert true_placeholder_shape.ndim <= 1
-        if true_placeholder_shape.ndim == 1 and len(true_placeholder_shape) > 1:
+        # in case OOB conversion batch_size placeholder shape is not required
+        # so use a shape specified in FIFOQueueV2 shapes list attribute
+        assert true_placeholder_shape is None or true_placeholder_shape.ndim <= 1
+        if true_placeholder_shape is not None and true_placeholder_shape.ndim == 1 and len(true_placeholder_shape) > 1:
             log.warning(
                 'Placeholder \'{}\' got non 0-dimensional shape {} in FIFOQueue pattern. Placeholder will have the '
                 'same shape after folding the pattern instead of {} shape which is original for the network.'
diff --git a/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py b/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
index 9e601554bd7..48b87bfc446 100644
--- a/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
+++ b/tools/mo/unit_tests/mo/front/tf/fifo_replacer_test.py
@@ -11,25 +11,43 @@ from openvino.tools.mo.utils.ir_engine.compare_graphs import compare_graphs
 from unit_tests.utils.graph import build_graph_with_edge_attrs
 
 
+def create_fifo_queue_graph(batch_size_shape: np.ndarray):
+    nodes = {
+        'placeholder': {'op': 'Parameter', 'data_type': np.int32, 'kind': 'op', 'shape': batch_size_shape},
+        'batch_join/fifo_queue': {'op': 'FIFOQueueV2', 'name': 'batch_join/fifo_queue',
+                                  'shapes': np.array([[1, 2, 3]]), 'types': np.array([np.float32]), 'kind': 'op'},
+        'batch_join': {'op': 'QueueDequeueUpToV2', 'kind': 'op'},
+        'image_batch': {'op': 'Identity', 'data_type': np.float32, 'kind': 'op'},
+        'label_batch': {'op': 'Identity', 'kind': 'op'},
+        'label_batch_op_output': {'op': 'Result', 'kind': 'op'},
+    }
+    edges = [
+        ('placeholder', 'batch_join', {'out': 0, 'in': 0}),
+        ('batch_join/fifo_queue', 'batch_join', {'out': 0, 'in': 1}),
+        ('batch_join', 'image_batch', {'out': 0, 'in': 0}),
+        ('batch_join', 'label_batch', {'out': 1, 'in': 0}),
+        ('label_batch', 'label_batch_op_output', {'out': 0, 'in': 0})
+    ]
+    graph = build_graph_with_edge_attrs(nodes, edges)
+    return graph
+
+
 class TestFIFOQueueReplacement(unittest.TestCase):
     def test_fifo_with_label_batch(self):
-        nodes = {
-            'placeholder': {'op': 'Parameter', 'data_type': np.int32, 'kind': 'op', 'shape': np.array(1)},
-            'batch_join/fifo_queue': {'op': 'FIFOQueueV2', 'name': 'batch_join/fifo_queue',
-                                      'shapes': np.array([[1, 2, 3]]), 'types': np.array([np.float32]), 'kind': 'op'},
-            'batch_join': {'op': 'QueueDequeueUpToV2', 'kind': 'op'},
-            'image_batch': {'op': 'Identity', 'data_type': np.float32, 'kind': 'op'},
-            'label_batch': {'op': 'Identity', 'kind': 'op'},
-            'label_batch_op_output': {'op': 'Result', 'kind': 'op'},
-        }
-        edges = [
-            ('placeholder', 'batch_join', {'out': 0, 'in': 0}),
-            ('batch_join/fifo_queue', 'batch_join', {'out': 0, 'in': 1}),
-            ('batch_join', 'image_batch', {'out': 0, 'in': 0}),
-            ('batch_join', 'label_batch', {'out': 1, 'in': 0}),
-            ('label_batch', 'label_batch_op_output', {'out': 0, 'in': 0})
-        ]
-        graph = build_graph_with_edge_attrs(nodes, edges)
+        graph = create_fifo_queue_graph(shape_array([1]))
+        tested_class = FIFOQueue()
+        tested_class.find_and_replace_pattern(graph=graph)
+        after_pattern = graph.nodes()
+        self.assertEqual(2, len(after_pattern))
+        try:
+            new_ph_dict = graph.node[[u for u, v in graph.in_edges('image_batch')][0]]
+        except Exception as e:
+            self.fail("Can't get new placeholder. Broken edge. Additional information: {}".format(e))
+        self.assertEqual(new_ph_dict['name'], 'batch_join/fifo_queue')
+        self.assertTrue(np.array_equal(new_ph_dict['shape'], [1, 2, 3]))
+
+    def test_fifo_with_undefined_batch_size(self):
+        graph = create_fifo_queue_graph(None)
         tested_class = FIFOQueue()
         tested_class.find_and_replace_pattern(graph=graph)
         after_pattern = graph.nodes()
@@ -128,7 +146,7 @@ class FIFOQueueDequeueCutTest(unittest.TestCase):
             {
                 'queue_dequeue': {'kind': 'op', 'op': 'QueueDequeue', 'shapes': [shape_array([2, 2]),
                                                                                  shape_array([1, 1])],
-                                 'types': [np.int32, np.float32]},
+                                  'types': [np.int32, np.float32]},
                 'sub': {'kind': 'op', 'op': 'Sub'},
                 'add': {'kind': 'op', 'op': 'Add'},
                 'concat': {'kind': 'op', 'op': 'Concat'}
@@ -167,7 +185,7 @@ class FIFOQueueDequeueCutTest(unittest.TestCase):
             {
                 'queue_dequeue': {'kind': 'op', 'op': 'QueueDequeueV2', 'shapes': [shape_array([2, 2]),
                                                                                    shape_array([1, 1])],
-                                 'types': [np.int32, np.float32]},
+                                  'types': [np.int32, np.float32]},
                 'sub': {'kind': 'op', 'op': 'Sub'},
                 'add': {'kind': 'op', 'op': 'Add'},
                 'concat': {'kind': 'op', 'op': 'Concat'}
@@ -199,4 +217,4 @@ class FIFOQueueDequeueCutTest(unittest.TestCase):
         FIFOQueueDequeueCut().find_and_replace_pattern(graph)
 
         flag, msg = compare_graphs(graph, graph_ref, last_node='concat', check_op_attrs=True)
-        self.assertTrue(flag, msg)
\ No newline at end of file
+        self.assertTrue(flag, msg)

From 73143b8c03fda00f60df2f04e491e43e0274db07 Mon Sep 17 00:00:00 2001
From: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>
Date: Sat, 25 Dec 2021 12:42:55 +0300
Subject: [PATCH 152/160] Add batch plugin to openvino wheel (#9432)

---
 inference-engine/ie_bridges/python/wheel/setup.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/inference-engine/ie_bridges/python/wheel/setup.py b/inference-engine/ie_bridges/python/wheel/setup.py
index eb8d573dfba..efa7e8a90d9 100644
--- a/inference-engine/ie_bridges/python/wheel/setup.py
+++ b/inference-engine/ie_bridges/python/wheel/setup.py
@@ -78,6 +78,11 @@ LIB_INSTALL_CFG = {
         'install_dir': OV_RUNTIME_LIBS_DIR,
         'rpath': LIBS_RPATH,
     },
+    'batch_plugin': {
+        'name': 'batch',
+        'prefix': 'libs.core',
+        'install_dir': OV_RUNTIME_LIBS_DIR,
+    },
     'tbb_libs': {
         'name': 'tbb',
         'prefix': 'libs.tbb',

From 2262692ce9ef1bac11f1498b41092724e34e6c62 Mon Sep 17 00:00:00 2001
From: Artur Kulikowski <artur.kulikowski@intel.com>
Date: Sun, 26 Dec 2021 17:12:01 +0100
Subject: [PATCH 153/160] Generate result names for ONNX models (#9413)

---
 src/frontends/onnx/frontend/src/core/graph.cpp | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/frontends/onnx/frontend/src/core/graph.cpp b/src/frontends/onnx/frontend/src/core/graph.cpp
index 212766fc550..be2f5799ca4 100644
--- a/src/frontends/onnx/frontend/src/core/graph.cpp
+++ b/src/frontends/onnx/frontend/src/core/graph.cpp
@@ -31,6 +31,12 @@ static std::string to_string(
     return result;
 }
 
+inline std::string generate_result_name(const std::string& onnx_output_name,
+                                        const std::shared_ptr<ov::Node>& result_node) {
+    auto output_index = result_node->input(0).get_source_output().get_index();
+    return onnx_output_name + "/sink_port_" + std::to_string(output_index);
+}
+
 /// \brief      Gets the operator represented by provided node unique identificator.
 ///
 /// \param[in]  node_proto  The node protobuf representation object.
@@ -232,8 +238,8 @@ std::shared_ptr<Function> Graph::create_function() {
     const auto& onnx_outputs = m_model->get_graph().output();
     for (std::size_t i{0}; i < function->get_output_size(); ++i) {
         // the suffix makes the Result's name unique in case the nodes in the model don't have a name
-        const auto suffix = "/sink_port_" + std::to_string(i);
-        function->get_output_op(i)->set_friendly_name(onnx_outputs.Get(i).name() + suffix);
+        auto ov_result = function->get_output_op(i);
+        ov_result->set_friendly_name(detail::generate_result_name(onnx_outputs.Get(i).name(), ov_result));
     }
     return function;
 }

From b454076a56ce84de4145a4b1d07bf4dfae30b92f Mon Sep 17 00:00:00 2001
From: Egor Shulman <egor.shulman@intel.com>
Date: Sun, 26 Dec 2021 21:25:33 +0300
Subject: [PATCH 154/160] [CPU] Fixed leftovers for
 ExperimentalDetectronTopKROIs and klocwork issue (#7885)

---
 .../mkldnn_experimental_detectron_detection_output_node.h     | 2 --
 ...erimental_detectron_generate_proposals_single_image_node.h | 2 --
 .../mkldnn_experimental_detectron_roifeatureextractor_node.h  | 2 --
 .../src/nodes/mkldnn_experimental_detectron_topkrois_node.cpp | 4 ++--
 src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.h            | 2 --
 5 files changed, 2 insertions(+), 10 deletions(-)

diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.h
index eeb9ccb9399..f918c3bc9ed 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.h
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_detection_output_node.h
@@ -41,8 +41,6 @@ private:
     int max_detections_per_image_;
     bool class_agnostic_box_regression_;
     std::vector<float> deltas_weights_;
-
-    std::string errorPrefix;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
index d00ec14601c..d617e928326 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h
@@ -45,8 +45,6 @@ private:
     float coordinates_offset;
 
     std::vector<int> roi_indices_;
-
-    std::string errorPrefix;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
index 4ebf1467366..db194e86ced 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
@@ -36,8 +36,6 @@ private:
     std::vector<int64_t> pyramid_scales_;
     int sampling_ratio_ = 0;
     bool aligned_ = false;
-
-    std::string errorPrefix;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.cpp b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.cpp
index 3dc472dece8..2785cfbe0d1 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.cpp
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_experimental_detectron_topkrois_node.cpp
@@ -43,8 +43,8 @@ MKLDNNExperimentalDetectronTopKROIsNode::MKLDNNExperimentalDetectronTopKROIsNode
     if (inputShapes.size() != 2 || outputShapes.size() != 1)
         IE_THROW() << errorPrefix << " has incorrect number of input/output edges!";
 
-    if (getInputShapeAtPort(INPUT_ROIS).getDims().size() != 2 || getInputShapeAtPort(INPUT_PROBS).getDims().size() != 1)
-        IE_THROW() << errorPrefix << " has nsupported input shape";
+    if (getInputShapeAtPort(INPUT_ROIS).getRank() != 2 || getInputShapeAtPort(INPUT_PROBS).getRank() != 1)
+        IE_THROW() << errorPrefix << " has unsupported input shape";
 
     max_rois_num_ = topKROI->get_max_rois();
 }
diff --git a/src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.h b/src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.h
index 7d288c4fb8b..d24dab63860 100644
--- a/src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.h
+++ b/src/plugins/intel_cpu/src/nodes/mkldnn_topk_node.h
@@ -94,8 +94,6 @@ private:
     int dim = 0;
     int before_num = 0;
 
-    std::string errorPrefix;
-
 #if defined(HAVE_AVX512F)
     const int count_vec = 32;
 #elif defined(HAVE_SSE) || defined(HAVE_AVX2)

From a9cee5f10192a32637da343c7daf811551e39f8c Mon Sep 17 00:00:00 2001
From: Vladimir Dudnik <vladimir.dudnik@intel.com>
Date: Mon, 27 Dec 2021 09:19:18 +0300
Subject: [PATCH 155/160] [IE Samples] OV2.0 API python
 ngraph_function_creation_sample (#9440)

* [IE Python Speech Sample] Migrate to OV 2.0 API

* improvements

* flake notes

* improved code style like as C++

* linters changes

* changed data.py

* sync output with C++ sample

Co-authored-by: Maxim Gordeev <maxim.gordeev@intel.com>
---
 .../ngraph_function_creation_sample/data.py   | 420 ++++++++++++++++++
 .../ngraph_function_creation_sample.py        | 239 ++++------
 2 files changed, 514 insertions(+), 145 deletions(-)
 create mode 100644 samples/python/ngraph_function_creation_sample/data.py

diff --git a/samples/python/ngraph_function_creation_sample/data.py b/samples/python/ngraph_function_creation_sample/data.py
new file mode 100644
index 00000000000..7fb968577fc
--- /dev/null
+++ b/samples/python/ngraph_function_creation_sample/data.py
@@ -0,0 +1,420 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+import numpy
+
+digits = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x0b, 0x96, 0xfd, 0xca, 0x1f, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0x25, 0xfb, 0xfb, 0xfd, 0x6b, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0x15, 0xc5, 0xfb, 0xfb, 0xfd, 0x6b, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x6e, 0xbe, 0xfb,
+                       0xfb, 0xfb, 0xfd, 0xa9, 0x6d, 0x3e, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0xfd, 0xfb, 0xfb, 0xfb, 0xfb, 0xfd, 0xfb, 0xfb, 0xdc, 0x33, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0xb6, 0xff, 0xfd, 0xfd, 0xfd,
+                       0xfd, 0xea, 0xde, 0xfd, 0xfd, 0xfd, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0x3f, 0xdd, 0xfd, 0xfb, 0xfb, 0xfb, 0x93, 0x4d, 0x3e, 0x80, 0xfb, 0xfb, 0x69, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x20, 0xe7, 0xfb, 0xfd, 0xfb, 0xdc, 0x89, 0x0a,
+                       0, 0, 0x1f, 0xe6, 0xfb, 0xf3, 0x71, 0x05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0x25, 0xfb, 0xfb, 0xfd, 0xbc, 0x14, 0, 0, 0, 0, 0, 0x6d, 0xfb, 0xfd, 0xfb, 0x23, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x25, 0xfb, 0xfb, 0xc9, 0x1e, 0, 0, 0, 0,
+                       0, 0, 0x1f, 0xc8, 0xfd, 0xfb, 0x23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0x25, 0xfd, 0xfd, 0, 0, 0, 0, 0, 0, 0, 0, 0x20, 0xca, 0xff, 0xfd, 0xa4, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0x8c, 0xfb, 0xfb, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0x6d, 0xfb, 0xfd, 0xfb, 0x23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0xd9,
+                       0xfb, 0xfb, 0, 0, 0, 0, 0, 0, 0x15, 0x3f, 0xe7, 0xfb, 0xfd, 0xe6, 0x1e, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0xd9, 0xfb, 0xfb, 0, 0, 0, 0, 0, 0, 0x90, 0xfb,
+                       0xfb, 0xfb, 0xdd, 0x3d, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0xd9, 0xfb,
+                       0xfb, 0, 0, 0, 0, 0, 0xb6, 0xdd, 0xfb, 0xfb, 0xfb, 0xb4, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0xda, 0xfd, 0xfd, 0x49, 0x49, 0xe4, 0xfd, 0xfd, 0xff, 0xfd, 0xfd, 0xfd,
+                       0xfd, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x71, 0xfb, 0xfb,
+                       0xfd, 0xfb, 0xfb, 0xfb, 0xfb, 0xfd, 0xfb, 0xfb, 0xfb, 0x93, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0x1f, 0xe6, 0xfb, 0xfd, 0xfb, 0xfb, 0xfb, 0xfb, 0xfd, 0xe6, 0xbd, 0x23, 0x0a,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x3e, 0x8e, 0xfd,
+                       0xfb, 0xfb, 0xfb, 0xfb, 0xfd, 0x6b, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0x48, 0xae, 0xfb, 0xad, 0x47, 0x48, 0x1e, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0],
+
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x26, 0xfe, 0x6d, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x57, 0xfc, 0x52, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x87, 0xf1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x2d, 0xf4, 0x96, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x54, 0xfe, 0x3f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0xca, 0xdf, 0x0b, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x20, 0xfe, 0xd8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x5f, 0xfe, 0xc3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x8c, 0xfe, 0x4d, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x39, 0xed, 0xcd, 0x08, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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+                       0, 0, 0xd2, 0x2b, 0x46, 0xfe, 0xfe, 0xfe, 0x15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0x48, 0xf3, 0xfe, 0xe4, 0x36, 0, 0, 0, 0, 0x03, 0x20, 0x74, 0xe1, 0xf2, 0xfe, 0xff, 0xa2, 0x05, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0x4b, 0xf0, 0xfe, 0xdf, 0x6d, 0x8a, 0xb2, 0xb2, 0xa9, 0xd2,
+                       0xfb, 0xe7, 0xfe, 0xfe, 0xfe, 0xe8, 0x26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x09,
+                       0xaf, 0xf4, 0xfd, 0xff, 0xfe, 0xfe, 0xfb, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfc, 0xab, 0x19, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x10, 0x88, 0xc3, 0xb0, 0x92, 0x99, 0xc8, 0xfe, 0xfe,
+                       0xfe, 0xfe, 0x96, 0x10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0xa2, 0xfe, 0xfe, 0xf1, 0x63, 0x03, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x76, 0xfa, 0xfe, 0xfe, 0x5a,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0x64, 0xf2, 0xfe, 0xfe, 0xd3, 0x07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x36, 0xf1, 0xfe, 0xfe, 0xf2, 0x3b, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0x83, 0xfe, 0xfe, 0xf4, 0x40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0x0d, 0xf9, 0xfe, 0xfe, 0x98, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x0c, 0xe4,
+                       0xfe, 0xfe, 0xd0, 0x08, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0x4e, 0xff, 0xfe, 0xfe, 0x42, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0xd1, 0xfe, 0xfe,
+                       0x89, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0xe3, 0xff, 0xe9, 0x19, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0x71, 0xff, 0x6c, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       0, 0, 0, 0, 0]])
diff --git a/samples/python/ngraph_function_creation_sample/ngraph_function_creation_sample.py b/samples/python/ngraph_function_creation_sample/ngraph_function_creation_sample.py
index 47fc46353b5..70cd4089dc6 100755
--- a/samples/python/ngraph_function_creation_sample/ngraph_function_creation_sample.py
+++ b/samples/python/ngraph_function_creation_sample/ngraph_function_creation_sample.py
@@ -2,110 +2,67 @@
 # -*- coding: utf-8 -*-
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-import argparse
 import logging as log
-import struct as st
 import sys
 import typing
 from functools import reduce
 
-import cv2
-import ngraph
-from ngraph.opset1 import max_pool
 import numpy as np
-from openvino.inference_engine import IECore, IENetwork
+from openvino.preprocess import PrePostProcessor
+from openvino.runtime import Core, Layout, Type, Model, Shape, PartialShape
+import openvino
+from data import digits
 
 
-def parse_args() -> argparse.Namespace:
-    """Parse and return command line arguments"""
-    parser = argparse.ArgumentParser(add_help=False)
-    args = parser.add_argument_group('Options')
-    # fmt: off
-    args.add_argument('-h', '--help', action='help', help='Show this help message and exit.')
-    args.add_argument('-m', '--model', required=True, type=str,
-                      help='Required. Path to a file with network weights.')
-    args.add_argument('-i', '--input', required=True, type=str, nargs='+', help='Required. Path to an image file.')
-    args.add_argument('-d', '--device', default='CPU', type=str,
-                      help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
-                      'is acceptable. The sample will look for a suitable plugin for device specified. '
-                      'Default value is CPU.')
-    args.add_argument('--labels', default=None, type=str, help='Optional. Path to a labels mapping file.')
-    args.add_argument('-nt', '--number_top', default=10, type=int, help='Optional. Number of top results.')
-    # fmt: on
-    return parser.parse_args()
-
-
-def read_image(image_path: str) -> np.ndarray:
-    """Read and return an image as grayscale (one channel)"""
-    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-
-    # Try to open image as ubyte
-    if image is None:
-        with open(image_path, 'rb') as f:
-            st.unpack('>4B', f.read(4))  # need to skip  4 bytes
-            nimg = st.unpack('>I', f.read(4))[0]  # number of images
-            nrow = st.unpack('>I', f.read(4))[0]  # number of rows
-            ncolumn = st.unpack('>I', f.read(4))[0]  # number of column
-            nbytes = nimg * nrow * ncolumn * 1  # each pixel data is 1 byte
-
-            if nimg != 1:
-                raise Exception('Sample supports ubyte files with 1 image inside')
-
-            image = np.asarray(st.unpack('>' + 'B' * nbytes, f.read(nbytes))).reshape((nrow, ncolumn))
-
-    return image
-
-
-def create_ngraph_function(args: argparse.Namespace) -> ngraph.impl.Function:
+def create_ngraph_function(model_path: str) -> Model:
     """Create a network on the fly from the source code using ngraph"""
 
     def shape_and_length(shape: list) -> typing.Tuple[list, int]:
         length = reduce(lambda x, y: x * y, shape)
         return shape, length
 
-    weights = np.fromfile(args.model, dtype=np.float32)
+    weights = np.fromfile(model_path, dtype=np.float32)
     weights_offset = 0
     padding_begin = padding_end = [0, 0]
 
     # input
     input_shape = [64, 1, 28, 28]
-    param_node = ngraph.parameter(input_shape, np.float32, 'Parameter')
+    param_node = openvino.runtime.op.Parameter(Type.f32, Shape(input_shape))
 
     # convolution 1
     conv_1_kernel_shape, conv_1_kernel_length = shape_and_length([20, 1, 5, 5])
-    conv_1_kernel = ngraph.constant(weights[0:conv_1_kernel_length].reshape(conv_1_kernel_shape))
+    conv_1_kernel = openvino.runtime.op.Constant(Type.f32, Shape(conv_1_kernel_shape), weights[0:conv_1_kernel_length].tolist())
     weights_offset += conv_1_kernel_length
-    conv_1_node = ngraph.convolution(param_node, conv_1_kernel, [1, 1], padding_begin, padding_end, [1, 1])
+    conv_1_node = openvino.runtime.opset8.convolution(param_node, conv_1_kernel, [1, 1], padding_begin, padding_end, [1, 1])
 
     # add 1
     add_1_kernel_shape, add_1_kernel_length = shape_and_length([1, 20, 1, 1])
-    add_1_kernel = ngraph.constant(
-        weights[weights_offset : weights_offset + add_1_kernel_length].reshape(add_1_kernel_shape),
-    )
+    add_1_kernel = openvino.runtime.op.Constant(Type.f32, Shape(add_1_kernel_shape),
+                                                weights[weights_offset : weights_offset + add_1_kernel_length])
     weights_offset += add_1_kernel_length
-    add_1_node = ngraph.add(conv_1_node, add_1_kernel)
+    add_1_node = openvino.runtime.opset8.add(conv_1_node, add_1_kernel)
 
     # maxpool 1
-    maxpool_1_node = max_pool(add_1_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')
+    maxpool_1_node = openvino.runtime.opset1.max_pool(add_1_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')
 
     # convolution 2
     conv_2_kernel_shape, conv_2_kernel_length = shape_and_length([50, 20, 5, 5])
-    conv_2_kernel = ngraph.constant(
-        weights[weights_offset : weights_offset + conv_2_kernel_length].reshape(conv_2_kernel_shape),
-    )
+    conv_2_kernel = openvino.runtime.op.Constant(Type.f32, Shape(conv_2_kernel_shape),
+                                                 weights[weights_offset : weights_offset + conv_2_kernel_length],
+                                                 )
     weights_offset += conv_2_kernel_length
-    conv_2_node = ngraph.convolution(maxpool_1_node, conv_2_kernel, [1, 1], padding_begin, padding_end, [1, 1])
+    conv_2_node = openvino.runtime.opset8.convolution(maxpool_1_node, conv_2_kernel, [1, 1], padding_begin, padding_end, [1, 1])
 
     # add 2
     add_2_kernel_shape, add_2_kernel_length = shape_and_length([1, 50, 1, 1])
-    add_2_kernel = ngraph.constant(
-        weights[weights_offset : weights_offset + add_2_kernel_length].reshape(add_2_kernel_shape),
-    )
+    add_2_kernel = openvino.runtime.op.Constant(Type.f32, Shape(add_2_kernel_shape),
+                                                weights[weights_offset : weights_offset + add_2_kernel_length],
+                                                )
     weights_offset += add_2_kernel_length
-    add_2_node = ngraph.add(conv_2_node, add_2_kernel)
+    add_2_node = openvino.runtime.opset8.add(conv_2_node, add_2_kernel)
 
     # maxpool 2
-    maxpool_2_node = max_pool(add_2_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')
+    maxpool_2_node = openvino.runtime.opset1.max_pool(add_2_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')
 
     # reshape 1
     reshape_1_dims, reshape_1_length = shape_and_length([2])
@@ -114,142 +71,134 @@ def create_ngraph_function(args: argparse.Namespace) -> ngraph.impl.Function:
         weights[weights_offset : weights_offset + 2 * reshape_1_length],
         dtype=np.int64,
     )
-    reshape_1_kernel = ngraph.constant(dtype_weights)
+    reshape_1_kernel = openvino.runtime.op.Constant(Type.i64, Shape(list(dtype_weights.shape)), dtype_weights)
     weights_offset += 2 * reshape_1_length
-    reshape_1_node = ngraph.reshape(maxpool_2_node, reshape_1_kernel, True)
+    reshape_1_node = openvino.runtime.opset8.reshape(maxpool_2_node, reshape_1_kernel, True)
 
     # matmul 1
     matmul_1_kernel_shape, matmul_1_kernel_length = shape_and_length([500, 800])
-    matmul_1_kernel = ngraph.constant(
-        weights[weights_offset : weights_offset + matmul_1_kernel_length].reshape(matmul_1_kernel_shape),
-    )
+    matmul_1_kernel = openvino.runtime.op.Constant(Type.f32, Shape(matmul_1_kernel_shape),
+                                                   weights[weights_offset : weights_offset + matmul_1_kernel_length],
+                                                   )
     weights_offset += matmul_1_kernel_length
-    matmul_1_node = ngraph.matmul(reshape_1_node, matmul_1_kernel, False, True)
+    matmul_1_node = openvino.runtime.opset8.matmul(reshape_1_node, matmul_1_kernel, False, True)
 
     # add 3
     add_3_kernel_shape, add_3_kernel_length = shape_and_length([1, 500])
-    add_3_kernel = ngraph.constant(
-        weights[weights_offset : weights_offset + add_3_kernel_length].reshape(add_3_kernel_shape),
-    )
+    add_3_kernel = openvino.runtime.op.Constant(Type.f32, Shape(add_3_kernel_shape),
+                                                weights[weights_offset : weights_offset + add_3_kernel_length],
+                                                )
     weights_offset += add_3_kernel_length
-    add_3_node = ngraph.add(matmul_1_node, add_3_kernel)
+    add_3_node = openvino.runtime.opset8.add(matmul_1_node, add_3_kernel)
 
     # ReLU
-    relu_node = ngraph.relu(add_3_node)
+    relu_node = openvino.runtime.opset8.relu(add_3_node)
 
     # reshape 2
-    reshape_2_kernel = ngraph.constant(dtype_weights)
-    reshape_2_node = ngraph.reshape(relu_node, reshape_2_kernel, True)
+    reshape_2_kernel = openvino.runtime.op.Constant(Type.i64, Shape(list(dtype_weights.shape)), dtype_weights)
+    reshape_2_node = openvino.runtime.opset8.reshape(relu_node, reshape_2_kernel, True)
 
     # matmul 2
     matmul_2_kernel_shape, matmul_2_kernel_length = shape_and_length([10, 500])
-    matmul_2_kernel = ngraph.constant(
-        weights[weights_offset : weights_offset + matmul_2_kernel_length].reshape(matmul_2_kernel_shape),
-    )
+    matmul_2_kernel = openvino.runtime.op.Constant(Type.f32, Shape(matmul_2_kernel_shape),
+                                                   weights[weights_offset : weights_offset + matmul_2_kernel_length],
+                                                   )
     weights_offset += matmul_2_kernel_length
-    matmul_2_node = ngraph.matmul(reshape_2_node, matmul_2_kernel, False, True)
+    matmul_2_node = openvino.runtime.opset8.matmul(reshape_2_node, matmul_2_kernel, False, True)
 
     # add 4
     add_4_kernel_shape, add_4_kernel_length = shape_and_length([1, 10])
-    add_4_kernel = ngraph.constant(
-        weights[weights_offset : weights_offset + add_4_kernel_length].reshape(add_4_kernel_shape),
-    )
+    add_4_kernel = openvino.runtime.op.Constant(Type.f32, Shape(add_4_kernel_shape),
+                                                weights[weights_offset : weights_offset + add_4_kernel_length],
+                                                )
     weights_offset += add_4_kernel_length
-    add_4_node = ngraph.add(matmul_2_node, add_4_kernel)
+    add_4_node = openvino.runtime.opset8.add(matmul_2_node, add_4_kernel)
 
     # softmax
     softmax_axis = 1
-    softmax_node = ngraph.softmax(add_4_node, softmax_axis)
+    softmax_node = openvino.runtime.opset8.softmax(add_4_node, softmax_axis)
 
-    # result
-    result_node = ngraph.result(softmax_node)
-    return ngraph.impl.Function(result_node, [param_node], 'lenet')
+    return Model(softmax_node, [param_node], 'lenet')
 
 
 def main():
     log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
-    args = parse_args()
+    # Parsing and validation of input arguments
+    if len(sys.argv) != 3:
+        log.info('Usage: <path_to_model> <device_name>')
+        return 1
 
+    model_path = sys.argv[1]
+    device_name = sys.argv[2]
+    labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+    number_top = 1
     # ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
-    log.info('Creating Inference Engine')
-    ie = IECore()
+    log.info('Creating OpenVINO Runtime Core')
+    core = Core()
 
     # ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation------------------------------
-    log.info(f'Loading the network using ngraph function with weights from {args.model}')
-    ngraph_function = create_ngraph_function(args)
-    net = IENetwork(ngraph.impl.Function.to_capsule(ngraph_function))
-
-    # ---------------------------Step 3. Configure input & output----------------------------------------------------------
-    log.info('Configuring input and output blobs')
+    log.info(f'Loading the network using ngraph function with weights from {model_path}')
+    model = create_ngraph_function(model_path)
+    # ---------------------------Step 3. Apply preprocessing----------------------------------------------------------
     # Get names of input and output blobs
-    input_blob = next(iter(net.input_info))
-    out_blob = next(iter(net.outputs))
+    ppp = PrePostProcessor(model)
+    # 1) Set input tensor information:
+    # - input() provides information about a single model input
+    # - precision of tensor is supposed to be 'u8'
+    # - layout of data is 'NHWC'
+    ppp.input().tensor() \
+        .set_element_type(Type.u8) \
+        .set_layout(Layout('NHWC'))  # noqa: N400
 
-    # Set input and output precision manually
-    net.input_info[input_blob].precision = 'U8'
-    net.outputs[out_blob].precision = 'FP32'
+    # 2) Here we suppose model has 'NCHW' layout for input
+    ppp.input().model().set_layout(Layout('NCHW'))
+    # 3) Set output tensor information:
+    # - precision of tensor is supposed to be 'f32'
+    ppp.output().tensor().set_element_type(Type.f32)
 
-    # Set a batch size to a equal number of input images
-    net.batch_size = len(args.input)
+    # 4) Apply preprocessing modifing the original 'model'
+    model = ppp.build()
+
+    # Set a batch size equal to number of input images
+    model.reshape({model.input().get_any_name(): PartialShape((digits.shape[0], model.input().shape[1], model.input().shape[2], model.input().shape[3]))})
 
     # ---------------------------Step 4. Loading model to the device-------------------------------------------------------
     log.info('Loading the model to the plugin')
-    exec_net = ie.load_network(network=net, device_name=args.device)
+    compiled_model = core.compile_model(model, device_name)
 
-    # ---------------------------Step 5. Create infer request--------------------------------------------------------------
-    # load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
-    # instance which stores infer requests. So you already created Infer requests in the previous step.
-
-    # ---------------------------Step 6. Prepare input---------------------------------------------------------------------
-    n, c, h, w = net.input_info[input_blob].input_data.shape
+    # ---------------------------Step 5. Prepare input---------------------------------------------------------------------
+    n, c, h, w = model.input().shape
     input_data = np.ndarray(shape=(n, c, h, w))
-
     for i in range(n):
-        image = read_image(args.input[i])
-
-        light_pixel_count = np.count_nonzero(image > 127)
-        dark_pixel_count = np.count_nonzero(image < 127)
-        is_light_image = (light_pixel_count - dark_pixel_count) > 0
-
-        if is_light_image:
-            log.warning(f'Image {args.input[i]} is inverted to white over black')
-            image = cv2.bitwise_not(image)
-
-        if image.shape != (h, w):
-            log.warning(f'Image {args.input[i]} is resized from {image.shape} to {(h, w)}')
-            image = cv2.resize(image, (w, h))
-
+        image = digits[i].reshape(28, 28)
+        image = image[:, :, np.newaxis]
         input_data[i] = image
 
-    # ---------------------------Step 7. Do inference----------------------------------------------------------------------
+    # ---------------------------Step 6. Do inference----------------------------------------------------------------------
     log.info('Starting inference in synchronous mode')
-    res = exec_net.infer(inputs={input_blob: input_data})
+    results = compiled_model.infer_new_request({0: input_data})
 
-    # ---------------------------Step 8. Process output--------------------------------------------------------------------
-    # Generate a label list
-    if args.labels:
-        with open(args.labels, 'r') as f:
-            labels = [line.split(',')[0].strip() for line in f]
-
-    res = res[out_blob]
+    # ---------------------------Step 7. Process output--------------------------------------------------------------------
+    predictions = next(iter(results.values()))
 
+    log.info(f'Top {number_top} results: ')
     for i in range(n):
-        probs = res[i]
-        # Get an array of args.number_top class IDs in descending order of probability
-        top_n_idexes = np.argsort(probs)[-args.number_top :][::-1]
+        probs = predictions[i]
+        # Get an array of number_top class IDs in descending order of probability
+        top_n_idexes = np.argsort(probs)[-number_top :][::-1]
 
         header = 'classid probability'
-        header = header + ' label' if args.labels else header
+        header = header + ' label' if labels else header
 
-        log.info(f'Image path: {args.input[i]}')
-        log.info(f'Top {args.number_top} results: ')
+        log.info(f'Image {i}')
+        log.info('')
         log.info(header)
         log.info('-' * len(header))
 
         for class_id in top_n_idexes:
             probability_indent = ' ' * (len('classid') - len(str(class_id)) + 1)
-            label_indent = ' ' * (len('probability') - 8) if args.labels else ''
-            label = labels[class_id] if args.labels else ''
+            label_indent = ' ' * (len('probability') - 8) if labels else ''
+            label = labels[class_id] if labels else ''
             log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}{label_indent}{label}')
         log.info('')
 

From d6dcf58846d33d27d02e112235f0d15830f0e9c0 Mon Sep 17 00:00:00 2001
From: Dmitry Pigasin <dmitry.pigasin@intel.com>
Date: Mon, 27 Dec 2021 09:22:15 +0300
Subject: [PATCH 156/160] [IE Python Speech Sample] Migrate to OV 2.0 API
 (#9348)

* Create mvp

* Implement new API & Refactoring

* Fix -oname for models whose name of output layer contains a port number

* Fix step numbers

* Create utils.py

* Remove shebang from utils.py

* Fix `-iname` option
---
 samples/python/speech_sample/speech_sample.py | 307 +++++++-----------
 samples/python/speech_sample/utils.py         |  80 +++++
 2 files changed, 204 insertions(+), 183 deletions(-)
 create mode 100644 samples/python/speech_sample/utils.py

diff --git a/samples/python/speech_sample/speech_sample.py b/samples/python/speech_sample/speech_sample.py
index 26d209f1e77..7382899e450 100755
--- a/samples/python/speech_sample/speech_sample.py
+++ b/samples/python/speech_sample/speech_sample.py
@@ -2,179 +2,106 @@
 # -*- coding: utf-8 -*-
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-import argparse
-import logging as log
+
 import re
 import sys
 from timeit import default_timer
-from typing import Union
+from typing import Dict
 
 import numpy as np
+from openvino.preprocess import PrePostProcessor
+from openvino.runtime import Core, InferRequest, Layout, Type, set_batch
+
 from arg_parser import parse_args
 from file_options import read_utterance_file, write_utterance_file
-from openvino.inference_engine import ExecutableNetwork, IECore, IENetwork
-
-# Operating Frequency for GNA HW devices for Core and Atom architecture
-GNA_CORE_FREQUENCY = 400
-GNA_ATOM_FREQUENCY = 200
+from utils import (GNA_ATOM_FREQUENCY, GNA_CORE_FREQUENCY,
+                   compare_with_reference, get_scale_factor, log,
+                   parse_outputs_from_args, parse_scale_factors,
+                   set_scale_factors)
 
 
-def get_scale_factor(matrix: np.ndarray) -> float:
-    """Get scale factor for quantization using utterance matrix"""
-    # Max to find scale factor
-    target_max = 16384
-    max_val = np.max(matrix)
-    if max_val == 0:
-        return 1.0
-    else:
-        return target_max / max_val
-
-
-def infer_data(
-    data: dict, exec_net: ExecutableNetwork, input_blobs: list, output_blobs: list, cw_l: int = 0, cw_r: int = 0,
-) -> np.ndarray:
+def infer_data(data: Dict[str, np.ndarray], infer_request: InferRequest, cw_l: int = 0, cw_r: int = 0) -> np.ndarray:
     """Do a synchronous matrix inference"""
-    matrix_shape = next(iter(data.values())).shape
+    frames_to_infer = {}
     result = {}
 
-    for blob_name in output_blobs:
-        output_shape = exec_net.outputs[blob_name].shape
-        batch_size = output_shape[0]
-        result[blob_name] = np.ndarray((matrix_shape[0], np.prod(output_shape[1:])))
+    batch_size = infer_request.get_input_tensor(0).shape[0]
+    num_of_frames = next(iter(data.values())).shape[0]
 
-    for i in range(-cw_l, matrix_shape[0] + cw_r, batch_size):
+    for output in infer_request.outputs:
+        result[output.any_name] = np.ndarray((num_of_frames, np.prod(tuple(output.shape)[1:])))
+
+    for i in range(-cw_l, num_of_frames + cw_r, batch_size):
         if i < 0:
             index = 0
-        elif i >= matrix_shape[0]:
-            index = matrix_shape[0] - 1
+        elif i >= num_of_frames:
+            index = num_of_frames - 1
         else:
             index = i
 
-        vectors = {blob_name: data[blob_name][index:index + batch_size] for blob_name in input_blobs}
+        for _input in infer_request.inputs:
+            frames_to_infer[_input.any_name] = data[_input.any_name][index:index + batch_size]
+            num_of_frames_to_infer = len(frames_to_infer[_input.any_name])
 
-        num_of_vectors = next(iter(vectors.values())).shape[0]
+            # Add [batch_size - num_of_frames_to_infer] zero rows to 2d numpy array
+            # Used to infer fewer frames than the batch size
+            frames_to_infer[_input.any_name] = np.pad(
+                frames_to_infer[_input.any_name],
+                [(0, batch_size - num_of_frames_to_infer), (0, 0)],
+            )
 
-        if num_of_vectors < batch_size:
-            temp = {blob_name: np.zeros((batch_size, vectors[blob_name].shape[1])) for blob_name in input_blobs}
+            frames_to_infer[_input.any_name] = frames_to_infer[_input.any_name].reshape(_input.tensor.shape)
 
-            for blob_name in input_blobs:
-                temp[blob_name][:num_of_vectors] = vectors[blob_name]
-
-            vectors = temp
-
-        for blob_name in input_blobs:
-            vectors[blob_name] = vectors[blob_name].reshape(exec_net.input_info[blob_name].input_data.shape)
-
-        vector_results = exec_net.infer(vectors)
+        frame_results = infer_request.infer(frames_to_infer)
 
         if i - cw_r < 0:
             continue
 
-        for blob_name in output_blobs:
-            vector_result = vector_results[blob_name].reshape((batch_size, result[blob_name].shape[1]))
-            result[blob_name][i - cw_r:i - cw_r + batch_size] = vector_result[:num_of_vectors]
+        for output in frame_results.keys():
+            vector_result = frame_results[output].reshape((batch_size, result[output.any_name].shape[1]))
+            result[output.any_name][i - cw_r:i - cw_r + batch_size] = vector_result[:num_of_frames_to_infer]
 
     return result
 
 
-def compare_with_reference(result: np.ndarray, reference: np.ndarray):
-    error_matrix = np.absolute(result - reference)
-
-    max_error = np.max(error_matrix)
-    sum_error = np.sum(error_matrix)
-    avg_error = sum_error / error_matrix.size
-    sum_square_error = np.sum(np.square(error_matrix))
-    avg_rms_error = np.sqrt(sum_square_error / error_matrix.size)
-    stdev_error = np.sqrt(sum_square_error / error_matrix.size - avg_error * avg_error)
-
-    log.info(f'max error: {max_error:.7f}')
-    log.info(f'avg error: {avg_error:.7f}')
-    log.info(f'avg rms error: {avg_rms_error:.7f}')
-    log.info(f'stdev error: {stdev_error:.7f}')
-
-
-def get_input_layer_list(net: Union[IENetwork, ExecutableNetwork], args: argparse.Namespace) -> list:
-    """Get a list of input layer names"""
-    return re.split(', |,', args.input_layers) if args.input_layers else [next(iter(net.input_info))]
-
-
-def get_output_layer_list(net: Union[IENetwork, ExecutableNetwork],
-                          args: argparse.Namespace, with_ports: bool) -> list:
-    """Get a list of output layer names"""
-    if args.output_layers:
-        output_name_port = [output.split(':') for output in re.split(', |,', args.output_layers)]
-        if with_ports:
-            try:
-                return [(blob_name, int(port)) for blob_name, port in output_name_port]
-            except ValueError:
-                log.error('Incorrect value for -oname/--output_layers option, please specify a port for output layer.')
-                sys.exit(-4)
-        else:
-            return [blob_name for blob_name, _ in output_name_port]
-    else:
-        return [list(net.outputs.keys())[-1]]
-
-
-def parse_scale_factors(args: argparse.Namespace) -> list:
-    """Get a list of scale factors for input files"""
-    input_files = re.split(', |,', args.input)
-    scale_factors = re.split(', |,', str(args.scale_factor))
-    scale_factors = list(map(float, scale_factors))
-
-    if len(input_files) != len(scale_factors):
-        log.error(f'Incorrect command line for multiple inputs: {len(scale_factors)} scale factors provided for '
-                  f'{len(input_files)} input files.')
-        sys.exit(-7)
-
-    for i, scale_factor in enumerate(scale_factors):
-        if float(scale_factor) < 0:
-            log.error(f'Scale factor for input #{i} (counting from zero) is out of range (must be positive).')
-            sys.exit(-8)
-
-    return scale_factors
-
-
-def set_scale_factors(plugin_config: dict, scale_factors: list):
-    """Set a scale factor provided for each input"""
-    for i, scale_factor in enumerate(scale_factors):
-        log.info(f'For input {i} using scale factor of {scale_factor:.7f}')
-        plugin_config[f'GNA_SCALE_FACTOR_{i}'] = str(scale_factor)
-
-
 def main():
-    log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
     args = parse_args()
 
-# ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
-    log.info('Creating Inference Engine')
-    ie = IECore()
+# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
+    log.info('Creating OpenVINO Runtime Core')
+    core = Core()
 
-# ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation---------------
+# --------------------------- Step 2. Read a model --------------------------------------------------------------------
     if args.model:
-        log.info(f'Reading the network: {args.model}')
-        # .xml and .bin files
-        net = ie.read_network(model=args.model)
+        log.info(f'Reading the model: {args.model}')
+        # (.xml and .bin files) or (.onnx file)
+        model = core.read_model(args.model)
 
-# ---------------------------Step 3. Configure input & output----------------------------------------------------------
-        log.info('Configuring input and output blobs')
-        # Mark layers from args.output_layers as outputs
+# --------------------------- Step 3. Apply preprocessing -------------------------------------------------------------
         if args.output_layers:
-            net.add_outputs(get_output_layer_list(net, args, with_ports=True))
+            output_names, output_ports = parse_outputs_from_args(args)
+            model.add_outputs(list(zip(output_names, output_ports)))
 
-        # Get names of input and output blobs
-        input_blobs = get_input_layer_list(net, args)
-        output_blobs = get_output_layer_list(net, args, with_ports=False)
+        ppp = PrePostProcessor(model)
 
-        # Set input and output precision manually
-        for blob_name in input_blobs:
-            net.input_info[blob_name].precision = 'FP32'
+        for i in range(len(model.inputs)):
+            ppp.input(i).tensor() \
+                .set_element_type(Type.f32) \
+                .set_layout(Layout('NC'))  # noqa: N400
 
-        for blob_name in output_blobs:
-            net.outputs[blob_name].precision = 'FP32'
+            ppp.input(i).model().set_layout(Layout('NC'))
 
-        net.batch_size = args.batch_size if args.context_window_left + args.context_window_right == 0 else 1
+        for i in range(len(model.outputs)):
+            ppp.output(i).tensor().set_element_type(Type.f32)
 
-# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
+        model = ppp.build()
+
+        if args.context_window_left == args.context_window_right == 0:
+            set_batch(model, args.batch_size)
+        else:
+            set_batch(model, 1)
+
+# ---------------------------Step 4. Configure plugin ---------------------------------------------------------
     devices = args.device.replace('HETERO:', '').split(',')
     plugin_config = {}
 
@@ -215,39 +142,17 @@ def main():
 
     device_str = f'HETERO:{",".join(devices)}' if 'HETERO' in args.device else devices[0]
 
+# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
     log.info('Loading the model to the plugin')
     if args.model:
-        exec_net = ie.load_network(net, device_str, plugin_config)
+        compiled_model = core.compile_model(model, device_str, plugin_config)
     else:
-        exec_net = ie.import_network(args.import_gna_model, device_str, plugin_config)
-        input_blobs = get_input_layer_list(exec_net, args)
-        output_blobs = get_output_layer_list(exec_net, args, with_ports=False)
-
-    if args.input:
-        input_files = re.split(', |,', args.input)
-
-        if len(input_blobs) != len(input_files):
-            log.error(f'Number of network inputs ({len(input_blobs)}) is not equal '
-                      f'to number of ark files ({len(input_files)})')
-            sys.exit(-3)
-
-    if args.reference:
-        reference_files = re.split(', |,', args.reference)
-
-        if len(output_blobs) != len(reference_files):
-            log.error('The number of reference files is not equal to the number of network outputs.')
-            sys.exit(-5)
-
-    if args.output:
-        output_files = re.split(', |,', args.output)
-
-        if len(output_blobs) != len(output_files):
-            log.error('The number of output files is not equal to the number of network outputs.')
-            sys.exit(-6)
+        compiled_model = core.import_model(args.import_gna_model, device_str, plugin_config)
 
+# --------------------------- Exporting GNA model using InferenceEngine AOT API ---------------------------------------
     if args.export_gna_model:
         log.info(f'Writing GNA Model to {args.export_gna_model}')
-        exec_net.export(args.export_gna_model)
+        compiled_model.export_model(args.export_gna_model)
         return 0
 
     if args.export_embedded_gna_model:
@@ -255,68 +160,104 @@ def main():
         log.info(f'GNA embedded model export done for GNA generation {args.embedded_gna_configuration}')
         return 0
 
-# ---------------------------Step 5. Create infer request--------------------------------------------------------------
-# load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
-# instance which stores infer requests. So you already created Infer requests in the previous step.
+# --------------------------- Step 6. Set up input --------------------------------------------------------------------
+    if args.input_layers:
+        input_names = re.split(', |,', args.input_layers)
+    else:
+        input_names = [_input.any_name for _input in compiled_model.inputs]
+
+    if args.output_layers:
+        output_names, output_ports = parse_outputs_from_args(args)
+        # If a name of output layer contains a port number then concatenate output_names and output_ports
+        if ':' in compiled_model.outputs[0].any_name:
+            output_names = [f'{output_names[i]}:{output_ports[i]}' for i in range(len(output_names))]
+    else:
+        output_names = [compiled_model.outputs[0].any_name]
+
+    if args.input:
+        input_files = re.split(', |,', args.input)
+
+        if len(input_names) != len(input_files):
+            log.error(f'Number of network inputs ({len(compiled_model.inputs)}) is not equal '
+                      f'to number of ark files ({len(input_files)})')
+            sys.exit(-3)
+
+    if args.reference:
+        reference_files = re.split(', |,', args.reference)
+
+        if len(output_names) != len(reference_files):
+            log.error('The number of reference files is not equal to the number of network outputs.')
+            sys.exit(-5)
+
+    if args.output:
+        output_files = re.split(', |,', args.output)
+
+        if len(output_names) != len(output_files):
+            log.error('The number of output files is not equal to the number of network outputs.')
+            sys.exit(-6)
 
-# ---------------------------Step 6. Prepare input---------------------------------------------------------------------
     file_data = [read_utterance_file(file_name) for file_name in input_files]
+
     input_data = {
         utterance_name: {
-            input_blobs[i]: file_data[i][utterance_name] for i in range(len(input_blobs))
+            input_names[i]: file_data[i][utterance_name] for i in range(len(input_names))
         }
         for utterance_name in file_data[0].keys()
     }
 
     if args.reference:
-        references = {output_blobs[i]: read_utterance_file(reference_files[i]) for i in range(len(output_blobs))}
+        references = {output_names[i]: read_utterance_file(reference_files[i]) for i in range(len(output_names))}
 
-# ---------------------------Step 7. Do inference----------------------------------------------------------------------
+# --------------------------- Step 7. Create infer request ------------------------------------------------------------
+    infer_request = compiled_model.create_infer_request()
+
+# --------------------------- Step 8. Do inference --------------------------------------------------------------------
     log.info('Starting inference in synchronous mode')
-    results = {blob_name: {} for blob_name in output_blobs}
+    results = {name: {} for name in output_names}
     total_infer_time = 0
 
     for i, key in enumerate(sorted(input_data)):
         start_infer_time = default_timer()
 
         # Reset states between utterance inferences to remove a memory impact
-        for request in exec_net.requests:
-            for state in request.query_state():
-                state.reset()
+        for state in infer_request.query_state():
+            state.reset()
 
         result = infer_data(
-            input_data[key], exec_net, input_blobs, output_blobs, args.context_window_left, args.context_window_right,
+            input_data[key],
+            infer_request,
+            args.context_window_left,
+            args.context_window_right,
         )
 
-        for blob_name in result.keys():
-            results[blob_name][key] = result[blob_name]
+        for name in output_names:
+            results[name][key] = result[name]
 
         infer_time = default_timer() - start_infer_time
         total_infer_time += infer_time
         num_of_frames = file_data[0][key].shape[0]
         avg_infer_time_per_frame = infer_time / num_of_frames
 
-# ---------------------------Step 8. Process output--------------------------------------------------------------------
+# --------------------------- Step 9. Process output ------------------------------------------------------------------
         log.info('')
         log.info(f'Utterance {i} ({key}):')
         log.info(f'Total time in Infer (HW and SW): {infer_time * 1000:.2f}ms')
         log.info(f'Frames in utterance: {num_of_frames}')
         log.info(f'Average Infer time per frame: {avg_infer_time_per_frame * 1000:.2f}ms')
 
-        for blob_name in output_blobs:
+        for name in output_names:
             log.info('')
-            log.info(f'Output blob name: {blob_name}')
-            log.info(f'Number scores per frame: {results[blob_name][key].shape[1]}')
+            log.info(f'Output blob name: {name}')
+            log.info(f'Number scores per frame: {results[name][key].shape[1]}')
 
             if args.reference:
                 log.info('')
-                compare_with_reference(results[blob_name][key], references[blob_name][key])
+                compare_with_reference(results[name][key], references[name][key])
 
         if args.performance_counter:
             if 'GNA' in args.device:
-                pc = exec_net.requests[0].get_perf_counts()
-                total_cycles = int(pc['1.1 Total scoring time in HW']['real_time'])
-                stall_cycles = int(pc['1.2 Stall scoring time in HW']['real_time'])
+                total_cycles = infer_request.profiling_info[0].real_time.total_seconds()
+                stall_cycles = infer_request.profiling_info[1].real_time.total_seconds()
                 active_cycles = total_cycles - stall_cycles
                 frequency = 10**6
                 if args.arch == 'CORE':
@@ -336,8 +277,8 @@ def main():
     log.info(f'Total sample time: {total_infer_time * 1000:.2f}ms')
 
     if args.output:
-        for i, blob_name in enumerate(results):
-            write_utterance_file(output_files[i], results[blob_name])
+        for i, name in enumerate(results):
+            write_utterance_file(output_files[i], results[name])
             log.info(f'File {output_files[i]} was created!')
 
 # ----------------------------------------------------------------------------------------------------------------------
diff --git a/samples/python/speech_sample/utils.py b/samples/python/speech_sample/utils.py
new file mode 100644
index 00000000000..91b75e1bc77
--- /dev/null
+++ b/samples/python/speech_sample/utils.py
@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import logging as log
+import re
+import sys
+from typing import List, Tuple
+
+import numpy as np
+
+# Operating Frequency for GNA HW devices for Core and Atom architecture
+GNA_CORE_FREQUENCY = 400
+GNA_ATOM_FREQUENCY = 200
+
+log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
+
+
+def compare_with_reference(result: np.ndarray, reference: np.ndarray):
+    error_matrix = np.absolute(result - reference)
+
+    max_error = np.max(error_matrix)
+    sum_error = np.sum(error_matrix)
+    avg_error = sum_error / error_matrix.size
+    sum_square_error = np.sum(np.square(error_matrix))
+    avg_rms_error = np.sqrt(sum_square_error / error_matrix.size)
+    stdev_error = np.sqrt(sum_square_error / error_matrix.size - avg_error * avg_error)
+
+    log.info(f'max error: {max_error:.7f}')
+    log.info(f'avg error: {avg_error:.7f}')
+    log.info(f'avg rms error: {avg_rms_error:.7f}')
+    log.info(f'stdev error: {stdev_error:.7f}')
+
+
+def get_scale_factor(matrix: np.ndarray) -> float:
+    """Get scale factor for quantization using utterance matrix"""
+    # Max to find scale factor
+    target_max = 16384
+    max_val = np.max(matrix)
+    if max_val == 0:
+        return 1.0
+    else:
+        return target_max / max_val
+
+
+def set_scale_factors(plugin_config: dict, scale_factors: list):
+    """Set a scale factor provided for each input"""
+    for i, scale_factor in enumerate(scale_factors):
+        log.info(f'For input {i} using scale factor of {scale_factor:.7f}')
+        plugin_config[f'GNA_SCALE_FACTOR_{i}'] = str(scale_factor)
+
+
+def parse_scale_factors(args: argparse.Namespace) -> list:
+    """Get a list of scale factors for input files"""
+    input_files = re.split(', |,', args.input)
+    scale_factors = re.split(', |,', str(args.scale_factor))
+    scale_factors = list(map(float, scale_factors))
+
+    if len(input_files) != len(scale_factors):
+        log.error(f'Incorrect command line for multiple inputs: {len(scale_factors)} scale factors provided for '
+                  f'{len(input_files)} input files.')
+        sys.exit(-7)
+
+    for i, scale_factor in enumerate(scale_factors):
+        if float(scale_factor) < 0:
+            log.error(f'Scale factor for input #{i} (counting from zero) is out of range (must be positive).')
+            sys.exit(-8)
+
+    return scale_factors
+
+
+def parse_outputs_from_args(args: argparse.Namespace) -> Tuple[List[str], List[int]]:
+    """Get a list of outputs specified in the args"""
+    name_and_port = [output.split(':') for output in re.split(', |,', args.output_layers)]
+    try:
+        return [name for name, _ in name_and_port], [int(port) for _, port in name_and_port]
+    except ValueError:
+        log.error('Incorrect value for -oname/--output_layers option, please specify a port for each output layer.')
+        sys.exit(-4)

From b050d39f891a2e8cd9849a0fba364d40d3aba08d Mon Sep 17 00:00:00 2001
From: Roman Lyamin <Roman.Lyamin@intel.com>
Date: Mon, 27 Dec 2021 09:46:37 +0300
Subject: [PATCH 157/160] [GPU] Add batching surface to new API (#9435)

---
 .../interface/ie_iplugin_internal.cpp         |   1 +
 .../intel_gpu/plugin/infer_request.hpp        |   3 +
 .../intel_gpu/src/plugin/infer_request.cpp    | 126 +++++++++++++++-
 .../src/plugin/ops/convert_color.cpp          |  36 ++++-
 .../intel_gpu/src/plugin/ops/parameter.cpp    |  24 ++-
 .../gpu_remote_tensor_tests.cpp               | 140 ++++++++++++++++++
 6 files changed, 315 insertions(+), 15 deletions(-)

diff --git a/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
index dd0a395f9ea..1933b50dded 100644
--- a/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
+++ b/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
@@ -324,6 +324,7 @@ void SetExeNetworkInfo(const std::shared_ptr<IExecutableNetworkInternal>& exeNet
         new_param->set_element_type(
             InferenceEngine::details::convertPrecision(inputsInfo.at(new_param->get_friendly_name())->getPrecision()));
         new_param->set_layout(param->get_layout());
+        new_param->output(0).get_rt_info() = param->output(0).get_rt_info();
         new_param->validate_and_infer_types();
         const_params.emplace_back(new_param);
     }
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/infer_request.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/infer_request.hpp
index d5aeacf95c3..3602a1e3f52 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/infer_request.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/infer_request.hpp
@@ -45,6 +45,7 @@ public:
 
     InferenceEngine::Blob::Ptr GetBlob(const std::string& name) override;
     void SetBlob(const std::string& name, const InferenceEngine::Blob::Ptr &data) override;
+    void SetBlobs(const std::string& name, const std::vector<InferenceEngine::Blob::Ptr> &data) override;
 
     void SetBatch(int batch = -1) override;
     void SetGraph(std::shared_ptr<Graph> graph);
@@ -72,6 +73,8 @@ private:
     std::map<std::string, cldnn::primitive_id> inputsMap;
     std::map<std::string, cldnn::primitive_id> outputsMap;
 
+    std::map<std::string, std::vector<InferenceEngine::Blob::Ptr>> inputTensorsMap;
+
     bool m_useProfiling = false;
     bool m_useStreams = false;
     bool m_useExternalQueue = false;
diff --git a/src/plugins/intel_gpu/src/plugin/infer_request.cpp b/src/plugins/intel_gpu/src/plugin/infer_request.cpp
index 2fc215403c3..97a56184387 100644
--- a/src/plugins/intel_gpu/src/plugin/infer_request.cpp
+++ b/src/plugins/intel_gpu/src/plugin/infer_request.cpp
@@ -13,6 +13,7 @@
 #include "intel_gpu/plugin/compiled_model.hpp"
 #include "intel_gpu/plugin/itt.hpp"
 #include "intel_gpu/runtime/debug_configuration.hpp"
+#include "openvino/core/preprocess/input_tensor_info.hpp"
 #include <ie_algorithm.hpp>
 #include <debug.h>
 
@@ -218,6 +219,9 @@ void InferRequest::SetBlob(const std::string& name, const Blob::Ptr& data) {
     if (0 == dataSize) {
         IE_THROW() << "Input data is empty. Input name: \'" << name << "\'";
     }
+    if (inputTensorsMap.find(name) != inputTensorsMap.end()) {
+        inputTensorsMap.erase(name);
+    }
     const bool compoundBlobPassed = data->is<CompoundBlob>();
 
     InputInfo::Ptr foundInput;
@@ -352,6 +356,88 @@ void InferRequest::SetBlob(const std::string& name, const Blob::Ptr& data) {
     }
 }
 
+void InferRequest::SetBlobs(const std::string& name, const std::vector<Blob::Ptr>& blobs) {
+    if (blobs.size() == 1) {
+        SetBlob(name, blobs[0]);
+        return;
+    }
+
+    if (name.empty()) {
+        IE_THROW(NotFound) << "Failed to set blobs with empty name";
+    }
+    if (blobs.empty()) {
+        IE_THROW(NotAllocated) << "Failed to set empty blobs with name: \'" << name << "\'";
+    }
+    bool empty_data = std::any_of(blobs.begin(), blobs.end(), [](const Blob::Ptr& blob) {
+        return blob->size() == 0;
+    });
+    if (empty_data) {
+        IE_THROW() << "At least one of the input blobs is empty. Input name: \'" << name << "\'";
+    }
+
+    bool is_compound = std::any_of(blobs.begin(), blobs.end(), [](const Blob::Ptr& blob) {
+        return blob->is<CompoundBlob>();
+    });
+    if (is_compound) {
+        IE_THROW(NotImplemented) << cannot_set_compound;
+    }
+
+    bool is_buffer = std::all_of(blobs.begin(), blobs.end(), [](const Blob::Ptr& blob) {
+        return blob->is<gpu::ClBufferBlob>();
+    });
+    bool is_surface = std::all_of(blobs.begin(), blobs.end(), [](const Blob::Ptr& blob) {
+        return blob->is<gpu::ClImage2DBlob>();
+    });
+    bool is_remote = is_buffer || is_surface;
+
+    bool is_host = std::all_of(blobs.begin(), blobs.end(), [](const Blob::Ptr& blob) {
+        return blob->is<InferenceEngine::MemoryBlob>();
+    });
+    is_host &= !is_remote;
+
+    if (!is_host && !is_remote) {
+        IE_THROW() << "Incorrect input blobs. All blobs must be of the same type";
+    }
+
+    InputInfo::Ptr foundInput;
+    DataPtr foundOutput;
+    bool is_input = findInputAndOutputBlobByName(name, foundInput, foundOutput);
+
+    if (!is_input) {
+        IE_THROW() << "SetBlobs method doesn't support outputs";
+    }
+
+    if (is_buffer) {
+        IE_THROW(NotImplemented) << "SetBlobs method doesn't support buffer blobs";
+    }
+
+    const TensorDesc& desc = foundInput->getTensorDesc();
+
+    size_t dataBinSize = blobs.front()->size() * blobs.front()->element_size() * blobs.size();
+    size_t netReqBinSize = std::accumulate(desc.getDims().begin(), desc.getDims().end(),
+                                           desc.getPrecision().size(),
+                                           std::multiplies<size_t>());
+    if (dataBinSize != netReqBinSize) {
+        IE_THROW() << "Incorrect binary data size for input blobs with name: \'" << name <<  "\' " <<
+                      "Current: " << dataBinSize << " Required: " << netReqBinSize;
+    }
+
+    if (_inputs.find(name) != _inputs.end()) {
+        _inputs.erase(name);
+    }
+
+    if (is_remote) {
+        for (auto& blob : blobs) {
+            auto impl = getBlobImpl(blob->as<gpu::ClBlob>());
+            if (!impl->is_allocated()) {
+                impl->allocate();
+            }
+        }
+    }
+
+    inputTensorsMap.insert({ name, blobs });
+}
+
 void InferRequest::checkBlobs() {
     OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "InferRequest::checkBlobs");
     for (auto const &input : _inputs) {
@@ -519,6 +605,24 @@ void InferRequest::enqueue() {
     // set input and output memory from request blob maps
     // into the network object primitives
     std::vector<cldnn::event::ptr> dependencies;
+
+    for (const auto& inputTensor : inputTensorsMap) {
+        const auto& blobs = inputTensor.second;
+        auto blobsDesc = blobs.front()->getTensorDesc();
+
+        bool is_surface = std::all_of(blobs.begin(), blobs.end(), [](const Blob::Ptr& blob) {
+            return blob->is<gpu::ClImage2DBlob>();
+        });
+
+        if (is_surface) {
+            for (size_t i = 0; i < blobs.size(); ++i) {
+                std::string new_name = inputTensor.first + "_" + std::to_string(i);
+                _inputs[new_name] = blobs[i];
+                _deviceInputs[new_name] = blobs[i];
+            }
+        }
+    }
+
     for (auto& item : _inputs) {
         std::string inputName = item.first;
         Blob::Ptr& inputBlob = item.second;
@@ -809,14 +913,30 @@ Blob::Ptr InferRequest::host_blob_from_device_blob(Blob::Ptr blobPtr) {
 void InferRequest::allocate_inputs() {
     OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "InferRequest::allocate_inputs");
     auto inputLayouts = m_graph->GetInputLayouts();
+
     // allocate inputs
     for (auto& ni : _networkInputs) {
         std::string name = ni.first;
         const TensorDesc& desc = ni.second->getTensorDesc();
 
-        if (ColorFormat::NV12 == ni.second->getPreProcess().getColorFormat() &&
-            m_graph->getConfig().nv12_two_inputs) {
-        } else {
+        bool is_nv12_input = ColorFormat::NV12 == ni.second->getPreProcess().getColorFormat() &&
+                             m_graph->getConfig().nv12_two_inputs;
+
+        auto parameter = std::find_if(_parameters.begin(), _parameters.end(), [&](const std::shared_ptr<const ov::Node>& node) {
+            return node->get_friendly_name() == name;
+        });
+
+        if (parameter != _parameters.end()) {
+            if (parameter->get()->output(0).get_rt_info().count(ov::preprocess::TensorInfoMemoryType::get_type_info_static())) {
+                std::string mem_type = parameter->get()->output(0).get_rt_info().at(ov::preprocess::TensorInfoMemoryType::get_type_info_static())
+                                                                                .as<ov::preprocess::TensorInfoMemoryType>().value;
+                if (mem_type.find(GPU_CONFIG_KEY(SURFACE)) != std::string::npos) {
+                    is_nv12_input = true;
+                }
+            }
+        }
+
+        if (!is_nv12_input) {
             auto litr = inputLayouts.find(name);
             if (litr == inputLayouts.end()) {
                 IE_THROW() << "Input layout for " << name << " is not found";
diff --git a/src/plugins/intel_gpu/src/plugin/ops/convert_color.cpp b/src/plugins/intel_gpu/src/plugin/ops/convert_color.cpp
index c867b58fe38..6d134fc501d 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/convert_color.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/convert_color.cpp
@@ -6,6 +6,7 @@
 #include "intel_gpu/plugin/common_utils.hpp"
 
 #include "intel_gpu/primitives/convert_color.hpp"
+#include "intel_gpu/primitives/concatenation.hpp"
 #include "openvino/core/preprocess/input_tensor_info.hpp"
 
 namespace ov {
@@ -32,13 +33,34 @@ static void CreateCommonConvertColorOp(Program& p, const std::shared_ptr<ngraph:
             memory_type = cldnn::convert_color::memory_type::image;
         }
     }
-    p.AddPrimitive(cldnn::convert_color(layerName,
-                                        inputPrimitives,
-                                        from_color,
-                                        to_color,
-                                        memory_type,
-                                        out_layout,
-                                        op->get_friendly_name()));
+
+    if (outShape.batch[0] > 1 && memory_type == cldnn::convert_color::memory_type::image) {
+        std::vector<cldnn::primitive_id> convert_color_names;
+        for (size_t b = 0; b < outShape.batch[0]; ++b) {
+            cldnn::primitive::primitive_id_arr batchedInputPrimitives = { inputPrimitives[0] + "_" + std::to_string(b),
+                                                                          inputPrimitives[1] + "_" + std::to_string(b)};
+            cldnn::primitive_id batched_prim_id = layerName + "_" + std::to_string(b);
+            convert_color_names.emplace_back(batched_prim_id);
+            out_layout.size.batch[0] = 1;
+
+            p.AddPrimitive(cldnn::convert_color(batched_prim_id,
+                                                batchedInputPrimitives,
+                                                from_color,
+                                                to_color,
+                                                memory_type,
+                                                out_layout,
+                                                op->get_friendly_name()));
+        }
+        p.AddPrimitive(cldnn::concatenation(layerName, convert_color_names, cldnn::concatenation::along_b, op->get_friendly_name()));
+    } else {
+        p.AddPrimitive(cldnn::convert_color(layerName,
+                                            inputPrimitives,
+                                            from_color,
+                                            to_color,
+                                            memory_type,
+                                            out_layout,
+                                            op->get_friendly_name()));
+    }
     p.AddPrimitiveToProfiler(op);
 }
 
diff --git a/src/plugins/intel_gpu/src/plugin/ops/parameter.cpp b/src/plugins/intel_gpu/src/plugin/ops/parameter.cpp
index 53e83826b82..2ceb9ffe99d 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/parameter.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/parameter.cpp
@@ -196,12 +196,26 @@ static void CreateParameterOp(Program& p, const std::shared_ptr<ngraph::op::v0::
                 networkInputLayout.format = cldnn::format::nv12;
             }
         }
-        networkInputLayout.size = { TensorValue(inputDims[0]), TensorValue(inputDims[3]),
-                                    TensorValue(inputDims[2]), TensorValue(inputDims[1]) };
 
-        p.inputLayouts.insert({ inputInfo->name(), networkInputLayout });
-        p.AddPrimitive(cldnn::input_layout(inputName, networkInputLayout, inputInfo->name()));
-        p.AddPrimitiveToProfiler(op);
+        if (networkInputLayout.format == cldnn::format::nv12 && networkInputLayout.size.batch[0] > 1) {
+            networkInputLayout.size = { 1, TensorValue(inputDims[3]), TensorValue(inputDims[2]), TensorValue(inputDims[1]) };
+
+            std::vector<cldnn::primitive_id> inputs;
+            for (size_t i = 0; i < inputDims[0]; ++i) {
+                std::string batched_name = inputName + "_" + std::to_string(i);
+                p.inputLayouts.insert({ inputInfo->name() + "_" + std::to_string(i), networkInputLayout });
+                inputs.emplace_back(batched_name);
+                p.AddPrimitive(cldnn::input_layout(batched_name, networkInputLayout, inputInfo->name()));
+                p.AddPrimitiveToProfiler(op);
+            }
+        } else {
+            networkInputLayout.size = { TensorValue(inputDims[0]), TensorValue(inputDims[3]),
+                                        TensorValue(inputDims[2]), TensorValue(inputDims[1]) };
+
+            p.inputLayouts.insert({ inputInfo->name(), networkInputLayout });
+            p.AddPrimitive(cldnn::input_layout(inputName, networkInputLayout, inputInfo->name()));
+            p.AddPrimitiveToProfiler(op);
+        }
     } else {
         if (ColorFormat::NV12 == preProcess.getColorFormat() && p.GetConfig().nv12_two_inputs) {
             // for NV12, create two input layouts with reorder instead of one,
diff --git a/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp b/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
index 8c4e43984ef..bcb7ab46dda 100644
--- a/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
+++ b/src/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
@@ -742,3 +742,143 @@ TEST_F(OVRemoteTensor_Test, NV12toBGR_buffer) {
     float thr = 0.1;
     FuncTestUtils::compare_tensor(out_tensor, output_tensor_regular, thr);
 }
+
+class OVRemoteTensorBatched_Test : public CommonTestUtils::TestsCommon, public testing::WithParamInterface<size_t> {
+    void SetUp() override {
+        num_batch = this->GetParam();
+    };
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<std::size_t> &obj) {
+        return "num_batch_" + std::to_string(obj.param);
+    }
+
+protected:
+    size_t num_batch;
+    std::vector<std::shared_ptr<ngraph::Function>> fn_ptrs;
+};
+
+TEST_P(OVRemoteTensorBatched_Test, NV12toBGR_image) {
+#if defined(ANDROID)
+    GTEST_SKIP();
+#endif
+    const int height = 16;
+    const int width = 16;
+
+    // ------------------------------------------------------
+    // Prepare input data
+    std::vector<ov::runtime::Tensor> fake_image_data_y, fake_image_data_uv;
+    for (int i = 0; i < num_batch; i++) {
+        fake_image_data_y.push_back(FuncTestUtils::create_and_fill_tensor(ov::element::u8, {1, 1, height, width}, 50, 0, 1, i));
+        fake_image_data_uv.push_back(FuncTestUtils::create_and_fill_tensor(ov::element::u8, {1, 2, height / 2, width / 2}, 256, 0, 1, i));
+    }
+
+    auto ie = ov::runtime::Core();
+
+    // ------------------------------------------------------
+    // inference using remote tensor
+    auto fn_ptr_remote = ngraph::builder::subgraph::makeConvPoolRelu({num_batch, 3, height, width});
+
+    using namespace ov::preprocess;
+    auto p = PrePostProcessor(fn_ptr_remote);
+    p.input().tensor().set_element_type(ov::element::u8)
+                      .set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES, {"y", "uv"})
+                      .set_memory_type(std::string(GPU_CONFIG_KEY(SURFACE)) + GPU_CONFIG_KEY(BATCHED));
+    p.input().preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
+    p.input().model().set_layout("NCHW");
+    auto function = p.build();
+
+    auto param_input_y = fn_ptr_remote->get_parameters().at(0);
+    auto param_input_uv = fn_ptr_remote->get_parameters().at(1);
+
+    auto exec_net_b = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto inf_req_remote = exec_net_b.create_infer_request();
+
+    auto cldnn_context = exec_net_b.get_context().as<ov::runtime::intel_gpu::ocl::ClContext>();
+    cl_context ctx = cldnn_context.get();
+    auto ocl_instance = std::make_shared<OpenCL>(ctx);
+    cl_int err;
+
+    std::vector<cl_mem> nv12_image_plane_y, nv12_image_plane_uv;
+    std::vector<cl::Image2D> img_y, img_uv;
+    std::vector<ov::runtime::Tensor> tensor_remote_y, tensor_remote_uv;
+
+    for (size_t i = 0; i < num_batch; ++i) {
+        cl_image_format image_format;
+        cl_image_desc image_desc = { 0 };
+        image_format.image_channel_order = CL_R;
+        image_format.image_channel_data_type = CL_UNORM_INT8;
+        image_desc.image_type = CL_MEM_OBJECT_IMAGE2D;
+        image_desc.image_width = width;
+        image_desc.image_height = height;
+        nv12_image_plane_y.emplace_back(clCreateImage(ocl_instance->_context.get(), CL_MEM_READ_WRITE, &image_format, &image_desc, NULL, &err));
+        ASSERT_EQ(err, 0);
+
+        image_format.image_channel_order = CL_RG;
+        image_desc.image_width = width / 2;
+        image_desc.image_height = height / 2;
+        nv12_image_plane_uv.emplace_back(clCreateImage(ocl_instance->_context.get(), CL_MEM_READ_WRITE, &image_format, &image_desc, NULL, &err));
+        ASSERT_EQ(err, 0);
+
+        size_t origin[3] = { 0, 0, 0 };
+        size_t y_region[3] = { (size_t)width, (size_t)height, 1 };
+        size_t uv_region[3] = { (size_t)width / 2, (size_t)height / 2, 1 };
+
+        err = clEnqueueWriteImage(ocl_instance->_queue.get(), nv12_image_plane_y[i],
+            true, origin, y_region, 0, 0, fake_image_data_y[i].data(), 0, NULL, NULL);
+        ASSERT_EQ(err, 0);
+
+        err = clEnqueueWriteImage(ocl_instance->_queue.get(), nv12_image_plane_uv[i],
+            true, origin, uv_region, 0, 0, fake_image_data_uv[i].data(), 0, NULL, NULL);
+        ASSERT_EQ(err, 0);
+
+        img_y.emplace_back(nv12_image_plane_y[i]);
+        img_uv.emplace_back(nv12_image_plane_uv[i]);
+
+        tensor_remote_y.emplace_back(cldnn_context.create_tensor(param_input_y->get_element_type(), fake_image_data_y[i].get_shape(), img_y[i]));
+        tensor_remote_uv.emplace_back(cldnn_context.create_tensor(param_input_uv->get_element_type(), fake_image_data_uv[i].get_shape(), img_uv[i]));
+    }
+
+    inf_req_remote.set_tensors(*param_input_y->output(0).get_tensor().get_names().begin(), tensor_remote_y);
+    inf_req_remote.set_tensors(*param_input_uv->output(0).get_tensor().get_names().begin(), tensor_remote_uv);
+
+    inf_req_remote.infer();
+
+    auto output_tensor_shared = inf_req_remote.get_tensor(function->get_results().at(0));
+    ASSERT_NO_THROW(output_tensor_shared.data());
+
+    // ------------------------------------------------------
+    // regular inference
+    auto fn_ptr_regular = ngraph::builder::subgraph::makeConvPoolRelu({1, 3, height, width});
+
+    using namespace ov::preprocess;
+    auto p_reg = PrePostProcessor(fn_ptr_regular);
+    p_reg.input().tensor().set_element_type(ov::element::u8)
+                          .set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES, {"y", "uv"})
+                          .set_memory_type(GPU_CONFIG_KEY(BUFFER));
+    p_reg.input().preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
+    p_reg.input().model().set_layout("NCHW");
+    auto function_regular = p_reg.build();
+
+    auto param_input_y_reg = fn_ptr_regular->get_parameters().at(0);
+    auto param_input_uv_reg = fn_ptr_regular->get_parameters().at(1);
+
+    auto exec_net_regular = ie.compile_model(function_regular, CommonTestUtils::DEVICE_GPU);
+    auto inf_req_regular = exec_net_regular.create_infer_request();
+
+    for (size_t i = 0; i < num_batch; ++i) {
+        inf_req_regular.set_tensor(param_input_y_reg, fake_image_data_y[i]);
+        inf_req_regular.set_tensor(param_input_uv_reg, fake_image_data_uv[i]);
+        inf_req_regular.infer();
+        auto output_tensor_regular = inf_req_regular.get_tensor(exec_net_regular.output());
+
+        ASSERT_EQ(output_tensor_regular.get_size() * num_batch, output_tensor_shared.get_size());
+        float thr = 0.1;
+
+        FuncTestUtils::compareRawBuffers<float>(static_cast<float*>(output_tensor_shared.data()) + i * output_tensor_regular.get_size(),
+                                                static_cast<float*>(output_tensor_regular.data()),
+                                                output_tensor_regular.get_size(), output_tensor_regular.get_size(), thr);
+    }
+}
+
+const std::vector<size_t> num_batches{ 1, 2, 4 };
+INSTANTIATE_TEST_SUITE_P(smoke_RemoteTensor, OVRemoteTensorBatched_Test, ::testing::ValuesIn(num_batches), OVRemoteTensorBatched_Test::getTestCaseName);

From 0fa226a0c254e2501f39940d6e820c842e8454a9 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Mon, 27 Dec 2021 09:46:52 +0300
Subject: [PATCH 158/160] [GPU] Fixed uninit vairable and exceptions from
 nothrow methods (#9426)

---
 .../convert_color/convert_color_kernel_base.h |  6 +--
 .../random_uniform_kernel_ref.h               |  4 +-
 .../thirdparty/clDNN/src/layout_optimizer.cpp |  2 -
 .../intel_gpu/plugin/remote_context.hpp       | 20 +++++++---
 src/plugins/intel_gpu/src/plugin/plugin.cpp   |  9 ++---
 .../intel_gpu/src/plugin/remote_context.cpp   | 40 ++++++++++++++-----
 6 files changed, 53 insertions(+), 28 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convert_color/convert_color_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convert_color/convert_color_kernel_base.h
index f6b7d155c5b..4a082b22c03 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convert_color/convert_color_kernel_base.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convert_color/convert_color_kernel_base.h
@@ -14,9 +14,9 @@ namespace kernel_selector {
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 struct convert_color_params : public base_params {
     convert_color_params() : base_params(KernelType::CONVERT_COLOR) {}
-    color_format input_color_format;
-    color_format output_color_format;
-    memory_type mem_type;
+    color_format input_color_format = color_format::BGR;
+    color_format output_color_format = color_format::BGR;
+    memory_type mem_type = memory_type::buffer;
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h
index 489f4b5a786..7743c539d7f 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/random_uniform/random_uniform_kernel_ref.h
@@ -20,8 +20,8 @@ struct random_uniform_params: public base_params {
     }
 
     // operation attributes
-    uint64_t global_seed;
-    uint64_t op_seed;
+    uint64_t global_seed = 0;
+    uint64_t op_seed = 0;
 };
 
 /**
diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
index eb6553e1e29..e4486e32e9c 100644
--- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
+++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
@@ -902,8 +902,6 @@ layout layout_optimizer::get_expected_layout(layout const& current_layout,
     if (use_onednn_impls) {
         std::function<bool(const program_node&)> has_any_convolutions_below;
         has_any_convolutions_below = [&](const program_node& node) -> bool {
-            if (node.get_users().empty())
-                return false;
             for (auto& usr : node.get_users()) {
                 if (usr->is_type<convolution>())
                     return true;
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_context.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_context.hpp
index 8f00b37bd97..cc0c76a35e9 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_context.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_context.hpp
@@ -236,7 +236,11 @@ public:
     void* lock(void* handle, InferenceEngine::LockOp = InferenceEngine::LOCK_FOR_WRITE) noexcept override {
         if (!_usm_host_blob)
             return nullptr;
-        return _usm_host_blob->get();
+        try {
+            return _usm_host_blob->get();
+        } catch (...) {
+            return nullptr;
+        }
     };
 
     /**
@@ -252,11 +256,15 @@ public:
     * @return Handle to the allocated resource
     */
     void* alloc(size_t size) noexcept override {
-        auto td = InferenceEngine::TensorDesc(InferenceEngine::Precision::U8, InferenceEngine::SizeVector{size}, InferenceEngine::Layout::C);
-        InferenceEngine::ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(USM_HOST_BUFFER)}};
-        _usm_host_blob = std::dynamic_pointer_cast<InferenceEngine::gpu::USMBlob>(_context->CreateBlob(td, params));
-        _usm_host_blob->allocate();
-        return _usm_host_blob->get();
+        try {
+            auto td = InferenceEngine::TensorDesc(InferenceEngine::Precision::U8, InferenceEngine::SizeVector{size}, InferenceEngine::Layout::C);
+            InferenceEngine::ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(USM_HOST_BUFFER)}};
+            _usm_host_blob = std::dynamic_pointer_cast<InferenceEngine::gpu::USMBlob>(_context->CreateBlob(td, params));
+            _usm_host_blob->allocate();
+            return _usm_host_blob->get();
+        } catch (...) {
+            return nullptr;
+        }
     }
 
     /**
diff --git a/src/plugins/intel_gpu/src/plugin/plugin.cpp b/src/plugins/intel_gpu/src/plugin/plugin.cpp
index 7450b67be0e..b2892e3caeb 100644
--- a/src/plugins/intel_gpu/src/plugin/plugin.cpp
+++ b/src/plugins/intel_gpu/src/plugin/plugin.cpp
@@ -667,9 +667,8 @@ Parameter Plugin::GetMetric(const std::string& name, const std::map<std::string,
     std::string device_id = GetConfig(CONFIG_KEY(DEVICE_ID), options);
 
     auto iter = device_map.find(device_id);
-    auto device_info = iter != device_map.end() ?
-        iter->second->get_info() :
-        device_map.begin()->second->get_info();
+    auto device = iter != device_map.end() ? iter->second : device_map.begin()->second;
+    auto device_info = device->get_info();
 
     if (name == METRIC_KEY(SUPPORTED_METRICS)) {
         std::vector<std::string> metrics;
@@ -899,8 +898,8 @@ Parameter Plugin::GetMetric(const std::string& name, const std::map<std::string,
 
         InferenceEngine::CNNNetwork network(model);
         size_t base_batch_size = 16; // empirically decided for DG1
-        auto engine_params = Plugin::GetParams(config, iter->second, nullptr);
-        auto engine = cldnn::engine::create(engine_params.engine_type, engine_params.runtime_type, iter->second,
+        auto engine_params = Plugin::GetParams(config, device, nullptr);
+        auto engine = cldnn::engine::create(engine_params.engine_type, engine_params.runtime_type, device,
                                 cldnn::engine_configuration(false, engine_params.queue_type, std::string(),
                                 config.queuePriority, config.queueThrottle, config.memory_pool_on,
                                 engine_params.use_unified_shared_memory, std::string(), config.throughput_streams),
diff --git a/src/plugins/intel_gpu/src/plugin/remote_context.cpp b/src/plugins/intel_gpu/src/plugin/remote_context.cpp
index 45ace3d9195..ea0f54fe238 100644
--- a/src/plugins/intel_gpu/src/plugin/remote_context.cpp
+++ b/src/plugins/intel_gpu/src/plugin/remote_context.cpp
@@ -185,28 +185,48 @@ void RemoteBlobImpl::unlock() const {
 }
 
 LockedMemory<void> RemoteBlobImpl::buffer() noexcept {
-    lock();
-    return LockedMemory<void>(reinterpret_cast<IAllocator*>(&m_allocator), _handle, 0);
+    try {
+        lock();
+        return LockedMemory<void>(reinterpret_cast<IAllocator*>(&m_allocator), _handle, 0);
+    } catch (...) {
+        return LockedMemory<void>(nullptr, nullptr, 0);
+    }
 }
 
 LockedMemory<const void> RemoteBlobImpl::cbuffer() const noexcept {
-    lock();
-    return LockedMemory<const void>(reinterpret_cast<IAllocator*>(&m_allocator), _handle, 0);
+    try {
+        lock();
+        return LockedMemory<const void>(reinterpret_cast<IAllocator*>(&m_allocator), _handle, 0);
+    } catch (...) {
+        return LockedMemory<const void>(nullptr, nullptr, 0);
+    }
 }
 
 LockedMemory<void> RemoteBlobImpl::rwmap()noexcept {
-    lock();
-    return LockedMemory<void>(reinterpret_cast<IAllocator *>(&m_allocator), _handle, 0);
+    try {
+        lock();
+        return LockedMemory<void>(reinterpret_cast<IAllocator *>(&m_allocator), _handle, 0);
+    } catch (...) {
+        return LockedMemory<void>(nullptr, nullptr, 0);
+    }
 }
 
 LockedMemory<const void> RemoteBlobImpl::rmap() const noexcept {
-    lock();
-    return LockedMemory<const void>(reinterpret_cast<IAllocator *>(&m_allocator), _handle, 0);
+    try {
+        lock();
+        return LockedMemory<const void>(reinterpret_cast<IAllocator *>(&m_allocator), _handle, 0);
+    } catch (...) {
+        return LockedMemory<const void>(nullptr, nullptr, 0);
+    }
 }
 
 LockedMemory<void> RemoteBlobImpl::wmap()noexcept {
-    lock();
-    return LockedMemory<void>(reinterpret_cast<IAllocator *>(&m_allocator), _handle, 0);
+    try {
+        lock();
+        return LockedMemory<void>(reinterpret_cast<IAllocator *>(&m_allocator), _handle, 0);
+    } catch (...) {
+        return LockedMemory<void>(nullptr, nullptr, 0);
+    }
 }
 
 void RemoteAllocator::regLockedBlob(void* handle, const RemoteBlobImpl* blob) {

From 95d86eb2bf6dfc0e8a3bea237285536386b1346d Mon Sep 17 00:00:00 2001
From: Sergey Shlyapnikov <sergey.shlyapnikov@intel.com>
Date: Mon, 27 Dec 2021 09:47:20 +0300
Subject: [PATCH 159/160]  [GPU] Add parallel quantizes optimization (#9370)

---
 .../graph_optimizer/prepare_quantization.cpp  | 97 +++++++++++++++++++
 .../clDNN/src/include/pass_manager.h          |  1 +
 2 files changed, 98 insertions(+)

diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp
index e3cc68e5152..4007e4a2ba2 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp
@@ -327,6 +327,9 @@ void  prepare_quantization::prepare_scale_shift_opt(program &p, quantize_node& q
 }
 
 void prepare_quantization::handle_quantize_node(program& p, quantize_node& quantize_node) {
+    if (optimize_quantize(p, quantize_node))
+        return;
+
     if (quantize_node.get_primitive()->levels == 2) {
         prepare_packed_quantize(p, quantize_node);
     } else if (quantize_node.get_primitive()->levels <= 256 && !quantize_node.get_scale_shift_opt() && !quantize_node.is_constant()) {
@@ -759,6 +762,100 @@ void prepare_quantization::prepare_asymmetric_quantization(program &p, convoluti
     new_conv_node.recalc_output_layout();
 }
 
+bool prepare_quantization::optimize_quantize(program &p, quantize_node& quantize_node) {
+    const auto& stream = p.get_stream();
+
+    auto& input = quantize_node.get_dependency(0);
+    auto parallel_quantizes_num = 0;
+    for (auto& usr : input.get_users()) {
+        if (usr->is_type<quantize>())
+            parallel_quantizes_num++;
+    }
+
+    if (parallel_quantizes_num < 2)
+        return false;
+
+    auto quantize_prim_first = quantize_node.get_primitive();
+
+    program_node &input_low_node_first = quantize_node.get_dependency(1);
+    program_node &input_high_node_first = quantize_node.get_dependency(2);
+    program_node &output_low_node_first = quantize_node.get_dependency(3);
+    program_node &output_high_node_first = quantize_node.get_dependency(4);
+
+    if (!input_low_node_first.is_type<data>() || !input_high_node_first.is_type<data>() ||
+        !output_low_node_first.is_type<data>() || !output_high_node_first.is_type<data>()) {
+        return false;
+    }
+
+    auto mem_input_low_first = input_low_node_first.as<data>().get_attached_memory_ptr();
+    auto mem_input_high_first = input_high_node_first.as<data>().get_attached_memory_ptr();
+    auto mem_output_low_first = output_low_node_first.as<data>().get_attached_memory_ptr();
+    auto mem_output_high_first = output_high_node_first.as<data>().get_attached_memory_ptr();
+
+    mem_lock<uint8_t, mem_lock_type::read> mem_input_low_lock_first{mem_input_low_first, stream};
+    mem_lock<uint8_t, mem_lock_type::read> mem_input_high_lock_first{mem_input_high_first, stream};
+    mem_lock<uint8_t, mem_lock_type::read> mem_output_low_lock_first{mem_output_low_first, stream};
+    mem_lock<uint8_t, mem_lock_type::read> mem_output_high_lock_first{mem_output_high_first, stream};
+
+    program_node* same_quantize = nullptr;
+    for (auto& usr : input.get_users()) {
+        if (!usr->is_type<quantize>() || usr == &quantize_node)
+            continue;
+
+        auto quantize_prim_second = usr->as<quantize>().get_primitive();
+
+        program_node &input_low_node_second = usr->get_dependency(1);
+        program_node &input_high_node_second = usr->get_dependency(2);
+        program_node &output_low_node_second = usr->get_dependency(3);
+        program_node &output_high_node_second = usr->get_dependency(4);
+
+        if (!input_low_node_second.is_type<data>() || !input_high_node_second.is_type<data>() ||
+            !output_low_node_second.is_type<data>() || !output_high_node_second.is_type<data>())
+            continue;
+
+        auto mem_input_low_second = input_low_node_second.as<data>().get_attached_memory_ptr();
+        auto mem_input_high_second = input_high_node_second.as<data>().get_attached_memory_ptr();
+        auto mem_output_low_second = output_low_node_second.as<data>().get_attached_memory_ptr();
+        auto mem_output_high_second = output_high_node_second.as<data>().get_attached_memory_ptr();
+
+        mem_lock<uint8_t, mem_lock_type::read> mem_input_low_lock_second{mem_input_low_second, stream};
+        mem_lock<uint8_t, mem_lock_type::read> mem_input_high_lock_second{mem_input_high_second, stream};
+        mem_lock<uint8_t, mem_lock_type::read> mem_output_low_lock_second{mem_output_low_second, stream};
+        mem_lock<uint8_t, mem_lock_type::read> mem_output_high_lock_second{mem_output_high_second, stream};
+
+        if (mem_input_low_first->count() != mem_input_low_second->count() || mem_input_high_first->count() != mem_input_high_second->count() ||
+            mem_output_low_first->count() != mem_output_low_second->count() || mem_output_high_first->count() != mem_output_high_second->count())
+            continue;
+
+        if (memcmp(mem_input_low_lock_first.data(), mem_input_low_lock_second.data(), mem_input_low_first->size()) != 0 ||
+            memcmp(mem_input_high_lock_first.data(), mem_input_high_lock_second.data(), mem_input_high_first->size()) != 0 ||
+            memcmp(mem_output_low_lock_first.data(), mem_output_low_lock_second.data(), mem_output_low_first->size()) != 0 ||
+            memcmp(mem_output_high_lock_first.data(), mem_output_high_lock_second.data(), mem_output_high_first->size()) != 0)
+            continue;
+
+        if (quantize_prim_first->output_data_type != quantize_prim_second->output_data_type ||
+            quantize_prim_first->levels != quantize_prim_second->levels)
+            continue;
+
+        same_quantize = usr;
+        break;
+    }
+
+    if (!same_quantize)
+        return false;
+
+    while (!quantize_node.get_dependencies().empty()) {
+        auto& dep = quantize_node.get_dependency(0);
+        p.remove_connection(dep, quantize_node);
+        p.remove_if_dangling(dep);
+    }
+
+    p.add_optimized_primitive_info(quantize_node.id(), {same_quantize->id()});
+    p.replace_all_usages(quantize_node, *same_quantize);
+
+    return true;
+}
+
 void prepare_quantization::run(program& p) {
     auto itr = p.get_processing_order().begin();
     while (itr != p.get_processing_order().end()) {
diff --git a/inference-engine/thirdparty/clDNN/src/include/pass_manager.h b/inference-engine/thirdparty/clDNN/src/include/pass_manager.h
index 2081e8dbe5e..d36d2b5cf71 100644
--- a/inference-engine/thirdparty/clDNN/src/include/pass_manager.h
+++ b/inference-engine/thirdparty/clDNN/src/include/pass_manager.h
@@ -159,6 +159,7 @@ private:
     void remove_fake_reorders(program& p, reorder_node& reorder_node);
     void prepare_asymmetric_quantization(program& p, convolution_node& convolution_node);
     void prepare_scale_shift_opt(program &p, quantize_node& quantize_node);
+    bool optimize_quantize(program &p, quantize_node& quantize_node);
 };
 
 class prepare_conv_eltw_fusing : public base_pass {

From 4188dbbf9ff6306d87073fa6bec67da710e9e39e Mon Sep 17 00:00:00 2001
From: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>
Date: Mon, 27 Dec 2021 13:06:12 +0300
Subject: [PATCH 160/160] install_openvino_dependencies.sh update (#9398)

* Remove opencv requirements from default components list

* Remove opencv requirements from default components list

* fix typo
---
 .../install_openvino_dependencies.sh          | 41 ++++---------------
 1 file changed, 8 insertions(+), 33 deletions(-)

diff --git a/scripts/install_dependencies/install_openvino_dependencies.sh b/scripts/install_dependencies/install_openvino_dependencies.sh
index 9caf825f244..0ee13109166 100755
--- a/scripts/install_dependencies/install_openvino_dependencies.sh
+++ b/scripts/install_dependencies/install_openvino_dependencies.sh
@@ -8,7 +8,8 @@ set -e
 #===================================================================================================
 # Option parsing
 
-all_comp=(opencv_req opencv_opt python dev myriad installer cl_compiler)
+default_comp=(dev python myriad cl_compiler)
+all_comp=(${default_comp[@]} opencv_req opencv_opt)
 os=${os:-auto}
 
 # public options
@@ -29,7 +30,7 @@ while :; do
             echo "  -y          non-interactive run (off)"
             echo "  -n          dry-run, assume no (off)"
             echo "  -c=<name>   install component <name>, can be repeated (${all_comp[*]})"
-            echo "  -e          add extra repositories (CentOS 7) (off)"
+            echo "  -e          add extra repositories (RHEL 8) (off)"
             echo "  -p          print package list and exit (off)"
             exit
             ;;
@@ -45,16 +46,16 @@ while :; do
     shift
 done
 
-# No components selected - install all
+# No components selected - install default
 if [ ${#comp[@]} -eq 0 ]; then
-    comp=(${all_comp[@]})
+    comp=(${default_comp[@]})
 fi
 
 #===================================================================================================
 #  Selftest
 
 if [ -n "$selftest" ] ; then
-    for image in centos:7 ubuntu:18.04 ubuntu:20.04 ; do
+    for image in ubuntu:18.04 ubuntu:20.04 redhat/ubi8 ; do
         for opt in  "-h" "-p" "-e -p" "-n" "-n -e" "-y" "-y -e" ; do
             echo "||"
             echo "|| Test $image / '$opt'"
@@ -86,7 +87,7 @@ if [ "$os" == "auto" ] ; then
       os="rhel8"
     fi
     case $os in
-        centos7|rhel8|ubuntu18.04|ubuntu20.04) [ -z "$print" ] && echo "Detected OS: ${os}" ;;
+        rhel8|ubuntu18.04|ubuntu20.04) [ -z "$print" ] && echo "Detected OS: ${os}" ;;
         *) echo "Unsupported OS: ${os:-detection failed}" >&2 ; exit 1 ;;
     esac
 fi
@@ -102,7 +103,6 @@ if [ "$os" == "ubuntu18.04" ] ; then
     pkgs_python=(python3 python3-dev python3-venv python3-setuptools python3-pip)
     pkgs_dev=(cmake g++ gcc libc6-dev make curl sudo)
     pkgs_myriad=(libusb-1.0-0)
-    pkgs_installer=(cpio)
     pkgs_cl_compiler=(libtinfo5)
     pkgs_opencv_opt=(
         gstreamer1.0-plugins-bad
@@ -124,7 +124,6 @@ elif [ "$os" == "ubuntu20.04" ] ; then
     pkgs_python=(python3 python3-dev python3-venv python3-setuptools python3-pip)
     pkgs_dev=(cmake g++ gcc libc6-dev make curl sudo)
     pkgs_myriad=(libusb-1.0-0)
-    pkgs_installer=(cpio)
     pkgs_cl_compiler=(libtinfo5)
     pkgs_opencv_opt=(
         gstreamer1.0-plugins-bad
@@ -146,7 +145,6 @@ elif [ "$os" == "rhel8" ] ; then
     pkgs_python=(python3 python3-devel python3-setuptools python3-pip)
     pkgs_dev=(gcc gcc-c++ make glibc libstdc++ libgcc cmake curl sudo)
     pkgs_myriad=()
-    pkgs_installer=()
     pkgs_opencv_opt=(
         gstreamer1
         gstreamer1-plugins-bad-free
@@ -155,29 +153,6 @@ elif [ "$os" == "rhel8" ] ; then
     )
     extra_repos+=(https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm)
 
-elif [ "$os" == "centos7" ] ; then
-
-    # find -name *.so -exec objdump -p {} \; | grep NEEDED | sort -u | cut -c 23- | xargs -t -n1 yum -q whatprovides
-
-    pkgs_opencv_req=(gtk2)
-    pkgs_python=(python3 python3-devel python3-setuptools python3-pip)
-    pkgs_dev=(gcc gcc-c++ make glibc libstdc++ libgcc cmake curl sudo)
-    pkgs_myriad=(libusbx)
-    pkgs_installer=()
-    pkgs_cl_compiler=()
-    pkgs_opencv_opt=(
-        gstreamer1
-        gstreamer1-plugins-bad-free
-        gstreamer1-plugins-good
-        gstreamer1-plugins-ugly-free
-    )
-
-    if [ -n "$extra" ] ; then
-        # 1 RPMFusion
-        extra_repos+=(https://mirrors.rpmfusion.org/free/el/rpmfusion-free-release-7.noarch.rpm)
-        pkgs_opencv_opt+=(ffmpeg-libs)
-    fi
-
 else
     echo "Internal script error: invalid OS after check (package selection)" >&2
     exit 3
@@ -226,7 +201,7 @@ if [ "$os" == "ubuntu18.04" ] || [ "$os" == "ubuntu20.04" ] ; then
 
     apt-get update && apt-get install --no-install-recommends $iopt ${pkgs[@]}
 
-elif [ "$os" == "centos7" ] || [ "$os" == "rhel8" ] ; then
+elif [ "$os" == "rhel8" ] ; then
 
     [ -z "$interactive" ] && iopt="--assumeyes"
     [ -n "$dry" ] && iopt="--downloadonly"